Jekyll2023-10-19T15:06:34+00:00https://danhdtruong.com/feed.xmlDanh Truong, PhDWorks by Danh!Danh TruongKeys to Success2023-08-24T00:00:00+00:002023-08-24T00:00:00+00:00https://danhdtruong.com/Keys-to-Success<h2 id="introduction">Introduction</h2>
<p>Since becoming a postdoc at MD Anderson Cancer Center, I have had more failures than successes. Although the countless failures seemed daunting, I used them as opportunities to learn and grow, which turned my failures into the experiences that I used to obtain success. As I reached the end of my tenure as a postdoc, I reflected on the keys to success that were turning points during my postdoc, which I want to share to improve the postdoctoral experience.</p>
<h2 id="define-goals-and-metrics-for-success">Define goals and metrics for success.</h2>
<p>A postdoctoral position can be quick or long but is ultimately temporary. When starting a postdoc, working with your mentor or postdoctoral office to define goals you want to achieve during your time as a postdoc is essential. In addition, you will want to define measurable success metrics so that you can track whether you are progressing on your goals. One way to be accountable for your goals is to keep a calendar for when your goals should be accomplished. This way, you record it to feel more responsible for progressing on time.</p>
<h2 id="learn-to-communicate-effectively">Learn to communicate effectively.</h2>
<p>Communication is crucial in your career, whether you stay in academia or continue elsewhere. When communicating, you must consider many audiences, such as your mentor, colleagues, the scientific community, and layperson. Communication can take many shapes and forms, like verbal, PowerPoint presentations, graphical abstracts, grant applications, and scientific papers. These aspects are crucial to consider when trying to deliver your message. Consider taking writing courses, using your institute’s scientific editing resources, and participating in journal clubs or symposiums since these activities will give you more exposure to communicating with different audiences and help you improve your communication skills.</p>
<p>Being able to deliver your message and ensuring that your audience is engaged takes significant work. Re-using the same abstract or presentation may seem the easiest, but it may not be the most effective. Different audiences will connect to varying styles of communication. Although you may have already prepared an excellent presentation, there is always room for improvement to better engage with your audience and effectively deliver your message.</p>
<h2 id="network">Network</h2>
<p>Networking may seem like a trivial task, but doing it effectively can make a significant impact on your career. You may think otherwise, but regardless of your career trajectory, you will always require the involvement of other folks in your career. Whether it is a letter of recommendation or hearing about a new job, networking enables you to be in a strategic position to take advantage of opportunities when they arise.</p>
<p>As a postdoc, some of the best networking opportunities are at poster symposiums and lunches with the speakers. In functions where people are more willing to talk, you can take this opportunity to have organic conversations where they could lead to solving a complex problem that you’ve had or enabling a new scientific collaboration. If you do not have the confidence to strike up a conversation, you can try to be the presenter at poster symposiums or seminars. Once you share your exciting research results, I am sure many folks will walk up to you to network.</p>
<h2 id="connect-and-work-with-the-office-of-postdoctoral-affairs">Connect and work with the Office of Postdoctoral Affairs.</h2>
<p>As a postdoc, there are institutional resources that you can take advantage of to boost your career development. Once you step on campus, the first thing you should do is find and learn what the Office of Postdoctoral Affairs offers. This includes career advice, funding sources, teaching opportunities, networking events, etc. The Office of Postdoctoral Affairs supports you and your career development. It is a free resource that only wants the best for you. If, for some reason, there is no Office of Postdoctoral Affairs, the National Postdoctoral Association offers similar resources that are accessible to organizational members.</p>
<h2 id="join-the-postdoctoral-association">Join the postdoctoral association.</h2>
<p>Another great resource to jumpstart your career is the postdoctoral association. This group of postdocs works to support and represent the interests of postdocs. This group also facilitates professional enrichment, career development, and networking, fostering a sense of community among postdocs.</p>
<p>Participating in the events will surely be beneficial for your training as a postdoc. However, if things still need to be added to your postdoctoral training experience, consider joining as a leader to enact the change you want to see. This will give you leadership opportunities and show prospective recruiters you can manage and complete non-research-related projects.</p>
<h2 id="develop-new-skills">Develop new skills.</h2>
<p>Your time as a postdoc is temporary, but the resources you have at hand for career development will be abundant and have a lasting impact on your trajectory. It would be best if you strived to take advantage of workshops offered by the Office of Postdoctoral Affairs or through your postdoctoral associations. In addition, when the opportunity arises in your lab to introduce a new technology, take the time to thoroughly learn it, as you may find out that you will become the resident expert in this new technology. These are all opportunities that you can leverage for your next career opportunity.</p>
<p>In addition to learning new technologies and research-related skills, ensure to spend time on soft or transferable skills. These include communication, time management, project management, budgeting, emotional intelligence, teamwork, etc. MD Anderson Cancer Center offers many courses and workshops on these skills as they are commonplace in the working environment.</p>
<h2 id="apply-for-intra--and-extra-mural-funding">Apply for intra- and extra-mural funding.</h2>
<p>Regardless of whether your mentor fully funds your position, it is always a great idea to pursue additional funding through your institution, foundations, or government agencies like the NCI. If you are awarded, the money allocated for your stipend can now be used elsewhere while you demonstrate that you can win competitive grants.</p>
<p>Although you may have yet to be awarded the funding, brainstorming, planning, and writing a proposal application are essential skills to obtain. Preparing a proposal incorporates many critical skills needed in the workplace, such as budgeting, written and visual communication, time management, and creativity.</p>
<h2 id="attend-both-large-and-small-conferences">Attend both large and small conferences.</h2>
<p>During your postdoctoral training, you will have opportunities to attend different types of conferences – large and small. Do not shy away from either since both can benefit your career.</p>
<p>Larger conferences often involve many folks and can span various topics. It is a great place to interact with folks from research topics that are tangential or different from yours. You can learn about other issues and incorporate these new ideas into your research. Also, at these conferences, vendors and pharmaceutical companies will be in attendance. These are chances to network to discuss potential new technologies and job opportunities.</p>
<p>Smaller conferences are more intimate. The topics are narrower, but there are more opportunities to discuss with collaborators and experts in the field. You can make a name for yourself at these types of conferences.</p>
<h2 id="take-care-of-yourself">Take care of yourself.</h2>
<p>Mental health is often overlooked – especially as a postdoc. You will be stressed to publish, write grants, and present research. Learning the proper ways to de-stress and saying “no” to things are essential for caring for your mental health. Taking time off and spending it with friends and family is necessary. Exercising is one of the best ways to take your mind off things and de-stress. Another thing you can do is complete tasks that have been on your to-do list for a while. Most important is to develop a sense of when to take a break.</p>
<h2 id="be-accountable-for-your-training">Be accountable for your training.</h2>
<p>The most important key to success is that the postdoctoral training is for you. It is not for anyone else but you. The only outcome that matters is that you know more about yourself and the world than when you started your postdoc.</p>
<p>There will be projects that you need to complete where you may need to learn new technology. If your primary mentor does not know to teach you these skills, you should not use this as an excuse to give up. It would be best if you tried to find a way by learning from other mentors or colleagues or adapting using a core facility service or a different technology.</p>Danh TruongIntroduction Since becoming a postdoc at MD Anderson Cancer Center, I have had more failures than successes. Although the countless failures seemed daunting, I used them as opportunities to learn and grow, which turned my failures into the experiences that I used to obtain success. As I reached the end of my tenure as a postdoc, I reflected on the keys to success that were turning points during my postdoc, which I want to share to improve the postdoctoral experience.Rendering Math from RMarkdown2022-02-25T00:00:00+00:002022-02-25T00:00:00+00:00https://danhdtruong.com/Rendering-Math-from-RMarkdown<p>In some of my posts, I have had trouble rendering math with MathJax. My
posts that include math equations were written in R markdown. Then I
used knitr to convert them into a markdown file before posting them to
my webpage. However, the math written using Latex ends up converted in a
way that is non-renderable.</p>
<p>My webpage and most others that have pages through GitHub are based on
<a href="https://jekyllrb.com">Jekyll</a>, which cannot parse the math even after
conversion. To handle this, I have come across a
<a href="https://tinyheero.github.io/2015/12/06/rmd-to-jekyll-protect-eqn.html">post</a>
by Fong Chun Chan that gives some insight. Essentially, you need to
protect your latex equations with HTML tags in the R markdown file, so
that when you perform the conversion, they are kept intact. Afterwards,
you can remove the HTML tags and MathJax will interpret the equations
and render them correctly.</p>
<h2 id="mathjax">MathJax</h2>
<p>First, we need to add the MathJax script to your website. For Jekyll,
you add it to <code class="language-plaintext highlighter-rouge">_includes/head.html</code>. I use the <a href="https://mmistakes.github.io/minimal-mistakes/">Minimal
Mistakes</a> theme so if you
have that, you can add it to <code class="language-plaintext highlighter-rouge">_layouts/single.html</code>.</p>
<div class="language-js highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="o"><</span><span class="nx">script</span> <span class="nx">id</span><span class="o">=</span><span class="dl">"</span><span class="s2">MathJax-script</span><span class="dl">"</span> <span class="k">async</span>
<span class="nx">src</span><span class="o">=</span><span class="dl">"</span><span class="s2">https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js</span><span class="dl">"</span><span class="o">></span>
<span class="o"><</span><span class="sr">/script</span><span class="err">>
</span></code></pre></div></div>
<p>This enables the MathJax javascript library so it can parse the math
equations and render them on your website.</p>
<h2 id="protecting-display-math-equations">Protecting display math equations</h2>
<p>In R markdown, a typical display equation would be:</p>
<div class="language-bash highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nv">$$</span> y <span class="o">=</span> mx + b <span class="nv">$$</span>
</code></pre></div></div>
<p><em>y</em> = <em>m**x</em> + <em>b</em></p>
<p>As you can see, it does not render properly. It should like like the one
below:</p>
\[y = mx + b\]
<p>To solve this, we can add HTML tags prior to the knitr conversion, where
knitr will not touch the equations, and then remove them later so that
MathJax can parse them. I found a
<a href="https://gist.github.com/emanuelhuber/11835e6840868029d7c4721b7f7bf465">script</a>
that can do just that. It will look like this with tags prior to markdown conversion.</p>
<div class="language-js highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="o"><</span><span class="nx">pre</span><span class="o">></span><span class="nx">$$</span> <span class="nx">y</span> <span class="o">=</span> <span class="nx">mx</span> <span class="o">+</span> <span class="nx">b</span> <span class="nx">$$</span><span class="o"><</span><span class="sr">/pre</span><span class="err">>
</span></code></pre></div></div>
<p>I have modified the script below. You can download it
<a href="https://github.com/danhtruong/danhtruong.github.io/blob/master/assets/files/r2jekyll.R">here</a>.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">library</span><span class="p">(</span><span class="n">rmarkdown</span><span class="p">)</span><span class="w">
</span><span class="n">library</span><span class="p">(</span><span class="n">dplyr</span><span class="p">)</span><span class="w">
</span><span class="n">library</span><span class="p">(</span><span class="n">stringr</span><span class="p">)</span><span class="w">
</span><span class="c1"># Get the filename given as an argument in the shell.</span><span class="w">
</span><span class="n">args</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">commandArgs</span><span class="p">(</span><span class="kc">TRUE</span><span class="p">)</span><span class="w">
</span><span class="n">filename</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">args</span><span class="p">[</span><span class="m">1</span><span class="p">]</span><span class="w">
</span><span class="c1"># Check that it's a .Rmd file.</span><span class="w">
</span><span class="k">if</span><span class="p">(</span><span class="o">!</span><span class="n">grepl</span><span class="p">(</span><span class="s2">".Rmd"</span><span class="p">,</span><span class="w"> </span><span class="n">filename</span><span class="p">))</span><span class="w"> </span><span class="p">{</span><span class="w">
</span><span class="n">stop</span><span class="p">(</span><span class="s2">"You must specify a .Rmd file."</span><span class="p">)</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="n">tempfile</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">sub</span><span class="p">(</span><span class="s1">'.Rmd'</span><span class="p">,</span><span class="w"> </span><span class="s1">'_deleteme.Rmd'</span><span class="p">,</span><span class="w"> </span><span class="n">filename</span><span class="p">)</span><span class="w">
</span><span class="n">mdtempfile</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">sub</span><span class="p">(</span><span class="s1">'.Rmd'</span><span class="p">,</span><span class="w"> </span><span class="s1">'_deleteme.md'</span><span class="p">,</span><span class="w"> </span><span class="n">filename</span><span class="p">)</span><span class="w">
</span><span class="n">mdfile</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">sub</span><span class="p">(</span><span class="s1">'.Rmd'</span><span class="p">,</span><span class="w"> </span><span class="s1">'.md'</span><span class="p">,</span><span class="w"> </span><span class="n">filename</span><span class="p">)</span><span class="w">
</span><span class="n">read_lines</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">readLines</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span><span class="w">
</span><span class="c1"># add pre tags around $...$</span><span class="w">
</span><span class="n">read_lines</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">gsub</span><span class="p">(</span><span class="s2">"(\\${2}(.+?)\\${2})"</span><span class="p">,</span><span class="w"> </span><span class="s2">"\\1<\\/pre>"</span><span class="p">,</span><span class="w"> </span><span class="n">read_lines</span><span class="p">)</span><span class="w">
</span><span class="n">writeLines</span><span class="p">(</span><span class="n">read_lines</span><span class="p">,</span><span class="w"> </span><span class="n">tempfile</span><span class="p">)</span><span class="w">
</span><span class="n">rmarkdown</span><span class="o">::</span><span class="n">render</span><span class="p">(</span><span class="n">tempfile</span><span class="p">,</span><span class="w"> </span><span class="n">output_format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"md_document"</span><span class="w">
</span><span class="p">,</span><span class="w"> </span><span class="n">output_file</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mdtempfile</span><span class="p">)</span><span class="w">
</span><span class="n">read_lines</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">readLines</span><span class="p">(</span><span class="n">mdtempfile</span><span class="p">)</span><span class="w">
</span><span class="c1"># remove pre tags</span><span class="w">
</span><span class="n">sel</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">grepl</span><span class="p">(</span><span class="s2">""</span><span class="p">,</span><span class="w"> </span><span class="n">read_lines</span><span class="p">)</span><span class="w">
</span><span class="n">read_lines</span><span class="p">[</span><span class="n">sel</span><span class="p">]</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">str_replace</span><span class="p">(</span><span class="n">read_lines</span><span class="p">[</span><span class="n">sel</span><span class="p">],</span><span class="w"> </span><span class="s1">''</span><span class="p">,</span><span class="w"> </span><span class="s1">''</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">str_replace</span><span class="p">(</span><span class="s1">'</pre>'</span><span class="p">,</span><span class="w"> </span><span class="s1">''</span><span class="p">)</span><span class="w">
</span><span class="c1"># remove multiple spaces from lists</span><span class="w">
</span><span class="n">sel</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">grepl</span><span class="p">(</span><span class="s2">"[[:space:]]{2}\\${1}(.+?)\\${1}"</span><span class="p">,</span><span class="w"> </span><span class="n">read_lines</span><span class="p">)</span><span class="w">
</span><span class="n">read_lines</span><span class="p">[</span><span class="n">sel</span><span class="p">]</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">gsub</span><span class="p">(</span><span class="s1">'\\s+'</span><span class="p">,</span><span class="w"> </span><span class="s1">' '</span><span class="p">,</span><span class="w"> </span><span class="n">read_lines</span><span class="p">[</span><span class="n">sel</span><span class="p">])</span><span class="w">
</span><span class="c1"># add correct path for files</span><span class="w">
</span><span class="n">read_lines</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">gsub</span><span class="p">(</span><span class="s1">'files/'</span><span class="p">,</span><span class="w"> </span><span class="s2">"/files/"</span><span class="w"> </span><span class="p">,</span><span class="w"> </span><span class="n">read_lines</span><span class="p">)</span><span class="w"> </span><span class="c1"># this one is for me to add correct file path when you have output files, but you can change it to where your files are. </span><span class="w">
</span><span class="n">writeLines</span><span class="p">(</span><span class="n">read_lines</span><span class="p">,</span><span class="w"> </span><span class="n">mdfile</span><span class="p">)</span><span class="w">
</span><span class="c1"># delete temp files</span><span class="w">
</span><span class="n">unlink</span><span class="p">(</span><span class="n">mdtempfile</span><span class="p">)</span><span class="w">
</span><span class="n">unlink</span><span class="p">(</span><span class="n">tempfile</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<p>Place the script in the same folder as your R markdown file. Then run
the following in your terminal:</p>
<div class="language-bash highlighter-rouge"><div class="highlight"><pre class="highlight"><code>Rscript <span class="nt">--vanilla</span> r2jekyll.R your_RMarkdownFile.Rmd
</code></pre></div></div>
<p>Before running, if you generate output files or figures, make sure to
add the following to the top of your R markdown notebook.</p>
<h2 id="enabling-inline-math-equations">Enabling inline math equations</h2>
<p>Now, for inline math equations. MathJax does not handle this unless you
properly
<a href="https://docs.mathjax.org/en/v2.7-latest/options/preprocessors/tex2jax.html">configure</a>
it. So simply add this right before where you added the MathJax script.
For instance, see below:</p>
<div class="language-js highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="o"><</span><span class="nx">script</span><span class="o">></span>
<span class="nx">MathJax</span> <span class="o">=</span> <span class="p">{</span>
<span class="na">tex</span><span class="p">:</span> <span class="p">{</span>
<span class="na">inlineMath</span><span class="p">:</span> <span class="p">[[</span><span class="dl">'</span><span class="s1">$</span><span class="dl">'</span><span class="p">,</span> <span class="dl">'</span><span class="s1">$</span><span class="dl">'</span><span class="p">],</span> <span class="p">[</span><span class="dl">'</span><span class="se">\\</span><span class="s1">(</span><span class="dl">'</span><span class="p">,</span> <span class="dl">'</span><span class="se">\\</span><span class="s1">)</span><span class="dl">'</span><span class="p">]]</span>
<span class="p">}</span>
<span class="p">};</span>
<span class="o"><</span><span class="sr">/script</span><span class="err">>
</span>
<span class="o"><</span><span class="nx">script</span> <span class="nx">id</span><span class="o">=</span><span class="dl">"</span><span class="s2">MathJax-script</span><span class="dl">"</span> <span class="k">async</span>
<span class="nx">src</span><span class="o">=</span><span class="dl">"</span><span class="s2">https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js</span><span class="dl">"</span><span class="o">></span>
<span class="o"><</span><span class="sr">/script</span><span class="err">>
</span></code></pre></div></div>
<h2 id="additional-resources">Additional Resources</h2>
<ul>
<li><a href="https://tinyheero.github.io/2015/12/06/rmd-to-jekyll-protect-eqn.html">R Markdown to Jekyll: “Protecting” Your Math Equations</a></li>
<li><a href="https://www.mathjax.org">MathJax</a></li>
</ul>Danh TruongIn some of my posts, I have had trouble rendering math with MathJax. My posts that include math equations were written in R markdown. Then I used knitr to convert them into a markdown file before posting them to my webpage. However, the math written using Latex ends up converted in a way that is non-renderable.Introduction to R and RStudio2022-02-21T00:00:00+00:002022-02-21T00:00:00+00:00https://danhdtruong.com/Introduction-to-R-and-RStudio<h2 id="what-is-r">What is R?</h2>
<p>R is a language and environment for statistical computing and graphics.
It is widely used for a variety of statistical analysis (i.e., linear
and nonlinear modeling, classical statistical tests, clustering, etc.).
We can also use R for data visualization and producing figures for
presentations. R is freely available and has a large collection of
developed packages of different tools. Overall, R is a powerful tool
that can be used to handle big data, perform statistics, and visualize
results, while enabling end-users to easily develop tools and packages
for customized functionality.</p>
<h2 id="what-is-rstudio">What is RStudio?</h2>
<p>While R, also called “base R”, itself is an interpreted computer
language and comes with a terminal, ease of use and more functionality
is introduced when using RStudio, an open source Integrated Development
Environment (IDE). Rather than using a terminal, RStudio provides a
graphical user interface that is platform agnostic and integrates
additional packages, project management, version control, and notebooks.</p>
<h2 id="getting-started-with-r-and-rstudio">Getting started with R and RStudio</h2>
<p><a href="https://cran.r-project.org">The Comprehensive R Archive Network</a> or
CRAN actively maintains and develops R. It also hosts a repository of R
packages that enhances the base functionality of R. To get started, base
R can be installed on either Linux, macOS, or Windows by going to the
<a href="https://cran.r-project.org">CRAN</a> homepage.</p>
<p>To use R, you need to open a terminal window and type <code class="language-plaintext highlighter-rouge">R</code> and press
enter. You will see some text denoting the R version and some helpful
functions. To quit, type <code class="language-plaintext highlighter-rouge">q()</code> and press enter.</p>
<p>Rather than work in a terminal, RStudio is the way to use R like how
many use Microsoft Word to prepare documents. Download RStudio for free
<a href="https://www.rstudio.com/products/rstudio/">here</a> and follow the
installation instructions. Once installed, proceed to opening RStudio,
typically done by clicking on the RStudio icon.</p>
<p>Once you open RStudio, you will see four main panes. Each will contain
different information. You can see my screen below.</p>
<p><img src="/images/RStudio.png" alt="" /></p>
<p>Starting from the top left pane and going from left to right, we have
the descriptions of each pane below:</p>
<ol>
<li><strong>Source Editor</strong>: This pane is where you can write R scripts or
notebooks. You can write in other programming languages if you
wanted to as well. Each document will have its own tab. Code written
here can be run with the <code class="language-plaintext highlighter-rouge">Run</code> command.</li>
<li><strong>Environment</strong>: This pane displays objects, variables, and
functions that are generated in your R session. There is also a
history of all code that was run.</li>
<li><strong>Console</strong>: This pane is where you can type commands and
interactively run R code. The output will display in the console.</li>
<li><strong>Files, Plots, Packages, Help, Viewer</strong>: This pane has several tabs
that are important.
<ul>
<li><em>Files</em>: This tab shows the structure and content of a directory
on your computer. This could be your working directory or a
directory that you manually navigated to.</li>
<li><em>Plots</em>: This tab will display plots or figures as an output
from the console.</li>
<li><em>Packages</em>: This tab contains a list of all packages that are
installed. Packages that are loaded in your R session will have
a checked box.</li>
<li><em>Help</em>: This tab reveals the help pages for an R package or
function.</li>
<li><em>Viewer</em>: This tab shows compiled R Markdown documents.</li>
</ul>
</li>
</ol>
<h2 id="starting-a-new-project-in-rstudio">Starting a new project in RStudio</h2>
<p>When working with R, it is always a good idea to create a new project
directory. This will help you keep your files and data organized by
project.</p>
<p>To get started: 1. Open RStudio 2. Go to the File menu and select New
Project. 3. In the New Project window, choose New Directory. Then,
choose New Project. Name your new directory. You can use a name like,
Intro-to-R, and then “Create the project as subdirectory of:” in a
location of your choice. 4. Click on Create Project. 5. The project
should open up automatically in Rstudio.</p>
<p>We can view our working directory by using the function <code class="language-plaintext highlighter-rouge">getwd()</code></p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">getwd</span><span class="p">()</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] "/Users/dtruong4/Desktop/Intro-to-R"
</code></pre></div></div>
<p>Your working directory will be the location where R will automatically
look for files. If you want to find files in a different location, you
will either need to provide the full path or type the path in relation
to the working directory. Files that you output will automatically save
into your working directory unless a path is provided.</p>
<p>To organize your working directory, it is highly recommended to generate
sub-folders like <code class="language-plaintext highlighter-rouge">data/</code> or <code class="language-plaintext highlighter-rouge">results/</code>. You can do so in the Files tab
and select <code class="language-plaintext highlighter-rouge">New Folder</code>.</p>
<h2 id="basic-math-calculations-in-r">Basic math calculations in R</h2>
<p>Below are some of the most common math calculations that can be done in
R.</p>
<table>
<thead>
<tr>
<th>Operation</th>
<th>Symbol</th>
</tr>
</thead>
<tbody>
<tr>
<td>Addition</td>
<td><code class="language-plaintext highlighter-rouge">a + b</code></td>
</tr>
<tr>
<td>Subtraction</td>
<td><code class="language-plaintext highlighter-rouge">a - b</code></td>
</tr>
<tr>
<td>Multiplication</td>
<td><code class="language-plaintext highlighter-rouge">a * b</code></td>
</tr>
<tr>
<td>Division</td>
<td><code class="language-plaintext highlighter-rouge">a / b</code></td>
</tr>
<tr>
<td>Exponent</td>
<td><code class="language-plaintext highlighter-rouge">a ^ b</code></td>
</tr>
<tr>
<td>Remainder</td>
<td><code class="language-plaintext highlighter-rouge">a %% b</code></td>
</tr>
<tr>
<td>Integer Division</td>
<td><code class="language-plaintext highlighter-rouge">a %/% b</code></td>
</tr>
</tbody>
</table>
<p>For instance, here is an example of addition.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="m">5</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="m">3</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 8
</code></pre></div></div>
<h2 id="functions-in-r">Functions in R</h2>
<p>R has several pre-built functions. For instance, we can use <code class="language-plaintext highlighter-rouge">sum()</code>
instead of the <code class="language-plaintext highlighter-rouge">+</code> symbol for addition.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nf">sum</span><span class="p">(</span><span class="m">1</span><span class="p">,</span><span class="m">3</span><span class="p">)</span><span class="w"> </span><span class="c1">#This gives the sum of 1 and 3</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 4
</code></pre></div></div>
<p>We can also call the <em>R documentation</em> for a function by using <code class="language-plaintext highlighter-rouge">?</code>
before the function name. The documentation will show up under the Help
tab. It contains information regarding description, usage, and arguments
for a function.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="o">?</span><span class="n">sum</span><span class="w"> </span><span class="c1">#Find the R Documentation for sum()</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/sum_help.png" alt="" /></p>
<p>We can see that <code class="language-plaintext highlighter-rouge">sum()</code> can return the sum of more than two values.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nf">sum</span><span class="p">(</span><span class="m">1</span><span class="p">,</span><span class="m">2</span><span class="p">,</span><span class="m">3</span><span class="p">,</span><span class="m">4</span><span class="p">,</span><span class="m">5</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 15
</code></pre></div></div>
<h2 id="math-functions-in-r">Math functions in R</h2>
<p>In addition to the basic calculations, there are many common pre-built
math functions in R.</p>
<table>
<thead>
<tr>
<th>Operation</th>
<th>Function</th>
</tr>
</thead>
<tbody>
<tr>
<td>Square root</td>
<td><code class="language-plaintext highlighter-rouge">sqrt()</code></td>
</tr>
<tr>
<td>Logarithm</td>
<td><code class="language-plaintext highlighter-rouge">log()</code></td>
</tr>
<tr>
<td>Logarithm, base 10</td>
<td><code class="language-plaintext highlighter-rouge">log10()</code></td>
</tr>
<tr>
<td>Exponential</td>
<td><code class="language-plaintext highlighter-rouge">exp()</code></td>
</tr>
<tr>
<td>Summation</td>
<td><code class="language-plaintext highlighter-rouge">sum()</code></td>
</tr>
<tr>
<td>Round</td>
<td><code class="language-plaintext highlighter-rouge">round()</code></td>
</tr>
<tr>
<td>Mean</td>
<td><code class="language-plaintext highlighter-rouge">mean()</code></td>
</tr>
<tr>
<td>Median</td>
<td><code class="language-plaintext highlighter-rouge">median()</code></td>
</tr>
<tr>
<td>Minimum</td>
<td><code class="language-plaintext highlighter-rouge">min()</code></td>
</tr>
<tr>
<td>Maximum</td>
<td><code class="language-plaintext highlighter-rouge">max()</code></td>
</tr>
</tbody>
</table>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#What is the square root of 4?</span><span class="w">
</span><span class="nf">sqrt</span><span class="p">(</span><span class="m">4</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 2
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#Can we round 3.14?</span><span class="w">
</span><span class="nf">round</span><span class="p">(</span><span class="m">3.14</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 3
</code></pre></div></div>
<h2 id="defining-variables">Defining variables</h2>
<p>A key aspect of programming is defining variables. We store data or
values as variables so that we can use in other functions or recall it
at a later time. It allows us to save time by storing the data and not
having to re-calculate it again. R has two <em>assignment operators</em> for
defining variables: <code class="language-plaintext highlighter-rouge"><-</code> and <code class="language-plaintext highlighter-rouge">=</code>. The operator <code class="language-plaintext highlighter-rouge"><-</code> can be used
anywhere, whereas the operator <code class="language-plaintext highlighter-rouge">=</code> is only allowed at the top level.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">x</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="m">1</span><span class="w">
</span><span class="c1">#What is in `x`?</span><span class="w">
</span><span class="n">x</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 1
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">15.3</span><span class="w">
</span><span class="c1">#What is in `y`?</span><span class="w">
</span><span class="n">y</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 15.3
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#What is in x + y?</span><span class="w">
</span><span class="n">x</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">y</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 16.3
</code></pre></div></div>
<p>We can also define variables within functions.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nf">sqrt</span><span class="p">(</span><span class="n">y</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="m">5</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 2.236068
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#What is in `y`?</span><span class="w">
</span><span class="n">y</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 5
</code></pre></div></div>
<p>However, we cannot use the <code class="language-plaintext highlighter-rouge">=</code> to do so. This is because functions
already have pre-defined arguments that the function is looking for. In
this case, <code class="language-plaintext highlighter-rouge">sqrt()</code> is looking for the argument <code class="language-plaintext highlighter-rouge">x</code>, not <code class="language-plaintext highlighter-rouge">y</code>.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nf">sqrt</span><span class="p">(</span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">5</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## Error in sqrt(y = 5): supplied argument name 'y' does not match 'x'
</code></pre></div></div>
<p>Instead, we can do the following:</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nf">sqrt</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="m">5</span><span class="p">)</span><span class="w"> </span><span class="c1">#Here we define y as 5 and pass y into x </span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 2.236068
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nf">sqrt</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">5</span><span class="p">)</span><span class="w"> </span><span class="c1">#Here we pass 5 into x</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 2.236068
</code></pre></div></div>
<p>Importantly, when defining variable names, ensure that you use an
<em>informative</em> name. This enables yourself and others when reviewing code
to know how the variable was used. For instance, we used <code class="language-plaintext highlighter-rouge">x</code> and <code class="language-plaintext highlighter-rouge">y</code> in
the examples, but their meaning is unknown. Something like
<code class="language-plaintext highlighter-rouge">country_population</code> or <code class="language-plaintext highlighter-rouge">room_capacity</code> provides better definition for a
variable.</p>
<h2 id="r-data-types">R Data Types</h2>
<p>There are different types of data in R, which can be stored as a
variable. Below is a table of some of the most commonly used data types.</p>
<table>
<thead>
<tr>
<th>Data Type</th>
<th>Definition</th>
<th>Example</th>
</tr>
</thead>
<tbody>
<tr>
<td>numeric</td>
<td>Any number value</td>
<td><code class="language-plaintext highlighter-rouge">3.14</code></td>
</tr>
<tr>
<td>integer</td>
<td>Any whole number value</td>
<td><code class="language-plaintext highlighter-rouge">42</code></td>
</tr>
<tr>
<td>character</td>
<td>Any number of ASCII characters</td>
<td><code class="language-plaintext highlighter-rouge">"Hello world!"</code></td>
</tr>
<tr>
<td> </td>
<td>defined within quotation marks</td>
<td> </td>
</tr>
<tr>
<td>logical</td>
<td>A value of <code class="language-plaintext highlighter-rouge">TRUE</code> or <code class="language-plaintext highlighter-rouge">FALSE</code></td>
<td><code class="language-plaintext highlighter-rouge">TRUE</code></td>
</tr>
<tr>
<td>factor</td>
<td>A categorical type of data</td>
<td><code class="language-plaintext highlighter-rouge">#> [1] Male Male Male Female Female</code></td>
</tr>
<tr>
<td> </td>
<td> </td>
<td><code class="language-plaintext highlighter-rouge">#> Levels: Male Female</code></td>
</tr>
</tbody>
</table>
<p>The function <code class="language-plaintext highlighter-rouge">class()</code> can be used to find out the type of data that you
are dealing with.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">x</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="m">3</span><span class="w">
</span><span class="nf">class</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] "numeric"
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">x</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="kc">TRUE</span><span class="w">
</span><span class="nf">class</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] "logical"
</code></pre></div></div>
<h2 id="vectors">Vectors</h2>
<p>Vectors are a data structure in R containing one or more values. In
fact, you may have noticed a <code class="language-plaintext highlighter-rouge">[1]</code> in the output of <code class="language-plaintext highlighter-rouge">x</code>. This indicates
that it is a vector of length <code class="language-plaintext highlighter-rouge">1</code>.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nf">length</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="w"> </span><span class="c1">#This function gives you the length of a vector</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 1
</code></pre></div></div>
<p>We use the function <code class="language-plaintext highlighter-rouge">c()</code> to define a vector with multiple elements. The
<code class="language-plaintext highlighter-rouge">c</code> stands for combine.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">my_first_vector</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="m">1</span><span class="p">,</span><span class="m">2</span><span class="p">,</span><span class="m">3</span><span class="p">,</span><span class="m">4</span><span class="p">,</span><span class="m">5</span><span class="p">)</span><span class="w"> </span><span class="c1">#We can also do this with the following, 1:5 instead of c()</span><span class="w">
</span><span class="n">my_first_vector</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 1 2 3 4 5
</code></pre></div></div>
<p>We can add more elements to the same vector.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">my_first_vector</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="n">my_first_vector</span><span class="p">,</span><span class="w"> </span><span class="m">6</span><span class="p">,</span><span class="m">7</span><span class="p">)</span><span class="w">
</span><span class="n">my_first_vector</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 1 2 3 4 5 6 7
</code></pre></div></div>
<p>We can call specific elements in a vector by using a process called
<em>indexing</em>. Basically, we can subset specific elements of a vector for
further analysis. We do this by defining which position we want in
brackets <code class="language-plaintext highlighter-rouge">[]</code> after the vector.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#Let's take out the 3rd element</span><span class="w">
</span><span class="n">my_first_vector</span><span class="p">[</span><span class="m">3</span><span class="p">]</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 3
</code></pre></div></div>
<p>What if we wanted to select multiple elements? We can use another vector
with the positions we want.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#Let's take out the 2nd and 4th elements</span><span class="w">
</span><span class="n">my_first_vector</span><span class="p">[</span><span class="nf">c</span><span class="p">(</span><span class="m">2</span><span class="p">,</span><span class="m">4</span><span class="p">)]</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 2 4
</code></pre></div></div>
<p>We can also do the opposite and select all elements but a single or
multiple element by using <code class="language-plaintext highlighter-rouge">-</code>.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#Let's keep all but the 5th element</span><span class="w">
</span><span class="n">my_first_vector</span><span class="p">[</span><span class="m">-5</span><span class="p">]</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 1 2 3 4 6 7
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#Let's keep all but the 1st and 3rd elements</span><span class="w">
</span><span class="n">my_first_vector</span><span class="p">[</span><span class="o">-</span><span class="nf">c</span><span class="p">(</span><span class="m">1</span><span class="p">,</span><span class="m">3</span><span class="p">)]</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 2 4 5 6 7
</code></pre></div></div>
<p>Importantly, R functions are typically <em>vectorized</em>. This means that the
function will perform its operation on all elements of the vector
without having to loop through for each element.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">my_first_vector</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 1 2 3 4 5 6 7
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">my_first_vector</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="m">2</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 2 4 6 8 10 12 14
</code></pre></div></div>
<p>We can also test some of math functions we listed above.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">mean</span><span class="p">(</span><span class="n">my_first_vector</span><span class="p">)</span><span class="w"> </span><span class="c1">#This gives the mean of a numeric vector</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 4
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nf">min</span><span class="p">(</span><span class="n">my_first_vector</span><span class="p">)</span><span class="w"> </span><span class="c1">#This gives the minimum numeric value in a vector</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 1
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nf">max</span><span class="p">(</span><span class="n">my_first_vector</span><span class="p">)</span><span class="w"> </span><span class="c1">#This gives the maximmum numeric value in a vector</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 7
</code></pre></div></div>
<h2 id="relational-and-logical-operators">Relational and Logical Operators</h2>
<p>In R, there are relational operators that compare values between two
variables. Typically, these are numerical equalities or inequalities.
The result of comparison is a Boolean value.</p>
<table>
<thead>
<tr>
<th>Operator</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code class="language-plaintext highlighter-rouge"><</code></td>
<td>less than</td>
</tr>
<tr>
<td><code class="language-plaintext highlighter-rouge"><=</code></td>
<td>less than or equal to</td>
</tr>
<tr>
<td><code class="language-plaintext highlighter-rouge">></code></td>
<td>greater than</td>
</tr>
<tr>
<td><code class="language-plaintext highlighter-rouge">>=</code></td>
<td>greater than or equal to</td>
</tr>
<tr>
<td><code class="language-plaintext highlighter-rouge">==</code></td>
<td>equal to</td>
</tr>
<tr>
<td><code class="language-plaintext highlighter-rouge">!=</code></td>
<td>not equal to</td>
</tr>
<tr>
<td><code class="language-plaintext highlighter-rouge">%in%</code></td>
<td>is ‘in’ a given vector</td>
</tr>
</tbody>
</table>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#Is 3 greater than 5?</span><span class="w">
</span><span class="m">3</span><span class="w"> </span><span class="o">></span><span class="w"> </span><span class="m">5</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] FALSE
</code></pre></div></div>
<p>We can use the <code class="language-plaintext highlighter-rouge">%in%</code> operator to determine if a given value is in a
vector.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#Is 5 in 1 through 10?</span><span class="w">
</span><span class="m">5</span><span class="w"> </span><span class="o">%in%</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="m">10</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] TRUE
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#Is 'a' in c('a','b','c')?</span><span class="w">
</span><span class="s1">'a'</span><span class="w"> </span><span class="o">%in%</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="s1">'a'</span><span class="p">,</span><span class="s1">'b'</span><span class="p">,</span><span class="s1">'c'</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] TRUE
</code></pre></div></div>
<p>There are also logical operators which connect two or more expressions
depending on the meaning of the operator. These are typically combined
with relational operators.</p>
<table>
<thead>
<tr>
<th>Operator</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code class="language-plaintext highlighter-rouge">|</code></td>
<td>OR</td>
</tr>
<tr>
<td><code class="language-plaintext highlighter-rouge">&</code></td>
<td>AND</td>
</tr>
<tr>
<td><code class="language-plaintext highlighter-rouge">!</code></td>
<td>NOT</td>
</tr>
</tbody>
</table>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#Is 3 greater than 1 and 5?</span><span class="w">
</span><span class="m">3</span><span class="w"> </span><span class="o">></span><span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="o">&</span><span class="w"> </span><span class="m">3</span><span class="w"> </span><span class="o">></span><span class="w"> </span><span class="m">5</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] FALSE
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#Is 3 greater than 1 or 5?</span><span class="w">
</span><span class="m">3</span><span class="w"> </span><span class="o">></span><span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="m">3</span><span class="w"> </span><span class="o">></span><span class="w"> </span><span class="m">5</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] TRUE
</code></pre></div></div>
<h2 id="user-written-functions">User-Written Functions</h2>
<p>We can build custom functions to perform operations that are not
pre-built in base R. Custom functions can include pre-built or other
custom functions. In fact, many pre-built functions are functions of
other pre-built functions.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">my_function</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span><span class="w"> </span><span class="n">arg2</span><span class="p">,</span><span class="n">...</span><span class="p">){</span><span class="w">
</span><span class="n">statements</span><span class="w">
</span><span class="nf">return</span><span class="p">(</span><span class="n">object</span><span class="p">)</span><span class="w">
</span><span class="p">}</span><span class="w">
</span></code></pre></div></div>
<p>Let’s make a function that finds the average of a numeric vector.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">average</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">numeric_vector</span><span class="p">){</span><span class="w">
</span><span class="n">out</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">sum</span><span class="p">(</span><span class="n">numeric_vector</span><span class="p">)</span><span class="o">/</span><span class="nf">length</span><span class="p">(</span><span class="n">numeric_vector</span><span class="p">)</span><span class="w">
</span><span class="nf">return</span><span class="p">(</span><span class="n">out</span><span class="p">)</span><span class="w">
</span><span class="p">}</span><span class="w">
</span></code></pre></div></div>
<p>Once we have created our function, you will see it under the <em>Functions</em>
section in the Environment tab.</p>
<p><img src="/images/functions.png" alt="" /></p>
<p>Let’s test our function below.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">average</span><span class="p">(</span><span class="nf">c</span><span class="p">(</span><span class="m">1</span><span class="p">,</span><span class="m">2</span><span class="p">,</span><span class="m">3</span><span class="p">))</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 2
</code></pre></div></div>
<p>What happens if we put a non-numeric vector?</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">average</span><span class="p">(</span><span class="nf">c</span><span class="p">(</span><span class="s1">'a'</span><span class="p">,</span><span class="w"> </span><span class="s1">'b'</span><span class="p">,</span><span class="w"> </span><span class="s1">'c'</span><span class="p">))</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## Error in sum(numeric_vector): invalid 'type' (character) of argument
</code></pre></div></div>
<p>While R has an error message for some cases, we can also build your own
error function with a custom message.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">average</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">numeric_vector</span><span class="p">){</span><span class="w">
</span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="nf">class</span><span class="p">(</span><span class="n">numeric_vector</span><span class="p">)</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="s1">'numeric'</span><span class="p">)</span><span class="w"> </span><span class="c1">#condition to throw the error</span><span class="w">
</span><span class="n">out</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="s1">'This is not a numeric vector'</span><span class="w"> </span><span class="c1">#The custom error message</span><span class="w">
</span><span class="k">else</span><span class="w">
</span><span class="n">out</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">sum</span><span class="p">(</span><span class="n">numeric_vector</span><span class="p">)</span><span class="o">/</span><span class="nf">length</span><span class="p">(</span><span class="n">numeric_vector</span><span class="p">)</span><span class="w">
</span><span class="nf">return</span><span class="p">(</span><span class="n">out</span><span class="p">)</span><span class="w">
</span><span class="p">}</span><span class="w">
</span></code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">average</span><span class="p">(</span><span class="nf">c</span><span class="p">(</span><span class="s1">'a'</span><span class="p">,</span><span class="w"> </span><span class="s1">'b'</span><span class="p">,</span><span class="w"> </span><span class="s1">'c'</span><span class="p">))</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] "This is not a numeric vector"
</code></pre></div></div>
<p>We can also view the code of any function by typing it without the <code class="language-plaintext highlighter-rouge">()</code>.
This can help you understand how a function works, especially if it
created by someone else.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">average</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## function(numeric_vector){
## if (class(numeric_vector) != 'numeric') #condition to throw the error
## out <- 'This is not a numeric vector' #The custom error message
## else
## out <- sum(numeric_vector)/length(numeric_vector)
## return(out)
## }
</code></pre></div></div>
<h2 id="best-practices">Best Practices</h2>
<ol>
<li>Document your code, thoughts, and decision making. Keep this in a
<code class="language-plaintext highlighter-rouge">.Rmd</code> or <code class="language-plaintext highlighter-rouge">.r</code> file. Use <code class="language-plaintext highlighter-rouge">#</code> for commenting. This will aid in
reproducible of your code for yourself and others.</li>
<li>Create and work inside an R project. This helps with code and data
organization.</li>
<li>Use informative naming for variables. Try to stay from <code class="language-plaintext highlighter-rouge">x</code>, <code class="language-plaintext highlighter-rouge">y</code>, or
similar.</li>
<li>Do create functions to simplify your code.</li>
<li>Practice and keep learning!</li>
</ol>
<h2 id="additional-resources">Additional Resources</h2>
<ul>
<li><a href="https://hbctraining.github.io/Training-modules/IntroR/lessons/01_Intro-to-R.html">Introduction to R and
Rstudio</a>:
Harvard Bioinformatics</li>
<li><a href="https://htmlpreview.github.io/?https://github.com/AlexsLemonade/training-modules/blob/master/intro-to-R-tidyverse/01-intro_to_base_R.nb.html">Introduction to R and
RStudio</a>:
Alex Lemonade Stand</li>
<li><a href="https://docs.ycrc.yale.edu/r-novice-gapminder/01-rstudio-intro/index.html">Introduction to R and
RStudio</a>:
Yale CRC</li>
</ul>Danh TruongWhat is R?K-means from scratch in R2022-01-28T00:00:00+00:002022-01-28T00:00:00+00:00https://danhdtruong.com/K-means-from-scratch-in-R<p>K-means is an unsupervised machine learning clustering algorithm. It can
be used to cluster a set of observations based on similarity between the
observations. K-means is one of the most popular clustering technique
and it is quite simple to understand.</p>
<h2 id="k-means-clustering-algorithm">K-means clustering algorithm</h2>
<p>The goal of this algorithm is to the find the optimal division of <code class="language-plaintext highlighter-rouge">n</code>
observations into <code class="language-plaintext highlighter-rouge">k</code> clusters, so that the total squared distance of
the group members to the cluster centroid is minimized.</p>
\[W(C_k) =\Sigma_{x_i\in C_k}(x_i-\mu_k)^2\]
<ul>
<li>$x_i$ is an observation assigned to cluster $C_k$</li>
<li>$\mu_k$ is the mean value of all observations assigned to cluster $C_k$ (i.e, the centroid)</li>
</ul>
<p>The K-means algorithm attempts to do the follow:</p>
<ol>
<li>Initialize <code class="language-plaintext highlighter-rouge">k</code> number of clusters.</li>
<li><code class="language-plaintext highlighter-rouge">k</code> number of observations are randomly selected to be the initial
centroids.</li>
<li>Determine the distance between observations and the centroids. This
can be done by a variety of distance metrics. A common one is the
Euclidean distance.</li>
<li>Assign each observation to the nearest centroid.</li>
<li>Recalculate new centroid position. This is done by updating the
centroid coordinates by taking the average of all values of each
observations that are part of the cluster.</li>
<li>Steps 3 - 5 are repeated iteratively until a maximum number of
iterations are reached or the observations no longer assigned to
another cluster.</li>
</ol>
<h2 id="computing-k-means-clustering">Computing k-means Clustering</h2>
<p>We can develop a simple K-means function using the above algorithm. Here
we have a data set <code class="language-plaintext highlighter-rouge">USArrests</code>, which contains statistics for arrests
per 100,000 residents in each state for either murder, assault, or rape.
In addition, the percentage of people living in urban areas is also
listed.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">library</span><span class="p">(</span><span class="n">ggplot2</span><span class="p">)</span><span class="w">
</span><span class="n">library</span><span class="p">(</span><span class="n">dplyr</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">library</span><span class="p">(</span><span class="n">tidyr</span><span class="p">)</span><span class="w">
</span><span class="n">data</span><span class="p">(</span><span class="s2">"USArrests"</span><span class="p">)</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">USArrests</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## Murder Assault UrbanPop Rape
## Alabama 13.2 236 58 21.2
## Alaska 10.0 263 48 44.5
## Arizona 8.1 294 80 31.0
## Arkansas 8.8 190 50 19.5
## California 9.0 276 91 40.6
## Colorado 7.9 204 78 38.7
</code></pre></div></div>
<p>The data is then scaled to standardize the values.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">USArrests_scaled</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">scale</span><span class="p">(</span><span class="n">USArrests</span><span class="p">)</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">USArrests_scaled</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## Murder Assault UrbanPop Rape
## Alabama 1.24256408 0.7828393 -0.5209066 -0.003416473
## Alaska 0.50786248 1.1068225 -1.2117642 2.484202941
## Arizona 0.07163341 1.4788032 0.9989801 1.042878388
## Arkansas 0.23234938 0.2308680 -1.0735927 -0.184916602
## California 0.27826823 1.2628144 1.7589234 2.067820292
## Colorado 0.02571456 0.3988593 0.8608085 1.864967207
</code></pre></div></div>
<p>We can use principal component analysis to generate a low-dimensional
representation of the graph.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">pca_USArrests</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">prcomp</span><span class="p">(</span><span class="n">USArrests_scaled</span><span class="p">,</span><span class="w"> </span><span class="n">scale.</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">F</span><span class="p">)</span><span class="w">
</span><span class="n">pca_USArrests_df</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">as.data.frame</span><span class="p">(</span><span class="n">pca_USArrests</span><span class="o">$</span><span class="n">x</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">dplyr</span><span class="o">::</span><span class="n">select</span><span class="p">(</span><span class="n">PC1</span><span class="p">,</span><span class="w"> </span><span class="n">PC2</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">cbind</span><span class="p">(</span><span class="n">States</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">rownames</span><span class="p">(</span><span class="n">USArrests</span><span class="p">))</span><span class="w">
</span><span class="n">ggplot</span><span class="p">(</span><span class="n">pca_USArrests_df</span><span class="p">,</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">PC1</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">PC2</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">geom_text</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">label</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">States</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">theme_classic</span><span class="p">()</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/k_means/unnamed-chunk-3-1.png" alt="" /></p>
<p>We can already see possible clusters or grouping of states with similar
statistics. Let’s start with an easy <code class="language-plaintext highlighter-rouge">k</code> value of <code class="language-plaintext highlighter-rouge">2</code>. We initialize <code class="language-plaintext highlighter-rouge">k</code>
and select for centroids.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">k</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">2</span><span class="w">
</span><span class="n">centroids</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sample.int</span><span class="p">(</span><span class="nf">dim</span><span class="p">(</span><span class="n">USArrests_scaled</span><span class="p">)[</span><span class="m">1</span><span class="p">],</span><span class="w"> </span><span class="n">k</span><span class="p">)</span><span class="w"> </span><span class="c1">#randomly select k integers from 1 to the length of the data.</span><span class="w">
</span><span class="n">centroid_points</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">USArrests_scaled</span><span class="p">[</span><span class="n">centroids</span><span class="p">,]</span><span class="w"> </span><span class="o">%>%</span><span class="w"> </span><span class="n">as.matrix</span><span class="p">()</span><span class="w"> </span><span class="c1">#use the selected integers as indices and select for them in the data frame.</span><span class="w">
</span><span class="n">centroid_points</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## Murder Assault UrbanPop Rape
## Oregon -0.6630682 -0.1411127 0.1008652 0.8613783
## Tennessee 1.2425641 0.2068693 -0.4518209 0.6051428
</code></pre></div></div>
<p>Next, we use a distance metric to compare the observation and the
centroids. This will result in a matrix that gauges dissimilarity.
Observations that are further apart from the centroid and less likely to
be part of that cluster. Choice of distance metric will affect the
formation of the clusters. Here we choose the Euclidean distance:</p>
\[d_{euc}(x,y) =\sqrt{\Sigma_{i=1}^{n}(x_i-y_i)^2}\]
<ul>
<li>
<p>$n$ is the number of observations</p>
</li>
<li>
<p>$y_i$ is the value of the centroid</p>
</li>
</ul>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">dataPoints</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">as.matrix</span><span class="p">(</span><span class="w"> </span><span class="n">USArrests_scaled</span><span class="p">)</span><span class="w">
</span><span class="n">dist_mat</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">matrix</span><span class="p">(</span><span class="m">0</span><span class="p">,</span><span class="w"> </span><span class="n">nrow</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">nrow</span><span class="p">(</span><span class="n">dataPoints</span><span class="p">),</span><span class="w"> </span><span class="n">ncol</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">k</span><span class="p">)</span><span class="w"> </span><span class="c1">#initialize an empty matrix</span><span class="w">
</span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">j</span><span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="n">k</span><span class="p">)</span><span class="w">
</span><span class="p">{</span><span class="w">
</span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">i</span><span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="n">nrow</span><span class="p">(</span><span class="n">dataPoints</span><span class="p">))</span><span class="w">
</span><span class="p">{</span><span class="w">
</span><span class="n">dist_mat</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">sqrt</span><span class="p">(</span><span class="nf">sum</span><span class="p">((</span><span class="n">dataPoints</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="m">1</span><span class="o">:</span><span class="n">ncol</span><span class="p">(</span><span class="n">dataPoints</span><span class="p">)]</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">centroid_points</span><span class="p">[</span><span class="n">j</span><span class="p">,</span><span class="m">1</span><span class="o">:</span><span class="n">ncol</span><span class="p">(</span><span class="n">centroid_points</span><span class="p">)])</span><span class="o">^</span><span class="m">2</span><span class="p">))</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">dist_mat</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [,1] [,2]
## [1,] 2.370568 0.8407489
## [2,] 2.699070 2.3362541
## [3,] 2.000866 2.2989846
## [4,] 1.847763 1.4254486
## [5,] 2.657402 3.0119267
## [6,] 1.533198 2.1972111
</code></pre></div></div>
<p>The cluster for each observation is chosen by the centroid with the
smallest distance to the observation. We can use the <code class="language-plaintext highlighter-rouge">which.min()</code>
function.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">cluster</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">factor</span><span class="p">(</span><span class="n">apply</span><span class="p">(</span><span class="n">dist_mat</span><span class="p">,</span><span class="w"> </span><span class="m">1</span><span class="p">,</span><span class="w"> </span><span class="n">which.min</span><span class="p">))</span><span class="w"> </span><span class="c1">#selects the column index with the smallest distance</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">cluster</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 2 2 1 2 1 1
## Levels: 1 2
</code></pre></div></div>
<p>Recall that we are minimizing the squared Euclidean distances between
the observation and the assigned centroid. This is also the
within-cluster sum of squares (WCSS).</p>
\[W(C_k) =\Sigma_{x_i\in C_k}(x_i-\mu_k)^2\]
<p>We define a total within-cluster sum of squares (total_WCSS) which
measures the compactness of the clustering. Minimizing this value
results in tighter clusters.</p>
\[totalWCSS =\Sigma^k_{k=1}W(C_k) =\Sigma^k_{k=1}\Sigma_{x_i\in C_k}(x_i-\mu_k)^2\]
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">dist_mat_cluster</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">list</span><span class="p">()</span><span class="w">
</span><span class="k">for</span><span class="p">(</span><span class="n">i</span><span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="n">k</span><span class="p">){</span><span class="w">
</span><span class="n">dist_mat_cluster</span><span class="p">[[</span><span class="n">i</span><span class="p">]]</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">dist_mat</span><span class="p">[</span><span class="n">which</span><span class="p">(</span><span class="n">cluster</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">i</span><span class="p">),</span><span class="n">i</span><span class="p">]</span><span class="o">^</span><span class="m">2</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="n">within_cluster_ss</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">unlist</span><span class="p">(</span><span class="n">lapply</span><span class="p">(</span><span class="n">dist_mat_cluster</span><span class="p">,</span><span class="w"> </span><span class="n">sum</span><span class="p">))</span><span class="w">
</span><span class="n">cat</span><span class="p">(</span><span class="s1">'Within-cluster sum of squares:'</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## Within-cluster sum of squares:
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">within_cluster_ss</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 133.74617 54.87014
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">total_WCSS</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">sum</span><span class="p">(</span><span class="n">within_cluster_ss</span><span class="p">)</span><span class="w">
</span><span class="n">cat</span><span class="p">(</span><span class="s1">'\nTotal within-cluster sum of squares:'</span><span class="p">,</span><span class="w"> </span><span class="n">total_WCSS</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>##
## Total within-cluster sum of squares: 188.6163
</code></pre></div></div>
<p>Using the PCA graph, we can observe how our clusters look and where the
initial centroids are located.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">pca_USArrests_df</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">as.data.frame</span><span class="p">(</span><span class="n">pca_USArrests</span><span class="o">$</span><span class="n">x</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">dplyr</span><span class="o">::</span><span class="n">select</span><span class="p">(</span><span class="n">PC1</span><span class="p">,</span><span class="w"> </span><span class="n">PC2</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">cbind</span><span class="p">(</span><span class="n">States</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">rownames</span><span class="p">(</span><span class="n">USArrests</span><span class="p">))</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">cbind</span><span class="p">(</span><span class="n">Clusters</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cluster</span><span class="p">)</span><span class="w">
</span><span class="n">centroid_points_unscaled</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">apply</span><span class="p">(</span><span class="n">centroid_points</span><span class="p">,</span><span class="w"> </span><span class="m">1</span><span class="p">,</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="w">
</span><span class="p">{</span><span class="w"> </span><span class="n">x</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">pca_USArrests</span><span class="o">$</span><span class="n">scale</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">pca_USArrests</span><span class="o">$</span><span class="n">center</span><span class="p">})</span><span class="w"> </span><span class="o">%>%</span><span class="w"> </span><span class="n">t</span><span class="p">()</span><span class="w">
</span><span class="n">rownames</span><span class="p">(</span><span class="n">centroid_points_unscaled</span><span class="p">)</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="m">1</span><span class="o">:</span><span class="n">k</span><span class="p">)</span><span class="w">
</span><span class="n">centroid_coord</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">predict</span><span class="p">(</span><span class="n">pca_USArrests</span><span class="p">,</span><span class="w"> </span><span class="n">centroid_points_unscaled</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w"> </span><span class="n">as.data.frame</span><span class="p">()</span><span class="w"> </span><span class="c1"># adding the centroid coordinates</span><span class="w">
</span><span class="n">ggplot</span><span class="p">(</span><span class="n">pca_USArrests_df</span><span class="p">,</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">PC1</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">PC2</span><span class="p">,))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">geom_text</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">label</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">States</span><span class="p">,</span><span class="w"> </span><span class="n">color</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Clusters</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="c1"># labeling the centroids</span><span class="w">
</span><span class="n">geom_point</span><span class="p">(</span><span class="n">data</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">centroid_coord</span><span class="p">,</span><span class="w">
</span><span class="n">mapping</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">PC1</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">PC2</span><span class="p">,</span><span class="w"> </span><span class="n">color</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">rownames</span><span class="p">(</span><span class="n">centroid_coord</span><span class="p">)),</span><span class="w">
</span><span class="n">size</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">3</span><span class="p">)</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">theme_classic</span><span class="p">()</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/k_means/unnamed-chunk-8-1.png" alt="" /></p>
<p>As you can see, the clustering is not that great since we have only
initialized the algorithm and had randomly selected <code class="language-plaintext highlighter-rouge">k</code> observations as
centroids. The next step is to form new centroids and iteratively assign
observations to clusters until a maximum number of iterations are
reached or the observations no longer assigned to another cluster.</p>
<p>We can generate new centroid values by taking the mean of all values of
each observations that are part of the cluster</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">new_centroid</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">USArrests_scaled</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">as.data.frame</span><span class="p">()</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">cbind</span><span class="p">(</span><span class="n">Clusters</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cluster</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">group_by</span><span class="p">(</span><span class="n">Clusters</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">summarise_all</span><span class="p">(</span><span class="n">mean</span><span class="p">)</span><span class="w">
</span><span class="n">new_centroid</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## # A tibble: 2 × 5
## Clusters Murder Assault UrbanPop Rape
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 1 -0.629 -0.517 0.0620 -0.328
## 2 2 1.12 0.919 -0.110 0.584
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">centroid_points</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">new_centroid</span><span class="p">[,</span><span class="m">-1</span><span class="p">]</span><span class="w"> </span><span class="o">%>%</span><span class="w"> </span><span class="n">as.matrix</span><span class="p">()</span><span class="w">
</span></code></pre></div></div>
<h2 id="creating-a-k-means-function">Creating a k-means function</h2>
<p>Rather than repeating the code over and over, we can write a function
that will do it for us.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">k_means_</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="n">k</span><span class="p">,</span><span class="w"> </span><span class="n">iters</span><span class="p">){</span><span class="w">
</span><span class="c1">#initialize random centroids</span><span class="w">
</span><span class="n">centroids</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sample.int</span><span class="p">(</span><span class="nf">dim</span><span class="p">(</span><span class="n">df</span><span class="p">)[</span><span class="m">1</span><span class="p">],</span><span class="w"> </span><span class="n">k</span><span class="p">)</span><span class="w">
</span><span class="n">centroid_points</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">df</span><span class="p">[</span><span class="n">centroids</span><span class="p">,]</span><span class="w"> </span><span class="o">%>%</span><span class="w"> </span><span class="n">as.matrix</span><span class="p">()</span><span class="w">
</span><span class="n">dataPoints</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">as.matrix</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="w">
</span><span class="c1">#initialize WCSS</span><span class="w">
</span><span class="n">within_cluster_ss</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">c</span><span class="p">()</span><span class="w">
</span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">i</span><span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="n">iters</span><span class="p">){</span><span class="w">
</span><span class="n">dist_mat</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">matrix</span><span class="p">(</span><span class="m">0</span><span class="p">,</span><span class="w"> </span><span class="n">nrow</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">nrow</span><span class="p">(</span><span class="n">dataPoints</span><span class="p">),</span><span class="w"> </span><span class="n">ncol</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">k</span><span class="p">)</span><span class="w">
</span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">j</span><span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="n">k</span><span class="p">)</span><span class="w">
</span><span class="p">{</span><span class="w">
</span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">i</span><span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="n">nrow</span><span class="p">(</span><span class="n">dataPoints</span><span class="p">))</span><span class="w">
</span><span class="p">{</span><span class="w">
</span><span class="n">dist_mat</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">sqrt</span><span class="p">(</span><span class="nf">sum</span><span class="p">((</span><span class="n">dataPoints</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="m">1</span><span class="o">:</span><span class="n">ncol</span><span class="p">(</span><span class="n">dataPoints</span><span class="p">)]</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">centroid_points</span><span class="p">[</span><span class="n">j</span><span class="p">,</span><span class="m">1</span><span class="o">:</span><span class="n">ncol</span><span class="p">(</span><span class="n">centroid_points</span><span class="p">)])</span><span class="o">^</span><span class="m">2</span><span class="p">))</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="n">cluster</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">factor</span><span class="p">(</span><span class="n">apply</span><span class="p">(</span><span class="n">dist_mat</span><span class="p">,</span><span class="w"> </span><span class="m">1</span><span class="p">,</span><span class="w"> </span><span class="n">which.min</span><span class="p">))</span><span class="w">
</span><span class="n">dist_mat_cluster</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">list</span><span class="p">()</span><span class="w">
</span><span class="k">for</span><span class="p">(</span><span class="n">i</span><span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="n">k</span><span class="p">){</span><span class="w">
</span><span class="n">dist_mat_cluster</span><span class="p">[[</span><span class="n">i</span><span class="p">]]</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">dist_mat</span><span class="p">[</span><span class="n">which</span><span class="p">(</span><span class="n">cluster</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">i</span><span class="p">),</span><span class="n">i</span><span class="p">]</span><span class="o">^</span><span class="m">2</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="n">within_cluster_ss_temp</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">unlist</span><span class="p">(</span><span class="n">lapply</span><span class="p">(</span><span class="n">dist_mat_cluster</span><span class="p">,</span><span class="w"> </span><span class="n">sum</span><span class="p">))</span><span class="w">
</span><span class="n">within_cluster_ss</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">append</span><span class="p">(</span><span class="n">within_cluster_ss</span><span class="p">,</span><span class="w"> </span><span class="n">within_cluster_ss_temp</span><span class="p">)</span><span class="w">
</span><span class="n">new_centroid</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">as.data.frame</span><span class="p">()</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">cbind</span><span class="p">(</span><span class="n">Clusters</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cluster</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">group_by</span><span class="p">(</span><span class="n">Clusters</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">summarise_all</span><span class="p">(</span><span class="n">mean</span><span class="p">)</span><span class="w">
</span><span class="n">centroid_points</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">new_centroid</span><span class="p">[,</span><span class="m">-1</span><span class="p">]</span><span class="w"> </span><span class="o">%>%</span><span class="w"> </span><span class="n">as.matrix</span><span class="p">()</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="n">within_cluster_ss</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">t</span><span class="p">(</span><span class="n">array</span><span class="p">(</span><span class="n">within_cluster_ss</span><span class="p">,</span><span class="w"> </span><span class="n">dim</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="n">k</span><span class="p">,</span><span class="w"> </span><span class="n">iters</span><span class="p">)))</span><span class="w">
</span><span class="nf">return</span><span class="p">(</span><span class="nf">list</span><span class="p">(</span><span class="n">Cluster</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cluster</span><span class="p">,</span><span class="w">
</span><span class="n">WCSS</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">within_cluster_ss</span><span class="p">))</span><span class="w">
</span><span class="p">}</span><span class="w">
</span></code></pre></div></div>
<p>We use the same parameters as before and pass our variables into our new
function <code class="language-plaintext highlighter-rouge">k_means_(</code>)`.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">iters</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">10</span><span class="w">
</span><span class="n">k</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">2</span><span class="w">
</span><span class="n">USArrests_scaled</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">scale</span><span class="p">(</span><span class="n">USArrests</span><span class="p">)</span><span class="w">
</span><span class="n">k_means</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">k_means_</span><span class="p">(</span><span class="n">USArrests_scaled</span><span class="p">,</span><span class="w"> </span><span class="n">k</span><span class="p">,</span><span class="w"> </span><span class="n">iters</span><span class="p">)</span><span class="w">
</span><span class="n">k_means</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## $Cluster
## [1] 2 2 2 1 2 2 1 1 2 2 1 1 2 1 1 1 1 2 1 2 1 2 1 2 2 1 1 2 1 1 2 2 2 1 1 1 1 1
## [39] 1 2 1 2 2 1 1 1 1 1 1 1
## Levels: 1 2
##
## $WCSS
## [,1] [,2]
## [1,] 147.75655 82.14443
## [2,] 64.39358 50.06885
## [3,] 56.22017 46.82608
## [4,] 56.11445 46.74796
## [5,] 56.11445 46.74796
## [6,] 56.11445 46.74796
## [7,] 56.11445 46.74796
## [8,] 56.11445 46.74796
## [9,] 56.11445 46.74796
## [10,] 56.11445 46.74796
</code></pre></div></div>
<p>The total WCSS is minimized after reaching the maximum iterations.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">df</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">rowSums</span><span class="p">(</span><span class="n">k_means</span><span class="o">$</span><span class="n">WCSS</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">as.data.frame</span><span class="p">()</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">cbind</span><span class="p">(</span><span class="n">iter</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="m">1</span><span class="o">:</span><span class="n">iters</span><span class="p">))</span><span class="w">
</span><span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="n">.</span><span class="p">,</span><span class="w"> </span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">iter</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">geom_line</span><span class="p">()</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">labs</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s1">'Iteration'</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s1">'Total WCSS'</span><span class="p">)</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">theme_classic</span><span class="p">()</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/k_means/unnamed-chunk-12-1.png" alt="" /></p>
<p>Using the PCA graph, we can observe how our clusters look after reaching
the maximum number of iterations.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">pca_USArrests_df</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">as.data.frame</span><span class="p">(</span><span class="n">pca_USArrests</span><span class="o">$</span><span class="n">x</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">dplyr</span><span class="o">::</span><span class="n">select</span><span class="p">(</span><span class="n">PC1</span><span class="p">,</span><span class="w"> </span><span class="n">PC2</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">cbind</span><span class="p">(</span><span class="n">States</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">rownames</span><span class="p">(</span><span class="n">USArrests</span><span class="p">))</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">cbind</span><span class="p">(</span><span class="n">Clusters</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">k_means</span><span class="o">$</span><span class="n">Cluster</span><span class="p">)</span><span class="w">
</span><span class="n">ggplot</span><span class="p">(</span><span class="n">pca_USArrests_df</span><span class="p">,</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">PC1</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">PC2</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">geom_text</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">label</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">States</span><span class="p">,</span><span class="w"> </span><span class="n">color</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Clusters</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">theme_classic</span><span class="p">()</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/k_means/unnamed-chunk-13-1.png" alt="" /></p>
<p>We can also observe the clustering with a scatter plot of two features
like <code class="language-plaintext highlighter-rouge">UrbanPop</code> and <code class="language-plaintext highlighter-rouge">Murder</code>.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">USArrests_df</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">USArrests_scaled</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">as.data.frame</span><span class="p">()</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">cbind</span><span class="p">(</span><span class="n">States</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">rownames</span><span class="p">(</span><span class="n">USArrests</span><span class="p">))</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">cbind</span><span class="p">(</span><span class="n">Clusters</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">k_means</span><span class="o">$</span><span class="n">Cluster</span><span class="p">)</span><span class="w">
</span><span class="n">ggplot</span><span class="p">(</span><span class="n">USArrests_df</span><span class="p">,</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">UrbanPop</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Murder</span><span class="p">,))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">geom_text</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">label</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">States</span><span class="p">,</span><span class="w"> </span><span class="n">color</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Clusters</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">theme_classic</span><span class="p">()</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/k_means/unnamed-chunk-14-1.png" alt="" /></p>
<h2 id="determining-the-optimal-number-of-clusters">Determining the optimal number of clusters</h2>
<p>Initially, we looked at 2 possible clusters. We can test out different
numbers for <code class="language-plaintext highlighter-rouge">k</code>. Let’s repeat the process but with 2,3,4, and 5
clusters. The results are below:</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">k_means_test</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">lapply</span><span class="p">(</span><span class="nf">c</span><span class="p">(</span><span class="m">2</span><span class="o">:</span><span class="m">5</span><span class="p">),</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">k</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="n">k_means_</span><span class="p">(</span><span class="n">USArrests_scaled</span><span class="p">,</span><span class="w"> </span><span class="n">k</span><span class="p">,</span><span class="w"> </span><span class="n">iters</span><span class="p">)})</span><span class="w">
</span><span class="n">cluster_list</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">lapply</span><span class="p">(</span><span class="n">k_means_test</span><span class="p">,</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="w"> </span><span class="n">x</span><span class="p">[[</span><span class="m">1</span><span class="p">]])</span><span class="w">
</span><span class="nf">names</span><span class="p">(</span><span class="n">cluster_list</span><span class="p">)</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">paste</span><span class="p">(</span><span class="s1">'k ='</span><span class="p">,</span><span class="nf">c</span><span class="p">(</span><span class="m">2</span><span class="o">:</span><span class="m">5</span><span class="p">))</span><span class="w">
</span><span class="n">cluster_list_df</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">do.call</span><span class="p">(</span><span class="n">cbind</span><span class="p">,</span><span class="w"> </span><span class="n">cluster_list</span><span class="p">)</span><span class="w">
</span><span class="n">pca_USArrests_df</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">as.data.frame</span><span class="p">(</span><span class="n">pca_USArrests</span><span class="o">$</span><span class="n">x</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">dplyr</span><span class="o">::</span><span class="n">select</span><span class="p">(</span><span class="n">PC1</span><span class="p">,</span><span class="w"> </span><span class="n">PC2</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">cbind</span><span class="p">(</span><span class="n">States</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">rownames</span><span class="p">(</span><span class="n">USArrests</span><span class="p">))</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">cbind</span><span class="p">(</span><span class="n">cluster_list_df</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w">
</span><span class="n">pivot_longer</span><span class="p">(</span><span class="n">cols</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">names</span><span class="p">(</span><span class="n">cluster_list</span><span class="p">))</span><span class="w">
</span><span class="n">ggplot</span><span class="p">(</span><span class="n">pca_USArrests_df</span><span class="p">,</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">PC1</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">PC2</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">geom_point</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">shape</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">factor</span><span class="p">(</span><span class="n">value</span><span class="p">),</span><span class="w"> </span><span class="n">color</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">factor</span><span class="p">(</span><span class="n">value</span><span class="p">)))</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">facet_wrap</span><span class="p">(</span><span class="o">~</span><span class="n">name</span><span class="p">)</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">labs</span><span class="p">(</span><span class="n">color</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"Cluster"</span><span class="p">,</span><span class="w"> </span><span class="n">shape</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"Cluster"</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/k_means/unnamed-chunk-15-1.png" alt="" /></p>
<p>Of course we can continue testing additional values of <code class="language-plaintext highlighter-rouge">k</code>. However, it
may be more advantageous to determine the optimal <code class="language-plaintext highlighter-rouge">k</code> value based on the
total within-cluster sum of squares. Recall that this value must be
minimized to find the optimal cluster assignments. We can also use this
to determine the optimal <code class="language-plaintext highlighter-rouge">k</code>.</p>
<ol>
<li>Compute the k-means cluster for different values for <code class="language-plaintext highlighter-rouge">k</code>. For
instance, we can vary <code class="language-plaintext highlighter-rouge">k</code> from 1 to 10.</li>
<li>For each value of <code class="language-plaintext highlighter-rouge">k</code>, the total within-cluster sum of squares.</li>
<li>Plot the the total within-cluster sum of squares against each value
of <code class="language-plaintext highlighter-rouge">k</code>.</li>
<li>Determine the location of a bend in the plot. This typically
indicates the optimal value for <code class="language-plaintext highlighter-rouge">k</code>.</li>
</ol>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">k_means_test</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">lapply</span><span class="p">(</span><span class="nf">c</span><span class="p">(</span><span class="m">1</span><span class="o">:</span><span class="m">10</span><span class="p">),</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">k</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="n">k_means_</span><span class="p">(</span><span class="n">USArrests_scaled</span><span class="p">,</span><span class="w"> </span><span class="n">k</span><span class="p">,</span><span class="w"> </span><span class="n">iters</span><span class="p">)})</span><span class="w">
</span><span class="n">WCSS_list</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">lapply</span><span class="p">(</span><span class="n">k_means_test</span><span class="p">,</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="w"> </span><span class="n">x</span><span class="p">[[</span><span class="m">2</span><span class="p">]][</span><span class="n">iters</span><span class="p">,])</span><span class="w">
</span><span class="n">total_WCSS_list</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">lapply</span><span class="p">(</span><span class="n">WCSS_list</span><span class="p">,</span><span class="w"> </span><span class="n">sum</span><span class="p">)</span><span class="w">
</span><span class="n">df</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">data.frame</span><span class="p">(</span><span class="n">Y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">unlist</span><span class="p">(</span><span class="n">total_WCSS_list</span><span class="p">),</span><span class="w"> </span><span class="n">X</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="m">1</span><span class="o">:</span><span class="m">10</span><span class="p">))</span><span class="w">
</span><span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">X</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Y</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">geom_line</span><span class="p">()</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">geom_point</span><span class="p">()</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">labs</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s1">'k clusters'</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s1">'Total WCSS'</span><span class="p">)</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">scale_x_continuous</span><span class="p">(</span><span class="n">breaks</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="m">1</span><span class="o">:</span><span class="m">10</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">theme_classic</span><span class="p">()</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/k_means/unnamed-chunk-16-1.png" alt="" /></p>
<p>The optimal <code class="language-plaintext highlighter-rouge">k</code> look to be either 4 or 5. As you can see, k-means
clustering is simple and quick. One caveat is choosing the number of
clusters. Another is the random initialization of centroids. This could
slow down the algorithm in very large data sets. One possible
improvement is to generate different initial centroids and select the
set that has the smallest total within-cluster sum of squares.</p>
<h1 id="additional-resources">Additional resources</h1>
<ul>
<li><a href="https://www.section.io/engineering-education/k-means-from-scratch-r/">K-means from Scratch
R</a></li>
<li><a href="https://uc-r.github.io/kmeans_clustering">K means Clustering</a></li>
<li><a href="https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Clustering/K-Means">Data Mining Algorithms In
R/Clustering/K-Means</a></li>
</ul>Danh TruongK-means is an unsupervised machine learning clustering algorithm. It can be used to cluster a set of observations based on similarity between the observations. K-means is one of the most popular clustering technique and it is quite simple to understand.Conditional Probability with R2022-01-20T00:00:00+00:002022-01-20T00:00:00+00:00https://danhdtruong.com/Conditional-Probability-with-R<p>In this post, we will explore conditional probability and how it relates
to Bayes’ Theorem.</p>
<h2 id="conditional-probability">Conditional Probability</h2>
<p>Conditional probability is the likelihood of an event or outcome
occurring based on the occurrence of a previous event or outcome. Below
is the equation for the conditional probability.</p>
<p>$P(A|B) = \frac{P(A \cap B) }{P(B)}$</p>
<p>where:</p>
<p><em>P</em>(<em>A</em> ∩ <em>B</em>) is the probability that event A and event B both occur.</p>
<p><em>P</em>(<em>B</em>) is the probability that event B occurs.</p>
<p>For example:</p>
<ul>
<li>Event A is a person completing their projects on time in a year.
There is 85% chance that this person will complete their work on
time.</li>
<li>Event B is that this person receiving a raise at the end of the
year. Raises are only given to to 45% of those that complete their
work on time in a year.</li>
<li>P(complete work and raise) = P(raise|complete work) P(complete
work) = 0.45 * 0.85 = 0.3825</li>
</ul>
<p>Looks like working hard does pay off. Conditional probability implies
there is a relationship between these two events, such as the
probability as completing your work on time in a year, and receiving a
raise. In other words, conditional probability depends on a previous
result. In this case, it is the probability of completing your work on
time.</p>
<p>Let’s take a look at a survey given to male and female students and
asking what their favorite past times are. Below a prepared a function
<code class="language-plaintext highlighter-rouge">rng_survey()</code> to generate the survey answers.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#helper function for our survey</span><span class="w">
</span><span class="n">rng_survey</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">max</span><span class="p">,</span><span class="w"> </span><span class="n">n</span><span class="p">){</span><span class="w">
</span><span class="c1">#max is the maximum number of surveys for a participant group (ie. Male)</span><span class="w">
</span><span class="c1">#n is the number of possible answers</span><span class="w">
</span><span class="n">total</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">0</span><span class="w">
</span><span class="k">while</span><span class="w"> </span><span class="p">(</span><span class="n">total</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="n">max</span><span class="p">){</span><span class="w">
</span><span class="n">x</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">sample</span><span class="p">(</span><span class="m">1</span><span class="o">:</span><span class="n">max</span><span class="p">,</span><span class="w"> </span><span class="n">n</span><span class="p">,</span><span class="w"> </span><span class="n">replace</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="kc">TRUE</span><span class="p">)</span><span class="w">
</span><span class="n">total</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">sum</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="nf">return</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="w">
</span><span class="p">}</span><span class="w">
</span></code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#male = rng_survey(150,4)</span><span class="w">
</span><span class="c1">#female = rng_survey(150,4)</span><span class="w">
</span><span class="c1">#cat("male answers:", male, "\n") </span><span class="w">
</span><span class="c1">#cat("female answers:", female, "\n") </span><span class="w">
</span><span class="c1">#male answers: 7 19 26 98 </span><span class="w">
</span><span class="c1">#female answers: 7 69 40 34 </span><span class="w">
</span></code></pre></div></div>
<p>Using the function, we create a data frame with the survey answers.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#create data frame for the survey responses</span><span class="w">
</span><span class="n">df</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">data.frame</span><span class="p">(</span><span class="n">gender</span><span class="o">=</span><span class="nf">rep</span><span class="p">(</span><span class="nf">c</span><span class="p">(</span><span class="s1">'Male'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Female'</span><span class="p">),</span><span class="w"> </span><span class="n">each</span><span class="o">=</span><span class="m">150</span><span class="p">),</span><span class="w">
</span><span class="n">sport</span><span class="o">=</span><span class="nf">rep</span><span class="p">(</span><span class="nf">c</span><span class="p">(</span><span class="s1">'Exercise'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Cooking'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Reading'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Television'</span><span class="p">,</span><span class="w">
</span><span class="s1">'Exercise'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Cooking'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Reading'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Television'</span><span class="p">),</span><span class="w">
</span><span class="n">times</span><span class="o">=</span><span class="nf">c</span><span class="p">(</span><span class="m">7</span><span class="p">,</span><span class="w"> </span><span class="m">19</span><span class="p">,</span><span class="w"> </span><span class="m">26</span><span class="p">,</span><span class="w"> </span><span class="m">98</span><span class="p">,</span><span class="w"> </span><span class="m">7</span><span class="p">,</span><span class="w"> </span><span class="m">69</span><span class="p">,</span><span class="w"> </span><span class="m">40</span><span class="p">,</span><span class="w"> </span><span class="m">34</span><span class="w"> </span><span class="p">)))</span><span class="w">
</span><span class="n">df</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## gender sport
## 1 Male Exercise
## 2 Male Exercise
## 3 Male Exercise
## 4 Male Exercise
## 5 Male Exercise
## 6 Male Exercise
## 7 Male Exercise
## 8 Male Cooking
## 9 Male Cooking
## 10 Male Cooking
## 11 Male Cooking
## 12 Male Cooking
## 13 Male Cooking
## 14 Male Cooking
## 15 Male Cooking
## 16 Male Cooking
## 17 Male Cooking
## 18 Male Cooking
## 19 Male Cooking
## 20 Male Cooking
## 21 Male Cooking
## 22 Male Cooking
## 23 Male Cooking
## 24 Male Cooking
## 25 Male Cooking
## 26 Male Cooking
## 27 Male Reading
## 28 Male Reading
## 29 Male Reading
## 30 Male Reading
## 31 Male Reading
## 32 Male Reading
## 33 Male Reading
## 34 Male Reading
## 35 Male Reading
## 36 Male Reading
## 37 Male Reading
## 38 Male Reading
## 39 Male Reading
## 40 Male Reading
## 41 Male Reading
## 42 Male Reading
## 43 Male Reading
## 44 Male Reading
## 45 Male Reading
## 46 Male Reading
## 47 Male Reading
## 48 Male Reading
## 49 Male Reading
## 50 Male Reading
## 51 Male Reading
## 52 Male Reading
## 53 Male Television
## 54 Male Television
## 55 Male Television
## 56 Male Television
## 57 Male Television
## 58 Male Television
## 59 Male Television
## 60 Male Television
## 61 Male Television
## 62 Male Television
## 63 Male Television
## 64 Male Television
## 65 Male Television
## 66 Male Television
## 67 Male Television
## 68 Male Television
## 69 Male Television
## 70 Male Television
## 71 Male Television
## 72 Male Television
## 73 Male Television
## 74 Male Television
## 75 Male Television
## 76 Male Television
## 77 Male Television
## 78 Male Television
## 79 Male Television
## 80 Male Television
## 81 Male Television
## 82 Male Television
## 83 Male Television
## 84 Male Television
## 85 Male Television
## 86 Male Television
## 87 Male Television
## 88 Male Television
## 89 Male Television
## 90 Male Television
## 91 Male Television
## 92 Male Television
## 93 Male Television
## 94 Male Television
## 95 Male Television
## 96 Male Television
## 97 Male Television
## 98 Male Television
## 99 Male Television
## 100 Male Television
## 101 Male Television
## 102 Male Television
## 103 Male Television
## 104 Male Television
## 105 Male Television
## 106 Male Television
## 107 Male Television
## 108 Male Television
## 109 Male Television
## 110 Male Television
## 111 Male Television
## 112 Male Television
## 113 Male Television
## 114 Male Television
## 115 Male Television
## 116 Male Television
## 117 Male Television
## 118 Male Television
## 119 Male Television
## 120 Male Television
## 121 Male Television
## 122 Male Television
## 123 Male Television
## 124 Male Television
## 125 Male Television
## 126 Male Television
## 127 Male Television
## 128 Male Television
## 129 Male Television
## 130 Male Television
## 131 Male Television
## 132 Male Television
## 133 Male Television
## 134 Male Television
## 135 Male Television
## 136 Male Television
## 137 Male Television
## 138 Male Television
## 139 Male Television
## 140 Male Television
## 141 Male Television
## 142 Male Television
## 143 Male Television
## 144 Male Television
## 145 Male Television
## 146 Male Television
## 147 Male Television
## 148 Male Television
## 149 Male Television
## 150 Male Television
## 151 Female Exercise
## 152 Female Exercise
## 153 Female Exercise
## 154 Female Exercise
## 155 Female Exercise
## 156 Female Exercise
## 157 Female Exercise
## 158 Female Cooking
## 159 Female Cooking
## 160 Female Cooking
## 161 Female Cooking
## 162 Female Cooking
## 163 Female Cooking
## 164 Female Cooking
## 165 Female Cooking
## 166 Female Cooking
## 167 Female Cooking
## 168 Female Cooking
## 169 Female Cooking
## 170 Female Cooking
## 171 Female Cooking
## 172 Female Cooking
## 173 Female Cooking
## 174 Female Cooking
## 175 Female Cooking
## 176 Female Cooking
## 177 Female Cooking
## 178 Female Cooking
## 179 Female Cooking
## 180 Female Cooking
## 181 Female Cooking
## 182 Female Cooking
## 183 Female Cooking
## 184 Female Cooking
## 185 Female Cooking
## 186 Female Cooking
## 187 Female Cooking
## 188 Female Cooking
## 189 Female Cooking
## 190 Female Cooking
## 191 Female Cooking
## 192 Female Cooking
## 193 Female Cooking
## 194 Female Cooking
## 195 Female Cooking
## 196 Female Cooking
## 197 Female Cooking
## 198 Female Cooking
## 199 Female Cooking
## 200 Female Cooking
## 201 Female Cooking
## 202 Female Cooking
## 203 Female Cooking
## 204 Female Cooking
## 205 Female Cooking
## 206 Female Cooking
## 207 Female Cooking
## 208 Female Cooking
## 209 Female Cooking
## 210 Female Cooking
## 211 Female Cooking
## 212 Female Cooking
## 213 Female Cooking
## 214 Female Cooking
## 215 Female Cooking
## 216 Female Cooking
## 217 Female Cooking
## 218 Female Cooking
## 219 Female Cooking
## 220 Female Cooking
## 221 Female Cooking
## 222 Female Cooking
## 223 Female Cooking
## 224 Female Cooking
## 225 Female Cooking
## 226 Female Cooking
## 227 Female Reading
## 228 Female Reading
## 229 Female Reading
## 230 Female Reading
## 231 Female Reading
## 232 Female Reading
## 233 Female Reading
## 234 Female Reading
## 235 Female Reading
## 236 Female Reading
## 237 Female Reading
## 238 Female Reading
## 239 Female Reading
## 240 Female Reading
## 241 Female Reading
## 242 Female Reading
## 243 Female Reading
## 244 Female Reading
## 245 Female Reading
## 246 Female Reading
## 247 Female Reading
## 248 Female Reading
## 249 Female Reading
## 250 Female Reading
## 251 Female Reading
## 252 Female Reading
## 253 Female Reading
## 254 Female Reading
## 255 Female Reading
## 256 Female Reading
## 257 Female Reading
## 258 Female Reading
## 259 Female Reading
## 260 Female Reading
## 261 Female Reading
## 262 Female Reading
## 263 Female Reading
## 264 Female Reading
## 265 Female Reading
## 266 Female Reading
## 267 Female Television
## 268 Female Television
## 269 Female Television
## 270 Female Television
## 271 Female Television
## 272 Female Television
## 273 Female Television
## 274 Female Television
## 275 Female Television
## 276 Female Television
## 277 Female Television
## 278 Female Television
## 279 Female Television
## 280 Female Television
## 281 Female Television
## 282 Female Television
## 283 Female Television
## 284 Female Television
## 285 Female Television
## 286 Female Television
## 287 Female Television
## 288 Female Television
## 289 Female Television
## 290 Female Television
## 291 Female Television
## 292 Female Television
## 293 Female Television
## 294 Female Television
## 295 Female Television
## 296 Female Television
## 297 Female Television
## 298 Female Television
## 299 Female Television
## 300 Female Television
</code></pre></div></div>
<p>We convert the data frame to a table.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#create two-way table from data frame</span><span class="w">
</span><span class="n">survey_data</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">addmargins</span><span class="p">(</span><span class="n">table</span><span class="p">(</span><span class="n">df</span><span class="o">$</span><span class="n">gender</span><span class="p">,</span><span class="w"> </span><span class="n">df</span><span class="o">$</span><span class="n">sport</span><span class="p">))</span><span class="w">
</span><span class="n">survey_data</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>##
## Cooking Exercise Reading Television Sum
## Female 69 7 40 34 150
## Male 19 7 26 98 150
## Sum 88 14 66 132 300
</code></pre></div></div>
<p>We can extract information from our table by calling a row and a column.
For instance, let’s ask for the number of males that prefer cooking.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">survey_data</span><span class="p">[</span><span class="s1">'Male'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Cooking'</span><span class="p">]</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 19
</code></pre></div></div>
<p>Now we can ask the probability of being male given that they prefer
cooking. We know that the probability of being male is <code class="language-plaintext highlighter-rouge">0.5</code>. We can
calculate the rest from the table.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">P_male</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">0.5</span><span class="w">
</span><span class="n">P_cooking_male</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">survey_data</span><span class="p">[</span><span class="s1">'Male'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Cooking'</span><span class="p">]</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">survey_data</span><span class="p">[</span><span class="s1">'Male'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Sum'</span><span class="p">]</span><span class="w"> </span><span class="c1">#probability of only males that prefer cooking</span><span class="w">
</span><span class="n">P_male_P_cooking_male</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">P_male</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">P_cooking_male</span><span class="w">
</span><span class="n">P_cooking</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">survey_data</span><span class="p">[</span><span class="s1">'Sum'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Cooking'</span><span class="p">]</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">survey_data</span><span class="p">[</span><span class="s1">'Sum'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Sum'</span><span class="p">]</span><span class="w"> </span><span class="c1">#probability of male and female that prefers cooking</span><span class="w">
</span><span class="n">P_male_cooking</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">P_male_P_cooking_male</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">P_cooking</span><span class="w">
</span><span class="n">P_male_cooking</span><span class="w"> </span><span class="c1">#probability of being male given they prefer cooking</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 0.2159091
</code></pre></div></div>
<p>Alternatively, we can use the table to easily answer the same problem.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">survey_data</span><span class="p">[</span><span class="s1">'Male'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Cooking'</span><span class="p">]</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">survey_data</span><span class="p">[</span><span class="s1">'Sum'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Cooking'</span><span class="p">]</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 0.2159091
</code></pre></div></div>
<p>Next, we can ask the probability of being female given that they prefer
reading.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">survey_data</span><span class="p">[</span><span class="s1">'Female'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Reading'</span><span class="p">]</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">survey_data</span><span class="p">[</span><span class="s1">'Sum'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Reading'</span><span class="p">]</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 0.6060606
</code></pre></div></div>
<h2 id="bayes-theorem">Bayes’ Theorem</h2>
<p>Suppose that we have the same survey but male and female was not
recorded by accident. How could we solve the probability of being male
given that they prefer cooking? Let us assume from a previous survey we
knew that 12.67% of males prefer to cook or is the probability of
preferring to cook given that they are male. We can use something called
Bayes’ Theorem,</p>
<p>$P(A|B) = \frac{P(B|A) P(A) }{P(B)}$.</p>
<p>Bayes’ Theorem describes the probability of an event based on prior
knowledge of conditions that may be related to the event.</p>
<p>Knowing that the equation for conditional probability is</p>
<p>$P(A|B) = \frac{P(A \cap B) }{P(B)}$</p>
<p>then</p>
<p><em>P</em>(<em>A</em> ∩ <em>B</em>) = <em>P</em>(<em>A</em>|<em>B</em>)<em>P</em>(<em>B</em>)</p>
<p>and</p>
<p><em>P</em>(<em>A</em> ∩ <em>B</em>) = <em>P</em>(<em>B</em>|<em>A</em>)<em>P</em>(<em>A</em>).</p>
<p>We can solve for <em>P</em>(<em>A</em> ∩ <em>B</em>) with substitution to yield</p>
<p>$P(A|B) = \frac{P(B|A)P(A)}{P(B)}$.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">#view table</span><span class="w">
</span><span class="n">survey_data</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>##
## Cooking Exercise Reading Television Sum
## Female 69 7 40 34 150
## Male 19 7 26 98 150
## Sum 88 14 66 132 300
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">P_male</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">0.5</span><span class="w">
</span><span class="n">P_cooking_male</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">0.1267</span><span class="w"> </span><span class="c1">#probability of prefering to cook given that they are male</span><span class="w">
</span><span class="n">P_cooking</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">survey_data</span><span class="p">[</span><span class="s1">'Sum'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Cooking'</span><span class="p">]</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">survey_data</span><span class="p">[</span><span class="s1">'Sum'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Sum'</span><span class="p">]</span><span class="w"> </span><span class="c1">#probability of male and female that prefers cooking</span><span class="w">
</span><span class="n">P_male_cooking</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="n">P_cooking_male</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">P_male</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">P_cooking</span><span class="w">
</span><span class="n">P_male_cooking</span><span class="w"> </span><span class="c1">#probability of being male given they prefer cooking</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 0.2159659
</code></pre></div></div>
<p>Let’s try to solve for the reverse, the probability of preferring to
cook given that they are male, using Bayes’ Theorem.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">P_male_cooking</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">P_cooking</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">P_male</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 0.1267
</code></pre></div></div>
<p>Is there a way to solve for the probability of preferring to cook given
that they are female using Bayes’ Theorem?</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">P_female</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">P_male</span><span class="w"> </span><span class="c1">#We have a binary choice and the probabilities sum up to 1</span><span class="w">
</span><span class="n">P_female_cooking</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">P_male_cooking</span><span class="w">
</span><span class="n">P_female_cooking</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">P_cooking</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">P_female</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 0.4599667
</code></pre></div></div>
<p>Looking back at the table, we can use Bayes’ Theorem to solve this
problem.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">survey_data</span><span class="p">[</span><span class="s1">'Female'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Cooking'</span><span class="p">]</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">survey_data</span><span class="p">[</span><span class="s1">'Female'</span><span class="p">,</span><span class="w"> </span><span class="s1">'Sum'</span><span class="p">]</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 0.46
</code></pre></div></div>
<h2 id="additional-resources">Additional Resources</h2>
<ul>
<li><a href="https://districtdatalabs.silvrback.com/conditional-probability-with-r">Conditional Probability with
R</a></li>
<li><a href="https://www.analyticsvidhya.com/blog/2017/03/conditional-probability-bayes-theorem/">Introduction to Conditional Probability and Bayes theorem in R for
data science
professionals</a></li>
</ul>Danh TruongIn this post, we will explore conditional probability and how it relates to Bayes’ Theorem.Exploratory Data Analysis in R2022-01-14T00:00:00+00:002022-01-14T00:00:00+00:00https://danhdtruong.com/Exploratory-Data-Analysis-in-R<p>In this post, we will be analyzing data as if we were looking at it for
the first time. This is called ‘Exploratory Data Analysis’. We will
visualize the data and gather insights from the <code class="language-plaintext highlighter-rouge">iris</code> data set.</p>
<h2 id="exploring-the-data">Exploring the data</h2>
<p>First, let’s load up the data and have a brief look at it. Using
<code class="language-plaintext highlighter-rouge">class()</code>, we can see that the data set is a <code class="language-plaintext highlighter-rouge">data.frame</code>.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">data</span><span class="p">(</span><span class="n">iris</span><span class="p">)</span><span class="w">
</span><span class="nf">class</span><span class="p">(</span><span class="n">iris</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] "data.frame"
</code></pre></div></div>
<p>Using <code class="language-plaintext highlighter-rouge">head()</code>, we can take a look at the first 5 rows. This may be
helpful when analyzing large data sets.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">head</span><span class="p">(</span><span class="n">iris</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
</code></pre></div></div>
<p>There are 4 columns that are made up of numbers and one with strings.
The fifth column <code class="language-plaintext highlighter-rouge">Species</code> suggests that each row corresponds to a data
from a single sample of that species. We can also use <code class="language-plaintext highlighter-rouge">dim()</code> to find
the full dimensions of the data.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nf">dim</span><span class="p">(</span><span class="n">iris</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 150 5
</code></pre></div></div>
<p>This tells us that there are 150 rows and 5 columns in this data frame.
It is also good practice to use the <code class="language-plaintext highlighter-rouge">str</code> function to briefly look at
the structure of the data.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">str</span><span class="p">(</span><span class="n">iris</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
</code></pre></div></div>
<p>Again, this confirmed our brief look earlier that there are 4 columns of
numeric values. We can also see that the fifth column is actually a
<code class="language-plaintext highlighter-rouge">Factor</code> with 3 levels. Using <code class="language-plaintext highlighter-rouge">summmary()</code>, we can acquire some insight
on the data distribution.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">summary</span><span class="p">(</span><span class="n">iris</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
</code></pre></div></div>
<h2 id="data-visualization">Data Visualization</h2>
<p>We can start by looking at the data point distribution of <code class="language-plaintext highlighter-rouge">Sepal.Length</code>
between the three species. We use the library <code class="language-plaintext highlighter-rouge">ggplot2</code> and create a
scatter plot with <code class="language-plaintext highlighter-rouge">geom_point()</code></p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">library</span><span class="p">(</span><span class="n">ggplot2</span><span class="p">)</span><span class="w">
</span><span class="n">ggplot</span><span class="p">(</span><span class="n">iris</span><span class="p">,</span><span class="w"> </span><span class="n">mapping</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Sepal.Length</span><span class="p">,</span><span class="w"> </span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Species</span><span class="p">,</span><span class="w"> </span><span class="n">color</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Species</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">geom_point</span><span class="p">()</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/EDA_files/figure-markdown_github/unnamed-chunk-6-1.png" alt="" /></p>
<p>It is tough to see the distribution in this manner, but it does look
like there may be differences between the <code class="language-plaintext highlighter-rouge">Species</code>. Using
<code class="language-plaintext highlighter-rouge">geom_histrogram()</code> we can build a distribution based on the number of
observation in each bin. In this case, the x-axis will be divided into
30 equally spaced bins with values between the minimum and maximum
<code class="language-plaintext highlighter-rouge">Sepal.Length</code>. This method works well for continuous variables.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">ggplot</span><span class="p">(</span><span class="n">iris</span><span class="p">,</span><span class="w"> </span><span class="n">mapping</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Sepal.Length</span><span class="p">,</span><span class="w"> </span><span class="n">fill</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Species</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">geom_histogram</span><span class="p">()</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
</code></pre></div></div>
<p><img src="/images/EDA_files/figure-markdown_github/unnamed-chunk-7-1.png" alt="" /></p>
<p>Let’s try using a box plot, also called box and whiskers, to look at the
distribution of <code class="language-plaintext highlighter-rouge">Sepal.Length</code> between the three species. A box plot
summarizes the data with five numbers:</p>
<ul>
<li>Minimum</li>
<li>First Quartile</li>
<li>Median</li>
<li>Third Quartile</li>
<li>Maximum</li>
</ul>
<p>Typically, the rectangle spans the first and third quartile of the data
set, which is also known as the interquartile range (IQR). The line in
the middle denotes the median, and the whiskers above and below denote
the maximum and minimum respectively. Outliers are also shown as data
points that fall 1.5 times the IQR from either edge of the box.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">ggplot</span><span class="p">(</span><span class="n">iris</span><span class="p">,</span><span class="w"> </span><span class="n">mapping</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Sepal.Length</span><span class="p">,</span><span class="w"> </span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Species</span><span class="p">,</span><span class="w"> </span><span class="n">fill</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Species</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">geom_boxplot</span><span class="p">()</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/EDA_files/figure-markdown_github/unnamed-chunk-8-1.png" alt="" /></p>
<p>We can also plot multiple graphs using <code class="language-plaintext highlighter-rouge">facet_wrap</code>, but first we need
to reshape the data frame in the <code class="language-plaintext highlighter-rouge">long</code> format.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">library</span><span class="p">(</span><span class="n">reshape2</span><span class="p">)</span><span class="w">
</span><span class="n">iris_melt</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">melt</span><span class="p">(</span><span class="n">iris</span><span class="p">,</span><span class="w"> </span><span class="n">id</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s1">'Species'</span><span class="p">)</span><span class="w"> </span><span class="c1">#reshaping the dataframe</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">iris_melt</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## Species variable value
## 1 setosa Sepal.Length 5.1
## 2 setosa Sepal.Length 4.9
## 3 setosa Sepal.Length 4.7
## 4 setosa Sepal.Length 4.6
## 5 setosa Sepal.Length 5.0
## 6 setosa Sepal.Length 5.4
</code></pre></div></div>
<p>Now the data is in a <code class="language-plaintext highlighter-rouge">long</code> format where each row provides a sample id
<code class="language-plaintext highlighter-rouge">Species</code>, a variable, like <code class="language-plaintext highlighter-rouge">Sepal.Length</code>, and the corresponding value.
We can use <code class="language-plaintext highlighter-rouge">facet_wrap</code> to generate multiple plots.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">ggplot</span><span class="p">(</span><span class="n">iris_melt</span><span class="p">,</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Species</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">value</span><span class="p">,</span><span class="w"> </span><span class="n">fill</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Species</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">geom_boxplot</span><span class="p">()</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">facet_wrap</span><span class="p">(</span><span class="o">~</span><span class="n">variable</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/EDA_files/figure-markdown_github/unnamed-chunk-10-1.png" alt="" /></p>
<h2 id="correlations">Correlations</h2>
<p>Interestingly, it looks like there is a correlation for some of the
variables. We can explore this by generating a correlation matrix.
First, we will exclude the fifth column since that is non-numeric. Next,
we will use the function <code class="language-plaintext highlighter-rouge">cor()</code>.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">iris_cor</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">cor</span><span class="p">(</span><span class="n">iris</span><span class="p">[,</span><span class="m">-5</span><span class="p">],</span><span class="n">iris</span><span class="p">[,</span><span class="m">-5</span><span class="p">])</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">iris_cor</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Sepal.Length 1.0000000 -0.1175698 0.8717538 0.8179411
## Sepal.Width -0.1175698 1.0000000 -0.4284401 -0.3661259
## Petal.Length 0.8717538 -0.4284401 1.0000000 0.9628654
## Petal.Width 0.8179411 -0.3661259 0.9628654 1.0000000
</code></pre></div></div>
<p>It seems that all the variables except for <code class="language-plaintext highlighter-rouge">Sepal.Width</code> correlate.
Let’s visualize this with <code class="language-plaintext highlighter-rouge">ggplot()</code>. We will need to reshape our data
first. We will also add a new color scale since the default color scale
isn’t too useful.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">iris_cor_melt</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">melt</span><span class="p">(</span><span class="n">iris_cor</span><span class="p">)</span><span class="w"> </span><span class="c1">#reshaping the data</span><span class="w">
</span><span class="n">ggplot</span><span class="p">(</span><span class="n">iris_cor_melt</span><span class="p">,</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Var1</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Var2</span><span class="p">,</span><span class="w"> </span><span class="n">fill</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">value</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">geom_tile</span><span class="p">(</span><span class="n">color</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s1">'white'</span><span class="p">)</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="c1">#geom_tile generates tiles which are colored based on a value</span><span class="w">
</span><span class="n">scale_fill_gradient2</span><span class="p">(</span><span class="n">low</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"blue"</span><span class="p">,</span><span class="w"> </span><span class="n">high</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"red"</span><span class="p">,</span><span class="w"> </span><span class="n">mid</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"white"</span><span class="p">,</span><span class="w">
</span><span class="n">midpoint</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">0</span><span class="p">,</span><span class="w"> </span><span class="n">limit</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="m">-1</span><span class="p">,</span><span class="m">1</span><span class="p">),</span><span class="w"> </span><span class="n">space</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"Lab"</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/EDA_files/figure-markdown_github/unnamed-chunk-12-1.png" alt="" /></p>
<p>It doesn’t look too pretty. We can generate a helper function to reorder
the matrix by correlation distance. This way we can easily see the
variables that highly correlate.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">reorder_cormat</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">cormat</span><span class="p">){</span><span class="w">
</span><span class="c1"># Use correlation between variables as distance</span><span class="w">
</span><span class="n">dd</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">as.dist</span><span class="p">((</span><span class="m">1</span><span class="o">-</span><span class="n">cormat</span><span class="p">)</span><span class="o">/</span><span class="m">2</span><span class="p">)</span><span class="w">
</span><span class="n">hc</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">hclust</span><span class="p">(</span><span class="n">dd</span><span class="p">)</span><span class="w">
</span><span class="n">cormat</span><span class="w"> </span><span class="o"><-</span><span class="n">cormat</span><span class="p">[</span><span class="n">hc</span><span class="o">$</span><span class="n">order</span><span class="p">,</span><span class="w"> </span><span class="n">hc</span><span class="o">$</span><span class="n">order</span><span class="p">]</span><span class="w">
</span><span class="p">}</span><span class="w">
</span></code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">iris_cor_ordered</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">reorder_cormat</span><span class="p">(</span><span class="n">iris_cor</span><span class="p">)</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">iris_cor_ordered</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## Sepal.Width Sepal.Length Petal.Length Petal.Width
## Sepal.Width 1.0000000 -0.1175698 -0.4284401 -0.3661259
## Sepal.Length -0.1175698 1.0000000 0.8717538 0.8179411
## Petal.Length -0.4284401 0.8717538 1.0000000 0.9628654
## Petal.Width -0.3661259 0.8179411 0.9628654 1.0000000
</code></pre></div></div>
<p>Now we reshape the data and use <code class="language-plaintext highlighter-rouge">ggplot</code> to visualize the ordered
correlation matrix.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">iris_cor_ordered_melt</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">melt</span><span class="p">(</span><span class="n">iris_cor_ordered</span><span class="p">,</span><span class="w"> </span><span class="n">na.rm</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="kc">TRUE</span><span class="p">)</span><span class="w">
</span><span class="n">ggplot</span><span class="p">(</span><span class="n">iris_cor_ordered_melt</span><span class="p">,</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Var1</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Var2</span><span class="p">,</span><span class="w"> </span><span class="n">fill</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">value</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">geom_tile</span><span class="p">(</span><span class="n">color</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s1">'white'</span><span class="p">)</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">scale_fill_gradient2</span><span class="p">(</span><span class="n">low</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"blue"</span><span class="p">,</span><span class="w"> </span><span class="n">high</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"red"</span><span class="p">,</span><span class="w"> </span><span class="n">mid</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"white"</span><span class="p">,</span><span class="w">
</span><span class="n">midpoint</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">0</span><span class="p">,</span><span class="w"> </span><span class="n">limit</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="m">-1</span><span class="p">,</span><span class="m">1</span><span class="p">),</span><span class="w"> </span><span class="n">space</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"Lab"</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/EDA_files/figure-markdown_github/unnamed-chunk-15-1.png" alt="" /></p>
<p>This can also be done using <code class="language-plaintext highlighter-rouge">pheatmap</code> with the added benefit of
pre-built ordering and cluster trees.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">library</span><span class="p">(</span><span class="n">pheatmap</span><span class="p">)</span><span class="w">
</span><span class="n">pheatmap</span><span class="p">(</span><span class="n">iris_cor</span><span class="p">,</span><span class="w">
</span><span class="n">color</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">colorRampPalette</span><span class="p">(</span><span class="nf">c</span><span class="p">(</span><span class="s1">'blue'</span><span class="p">,</span><span class="w"> </span><span class="s1">'white'</span><span class="p">,</span><span class="w"> </span><span class="s1">'red'</span><span class="p">))(</span><span class="m">100</span><span class="p">),</span><span class="w">
</span><span class="n">breaks</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">seq</span><span class="p">(</span><span class="m">-1</span><span class="p">,</span><span class="m">1</span><span class="p">,</span><span class="w"> </span><span class="n">length.out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">100</span><span class="p">))</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/EDA_files/figure-markdown_github/unnamed-chunk-16-1.png" alt="" /></p>
<h2 id="statistical-testing">Statistical Testing</h2>
<p>Based on the data, it looks like <code class="language-plaintext highlighter-rouge">virginica</code> has the longest
<code class="language-plaintext highlighter-rouge">Sepal.Length</code>. We can test for this using basic statistics. Let’s
perform a simple Student’s t-Test between <code class="language-plaintext highlighter-rouge">virginica</code> and <code class="language-plaintext highlighter-rouge">setosa</code>. We
will use the reshaped data frame and remove the <code class="language-plaintext highlighter-rouge">versicolor</code> species.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">iris_subset_melt</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">subset</span><span class="p">(</span><span class="n">iris_melt</span><span class="p">,</span><span class="w"> </span><span class="n">subset</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Species</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="s1">'virginica'</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="n">Species</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="s1">'setosa'</span><span class="w"> </span><span class="p">)</span><span class="w"> </span><span class="c1">#removing versicolor so we have the two groups we are comparing</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">iris_subset_melt</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## Species variable value
## 1 setosa Sepal.Length 5.1
## 2 setosa Sepal.Length 4.9
## 3 setosa Sepal.Length 4.7
## 4 setosa Sepal.Length 4.6
## 5 setosa Sepal.Length 5.0
## 6 setosa Sepal.Length 5.4
</code></pre></div></div>
<p>Let’s do some more data wrangling to set up our data. Essentially, we
will create two numeric vectors corresponding to <code class="language-plaintext highlighter-rouge">Sepal.Length</code> for both
species.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">setosa_sepal_length</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">subset</span><span class="p">(</span><span class="n">iris_subset_melt</span><span class="p">,</span><span class="w">
</span><span class="n">subset</span><span class="w"> </span><span class="o">=</span><span class="w">
</span><span class="n">variable</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="s1">'Sepal.Length'</span><span class="w"> </span><span class="o">&</span><span class="w">
</span><span class="n">Species</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="s1">'setosa'</span><span class="p">)</span><span class="o">$</span><span class="n">value</span><span class="w"> </span><span class="c1">#selecting only values for setosa</span><span class="w">
</span><span class="n">virginica_sepal_length</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">subset</span><span class="p">(</span><span class="n">iris_subset_melt</span><span class="p">,</span><span class="w">
</span><span class="n">subset</span><span class="w"> </span><span class="o">=</span><span class="w">
</span><span class="n">variable</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="s1">'Sepal.Length'</span><span class="w"> </span><span class="o">&</span><span class="w">
</span><span class="n">Species</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="s1">'virginica'</span><span class="p">)</span><span class="o">$</span><span class="n">value</span><span class="w"> </span><span class="c1">#selecting only values for virginica</span><span class="w">
</span></code></pre></div></div>
<p>With that, we are ready to perform the Student’s t-Test for two samples.
Essentially, we are comparing the means of two groups with a single
variable.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">t.test</span><span class="p">(</span><span class="n">setosa_sepal_length</span><span class="p">,</span><span class="w"> </span><span class="n">virginica_sepal_length</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>##
## Welch Two Sample t-test
##
## data: setosa_sepal_length and virginica_sepal_length
## t = -15.386, df = 76.516, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.78676 -1.37724
## sample estimates:
## mean of x mean of y
## 5.006 6.588
</code></pre></div></div>
<p>The graph already suggested that the two species were different based on
the <code class="language-plaintext highlighter-rouge">Petal.Length</code>. Here the <code class="language-plaintext highlighter-rouge">p-value</code> is less than 2.2e-16, where a
typical alpha is 0.05. Therefore, the null hypothesis where there is no
difference is rejected.</p>
<p>If we want to continue performing more comparisons, we will run into the
multiple testing problem. In other words, if we keep testing different
variables, we will eventually find a difference. Since, we are expecting
a 5% chance of incorrectly rejecting the null hypothesis, then
performing 100 multiple comparisons will result in 5 incorrect
rejections or false positives. A simple way to correct for this is
<a href="https://mathworld.wolfram.com/BonferroniCorrection.html">Bonferroni
correction</a>,
which just divides the alpha by the number of total comparisons. There
are additional methods, which you can read more
<a href="https://www.biochemia-medica.com/en/journal/21/3/10.11613/BM.2011.029/fullArticle">here</a>.</p>
<p>To compare multiple groups, we will perform a One-Way ANOVA. While
t-tests compare only two groups, an ANOVA can compare three or more
groups. One more note is that an ANOVA only tests if one or more mean is
different. Post-hoc comparison test with multiple comparison correction
will be needed to find which pairwise comparison was statically
different.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">iris_anova</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">aov</span><span class="p">(</span><span class="n">Sepal.Width</span><span class="w"> </span><span class="o">~</span><span class="w"> </span><span class="n">Species</span><span class="p">,</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">iris</span><span class="p">)</span><span class="w">
</span><span class="n">summary</span><span class="p">(</span><span class="n">iris_anova</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## Df Sum Sq Mean Sq F value Pr(>F)
## Species 2 11.35 5.672 49.16 <2e-16 ***
## Residuals 147 16.96 0.115
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
</code></pre></div></div>
<p>There is a difference between <code class="language-plaintext highlighter-rouge">Species</code> just on <code class="language-plaintext highlighter-rouge">Sepal.Length</code> alone. It
is significant with a p-value <2e-16. However, we do not know which
direction or between which <code class="language-plaintext highlighter-rouge">Species</code>. We can use a pairwise t-test to
find out. First, we will try without any multiple testing correction.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">pairwise.t.test</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">iris</span><span class="o">$</span><span class="n">Sepal.Length</span><span class="p">,</span><span class="w"> </span><span class="n">g</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">iris</span><span class="o">$</span><span class="n">Species</span><span class="p">,</span><span class="w"> </span><span class="n">p.adj</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s1">'none'</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>##
## Pairwise comparisons using t tests with pooled SD
##
## data: iris$Sepal.Length and iris$Species
##
## setosa versicolor
## versicolor 8.8e-16 -
## virginica < 2e-16 2.8e-09
##
## P value adjustment method: none
</code></pre></div></div>
<p>Now with we can try with Bonferroni correction.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">pairwise.t.test</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">iris</span><span class="o">$</span><span class="n">Sepal.Length</span><span class="p">,</span><span class="w"> </span><span class="n">g</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">iris</span><span class="o">$</span><span class="n">Species</span><span class="p">,</span><span class="w"> </span><span class="n">p.adj</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s1">'bonf'</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>##
## Pairwise comparisons using t tests with pooled SD
##
## data: iris$Sepal.Length and iris$Species
##
## setosa versicolor
## versicolor 2.6e-15 -
## virginica < 2e-16 8.3e-09
##
## P value adjustment method: bonferroni
</code></pre></div></div>
<p>The result is still the same, but it may not always be the case. See
<a href="https://benwhalley.github.io/just-enough-r/multiple-comparisons.html">here</a>.</p>
<p>If we wanted to plot the resulting p-values, then we can use the package
<code class="language-plaintext highlighter-rouge">ggpubr</code>.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">library</span><span class="p">(</span><span class="n">ggpubr</span><span class="p">)</span><span class="w">
</span><span class="n">ggplot</span><span class="p">(</span><span class="n">iris</span><span class="p">,</span><span class="w"> </span><span class="n">mapping</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Sepal.Length</span><span class="p">,</span><span class="w"> </span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Species</span><span class="p">,</span><span class="w"> </span><span class="n">fill</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Species</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">geom_boxplot</span><span class="p">()</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">stat_compare_means</span><span class="p">()</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/EDA_files/figure-markdown_github/unnamed-chunk-23-1.png" alt="" /></p>
<p>As you can see, we can plot the p-value from an ANOVA directly onto the
plot, which only tells you that there is a difference but not between
which pair of <code class="language-plaintext highlighter-rouge">Species</code>. To perform the pairwise comparisons, you have
to manually generate a <code class="language-plaintext highlighter-rouge">list</code> of pairwise comparisons.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">my_comparisons</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">list</span><span class="p">(</span><span class="nf">c</span><span class="p">(</span><span class="s1">'setosa'</span><span class="p">,</span><span class="s1">'virginica'</span><span class="p">),</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="s1">'setosa'</span><span class="p">,</span><span class="s1">'versicolor'</span><span class="p">),</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="s1">'versicolor'</span><span class="p">,</span><span class="s1">'virginica'</span><span class="p">))</span><span class="w">
</span><span class="n">ggplot</span><span class="p">(</span><span class="n">iris</span><span class="p">,</span><span class="w"> </span><span class="n">mapping</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Sepal.Length</span><span class="p">,</span><span class="w"> </span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Species</span><span class="p">,</span><span class="w"> </span><span class="n">fill</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Species</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">geom_boxplot</span><span class="p">()</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">stat_compare_means</span><span class="p">(</span><span class="n">comparisons</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">my_comparisons</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/EDA_files/figure-markdown_github/unnamed-chunk-24-1.png" alt="" /></p>
<p>We can also repeat this with all variables as well. It can get a bit
messy so we will switch the p-value to “p.signif”. This essentially uses
the <code class="language-plaintext highlighter-rouge">*</code> symbol for the following:</p>
<ul>
<li>ns: p > 0.05</li>
<li>*: p <= 0.05</li>
<li>**: p <= 0.01</li>
<li>***: p <= 0.001</li>
<li>****: p <= 0.0001</li>
</ul>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">ggplot</span><span class="p">(</span><span class="n">iris_melt</span><span class="p">,</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Species</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">value</span><span class="p">,</span><span class="w"> </span><span class="n">fill</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Species</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">geom_boxplot</span><span class="p">()</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">facet_wrap</span><span class="p">(</span><span class="o">~</span><span class="n">variable</span><span class="p">)</span><span class="w"> </span><span class="o">+</span><span class="w">
</span><span class="n">stat_compare_means</span><span class="p">(</span><span class="n">comparisons</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">my_comparisons</span><span class="p">,</span><span class="w"> </span><span class="n">label</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s1">'p.signif'</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/EDA_files/figure-markdown_github/unnamed-chunk-25-1.png" alt="" /></p>
<h2 id="additional-resources">Additional Resources</h2>
<ul>
<li><a href="https://r4ds.had.co.nz/exploratory-data-analysis.html">R for Data Science: Exploratory Data
Analysis</a></li>
<li><a href="https://statsandr.com/blog/how-to-do-a-t-test-or-anova-for-many-variables-at-once-in-r-and-communicate-the-results-in-a-better-way/#anova">How
to do a t-test or ANOVA for more than one variable at once in
R</a></li>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6813708/">Application of Student’s t-test, Analysis of Variance, and
Covariance</a></li>
</ul>Danh TruongIn this post, we will be analyzing data as if we were looking at it for the first time. This is called ‘Exploratory Data Analysis’. We will visualize the data and gather insights from the iris data set.Text mining using TidyText2021-04-25T00:00:00+00:002021-04-25T00:00:00+00:00https://danhdtruong.com/Text-mining-using-TidyText<html>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
<script>/*! jQuery v1.11.3 | (c) 2005, 2015 jQuery Foundation, Inc. | jquery.org/license */
!function(a,b){"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(a)}("undefined"!=typeof window?window:this,function(a,b){var c=[],d=c.slice,e=c.concat,f=c.push,g=c.indexOf,h={},i=h.toString,j=h.hasOwnProperty,k={},l="1.11.3",m=function(a,b){return new m.fn.init(a,b)},n=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,o=/^-ms-/,p=/-([\da-z])/gi,q=function(a,b){return b.toUpperCase()};m.fn=m.prototype={jquery:l,constructor:m,selector:"",length:0,toArray:function(){return d.call(this)},get:function(a){return null!=a?0>a?this[a+this.length]:this[a]:d.call(this)},pushStack:function(a){var b=m.merge(this.constructor(),a);return b.prevObject=this,b.context=this.context,b},each:function(a,b){return m.each(this,a,b)},map:function(a){return this.pushStack(m.map(this,function(b,c){return a.call(b,c,b)}))},slice:function(){return this.pushStack(d.apply(this,arguments))},first:function(){return this.eq(0)},last:function(){return this.eq(-1)},eq:function(a){var b=this.length,c=+a+(0>a?b:0);return this.pushStack(c>=0&&b>c?[this[c]]:[])},end:function(){return this.prevObject||this.constructor(null)},push:f,sort:c.sort,splice:c.splice},m.extend=m.fn.extend=function(){var a,b,c,d,e,f,g=arguments[0]||{},h=1,i=arguments.length,j=!1;for("boolean"==typeof g&&(j=g,g=arguments[h]||{},h++),"object"==typeof g||m.isFunction(g)||(g={}),h===i&&(g=this,h--);i>h;h++)if(null!=(e=arguments[h]))for(d in e)a=g[d],c=e[d],g!==c&&(j&&c&&(m.isPlainObject(c)||(b=m.isArray(c)))?(b?(b=!1,f=a&&m.isArray(a)?a:[]):f=a&&m.isPlainObject(a)?a:{},g[d]=m.extend(j,f,c)):void 0!==c&&(g[d]=c));return g},m.extend({expando:"jQuery"+(l+Math.random()).replace(/\D/g,""),isReady:!0,error:function(a){throw new Error(a)},noop:function(){},isFunction:function(a){return"function"===m.type(a)},isArray:Array.isArray||function(a){return"array"===m.type(a)},isWindow:function(a){return null!=a&&a==a.window},isNumeric:function(a){return!m.isArray(a)&&a-parseFloat(a)+1>=0},isEmptyObject:function(a){var b;for(b in a)return!1;return!0},isPlainObject:function(a){var b;if(!a||"object"!==m.type(a)||a.nodeType||m.isWindow(a))return!1;try{if(a.constructor&&!j.call(a,"constructor")&&!j.call(a.constructor.prototype,"isPrototypeOf"))return!1}catch(c){return!1}if(k.ownLast)for(b in a)return j.call(a,b);for(b in a);return void 0===b||j.call(a,b)},type:function(a){return null==a?a+"":"object"==typeof a||"function"==typeof a?h[i.call(a)]||"object":typeof a},globalEval:function(b){b&&m.trim(b)&&(a.execScript||function(b){a.eval.call(a,b)})(b)},camelCase:function(a){return a.replace(o,"ms-").replace(p,q)},nodeName:function(a,b){return a.nodeName&&a.nodeName.toLowerCase()===b.toLowerCase()},each:function(a,b,c){var d,e=0,f=a.length,g=r(a);if(c){if(g){for(;f>e;e++)if(d=b.apply(a[e],c),d===!1)break}else for(e in a)if(d=b.apply(a[e],c),d===!1)break}else if(g){for(;f>e;e++)if(d=b.call(a[e],e,a[e]),d===!1)break}else for(e in a)if(d=b.call(a[e],e,a[e]),d===!1)break;return a},trim:function(a){return null==a?"":(a+"").replace(n,"")},makeArray:function(a,b){var c=b||[];return null!=a&&(r(Object(a))?m.merge(c,"string"==typeof a?[a]:a):f.call(c,a)),c},inArray:function(a,b,c){var d;if(b){if(g)return g.call(b,a,c);for(d=b.length,c=c?0>c?Math.max(0,d+c):c:0;d>c;c++)if(c in b&&b[c]===a)return c}return-1},merge:function(a,b){var c=+b.length,d=0,e=a.length;while(c>d)a[e++]=b[d++];if(c!==c)while(void 0!==b[d])a[e++]=b[d++];return a.length=e,a},grep:function(a,b,c){for(var d,e=[],f=0,g=a.length,h=!c;g>f;f++)d=!b(a[f],f),d!==h&&e.push(a[f]);return e},map:function(a,b,c){var d,f=0,g=a.length,h=r(a),i=[];if(h)for(;g>f;f++)d=b(a[f],f,c),null!=d&&i.push(d);else for(f in a)d=b(a[f],f,c),null!=d&&i.push(d);return e.apply([],i)},guid:1,proxy:function(a,b){var c,e,f;return"string"==typeof b&&(f=a[b],b=a,a=f),m.isFunction(a)?(c=d.call(arguments,2),e=function(){return a.apply(b||this,c.concat(d.call(arguments)))},e.guid=a.guid=a.guid||m.guid++,e):void 0},now:function(){return+new Date},support:k}),m.each("Boolean Number String Function Array Date RegExp Object Error".split(" "),function(a,b){h["[object "+b+"]"]=b.toLowerCase()});function r(a){var b="length"in a&&a.length,c=m.type(a);return"function"===c||m.isWindow(a)?!1:1===a.nodeType&&b?!0:"array"===c||0===b||"number"==typeof b&&b>0&&b-1 in a}var s=function(a){var b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u="sizzle"+1*new Date,v=a.document,w=0,x=0,y=ha(),z=ha(),A=ha(),B=function(a,b){return a===b&&(l=!0),0},C=1<<31,D={}.hasOwnProperty,E=[],F=E.pop,G=E.push,H=E.push,I=E.slice,J=function(a,b){for(var c=0,d=a.length;d>c;c++)if(a[c]===b)return c;return-1},K="checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",L="[\\x20\\t\\r\\n\\f]",M="(?:\\\\.|[\\w-]|[^\\x00-\\xa0])+",N=M.replace("w","w#"),O="\\["+L+"*("+M+")(?:"+L+"*([*^$|!~]?=)"+L+"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|("+N+"))|)"+L+"*\\]",P=":("+M+")(?:\\((('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|((?:\\\\.|[^\\\\()[\\]]|"+O+")*)|.*)\\)|)",Q=new RegExp(L+"+","g"),R=new RegExp("^"+L+"+|((?:^|[^\\\\])(?:\\\\.)*)"+L+"+$","g"),S=new RegExp("^"+L+"*,"+L+"*"),T=new RegExp("^"+L+"*([>+~]|"+L+")"+L+"*"),U=new RegExp("="+L+"*([^\\]'\"]*?)"+L+"*\\]","g"),V=new RegExp(P),W=new RegExp("^"+N+"$"),X={ID:new RegExp("^#("+M+")"),CLASS:new RegExp("^\\.("+M+")"),TAG:new RegExp("^("+M.replace("w","w*")+")"),ATTR:new RegExp("^"+O),PSEUDO:new RegExp("^"+P),CHILD:new RegExp("^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\("+L+"*(even|odd|(([+-]|)(\\d*)n|)"+L+"*(?:([+-]|)"+L+"*(\\d+)|))"+L+"*\\)|)","i"),bool:new RegExp("^(?:"+K+")$","i"),needsContext:new RegExp("^"+L+"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\("+L+"*((?:-\\d)?\\d*)"+L+"*\\)|)(?=[^-]|$)","i")},Y=/^(?:input|select|textarea|button)$/i,Z=/^h\d$/i,$=/^[^{]+\{\s*\[native \w/,_=/^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,aa=/[+~]/,ba=/'|\\/g,ca=new RegExp("\\\\([\\da-f]{1,6}"+L+"?|("+L+")|.)","ig"),da=function(a,b,c){var d="0x"+b-65536;return d!==d||c?b:0>d?String.fromCharCode(d+65536):String.fromCharCode(d>>10|55296,1023&d|56320)},ea=function(){m()};try{H.apply(E=I.call(v.childNodes),v.childNodes),E[v.childNodes.length].nodeType}catch(fa){H={apply:E.length?function(a,b){G.apply(a,I.call(b))}:function(a,b){var c=a.length,d=0;while(a[c++]=b[d++]);a.length=c-1}}}function ga(a,b,d,e){var f,h,j,k,l,o,r,s,w,x;if((b?b.ownerDocument||b:v)!==n&&m(b),b=b||n,d=d||[],k=b.nodeType,"string"!=typeof a||!a||1!==k&&9!==k&&11!==k)return d;if(!e&&p){if(11!==k&&(f=_.exec(a)))if(j=f[1]){if(9===k){if(h=b.getElementById(j),!h||!h.parentNode)return d;if(h.id===j)return d.push(h),d}else if(b.ownerDocument&&(h=b.ownerDocument.getElementById(j))&&t(b,h)&&h.id===j)return d.push(h),d}else{if(f[2])return H.apply(d,b.getElementsByTagName(a)),d;if((j=f[3])&&c.getElementsByClassName)return H.apply(d,b.getElementsByClassName(j)),d}if(c.qsa&&(!q||!q.test(a))){if(s=r=u,w=b,x=1!==k&&a,1===k&&"object"!==b.nodeName.toLowerCase()){o=g(a),(r=b.getAttribute("id"))?s=r.replace(ba,"\\$&"):b.setAttribute("id",s),s="[id='"+s+"'] ",l=o.length;while(l--)o[l]=s+ra(o[l]);w=aa.test(a)&&pa(b.parentNode)||b,x=o.join(",")}if(x)try{return H.apply(d,w.querySelectorAll(x)),d}catch(y){}finally{r||b.removeAttribute("id")}}}return i(a.replace(R,"$1"),b,d,e)}function ha(){var a=[];function b(c,e){return a.push(c+" ")>d.cacheLength&&delete b[a.shift()],b[c+" "]=e}return b}function ia(a){return a[u]=!0,a}function ja(a){var b=n.createElement("div");try{return!!a(b)}catch(c){return!1}finally{b.parentNode&&b.parentNode.removeChild(b),b=null}}function ka(a,b){var c=a.split("|"),e=a.length;while(e--)d.attrHandle[c[e]]=b}function la(a,b){var c=b&&a,d=c&&1===a.nodeType&&1===b.nodeType&&(~b.sourceIndex||C)-(~a.sourceIndex||C);if(d)return d;if(c)while(c=c.nextSibling)if(c===b)return-1;return a?1:-1}function ma(a){return function(b){var c=b.nodeName.toLowerCase();return"input"===c&&b.type===a}}function na(a){return function(b){var c=b.nodeName.toLowerCase();return("input"===c||"button"===c)&&b.type===a}}function oa(a){return ia(function(b){return b=+b,ia(function(c,d){var e,f=a([],c.length,b),g=f.length;while(g--)c[e=f[g]]&&(c[e]=!(d[e]=c[e]))})})}function pa(a){return a&&"undefined"!=typeof a.getElementsByTagName&&a}c=ga.support={},f=ga.isXML=function(a){var b=a&&(a.ownerDocument||a).documentElement;return b?"HTML"!==b.nodeName:!1},m=ga.setDocument=function(a){var b,e,g=a?a.ownerDocument||a:v;return g!==n&&9===g.nodeType&&g.documentElement?(n=g,o=g.documentElement,e=g.defaultView,e&&e!==e.top&&(e.addEventListener?e.addEventListener("unload",ea,!1):e.attachEvent&&e.attachEvent("onunload",ea)),p=!f(g),c.attributes=ja(function(a){return a.className="i",!a.getAttribute("className")}),c.getElementsByTagName=ja(function(a){return a.appendChild(g.createComment("")),!a.getElementsByTagName("*").length}),c.getElementsByClassName=$.test(g.getElementsByClassName),c.getById=ja(function(a){return o.appendChild(a).id=u,!g.getElementsByName||!g.getElementsByName(u).length}),c.getById?(d.find.ID=function(a,b){if("undefined"!=typeof b.getElementById&&p){var c=b.getElementById(a);return c&&c.parentNode?[c]:[]}},d.filter.ID=function(a){var b=a.replace(ca,da);return function(a){return a.getAttribute("id")===b}}):(delete d.find.ID,d.filter.ID=function(a){var b=a.replace(ca,da);return function(a){var c="undefined"!=typeof a.getAttributeNode&&a.getAttributeNode("id");return c&&c.value===b}}),d.find.TAG=c.getElementsByTagName?function(a,b){return"undefined"!=typeof b.getElementsByTagName?b.getElementsByTagName(a):c.qsa?b.querySelectorAll(a):void 0}:function(a,b){var c,d=[],e=0,f=b.getElementsByTagName(a);if("*"===a){while(c=f[e++])1===c.nodeType&&d.push(c);return d}return f},d.find.CLASS=c.getElementsByClassName&&function(a,b){return p?b.getElementsByClassName(a):void 0},r=[],q=[],(c.qsa=$.test(g.querySelectorAll))&&(ja(function(a){o.appendChild(a).innerHTML="<a id='"+u+"'></a><select id='"+u+"-\f]' msallowcapture=''><option selected=''></option></select>",a.querySelectorAll("[msallowcapture^='']").length&&q.push("[*^$]="+L+"*(?:''|\"\")"),a.querySelectorAll("[selected]").length||q.push("\\["+L+"*(?:value|"+K+")"),a.querySelectorAll("[id~="+u+"-]").length||q.push("~="),a.querySelectorAll(":checked").length||q.push(":checked"),a.querySelectorAll("a#"+u+"+*").length||q.push(".#.+[+~]")}),ja(function(a){var b=g.createElement("input");b.setAttribute("type","hidden"),a.appendChild(b).setAttribute("name","D"),a.querySelectorAll("[name=d]").length&&q.push("name"+L+"*[*^$|!~]?="),a.querySelectorAll(":enabled").length||q.push(":enabled",":disabled"),a.querySelectorAll("*,:x"),q.push(",.*:")})),(c.matchesSelector=$.test(s=o.matches||o.webkitMatchesSelector||o.mozMatchesSelector||o.oMatchesSelector||o.msMatchesSelector))&&ja(function(a){c.disconnectedMatch=s.call(a,"div"),s.call(a,"[s!='']:x"),r.push("!=",P)}),q=q.length&&new RegExp(q.join("|")),r=r.length&&new RegExp(r.join("|")),b=$.test(o.compareDocumentPosition),t=b||$.test(o.contains)?function(a,b){var c=9===a.nodeType?a.documentElement:a,d=b&&b.parentNode;return a===d||!(!d||1!==d.nodeType||!(c.contains?c.contains(d):a.compareDocumentPosition&&16&a.compareDocumentPosition(d)))}:function(a,b){if(b)while(b=b.parentNode)if(b===a)return!0;return!1},B=b?function(a,b){if(a===b)return l=!0,0;var d=!a.compareDocumentPosition-!b.compareDocumentPosition;return d?d:(d=(a.ownerDocument||a)===(b.ownerDocument||b)?a.compareDocumentPosition(b):1,1&d||!c.sortDetached&&b.compareDocumentPosition(a)===d?a===g||a.ownerDocument===v&&t(v,a)?-1:b===g||b.ownerDocument===v&&t(v,b)?1:k?J(k,a)-J(k,b):0:4&d?-1:1)}:function(a,b){if(a===b)return l=!0,0;var c,d=0,e=a.parentNode,f=b.parentNode,h=[a],i=[b];if(!e||!f)return a===g?-1:b===g?1:e?-1:f?1:k?J(k,a)-J(k,b):0;if(e===f)return la(a,b);c=a;while(c=c.parentNode)h.unshift(c);c=b;while(c=c.parentNode)i.unshift(c);while(h[d]===i[d])d++;return d?la(h[d],i[d]):h[d]===v?-1:i[d]===v?1:0},g):n},ga.matches=function(a,b){return ga(a,null,null,b)},ga.matchesSelector=function(a,b){if((a.ownerDocument||a)!==n&&m(a),b=b.replace(U,"='$1']"),!(!c.matchesSelector||!p||r&&r.test(b)||q&&q.test(b)))try{var d=s.call(a,b);if(d||c.disconnectedMatch||a.document&&11!==a.document.nodeType)return d}catch(e){}return ga(b,n,null,[a]).length>0},ga.contains=function(a,b){return(a.ownerDocument||a)!==n&&m(a),t(a,b)},ga.attr=function(a,b){(a.ownerDocument||a)!==n&&m(a);var e=d.attrHandle[b.toLowerCase()],f=e&&D.call(d.attrHandle,b.toLowerCase())?e(a,b,!p):void 0;return void 0!==f?f:c.attributes||!p?a.getAttribute(b):(f=a.getAttributeNode(b))&&f.specified?f.value:null},ga.error=function(a){throw new Error("Syntax error, unrecognized expression: "+a)},ga.uniqueSort=function(a){var b,d=[],e=0,f=0;if(l=!c.detectDuplicates,k=!c.sortStable&&a.slice(0),a.sort(B),l){while(b=a[f++])b===a[f]&&(e=d.push(f));while(e--)a.splice(d[e],1)}return k=null,a},e=ga.getText=function(a){var b,c="",d=0,f=a.nodeType;if(f){if(1===f||9===f||11===f){if("string"==typeof a.textContent)return a.textContent;for(a=a.firstChild;a;a=a.nextSibling)c+=e(a)}else if(3===f||4===f)return a.nodeValue}else while(b=a[d++])c+=e(b);return c},d=ga.selectors={cacheLength:50,createPseudo:ia,match:X,attrHandle:{},find:{},relative:{">":{dir:"parentNode",first:!0}," ":{dir:"parentNode"},"+":{dir:"previousSibling",first:!0},"~":{dir:"previousSibling"}},preFilter:{ATTR:function(a){return a[1]=a[1].replace(ca,da),a[3]=(a[3]||a[4]||a[5]||"").replace(ca,da),"~="===a[2]&&(a[3]=" "+a[3]+" "),a.slice(0,4)},CHILD:function(a){return a[1]=a[1].toLowerCase(),"nth"===a[1].slice(0,3)?(a[3]||ga.error(a[0]),a[4]=+(a[4]?a[5]+(a[6]||1):2*("even"===a[3]||"odd"===a[3])),a[5]=+(a[7]+a[8]||"odd"===a[3])):a[3]&&ga.error(a[0]),a},PSEUDO:function(a){var b,c=!a[6]&&a[2];return X.CHILD.test(a[0])?null:(a[3]?a[2]=a[4]||a[5]||"":c&&V.test(c)&&(b=g(c,!0))&&(b=c.indexOf(")",c.length-b)-c.length)&&(a[0]=a[0].slice(0,b),a[2]=c.slice(0,b)),a.slice(0,3))}},filter:{TAG:function(a){var b=a.replace(ca,da).toLowerCase();return"*"===a?function(){return!0}:function(a){return a.nodeName&&a.nodeName.toLowerCase()===b}},CLASS:function(a){var b=y[a+" "];return b||(b=new RegExp("(^|"+L+")"+a+"("+L+"|$)"))&&y(a,function(a){return b.test("string"==typeof a.className&&a.className||"undefined"!=typeof a.getAttribute&&a.getAttribute("class")||"")})},ATTR:function(a,b,c){return function(d){var e=ga.attr(d,a);return null==e?"!="===b:b?(e+="","="===b?e===c:"!="===b?e!==c:"^="===b?c&&0===e.indexOf(c):"*="===b?c&&e.indexOf(c)>-1:"$="===b?c&&e.slice(-c.length)===c:"~="===b?(" "+e.replace(Q," ")+" ").indexOf(c)>-1:"|="===b?e===c||e.slice(0,c.length+1)===c+"-":!1):!0}},CHILD:function(a,b,c,d,e){var f="nth"!==a.slice(0,3),g="last"!==a.slice(-4),h="of-type"===b;return 1===d&&0===e?function(a){return!!a.parentNode}:function(b,c,i){var j,k,l,m,n,o,p=f!==g?"nextSibling":"previousSibling",q=b.parentNode,r=h&&b.nodeName.toLowerCase(),s=!i&&!h;if(q){if(f){while(p){l=b;while(l=l[p])if(h?l.nodeName.toLowerCase()===r:1===l.nodeType)return!1;o=p="only"===a&&!o&&"nextSibling"}return!0}if(o=[g?q.firstChild:q.lastChild],g&&s){k=q[u]||(q[u]={}),j=k[a]||[],n=j[0]===w&&j[1],m=j[0]===w&&j[2],l=n&&q.childNodes[n];while(l=++n&&l&&l[p]||(m=n=0)||o.pop())if(1===l.nodeType&&++m&&l===b){k[a]=[w,n,m];break}}else if(s&&(j=(b[u]||(b[u]={}))[a])&&j[0]===w)m=j[1];else while(l=++n&&l&&l[p]||(m=n=0)||o.pop())if((h?l.nodeName.toLowerCase()===r:1===l.nodeType)&&++m&&(s&&((l[u]||(l[u]={}))[a]=[w,m]),l===b))break;return m-=e,m===d||m%d===0&&m/d>=0}}},PSEUDO:function(a,b){var c,e=d.pseudos[a]||d.setFilters[a.toLowerCase()]||ga.error("unsupported pseudo: "+a);return e[u]?e(b):e.length>1?(c=[a,a,"",b],d.setFilters.hasOwnProperty(a.toLowerCase())?ia(function(a,c){var d,f=e(a,b),g=f.length;while(g--)d=J(a,f[g]),a[d]=!(c[d]=f[g])}):function(a){return e(a,0,c)}):e}},pseudos:{not:ia(function(a){var b=[],c=[],d=h(a.replace(R,"$1"));return d[u]?ia(function(a,b,c,e){var f,g=d(a,null,e,[]),h=a.length;while(h--)(f=g[h])&&(a[h]=!(b[h]=f))}):function(a,e,f){return b[0]=a,d(b,null,f,c),b[0]=null,!c.pop()}}),has:ia(function(a){return function(b){return ga(a,b).length>0}}),contains:ia(function(a){return a=a.replace(ca,da),function(b){return(b.textContent||b.innerText||e(b)).indexOf(a)>-1}}),lang:ia(function(a){return W.test(a||"")||ga.error("unsupported lang: "+a),a=a.replace(ca,da).toLowerCase(),function(b){var c;do if(c=p?b.lang:b.getAttribute("xml:lang")||b.getAttribute("lang"))return c=c.toLowerCase(),c===a||0===c.indexOf(a+"-");while((b=b.parentNode)&&1===b.nodeType);return!1}}),target:function(b){var c=a.location&&a.location.hash;return c&&c.slice(1)===b.id},root:function(a){return a===o},focus:function(a){return a===n.activeElement&&(!n.hasFocus||n.hasFocus())&&!!(a.type||a.href||~a.tabIndex)},enabled:function(a){return a.disabled===!1},disabled:function(a){return a.disabled===!0},checked:function(a){var b=a.nodeName.toLowerCase();return"input"===b&&!!a.checked||"option"===b&&!!a.selected},selected:function(a){return a.parentNode&&a.parentNode.selectedIndex,a.selected===!0},empty:function(a){for(a=a.firstChild;a;a=a.nextSibling)if(a.nodeType<6)return!1;return!0},parent:function(a){return!d.pseudos.empty(a)},header:function(a){return Z.test(a.nodeName)},input:function(a){return Y.test(a.nodeName)},button:function(a){var b=a.nodeName.toLowerCase();return"input"===b&&"button"===a.type||"button"===b},text:function(a){var b;return"input"===a.nodeName.toLowerCase()&&"text"===a.type&&(null==(b=a.getAttribute("type"))||"text"===b.toLowerCase())},first:oa(function(){return[0]}),last:oa(function(a,b){return[b-1]}),eq:oa(function(a,b,c){return[0>c?c+b:c]}),even:oa(function(a,b){for(var c=0;b>c;c+=2)a.push(c);return a}),odd:oa(function(a,b){for(var c=1;b>c;c+=2)a.push(c);return a}),lt:oa(function(a,b,c){for(var d=0>c?c+b:c;--d>=0;)a.push(d);return a}),gt:oa(function(a,b,c){for(var d=0>c?c+b:c;++d<b;)a.push(d);return a})}},d.pseudos.nth=d.pseudos.eq;for(b in{radio:!0,checkbox:!0,file:!0,password:!0,image:!0})d.pseudos[b]=ma(b);for(b in{submit:!0,reset:!0})d.pseudos[b]=na(b);function qa(){}qa.prototype=d.filters=d.pseudos,d.setFilters=new qa,g=ga.tokenize=function(a,b){var c,e,f,g,h,i,j,k=z[a+" "];if(k)return b?0:k.slice(0);h=a,i=[],j=d.preFilter;while(h){(!c||(e=S.exec(h)))&&(e&&(h=h.slice(e[0].length)||h),i.push(f=[])),c=!1,(e=T.exec(h))&&(c=e.shift(),f.push({value:c,type:e[0].replace(R," ")}),h=h.slice(c.length));for(g in d.filter)!(e=X[g].exec(h))||j[g]&&!(e=j[g](e))||(c=e.shift(),f.push({value:c,type:g,matches:e}),h=h.slice(c.length));if(!c)break}return b?h.length:h?ga.error(a):z(a,i).slice(0)};function ra(a){for(var b=0,c=a.length,d="";c>b;b++)d+=a[b].value;return d}function sa(a,b,c){var d=b.dir,e=c&&"parentNode"===d,f=x++;return b.first?function(b,c,f){while(b=b[d])if(1===b.nodeType||e)return a(b,c,f)}:function(b,c,g){var h,i,j=[w,f];if(g){while(b=b[d])if((1===b.nodeType||e)&&a(b,c,g))return!0}else while(b=b[d])if(1===b.nodeType||e){if(i=b[u]||(b[u]={}),(h=i[d])&&h[0]===w&&h[1]===f)return j[2]=h[2];if(i[d]=j,j[2]=a(b,c,g))return!0}}}function ta(a){return a.length>1?function(b,c,d){var e=a.length;while(e--)if(!a[e](b,c,d))return!1;return!0}:a[0]}function ua(a,b,c){for(var d=0,e=b.length;e>d;d++)ga(a,b[d],c);return c}function va(a,b,c,d,e){for(var f,g=[],h=0,i=a.length,j=null!=b;i>h;h++)(f=a[h])&&(!c||c(f,d,e))&&(g.push(f),j&&b.push(h));return g}function wa(a,b,c,d,e,f){return d&&!d[u]&&(d=wa(d)),e&&!e[u]&&(e=wa(e,f)),ia(function(f,g,h,i){var j,k,l,m=[],n=[],o=g.length,p=f||ua(b||"*",h.nodeType?[h]:h,[]),q=!a||!f&&b?p:va(p,m,a,h,i),r=c?e||(f?a:o||d)?[]:g:q;if(c&&c(q,r,h,i),d){j=va(r,n),d(j,[],h,i),k=j.length;while(k--)(l=j[k])&&(r[n[k]]=!(q[n[k]]=l))}if(f){if(e||a){if(e){j=[],k=r.length;while(k--)(l=r[k])&&j.push(q[k]=l);e(null,r=[],j,i)}k=r.length;while(k--)(l=r[k])&&(j=e?J(f,l):m[k])>-1&&(f[j]=!(g[j]=l))}}else r=va(r===g?r.splice(o,r.length):r),e?e(null,g,r,i):H.apply(g,r)})}function xa(a){for(var b,c,e,f=a.length,g=d.relative[a[0].type],h=g||d.relative[" "],i=g?1:0,k=sa(function(a){return a===b},h,!0),l=sa(function(a){return J(b,a)>-1},h,!0),m=[function(a,c,d){var e=!g&&(d||c!==j)||((b=c).nodeType?k(a,c,d):l(a,c,d));return b=null,e}];f>i;i++)if(c=d.relative[a[i].type])m=[sa(ta(m),c)];else{if(c=d.filter[a[i].type].apply(null,a[i].matches),c[u]){for(e=++i;f>e;e++)if(d.relative[a[e].type])break;return wa(i>1&&ta(m),i>1&&ra(a.slice(0,i-1).concat({value:" "===a[i-2].type?"*":""})).replace(R,"$1"),c,e>i&&xa(a.slice(i,e)),f>e&&xa(a=a.slice(e)),f>e&&ra(a))}m.push(c)}return ta(m)}function ya(a,b){var c=b.length>0,e=a.length>0,f=function(f,g,h,i,k){var l,m,o,p=0,q="0",r=f&&[],s=[],t=j,u=f||e&&d.find.TAG("*",k),v=w+=null==t?1:Math.random()||.1,x=u.length;for(k&&(j=g!==n&&g);q!==x&&null!=(l=u[q]);q++){if(e&&l){m=0;while(o=a[m++])if(o(l,g,h)){i.push(l);break}k&&(w=v)}c&&((l=!o&&l)&&p--,f&&r.push(l))}if(p+=q,c&&q!==p){m=0;while(o=b[m++])o(r,s,g,h);if(f){if(p>0)while(q--)r[q]||s[q]||(s[q]=F.call(i));s=va(s)}H.apply(i,s),k&&!f&&s.length>0&&p+b.length>1&&ga.uniqueSort(i)}return k&&(w=v,j=t),r};return c?ia(f):f}return h=ga.compile=function(a,b){var c,d=[],e=[],f=A[a+" "];if(!f){b||(b=g(a)),c=b.length;while(c--)f=xa(b[c]),f[u]?d.push(f):e.push(f);f=A(a,ya(e,d)),f.selector=a}return f},i=ga.select=function(a,b,e,f){var i,j,k,l,m,n="function"==typeof a&&a,o=!f&&g(a=n.selector||a);if(e=e||[],1===o.length){if(j=o[0]=o[0].slice(0),j.length>2&&"ID"===(k=j[0]).type&&c.getById&&9===b.nodeType&&p&&d.relative[j[1].type]){if(b=(d.find.ID(k.matches[0].replace(ca,da),b)||[])[0],!b)return e;n&&(b=b.parentNode),a=a.slice(j.shift().value.length)}i=X.needsContext.test(a)?0:j.length;while(i--){if(k=j[i],d.relative[l=k.type])break;if((m=d.find[l])&&(f=m(k.matches[0].replace(ca,da),aa.test(j[0].type)&&pa(b.parentNode)||b))){if(j.splice(i,1),a=f.length&&ra(j),!a)return H.apply(e,f),e;break}}}return(n||h(a,o))(f,b,!p,e,aa.test(a)&&pa(b.parentNode)||b),e},c.sortStable=u.split("").sort(B).join("")===u,c.detectDuplicates=!!l,m(),c.sortDetached=ja(function(a){return 1&a.compareDocumentPosition(n.createElement("div"))}),ja(function(a){return a.innerHTML="<a href='#'></a>","#"===a.firstChild.getAttribute("href")})||ka("type|href|height|width",function(a,b,c){return c?void 0:a.getAttribute(b,"type"===b.toLowerCase()?1:2)}),c.attributes&&ja(function(a){return a.innerHTML="<input/>",a.firstChild.setAttribute("value",""),""===a.firstChild.getAttribute("value")})||ka("value",function(a,b,c){return c||"input"!==a.nodeName.toLowerCase()?void 0:a.defaultValue}),ja(function(a){return null==a.getAttribute("disabled")})||ka(K,function(a,b,c){var d;return c?void 0:a[b]===!0?b.toLowerCase():(d=a.getAttributeNode(b))&&d.specified?d.value:null}),ga}(a);m.find=s,m.expr=s.selectors,m.expr[":"]=m.expr.pseudos,m.unique=s.uniqueSort,m.text=s.getText,m.isXMLDoc=s.isXML,m.contains=s.contains;var t=m.expr.match.needsContext,u=/^<(\w+)\s*\/?>(?:<\/\1>|)$/,v=/^.[^:#\[\.,]*$/;function w(a,b,c){if(m.isFunction(b))return m.grep(a,function(a,d){return!!b.call(a,d,a)!==c});if(b.nodeType)return m.grep(a,function(a){return a===b!==c});if("string"==typeof b){if(v.test(b))return m.filter(b,a,c);b=m.filter(b,a)}return m.grep(a,function(a){return m.inArray(a,b)>=0!==c})}m.filter=function(a,b,c){var d=b[0];return c&&(a=":not("+a+")"),1===b.length&&1===d.nodeType?m.find.matchesSelector(d,a)?[d]:[]:m.find.matches(a,m.grep(b,function(a){return 1===a.nodeType}))},m.fn.extend({find:function(a){var b,c=[],d=this,e=d.length;if("string"!=typeof a)return this.pushStack(m(a).filter(function(){for(b=0;e>b;b++)if(m.contains(d[b],this))return!0}));for(b=0;e>b;b++)m.find(a,d[b],c);return c=this.pushStack(e>1?m.unique(c):c),c.selector=this.selector?this.selector+" "+a:a,c},filter:function(a){return this.pushStack(w(this,a||[],!1))},not:function(a){return this.pushStack(w(this,a||[],!0))},is:function(a){return!!w(this,"string"==typeof a&&t.test(a)?m(a):a||[],!1).length}});var x,y=a.document,z=/^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]*))$/,A=m.fn.init=function(a,b){var c,d;if(!a)return this;if("string"==typeof a){if(c="<"===a.charAt(0)&&">"===a.charAt(a.length-1)&&a.length>=3?[null,a,null]:z.exec(a),!c||!c[1]&&b)return!b||b.jquery?(b||x).find(a):this.constructor(b).find(a);if(c[1]){if(b=b instanceof m?b[0]:b,m.merge(this,m.parseHTML(c[1],b&&b.nodeType?b.ownerDocument||b:y,!0)),u.test(c[1])&&m.isPlainObject(b))for(c in b)m.isFunction(this[c])?this[c](b[c]):this.attr(c,b[c]);return this}if(d=y.getElementById(c[2]),d&&d.parentNode){if(d.id!==c[2])return x.find(a);this.length=1,this[0]=d}return this.context=y,this.selector=a,this}return a.nodeType?(this.context=this[0]=a,this.length=1,this):m.isFunction(a)?"undefined"!=typeof x.ready?x.ready(a):a(m):(void 0!==a.selector&&(this.selector=a.selector,this.context=a.context),m.makeArray(a,this))};A.prototype=m.fn,x=m(y);var B=/^(?:parents|prev(?:Until|All))/,C={children:!0,contents:!0,next:!0,prev:!0};m.extend({dir:function(a,b,c){var d=[],e=a[b];while(e&&9!==e.nodeType&&(void 0===c||1!==e.nodeType||!m(e).is(c)))1===e.nodeType&&d.push(e),e=e[b];return d},sibling:function(a,b){for(var c=[];a;a=a.nextSibling)1===a.nodeType&&a!==b&&c.push(a);return c}}),m.fn.extend({has:function(a){var b,c=m(a,this),d=c.length;return this.filter(function(){for(b=0;d>b;b++)if(m.contains(this,c[b]))return!0})},closest:function(a,b){for(var c,d=0,e=this.length,f=[],g=t.test(a)||"string"!=typeof a?m(a,b||this.context):0;e>d;d++)for(c=this[d];c&&c!==b;c=c.parentNode)if(c.nodeType<11&&(g?g.index(c)>-1:1===c.nodeType&&m.find.matchesSelector(c,a))){f.push(c);break}return this.pushStack(f.length>1?m.unique(f):f)},index:function(a){return a?"string"==typeof a?m.inArray(this[0],m(a)):m.inArray(a.jquery?a[0]:a,this):this[0]&&this[0].parentNode?this.first().prevAll().length:-1},add:function(a,b){return this.pushStack(m.unique(m.merge(this.get(),m(a,b))))},addBack:function(a){return this.add(null==a?this.prevObject:this.prevObject.filter(a))}});function D(a,b){do a=a[b];while(a&&1!==a.nodeType);return a}m.each({parent:function(a){var b=a.parentNode;return b&&11!==b.nodeType?b:null},parents:function(a){return m.dir(a,"parentNode")},parentsUntil:function(a,b,c){return m.dir(a,"parentNode",c)},next:function(a){return D(a,"nextSibling")},prev:function(a){return D(a,"previousSibling")},nextAll:function(a){return m.dir(a,"nextSibling")},prevAll:function(a){return m.dir(a,"previousSibling")},nextUntil:function(a,b,c){return m.dir(a,"nextSibling",c)},prevUntil:function(a,b,c){return m.dir(a,"previousSibling",c)},siblings:function(a){return m.sibling((a.parentNode||{}).firstChild,a)},children:function(a){return m.sibling(a.firstChild)},contents:function(a){return m.nodeName(a,"iframe")?a.contentDocument||a.contentWindow.document:m.merge([],a.childNodes)}},function(a,b){m.fn[a]=function(c,d){var e=m.map(this,b,c);return"Until"!==a.slice(-5)&&(d=c),d&&"string"==typeof d&&(e=m.filter(d,e)),this.length>1&&(C[a]||(e=m.unique(e)),B.test(a)&&(e=e.reverse())),this.pushStack(e)}});var E=/\S+/g,F={};function G(a){var b=F[a]={};return m.each(a.match(E)||[],function(a,c){b[c]=!0}),b}m.Callbacks=function(a){a="string"==typeof a?F[a]||G(a):m.extend({},a);var b,c,d,e,f,g,h=[],i=!a.once&&[],j=function(l){for(c=a.memory&&l,d=!0,f=g||0,g=0,e=h.length,b=!0;h&&e>f;f++)if(h[f].apply(l[0],l[1])===!1&&a.stopOnFalse){c=!1;break}b=!1,h&&(i?i.length&&j(i.shift()):c?h=[]:k.disable())},k={add:function(){if(h){var d=h.length;!function f(b){m.each(b,function(b,c){var d=m.type(c);"function"===d?a.unique&&k.has(c)||h.push(c):c&&c.length&&"string"!==d&&f(c)})}(arguments),b?e=h.length:c&&(g=d,j(c))}return this},remove:function(){return h&&m.each(arguments,function(a,c){var d;while((d=m.inArray(c,h,d))>-1)h.splice(d,1),b&&(e>=d&&e--,f>=d&&f--)}),this},has:function(a){return a?m.inArray(a,h)>-1:!(!h||!h.length)},empty:function(){return h=[],e=0,this},disable:function(){return h=i=c=void 0,this},disabled:function(){return!h},lock:function(){return i=void 0,c||k.disable(),this},locked:function(){return!i},fireWith:function(a,c){return!h||d&&!i||(c=c||[],c=[a,c.slice?c.slice():c],b?i.push(c):j(c)),this},fire:function(){return k.fireWith(this,arguments),this},fired:function(){return!!d}};return k},m.extend({Deferred:function(a){var b=[["resolve","done",m.Callbacks("once memory"),"resolved"],["reject","fail",m.Callbacks("once memory"),"rejected"],["notify","progress",m.Callbacks("memory")]],c="pending",d={state:function(){return c},always:function(){return e.done(arguments).fail(arguments),this},then:function(){var a=arguments;return m.Deferred(function(c){m.each(b,function(b,f){var g=m.isFunction(a[b])&&a[b];e[f[1]](function(){var a=g&&g.apply(this,arguments);a&&m.isFunction(a.promise)?a.promise().done(c.resolve).fail(c.reject).progress(c.notify):c[f[0]+"With"](this===d?c.promise():this,g?[a]:arguments)})}),a=null}).promise()},promise:function(a){return null!=a?m.extend(a,d):d}},e={};return d.pipe=d.then,m.each(b,function(a,f){var g=f[2],h=f[3];d[f[1]]=g.add,h&&g.add(function(){c=h},b[1^a][2].disable,b[2][2].lock),e[f[0]]=function(){return e[f[0]+"With"](this===e?d:this,arguments),this},e[f[0]+"With"]=g.fireWith}),d.promise(e),a&&a.call(e,e),e},when:function(a){var b=0,c=d.call(arguments),e=c.length,f=1!==e||a&&m.isFunction(a.promise)?e:0,g=1===f?a:m.Deferred(),h=function(a,b,c){return function(e){b[a]=this,c[a]=arguments.length>1?d.call(arguments):e,c===i?g.notifyWith(b,c):--f||g.resolveWith(b,c)}},i,j,k;if(e>1)for(i=new Array(e),j=new Array(e),k=new Array(e);e>b;b++)c[b]&&m.isFunction(c[b].promise)?c[b].promise().done(h(b,k,c)).fail(g.reject).progress(h(b,j,i)):--f;return f||g.resolveWith(k,c),g.promise()}});var H;m.fn.ready=function(a){return m.ready.promise().done(a),this},m.extend({isReady:!1,readyWait:1,holdReady:function(a){a?m.readyWait++:m.ready(!0)},ready:function(a){if(a===!0?!--m.readyWait:!m.isReady){if(!y.body)return setTimeout(m.ready);m.isReady=!0,a!==!0&&--m.readyWait>0||(H.resolveWith(y,[m]),m.fn.triggerHandler&&(m(y).triggerHandler("ready"),m(y).off("ready")))}}});function I(){y.addEventListener?(y.removeEventListener("DOMContentLoaded",J,!1),a.removeEventListener("load",J,!1)):(y.detachEvent("onreadystatechange",J),a.detachEvent("onload",J))}function J(){(y.addEventListener||"load"===event.type||"complete"===y.readyState)&&(I(),m.ready())}m.ready.promise=function(b){if(!H)if(H=m.Deferred(),"complete"===y.readyState)setTimeout(m.ready);else if(y.addEventListener)y.addEventListener("DOMContentLoaded",J,!1),a.addEventListener("load",J,!1);else{y.attachEvent("onreadystatechange",J),a.attachEvent("onload",J);var c=!1;try{c=null==a.frameElement&&y.documentElement}catch(d){}c&&c.doScroll&&!function e(){if(!m.isReady){try{c.doScroll("left")}catch(a){return setTimeout(e,50)}I(),m.ready()}}()}return H.promise(b)};var K="undefined",L;for(L in m(k))break;k.ownLast="0"!==L,k.inlineBlockNeedsLayout=!1,m(function(){var a,b,c,d;c=y.getElementsByTagName("body")[0],c&&c.style&&(b=y.createElement("div"),d=y.createElement("div"),d.style.cssText="position:absolute;border:0;width:0;height:0;top:0;left:-9999px",c.appendChild(d).appendChild(b),typeof b.style.zoom!==K&&(b.style.cssText="display:inline;margin:0;border:0;padding:1px;width:1px;zoom:1",k.inlineBlockNeedsLayout=a=3===b.offsetWidth,a&&(c.style.zoom=1)),c.removeChild(d))}),function(){var a=y.createElement("div");if(null==k.deleteExpando){k.deleteExpando=!0;try{delete a.test}catch(b){k.deleteExpando=!1}}a=null}(),m.acceptData=function(a){var b=m.noData[(a.nodeName+" ").toLowerCase()],c=+a.nodeType||1;return 1!==c&&9!==c?!1:!b||b!==!0&&a.getAttribute("classid")===b};var M=/^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,N=/([A-Z])/g;function O(a,b,c){if(void 0===c&&1===a.nodeType){var d="data-"+b.replace(N,"-$1").toLowerCase();if(c=a.getAttribute(d),"string"==typeof c){try{c="true"===c?!0:"false"===c?!1:"null"===c?null:+c+""===c?+c:M.test(c)?m.parseJSON(c):c}catch(e){}m.data(a,b,c)}else c=void 0}return c}function P(a){var b;for(b in a)if(("data"!==b||!m.isEmptyObject(a[b]))&&"toJSON"!==b)return!1;
return!0}function Q(a,b,d,e){if(m.acceptData(a)){var f,g,h=m.expando,i=a.nodeType,j=i?m.cache:a,k=i?a[h]:a[h]&&h;if(k&&j[k]&&(e||j[k].data)||void 0!==d||"string"!=typeof b)return k||(k=i?a[h]=c.pop()||m.guid++:h),j[k]||(j[k]=i?{}:{toJSON:m.noop}),("object"==typeof b||"function"==typeof b)&&(e?j[k]=m.extend(j[k],b):j[k].data=m.extend(j[k].data,b)),g=j[k],e||(g.data||(g.data={}),g=g.data),void 0!==d&&(g[m.camelCase(b)]=d),"string"==typeof b?(f=g[b],null==f&&(f=g[m.camelCase(b)])):f=g,f}}function R(a,b,c){if(m.acceptData(a)){var d,e,f=a.nodeType,g=f?m.cache:a,h=f?a[m.expando]:m.expando;if(g[h]){if(b&&(d=c?g[h]:g[h].data)){m.isArray(b)?b=b.concat(m.map(b,m.camelCase)):b in d?b=[b]:(b=m.camelCase(b),b=b in d?[b]:b.split(" ")),e=b.length;while(e--)delete d[b[e]];if(c?!P(d):!m.isEmptyObject(d))return}(c||(delete g[h].data,P(g[h])))&&(f?m.cleanData([a],!0):k.deleteExpando||g!=g.window?delete g[h]:g[h]=null)}}}m.extend({cache:{},noData:{"applet ":!0,"embed ":!0,"object ":"clsid:D27CDB6E-AE6D-11cf-96B8-444553540000"},hasData:function(a){return a=a.nodeType?m.cache[a[m.expando]]:a[m.expando],!!a&&!P(a)},data:function(a,b,c){return Q(a,b,c)},removeData:function(a,b){return R(a,b)},_data:function(a,b,c){return Q(a,b,c,!0)},_removeData:function(a,b){return R(a,b,!0)}}),m.fn.extend({data:function(a,b){var c,d,e,f=this[0],g=f&&f.attributes;if(void 0===a){if(this.length&&(e=m.data(f),1===f.nodeType&&!m._data(f,"parsedAttrs"))){c=g.length;while(c--)g[c]&&(d=g[c].name,0===d.indexOf("data-")&&(d=m.camelCase(d.slice(5)),O(f,d,e[d])));m._data(f,"parsedAttrs",!0)}return e}return"object"==typeof a?this.each(function(){m.data(this,a)}):arguments.length>1?this.each(function(){m.data(this,a,b)}):f?O(f,a,m.data(f,a)):void 0},removeData:function(a){return this.each(function(){m.removeData(this,a)})}}),m.extend({queue:function(a,b,c){var d;return a?(b=(b||"fx")+"queue",d=m._data(a,b),c&&(!d||m.isArray(c)?d=m._data(a,b,m.makeArray(c)):d.push(c)),d||[]):void 0},dequeue:function(a,b){b=b||"fx";var c=m.queue(a,b),d=c.length,e=c.shift(),f=m._queueHooks(a,b),g=function(){m.dequeue(a,b)};"inprogress"===e&&(e=c.shift(),d--),e&&("fx"===b&&c.unshift("inprogress"),delete f.stop,e.call(a,g,f)),!d&&f&&f.empty.fire()},_queueHooks:function(a,b){var c=b+"queueHooks";return m._data(a,c)||m._data(a,c,{empty:m.Callbacks("once memory").add(function(){m._removeData(a,b+"queue"),m._removeData(a,c)})})}}),m.fn.extend({queue:function(a,b){var c=2;return"string"!=typeof a&&(b=a,a="fx",c--),arguments.length<c?m.queue(this[0],a):void 0===b?this:this.each(function(){var c=m.queue(this,a,b);m._queueHooks(this,a),"fx"===a&&"inprogress"!==c[0]&&m.dequeue(this,a)})},dequeue:function(a){return this.each(function(){m.dequeue(this,a)})},clearQueue:function(a){return this.queue(a||"fx",[])},promise:function(a,b){var c,d=1,e=m.Deferred(),f=this,g=this.length,h=function(){--d||e.resolveWith(f,[f])};"string"!=typeof a&&(b=a,a=void 0),a=a||"fx";while(g--)c=m._data(f[g],a+"queueHooks"),c&&c.empty&&(d++,c.empty.add(h));return h(),e.promise(b)}});var S=/[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/.source,T=["Top","Right","Bottom","Left"],U=function(a,b){return a=b||a,"none"===m.css(a,"display")||!m.contains(a.ownerDocument,a)},V=m.access=function(a,b,c,d,e,f,g){var h=0,i=a.length,j=null==c;if("object"===m.type(c)){e=!0;for(h in c)m.access(a,b,h,c[h],!0,f,g)}else if(void 0!==d&&(e=!0,m.isFunction(d)||(g=!0),j&&(g?(b.call(a,d),b=null):(j=b,b=function(a,b,c){return j.call(m(a),c)})),b))for(;i>h;h++)b(a[h],c,g?d:d.call(a[h],h,b(a[h],c)));return e?a:j?b.call(a):i?b(a[0],c):f},W=/^(?:checkbox|radio)$/i;!function(){var a=y.createElement("input"),b=y.createElement("div"),c=y.createDocumentFragment();if(b.innerHTML=" <link/><table></table><a href='/a'>a</a><input type='checkbox'/>",k.leadingWhitespace=3===b.firstChild.nodeType,k.tbody=!b.getElementsByTagName("tbody").length,k.htmlSerialize=!!b.getElementsByTagName("link").length,k.html5Clone="<:nav></:nav>"!==y.createElement("nav").cloneNode(!0).outerHTML,a.type="checkbox",a.checked=!0,c.appendChild(a),k.appendChecked=a.checked,b.innerHTML="<textarea>x</textarea>",k.noCloneChecked=!!b.cloneNode(!0).lastChild.defaultValue,c.appendChild(b),b.innerHTML="<input type='radio' checked='checked' name='t'/>",k.checkClone=b.cloneNode(!0).cloneNode(!0).lastChild.checked,k.noCloneEvent=!0,b.attachEvent&&(b.attachEvent("onclick",function(){k.noCloneEvent=!1}),b.cloneNode(!0).click()),null==k.deleteExpando){k.deleteExpando=!0;try{delete b.test}catch(d){k.deleteExpando=!1}}}(),function(){var b,c,d=y.createElement("div");for(b in{submit:!0,change:!0,focusin:!0})c="on"+b,(k[b+"Bubbles"]=c in a)||(d.setAttribute(c,"t"),k[b+"Bubbles"]=d.attributes[c].expando===!1);d=null}();var X=/^(?:input|select|textarea)$/i,Y=/^key/,Z=/^(?:mouse|pointer|contextmenu)|click/,$=/^(?:focusinfocus|focusoutblur)$/,_=/^([^.]*)(?:\.(.+)|)$/;function aa(){return!0}function ba(){return!1}function ca(){try{return y.activeElement}catch(a){}}m.event={global:{},add:function(a,b,c,d,e){var f,g,h,i,j,k,l,n,o,p,q,r=m._data(a);if(r){c.handler&&(i=c,c=i.handler,e=i.selector),c.guid||(c.guid=m.guid++),(g=r.events)||(g=r.events={}),(k=r.handle)||(k=r.handle=function(a){return typeof m===K||a&&m.event.triggered===a.type?void 0:m.event.dispatch.apply(k.elem,arguments)},k.elem=a),b=(b||"").match(E)||[""],h=b.length;while(h--)f=_.exec(b[h])||[],o=q=f[1],p=(f[2]||"").split(".").sort(),o&&(j=m.event.special[o]||{},o=(e?j.delegateType:j.bindType)||o,j=m.event.special[o]||{},l=m.extend({type:o,origType:q,data:d,handler:c,guid:c.guid,selector:e,needsContext:e&&m.expr.match.needsContext.test(e),namespace:p.join(".")},i),(n=g[o])||(n=g[o]=[],n.delegateCount=0,j.setup&&j.setup.call(a,d,p,k)!==!1||(a.addEventListener?a.addEventListener(o,k,!1):a.attachEvent&&a.attachEvent("on"+o,k))),j.add&&(j.add.call(a,l),l.handler.guid||(l.handler.guid=c.guid)),e?n.splice(n.delegateCount++,0,l):n.push(l),m.event.global[o]=!0);a=null}},remove:function(a,b,c,d,e){var f,g,h,i,j,k,l,n,o,p,q,r=m.hasData(a)&&m._data(a);if(r&&(k=r.events)){b=(b||"").match(E)||[""],j=b.length;while(j--)if(h=_.exec(b[j])||[],o=q=h[1],p=(h[2]||"").split(".").sort(),o){l=m.event.special[o]||{},o=(d?l.delegateType:l.bindType)||o,n=k[o]||[],h=h[2]&&new RegExp("(^|\\.)"+p.join("\\.(?:.*\\.|)")+"(\\.|$)"),i=f=n.length;while(f--)g=n[f],!e&&q!==g.origType||c&&c.guid!==g.guid||h&&!h.test(g.namespace)||d&&d!==g.selector&&("**"!==d||!g.selector)||(n.splice(f,1),g.selector&&n.delegateCount--,l.remove&&l.remove.call(a,g));i&&!n.length&&(l.teardown&&l.teardown.call(a,p,r.handle)!==!1||m.removeEvent(a,o,r.handle),delete k[o])}else for(o in k)m.event.remove(a,o+b[j],c,d,!0);m.isEmptyObject(k)&&(delete r.handle,m._removeData(a,"events"))}},trigger:function(b,c,d,e){var f,g,h,i,k,l,n,o=[d||y],p=j.call(b,"type")?b.type:b,q=j.call(b,"namespace")?b.namespace.split("."):[];if(h=l=d=d||y,3!==d.nodeType&&8!==d.nodeType&&!$.test(p+m.event.triggered)&&(p.indexOf(".")>=0&&(q=p.split("."),p=q.shift(),q.sort()),g=p.indexOf(":")<0&&"on"+p,b=b[m.expando]?b:new m.Event(p,"object"==typeof b&&b),b.isTrigger=e?2:3,b.namespace=q.join("."),b.namespace_re=b.namespace?new RegExp("(^|\\.)"+q.join("\\.(?:.*\\.|)")+"(\\.|$)"):null,b.result=void 0,b.target||(b.target=d),c=null==c?[b]:m.makeArray(c,[b]),k=m.event.special[p]||{},e||!k.trigger||k.trigger.apply(d,c)!==!1)){if(!e&&!k.noBubble&&!m.isWindow(d)){for(i=k.delegateType||p,$.test(i+p)||(h=h.parentNode);h;h=h.parentNode)o.push(h),l=h;l===(d.ownerDocument||y)&&o.push(l.defaultView||l.parentWindow||a)}n=0;while((h=o[n++])&&!b.isPropagationStopped())b.type=n>1?i:k.bindType||p,f=(m._data(h,"events")||{})[b.type]&&m._data(h,"handle"),f&&f.apply(h,c),f=g&&h[g],f&&f.apply&&m.acceptData(h)&&(b.result=f.apply(h,c),b.result===!1&&b.preventDefault());if(b.type=p,!e&&!b.isDefaultPrevented()&&(!k._default||k._default.apply(o.pop(),c)===!1)&&m.acceptData(d)&&g&&d[p]&&!m.isWindow(d)){l=d[g],l&&(d[g]=null),m.event.triggered=p;try{d[p]()}catch(r){}m.event.triggered=void 0,l&&(d[g]=l)}return b.result}},dispatch:function(a){a=m.event.fix(a);var b,c,e,f,g,h=[],i=d.call(arguments),j=(m._data(this,"events")||{})[a.type]||[],k=m.event.special[a.type]||{};if(i[0]=a,a.delegateTarget=this,!k.preDispatch||k.preDispatch.call(this,a)!==!1){h=m.event.handlers.call(this,a,j),b=0;while((f=h[b++])&&!a.isPropagationStopped()){a.currentTarget=f.elem,g=0;while((e=f.handlers[g++])&&!a.isImmediatePropagationStopped())(!a.namespace_re||a.namespace_re.test(e.namespace))&&(a.handleObj=e,a.data=e.data,c=((m.event.special[e.origType]||{}).handle||e.handler).apply(f.elem,i),void 0!==c&&(a.result=c)===!1&&(a.preventDefault(),a.stopPropagation()))}return k.postDispatch&&k.postDispatch.call(this,a),a.result}},handlers:function(a,b){var c,d,e,f,g=[],h=b.delegateCount,i=a.target;if(h&&i.nodeType&&(!a.button||"click"!==a.type))for(;i!=this;i=i.parentNode||this)if(1===i.nodeType&&(i.disabled!==!0||"click"!==a.type)){for(e=[],f=0;h>f;f++)d=b[f],c=d.selector+" ",void 0===e[c]&&(e[c]=d.needsContext?m(c,this).index(i)>=0:m.find(c,this,null,[i]).length),e[c]&&e.push(d);e.length&&g.push({elem:i,handlers:e})}return h<b.length&&g.push({elem:this,handlers:b.slice(h)}),g},fix:function(a){if(a[m.expando])return a;var b,c,d,e=a.type,f=a,g=this.fixHooks[e];g||(this.fixHooks[e]=g=Z.test(e)?this.mouseHooks:Y.test(e)?this.keyHooks:{}),d=g.props?this.props.concat(g.props):this.props,a=new m.Event(f),b=d.length;while(b--)c=d[b],a[c]=f[c];return a.target||(a.target=f.srcElement||y),3===a.target.nodeType&&(a.target=a.target.parentNode),a.metaKey=!!a.metaKey,g.filter?g.filter(a,f):a},props:"altKey bubbles cancelable ctrlKey currentTarget eventPhase metaKey relatedTarget shiftKey target timeStamp view which".split(" "),fixHooks:{},keyHooks:{props:"char charCode key keyCode".split(" "),filter:function(a,b){return null==a.which&&(a.which=null!=b.charCode?b.charCode:b.keyCode),a}},mouseHooks:{props:"button buttons clientX clientY fromElement offsetX offsetY pageX pageY screenX screenY toElement".split(" "),filter:function(a,b){var c,d,e,f=b.button,g=b.fromElement;return null==a.pageX&&null!=b.clientX&&(d=a.target.ownerDocument||y,e=d.documentElement,c=d.body,a.pageX=b.clientX+(e&&e.scrollLeft||c&&c.scrollLeft||0)-(e&&e.clientLeft||c&&c.clientLeft||0),a.pageY=b.clientY+(e&&e.scrollTop||c&&c.scrollTop||0)-(e&&e.clientTop||c&&c.clientTop||0)),!a.relatedTarget&&g&&(a.relatedTarget=g===a.target?b.toElement:g),a.which||void 0===f||(a.which=1&f?1:2&f?3:4&f?2:0),a}},special:{load:{noBubble:!0},focus:{trigger:function(){if(this!==ca()&&this.focus)try{return this.focus(),!1}catch(a){}},delegateType:"focusin"},blur:{trigger:function(){return this===ca()&&this.blur?(this.blur(),!1):void 0},delegateType:"focusout"},click:{trigger:function(){return m.nodeName(this,"input")&&"checkbox"===this.type&&this.click?(this.click(),!1):void 0},_default:function(a){return m.nodeName(a.target,"a")}},beforeunload:{postDispatch:function(a){void 0!==a.result&&a.originalEvent&&(a.originalEvent.returnValue=a.result)}}},simulate:function(a,b,c,d){var e=m.extend(new m.Event,c,{type:a,isSimulated:!0,originalEvent:{}});d?m.event.trigger(e,null,b):m.event.dispatch.call(b,e),e.isDefaultPrevented()&&c.preventDefault()}},m.removeEvent=y.removeEventListener?function(a,b,c){a.removeEventListener&&a.removeEventListener(b,c,!1)}:function(a,b,c){var d="on"+b;a.detachEvent&&(typeof a[d]===K&&(a[d]=null),a.detachEvent(d,c))},m.Event=function(a,b){return this instanceof m.Event?(a&&a.type?(this.originalEvent=a,this.type=a.type,this.isDefaultPrevented=a.defaultPrevented||void 0===a.defaultPrevented&&a.returnValue===!1?aa:ba):this.type=a,b&&m.extend(this,b),this.timeStamp=a&&a.timeStamp||m.now(),void(this[m.expando]=!0)):new m.Event(a,b)},m.Event.prototype={isDefaultPrevented:ba,isPropagationStopped:ba,isImmediatePropagationStopped:ba,preventDefault:function(){var a=this.originalEvent;this.isDefaultPrevented=aa,a&&(a.preventDefault?a.preventDefault():a.returnValue=!1)},stopPropagation:function(){var a=this.originalEvent;this.isPropagationStopped=aa,a&&(a.stopPropagation&&a.stopPropagation(),a.cancelBubble=!0)},stopImmediatePropagation:function(){var a=this.originalEvent;this.isImmediatePropagationStopped=aa,a&&a.stopImmediatePropagation&&a.stopImmediatePropagation(),this.stopPropagation()}},m.each({mouseenter:"mouseover",mouseleave:"mouseout",pointerenter:"pointerover",pointerleave:"pointerout"},function(a,b){m.event.special[a]={delegateType:b,bindType:b,handle:function(a){var c,d=this,e=a.relatedTarget,f=a.handleObj;return(!e||e!==d&&!m.contains(d,e))&&(a.type=f.origType,c=f.handler.apply(this,arguments),a.type=b),c}}}),k.submitBubbles||(m.event.special.submit={setup:function(){return m.nodeName(this,"form")?!1:void m.event.add(this,"click._submit keypress._submit",function(a){var b=a.target,c=m.nodeName(b,"input")||m.nodeName(b,"button")?b.form:void 0;c&&!m._data(c,"submitBubbles")&&(m.event.add(c,"submit._submit",function(a){a._submit_bubble=!0}),m._data(c,"submitBubbles",!0))})},postDispatch:function(a){a._submit_bubble&&(delete a._submit_bubble,this.parentNode&&!a.isTrigger&&m.event.simulate("submit",this.parentNode,a,!0))},teardown:function(){return m.nodeName(this,"form")?!1:void m.event.remove(this,"._submit")}}),k.changeBubbles||(m.event.special.change={setup:function(){return X.test(this.nodeName)?(("checkbox"===this.type||"radio"===this.type)&&(m.event.add(this,"propertychange._change",function(a){"checked"===a.originalEvent.propertyName&&(this._just_changed=!0)}),m.event.add(this,"click._change",function(a){this._just_changed&&!a.isTrigger&&(this._just_changed=!1),m.event.simulate("change",this,a,!0)})),!1):void m.event.add(this,"beforeactivate._change",function(a){var b=a.target;X.test(b.nodeName)&&!m._data(b,"changeBubbles")&&(m.event.add(b,"change._change",function(a){!this.parentNode||a.isSimulated||a.isTrigger||m.event.simulate("change",this.parentNode,a,!0)}),m._data(b,"changeBubbles",!0))})},handle:function(a){var b=a.target;return this!==b||a.isSimulated||a.isTrigger||"radio"!==b.type&&"checkbox"!==b.type?a.handleObj.handler.apply(this,arguments):void 0},teardown:function(){return m.event.remove(this,"._change"),!X.test(this.nodeName)}}),k.focusinBubbles||m.each({focus:"focusin",blur:"focusout"},function(a,b){var c=function(a){m.event.simulate(b,a.target,m.event.fix(a),!0)};m.event.special[b]={setup:function(){var d=this.ownerDocument||this,e=m._data(d,b);e||d.addEventListener(a,c,!0),m._data(d,b,(e||0)+1)},teardown:function(){var d=this.ownerDocument||this,e=m._data(d,b)-1;e?m._data(d,b,e):(d.removeEventListener(a,c,!0),m._removeData(d,b))}}}),m.fn.extend({on:function(a,b,c,d,e){var f,g;if("object"==typeof a){"string"!=typeof b&&(c=c||b,b=void 0);for(f in a)this.on(f,b,c,a[f],e);return this}if(null==c&&null==d?(d=b,c=b=void 0):null==d&&("string"==typeof b?(d=c,c=void 0):(d=c,c=b,b=void 0)),d===!1)d=ba;else if(!d)return this;return 1===e&&(g=d,d=function(a){return m().off(a),g.apply(this,arguments)},d.guid=g.guid||(g.guid=m.guid++)),this.each(function(){m.event.add(this,a,d,c,b)})},one:function(a,b,c,d){return this.on(a,b,c,d,1)},off:function(a,b,c){var d,e;if(a&&a.preventDefault&&a.handleObj)return d=a.handleObj,m(a.delegateTarget).off(d.namespace?d.origType+"."+d.namespace:d.origType,d.selector,d.handler),this;if("object"==typeof a){for(e in a)this.off(e,b,a[e]);return this}return(b===!1||"function"==typeof b)&&(c=b,b=void 0),c===!1&&(c=ba),this.each(function(){m.event.remove(this,a,c,b)})},trigger:function(a,b){return this.each(function(){m.event.trigger(a,b,this)})},triggerHandler:function(a,b){var c=this[0];return c?m.event.trigger(a,b,c,!0):void 0}});function da(a){var b=ea.split("|"),c=a.createDocumentFragment();if(c.createElement)while(b.length)c.createElement(b.pop());return c}var ea="abbr|article|aside|audio|bdi|canvas|data|datalist|details|figcaption|figure|footer|header|hgroup|mark|meter|nav|output|progress|section|summary|time|video",fa=/ jQuery\d+="(?:null|\d+)"/g,ga=new RegExp("<(?:"+ea+")[\\s/>]","i"),ha=/^\s+/,ia=/<(?!area|br|col|embed|hr|img|input|link|meta|param)(([\w:]+)[^>]*)\/>/gi,ja=/<([\w:]+)/,ka=/<tbody/i,la=/<|&#?\w+;/,ma=/<(?:script|style|link)/i,na=/checked\s*(?:[^=]|=\s*.checked.)/i,oa=/^$|\/(?:java|ecma)script/i,pa=/^true\/(.*)/,qa=/^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g,ra={option:[1,"<select multiple='multiple'>","</select>"],legend:[1,"<fieldset>","</fieldset>"],area:[1,"<map>","</map>"],param:[1,"<object>","</object>"],thead:[1,"<table>","</table>"],tr:[2,"<table><tbody>","</tbody></table>"],col:[2,"<table><tbody></tbody><colgroup>","</colgroup></table>"],td:[3,"<table><tbody><tr>","</tr></tbody></table>"],_default:k.htmlSerialize?[0,"",""]:[1,"X<div>","</div>"]},sa=da(y),ta=sa.appendChild(y.createElement("div"));ra.optgroup=ra.option,ra.tbody=ra.tfoot=ra.colgroup=ra.caption=ra.thead,ra.th=ra.td;function ua(a,b){var c,d,e=0,f=typeof a.getElementsByTagName!==K?a.getElementsByTagName(b||"*"):typeof a.querySelectorAll!==K?a.querySelectorAll(b||"*"):void 0;if(!f)for(f=[],c=a.childNodes||a;null!=(d=c[e]);e++)!b||m.nodeName(d,b)?f.push(d):m.merge(f,ua(d,b));return void 0===b||b&&m.nodeName(a,b)?m.merge([a],f):f}function va(a){W.test(a.type)&&(a.defaultChecked=a.checked)}function wa(a,b){return m.nodeName(a,"table")&&m.nodeName(11!==b.nodeType?b:b.firstChild,"tr")?a.getElementsByTagName("tbody")[0]||a.appendChild(a.ownerDocument.createElement("tbody")):a}function xa(a){return a.type=(null!==m.find.attr(a,"type"))+"/"+a.type,a}function ya(a){var b=pa.exec(a.type);return b?a.type=b[1]:a.removeAttribute("type"),a}function za(a,b){for(var c,d=0;null!=(c=a[d]);d++)m._data(c,"globalEval",!b||m._data(b[d],"globalEval"))}function Aa(a,b){if(1===b.nodeType&&m.hasData(a)){var c,d,e,f=m._data(a),g=m._data(b,f),h=f.events;if(h){delete g.handle,g.events={};for(c in h)for(d=0,e=h[c].length;e>d;d++)m.event.add(b,c,h[c][d])}g.data&&(g.data=m.extend({},g.data))}}function Ba(a,b){var c,d,e;if(1===b.nodeType){if(c=b.nodeName.toLowerCase(),!k.noCloneEvent&&b[m.expando]){e=m._data(b);for(d in e.events)m.removeEvent(b,d,e.handle);b.removeAttribute(m.expando)}"script"===c&&b.text!==a.text?(xa(b).text=a.text,ya(b)):"object"===c?(b.parentNode&&(b.outerHTML=a.outerHTML),k.html5Clone&&a.innerHTML&&!m.trim(b.innerHTML)&&(b.innerHTML=a.innerHTML)):"input"===c&&W.test(a.type)?(b.defaultChecked=b.checked=a.checked,b.value!==a.value&&(b.value=a.value)):"option"===c?b.defaultSelected=b.selected=a.defaultSelected:("input"===c||"textarea"===c)&&(b.defaultValue=a.defaultValue)}}m.extend({clone:function(a,b,c){var d,e,f,g,h,i=m.contains(a.ownerDocument,a);if(k.html5Clone||m.isXMLDoc(a)||!ga.test("<"+a.nodeName+">")?f=a.cloneNode(!0):(ta.innerHTML=a.outerHTML,ta.removeChild(f=ta.firstChild)),!(k.noCloneEvent&&k.noCloneChecked||1!==a.nodeType&&11!==a.nodeType||m.isXMLDoc(a)))for(d=ua(f),h=ua(a),g=0;null!=(e=h[g]);++g)d[g]&&Ba(e,d[g]);if(b)if(c)for(h=h||ua(a),d=d||ua(f),g=0;null!=(e=h[g]);g++)Aa(e,d[g]);else Aa(a,f);return d=ua(f,"script"),d.length>0&&za(d,!i&&ua(a,"script")),d=h=e=null,f},buildFragment:function(a,b,c,d){for(var e,f,g,h,i,j,l,n=a.length,o=da(b),p=[],q=0;n>q;q++)if(f=a[q],f||0===f)if("object"===m.type(f))m.merge(p,f.nodeType?[f]:f);else if(la.test(f)){h=h||o.appendChild(b.createElement("div")),i=(ja.exec(f)||["",""])[1].toLowerCase(),l=ra[i]||ra._default,h.innerHTML=l[1]+f.replace(ia,"<$1></$2>")+l[2],e=l[0];while(e--)h=h.lastChild;if(!k.leadingWhitespace&&ha.test(f)&&p.push(b.createTextNode(ha.exec(f)[0])),!k.tbody){f="table"!==i||ka.test(f)?"<table>"!==l[1]||ka.test(f)?0:h:h.firstChild,e=f&&f.childNodes.length;while(e--)m.nodeName(j=f.childNodes[e],"tbody")&&!j.childNodes.length&&f.removeChild(j)}m.merge(p,h.childNodes),h.textContent="";while(h.firstChild)h.removeChild(h.firstChild);h=o.lastChild}else p.push(b.createTextNode(f));h&&o.removeChild(h),k.appendChecked||m.grep(ua(p,"input"),va),q=0;while(f=p[q++])if((!d||-1===m.inArray(f,d))&&(g=m.contains(f.ownerDocument,f),h=ua(o.appendChild(f),"script"),g&&za(h),c)){e=0;while(f=h[e++])oa.test(f.type||"")&&c.push(f)}return h=null,o},cleanData:function(a,b){for(var d,e,f,g,h=0,i=m.expando,j=m.cache,l=k.deleteExpando,n=m.event.special;null!=(d=a[h]);h++)if((b||m.acceptData(d))&&(f=d[i],g=f&&j[f])){if(g.events)for(e in g.events)n[e]?m.event.remove(d,e):m.removeEvent(d,e,g.handle);j[f]&&(delete j[f],l?delete d[i]:typeof d.removeAttribute!==K?d.removeAttribute(i):d[i]=null,c.push(f))}}}),m.fn.extend({text:function(a){return V(this,function(a){return void 0===a?m.text(this):this.empty().append((this[0]&&this[0].ownerDocument||y).createTextNode(a))},null,a,arguments.length)},append:function(){return this.domManip(arguments,function(a){if(1===this.nodeType||11===this.nodeType||9===this.nodeType){var b=wa(this,a);b.appendChild(a)}})},prepend:function(){return this.domManip(arguments,function(a){if(1===this.nodeType||11===this.nodeType||9===this.nodeType){var b=wa(this,a);b.insertBefore(a,b.firstChild)}})},before:function(){return this.domManip(arguments,function(a){this.parentNode&&this.parentNode.insertBefore(a,this)})},after:function(){return this.domManip(arguments,function(a){this.parentNode&&this.parentNode.insertBefore(a,this.nextSibling)})},remove:function(a,b){for(var c,d=a?m.filter(a,this):this,e=0;null!=(c=d[e]);e++)b||1!==c.nodeType||m.cleanData(ua(c)),c.parentNode&&(b&&m.contains(c.ownerDocument,c)&&za(ua(c,"script")),c.parentNode.removeChild(c));return this},empty:function(){for(var a,b=0;null!=(a=this[b]);b++){1===a.nodeType&&m.cleanData(ua(a,!1));while(a.firstChild)a.removeChild(a.firstChild);a.options&&m.nodeName(a,"select")&&(a.options.length=0)}return this},clone:function(a,b){return a=null==a?!1:a,b=null==b?a:b,this.map(function(){return m.clone(this,a,b)})},html:function(a){return V(this,function(a){var b=this[0]||{},c=0,d=this.length;if(void 0===a)return 1===b.nodeType?b.innerHTML.replace(fa,""):void 0;if(!("string"!=typeof a||ma.test(a)||!k.htmlSerialize&&ga.test(a)||!k.leadingWhitespace&&ha.test(a)||ra[(ja.exec(a)||["",""])[1].toLowerCase()])){a=a.replace(ia,"<$1></$2>");try{for(;d>c;c++)b=this[c]||{},1===b.nodeType&&(m.cleanData(ua(b,!1)),b.innerHTML=a);b=0}catch(e){}}b&&this.empty().append(a)},null,a,arguments.length)},replaceWith:function(){var a=arguments[0];return this.domManip(arguments,function(b){a=this.parentNode,m.cleanData(ua(this)),a&&a.replaceChild(b,this)}),a&&(a.length||a.nodeType)?this:this.remove()},detach:function(a){return this.remove(a,!0)},domManip:function(a,b){a=e.apply([],a);var c,d,f,g,h,i,j=0,l=this.length,n=this,o=l-1,p=a[0],q=m.isFunction(p);if(q||l>1&&"string"==typeof p&&!k.checkClone&&na.test(p))return this.each(function(c){var d=n.eq(c);q&&(a[0]=p.call(this,c,d.html())),d.domManip(a,b)});if(l&&(i=m.buildFragment(a,this[0].ownerDocument,!1,this),c=i.firstChild,1===i.childNodes.length&&(i=c),c)){for(g=m.map(ua(i,"script"),xa),f=g.length;l>j;j++)d=i,j!==o&&(d=m.clone(d,!0,!0),f&&m.merge(g,ua(d,"script"))),b.call(this[j],d,j);if(f)for(h=g[g.length-1].ownerDocument,m.map(g,ya),j=0;f>j;j++)d=g[j],oa.test(d.type||"")&&!m._data(d,"globalEval")&&m.contains(h,d)&&(d.src?m._evalUrl&&m._evalUrl(d.src):m.globalEval((d.text||d.textContent||d.innerHTML||"").replace(qa,"")));i=c=null}return this}}),m.each({appendTo:"append",prependTo:"prepend",insertBefore:"before",insertAfter:"after",replaceAll:"replaceWith"},function(a,b){m.fn[a]=function(a){for(var c,d=0,e=[],g=m(a),h=g.length-1;h>=d;d++)c=d===h?this:this.clone(!0),m(g[d])[b](c),f.apply(e,c.get());return this.pushStack(e)}});var Ca,Da={};function Ea(b,c){var d,e=m(c.createElement(b)).appendTo(c.body),f=a.getDefaultComputedStyle&&(d=a.getDefaultComputedStyle(e[0]))?d.display:m.css(e[0],"display");return e.detach(),f}function Fa(a){var b=y,c=Da[a];return c||(c=Ea(a,b),"none"!==c&&c||(Ca=(Ca||m("<iframe frameborder='0' width='0' height='0'/>")).appendTo(b.documentElement),b=(Ca[0].contentWindow||Ca[0].contentDocument).document,b.write(),b.close(),c=Ea(a,b),Ca.detach()),Da[a]=c),c}!function(){var a;k.shrinkWrapBlocks=function(){if(null!=a)return a;a=!1;var b,c,d;return c=y.getElementsByTagName("body")[0],c&&c.style?(b=y.createElement("div"),d=y.createElement("div"),d.style.cssText="position:absolute;border:0;width:0;height:0;top:0;left:-9999px",c.appendChild(d).appendChild(b),typeof b.style.zoom!==K&&(b.style.cssText="-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;display:block;margin:0;border:0;padding:1px;width:1px;zoom:1",b.appendChild(y.createElement("div")).style.width="5px",a=3!==b.offsetWidth),c.removeChild(d),a):void 0}}();var Ga=/^margin/,Ha=new RegExp("^("+S+")(?!px)[a-z%]+$","i"),Ia,Ja,Ka=/^(top|right|bottom|left)$/;a.getComputedStyle?(Ia=function(b){return b.ownerDocument.defaultView.opener?b.ownerDocument.defaultView.getComputedStyle(b,null):a.getComputedStyle(b,null)},Ja=function(a,b,c){var d,e,f,g,h=a.style;return c=c||Ia(a),g=c?c.getPropertyValue(b)||c[b]:void 0,c&&(""!==g||m.contains(a.ownerDocument,a)||(g=m.style(a,b)),Ha.test(g)&&Ga.test(b)&&(d=h.width,e=h.minWidth,f=h.maxWidth,h.minWidth=h.maxWidth=h.width=g,g=c.width,h.width=d,h.minWidth=e,h.maxWidth=f)),void 0===g?g:g+""}):y.documentElement.currentStyle&&(Ia=function(a){return a.currentStyle},Ja=function(a,b,c){var d,e,f,g,h=a.style;return c=c||Ia(a),g=c?c[b]:void 0,null==g&&h&&h[b]&&(g=h[b]),Ha.test(g)&&!Ka.test(b)&&(d=h.left,e=a.runtimeStyle,f=e&&e.left,f&&(e.left=a.currentStyle.left),h.left="fontSize"===b?"1em":g,g=h.pixelLeft+"px",h.left=d,f&&(e.left=f)),void 0===g?g:g+""||"auto"});function La(a,b){return{get:function(){var c=a();if(null!=c)return c?void delete this.get:(this.get=b).apply(this,arguments)}}}!function(){var b,c,d,e,f,g,h;if(b=y.createElement("div"),b.innerHTML=" <link/><table></table><a href='/a'>a</a><input type='checkbox'/>",d=b.getElementsByTagName("a")[0],c=d&&d.style){c.cssText="float:left;opacity:.5",k.opacity="0.5"===c.opacity,k.cssFloat=!!c.cssFloat,b.style.backgroundClip="content-box",b.cloneNode(!0).style.backgroundClip="",k.clearCloneStyle="content-box"===b.style.backgroundClip,k.boxSizing=""===c.boxSizing||""===c.MozBoxSizing||""===c.WebkitBoxSizing,m.extend(k,{reliableHiddenOffsets:function(){return null==g&&i(),g},boxSizingReliable:function(){return null==f&&i(),f},pixelPosition:function(){return null==e&&i(),e},reliableMarginRight:function(){return null==h&&i(),h}});function i(){var b,c,d,i;c=y.getElementsByTagName("body")[0],c&&c.style&&(b=y.createElement("div"),d=y.createElement("div"),d.style.cssText="position:absolute;border:0;width:0;height:0;top:0;left:-9999px",c.appendChild(d).appendChild(b),b.style.cssText="-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;display:block;margin-top:1%;top:1%;border:1px;padding:1px;width:4px;position:absolute",e=f=!1,h=!0,a.getComputedStyle&&(e="1%"!==(a.getComputedStyle(b,null)||{}).top,f="4px"===(a.getComputedStyle(b,null)||{width:"4px"}).width,i=b.appendChild(y.createElement("div")),i.style.cssText=b.style.cssText="-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;display:block;margin:0;border:0;padding:0",i.style.marginRight=i.style.width="0",b.style.width="1px",h=!parseFloat((a.getComputedStyle(i,null)||{}).marginRight),b.removeChild(i)),b.innerHTML="<table><tr><td></td><td>t</td></tr></table>",i=b.getElementsByTagName("td"),i[0].style.cssText="margin:0;border:0;padding:0;display:none",g=0===i[0].offsetHeight,g&&(i[0].style.display="",i[1].style.display="none",g=0===i[0].offsetHeight),c.removeChild(d))}}}(),m.swap=function(a,b,c,d){var e,f,g={};for(f in b)g[f]=a.style[f],a.style[f]=b[f];e=c.apply(a,d||[]);for(f in b)a.style[f]=g[f];return e};var Ma=/alpha\([^)]*\)/i,Na=/opacity\s*=\s*([^)]*)/,Oa=/^(none|table(?!-c[ea]).+)/,Pa=new RegExp("^("+S+")(.*)$","i"),Qa=new RegExp("^([+-])=("+S+")","i"),Ra={position:"absolute",visibility:"hidden",display:"block"},Sa={letterSpacing:"0",fontWeight:"400"},Ta=["Webkit","O","Moz","ms"];function Ua(a,b){if(b in a)return b;var c=b.charAt(0).toUpperCase()+b.slice(1),d=b,e=Ta.length;while(e--)if(b=Ta[e]+c,b in a)return b;return d}function Va(a,b){for(var c,d,e,f=[],g=0,h=a.length;h>g;g++)d=a[g],d.style&&(f[g]=m._data(d,"olddisplay"),c=d.style.display,b?(f[g]||"none"!==c||(d.style.display=""),""===d.style.display&&U(d)&&(f[g]=m._data(d,"olddisplay",Fa(d.nodeName)))):(e=U(d),(c&&"none"!==c||!e)&&m._data(d,"olddisplay",e?c:m.css(d,"display"))));for(g=0;h>g;g++)d=a[g],d.style&&(b&&"none"!==d.style.display&&""!==d.style.display||(d.style.display=b?f[g]||"":"none"));return a}function Wa(a,b,c){var d=Pa.exec(b);return d?Math.max(0,d[1]-(c||0))+(d[2]||"px"):b}function Xa(a,b,c,d,e){for(var f=c===(d?"border":"content")?4:"width"===b?1:0,g=0;4>f;f+=2)"margin"===c&&(g+=m.css(a,c+T[f],!0,e)),d?("content"===c&&(g-=m.css(a,"padding"+T[f],!0,e)),"margin"!==c&&(g-=m.css(a,"border"+T[f]+"Width",!0,e))):(g+=m.css(a,"padding"+T[f],!0,e),"padding"!==c&&(g+=m.css(a,"border"+T[f]+"Width",!0,e)));return g}function Ya(a,b,c){var d=!0,e="width"===b?a.offsetWidth:a.offsetHeight,f=Ia(a),g=k.boxSizing&&"border-box"===m.css(a,"boxSizing",!1,f);if(0>=e||null==e){if(e=Ja(a,b,f),(0>e||null==e)&&(e=a.style[b]),Ha.test(e))return e;d=g&&(k.boxSizingReliable()||e===a.style[b]),e=parseFloat(e)||0}return e+Xa(a,b,c||(g?"border":"content"),d,f)+"px"}m.extend({cssHooks:{opacity:{get:function(a,b){if(b){var c=Ja(a,"opacity");return""===c?"1":c}}}},cssNumber:{columnCount:!0,fillOpacity:!0,flexGrow:!0,flexShrink:!0,fontWeight:!0,lineHeight:!0,opacity:!0,order:!0,orphans:!0,widows:!0,zIndex:!0,zoom:!0},cssProps:{"float":k.cssFloat?"cssFloat":"styleFloat"},style:function(a,b,c,d){if(a&&3!==a.nodeType&&8!==a.nodeType&&a.style){var e,f,g,h=m.camelCase(b),i=a.style;if(b=m.cssProps[h]||(m.cssProps[h]=Ua(i,h)),g=m.cssHooks[b]||m.cssHooks[h],void 0===c)return g&&"get"in g&&void 0!==(e=g.get(a,!1,d))?e:i[b];if(f=typeof c,"string"===f&&(e=Qa.exec(c))&&(c=(e[1]+1)*e[2]+parseFloat(m.css(a,b)),f="number"),null!=c&&c===c&&("number"!==f||m.cssNumber[h]||(c+="px"),k.clearCloneStyle||""!==c||0!==b.indexOf("background")||(i[b]="inherit"),!(g&&"set"in g&&void 0===(c=g.set(a,c,d)))))try{i[b]=c}catch(j){}}},css:function(a,b,c,d){var e,f,g,h=m.camelCase(b);return b=m.cssProps[h]||(m.cssProps[h]=Ua(a.style,h)),g=m.cssHooks[b]||m.cssHooks[h],g&&"get"in g&&(f=g.get(a,!0,c)),void 0===f&&(f=Ja(a,b,d)),"normal"===f&&b in Sa&&(f=Sa[b]),""===c||c?(e=parseFloat(f),c===!0||m.isNumeric(e)?e||0:f):f}}),m.each(["height","width"],function(a,b){m.cssHooks[b]={get:function(a,c,d){return c?Oa.test(m.css(a,"display"))&&0===a.offsetWidth?m.swap(a,Ra,function(){return Ya(a,b,d)}):Ya(a,b,d):void 0},set:function(a,c,d){var e=d&&Ia(a);return Wa(a,c,d?Xa(a,b,d,k.boxSizing&&"border-box"===m.css(a,"boxSizing",!1,e),e):0)}}}),k.opacity||(m.cssHooks.opacity={get:function(a,b){return Na.test((b&&a.currentStyle?a.currentStyle.filter:a.style.filter)||"")?.01*parseFloat(RegExp.$1)+"":b?"1":""},set:function(a,b){var c=a.style,d=a.currentStyle,e=m.isNumeric(b)?"alpha(opacity="+100*b+")":"",f=d&&d.filter||c.filter||"";c.zoom=1,(b>=1||""===b)&&""===m.trim(f.replace(Ma,""))&&c.removeAttribute&&(c.removeAttribute("filter"),""===b||d&&!d.filter)||(c.filter=Ma.test(f)?f.replace(Ma,e):f+" "+e)}}),m.cssHooks.marginRight=La(k.reliableMarginRight,function(a,b){return b?m.swap(a,{display:"inline-block"},Ja,[a,"marginRight"]):void 0}),m.each({margin:"",padding:"",border:"Width"},function(a,b){m.cssHooks[a+b]={expand:function(c){for(var d=0,e={},f="string"==typeof c?c.split(" "):[c];4>d;d++)e[a+T[d]+b]=f[d]||f[d-2]||f[0];return e}},Ga.test(a)||(m.cssHooks[a+b].set=Wa)}),m.fn.extend({css:function(a,b){return V(this,function(a,b,c){var d,e,f={},g=0;if(m.isArray(b)){for(d=Ia(a),e=b.length;e>g;g++)f[b[g]]=m.css(a,b[g],!1,d);return f}return void 0!==c?m.style(a,b,c):m.css(a,b)},a,b,arguments.length>1)},show:function(){return Va(this,!0)},hide:function(){return Va(this)},toggle:function(a){return"boolean"==typeof a?a?this.show():this.hide():this.each(function(){U(this)?m(this).show():m(this).hide()})}});function Za(a,b,c,d,e){
return new Za.prototype.init(a,b,c,d,e)}m.Tween=Za,Za.prototype={constructor:Za,init:function(a,b,c,d,e,f){this.elem=a,this.prop=c,this.easing=e||"swing",this.options=b,this.start=this.now=this.cur(),this.end=d,this.unit=f||(m.cssNumber[c]?"":"px")},cur:function(){var a=Za.propHooks[this.prop];return a&&a.get?a.get(this):Za.propHooks._default.get(this)},run:function(a){var b,c=Za.propHooks[this.prop];return this.options.duration?this.pos=b=m.easing[this.easing](a,this.options.duration*a,0,1,this.options.duration):this.pos=b=a,this.now=(this.end-this.start)*b+this.start,this.options.step&&this.options.step.call(this.elem,this.now,this),c&&c.set?c.set(this):Za.propHooks._default.set(this),this}},Za.prototype.init.prototype=Za.prototype,Za.propHooks={_default:{get:function(a){var b;return null==a.elem[a.prop]||a.elem.style&&null!=a.elem.style[a.prop]?(b=m.css(a.elem,a.prop,""),b&&"auto"!==b?b:0):a.elem[a.prop]},set:function(a){m.fx.step[a.prop]?m.fx.step[a.prop](a):a.elem.style&&(null!=a.elem.style[m.cssProps[a.prop]]||m.cssHooks[a.prop])?m.style(a.elem,a.prop,a.now+a.unit):a.elem[a.prop]=a.now}}},Za.propHooks.scrollTop=Za.propHooks.scrollLeft={set:function(a){a.elem.nodeType&&a.elem.parentNode&&(a.elem[a.prop]=a.now)}},m.easing={linear:function(a){return a},swing:function(a){return.5-Math.cos(a*Math.PI)/2}},m.fx=Za.prototype.init,m.fx.step={};var $a,_a,ab=/^(?:toggle|show|hide)$/,bb=new RegExp("^(?:([+-])=|)("+S+")([a-z%]*)$","i"),cb=/queueHooks$/,db=[ib],eb={"*":[function(a,b){var c=this.createTween(a,b),d=c.cur(),e=bb.exec(b),f=e&&e[3]||(m.cssNumber[a]?"":"px"),g=(m.cssNumber[a]||"px"!==f&&+d)&&bb.exec(m.css(c.elem,a)),h=1,i=20;if(g&&g[3]!==f){f=f||g[3],e=e||[],g=+d||1;do h=h||".5",g/=h,m.style(c.elem,a,g+f);while(h!==(h=c.cur()/d)&&1!==h&&--i)}return e&&(g=c.start=+g||+d||0,c.unit=f,c.end=e[1]?g+(e[1]+1)*e[2]:+e[2]),c}]};function fb(){return setTimeout(function(){$a=void 0}),$a=m.now()}function gb(a,b){var c,d={height:a},e=0;for(b=b?1:0;4>e;e+=2-b)c=T[e],d["margin"+c]=d["padding"+c]=a;return b&&(d.opacity=d.width=a),d}function hb(a,b,c){for(var d,e=(eb[b]||[]).concat(eb["*"]),f=0,g=e.length;g>f;f++)if(d=e[f].call(c,b,a))return d}function ib(a,b,c){var d,e,f,g,h,i,j,l,n=this,o={},p=a.style,q=a.nodeType&&U(a),r=m._data(a,"fxshow");c.queue||(h=m._queueHooks(a,"fx"),null==h.unqueued&&(h.unqueued=0,i=h.empty.fire,h.empty.fire=function(){h.unqueued||i()}),h.unqueued++,n.always(function(){n.always(function(){h.unqueued--,m.queue(a,"fx").length||h.empty.fire()})})),1===a.nodeType&&("height"in b||"width"in b)&&(c.overflow=[p.overflow,p.overflowX,p.overflowY],j=m.css(a,"display"),l="none"===j?m._data(a,"olddisplay")||Fa(a.nodeName):j,"inline"===l&&"none"===m.css(a,"float")&&(k.inlineBlockNeedsLayout&&"inline"!==Fa(a.nodeName)?p.zoom=1:p.display="inline-block")),c.overflow&&(p.overflow="hidden",k.shrinkWrapBlocks()||n.always(function(){p.overflow=c.overflow[0],p.overflowX=c.overflow[1],p.overflowY=c.overflow[2]}));for(d in b)if(e=b[d],ab.exec(e)){if(delete b[d],f=f||"toggle"===e,e===(q?"hide":"show")){if("show"!==e||!r||void 0===r[d])continue;q=!0}o[d]=r&&r[d]||m.style(a,d)}else j=void 0;if(m.isEmptyObject(o))"inline"===("none"===j?Fa(a.nodeName):j)&&(p.display=j);else{r?"hidden"in r&&(q=r.hidden):r=m._data(a,"fxshow",{}),f&&(r.hidden=!q),q?m(a).show():n.done(function(){m(a).hide()}),n.done(function(){var b;m._removeData(a,"fxshow");for(b in o)m.style(a,b,o[b])});for(d in o)g=hb(q?r[d]:0,d,n),d in r||(r[d]=g.start,q&&(g.end=g.start,g.start="width"===d||"height"===d?1:0))}}function jb(a,b){var c,d,e,f,g;for(c in a)if(d=m.camelCase(c),e=b[d],f=a[c],m.isArray(f)&&(e=f[1],f=a[c]=f[0]),c!==d&&(a[d]=f,delete a[c]),g=m.cssHooks[d],g&&"expand"in g){f=g.expand(f),delete a[d];for(c in f)c in a||(a[c]=f[c],b[c]=e)}else b[d]=e}function kb(a,b,c){var d,e,f=0,g=db.length,h=m.Deferred().always(function(){delete i.elem}),i=function(){if(e)return!1;for(var b=$a||fb(),c=Math.max(0,j.startTime+j.duration-b),d=c/j.duration||0,f=1-d,g=0,i=j.tweens.length;i>g;g++)j.tweens[g].run(f);return h.notifyWith(a,[j,f,c]),1>f&&i?c:(h.resolveWith(a,[j]),!1)},j=h.promise({elem:a,props:m.extend({},b),opts:m.extend(!0,{specialEasing:{}},c),originalProperties:b,originalOptions:c,startTime:$a||fb(),duration:c.duration,tweens:[],createTween:function(b,c){var d=m.Tween(a,j.opts,b,c,j.opts.specialEasing[b]||j.opts.easing);return j.tweens.push(d),d},stop:function(b){var c=0,d=b?j.tweens.length:0;if(e)return this;for(e=!0;d>c;c++)j.tweens[c].run(1);return b?h.resolveWith(a,[j,b]):h.rejectWith(a,[j,b]),this}}),k=j.props;for(jb(k,j.opts.specialEasing);g>f;f++)if(d=db[f].call(j,a,k,j.opts))return d;return m.map(k,hb,j),m.isFunction(j.opts.start)&&j.opts.start.call(a,j),m.fx.timer(m.extend(i,{elem:a,anim:j,queue:j.opts.queue})),j.progress(j.opts.progress).done(j.opts.done,j.opts.complete).fail(j.opts.fail).always(j.opts.always)}m.Animation=m.extend(kb,{tweener:function(a,b){m.isFunction(a)?(b=a,a=["*"]):a=a.split(" ");for(var c,d=0,e=a.length;e>d;d++)c=a[d],eb[c]=eb[c]||[],eb[c].unshift(b)},prefilter:function(a,b){b?db.unshift(a):db.push(a)}}),m.speed=function(a,b,c){var d=a&&"object"==typeof a?m.extend({},a):{complete:c||!c&&b||m.isFunction(a)&&a,duration:a,easing:c&&b||b&&!m.isFunction(b)&&b};return d.duration=m.fx.off?0:"number"==typeof d.duration?d.duration:d.duration in m.fx.speeds?m.fx.speeds[d.duration]:m.fx.speeds._default,(null==d.queue||d.queue===!0)&&(d.queue="fx"),d.old=d.complete,d.complete=function(){m.isFunction(d.old)&&d.old.call(this),d.queue&&m.dequeue(this,d.queue)},d},m.fn.extend({fadeTo:function(a,b,c,d){return this.filter(U).css("opacity",0).show().end().animate({opacity:b},a,c,d)},animate:function(a,b,c,d){var e=m.isEmptyObject(a),f=m.speed(b,c,d),g=function(){var b=kb(this,m.extend({},a),f);(e||m._data(this,"finish"))&&b.stop(!0)};return g.finish=g,e||f.queue===!1?this.each(g):this.queue(f.queue,g)},stop:function(a,b,c){var d=function(a){var b=a.stop;delete a.stop,b(c)};return"string"!=typeof a&&(c=b,b=a,a=void 0),b&&a!==!1&&this.queue(a||"fx",[]),this.each(function(){var b=!0,e=null!=a&&a+"queueHooks",f=m.timers,g=m._data(this);if(e)g[e]&&g[e].stop&&d(g[e]);else for(e in g)g[e]&&g[e].stop&&cb.test(e)&&d(g[e]);for(e=f.length;e--;)f[e].elem!==this||null!=a&&f[e].queue!==a||(f[e].anim.stop(c),b=!1,f.splice(e,1));(b||!c)&&m.dequeue(this,a)})},finish:function(a){return a!==!1&&(a=a||"fx"),this.each(function(){var b,c=m._data(this),d=c[a+"queue"],e=c[a+"queueHooks"],f=m.timers,g=d?d.length:0;for(c.finish=!0,m.queue(this,a,[]),e&&e.stop&&e.stop.call(this,!0),b=f.length;b--;)f[b].elem===this&&f[b].queue===a&&(f[b].anim.stop(!0),f.splice(b,1));for(b=0;g>b;b++)d[b]&&d[b].finish&&d[b].finish.call(this);delete c.finish})}}),m.each(["toggle","show","hide"],function(a,b){var c=m.fn[b];m.fn[b]=function(a,d,e){return null==a||"boolean"==typeof a?c.apply(this,arguments):this.animate(gb(b,!0),a,d,e)}}),m.each({slideDown:gb("show"),slideUp:gb("hide"),slideToggle:gb("toggle"),fadeIn:{opacity:"show"},fadeOut:{opacity:"hide"},fadeToggle:{opacity:"toggle"}},function(a,b){m.fn[a]=function(a,c,d){return this.animate(b,a,c,d)}}),m.timers=[],m.fx.tick=function(){var a,b=m.timers,c=0;for($a=m.now();c<b.length;c++)a=b[c],a()||b[c]!==a||b.splice(c--,1);b.length||m.fx.stop(),$a=void 0},m.fx.timer=function(a){m.timers.push(a),a()?m.fx.start():m.timers.pop()},m.fx.interval=13,m.fx.start=function(){_a||(_a=setInterval(m.fx.tick,m.fx.interval))},m.fx.stop=function(){clearInterval(_a),_a=null},m.fx.speeds={slow:600,fast:200,_default:400},m.fn.delay=function(a,b){return a=m.fx?m.fx.speeds[a]||a:a,b=b||"fx",this.queue(b,function(b,c){var d=setTimeout(b,a);c.stop=function(){clearTimeout(d)}})},function(){var a,b,c,d,e;b=y.createElement("div"),b.setAttribute("className","t"),b.innerHTML=" <link/><table></table><a href='/a'>a</a><input type='checkbox'/>",d=b.getElementsByTagName("a")[0],c=y.createElement("select"),e=c.appendChild(y.createElement("option")),a=b.getElementsByTagName("input")[0],d.style.cssText="top:1px",k.getSetAttribute="t"!==b.className,k.style=/top/.test(d.getAttribute("style")),k.hrefNormalized="/a"===d.getAttribute("href"),k.checkOn=!!a.value,k.optSelected=e.selected,k.enctype=!!y.createElement("form").enctype,c.disabled=!0,k.optDisabled=!e.disabled,a=y.createElement("input"),a.setAttribute("value",""),k.input=""===a.getAttribute("value"),a.value="t",a.setAttribute("type","radio"),k.radioValue="t"===a.value}();var lb=/\r/g;m.fn.extend({val:function(a){var b,c,d,e=this[0];{if(arguments.length)return d=m.isFunction(a),this.each(function(c){var e;1===this.nodeType&&(e=d?a.call(this,c,m(this).val()):a,null==e?e="":"number"==typeof e?e+="":m.isArray(e)&&(e=m.map(e,function(a){return null==a?"":a+""})),b=m.valHooks[this.type]||m.valHooks[this.nodeName.toLowerCase()],b&&"set"in b&&void 0!==b.set(this,e,"value")||(this.value=e))});if(e)return b=m.valHooks[e.type]||m.valHooks[e.nodeName.toLowerCase()],b&&"get"in b&&void 0!==(c=b.get(e,"value"))?c:(c=e.value,"string"==typeof c?c.replace(lb,""):null==c?"":c)}}}),m.extend({valHooks:{option:{get:function(a){var b=m.find.attr(a,"value");return null!=b?b:m.trim(m.text(a))}},select:{get:function(a){for(var b,c,d=a.options,e=a.selectedIndex,f="select-one"===a.type||0>e,g=f?null:[],h=f?e+1:d.length,i=0>e?h:f?e:0;h>i;i++)if(c=d[i],!(!c.selected&&i!==e||(k.optDisabled?c.disabled:null!==c.getAttribute("disabled"))||c.parentNode.disabled&&m.nodeName(c.parentNode,"optgroup"))){if(b=m(c).val(),f)return b;g.push(b)}return g},set:function(a,b){var c,d,e=a.options,f=m.makeArray(b),g=e.length;while(g--)if(d=e[g],m.inArray(m.valHooks.option.get(d),f)>=0)try{d.selected=c=!0}catch(h){d.scrollHeight}else d.selected=!1;return c||(a.selectedIndex=-1),e}}}}),m.each(["radio","checkbox"],function(){m.valHooks[this]={set:function(a,b){return m.isArray(b)?a.checked=m.inArray(m(a).val(),b)>=0:void 0}},k.checkOn||(m.valHooks[this].get=function(a){return null===a.getAttribute("value")?"on":a.value})});var mb,nb,ob=m.expr.attrHandle,pb=/^(?:checked|selected)$/i,qb=k.getSetAttribute,rb=k.input;m.fn.extend({attr:function(a,b){return V(this,m.attr,a,b,arguments.length>1)},removeAttr:function(a){return this.each(function(){m.removeAttr(this,a)})}}),m.extend({attr:function(a,b,c){var d,e,f=a.nodeType;if(a&&3!==f&&8!==f&&2!==f)return typeof a.getAttribute===K?m.prop(a,b,c):(1===f&&m.isXMLDoc(a)||(b=b.toLowerCase(),d=m.attrHooks[b]||(m.expr.match.bool.test(b)?nb:mb)),void 0===c?d&&"get"in d&&null!==(e=d.get(a,b))?e:(e=m.find.attr(a,b),null==e?void 0:e):null!==c?d&&"set"in d&&void 0!==(e=d.set(a,c,b))?e:(a.setAttribute(b,c+""),c):void m.removeAttr(a,b))},removeAttr:function(a,b){var c,d,e=0,f=b&&b.match(E);if(f&&1===a.nodeType)while(c=f[e++])d=m.propFix[c]||c,m.expr.match.bool.test(c)?rb&&qb||!pb.test(c)?a[d]=!1:a[m.camelCase("default-"+c)]=a[d]=!1:m.attr(a,c,""),a.removeAttribute(qb?c:d)},attrHooks:{type:{set:function(a,b){if(!k.radioValue&&"radio"===b&&m.nodeName(a,"input")){var c=a.value;return a.setAttribute("type",b),c&&(a.value=c),b}}}}}),nb={set:function(a,b,c){return b===!1?m.removeAttr(a,c):rb&&qb||!pb.test(c)?a.setAttribute(!qb&&m.propFix[c]||c,c):a[m.camelCase("default-"+c)]=a[c]=!0,c}},m.each(m.expr.match.bool.source.match(/\w+/g),function(a,b){var c=ob[b]||m.find.attr;ob[b]=rb&&qb||!pb.test(b)?function(a,b,d){var e,f;return d||(f=ob[b],ob[b]=e,e=null!=c(a,b,d)?b.toLowerCase():null,ob[b]=f),e}:function(a,b,c){return c?void 0:a[m.camelCase("default-"+b)]?b.toLowerCase():null}}),rb&&qb||(m.attrHooks.value={set:function(a,b,c){return m.nodeName(a,"input")?void(a.defaultValue=b):mb&&mb.set(a,b,c)}}),qb||(mb={set:function(a,b,c){var d=a.getAttributeNode(c);return d||a.setAttributeNode(d=a.ownerDocument.createAttribute(c)),d.value=b+="","value"===c||b===a.getAttribute(c)?b:void 0}},ob.id=ob.name=ob.coords=function(a,b,c){var d;return c?void 0:(d=a.getAttributeNode(b))&&""!==d.value?d.value:null},m.valHooks.button={get:function(a,b){var c=a.getAttributeNode(b);return c&&c.specified?c.value:void 0},set:mb.set},m.attrHooks.contenteditable={set:function(a,b,c){mb.set(a,""===b?!1:b,c)}},m.each(["width","height"],function(a,b){m.attrHooks[b]={set:function(a,c){return""===c?(a.setAttribute(b,"auto"),c):void 0}}})),k.style||(m.attrHooks.style={get:function(a){return a.style.cssText||void 0},set:function(a,b){return a.style.cssText=b+""}});var sb=/^(?:input|select|textarea|button|object)$/i,tb=/^(?:a|area)$/i;m.fn.extend({prop:function(a,b){return V(this,m.prop,a,b,arguments.length>1)},removeProp:function(a){return a=m.propFix[a]||a,this.each(function(){try{this[a]=void 0,delete this[a]}catch(b){}})}}),m.extend({propFix:{"for":"htmlFor","class":"className"},prop:function(a,b,c){var d,e,f,g=a.nodeType;if(a&&3!==g&&8!==g&&2!==g)return f=1!==g||!m.isXMLDoc(a),f&&(b=m.propFix[b]||b,e=m.propHooks[b]),void 0!==c?e&&"set"in e&&void 0!==(d=e.set(a,c,b))?d:a[b]=c:e&&"get"in e&&null!==(d=e.get(a,b))?d:a[b]},propHooks:{tabIndex:{get:function(a){var b=m.find.attr(a,"tabindex");return b?parseInt(b,10):sb.test(a.nodeName)||tb.test(a.nodeName)&&a.href?0:-1}}}}),k.hrefNormalized||m.each(["href","src"],function(a,b){m.propHooks[b]={get:function(a){return a.getAttribute(b,4)}}}),k.optSelected||(m.propHooks.selected={get:function(a){var b=a.parentNode;return b&&(b.selectedIndex,b.parentNode&&b.parentNode.selectedIndex),null}}),m.each(["tabIndex","readOnly","maxLength","cellSpacing","cellPadding","rowSpan","colSpan","useMap","frameBorder","contentEditable"],function(){m.propFix[this.toLowerCase()]=this}),k.enctype||(m.propFix.enctype="encoding");var ub=/[\t\r\n\f]/g;m.fn.extend({addClass:function(a){var b,c,d,e,f,g,h=0,i=this.length,j="string"==typeof a&&a;if(m.isFunction(a))return this.each(function(b){m(this).addClass(a.call(this,b,this.className))});if(j)for(b=(a||"").match(E)||[];i>h;h++)if(c=this[h],d=1===c.nodeType&&(c.className?(" "+c.className+" ").replace(ub," "):" ")){f=0;while(e=b[f++])d.indexOf(" "+e+" ")<0&&(d+=e+" ");g=m.trim(d),c.className!==g&&(c.className=g)}return this},removeClass:function(a){var b,c,d,e,f,g,h=0,i=this.length,j=0===arguments.length||"string"==typeof a&&a;if(m.isFunction(a))return this.each(function(b){m(this).removeClass(a.call(this,b,this.className))});if(j)for(b=(a||"").match(E)||[];i>h;h++)if(c=this[h],d=1===c.nodeType&&(c.className?(" "+c.className+" ").replace(ub," "):"")){f=0;while(e=b[f++])while(d.indexOf(" "+e+" ")>=0)d=d.replace(" "+e+" "," ");g=a?m.trim(d):"",c.className!==g&&(c.className=g)}return this},toggleClass:function(a,b){var c=typeof a;return"boolean"==typeof b&&"string"===c?b?this.addClass(a):this.removeClass(a):this.each(m.isFunction(a)?function(c){m(this).toggleClass(a.call(this,c,this.className,b),b)}:function(){if("string"===c){var b,d=0,e=m(this),f=a.match(E)||[];while(b=f[d++])e.hasClass(b)?e.removeClass(b):e.addClass(b)}else(c===K||"boolean"===c)&&(this.className&&m._data(this,"__className__",this.className),this.className=this.className||a===!1?"":m._data(this,"__className__")||"")})},hasClass:function(a){for(var b=" "+a+" ",c=0,d=this.length;d>c;c++)if(1===this[c].nodeType&&(" "+this[c].className+" ").replace(ub," ").indexOf(b)>=0)return!0;return!1}}),m.each("blur focus focusin focusout load resize scroll unload click dblclick mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave change select submit keydown keypress keyup error contextmenu".split(" "),function(a,b){m.fn[b]=function(a,c){return arguments.length>0?this.on(b,null,a,c):this.trigger(b)}}),m.fn.extend({hover:function(a,b){return this.mouseenter(a).mouseleave(b||a)},bind:function(a,b,c){return this.on(a,null,b,c)},unbind:function(a,b){return this.off(a,null,b)},delegate:function(a,b,c,d){return this.on(b,a,c,d)},undelegate:function(a,b,c){return 1===arguments.length?this.off(a,"**"):this.off(b,a||"**",c)}});var vb=m.now(),wb=/\?/,xb=/(,)|(\[|{)|(}|])|"(?:[^"\\\r\n]|\\["\\\/bfnrt]|\\u[\da-fA-F]{4})*"\s*:?|true|false|null|-?(?!0\d)\d+(?:\.\d+|)(?:[eE][+-]?\d+|)/g;m.parseJSON=function(b){if(a.JSON&&a.JSON.parse)return a.JSON.parse(b+"");var c,d=null,e=m.trim(b+"");return e&&!m.trim(e.replace(xb,function(a,b,e,f){return c&&b&&(d=0),0===d?a:(c=e||b,d+=!f-!e,"")}))?Function("return "+e)():m.error("Invalid JSON: "+b)},m.parseXML=function(b){var c,d;if(!b||"string"!=typeof b)return null;try{a.DOMParser?(d=new DOMParser,c=d.parseFromString(b,"text/xml")):(c=new ActiveXObject("Microsoft.XMLDOM"),c.async="false",c.loadXML(b))}catch(e){c=void 0}return c&&c.documentElement&&!c.getElementsByTagName("parsererror").length||m.error("Invalid XML: "+b),c};var yb,zb,Ab=/#.*$/,Bb=/([?&])_=[^&]*/,Cb=/^(.*?):[ \t]*([^\r\n]*)\r?$/gm,Db=/^(?:about|app|app-storage|.+-extension|file|res|widget):$/,Eb=/^(?:GET|HEAD)$/,Fb=/^\/\//,Gb=/^([\w.+-]+:)(?:\/\/(?:[^\/?#]*@|)([^\/?#:]*)(?::(\d+)|)|)/,Hb={},Ib={},Jb="*/".concat("*");try{zb=location.href}catch(Kb){zb=y.createElement("a"),zb.href="",zb=zb.href}yb=Gb.exec(zb.toLowerCase())||[];function Lb(a){return function(b,c){"string"!=typeof b&&(c=b,b="*");var d,e=0,f=b.toLowerCase().match(E)||[];if(m.isFunction(c))while(d=f[e++])"+"===d.charAt(0)?(d=d.slice(1)||"*",(a[d]=a[d]||[]).unshift(c)):(a[d]=a[d]||[]).push(c)}}function Mb(a,b,c,d){var e={},f=a===Ib;function g(h){var i;return e[h]=!0,m.each(a[h]||[],function(a,h){var j=h(b,c,d);return"string"!=typeof j||f||e[j]?f?!(i=j):void 0:(b.dataTypes.unshift(j),g(j),!1)}),i}return g(b.dataTypes[0])||!e["*"]&&g("*")}function Nb(a,b){var c,d,e=m.ajaxSettings.flatOptions||{};for(d in b)void 0!==b[d]&&((e[d]?a:c||(c={}))[d]=b[d]);return c&&m.extend(!0,a,c),a}function Ob(a,b,c){var d,e,f,g,h=a.contents,i=a.dataTypes;while("*"===i[0])i.shift(),void 0===e&&(e=a.mimeType||b.getResponseHeader("Content-Type"));if(e)for(g in h)if(h[g]&&h[g].test(e)){i.unshift(g);break}if(i[0]in c)f=i[0];else{for(g in c){if(!i[0]||a.converters[g+" "+i[0]]){f=g;break}d||(d=g)}f=f||d}return f?(f!==i[0]&&i.unshift(f),c[f]):void 0}function Pb(a,b,c,d){var e,f,g,h,i,j={},k=a.dataTypes.slice();if(k[1])for(g in a.converters)j[g.toLowerCase()]=a.converters[g];f=k.shift();while(f)if(a.responseFields[f]&&(c[a.responseFields[f]]=b),!i&&d&&a.dataFilter&&(b=a.dataFilter(b,a.dataType)),i=f,f=k.shift())if("*"===f)f=i;else if("*"!==i&&i!==f){if(g=j[i+" "+f]||j["* "+f],!g)for(e in j)if(h=e.split(" "),h[1]===f&&(g=j[i+" "+h[0]]||j["* "+h[0]])){g===!0?g=j[e]:j[e]!==!0&&(f=h[0],k.unshift(h[1]));break}if(g!==!0)if(g&&a["throws"])b=g(b);else try{b=g(b)}catch(l){return{state:"parsererror",error:g?l:"No conversion from "+i+" to "+f}}}return{state:"success",data:b}}m.extend({active:0,lastModified:{},etag:{},ajaxSettings:{url:zb,type:"GET",isLocal:Db.test(yb[1]),global:!0,processData:!0,async:!0,contentType:"application/x-www-form-urlencoded; charset=UTF-8",accepts:{"*":Jb,text:"text/plain",html:"text/html",xml:"application/xml, text/xml",json:"application/json, text/javascript"},contents:{xml:/xml/,html:/html/,json:/json/},responseFields:{xml:"responseXML",text:"responseText",json:"responseJSON"},converters:{"* text":String,"text html":!0,"text json":m.parseJSON,"text xml":m.parseXML},flatOptions:{url:!0,context:!0}},ajaxSetup:function(a,b){return b?Nb(Nb(a,m.ajaxSettings),b):Nb(m.ajaxSettings,a)},ajaxPrefilter:Lb(Hb),ajaxTransport:Lb(Ib),ajax:function(a,b){"object"==typeof a&&(b=a,a=void 0),b=b||{};var c,d,e,f,g,h,i,j,k=m.ajaxSetup({},b),l=k.context||k,n=k.context&&(l.nodeType||l.jquery)?m(l):m.event,o=m.Deferred(),p=m.Callbacks("once memory"),q=k.statusCode||{},r={},s={},t=0,u="canceled",v={readyState:0,getResponseHeader:function(a){var b;if(2===t){if(!j){j={};while(b=Cb.exec(f))j[b[1].toLowerCase()]=b[2]}b=j[a.toLowerCase()]}return null==b?null:b},getAllResponseHeaders:function(){return 2===t?f:null},setRequestHeader:function(a,b){var c=a.toLowerCase();return t||(a=s[c]=s[c]||a,r[a]=b),this},overrideMimeType:function(a){return t||(k.mimeType=a),this},statusCode:function(a){var b;if(a)if(2>t)for(b in a)q[b]=[q[b],a[b]];else v.always(a[v.status]);return this},abort:function(a){var b=a||u;return i&&i.abort(b),x(0,b),this}};if(o.promise(v).complete=p.add,v.success=v.done,v.error=v.fail,k.url=((a||k.url||zb)+"").replace(Ab,"").replace(Fb,yb[1]+"//"),k.type=b.method||b.type||k.method||k.type,k.dataTypes=m.trim(k.dataType||"*").toLowerCase().match(E)||[""],null==k.crossDomain&&(c=Gb.exec(k.url.toLowerCase()),k.crossDomain=!(!c||c[1]===yb[1]&&c[2]===yb[2]&&(c[3]||("http:"===c[1]?"80":"443"))===(yb[3]||("http:"===yb[1]?"80":"443")))),k.data&&k.processData&&"string"!=typeof k.data&&(k.data=m.param(k.data,k.traditional)),Mb(Hb,k,b,v),2===t)return v;h=m.event&&k.global,h&&0===m.active++&&m.event.trigger("ajaxStart"),k.type=k.type.toUpperCase(),k.hasContent=!Eb.test(k.type),e=k.url,k.hasContent||(k.data&&(e=k.url+=(wb.test(e)?"&":"?")+k.data,delete k.data),k.cache===!1&&(k.url=Bb.test(e)?e.replace(Bb,"$1_="+vb++):e+(wb.test(e)?"&":"?")+"_="+vb++)),k.ifModified&&(m.lastModified[e]&&v.setRequestHeader("If-Modified-Since",m.lastModified[e]),m.etag[e]&&v.setRequestHeader("If-None-Match",m.etag[e])),(k.data&&k.hasContent&&k.contentType!==!1||b.contentType)&&v.setRequestHeader("Content-Type",k.contentType),v.setRequestHeader("Accept",k.dataTypes[0]&&k.accepts[k.dataTypes[0]]?k.accepts[k.dataTypes[0]]+("*"!==k.dataTypes[0]?", "+Jb+"; q=0.01":""):k.accepts["*"]);for(d in k.headers)v.setRequestHeader(d,k.headers[d]);if(k.beforeSend&&(k.beforeSend.call(l,v,k)===!1||2===t))return v.abort();u="abort";for(d in{success:1,error:1,complete:1})v[d](k[d]);if(i=Mb(Ib,k,b,v)){v.readyState=1,h&&n.trigger("ajaxSend",[v,k]),k.async&&k.timeout>0&&(g=setTimeout(function(){v.abort("timeout")},k.timeout));try{t=1,i.send(r,x)}catch(w){if(!(2>t))throw w;x(-1,w)}}else x(-1,"No Transport");function x(a,b,c,d){var j,r,s,u,w,x=b;2!==t&&(t=2,g&&clearTimeout(g),i=void 0,f=d||"",v.readyState=a>0?4:0,j=a>=200&&300>a||304===a,c&&(u=Ob(k,v,c)),u=Pb(k,u,v,j),j?(k.ifModified&&(w=v.getResponseHeader("Last-Modified"),w&&(m.lastModified[e]=w),w=v.getResponseHeader("etag"),w&&(m.etag[e]=w)),204===a||"HEAD"===k.type?x="nocontent":304===a?x="notmodified":(x=u.state,r=u.data,s=u.error,j=!s)):(s=x,(a||!x)&&(x="error",0>a&&(a=0))),v.status=a,v.statusText=(b||x)+"",j?o.resolveWith(l,[r,x,v]):o.rejectWith(l,[v,x,s]),v.statusCode(q),q=void 0,h&&n.trigger(j?"ajaxSuccess":"ajaxError",[v,k,j?r:s]),p.fireWith(l,[v,x]),h&&(n.trigger("ajaxComplete",[v,k]),--m.active||m.event.trigger("ajaxStop")))}return v},getJSON:function(a,b,c){return m.get(a,b,c,"json")},getScript:function(a,b){return m.get(a,void 0,b,"script")}}),m.each(["get","post"],function(a,b){m[b]=function(a,c,d,e){return m.isFunction(c)&&(e=e||d,d=c,c=void 0),m.ajax({url:a,type:b,dataType:e,data:c,success:d})}}),m._evalUrl=function(a){return m.ajax({url:a,type:"GET",dataType:"script",async:!1,global:!1,"throws":!0})},m.fn.extend({wrapAll:function(a){if(m.isFunction(a))return this.each(function(b){m(this).wrapAll(a.call(this,b))});if(this[0]){var b=m(a,this[0].ownerDocument).eq(0).clone(!0);this[0].parentNode&&b.insertBefore(this[0]),b.map(function(){var a=this;while(a.firstChild&&1===a.firstChild.nodeType)a=a.firstChild;return a}).append(this)}return this},wrapInner:function(a){return this.each(m.isFunction(a)?function(b){m(this).wrapInner(a.call(this,b))}:function(){var b=m(this),c=b.contents();c.length?c.wrapAll(a):b.append(a)})},wrap:function(a){var b=m.isFunction(a);return this.each(function(c){m(this).wrapAll(b?a.call(this,c):a)})},unwrap:function(){return this.parent().each(function(){m.nodeName(this,"body")||m(this).replaceWith(this.childNodes)}).end()}}),m.expr.filters.hidden=function(a){return a.offsetWidth<=0&&a.offsetHeight<=0||!k.reliableHiddenOffsets()&&"none"===(a.style&&a.style.display||m.css(a,"display"))},m.expr.filters.visible=function(a){return!m.expr.filters.hidden(a)};var Qb=/%20/g,Rb=/\[\]$/,Sb=/\r?\n/g,Tb=/^(?:submit|button|image|reset|file)$/i,Ub=/^(?:input|select|textarea|keygen)/i;function Vb(a,b,c,d){var e;if(m.isArray(b))m.each(b,function(b,e){c||Rb.test(a)?d(a,e):Vb(a+"["+("object"==typeof e?b:"")+"]",e,c,d)});else if(c||"object"!==m.type(b))d(a,b);else for(e in b)Vb(a+"["+e+"]",b[e],c,d)}m.param=function(a,b){var c,d=[],e=function(a,b){b=m.isFunction(b)?b():null==b?"":b,d[d.length]=encodeURIComponent(a)+"="+encodeURIComponent(b)};if(void 0===b&&(b=m.ajaxSettings&&m.ajaxSettings.traditional),m.isArray(a)||a.jquery&&!m.isPlainObject(a))m.each(a,function(){e(this.name,this.value)});else for(c in a)Vb(c,a[c],b,e);return d.join("&").replace(Qb,"+")},m.fn.extend({serialize:function(){return m.param(this.serializeArray())},serializeArray:function(){return this.map(function(){var a=m.prop(this,"elements");return a?m.makeArray(a):this}).filter(function(){var a=this.type;return this.name&&!m(this).is(":disabled")&&Ub.test(this.nodeName)&&!Tb.test(a)&&(this.checked||!W.test(a))}).map(function(a,b){var c=m(this).val();return null==c?null:m.isArray(c)?m.map(c,function(a){return{name:b.name,value:a.replace(Sb,"\r\n")}}):{name:b.name,value:c.replace(Sb,"\r\n")}}).get()}}),m.ajaxSettings.xhr=void 0!==a.ActiveXObject?function(){return!this.isLocal&&/^(get|post|head|put|delete|options)$/i.test(this.type)&&Zb()||$b()}:Zb;var Wb=0,Xb={},Yb=m.ajaxSettings.xhr();a.attachEvent&&a.attachEvent("onunload",function(){for(var a in Xb)Xb[a](void 0,!0)}),k.cors=!!Yb&&"withCredentials"in Yb,Yb=k.ajax=!!Yb,Yb&&m.ajaxTransport(function(a){if(!a.crossDomain||k.cors){var b;return{send:function(c,d){var e,f=a.xhr(),g=++Wb;if(f.open(a.type,a.url,a.async,a.username,a.password),a.xhrFields)for(e in a.xhrFields)f[e]=a.xhrFields[e];a.mimeType&&f.overrideMimeType&&f.overrideMimeType(a.mimeType),a.crossDomain||c["X-Requested-With"]||(c["X-Requested-With"]="XMLHttpRequest");for(e in c)void 0!==c[e]&&f.setRequestHeader(e,c[e]+"");f.send(a.hasContent&&a.data||null),b=function(c,e){var h,i,j;if(b&&(e||4===f.readyState))if(delete Xb[g],b=void 0,f.onreadystatechange=m.noop,e)4!==f.readyState&&f.abort();else{j={},h=f.status,"string"==typeof f.responseText&&(j.text=f.responseText);try{i=f.statusText}catch(k){i=""}h||!a.isLocal||a.crossDomain?1223===h&&(h=204):h=j.text?200:404}j&&d(h,i,j,f.getAllResponseHeaders())},a.async?4===f.readyState?setTimeout(b):f.onreadystatechange=Xb[g]=b:b()},abort:function(){b&&b(void 0,!0)}}}});function Zb(){try{return new a.XMLHttpRequest}catch(b){}}function $b(){try{return new a.ActiveXObject("Microsoft.XMLHTTP")}catch(b){}}m.ajaxSetup({accepts:{script:"text/javascript, application/javascript, application/ecmascript, application/x-ecmascript"},contents:{script:/(?:java|ecma)script/},converters:{"text script":function(a){return m.globalEval(a),a}}}),m.ajaxPrefilter("script",function(a){void 0===a.cache&&(a.cache=!1),a.crossDomain&&(a.type="GET",a.global=!1)}),m.ajaxTransport("script",function(a){if(a.crossDomain){var b,c=y.head||m("head")[0]||y.documentElement;return{send:function(d,e){b=y.createElement("script"),b.async=!0,a.scriptCharset&&(b.charset=a.scriptCharset),b.src=a.url,b.onload=b.onreadystatechange=function(a,c){(c||!b.readyState||/loaded|complete/.test(b.readyState))&&(b.onload=b.onreadystatechange=null,b.parentNode&&b.parentNode.removeChild(b),b=null,c||e(200,"success"))},c.insertBefore(b,c.firstChild)},abort:function(){b&&b.onload(void 0,!0)}}}});var _b=[],ac=/(=)\?(?=&|$)|\?\?/;m.ajaxSetup({jsonp:"callback",jsonpCallback:function(){var a=_b.pop()||m.expando+"_"+vb++;return this[a]=!0,a}}),m.ajaxPrefilter("json jsonp",function(b,c,d){var e,f,g,h=b.jsonp!==!1&&(ac.test(b.url)?"url":"string"==typeof b.data&&!(b.contentType||"").indexOf("application/x-www-form-urlencoded")&&ac.test(b.data)&&"data");return h||"jsonp"===b.dataTypes[0]?(e=b.jsonpCallback=m.isFunction(b.jsonpCallback)?b.jsonpCallback():b.jsonpCallback,h?b[h]=b[h].replace(ac,"$1"+e):b.jsonp!==!1&&(b.url+=(wb.test(b.url)?"&":"?")+b.jsonp+"="+e),b.converters["script json"]=function(){return g||m.error(e+" was not called"),g[0]},b.dataTypes[0]="json",f=a[e],a[e]=function(){g=arguments},d.always(function(){a[e]=f,b[e]&&(b.jsonpCallback=c.jsonpCallback,_b.push(e)),g&&m.isFunction(f)&&f(g[0]),g=f=void 0}),"script"):void 0}),m.parseHTML=function(a,b,c){if(!a||"string"!=typeof a)return null;"boolean"==typeof b&&(c=b,b=!1),b=b||y;var d=u.exec(a),e=!c&&[];return d?[b.createElement(d[1])]:(d=m.buildFragment([a],b,e),e&&e.length&&m(e).remove(),m.merge([],d.childNodes))};var bc=m.fn.load;m.fn.load=function(a,b,c){if("string"!=typeof a&&bc)return bc.apply(this,arguments);var d,e,f,g=this,h=a.indexOf(" ");return h>=0&&(d=m.trim(a.slice(h,a.length)),a=a.slice(0,h)),m.isFunction(b)?(c=b,b=void 0):b&&"object"==typeof b&&(f="POST"),g.length>0&&m.ajax({url:a,type:f,dataType:"html",data:b}).done(function(a){e=arguments,g.html(d?m("<div>").append(m.parseHTML(a)).find(d):a)}).complete(c&&function(a,b){g.each(c,e||[a.responseText,b,a])}),this},m.each(["ajaxStart","ajaxStop","ajaxComplete","ajaxError","ajaxSuccess","ajaxSend"],function(a,b){m.fn[b]=function(a){return this.on(b,a)}}),m.expr.filters.animated=function(a){return m.grep(m.timers,function(b){return a===b.elem}).length};var cc=a.document.documentElement;function dc(a){return m.isWindow(a)?a:9===a.nodeType?a.defaultView||a.parentWindow:!1}m.offset={setOffset:function(a,b,c){var d,e,f,g,h,i,j,k=m.css(a,"position"),l=m(a),n={};"static"===k&&(a.style.position="relative"),h=l.offset(),f=m.css(a,"top"),i=m.css(a,"left"),j=("absolute"===k||"fixed"===k)&&m.inArray("auto",[f,i])>-1,j?(d=l.position(),g=d.top,e=d.left):(g=parseFloat(f)||0,e=parseFloat(i)||0),m.isFunction(b)&&(b=b.call(a,c,h)),null!=b.top&&(n.top=b.top-h.top+g),null!=b.left&&(n.left=b.left-h.left+e),"using"in b?b.using.call(a,n):l.css(n)}},m.fn.extend({offset:function(a){if(arguments.length)return void 0===a?this:this.each(function(b){m.offset.setOffset(this,a,b)});var b,c,d={top:0,left:0},e=this[0],f=e&&e.ownerDocument;if(f)return b=f.documentElement,m.contains(b,e)?(typeof e.getBoundingClientRect!==K&&(d=e.getBoundingClientRect()),c=dc(f),{top:d.top+(c.pageYOffset||b.scrollTop)-(b.clientTop||0),left:d.left+(c.pageXOffset||b.scrollLeft)-(b.clientLeft||0)}):d},position:function(){if(this[0]){var a,b,c={top:0,left:0},d=this[0];return"fixed"===m.css(d,"position")?b=d.getBoundingClientRect():(a=this.offsetParent(),b=this.offset(),m.nodeName(a[0],"html")||(c=a.offset()),c.top+=m.css(a[0],"borderTopWidth",!0),c.left+=m.css(a[0],"borderLeftWidth",!0)),{top:b.top-c.top-m.css(d,"marginTop",!0),left:b.left-c.left-m.css(d,"marginLeft",!0)}}},offsetParent:function(){return this.map(function(){var a=this.offsetParent||cc;while(a&&!m.nodeName(a,"html")&&"static"===m.css(a,"position"))a=a.offsetParent;return a||cc})}}),m.each({scrollLeft:"pageXOffset",scrollTop:"pageYOffset"},function(a,b){var c=/Y/.test(b);m.fn[a]=function(d){return V(this,function(a,d,e){var f=dc(a);return void 0===e?f?b in f?f[b]:f.document.documentElement[d]:a[d]:void(f?f.scrollTo(c?m(f).scrollLeft():e,c?e:m(f).scrollTop()):a[d]=e)},a,d,arguments.length,null)}}),m.each(["top","left"],function(a,b){m.cssHooks[b]=La(k.pixelPosition,function(a,c){return c?(c=Ja(a,b),Ha.test(c)?m(a).position()[b]+"px":c):void 0})}),m.each({Height:"height",Width:"width"},function(a,b){m.each({padding:"inner"+a,content:b,"":"outer"+a},function(c,d){m.fn[d]=function(d,e){var f=arguments.length&&(c||"boolean"!=typeof d),g=c||(d===!0||e===!0?"margin":"border");return V(this,function(b,c,d){var e;return m.isWindow(b)?b.document.documentElement["client"+a]:9===b.nodeType?(e=b.documentElement,Math.max(b.body["scroll"+a],e["scroll"+a],b.body["offset"+a],e["offset"+a],e["client"+a])):void 0===d?m.css(b,c,g):m.style(b,c,d,g)},b,f?d:void 0,f,null)}})}),m.fn.size=function(){return this.length},m.fn.andSelf=m.fn.addBack,"function"==typeof define&&define.amd&&define("jquery",[],function(){return m});var ec=a.jQuery,fc=a.$;return m.noConflict=function(b){return a.$===m&&(a.$=fc),b&&a.jQuery===m&&(a.jQuery=ec),m},typeof b===K&&(a.jQuery=a.$=m),m});
</script>
<meta name="viewport" content="width=device-width, initial-scale=1" />
<style type="text/css">html{font-family:sans-serif;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,strong{font-weight:700}dfn{font-style:italic}h1{margin:.67em 0;font-size:2em}mark{color:#000;background:#ff0}small{font-size:80%}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{height:0;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}button,input,optgroup,select,textarea{margin:0;font:inherit;color:inherit}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}input{line-height:normal}input[type=checkbox],input[type=radio]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}fieldset{padding:.35em .625em .75em;margin:0 2px;border:1px solid silver}legend{padding:0;border:0}textarea{overflow:auto}optgroup{font-weight:700}table{border-spacing:0;border-collapse:collapse}td,th{padding:0}@media print{*,:after,:before{color:#000!important;text-shadow:none!important;background:0 0!important;-webkit-box-shadow:none!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href) ")"}abbr[title]:after{content:" (" attr(title) ")"}a[href^="javascript:"]:after,a[href^="#"]:after{content:""}blockquote,pre{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}img,tr{page-break-inside:avoid}img{max-width:100%!important}h2,h3,p{orphans:3;widows:3}h2,h3{page-break-after:avoid}.navbar{display:none}.btn>.caret,.dropup>.btn>.caret{border-top-color:#000!important}.label{border:1px solid #000}.table{border-collapse:collapse!important}.table td,.table th{background-color:#fff!important}.table-bordered td,.table-bordered th{border:1px solid #ddd!important}}@font-face{font-family:'Glyphicons Halflings';src:url(data:application/vnd.ms-fontobject;base64,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);src:url(data:application/vnd.ms-fontobject;base64,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) format('embedded-opentype'),url(data:application/font-woff;base64,d09GRgABAAAAAFuAAA8AAAAAsVwAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAABGRlRNAAABWAAAABwAAAAcbSqX3EdERUYAAAF0AAAAHwAAACABRAAET1MvMgAAAZQAAABFAAAAYGe5a4ljbWFwAAAB3AAAAsAAAAZy2q3jgWN2dCAAAAScAAAABAAAAAQAKAL4Z2FzcAAABKAAAAAIAAAACP//AANnbHlmAAAEqAAATRcAAJSkfV3Cb2hlYWQAAFHAAAAANAAAADYFTS/YaGhlYQAAUfQAAAAcAAAAJApEBBFobXR4AABSEAAAAU8AAAN00scgYGxvY2EAAFNgAAACJwAAAjBv+5XObWF4cAAAVYgAAAAgAAAAIAFqANhuYW1lAABVqAAAAZ4AAAOisyygm3Bvc3QAAFdIAAAELQAACtG6o+U1d2ViZgAAW3gAAAAGAAAABsMYVFAAAAABAAAAAMw9os8AAAAA0HaBdQAAAADQdnOXeNpjYGRgYOADYgkGEGBiYGRgZBQDkixgHgMABUgASgB42mNgZulmnMDAysDCzMN0gYGBIQpCMy5hMGLaAeQDpRCACYkd6h3ux+DAoPD/P/OB/wJAdSIM1UBhRiQlCgyMADGWCwwAAAB42u2UP2hTQRzHf5ekaVPExv6JjW3fvTQ0sa3QLA5xylBLgyBx0gzSWEUaXbIoBBQyCQGHLqXUqYNdtIIgIg5FHJxEtwqtpbnfaV1E1KFaSvX5vVwGEbW6OPngk8/vvXfv7pt3v4SImojIDw6BViKxRgIVBaZwVdSv+xvXA+Iuzqcog2cOkkvDNE8Lbqs74k64i+5Sf3u8Z2AnIRLbyVCyTflVSEXVoEqrrMqrgiqqsqqqWQ5xlAc5zWOc5TwXucxVnuE5HdQhHdFRHdNJndZZndeFLc/zsKJLQ/WV6BcrCdWkwspVKZVROaw0qUqqoqZZcJhdTnGGxznHBS5xhad5VhNWCuturBTXKZ3RObuS98pb9c57k6ql9rp2v1as5deb1r6s9q1GV2IrHSt73T631424YXzjgPwqt+Rn+VG+lRvyirwsS/KCPCfPytPypDwhj8mjctRZd9acF86y89x55jxxHjkPnXstXfbt/pNjj/nwXW+cHa6/SYvZ7yEwbDYazDcIgoUGzY3h2HtqgUcs1AFPWKgTXrRQF7xkoQhRf7uF9hPFeyzUTTSwY6EoUUJY6AC8bSGMS4Ys1Au3WaiPSGGsMtkdGH2rzJgYHAaYjxIwQqtB1CnYkEZ9BM6ALOpROAfyqI/DBQudgidBETXuqRIooz4DV0AV9UV4GsyivkTEyMMmw1UYGdhkuAYjA5sMGMvIwCbDDRgZeAz1TXgcmDy3YeRhk+cOjCxsMjyAkYFNhscwMrDJ8BQ2886gXoaRhedQvyTSkDZ7uA6HLLQBI5vGntAbGHugTc53cMxC7+E4SKL+ACOzNpk3YWTWJid+iRo5NXIKM3fBItAPW55FdJLY3FeHBDr90606JCIU9Jk+Ms3/Y/8L8jUq3y79bJ/0/+ROoP4v9v/4/mj+i7HBXUd0/elU6IHfHt8Aj9EPGAAoAvgAAAAB//8AAnjaxb0JfBvVtTA+dxaN1hltI1m2ZVuSJVneLVlSHCdy9oTEWchqtrBEJRAgCYEsQNhC2EsbWmpI2dqkQBoSYgKlpaQthVL0yusrpW77aEubfq/ly+ujvJampSTW5Dvnzmi1E+jr//3+Xmbu3Llz77nnbuece865DMu0MAy5jGtiOEZkOp8lTNeUwyLP/DH+rEH41ZTDHAtB5lkOowWMPiwayNiUwwTjE46AI5xwhFrINPXYn/7ENY0dbWHfZAiTZbL8ID/InAd5xz2NpIH4STpDGonHIJNE3OP1KG4ISaSNeBuITAyRLgIxoiEUhFAnmUpEiXSRSGqAQEw0kuyFUIb0k2gnGSApyBFi0il2SI5YLGb5MdFjXCey4mNHzQ7WwLGEdZiPPgYR64we8THZHAt+wnT84D/x8YTpGPgheKH4CMEDVF9xBOIeP3EbQgGH29BGgpGkIxCMTCW9qUTA0Zsir+QUP1mt+P2KusevwIO6Bx/Iaj8/OD5O0VNrZW2EsqZBWbO1skRiEKE0DdlKKaSVO5VAuRpqk8VQJAqY7ydxaK44YJvrO2EWjOoDBoFYzQbDNkON+UbiKoRkywMWWf1j4bEY2iIY1AeMgvmEz/kVo9v4FSc/aMZMrFbjl4zWLL0+Y5FlyzNlEVYDudJohg8gPUP7kcB/mn+G6cd+5PV4Q72dXCgocWJADBgUuDTwiXiGSyZo14HOEQ2lE6k0XDIEusexDzZOMXwt1Dutz+tqmxTvlskNWXXUQIbhaurum9GrePqm9Yaeabjkiqf+bUvzDOvb2Y1E+EX2DnemcTP/zLcuu7xjQXdAtjR0Lo5n4/Hs/GtntMlysHt+29NXbH6se//WbFcyu+r28H0MwzI30DYeYTLMXIA2EG8QlHpAsyS0EfEToR0a3utIxFPJ3kiIHCCrZ66b0e2xEmL1dM9YN/MwS5p01N5jMX/BLKt/1R83l0LyC29M6+iYxo/UNg/EF7c2WyyW5tYl8WnhWg2/hyySbD5UhnDyS7OcU0dnrFw+DfGdI7v4QfYIIzOMq9hFtY55gmvC7jZ2FK7sEdrn6IXBuucYhjsGdQ8z0yEbWkkczjjsE5hNAIZrPx2zOLZDmKNXcXtg7EMqidAEEWg+SJCBBNwxvxJfc/bZa+KKf+xoKZybnq5vaqpPTye7CiF+ZFjxZ8/7Qij0hfOG/cowPA1rT1l4ymWnrKmxxqfErTVrpgwPlz1kC+Oy8NMDz6c+IO38K/x0xkPnLW8Kx6qGAoQdL+TD9V9rb+/ctn//trxz8dUrZrD/zk/ferF0cNt1BzctmX2FZPXt/jnFCQNz4Ah/iKllGiCMs1w5Lkg0kiEwj6VTXCDKsX9rMpnvIj9pcDecXAIXMnqn2dTUbN6w0XQ9ue6FV/nnXCH7S3lPWGltVcLsH75ub3ab7A8M28caNrIeOr3o5Q0yFsYL80xaa0EY/UEczV7icUMY5pnelAkmUAXmHYjvFWFGxuqlSaow3OM+/iYY7/l/hVELF4EjRqNR/bvRbOY+DUGzGR/Oh3EqmE/ugIQQguGt/eMYz/+L0cimjeZfQDI3phXMbMQsqH+CjwVz/hf4idHovgVmB8gLvjbicDcC/NypP536E/9N/puMibExdohBmNwyiaZdJGoigos7GpF222xrfnZhML/7Z+ylaqP63Hr+m7bdUkQ6/2cXqdfmvwixY+s2ksXFeXcE+iX0Z+Iow76DBNgjJ7TOdUK18iPsPflfQD+DPsZG2Aj9VmKMMJ4fYRrhIaxhTDR0Elh2vA6h/AE6xUb29mj3sjmL72petXjejPy+oel60M99tFduCI59N3221xe7apOvxs6aHs7vab1IqY2tv7q2xsHeHGml/cV06u/8S/xTjJ+JYc0bWEX0ukW6YmIbGkJRMdjJ9mYIH5QIdJF4hvRGyK7cC7ctImQRcUET99fGXOoft35GYLMQu+g2smnkgZUrH8AL/9Si217IssJ916nv14ZrJrvdxLkQvrvtBcjgPC0NXOicO8Qf4mcxPqh3hgUw3DDfdvLJXngg7N3dN2zbPJSaed3OfZnMU7dvmznp3C3bruO+Nmue0LFsy7S+6265+fCKFYdvvuW6vmlblnUI8xCXp37CrOZv4B9gauDBlYp7adcUXB5DNCwYImlXOJJKkAdvExXxVvKEYnCo+3eIskP9qrrfIYs71CccBjfXRC52udTHHdaP1A1ui/VvH1otbrLrpNXBsGX5B89QghDyimlvNB2KfkxZ5C9/em3+d1+d//IfFp2+2Oxn/s+9n/79p39S3s8idN6g0yZObwJOgKUpNB3GyU0Ls0PbRzIRq4lcarLKOJBkLRzJQD4j2090XrbA7DW8K3jNF5hlGS5e4V2D17zgss4T20egOJte5iD0bReM9yjTxnQxCRj3c5kFzGJmGbNKmwGw39IJDJcXJZGMkaAB4jyJAKw0jt5IAuIE+A+U3cVAZZrq9zhDyBrU8oosuxcGNTzCKJfla7JjNVmuSb/+tuzN2H+X4vlB+PpdfMXXmuVsNiub1T34SFbjYw5itEvVi0K0Nt9pNJUMI7SLGRhf2xipfCYf8z5OdlGKayOucFeVPeS/dbo3lBrbSMmwUiQN5/ed7g0Ds1s17IuZC5kNzM3MZ6EWCa0DtekdJfAxz+R/OX28sND7yRMTBcf++s8mQCQWHya4qBv/ufeMoWyslPA9DtMxUknxkH/yfTnm2CMYzs+Cq3r7PxY/MXomrvTEsRpfEGHa+WN8E1AHjElb7d06ddA7oK/+5Mdsv9EtPms0jv0Z5kf1FqPxWdFtfFr0kHfgDX0Y+5PRSG7RUj0tQr7rmfX8DH4G5W28kKeJLtmQsQkuwMP1pk16EV4sl7vrMJATfyUWo/GwEco4rh4XFQgaiUX9qxZHrMQqKnz/c2d8b9TysYrAuXpP/Rf/Gr8b1qwwc5a+euLa6S6sneNXToG2XrEJi4R5SGs8Sq2S3d97bsfCRaTdaLwKClRHt37mkudvXbjwVrLhuYeGhh56bvfQkHpk2CwvwClqgWwuBfndC3c8dwmstj81KkagcUgbfPY8Zje0W/82VPWJHmSq6pP8hPWpotc/EexDOK3qU+wngPhOCiO9MJRm8TJefjelrzoKnG2Bn+1NCUmPE4gHFmBN9jrTigRIpsACrc9Gstg58ULkp9467+Gf/eFnD5/31lNrt2967dhrm7bzI+VT5m+fzKhvf2MzpICEm79Bopkn07lt1762adNr127LwVqQLdJ5+lpQDcvHPQtVY5knhYrK6q8/JsiP6EuhGZdFdaNszjvpqvc+PI0CdjN0AXsFOC3ZfALDJwr4q2Xq+GF+GNbsxUg5NLLIEXi8otcDQcUts0D8eQ1iVDRAMBTsYiNdRIxE09EIBJO9A2xqgERTaW86BUFn0OD2xFO97FAgFhF6OoQ7prYt4XwSeUgQHiJyDbeke9IdQntciLQ1FlJMaYcUNvZBg+FB1ubjlnRNvl3o6IEU2w7fdNPhm/hh+FLysUu6++DLHkOkrSHYEjH0tEPe7WdD3uyDgvAgK/m4szFFR7ch0toUgBTdWHr7EpaWru6+6dmbbnqWEbV2EtxAsXiZAPTtGPSbHsotI2leoM8TePEqgSQprs7AGFf8kuOkPdZPXGb55POAW1d/jLST9v5YflasP6v/CO7+GNAPC2BMZWmsOjp2NNbfHwMCJD+LPVL+D/OYlWEEI/9jpPddOFkB5d1GSuKZYggmCCd7JUxD7EXAzxyirYnNDLdDZoFdx14kivkvGc3579Jm36reTTvDgBnaO6vzyQ6chQmlsMoIkIQ2+bBDWBud1Va4pcCn8CPqxlh/fgtG8IPaPH8C5wk6/nZDv69jurV5QhtwE0x2iqOsj9Mx8B9/0EaUdiPfOYYDCi/q9jhWRuupMDEU0+CtX0sDFxv07T/K5niBPqN9+tQjgEc31NGCXFeMcCEuQBIc/BK4CO78u7EPYvl3yaEfK3vcb6qP1R2tI7vUjVDDUdKubsSrNjYKY1qBEa2P50SJoaXiksIoLiCwnxS6EBuBde87botNfdEWwYvF/R0/u5yCqhGeEOR2ynSeyXjt6ka7neyye8kryBSWE52y+RBgogrXPZ8E1yIHoHIFUM+AbJhE7lbMtt8ApL+xmZW7PwbjAO0fAVoXQOuiSP/ksIVdFZ0aulsamKUzwPZ/NYDMJRBPCxsBqLzqHyneXF6Ej9HlIFo7+pg+jUb3unRmGpstGkm6etOuDBGA5wCMefp1gTHcdZlvPBXlOslvYTp1cd8UjYLVd/J5awNrIOKLnIt9MD9qdrKrWCvA6ALm3QV9VrsPm60Q7+RHJHP+2hqfugo/MvI2H/mqr4b9tFnKSRY1Y5Ek80Nm/WIhr1ikKnxGz9TWXrokf9xwujfvcOTtNTWnxd0F37Y2W79tteBqZ4G5qLCuomw+nSr28QESCRVLTyYKILGJOPfcnaIFOsewhRdvv+rWa/Wih0vlbX6Zb75T5C0qNKVFvH1QL/vazSWgC2s6oWXXIuUxQelKiJbowuJDQViatLmLijg9CQBMg8WiPgiw3LEeYRmm5f+XdnvkDnxLLjMLxtvX74C3OlwPQqx4xwIdpPx38LrlDphiyWUWHWKAzzxurS/xTo+P5wGFak62ap1PVFFN4v/y+xuR39WnIO7lsWfwgVsK17wxrs9K8ltIKuhkw7f/6dhK6gQokFKhWX3urrjk/rnI0pgfpGMeuQIUaEM7+GF5q2iMkCaMQwxxOzcvU0eXbsnS9XknXvP7Gtw5dwPXlFu2ecvSHEZgNDsU6x/GdXBYXyOQjzZReSedeEPY6nEv9gJR4oBQJtFO6Kd0fwC6BO4LNHDeBujB6dSNcUQC9zIv2LnAzGk99bUDrdFY+9yGFQtEo0GQPNv6vS2drj4+1jHbv3aJSMUWP+QTZrmbNTjU8wyG/iXNNpskybLcJ3CiTF5Ir+JYzmJwE0mSVhlxbtbmvweB3ulB6Til5UuUZydpgiFVeobhU0WaBqpJ198d+/XeNRTZ9/1OPfG7+2hwzd5W3D+hmyjsRcUg/+Cavb++Vh2ls3L7zT/etOnHNxeerv313vzLVqPai4nJv+K1FC6040/4udw7sAb3laSg0XCkAAs0npBO6VJabS4Elk/U+D4gTXW+j0wnrMlqNamq4tMIYB87tE10i0FR3LZNhJsb7/R561btmes8YBCRkhYNByRtKd55mqTas9FYhJnbRGHuOh3M4QTdgQSqmgRxuzGdSvZGcbMxNQGk5C3ebLjoXIOFM4l+WKHmLTJwRv9E8GWJ6dYvf/FmEyEGr+gyrr1p5zrgkz0Cw2j94Hv8Jdx7dIVegBSNtgsqGsRQEYiIBoXwD0LNvQ5d7s5Z00QzwNhqZA0b+tMG1tQq5nd84uq8R0zPvX35G8uRaze4jcOHzz0w1+Q2BIRvf6J6Kgatnrbiem+CFvAxfkrndzD9MFPP1GWTUHclpASUkCNAQkpCCcCgDSUDAhDZ+CuEkgn8J7i9nMA7pA4lISappxILKfAeSAbIcSDuN2bJcfZILqeO5rLs0MnngSHYRdrHjmaz7JEsEPw51ZqDJDmUIOZIe34WaQeegNsJn1qz8AIpT3yCjyEih/xELkuJ0lEMYTLVCiWpo5oYMleMH6USyYJcD+uOe+kWKpn1Qns34iyYDjkSLvgnZXcgVQNeqINXr48m3iS7cjm8tedyY0f1QvTnHHdsrKby/+SSbPY8/NH6vpl/Esq3Ae4ZU1HC44KFiI9o7CEgab/RqHbj7s5KAg06s39ZP/zxI/mVuF/TbTSy+3Fb8If9/cv7+wt91yy8RfP1QXtW5RzQn7qIiZyuFM5QfJ5E9uVnqT85TanFx0lkP3ukBAMprvsRyi/C8NAJL1xbIIirSvnSj4O5netb4JxmNANHPssHAcHMHsFRgEug816gDBeMbdfiuRcghqYcm0+Xxx/5IAEtN3fqFF3LzAXqwoT0PN0OVTNqxo8sxMkd5Ig6k79Zk7VxxX6gMLOZFQgvpW2RrMW1D0BDihaXQ9wVRoBxPLfpknmkeMtoB/qM9cRc9IqmMD2XUmdZ7GSRKPUZvChf8BoykriM2MnKYbOHX8R7cLdNCxSFFVQqoYswnlWtlFS2mNkhswVpZiQW1J/UKFfipHGlUkM6UKBhMz1istELIHJLMSctu3ugzfaVSOjKvUgc/THK4Sdg2Wscz69leKIkkrwuuWiOe9yGYKQXRumkC3qbRcMwrvhjNXgdZk3RxAUEhuSPvn3nnd++U/3vlVOmrJzCD8JLxV1OHRjrZifbcFDOuRNTGqdgQm1tSNJ2OcQ04YiEXuxtII1ECSQRoQGYioEsgCfchB4ghAtw7FfJre4WZ9hkVi9MtjuWqtdNDlpMrfEG9fOT6q21okg+e4As38MfGquNt7oUws6Ysarj1/efE+yst86YUVNvDdts3Pv5c8m/aP0C+f8/Qb+IMnGq09BgwN01oIOAnAdagI8mBSrqk1gxTDUBOtk2ousEtBH2z4Ir2d3f6k8PXXVlt2qN9RODxRuoJT/v27wm09jRYVc/e++iyx2tyzJb/n3J0htXP87eSsQaf2Ly0s6Zmxela88REy1cf4273mI3iXNJ7KxrZibOm9xm6rl4fqy/t27smU8tOfdW2ucBzg2UfmOIVyLIl3kpYlwphDISTXJXsctmiDtN7fNV6zelgxwnWxsVr83Aj/S5ki1jL/a0GC6+2L6Um+aoddlNFuj+bJ8mH/iaLh8I0/U51NspIEfq0dohwyFXKgm4NggwQ4rRhCOUFtxxo8XnitT4cnGfT93IS8FaT85XE3H5LMY4zIEPL1hw443wz+1UmhTJyJGxZzw+wsKkKZgUiVtKOKMEb2AKHTv61FNc01PQFwKnvsZ/9pPA4RKTASWahmh+8MxwzHxKy74IRn5LGRjsPUUwTu64UYNY38caqd7HKucZ/tHnODtENw/2UfHRMaq1UUPDJQ0OKkWCeet5fYOhII1VRz8+/Elg5j4Gxur3J8o2PJ4rg+2d08T/fwEzSVbyZ9XPro95T477lRKqUSRXQnauHNsISAl27oWi6Fv9z48JMv8r/aMMj8onCP/DuDZOuN+GPPr/+p7bx+7JlbYdppcNhzKU/1Px5aiaGDn/s1iGMaBcleKUo/v9rcxkZj7DBEKOfrayytXNLYiUdBY+pleQXdnscKlQcpzuWluxsieeyuXIK6SdxozitWyGOV3vOHHjguyCQ6fpIYy2JwvrQEF/Qa9Pdf/QqOSqCiE/EE1/XIVKTc2tzWbHnimrEd+Vyz311Ml3P0GVTj7PD5aDnsvCvH36alEaPMePcMegXs7x8igTu4B9v7G9vTHvhCu/kzIdx+BxC0ay9zRSvoS0F2lIxI+X7klU63I40gLQ3w5ep5na+SFnba3z5D64zv+QtM4n4ffG3tq4aNHGRfxgrXPMim+5487abL7xhdseIRn1KDl+7aINixdv0OD+JSPwKf5+xoP6aiTeQIDVlIhMcL1H5R9PYXvprs3fv2bO7MOplCmweuiq2JRZ1zz+9a/v2PH1Hfz9236w+ZrPXvWfAxlj4NLLHpq3c/PQ3uvmvbrjG7fe+o2y/cLdtE6VUlXi0ASb1VLUBVSUWSU4HdvAraTyS8xzM8NxvxFkXV6pUVRiJwcgC5zEeht4rwcp7ki0k41G0qlQhG1Vzlq8alEmnFi58caB5Q9vn988MLhqyVlHvLEWjtQFeupdiocF/tkkOGPW2ibWaBTkeZ/dvPWazXfOnnvL6jkRXpi85sFzZt+55ZptW3bl1cCCHZPD06MhySha7UFzjcjbp8fOecFCirzAG/yVjBX6OFIaadSjQq1nNhyIe8tVbaaSdHlXIWKacMeuZA1uxS95zILhyrxAdsXTL6m7kNQlx2P9uZf2qhufePFFbpI6/OU0WcP99RrCsrwseVot5mtytpf6Y0gm9sdeyKnPQ7onyK4nXlR/rg7H95M1upzu89DH6pgUcikoiihJ6NJKmRxV1x+MJiOA3YwhDRQrWU0u/0rvq0VYXnyCwsLeTJYBq3dAtJDavuzyoVpzZ99Z0+a0uoiFH/xcqgDR7rUFeOrUn6Cywb8ZeNMbhLV5ugP9l0zv9UN5b5mFkjzxUcpPJCn3V402pRxtJd2GrnLdhtVk9ZSZh9W91fCSH5B7ofxPiWL+j3D/uwhBRdyAyozeZwvQzs79soi+BKSnafLviZCcfrpBpLyimfLfTyJtbyruIQKD01tUwJyKEo/ybaxkSNFUMdMkhQoJyRBQFhnUkDQSXhTM+3NmY0EDM7ffLIjqWEGt8lCO6mLia3PukFnghosJD5p5SIho/VDkzQfLE+IrYoJXkD19pdP7OwG/voIUtagiWiZ4PAFTHHlTVhRZ7dYmPar+NJ+8JhmR6DFK5DV1foHoLNO/pHrvZfmWZ15RQlwvoVDKhCWNK3CCch9lfFBuAqUgpFSShmNaPj+i5++WZfKeViJfW5HnUakVL4UCNVkA4+ETfIqx4B5xSaP2L1yn0zn2ltPn4+OqZGmwwEVCaCSqG53ldtL1oLGAhdMLd09MpCCF6tD6ZnAZBY9hDaYsP0jzZ0j5ZjKsF4i1UmLuhbJMCnYJPt5VwFNvmZawXjEvLJqIH8STonZjq7BZ8gKgR20C9MDFqJAX1H64QW2NEup6qgzLP8cvppL/NNTOBTCJABOHeWoXzLhw4Wuy7gaBtjKr9kgKq8ZlRYBS32Lpxc8vIhpNDTfyNXWybMJbn2RyQ5EmWc2QF9wmSZ0KYCE+cPuYO6b15Uotj2Kd4MItLS7gtFbkTdrFND6pvEZqv5Yv7jXAus7Pg7avo7KDot50NX3CPkP+Kps8J9/3mGQIteY/LGPC+L7872SPR2br5fy8MtKBMHedGuM28/MZmPJMrGgi3Gb1S+Si1/L/zrZwO9XH1ce/z7ZQ1WSoY/+pMb5FT4ua0Wm+Jf/298nFmChEQ+Ti71est4mq9VYI6RsymoRJKYidElT2FGnDTZvqtfhGAFTbeqEw68GqtfmbVa/1IFO1/jdWr/8BDRRtQh9XNjubEm4aWVpVonpTGR7PVGc+KJNoBIWF7kYi4gUV3r1U6723i6TxUl3n3/tM27aZfKb7THiHW9VzFSwHJ05VfK6Ar7kaB0XgPPE0BSkSFKsBUpaLihEWoA9wBt8qirh2VSOkZwXEwyrxZ5jyt2rJmSo9gX7cg6jsEUGJU9z9xJPOEM3uQQxKgkh35DNATnVyrmJ3mbCNyIB/yox4wH1bg2DwN7q9kov4pFqny8oSm3RQbGgJ1QQTs6ZMLilOVYJ9v6Wha3HcJ9jddsXp9YhGUXLXt/qMDnvLpPNTXfNa60z5/yjXQOMq+lNmwh5egpYrdfZQZV9rI47xlRkuyTjpzsmCBSWNkAXVoK8sgYWqQJWbo1RLo6QH0YW6pxqfCnRgkd+RiFjUQUQ7poIaYoakgXxwFd9BuuI38H1xBxXSFb/pBDIKQFn7YB3dB36l7sG1FLaKiBdp1KxLvfswap/30lnVESgNnvjbUoT6w9N+Xoio0qcYOIM+heg940YimsucQVvli9NEcft2UZwGQwLuilj1fFr1i3NP94X+PE7Hpvtj6lBJfJ4R6NvWiaL6MgzWHxiN66DExa+dAdAbMYX6HVF8A+7rjEZIXAVbDe7PVI9rmN69JOLV1DOSvRPxWNPZBZf/Nf+Ny65BhYxxxV+77XJ2wfQ389/IQPgajXbwMsuAz/0IaQcXJavKbRqR2IqyZruXjVC2+hdee/5vdnYOedpmVtR3NGXldxSzDSIiBVpkGb9by89UpEPKrSLZmyFDzMab/wXl2CNe7s/qCtTvWgG5kpBmCBlSzDS/r8N4uwBwohRW63JTS1y32f0TQsPfXVGEHQrV8/NCfiOUVirYcBbIeA2+iF68rQIo3B/S628vYESr79ehzS7Q9LEL9UXmik9XVHb1yBO3Ngvt5935+k1efkV51mzzrM0LL3/20avnwMeKuWyOUZg2TasSqZ+KcZQiOn1Iu2Vh497ALUVZiCKt/gh6IvTIj1ZLRjWAkpHKOKovNwp00eqPROiAbiNEKieXwMLcXhVJ1/uzmLP4tfxaHR59cBdJVG1kTAgl9ze9QKUEQ946Hkb+okJ5JRDyf54Axur1D+WS49cLr0tTPEu7UmXrxcSr3XNvumv4yXzInXKH4F7Tc7p17Zt+t/qW2+93k063X7VW6lALxTY7i1nBXMxcxmzQbabxz+tJo+wijYaIGMNS8AoSMgAPt84DdHOoMPfjXhF+kuH1tZvuFQrRCN07xGcXRX9MYxYchDe5BcHj+Z4i+42WyPc8Xofi7bbZJN5nJLJ5qr6IqRtzqNlM17SpFsnkEyTWoABEjz4JXOQvzWYuwdnV5LNGOwTM5v9r4RpQ8ZXsYodks3o31JBlzbYtNotisnm22MxiwGFXam5oN1n0TA/hRvshvTSDwHff4nNzRo9Dum6PaJbMXzDz+x+Fkj4L4bFNBb1asqsgH7Dyh4DvbkPtf5yMDKzEwyoaESMSNS9P9gJVA3/RTlwoMwZvxECFWxIPNw9gi01nOHjP32esZTtmXHnxvZd8ZtakqQ7ekajbXetpNa6ocTVxJtY+uSe69OLz77zh5bDR3xjZMzUz6fxrz1nqrZGcHQHfPVefN+fiK86LeXj+Sc5lPKy+k/vCUI/DaLFYCWHr6nbXuILTIsb5imNKY/rCm28fSMxPhkN1XbNMNZGuqwOBhtTSxWuTk6bw0ZaG86b1hKddePOKuBvmiguYBn4T/yOqOyGRBt7bKUI1GjioBC8aUKwF7Q319UgcmtFGIzCJGBqwQij0ynDsfdFGc3TS3BlNfJ25xmzniMkpXXTPvCaD3ZaZvyzjmZdudBostmhb0ORZNN2sJBeed1HXkrUsywueQH+L0eCPxmsa5ZpgRJSDZ11yDv+jmbd86vxZfc1WcZJ3UkMq1BOOOVtvu/+pB+en186d3GTwWAw2jheaJs09/+LNfZft37DALyrNj1wABMuUKbODyTVnT/KYbJ3Tpq8IrNh92dkxOj5P/YpZx4/ycyiVcDYdn4JbEoKdQi9054iBKsygLW46FRGxAb0NPNCm8BSNCPjoKcj6EAus4SuP3rB+cV99/eTF6294dA8+TK6v74MHVpYNRt/I30e8QGTOOdfGWzzxcy+87a7bLjw37rHw1nPzp0KyyRSeZO+QQhInt3dYgvycjrPOv+T8s1rptaP84VeywdWX2T4ysr0/7TLIs6+x9zib56ye1dM9e/XsZmePY3NDs9zlnNVt4+WgHJbbz3Livg4P9WWgviOMm4kCRT6I8vw0NbUUEnFvOuFKoxQW1gTsvFirsF5pb7qTUCx4i7VmtToveaDxvK9uOaedVvPRpVOnNz0Q6bry7uiSdQ8t7Vy4JQKVS+XPplV2ts4bvCwZu+KzgITtxepaPRzWdpv74muvv6RO0SorX6cu/dqKn/XWnrtp/Zragz13DUCl5myiFW2Ycvb0PtsXnU+tx8pvLFbUspLX68mdegwmOif/NPDONajTGoUh6tU56HBJCTBASVvNUB5VIiKpc9kd7kludodSFz7xQbiOmMk5dOYk56gzL6uaf7N8a6MQOHm0ae6snZpFDfuT3/jdYzjzwkXXIVHoXNuCfQslQZqBZjTsoHMqrkE4jaYdgkGz2ATOgB3cPkSukD01DnV3ttb1wx+6arPqbkcNAHoFPzKUUQ+qL0k97pjbZv1I/egC9zTFbrrlFpNdmea+gIgfWW3wqkcis8ky5FAcRd1If5nNZrl2FFpungc8wpoCl1BpQV/ScS+zjlASyUTVv/AJ46gkJI4bHX4lTnloctxPZE1ckS3+jG2fKIjkQFyzuo8jvYQG1OrGvJPSTu/nSp9PHNTl4z5hK/8gtXVKF6gEKiglgcKiRlCESsQCV5QIlKWKpr34lt/wkSx/JCmP5/cBKQfl/5gd+rOS/+p91/+YCg5CXK2W4M9fu+/6xxX+vnelVuldIDCG0VQTpU9Dw4pRfei+6zWx0MLie0gPbyrkmRU7OwT16JGeyXLHqOLqAfVN1GPlBzWtFNzj0TRTCjogtP1NjIvu5habN5Aoa1k66wGpqriVetJgiGdwDZtKhnN0y4n9sXYnsqGmZfDSR15+5NLBlhoDaedEm7sxmpqRija6ZEEg2EAnTiAC8IrmFbGz1q08P9PSkjl/5bqzYqT9hMmptEXDgTqP3Wiye+sD4Wir4jCeoHbbp5hRfpB7BakUIppIlPCD30dR1GtslDz8OsqbXmejFC/v8wu5X2myq7SJ8Avzv9DFUJySf5uNvq4+Ti7W9D/OZrLChdwxmPNiBRqVjnpK/aGxRCDspVYKAW9AN1JANoo8wP4BJUlGqdgw6m1qPQ2QW3+OfU5/ieLS/NuKpDU3uf8bcAXyBal5jMR2NEAbPAZt0K3hvxHBEDlUxfIGcD+N2gNSNx36nfqlAYow0puatNpRz0e4W2oahKzQHsjf2c16ad/3t2KTtPobnX6D8C8pd0MDP+Kx7wnXqGGlLQcvikMErm6TmfsuxJXbSAxqNjOogJLQBLiKEHAE+JGTS3JoEhTrz8/CB+5YlupJ58aOat8Kv4JvregxwcU5Cp8GFAFm1FyOfto6GS2m1NGTS6CPNKkbsTdCBlnN9onMho55BX8IJZtEQ35lk+htwN5A0V3RCPoD/yXAcv6pAtbZczRUA64JmcUf4q7Q89ZHLeJVZ5D1Ps/t+0iCT3AHVtZC7JDCXfR7OSb/Xja5H3zQbZL1B+ULX1BMTEk3AseSpmnKEK4T9ekMIidUCRQFfcbj7z8gNLvzF7mbhQN8h6ZbRset+nQWdS/ZX3k7WpS8P9sfo0iGS64wV516pOhjI6TZ2dApgI5+LhxywYoWxKUrykKJsIoDsR4mSrCTg0egMPnLW/3Q5Nn8BZEuzqEI7HK3n0+zFmuO3TtWQ5WJoG9YqCD6Gc32SxnbnVPfsxvrFXK2dILl7bLthDp6glhcsfp4bYvbSmj/mQ94uBTw0E73x2jbNRCvC6VL6GCFDwU7eWQDcC5FY5s0slieRDwtAbRsbLXbaXAuu14e2OJw1dc6jQ3ZdY8v7rv2/BWZLqvFWVvvcmwZkK9f5jS4muO9yR5res4kfkRxhV03L1RfPOiPtYi8pd7jNEsOpyTwxpaY/yCZu/Amd5Or9uS3DYaeqVOhH7gZN/8I/wi1fEuLXvyNivibjuKvN+1Nc01HF/3h+ef/sOhox8MPd5SFucPjorQwXT+ytA8EmA5mamHNFDVhBI5pjZbQpugBNkO8MvRub8KVDKST1Wag7D3xlin1ZF7LFP/79nbvCXFOY+PUjrT7/otsPXXZ4exdPzuhZuL5LUXVAn7k7PbhG89uz3b41X01gbjP1xwlu5rrvvf9+pbs6E/Vu7Nk642/PYRaAiUBdrmO6CDTBLPQFA1ur0uXoBR1INDMkypKpoTqnSMx5GiEdTEaSHLs0Alvu/19/5QW9Rv1U1ridT22i+53pzumbs+XFFXYC++CGsTj5JUT/GCgRt3n78i2n71FHG4/u6X++9+raya7os3ZbDmgWfXun44e+u2NZKuGZ0HiF8M4TlMPR+EU6rPKRJ8wOU2RFUFLex3egEsz3YqEAq0cqhAAW19dBZIlVzR61tuIdTnpXH7l+uXrbjPUyep+8cl6aXKWhPHpDcXl9KiTWDNr4mBQc8Tq+NzK/OKSbsfl79o9G20R+brBXYvUg0rLHhtrc4TN81TTOWSZ0gL1ZVlOYH2ery/7XVUjFMbzYpg7UswcqJPQwBd0LKLabJ8IaCr2otcjSkIrGwootKECaUd4XH1+SdazRrfddkBU98t1htvWrbjqSqjaCguxrffM/5zDCpBALUycmajhd+R6ww4SWafuZ5eU+tPid4lgd3gt+b/Y9rQoZNmiXYPXyRHbRs8zX/f4WIFjWZJtUdSD55AP3xtXH+ZipC0EqdBGDA4CoYEU6gRLGPU11QhkLTBiEYPiqOeQgwTCl9aok1Qr5pFf71qEeNxjy/8F0GoqYPv75Yh9j3x4DuJ+uEzHRpAq2lMqb+qfTdiq6kGtzfOWsv0c7lSeMXDHBDe1MT+LUgx0Pg/p87u2UicdIvqQi8DkxhcUwUXCedMpb4NQjwY3npTmgsURJavLwCRyEcN2HfWsDVGfv/u9ZUWUx+PYFueUKwaNvbtu+Xps3eVWbN1GcgVrdMnWJ7WmJz9SD66EBidag0NF1Ukep0t5A7sFCWdhzvYwHv6L/BehXuHqfaBwBEU7hfVLcXvS4VQv+T/vaSIl7cbeMc7ekv9i8S3e1L5xxpvMGcu1EYPbKyCiijjGXcDKckm43PqU2qNWlXusZMiqF82cuVzolUHN9NNR0HZPxFPV9V0wLtvq+k4DqOwVWDlzuQLVdqFiP08cRX7aRlBVfR8cb55bWe5LExnlcsDp1vAP8Q9BucPMk1Ulh4GnN0SAdxcNHv3q9ohx1Ati4S/tkWjIDe3hQdkUGrGRaFBiUdiTSkI41UkMuuQHP+EaSQYlPQTFWJF03BNPpTu5KFAdkWgDukzsZKMG0Q1TAQQglScOaP/dsZ8+fP75D/9Uu5Gs3FY/2SxPld0DHOciXI9gqjcEidXjE+3BLosy0OcX3T7O5g65ROGyzQ2BZs7WbZVnO5ydLe32hMwTQ4wnnKXW6XW5LAa7oaXOIHoUl0FgLQLH2by8wSTWeAx2Y5PDazK3BqZbeJZwXGPaYhX87ZNszoDdaRxotXO1nNlpdvAPFWHDm8PqEE0sZxDEqGzxisFNnuCWetPcGrObN0p23tTZwMuRVodSV8+LTrOV3eRvzjQZiSjaLYS1WEJe0kNsJlZu9LFun7++wW4gRDRbaxw2nrOGm+xOj9cmtbp9ZqeTM1m8UXfQQCSTVSQox6pvtjot/FpHvIUjJovFEoYvHYV9C5Y/xN9OfcalvII37UEhTbTg/AQIaPb4Vz6j5u8/aViycMod/fkDcpu8QZbZoeBi/vbzP3XPsZvOubMtaPHkD9jt6+U2O7vqU/9C9SMvgrXpQNG/E0oJxun+CiElUa0IKQSUwERxOntKSV7ekcuh9VBZBBo3VUcB58ofKBHCwLyf9qFosz9Ibf8dGqwaBMjRig4SGOZ2UkWI7UiO9OfUPdxOYFApUZyfpY7mgEc5rtNGGk2H1lPhAk1Hp/VAMqQEHEUfEYkkUQq1JMdzsX7kklRrTrUi1wMcDjmu1YYfATj7Y+pGpPEBXuoQIj8rR9mgCl4C9yqmF7xnVWxGVniNqtpVmXBvQ6iwni5YQ8a1jYrXtc2J13HvgkvqWxuva1sbr+P2S5ceKGyBwDv2DbrToe1u6BkAJV7xnVLUaq0sJB8pFqcUIPi3yuwxi4JuLr+P30f3OkPQ72aO0xYo3/EsmO3QO5qEF8S0qQH0UsKXv0brnl9+8M7jF174+DsfvPOl1au/RL5/9DsbNnwHL2pHR1NTRxMZhJtHktOOxLxErPF6YlLvpC9YP73x+4ofw+3xVdrHcDE0dQQCmCRgvt9b35xINDf1CDcRSfJ+pYl+Sf8YcurfmXP5F/kj6J82jNsrkWiEuhVlgFfyNkB3S5MUzLhoNiwSCYcxQ7Ui4J0Xh7fmqRbaPa1tzujxkBRlsEHy0/OM4pYLPb7g9O6BQJN6l9zQ0OGyCaZz0vMTbHOzXfQ7a2tsterTcqxeInODoemdktw+1SbVhKwtW9ffe8VKadK0OVuC3bWzyKm5LeddsWTeorWyY9IMtUFutdu5g+Rn533qkocdvLs2HmhU75br/MmWtD8zA3OP2t1ea636jEzqYxJZGAwFiDEd61oTsrRuW3/3pYNi3bS+Rd+GjOfVpAPNd6y64Gsz1GaZleWIPoYL/v9mTeQBENVEguiF1aC4YeXxFETw6QyPfn0m9g8IrMFAvKM1EI11DARnbqibHk/Iojy5rSdgCyZi06y8sS024PeuO4MfwQ5Y9yKRZCqyYaF30vzeHlmUprR21tR0t0yz8KZY66zWuGvxVQB/36kP+K38t2Hu6NQ9SFJfw0AdpqPEK2qTMpf2VCqJwqPoJezTL824b8akoL+x03nhh+oNo5e77psxg9Q5LzebIKD+fsY34f2MtB9fk9v5b8PT6tYrgv4kRPwd0q9z3gdJSJ0653KjCYPwCaR5aUY63eW48O/kdo33yxX9wCiMv2QTrk8eGSI6Ag6moG9t2P/F7GRNlDjl0gw7pJ5aOXXqyqn8SENnXBmbSwUYLyqJjv3UmY1nKr4t80no0faXsaIEiF/BRaIBnItSce4OUif7W6Vm9T9H1X9Vj71BEm+RdmIJQST/ZfVdudUvh9S/qqNvqT98g9SQ3lHibZY0mRVHooyDN/FHmTgzjdozKw28NwQ0hwN6BCoPKaEk3YtKwNhwRLXuk076CGoZNXDQcRwZvreTZY9EZi+d0s4+ztv8iei04JQl6ZbDD2eHV7X4uHuFVfPrOmcs6m6Kr7hssr+1VZFcEZ/PdJkn1hOs8SXS/NFFgqt94PIZzZ3tdaL6Q5vo6piSzdy737pwsX1VyxUrF15iJ4uNkq+rbyg1Z+O8VsNC1UmcvORPRfxtPrfRwL2p/oA1eZp6Z/aGffoewaXcA/xBlKlQLfhQL/oPgBGP3qsA7IQS8qDVNswHKRSheDUvA3Q7MZoRcJMxlEygujn1QdyzfPfq3dEp/bXh5e5YXW2Ngfvza0ZF6UgFL/E0fTq4LBlvTE2qb/KuuzYSXVnjTfM1osvqMHVbm9950quIZlbqaL6YP7jk3kUtA0GnX2nvq53f3WoSsvEdDRnULgo2fN7lNZJgI8/VWi33c3bBZnGY05+dm+3qc7fNmj4YGKLj2nfqFP+g7jdDlxEV5XsJQZP6hYrS1l0VQr4c69Xueixp90gnZPmE5OF22j+SYEWHlZ0K/Hgsh/Ztsbh6h2DNRlvv6jJh9XaJaHCZDiUDKNTMkvb8vsqCyf3ZNdSmO0fa0Y4baJTtpbKzuVzeeSI7fCKr2Z0WypapnXJ4gnoWy3PoUIlIQ1TXdqhQJIXp9Wx5fYdpeWh2TY5D+YVyKd0jw3iumwi/BC3cEy4o83QlZnW79MrCgCjbhWXBlRZVVZZv4rIKpXC01HFlHdHLoeWVl6UVc/J5uGm6CViW5mulYMk+HqNYr0AyUPivLg2oMs2MPqtuhHyRyiwvNJej1Br+fcLyoAyu8D9B7bgmzUqfFobF5nKnK4+t8MPJkI/xHUNWk117jugWF+xazTAALQn6+UE9lhoI5ApGA/iuJOsrlNP28SVVuBVajXmircLel46w2bJS1Q0Ft0KDuikDFL/3pYrid1Q4FvofwRIo4R9h2ftSwc6jHAMqLcCql8YPHtlzGoByNXYN6v8hXnRaOhUvx0sVLCexwupGDR4NOYC7PePa5keIPACnuAdD7dEadRuTIiS6Lb7uskb381My5yjzF8lGCjBRqdwrWJCagfB3yCy7XT1i92hbcZ5Ci1FJkgYMDf6n+jspIsHFjJrTOdzSMuOa9DbDcj/nH9N9bIoGVgzHPWIQuFuYtaMRaq8eCKI0gEF6lPOZjBz3EEvaaxwSUT9U/8JbJZPJJLBLolH1La/RbF9AbC8JJjv/mMnssKjLRBJyqj9QXxNko0Ux/X79epfiXkm6fmKwF/en1HLc6LxloXWKvGa5rVCVL83VuiPcDEX/K5pTXOxHfx6HHB0t2FI0qI2rCZFTrvPWU67zVuS/kTsLnc7IKhFg30e4FOkqNSfH5PtkmUy6Cpiv/36k2sbqCeCFNa+URpoY0sZoYmCgCr3qgZz6s8I0gP1bYiR+D79H56NOz0EVWCTy2/fffvSCCx59W7uRV9995eqrX8GLesOXNm360iZ+T/El3uZqL+FyzSZ8XxpTiI/G0nkT4zznFZ0t4ipMz5v4q9ssqbdKUZt6u82knPCrt6PZwsnn0XySVnyPR1ZXAn72yx48bWJsu7apnI3Hy8bygUK5Js32qcytapqgmn95uexccj205vGgJ+euOeG2SORmKZr/qKzcx9SFctMJdwMUFZDJITs7dnOp1EKZCxg304Cevyfya+vlKqv6aXK1qIj3imL+L6hL+yvUlFfE0VKZ7E8gBY3M/8VoJCFgizH1W6VyC76nH6b7jiibYVxUmVIEspry/LgZIlCeP11Z4zs/AwvVwtGFEut5S1JY4lfyT0N/evOLo+rUEgjcqc9IkGpQbv3iW7Co5b+KgjvpzYdH85PLcc4X21ouwEGl/S4qnUAvoSlXUUhR1eKr2VWFTB+GMl6FsiQsVD1R3urlAAIoSn7JQkmiVVCHSpCwDH/qPepXQ0Db77CJOAImohB+RPWr31ev5g/kE+zTa4lbvZo8xdWPffQu9yJTPCNB66s+zXoJt/0L6hSoCuBIoK8fnBGG87OoRckJpLqyWe4YbpGi50g0+3I3UD85Oa0fzubfoXxPLbW3FDWzigmyJeM0tQkax7PqTy80+UxfUHPlBZIRVNQ+v0xRm8REKPoLmNr0+Uo48v9GFbXPKylqQ2IKm00QddgyWGMROCTxdLB9nCY8P7j2DjlsV/+mfr0C0r/NkeXbbpPlOTBBwT0mVz1zx9S/wJecBF9Wgv3p032iP2v4VSgfgW2G+HUEdEXU6iq4CtpLJfIN9XQG8dwa1VoO8XC2SrPDDyCOQptXgbcPvlAgBfxBoGwftQKeKFrNTASPt3pGGqDt/QRasn2kri+H6L80MJRsmVYJrAKyDItpJUy3/15WYIJqcJ9Q5N/LFJ4c3dc1URpWl9hW6mu50MUIelg4ucTPf15zs5DFo1c0VSp1tKB9jkwIyuM45kb+IP8gHed+6jO3v0KbIknzLy636E8KPTdCuUpB0wLo9JKnAO6pv0vS31EtBha/fJemkgLVVnd8KCk4qBTpQ5m7FbifBKrPJcq0pZAFVG/XbOFz+Tcq2MLrcmV28Nmi/OHskh82bau0k8eWCaPijQPWQ5lUvslwVCfHkXBMIehqUgtDNLeauH1huvZTbYmw+luPjyWoNGEuxRLR7LK5fSyXFUyK7PURQv2v8D3XOt2NJ6liBbmPGOsakw1kbeOs+31Wm5qpH+iJWSzqdPr2O7zc2TmtnrzCig6bBd/vgQmzOlz0STWIlmZEQfupogOZFHUZ7EkUnMn0RrpIMqAgHRJAOjIJ3yGw1I/MAp9q9S3Q/clADNm1wEeO+xbwg5OIYHZLY3ehG5lJk2xhco+6JWybpEVz2wrR6hZyD0QXZbeDVB+onmlimpkWprdAs4WEZDSQppsDlcdCBJJESIYFuAtUnC4GIF2C3Uu2Kv7L1bdz6FxtqxpG4TqQOqOUNAJ2HLvPWA2GgDy4O4vaDrtyl6P+1fAll+SyFcQ28GHqh7fvvf37udylf0fNwhzgz87Y+cf5x9GnF6ygHu18sAbipWeF0YPBgp2GaKeQduxxdEr3SgbH1kvH7tvqSLhedomOvZyts2dw8acu3dY/f+ucuMtCuP/e4zC4XnH3OLZ8ZuxTWxy8dJfU5dhDeKPSlJy5pn/+7u3XrJhmr9C5CuleGflGQocKnlAUaRKp0BAHV0ZwUt9VCqk6zYOgRIuMfePJzdmBdpPJ7/6B23+f+sp9NMDZevovvfYHG5dGPISQq1DojqNckchVrCcCYz/Q0hI0m3NKDRfkgsrnamo+p0CAq1FyvC3a3Nak/s5VX282x9Ufy3E39VAx6o7LpCvO2wK+ch9jNqpJCutcIOooKnYWtDK8gTRVYygRQfwgzKM5+jP2jOZdx3r32Py7rQUPOzAnoRs95NvRAR0qLGU11Taqu1bUYSzMcWjMEir067JQQHfIrLBHsrgv00/Wavd8HRLMEEYFSW3HCSNQehnrHztKqHcDyo4VfZ6gPKCR+gufwA8GegxUEo4A+gd0BASHiH6jYMLIsUdQJTs/C641KN4oCHWolCMLlMfIdtWKScjx7SM5LD9HnfmhrGI0S139UWfUnxgOXdJFW+AMcGjKr6eHAttHF5sUoeArYKDcxMSYcKA/xUDhPiEOEAPafSIUFArN0r24ynI91EPARDXvIDYyvqZaWeroBOUABQA/E+DXC7PWafDLQY2oiwpUEyj4RQtVlUp1GrM7In2p2A7VuiOW6otMiGOo5Mrp05ejVuTy6dNX/k/7mybZQ0nUmfrbx3U4KueDnlHm5wdh8FFeKnoaKKh/TK18StOPhwG9Xo5mqXAxvw/79YQwwDR+nAKQQ4izVXioB84qcppWB7IqjU45z4CE17OvF1Dw+oTFqxtz8dxwtogBnF9MjIl/in+K8s3hM9laIn0TiCbTAXL0T798bPXqx36p3chrv0O+GC9Xaj48Ecv8U8UEeBvUEsDlTepiU5OvlpeNGvpnKF0RvUooWhIjnx6GeBapXCQYTw9DNg6/OC3gZjp76oNTj9Kz6Jqobxb9NDqc08vcKReOpcsQV2K8InXFaXW3aI6Ofr1k48rp7CX7rx+v1UKPsfvzQU0Kc83i2VdILmd2/yX55zT9luN2+Cu4nKfwPcK/CvDVU+pHh8+LaldIf1fA5h3ndT6Fln9/W/9Ce1vndfvJtnPVO2xhm3qbafHVCN1X363UXHq9xuVD8OSD29Z8pZ5cZrern9cAdGW/uib/ud+VK0L9a42r6C90kL8KzxwLQw9NkIQJL0ASU8M+VG0KsUdgdvpgP/6NqqP0/gHZFUfGEijZLHpiIgvV5/Bltrj8Qd7XQd5p4P+7tJo30NMO6VGBwahSPMYiaaBYoLY6uEnciyhhh1Z/vvacG/rjpsvnpzs0B1Id6fmX8119l88XnOxe/uGrzzHcdu7UtY3+2vmXN5zUyj3ZcPl8p1sZSs6/nGXtwrV7Ka0XZdz83fwjjINpZWYw85lL8BRK4nGyIir2RiOsEyipuEcIakpGjWgBjLiHWOgj0Yi34gW1kKPxHt2Na5q+lwg1RdRSpFDNzosb44YJXnAfoEOpZW//6u1lhYA6leevezbI26zNHO811M2dc5HFxpk4i1jPC0s21/BWW5DnPQbn2X1WK43/aM2n18DfSoybbNHijFpamzXI31eRibGUOxSu/lT96YZlq1Yt20DaSBuG6knw2eusHs5EPBfNmVvHKdaQzcDfz9ZsXmLDWGXy2U5OsYSsIn8CS12jQIyD12KKqZrLPy7mSPdICmd6WGHG8NDZkkHuE4h9TU8FpmUO/VjC/EinToFyoNDz2p9XD6g78WgQdPG7Z3R0T/Z5dTM9lsL8Ktek7szl2L+gQwGgwkZHc2g5Su7NvVqwGy2Ua4KSXUwt1X4PaM5paaEu6jQ5zVFyNabxvUksVt2T/4VeamYPlLtffdQsk+2sUTY/zDXl/05W53/Bz9UK3p7LjapZ2ZxOm+UlZXrL3HHGqO8+wVroDaCTTnTxitMxmiAAYQzVJQH+nj3oIHnPaN6Zq6sNSLjBl8tKgVr2mj/9CWi9dnKca8rBQBsd5R1tzVlgrl5pbnPw6kZclCr2CHxMnHohLz+3KRQokzALyeIKFU1TNCiayJdoHvDYe7K6mZLm8S3uJ9dojuaJ62/qN/tjQxnSnhnKPw+LNrLi8ZKyJ3x1YhiI1aNAtP6NzCGzYv3DmaGh/LvQZnt0evgIhTFV0kE/PYxAnOHhCQUZdCWY5JWJwMzlAGl1mpNbDU7yyGnhRMILsYhH3VRAijrPcBU8/Cj1Y9NY6cnGVW0CjTLaz7E3epvaT/LtTV72Rs+0WVVmd0dz/MGTI5F0OsIviaqDlbbO5X6xT3PeXbXHRtf/z+fdka+eKPr8KF7IF4vBsT9MFPuPJMBTBMq9hQxXelQ+bewnf18ap4Ib+mSMrtDU5zqlD8QANa5MBGh/OwOvSDfcV2d66mfEWsbGWmIz6nsyZDWQSmqmxDneYyvjHPmRXHZxeueyRGLZzvRioKnGto9nIPkibAJA16adcOZRQr1iAP3bUyBR7T4RgAWTKxhkCYFwshq+7iV9r0whk50cmRcTg4fy5x4OmmNkHndIA2+YuMbmE9dwGYB4KFTsvnDE6Ah47r/fE3AYI+oXADpkdlENcZ8OZEEf8FFGZNxMs6ZLpG3SUFLL7Q2kcFU/A/Jsw+vWDa/7emewLaoeibaF1B9qUNnuqWK3+UfXYVL1v/omD15xxeDkPnXTOKSVcCbDGtOu0YQNpGAP7U1HU58UrqGu8xIbHtkQ3LVhb7Dx46ET3Ffcm1q0YcOizNmf3bC3VjWfAcpSv3MyTlgJ23FHQgmgvk+gk8pL0mcCDOn08MDAQlf+/SlTZ1z12fnqntOhbOTL9/ZdevbAPN+yby1f/uUtC/ixm8ZBo59LTXEW060hGrTDplNprWd58fwB/b/E27BdS/s7U+rGVCeQ46nzaw9QccnmZerGZZs3Yw9aVHt+Kh6HN4ti6lxIhT/wahnZtWwzlY9QHQ2c79C+dxzvVDKy8GqKWQERO9YAKbpsDUTLdWV5dE8PVPjvj9pqw7ah/PFVtkit7aj6G5xY9mfJrCz1j1e0BcnPol4UjtrCdbahIVtd2HaURujnFJR8CuOuUUfhrGhgKKgjCYNSvCc1WKlEp8wHUaAYynFNyzZn+2MnYv36dbMDBTonl/T/ma5IKAyEGz+4eRnVtaX6tss2o34u8mWorFtuFgm4A6qK/yp/gLEBVat5WnPDdKA574ubuFJ/IUfZ/Y2Nt6mN+ZNNTSTaeI56gKwkXerTe9DDHUw8/H35FY3nNN7GGuBKWhrV9ep+0k1WjNWVaHkW1yA+QHWNu8rtBw2a5YXuE40rs7/GA+j09V3hA98yRnFPOGr8ltGlsFdD/7tRce3LH6Trcneuiy7K7J3khKu+3qUaXPWaX7T6/Kfj9BX2eZq2XAcZT79u1ClJzUtHUqfqSMWBcZS43Ena0cUGLgpkKxB1QM+0Fxz10wgg6r5rltnFpH05pepUq3Y2HfYqeKRntmUFNz+XmcOs1H31U6cC6RTVLfCg7RNBF1UF2/wBgu0fFQtPEU1sSg3VcNsR7dWq3af87tUFn1l3ltXpaJxpNvtcZkH2WmMst3JqRpxUH+WC0E1qOGtP66s1MYv+VLu8/XFXvV/ZbunYYBeVN64ls0ur6NzpV9xzlmQwB5qC4Tq70WC0tk8dWJXeHvkD0h9zJOM0vD86/1NJMaIAolctvlByferCsqOKDKceOfUu1PsmoFCamV5mCrMUOCi6V6FJosMF22AcrKJgQDVhfYh6tepp/lYgvnCEAbJQ1L0rOpajEmRcasMiPfxhgGoVo4rwreQpV6fUJHH2e8fa1s2c13Apl1b89a58ozdoap2sjgLN9uISl7P1DrulyeIkt0zr6JjWocoPOZsaXPb6jtqBblsgsaRre2xHi4nELm0MhG1+x1SXwLpFi53b+aHRYo/IrbZtuWAKu5cSEXfybnnmUCaXGTpQr0xK2O2WWY76f+nAjNVf7nCZHU5XqIkTnpt6VtvsFlPXg1031g/VRdpkkyVpD7jnmax88QwDvg/66NnMRdRXTcGTmQc3cuINwN5IQqi0yzb+YFVHuVqI5s4ADfg5oE4ybDLd28mFSFmYvRoomsWXEdLU2Wl3GJy93ZNb/d5gqmNaqJZSO1l6PVRy0nZIj/45EetjLguh1rLqR+SK0hO6NrsqcNX8zoUdjQYDJ7tb4os6+i+Y0qpY2AWlnLRDWdGFTfGY1gV0zNAtJ7pdo24se0D88AwLY/gZmE9iuP4V5v7CSR/RThaHLh+UeBkXwU6BC7lGOevK65udTv+tS/PfW7qj3ljTcj3b9OkbV85t8xsMj7Ddj7DGpthZKwKPvso/c/1K9aLE12fMWLV1y1D9ua8lyJdWXr/bG+noCFutf/mLILe39ITUV4igr3876fpX5g2zeB52sWnIL4fXHlgeUzOx5QfIvJQyrKQE9wHUqVq+PEaOrz0wVvNbJZVSfsuMzxN4l9PkedFzw9V5Dj+nzpgoT4ZxCxJfC5RWLc74YVHxKlExCYt0JAOMatREhHBSCAtSfod6x6Ls8HCWECLwXZ9nd5Dz1T24JUdWs6fU3++fcnT49Qe+kBs+wdsMZgPXMp3U5S958snPP/EE7bvkOPCuTUDTUQ/UzirLhML9yPahoe1D5Fj5jWsaoveyP00PehdUAHk/seDVWsvDWXXXsyn/4wfpXc2V3/Qxli3jl/5hj/83avSCfpTNxOEKLmTjxOEKuxgNlsQn0xgct724mhynupNW1Ph6o3RYS3/+2TJrzLlkFz+ip3qCHKf6eqW02QJLjBYuuj4sobhCWqa/YHGEHpcnumuWSOhxeaL7sOakNR6vvmo+YcfFA8UFXEPZf9UjyudIOyNwx/i90DdsujS/FX2UAwvWSVK4NxaMhAGw3oowp/uc8CTi7D2rBgZWwb/60faR7SPsEbjkXy4G0XaqhXPwe2cePjxjxuHD6ssQuR1fq6PF0E+o2t1nePTn8TUmxz/A3crMoCc7egESuoTHYc7mYdg6etORoOhR7BBGD+qJopELrl4S6cJNRtEAsLP/OdvnJq0Wo0GolY2Et9VFB2Kf+4bZvVyxfOMz3WdFfSIryj6DwWghre7aQbdiDrkTL3A3vNDuDpk93HqXwam+bWmUJZfNn5ozKV5Pmmq8PF/jVY+2Tlk2M2RzSXKjmbQ4RZcQavEYrN/9rlXwtIQqzxQNMzPPfHYLvuPoO9TbT8bpGw5CQPGd+SyX/Cyf0Vxjd2R9NmsunnXYa8xGHzn+sSfM5J0y0DZEXWWxkXjcR75KBLNLHi7XvX2G8VOrf4Ykg0AMdBESIpo7MgAfyakA6rkqpI6UjNs0px7cMV+D5BF49Tez1VGnYmq0WIijp985m4Sn2gJR9b07riPPFo97OYbUZbxJCpot7H/lpZBicglCPN7WOfJkcHqc3ElWqvvz/1E6bIQrG+tz6WkM1SM9FBTR7FSs8KyBBytSmNEoquJNFN5EQyTiCrnKDx1h58yxCepPHU5nxGoxEQeeOZi2m80DxNxncVhr6BmEfUarxejw+WSiHhWk19bSY7aKR5MsteblJpfTLtjimBouXsm3d3djjYM+wEW0El9dM/ueVRWIsXwe43R7SgbVZqrnqoJ1X/kuF7pcgf8duv4q6vayV5U9zMV91GxO59UUjW8rHV6u799WzKMT7umRCXbYUKM+foaCcwgaoqZUtmodV3p+X7akb4dnU9B9La38RPFUG2SCC90tVA4XwEFhyOpZZrUCsgWYHsczLFBBVGNtstoN1bw0Z+O4fYIbvZVt4EUcJEKOhHeincWqONw+q6w5Go+WGOSR7LhKV+KBqbBPpfUvOf9QqkpDyVhBeyyZQGMsdA5FBUqvFMtUyGq9vjnsAJU4UcrxldP1CCaofyDkSAifoP5QwWx+SyUGxp75BzGAvtG7uQ38LehlyEQMeh0TeE6Bm7tYdXqdkt0uOb3kfYlNwmOdDyacOq/qlFo1v+PTmTi3E/glC9W11b34A22zmLzvb231Q0L2Bgg60OTW4YdstO+YOJnO38TtpH7zy9ymokWyA79qlVSn38HtpFlImFnhu3b4boNWXklOXV0Iwo7lQ1hrZyPFcwtjwFP7iEKSHSSJw509kh8kj6pr+H1jR7km9vcvqN9657vffefkv+fKxge1X+7RdjYUPIESN7gTvRkB/RMYtEkaVkdHApmdBPpnKmz0n1xSWFOyVIuLrinZwpoCRe6kyiVZoHX088F+UX4+WKS4iBTP0IWxGtZgOdMaV4KTayqHQF/VihBwTbgDXTCmKoOBJeNhwJMzEVjtjIFLuU38fPR7hqNG1JS7g/qRCuy3vmQ3W9Vu8qbVbP+SzazGRJH83MzP90Ck2m31mMjP8TiLn5uwD2Ugr2PFvPQjB5BnSJvQxGQZZEB+LopqzGzDbMmbkAPkZVJjeO5FzOSBKCgJze2ZS4Gemc9twrwY6u9H61iUQTcRvtdT9RW3tRxAWwFs2tcuJRnI6xjmBdWjbgFNRHMHiF1uHYBfUR/ut5Ug2jXAaT96+9RH/FToRwIzGbKmVJ1AZQnoabSB1yyIg7ByAridHApPMjyw0OiV6RjSbCuzwLAvFizBliWJua1tsuAgvNPbmljYbpt8lkWam7b3XZiOiKJskMOtmfScnsbPW208knwjuXrXK4Q1iKIgNyYXXDVT9C2Ye/78GQ5BEEXfFdde2RwauOysdJNL5AzCy84ard/nGAVN8alecnFdgu5Gbd5DJTL+hHZK0vApVy3OfU8XTSJg1TlssivsPYUlIqvn66PzrVTymCc4wgF6SDNR0pDf+9Gp+VnsUH5WtpHYsuhOaey8zdwLN47V8MTbm78g687+P3cx6tcAeNpjYGRgYGBk8s0/zBIfz2/zlUGeZQNQhOFCWfF0GP0/8P8c1jusIkAuBwMTSBQAYwQM6HjaY2BkYGAV+d8KJgP/XWG9wwAUQQGLAYqPBl942n1TvUoDQRCe1VM8kWARjNrZGIurBAsRBIuA2vkAFsJiKTYW4guIjT5ARMgTxCLoA1hcb5OgDyGHrY7f7M65e8fpLF++2W/nZ2eTmGfaIJi5I0qGDlZZcD51QzTTJirZPAI9JIwVA+wT8L5nOdMaV0AuMJ+icRHq8of6LSD18fzq8ds7xjpwBnQiSI9V5QVl6NwPvgM15NXn/AtWZyj3W0HjEXitOc/dIdbetPdFTZ+P6t+X7xU0/k6GJtOe1/B3arN0/pmz1J4UZc+D6ExwjD7vioeGd5HvhvU+R+DZcGZ6YBPNfAi0G97iBPwFXqph2cW8+D7kjMfwtinHb6kLb6Wygk3cZytSEoptGrlScdHtLPeri1JKueACMZfU1ViJG1Sq5E43dIt7SZZFl1zuRhb/GOs44xFVDbrJzB5tYs35OmaXTrEmkv0DajnMWQB42mNgYNCCwk0MLxheMPrhgUuY2JiUmOqY2pjWMD1hdmPOY+5hPsLCwWLEksSyiOUOawzrLrYiti/sCuxJ7Kc45DiSOPZxmnG2cG7jvMelweXDNYXrEbcBdxf3KR4OngheLd443g18fHwZfFv4NfiX8T8TEBIIEZggsEpQS7BMcJsQl5CFUI3QAWEp4RLhCyJaIldEbURXiJ4RYxEzE0sQ2yD2TzxIfJkEk4SeRJbENIkNEg8k/klqSGZITpE8InlL8p2UmVSG1A6pb9Jx0ltkjGSmyDySlZF1kc2RnSK7R/aZnJ5cmdwB+ST5SwpuCvsUjRTLFHcoOShNU9qhzKespGyhXKV8SPmBCpOKgUqcyjSVR6omqgmqe9RE1OrUnqkHqO9R/6FholGgsUZzgeYZLTUtL60WbS7tKh0OnQydXTpvdGV0O3S/6Gnopekt0ruhz6fvpl+nv0n/h4GdQYvBJUMhwwTDdYYvjFSM4oxmGd0zVjK2M84w3mYiYZJgssLkkqmO6TzTF2Z2ZjVmd8ylzP3MJ5lfsRCwcLJoszhhyWXpZdlhecZKxirHapbVPesF1ndsJGwCbBbZ/LA1sn1jZ2XXY3fFXsM+z36V/S8HD4cGh2OOTI51ThJOK5zeOUs4OzmXOS9wPuUi4JLgss7lm2uU6zY3NrcSty1u39zN3Mvct7l/8xDzMPLw88jyaPM44ynkaeEZ59niucqLyUvPKwgAn3OqOQAAAQAAARcApwARAAAAAAACAAAAAQABAAAAQAAuAAAAAHjarZK9TgJBEMf/d6CRaAyRhMLqCgsbL4ciglTGRPEjSiSKlnLycXJ86CEniU/hM9jYWPgIFkYfwd6nsDD+d1mBIIUx3mZnfzs3MzszuwDCeIYG8UUwQxmAFgxxPeeuyxrmcaNYxzTuFAewi0fFQSTxqXgM11pC8TgS2oPiCUS1d8Uh8ofiSczpYcVT5LjiCPlY8Qui+ncOr7D02y6/BTCrP/m+b5bdTrPi2I26Z9qNGtbRQBMdXMJBGRW0YOCecxEWYoiTCvxrYBunqHPdoX2bLOyrMKlZg8thDETw5K7Itci1TXlGy0124QRZZLDFU/exhxztMozlosTpMH6ZPge0L+OKGnFKjJ4WRwppHPL0PP3SI2P9jLQwFOu3GRhDfkeyDo//G7IHgzllZQxLdquvrdCyBVvat3seJlYo06gxapUxhU2JWnFygR03sSxnEkvcpf5Y5eibGq315TDp7fKWm8zbUVl71Aqq/ZtNnlkWmLnQtno9ycvXYbA6W2pF3aKfCayyC0Ja7Fr/PW70/HO4YM0OKxFvzf0C1MyPjwAAeNpt1VWUU2cYRuHsgxenQt1d8/3JOUnqAyR1d/cCLQVKO22pu7tQd3d3d3d3d3cXmGzumrWy3pWLs/NdPDMpZaWu1783l1Lpf14MnfzO6FbqVupfGkD30iR60JNe9KYP09CXfvRnAAMZxGCGMG3pW6ZjemZgKDMyEzMzC7MyG7MzB3MyF3MzD/MyH/OzAAuyEAuzCIuyGIuzBGWCRIUqOQU16jRYkqVYmmVYluVYng6GMZwRNGmxAiuyEiuzCquyGquzBmuyFmuzDuuyHuuzARuyERuzCZuyGZuzBVuyFVuzDduyHdszklGMZgd2ZAw7MZZxjGdnJrALu9LJbuzOHkxkT/Zib/ZhX/Zjfw7gQA7iYA7hUA7jcI7gSI7iaI7hWI7jeE7gRE7iZE5hEqdyGqdzBmdyFmdzDudyHudzARdyERdzCZdyGZdzBVdyFVdzDddyHddzAzdyEzdzC7dyG7dzB3dyF3dzD/dyH/fzAA/yEA/zCI/yGI/zBE/yFE/zDM/yHM/zAi/yEi/zCq/yGq/zBm/yFm/zDu/yHu/zAR/yER/zCZ/yGZ/zBV/yFV/zDd/yHd/zAz/yEz/zC7/yG7/zB3/yF3/zD/9mpYwsy7pl3bMeWc+sV9Y765NNk/XN+mX9swHZwGxQNjgb0nPkmInjR0V7Uq/OsaPL5Y7ylE3l8tQNN7kVt+rmbuHW3LrbcDvam1rtzVvdm50TxrU/DBvRtZUY1rV5a3jXFn550Wo/XDNWK3dFmh7X9LimxzU9qulRTY9qelTTo5rlKLt2wk7YiaprL+yFvbAX9pK9ZC/ZS/aSvWQv2Uv2kr1kr2KvYq9ir2KvYq9ir2KvYq9ir2Kvaq9qr2qvaq9qr2qvaq9qr2qvai+3l9vL7eX2cnu5vdxebi+3l9sr7BV2CjuFncJOYaewU9gp7NTs1LyrZq9mr2avZq9mr2avZq9mr26vbq9ur26vbq9ur26vbq9ur26vYa9hr2GvYa9hr2GvYa/R7oXuQ/eh+2j/UU7e3C3cqc/V3fYdof/Qf+g/9B/6D/2H/kP/of/Qf+g/9B/6D/2H/kP/of/Qf+g/9B/6D/2H/kP/of/Qf+g/9B/6D/2H/kP/of/Qf+g/9B/6D92H7kP3ofvQfeg+dB+6D92H7kP3ofvQfRT29B/6D/2H/kP/of/Qf+g/9B/6D/2H/kP/of/Qf+g/9B/6D/2H/kP/of/Qf+g/9B/6D/2H/kP/of/Qf+g/9B/6j6nuG3Ya7U5q/0hN3nCTW3Grbu4Wrs/rP+k/6T/pP+k/6T/pP+k+6T7pPek86TzpPOk86TzpOuk66TrpOuk66TrpOlWmPu/36zrpOuk66TrpOuk66TrpOvl/Pek76TvpO+k76TvpO+k76TvpO+k76TvpO7V9t+qtVs/OaOURU6bo6PgPt6rZbwAAAAABVFDDFwAA) format('woff'),url(data:application/x-font-truetype;base64,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) format('truetype'),url(data:image/svg+xml;base64,<?xml version="1.0" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
<svg xmlns="http://www.w3.org/2000/svg">
<metadata></metadata>
<defs>
<font id="glyphicons_halflingsregular" horiz-adv-x="1200" >
<font-face units-per-em="1200" ascent="960" descent="-240" />
<missing-glyph horiz-adv-x="500" />
<glyph horiz-adv-x="0" />
<glyph horiz-adv-x="400" />
<glyph unicode=" " />
<glyph unicode="*" d="M600 1100q15 0 34 -1.5t30 -3.5l11 -1q10 -2 17.5 -10.5t7.5 -18.5v-224l158 158q7 7 18 8t19 -6l106 -106q7 -8 6 -19t-8 -18l-158 -158h224q10 0 18.5 -7.5t10.5 -17.5q6 -41 6 -75q0 -15 -1.5 -34t-3.5 -30l-1 -11q-2 -10 -10.5 -17.5t-18.5 -7.5h-224l158 -158 q7 -7 8 -18t-6 -19l-106 -106q-8 -7 -19 -6t-18 8l-158 158v-224q0 -10 -7.5 -18.5t-17.5 -10.5q-41 -6 -75 -6q-15 0 -34 1.5t-30 3.5l-11 1q-10 2 -17.5 10.5t-7.5 18.5v224l-158 -158q-7 -7 -18 -8t-19 6l-106 106q-7 8 -6 19t8 18l158 158h-224q-10 0 -18.5 7.5 t-10.5 17.5q-6 41 -6 75q0 15 1.5 34t3.5 30l1 11q2 10 10.5 17.5t18.5 7.5h224l-158 158q-7 7 -8 18t6 19l106 106q8 7 19 6t18 -8l158 -158v224q0 10 7.5 18.5t17.5 10.5q41 6 75 6z" />
<glyph unicode="+" d="M450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-350h350q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-350v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v350h-350q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5 h350v350q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xa0;" />
<glyph unicode="&#xa5;" d="M825 1100h250q10 0 12.5 -5t-5.5 -13l-364 -364q-6 -6 -11 -18h268q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-100h275q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-174q0 -11 -7.5 -18.5t-18.5 -7.5h-148q-11 0 -18.5 7.5t-7.5 18.5v174 h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h125v100h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h118q-5 12 -11 18l-364 364q-8 8 -5.5 13t12.5 5h250q25 0 43 -18l164 -164q8 -8 18 -8t18 8l164 164q18 18 43 18z" />
<glyph unicode="&#x2000;" horiz-adv-x="650" />
<glyph unicode="&#x2001;" horiz-adv-x="1300" />
<glyph unicode="&#x2002;" horiz-adv-x="650" />
<glyph unicode="&#x2003;" horiz-adv-x="1300" />
<glyph unicode="&#x2004;" horiz-adv-x="433" />
<glyph unicode="&#x2005;" horiz-adv-x="325" />
<glyph unicode="&#x2006;" horiz-adv-x="216" />
<glyph unicode="&#x2007;" horiz-adv-x="216" />
<glyph unicode="&#x2008;" horiz-adv-x="162" />
<glyph unicode="&#x2009;" horiz-adv-x="260" />
<glyph unicode="&#x200a;" horiz-adv-x="72" />
<glyph unicode="&#x202f;" horiz-adv-x="260" />
<glyph unicode="&#x205f;" horiz-adv-x="325" />
<glyph unicode="&#x20ac;" d="M744 1198q242 0 354 -189q60 -104 66 -209h-181q0 45 -17.5 82.5t-43.5 61.5t-58 40.5t-60.5 24t-51.5 7.5q-19 0 -40.5 -5.5t-49.5 -20.5t-53 -38t-49 -62.5t-39 -89.5h379l-100 -100h-300q-6 -50 -6 -100h406l-100 -100h-300q9 -74 33 -132t52.5 -91t61.5 -54.5t59 -29 t47 -7.5q22 0 50.5 7.5t60.5 24.5t58 41t43.5 61t17.5 80h174q-30 -171 -128 -278q-107 -117 -274 -117q-206 0 -324 158q-36 48 -69 133t-45 204h-217l100 100h112q1 47 6 100h-218l100 100h134q20 87 51 153.5t62 103.5q117 141 297 141z" />
<glyph unicode="&#x20bd;" d="M428 1200h350q67 0 120 -13t86 -31t57 -49.5t35 -56.5t17 -64.5t6.5 -60.5t0.5 -57v-16.5v-16.5q0 -36 -0.5 -57t-6.5 -61t-17 -65t-35 -57t-57 -50.5t-86 -31.5t-120 -13h-178l-2 -100h288q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-138v-175q0 -11 -5.5 -18 t-15.5 -7h-149q-10 0 -17.5 7.5t-7.5 17.5v175h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v100h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v475q0 10 7.5 17.5t17.5 7.5zM600 1000v-300h203q64 0 86.5 33t22.5 119q0 84 -22.5 116t-86.5 32h-203z" />
<glyph unicode="&#x2212;" d="M250 700h800q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#x231b;" d="M1000 1200v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-50v-100q0 -91 -49.5 -165.5t-130.5 -109.5q81 -35 130.5 -109.5t49.5 -165.5v-150h50q21 0 35.5 -14.5t14.5 -35.5v-150h-800v150q0 21 14.5 35.5t35.5 14.5h50v150q0 91 49.5 165.5t130.5 109.5q-81 35 -130.5 109.5 t-49.5 165.5v100h-50q-21 0 -35.5 14.5t-14.5 35.5v150h800zM400 1000v-100q0 -60 32.5 -109.5t87.5 -73.5q28 -12 44 -37t16 -55t-16 -55t-44 -37q-55 -24 -87.5 -73.5t-32.5 -109.5v-150h400v150q0 60 -32.5 109.5t-87.5 73.5q-28 12 -44 37t-16 55t16 55t44 37 q55 24 87.5 73.5t32.5 109.5v100h-400z" />
<glyph unicode="&#x25fc;" horiz-adv-x="500" d="M0 0z" />
<glyph unicode="&#x2601;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -206.5q0 -121 -85 -207.5t-205 -86.5h-750q-79 0 -135.5 57t-56.5 137q0 69 42.5 122.5t108.5 67.5q-2 12 -2 37q0 153 108 260.5t260 107.5z" />
<glyph unicode="&#x26fa;" d="M774 1193.5q16 -9.5 20.5 -27t-5.5 -33.5l-136 -187l467 -746h30q20 0 35 -18.5t15 -39.5v-42h-1200v42q0 21 15 39.5t35 18.5h30l468 746l-135 183q-10 16 -5.5 34t20.5 28t34 5.5t28 -20.5l111 -148l112 150q9 16 27 20.5t34 -5zM600 200h377l-182 112l-195 534v-646z " />
<glyph unicode="&#x2709;" d="M25 1100h1150q10 0 12.5 -5t-5.5 -13l-564 -567q-8 -8 -18 -8t-18 8l-564 567q-8 8 -5.5 13t12.5 5zM18 882l264 -264q8 -8 8 -18t-8 -18l-264 -264q-8 -8 -13 -5.5t-5 12.5v550q0 10 5 12.5t13 -5.5zM918 618l264 264q8 8 13 5.5t5 -12.5v-550q0 -10 -5 -12.5t-13 5.5 l-264 264q-8 8 -8 18t8 18zM818 482l364 -364q8 -8 5.5 -13t-12.5 -5h-1150q-10 0 -12.5 5t5.5 13l364 364q8 8 18 8t18 -8l164 -164q8 -8 18 -8t18 8l164 164q8 8 18 8t18 -8z" />
<glyph unicode="&#x270f;" d="M1011 1210q19 0 33 -13l153 -153q13 -14 13 -33t-13 -33l-99 -92l-214 214l95 96q13 14 32 14zM1013 800l-615 -614l-214 214l614 614zM317 96l-333 -112l110 335z" />
<glyph unicode="&#xe001;" d="M700 650v-550h250q21 0 35.5 -14.5t14.5 -35.5v-50h-800v50q0 21 14.5 35.5t35.5 14.5h250v550l-500 550h1200z" />
<glyph unicode="&#xe002;" d="M368 1017l645 163q39 15 63 0t24 -49v-831q0 -55 -41.5 -95.5t-111.5 -63.5q-79 -25 -147 -4.5t-86 75t25.5 111.5t122.5 82q72 24 138 8v521l-600 -155v-606q0 -42 -44 -90t-109 -69q-79 -26 -147 -5.5t-86 75.5t25.5 111.5t122.5 82.5q72 24 138 7v639q0 38 14.5 59 t53.5 34z" />
<glyph unicode="&#xe003;" d="M500 1191q100 0 191 -39t156.5 -104.5t104.5 -156.5t39 -191l-1 -2l1 -5q0 -141 -78 -262l275 -274q23 -26 22.5 -44.5t-22.5 -42.5l-59 -58q-26 -20 -46.5 -20t-39.5 20l-275 274q-119 -77 -261 -77l-5 1l-2 -1q-100 0 -191 39t-156.5 104.5t-104.5 156.5t-39 191 t39 191t104.5 156.5t156.5 104.5t191 39zM500 1022q-88 0 -162 -43t-117 -117t-43 -162t43 -162t117 -117t162 -43t162 43t117 117t43 162t-43 162t-117 117t-162 43z" />
<glyph unicode="&#xe005;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104z" />
<glyph unicode="&#xe006;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429z" />
<glyph unicode="&#xe007;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429zM477 700h-240l197 -142l-74 -226 l193 139l195 -140l-74 229l192 140h-234l-78 211z" />
<glyph unicode="&#xe008;" d="M600 1200q124 0 212 -88t88 -212v-250q0 -46 -31 -98t-69 -52v-75q0 -10 6 -21.5t15 -17.5l358 -230q9 -5 15 -16.5t6 -21.5v-93q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v93q0 10 6 21.5t15 16.5l358 230q9 6 15 17.5t6 21.5v75q-38 0 -69 52 t-31 98v250q0 124 88 212t212 88z" />
<glyph unicode="&#xe009;" d="M25 1100h1150q10 0 17.5 -7.5t7.5 -17.5v-1050q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v1050q0 10 7.5 17.5t17.5 7.5zM100 1000v-100h100v100h-100zM875 1000h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5t17.5 -7.5h550 q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM1000 1000v-100h100v100h-100zM100 800v-100h100v100h-100zM1000 800v-100h100v100h-100zM100 600v-100h100v100h-100zM1000 600v-100h100v100h-100zM875 500h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5 t17.5 -7.5h550q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM100 400v-100h100v100h-100zM1000 400v-100h100v100h-100zM100 200v-100h100v100h-100zM1000 200v-100h100v100h-100z" />
<glyph unicode="&#xe010;" d="M50 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM50 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe011;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM850 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 700h200q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h200 q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5 t35.5 14.5z" />
<glyph unicode="&#xe012;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h700q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe013;" d="M465 477l571 571q8 8 18 8t17 -8l177 -177q8 -7 8 -17t-8 -18l-783 -784q-7 -8 -17.5 -8t-17.5 8l-384 384q-8 8 -8 18t8 17l177 177q7 8 17 8t18 -8l171 -171q7 -7 18 -7t18 7z" />
<glyph unicode="&#xe014;" d="M904 1083l178 -179q8 -8 8 -18.5t-8 -17.5l-267 -268l267 -268q8 -7 8 -17.5t-8 -18.5l-178 -178q-8 -8 -18.5 -8t-17.5 8l-268 267l-268 -267q-7 -8 -17.5 -8t-18.5 8l-178 178q-8 8 -8 18.5t8 17.5l267 268l-267 268q-8 7 -8 17.5t8 18.5l178 178q8 8 18.5 8t17.5 -8 l268 -267l268 268q7 7 17.5 7t18.5 -7z" />
<glyph unicode="&#xe015;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM425 900h150q10 0 17.5 -7.5t7.5 -17.5v-75h75q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5 t-17.5 -7.5h-75v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-75q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v75q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe016;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM325 800h350q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-350q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe017;" d="M550 1200h100q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM800 975v166q167 -62 272 -209.5t105 -331.5q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5 t-184.5 123t-123 184.5t-45.5 224q0 184 105 331.5t272 209.5v-166q-103 -55 -165 -155t-62 -220q0 -116 57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5q0 120 -62 220t-165 155z" />
<glyph unicode="&#xe018;" d="M1025 1200h150q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM725 800h150q10 0 17.5 -7.5t7.5 -17.5v-750q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v750 q0 10 7.5 17.5t17.5 7.5zM425 500h150q10 0 17.5 -7.5t7.5 -17.5v-450q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v450q0 10 7.5 17.5t17.5 7.5zM125 300h150q10 0 17.5 -7.5t7.5 -17.5v-250q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5 v250q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe019;" d="M600 1174q33 0 74 -5l38 -152l5 -1q49 -14 94 -39l5 -2l134 80q61 -48 104 -105l-80 -134l3 -5q25 -44 39 -93l1 -6l152 -38q5 -43 5 -73q0 -34 -5 -74l-152 -38l-1 -6q-15 -49 -39 -93l-3 -5l80 -134q-48 -61 -104 -105l-134 81l-5 -3q-44 -25 -94 -39l-5 -2l-38 -151 q-43 -5 -74 -5q-33 0 -74 5l-38 151l-5 2q-49 14 -94 39l-5 3l-134 -81q-60 48 -104 105l80 134l-3 5q-25 45 -38 93l-2 6l-151 38q-6 42 -6 74q0 33 6 73l151 38l2 6q13 48 38 93l3 5l-80 134q47 61 105 105l133 -80l5 2q45 25 94 39l5 1l38 152q43 5 74 5zM600 815 q-89 0 -152 -63t-63 -151.5t63 -151.5t152 -63t152 63t63 151.5t-63 151.5t-152 63z" />
<glyph unicode="&#xe020;" d="M500 1300h300q41 0 70.5 -29.5t29.5 -70.5v-100h275q10 0 17.5 -7.5t7.5 -17.5v-75h-1100v75q0 10 7.5 17.5t17.5 7.5h275v100q0 41 29.5 70.5t70.5 29.5zM500 1200v-100h300v100h-300zM1100 900v-800q0 -41 -29.5 -70.5t-70.5 -29.5h-700q-41 0 -70.5 29.5t-29.5 70.5 v800h900zM300 800v-700h100v700h-100zM500 800v-700h100v700h-100zM700 800v-700h100v700h-100zM900 800v-700h100v700h-100z" />
<glyph unicode="&#xe021;" d="M18 618l620 608q8 7 18.5 7t17.5 -7l608 -608q8 -8 5.5 -13t-12.5 -5h-175v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v375h-300v-375q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v575h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe022;" d="M600 1200v-400q0 -41 29.5 -70.5t70.5 -29.5h300v-650q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5h450zM1000 800h-250q-21 0 -35.5 14.5t-14.5 35.5v250z" />
<glyph unicode="&#xe023;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h50q10 0 17.5 -7.5t7.5 -17.5v-275h175q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe024;" d="M1300 0h-538l-41 400h-242l-41 -400h-538l431 1200h209l-21 -300h162l-20 300h208zM515 800l-27 -300h224l-27 300h-170z" />
<glyph unicode="&#xe025;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-450h191q20 0 25.5 -11.5t-7.5 -27.5l-327 -400q-13 -16 -32 -16t-32 16l-327 400q-13 16 -7.5 27.5t25.5 11.5h191v450q0 21 14.5 35.5t35.5 14.5zM1125 400h50q10 0 17.5 -7.5t7.5 -17.5v-350q0 -10 -7.5 -17.5t-17.5 -7.5 h-1050q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h50q10 0 17.5 -7.5t7.5 -17.5v-175h900v175q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe026;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -275q-13 -16 -32 -16t-32 16l-223 275q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z " />
<glyph unicode="&#xe027;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM632 914l223 -275q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5l223 275q13 16 32 16 t32 -16z" />
<glyph unicode="&#xe028;" d="M225 1200h750q10 0 19.5 -7t12.5 -17l186 -652q7 -24 7 -49v-425q0 -12 -4 -27t-9 -17q-12 -6 -37 -6h-1100q-12 0 -27 4t-17 8q-6 13 -6 38l1 425q0 25 7 49l185 652q3 10 12.5 17t19.5 7zM878 1000h-556q-10 0 -19 -7t-11 -18l-87 -450q-2 -11 4 -18t16 -7h150 q10 0 19.5 -7t11.5 -17l38 -152q2 -10 11.5 -17t19.5 -7h250q10 0 19.5 7t11.5 17l38 152q2 10 11.5 17t19.5 7h150q10 0 16 7t4 18l-87 450q-2 11 -11 18t-19 7z" />
<glyph unicode="&#xe029;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM540 820l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe030;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-362q0 -10 -7.5 -17.5t-17.5 -7.5h-362q-11 0 -13 5.5t5 12.5l133 133q-109 76 -238 76q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5h150q0 -117 -45.5 -224 t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117z" />
<glyph unicode="&#xe031;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-361q0 -11 -7.5 -18.5t-18.5 -7.5h-361q-11 0 -13 5.5t5 12.5l134 134q-110 75 -239 75q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5h-150q0 117 45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117zM1027 600h150 q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5q-192 0 -348 118l-134 -134q-7 -8 -12.5 -5.5t-5.5 12.5v360q0 11 7.5 18.5t18.5 7.5h360q10 0 12.5 -5.5t-5.5 -12.5l-133 -133q110 -76 240 -76q116 0 214.5 57t155.5 155.5t57 214.5z" />
<glyph unicode="&#xe032;" d="M125 1200h1050q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-1050q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM1075 1000h-850q-10 0 -17.5 -7.5t-7.5 -17.5v-850q0 -10 7.5 -17.5t17.5 -7.5h850q10 0 17.5 7.5t7.5 17.5v850 q0 10 -7.5 17.5t-17.5 7.5zM325 900h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 900h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 700h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 700h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 500h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 500h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 300h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 300h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe033;" d="M900 800v200q0 83 -58.5 141.5t-141.5 58.5h-300q-82 0 -141 -59t-59 -141v-200h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h900q41 0 70.5 29.5t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5h-100zM400 800v150q0 21 15 35.5t35 14.5h200 q20 0 35 -14.5t15 -35.5v-150h-300z" />
<glyph unicode="&#xe034;" d="M125 1100h50q10 0 17.5 -7.5t7.5 -17.5v-1075h-100v1075q0 10 7.5 17.5t17.5 7.5zM1075 1052q4 0 9 -2q16 -6 16 -23v-421q0 -6 -3 -12q-33 -59 -66.5 -99t-65.5 -58t-56.5 -24.5t-52.5 -6.5q-26 0 -57.5 6.5t-52.5 13.5t-60 21q-41 15 -63 22.5t-57.5 15t-65.5 7.5 q-85 0 -160 -57q-7 -5 -15 -5q-6 0 -11 3q-14 7 -14 22v438q22 55 82 98.5t119 46.5q23 2 43 0.5t43 -7t32.5 -8.5t38 -13t32.5 -11q41 -14 63.5 -21t57 -14t63.5 -7q103 0 183 87q7 8 18 8z" />
<glyph unicode="&#xe035;" d="M600 1175q116 0 227 -49.5t192.5 -131t131 -192.5t49.5 -227v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v300q0 127 -70.5 231.5t-184.5 161.5t-245 57t-245 -57t-184.5 -161.5t-70.5 -231.5v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50 q-10 0 -17.5 7.5t-7.5 17.5v300q0 116 49.5 227t131 192.5t192.5 131t227 49.5zM220 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460q0 8 6 14t14 6zM820 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460 q0 8 6 14t14 6z" />
<glyph unicode="&#xe036;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM900 668l120 120q7 7 17 7t17 -7l34 -34q7 -7 7 -17t-7 -17l-120 -120l120 -120q7 -7 7 -17 t-7 -17l-34 -34q-7 -7 -17 -7t-17 7l-120 119l-120 -119q-7 -7 -17 -7t-17 7l-34 34q-7 7 -7 17t7 17l119 120l-119 120q-7 7 -7 17t7 17l34 34q7 8 17 8t17 -8z" />
<glyph unicode="&#xe037;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6 l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238q-6 8 -4.5 18t9.5 17l29 22q7 5 15 5z" />
<glyph unicode="&#xe038;" d="M967 1004h3q11 -1 17 -10q135 -179 135 -396q0 -105 -34 -206.5t-98 -185.5q-7 -9 -17 -10h-3q-9 0 -16 6l-42 34q-8 6 -9 16t5 18q111 150 111 328q0 90 -29.5 176t-84.5 157q-6 9 -5 19t10 16l42 33q7 5 15 5zM321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5 t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238 q-6 8 -4.5 18.5t9.5 16.5l29 22q7 5 15 5z" />
<glyph unicode="&#xe039;" d="M500 900h100v-100h-100v-100h-400v-100h-100v600h500v-300zM1200 700h-200v-100h200v-200h-300v300h-200v300h-100v200h600v-500zM100 1100v-300h300v300h-300zM800 1100v-300h300v300h-300zM300 900h-100v100h100v-100zM1000 900h-100v100h100v-100zM300 500h200v-500 h-500v500h200v100h100v-100zM800 300h200v-100h-100v-100h-200v100h-100v100h100v200h-200v100h300v-300zM100 400v-300h300v300h-300zM300 200h-100v100h100v-100zM1200 200h-100v100h100v-100zM700 0h-100v100h100v-100zM1200 0h-300v100h300v-100z" />
<glyph unicode="&#xe040;" d="M100 200h-100v1000h100v-1000zM300 200h-100v1000h100v-1000zM700 200h-200v1000h200v-1000zM900 200h-100v1000h100v-1000zM1200 200h-200v1000h200v-1000zM400 0h-300v100h300v-100zM600 0h-100v91h100v-91zM800 0h-100v91h100v-91zM1100 0h-200v91h200v-91z" />
<glyph unicode="&#xe041;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe042;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM800 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-56 56l424 426l-700 700h150zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5 t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe043;" d="M300 1200h825q75 0 75 -75v-900q0 -25 -18 -43l-64 -64q-8 -8 -13 -5.5t-5 12.5v950q0 10 -7.5 17.5t-17.5 7.5h-700q-25 0 -43 -18l-64 -64q-8 -8 -5.5 -13t12.5 -5h700q10 0 17.5 -7.5t7.5 -17.5v-950q0 -10 -7.5 -17.5t-17.5 -7.5h-850q-10 0 -17.5 7.5t-7.5 17.5v975 q0 25 18 43l139 139q18 18 43 18z" />
<glyph unicode="&#xe044;" d="M250 1200h800q21 0 35.5 -14.5t14.5 -35.5v-1150l-450 444l-450 -445v1151q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe045;" d="M822 1200h-444q-11 0 -19 -7.5t-9 -17.5l-78 -301q-7 -24 7 -45l57 -108q6 -9 17.5 -15t21.5 -6h450q10 0 21.5 6t17.5 15l62 108q14 21 7 45l-83 301q-1 10 -9 17.5t-19 7.5zM1175 800h-150q-10 0 -21 -6.5t-15 -15.5l-78 -156q-4 -9 -15 -15.5t-21 -6.5h-550 q-10 0 -21 6.5t-15 15.5l-78 156q-4 9 -15 15.5t-21 6.5h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-650q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h750q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5 t7.5 17.5v650q0 10 -7.5 17.5t-17.5 7.5zM850 200h-500q-10 0 -19.5 -7t-11.5 -17l-38 -152q-2 -10 3.5 -17t15.5 -7h600q10 0 15.5 7t3.5 17l-38 152q-2 10 -11.5 17t-19.5 7z" />
<glyph unicode="&#xe046;" d="M500 1100h200q56 0 102.5 -20.5t72.5 -50t44 -59t25 -50.5l6 -20h150q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5h150q2 8 6.5 21.5t24 48t45 61t72 48t102.5 21.5zM900 800v-100 h100v100h-100zM600 730q-95 0 -162.5 -67.5t-67.5 -162.5t67.5 -162.5t162.5 -67.5t162.5 67.5t67.5 162.5t-67.5 162.5t-162.5 67.5zM600 603q43 0 73 -30t30 -73t-30 -73t-73 -30t-73 30t-30 73t30 73t73 30z" />
<glyph unicode="&#xe047;" d="M681 1199l385 -998q20 -50 60 -92q18 -19 36.5 -29.5t27.5 -11.5l10 -2v-66h-417v66q53 0 75 43.5t5 88.5l-82 222h-391q-58 -145 -92 -234q-11 -34 -6.5 -57t25.5 -37t46 -20t55 -6v-66h-365v66q56 24 84 52q12 12 25 30.5t20 31.5l7 13l399 1006h93zM416 521h340 l-162 457z" />
<glyph unicode="&#xe048;" d="M753 641q5 -1 14.5 -4.5t36 -15.5t50.5 -26.5t53.5 -40t50.5 -54.5t35.5 -70t14.5 -87q0 -67 -27.5 -125.5t-71.5 -97.5t-98.5 -66.5t-108.5 -40.5t-102 -13h-500v89q41 7 70.5 32.5t29.5 65.5v827q0 24 -0.5 34t-3.5 24t-8.5 19.5t-17 13.5t-28 12.5t-42.5 11.5v71 l471 -1q57 0 115.5 -20.5t108 -57t80.5 -94t31 -124.5q0 -51 -15.5 -96.5t-38 -74.5t-45 -50.5t-38.5 -30.5zM400 700h139q78 0 130.5 48.5t52.5 122.5q0 41 -8.5 70.5t-29.5 55.5t-62.5 39.5t-103.5 13.5h-118v-350zM400 200h216q80 0 121 50.5t41 130.5q0 90 -62.5 154.5 t-156.5 64.5h-159v-400z" />
<glyph unicode="&#xe049;" d="M877 1200l2 -57q-83 -19 -116 -45.5t-40 -66.5l-132 -839q-9 -49 13 -69t96 -26v-97h-500v97q186 16 200 98l173 832q3 17 3 30t-1.5 22.5t-9 17.5t-13.5 12.5t-21.5 10t-26 8.5t-33.5 10q-13 3 -19 5v57h425z" />
<glyph unicode="&#xe050;" d="M1300 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM175 1000h-75v-800h75l-125 -167l-125 167h75v800h-75l125 167z" />
<glyph unicode="&#xe051;" d="M1100 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-650q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v650h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM1167 50l-167 -125v75h-800v-75l-167 125l167 125v-75h800v75z" />
<glyph unicode="&#xe052;" d="M50 1100h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe053;" d="M250 1100h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM250 500h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe054;" d="M500 950v100q0 21 14.5 35.5t35.5 14.5h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5zM100 650v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000 q-21 0 -35.5 14.5t-14.5 35.5zM300 350v100q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5zM0 50v100q0 21 14.5 35.5t35.5 14.5h1100q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5z" />
<glyph unicode="&#xe055;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe056;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 1100h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 800h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 500h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 500h800q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 200h800 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe057;" d="M400 0h-100v1100h100v-1100zM550 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM267 550l-167 -125v75h-200v100h200v75zM550 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe058;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM900 0h-100v1100h100v-1100zM50 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM1100 600h200v-100h-200v-75l-167 125l167 125v-75zM50 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe059;" d="M75 1000h750q31 0 53 -22t22 -53v-650q0 -31 -22 -53t-53 -22h-750q-31 0 -53 22t-22 53v650q0 31 22 53t53 22zM1200 300l-300 300l300 300v-600z" />
<glyph unicode="&#xe060;" d="M44 1100h1112q18 0 31 -13t13 -31v-1012q0 -18 -13 -31t-31 -13h-1112q-18 0 -31 13t-13 31v1012q0 18 13 31t31 13zM100 1000v-737l247 182l298 -131l-74 156l293 318l236 -288v500h-1000zM342 884q56 0 95 -39t39 -94.5t-39 -95t-95 -39.5t-95 39.5t-39 95t39 94.5 t95 39z" />
<glyph unicode="&#xe062;" d="M648 1169q117 0 216 -60t156.5 -161t57.5 -218q0 -115 -70 -258q-69 -109 -158 -225.5t-143 -179.5l-54 -62q-9 8 -25.5 24.5t-63.5 67.5t-91 103t-98.5 128t-95.5 148q-60 132 -60 249q0 88 34 169.5t91.5 142t137 96.5t166.5 36zM652.5 974q-91.5 0 -156.5 -65 t-65 -157t65 -156.5t156.5 -64.5t156.5 64.5t65 156.5t-65 157t-156.5 65z" />
<glyph unicode="&#xe063;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 173v854q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57z" />
<glyph unicode="&#xe064;" d="M554 1295q21 -72 57.5 -143.5t76 -130t83 -118t82.5 -117t70 -116t49.5 -126t18.5 -136.5q0 -71 -25.5 -135t-68.5 -111t-99 -82t-118.5 -54t-125.5 -23q-84 5 -161.5 34t-139.5 78.5t-99 125t-37 164.5q0 69 18 136.5t49.5 126.5t69.5 116.5t81.5 117.5t83.5 119 t76.5 131t58.5 143zM344 710q-23 -33 -43.5 -70.5t-40.5 -102.5t-17 -123q1 -37 14.5 -69.5t30 -52t41 -37t38.5 -24.5t33 -15q21 -7 32 -1t13 22l6 34q2 10 -2.5 22t-13.5 19q-5 4 -14 12t-29.5 40.5t-32.5 73.5q-26 89 6 271q2 11 -6 11q-8 1 -15 -10z" />
<glyph unicode="&#xe065;" d="M1000 1013l108 115q2 1 5 2t13 2t20.5 -1t25 -9.5t28.5 -21.5q22 -22 27 -43t0 -32l-6 -10l-108 -115zM350 1100h400q50 0 105 -13l-187 -187h-368q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v182l200 200v-332 q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM1009 803l-362 -362l-161 -50l55 170l355 355z" />
<glyph unicode="&#xe066;" d="M350 1100h361q-164 -146 -216 -200h-195q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5l200 153v-103q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M824 1073l339 -301q8 -7 8 -17.5t-8 -17.5l-340 -306q-7 -6 -12.5 -4t-6.5 11v203q-26 1 -54.5 0t-78.5 -7.5t-92 -17.5t-86 -35t-70 -57q10 59 33 108t51.5 81.5t65 58.5t68.5 40.5t67 24.5t56 13.5t40 4.5v210q1 10 6.5 12.5t13.5 -4.5z" />
<glyph unicode="&#xe067;" d="M350 1100h350q60 0 127 -23l-178 -177h-349q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v69l200 200v-219q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M643 639l395 395q7 7 17.5 7t17.5 -7l101 -101q7 -7 7 -17.5t-7 -17.5l-531 -532q-7 -7 -17.5 -7t-17.5 7l-248 248q-7 7 -7 17.5t7 17.5l101 101q7 7 17.5 7t17.5 -7l111 -111q8 -7 18 -7t18 7z" />
<glyph unicode="&#xe068;" d="M318 918l264 264q8 8 18 8t18 -8l260 -264q7 -8 4.5 -13t-12.5 -5h-170v-200h200v173q0 10 5 12t13 -5l264 -260q8 -7 8 -17.5t-8 -17.5l-264 -265q-8 -7 -13 -5t-5 12v173h-200v-200h170q10 0 12.5 -5t-4.5 -13l-260 -264q-8 -8 -18 -8t-18 8l-264 264q-8 8 -5.5 13 t12.5 5h175v200h-200v-173q0 -10 -5 -12t-13 5l-264 265q-8 7 -8 17.5t8 17.5l264 260q8 7 13 5t5 -12v-173h200v200h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe069;" d="M250 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe070;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5 t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe071;" d="M1200 1050v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-492 480q-15 14 -15 35t15 35l492 480q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25z" />
<glyph unicode="&#xe072;" d="M243 1074l814 -498q18 -11 18 -26t-18 -26l-814 -498q-18 -11 -30.5 -4t-12.5 28v1000q0 21 12.5 28t30.5 -4z" />
<glyph unicode="&#xe073;" d="M250 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM650 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe074;" d="M1100 950v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe075;" d="M500 612v438q0 21 10.5 25t25.5 -10l492 -480q15 -14 15 -35t-15 -35l-492 -480q-15 -14 -25.5 -10t-10.5 25v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10z" />
<glyph unicode="&#xe076;" d="M1048 1102l100 1q20 0 35 -14.5t15 -35.5l5 -1000q0 -21 -14.5 -35.5t-35.5 -14.5l-100 -1q-21 0 -35.5 14.5t-14.5 35.5l-2 437l-463 -454q-14 -15 -24.5 -10.5t-10.5 25.5l-2 437l-462 -455q-15 -14 -25.5 -9.5t-10.5 24.5l-5 1000q0 21 10.5 25.5t25.5 -10.5l466 -450 l-2 438q0 20 10.5 24.5t25.5 -9.5l466 -451l-2 438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe077;" d="M850 1100h100q21 0 35.5 -14.5t14.5 -35.5v-1000q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10l464 -453v438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe078;" d="M686 1081l501 -540q15 -15 10.5 -26t-26.5 -11h-1042q-22 0 -26.5 11t10.5 26l501 540q15 15 36 15t36 -15zM150 400h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe079;" d="M885 900l-352 -353l352 -353l-197 -198l-552 552l552 550z" />
<glyph unicode="&#xe080;" d="M1064 547l-551 -551l-198 198l353 353l-353 353l198 198z" />
<glyph unicode="&#xe081;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM650 900h-100q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-150 q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5h150v-150q0 -21 14.5 -35.5t35.5 -14.5h100q21 0 35.5 14.5t14.5 35.5v150h150q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-150v150q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe082;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM850 700h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5 t35.5 -14.5h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe083;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM741.5 913q-12.5 0 -21.5 -9l-120 -120l-120 120q-9 9 -21.5 9 t-21.5 -9l-141 -141q-9 -9 -9 -21.5t9 -21.5l120 -120l-120 -120q-9 -9 -9 -21.5t9 -21.5l141 -141q9 -9 21.5 -9t21.5 9l120 120l120 -120q9 -9 21.5 -9t21.5 9l141 141q9 9 9 21.5t-9 21.5l-120 120l120 120q9 9 9 21.5t-9 21.5l-141 141q-9 9 -21.5 9z" />
<glyph unicode="&#xe084;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM546 623l-84 85q-7 7 -17.5 7t-18.5 -7l-139 -139q-7 -8 -7 -18t7 -18 l242 -241q7 -8 17.5 -8t17.5 8l375 375q7 7 7 17.5t-7 18.5l-139 139q-7 7 -17.5 7t-17.5 -7z" />
<glyph unicode="&#xe085;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM588 941q-29 0 -59 -5.5t-63 -20.5t-58 -38.5t-41.5 -63t-16.5 -89.5 q0 -25 20 -25h131q30 -5 35 11q6 20 20.5 28t45.5 8q20 0 31.5 -10.5t11.5 -28.5q0 -23 -7 -34t-26 -18q-1 0 -13.5 -4t-19.5 -7.5t-20 -10.5t-22 -17t-18.5 -24t-15.5 -35t-8 -46q-1 -8 5.5 -16.5t20.5 -8.5h173q7 0 22 8t35 28t37.5 48t29.5 74t12 100q0 47 -17 83 t-42.5 57t-59.5 34.5t-64 18t-59 4.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe086;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM675 1000h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5 t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5zM675 700h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h75v-200h-75q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h350q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5 t-17.5 7.5h-75v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe087;" d="M525 1200h150q10 0 17.5 -7.5t7.5 -17.5v-194q103 -27 178.5 -102.5t102.5 -178.5h194q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-194q-27 -103 -102.5 -178.5t-178.5 -102.5v-194q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v194 q-103 27 -178.5 102.5t-102.5 178.5h-194q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h194q27 103 102.5 178.5t178.5 102.5v194q0 10 7.5 17.5t17.5 7.5zM700 893v-168q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v168q-68 -23 -119 -74 t-74 -119h168q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-168q23 -68 74 -119t119 -74v168q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-168q68 23 119 74t74 119h-168q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h168 q-23 68 -74 119t-119 74z" />
<glyph unicode="&#xe088;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM759 823l64 -64q7 -7 7 -17.5t-7 -17.5l-124 -124l124 -124q7 -7 7 -17.5t-7 -17.5l-64 -64q-7 -7 -17.5 -7t-17.5 7l-124 124l-124 -124q-7 -7 -17.5 -7t-17.5 7l-64 64 q-7 7 -7 17.5t7 17.5l124 124l-124 124q-7 7 -7 17.5t7 17.5l64 64q7 7 17.5 7t17.5 -7l124 -124l124 124q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe089;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM782 788l106 -106q7 -7 7 -17.5t-7 -17.5l-320 -321q-8 -7 -18 -7t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l197 197q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe090;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5q0 -120 65 -225 l587 587q-105 65 -225 65zM965 819l-584 -584q104 -62 219 -62q116 0 214.5 57t155.5 155.5t57 214.5q0 115 -62 219z" />
<glyph unicode="&#xe091;" d="M39 582l522 427q16 13 27.5 8t11.5 -26v-291h550q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-550v-291q0 -21 -11.5 -26t-27.5 8l-522 427q-16 13 -16 32t16 32z" />
<glyph unicode="&#xe092;" d="M639 1009l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291h-550q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h550v291q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe093;" d="M682 1161l427 -522q13 -16 8 -27.5t-26 -11.5h-291v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v550h-291q-21 0 -26 11.5t8 27.5l427 522q13 16 32 16t32 -16z" />
<glyph unicode="&#xe094;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-550h291q21 0 26 -11.5t-8 -27.5l-427 -522q-13 -16 -32 -16t-32 16l-427 522q-13 16 -8 27.5t26 11.5h291v550q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe095;" d="M639 1109l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291q-94 -2 -182 -20t-170.5 -52t-147 -92.5t-100.5 -135.5q5 105 27 193.5t67.5 167t113 135t167 91.5t225.5 42v262q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe096;" d="M850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5zM350 0h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249 q8 7 18 7t18 -7l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5z" />
<glyph unicode="&#xe097;" d="M1014 1120l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249q8 7 18 7t18 -7zM250 600h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5z" />
<glyph unicode="&#xe101;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM704 900h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5 t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe102;" d="M260 1200q9 0 19 -2t15 -4l5 -2q22 -10 44 -23l196 -118q21 -13 36 -24q29 -21 37 -12q11 13 49 35l196 118q22 13 45 23q17 7 38 7q23 0 47 -16.5t37 -33.5l13 -16q14 -21 18 -45l25 -123l8 -44q1 -9 8.5 -14.5t17.5 -5.5h61q10 0 17.5 -7.5t7.5 -17.5v-50 q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 -7.5t-7.5 -17.5v-175h-400v300h-200v-300h-400v175q0 10 -7.5 17.5t-17.5 7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5h61q11 0 18 3t7 8q0 4 9 52l25 128q5 25 19 45q2 3 5 7t13.5 15t21.5 19.5t26.5 15.5 t29.5 7zM915 1079l-166 -162q-7 -7 -5 -12t12 -5h219q10 0 15 7t2 17l-51 149q-3 10 -11 12t-15 -6zM463 917l-177 157q-8 7 -16 5t-11 -12l-51 -143q-3 -10 2 -17t15 -7h231q11 0 12.5 5t-5.5 12zM500 0h-375q-10 0 -17.5 7.5t-7.5 17.5v375h400v-400zM1100 400v-375 q0 -10 -7.5 -17.5t-17.5 -7.5h-375v400h400z" />
<glyph unicode="&#xe103;" d="M1165 1190q8 3 21 -6.5t13 -17.5q-2 -178 -24.5 -323.5t-55.5 -245.5t-87 -174.5t-102.5 -118.5t-118 -68.5t-118.5 -33t-120 -4.5t-105 9.5t-90 16.5q-61 12 -78 11q-4 1 -12.5 0t-34 -14.5t-52.5 -40.5l-153 -153q-26 -24 -37 -14.5t-11 43.5q0 64 42 102q8 8 50.5 45 t66.5 58q19 17 35 47t13 61q-9 55 -10 102.5t7 111t37 130t78 129.5q39 51 80 88t89.5 63.5t94.5 45t113.5 36t129 31t157.5 37t182 47.5zM1116 1098q-8 9 -22.5 -3t-45.5 -50q-38 -47 -119 -103.5t-142 -89.5l-62 -33q-56 -30 -102 -57t-104 -68t-102.5 -80.5t-85.5 -91 t-64 -104.5q-24 -56 -31 -86t2 -32t31.5 17.5t55.5 59.5q25 30 94 75.5t125.5 77.5t147.5 81q70 37 118.5 69t102 79.5t99 111t86.5 148.5q22 50 24 60t-6 19z" />
<glyph unicode="&#xe104;" d="M653 1231q-39 -67 -54.5 -131t-10.5 -114.5t24.5 -96.5t47.5 -80t63.5 -62.5t68.5 -46.5t65 -30q-4 7 -17.5 35t-18.5 39.5t-17 39.5t-17 43t-13 42t-9.5 44.5t-2 42t4 43t13.5 39t23 38.5q96 -42 165 -107.5t105 -138t52 -156t13 -159t-19 -149.5q-13 -55 -44 -106.5 t-68 -87t-78.5 -64.5t-72.5 -45t-53 -22q-72 -22 -127 -11q-31 6 -13 19q6 3 17 7q13 5 32.5 21t41 44t38.5 63.5t21.5 81.5t-6.5 94.5t-50 107t-104 115.5q10 -104 -0.5 -189t-37 -140.5t-65 -93t-84 -52t-93.5 -11t-95 24.5q-80 36 -131.5 114t-53.5 171q-2 23 0 49.5 t4.5 52.5t13.5 56t27.5 60t46 64.5t69.5 68.5q-8 -53 -5 -102.5t17.5 -90t34 -68.5t44.5 -39t49 -2q31 13 38.5 36t-4.5 55t-29 64.5t-36 75t-26 75.5q-15 85 2 161.5t53.5 128.5t85.5 92.5t93.5 61t81.5 25.5z" />
<glyph unicode="&#xe105;" d="M600 1094q82 0 160.5 -22.5t140 -59t116.5 -82.5t94.5 -95t68 -95t42.5 -82.5t14 -57.5t-14 -57.5t-43 -82.5t-68.5 -95t-94.5 -95t-116.5 -82.5t-140 -59t-159.5 -22.5t-159.5 22.5t-140 59t-116.5 82.5t-94.5 95t-68.5 95t-43 82.5t-14 57.5t14 57.5t42.5 82.5t68 95 t94.5 95t116.5 82.5t140 59t160.5 22.5zM888 829q-15 15 -18 12t5 -22q25 -57 25 -119q0 -124 -88 -212t-212 -88t-212 88t-88 212q0 59 23 114q8 19 4.5 22t-17.5 -12q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q22 -36 47 -71t70 -82t92.5 -81t113 -58.5t133.5 -24.5 t133.5 24t113 58.5t92.5 81.5t70 81.5t47 70.5q11 18 9 42.5t-14 41.5q-90 117 -163 189zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l35 34q14 15 12.5 33.5t-16.5 33.5q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe106;" d="M592 0h-148l31 120q-91 20 -175.5 68.5t-143.5 106.5t-103.5 119t-66.5 110t-22 76q0 21 14 57.5t42.5 82.5t68 95t94.5 95t116.5 82.5t140 59t160.5 22.5q61 0 126 -15l32 121h148zM944 770l47 181q108 -85 176.5 -192t68.5 -159q0 -26 -19.5 -71t-59.5 -102t-93 -112 t-129 -104.5t-158 -75.5l46 173q77 49 136 117t97 131q11 18 9 42.5t-14 41.5q-54 70 -107 130zM310 824q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q18 -30 39 -60t57 -70.5t74 -73t90 -61t105 -41.5l41 154q-107 18 -178.5 101.5t-71.5 193.5q0 59 23 114q8 19 4.5 22 t-17.5 -12zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l12 11l22 86l-3 4q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe107;" d="M-90 100l642 1066q20 31 48 28.5t48 -35.5l642 -1056q21 -32 7.5 -67.5t-50.5 -35.5h-1294q-37 0 -50.5 34t7.5 66zM155 200h345v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h345l-445 723zM496 700h208q20 0 32 -14.5t8 -34.5l-58 -252 q-4 -20 -21.5 -34.5t-37.5 -14.5h-54q-20 0 -37.5 14.5t-21.5 34.5l-58 252q-4 20 8 34.5t32 14.5z" />
<glyph unicode="&#xe108;" d="M650 1200q62 0 106 -44t44 -106v-339l363 -325q15 -14 26 -38.5t11 -44.5v-41q0 -20 -12 -26.5t-29 5.5l-359 249v-263q100 -93 100 -113v-64q0 -21 -13 -29t-32 1l-205 128l-205 -128q-19 -9 -32 -1t-13 29v64q0 20 100 113v263l-359 -249q-17 -12 -29 -5.5t-12 26.5v41 q0 20 11 44.5t26 38.5l363 325v339q0 62 44 106t106 44z" />
<glyph unicode="&#xe109;" d="M850 1200h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-150h-1100v150q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5h100q21 0 35.5 -14.5t14.5 -35.5v-50h500v50q0 21 14.5 35.5t35.5 14.5zM1100 800v-750q0 -21 -14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v750h1100zM100 600v-100h100v100h-100zM300 600v-100h100v100h-100zM500 600v-100h100v100h-100zM700 600v-100h100v100h-100zM900 600v-100h100v100h-100zM100 400v-100h100v100h-100zM300 400v-100h100v100h-100zM500 400 v-100h100v100h-100zM700 400v-100h100v100h-100zM900 400v-100h100v100h-100zM100 200v-100h100v100h-100zM300 200v-100h100v100h-100zM500 200v-100h100v100h-100zM700 200v-100h100v100h-100zM900 200v-100h100v100h-100z" />
<glyph unicode="&#xe110;" d="M1135 1165l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-159l-600 -600h-291q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h209l600 600h241v150q0 21 10.5 25t24.5 -10zM522 819l-141 -141l-122 122h-209q-21 0 -35.5 14.5 t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h291zM1135 565l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-241l-181 181l141 141l122 -122h159v150q0 21 10.5 25t24.5 -10z" />
<glyph unicode="&#xe111;" d="M100 1100h1000q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-596l-304 -300v300h-100q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5z" />
<glyph unicode="&#xe112;" d="M150 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM850 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM1100 800v-300q0 -41 -3 -77.5t-15 -89.5t-32 -96t-58 -89t-89 -77t-129 -51t-174 -20t-174 20 t-129 51t-89 77t-58 89t-32 96t-15 89.5t-3 77.5v300h300v-250v-27v-42.5t1.5 -41t5 -38t10 -35t16.5 -30t25.5 -24.5t35 -19t46.5 -12t60 -4t60 4.5t46.5 12.5t35 19.5t25 25.5t17 30.5t10 35t5 38t2 40.5t-0.5 42v25v250h300z" />
<glyph unicode="&#xe113;" d="M1100 411l-198 -199l-353 353l-353 -353l-197 199l551 551z" />
<glyph unicode="&#xe114;" d="M1101 789l-550 -551l-551 551l198 199l353 -353l353 353z" />
<glyph unicode="&#xe115;" d="M404 1000h746q21 0 35.5 -14.5t14.5 -35.5v-551h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v401h-381zM135 984l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-400h385l215 -200h-750q-21 0 -35.5 14.5 t-14.5 35.5v550h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe116;" d="M56 1200h94q17 0 31 -11t18 -27l38 -162h896q24 0 39 -18.5t10 -42.5l-100 -475q-5 -21 -27 -42.5t-55 -21.5h-633l48 -200h535q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-50q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-300v-50 q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-31q-18 0 -32.5 10t-20.5 19l-5 10l-201 961h-54q-20 0 -35 14.5t-15 35.5t15 35.5t35 14.5z" />
<glyph unicode="&#xe117;" d="M1200 1000v-100h-1200v100h200q0 41 29.5 70.5t70.5 29.5h300q41 0 70.5 -29.5t29.5 -70.5h500zM0 800h1200v-800h-1200v800z" />
<glyph unicode="&#xe118;" d="M200 800l-200 -400v600h200q0 41 29.5 70.5t70.5 29.5h300q42 0 71 -29.5t29 -70.5h500v-200h-1000zM1500 700l-300 -700h-1200l300 700h1200z" />
<glyph unicode="&#xe119;" d="M635 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-601h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v601h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe120;" d="M936 864l249 -229q14 -15 14 -35.5t-14 -35.5l-249 -229q-15 -15 -25.5 -10.5t-10.5 24.5v151h-600v-151q0 -20 -10.5 -24.5t-25.5 10.5l-249 229q-14 15 -14 35.5t14 35.5l249 229q15 15 25.5 10.5t10.5 -25.5v-149h600v149q0 21 10.5 25.5t25.5 -10.5z" />
<glyph unicode="&#xe121;" d="M1169 400l-172 732q-5 23 -23 45.5t-38 22.5h-672q-20 0 -38 -20t-23 -41l-172 -739h1138zM1100 300h-1000q-41 0 -70.5 -29.5t-29.5 -70.5v-100q0 -41 29.5 -70.5t70.5 -29.5h1000q41 0 70.5 29.5t29.5 70.5v100q0 41 -29.5 70.5t-70.5 29.5zM800 100v100h100v-100h-100 zM1000 100v100h100v-100h-100z" />
<glyph unicode="&#xe122;" d="M1150 1100q21 0 35.5 -14.5t14.5 -35.5v-850q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v850q0 21 14.5 35.5t35.5 14.5zM1000 200l-675 200h-38l47 -276q3 -16 -5.5 -20t-29.5 -4h-7h-84q-20 0 -34.5 14t-18.5 35q-55 337 -55 351v250v6q0 16 1 23.5t6.5 14 t17.5 6.5h200l675 250v-850zM0 750v-250q-4 0 -11 0.5t-24 6t-30 15t-24 30t-11 48.5v50q0 26 10.5 46t25 30t29 16t25.5 7z" />
<glyph unicode="&#xe123;" d="M553 1200h94q20 0 29 -10.5t3 -29.5l-18 -37q83 -19 144 -82.5t76 -140.5l63 -327l118 -173h17q19 0 33 -14.5t14 -35t-13 -40.5t-31 -27q-8 -4 -23 -9.5t-65 -19.5t-103 -25t-132.5 -20t-158.5 -9q-57 0 -115 5t-104 12t-88.5 15.5t-73.5 17.5t-54.5 16t-35.5 12l-11 4 q-18 8 -31 28t-13 40.5t14 35t33 14.5h17l118 173l63 327q15 77 76 140t144 83l-18 32q-6 19 3.5 32t28.5 13zM498 110q50 -6 102 -6q53 0 102 6q-12 -49 -39.5 -79.5t-62.5 -30.5t-63 30.5t-39 79.5z" />
<glyph unicode="&#xe124;" d="M800 946l224 78l-78 -224l234 -45l-180 -155l180 -155l-234 -45l78 -224l-224 78l-45 -234l-155 180l-155 -180l-45 234l-224 -78l78 224l-234 45l180 155l-180 155l234 45l-78 224l224 -78l45 234l155 -180l155 180z" />
<glyph unicode="&#xe125;" d="M650 1200h50q40 0 70 -40.5t30 -84.5v-150l-28 -125h328q40 0 70 -40.5t30 -84.5v-100q0 -45 -29 -74l-238 -344q-16 -24 -38 -40.5t-45 -16.5h-250q-7 0 -42 25t-66 50l-31 25h-61q-45 0 -72.5 18t-27.5 57v400q0 36 20 63l145 196l96 198q13 28 37.5 48t51.5 20z M650 1100l-100 -212l-150 -213v-375h100l136 -100h214l250 375v125h-450l50 225v175h-50zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe126;" d="M600 1100h250q23 0 45 -16.5t38 -40.5l238 -344q29 -29 29 -74v-100q0 -44 -30 -84.5t-70 -40.5h-328q28 -118 28 -125v-150q0 -44 -30 -84.5t-70 -40.5h-50q-27 0 -51.5 20t-37.5 48l-96 198l-145 196q-20 27 -20 63v400q0 39 27.5 57t72.5 18h61q124 100 139 100z M50 1000h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM636 1000l-136 -100h-100v-375l150 -213l100 -212h50v175l-50 225h450v125l-250 375h-214z" />
<glyph unicode="&#xe127;" d="M356 873l363 230q31 16 53 -6l110 -112q13 -13 13.5 -32t-11.5 -34l-84 -121h302q84 0 138 -38t54 -110t-55 -111t-139 -39h-106l-131 -339q-6 -21 -19.5 -41t-28.5 -20h-342q-7 0 -90 81t-83 94v525q0 17 14 35.5t28 28.5zM400 792v-503l100 -89h293l131 339 q6 21 19.5 41t28.5 20h203q21 0 30.5 25t0.5 50t-31 25h-456h-7h-6h-5.5t-6 0.5t-5 1.5t-5 2t-4 2.5t-4 4t-2.5 4.5q-12 25 5 47l146 183l-86 83zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe128;" d="M475 1103l366 -230q2 -1 6 -3.5t14 -10.5t18 -16.5t14.5 -20t6.5 -22.5v-525q0 -13 -86 -94t-93 -81h-342q-15 0 -28.5 20t-19.5 41l-131 339h-106q-85 0 -139.5 39t-54.5 111t54 110t138 38h302l-85 121q-11 15 -10.5 34t13.5 32l110 112q22 22 53 6zM370 945l146 -183 q17 -22 5 -47q-2 -2 -3.5 -4.5t-4 -4t-4 -2.5t-5 -2t-5 -1.5t-6 -0.5h-6h-6.5h-6h-475v-100h221q15 0 29 -20t20 -41l130 -339h294l106 89v503l-342 236zM1050 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5 v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe129;" d="M550 1294q72 0 111 -55t39 -139v-106l339 -131q21 -6 41 -19.5t20 -28.5v-342q0 -7 -81 -90t-94 -83h-525q-17 0 -35.5 14t-28.5 28l-9 14l-230 363q-16 31 6 53l112 110q13 13 32 13.5t34 -11.5l121 -84v302q0 84 38 138t110 54zM600 972v203q0 21 -25 30.5t-50 0.5 t-25 -31v-456v-7v-6v-5.5t-0.5 -6t-1.5 -5t-2 -5t-2.5 -4t-4 -4t-4.5 -2.5q-25 -12 -47 5l-183 146l-83 -86l236 -339h503l89 100v293l-339 131q-21 6 -41 19.5t-20 28.5zM450 200h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe130;" d="M350 1100h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5zM600 306v-106q0 -84 -39 -139t-111 -55t-110 54t-38 138v302l-121 -84q-15 -12 -34 -11.5t-32 13.5l-112 110 q-22 22 -6 53l230 363q1 2 3.5 6t10.5 13.5t16.5 17t20 13.5t22.5 6h525q13 0 94 -83t81 -90v-342q0 -15 -20 -28.5t-41 -19.5zM308 900l-236 -339l83 -86l183 146q22 17 47 5q2 -1 4.5 -2.5t4 -4t2.5 -4t2 -5t1.5 -5t0.5 -6v-5.5v-6v-7v-456q0 -22 25 -31t50 0.5t25 30.5 v203q0 15 20 28.5t41 19.5l339 131v293l-89 100h-503z" />
<glyph unicode="&#xe131;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM914 632l-275 223q-16 13 -27.5 8t-11.5 -26v-137h-275 q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h275v-137q0 -21 11.5 -26t27.5 8l275 223q16 13 16 32t-16 32z" />
<glyph unicode="&#xe132;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM561 855l-275 -223q-16 -13 -16 -32t16 -32l275 -223q16 -13 27.5 -8 t11.5 26v137h275q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5h-275v137q0 21 -11.5 26t-27.5 -8z" />
<glyph unicode="&#xe133;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM855 639l-223 275q-13 16 -32 16t-32 -16l-223 -275q-13 -16 -8 -27.5 t26 -11.5h137v-275q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v275h137q21 0 26 11.5t-8 27.5z" />
<glyph unicode="&#xe134;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM675 900h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-275h-137q-21 0 -26 -11.5 t8 -27.5l223 -275q13 -16 32 -16t32 16l223 275q13 16 8 27.5t-26 11.5h-137v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe135;" d="M600 1176q116 0 222.5 -46t184 -123.5t123.5 -184t46 -222.5t-46 -222.5t-123.5 -184t-184 -123.5t-222.5 -46t-222.5 46t-184 123.5t-123.5 184t-46 222.5t46 222.5t123.5 184t184 123.5t222.5 46zM627 1101q-15 -12 -36.5 -20.5t-35.5 -12t-43 -8t-39 -6.5 q-15 -3 -45.5 0t-45.5 -2q-20 -7 -51.5 -26.5t-34.5 -34.5q-3 -11 6.5 -22.5t8.5 -18.5q-3 -34 -27.5 -91t-29.5 -79q-9 -34 5 -93t8 -87q0 -9 17 -44.5t16 -59.5q12 0 23 -5t23.5 -15t19.5 -14q16 -8 33 -15t40.5 -15t34.5 -12q21 -9 52.5 -32t60 -38t57.5 -11 q7 -15 -3 -34t-22.5 -40t-9.5 -38q13 -21 23 -34.5t27.5 -27.5t36.5 -18q0 -7 -3.5 -16t-3.5 -14t5 -17q104 -2 221 112q30 29 46.5 47t34.5 49t21 63q-13 8 -37 8.5t-36 7.5q-15 7 -49.5 15t-51.5 19q-18 0 -41 -0.5t-43 -1.5t-42 -6.5t-38 -16.5q-51 -35 -66 -12 q-4 1 -3.5 25.5t0.5 25.5q-6 13 -26.5 17.5t-24.5 6.5q1 15 -0.5 30.5t-7 28t-18.5 11.5t-31 -21q-23 -25 -42 4q-19 28 -8 58q6 16 22 22q6 -1 26 -1.5t33.5 -4t19.5 -13.5q7 -12 18 -24t21.5 -20.5t20 -15t15.5 -10.5l5 -3q2 12 7.5 30.5t8 34.5t-0.5 32q-3 18 3.5 29 t18 22.5t15.5 24.5q6 14 10.5 35t8 31t15.5 22.5t34 22.5q-6 18 10 36q8 0 24 -1.5t24.5 -1.5t20 4.5t20.5 15.5q-10 23 -31 42.5t-37.5 29.5t-49 27t-43.5 23q0 1 2 8t3 11.5t1.5 10.5t-1 9.5t-4.5 4.5q31 -13 58.5 -14.5t38.5 2.5l12 5q5 28 -9.5 46t-36.5 24t-50 15 t-41 20q-18 -4 -37 0zM613 994q0 -17 8 -42t17 -45t9 -23q-8 1 -39.5 5.5t-52.5 10t-37 16.5q3 11 16 29.5t16 25.5q10 -10 19 -10t14 6t13.5 14.5t16.5 12.5z" />
<glyph unicode="&#xe136;" d="M756 1157q164 92 306 -9l-259 -138l145 -232l251 126q6 -89 -34 -156.5t-117 -110.5q-60 -34 -127 -39.5t-126 16.5l-596 -596q-15 -16 -36.5 -16t-36.5 16l-111 110q-15 15 -15 36.5t15 37.5l600 599q-34 101 5.5 201.5t135.5 154.5z" />
<glyph unicode="&#xe137;" horiz-adv-x="1220" d="M100 1196h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 1096h-200v-100h200v100zM100 796h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 696h-500v-100h500v100zM100 396h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 296h-300v-100h300v100z " />
<glyph unicode="&#xe138;" d="M150 1200h900q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM700 500v-300l-200 -200v500l-350 500h900z" />
<glyph unicode="&#xe139;" d="M500 1200h200q41 0 70.5 -29.5t29.5 -70.5v-100h300q41 0 70.5 -29.5t29.5 -70.5v-400h-500v100h-200v-100h-500v400q0 41 29.5 70.5t70.5 29.5h300v100q0 41 29.5 70.5t70.5 29.5zM500 1100v-100h200v100h-200zM1200 400v-200q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v200h1200z" />
<glyph unicode="&#xe140;" d="M50 1200h300q21 0 25 -10.5t-10 -24.5l-94 -94l199 -199q7 -8 7 -18t-7 -18l-106 -106q-8 -7 -18 -7t-18 7l-199 199l-94 -94q-14 -14 -24.5 -10t-10.5 25v300q0 21 14.5 35.5t35.5 14.5zM850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-199 -199q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l199 199l-94 94q-14 14 -10 24.5t25 10.5zM364 470l106 -106q7 -8 7 -18t-7 -18l-199 -199l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l199 199 q8 7 18 7t18 -7zM1071 271l94 94q14 14 24.5 10t10.5 -25v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -25 10.5t10 24.5l94 94l-199 199q-7 8 -7 18t7 18l106 106q8 7 18 7t18 -7z" />
<glyph unicode="&#xe141;" d="M596 1192q121 0 231.5 -47.5t190 -127t127 -190t47.5 -231.5t-47.5 -231.5t-127 -190.5t-190 -127t-231.5 -47t-231.5 47t-190.5 127t-127 190.5t-47 231.5t47 231.5t127 190t190.5 127t231.5 47.5zM596 1010q-112 0 -207.5 -55.5t-151 -151t-55.5 -207.5t55.5 -207.5 t151 -151t207.5 -55.5t207.5 55.5t151 151t55.5 207.5t-55.5 207.5t-151 151t-207.5 55.5zM454.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38.5 -16.5t-38.5 16.5t-16 39t16 38.5t38.5 16zM754.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38 -16.5q-14 0 -29 10l-55 -145 q17 -23 17 -51q0 -36 -25.5 -61.5t-61.5 -25.5t-61.5 25.5t-25.5 61.5q0 32 20.5 56.5t51.5 29.5l122 126l1 1q-9 14 -9 28q0 23 16 39t38.5 16zM345.5 709q22.5 0 38.5 -16t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16zM854.5 709q22.5 0 38.5 -16 t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16z" />
<glyph unicode="&#xe142;" d="M546 173l469 470q91 91 99 192q7 98 -52 175.5t-154 94.5q-22 4 -47 4q-34 0 -66.5 -10t-56.5 -23t-55.5 -38t-48 -41.5t-48.5 -47.5q-376 -375 -391 -390q-30 -27 -45 -41.5t-37.5 -41t-32 -46.5t-16 -47.5t-1.5 -56.5q9 -62 53.5 -95t99.5 -33q74 0 125 51l548 548 q36 36 20 75q-7 16 -21.5 26t-32.5 10q-26 0 -50 -23q-13 -12 -39 -38l-341 -338q-15 -15 -35.5 -15.5t-34.5 13.5t-14 34.5t14 34.5q327 333 361 367q35 35 67.5 51.5t78.5 16.5q14 0 29 -1q44 -8 74.5 -35.5t43.5 -68.5q14 -47 2 -96.5t-47 -84.5q-12 -11 -32 -32 t-79.5 -81t-114.5 -115t-124.5 -123.5t-123 -119.5t-96.5 -89t-57 -45q-56 -27 -120 -27q-70 0 -129 32t-93 89q-48 78 -35 173t81 163l511 511q71 72 111 96q91 55 198 55q80 0 152 -33q78 -36 129.5 -103t66.5 -154q17 -93 -11 -183.5t-94 -156.5l-482 -476 q-15 -15 -36 -16t-37 14t-17.5 34t14.5 35z" />
<glyph unicode="&#xe143;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104zM896 972q-33 0 -64.5 -19t-56.5 -46t-47.5 -53.5t-43.5 -45.5t-37.5 -19t-36 19t-40 45.5t-43 53.5t-54 46t-65.5 19q-67 0 -122.5 -55.5t-55.5 -132.5q0 -23 13.5 -51t46 -65t57.5 -63t76 -75l22 -22q15 -14 44 -44t50.5 -51t46 -44t41 -35t23 -12 t23.5 12t42.5 36t46 44t52.5 52t44 43q4 4 12 13q43 41 63.5 62t52 55t46 55t26 46t11.5 44q0 79 -53 133.5t-120 54.5z" />
<glyph unicode="&#xe144;" d="M776.5 1214q93.5 0 159.5 -66l141 -141q66 -66 66 -160q0 -42 -28 -95.5t-62 -87.5l-29 -29q-31 53 -77 99l-18 18l95 95l-247 248l-389 -389l212 -212l-105 -106l-19 18l-141 141q-66 66 -66 159t66 159l283 283q65 66 158.5 66zM600 706l105 105q10 -8 19 -17l141 -141 q66 -66 66 -159t-66 -159l-283 -283q-66 -66 -159 -66t-159 66l-141 141q-66 66 -66 159.5t66 159.5l55 55q29 -55 75 -102l18 -17l-95 -95l247 -248l389 389z" />
<glyph unicode="&#xe145;" d="M603 1200q85 0 162 -15t127 -38t79 -48t29 -46v-953q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-41 0 -70.5 29.5t-29.5 70.5v953q0 21 30 46.5t81 48t129 37.5t163 15zM300 1000v-700h600v700h-600zM600 254q-43 0 -73.5 -30.5t-30.5 -73.5t30.5 -73.5t73.5 -30.5t73.5 30.5 t30.5 73.5t-30.5 73.5t-73.5 30.5z" />
<glyph unicode="&#xe146;" d="M902 1185l283 -282q15 -15 15 -36t-14.5 -35.5t-35.5 -14.5t-35 15l-36 35l-279 -267v-300l-212 210l-308 -307l-280 -203l203 280l307 308l-210 212h300l267 279l-35 36q-15 14 -15 35t14.5 35.5t35.5 14.5t35 -15z" />
<glyph unicode="&#xe148;" d="M700 1248v-78q38 -5 72.5 -14.5t75.5 -31.5t71 -53.5t52 -84t24 -118.5h-159q-4 36 -10.5 59t-21 45t-40 35.5t-64.5 20.5v-307l64 -13q34 -7 64 -16.5t70 -32t67.5 -52.5t47.5 -80t20 -112q0 -139 -89 -224t-244 -97v-77h-100v79q-150 16 -237 103q-40 40 -52.5 93.5 t-15.5 139.5h139q5 -77 48.5 -126t117.5 -65v335l-27 8q-46 14 -79 26.5t-72 36t-63 52t-40 72.5t-16 98q0 70 25 126t67.5 92t94.5 57t110 27v77h100zM600 754v274q-29 -4 -50 -11t-42 -21.5t-31.5 -41.5t-10.5 -65q0 -29 7 -50.5t16.5 -34t28.5 -22.5t31.5 -14t37.5 -10 q9 -3 13 -4zM700 547v-310q22 2 42.5 6.5t45 15.5t41.5 27t29 42t12 59.5t-12.5 59.5t-38 44.5t-53 31t-66.5 24.5z" />
<glyph unicode="&#xe149;" d="M561 1197q84 0 160.5 -40t123.5 -109.5t47 -147.5h-153q0 40 -19.5 71.5t-49.5 48.5t-59.5 26t-55.5 9q-37 0 -79 -14.5t-62 -35.5q-41 -44 -41 -101q0 -26 13.5 -63t26.5 -61t37 -66q6 -9 9 -14h241v-100h-197q8 -50 -2.5 -115t-31.5 -95q-45 -62 -99 -112 q34 10 83 17.5t71 7.5q32 1 102 -16t104 -17q83 0 136 30l50 -147q-31 -19 -58 -30.5t-55 -15.5t-42 -4.5t-46 -0.5q-23 0 -76 17t-111 32.5t-96 11.5q-39 -3 -82 -16t-67 -25l-23 -11l-55 145q4 3 16 11t15.5 10.5t13 9t15.5 12t14.5 14t17.5 18.5q48 55 54 126.5 t-30 142.5h-221v100h166q-23 47 -44 104q-7 20 -12 41.5t-6 55.5t6 66.5t29.5 70.5t58.5 71q97 88 263 88z" />
<glyph unicode="&#xe150;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM935 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-900h-200v900h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe151;" d="M1000 700h-100v100h-100v-100h-100v500h300v-500zM400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM801 1100v-200h100v200h-100zM1000 350l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150z " />
<glyph unicode="&#xe152;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 1050l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150zM1000 0h-100v100h-100v-100h-100v500h300v-500zM801 400v-200h100v200h-100z " />
<glyph unicode="&#xe153;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 700h-100v400h-100v100h200v-500zM1100 0h-100v100h-200v400h300v-500zM901 400v-200h100v200h-100z" />
<glyph unicode="&#xe154;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1100 700h-100v100h-200v400h300v-500zM901 1100v-200h100v200h-100zM1000 0h-100v400h-100v100h200v-500z" />
<glyph unicode="&#xe155;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM900 1000h-200v200h200v-200zM1000 700h-300v200h300v-200zM1100 400h-400v200h400v-200zM1200 100h-500v200h500v-200z" />
<glyph unicode="&#xe156;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1200 1000h-500v200h500v-200zM1100 700h-400v200h400v-200zM1000 400h-300v200h300v-200zM900 100h-200v200h200v-200z" />
<glyph unicode="&#xe157;" d="M350 1100h400q162 0 256 -93.5t94 -256.5v-400q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5z" />
<glyph unicode="&#xe158;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-163 0 -256.5 92.5t-93.5 257.5v400q0 163 94 256.5t256 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM440 770l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe159;" d="M350 1100h400q163 0 256.5 -94t93.5 -256v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 163 92.5 256.5t257.5 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM350 700h400q21 0 26.5 -12t-6.5 -28l-190 -253q-12 -17 -30 -17t-30 17l-190 253q-12 16 -6.5 28t26.5 12z" />
<glyph unicode="&#xe160;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -163 -92.5 -256.5t-257.5 -93.5h-400q-163 0 -256.5 94t-93.5 256v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM580 693l190 -253q12 -16 6.5 -28t-26.5 -12h-400q-21 0 -26.5 12t6.5 28l190 253q12 17 30 17t30 -17z" />
<glyph unicode="&#xe161;" d="M550 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h450q41 0 70.5 29.5t29.5 70.5v500q0 41 -29.5 70.5t-70.5 29.5h-450q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM338 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe162;" d="M793 1182l9 -9q8 -10 5 -27q-3 -11 -79 -225.5t-78 -221.5l300 1q24 0 32.5 -17.5t-5.5 -35.5q-1 0 -133.5 -155t-267 -312.5t-138.5 -162.5q-12 -15 -26 -15h-9l-9 8q-9 11 -4 32q2 9 42 123.5t79 224.5l39 110h-302q-23 0 -31 19q-10 21 6 41q75 86 209.5 237.5 t228 257t98.5 111.5q9 16 25 16h9z" />
<glyph unicode="&#xe163;" d="M350 1100h400q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-450q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h450q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400 q0 165 92.5 257.5t257.5 92.5zM938 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe164;" d="M750 1200h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -10.5 -25t-24.5 10l-109 109l-312 -312q-15 -15 -35.5 -15t-35.5 15l-141 141q-15 15 -15 35.5t15 35.5l312 312l-109 109q-14 14 -10 24.5t25 10.5zM456 900h-156q-41 0 -70.5 -29.5t-29.5 -70.5v-500 q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v148l200 200v-298q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5h300z" />
<glyph unicode="&#xe165;" d="M600 1186q119 0 227.5 -46.5t187 -125t125 -187t46.5 -227.5t-46.5 -227.5t-125 -187t-187 -125t-227.5 -46.5t-227.5 46.5t-187 125t-125 187t-46.5 227.5t46.5 227.5t125 187t187 125t227.5 46.5zM600 1022q-115 0 -212 -56.5t-153.5 -153.5t-56.5 -212t56.5 -212 t153.5 -153.5t212 -56.5t212 56.5t153.5 153.5t56.5 212t-56.5 212t-153.5 153.5t-212 56.5zM600 794q80 0 137 -57t57 -137t-57 -137t-137 -57t-137 57t-57 137t57 137t137 57z" />
<glyph unicode="&#xe166;" d="M450 1200h200q21 0 35.5 -14.5t14.5 -35.5v-350h245q20 0 25 -11t-9 -26l-383 -426q-14 -15 -33.5 -15t-32.5 15l-379 426q-13 15 -8.5 26t25.5 11h250v350q0 21 14.5 35.5t35.5 14.5zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe167;" d="M583 1182l378 -435q14 -15 9 -31t-26 -16h-244v-250q0 -20 -17 -35t-39 -15h-200q-20 0 -32 14.5t-12 35.5v250h-250q-20 0 -25.5 16.5t8.5 31.5l383 431q14 16 33.5 17t33.5 -14zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe168;" d="M396 723l369 369q7 7 17.5 7t17.5 -7l139 -139q7 -8 7 -18.5t-7 -17.5l-525 -525q-7 -8 -17.5 -8t-17.5 8l-292 291q-7 8 -7 18t7 18l139 139q8 7 18.5 7t17.5 -7zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50 h-100z" />
<glyph unicode="&#xe169;" d="M135 1023l142 142q14 14 35 14t35 -14l77 -77l-212 -212l-77 76q-14 15 -14 36t14 35zM655 855l210 210q14 14 24.5 10t10.5 -25l-2 -599q-1 -20 -15.5 -35t-35.5 -15l-597 -1q-21 0 -25 10.5t10 24.5l208 208l-154 155l212 212zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5 v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe170;" d="M350 1200l599 -2q20 -1 35 -15.5t15 -35.5l1 -597q0 -21 -10.5 -25t-24.5 10l-208 208l-155 -154l-212 212l155 154l-210 210q-14 14 -10 24.5t25 10.5zM524 512l-76 -77q-15 -14 -36 -14t-35 14l-142 142q-14 14 -14 35t14 35l77 77zM50 300h1000q21 0 35.5 -14.5 t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe171;" d="M1200 103l-483 276l-314 -399v423h-399l1196 796v-1096zM483 424v-230l683 953z" />
<glyph unicode="&#xe172;" d="M1100 1000v-850q0 -21 -14.5 -35.5t-35.5 -14.5h-150v400h-700v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200z" />
<glyph unicode="&#xe173;" d="M1100 1000l-2 -149l-299 -299l-95 95q-9 9 -21.5 9t-21.5 -9l-149 -147h-312v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1132 638l106 -106q7 -7 7 -17.5t-7 -17.5l-420 -421q-8 -7 -18 -7 t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l297 297q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe174;" d="M1100 1000v-269l-103 -103l-134 134q-15 15 -33.5 16.5t-34.5 -12.5l-266 -266h-329v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1202 572l70 -70q15 -15 15 -35.5t-15 -35.5l-131 -131 l131 -131q15 -15 15 -35.5t-15 -35.5l-70 -70q-15 -15 -35.5 -15t-35.5 15l-131 131l-131 -131q-15 -15 -35.5 -15t-35.5 15l-70 70q-15 15 -15 35.5t15 35.5l131 131l-131 131q-15 15 -15 35.5t15 35.5l70 70q15 15 35.5 15t35.5 -15l131 -131l131 131q15 15 35.5 15 t35.5 -15z" />
<glyph unicode="&#xe175;" d="M1100 1000v-300h-350q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-500v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM850 600h100q21 0 35.5 -14.5t14.5 -35.5v-250h150q21 0 25 -10.5t-10 -24.5 l-230 -230q-14 -14 -35 -14t-35 14l-230 230q-14 14 -10 24.5t25 10.5h150v250q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe176;" d="M1100 1000v-400l-165 165q-14 15 -35 15t-35 -15l-263 -265h-402v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM935 565l230 -229q14 -15 10 -25.5t-25 -10.5h-150v-250q0 -20 -14.5 -35 t-35.5 -15h-100q-21 0 -35.5 15t-14.5 35v250h-150q-21 0 -25 10.5t10 25.5l230 229q14 15 35 15t35 -15z" />
<glyph unicode="&#xe177;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-150h-1200v150q0 21 14.5 35.5t35.5 14.5zM1200 800v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v550h1200zM100 500v-200h400v200h-400z" />
<glyph unicode="&#xe178;" d="M935 1165l248 -230q14 -14 14 -35t-14 -35l-248 -230q-14 -14 -24.5 -10t-10.5 25v150h-400v200h400v150q0 21 10.5 25t24.5 -10zM200 800h-50q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v-200zM400 800h-100v200h100v-200zM18 435l247 230 q14 14 24.5 10t10.5 -25v-150h400v-200h-400v-150q0 -21 -10.5 -25t-24.5 10l-247 230q-15 14 -15 35t15 35zM900 300h-100v200h100v-200zM1000 500h51q20 0 34.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-34.5 -14.5h-51v200z" />
<glyph unicode="&#xe179;" d="M862 1073l276 116q25 18 43.5 8t18.5 -41v-1106q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v397q-4 1 -11 5t-24 17.5t-30 29t-24 42t-11 56.5v359q0 31 18.5 65t43.5 52zM550 1200q22 0 34.5 -12.5t14.5 -24.5l1 -13v-450q0 -28 -10.5 -59.5 t-25 -56t-29 -45t-25.5 -31.5l-10 -11v-447q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v447q-4 4 -11 11.5t-24 30.5t-30 46t-24 55t-11 60v450q0 2 0.5 5.5t4 12t8.5 15t14.5 12t22.5 5.5q20 0 32.5 -12.5t14.5 -24.5l3 -13v-350h100v350v5.5t2.5 12 t7 15t15 12t25.5 5.5q23 0 35.5 -12.5t13.5 -24.5l1 -13v-350h100v350q0 2 0.5 5.5t3 12t7 15t15 12t24.5 5.5z" />
<glyph unicode="&#xe180;" d="M1200 1100v-56q-4 0 -11 -0.5t-24 -3t-30 -7.5t-24 -15t-11 -24v-888q0 -22 25 -34.5t50 -13.5l25 -2v-56h-400v56q75 0 87.5 6.5t12.5 43.5v394h-500v-394q0 -37 12.5 -43.5t87.5 -6.5v-56h-400v56q4 0 11 0.5t24 3t30 7.5t24 15t11 24v888q0 22 -25 34.5t-50 13.5 l-25 2v56h400v-56q-75 0 -87.5 -6.5t-12.5 -43.5v-394h500v394q0 37 -12.5 43.5t-87.5 6.5v56h400z" />
<glyph unicode="&#xe181;" d="M675 1000h375q21 0 35.5 -14.5t14.5 -35.5v-150h-105l-295 -98v98l-200 200h-400l100 100h375zM100 900h300q41 0 70.5 -29.5t29.5 -70.5v-500q0 -41 -29.5 -70.5t-70.5 -29.5h-300q-41 0 -70.5 29.5t-29.5 70.5v500q0 41 29.5 70.5t70.5 29.5zM100 800v-200h300v200 h-300zM1100 535l-400 -133v163l400 133v-163zM100 500v-200h300v200h-300zM1100 398v-248q0 -21 -14.5 -35.5t-35.5 -14.5h-375l-100 -100h-375l-100 100h400l200 200h105z" />
<glyph unicode="&#xe182;" d="M17 1007l162 162q17 17 40 14t37 -22l139 -194q14 -20 11 -44.5t-20 -41.5l-119 -118q102 -142 228 -268t267 -227l119 118q17 17 42.5 19t44.5 -12l192 -136q19 -14 22.5 -37.5t-13.5 -40.5l-163 -162q-3 -1 -9.5 -1t-29.5 2t-47.5 6t-62.5 14.5t-77.5 26.5t-90 42.5 t-101.5 60t-111 83t-119 108.5q-74 74 -133.5 150.5t-94.5 138.5t-60 119.5t-34.5 100t-15 74.5t-4.5 48z" />
<glyph unicode="&#xe183;" d="M600 1100q92 0 175 -10.5t141.5 -27t108.5 -36.5t81.5 -40t53.5 -37t31 -27l9 -10v-200q0 -21 -14.5 -33t-34.5 -9l-202 34q-20 3 -34.5 20t-14.5 38v146q-141 24 -300 24t-300 -24v-146q0 -21 -14.5 -38t-34.5 -20l-202 -34q-20 -3 -34.5 9t-14.5 33v200q3 4 9.5 10.5 t31 26t54 37.5t80.5 39.5t109 37.5t141 26.5t175 10.5zM600 795q56 0 97 -9.5t60 -23.5t30 -28t12 -24l1 -10v-50l365 -303q14 -15 24.5 -40t10.5 -45v-212q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v212q0 20 10.5 45t24.5 40l365 303v50 q0 4 1 10.5t12 23t30 29t60 22.5t97 10z" />
<glyph unicode="&#xe184;" d="M1100 700l-200 -200h-600l-200 200v500h200v-200h200v200h200v-200h200v200h200v-500zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5 t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe185;" d="M700 1100h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-1000h300v1000q0 41 -29.5 70.5t-70.5 29.5zM1100 800h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-700h300v700q0 41 -29.5 70.5t-70.5 29.5zM400 0h-300v400q0 41 29.5 70.5t70.5 29.5h100q41 0 70.5 -29.5t29.5 -70.5v-400z " />
<glyph unicode="&#xe186;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe187;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 300h-100v200h-100v-200h-100v500h100v-200h100v200h100v-500zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe188;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-300h200v-100h-300v500h300v-100zM900 700h-200v-300h200v-100h-300v500h300v-100z" />
<glyph unicode="&#xe189;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 400l-300 150l300 150v-300zM900 550l-300 -150v300z" />
<glyph unicode="&#xe190;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM900 300h-700v500h700v-500zM800 700h-130q-38 0 -66.5 -43t-28.5 -108t27 -107t68 -42h130v300zM300 700v-300 h130q41 0 68 42t27 107t-28.5 108t-66.5 43h-130z" />
<glyph unicode="&#xe191;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 300h-100v400h-100v100h200v-500z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe192;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM300 700h200v-400h-300v500h100v-100zM900 300h-100v400h-100v100h200v-500zM300 600v-200h100v200h-100z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe193;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 500l-199 -200h-100v50l199 200v150h-200v100h300v-300zM900 300h-100v400h-100v100h200v-500zM701 300h-100 v100h100v-100z" />
<glyph unicode="&#xe194;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700h-300v-200h300v-100h-300l-100 100v200l100 100h300v-100z" />
<glyph unicode="&#xe195;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700v-100l-50 -50l100 -100v-50h-100l-100 100h-150v-100h-100v400h300zM500 700v-100h200v100h-200z" />
<glyph unicode="&#xe197;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -207t-85 -207t-205 -86.5h-128v250q0 21 -14.5 35.5t-35.5 14.5h-300q-21 0 -35.5 -14.5t-14.5 -35.5v-250h-222q-80 0 -136 57.5t-56 136.5q0 69 43 122.5t108 67.5q-2 19 -2 37q0 100 49 185 t134 134t185 49zM525 500h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -244q-13 -16 -32 -16t-32 16l-223 244q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe198;" d="M502 1089q110 0 201 -59.5t135 -156.5q43 15 89 15q121 0 206 -86.5t86 -206.5q0 -99 -60 -181t-150 -110l-378 360q-13 16 -31.5 16t-31.5 -16l-381 -365h-9q-79 0 -135.5 57.5t-56.5 136.5q0 69 43 122.5t108 67.5q-2 19 -2 38q0 100 49 184.5t133.5 134t184.5 49.5z M632 467l223 -228q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5q199 204 223 228q19 19 31.5 19t32.5 -19z" />
<glyph unicode="&#xe199;" d="M700 100v100h400l-270 300h170l-270 300h170l-300 333l-300 -333h170l-270 -300h170l-270 -300h400v-100h-50q-21 0 -35.5 -14.5t-14.5 -35.5v-50h400v50q0 21 -14.5 35.5t-35.5 14.5h-50z" />
<glyph unicode="&#xe200;" d="M600 1179q94 0 167.5 -56.5t99.5 -145.5q89 -6 150.5 -71.5t61.5 -155.5q0 -61 -29.5 -112.5t-79.5 -82.5q9 -29 9 -55q0 -74 -52.5 -126.5t-126.5 -52.5q-55 0 -100 30v-251q21 0 35.5 -14.5t14.5 -35.5v-50h-300v50q0 21 14.5 35.5t35.5 14.5v251q-45 -30 -100 -30 q-74 0 -126.5 52.5t-52.5 126.5q0 18 4 38q-47 21 -75.5 65t-28.5 97q0 74 52.5 126.5t126.5 52.5q5 0 23 -2q0 2 -1 10t-1 13q0 116 81.5 197.5t197.5 81.5z" />
<glyph unicode="&#xe201;" d="M1010 1010q111 -111 150.5 -260.5t0 -299t-150.5 -260.5q-83 -83 -191.5 -126.5t-218.5 -43.5t-218.5 43.5t-191.5 126.5q-111 111 -150.5 260.5t0 299t150.5 260.5q83 83 191.5 126.5t218.5 43.5t218.5 -43.5t191.5 -126.5zM476 1065q-4 0 -8 -1q-121 -34 -209.5 -122.5 t-122.5 -209.5q-4 -12 2.5 -23t18.5 -14l36 -9q3 -1 7 -1q23 0 29 22q27 96 98 166q70 71 166 98q11 3 17.5 13.5t3.5 22.5l-9 35q-3 13 -14 19q-7 4 -15 4zM512 920q-4 0 -9 -2q-80 -24 -138.5 -82.5t-82.5 -138.5q-4 -13 2 -24t19 -14l34 -9q4 -1 8 -1q22 0 28 21 q18 58 58.5 98.5t97.5 58.5q12 3 18 13.5t3 21.5l-9 35q-3 12 -14 19q-7 4 -15 4zM719.5 719.5q-49.5 49.5 -119.5 49.5t-119.5 -49.5t-49.5 -119.5t49.5 -119.5t119.5 -49.5t119.5 49.5t49.5 119.5t-49.5 119.5zM855 551q-22 0 -28 -21q-18 -58 -58.5 -98.5t-98.5 -57.5 q-11 -4 -17 -14.5t-3 -21.5l9 -35q3 -12 14 -19q7 -4 15 -4q4 0 9 2q80 24 138.5 82.5t82.5 138.5q4 13 -2.5 24t-18.5 14l-34 9q-4 1 -8 1zM1000 515q-23 0 -29 -22q-27 -96 -98 -166q-70 -71 -166 -98q-11 -3 -17.5 -13.5t-3.5 -22.5l9 -35q3 -13 14 -19q7 -4 15 -4 q4 0 8 1q121 34 209.5 122.5t122.5 209.5q4 12 -2.5 23t-18.5 14l-36 9q-3 1 -7 1z" />
<glyph unicode="&#xe202;" d="M700 800h300v-380h-180v200h-340v-200h-380v755q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM700 300h162l-212 -212l-212 212h162v200h100v-200zM520 0h-395q-10 0 -17.5 7.5t-7.5 17.5v395zM1000 220v-195q0 -10 -7.5 -17.5t-17.5 -7.5h-195z" />
<glyph unicode="&#xe203;" d="M700 800h300v-520l-350 350l-550 -550v1095q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM862 200h-162v-200h-100v200h-162l212 212zM480 0h-355q-10 0 -17.5 7.5t-7.5 17.5v55h380v-80zM1000 80v-55q0 -10 -7.5 -17.5t-17.5 -7.5h-155v80h180z" />
<glyph unicode="&#xe204;" d="M1162 800h-162v-200h100l100 -100h-300v300h-162l212 212zM200 800h200q27 0 40 -2t29.5 -10.5t23.5 -30t7 -57.5h300v-100h-600l-200 -350v450h100q0 36 7 57.5t23.5 30t29.5 10.5t40 2zM800 400h240l-240 -400h-800l300 500h500v-100z" />
<glyph unicode="&#xe205;" d="M650 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM1000 850v150q41 0 70.5 -29.5t29.5 -70.5v-800 q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-1 0 -20 4l246 246l-326 326v324q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM412 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe206;" d="M450 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM800 850v150q41 0 70.5 -29.5t29.5 -70.5v-500 h-200v-300h200q0 -36 -7 -57.5t-23.5 -30t-29.5 -10.5t-40 -2h-600q-41 0 -70.5 29.5t-29.5 70.5v800q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM1212 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe209;" d="M658 1197l637 -1104q23 -38 7 -65.5t-60 -27.5h-1276q-44 0 -60 27.5t7 65.5l637 1104q22 39 54 39t54 -39zM704 800h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM500 300v-100h200 v100h-200z" />
<glyph unicode="&#xe210;" d="M425 1100h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM825 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM25 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5zM425 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5 v150q0 10 7.5 17.5t17.5 7.5zM25 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe211;" d="M700 1200h100v-200h-100v-100h350q62 0 86.5 -39.5t-3.5 -94.5l-66 -132q-41 -83 -81 -134h-772q-40 51 -81 134l-66 132q-28 55 -3.5 94.5t86.5 39.5h350v100h-100v200h100v100h200v-100zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100 h-950l138 100h-13q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe212;" d="M600 1300q40 0 68.5 -29.5t28.5 -70.5h-194q0 41 28.5 70.5t68.5 29.5zM443 1100h314q18 -37 18 -75q0 -8 -3 -25h328q41 0 44.5 -16.5t-30.5 -38.5l-175 -145h-678l-178 145q-34 22 -29 38.5t46 16.5h328q-3 17 -3 25q0 38 18 75zM250 700h700q21 0 35.5 -14.5 t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-150v-200l275 -200h-950l275 200v200h-150q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe213;" d="M600 1181q75 0 128 -53t53 -128t-53 -128t-128 -53t-128 53t-53 128t53 128t128 53zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13 l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe214;" d="M600 1300q47 0 92.5 -53.5t71 -123t25.5 -123.5q0 -78 -55.5 -133.5t-133.5 -55.5t-133.5 55.5t-55.5 133.5q0 62 34 143l144 -143l111 111l-163 163q34 26 63 26zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45 zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe215;" d="M600 1200l300 -161v-139h-300q0 -57 18.5 -108t50 -91.5t63 -72t70 -67.5t57.5 -61h-530q-60 83 -90.5 177.5t-30.5 178.5t33 164.5t87.5 139.5t126 96.5t145.5 41.5v-98zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100 h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe216;" d="M600 1300q41 0 70.5 -29.5t29.5 -70.5v-78q46 -26 73 -72t27 -100v-50h-400v50q0 54 27 100t73 72v78q0 41 29.5 70.5t70.5 29.5zM400 800h400q54 0 100 -27t72 -73h-172v-100h200v-100h-200v-100h200v-100h-200v-100h200q0 -83 -58.5 -141.5t-141.5 -58.5h-400 q-83 0 -141.5 58.5t-58.5 141.5v400q0 83 58.5 141.5t141.5 58.5z" />
<glyph unicode="&#xe218;" d="M150 1100h900q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM125 400h950q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-283l224 -224q13 -13 13 -31.5t-13 -32 t-31.5 -13.5t-31.5 13l-88 88h-524l-87 -88q-13 -13 -32 -13t-32 13.5t-13 32t13 31.5l224 224h-289q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM541 300l-100 -100h324l-100 100h-124z" />
<glyph unicode="&#xe219;" d="M200 1100h800q83 0 141.5 -58.5t58.5 -141.5v-200h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100v200q0 83 58.5 141.5t141.5 58.5zM100 600h1000q41 0 70.5 -29.5 t29.5 -70.5v-300h-1200v300q0 41 29.5 70.5t70.5 29.5zM300 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200zM1100 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200z" />
<glyph unicode="&#xe221;" d="M480 1165l682 -683q31 -31 31 -75.5t-31 -75.5l-131 -131h-481l-517 518q-32 31 -32 75.5t32 75.5l295 296q31 31 75.5 31t76.5 -31zM108 794l342 -342l303 304l-341 341zM250 100h800q21 0 35.5 -14.5t14.5 -35.5v-50h-900v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe223;" d="M1057 647l-189 506q-8 19 -27.5 33t-40.5 14h-400q-21 0 -40.5 -14t-27.5 -33l-189 -506q-8 -19 1.5 -33t30.5 -14h625v-150q0 -21 14.5 -35.5t35.5 -14.5t35.5 14.5t14.5 35.5v150h125q21 0 30.5 14t1.5 33zM897 0h-595v50q0 21 14.5 35.5t35.5 14.5h50v50 q0 21 14.5 35.5t35.5 14.5h48v300h200v-300h47q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-50z" />
<glyph unicode="&#xe224;" d="M900 800h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-375v591l-300 300v84q0 10 7.5 17.5t17.5 7.5h375v-400zM1200 900h-200v200zM400 600h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-650q-10 0 -17.5 7.5t-7.5 17.5v950q0 10 7.5 17.5t17.5 7.5h375v-400zM700 700h-200v200z " />
<glyph unicode="&#xe225;" d="M484 1095h195q75 0 146 -32.5t124 -86t89.5 -122.5t48.5 -142q18 -14 35 -20q31 -10 64.5 6.5t43.5 48.5q10 34 -15 71q-19 27 -9 43q5 8 12.5 11t19 -1t23.5 -16q41 -44 39 -105q-3 -63 -46 -106.5t-104 -43.5h-62q-7 -55 -35 -117t-56 -100l-39 -234q-3 -20 -20 -34.5 t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l12 70q-49 -14 -91 -14h-195q-24 0 -65 8l-11 -64q-3 -20 -20 -34.5t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l26 157q-84 74 -128 175l-159 53q-19 7 -33 26t-14 40v50q0 21 14.5 35.5t35.5 14.5h124q11 87 56 166l-111 95 q-16 14 -12.5 23.5t24.5 9.5h203q116 101 250 101zM675 1000h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h250q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe226;" d="M641 900l423 247q19 8 42 2.5t37 -21.5l32 -38q14 -15 12.5 -36t-17.5 -34l-139 -120h-390zM50 1100h106q67 0 103 -17t66 -71l102 -212h823q21 0 35.5 -14.5t14.5 -35.5v-50q0 -21 -14 -40t-33 -26l-737 -132q-23 -4 -40 6t-26 25q-42 67 -100 67h-300q-62 0 -106 44 t-44 106v200q0 62 44 106t106 44zM173 928h-80q-19 0 -28 -14t-9 -35v-56q0 -51 42 -51h134q16 0 21.5 8t5.5 24q0 11 -16 45t-27 51q-18 28 -43 28zM550 727q-32 0 -54.5 -22.5t-22.5 -54.5t22.5 -54.5t54.5 -22.5t54.5 22.5t22.5 54.5t-22.5 54.5t-54.5 22.5zM130 389 l152 130q18 19 34 24t31 -3.5t24.5 -17.5t25.5 -28q28 -35 50.5 -51t48.5 -13l63 5l48 -179q13 -61 -3.5 -97.5t-67.5 -79.5l-80 -69q-47 -40 -109 -35.5t-103 51.5l-130 151q-40 47 -35.5 109.5t51.5 102.5zM380 377l-102 -88q-31 -27 2 -65l37 -43q13 -15 27.5 -19.5 t31.5 6.5l61 53q19 16 14 49q-2 20 -12 56t-17 45q-11 12 -19 14t-23 -8z" />
<glyph unicode="&#xe227;" d="M625 1200h150q10 0 17.5 -7.5t7.5 -17.5v-109q79 -33 131 -87.5t53 -128.5q1 -46 -15 -84.5t-39 -61t-46 -38t-39 -21.5l-17 -6q6 0 15 -1.5t35 -9t50 -17.5t53 -30t50 -45t35.5 -64t14.5 -84q0 -59 -11.5 -105.5t-28.5 -76.5t-44 -51t-49.5 -31.5t-54.5 -16t-49.5 -6.5 t-43.5 -1v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-100v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-175q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v600h-75q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5h175v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h100v75q0 10 7.5 17.5t17.5 7.5zM400 900v-200h263q28 0 48.5 10.5t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-263zM400 500v-200h363q28 0 48.5 10.5 t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-363z" />
<glyph unicode="&#xe230;" d="M212 1198h780q86 0 147 -61t61 -147v-416q0 -51 -18 -142.5t-36 -157.5l-18 -66q-29 -87 -93.5 -146.5t-146.5 -59.5h-572q-82 0 -147 59t-93 147q-8 28 -20 73t-32 143.5t-20 149.5v416q0 86 61 147t147 61zM600 1045q-70 0 -132.5 -11.5t-105.5 -30.5t-78.5 -41.5 t-57 -45t-36 -41t-20.5 -30.5l-6 -12l156 -243h560l156 243q-2 5 -6 12.5t-20 29.5t-36.5 42t-57 44.5t-79 42t-105 29.5t-132.5 12zM762 703h-157l195 261z" />
<glyph unicode="&#xe231;" d="M475 1300h150q103 0 189 -86t86 -189v-500q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe232;" d="M475 1300h96q0 -150 89.5 -239.5t239.5 -89.5v-446q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe233;" d="M1294 767l-638 -283l-378 170l-78 -60v-224l100 -150v-199l-150 148l-150 -149v200l100 150v250q0 4 -0.5 10.5t0 9.5t1 8t3 8t6.5 6l47 40l-147 65l642 283zM1000 380l-350 -166l-350 166v147l350 -165l350 165v-147z" />
<glyph unicode="&#xe234;" d="M250 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM650 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM1050 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe235;" d="M550 1100q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 700q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 300q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe236;" d="M125 1100h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM125 700h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM125 300h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe237;" d="M350 1200h500q162 0 256 -93.5t94 -256.5v-500q0 -165 -93.5 -257.5t-256.5 -92.5h-500q-165 0 -257.5 92.5t-92.5 257.5v500q0 165 92.5 257.5t257.5 92.5zM900 1000h-600q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h600q41 0 70.5 29.5 t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5zM350 900h500q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-500q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 14.5 35.5t35.5 14.5zM400 800v-200h400v200h-400z" />
<glyph unicode="&#xe238;" d="M150 1100h1000q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe239;" d="M650 1187q87 -67 118.5 -156t0 -178t-118.5 -155q-87 66 -118.5 155t0 178t118.5 156zM300 800q124 0 212 -88t88 -212q-124 0 -212 88t-88 212zM1000 800q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM300 500q124 0 212 -88t88 -212q-124 0 -212 88t-88 212z M1000 500q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM700 199v-144q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v142q40 -4 43 -4q17 0 57 6z" />
<glyph unicode="&#xe240;" d="M745 878l69 19q25 6 45 -12l298 -295q11 -11 15 -26.5t-2 -30.5q-5 -14 -18 -23.5t-28 -9.5h-8q1 0 1 -13q0 -29 -2 -56t-8.5 -62t-20 -63t-33 -53t-51 -39t-72.5 -14h-146q-184 0 -184 288q0 24 10 47q-20 4 -62 4t-63 -4q11 -24 11 -47q0 -288 -184 -288h-142 q-48 0 -84.5 21t-56 51t-32 71.5t-16 75t-3.5 68.5q0 13 2 13h-7q-15 0 -27.5 9.5t-18.5 23.5q-6 15 -2 30.5t15 25.5l298 296q20 18 46 11l76 -19q20 -5 30.5 -22.5t5.5 -37.5t-22.5 -31t-37.5 -5l-51 12l-182 -193h891l-182 193l-44 -12q-20 -5 -37.5 6t-22.5 31t6 37.5 t31 22.5z" />
<glyph unicode="&#xe241;" d="M1200 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM500 450h-25q0 15 -4 24.5t-9 14.5t-17 7.5t-20 3t-25 0.5h-100v-425q0 -11 12.5 -17.5t25.5 -7.5h12v-50h-200v50q50 0 50 25v425h-100q-17 0 -25 -0.5t-20 -3t-17 -7.5t-9 -14.5t-4 -24.5h-25v150h500v-150z" />
<glyph unicode="&#xe242;" d="M1000 300v50q-25 0 -55 32q-14 14 -25 31t-16 27l-4 11l-289 747h-69l-300 -754q-18 -35 -39 -56q-9 -9 -24.5 -18.5t-26.5 -14.5l-11 -5v-50h273v50q-49 0 -78.5 21.5t-11.5 67.5l69 176h293l61 -166q13 -34 -3.5 -66.5t-55.5 -32.5v-50h312zM412 691l134 342l121 -342 h-255zM1100 150v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe243;" d="M50 1200h1100q21 0 35.5 -14.5t14.5 -35.5v-1100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5zM611 1118h-70q-13 0 -18 -12l-299 -753q-17 -32 -35 -51q-18 -18 -56 -34q-12 -5 -12 -18v-50q0 -8 5.5 -14t14.5 -6 h273q8 0 14 6t6 14v50q0 8 -6 14t-14 6q-55 0 -71 23q-10 14 0 39l63 163h266l57 -153q11 -31 -6 -55q-12 -17 -36 -17q-8 0 -14 -6t-6 -14v-50q0 -8 6 -14t14 -6h313q8 0 14 6t6 14v50q0 7 -5.5 13t-13.5 7q-17 0 -42 25q-25 27 -40 63h-1l-288 748q-5 12 -19 12zM639 611 h-197l103 264z" />
<glyph unicode="&#xe244;" d="M1200 1100h-1200v100h1200v-100zM50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 1000h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM700 900v-300h300v300h-300z" />
<glyph unicode="&#xe245;" d="M50 1200h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 700h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM700 600v-300h300v300h-300zM1200 0h-1200v100h1200v-100z" />
<glyph unicode="&#xe246;" d="M50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-350h100v150q0 21 14.5 35.5t35.5 14.5h400q21 0 35.5 -14.5t14.5 -35.5v-150h100v-100h-100v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v150h-100v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM700 700v-300h300v300h-300z" />
<glyph unicode="&#xe247;" d="M100 0h-100v1200h100v-1200zM250 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM300 1000v-300h300v300h-300zM250 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe248;" d="M600 1100h150q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-100h450q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h350v100h-150q-21 0 -35.5 14.5 t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h150v100h100v-100zM400 1000v-300h300v300h-300z" />
<glyph unicode="&#xe249;" d="M1200 0h-100v1200h100v-1200zM550 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM600 1000v-300h300v300h-300zM50 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe250;" d="M865 565l-494 -494q-23 -23 -41 -23q-14 0 -22 13.5t-8 38.5v1000q0 25 8 38.5t22 13.5q18 0 41 -23l494 -494q14 -14 14 -35t-14 -35z" />
<glyph unicode="&#xe251;" d="M335 635l494 494q29 29 50 20.5t21 -49.5v-1000q0 -41 -21 -49.5t-50 20.5l-494 494q-14 14 -14 35t14 35z" />
<glyph unicode="&#xe252;" d="M100 900h1000q41 0 49.5 -21t-20.5 -50l-494 -494q-14 -14 -35 -14t-35 14l-494 494q-29 29 -20.5 50t49.5 21z" />
<glyph unicode="&#xe253;" d="M635 865l494 -494q29 -29 20.5 -50t-49.5 -21h-1000q-41 0 -49.5 21t20.5 50l494 494q14 14 35 14t35 -14z" />
<glyph unicode="&#xe254;" d="M700 741v-182l-692 -323v221l413 193l-413 193v221zM1200 0h-800v200h800v-200z" />
<glyph unicode="&#xe255;" d="M1200 900h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300zM0 700h50q0 21 4 37t9.5 26.5t18 17.5t22 11t28.5 5.5t31 2t37 0.5h100v-550q0 -22 -25 -34.5t-50 -13.5l-25 -2v-100h400v100q-4 0 -11 0.5t-24 3t-30 7t-24 15t-11 24.5v550h100q25 0 37 -0.5t31 -2 t28.5 -5.5t22 -11t18 -17.5t9.5 -26.5t4 -37h50v300h-800v-300z" />
<glyph unicode="&#xe256;" d="M800 700h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-100v-550q0 -22 25 -34.5t50 -14.5l25 -1v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v550h-100q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h800v-300zM1100 200h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300z" />
<glyph unicode="&#xe257;" d="M701 1098h160q16 0 21 -11t-7 -23l-464 -464l464 -464q12 -12 7 -23t-21 -11h-160q-13 0 -23 9l-471 471q-7 8 -7 18t7 18l471 471q10 9 23 9z" />
<glyph unicode="&#xe258;" d="M339 1098h160q13 0 23 -9l471 -471q7 -8 7 -18t-7 -18l-471 -471q-10 -9 -23 -9h-160q-16 0 -21 11t7 23l464 464l-464 464q-12 12 -7 23t21 11z" />
<glyph unicode="&#xe259;" d="M1087 882q11 -5 11 -21v-160q0 -13 -9 -23l-471 -471q-8 -7 -18 -7t-18 7l-471 471q-9 10 -9 23v160q0 16 11 21t23 -7l464 -464l464 464q12 12 23 7z" />
<glyph unicode="&#xe260;" d="M618 993l471 -471q9 -10 9 -23v-160q0 -16 -11 -21t-23 7l-464 464l-464 -464q-12 -12 -23 -7t-11 21v160q0 13 9 23l471 471q8 7 18 7t18 -7z" />
<glyph unicode="&#xf8ff;" d="M1000 1200q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM450 1000h100q21 0 40 -14t26 -33l79 -194q5 1 16 3q34 6 54 9.5t60 7t65.5 1t61 -10t56.5 -23t42.5 -42t29 -64t5 -92t-19.5 -121.5q-1 -7 -3 -19.5t-11 -50t-20.5 -73t-32.5 -81.5t-46.5 -83t-64 -70 t-82.5 -50q-13 -5 -42 -5t-65.5 2.5t-47.5 2.5q-14 0 -49.5 -3.5t-63 -3.5t-43.5 7q-57 25 -104.5 78.5t-75 111.5t-46.5 112t-26 90l-7 35q-15 63 -18 115t4.5 88.5t26 64t39.5 43.5t52 25.5t58.5 13t62.5 2t59.5 -4.5t55.5 -8l-147 192q-12 18 -5.5 30t27.5 12z" />
<glyph unicode="&#x1f511;" d="M250 1200h600q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-500l-255 -178q-19 -9 -32 -1t-13 29v650h-150q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM400 1100v-100h300v100h-300z" />
<glyph unicode="&#x1f6aa;" d="M250 1200h750q39 0 69.5 -40.5t30.5 -84.5v-933l-700 -117v950l600 125h-700v-1000h-100v1025q0 23 15.5 49t34.5 26zM500 525v-100l100 20v100z" />
</font>
</defs></svg> ) format('svg')}.glyphicon{position:relative;top:1px;display:inline-block;font-family:'Glyphicons Halflings';font-style:normal;font-weight:400;line-height:1;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}.glyphicon-asterisk:before{content:"\2a"}.glyphicon-plus:before{content:"\2b"}.glyphicon-eur:before,.glyphicon-euro:before{content:"\20ac"}.glyphicon-minus:before{content:"\2212"}.glyphicon-cloud:before{content:"\2601"}.glyphicon-envelope:before{content:"\2709"}.glyphicon-pencil:before{content:"\270f"}.glyphicon-glass:before{content:"\e001"}.glyphicon-music:before{content:"\e002"}.glyphicon-search:before{content:"\e003"}.glyphicon-heart:before{content:"\e005"}.glyphicon-star:before{content:"\e006"}.glyphicon-star-empty:before{content:"\e007"}.glyphicon-user:before{content:"\e008"}.glyphicon-film:before{content:"\e009"}.glyphicon-th-large:before{content:"\e010"}.glyphicon-th:before{content:"\e011"}.glyphicon-th-list:before{content:"\e012"}.glyphicon-ok:before{content:"\e013"}.glyphicon-remove:before{content:"\e014"}.glyphicon-zoom-in:before{content:"\e015"}.glyphicon-zoom-out:before{content:"\e016"}.glyphicon-off:before{content:"\e017"}.glyphicon-signal:before{content:"\e018"}.glyphicon-cog:before{content:"\e019"}.glyphicon-trash:before{content:"\e020"}.glyphicon-home:before{content:"\e021"}.glyphicon-file:before{content:"\e022"}.glyphicon-time:before{content:"\e023"}.glyphicon-road:before{content:"\e024"}.glyphicon-download-alt:before{content:"\e025"}.glyphicon-download:before{content:"\e026"}.glyphicon-upload:before{content:"\e027"}.glyphicon-inbox:before{content:"\e028"}.glyphicon-play-circle:before{content:"\e029"}.glyphicon-repeat:before{content:"\e030"}.glyphicon-refresh:before{content:"\e031"}.glyphicon-list-alt:before{content:"\e032"}.glyphicon-lock:before{content:"\e033"}.glyphicon-flag:before{content:"\e034"}.glyphicon-headphones:before{content:"\e035"}.glyphicon-volume-off:before{content:"\e036"}.glyphicon-volume-down:before{content:"\e037"}.glyphicon-volume-up:before{content:"\e038"}.glyphicon-qrcode:before{content:"\e039"}.glyphicon-barcode:before{content:"\e040"}.glyphicon-tag:before{content:"\e041"}.glyphicon-tags:before{content:"\e042"}.glyphicon-book:before{content:"\e043"}.glyphicon-bookmark:before{content:"\e044"}.glyphicon-print:before{content:"\e045"}.glyphicon-camera:before{content:"\e046"}.glyphicon-font:before{content:"\e047"}.glyphicon-bold:before{content:"\e048"}.glyphicon-italic:before{content:"\e049"}.glyphicon-text-height:before{content:"\e050"}.glyphicon-text-width:before{content:"\e051"}.glyphicon-align-left:before{content:"\e052"}.glyphicon-align-center:before{content:"\e053"}.glyphicon-align-right:before{content:"\e054"}.glyphicon-align-justify:before{content:"\e055"}.glyphicon-list:before{content:"\e056"}.glyphicon-indent-left:before{content:"\e057"}.glyphicon-indent-right:before{content:"\e058"}.glyphicon-facetime-video:before{content:"\e059"}.glyphicon-picture:before{content:"\e060"}.glyphicon-map-marker:before{content:"\e062"}.glyphicon-adjust:before{content:"\e063"}.glyphicon-tint:before{content:"\e064"}.glyphicon-edit:before{content:"\e065"}.glyphicon-share:before{content:"\e066"}.glyphicon-check:before{content:"\e067"}.glyphicon-move:before{content:"\e068"}.glyphicon-step-backward:before{content:"\e069"}.glyphicon-fast-backward:before{content:"\e070"}.glyphicon-backward:before{content:"\e071"}.glyphicon-play:before{content:"\e072"}.glyphicon-pause:before{content:"\e073"}.glyphicon-stop:before{content:"\e074"}.glyphicon-forward:before{content:"\e075"}.glyphicon-fast-forward:before{content:"\e076"}.glyphicon-step-forward:before{content:"\e077"}.glyphicon-eject:before{content:"\e078"}.glyphicon-chevron-left:before{content:"\e079"}.glyphicon-chevron-right:before{content:"\e080"}.glyphicon-plus-sign:before{content:"\e081"}.glyphicon-minus-sign:before{content:"\e082"}.glyphicon-remove-sign:before{content:"\e083"}.glyphicon-ok-sign:before{content:"\e084"}.glyphicon-question-sign:before{content:"\e085"}.glyphicon-info-sign:before{content:"\e086"}.glyphicon-screenshot:before{content:"\e087"}.glyphicon-remove-circle:before{content:"\e088"}.glyphicon-ok-circle:before{content:"\e089"}.glyphicon-ban-circle:before{content:"\e090"}.glyphicon-arrow-left:before{content:"\e091"}.glyphicon-arrow-right:before{content:"\e092"}.glyphicon-arrow-up:before{content:"\e093"}.glyphicon-arrow-down:before{content:"\e094"}.glyphicon-share-alt:before{content:"\e095"}.glyphicon-resize-full:before{content:"\e096"}.glyphicon-resize-small:before{content:"\e097"}.glyphicon-exclamation-sign:before{content:"\e101"}.glyphicon-gift:before{content:"\e102"}.glyphicon-leaf:before{content:"\e103"}.glyphicon-fire:before{content:"\e104"}.glyphicon-eye-open:before{content:"\e105"}.glyphicon-eye-close:before{content:"\e106"}.glyphicon-warning-sign:before{content:"\e107"}.glyphicon-plane:before{content:"\e108"}.glyphicon-calendar:before{content:"\e109"}.glyphicon-random:before{content:"\e110"}.glyphicon-comment:before{content:"\e111"}.glyphicon-magnet:before{content:"\e112"}.glyphicon-chevron-up:before{content:"\e113"}.glyphicon-chevron-down:before{content:"\e114"}.glyphicon-retweet:before{content:"\e115"}.glyphicon-shopping-cart:before{content:"\e116"}.glyphicon-folder-close:before{content:"\e117"}.glyphicon-folder-open:before{content:"\e118"}.glyphicon-resize-vertical:before{content:"\e119"}.glyphicon-resize-horizontal:before{content:"\e120"}.glyphicon-hdd:before{content:"\e121"}.glyphicon-bullhorn:before{content:"\e122"}.glyphicon-bell:before{content:"\e123"}.glyphicon-certificate:before{content:"\e124"}.glyphicon-thumbs-up:before{content:"\e125"}.glyphicon-thumbs-down:before{content:"\e126"}.glyphicon-hand-right:before{content:"\e127"}.glyphicon-hand-left:before{content:"\e128"}.glyphicon-hand-up:before{content:"\e129"}.glyphicon-hand-down:before{content:"\e130"}.glyphicon-circle-arrow-right:before{content:"\e131"}.glyphicon-circle-arrow-left:before{content:"\e132"}.glyphicon-circle-arrow-up:before{content:"\e133"}.glyphicon-circle-arrow-down:before{content:"\e134"}.glyphicon-globe:before{content:"\e135"}.glyphicon-wrench:before{content:"\e136"}.glyphicon-tasks:before{content:"\e137"}.glyphicon-filter:before{content:"\e138"}.glyphicon-briefcase:before{content:"\e139"}.glyphicon-fullscreen:before{content:"\e140"}.glyphicon-dashboard:before{content:"\e141"}.glyphicon-paperclip:before{content:"\e142"}.glyphicon-heart-empty:before{content:"\e143"}.glyphicon-link:before{content:"\e144"}.glyphicon-phone:before{content:"\e145"}.glyphicon-pushpin:before{content:"\e146"}.glyphicon-usd:before{content:"\e148"}.glyphicon-gbp:before{content:"\e149"}.glyphicon-sort:before{content:"\e150"}.glyphicon-sort-by-alphabet:before{content:"\e151"}.glyphicon-sort-by-alphabet-alt:before{content:"\e152"}.glyphicon-sort-by-order:before{content:"\e153"}.glyphicon-sort-by-order-alt:before{content:"\e154"}.glyphicon-sort-by-attributes:before{content:"\e155"}.glyphicon-sort-by-attributes-alt:before{content:"\e156"}.glyphicon-unchecked:before{content:"\e157"}.glyphicon-expand:before{content:"\e158"}.glyphicon-collapse-down:before{content:"\e159"}.glyphicon-collapse-up:before{content:"\e160"}.glyphicon-log-in:before{content:"\e161"}.glyphicon-flash:before{content:"\e162"}.glyphicon-log-out:before{content:"\e163"}.glyphicon-new-window:before{content:"\e164"}.glyphicon-record:before{content:"\e165"}.glyphicon-save:before{content:"\e166"}.glyphicon-open:before{content:"\e167"}.glyphicon-saved:before{content:"\e168"}.glyphicon-import:before{content:"\e169"}.glyphicon-export:before{content:"\e170"}.glyphicon-send:before{content:"\e171"}.glyphicon-floppy-disk:before{content:"\e172"}.glyphicon-floppy-saved:before{content:"\e173"}.glyphicon-floppy-remove:before{content:"\e174"}.glyphicon-floppy-save:before{content:"\e175"}.glyphicon-floppy-open:before{content:"\e176"}.glyphicon-credit-card:before{content:"\e177"}.glyphicon-transfer:before{content:"\e178"}.glyphicon-cutlery:before{content:"\e179"}.glyphicon-header:before{content:"\e180"}.glyphicon-compressed:before{content:"\e181"}.glyphicon-earphone:before{content:"\e182"}.glyphicon-phone-alt:before{content:"\e183"}.glyphicon-tower:before{content:"\e184"}.glyphicon-stats:before{content:"\e185"}.glyphicon-sd-video:before{content:"\e186"}.glyphicon-hd-video:before{content:"\e187"}.glyphicon-subtitles:before{content:"\e188"}.glyphicon-sound-stereo:before{content:"\e189"}.glyphicon-sound-dolby:before{content:"\e190"}.glyphicon-sound-5-1:before{content:"\e191"}.glyphicon-sound-6-1:before{content:"\e192"}.glyphicon-sound-7-1:before{content:"\e193"}.glyphicon-copyright-mark:before{content:"\e194"}.glyphicon-registration-mark:before{content:"\e195"}.glyphicon-cloud-download:before{content:"\e197"}.glyphicon-cloud-upload:before{content:"\e198"}.glyphicon-tree-conifer:before{content:"\e199"}.glyphicon-tree-deciduous:before{content:"\e200"}.glyphicon-cd:before{content:"\e201"}.glyphicon-save-file:before{content:"\e202"}.glyphicon-open-file:before{content:"\e203"}.glyphicon-level-up:before{content:"\e204"}.glyphicon-copy:before{content:"\e205"}.glyphicon-paste:before{content:"\e206"}.glyphicon-alert:before{content:"\e209"}.glyphicon-equalizer:before{content:"\e210"}.glyphicon-king:before{content:"\e211"}.glyphicon-queen:before{content:"\e212"}.glyphicon-pawn:before{content:"\e213"}.glyphicon-bishop:before{content:"\e214"}.glyphicon-knight:before{content:"\e215"}.glyphicon-baby-formula:before{content:"\e216"}.glyphicon-tent:before{content:"\26fa"}.glyphicon-blackboard:before{content:"\e218"}.glyphicon-bed:before{content:"\e219"}.glyphicon-apple:before{content:"\f8ff"}.glyphicon-erase:before{content:"\e221"}.glyphicon-hourglass:before{content:"\231b"}.glyphicon-lamp:before{content:"\e223"}.glyphicon-duplicate:before{content:"\e224"}.glyphicon-piggy-bank:before{content:"\e225"}.glyphicon-scissors:before{content:"\e226"}.glyphicon-bitcoin:before{content:"\e227"}.glyphicon-btc:before{content:"\e227"}.glyphicon-xbt:before{content:"\e227"}.glyphicon-yen:before{content:"\00a5"}.glyphicon-jpy:before{content:"\00a5"}.glyphicon-ruble:before{content:"\20bd"}.glyphicon-rub:before{content:"\20bd"}.glyphicon-scale:before{content:"\e230"}.glyphicon-ice-lolly:before{content:"\e231"}.glyphicon-ice-lolly-tasted:before{content:"\e232"}.glyphicon-education:before{content:"\e233"}.glyphicon-option-horizontal:before{content:"\e234"}.glyphicon-option-vertical:before{content:"\e235"}.glyphicon-menu-hamburger:before{content:"\e236"}.glyphicon-modal-window:before{content:"\e237"}.glyphicon-oil:before{content:"\e238"}.glyphicon-grain:before{content:"\e239"}.glyphicon-sunglasses:before{content:"\e240"}.glyphicon-text-size:before{content:"\e241"}.glyphicon-text-color:before{content:"\e242"}.glyphicon-text-background:before{content:"\e243"}.glyphicon-object-align-top:before{content:"\e244"}.glyphicon-object-align-bottom:before{content:"\e245"}.glyphicon-object-align-horizontal:before{content:"\e246"}.glyphicon-object-align-left:before{content:"\e247"}.glyphicon-object-align-vertical:before{content:"\e248"}.glyphicon-object-align-right:before{content:"\e249"}.glyphicon-triangle-right:before{content:"\e250"}.glyphicon-triangle-left:before{content:"\e251"}.glyphicon-triangle-bottom:before{content:"\e252"}.glyphicon-triangle-top:before{content:"\e253"}.glyphicon-console:before{content:"\e254"}.glyphicon-superscript:before{content:"\e255"}.glyphicon-subscript:before{content:"\e256"}.glyphicon-menu-left:before{content:"\e257"}.glyphicon-menu-right:before{content:"\e258"}.glyphicon-menu-down:before{content:"\e259"}.glyphicon-menu-up:before{content:"\e260"}*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}:after,:before{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}html{font-size:10px;-webkit-tap-highlight-color:rgba(0,0,0,0)}body{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:1.42857143;color:#333;background-color:#fff}button,input,select,textarea{font-family:inherit;font-size:inherit;line-height:inherit}a{color:#337ab7;text-decoration:none}a:focus,a:hover{color:#23527c;text-decoration:underline}a:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}figure{margin:0}img{vertical-align:middle}.carousel-inner>.item>a>img,.carousel-inner>.item>img,.img-responsive,.thumbnail a>img,.thumbnail>img{display:block;max-width:100%;height:auto}.img-rounded{border-radius:6px}.img-thumbnail{display:inline-block;max-width:100%;height:auto;padding:4px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}.img-circle{border-radius:50%}hr{margin-top:20px;margin-bottom:20px;border:0;border-top:1px solid #eee}.sr-only{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);border:0}.sr-only-focusable:active,.sr-only-focusable:focus{position:static;width:auto;height:auto;margin:0;overflow:visible;clip:auto}[role=button]{cursor:pointer}.h1,.h2,.h3,.h4,.h5,.h6,h1,h2,h3,h4,h5,h6{font-family:inherit;font-weight:500;line-height:1.1;color:inherit}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-weight:400;line-height:1;color:#777}.h1,.h2,.h3,h1,h2,h3{margin-top:20px;margin-bottom:10px}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small{font-size:65%}.h4,.h5,.h6,h4,h5,h6{margin-top:10px;margin-bottom:10px}.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-size:75%}.h1,h1{font-size:36px}.h2,h2{font-size:30px}.h3,h3{font-size:24px}.h4,h4{font-size:18px}.h5,h5{font-size:14px}.h6,h6{font-size:12px}p{margin:0 0 10px}.lead{margin-bottom:20px;font-size:16px;font-weight:300;line-height:1.4}@media (min-width:768px){.lead{font-size:21px}}.small,small{font-size:85%}.mark,mark{padding:.2em;background-color:#fcf8e3}.text-left{text-align:left}.text-right{text-align:right}.text-center{text-align:center}.text-justify{text-align:justify}.text-nowrap{white-space:nowrap}.text-lowercase{text-transform:lowercase}.text-uppercase{text-transform:uppercase}.text-capitalize{text-transform:capitalize}.text-muted{color:#777}.text-primary{color:#337ab7}a.text-primary:focus,a.text-primary:hover{color:#286090}.text-success{color:#3c763d}a.text-success:focus,a.text-success:hover{color:#2b542c}.text-info{color:#31708f}a.text-info:focus,a.text-info:hover{color:#245269}.text-warning{color:#8a6d3b}a.text-warning:focus,a.text-warning:hover{color:#66512c}.text-danger{color:#a94442}a.text-danger:focus,a.text-danger:hover{color:#843534}.bg-primary{color:#fff;background-color:#337ab7}a.bg-primary:focus,a.bg-primary:hover{background-color:#286090}.bg-success{background-color:#dff0d8}a.bg-success:focus,a.bg-success:hover{background-color:#c1e2b3}.bg-info{background-color:#d9edf7}a.bg-info:focus,a.bg-info:hover{background-color:#afd9ee}.bg-warning{background-color:#fcf8e3}a.bg-warning:focus,a.bg-warning:hover{background-color:#f7ecb5}.bg-danger{background-color:#f2dede}a.bg-danger:focus,a.bg-danger:hover{background-color:#e4b9b9}.page-header{padding-bottom:9px;margin:40px 0 20px;border-bottom:1px solid #eee}ol,ul{margin-top:0;margin-bottom:10px}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}.list-unstyled{padding-left:0;list-style:none}.list-inline{padding-left:0;margin-left:-5px;list-style:none}.list-inline>li{display:inline-block;padding-right:5px;padding-left:5px}dl{margin-top:0;margin-bottom:20px}dd,dt{line-height:1.42857143}dt{font-weight:700}dd{margin-left:0}@media (min-width:768px){.dl-horizontal dt{float:left;width:160px;overflow:hidden;clear:left;text-align:right;text-overflow:ellipsis;white-space:nowrap}.dl-horizontal dd{margin-left:180px}}abbr[data-original-title],abbr[title]{cursor:help;border-bottom:1px dotted #777}.initialism{font-size:90%;text-transform:uppercase}blockquote{padding:10px 20px;margin:0 0 20px;font-size:17.5px;border-left:5px solid #eee}blockquote ol:last-child,blockquote p:last-child,blockquote ul:last-child{margin-bottom:0}blockquote .small,blockquote footer,blockquote small{display:block;font-size:80%;line-height:1.42857143;color:#777}blockquote .small:before,blockquote footer:before,blockquote small:before{content:'\2014 \00A0'}.blockquote-reverse,blockquote.pull-right{padding-right:15px;padding-left:0;text-align:right;border-right:5px solid #eee;border-left:0}.blockquote-reverse .small:before,.blockquote-reverse footer:before,.blockquote-reverse small:before,blockquote.pull-right .small:before,blockquote.pull-right footer:before,blockquote.pull-right small:before{content:''}.blockquote-reverse .small:after,.blockquote-reverse footer:after,.blockquote-reverse small:after,blockquote.pull-right .small:after,blockquote.pull-right footer:after,blockquote.pull-right small:after{content:'\00A0 \2014'}address{margin-bottom:20px;font-style:normal;line-height:1.42857143}code,kbd,pre,samp{font-family:monospace}code{padding:2px 4px;font-size:90%;color:#c7254e;background-color:#f9f2f4;border-radius:4px}kbd{padding:2px 4px;font-size:90%;color:#fff;background-color:#333;border-radius:3px;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.25);box-shadow:inset 0 -1px 0 rgba(0,0,0,.25)}kbd kbd{padding:0;font-size:100%;font-weight:700;-webkit-box-shadow:none;box-shadow:none}pre{display:block;padding:9.5px;margin:0 0 10px;font-size:13px;line-height:1.42857143;color:#333;word-break:break-all;word-wrap:break-word;background-color:#f5f5f5;border:1px solid #ccc;border-radius:4px}pre code{padding:0;font-size:inherit;color:inherit;white-space:pre-wrap;background-color:transparent;border-radius:0}.pre-scrollable{max-height:340px;overflow-y:scroll}.container{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}@media (min-width:768px){.container{width:750px}}@media (min-width:992px){.container{width:970px}}@media (min-width:1200px){.container{width:1170px}}.container-fluid{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}.row{margin-right:-15px;margin-left:-15px}.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9,.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9,.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9,.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{position:relative;min-height:1px;padding-right:15px;padding-left:15px}.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{float:left}.col-xs-12{width:100%}.col-xs-11{width:91.66666667%}.col-xs-10{width:83.33333333%}.col-xs-9{width:75%}.col-xs-8{width:66.66666667%}.col-xs-7{width:58.33333333%}.col-xs-6{width:50%}.col-xs-5{width:41.66666667%}.col-xs-4{width:33.33333333%}.col-xs-3{width:25%}.col-xs-2{width:16.66666667%}.col-xs-1{width:8.33333333%}.col-xs-pull-12{right:100%}.col-xs-pull-11{right:91.66666667%}.col-xs-pull-10{right:83.33333333%}.col-xs-pull-9{right:75%}.col-xs-pull-8{right:66.66666667%}.col-xs-pull-7{right:58.33333333%}.col-xs-pull-6{right:50%}.col-xs-pull-5{right:41.66666667%}.col-xs-pull-4{right:33.33333333%}.col-xs-pull-3{right:25%}.col-xs-pull-2{right:16.66666667%}.col-xs-pull-1{right:8.33333333%}.col-xs-pull-0{right:auto}.col-xs-push-12{left:100%}.col-xs-push-11{left:91.66666667%}.col-xs-push-10{left:83.33333333%}.col-xs-push-9{left:75%}.col-xs-push-8{left:66.66666667%}.col-xs-push-7{left:58.33333333%}.col-xs-push-6{left:50%}.col-xs-push-5{left:41.66666667%}.col-xs-push-4{left:33.33333333%}.col-xs-push-3{left:25%}.col-xs-push-2{left:16.66666667%}.col-xs-push-1{left:8.33333333%}.col-xs-push-0{left:auto}.col-xs-offset-12{margin-left:100%}.col-xs-offset-11{margin-left:91.66666667%}.col-xs-offset-10{margin-left:83.33333333%}.col-xs-offset-9{margin-left:75%}.col-xs-offset-8{margin-left:66.66666667%}.col-xs-offset-7{margin-left:58.33333333%}.col-xs-offset-6{margin-left:50%}.col-xs-offset-5{margin-left:41.66666667%}.col-xs-offset-4{margin-left:33.33333333%}.col-xs-offset-3{margin-left:25%}.col-xs-offset-2{margin-left:16.66666667%}.col-xs-offset-1{margin-left:8.33333333%}.col-xs-offset-0{margin-left:0}@media (min-width:768px){.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9{float:left}.col-sm-12{width:100%}.col-sm-11{width:91.66666667%}.col-sm-10{width:83.33333333%}.col-sm-9{width:75%}.col-sm-8{width:66.66666667%}.col-sm-7{width:58.33333333%}.col-sm-6{width:50%}.col-sm-5{width:41.66666667%}.col-sm-4{width:33.33333333%}.col-sm-3{width:25%}.col-sm-2{width:16.66666667%}.col-sm-1{width:8.33333333%}.col-sm-pull-12{right:100%}.col-sm-pull-11{right:91.66666667%}.col-sm-pull-10{right:83.33333333%}.col-sm-pull-9{right:75%}.col-sm-pull-8{right:66.66666667%}.col-sm-pull-7{right:58.33333333%}.col-sm-pull-6{right:50%}.col-sm-pull-5{right:41.66666667%}.col-sm-pull-4{right:33.33333333%}.col-sm-pull-3{right:25%}.col-sm-pull-2{right:16.66666667%}.col-sm-pull-1{right:8.33333333%}.col-sm-pull-0{right:auto}.col-sm-push-12{left:100%}.col-sm-push-11{left:91.66666667%}.col-sm-push-10{left:83.33333333%}.col-sm-push-9{left:75%}.col-sm-push-8{left:66.66666667%}.col-sm-push-7{left:58.33333333%}.col-sm-push-6{left:50%}.col-sm-push-5{left:41.66666667%}.col-sm-push-4{left:33.33333333%}.col-sm-push-3{left:25%}.col-sm-push-2{left:16.66666667%}.col-sm-push-1{left:8.33333333%}.col-sm-push-0{left:auto}.col-sm-offset-12{margin-left:100%}.col-sm-offset-11{margin-left:91.66666667%}.col-sm-offset-10{margin-left:83.33333333%}.col-sm-offset-9{margin-left:75%}.col-sm-offset-8{margin-left:66.66666667%}.col-sm-offset-7{margin-left:58.33333333%}.col-sm-offset-6{margin-left:50%}.col-sm-offset-5{margin-left:41.66666667%}.col-sm-offset-4{margin-left:33.33333333%}.col-sm-offset-3{margin-left:25%}.col-sm-offset-2{margin-left:16.66666667%}.col-sm-offset-1{margin-left:8.33333333%}.col-sm-offset-0{margin-left:0}}@media (min-width:992px){.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9{float:left}.col-md-12{width:100%}.col-md-11{width:91.66666667%}.col-md-10{width:83.33333333%}.col-md-9{width:75%}.col-md-8{width:66.66666667%}.col-md-7{width:58.33333333%}.col-md-6{width:50%}.col-md-5{width:41.66666667%}.col-md-4{width:33.33333333%}.col-md-3{width:25%}.col-md-2{width:16.66666667%}.col-md-1{width:8.33333333%}.col-md-pull-12{right:100%}.col-md-pull-11{right:91.66666667%}.col-md-pull-10{right:83.33333333%}.col-md-pull-9{right:75%}.col-md-pull-8{right:66.66666667%}.col-md-pull-7{right:58.33333333%}.col-md-pull-6{right:50%}.col-md-pull-5{right:41.66666667%}.col-md-pull-4{right:33.33333333%}.col-md-pull-3{right:25%}.col-md-pull-2{right:16.66666667%}.col-md-pull-1{right:8.33333333%}.col-md-pull-0{right:auto}.col-md-push-12{left:100%}.col-md-push-11{left:91.66666667%}.col-md-push-10{left:83.33333333%}.col-md-push-9{left:75%}.col-md-push-8{left:66.66666667%}.col-md-push-7{left:58.33333333%}.col-md-push-6{left:50%}.col-md-push-5{left:41.66666667%}.col-md-push-4{left:33.33333333%}.col-md-push-3{left:25%}.col-md-push-2{left:16.66666667%}.col-md-push-1{left:8.33333333%}.col-md-push-0{left:auto}.col-md-offset-12{margin-left:100%}.col-md-offset-11{margin-left:91.66666667%}.col-md-offset-10{margin-left:83.33333333%}.col-md-offset-9{margin-left:75%}.col-md-offset-8{margin-left:66.66666667%}.col-md-offset-7{margin-left:58.33333333%}.col-md-offset-6{margin-left:50%}.col-md-offset-5{margin-left:41.66666667%}.col-md-offset-4{margin-left:33.33333333%}.col-md-offset-3{margin-left:25%}.col-md-offset-2{margin-left:16.66666667%}.col-md-offset-1{margin-left:8.33333333%}.col-md-offset-0{margin-left:0}}@media (min-width:1200px){.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9{float:left}.col-lg-12{width:100%}.col-lg-11{width:91.66666667%}.col-lg-10{width:83.33333333%}.col-lg-9{width:75%}.col-lg-8{width:66.66666667%}.col-lg-7{width:58.33333333%}.col-lg-6{width:50%}.col-lg-5{width:41.66666667%}.col-lg-4{width:33.33333333%}.col-lg-3{width:25%}.col-lg-2{width:16.66666667%}.col-lg-1{width:8.33333333%}.col-lg-pull-12{right:100%}.col-lg-pull-11{right:91.66666667%}.col-lg-pull-10{right:83.33333333%}.col-lg-pull-9{right:75%}.col-lg-pull-8{right:66.66666667%}.col-lg-pull-7{right:58.33333333%}.col-lg-pull-6{right:50%}.col-lg-pull-5{right:41.66666667%}.col-lg-pull-4{right:33.33333333%}.col-lg-pull-3{right:25%}.col-lg-pull-2{right:16.66666667%}.col-lg-pull-1{right:8.33333333%}.col-lg-pull-0{right:auto}.col-lg-push-12{left:100%}.col-lg-push-11{left:91.66666667%}.col-lg-push-10{left:83.33333333%}.col-lg-push-9{left:75%}.col-lg-push-8{left:66.66666667%}.col-lg-push-7{left:58.33333333%}.col-lg-push-6{left:50%}.col-lg-push-5{left:41.66666667%}.col-lg-push-4{left:33.33333333%}.col-lg-push-3{left:25%}.col-lg-push-2{left:16.66666667%}.col-lg-push-1{left:8.33333333%}.col-lg-push-0{left:auto}.col-lg-offset-12{margin-left:100%}.col-lg-offset-11{margin-left:91.66666667%}.col-lg-offset-10{margin-left:83.33333333%}.col-lg-offset-9{margin-left:75%}.col-lg-offset-8{margin-left:66.66666667%}.col-lg-offset-7{margin-left:58.33333333%}.col-lg-offset-6{margin-left:50%}.col-lg-offset-5{margin-left:41.66666667%}.col-lg-offset-4{margin-left:33.33333333%}.col-lg-offset-3{margin-left:25%}.col-lg-offset-2{margin-left:16.66666667%}.col-lg-offset-1{margin-left:8.33333333%}.col-lg-offset-0{margin-left:0}}table{background-color:transparent}caption{padding-top:8px;padding-bottom:8px;color:#777;text-align:left}th{}.table{width:100%;max-width:100%;margin-bottom:20px}.table>tbody>tr>td,.table>tbody>tr>th,.table>tfoot>tr>td,.table>tfoot>tr>th,.table>thead>tr>td,.table>thead>tr>th{padding:8px;line-height:1.42857143;vertical-align:top;border-top:1px solid #ddd}.table>thead>tr>th{vertical-align:bottom;border-bottom:2px solid #ddd}.table>caption+thead>tr:first-child>td,.table>caption+thead>tr:first-child>th,.table>colgroup+thead>tr:first-child>td,.table>colgroup+thead>tr:first-child>th,.table>thead:first-child>tr:first-child>td,.table>thead:first-child>tr:first-child>th{border-top:0}.table>tbody+tbody{border-top:2px solid #ddd}.table .table{background-color:#fff}.table-condensed>tbody>tr>td,.table-condensed>tbody>tr>th,.table-condensed>tfoot>tr>td,.table-condensed>tfoot>tr>th,.table-condensed>thead>tr>td,.table-condensed>thead>tr>th{padding:5px}.table-bordered{border:1px solid #ddd}.table-bordered>tbody>tr>td,.table-bordered>tbody>tr>th,.table-bordered>tfoot>tr>td,.table-bordered>tfoot>tr>th,.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border:1px solid #ddd}.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border-bottom-width:2px}.table-striped>tbody>tr:nth-of-type(odd){background-color:#f9f9f9}.table-hover>tbody>tr:hover{background-color:#f5f5f5}table col[class*=col-]{position:static;display:table-column;float:none}table td[class*=col-],table th[class*=col-]{position:static;display:table-cell;float:none}.table>tbody>tr.active>td,.table>tbody>tr.active>th,.table>tbody>tr>td.active,.table>tbody>tr>th.active,.table>tfoot>tr.active>td,.table>tfoot>tr.active>th,.table>tfoot>tr>td.active,.table>tfoot>tr>th.active,.table>thead>tr.active>td,.table>thead>tr.active>th,.table>thead>tr>td.active,.table>thead>tr>th.active{background-color:#f5f5f5}.table-hover>tbody>tr.active:hover>td,.table-hover>tbody>tr.active:hover>th,.table-hover>tbody>tr:hover>.active,.table-hover>tbody>tr>td.active:hover,.table-hover>tbody>tr>th.active:hover{background-color:#e8e8e8}.table>tbody>tr.success>td,.table>tbody>tr.success>th,.table>tbody>tr>td.success,.table>tbody>tr>th.success,.table>tfoot>tr.success>td,.table>tfoot>tr.success>th,.table>tfoot>tr>td.success,.table>tfoot>tr>th.success,.table>thead>tr.success>td,.table>thead>tr.success>th,.table>thead>tr>td.success,.table>thead>tr>th.success{background-color:#dff0d8}.table-hover>tbody>tr.success:hover>td,.table-hover>tbody>tr.success:hover>th,.table-hover>tbody>tr:hover>.success,.table-hover>tbody>tr>td.success:hover,.table-hover>tbody>tr>th.success:hover{background-color:#d0e9c6}.table>tbody>tr.info>td,.table>tbody>tr.info>th,.table>tbody>tr>td.info,.table>tbody>tr>th.info,.table>tfoot>tr.info>td,.table>tfoot>tr.info>th,.table>tfoot>tr>td.info,.table>tfoot>tr>th.info,.table>thead>tr.info>td,.table>thead>tr.info>th,.table>thead>tr>td.info,.table>thead>tr>th.info{background-color:#d9edf7}.table-hover>tbody>tr.info:hover>td,.table-hover>tbody>tr.info:hover>th,.table-hover>tbody>tr:hover>.info,.table-hover>tbody>tr>td.info:hover,.table-hover>tbody>tr>th.info:hover{background-color:#c4e3f3}.table>tbody>tr.warning>td,.table>tbody>tr.warning>th,.table>tbody>tr>td.warning,.table>tbody>tr>th.warning,.table>tfoot>tr.warning>td,.table>tfoot>tr.warning>th,.table>tfoot>tr>td.warning,.table>tfoot>tr>th.warning,.table>thead>tr.warning>td,.table>thead>tr.warning>th,.table>thead>tr>td.warning,.table>thead>tr>th.warning{background-color:#fcf8e3}.table-hover>tbody>tr.warning:hover>td,.table-hover>tbody>tr.warning:hover>th,.table-hover>tbody>tr:hover>.warning,.table-hover>tbody>tr>td.warning:hover,.table-hover>tbody>tr>th.warning:hover{background-color:#faf2cc}.table>tbody>tr.danger>td,.table>tbody>tr.danger>th,.table>tbody>tr>td.danger,.table>tbody>tr>th.danger,.table>tfoot>tr.danger>td,.table>tfoot>tr.danger>th,.table>tfoot>tr>td.danger,.table>tfoot>tr>th.danger,.table>thead>tr.danger>td,.table>thead>tr.danger>th,.table>thead>tr>td.danger,.table>thead>tr>th.danger{background-color:#f2dede}.table-hover>tbody>tr.danger:hover>td,.table-hover>tbody>tr.danger:hover>th,.table-hover>tbody>tr:hover>.danger,.table-hover>tbody>tr>td.danger:hover,.table-hover>tbody>tr>th.danger:hover{background-color:#ebcccc}.table-responsive{min-height:.01%;overflow-x:auto}@media screen and (max-width:767px){.table-responsive{width:100%;margin-bottom:15px;overflow-y:hidden;-ms-overflow-style:-ms-autohiding-scrollbar;border:1px solid #ddd}.table-responsive>.table{margin-bottom:0}.table-responsive>.table>tbody>tr>td,.table-responsive>.table>tbody>tr>th,.table-responsive>.table>tfoot>tr>td,.table-responsive>.table>tfoot>tr>th,.table-responsive>.table>thead>tr>td,.table-responsive>.table>thead>tr>th{white-space:nowrap}.table-responsive>.table-bordered{border:0}.table-responsive>.table-bordered>tbody>tr>td:first-child,.table-responsive>.table-bordered>tbody>tr>th:first-child,.table-responsive>.table-bordered>tfoot>tr>td:first-child,.table-responsive>.table-bordered>tfoot>tr>th:first-child,.table-responsive>.table-bordered>thead>tr>td:first-child,.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.table-responsive>.table-bordered>tbody>tr>td:last-child,.table-responsive>.table-bordered>tbody>tr>th:last-child,.table-responsive>.table-bordered>tfoot>tr>td:last-child,.table-responsive>.table-bordered>tfoot>tr>th:last-child,.table-responsive>.table-bordered>thead>tr>td:last-child,.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.table-responsive>.table-bordered>tbody>tr:last-child>td,.table-responsive>.table-bordered>tbody>tr:last-child>th,.table-responsive>.table-bordered>tfoot>tr:last-child>td,.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}}fieldset{min-width:0;padding:0;margin:0;border:0}legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:21px;line-height:inherit;color:#333;border:0;border-bottom:1px solid #e5e5e5}label{display:inline-block;max-width:100%;margin-bottom:5px;font-weight:700}input[type=search]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}input[type=checkbox],input[type=radio]{margin:4px 0 0;margin-top:1px\9;line-height:normal}input[type=file]{display:block}input[type=range]{display:block;width:100%}select[multiple],select[size]{height:auto}input[type=file]:focus,input[type=checkbox]:focus,input[type=radio]:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}output{display:block;padding-top:7px;font-size:14px;line-height:1.42857143;color:#555}.form-control{display:block;width:100%;height:34px;padding:6px 12px;font-size:14px;line-height:1.42857143;color:#555;background-color:#fff;background-image:none;border:1px solid #ccc;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075);-webkit-transition:border-color ease-in-out .15s,-webkit-box-shadow ease-in-out .15s;-o-transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s;transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s}.form-control:focus{border-color:#66afe9;outline:0;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6)}.form-control::-moz-placeholder{color:#999;opacity:1}.form-control:-ms-input-placeholder{color:#999}.form-control::-webkit-input-placeholder{color:#999}.form-control[disabled],.form-control[readonly],fieldset[disabled] .form-control{background-color:#eee;opacity:1}.form-control[disabled],fieldset[disabled] .form-control{cursor:not-allowed}textarea.form-control{height:auto}input[type=search]{-webkit-appearance:none}@media screen and (-webkit-min-device-pixel-ratio:0){input[type=date].form-control,input[type=time].form-control,input[type=datetime-local].form-control,input[type=month].form-control{line-height:34px}.input-group-sm input[type=date],.input-group-sm input[type=time],.input-group-sm input[type=datetime-local],.input-group-sm input[type=month],input[type=date].input-sm,input[type=time].input-sm,input[type=datetime-local].input-sm,input[type=month].input-sm{line-height:30px}.input-group-lg input[type=date],.input-group-lg input[type=time],.input-group-lg input[type=datetime-local],.input-group-lg input[type=month],input[type=date].input-lg,input[type=time].input-lg,input[type=datetime-local].input-lg,input[type=month].input-lg{line-height:46px}}.form-group{margin-bottom:15px}.checkbox,.radio{position:relative;display:block;margin-top:10px;margin-bottom:10px}.checkbox label,.radio label{min-height:20px;padding-left:20px;margin-bottom:0;font-weight:400;cursor:pointer}.checkbox input[type=checkbox],.checkbox-inline input[type=checkbox],.radio input[type=radio],.radio-inline input[type=radio]{position:absolute;margin-top:4px\9;margin-left:-20px}.checkbox+.checkbox,.radio+.radio{margin-top:-5px}.checkbox-inline,.radio-inline{position:relative;display:inline-block;padding-left:20px;margin-bottom:0;font-weight:400;vertical-align:middle;cursor:pointer}.checkbox-inline+.checkbox-inline,.radio-inline+.radio-inline{margin-top:0;margin-left:10px}fieldset[disabled] input[type=checkbox],fieldset[disabled] input[type=radio],input[type=checkbox].disabled,input[type=checkbox][disabled],input[type=radio].disabled,input[type=radio][disabled]{cursor:not-allowed}.checkbox-inline.disabled,.radio-inline.disabled,fieldset[disabled] .checkbox-inline,fieldset[disabled] .radio-inline{cursor:not-allowed}.checkbox.disabled label,.radio.disabled label,fieldset[disabled] .checkbox label,fieldset[disabled] .radio label{cursor:not-allowed}.form-control-static{min-height:34px;padding-top:7px;padding-bottom:7px;margin-bottom:0}.form-control-static.input-lg,.form-control-static.input-sm{padding-right:0;padding-left:0}.input-sm{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-sm{height:30px;line-height:30px}select[multiple].input-sm,textarea.input-sm{height:auto}.form-group-sm .form-control{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.form-group-sm select.form-control{height:30px;line-height:30px}.form-group-sm select[multiple].form-control,.form-group-sm textarea.form-control{height:auto}.form-group-sm .form-control-static{height:30px;min-height:32px;padding:6px 10px;font-size:12px;line-height:1.5}.input-lg{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-lg{height:46px;line-height:46px}select[multiple].input-lg,textarea.input-lg{height:auto}.form-group-lg .form-control{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.form-group-lg select.form-control{height:46px;line-height:46px}.form-group-lg select[multiple].form-control,.form-group-lg textarea.form-control{height:auto}.form-group-lg .form-control-static{height:46px;min-height:38px;padding:11px 16px;font-size:18px;line-height:1.3333333}.has-feedback{position:relative}.has-feedback .form-control{padding-right:42.5px}.form-control-feedback{position:absolute;top:0;right:0;z-index:2;display:block;width:34px;height:34px;line-height:34px;text-align:center;pointer-events:none}.form-group-lg .form-control+.form-control-feedback,.input-group-lg+.form-control-feedback,.input-lg+.form-control-feedback{width:46px;height:46px;line-height:46px}.form-group-sm .form-control+.form-control-feedback,.input-group-sm+.form-control-feedback,.input-sm+.form-control-feedback{width:30px;height:30px;line-height:30px}.has-success .checkbox,.has-success .checkbox-inline,.has-success .control-label,.has-success .help-block,.has-success .radio,.has-success .radio-inline,.has-success.checkbox label,.has-success.checkbox-inline label,.has-success.radio label,.has-success.radio-inline label{color:#3c763d}.has-success .form-control{border-color:#3c763d;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-success .form-control:focus{border-color:#2b542c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168}.has-success .input-group-addon{color:#3c763d;background-color:#dff0d8;border-color:#3c763d}.has-success .form-control-feedback{color:#3c763d}.has-warning .checkbox,.has-warning .checkbox-inline,.has-warning .control-label,.has-warning .help-block,.has-warning .radio,.has-warning .radio-inline,.has-warning.checkbox label,.has-warning.checkbox-inline label,.has-warning.radio label,.has-warning.radio-inline label{color:#8a6d3b}.has-warning .form-control{border-color:#8a6d3b;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-warning .form-control:focus{border-color:#66512c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b}.has-warning .input-group-addon{color:#8a6d3b;background-color:#fcf8e3;border-color:#8a6d3b}.has-warning .form-control-feedback{color:#8a6d3b}.has-error .checkbox,.has-error .checkbox-inline,.has-error .control-label,.has-error .help-block,.has-error .radio,.has-error .radio-inline,.has-error.checkbox label,.has-error.checkbox-inline label,.has-error.radio label,.has-error.radio-inline label{color:#a94442}.has-error .form-control{border-color:#a94442;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-error .form-control:focus{border-color:#843534;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483}.has-error .input-group-addon{color:#a94442;background-color:#f2dede;border-color:#a94442}.has-error .form-control-feedback{color:#a94442}.has-feedback label~.form-control-feedback{top:25px}.has-feedback label.sr-only~.form-control-feedback{top:0}.help-block{display:block;margin-top:5px;margin-bottom:10px;color:#737373}@media (min-width:768px){.form-inline .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.form-inline .form-control{display:inline-block;width:auto;vertical-align:middle}.form-inline .form-control-static{display:inline-block}.form-inline .input-group{display:inline-table;vertical-align:middle}.form-inline .input-group .form-control,.form-inline .input-group .input-group-addon,.form-inline .input-group .input-group-btn{width:auto}.form-inline .input-group>.form-control{width:100%}.form-inline .control-label{margin-bottom:0;vertical-align:middle}.form-inline .checkbox,.form-inline .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.form-inline .checkbox label,.form-inline .radio label{padding-left:0}.form-inline .checkbox input[type=checkbox],.form-inline .radio input[type=radio]{position:relative;margin-left:0}.form-inline .has-feedback .form-control-feedback{top:0}}.form-horizontal .checkbox,.form-horizontal .checkbox-inline,.form-horizontal .radio,.form-horizontal .radio-inline{padding-top:7px;margin-top:0;margin-bottom:0}.form-horizontal .checkbox,.form-horizontal .radio{min-height:27px}.form-horizontal .form-group{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.form-horizontal .control-label{padding-top:7px;margin-bottom:0;text-align:right}}.form-horizontal .has-feedback .form-control-feedback{right:15px}@media (min-width:768px){.form-horizontal .form-group-lg .control-label{padding-top:14.33px;font-size:18px}}@media (min-width:768px){.form-horizontal .form-group-sm .control-label{padding-top:6px;font-size:12px}}.btn{display:inline-block;padding:6px 12px;margin-bottom:0;font-size:14px;font-weight:400;line-height:1.42857143;text-align:center;white-space:nowrap;vertical-align:middle;-ms-touch-action:manipulation;touch-action:manipulation;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;background-image:none;border:1px solid transparent;border-radius:4px}.btn.active.focus,.btn.active:focus,.btn.focus,.btn:active.focus,.btn:active:focus,.btn:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.btn.focus,.btn:focus,.btn:hover{color:#333;text-decoration:none}.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn.disabled,.btn[disabled],fieldset[disabled] .btn{cursor:not-allowed;filter:alpha(opacity=65);-webkit-box-shadow:none;box-shadow:none;opacity:.65}a.btn.disabled,fieldset[disabled] a.btn{pointer-events:none}.btn-default{color:#333;background-color:#fff;border-color:#ccc}.btn-default.focus,.btn-default:focus{color:#333;background-color:#e6e6e6;border-color:#8c8c8c}.btn-default:hover{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active.focus,.btn-default.active:focus,.btn-default.active:hover,.btn-default:active.focus,.btn-default:active:focus,.btn-default:active:hover,.open>.dropdown-toggle.btn-default.focus,.open>.dropdown-toggle.btn-default:focus,.open>.dropdown-toggle.btn-default:hover{color:#333;background-color:#d4d4d4;border-color:#8c8c8c}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{background-image:none}.btn-default.disabled,.btn-default.disabled.active,.btn-default.disabled.focus,.btn-default.disabled:active,.btn-default.disabled:focus,.btn-default.disabled:hover,.btn-default[disabled],.btn-default[disabled].active,.btn-default[disabled].focus,.btn-default[disabled]:active,.btn-default[disabled]:focus,.btn-default[disabled]:hover,fieldset[disabled] .btn-default,fieldset[disabled] .btn-default.active,fieldset[disabled] .btn-default.focus,fieldset[disabled] .btn-default:active,fieldset[disabled] .btn-default:focus,fieldset[disabled] .btn-default:hover{background-color:#fff;border-color:#ccc}.btn-default .badge{color:#fff;background-color:#333}.btn-primary{color:#fff;background-color:#337ab7;border-color:#2e6da4}.btn-primary.focus,.btn-primary:focus{color:#fff;background-color:#286090;border-color:#122b40}.btn-primary:hover{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active.focus,.btn-primary.active:focus,.btn-primary.active:hover,.btn-primary:active.focus,.btn-primary:active:focus,.btn-primary:active:hover,.open>.dropdown-toggle.btn-primary.focus,.open>.dropdown-toggle.btn-primary:focus,.open>.dropdown-toggle.btn-primary:hover{color:#fff;background-color:#204d74;border-color:#122b40}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{background-image:none}.btn-primary.disabled,.btn-primary.disabled.active,.btn-primary.disabled.focus,.btn-primary.disabled:active,.btn-primary.disabled:focus,.btn-primary.disabled:hover,.btn-primary[disabled],.btn-primary[disabled].active,.btn-primary[disabled].focus,.btn-primary[disabled]:active,.btn-primary[disabled]:focus,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-primary.active,fieldset[disabled] .btn-primary.focus,fieldset[disabled] .btn-primary:active,fieldset[disabled] .btn-primary:focus,fieldset[disabled] .btn-primary:hover{background-color:#337ab7;border-color:#2e6da4}.btn-primary .badge{color:#337ab7;background-color:#fff}.btn-success{color:#fff;background-color:#5cb85c;border-color:#4cae4c}.btn-success.focus,.btn-success:focus{color:#fff;background-color:#449d44;border-color:#255625}.btn-success:hover{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active.focus,.btn-success.active:focus,.btn-success.active:hover,.btn-success:active.focus,.btn-success:active:focus,.btn-success:active:hover,.open>.dropdown-toggle.btn-success.focus,.open>.dropdown-toggle.btn-success:focus,.open>.dropdown-toggle.btn-success:hover{color:#fff;background-color:#398439;border-color:#255625}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{background-image:none}.btn-success.disabled,.btn-success.disabled.active,.btn-success.disabled.focus,.btn-success.disabled:active,.btn-success.disabled:focus,.btn-success.disabled:hover,.btn-success[disabled],.btn-success[disabled].active,.btn-success[disabled].focus,.btn-success[disabled]:active,.btn-success[disabled]:focus,.btn-success[disabled]:hover,fieldset[disabled] .btn-success,fieldset[disabled] .btn-success.active,fieldset[disabled] .btn-success.focus,fieldset[disabled] .btn-success:active,fieldset[disabled] .btn-success:focus,fieldset[disabled] .btn-success:hover{background-color:#5cb85c;border-color:#4cae4c}.btn-success .badge{color:#5cb85c;background-color:#fff}.btn-info{color:#fff;background-color:#5bc0de;border-color:#46b8da}.btn-info.focus,.btn-info:focus{color:#fff;background-color:#31b0d5;border-color:#1b6d85}.btn-info:hover{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active.focus,.btn-info.active:focus,.btn-info.active:hover,.btn-info:active.focus,.btn-info:active:focus,.btn-info:active:hover,.open>.dropdown-toggle.btn-info.focus,.open>.dropdown-toggle.btn-info:focus,.open>.dropdown-toggle.btn-info:hover{color:#fff;background-color:#269abc;border-color:#1b6d85}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{background-image:none}.btn-info.disabled,.btn-info.disabled.active,.btn-info.disabled.focus,.btn-info.disabled:active,.btn-info.disabled:focus,.btn-info.disabled:hover,.btn-info[disabled],.btn-info[disabled].active,.btn-info[disabled].focus,.btn-info[disabled]:active,.btn-info[disabled]:focus,.btn-info[disabled]:hover,fieldset[disabled] .btn-info,fieldset[disabled] .btn-info.active,fieldset[disabled] .btn-info.focus,fieldset[disabled] .btn-info:active,fieldset[disabled] .btn-info:focus,fieldset[disabled] .btn-info:hover{background-color:#5bc0de;border-color:#46b8da}.btn-info .badge{color:#5bc0de;background-color:#fff}.btn-warning{color:#fff;background-color:#f0ad4e;border-color:#eea236}.btn-warning.focus,.btn-warning:focus{color:#fff;background-color:#ec971f;border-color:#985f0d}.btn-warning:hover{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active.focus,.btn-warning.active:focus,.btn-warning.active:hover,.btn-warning:active.focus,.btn-warning:active:focus,.btn-warning:active:hover,.open>.dropdown-toggle.btn-warning.focus,.open>.dropdown-toggle.btn-warning:focus,.open>.dropdown-toggle.btn-warning:hover{color:#fff;background-color:#d58512;border-color:#985f0d}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{background-image:none}.btn-warning.disabled,.btn-warning.disabled.active,.btn-warning.disabled.focus,.btn-warning.disabled:active,.btn-warning.disabled:focus,.btn-warning.disabled:hover,.btn-warning[disabled],.btn-warning[disabled].active,.btn-warning[disabled].focus,.btn-warning[disabled]:active,.btn-warning[disabled]:focus,.btn-warning[disabled]:hover,fieldset[disabled] .btn-warning,fieldset[disabled] .btn-warning.active,fieldset[disabled] .btn-warning.focus,fieldset[disabled] .btn-warning:active,fieldset[disabled] .btn-warning:focus,fieldset[disabled] .btn-warning:hover{background-color:#f0ad4e;border-color:#eea236}.btn-warning .badge{color:#f0ad4e;background-color:#fff}.btn-danger{color:#fff;background-color:#d9534f;border-color:#d43f3a}.btn-danger.focus,.btn-danger:focus{color:#fff;background-color:#c9302c;border-color:#761c19}.btn-danger:hover{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active.focus,.btn-danger.active:focus,.btn-danger.active:hover,.btn-danger:active.focus,.btn-danger:active:focus,.btn-danger:active:hover,.open>.dropdown-toggle.btn-danger.focus,.open>.dropdown-toggle.btn-danger:focus,.open>.dropdown-toggle.btn-danger:hover{color:#fff;background-color:#ac2925;border-color:#761c19}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{background-image:none}.btn-danger.disabled,.btn-danger.disabled.active,.btn-danger.disabled.focus,.btn-danger.disabled:active,.btn-danger.disabled:focus,.btn-danger.disabled:hover,.btn-danger[disabled],.btn-danger[disabled].active,.btn-danger[disabled].focus,.btn-danger[disabled]:active,.btn-danger[disabled]:focus,.btn-danger[disabled]:hover,fieldset[disabled] .btn-danger,fieldset[disabled] .btn-danger.active,fieldset[disabled] .btn-danger.focus,fieldset[disabled] .btn-danger:active,fieldset[disabled] .btn-danger:focus,fieldset[disabled] .btn-danger:hover{background-color:#d9534f;border-color:#d43f3a}.btn-danger .badge{color:#d9534f;background-color:#fff}.btn-link{font-weight:400;color:#337ab7;border-radius:0}.btn-link,.btn-link.active,.btn-link:active,.btn-link[disabled],fieldset[disabled] .btn-link{background-color:transparent;-webkit-box-shadow:none;box-shadow:none}.btn-link,.btn-link:active,.btn-link:focus,.btn-link:hover{border-color:transparent}.btn-link:focus,.btn-link:hover{color:#23527c;text-decoration:underline;background-color:transparent}.btn-link[disabled]:focus,.btn-link[disabled]:hover,fieldset[disabled] .btn-link:focus,fieldset[disabled] .btn-link:hover{color:#777;text-decoration:none}.btn-group-lg>.btn,.btn-lg{padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.btn-group-sm>.btn,.btn-sm{padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.btn-group-xs>.btn,.btn-xs{padding:1px 5px;font-size:12px;line-height:1.5;border-radius:3px}.btn-block{display:block;width:100%}.btn-block+.btn-block{margin-top:5px}input[type=button].btn-block,input[type=reset].btn-block,input[type=submit].btn-block{width:100%}.fade{opacity:0;-webkit-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}.collapse{display:none}.collapse.in{display:block}tr.collapse.in{display:table-row}tbody.collapse.in{display:table-row-group}.collapsing{position:relative;height:0;overflow:hidden;-webkit-transition-timing-function:ease;-o-transition-timing-function:ease;transition-timing-function:ease;-webkit-transition-duration:.35s;-o-transition-duration:.35s;transition-duration:.35s;-webkit-transition-property:height,visibility;-o-transition-property:height,visibility;transition-property:height,visibility}.caret{display:inline-block;width:0;height:0;margin-left:2px;vertical-align:middle;border-top:4px dashed;border-top:4px solid\9;border-right:4px solid transparent;border-left:4px solid transparent}.dropdown,.dropup{position:relative}.dropdown-toggle:focus{outline:0}.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;font-size:14px;text-align:left;list-style:none;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.15);border-radius:4px;-webkit-box-shadow:0 6px 12px rgba(0,0,0,.175);box-shadow:0 6px 12px rgba(0,0,0,.175)}.dropdown-menu.pull-right{right:0;left:auto}.dropdown-menu .divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:400;line-height:1.42857143;color:#333;white-space:nowrap}.dropdown-menu>li>a:focus,.dropdown-menu>li>a:hover{color:#262626;text-decoration:none;background-color:#f5f5f5}.dropdown-menu>.active>a,.dropdown-menu>.active>a:focus,.dropdown-menu>.active>a:hover{color:#fff;text-decoration:none;background-color:#337ab7;outline:0}.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{color:#777}.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{text-decoration:none;cursor:not-allowed;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.open>.dropdown-menu{display:block}.open>a{outline:0}.dropdown-menu-right{right:0;left:auto}.dropdown-menu-left{right:auto;left:0}.dropdown-header{display:block;padding:3px 20px;font-size:12px;line-height:1.42857143;color:#777;white-space:nowrap}.dropdown-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:990}.pull-right>.dropdown-menu{right:0;left:auto}.dropup .caret,.navbar-fixed-bottom .dropdown .caret{content:"";border-top:0;border-bottom:4px dashed;border-bottom:4px solid\9}.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:2px}@media (min-width:768px){.navbar-right .dropdown-menu{right:0;left:auto}.navbar-right .dropdown-menu-left{right:auto;left:0}}.btn-group,.btn-group-vertical{position:relative;display:inline-block;vertical-align:middle}.btn-group-vertical>.btn,.btn-group>.btn{position:relative;float:left}.btn-group-vertical>.btn.active,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:hover,.btn-group>.btn.active,.btn-group>.btn:active,.btn-group>.btn:focus,.btn-group>.btn:hover{z-index:2}.btn-group .btn+.btn,.btn-group .btn+.btn-group,.btn-group .btn-group+.btn,.btn-group .btn-group+.btn-group{margin-left:-1px}.btn-toolbar{margin-left:-5px}.btn-toolbar .btn,.btn-toolbar .btn-group,.btn-toolbar .input-group{float:left}.btn-toolbar>.btn,.btn-toolbar>.btn-group,.btn-toolbar>.input-group{margin-left:5px}.btn-group>.btn:not(:first-child):not(:last-child):not(.dropdown-toggle){border-radius:0}.btn-group>.btn:first-child{margin-left:0}.btn-group>.btn:first-child:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:last-child:not(:first-child),.btn-group>.dropdown-toggle:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.btn-group>.btn-group{float:left}.btn-group>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-bottom-left-radius:0}.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}.btn-group>.btn+.dropdown-toggle{padding-right:8px;padding-left:8px}.btn-group>.btn-lg+.dropdown-toggle{padding-right:12px;padding-left:12px}.btn-group.open .dropdown-toggle{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-group.open .dropdown-toggle.btn-link{-webkit-box-shadow:none;box-shadow:none}.btn .caret{margin-left:0}.btn-lg .caret{border-width:5px 5px 0;border-bottom-width:0}.dropup .btn-lg .caret{border-width:0 5px 5px}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group,.btn-group-vertical>.btn-group>.btn{display:block;float:none;width:100%;max-width:100%}.btn-group-vertical>.btn-group>.btn{float:none}.btn-group-vertical>.btn+.btn,.btn-group-vertical>.btn+.btn-group,.btn-group-vertical>.btn-group+.btn,.btn-group-vertical>.btn-group+.btn-group{margin-top:-1px;margin-left:0}.btn-group-vertical>.btn:not(:first-child):not(:last-child){border-radius:0}.btn-group-vertical>.btn:first-child:not(:last-child){border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn:last-child:not(:first-child){border-top-left-radius:0;border-top-right-radius:0;border-bottom-left-radius:4px}.btn-group-vertical>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group-vertical>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group-vertical>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-top-right-radius:0}.btn-group-justified{display:table;width:100%;table-layout:fixed;border-collapse:separate}.btn-group-justified>.btn,.btn-group-justified>.btn-group{display:table-cell;float:none;width:1%}.btn-group-justified>.btn-group .btn{width:100%}.btn-group-justified>.btn-group .dropdown-menu{left:auto}[data-toggle=buttons]>.btn input[type=checkbox],[data-toggle=buttons]>.btn input[type=radio],[data-toggle=buttons]>.btn-group>.btn input[type=checkbox],[data-toggle=buttons]>.btn-group>.btn input[type=radio]{position:absolute;clip:rect(0,0,0,0);pointer-events:none}.input-group{position:relative;display:table;border-collapse:separate}.input-group[class*=col-]{float:none;padding-right:0;padding-left:0}.input-group .form-control{position:relative;z-index:2;float:left;width:100%;margin-bottom:0}.input-group-lg>.form-control,.input-group-lg>.input-group-addon,.input-group-lg>.input-group-btn>.btn{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-group-lg>.form-control,select.input-group-lg>.input-group-addon,select.input-group-lg>.input-group-btn>.btn{height:46px;line-height:46px}select[multiple].input-group-lg>.form-control,select[multiple].input-group-lg>.input-group-addon,select[multiple].input-group-lg>.input-group-btn>.btn,textarea.input-group-lg>.form-control,textarea.input-group-lg>.input-group-addon,textarea.input-group-lg>.input-group-btn>.btn{height:auto}.input-group-sm>.form-control,.input-group-sm>.input-group-addon,.input-group-sm>.input-group-btn>.btn{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-group-sm>.form-control,select.input-group-sm>.input-group-addon,select.input-group-sm>.input-group-btn>.btn{height:30px;line-height:30px}select[multiple].input-group-sm>.form-control,select[multiple].input-group-sm>.input-group-addon,select[multiple].input-group-sm>.input-group-btn>.btn,textarea.input-group-sm>.form-control,textarea.input-group-sm>.input-group-addon,textarea.input-group-sm>.input-group-btn>.btn{height:auto}.input-group .form-control,.input-group-addon,.input-group-btn{display:table-cell}.input-group .form-control:not(:first-child):not(:last-child),.input-group-addon:not(:first-child):not(:last-child),.input-group-btn:not(:first-child):not(:last-child){border-radius:0}.input-group-addon,.input-group-btn{width:1%;white-space:nowrap;vertical-align:middle}.input-group-addon{padding:6px 12px;font-size:14px;font-weight:400;line-height:1;color:#555;text-align:center;background-color:#eee;border:1px solid #ccc;border-radius:4px}.input-group-addon.input-sm{padding:5px 10px;font-size:12px;border-radius:3px}.input-group-addon.input-lg{padding:10px 16px;font-size:18px;border-radius:6px}.input-group-addon input[type=checkbox],.input-group-addon input[type=radio]{margin-top:0}.input-group .form-control:first-child,.input-group-addon:first-child,.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group>.btn,.input-group-btn:first-child>.dropdown-toggle,.input-group-btn:last-child>.btn-group:not(:last-child)>.btn,.input-group-btn:last-child>.btn:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.input-group-addon:first-child{border-right:0}.input-group .form-control:last-child,.input-group-addon:last-child,.input-group-btn:first-child>.btn-group:not(:first-child)>.btn,.input-group-btn:first-child>.btn:not(:first-child),.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group>.btn,.input-group-btn:last-child>.dropdown-toggle{border-top-left-radius:0;border-bottom-left-radius:0}.input-group-addon:last-child{border-left:0}.input-group-btn{position:relative;font-size:0;white-space:nowrap}.input-group-btn>.btn{position:relative}.input-group-btn>.btn+.btn{margin-left:-1px}.input-group-btn>.btn:active,.input-group-btn>.btn:focus,.input-group-btn>.btn:hover{z-index:2}.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group{margin-right:-1px}.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group{z-index:2;margin-left:-1px}.nav{padding-left:0;margin-bottom:0;list-style:none}.nav>li{position:relative;display:block}.nav>li>a{position:relative;display:block;padding:10px 15px}.nav>li>a:focus,.nav>li>a:hover{text-decoration:none;background-color:#eee}.nav>li.disabled>a{color:#777}.nav>li.disabled>a:focus,.nav>li.disabled>a:hover{color:#777;text-decoration:none;cursor:not-allowed;background-color:transparent}.nav .open>a,.nav .open>a:focus,.nav .open>a:hover{background-color:#eee;border-color:#337ab7}.nav .nav-divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.nav>li>a>img{max-width:none}.nav-tabs{border-bottom:1px solid #ddd}.nav-tabs>li{float:left;margin-bottom:-1px}.nav-tabs>li>a{margin-right:2px;line-height:1.42857143;border:1px solid transparent;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover{border-color:#eee #eee #ddd}.nav-tabs>li.active>a,.nav-tabs>li.active>a:focus,.nav-tabs>li.active>a:hover{color:#555;cursor:default;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent}.nav-tabs.nav-justified{width:100%;border-bottom:0}.nav-tabs.nav-justified>li{float:none}.nav-tabs.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-tabs.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-tabs.nav-justified>li{display:table-cell;width:1%}.nav-tabs.nav-justified>li>a{margin-bottom:0}}.nav-tabs.nav-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs.nav-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border-bottom-color:#fff}}.nav-pills>li{float:left}.nav-pills>li>a{border-radius:4px}.nav-pills>li+li{margin-left:2px}.nav-pills>li.active>a,.nav-pills>li.active>a:focus,.nav-pills>li.active>a:hover{color:#fff;background-color:#337ab7}.nav-stacked>li{float:none}.nav-stacked>li+li{margin-top:2px;margin-left:0}.nav-justified{width:100%}.nav-justified>li{float:none}.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-justified>li{display:table-cell;width:1%}.nav-justified>li>a{margin-bottom:0}}.nav-tabs-justified{border-bottom:0}.nav-tabs-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border-bottom-color:#fff}}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.nav-tabs .dropdown-menu{margin-top:-1px;border-top-left-radius:0;border-top-right-radius:0}.navbar{position:relative;min-height:50px;margin-bottom:20px;border:1px solid transparent}@media (min-width:768px){.navbar{border-radius:4px}}@media (min-width:768px){.navbar-header{float:left}}.navbar-collapse{padding-right:15px;padding-left:15px;overflow-x:visible;-webkit-overflow-scrolling:touch;border-top:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1)}.navbar-collapse.in{overflow-y:auto}@media (min-width:768px){.navbar-collapse{width:auto;border-top:0;-webkit-box-shadow:none;box-shadow:none}.navbar-collapse.collapse{display:block!important;height:auto!important;padding-bottom:0;overflow:visible!important}.navbar-collapse.in{overflow-y:visible}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse,.navbar-static-top .navbar-collapse{padding-right:0;padding-left:0}}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:340px}@media (max-device-width:480px) and (orientation:landscape){.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:200px}}.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:0;margin-left:0}}.navbar-static-top{z-index:1000;border-width:0 0 1px}@media (min-width:768px){.navbar-static-top{border-radius:0}}.navbar-fixed-bottom,.navbar-fixed-top{position:fixed;right:0;left:0;z-index:1030}@media (min-width:768px){.navbar-fixed-bottom,.navbar-fixed-top{border-radius:0}}.navbar-fixed-top{top:0;border-width:0 0 1px}.navbar-fixed-bottom{bottom:0;margin-bottom:0;border-width:1px 0 0}.navbar-brand{float:left;height:50px;padding:15px 15px;font-size:18px;line-height:20px}.navbar-brand:focus,.navbar-brand:hover{text-decoration:none}.navbar-brand>img{display:block}@media (min-width:768px){.navbar>.container .navbar-brand,.navbar>.container-fluid .navbar-brand{margin-left:-15px}}.navbar-toggle{position:relative;float:right;padding:9px 10px;margin-top:8px;margin-right:15px;margin-bottom:8px;background-color:transparent;background-image:none;border:1px solid transparent;border-radius:4px}.navbar-toggle:focus{outline:0}.navbar-toggle .icon-bar{display:block;width:22px;height:2px;border-radius:1px}.navbar-toggle .icon-bar+.icon-bar{margin-top:4px}@media (min-width:768px){.navbar-toggle{display:none}}.navbar-nav{margin:7.5px -15px}.navbar-nav>li>a{padding-top:10px;padding-bottom:10px;line-height:20px}@media (max-width:767px){.navbar-nav .open .dropdown-menu{position:static;float:none;width:auto;margin-top:0;background-color:transparent;border:0;-webkit-box-shadow:none;box-shadow:none}.navbar-nav .open .dropdown-menu .dropdown-header,.navbar-nav .open .dropdown-menu>li>a{padding:5px 15px 5px 25px}.navbar-nav .open .dropdown-menu>li>a{line-height:20px}.navbar-nav .open .dropdown-menu>li>a:focus,.navbar-nav .open .dropdown-menu>li>a:hover{background-image:none}}@media (min-width:768px){.navbar-nav{float:left;margin:0}.navbar-nav>li{float:left}.navbar-nav>li>a{padding-top:15px;padding-bottom:15px}}.navbar-form{padding:10px 15px;margin-top:8px;margin-right:-15px;margin-bottom:8px;margin-left:-15px;border-top:1px solid transparent;border-bottom:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1)}@media (min-width:768px){.navbar-form .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.navbar-form .form-control{display:inline-block;width:auto;vertical-align:middle}.navbar-form .form-control-static{display:inline-block}.navbar-form .input-group{display:inline-table;vertical-align:middle}.navbar-form .input-group .form-control,.navbar-form .input-group .input-group-addon,.navbar-form .input-group .input-group-btn{width:auto}.navbar-form .input-group>.form-control{width:100%}.navbar-form .control-label{margin-bottom:0;vertical-align:middle}.navbar-form .checkbox,.navbar-form .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.navbar-form .checkbox label,.navbar-form .radio label{padding-left:0}.navbar-form .checkbox input[type=checkbox],.navbar-form .radio input[type=radio]{position:relative;margin-left:0}.navbar-form .has-feedback .form-control-feedback{top:0}}@media (max-width:767px){.navbar-form .form-group{margin-bottom:5px}.navbar-form .form-group:last-child{margin-bottom:0}}@media (min-width:768px){.navbar-form{width:auto;padding-top:0;padding-bottom:0;margin-right:0;margin-left:0;border:0;-webkit-box-shadow:none;box-shadow:none}}.navbar-nav>li>.dropdown-menu{margin-top:0;border-top-left-radius:0;border-top-right-radius:0}.navbar-fixed-bottom .navbar-nav>li>.dropdown-menu{margin-bottom:0;border-top-left-radius:4px;border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.navbar-btn{margin-top:8px;margin-bottom:8px}.navbar-btn.btn-sm{margin-top:10px;margin-bottom:10px}.navbar-btn.btn-xs{margin-top:14px;margin-bottom:14px}.navbar-text{margin-top:15px;margin-bottom:15px}@media (min-width:768px){.navbar-text{float:left;margin-right:15px;margin-left:15px}}@media (min-width:768px){.navbar-left{float:left!important}.navbar-right{float:right!important;margin-right:-15px}.navbar-right~.navbar-right{margin-right:0}}.navbar-default{background-color:#f8f8f8;border-color:#e7e7e7}.navbar-default .navbar-brand{color:#777}.navbar-default .navbar-brand:focus,.navbar-default .navbar-brand:hover{color:#5e5e5e;background-color:transparent}.navbar-default .navbar-text{color:#777}.navbar-default .navbar-nav>li>a{color:#777}.navbar-default .navbar-nav>li>a:focus,.navbar-default .navbar-nav>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav>.active>a,.navbar-default .navbar-nav>.active>a:focus,.navbar-default .navbar-nav>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav>.disabled>a,.navbar-default .navbar-nav>.disabled>a:focus,.navbar-default .navbar-nav>.disabled>a:hover{color:#ccc;background-color:transparent}.navbar-default .navbar-toggle{border-color:#ddd}.navbar-default .navbar-toggle:focus,.navbar-default .navbar-toggle:hover{background-color:#ddd}.navbar-default .navbar-toggle .icon-bar{background-color:#888}.navbar-default .navbar-collapse,.navbar-default .navbar-form{border-color:#e7e7e7}.navbar-default .navbar-nav>.open>a,.navbar-default .navbar-nav>.open>a:focus,.navbar-default .navbar-nav>.open>a:hover{color:#555;background-color:#e7e7e7}@media (max-width:767px){.navbar-default .navbar-nav .open .dropdown-menu>li>a{color:#777}.navbar-default .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav .open .dropdown-menu>.active>a,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#ccc;background-color:transparent}}.navbar-default .navbar-link{color:#777}.navbar-default .navbar-link:hover{color:#333}.navbar-default .btn-link{color:#777}.navbar-default .btn-link:focus,.navbar-default .btn-link:hover{color:#333}.navbar-default .btn-link[disabled]:focus,.navbar-default .btn-link[disabled]:hover,fieldset[disabled] .navbar-default .btn-link:focus,fieldset[disabled] .navbar-default .btn-link:hover{color:#ccc}.navbar-inverse{background-color:#222;border-color:#080808}.navbar-inverse .navbar-brand{color:#9d9d9d}.navbar-inverse .navbar-brand:focus,.navbar-inverse .navbar-brand:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-text{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a:focus,.navbar-inverse .navbar-nav>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav>.active>a,.navbar-inverse .navbar-nav>.active>a:focus,.navbar-inverse .navbar-nav>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav>.disabled>a,.navbar-inverse .navbar-nav>.disabled>a:focus,.navbar-inverse .navbar-nav>.disabled>a:hover{color:#444;background-color:transparent}.navbar-inverse .navbar-toggle{border-color:#333}.navbar-inverse .navbar-toggle:focus,.navbar-inverse .navbar-toggle:hover{background-color:#333}.navbar-inverse .navbar-toggle .icon-bar{background-color:#fff}.navbar-inverse .navbar-collapse,.navbar-inverse .navbar-form{border-color:#101010}.navbar-inverse .navbar-nav>.open>a,.navbar-inverse .navbar-nav>.open>a:focus,.navbar-inverse .navbar-nav>.open>a:hover{color:#fff;background-color:#080808}@media (max-width:767px){.navbar-inverse .navbar-nav .open .dropdown-menu>.dropdown-header{border-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu .divider{background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#444;background-color:transparent}}.navbar-inverse .navbar-link{color:#9d9d9d}.navbar-inverse .navbar-link:hover{color:#fff}.navbar-inverse .btn-link{color:#9d9d9d}.navbar-inverse .btn-link:focus,.navbar-inverse .btn-link:hover{color:#fff}.navbar-inverse .btn-link[disabled]:focus,.navbar-inverse .btn-link[disabled]:hover,fieldset[disabled] .navbar-inverse .btn-link:focus,fieldset[disabled] .navbar-inverse .btn-link:hover{color:#444}.breadcrumb{padding:8px 15px;margin-bottom:20px;list-style:none;background-color:#f5f5f5;border-radius:4px}.breadcrumb>li{display:inline-block}.breadcrumb>li+li:before{padding:0 5px;color:#ccc;content:"/\00a0"}.breadcrumb>.active{color:#777}.pagination{display:inline-block;padding-left:0;margin:20px 0;border-radius:4px}.pagination>li{display:inline}.pagination>li>a,.pagination>li>span{position:relative;float:left;padding:6px 12px;margin-left:-1px;line-height:1.42857143;color:#337ab7;text-decoration:none;background-color:#fff;border:1px solid #ddd}.pagination>li:first-child>a,.pagination>li:first-child>span{margin-left:0;border-top-left-radius:4px;border-bottom-left-radius:4px}.pagination>li:last-child>a,.pagination>li:last-child>span{border-top-right-radius:4px;border-bottom-right-radius:4px}.pagination>li>a:focus,.pagination>li>a:hover,.pagination>li>span:focus,.pagination>li>span:hover{z-index:3;color:#23527c;background-color:#eee;border-color:#ddd}.pagination>.active>a,.pagination>.active>a:focus,.pagination>.active>a:hover,.pagination>.active>span,.pagination>.active>span:focus,.pagination>.active>span:hover{z-index:2;color:#fff;cursor:default;background-color:#337ab7;border-color:#337ab7}.pagination>.disabled>a,.pagination>.disabled>a:focus,.pagination>.disabled>a:hover,.pagination>.disabled>span,.pagination>.disabled>span:focus,.pagination>.disabled>span:hover{color:#777;cursor:not-allowed;background-color:#fff;border-color:#ddd}.pagination-lg>li>a,.pagination-lg>li>span{padding:10px 16px;font-size:18px;line-height:1.3333333}.pagination-lg>li:first-child>a,.pagination-lg>li:first-child>span{border-top-left-radius:6px;border-bottom-left-radius:6px}.pagination-lg>li:last-child>a,.pagination-lg>li:last-child>span{border-top-right-radius:6px;border-bottom-right-radius:6px}.pagination-sm>li>a,.pagination-sm>li>span{padding:5px 10px;font-size:12px;line-height:1.5}.pagination-sm>li:first-child>a,.pagination-sm>li:first-child>span{border-top-left-radius:3px;border-bottom-left-radius:3px}.pagination-sm>li:last-child>a,.pagination-sm>li:last-child>span{border-top-right-radius:3px;border-bottom-right-radius:3px}.pager{padding-left:0;margin:20px 0;text-align:center;list-style:none}.pager li{display:inline}.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px}.pager li>a:focus,.pager li>a:hover{text-decoration:none;background-color:#eee}.pager .next>a,.pager .next>span{float:right}.pager .previous>a,.pager .previous>span{float:left}.pager .disabled>a,.pager .disabled>a:focus,.pager .disabled>a:hover,.pager .disabled>span{color:#777;cursor:not-allowed;background-color:#fff}.label{display:inline;padding:.2em .6em .3em;font-size:75%;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:.25em}a.label:focus,a.label:hover{color:#fff;text-decoration:none;cursor:pointer}.label:empty{display:none}.btn .label{position:relative;top:-1px}.label-default{background-color:#777}.label-default[href]:focus,.label-default[href]:hover{background-color:#5e5e5e}.label-primary{background-color:#337ab7}.label-primary[href]:focus,.label-primary[href]:hover{background-color:#286090}.label-success{background-color:#5cb85c}.label-success[href]:focus,.label-success[href]:hover{background-color:#449d44}.label-info{background-color:#5bc0de}.label-info[href]:focus,.label-info[href]:hover{background-color:#31b0d5}.label-warning{background-color:#f0ad4e}.label-warning[href]:focus,.label-warning[href]:hover{background-color:#ec971f}.label-danger{background-color:#d9534f}.label-danger[href]:focus,.label-danger[href]:hover{background-color:#c9302c}.badge{display:inline-block;min-width:10px;padding:3px 7px;font-size:12px;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:middle;background-color:#777;border-radius:10px}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.btn-group-xs>.btn .badge,.btn-xs .badge{top:0;padding:1px 5px}a.badge:focus,a.badge:hover{color:#fff;text-decoration:none;cursor:pointer}.list-group-item.active>.badge,.nav-pills>.active>a>.badge{color:#337ab7;background-color:#fff}.list-group-item>.badge{float:right}.list-group-item>.badge+.badge{margin-right:5px}.nav-pills>li>a>.badge{margin-left:3px}.jumbotron{padding-top:30px;padding-bottom:30px;margin-bottom:30px;color:inherit;background-color:#eee}.jumbotron .h1,.jumbotron h1{color:inherit}.jumbotron p{margin-bottom:15px;font-size:21px;font-weight:200}.jumbotron>hr{border-top-color:#d5d5d5}.container .jumbotron,.container-fluid .jumbotron{border-radius:6px}.jumbotron .container{max-width:100%}@media screen and (min-width:768px){.jumbotron{padding-top:48px;padding-bottom:48px}.container .jumbotron,.container-fluid .jumbotron{padding-right:60px;padding-left:60px}.jumbotron .h1,.jumbotron h1{font-size:63px}}.thumbnail{display:block;padding:4px;margin-bottom:20px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:border .2s ease-in-out;-o-transition:border .2s ease-in-out;transition:border .2s ease-in-out}.thumbnail a>img,.thumbnail>img{margin-right:auto;margin-left:auto}a.thumbnail.active,a.thumbnail:focus,a.thumbnail:hover{border-color:#337ab7}.thumbnail .caption{padding:9px;color:#333}.alert{padding:15px;margin-bottom:20px;border:1px solid transparent;border-radius:4px}.alert h4{margin-top:0;color:inherit}.alert .alert-link{font-weight:700}.alert>p,.alert>ul{margin-bottom:0}.alert>p+p{margin-top:5px}.alert-dismissable,.alert-dismissible{padding-right:35px}.alert-dismissable .close,.alert-dismissible .close{position:relative;top:-2px;right:-21px;color:inherit}.alert-success{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.alert-success hr{border-top-color:#c9e2b3}.alert-success .alert-link{color:#2b542c}.alert-info{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.alert-info hr{border-top-color:#a6e1ec}.alert-info .alert-link{color:#245269}.alert-warning{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.alert-warning hr{border-top-color:#f7e1b5}.alert-warning .alert-link{color:#66512c}.alert-danger{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.alert-danger hr{border-top-color:#e4b9c0}.alert-danger .alert-link{color:#843534}@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}.progress{height:20px;margin-bottom:20px;overflow:hidden;background-color:#f5f5f5;border-radius:4px;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1);box-shadow:inset 0 1px 2px rgba(0,0,0,.1)}.progress-bar{float:left;width:0;height:100%;font-size:12px;line-height:20px;color:#fff;text-align:center;background-color:#337ab7;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);-webkit-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}.progress-bar-striped,.progress-striped .progress-bar{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);-webkit-background-size:40px 40px;background-size:40px 40px}.progress-bar.active,.progress.active .progress-bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}.progress-bar-success{background-color:#5cb85c}.progress-striped .progress-bar-success{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-info{background-color:#5bc0de}.progress-striped .progress-bar-info{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-warning{background-color:#f0ad4e}.progress-striped .progress-bar-warning{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-danger{background-color:#d9534f}.progress-striped .progress-bar-danger{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.media{margin-top:15px}.media:first-child{margin-top:0}.media,.media-body{overflow:hidden;zoom:1}.media-body{width:10000px}.media-object{display:block}.media-object.img-thumbnail{max-width:none}.media-right,.media>.pull-right{padding-left:10px}.media-left,.media>.pull-left{padding-right:10px}.media-body,.media-left,.media-right{display:table-cell;vertical-align:top}.media-middle{vertical-align:middle}.media-bottom{vertical-align:bottom}.media-heading{margin-top:0;margin-bottom:5px}.media-list{padding-left:0;list-style:none}.list-group{padding-left:0;margin-bottom:20px}.list-group-item{position:relative;display:block;padding:10px 15px;margin-bottom:-1px;background-color:#fff;border:1px solid #ddd}.list-group-item:first-child{border-top-left-radius:4px;border-top-right-radius:4px}.list-group-item:last-child{margin-bottom:0;border-bottom-right-radius:4px;border-bottom-left-radius:4px}a.list-group-item,button.list-group-item{color:#555}a.list-group-item .list-group-item-heading,button.list-group-item .list-group-item-heading{color:#333}a.list-group-item:focus,a.list-group-item:hover,button.list-group-item:focus,button.list-group-item:hover{color:#555;text-decoration:none;background-color:#f5f5f5}button.list-group-item{width:100%;text-align:left}.list-group-item.disabled,.list-group-item.disabled:focus,.list-group-item.disabled:hover{color:#777;cursor:not-allowed;background-color:#eee}.list-group-item.disabled .list-group-item-heading,.list-group-item.disabled:focus .list-group-item-heading,.list-group-item.disabled:hover .list-group-item-heading{color:inherit}.list-group-item.disabled .list-group-item-text,.list-group-item.disabled:focus .list-group-item-text,.list-group-item.disabled:hover .list-group-item-text{color:#777}.list-group-item.active,.list-group-item.active:focus,.list-group-item.active:hover{z-index:2;color:#fff;background-color:#337ab7;border-color:#337ab7}.list-group-item.active .list-group-item-heading,.list-group-item.active .list-group-item-heading>.small,.list-group-item.active .list-group-item-heading>small,.list-group-item.active:focus .list-group-item-heading,.list-group-item.active:focus .list-group-item-heading>.small,.list-group-item.active:focus .list-group-item-heading>small,.list-group-item.active:hover .list-group-item-heading,.list-group-item.active:hover .list-group-item-heading>.small,.list-group-item.active:hover .list-group-item-heading>small{color:inherit}.list-group-item.active .list-group-item-text,.list-group-item.active:focus .list-group-item-text,.list-group-item.active:hover .list-group-item-text{color:#c7ddef}.list-group-item-success{color:#3c763d;background-color:#dff0d8}a.list-group-item-success,button.list-group-item-success{color:#3c763d}a.list-group-item-success .list-group-item-heading,button.list-group-item-success .list-group-item-heading{color:inherit}a.list-group-item-success:focus,a.list-group-item-success:hover,button.list-group-item-success:focus,button.list-group-item-success:hover{color:#3c763d;background-color:#d0e9c6}a.list-group-item-success.active,a.list-group-item-success.active:focus,a.list-group-item-success.active:hover,button.list-group-item-success.active,button.list-group-item-success.active:focus,button.list-group-item-success.active:hover{color:#fff;background-color:#3c763d;border-color:#3c763d}.list-group-item-info{color:#31708f;background-color:#d9edf7}a.list-group-item-info,button.list-group-item-info{color:#31708f}a.list-group-item-info .list-group-item-heading,button.list-group-item-info .list-group-item-heading{color:inherit}a.list-group-item-info:focus,a.list-group-item-info:hover,button.list-group-item-info:focus,button.list-group-item-info:hover{color:#31708f;background-color:#c4e3f3}a.list-group-item-info.active,a.list-group-item-info.active:focus,a.list-group-item-info.active:hover,button.list-group-item-info.active,button.list-group-item-info.active:focus,button.list-group-item-info.active:hover{color:#fff;background-color:#31708f;border-color:#31708f}.list-group-item-warning{color:#8a6d3b;background-color:#fcf8e3}a.list-group-item-warning,button.list-group-item-warning{color:#8a6d3b}a.list-group-item-warning .list-group-item-heading,button.list-group-item-warning .list-group-item-heading{color:inherit}a.list-group-item-warning:focus,a.list-group-item-warning:hover,button.list-group-item-warning:focus,button.list-group-item-warning:hover{color:#8a6d3b;background-color:#faf2cc}a.list-group-item-warning.active,a.list-group-item-warning.active:focus,a.list-group-item-warning.active:hover,button.list-group-item-warning.active,button.list-group-item-warning.active:focus,button.list-group-item-warning.active:hover{color:#fff;background-color:#8a6d3b;border-color:#8a6d3b}.list-group-item-danger{color:#a94442;background-color:#f2dede}a.list-group-item-danger,button.list-group-item-danger{color:#a94442}a.list-group-item-danger .list-group-item-heading,button.list-group-item-danger .list-group-item-heading{color:inherit}a.list-group-item-danger:focus,a.list-group-item-danger:hover,button.list-group-item-danger:focus,button.list-group-item-danger:hover{color:#a94442;background-color:#ebcccc}a.list-group-item-danger.active,a.list-group-item-danger.active:focus,a.list-group-item-danger.active:hover,button.list-group-item-danger.active,button.list-group-item-danger.active:focus,button.list-group-item-danger.active:hover{color:#fff;background-color:#a94442;border-color:#a94442}.list-group-item-heading{margin-top:0;margin-bottom:5px}.list-group-item-text{margin-bottom:0;line-height:1.3}.panel{margin-bottom:20px;background-color:#fff;border:1px solid transparent;border-radius:4px;-webkit-box-shadow:0 1px 1px rgba(0,0,0,.05);box-shadow:0 1px 1px rgba(0,0,0,.05)}.panel-body{padding:15px}.panel-heading{padding:10px 15px;border-bottom:1px solid transparent;border-top-left-radius:3px;border-top-right-radius:3px}.panel-heading>.dropdown .dropdown-toggle{color:inherit}.panel-title{margin-top:0;margin-bottom:0;font-size:16px;color:inherit}.panel-title>.small,.panel-title>.small>a,.panel-title>a,.panel-title>small,.panel-title>small>a{color:inherit}.panel-footer{padding:10px 15px;background-color:#f5f5f5;border-top:1px solid #ddd;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.list-group,.panel>.panel-collapse>.list-group{margin-bottom:0}.panel>.list-group .list-group-item,.panel>.panel-collapse>.list-group .list-group-item{border-width:1px 0;border-radius:0}.panel>.list-group:first-child .list-group-item:first-child,.panel>.panel-collapse>.list-group:first-child .list-group-item:first-child{border-top:0;border-top-left-radius:3px;border-top-right-radius:3px}.panel>.list-group:last-child .list-group-item:last-child,.panel>.panel-collapse>.list-group:last-child .list-group-item:last-child{border-bottom:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.panel-heading+.panel-collapse>.list-group .list-group-item:first-child{border-top-left-radius:0;border-top-right-radius:0}.panel-heading+.list-group .list-group-item:first-child{border-top-width:0}.list-group+.panel-footer{border-top-width:0}.panel>.panel-collapse>.table,.panel>.table,.panel>.table-responsive>.table{margin-bottom:0}.panel>.panel-collapse>.table caption,.panel>.table caption,.panel>.table-responsive>.table caption{padding-right:15px;padding-left:15px}.panel>.table-responsive:first-child>.table:first-child,.panel>.table:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child,.panel>.table:first-child>thead:first-child>tr:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table:first-child>thead:first-child>tr:first-child th:first-child{border-top-left-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table:first-child>thead:first-child>tr:first-child th:last-child{border-top-right-radius:3px}.panel>.table-responsive:last-child>.table:last-child,.panel>.table:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:first-child{border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:last-child{border-bottom-right-radius:3px}.panel>.panel-body+.table,.panel>.panel-body+.table-responsive,.panel>.table+.panel-body,.panel>.table-responsive+.panel-body{border-top:1px solid #ddd}.panel>.table>tbody:first-child>tr:first-child td,.panel>.table>tbody:first-child>tr:first-child th{border-top:0}.panel>.table-bordered,.panel>.table-responsive>.table-bordered{border:0}.panel>.table-bordered>tbody>tr>td:first-child,.panel>.table-bordered>tbody>tr>th:first-child,.panel>.table-bordered>tfoot>tr>td:first-child,.panel>.table-bordered>tfoot>tr>th:first-child,.panel>.table-bordered>thead>tr>td:first-child,.panel>.table-bordered>thead>tr>th:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:first-child,.panel>.table-responsive>.table-bordered>thead>tr>td:first-child,.panel>.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.panel>.table-bordered>tbody>tr>td:last-child,.panel>.table-bordered>tbody>tr>th:last-child,.panel>.table-bordered>tfoot>tr>td:last-child,.panel>.table-bordered>tfoot>tr>th:last-child,.panel>.table-bordered>thead>tr>td:last-child,.panel>.table-bordered>thead>tr>th:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:last-child,.panel>.table-responsive>.table-bordered>thead>tr>td:last-child,.panel>.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.panel>.table-bordered>tbody>tr:first-child>td,.panel>.table-bordered>tbody>tr:first-child>th,.panel>.table-bordered>thead>tr:first-child>td,.panel>.table-bordered>thead>tr:first-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>th,.panel>.table-responsive>.table-bordered>thead>tr:first-child>td,.panel>.table-responsive>.table-bordered>thead>tr:first-child>th{border-bottom:0}.panel>.table-bordered>tbody>tr:last-child>td,.panel>.table-bordered>tbody>tr:last-child>th,.panel>.table-bordered>tfoot>tr:last-child>td,.panel>.table-bordered>tfoot>tr:last-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>th,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>td,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}.panel>.table-responsive{margin-bottom:0;border:0}.panel-group{margin-bottom:20px}.panel-group .panel{margin-bottom:0;border-radius:4px}.panel-group .panel+.panel{margin-top:5px}.panel-group .panel-heading{border-bottom:0}.panel-group .panel-heading+.panel-collapse>.list-group,.panel-group .panel-heading+.panel-collapse>.panel-body{border-top:1px solid #ddd}.panel-group .panel-footer{border-top:0}.panel-group .panel-footer+.panel-collapse .panel-body{border-bottom:1px solid #ddd}.panel-default{border-color:#ddd}.panel-default>.panel-heading{color:#333;background-color:#f5f5f5;border-color:#ddd}.panel-default>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ddd}.panel-default>.panel-heading .badge{color:#f5f5f5;background-color:#333}.panel-default>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ddd}.panel-primary{border-color:#337ab7}.panel-primary>.panel-heading{color:#fff;background-color:#337ab7;border-color:#337ab7}.panel-primary>.panel-heading+.panel-collapse>.panel-body{border-top-color:#337ab7}.panel-primary>.panel-heading .badge{color:#337ab7;background-color:#fff}.panel-primary>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#337ab7}.panel-success{border-color:#d6e9c6}.panel-success>.panel-heading{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.panel-success>.panel-heading+.panel-collapse>.panel-body{border-top-color:#d6e9c6}.panel-success>.panel-heading .badge{color:#dff0d8;background-color:#3c763d}.panel-success>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#d6e9c6}.panel-info{border-color:#bce8f1}.panel-info>.panel-heading{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.panel-info>.panel-heading+.panel-collapse>.panel-body{border-top-color:#bce8f1}.panel-info>.panel-heading .badge{color:#d9edf7;background-color:#31708f}.panel-info>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#bce8f1}.panel-warning{border-color:#faebcc}.panel-warning>.panel-heading{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.panel-warning>.panel-heading+.panel-collapse>.panel-body{border-top-color:#faebcc}.panel-warning>.panel-heading .badge{color:#fcf8e3;background-color:#8a6d3b}.panel-warning>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#faebcc}.panel-danger{border-color:#ebccd1}.panel-danger>.panel-heading{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.panel-danger>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ebccd1}.panel-danger>.panel-heading .badge{color:#f2dede;background-color:#a94442}.panel-danger>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ebccd1}.embed-responsive{position:relative;display:block;height:0;padding:0;overflow:hidden}.embed-responsive .embed-responsive-item,.embed-responsive embed,.embed-responsive iframe,.embed-responsive object,.embed-responsive video{position:absolute;top:0;bottom:0;left:0;width:100%;height:100%;border:0}.embed-responsive-16by9{padding-bottom:56.25%}.embed-responsive-4by3{padding-bottom:75%}.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.05);box-shadow:inset 0 1px 1px rgba(0,0,0,.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,.15)}.well-lg{padding:24px;border-radius:6px}.well-sm{padding:9px;border-radius:3px}.close{float:right;font-size:21px;font-weight:700;line-height:1;color:#000;text-shadow:0 1px 0 #fff;filter:alpha(opacity=20);opacity:.2}.close:focus,.close:hover{color:#000;text-decoration:none;cursor:pointer;filter:alpha(opacity=50);opacity:.5}button.close{-webkit-appearance:none;padding:0;cursor:pointer;background:0 0;border:0}.modal-open{overflow:hidden}.modal{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1050;display:none;overflow:hidden;-webkit-overflow-scrolling:touch;outline:0}.modal.fade .modal-dialog{-webkit-transition:-webkit-transform .3s ease-out;-o-transition:-o-transform .3s ease-out;transition:transform .3s ease-out;-webkit-transform:translate(0,-25%);-ms-transform:translate(0,-25%);-o-transform:translate(0,-25%);transform:translate(0,-25%)}.modal.in .modal-dialog{-webkit-transform:translate(0,0);-ms-transform:translate(0,0);-o-transform:translate(0,0);transform:translate(0,0)}.modal-open .modal{overflow-x:hidden;overflow-y:auto}.modal-dialog{position:relative;width:auto;margin:10px}.modal-content{position:relative;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #999;border:1px solid rgba(0,0,0,.2);border-radius:6px;outline:0;-webkit-box-shadow:0 3px 9px rgba(0,0,0,.5);box-shadow:0 3px 9px rgba(0,0,0,.5)}.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{filter:alpha(opacity=0);opacity:0}.modal-backdrop.in{filter:alpha(opacity=50);opacity:.5}.modal-header{min-height:16.43px;padding:15px;border-bottom:1px solid #e5e5e5}.modal-header .close{margin-top:-2px}.modal-title{margin:0;line-height:1.42857143}.modal-body{position:relative;padding:15px}.modal-footer{padding:15px;text-align:right;border-top:1px solid #e5e5e5}.modal-footer .btn+.btn{margin-bottom:0;margin-left:5px}.modal-footer .btn-group .btn+.btn{margin-left:-1px}.modal-footer .btn-block+.btn-block{margin-left:0}.modal-scrollbar-measure{position:absolute;top:-9999px;width:50px;height:50px;overflow:scroll}@media (min-width:768px){.modal-dialog{width:600px;margin:30px auto}.modal-content{-webkit-box-shadow:0 5px 15px rgba(0,0,0,.5);box-shadow:0 5px 15px rgba(0,0,0,.5)}.modal-sm{width:300px}}@media (min-width:992px){.modal-lg{width:900px}}.tooltip{position:absolute;z-index:1070;display:block;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:12px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;filter:alpha(opacity=0);opacity:0;line-break:auto}.tooltip.in{filter:alpha(opacity=90);opacity:.9}.tooltip.top{padding:5px 0;margin-top:-3px}.tooltip.right{padding:0 5px;margin-left:3px}.tooltip.bottom{padding:5px 0;margin-top:3px}.tooltip.left{padding:0 5px;margin-left:-3px}.tooltip-inner{max-width:200px;padding:3px 8px;color:#fff;text-align:center;background-color:#000;border-radius:4px}.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-left .tooltip-arrow{right:5px;bottom:0;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-right .tooltip-arrow{bottom:0;left:5px;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-left .tooltip-arrow{top:0;right:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-right .tooltip-arrow{top:0;left:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.popover{position:absolute;top:0;left:0;z-index:1060;display:none;max-width:276px;padding:1px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.2);border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,.2);box-shadow:0 5px 10px rgba(0,0,0,.2);line-break:auto}.popover.top{margin-top:-10px}.popover.right{margin-left:10px}.popover.bottom{margin-top:10px}.popover.left{margin-left:-10px}.popover-title{padding:8px 14px;margin:0;font-size:14px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0}.popover-content{padding:9px 14px}.popover>.arrow,.popover>.arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}.popover>.arrow{border-width:11px}.popover>.arrow:after{content:"";border-width:10px}.popover.top>.arrow{bottom:-11px;left:50%;margin-left:-11px;border-top-color:#999;border-top-color:rgba(0,0,0,.25);border-bottom-width:0}.popover.top>.arrow:after{bottom:1px;margin-left:-10px;content:" ";border-top-color:#fff;border-bottom-width:0}.popover.right>.arrow{top:50%;left:-11px;margin-top:-11px;border-right-color:#999;border-right-color:rgba(0,0,0,.25);border-left-width:0}.popover.right>.arrow:after{bottom:-10px;left:1px;content:" ";border-right-color:#fff;border-left-width:0}.popover.bottom>.arrow{top:-11px;left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,.25)}.popover.bottom>.arrow:after{top:1px;margin-left:-10px;content:" ";border-top-width:0;border-bottom-color:#fff}.popover.left>.arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,.25)}.popover.left>.arrow:after{right:1px;bottom:-10px;content:" ";border-right-width:0;border-left-color:#fff}.carousel{position:relative}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner>.item{position:relative;display:none;-webkit-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>a>img,.carousel-inner>.item>img{line-height:1}@media all and (transform-3d),(-webkit-transform-3d){.carousel-inner>.item{-webkit-transition:-webkit-transform .6s ease-in-out;-o-transition:-o-transform .6s ease-in-out;transition:transform .6s ease-in-out;-webkit-backface-visibility:hidden;backface-visibility:hidden;-webkit-perspective:1000px;perspective:1000px}.carousel-inner>.item.active.right,.carousel-inner>.item.next{left:0;-webkit-transform:translate3d(100%,0,0);transform:translate3d(100%,0,0)}.carousel-inner>.item.active.left,.carousel-inner>.item.prev{left:0;-webkit-transform:translate3d(-100%,0,0);transform:translate3d(-100%,0,0)}.carousel-inner>.item.active,.carousel-inner>.item.next.left,.carousel-inner>.item.prev.right{left:0;-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0)}}.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}.carousel-inner>.active{left:0}.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}.carousel-inner>.next{left:100%}.carousel-inner>.prev{left:-100%}.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}.carousel-inner>.active.left{left:-100%}.carousel-inner>.active.right{left:100%}.carousel-control{position:absolute;top:0;bottom:0;left:0;width:15%;font-size:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6);filter:alpha(opacity=50);opacity:.5}.carousel-control.left{background-image:-webkit-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.5)),to(rgba(0,0,0,.0001)));background-image:linear-gradient(to right,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);background-repeat:repeat-x}.carousel-control.right{right:0;left:auto;background-image:-webkit-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.0001)),to(rgba(0,0,0,.5)));background-image:linear-gradient(to right,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);background-repeat:repeat-x}.carousel-control:focus,.carousel-control:hover{color:#fff;text-decoration:none;filter:alpha(opacity=90);outline:0;opacity:.9}.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{position:absolute;top:50%;z-index:5;display:inline-block;margin-top:-10px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{left:50%;margin-left:-10px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{right:50%;margin-right:-10px}.carousel-control .icon-next,.carousel-control .icon-prev{width:20px;height:20px;font-family:serif;line-height:1}.carousel-control .icon-prev:before{content:'\2039'}.carousel-control .icon-next:before{content:'\203a'}.carousel-indicators{position:absolute;bottom:10px;left:50%;z-index:15;width:60%;padding-left:0;margin-left:-30%;text-align:center;list-style:none}.carousel-indicators li{display:inline-block;width:10px;height:10px;margin:1px;text-indent:-999px;cursor:pointer;background-color:#000\9;background-color:rgba(0,0,0,0);border:1px solid #fff;border-radius:10px}.carousel-indicators .active{width:12px;height:12px;margin:0;background-color:#fff}.carousel-caption{position:absolute;right:15%;bottom:20px;left:15%;z-index:10;padding-top:20px;padding-bottom:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6)}.carousel-caption .btn{text-shadow:none}@media screen and (min-width:768px){.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{width:30px;height:30px;margin-top:-15px;font-size:30px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{margin-left:-15px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{margin-right:-15px}.carousel-caption{right:20%;left:20%;padding-bottom:30px}.carousel-indicators{bottom:20px}}.btn-group-vertical>.btn-group:after,.btn-group-vertical>.btn-group:before,.btn-toolbar:after,.btn-toolbar:before,.clearfix:after,.clearfix:before,.container-fluid:after,.container-fluid:before,.container:after,.container:before,.dl-horizontal dd:after,.dl-horizontal dd:before,.form-horizontal .form-group:after,.form-horizontal .form-group:before,.modal-footer:after,.modal-footer:before,.nav:after,.nav:before,.navbar-collapse:after,.navbar-collapse:before,.navbar-header:after,.navbar-header:before,.navbar:after,.navbar:before,.pager:after,.pager:before,.panel-body:after,.panel-body:before,.row:after,.row:before{display:table;content:" "}.btn-group-vertical>.btn-group:after,.btn-toolbar:after,.clearfix:after,.container-fluid:after,.container:after,.dl-horizontal dd:after,.form-horizontal .form-group:after,.modal-footer:after,.nav:after,.navbar-collapse:after,.navbar-header:after,.navbar:after,.pager:after,.panel-body:after,.row:after{clear:both}.center-block{display:block;margin-right:auto;margin-left:auto}.pull-right{float:right!important}.pull-left{float:left!important}.hide{display:none!important}.show{display:block!important}.invisible{visibility:hidden}.text-hide{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.hidden{display:none!important}.affix{position:fixed}@-ms-viewport{width:device-width}.visible-lg,.visible-md,.visible-sm,.visible-xs{display:none!important}.visible-lg-block,.visible-lg-inline,.visible-lg-inline-block,.visible-md-block,.visible-md-inline,.visible-md-inline-block,.visible-sm-block,.visible-sm-inline,.visible-sm-inline-block,.visible-xs-block,.visible-xs-inline,.visible-xs-inline-block{display:none!important}@media (max-width:767px){.visible-xs{display:block!important}table.visible-xs{display:table!important}tr.visible-xs{display:table-row!important}td.visible-xs,th.visible-xs{display:table-cell!important}}@media (max-width:767px){.visible-xs-block{display:block!important}}@media (max-width:767px){.visible-xs-inline{display:inline!important}}@media (max-width:767px){.visible-xs-inline-block{display:inline-block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm{display:block!important}table.visible-sm{display:table!important}tr.visible-sm{display:table-row!important}td.visible-sm,th.visible-sm{display:table-cell!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-block{display:block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline{display:inline!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline-block{display:inline-block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md{display:block!important}table.visible-md{display:table!important}tr.visible-md{display:table-row!important}td.visible-md,th.visible-md{display:table-cell!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-block{display:block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline{display:inline!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline-block{display:inline-block!important}}@media (min-width:1200px){.visible-lg{display:block!important}table.visible-lg{display:table!important}tr.visible-lg{display:table-row!important}td.visible-lg,th.visible-lg{display:table-cell!important}}@media (min-width:1200px){.visible-lg-block{display:block!important}}@media (min-width:1200px){.visible-lg-inline{display:inline!important}}@media (min-width:1200px){.visible-lg-inline-block{display:inline-block!important}}@media (max-width:767px){.hidden-xs{display:none!important}}@media (min-width:768px) and (max-width:991px){.hidden-sm{display:none!important}}@media (min-width:992px) and (max-width:1199px){.hidden-md{display:none!important}}@media (min-width:1200px){.hidden-lg{display:none!important}}.visible-print{display:none!important}@media print{.visible-print{display:block!important}table.visible-print{display:table!important}tr.visible-print{display:table-row!important}td.visible-print,th.visible-print{display:table-cell!important}}.visible-print-block{display:none!important}@media print{.visible-print-block{display:block!important}}.visible-print-inline{display:none!important}@media print{.visible-print-inline{display:inline!important}}.visible-print-inline-block{display:none!important}@media print{.visible-print-inline-block{display:inline-block!important}}@media print{.hidden-print{display:none!important}}
</style>
<script>/*!
* Bootstrap v3.3.5 (http://getbootstrap.com)
* Copyright 2011-2015 Twitter, Inc.
* Licensed under the MIT license
*/
if("undefined"==typeof jQuery)throw new Error("Bootstrap's JavaScript requires jQuery");+function(a){"use strict";var b=a.fn.jquery.split(" ")[0].split(".");if(b[0]<2&&b[1]<9||1==b[0]&&9==b[1]&&b[2]<1)throw new Error("Bootstrap's JavaScript requires jQuery version 1.9.1 or higher")}(jQuery),+function(a){"use strict";function b(){var a=document.createElement("bootstrap"),b={WebkitTransition:"webkitTransitionEnd",MozTransition:"transitionend",OTransition:"oTransitionEnd otransitionend",transition:"transitionend"};for(var c in b)if(void 0!==a.style[c])return{end:b[c]};return!1}a.fn.emulateTransitionEnd=function(b){var c=!1,d=this;a(this).one("bsTransitionEnd",function(){c=!0});var e=function(){c||a(d).trigger(a.support.transition.end)};return setTimeout(e,b),this},a(function(){a.support.transition=b(),a.support.transition&&(a.event.special.bsTransitionEnd={bindType:a.support.transition.end,delegateType:a.support.transition.end,handle:function(b){return a(b.target).is(this)?b.handleObj.handler.apply(this,arguments):void 0}})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var c=a(this),e=c.data("bs.alert");e||c.data("bs.alert",e=new d(this)),"string"==typeof b&&e[b].call(c)})}var c='[data-dismiss="alert"]',d=function(b){a(b).on("click",c,this.close)};d.VERSION="3.3.5",d.TRANSITION_DURATION=150,d.prototype.close=function(b){function c(){g.detach().trigger("closed.bs.alert").remove()}var e=a(this),f=e.attr("data-target");f||(f=e.attr("href"),f=f&&f.replace(/.*(?=#[^\s]*$)/,""));var g=a(f);b&&b.preventDefault(),g.length||(g=e.closest(".alert")),g.trigger(b=a.Event("close.bs.alert")),b.isDefaultPrevented()||(g.removeClass("in"),a.support.transition&&g.hasClass("fade")?g.one("bsTransitionEnd",c).emulateTransitionEnd(d.TRANSITION_DURATION):c())};var e=a.fn.alert;a.fn.alert=b,a.fn.alert.Constructor=d,a.fn.alert.noConflict=function(){return a.fn.alert=e,this},a(document).on("click.bs.alert.data-api",c,d.prototype.close)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.button"),f="object"==typeof b&&b;e||d.data("bs.button",e=new c(this,f)),"toggle"==b?e.toggle():b&&e.setState(b)})}var c=function(b,d){this.$element=a(b),this.options=a.extend({},c.DEFAULTS,d),this.isLoading=!1};c.VERSION="3.3.5",c.DEFAULTS={loadingText:"loading..."},c.prototype.setState=function(b){var c="disabled",d=this.$element,e=d.is("input")?"val":"html",f=d.data();b+="Text",null==f.resetText&&d.data("resetText",d[e]()),setTimeout(a.proxy(function(){d[e](null==f[b]?this.options[b]:f[b]),"loadingText"==b?(this.isLoading=!0,d.addClass(c).attr(c,c)):this.isLoading&&(this.isLoading=!1,d.removeClass(c).removeAttr(c))},this),0)},c.prototype.toggle=function(){var a=!0,b=this.$element.closest('[data-toggle="buttons"]');if(b.length){var c=this.$element.find("input");"radio"==c.prop("type")?(c.prop("checked")&&(a=!1),b.find(".active").removeClass("active"),this.$element.addClass("active")):"checkbox"==c.prop("type")&&(c.prop("checked")!==this.$element.hasClass("active")&&(a=!1),this.$element.toggleClass("active")),c.prop("checked",this.$element.hasClass("active")),a&&c.trigger("change")}else this.$element.attr("aria-pressed",!this.$element.hasClass("active")),this.$element.toggleClass("active")};var d=a.fn.button;a.fn.button=b,a.fn.button.Constructor=c,a.fn.button.noConflict=function(){return a.fn.button=d,this},a(document).on("click.bs.button.data-api",'[data-toggle^="button"]',function(c){var d=a(c.target);d.hasClass("btn")||(d=d.closest(".btn")),b.call(d,"toggle"),a(c.target).is('input[type="radio"]')||a(c.target).is('input[type="checkbox"]')||c.preventDefault()}).on("focus.bs.button.data-api blur.bs.button.data-api",'[data-toggle^="button"]',function(b){a(b.target).closest(".btn").toggleClass("focus",/^focus(in)?$/.test(b.type))})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.carousel"),f=a.extend({},c.DEFAULTS,d.data(),"object"==typeof b&&b),g="string"==typeof b?b:f.slide;e||d.data("bs.carousel",e=new c(this,f)),"number"==typeof b?e.to(b):g?e[g]():f.interval&&e.pause().cycle()})}var c=function(b,c){this.$element=a(b),this.$indicators=this.$element.find(".carousel-indicators"),this.options=c,this.paused=null,this.sliding=null,this.interval=null,this.$active=null,this.$items=null,this.options.keyboard&&this.$element.on("keydown.bs.carousel",a.proxy(this.keydown,this)),"hover"==this.options.pause&&!("ontouchstart"in document.documentElement)&&this.$element.on("mouseenter.bs.carousel",a.proxy(this.pause,this)).on("mouseleave.bs.carousel",a.proxy(this.cycle,this))};c.VERSION="3.3.5",c.TRANSITION_DURATION=600,c.DEFAULTS={interval:5e3,pause:"hover",wrap:!0,keyboard:!0},c.prototype.keydown=function(a){if(!/input|textarea/i.test(a.target.tagName)){switch(a.which){case 37:this.prev();break;case 39:this.next();break;default:return}a.preventDefault()}},c.prototype.cycle=function(b){return b||(this.paused=!1),this.interval&&clearInterval(this.interval),this.options.interval&&!this.paused&&(this.interval=setInterval(a.proxy(this.next,this),this.options.interval)),this},c.prototype.getItemIndex=function(a){return this.$items=a.parent().children(".item"),this.$items.index(a||this.$active)},c.prototype.getItemForDirection=function(a,b){var c=this.getItemIndex(b),d="prev"==a&&0===c||"next"==a&&c==this.$items.length-1;if(d&&!this.options.wrap)return b;var e="prev"==a?-1:1,f=(c+e)%this.$items.length;return this.$items.eq(f)},c.prototype.to=function(a){var b=this,c=this.getItemIndex(this.$active=this.$element.find(".item.active"));return a>this.$items.length-1||0>a?void 0:this.sliding?this.$element.one("slid.bs.carousel",function(){b.to(a)}):c==a?this.pause().cycle():this.slide(a>c?"next":"prev",this.$items.eq(a))},c.prototype.pause=function(b){return b||(this.paused=!0),this.$element.find(".next, .prev").length&&a.support.transition&&(this.$element.trigger(a.support.transition.end),this.cycle(!0)),this.interval=clearInterval(this.interval),this},c.prototype.next=function(){return this.sliding?void 0:this.slide("next")},c.prototype.prev=function(){return this.sliding?void 0:this.slide("prev")},c.prototype.slide=function(b,d){var e=this.$element.find(".item.active"),f=d||this.getItemForDirection(b,e),g=this.interval,h="next"==b?"left":"right",i=this;if(f.hasClass("active"))return this.sliding=!1;var j=f[0],k=a.Event("slide.bs.carousel",{relatedTarget:j,direction:h});if(this.$element.trigger(k),!k.isDefaultPrevented()){if(this.sliding=!0,g&&this.pause(),this.$indicators.length){this.$indicators.find(".active").removeClass("active");var l=a(this.$indicators.children()[this.getItemIndex(f)]);l&&l.addClass("active")}var m=a.Event("slid.bs.carousel",{relatedTarget:j,direction:h});return a.support.transition&&this.$element.hasClass("slide")?(f.addClass(b),f[0].offsetWidth,e.addClass(h),f.addClass(h),e.one("bsTransitionEnd",function(){f.removeClass([b,h].join(" ")).addClass("active"),e.removeClass(["active",h].join(" ")),i.sliding=!1,setTimeout(function(){i.$element.trigger(m)},0)}).emulateTransitionEnd(c.TRANSITION_DURATION)):(e.removeClass("active"),f.addClass("active"),this.sliding=!1,this.$element.trigger(m)),g&&this.cycle(),this}};var d=a.fn.carousel;a.fn.carousel=b,a.fn.carousel.Constructor=c,a.fn.carousel.noConflict=function(){return a.fn.carousel=d,this};var e=function(c){var d,e=a(this),f=a(e.attr("data-target")||(d=e.attr("href"))&&d.replace(/.*(?=#[^\s]+$)/,""));if(f.hasClass("carousel")){var g=a.extend({},f.data(),e.data()),h=e.attr("data-slide-to");h&&(g.interval=!1),b.call(f,g),h&&f.data("bs.carousel").to(h),c.preventDefault()}};a(document).on("click.bs.carousel.data-api","[data-slide]",e).on("click.bs.carousel.data-api","[data-slide-to]",e),a(window).on("load",function(){a('[data-ride="carousel"]').each(function(){var c=a(this);b.call(c,c.data())})})}(jQuery),+function(a){"use strict";function b(b){var c,d=b.attr("data-target")||(c=b.attr("href"))&&c.replace(/.*(?=#[^\s]+$)/,"");return a(d)}function c(b){return this.each(function(){var c=a(this),e=c.data("bs.collapse"),f=a.extend({},d.DEFAULTS,c.data(),"object"==typeof b&&b);!e&&f.toggle&&/show|hide/.test(b)&&(f.toggle=!1),e||c.data("bs.collapse",e=new d(this,f)),"string"==typeof b&&e[b]()})}var d=function(b,c){this.$element=a(b),this.options=a.extend({},d.DEFAULTS,c),this.$trigger=a('[data-toggle="collapse"][href="#'+b.id+'"],[data-toggle="collapse"][data-target="#'+b.id+'"]'),this.transitioning=null,this.options.parent?this.$parent=this.getParent():this.addAriaAndCollapsedClass(this.$element,this.$trigger),this.options.toggle&&this.toggle()};d.VERSION="3.3.5",d.TRANSITION_DURATION=350,d.DEFAULTS={toggle:!0},d.prototype.dimension=function(){var a=this.$element.hasClass("width");return a?"width":"height"},d.prototype.show=function(){if(!this.transitioning&&!this.$element.hasClass("in")){var b,e=this.$parent&&this.$parent.children(".panel").children(".in, .collapsing");if(!(e&&e.length&&(b=e.data("bs.collapse"),b&&b.transitioning))){var f=a.Event("show.bs.collapse");if(this.$element.trigger(f),!f.isDefaultPrevented()){e&&e.length&&(c.call(e,"hide"),b||e.data("bs.collapse",null));var g=this.dimension();this.$element.removeClass("collapse").addClass("collapsing")[g](0).attr("aria-expanded",!0),this.$trigger.removeClass("collapsed").attr("aria-expanded",!0),this.transitioning=1;var h=function(){this.$element.removeClass("collapsing").addClass("collapse in")[g](""),this.transitioning=0,this.$element.trigger("shown.bs.collapse")};if(!a.support.transition)return h.call(this);var i=a.camelCase(["scroll",g].join("-"));this.$element.one("bsTransitionEnd",a.proxy(h,this)).emulateTransitionEnd(d.TRANSITION_DURATION)[g](this.$element[0][i])}}}},d.prototype.hide=function(){if(!this.transitioning&&this.$element.hasClass("in")){var b=a.Event("hide.bs.collapse");if(this.$element.trigger(b),!b.isDefaultPrevented()){var c=this.dimension();this.$element[c](this.$element[c]())[0].offsetHeight,this.$element.addClass("collapsing").removeClass("collapse in").attr("aria-expanded",!1),this.$trigger.addClass("collapsed").attr("aria-expanded",!1),this.transitioning=1;var e=function(){this.transitioning=0,this.$element.removeClass("collapsing").addClass("collapse").trigger("hidden.bs.collapse")};return a.support.transition?void this.$element[c](0).one("bsTransitionEnd",a.proxy(e,this)).emulateTransitionEnd(d.TRANSITION_DURATION):e.call(this)}}},d.prototype.toggle=function(){this[this.$element.hasClass("in")?"hide":"show"]()},d.prototype.getParent=function(){return a(this.options.parent).find('[data-toggle="collapse"][data-parent="'+this.options.parent+'"]').each(a.proxy(function(c,d){var e=a(d);this.addAriaAndCollapsedClass(b(e),e)},this)).end()},d.prototype.addAriaAndCollapsedClass=function(a,b){var c=a.hasClass("in");a.attr("aria-expanded",c),b.toggleClass("collapsed",!c).attr("aria-expanded",c)};var e=a.fn.collapse;a.fn.collapse=c,a.fn.collapse.Constructor=d,a.fn.collapse.noConflict=function(){return a.fn.collapse=e,this},a(document).on("click.bs.collapse.data-api",'[data-toggle="collapse"]',function(d){var e=a(this);e.attr("data-target")||d.preventDefault();var f=b(e),g=f.data("bs.collapse"),h=g?"toggle":e.data();c.call(f,h)})}(jQuery),+function(a){"use strict";function b(b){var c=b.attr("data-target");c||(c=b.attr("href"),c=c&&/#[A-Za-z]/.test(c)&&c.replace(/.*(?=#[^\s]*$)/,""));var d=c&&a(c);return d&&d.length?d:b.parent()}function c(c){c&&3===c.which||(a(e).remove(),a(f).each(function(){var d=a(this),e=b(d),f={relatedTarget:this};e.hasClass("open")&&(c&&"click"==c.type&&/input|textarea/i.test(c.target.tagName)&&a.contains(e[0],c.target)||(e.trigger(c=a.Event("hide.bs.dropdown",f)),c.isDefaultPrevented()||(d.attr("aria-expanded","false"),e.removeClass("open").trigger("hidden.bs.dropdown",f))))}))}function d(b){return this.each(function(){var c=a(this),d=c.data("bs.dropdown");d||c.data("bs.dropdown",d=new g(this)),"string"==typeof b&&d[b].call(c)})}var e=".dropdown-backdrop",f='[data-toggle="dropdown"]',g=function(b){a(b).on("click.bs.dropdown",this.toggle)};g.VERSION="3.3.5",g.prototype.toggle=function(d){var e=a(this);if(!e.is(".disabled, :disabled")){var f=b(e),g=f.hasClass("open");if(c(),!g){"ontouchstart"in document.documentElement&&!f.closest(".navbar-nav").length&&a(document.createElement("div")).addClass("dropdown-backdrop").insertAfter(a(this)).on("click",c);var h={relatedTarget:this};if(f.trigger(d=a.Event("show.bs.dropdown",h)),d.isDefaultPrevented())return;e.trigger("focus").attr("aria-expanded","true"),f.toggleClass("open").trigger("shown.bs.dropdown",h)}return!1}},g.prototype.keydown=function(c){if(/(38|40|27|32)/.test(c.which)&&!/input|textarea/i.test(c.target.tagName)){var d=a(this);if(c.preventDefault(),c.stopPropagation(),!d.is(".disabled, :disabled")){var e=b(d),g=e.hasClass("open");if(!g&&27!=c.which||g&&27==c.which)return 27==c.which&&e.find(f).trigger("focus"),d.trigger("click");var h=" li:not(.disabled):visible a",i=e.find(".dropdown-menu"+h);if(i.length){var j=i.index(c.target);38==c.which&&j>0&&j--,40==c.which&&j<i.length-1&&j++,~j||(j=0),i.eq(j).trigger("focus")}}}};var h=a.fn.dropdown;a.fn.dropdown=d,a.fn.dropdown.Constructor=g,a.fn.dropdown.noConflict=function(){return a.fn.dropdown=h,this},a(document).on("click.bs.dropdown.data-api",c).on("click.bs.dropdown.data-api",".dropdown form",function(a){a.stopPropagation()}).on("click.bs.dropdown.data-api",f,g.prototype.toggle).on("keydown.bs.dropdown.data-api",f,g.prototype.keydown).on("keydown.bs.dropdown.data-api",".dropdown-menu",g.prototype.keydown)}(jQuery),+function(a){"use strict";function b(b,d){return this.each(function(){var e=a(this),f=e.data("bs.modal"),g=a.extend({},c.DEFAULTS,e.data(),"object"==typeof b&&b);f||e.data("bs.modal",f=new c(this,g)),"string"==typeof b?f[b](d):g.show&&f.show(d)})}var c=function(b,c){this.options=c,this.$body=a(document.body),this.$element=a(b),this.$dialog=this.$element.find(".modal-dialog"),this.$backdrop=null,this.isShown=null,this.originalBodyPad=null,this.scrollbarWidth=0,this.ignoreBackdropClick=!1,this.options.remote&&this.$element.find(".modal-content").load(this.options.remote,a.proxy(function(){this.$element.trigger("loaded.bs.modal")},this))};c.VERSION="3.3.5",c.TRANSITION_DURATION=300,c.BACKDROP_TRANSITION_DURATION=150,c.DEFAULTS={backdrop:!0,keyboard:!0,show:!0},c.prototype.toggle=function(a){return this.isShown?this.hide():this.show(a)},c.prototype.show=function(b){var d=this,e=a.Event("show.bs.modal",{relatedTarget:b});this.$element.trigger(e),this.isShown||e.isDefaultPrevented()||(this.isShown=!0,this.checkScrollbar(),this.setScrollbar(),this.$body.addClass("modal-open"),this.escape(),this.resize(),this.$element.on("click.dismiss.bs.modal",'[data-dismiss="modal"]',a.proxy(this.hide,this)),this.$dialog.on("mousedown.dismiss.bs.modal",function(){d.$element.one("mouseup.dismiss.bs.modal",function(b){a(b.target).is(d.$element)&&(d.ignoreBackdropClick=!0)})}),this.backdrop(function(){var e=a.support.transition&&d.$element.hasClass("fade");d.$element.parent().length||d.$element.appendTo(d.$body),d.$element.show().scrollTop(0),d.adjustDialog(),e&&d.$element[0].offsetWidth,d.$element.addClass("in"),d.enforceFocus();var f=a.Event("shown.bs.modal",{relatedTarget:b});e?d.$dialog.one("bsTransitionEnd",function(){d.$element.trigger("focus").trigger(f)}).emulateTransitionEnd(c.TRANSITION_DURATION):d.$element.trigger("focus").trigger(f)}))},c.prototype.hide=function(b){b&&b.preventDefault(),b=a.Event("hide.bs.modal"),this.$element.trigger(b),this.isShown&&!b.isDefaultPrevented()&&(this.isShown=!1,this.escape(),this.resize(),a(document).off("focusin.bs.modal"),this.$element.removeClass("in").off("click.dismiss.bs.modal").off("mouseup.dismiss.bs.modal"),this.$dialog.off("mousedown.dismiss.bs.modal"),a.support.transition&&this.$element.hasClass("fade")?this.$element.one("bsTransitionEnd",a.proxy(this.hideModal,this)).emulateTransitionEnd(c.TRANSITION_DURATION):this.hideModal())},c.prototype.enforceFocus=function(){a(document).off("focusin.bs.modal").on("focusin.bs.modal",a.proxy(function(a){this.$element[0]===a.target||this.$element.has(a.target).length||this.$element.trigger("focus")},this))},c.prototype.escape=function(){this.isShown&&this.options.keyboard?this.$element.on("keydown.dismiss.bs.modal",a.proxy(function(a){27==a.which&&this.hide()},this)):this.isShown||this.$element.off("keydown.dismiss.bs.modal")},c.prototype.resize=function(){this.isShown?a(window).on("resize.bs.modal",a.proxy(this.handleUpdate,this)):a(window).off("resize.bs.modal")},c.prototype.hideModal=function(){var a=this;this.$element.hide(),this.backdrop(function(){a.$body.removeClass("modal-open"),a.resetAdjustments(),a.resetScrollbar(),a.$element.trigger("hidden.bs.modal")})},c.prototype.removeBackdrop=function(){this.$backdrop&&this.$backdrop.remove(),this.$backdrop=null},c.prototype.backdrop=function(b){var d=this,e=this.$element.hasClass("fade")?"fade":"";if(this.isShown&&this.options.backdrop){var f=a.support.transition&&e;if(this.$backdrop=a(document.createElement("div")).addClass("modal-backdrop "+e).appendTo(this.$body),this.$element.on("click.dismiss.bs.modal",a.proxy(function(a){return this.ignoreBackdropClick?void(this.ignoreBackdropClick=!1):void(a.target===a.currentTarget&&("static"==this.options.backdrop?this.$element[0].focus():this.hide()))},this)),f&&this.$backdrop[0].offsetWidth,this.$backdrop.addClass("in"),!b)return;f?this.$backdrop.one("bsTransitionEnd",b).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):b()}else if(!this.isShown&&this.$backdrop){this.$backdrop.removeClass("in");var g=function(){d.removeBackdrop(),b&&b()};a.support.transition&&this.$element.hasClass("fade")?this.$backdrop.one("bsTransitionEnd",g).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):g()}else b&&b()},c.prototype.handleUpdate=function(){this.adjustDialog()},c.prototype.adjustDialog=function(){var a=this.$element[0].scrollHeight>document.documentElement.clientHeight;this.$element.css({paddingLeft:!this.bodyIsOverflowing&&a?this.scrollbarWidth:"",paddingRight:this.bodyIsOverflowing&&!a?this.scrollbarWidth:""})},c.prototype.resetAdjustments=function(){this.$element.css({paddingLeft:"",paddingRight:""})},c.prototype.checkScrollbar=function(){var a=window.innerWidth;if(!a){var b=document.documentElement.getBoundingClientRect();a=b.right-Math.abs(b.left)}this.bodyIsOverflowing=document.body.clientWidth<a,this.scrollbarWidth=this.measureScrollbar()},c.prototype.setScrollbar=function(){var a=parseInt(this.$body.css("padding-right")||0,10);this.originalBodyPad=document.body.style.paddingRight||"",this.bodyIsOverflowing&&this.$body.css("padding-right",a+this.scrollbarWidth)},c.prototype.resetScrollbar=function(){this.$body.css("padding-right",this.originalBodyPad)},c.prototype.measureScrollbar=function(){var a=document.createElement("div");a.className="modal-scrollbar-measure",this.$body.append(a);var b=a.offsetWidth-a.clientWidth;return this.$body[0].removeChild(a),b};var d=a.fn.modal;a.fn.modal=b,a.fn.modal.Constructor=c,a.fn.modal.noConflict=function(){return a.fn.modal=d,this},a(document).on("click.bs.modal.data-api",'[data-toggle="modal"]',function(c){var d=a(this),e=d.attr("href"),f=a(d.attr("data-target")||e&&e.replace(/.*(?=#[^\s]+$)/,"")),g=f.data("bs.modal")?"toggle":a.extend({remote:!/#/.test(e)&&e},f.data(),d.data());d.is("a")&&c.preventDefault(),f.one("show.bs.modal",function(a){a.isDefaultPrevented()||f.one("hidden.bs.modal",function(){d.is(":visible")&&d.trigger("focus")})}),b.call(f,g,this)})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tooltip"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.tooltip",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.type=null,this.options=null,this.enabled=null,this.timeout=null,this.hoverState=null,this.$element=null,this.inState=null,this.init("tooltip",a,b)};c.VERSION="3.3.5",c.TRANSITION_DURATION=150,c.DEFAULTS={animation:!0,placement:"top",selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,container:!1,viewport:{selector:"body",padding:0}},c.prototype.init=function(b,c,d){if(this.enabled=!0,this.type=b,this.$element=a(c),this.options=this.getOptions(d),this.$viewport=this.options.viewport&&a(a.isFunction(this.options.viewport)?this.options.viewport.call(this,this.$element):this.options.viewport.selector||this.options.viewport),this.inState={click:!1,hover:!1,focus:!1},this.$element[0]instanceof document.constructor&&!this.options.selector)throw new Error("`selector` option must be specified when initializing "+this.type+" on the window.document object!");for(var e=this.options.trigger.split(" "),f=e.length;f--;){var g=e[f];if("click"==g)this.$element.on("click."+this.type,this.options.selector,a.proxy(this.toggle,this));else if("manual"!=g){var h="hover"==g?"mouseenter":"focusin",i="hover"==g?"mouseleave":"focusout";this.$element.on(h+"."+this.type,this.options.selector,a.proxy(this.enter,this)),this.$element.on(i+"."+this.type,this.options.selector,a.proxy(this.leave,this))}}this.options.selector?this._options=a.extend({},this.options,{trigger:"manual",selector:""}):this.fixTitle()},c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.getOptions=function(b){return b=a.extend({},this.getDefaults(),this.$element.data(),b),b.delay&&"number"==typeof b.delay&&(b.delay={show:b.delay,hide:b.delay}),b},c.prototype.getDelegateOptions=function(){var b={},c=this.getDefaults();return this._options&&a.each(this._options,function(a,d){c[a]!=d&&(b[a]=d)}),b},c.prototype.enter=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);return c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusin"==b.type?"focus":"hover"]=!0),c.tip().hasClass("in")||"in"==c.hoverState?void(c.hoverState="in"):(clearTimeout(c.timeout),c.hoverState="in",c.options.delay&&c.options.delay.show?void(c.timeout=setTimeout(function(){"in"==c.hoverState&&c.show()},c.options.delay.show)):c.show())},c.prototype.isInStateTrue=function(){for(var a in this.inState)if(this.inState[a])return!0;return!1},c.prototype.leave=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);return c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusout"==b.type?"focus":"hover"]=!1),c.isInStateTrue()?void 0:(clearTimeout(c.timeout),c.hoverState="out",c.options.delay&&c.options.delay.hide?void(c.timeout=setTimeout(function(){"out"==c.hoverState&&c.hide()},c.options.delay.hide)):c.hide())},c.prototype.show=function(){var b=a.Event("show.bs."+this.type);if(this.hasContent()&&this.enabled){this.$element.trigger(b);var d=a.contains(this.$element[0].ownerDocument.documentElement,this.$element[0]);if(b.isDefaultPrevented()||!d)return;var e=this,f=this.tip(),g=this.getUID(this.type);this.setContent(),f.attr("id",g),this.$element.attr("aria-describedby",g),this.options.animation&&f.addClass("fade");var h="function"==typeof this.options.placement?this.options.placement.call(this,f[0],this.$element[0]):this.options.placement,i=/\s?auto?\s?/i,j=i.test(h);j&&(h=h.replace(i,"")||"top"),f.detach().css({top:0,left:0,display:"block"}).addClass(h).data("bs."+this.type,this),this.options.container?f.appendTo(this.options.container):f.insertAfter(this.$element),this.$element.trigger("inserted.bs."+this.type);var k=this.getPosition(),l=f[0].offsetWidth,m=f[0].offsetHeight;if(j){var n=h,o=this.getPosition(this.$viewport);h="bottom"==h&&k.bottom+m>o.bottom?"top":"top"==h&&k.top-m<o.top?"bottom":"right"==h&&k.right+l>o.width?"left":"left"==h&&k.left-l<o.left?"right":h,f.removeClass(n).addClass(h)}var p=this.getCalculatedOffset(h,k,l,m);this.applyPlacement(p,h);var q=function(){var a=e.hoverState;e.$element.trigger("shown.bs."+e.type),e.hoverState=null,"out"==a&&e.leave(e)};a.support.transition&&this.$tip.hasClass("fade")?f.one("bsTransitionEnd",q).emulateTransitionEnd(c.TRANSITION_DURATION):q()}},c.prototype.applyPlacement=function(b,c){var d=this.tip(),e=d[0].offsetWidth,f=d[0].offsetHeight,g=parseInt(d.css("margin-top"),10),h=parseInt(d.css("margin-left"),10);isNaN(g)&&(g=0),isNaN(h)&&(h=0),b.top+=g,b.left+=h,a.offset.setOffset(d[0],a.extend({using:function(a){d.css({top:Math.round(a.top),left:Math.round(a.left)})}},b),0),d.addClass("in");var i=d[0].offsetWidth,j=d[0].offsetHeight;"top"==c&&j!=f&&(b.top=b.top+f-j);var k=this.getViewportAdjustedDelta(c,b,i,j);k.left?b.left+=k.left:b.top+=k.top;var l=/top|bottom/.test(c),m=l?2*k.left-e+i:2*k.top-f+j,n=l?"offsetWidth":"offsetHeight";d.offset(b),this.replaceArrow(m,d[0][n],l)},c.prototype.replaceArrow=function(a,b,c){this.arrow().css(c?"left":"top",50*(1-a/b)+"%").css(c?"top":"left","")},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle();a.find(".tooltip-inner")[this.options.html?"html":"text"](b),a.removeClass("fade in top bottom left right")},c.prototype.hide=function(b){function d(){"in"!=e.hoverState&&f.detach(),e.$element.removeAttr("aria-describedby").trigger("hidden.bs."+e.type),b&&b()}var e=this,f=a(this.$tip),g=a.Event("hide.bs."+this.type);return this.$element.trigger(g),g.isDefaultPrevented()?void 0:(f.removeClass("in"),a.support.transition&&f.hasClass("fade")?f.one("bsTransitionEnd",d).emulateTransitionEnd(c.TRANSITION_DURATION):d(),this.hoverState=null,this)},c.prototype.fixTitle=function(){var a=this.$element;(a.attr("title")||"string"!=typeof a.attr("data-original-title"))&&a.attr("data-original-title",a.attr("title")||"").attr("title","")},c.prototype.hasContent=function(){return this.getTitle()},c.prototype.getPosition=function(b){b=b||this.$element;var c=b[0],d="BODY"==c.tagName,e=c.getBoundingClientRect();null==e.width&&(e=a.extend({},e,{width:e.right-e.left,height:e.bottom-e.top}));var f=d?{top:0,left:0}:b.offset(),g={scroll:d?document.documentElement.scrollTop||document.body.scrollTop:b.scrollTop()},h=d?{width:a(window).width(),height:a(window).height()}:null;return a.extend({},e,g,h,f)},c.prototype.getCalculatedOffset=function(a,b,c,d){return"bottom"==a?{top:b.top+b.height,left:b.left+b.width/2-c/2}:"top"==a?{top:b.top-d,left:b.left+b.width/2-c/2}:"left"==a?{top:b.top+b.height/2-d/2,left:b.left-c}:{top:b.top+b.height/2-d/2,left:b.left+b.width}},c.prototype.getViewportAdjustedDelta=function(a,b,c,d){var e={top:0,left:0};if(!this.$viewport)return e;var f=this.options.viewport&&this.options.viewport.padding||0,g=this.getPosition(this.$viewport);if(/right|left/.test(a)){var h=b.top-f-g.scroll,i=b.top+f-g.scroll+d;h<g.top?e.top=g.top-h:i>g.top+g.height&&(e.top=g.top+g.height-i)}else{var j=b.left-f,k=b.left+f+c;j<g.left?e.left=g.left-j:k>g.right&&(e.left=g.left+g.width-k)}return e},c.prototype.getTitle=function(){var a,b=this.$element,c=this.options;return a=b.attr("data-original-title")||("function"==typeof c.title?c.title.call(b[0]):c.title)},c.prototype.getUID=function(a){do a+=~~(1e6*Math.random());while(document.getElementById(a));return a},c.prototype.tip=function(){if(!this.$tip&&(this.$tip=a(this.options.template),1!=this.$tip.length))throw new Error(this.type+" `template` option must consist of exactly 1 top-level element!");return this.$tip},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".tooltip-arrow")},c.prototype.enable=function(){this.enabled=!0},c.prototype.disable=function(){this.enabled=!1},c.prototype.toggleEnabled=function(){this.enabled=!this.enabled},c.prototype.toggle=function(b){var c=this;b&&(c=a(b.currentTarget).data("bs."+this.type),c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c))),b?(c.inState.click=!c.inState.click,c.isInStateTrue()?c.enter(c):c.leave(c)):c.tip().hasClass("in")?c.leave(c):c.enter(c)},c.prototype.destroy=function(){var a=this;clearTimeout(this.timeout),this.hide(function(){a.$element.off("."+a.type).removeData("bs."+a.type),a.$tip&&a.$tip.detach(),a.$tip=null,a.$arrow=null,a.$viewport=null})};var d=a.fn.tooltip;a.fn.tooltip=b,a.fn.tooltip.Constructor=c,a.fn.tooltip.noConflict=function(){return a.fn.tooltip=d,this}}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.popover"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.popover",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.init("popover",a,b)};if(!a.fn.tooltip)throw new Error("Popover requires tooltip.js");c.VERSION="3.3.5",c.DEFAULTS=a.extend({},a.fn.tooltip.Constructor.DEFAULTS,{placement:"right",trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="arrow"></div><h3 class="popover-title"></h3><div class="popover-content"></div></div>'}),c.prototype=a.extend({},a.fn.tooltip.Constructor.prototype),c.prototype.constructor=c,c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle(),c=this.getContent();a.find(".popover-title")[this.options.html?"html":"text"](b),a.find(".popover-content").children().detach().end()[this.options.html?"string"==typeof c?"html":"append":"text"](c),a.removeClass("fade top bottom left right in"),a.find(".popover-title").html()||a.find(".popover-title").hide()},c.prototype.hasContent=function(){return this.getTitle()||this.getContent()},c.prototype.getContent=function(){var a=this.$element,b=this.options;return a.attr("data-content")||("function"==typeof b.content?b.content.call(a[0]):b.content)},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".arrow")};var d=a.fn.popover;a.fn.popover=b,a.fn.popover.Constructor=c,a.fn.popover.noConflict=function(){return a.fn.popover=d,this}}(jQuery),+function(a){"use strict";function b(c,d){this.$body=a(document.body),this.$scrollElement=a(a(c).is(document.body)?window:c),this.options=a.extend({},b.DEFAULTS,d),this.selector=(this.options.target||"")+" .nav li > a",this.offsets=[],this.targets=[],this.activeTarget=null,this.scrollHeight=0,this.$scrollElement.on("scroll.bs.scrollspy",a.proxy(this.process,this)),this.refresh(),this.process()}function c(c){return this.each(function(){var d=a(this),e=d.data("bs.scrollspy"),f="object"==typeof c&&c;e||d.data("bs.scrollspy",e=new b(this,f)),"string"==typeof c&&e[c]()})}b.VERSION="3.3.5",b.DEFAULTS={offset:10},b.prototype.getScrollHeight=function(){return this.$scrollElement[0].scrollHeight||Math.max(this.$body[0].scrollHeight,document.documentElement.scrollHeight)},b.prototype.refresh=function(){var b=this,c="offset",d=0;this.offsets=[],this.targets=[],this.scrollHeight=this.getScrollHeight(),a.isWindow(this.$scrollElement[0])||(c="position",d=this.$scrollElement.scrollTop()),this.$body.find(this.selector).map(function(){var b=a(this),e=b.data("target")||b.attr("href"),f=/^#./.test(e)&&a(e);return f&&f.length&&f.is(":visible")&&[[f[c]().top+d,e]]||null}).sort(function(a,b){return a[0]-b[0]}).each(function(){b.offsets.push(this[0]),b.targets.push(this[1])})},b.prototype.process=function(){var a,b=this.$scrollElement.scrollTop()+this.options.offset,c=this.getScrollHeight(),d=this.options.offset+c-this.$scrollElement.height(),e=this.offsets,f=this.targets,g=this.activeTarget;if(this.scrollHeight!=c&&this.refresh(),b>=d)return g!=(a=f[f.length-1])&&this.activate(a);if(g&&b<e[0])return this.activeTarget=null,this.clear();for(a=e.length;a--;)g!=f[a]&&b>=e[a]&&(void 0===e[a+1]||b<e[a+1])&&this.activate(f[a])},b.prototype.activate=function(b){this.activeTarget=b,this.clear();var c=this.selector+'[data-target="'+b+'"],'+this.selector+'[href="'+b+'"]',d=a(c).parents("li").addClass("active");d.parent(".dropdown-menu").length&&(d=d.closest("li.dropdown").addClass("active")),
d.trigger("activate.bs.scrollspy")},b.prototype.clear=function(){a(this.selector).parentsUntil(this.options.target,".active").removeClass("active")};var d=a.fn.scrollspy;a.fn.scrollspy=c,a.fn.scrollspy.Constructor=b,a.fn.scrollspy.noConflict=function(){return a.fn.scrollspy=d,this},a(window).on("load.bs.scrollspy.data-api",function(){a('[data-spy="scroll"]').each(function(){var b=a(this);c.call(b,b.data())})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tab");e||d.data("bs.tab",e=new c(this)),"string"==typeof b&&e[b]()})}var c=function(b){this.element=a(b)};c.VERSION="3.3.5",c.TRANSITION_DURATION=150,c.prototype.show=function(){var b=this.element,c=b.closest("ul:not(.dropdown-menu)"),d=b.data("target");if(d||(d=b.attr("href"),d=d&&d.replace(/.*(?=#[^\s]*$)/,"")),!b.parent("li").hasClass("active")){var e=c.find(".active:last a"),f=a.Event("hide.bs.tab",{relatedTarget:b[0]}),g=a.Event("show.bs.tab",{relatedTarget:e[0]});if(e.trigger(f),b.trigger(g),!g.isDefaultPrevented()&&!f.isDefaultPrevented()){var h=a(d);this.activate(b.closest("li"),c),this.activate(h,h.parent(),function(){e.trigger({type:"hidden.bs.tab",relatedTarget:b[0]}),b.trigger({type:"shown.bs.tab",relatedTarget:e[0]})})}}},c.prototype.activate=function(b,d,e){function f(){g.removeClass("active").find("> .dropdown-menu > .active").removeClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!1),b.addClass("active").find('[data-toggle="tab"]').attr("aria-expanded",!0),h?(b[0].offsetWidth,b.addClass("in")):b.removeClass("fade"),b.parent(".dropdown-menu").length&&b.closest("li.dropdown").addClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!0),e&&e()}var g=d.find("> .active"),h=e&&a.support.transition&&(g.length&&g.hasClass("fade")||!!d.find("> .fade").length);g.length&&h?g.one("bsTransitionEnd",f).emulateTransitionEnd(c.TRANSITION_DURATION):f(),g.removeClass("in")};var d=a.fn.tab;a.fn.tab=b,a.fn.tab.Constructor=c,a.fn.tab.noConflict=function(){return a.fn.tab=d,this};var e=function(c){c.preventDefault(),b.call(a(this),"show")};a(document).on("click.bs.tab.data-api",'[data-toggle="tab"]',e).on("click.bs.tab.data-api",'[data-toggle="pill"]',e)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.affix"),f="object"==typeof b&&b;e||d.data("bs.affix",e=new c(this,f)),"string"==typeof b&&e[b]()})}var c=function(b,d){this.options=a.extend({},c.DEFAULTS,d),this.$target=a(this.options.target).on("scroll.bs.affix.data-api",a.proxy(this.checkPosition,this)).on("click.bs.affix.data-api",a.proxy(this.checkPositionWithEventLoop,this)),this.$element=a(b),this.affixed=null,this.unpin=null,this.pinnedOffset=null,this.checkPosition()};c.VERSION="3.3.5",c.RESET="affix affix-top affix-bottom",c.DEFAULTS={offset:0,target:window},c.prototype.getState=function(a,b,c,d){var e=this.$target.scrollTop(),f=this.$element.offset(),g=this.$target.height();if(null!=c&&"top"==this.affixed)return c>e?"top":!1;if("bottom"==this.affixed)return null!=c?e+this.unpin<=f.top?!1:"bottom":a-d>=e+g?!1:"bottom";var h=null==this.affixed,i=h?e:f.top,j=h?g:b;return null!=c&&c>=e?"top":null!=d&&i+j>=a-d?"bottom":!1},c.prototype.getPinnedOffset=function(){if(this.pinnedOffset)return this.pinnedOffset;this.$element.removeClass(c.RESET).addClass("affix");var a=this.$target.scrollTop(),b=this.$element.offset();return this.pinnedOffset=b.top-a},c.prototype.checkPositionWithEventLoop=function(){setTimeout(a.proxy(this.checkPosition,this),1)},c.prototype.checkPosition=function(){if(this.$element.is(":visible")){var b=this.$element.height(),d=this.options.offset,e=d.top,f=d.bottom,g=Math.max(a(document).height(),a(document.body).height());"object"!=typeof d&&(f=e=d),"function"==typeof e&&(e=d.top(this.$element)),"function"==typeof f&&(f=d.bottom(this.$element));var h=this.getState(g,b,e,f);if(this.affixed!=h){null!=this.unpin&&this.$element.css("top","");var i="affix"+(h?"-"+h:""),j=a.Event(i+".bs.affix");if(this.$element.trigger(j),j.isDefaultPrevented())return;this.affixed=h,this.unpin="bottom"==h?this.getPinnedOffset():null,this.$element.removeClass(c.RESET).addClass(i).trigger(i.replace("affix","affixed")+".bs.affix")}"bottom"==h&&this.$element.offset({top:g-b-f})}};var d=a.fn.affix;a.fn.affix=b,a.fn.affix.Constructor=c,a.fn.affix.noConflict=function(){return a.fn.affix=d,this},a(window).on("load",function(){a('[data-spy="affix"]').each(function(){var c=a(this),d=c.data();d.offset=d.offset||{},null!=d.offsetBottom&&(d.offset.bottom=d.offsetBottom),null!=d.offsetTop&&(d.offset.top=d.offsetTop),b.call(c,d)})})}(jQuery);</script>
<script>/**
* @preserve HTML5 Shiv 3.7.2 | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
*/
// Only run this code in IE 8
if (!!window.navigator.userAgent.match("MSIE 8")) {
!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=t.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=t.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),t.elements=c+" "+a,j(b)}function f(a){var b=s[a[q]];return b||(b={},r++,a[q]=r,s[r]=b),b}function g(a,c,d){if(c||(c=b),l)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():p.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||o.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),l)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return t.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(t,b.frag)}function j(a){a||(a=b);var d=f(a);return!t.shivCSS||k||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),l||i(a,d),a}var k,l,m="3.7.2",n=a.html5||{},o=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,p=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,q="_html5shiv",r=0,s={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",k="hidden"in a,l=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){k=!0,l=!0}}();var t={elements:n.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:m,shivCSS:n.shivCSS!==!1,supportsUnknownElements:l,shivMethods:n.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=t,j(b)}(this,document);
};
</script>
<script>/*! Respond.js v1.4.2: min/max-width media query polyfill * Copyright 2013 Scott Jehl
* Licensed under https://github.com/scottjehl/Respond/blob/master/LICENSE-MIT
* */
// Only run this code in IE 8
if (!!window.navigator.userAgent.match("MSIE 8")) {
!function(a){"use strict";a.matchMedia=a.matchMedia||function(a){var b,c=a.documentElement,d=c.firstElementChild||c.firstChild,e=a.createElement("body"),f=a.createElement("div");return f.id="mq-test-1",f.style.cssText="position:absolute;top:-100em",e.style.background="none",e.appendChild(f),function(a){return f.innerHTML='­<style media="'+a+'"> #mq-test-1 { width: 42px; }</style>',c.insertBefore(e,d),b=42===f.offsetWidth,c.removeChild(e),{matches:b,media:a}}}(a.document)}(this),function(a){"use strict";function b(){u(!0)}var c={};a.respond=c,c.update=function(){};var d=[],e=function(){var b=!1;try{b=new a.XMLHttpRequest}catch(c){b=new a.ActiveXObject("Microsoft.XMLHTTP")}return function(){return b}}(),f=function(a,b){var c=e();c&&(c.open("GET",a,!0),c.onreadystatechange=function(){4!==c.readyState||200!==c.status&&304!==c.status||b(c.responseText)},4!==c.readyState&&c.send(null))};if(c.ajax=f,c.queue=d,c.regex={media:/@media[^\{]+\{([^\{\}]*\{[^\}\{]*\})+/gi,keyframes:/@(?:\-(?:o|moz|webkit)\-)?keyframes[^\{]+\{(?:[^\{\}]*\{[^\}\{]*\})+[^\}]*\}/gi,urls:/(url\()['"]?([^\/\)'"][^:\)'"]+)['"]?(\))/g,findStyles:/@media *([^\{]+)\{([\S\s]+?)$/,only:/(only\s+)?([a-zA-Z]+)\s?/,minw:/\([\s]*min\-width\s*:[\s]*([\s]*[0-9\.]+)(px|em)[\s]*\)/,maxw:/\([\s]*max\-width\s*:[\s]*([\s]*[0-9\.]+)(px|em)[\s]*\)/},c.mediaQueriesSupported=a.matchMedia&&null!==a.matchMedia("only all")&&a.matchMedia("only all").matches,!c.mediaQueriesSupported){var g,h,i,j=a.document,k=j.documentElement,l=[],m=[],n=[],o={},p=30,q=j.getElementsByTagName("head")[0]||k,r=j.getElementsByTagName("base")[0],s=q.getElementsByTagName("link"),t=function(){var a,b=j.createElement("div"),c=j.body,d=k.style.fontSize,e=c&&c.style.fontSize,f=!1;return b.style.cssText="position:absolute;font-size:1em;width:1em",c||(c=f=j.createElement("body"),c.style.background="none"),k.style.fontSize="100%",c.style.fontSize="100%",c.appendChild(b),f&&k.insertBefore(c,k.firstChild),a=b.offsetWidth,f?k.removeChild(c):c.removeChild(b),k.style.fontSize=d,e&&(c.style.fontSize=e),a=i=parseFloat(a)},u=function(b){var c="clientWidth",d=k[c],e="CSS1Compat"===j.compatMode&&d||j.body[c]||d,f={},o=s[s.length-1],r=(new Date).getTime();if(b&&g&&p>r-g)return a.clearTimeout(h),h=a.setTimeout(u,p),void 0;g=r;for(var v in l)if(l.hasOwnProperty(v)){var w=l[v],x=w.minw,y=w.maxw,z=null===x,A=null===y,B="em";x&&(x=parseFloat(x)*(x.indexOf(B)>-1?i||t():1)),y&&(y=parseFloat(y)*(y.indexOf(B)>-1?i||t():1)),w.hasquery&&(z&&A||!(z||e>=x)||!(A||y>=e))||(f[w.media]||(f[w.media]=[]),f[w.media].push(m[w.rules]))}for(var C in n)n.hasOwnProperty(C)&&n[C]&&n[C].parentNode===q&&q.removeChild(n[C]);n.length=0;for(var D in f)if(f.hasOwnProperty(D)){var E=j.createElement("style"),F=f[D].join("\n");E.type="text/css",E.media=D,q.insertBefore(E,o.nextSibling),E.styleSheet?E.styleSheet.cssText=F:E.appendChild(j.createTextNode(F)),n.push(E)}},v=function(a,b,d){var e=a.replace(c.regex.keyframes,"").match(c.regex.media),f=e&&e.length||0;b=b.substring(0,b.lastIndexOf("/"));var g=function(a){return a.replace(c.regex.urls,"$1"+b+"$2$3")},h=!f&&d;b.length&&(b+="/"),h&&(f=1);for(var i=0;f>i;i++){var j,k,n,o;h?(j=d,m.push(g(a))):(j=e[i].match(c.regex.findStyles)&&RegExp.$1,m.push(RegExp.$2&&g(RegExp.$2))),n=j.split(","),o=n.length;for(var p=0;o>p;p++)k=n[p],l.push({media:k.split("(")[0].match(c.regex.only)&&RegExp.$2||"all",rules:m.length-1,hasquery:k.indexOf("(")>-1,minw:k.match(c.regex.minw)&&parseFloat(RegExp.$1)+(RegExp.$2||""),maxw:k.match(c.regex.maxw)&&parseFloat(RegExp.$1)+(RegExp.$2||"")})}u()},w=function(){if(d.length){var b=d.shift();f(b.href,function(c){v(c,b.href,b.media),o[b.href]=!0,a.setTimeout(function(){w()},0)})}},x=function(){for(var b=0;b<s.length;b++){var c=s[b],e=c.href,f=c.media,g=c.rel&&"stylesheet"===c.rel.toLowerCase();e&&g&&!o[e]&&(c.styleSheet&&c.styleSheet.rawCssText?(v(c.styleSheet.rawCssText,e,f),o[e]=!0):(!/^([a-zA-Z:]*\/\/)/.test(e)&&!r||e.replace(RegExp.$1,"").split("/")[0]===a.location.host)&&("//"===e.substring(0,2)&&(e=a.location.protocol+e),d.push({href:e,media:f})))}w()};x(),c.update=x,c.getEmValue=t,a.addEventListener?a.addEventListener("resize",b,!1):a.attachEvent&&a.attachEvent("onresize",b)}}(this);
};
</script>
<style>h1 {font-size: 34px;}
h1.title {font-size: 38px;}
h2 {font-size: 30px;}
h3 {font-size: 24px;}
h4 {font-size: 18px;}
h5 {font-size: 16px;}
h6 {font-size: 12px;}
code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
pre:not([class]) { background-color: white }</style>
<script>
/**
* jQuery Plugin: Sticky Tabs
*
* @author Aidan Lister <aidan@php.net>
* adapted by Ruben Arslan to activate parent tabs too
* http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
*/
(function($) {
"use strict";
$.fn.rmarkdownStickyTabs = function() {
var context = this;
// Show the tab corresponding with the hash in the URL, or the first tab
var showStuffFromHash = function() {
var hash = window.location.hash;
var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a';
var $selector = $(selector, context);
if($selector.data('toggle') === "tab") {
$selector.tab('show');
// walk up the ancestors of this element, show any hidden tabs
$selector.parents('.section.tabset').each(function(i, elm) {
var link = $('a[href="#' + $(elm).attr('id') + '"]');
if(link.data('toggle') === "tab") {
link.tab("show");
}
});
}
};
// Set the correct tab when the page loads
showStuffFromHash(context);
// Set the correct tab when a user uses their back/forward button
$(window).on('hashchange', function() {
showStuffFromHash(context);
});
// Change the URL when tabs are clicked
$('a', context).on('click', function(e) {
history.pushState(null, null, this.href);
showStuffFromHash(context);
});
return this;
};
}(jQuery));
window.buildTabsets = function(tocID) {
// build a tabset from a section div with the .tabset class
function buildTabset(tabset) {
// check for fade and pills options
var fade = tabset.hasClass("tabset-fade");
var pills = tabset.hasClass("tabset-pills");
var navClass = pills ? "nav-pills" : "nav-tabs";
// determine the heading level of the tabset and tabs
var match = tabset.attr('class').match(/level(\d) /);
if (match === null)
return;
var tabsetLevel = Number(match[1]);
var tabLevel = tabsetLevel + 1;
// find all subheadings immediately below
var tabs = tabset.find("div.section.level" + tabLevel);
if (!tabs.length)
return;
// create tablist and tab-content elements
var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
$(tabs[0]).before(tabList);
var tabContent = $('<div class="tab-content"></div>');
$(tabs[0]).before(tabContent);
// build the tabset
var activeTab = 0;
tabs.each(function(i) {
// get the tab div
var tab = $(tabs[i]);
// get the id then sanitize it for use with bootstrap tabs
var id = tab.attr('id');
// see if this is marked as the active tab
if (tab.hasClass('active'))
activeTab = i;
// remove any table of contents entries associated with
// this ID (since we'll be removing the heading element)
$("div#" + tocID + " li a[href='#" + id + "']").parent().remove();
// sanitize the id for use with bootstrap tabs
id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
tab.attr('id', id);
// get the heading element within it, grab it's text, then remove it
var heading = tab.find('h' + tabLevel + ':first');
var headingText = heading.html();
heading.remove();
// build and append the tab list item
var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>');
a.attr('href', '#' + id);
a.attr('aria-controls', id);
var li = $('<li role="presentation"></li>');
li.append(a);
tabList.append(li);
// set it's attributes
tab.attr('role', 'tabpanel');
tab.addClass('tab-pane');
tab.addClass('tabbed-pane');
if (fade)
tab.addClass('fade');
// move it into the tab content div
tab.detach().appendTo(tabContent);
});
// set active tab
$(tabList.children('li')[activeTab]).addClass('active');
var active = $(tabContent.children('div.section')[activeTab]);
active.addClass('active');
if (fade)
active.addClass('in');
if (tabset.hasClass("tabset-sticky"))
tabset.rmarkdownStickyTabs();
}
// convert section divs with the .tabset class to tabsets
var tabsets = $("div.section.tabset");
tabsets.each(function(i) {
buildTabset($(tabsets[i]));
});
};
</script>
<style type="text/css">.hljs-literal {
color: #990073;
}
.hljs-number {
color: #099;
}
.hljs-comment {
color: #998;
font-style: italic;
}
.hljs-keyword {
color: #900;
font-weight: bold;
}
.hljs-string {
color: #d14;
}
</style>
<script src="data:application/javascript;base64,/*! highlight.js v9.12.0 | BSD3 License | git.io/hljslicense */
!function(e){var n="object"==typeof window&&window||"object"==typeof self&&self;"undefined"!=typeof exports?e(exports):n&&(n.hljs=e({}),"function"==typeof define&&define.amd&&define([],function(){return n.hljs}))}(function(e){function n(e){return e.replace(/&/g,"&amp;").replace(/</g,"&lt;").replace(/>/g,"&gt;")}function t(e){return e.nodeName.toLowerCase()}function r(e,n){var t=e&&e.exec(n);return t&&0===t.index}function a(e){return k.test(e)}function i(e){var n,t,r,i,o=e.className+" ";if(o+=e.parentNode?e.parentNode.className:"",t=B.exec(o))return w(t[1])?t[1]:"no-highlight";for(o=o.split(/\s+/),n=0,r=o.length;r>n;n++)if(i=o[n],a(i)||w(i))return i}function o(e){var n,t={},r=Array.prototype.slice.call(arguments,1);for(n in e)t[n]=e[n];return r.forEach(function(e){for(n in e)t[n]=e[n]}),t}function u(e){var n=[];return function r(e,a){for(var i=e.firstChild;i;i=i.nextSibling)3===i.nodeType?a+=i.nodeValue.length:1===i.nodeType&&(n.push({event:"start",offset:a,node:i}),a=r(i,a),t(i).match(/br|hr|img|input/)||n.push({event:"stop",offset:a,node:i}));return a}(e,0),n}function c(e,r,a){function i(){return e.length&&r.length?e[0].offset!==r[0].offset?e[0].offset<r[0].offset?e:r:"start"===r[0].event?e:r:e.length?e:r}function o(e){function r(e){return" "+e.nodeName+'="'+n(e.value).replace('"',"&quot;")+'"'}s+="<"+t(e)+E.map.call(e.attributes,r).join("")+">"}function u(e){s+="</"+t(e)+">"}function c(e){("start"===e.event?o:u)(e.node)}for(var l=0,s="",f=[];e.length||r.length;){var g=i();if(s+=n(a.substring(l,g[0].offset)),l=g[0].offset,g===e){f.reverse().forEach(u);do c(g.splice(0,1)[0]),g=i();while(g===e&&g.length&&g[0].offset===l);f.reverse().forEach(o)}else"start"===g[0].event?f.push(g[0].node):f.pop(),c(g.splice(0,1)[0])}return s+n(a.substr(l))}function l(e){return e.v&&!e.cached_variants&&(e.cached_variants=e.v.map(function(n){return o(e,{v:null},n)})),e.cached_variants||e.eW&&[o(e)]||[e]}function s(e){function n(e){return e&&e.source||e}function t(t,r){return new RegExp(n(t),"m"+(e.cI?"i":"")+(r?"g":""))}function r(a,i){if(!a.compiled){if(a.compiled=!0,a.k=a.k||a.bK,a.k){var o={},u=function(n,t){e.cI&&(t=t.toLowerCase()),t.split(" ").forEach(function(e){var t=e.split("|");o[t[0]]=[n,t[1]?Number(t[1]):1]})};"string"==typeof a.k?u("keyword",a.k):x(a.k).forEach(function(e){u(e,a.k[e])}),a.k=o}a.lR=t(a.l||/\w+/,!0),i&&(a.bK&&(a.b="\\b("+a.bK.split(" ").join("|")+")\\b"),a.b||(a.b=/\B|\b/),a.bR=t(a.b),a.e||a.eW||(a.e=/\B|\b/),a.e&&(a.eR=t(a.e)),a.tE=n(a.e)||"",a.eW&&i.tE&&(a.tE+=(a.e?"|":"")+i.tE)),a.i&&(a.iR=t(a.i)),null==a.r&&(a.r=1),a.c||(a.c=[]),a.c=Array.prototype.concat.apply([],a.c.map(function(e){return l("self"===e?a:e)})),a.c.forEach(function(e){r(e,a)}),a.starts&&r(a.starts,i);var c=a.c.map(function(e){return e.bK?"\\.?("+e.b+")\\.?":e.b}).concat([a.tE,a.i]).map(n).filter(Boolean);a.t=c.length?t(c.join("|"),!0):{exec:function(){return null}}}}r(e)}function f(e,t,a,i){function o(e,n){var t,a;for(t=0,a=n.c.length;a>t;t++)if(r(n.c[t].bR,e))return n.c[t]}function u(e,n){if(r(e.eR,n)){for(;e.endsParent&&e.parent;)e=e.parent;return e}return e.eW?u(e.parent,n):void 0}function c(e,n){return!a&&r(n.iR,e)}function l(e,n){var t=N.cI?n[0].toLowerCase():n[0];return e.k.hasOwnProperty(t)&&e.k[t]}function p(e,n,t,r){var a=r?"":I.classPrefix,i='<span class="'+a,o=t?"":C;return i+=e+'">',i+n+o}function h(){var e,t,r,a;if(!E.k)return n(k);for(a="",t=0,E.lR.lastIndex=0,r=E.lR.exec(k);r;)a+=n(k.substring(t,r.index)),e=l(E,r),e?(B+=e[1],a+=p(e[0],n(r[0]))):a+=n(r[0]),t=E.lR.lastIndex,r=E.lR.exec(k);return a+n(k.substr(t))}function d(){var e="string"==typeof E.sL;if(e&&!y[E.sL])return n(k);var t=e?f(E.sL,k,!0,x[E.sL]):g(k,E.sL.length?E.sL:void 0);return E.r>0&&(B+=t.r),e&&(x[E.sL]=t.top),p(t.language,t.value,!1,!0)}function b(){L+=null!=E.sL?d():h(),k=""}function v(e){L+=e.cN?p(e.cN,"",!0):"",E=Object.create(e,{parent:{value:E}})}function m(e,n){if(k+=e,null==n)return b(),0;var t=o(n,E);if(t)return t.skip?k+=n:(t.eB&&(k+=n),b(),t.rB||t.eB||(k=n)),v(t,n),t.rB?0:n.length;var r=u(E,n);if(r){var a=E;a.skip?k+=n:(a.rE||a.eE||(k+=n),b(),a.eE&&(k=n));do E.cN&&(L+=C),E.skip||(B+=E.r),E=E.parent;while(E!==r.parent);return r.starts&&v(r.starts,""),a.rE?0:n.length}if(c(n,E))throw new Error('Illegal lexeme "'+n+'" for mode "'+(E.cN||"<unnamed>")+'"');return k+=n,n.length||1}var N=w(e);if(!N)throw new Error('Unknown language: "'+e+'"');s(N);var R,E=i||N,x={},L="";for(R=E;R!==N;R=R.parent)R.cN&&(L=p(R.cN,"",!0)+L);var k="",B=0;try{for(var M,j,O=0;;){if(E.t.lastIndex=O,M=E.t.exec(t),!M)break;j=m(t.substring(O,M.index),M[0]),O=M.index+j}for(m(t.substr(O)),R=E;R.parent;R=R.parent)R.cN&&(L+=C);return{r:B,value:L,language:e,top:E}}catch(T){if(T.message&&-1!==T.message.indexOf("Illegal"))return{r:0,value:n(t)};throw T}}function g(e,t){t=t||I.languages||x(y);var r={r:0,value:n(e)},a=r;return t.filter(w).forEach(function(n){var t=f(n,e,!1);t.language=n,t.r>a.r&&(a=t),t.r>r.r&&(a=r,r=t)}),a.language&&(r.second_best=a),r}function p(e){return I.tabReplace||I.useBR?e.replace(M,function(e,n){return I.useBR&&"\n"===e?"<br>":I.tabReplace?n.replace(/\t/g,I.tabReplace):""}):e}function h(e,n,t){var r=n?L[n]:t,a=[e.trim()];return e.match(/\bhljs\b/)||a.push("hljs"),-1===e.indexOf(r)&&a.push(r),a.join(" ").trim()}function d(e){var n,t,r,o,l,s=i(e);a(s)||(I.useBR?(n=document.createElementNS("http://www.w3.org/1999/xhtml","div"),n.innerHTML=e.innerHTML.replace(/\n/g,"").replace(/<br[ \/]*>/g,"\n")):n=e,l=n.textContent,r=s?f(s,l,!0):g(l),t=u(n),t.length&&(o=document.createElementNS("http://www.w3.org/1999/xhtml","div"),o.innerHTML=r.value,r.value=c(t,u(o),l)),r.value=p(r.value),e.innerHTML=r.value,e.className=h(e.className,s,r.language),e.result={language:r.language,re:r.r},r.second_best&&(e.second_best={language:r.second_best.language,re:r.second_best.r}))}function b(e){I=o(I,e)}function v(){if(!v.called){v.called=!0;var e=document.querySelectorAll("pre code");E.forEach.call(e,d)}}function m(){addEventListener("DOMContentLoaded",v,!1),addEventListener("load",v,!1)}function N(n,t){var r=y[n]=t(e);r.aliases&&r.aliases.forEach(function(e){L[e]=n})}function R(){return x(y)}function w(e){return e=(e||"").toLowerCase(),y[e]||y[L[e]]}var E=[],x=Object.keys,y={},L={},k=/^(no-?highlight|plain|text)$/i,B=/\blang(?:uage)?-([\w-]+)\b/i,M=/((^(<[^>]+>|\t|)+|(?:\n)))/gm,C="</span>",I={classPrefix:"hljs-",tabReplace:null,useBR:!1,languages:void 0};return e.highlight=f,e.highlightAuto=g,e.fixMarkup=p,e.highlightBlock=d,e.configure=b,e.initHighlighting=v,e.initHighlightingOnLoad=m,e.registerLanguage=N,e.listLanguages=R,e.getLanguage=w,e.inherit=o,e.IR="[a-zA-Z]\\w*",e.UIR="[a-zA-Z_]\\w*",e.NR="\\b\\d+(\\.\\d+)?",e.CNR="(-?)(\\b0[xX][a-fA-F0-9]+|(\\b\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)",e.BNR="\\b(0b[01]+)",e.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|-|-=|/=|/|:|;|<<|<<=|<=|<|===|==|=|>>>=|>>=|>=|>>>|>>|>|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~",e.BE={b:"\\\\[\\s\\S]",r:0},e.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[e.BE]},e.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[e.BE]},e.PWM={b:/\b(a|an|the|are|I'm|isn't|don't|doesn't|won't|but|just|should|pretty|simply|enough|gonna|going|wtf|so|such|will|you|your|they|like|more)\b/},e.C=function(n,t,r){var a=e.inherit({cN:"comment",b:n,e:t,c:[]},r||{});return a.c.push(e.PWM),a.c.push({cN:"doctag",b:"(?:TODO|FIXME|NOTE|BUG|XXX):",r:0}),a},e.CLCM=e.C("//","$"),e.CBCM=e.C("/\\*","\\*/"),e.HCM=e.C("#","$"),e.NM={cN:"number",b:e.NR,r:0},e.CNM={cN:"number",b:e.CNR,r:0},e.BNM={cN:"number",b:e.BNR,r:0},e.CSSNM={cN:"number",b:e.NR+"(%|em|ex|ch|rem|vw|vh|vmin|vmax|cm|mm|in|pt|pc|px|deg|grad|rad|turn|s|ms|Hz|kHz|dpi|dpcm|dppx)?",r:0},e.RM={cN:"regexp",b:/\//,e:/\/[gimuy]*/,i:/\n/,c:[e.BE,{b:/\[/,e:/\]/,r:0,c:[e.BE]}]},e.TM={cN:"title",b:e.IR,r:0},e.UTM={cN:"title",b:e.UIR,r:0},e.METHOD_GUARD={b:"\\.\\s*"+e.UIR,r:0},e});hljs.registerLanguage("sql",function(e){var t=e.C("--","$");return{cI:!0,i:/[<>{}*#]/,c:[{bK:"begin end start commit rollback savepoint lock alter create drop rename call delete do handler insert load replace select truncate update set show pragma grant merge describe use explain help declare prepare execute deallocate release unlock purge reset change stop analyze cache flush optimize repair kill install uninstall checksum restore check backup revoke comment",e:/;/,eW:!0,l:/[\w\.]+/,k:{keyword:"abort abs absolute acc acce accep accept access accessed accessible account acos action activate add addtime admin administer advanced advise aes_decrypt aes_encrypt after agent aggregate ali alia alias allocate allow alter always analyze ancillary and any anydata anydataset anyschema anytype apply archive archived archivelog are as asc ascii asin assembly assertion associate asynchronous at atan atn2 attr attri attrib attribu attribut attribute attributes audit authenticated authentication authid authors auto autoallocate autodblink autoextend automatic availability avg backup badfile basicfile before begin beginning benchmark between bfile bfile_base big bigfile bin binary_double binary_float binlog bit_and bit_count bit_length bit_or bit_xor bitmap blob_base block blocksize body both bound buffer_cache buffer_pool build bulk by byte byteordermark bytes cache caching call calling cancel capacity cascade cascaded case cast catalog category ceil ceiling chain change changed char_base char_length character_length characters characterset charindex charset charsetform charsetid check checksum checksum_agg child choose chr chunk class cleanup clear client clob clob_base clone close cluster_id cluster_probability cluster_set clustering coalesce coercibility col collate collation collect colu colum column column_value columns columns_updated comment commit compact compatibility compiled complete composite_limit compound compress compute concat concat_ws concurrent confirm conn connec connect connect_by_iscycle connect_by_isleaf connect_by_root connect_time connection consider consistent constant constraint constraints constructor container content contents context contributors controlfile conv convert convert_tz corr corr_k corr_s corresponding corruption cos cost count count_big counted covar_pop covar_samp cpu_per_call cpu_per_session crc32 create creation critical cross cube cume_dist curdate current current_date current_time current_timestamp current_user cursor curtime customdatum cycle data database databases datafile datafiles datalength date_add date_cache date_format date_sub dateadd datediff datefromparts datename datepart datetime2fromparts day day_to_second dayname dayofmonth dayofweek dayofyear days db_role_change dbtimezone ddl deallocate declare decode decompose decrement decrypt deduplicate def defa defau defaul default defaults deferred defi defin define degrees delayed delegate delete delete_all delimited demand dense_rank depth dequeue des_decrypt des_encrypt des_key_file desc descr descri describ describe descriptor deterministic diagnostics difference dimension direct_load directory disable disable_all disallow disassociate discardfile disconnect diskgroup distinct distinctrow distribute distributed div do document domain dotnet double downgrade drop dumpfile duplicate duration each edition editionable editions element ellipsis else elsif elt empty enable enable_all enclosed encode encoding encrypt end end-exec endian enforced engine engines enqueue enterprise entityescaping eomonth error errors escaped evalname evaluate event eventdata events except exception exceptions exchange exclude excluding execu execut execute exempt exists exit exp expire explain export export_set extended extent external external_1 external_2 externally extract failed failed_login_attempts failover failure far fast feature_set feature_value fetch field fields file file_name_convert filesystem_like_logging final finish first first_value fixed flash_cache flashback floor flush following follows for forall force form forma format found found_rows freelist freelists freepools fresh from from_base64 from_days ftp full function general generated get get_format get_lock getdate getutcdate global global_name globally go goto grant grants greatest group group_concat group_id grouping grouping_id groups gtid_subtract guarantee guard handler hash hashkeys having hea head headi headin heading heap help hex hierarchy high high_priority hosts hour http id ident_current ident_incr ident_seed identified identity idle_time if ifnull ignore iif ilike ilm immediate import in include including increment index indexes indexing indextype indicator indices inet6_aton inet6_ntoa inet_aton inet_ntoa infile initial initialized initially initrans inmemory inner innodb input insert install instance instantiable instr interface interleaved intersect into invalidate invisible is is_free_lock is_ipv4 is_ipv4_compat is_not is_not_null is_used_lock isdate isnull isolation iterate java join json json_exists keep keep_duplicates key keys kill language large last last_day last_insert_id last_value lax lcase lead leading least leaves left len lenght length less level levels library like like2 like4 likec limit lines link list listagg little ln load load_file lob lobs local localtime localtimestamp locate locator lock locked log log10 log2 logfile logfiles logging logical logical_reads_per_call logoff logon logs long loop low low_priority lower lpad lrtrim ltrim main make_set makedate maketime managed management manual map mapping mask master master_pos_wait match matched materialized max maxextents maximize maxinstances maxlen maxlogfiles maxloghistory maxlogmembers maxsize maxtrans md5 measures median medium member memcompress memory merge microsecond mid migration min minextents minimum mining minus minute minvalue missing mod mode model modification modify module monitoring month months mount move movement multiset mutex name name_const names nan national native natural nav nchar nclob nested never new newline next nextval no no_write_to_binlog noarchivelog noaudit nobadfile nocheck nocompress nocopy nocycle nodelay nodiscardfile noentityescaping noguarantee nokeep nologfile nomapping nomaxvalue nominimize nominvalue nomonitoring none noneditionable nonschema noorder nopr nopro noprom nopromp noprompt norely noresetlogs noreverse normal norowdependencies noschemacheck noswitch not nothing notice notrim novalidate now nowait nth_value nullif nulls num numb numbe nvarchar nvarchar2 object ocicoll ocidate ocidatetime ociduration ociinterval ociloblocator ocinumber ociref ocirefcursor ocirowid ocistring ocitype oct octet_length of off offline offset oid oidindex old on online only opaque open operations operator optimal optimize option optionally or oracle oracle_date oradata ord ordaudio orddicom orddoc order ordimage ordinality ordvideo organization orlany orlvary out outer outfile outline output over overflow overriding package pad parallel parallel_enable parameters parent parse partial partition partitions pascal passing password password_grace_time password_lock_time password_reuse_max password_reuse_time password_verify_function patch path patindex pctincrease pctthreshold pctused pctversion percent percent_rank percentile_cont percentile_disc performance period period_add period_diff permanent physical pi pipe pipelined pivot pluggable plugin policy position post_transaction pow power pragma prebuilt precedes preceding precision prediction prediction_cost prediction_details prediction_probability prediction_set prepare present preserve prior priority private private_sga privileges procedural procedure procedure_analyze processlist profiles project prompt protection public publishingservername purge quarter query quick quiesce quota quotename radians raise rand range rank raw read reads readsize rebuild record records recover recovery recursive recycle redo reduced ref reference referenced references referencing refresh regexp_like register regr_avgx regr_avgy regr_count regr_intercept regr_r2 regr_slope regr_sxx regr_sxy reject rekey relational relative relaylog release release_lock relies_on relocate rely rem remainder rename repair repeat replace replicate replication required reset resetlogs resize resource respect restore restricted result result_cache resumable resume retention return returning returns reuse reverse revoke right rlike role roles rollback rolling rollup round row row_count rowdependencies rowid rownum rows rtrim rules safe salt sample save savepoint sb1 sb2 sb4 scan schema schemacheck scn scope scroll sdo_georaster sdo_topo_geometry search sec_to_time second section securefile security seed segment select self sequence sequential serializable server servererror session session_user sessions_per_user set sets settings sha sha1 sha2 share shared shared_pool short show shrink shutdown si_averagecolor si_colorhistogram si_featurelist si_positionalcolor si_stillimage si_texture siblings sid sign sin size size_t sizes skip slave sleep smalldatetimefromparts smallfile snapshot some soname sort soundex source space sparse spfile split sql sql_big_result sql_buffer_result sql_cache sql_calc_found_rows sql_small_result sql_variant_property sqlcode sqldata sqlerror sqlname sqlstate sqrt square standalone standby start starting startup statement static statistics stats_binomial_test stats_crosstab stats_ks_test stats_mode stats_mw_test stats_one_way_anova stats_t_test_ stats_t_test_indep stats_t_test_one stats_t_test_paired stats_wsr_test status std stddev stddev_pop stddev_samp stdev stop storage store stored str str_to_date straight_join strcmp strict string struct stuff style subdate subpartition subpartitions substitutable substr substring subtime subtring_index subtype success sum suspend switch switchoffset switchover sync synchronous synonym sys sys_xmlagg sysasm sysaux sysdate sysdatetimeoffset sysdba sysoper system system_user sysutcdatetime table tables tablespace tan tdo template temporary terminated tertiary_weights test than then thread through tier ties time time_format time_zone timediff timefromparts timeout timestamp timestampadd timestampdiff timezone_abbr timezone_minute timezone_region to to_base64 to_date to_days to_seconds todatetimeoffset trace tracking transaction transactional translate translation treat trigger trigger_nestlevel triggers trim truncate try_cast try_convert try_parse type ub1 ub2 ub4 ucase unarchived unbounded uncompress under undo unhex unicode uniform uninstall union unique unix_timestamp unknown unlimited unlock unpivot unrecoverable unsafe unsigned until untrusted unusable unused update updated upgrade upped upper upsert url urowid usable usage use use_stored_outlines user user_data user_resources users using utc_date utc_timestamp uuid uuid_short validate validate_password_strength validation valist value values var var_samp varcharc vari varia variab variabl variable variables variance varp varraw varrawc varray verify version versions view virtual visible void wait wallet warning warnings week weekday weekofyear wellformed when whene whenev wheneve whenever where while whitespace with within without work wrapped xdb xml xmlagg xmlattributes xmlcast xmlcolattval xmlelement xmlexists xmlforest xmlindex xmlnamespaces xmlpi xmlquery xmlroot xmlschema xmlserialize xmltable xmltype xor year year_to_month years yearweek",literal:"true false null",built_in:"array bigint binary bit blob boolean char character date dec decimal float int int8 integer interval number numeric real record serial serial8 smallint text varchar varying void"},c:[{cN:"string",b:"'",e:"'",c:[e.BE,{b:"''"}]},{cN:"string",b:'"',e:'"',c:[e.BE,{b:'""'}]},{cN:"string",b:"`",e:"`",c:[e.BE]},e.CNM,e.CBCM,t]},e.CBCM,t]}});hljs.registerLanguage("r",function(e){var r="([a-zA-Z]|\\.[a-zA-Z.])[a-zA-Z0-9._]*";return{c:[e.HCM,{b:r,l:r,k:{keyword:"function if in break next repeat else for return switch while try tryCatch stop warning require library attach detach source setMethod setGeneric setGroupGeneric setClass ...",literal:"NULL NA TRUE FALSE T F Inf NaN NA_integer_|10 NA_real_|10 NA_character_|10 NA_complex_|10"},r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"\\d+(?:[eE][+\\-]?\\d*)?L\\b",r:0},{cN:"number",b:"\\d+\\.(?!\\d)(?:i\\b)?",r:0},{cN:"number",b:"\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{b:"`",e:"`",r:0},{cN:"string",c:[e.BE],v:[{b:'"',e:'"'},{b:"'",e:"'"}]}]}});hljs.registerLanguage("perl",function(e){var t="getpwent getservent quotemeta msgrcv scalar kill dbmclose undef lc ma syswrite tr send umask sysopen shmwrite vec qx utime local oct semctl localtime readpipe do return format read sprintf dbmopen pop getpgrp not getpwnam rewinddir qqfileno qw endprotoent wait sethostent bless s|0 opendir continue each sleep endgrent shutdown dump chomp connect getsockname die socketpair close flock exists index shmgetsub for endpwent redo lstat msgctl setpgrp abs exit select print ref gethostbyaddr unshift fcntl syscall goto getnetbyaddr join gmtime symlink semget splice x|0 getpeername recv log setsockopt cos last reverse gethostbyname getgrnam study formline endhostent times chop length gethostent getnetent pack getprotoent getservbyname rand mkdir pos chmod y|0 substr endnetent printf next open msgsnd readdir use unlink getsockopt getpriority rindex wantarray hex system getservbyport endservent int chr untie rmdir prototype tell listen fork shmread ucfirst setprotoent else sysseek link getgrgid shmctl waitpid unpack getnetbyname reset chdir grep split require caller lcfirst until warn while values shift telldir getpwuid my getprotobynumber delete and sort uc defined srand accept package seekdir getprotobyname semop our rename seek if q|0 chroot sysread setpwent no crypt getc chown sqrt write setnetent setpriority foreach tie sin msgget map stat getlogin unless elsif truncate exec keys glob tied closedirioctl socket readlink eval xor readline binmode setservent eof ord bind alarm pipe atan2 getgrent exp time push setgrent gt lt or ne m|0 break given say state when",r={cN:"subst",b:"[$@]\\{",e:"\\}",k:t},s={b:"->{",e:"}"},n={v:[{b:/\$\d/},{b:/[\$%@](\^\w\b|#\w+(::\w+)*|{\w+}|\w+(::\w*)*)/},{b:/[\$%@][^\s\w{]/,r:0}]},i=[e.BE,r,n],o=[n,e.HCM,e.C("^\\=\\w","\\=cut",{eW:!0}),s,{cN:"string",c:i,v:[{b:"q[qwxr]?\\s*\\(",e:"\\)",r:5},{b:"q[qwxr]?\\s*\\[",e:"\\]",r:5},{b:"q[qwxr]?\\s*\\{",e:"\\}",r:5},{b:"q[qwxr]?\\s*\\|",e:"\\|",r:5},{b:"q[qwxr]?\\s*\\<",e:"\\>",r:5},{b:"qw\\s+q",e:"q",r:5},{b:"'",e:"'",c:[e.BE]},{b:'"',e:'"'},{b:"`",e:"`",c:[e.BE]},{b:"{\\w+}",c:[],r:0},{b:"-?\\w+\\s*\\=\\>",c:[],r:0}]},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"(\\/\\/|"+e.RSR+"|\\b(split|return|print|reverse|grep)\\b)\\s*",k:"split return print reverse grep",r:0,c:[e.HCM,{cN:"regexp",b:"(s|tr|y)/(\\\\.|[^/])*/(\\\\.|[^/])*/[a-z]*",r:10},{cN:"regexp",b:"(m|qr)?/",e:"/[a-z]*",c:[e.BE],r:0}]},{cN:"function",bK:"sub",e:"(\\s*\\(.*?\\))?[;{]",eE:!0,r:5,c:[e.TM]},{b:"-\\w\\b",r:0},{b:"^__DATA__$",e:"^__END__$",sL:"mojolicious",c:[{b:"^@@.*",e:"$",cN:"comment"}]}];return r.c=o,s.c=o,{aliases:["pl","pm"],l:/[\w\.]+/,k:t,c:o}});hljs.registerLanguage("ini",function(e){var b={cN:"string",c:[e.BE],v:[{b:"'''",e:"'''",r:10},{b:'"""',e:'"""',r:10},{b:'"',e:'"'},{b:"'",e:"'"}]};return{aliases:["toml"],cI:!0,i:/\S/,c:[e.C(";","$"),e.HCM,{cN:"section",b:/^\s*\[+/,e:/\]+/},{b:/^[a-z0-9\[\]_-]+\s*=\s*/,e:"$",rB:!0,c:[{cN:"attr",b:/[a-z0-9\[\]_-]+/},{b:/=/,eW:!0,r:0,c:[{cN:"literal",b:/\bon|off|true|false|yes|no\b/},{cN:"variable",v:[{b:/\$[\w\d"][\w\d_]*/},{b:/\$\{(.*?)}/}]},b,{cN:"number",b:/([\+\-]+)?[\d]+_[\d_]+/},e.NM]}]}]}});hljs.registerLanguage("diff",function(e){return{aliases:["patch"],c:[{cN:"meta",r:10,v:[{b:/^@@ +\-\d+,\d+ +\+\d+,\d+ +@@$/},{b:/^\*\*\* +\d+,\d+ +\*\*\*\*$/},{b:/^\-\-\- +\d+,\d+ +\-\-\-\-$/}]},{cN:"comment",v:[{b:/Index: /,e:/$/},{b:/={3,}/,e:/$/},{b:/^\-{3}/,e:/$/},{b:/^\*{3} /,e:/$/},{b:/^\+{3}/,e:/$/},{b:/\*{5}/,e:/\*{5}$/}]},{cN:"addition",b:"^\\+",e:"$"},{cN:"deletion",b:"^\\-",e:"$"},{cN:"addition",b:"^\\!",e:"$"}]}});hljs.registerLanguage("go",function(e){var t={keyword:"break default func interface select case map struct chan else goto package switch const fallthrough if range type continue for import return var go defer bool byte complex64 complex128 float32 float64 int8 int16 int32 int64 string uint8 uint16 uint32 uint64 int uint uintptr rune",literal:"true false iota nil",built_in:"append cap close complex copy imag len make new panic print println real recover delete"};return{aliases:["golang"],k:t,i:"</",c:[e.CLCM,e.CBCM,{cN:"string",v:[e.QSM,{b:"'",e:"[^\\\\]'"},{b:"`",e:"`"}]},{cN:"number",v:[{b:e.CNR+"[dflsi]",r:1},e.CNM]},{b:/:=/},{cN:"function",bK:"func",e:/\s*\{/,eE:!0,c:[e.TM,{cN:"params",b:/\(/,e:/\)/,k:t,i:/["']/}]}]}});hljs.registerLanguage("bash",function(e){var t={cN:"variable",v:[{b:/\$[\w\d#@][\w\d_]*/},{b:/\$\{(.*?)}/}]},s={cN:"string",b:/"/,e:/"/,c:[e.BE,t,{cN:"variable",b:/\$\(/,e:/\)/,c:[e.BE]}]},a={cN:"string",b:/'/,e:/'/};return{aliases:["sh","zsh"],l:/\b-?[a-z\._]+\b/,k:{keyword:"if then else elif fi for while in do done case esac function",literal:"true false",built_in:"break cd continue eval exec exit export getopts hash pwd readonly return shift test times trap umask unset alias bind builtin caller command declare echo enable help let local logout mapfile printf read readarray source type typeset ulimit unalias set shopt autoload bg bindkey bye cap chdir clone comparguments compcall compctl compdescribe compfiles compgroups compquote comptags comptry compvalues dirs disable disown echotc echoti emulate fc fg float functions getcap getln history integer jobs kill limit log noglob popd print pushd pushln rehash sched setcap setopt stat suspend ttyctl unfunction unhash unlimit unsetopt vared wait whence where which zcompile zformat zftp zle zmodload zparseopts zprof zpty zregexparse zsocket zstyle ztcp",_:"-ne -eq -lt -gt -f -d -e -s -l -a"},c:[{cN:"meta",b:/^#![^\n]+sh\s*$/,r:10},{cN:"function",b:/\w[\w\d_]*\s*\(\s*\)\s*\{/,rB:!0,c:[e.inherit(e.TM,{b:/\w[\w\d_]*/})],r:0},e.HCM,s,a,t]}});hljs.registerLanguage("python",function(e){var r={keyword:"and elif is global as in if from raise for except finally print import pass return exec else break not with class assert yield try while continue del or def lambda async await nonlocal|10 None True False",built_in:"Ellipsis NotImplemented"},b={cN:"meta",b:/^(>>>|\.\.\.) /},c={cN:"subst",b:/\{/,e:/\}/,k:r,i:/#/},a={cN:"string",c:[e.BE],v:[{b:/(u|b)?r?'''/,e:/'''/,c:[b],r:10},{b:/(u|b)?r?"""/,e:/"""/,c:[b],r:10},{b:/(fr|rf|f)'''/,e:/'''/,c:[b,c]},{b:/(fr|rf|f)"""/,e:/"""/,c:[b,c]},{b:/(u|r|ur)'/,e:/'/,r:10},{b:/(u|r|ur)"/,e:/"/,r:10},{b:/(b|br)'/,e:/'/},{b:/(b|br)"/,e:/"/},{b:/(fr|rf|f)'/,e:/'/,c:[c]},{b:/(fr|rf|f)"/,e:/"/,c:[c]},e.ASM,e.QSM]},s={cN:"number",r:0,v:[{b:e.BNR+"[lLjJ]?"},{b:"\\b(0o[0-7]+)[lLjJ]?"},{b:e.CNR+"[lLjJ]?"}]},i={cN:"params",b:/\(/,e:/\)/,c:["self",b,s,a]};return c.c=[a,s,b],{aliases:["py","gyp"],k:r,i:/(<\/|->|\?)|=>/,c:[b,s,a,e.HCM,{v:[{cN:"function",bK:"def"},{cN:"class",bK:"class"}],e:/:/,i:/[${=;\n,]/,c:[e.UTM,i,{b:/->/,eW:!0,k:"None"}]},{cN:"meta",b:/^[\t ]*@/,e:/$/},{b:/\b(print|exec)\(/}]}});hljs.registerLanguage("julia",function(e){var r={keyword:"in isa where baremodule begin break catch ccall const continue do else elseif end export false finally for function global if import importall let local macro module quote return true try using while type immutable abstract bitstype typealias ",literal:"true false ARGS C_NULL DevNull ENDIAN_BOM ENV I Inf Inf16 Inf32 Inf64 InsertionSort JULIA_HOME LOAD_PATH MergeSort NaN NaN16 NaN32 NaN64 PROGRAM_FILE QuickSort RoundDown RoundFromZero RoundNearest RoundNearestTiesAway RoundNearestTiesUp RoundToZero RoundUp STDERR STDIN STDOUT VERSION catalan e|0 eu|0 eulergamma golden im nothing pi γ π φ ",built_in:"ANY AbstractArray AbstractChannel AbstractFloat AbstractMatrix AbstractRNG AbstractSerializer AbstractSet AbstractSparseArray AbstractSparseMatrix AbstractSparseVector AbstractString AbstractUnitRange AbstractVecOrMat AbstractVector Any ArgumentError Array AssertionError Associative Base64DecodePipe Base64EncodePipe Bidiagonal BigFloat BigInt BitArray BitMatrix BitVector Bool BoundsError BufferStream CachingPool CapturedException CartesianIndex CartesianRange Cchar Cdouble Cfloat Channel Char Cint Cintmax_t Clong Clonglong ClusterManager Cmd CodeInfo Colon Complex Complex128 Complex32 Complex64 CompositeException Condition ConjArray ConjMatrix ConjVector Cptrdiff_t Cshort Csize_t Cssize_t Cstring Cuchar Cuint Cuintmax_t Culong Culonglong Cushort Cwchar_t Cwstring DataType Date DateFormat DateTime DenseArray DenseMatrix DenseVecOrMat DenseVector Diagonal Dict DimensionMismatch Dims DirectIndexString Display DivideError DomainError EOFError EachLine Enum Enumerate ErrorException Exception ExponentialBackOff Expr Factorization FileMonitor Float16 Float32 Float64 Function Future GlobalRef GotoNode HTML Hermitian IO IOBuffer IOContext IOStream IPAddr IPv4 IPv6 IndexCartesian IndexLinear IndexStyle InexactError InitError Int Int128 Int16 Int32 Int64 Int8 IntSet Integer InterruptException InvalidStateException Irrational KeyError LabelNode LinSpace LineNumberNode LoadError LowerTriangular MIME Matrix MersenneTwister Method MethodError MethodTable Module NTuple NewvarNode NullException Nullable Number ObjectIdDict OrdinalRange OutOfMemoryError OverflowError Pair ParseError PartialQuickSort PermutedDimsArray Pipe PollingFileWatcher ProcessExitedException Ptr QuoteNode RandomDevice Range RangeIndex Rational RawFD ReadOnlyMemoryError Real ReentrantLock Ref Regex RegexMatch RemoteChannel RemoteException RevString RoundingMode RowVector SSAValue SegmentationFault SerializationState Set SharedArray SharedMatrix SharedVector Signed SimpleVector Slot SlotNumber SparseMatrixCSC SparseVector StackFrame StackOverflowError StackTrace StepRange StepRangeLen StridedArray StridedMatrix StridedVecOrMat StridedVector String SubArray SubString SymTridiagonal Symbol Symmetric SystemError TCPSocket Task Text TextDisplay Timer Tridiagonal Tuple Type TypeError TypeMapEntry TypeMapLevel TypeName TypeVar TypedSlot UDPSocket UInt UInt128 UInt16 UInt32 UInt64 UInt8 UndefRefError UndefVarError UnicodeError UniformScaling Union UnionAll UnitRange Unsigned UpperTriangular Val Vararg VecElement VecOrMat Vector VersionNumber Void WeakKeyDict WeakRef WorkerConfig WorkerPool "},t="[A-Za-z_\\u00A1-\\uFFFF][A-Za-z_0-9\\u00A1-\\uFFFF]*",a={l:t,k:r,i:/<\//},n={cN:"number",b:/(\b0x[\d_]*(\.[\d_]*)?|0x\.\d[\d_]*)p[-+]?\d+|\b0[box][a-fA-F0-9][a-fA-F0-9_]*|(\b\d[\d_]*(\.[\d_]*)?|\.\d[\d_]*)([eEfF][-+]?\d+)?/,r:0},o={cN:"string",b:/'(.|\\[xXuU][a-zA-Z0-9]+)'/},i={cN:"subst",b:/\$\(/,e:/\)/,k:r},l={cN:"variable",b:"\\$"+t},c={cN:"string",c:[e.BE,i,l],v:[{b:/\w*"""/,e:/"""\w*/,r:10},{b:/\w*"/,e:/"\w*/}]},s={cN:"string",c:[e.BE,i,l],b:"`",e:"`"},d={cN:"meta",b:"@"+t},u={cN:"comment",v:[{b:"#=",e:"=#",r:10},{b:"#",e:"$"}]};return a.c=[n,o,c,s,d,u,e.HCM,{cN:"keyword",b:"\\b(((abstract|primitive)\\s+)type|(mutable\\s+)?struct)\\b"},{b:/<:/}],i.c=a.c,a});hljs.registerLanguage("coffeescript",function(e){var c={keyword:"in if for while finally new do return else break catch instanceof throw try this switch continue typeof delete debugger super yield import export from as default await then unless until loop of by when and or is isnt not",literal:"true false null undefined yes no on off",built_in:"npm require console print module global window document"},n="[A-Za-z$_][0-9A-Za-z$_]*",r={cN:"subst",b:/#\{/,e:/}/,k:c},i=[e.BNM,e.inherit(e.CNM,{starts:{e:"(\\s*/)?",r:0}}),{cN:"string",v:[{b:/'''/,e:/'''/,c:[e.BE]},{b:/'/,e:/'/,c:[e.BE]},{b:/"""/,e:/"""/,c:[e.BE,r]},{b:/"/,e:/"/,c:[e.BE,r]}]},{cN:"regexp",v:[{b:"///",e:"///",c:[r,e.HCM]},{b:"//[gim]*",r:0},{b:/\/(?![ *])(\\\/|.)*?\/[gim]*(?=\W|$)/}]},{b:"@"+n},{sL:"javascript",eB:!0,eE:!0,v:[{b:"```",e:"```"},{b:"`",e:"`"}]}];r.c=i;var s=e.inherit(e.TM,{b:n}),t="(\\(.*\\))?\\s*\\B[-=]>",o={cN:"params",b:"\\([^\\(]",rB:!0,c:[{b:/\(/,e:/\)/,k:c,c:["self"].concat(i)}]};return{aliases:["coffee","cson","iced"],k:c,i:/\/\*/,c:i.concat([e.C("###","###"),e.HCM,{cN:"function",b:"^\\s*"+n+"\\s*=\\s*"+t,e:"[-=]>",rB:!0,c:[s,o]},{b:/[:\(,=]\s*/,r:0,c:[{cN:"function",b:t,e:"[-=]>",rB:!0,c:[o]}]},{cN:"class",bK:"class",e:"$",i:/[:="\[\]]/,c:[{bK:"extends",eW:!0,i:/[:="\[\]]/,c:[s]},s]},{b:n+":",e:":",rB:!0,rE:!0,r:0}])}});hljs.registerLanguage("cpp",function(t){var e={cN:"keyword",b:"\\b[a-z\\d_]*_t\\b"},r={cN:"string",v:[{b:'(u8?|U)?L?"',e:'"',i:"\\n",c:[t.BE]},{b:'(u8?|U)?R"',e:'"',c:[t.BE]},{b:"'\\\\?.",e:"'",i:"."}]},s={cN:"number",v:[{b:"\\b(0b[01']+)"},{b:"(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)(u|U|l|L|ul|UL|f|F|b|B)"},{b:"(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)"}],r:0},i={cN:"meta",b:/#\s*[a-z]+\b/,e:/$/,k:{"meta-keyword":"if else elif endif define undef warning error line pragma ifdef ifndef include"},c:[{b:/\\\n/,r:0},t.inherit(r,{cN:"meta-string"}),{cN:"meta-string",b:/<[^\n>]*>/,e:/$/,i:"\\n"},t.CLCM,t.CBCM]},a=t.IR+"\\s*\\(",c={keyword:"int float while private char catch import module export virtual operator sizeof dynamic_cast|10 typedef const_cast|10 const for static_cast|10 union namespace unsigned long volatile static protected bool template mutable if public friend do goto auto void enum else break extern using asm case typeid short reinterpret_cast|10 default double register explicit signed typename try this switch continue inline delete alignof constexpr decltype noexcept static_assert thread_local restrict _Bool complex _Complex _Imaginary atomic_bool atomic_char atomic_schar atomic_uchar atomic_short atomic_ushort atomic_int atomic_uint atomic_long atomic_ulong atomic_llong atomic_ullong new throw return and or not",built_in:"std string cin cout cerr clog stdin stdout stderr stringstream istringstream ostringstream auto_ptr deque list queue stack vector map set bitset multiset multimap unordered_set unordered_map unordered_multiset unordered_multimap array shared_ptr abort abs acos asin atan2 atan calloc ceil cosh cos exit exp fabs floor fmod fprintf fputs free frexp fscanf isalnum isalpha iscntrl isdigit isgraph islower isprint ispunct isspace isupper isxdigit tolower toupper labs ldexp log10 log malloc realloc memchr memcmp memcpy memset modf pow printf putchar puts scanf sinh sin snprintf sprintf sqrt sscanf strcat strchr strcmp strcpy strcspn strlen strncat strncmp strncpy strpbrk strrchr strspn strstr tanh tan vfprintf vprintf vsprintf endl initializer_list unique_ptr",literal:"true false nullptr NULL"},n=[e,t.CLCM,t.CBCM,s,r];return{aliases:["c","cc","h","c++","h++","hpp"],k:c,i:"</",c:n.concat([i,{b:"\\b(deque|list|queue|stack|vector|map|set|bitset|multiset|multimap|unordered_map|unordered_set|unordered_multiset|unordered_multimap|array)\\s*<",e:">",k:c,c:["self",e]},{b:t.IR+"::",k:c},{v:[{b:/=/,e:/;/},{b:/\(/,e:/\)/},{bK:"new throw return else",e:/;/}],k:c,c:n.concat([{b:/\(/,e:/\)/,k:c,c:n.concat(["self"]),r:0}]),r:0},{cN:"function",b:"("+t.IR+"[\\*&\\s]+)+"+a,rB:!0,e:/[{;=]/,eE:!0,k:c,i:/[^\w\s\*&]/,c:[{b:a,rB:!0,c:[t.TM],r:0},{cN:"params",b:/\(/,e:/\)/,k:c,r:0,c:[t.CLCM,t.CBCM,r,s,e]},t.CLCM,t.CBCM,i]},{cN:"class",bK:"class struct",e:/[{;:]/,c:[{b:/</,e:/>/,c:["self"]},t.TM]}]),exports:{preprocessor:i,strings:r,k:c}}});hljs.registerLanguage("ruby",function(e){var b="[a-zA-Z_]\\w*[!?=]?|[-+~]\\@|<<|>>|=~|===?|<=>|[<>]=?|\\*\\*|[-/+%^&*~`|]|\\[\\]=?",r={keyword:"and then defined module in return redo if BEGIN retry end for self when next until do begin unless END rescue else break undef not super class case require yield alias while ensure elsif or include attr_reader attr_writer attr_accessor",literal:"true false nil"},c={cN:"doctag",b:"@[A-Za-z]+"},a={b:"#<",e:">"},s=[e.C("#","$",{c:[c]}),e.C("^\\=begin","^\\=end",{c:[c],r:10}),e.C("^__END__","\\n$")],n={cN:"subst",b:"#\\{",e:"}",k:r},t={cN:"string",c:[e.BE,n],v:[{b:/'/,e:/'/},{b:/"/,e:/"/},{b:/`/,e:/`/},{b:"%[qQwWx]?\\(",e:"\\)"},{b:"%[qQwWx]?\\[",e:"\\]"},{b:"%[qQwWx]?{",e:"}"},{b:"%[qQwWx]?<",e:">"},{b:"%[qQwWx]?/",e:"/"},{b:"%[qQwWx]?%",e:"%"},{b:"%[qQwWx]?-",e:"-"},{b:"%[qQwWx]?\\|",e:"\\|"},{b:/\B\?(\\\d{1,3}|\\x[A-Fa-f0-9]{1,2}|\\u[A-Fa-f0-9]{4}|\\?\S)\b/},{b:/<<(-?)\w+$/,e:/^\s*\w+$/}]},i={cN:"params",b:"\\(",e:"\\)",endsParent:!0,k:r},d=[t,a,{cN:"class",bK:"class module",e:"$|;",i:/=/,c:[e.inherit(e.TM,{b:"[A-Za-z_]\\w*(::\\w+)*(\\?|\\!)?"}),{b:"<\\s*",c:[{b:"("+e.IR+"::)?"+e.IR}]}].concat(s)},{cN:"function",bK:"def",e:"$|;",c:[e.inherit(e.TM,{b:b}),i].concat(s)},{b:e.IR+"::"},{cN:"symbol",b:e.UIR+"(\\!|\\?)?:",r:0},{cN:"symbol",b:":(?!\\s)",c:[t,{b:b}],r:0},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"(\\$\\W)|((\\$|\\@\\@?)(\\w+))"},{cN:"params",b:/\|/,e:/\|/,k:r},{b:"("+e.RSR+"|unless)\\s*",k:"unless",c:[a,{cN:"regexp",c:[e.BE,n],i:/\n/,v:[{b:"/",e:"/[a-z]*"},{b:"%r{",e:"}[a-z]*"},{b:"%r\\(",e:"\\)[a-z]*"},{b:"%r!",e:"![a-z]*"},{b:"%r\\[",e:"\\][a-z]*"}]}].concat(s),r:0}].concat(s);n.c=d,i.c=d;var l="[>?]>",o="[\\w#]+\\(\\w+\\):\\d+:\\d+>",u="(\\w+-)?\\d+\\.\\d+\\.\\d(p\\d+)?[^>]+>",w=[{b:/^\s*=>/,starts:{e:"$",c:d}},{cN:"meta",b:"^("+l+"|"+o+"|"+u+")",starts:{e:"$",c:d}}];return{aliases:["rb","gemspec","podspec","thor","irb"],k:r,i:/\/\*/,c:s.concat(w).concat(d)}});hljs.registerLanguage("yaml",function(e){var b="true false yes no null",a="^[ \\-]*",r="[a-zA-Z_][\\w\\-]*",t={cN:"attr",v:[{b:a+r+":"},{b:a+'"'+r+'":'},{b:a+"'"+r+"':"}]},c={cN:"template-variable",v:[{b:"{{",e:"}}"},{b:"%{",e:"}"}]},l={cN:"string",r:0,v:[{b:/'/,e:/'/},{b:/"/,e:/"/},{b:/\S+/}],c:[e.BE,c]};return{cI:!0,aliases:["yml","YAML","yaml"],c:[t,{cN:"meta",b:"^---s*$",r:10},{cN:"string",b:"[\\|>] *$",rE:!0,c:l.c,e:t.v[0].b},{b:"<%[%=-]?",e:"[%-]?%>",sL:"ruby",eB:!0,eE:!0,r:0},{cN:"type",b:"!!"+e.UIR},{cN:"meta",b:"&"+e.UIR+"$"},{cN:"meta",b:"\\*"+e.UIR+"$"},{cN:"bullet",b:"^ *-",r:0},e.HCM,{bK:b,k:{literal:b}},e.CNM,l]}});hljs.registerLanguage("css",function(e){var c="[a-zA-Z-][a-zA-Z0-9_-]*",t={b:/[A-Z\_\.\-]+\s*:/,rB:!0,e:";",eW:!0,c:[{cN:"attribute",b:/\S/,e:":",eE:!0,starts:{eW:!0,eE:!0,c:[{b:/[\w-]+\(/,rB:!0,c:[{cN:"built_in",b:/[\w-]+/},{b:/\(/,e:/\)/,c:[e.ASM,e.QSM]}]},e.CSSNM,e.QSM,e.ASM,e.CBCM,{cN:"number",b:"#[0-9A-Fa-f]+"},{cN:"meta",b:"!important"}]}}]};return{cI:!0,i:/[=\/|'\$]/,c:[e.CBCM,{cN:"selector-id",b:/#[A-Za-z0-9_-]+/},{cN:"selector-class",b:/\.[A-Za-z0-9_-]+/},{cN:"selector-attr",b:/\[/,e:/\]/,i:"$"},{cN:"selector-pseudo",b:/:(:)?[a-zA-Z0-9\_\-\+\(\)"'.]+/},{b:"@(font-face|page)",l:"[a-z-]+",k:"font-face page"},{b:"@",e:"[{;]",i:/:/,c:[{cN:"keyword",b:/\w+/},{b:/\s/,eW:!0,eE:!0,r:0,c:[e.ASM,e.QSM,e.CSSNM]}]},{cN:"selector-tag",b:c,r:0},{b:"{",e:"}",i:/\S/,c:[e.CBCM,t]}]}});hljs.registerLanguage("fortran",function(e){var t={cN:"params",b:"\\(",e:"\\)"},n={literal:".False. .True.",keyword:"kind do while private call intrinsic where elsewhere type endtype endmodule endselect endinterface end enddo endif if forall endforall only contains default return stop then public subroutine|10 function program .and. .or. .not. .le. .eq. .ge. .gt. .lt. goto save else use module select case access blank direct exist file fmt form formatted iostat name named nextrec number opened rec recl sequential status unformatted unit continue format pause cycle exit c_null_char c_alert c_backspace c_form_feed flush wait decimal round iomsg synchronous nopass non_overridable pass protected volatile abstract extends import non_intrinsic value deferred generic final enumerator class associate bind enum c_int c_short c_long c_long_long c_signed_char c_size_t c_int8_t c_int16_t c_int32_t c_int64_t c_int_least8_t c_int_least16_t c_int_least32_t c_int_least64_t c_int_fast8_t c_int_fast16_t c_int_fast32_t c_int_fast64_t c_intmax_t C_intptr_t c_float c_double c_long_double c_float_complex c_double_complex c_long_double_complex c_bool c_char c_null_ptr c_null_funptr c_new_line c_carriage_return c_horizontal_tab c_vertical_tab iso_c_binding c_loc c_funloc c_associated  c_f_pointer c_ptr c_funptr iso_fortran_env character_storage_size error_unit file_storage_size input_unit iostat_end iostat_eor numeric_storage_size output_unit c_f_procpointer ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode newunit contiguous recursive pad position action delim readwrite eor advance nml interface procedure namelist include sequence elemental pure integer real character complex logical dimension allocatable|10 parameter external implicit|10 none double precision assign intent optional pointer target in out common equivalence data",built_in:"alog alog10 amax0 amax1 amin0 amin1 amod cabs ccos cexp clog csin csqrt dabs dacos dasin datan datan2 dcos dcosh ddim dexp dint dlog dlog10 dmax1 dmin1 dmod dnint dsign dsin dsinh dsqrt dtan dtanh float iabs idim idint idnint ifix isign max0 max1 min0 min1 sngl algama cdabs cdcos cdexp cdlog cdsin cdsqrt cqabs cqcos cqexp cqlog cqsin cqsqrt dcmplx dconjg derf derfc dfloat dgamma dimag dlgama iqint qabs qacos qasin qatan qatan2 qcmplx qconjg qcos qcosh qdim qerf qerfc qexp qgamma qimag qlgama qlog qlog10 qmax1 qmin1 qmod qnint qsign qsin qsinh qsqrt qtan qtanh abs acos aimag aint anint asin atan atan2 char cmplx conjg cos cosh exp ichar index int log log10 max min nint sign sin sinh sqrt tan tanh print write dim lge lgt lle llt mod nullify allocate deallocate adjustl adjustr all allocated any associated bit_size btest ceiling count cshift date_and_time digits dot_product eoshift epsilon exponent floor fraction huge iand ibclr ibits ibset ieor ior ishft ishftc lbound len_trim matmul maxexponent maxloc maxval merge minexponent minloc minval modulo mvbits nearest pack present product radix random_number random_seed range repeat reshape rrspacing scale scan selected_int_kind selected_real_kind set_exponent shape size spacing spread sum system_clock tiny transpose trim ubound unpack verify achar iachar transfer dble entry dprod cpu_time command_argument_count get_command get_command_argument get_environment_variable is_iostat_end ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode is_iostat_eor move_alloc new_line selected_char_kind same_type_as extends_type_ofacosh asinh atanh bessel_j0 bessel_j1 bessel_jn bessel_y0 bessel_y1 bessel_yn erf erfc erfc_scaled gamma log_gamma hypot norm2 atomic_define atomic_ref execute_command_line leadz trailz storage_size merge_bits bge bgt ble blt dshiftl dshiftr findloc iall iany iparity image_index lcobound ucobound maskl maskr num_images parity popcnt poppar shifta shiftl shiftr this_image"};return{cI:!0,aliases:["f90","f95"],k:n,i:/\/\*/,c:[e.inherit(e.ASM,{cN:"string",r:0}),e.inherit(e.QSM,{cN:"string",r:0}),{cN:"function",bK:"subroutine function program",i:"[${=\\n]",c:[e.UTM,t]},e.C("!","$",{r:0}),{cN:"number",b:"(?=\\b|\\+|\\-|\\.)(?=\\.\\d|\\d)(?:\\d+)?(?:\\.?\\d*)(?:[de][+-]?\\d+)?\\b\\.?",r:0}]}});hljs.registerLanguage("awk",function(e){var r={cN:"variable",v:[{b:/\$[\w\d#@][\w\d_]*/},{b:/\$\{(.*?)}/}]},b="BEGIN END if else while do for in break continue delete next nextfile function func exit|10",n={cN:"string",c:[e.BE],v:[{b:/(u|b)?r?'''/,e:/'''/,r:10},{b:/(u|b)?r?"""/,e:/"""/,r:10},{b:/(u|r|ur)'/,e:/'/,r:10},{b:/(u|r|ur)"/,e:/"/,r:10},{b:/(b|br)'/,e:/'/},{b:/(b|br)"/,e:/"/},e.ASM,e.QSM]};return{k:{keyword:b},c:[r,n,e.RM,e.HCM,e.NM]}});hljs.registerLanguage("makefile",function(e){var i={cN:"variable",v:[{b:"\\$\\("+e.UIR+"\\)",c:[e.BE]},{b:/\$[@%<?\^\+\*]/}]},r={cN:"string",b:/"/,e:/"/,c:[e.BE,i]},a={cN:"variable",b:/\$\([\w-]+\s/,e:/\)/,k:{built_in:"subst patsubst strip findstring filter filter-out sort word wordlist firstword lastword dir notdir suffix basename addsuffix addprefix join wildcard realpath abspath error warning shell origin flavor foreach if or and call eval file value"},c:[i]},n={b:"^"+e.UIR+"\\s*[:+?]?=",i:"\\n",rB:!0,c:[{b:"^"+e.UIR,e:"[:+?]?=",eE:!0}]},t={cN:"meta",b:/^\.PHONY:/,e:/$/,k:{"meta-keyword":".PHONY"},l:/[\.\w]+/},l={cN:"section",b:/^[^\s]+:/,e:/$/,c:[i]};return{aliases:["mk","mak"],k:"define endef undefine ifdef ifndef ifeq ifneq else endif include -include sinclude override export unexport private vpath",l:/[\w-]+/,c:[e.HCM,i,r,a,n,t,l]}});hljs.registerLanguage("java",function(e){var a="[À-ʸa-zA-Z_$][À-ʸa-zA-Z_$0-9]*",t=a+"(<"+a+"(\\s*,\\s*"+a+")*>)?",r="false synchronized int abstract float private char boolean static null if const for true while long strictfp finally protected import native final void enum else break transient catch instanceof byte super volatile case assert short package default double public try this switch continue throws protected public private module requires exports do",s="\\b(0[bB]([01]+[01_]+[01]+|[01]+)|0[xX]([a-fA-F0-9]+[a-fA-F0-9_]+[a-fA-F0-9]+|[a-fA-F0-9]+)|(([\\d]+[\\d_]+[\\d]+|[\\d]+)(\\.([\\d]+[\\d_]+[\\d]+|[\\d]+))?|\\.([\\d]+[\\d_]+[\\d]+|[\\d]+))([eE][-+]?\\d+)?)[lLfF]?",c={cN:"number",b:s,r:0};return{aliases:["jsp"],k:r,i:/<\/|#/,c:[e.C("/\\*\\*","\\*/",{r:0,c:[{b:/\w+@/,r:0},{cN:"doctag",b:"@[A-Za-z]+"}]}),e.CLCM,e.CBCM,e.ASM,e.QSM,{cN:"class",bK:"class interface",e:/[{;=]/,eE:!0,k:"class interface",i:/[:"\[\]]/,c:[{bK:"extends implements"},e.UTM]},{bK:"new throw return else",r:0},{cN:"function",b:"("+t+"\\s+)+"+e.UIR+"\\s*\\(",rB:!0,e:/[{;=]/,eE:!0,k:r,c:[{b:e.UIR+"\\s*\\(",rB:!0,r:0,c:[e.UTM]},{cN:"params",b:/\(/,e:/\)/,k:r,r:0,c:[e.ASM,e.QSM,e.CNM,e.CBCM]},e.CLCM,e.CBCM]},c,{cN:"meta",b:"@[A-Za-z]+"}]}});hljs.registerLanguage("stan",function(e){return{c:[e.HCM,e.CLCM,e.CBCM,{b:e.UIR,l:e.UIR,k:{name:"for in while repeat until if then else",symbol:"bernoulli bernoulli_logit binomial binomial_logit beta_binomial hypergeometric categorical categorical_logit ordered_logistic neg_binomial neg_binomial_2 neg_binomial_2_log poisson poisson_log multinomial normal exp_mod_normal skew_normal student_t cauchy double_exponential logistic gumbel lognormal chi_square inv_chi_square scaled_inv_chi_square exponential inv_gamma weibull frechet rayleigh wiener pareto pareto_type_2 von_mises uniform multi_normal multi_normal_prec multi_normal_cholesky multi_gp multi_gp_cholesky multi_student_t gaussian_dlm_obs dirichlet lkj_corr lkj_corr_cholesky wishart inv_wishart","selector-tag":"int real vector simplex unit_vector ordered positive_ordered row_vector matrix cholesky_factor_corr cholesky_factor_cov corr_matrix cov_matrix",title:"functions model data parameters quantities transformed generated",literal:"true false"},r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"\\d+(?:[eE][+\\-]?\\d*)?L\\b",r:0},{cN:"number",b:"\\d+\\.(?!\\d)(?:i\\b)?",r:0},{cN:"number",b:"\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",r:0}]}});hljs.registerLanguage("javascript",function(e){var r="[A-Za-z$_][0-9A-Za-z$_]*",t={keyword:"in of if for while finally var new function do return void else break catch instanceof with throw case default try this switch continue typeof delete let yield const export super debugger as async await static import from as",literal:"true false null undefined NaN Infinity",built_in:"eval isFinite isNaN parseFloat parseInt decodeURI decodeURIComponent encodeURI encodeURIComponent escape unescape Object Function Boolean Error EvalError InternalError RangeError ReferenceError StopIteration SyntaxError TypeError URIError Number Math Date String RegExp Array Float32Array Float64Array Int16Array Int32Array Int8Array Uint16Array Uint32Array Uint8Array Uint8ClampedArray ArrayBuffer DataView JSON Intl arguments require module console window document Symbol Set Map WeakSet WeakMap Proxy Reflect Promise"},a={cN:"number",v:[{b:"\\b(0[bB][01]+)"},{b:"\\b(0[oO][0-7]+)"},{b:e.CNR}],r:0},n={cN:"subst",b:"\\$\\{",e:"\\}",k:t,c:[]},c={cN:"string",b:"`",e:"`",c:[e.BE,n]};n.c=[e.ASM,e.QSM,c,a,e.RM];var s=n.c.concat([e.CBCM,e.CLCM]);return{aliases:["js","jsx"],k:t,c:[{cN:"meta",r:10,b:/^\s*['"]use (strict|asm)['"]/},{cN:"meta",b:/^#!/,e:/$/},e.ASM,e.QSM,c,e.CLCM,e.CBCM,a,{b:/[{,]\s*/,r:0,c:[{b:r+"\\s*:",rB:!0,r:0,c:[{cN:"attr",b:r,r:0}]}]},{b:"("+e.RSR+"|\\b(case|return|throw)\\b)\\s*",k:"return throw case",c:[e.CLCM,e.CBCM,e.RM,{cN:"function",b:"(\\(.*?\\)|"+r+")\\s*=>",rB:!0,e:"\\s*=>",c:[{cN:"params",v:[{b:r},{b:/\(\s*\)/},{b:/\(/,e:/\)/,eB:!0,eE:!0,k:t,c:s}]}]},{b:/</,e:/(\/\w+|\w+\/)>/,sL:"xml",c:[{b:/<\w+\s*\/>/,skip:!0},{b:/<\w+/,e:/(\/\w+|\w+\/)>/,skip:!0,c:[{b:/<\w+\s*\/>/,skip:!0},"self"]}]}],r:0},{cN:"function",bK:"function",e:/\{/,eE:!0,c:[e.inherit(e.TM,{b:r}),{cN:"params",b:/\(/,e:/\)/,eB:!0,eE:!0,c:s}],i:/\[|%/},{b:/\$[(.]/},e.METHOD_GUARD,{cN:"class",bK:"class",e:/[{;=]/,eE:!0,i:/[:"\[\]]/,c:[{bK:"extends"},e.UTM]},{bK:"constructor",e:/\{/,eE:!0}],i:/#(?!!)/}});hljs.registerLanguage("tex",function(c){var e={cN:"tag",b:/\\/,r:0,c:[{cN:"name",v:[{b:/[a-zA-Zа-яА-я]+[*]?/},{b:/[^a-zA-Zа-яА-я0-9]/}],starts:{eW:!0,r:0,c:[{cN:"string",v:[{b:/\[/,e:/\]/},{b:/\{/,e:/\}/}]},{b:/\s*=\s*/,eW:!0,r:0,c:[{cN:"number",b:/-?\d*\.?\d+(pt|pc|mm|cm|in|dd|cc|ex|em)?/}]}]}}]};return{c:[e,{cN:"formula",c:[e],r:0,v:[{b:/\$\$/,e:/\$\$/},{b:/\$/,e:/\$/}]},c.C("%","$",{r:0})]}});hljs.registerLanguage("xml",function(s){var e="[A-Za-z0-9\\._:-]+",t={eW:!0,i:/</,r:0,c:[{cN:"attr",b:e,r:0},{b:/=\s*/,r:0,c:[{cN:"string",endsParent:!0,v:[{b:/"/,e:/"/},{b:/'/,e:/'/},{b:/[^\s"'=<>`]+/}]}]}]};return{aliases:["html","xhtml","rss","atom","xjb","xsd","xsl","plist"],cI:!0,c:[{cN:"meta",b:"<!DOCTYPE",e:">",r:10,c:[{b:"\\[",e:"\\]"}]},s.C("<!--","-->",{r:10}),{b:"<\\!\\[CDATA\\[",e:"\\]\\]>",r:10},{b:/<\?(php)?/,e:/\?>/,sL:"php",c:[{b:"/\\*",e:"\\*/",skip:!0}]},{cN:"tag",b:"<style(?=\\s|>|$)",e:">",k:{name:"style"},c:[t],starts:{e:"</style>",rE:!0,sL:["css","xml"]}},{cN:"tag",b:"<script(?=\\s|>|$)",e:">",k:{name:"script"},c:[t],starts:{e:"</script>",rE:!0,sL:["actionscript","javascript","handlebars","xml"]}},{cN:"meta",v:[{b:/<\?xml/,e:/\?>/,r:10},{b:/<\?\w+/,e:/\?>/}]},{cN:"tag",b:"</?",e:"/?>",c:[{cN:"name",b:/[^\/><\s]+/,r:0},t]}]}});hljs.registerLanguage("markdown",function(e){return{aliases:["md","mkdown","mkd"],c:[{cN:"section",v:[{b:"^#{1,6}",e:"$"},{b:"^.+?\\n[=-]{2,}$"}]},{b:"<",e:">",sL:"xml",r:0},{cN:"bullet",b:"^([*+-]|(\\d+\\.))\\s+"},{cN:"strong",b:"[*_]{2}.+?[*_]{2}"},{cN:"emphasis",v:[{b:"\\*.+?\\*"},{b:"_.+?_",r:0}]},{cN:"quote",b:"^>\\s+",e:"$"},{cN:"code",v:[{b:"^```w*s*$",e:"^```s*$"},{b:"`.+?`"},{b:"^( {4}|	)",e:"$",r:0}]},{b:"^[-\\*]{3,}",e:"$"},{b:"\\[.+?\\][\\(\\[].*?[\\)\\]]",rB:!0,c:[{cN:"string",b:"\\[",e:"\\]",eB:!0,rE:!0,r:0},{cN:"link",b:"\\]\\(",e:"\\)",eB:!0,eE:!0},{cN:"symbol",b:"\\]\\[",e:"\\]",eB:!0,eE:!0}],r:10},{b:/^\[[^\n]+\]:/,rB:!0,c:[{cN:"symbol",b:/\[/,e:/\]/,eB:!0,eE:!0},{cN:"link",b:/:\s*/,e:/$/,eB:!0}]}]}});hljs.registerLanguage("json",function(e){var i={literal:"true false null"},n=[e.QSM,e.CNM],r={e:",",eW:!0,eE:!0,c:n,k:i},t={b:"{",e:"}",c:[{cN:"attr",b:/"/,e:/"/,c:[e.BE],i:"\\n"},e.inherit(r,{b:/:/})],i:"\\S"},c={b:"\\[",e:"\\]",c:[e.inherit(r)],i:"\\S"};return n.splice(n.length,0,t,c),{c:n,k:i,i:"\\S"}});"></script>
<style type="text/css">.pagedtable {
overflow: auto;
padding-left: 8px;
padding-right: 8px;
}
.pagedtable-wrapper {
border: 1px solid #ccc;
border-radius: 4px;
margin-bottom: 10px;
}
.pagedtable table {
width: 100%;
max-width: 100%;
margin: 0;
}
.pagedtable th {
padding: 0 5px 0 5px;
border: none;
border-bottom: 2px solid #dddddd;
min-width: 45px;
}
.pagedtable-empty th {
display: none;
}
.pagedtable td {
padding: 0 4px 0 4px;
}
.pagedtable .even {
background-color: rgba(140, 140, 140, 0.1);
}
.pagedtable-padding-col {
display: none;
}
.pagedtable a {
-webkit-touch-callout: none;
-webkit-user-select: none;
-khtml-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.pagedtable-index-nav {
cursor: pointer;
padding: 0 5px 0 5px;
float: right;
border: 0;
}
.pagedtable-index-nav-disabled {
cursor: default;
text-decoration: none;
color: #999;
}
a.pagedtable-index-nav-disabled:hover {
text-decoration: none;
color: #999;
}
.pagedtable-indexes {
cursor: pointer;
float: right;
border: 0;
}
.pagedtable-index-current {
cursor: default;
text-decoration: none;
font-weight: bold;
color: #333;
border: 0;
}
a.pagedtable-index-current:hover {
text-decoration: none;
font-weight: bold;
color: #333;
}
.pagedtable-index {
width: 30px;
display: inline-block;
text-align: center;
border: 0;
}
.pagedtable-index-separator-left {
display: inline-block;
color: #333;
font-size: 9px;
padding: 0 0 0 0;
cursor: default;
}
.pagedtable-index-separator-right {
display: inline-block;
color: #333;
font-size: 9px;
padding: 0 4px 0 0;
cursor: default;
}
.pagedtable-footer {
padding-top: 4px;
padding-bottom: 5px;
}
.pagedtable-not-empty .pagedtable-footer {
border-top: 2px solid #dddddd;
}
.pagedtable-info {
overflow: hidden;
color: #999;
white-space: nowrap;
text-overflow: ellipsis;
}
.pagedtable-header-name {
overflow: hidden;
text-overflow: ellipsis;
}
.pagedtable-header-type {
color: #999;
font-weight: 400;
}
.pagedtable-na-cell {
font-style: italic;
opacity: 0.3;
}
</style>
<script>// Production steps of ECMA-262, Edition 5, 15.4.4.18
// Reference: http://es5.github.io/#x15.4.4.18
if (!Array.prototype.forEach) {
Array.prototype.forEach = function(callback, thisArg) {
var T, k;
if (this === null) {
throw new TypeError(' this is null or not defined');
}
// 1. Let O be the result of calling toObject() passing the
// |this| value as the argument.
var O = Object(this);
// 2. Let lenValue be the result of calling the Get() internal
// method of O with the argument "length".
// 3. Let len be toUint32(lenValue).
var len = O.length >>> 0;
// 4. If isCallable(callback) is false, throw a TypeError exception.
// See: http://es5.github.com/#x9.11
if (typeof callback !== "function") {
throw new TypeError(callback + ' is not a function');
}
// 5. If thisArg was supplied, let T be thisArg; else let
// T be undefined.
if (arguments.length > 1) {
T = thisArg;
}
// 6. Let k be 0
k = 0;
// 7. Repeat, while k < len
while (k < len) {
var kValue;
// a. Let Pk be ToString(k).
// This is implicit for LHS operands of the in operator
// b. Let kPresent be the result of calling the HasProperty
// internal method of O with argument Pk.
// This step can be combined with c
// c. If kPresent is true, then
if (k in O) {
// i. Let kValue be the result of calling the Get internal
// method of O with argument Pk.
kValue = O[k];
// ii. Call the Call internal method of callback with T as
// the this value and argument list containing kValue, k, and O.
callback.call(T, kValue, k, O);
}
// d. Increase k by 1.
k++;
}
// 8. return undefined
};
}
// Production steps of ECMA-262, Edition 5, 15.4.4.19
// Reference: http://es5.github.io/#x15.4.4.19
if (!Array.prototype.map) {
Array.prototype.map = function(callback, thisArg) {
var T, A, k;
if (this == null) {
throw new TypeError(' this is null or not defined');
}
// 1. Let O be the result of calling ToObject passing the |this|
// value as the argument.
var O = Object(this);
// 2. Let lenValue be the result of calling the Get internal
// method of O with the argument "length".
// 3. Let len be ToUint32(lenValue).
var len = O.length >>> 0;
// 4. If IsCallable(callback) is false, throw a TypeError exception.
// See: http://es5.github.com/#x9.11
if (typeof callback !== 'function') {
throw new TypeError(callback + ' is not a function');
}
// 5. If thisArg was supplied, let T be thisArg; else let T be undefined.
if (arguments.length > 1) {
T = thisArg;
}
// 6. Let A be a new array created as if by the expression new Array(len)
// where Array is the standard built-in constructor with that name and
// len is the value of len.
A = new Array(len);
// 7. Let k be 0
k = 0;
// 8. Repeat, while k < len
while (k < len) {
var kValue, mappedValue;
// a. Let Pk be ToString(k).
// This is implicit for LHS operands of the in operator
// b. Let kPresent be the result of calling the HasProperty internal
// method of O with argument Pk.
// This step can be combined with c
// c. If kPresent is true, then
if (k in O) {
// i. Let kValue be the result of calling the Get internal
// method of O with argument Pk.
kValue = O[k];
// ii. Let mappedValue be the result of calling the Call internal
// method of callback with T as the this value and argument
// list containing kValue, k, and O.
mappedValue = callback.call(T, kValue, k, O);
// iii. Call the DefineOwnProperty internal method of A with arguments
// Pk, Property Descriptor
// { Value: mappedValue,
// Writable: true,
// Enumerable: true,
// Configurable: true },
// and false.
// In browsers that support Object.defineProperty, use the following:
// Object.defineProperty(A, k, {
// value: mappedValue,
// writable: true,
// enumerable: true,
// configurable: true
// });
// For best browser support, use the following:
A[k] = mappedValue;
}
// d. Increase k by 1.
k++;
}
// 9. return A
return A;
};
}
var PagedTable = function (pagedTable) {
var me = this;
var source = function(pagedTable) {
var sourceElems = [].slice.call(pagedTable.children).filter(function(e) {
return e.hasAttribute("data-pagedtable-source");
});
if (sourceElems === null || sourceElems.length !== 1) {
throw("A single data-pagedtable-source was not found");
}
return JSON.parse(sourceElems[0].innerHTML);
}(pagedTable);
var options = function(source) {
var options = typeof(source.options) !== "undefined" &&
source.options !== null ? source.options : {};
var columns = typeof(options.columns) !== "undefined" ? options.columns : {};
var rows = typeof(options.rows) !== "undefined" ? options.rows : {};
var positiveIntOrNull = function(value) {
return parseInt(value) >= 0 ? parseInt(value) : null;
};
return {
pages: positiveIntOrNull(options.pages),
rows: {
min: positiveIntOrNull(rows.min),
max: positiveIntOrNull(rows.max),
total: positiveIntOrNull(rows.total)
},
columns: {
min: positiveIntOrNull(columns.min),
max: positiveIntOrNull(columns.max),
total: positiveIntOrNull(columns.total)
}
};
}(source);
var Measurer = function() {
// set some default initial values that will get adjusted in runtime
me.measures = {
padding: 12,
character: 8,
height: 15,
defaults: true
};
me.calculate = function(measuresCell) {
if (!me.measures.defaults)
return;
var measuresCellStyle = window.getComputedStyle(measuresCell, null);
var newPadding = parsePadding(measuresCellStyle.paddingLeft) +
parsePadding(measuresCellStyle.paddingRight);
var sampleString = "ABCDEFGHIJ0123456789";
var newCharacter = Math.ceil(measuresCell.clientWidth / sampleString.length);
if (newPadding <= 0 || newCharacter <= 0)
return;
me.measures.padding = newPadding;
me.measures.character = newCharacter;
me.measures.height = measuresCell.clientHeight;
me.measures.defaults = false;
};
return me;
};
var Page = function(data, options) {
var me = this;
var defaults = {
max: 7,
rows: 10
};
var totalPages = function() {
return Math.ceil(data.length / me.rows);
};
me.number = 0;
me.max = options.pages !== null ? options.pages : defaults.max;
me.visible = me.max;
me.rows = options.rows.min !== null ? options.rows.min : defaults.rows;
me.total = totalPages();
me.setRows = function(newRows) {
me.rows = newRows;
me.total = totalPages();
};
me.setPageNumber = function(newPageNumber) {
if (newPageNumber < 0) newPageNumber = 0;
if (newPageNumber >= me.total) newPageNumber = me.total - 1;
me.number = newPageNumber;
};
me.setVisiblePages = function(visiblePages) {
me.visible = Math.min(me.max, visiblePages);
me.setPageNumber(me.number);
};
me.getVisiblePageRange = function() {
var start = me.number - Math.max(Math.floor((me.visible - 1) / 2), 0);
var end = me.number + Math.floor(me.visible / 2) + 1;
var pageCount = me.total;
if (start < 0) {
var diffToStart = 0 - start;
start += diffToStart;
end += diffToStart;
}
if (end > pageCount) {
var diffToEnd = end - pageCount;
start -= diffToEnd;
end -= diffToEnd;
}
start = start < 0 ? 0 : start;
end = end >= pageCount ? pageCount : end;
var first = false;
var last = false;
if (start > 0 && me.visible > 1) {
start = start + 1;
first = true;
}
if (end < pageCount && me.visible > 2) {
end = end - 1;
last = true;
}
return {
first: first,
start: start,
end: end,
last: last
};
};
me.getRowStart = function() {
var rowStart = page.number * page.rows;
if (rowStart < 0)
rowStart = 0;
return rowStart;
};
me.getRowEnd = function() {
var rowStart = me.getRowStart();
return Math.min(rowStart + me.rows, data.length);
};
me.getPaddingRows = function() {
var rowStart = me.getRowStart();
var rowEnd = me.getRowEnd();
return data.length > me.rows ? me.rows - (rowEnd - rowStart) : 0;
};
};
var Columns = function(data, columns, options) {
var me = this;
me.defaults = {
min: 5
};
me.number = 0;
me.visible = 0;
me.total = columns.length;
me.subset = [];
me.padding = 0;
me.min = options.columns.min !== null ? options.columns.min : me.defaults.min;
me.max = options.columns.max !== null ? options.columns.max : null;
me.widths = {};
var widthsLookAhead = Math.max(100, options.rows.min);
var paddingColChars = 10;
me.emptyNames = function() {
columns.forEach(function(column) {
if (columns.label !== null && columns.label !== "")
return false;
});
return true;
};
var parsePadding = function(value) {
return parseInt(value) >= 0 ? parseInt(value) : 0;
};
me.calculateWidths = function(measures) {
columns.forEach(function(column) {
var maxChars = Math.max(
column.label.toString().length,
column.type.toString().length
);
for (var idxRow = 0; idxRow < Math.min(widthsLookAhead, data.length); idxRow++) {
maxChars = Math.max(maxChars, data[idxRow][column.name.toString()].length);
}
me.widths[column.name] = {
// width in characters
chars: maxChars,
// width for the inner html columns
inner: maxChars * measures.character,
// width adding outer styles like padding
outer: maxChars * measures.character + measures.padding
};
});
};
me.getWidth = function() {
var widthOuter = 0;
for (var idxCol = 0; idxCol < me.subset.length; idxCol++) {
var columnName = me.subset[idxCol].name;
widthOuter = widthOuter + me.widths[columnName].outer;
}
widthOuter = widthOuter + me.padding * paddingColChars * measurer.measures.character;
if (me.hasMoreLeftColumns()) {
widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding;
}
if (me.hasMoreRightColumns()) {
widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding;
}
return widthOuter;
};
me.updateSlice = function() {
if (me.number + me.visible >= me.total)
me.number = me.total - me.visible;
if (me.number < 0) me.number = 0;
me.subset = columns.slice(me.number, Math.min(me.number + me.visible, me.total));
me.subset = me.subset.map(function(column) {
Object.keys(column).forEach(function(colKey) {
column[colKey] = column[colKey] === null ? "" : column[colKey].toString();
});
column.width = null;
return column;
});
};
me.setVisibleColumns = function(columnNumber, newVisibleColumns, paddingCount) {
me.number = columnNumber;
me.visible = newVisibleColumns;
me.padding = paddingCount;
me.updateSlice();
};
me.incColumnNumber = function(increment) {
me.number = me.number + increment;
};
me.setColumnNumber = function(newNumber) {
me.number = newNumber;
};
me.setPaddingCount = function(newPadding) {
me.padding = newPadding;
};
me.getPaddingCount = function() {
return me.padding;
};
me.hasMoreLeftColumns = function() {
return me.number > 0;
};
me.hasMoreRightColumns = function() {
return me.number + me.visible < me.total;
};
me.updateSlice(0);
return me;
};
var data = source.data;
var page = new Page(data, options);
var measurer = new Measurer(data, options);
var columns = new Columns(data, source.columns, options);
var table = null;
var tableDiv = null;
var header = null;
var footer = null;
var tbody = null;
// Caches pagedTable.clientWidth, specially for webkit
var cachedPagedTableClientWidth = null;
var onChangeCallbacks = [];
var clearSelection = function() {
if(document.selection && document.selection.empty) {
document.selection.empty();
} else if(window.getSelection) {
var sel = window.getSelection();
sel.removeAllRanges();
}
};
var columnNavigationWidthPX = 5;
var renderColumnNavigation = function(increment, backwards) {
var arrow = document.createElement("div");
arrow.setAttribute("style",
"border-top: " + columnNavigationWidthPX + "px solid transparent;" +
"border-bottom: " + columnNavigationWidthPX + "px solid transparent;" +
"border-" + (backwards ? "right" : "left") + ": " + columnNavigationWidthPX + "px solid;");
var header = document.createElement("th");
header.appendChild(arrow);
header.setAttribute("style",
"cursor: pointer;" +
"vertical-align: middle;" +
"min-width: " + columnNavigationWidthPX + "px;" +
"width: " + columnNavigationWidthPX + "px;");
header.onclick = function() {
columns.incColumnNumber(backwards ? -1 : increment);
me.animateColumns(backwards);
renderFooter();
clearSelection();
triggerOnChange();
};
return header;
};
var maxColumnWidth = function(width) {
var padding = 80;
var columnMax = Math.max(cachedPagedTableClientWidth - padding, 0);
return parseInt(width) > 0 ?
Math.min(columnMax, parseInt(width)) + "px" :
columnMax + "px";
};
var clearHeader = function() {
var thead = pagedTable.querySelectorAll("thead")[0];
thead.innerHTML = "";
};
var renderHeader = function(clear) {
cachedPagedTableClientWidth = pagedTable.clientWidth;
var fragment = document.createDocumentFragment();
header = document.createElement("tr");
fragment.appendChild(header);
if (columns.number > 0)
header.appendChild(renderColumnNavigation(-columns.visible, true));
columns.subset = columns.subset.map(function(columnData) {
var column = document.createElement("th");
column.setAttribute("align", columnData.align);
column.style.textAlign = columnData.align;
column.style.maxWidth = maxColumnWidth(null);
if (columnData.width) {
column.style.minWidth =
column.style.maxWidth = maxColumnWidth(columnData.width);
}
var columnName = document.createElement("div");
columnName.setAttribute("class", "pagedtable-header-name");
if (columnData.label === "") {
columnName.innerHTML = " ";
}
else {
columnName.appendChild(document.createTextNode(columnData.label));
}
column.appendChild(columnName);
var columnType = document.createElement("div");
columnType.setAttribute("class", "pagedtable-header-type");
if (columnData.type === "") {
columnType.innerHTML = " ";
}
else {
columnType.appendChild(document.createTextNode("<" + columnData.type + ">"));
}
column.appendChild(columnType);
header.appendChild(column);
columnData.element = column;
return columnData;
});
for (var idx = 0; idx < columns.getPaddingCount(); idx++) {
var paddingCol = document.createElement("th");
paddingCol.setAttribute("class", "pagedtable-padding-col");
header.appendChild(paddingCol);
}
if (columns.number + columns.visible < columns.total)
header.appendChild(renderColumnNavigation(columns.visible, false));
if (typeof(clear) == "undefined" || clear) clearHeader();
var thead = pagedTable.querySelectorAll("thead")[0];
thead.appendChild(fragment);
};
me.animateColumns = function(backwards) {
var thead = pagedTable.querySelectorAll("thead")[0];
var headerOld = thead.querySelectorAll("tr")[0];
var tbodyOld = table.querySelectorAll("tbody")[0];
me.fitColumns(backwards);
renderHeader(false);
header.style.opacity = "0";
header.style.transform = backwards ? "translateX(-30px)" : "translateX(30px)";
header.style.transition = "transform 200ms linear, opacity 200ms";
header.style.transitionDelay = "0";
renderBody(false);
if (headerOld) {
headerOld.style.position = "absolute";
headerOld.style.transform = "translateX(0px)";
headerOld.style.opacity = "1";
headerOld.style.transition = "transform 100ms linear, opacity 100ms";
headerOld.setAttribute("class", "pagedtable-remove-head");
if (headerOld.style.transitionEnd) {
headerOld.addEventListener("transitionend", function() {
var headerOldByClass = thead.querySelector(".pagedtable-remove-head");
if (headerOldByClass) thead.removeChild(headerOldByClass);
});
}
else {
thead.removeChild(headerOld);
}
}
if (tbodyOld) table.removeChild(tbodyOld);
tbody.style.opacity = "0";
tbody.style.transition = "transform 200ms linear, opacity 200ms";
tbody.style.transitionDelay = "0ms";
// force relayout
window.getComputedStyle(header).opacity;
window.getComputedStyle(tbody).opacity;
if (headerOld) {
headerOld.style.transform = backwards ? "translateX(20px)" : "translateX(-30px)";
headerOld.style.opacity = "0";
}
header.style.transform = "translateX(0px)";
header.style.opacity = "1";
tbody.style.opacity = "1";
}
me.onChange = function(callback) {
onChangeCallbacks.push(callback);
};
var triggerOnChange = function() {
onChangeCallbacks.forEach(function(onChange) {
onChange();
});
};
var clearBody = function() {
if (tbody) {
table.removeChild(tbody);
tbody = null;
}
};
var renderBody = function(clear) {
cachedPagedTableClientWidth = pagedTable.clientWidth
var fragment = document.createDocumentFragment();
var pageData = data.slice(page.getRowStart(), page.getRowEnd());
pageData.forEach(function(dataRow, idxRow) {
var htmlRow = document.createElement("tr");
htmlRow.setAttribute("class", (idxRow % 2 !==0) ? "even" : "odd");
if (columns.hasMoreLeftColumns())
htmlRow.appendChild(document.createElement("td"));
columns.subset.forEach(function(columnData) {
var cellName = columnData.name;
var dataCell = dataRow[cellName];
var htmlCell = document.createElement("td");
if (dataCell === "NA") htmlCell.setAttribute("class", "pagedtable-na-cell");
if (dataCell === "__NA__") dataCell = "NA";
var cellText = document.createTextNode(dataCell);
htmlCell.appendChild(cellText);
if (dataCell.length > 50) {
htmlCell.setAttribute("title", dataCell);
}
htmlCell.setAttribute("align", columnData.align);
htmlCell.style.textAlign = columnData.align;
htmlCell.style.maxWidth = maxColumnWidth(null);
if (columnData.width) {
htmlCell.style.minWidth = htmlCell.style.maxWidth = maxColumnWidth(columnData.width);
}
htmlRow.appendChild(htmlCell);
});
for (var idx = 0; idx < columns.getPaddingCount(); idx++) {
var paddingCol = document.createElement("td");
paddingCol.setAttribute("class", "pagedtable-padding-col");
htmlRow.appendChild(paddingCol);
}
if (columns.hasMoreRightColumns())
htmlRow.appendChild(document.createElement("td"));
fragment.appendChild(htmlRow);
});
for (var idxPadding = 0; idxPadding < page.getPaddingRows(); idxPadding++) {
var paddingRow = document.createElement("tr");
var paddingCellRow = document.createElement("td");
paddingCellRow.innerHTML = " ";
paddingCellRow.setAttribute("colspan", "100%");
paddingRow.appendChild(paddingCellRow);
fragment.appendChild(paddingRow);
}
if (typeof(clear) == "undefined" || clear) clearBody();
tbody = document.createElement("tbody");
tbody.appendChild(fragment);
table.appendChild(tbody);
};
var getLabelInfo = function() {
var pageStart = page.getRowStart();
var pageEnd = page.getRowEnd();
var totalRows = data.length;
var totalRowsLabel = options.rows.total ? options.rows.total : totalRows;
var totalRowsLabelFormat = totalRowsLabel.toString().replace(/(\d)(?=(\d\d\d)+(?!\d))/g, '$1,');
var infoText = (pageStart + 1) + "-" + pageEnd + " of " + totalRowsLabelFormat + " rows";
if (totalRows < page.rows) {
infoText = totalRowsLabel + " row" + (totalRows != 1 ? "s" : "");
}
if (columns.total > columns.visible) {
var totalColumnsLabel = options.columns.total ? options.columns.total : columns.total;
infoText = infoText + " | " + (columns.number + 1) + "-" +
(Math.min(columns.number + columns.visible, columns.total)) +
" of " + totalColumnsLabel + " columns";
}
return infoText;
};
var clearFooter = function() {
footer = pagedTable.querySelectorAll("div.pagedtable-footer")[0];
footer.innerHTML = "";
return footer;
};
var createPageLink = function(idxPage) {
var pageLink = document.createElement("a");
pageLinkClass = idxPage === page.number ? "pagedtable-index pagedtable-index-current" : "pagedtable-index";
pageLink.setAttribute("class", pageLinkClass);
pageLink.setAttribute("data-page-index", idxPage);
pageLink.onclick = function() {
page.setPageNumber(parseInt(this.getAttribute("data-page-index")));
renderBody();
renderFooter();
triggerOnChange();
};
pageLink.appendChild(document.createTextNode(idxPage + 1));
return pageLink;
}
var renderFooter = function() {
footer = clearFooter();
var next = document.createElement("a");
next.appendChild(document.createTextNode("Next"));
next.onclick = function() {
page.setPageNumber(page.number + 1);
renderBody();
renderFooter();
triggerOnChange();
};
if (data.length > page.rows) footer.appendChild(next);
var pageNumbers = document.createElement("div");
pageNumbers.setAttribute("class", "pagedtable-indexes");
var pageRange = page.getVisiblePageRange();
if (pageRange.first) {
var pageLink = createPageLink(0);
pageNumbers.appendChild(pageLink);
var pageSeparator = document.createElement("div");
pageSeparator.setAttribute("class", "pagedtable-index-separator-left");
pageSeparator.appendChild(document.createTextNode("..."))
pageNumbers.appendChild(pageSeparator);
}
for (var idxPage = pageRange.start; idxPage < pageRange.end; idxPage++) {
var pageLink = createPageLink(idxPage);
pageNumbers.appendChild(pageLink);
}
if (pageRange.last) {
var pageSeparator = document.createElement("div");
pageSeparator.setAttribute("class", "pagedtable-index-separator-right");
pageSeparator.appendChild(document.createTextNode("..."))
pageNumbers.appendChild(pageSeparator);
var pageLink = createPageLink(page.total - 1);
pageNumbers.appendChild(pageLink);
}
if (data.length > page.rows) footer.appendChild(pageNumbers);
var previous = document.createElement("a");
previous.appendChild(document.createTextNode("Previous"));
previous.onclick = function() {
page.setPageNumber(page.number - 1);
renderBody();
renderFooter();
triggerOnChange();
};
if (data.length > page.rows) footer.appendChild(previous);
var infoLabel = document.createElement("div");
infoLabel.setAttribute("class", "pagedtable-info");
infoLabel.setAttribute("title", getLabelInfo());
infoLabel.appendChild(document.createTextNode(getLabelInfo()));
footer.appendChild(infoLabel);
var enabledClass = "pagedtable-index-nav";
var disabledClass = "pagedtable-index-nav pagedtable-index-nav-disabled";
previous.setAttribute("class", page.number <= 0 ? disabledClass : enabledClass);
next.setAttribute("class", (page.number + 1) * page.rows >= data.length ? disabledClass : enabledClass);
};
var measuresCell = null;
var renderMeasures = function() {
var measuresTable = document.createElement("table");
measuresTable.style.visibility = "hidden";
measuresTable.style.position = "absolute";
measuresTable.style.whiteSpace = "nowrap";
measuresTable.style.height = "auto";
measuresTable.style.width = "auto";
var measuresRow = document.createElement("tr");
measuresTable.appendChild(measuresRow);
measuresCell = document.createElement("td");
var sampleString = "ABCDEFGHIJ0123456789";
measuresCell.appendChild(document.createTextNode(sampleString));
measuresRow.appendChild(measuresCell);
tableDiv.appendChild(measuresTable);
}
me.init = function() {
tableDiv = document.createElement("div");
pagedTable.appendChild(tableDiv);
var pagedTableClass = data.length > 0 ?
"pagedtable pagedtable-not-empty" :
"pagedtable pagedtable-empty";
if (columns.total == 0 || (columns.emptyNames() && data.length == 0)) {
pagedTableClass = pagedTableClass + " pagedtable-empty-columns";
}
tableDiv.setAttribute("class", pagedTableClass);
renderMeasures();
measurer.calculate(measuresCell);
columns.calculateWidths(measurer.measures);
table = document.createElement("table");
table.setAttribute("cellspacing", "0");
table.setAttribute("class", "table table-condensed");
tableDiv.appendChild(table);
table.appendChild(document.createElement("thead"));
var footerDiv = document.createElement("div");
footerDiv.setAttribute("class", "pagedtable-footer");
tableDiv.appendChild(footerDiv);
// if the host has not yet provided horizontal space, render hidden
if (tableDiv.clientWidth <= 0) {
tableDiv.style.opacity = "0";
}
me.render();
// retry seizing columns later if the host has not provided space
function retryFit() {
if (tableDiv.clientWidth <= 0) {
setTimeout(retryFit, 100);
} else {
me.render();
triggerOnChange();
}
}
if (tableDiv.clientWidth <= 0) {
retryFit();
}
};
var registerWidths = function() {
columns.subset = columns.subset.map(function(column) {
column.width = columns.widths[column.name].inner;
return column;
});
};
var parsePadding = function(value) {
return parseInt(value) >= 0 ? parseInt(value) : 0;
};
me.fixedHeight = function() {
return options.rows.max != null;
}
me.fitRows = function() {
if (me.fixedHeight())
return;
measurer.calculate(measuresCell);
var rows = options.rows.min !== null ? options.rows.min : 0;
var headerHeight = header !== null && header.offsetHeight > 0 ? header.offsetHeight : 0;
var footerHeight = footer !== null && footer.offsetHeight > 0 ? footer.offsetHeight : 0;
if (pagedTable.offsetHeight > 0) {
var availableHeight = pagedTable.offsetHeight - headerHeight - footerHeight;
rows = Math.floor((availableHeight) / measurer.measures.height);
}
rows = options.rows.min !== null ? Math.max(options.rows.min, rows) : rows;
page.setRows(rows);
}
// The goal of this function is to add as many columns as possible
// starting from left-to-right, when the right most limit is reached
// it tries to add columns from the left as well.
//
// When startBackwards is true columns are added from right-to-left
me.fitColumns = function(startBackwards) {
measurer.calculate(measuresCell);
columns.calculateWidths(measurer.measures);
if (tableDiv.clientWidth > 0) {
tableDiv.style.opacity = 1;
}
var visibleColumns = tableDiv.clientWidth <= 0 ? Math.max(columns.min, 1) : 1;
var columnNumber = columns.number;
var paddingCount = 0;
// track a list of added columns as we build the visible ones to allow us
// to remove columns when they don't fit anymore.
var columnHistory = [];
var lastTableHeight = 0;
var backwards = startBackwards;
var tableDivStyle = window.getComputedStyle(tableDiv, null);
var tableDivPadding = parsePadding(tableDivStyle.paddingLeft) +
parsePadding(tableDivStyle.paddingRight);
var addPaddingCol = false;
var currentWidth = 0;
while (true) {
columns.setVisibleColumns(columnNumber, visibleColumns, paddingCount);
currentWidth = columns.getWidth();
if (tableDiv.clientWidth - tableDivPadding < currentWidth) {
break;
}
columnHistory.push({
columnNumber: columnNumber,
visibleColumns: visibleColumns,
paddingCount: paddingCount
});
if (columnHistory.length > 100) {
console.error("More than 100 tries to fit columns, aborting");
break;
}
if (columns.max !== null &&
columns.visible + columns.getPaddingCount() >= columns.max) {
break;
}
// if we run out of right-columns
if (!backwards && columnNumber + columns.visible >= columns.total) {
// if we started adding right-columns, try adding left-columns
if (!startBackwards && columnNumber > 0) {
backwards = true;
}
else if (columns.min === null || visibleColumns + columns.getPaddingCount() >= columns.min) {
break;
}
else {
paddingCount = paddingCount + 1;
}
}
// if we run out of left-columns
if (backwards && columnNumber == 0) {
// if we started adding left-columns, try adding right-columns
if (startBackwards && columnNumber + columns.visible < columns.total) {
backwards = false;
}
else if (columns.min === null || visibleColumns + columns.getPaddingCount() >= columns.min) {
break;
}
else {
paddingCount = paddingCount + 1;
}
}
// when moving backwards try fitting left columns first
if (backwards && columnNumber > 0) {
columnNumber = columnNumber - 1;
}
if (columnNumber + visibleColumns < columns.total) {
visibleColumns = visibleColumns + 1;
}
}
var lastRenderableColumn = {
columnNumber: columnNumber,
visibleColumns: visibleColumns,
paddingCount: paddingCount
};
if (columnHistory.length > 0) {
lastRenderableColumn = columnHistory[columnHistory.length - 1];
}
columns.setVisibleColumns(
lastRenderableColumn.columnNumber,
lastRenderableColumn.visibleColumns,
lastRenderableColumn.paddingCount);
if (pagedTable.offsetWidth > 0) {
page.setVisiblePages(Math.max(Math.ceil(1.0 * (pagedTable.offsetWidth - 250) / 40), 2));
}
registerWidths();
};
me.fit = function(startBackwards) {
me.fitRows();
me.fitColumns(startBackwards);
}
me.render = function() {
me.fitColumns(false);
// render header/footer to measure height accurately
renderHeader();
renderFooter();
me.fitRows();
renderBody();
// re-render footer to match new rows
renderFooter();
}
var resizeLastWidth = -1;
var resizeLastHeight = -1;
var resizeNewWidth = -1;
var resizeNewHeight = -1;
var resizePending = false;
me.resize = function(newWidth, newHeight) {
function resizeDelayed() {
resizePending = false;
if (
(resizeNewWidth !== resizeLastWidth) ||
(!me.fixedHeight() && resizeNewHeight !== resizeLastHeight)
) {
resizeLastWidth = resizeNewWidth;
resizeLastHeight = resizeNewHeight;
setTimeout(resizeDelayed, 200);
resizePending = true;
} else {
me.render();
triggerOnChange();
resizeLastWidth = -1;
resizeLastHeight = -1;
}
}
resizeNewWidth = newWidth;
resizeNewHeight = newHeight;
if (!resizePending) resizeDelayed();
};
};
var PagedTableDoc;
(function (PagedTableDoc) {
var allPagedTables = [];
PagedTableDoc.initAll = function() {
allPagedTables = [];
var pagedTables = [].slice.call(document.querySelectorAll('[data-pagedtable="false"],[data-pagedtable=""]'));
pagedTables.forEach(function(pagedTable, idx) {
pagedTable.setAttribute("data-pagedtable", "true");
pagedTable.setAttribute("pagedtable-page", 0);
pagedTable.setAttribute("class", "pagedtable-wrapper");
var pagedTableInstance = new PagedTable(pagedTable);
pagedTableInstance.init();
allPagedTables.push(pagedTableInstance);
});
};
PagedTableDoc.resizeAll = function() {
allPagedTables.forEach(function(pagedTable) {
pagedTable.render();
});
};
window.addEventListener("resize", PagedTableDoc.resizeAll);
return PagedTableDoc;
})(PagedTableDoc || (PagedTableDoc = {}));
window.onload = function() {
PagedTableDoc.initAll();
};
</script>
<style type="text/css">
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
span.underline{text-decoration: underline;}
div.column{display: inline-block; vertical-align: top; width: 50%;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
</style>
<style type="text/css">code{white-space: pre;}</style>
<script type="text/javascript">
if (window.hljs) {
hljs.configure({languages: []});
hljs.initHighlightingOnLoad();
if (document.readyState && document.readyState === "complete") {
window.setTimeout(function() { hljs.initHighlighting(); }, 0);
}
}
</script>
<style type="text/css">
.main-container {
max-width: 940px;
margin-left: auto;
margin-right: auto;
}
img {
max-width:100%;
}
.tabbed-pane {
padding-top: 12px;
}
.html-widget {
margin-bottom: 20px;
}
button.code-folding-btn:focus {
outline: none;
}
summary {
display: list-item;
}
pre code {
padding: 0;
}
</style>
<!-- tabsets -->
<style type="text/css">
.tabset-dropdown > .nav-tabs {
display: inline-table;
max-height: 500px;
min-height: 44px;
overflow-y: auto;
border: 1px solid #ddd;
border-radius: 4px;
}
.tabset-dropdown > .nav-tabs > li.active:before {
content: "";
font-family: 'Glyphicons Halflings';
display: inline-block;
padding: 10px;
border-right: 1px solid #ddd;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before {
content: "";
border: none;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open:before {
content: "";
font-family: 'Glyphicons Halflings';
display: inline-block;
padding: 10px;
border-right: 1px solid #ddd;
}
.tabset-dropdown > .nav-tabs > li.active {
display: block;
}
.tabset-dropdown > .nav-tabs > li > a,
.tabset-dropdown > .nav-tabs > li > a:focus,
.tabset-dropdown > .nav-tabs > li > a:hover {
border: none;
display: inline-block;
border-radius: 4px;
background-color: transparent;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open > li {
display: block;
float: none;
}
.tabset-dropdown > .nav-tabs > li {
display: none;
}
</style>
<!-- code folding -->
</head>
<body>
<div class="container-fluid main-container">
<div id="header">
</div>
<h2>
Introduction
</h2>
<p>Text mining is a process of discovering new and latent features within a body of text. It uses Natural Language Processing (NLP) to enable computers to digest human language. It has many uses in the real world. A couple of examples included are:</p>
<ul>
<li>Spam Filtering</li>
<li>Sentiment Analysis</li>
<li>Fraud Detection</li>
<li>Knowledge Management</li>
</ul>
<p>In this post, we will use the package <code>tidytext</code>, which is part of the <code>tidyverse</code>. You can read more about it <a href="https://www.tidyverse.org/">here</a>. <code>tidytext</code> is a package that uses tidy data principles to wrangle and visualize text data. In this post, we will process some text and maybe discover new information. First, make sure you have the following libraries installed, <code>tidytext</code>, <code>dplyr</code>, <code>ggplot2</code>,<code>stringi</code>, <code>textclean</code>, and <code>forcats</code>.</p>
<h2>
Importing the text
</h2>
<pre class="r"><code>#install.packages(c('dplyr','ggplot2','stringi','forcats', 'tidytext','textclean', 'tidyr'))
library(dplyr)</code></pre>
<pre><code>##
## Attaching package: 'dplyr'</code></pre>
<pre><code>## The following objects are masked from 'package:stats':
##
## filter, lag</code></pre>
<pre><code>## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union</code></pre>
<pre class="r"><code>library(ggplot2)
library(stringi)
library(textclean)
library(tidytext)
library(forcats)
library(tidyr)</code></pre>
<p>We can gather data by mining text from Wikipedia. Let’s import nine articles with topics including math, sports, and fantasy books. The specific topics will be important later when we try to perform text analytics. We will use the function <code>readLines</code> to directly connect to a Wikipedia URL and read all text lines.</p>
<pre class="r"><code>wiki <- "http://en.wikipedia.org/wiki/"
titles <- c("Integral", "Derivative",
"Calculus", "Football", "Soccer", "Basketball",
"A_Song_of_Ice_and_Fire", "The_Lord_of_the_Rings", "His_Dark_Materials")
articles <- character(length(titles))
for (i in 1:length(titles)) {
articles[i] <- stri_flatten(readLines(stri_paste(wiki, titles[i]), warn = F), col = " ")
}
length(articles)</code></pre>
<pre><code>## [1] 9</code></pre>
<h2>
Text processing
</h2>
<p>Now that we have imported our articles, which we will now call <em>documents</em>. We can the documents are represented as a large character vector with a length of 9. For each document, we need to separate each word and then count them. To do so, we use a process called <em>tokenization</em>. While a computer will read strings as a series of character, humans will read it as a sentence of words. To reproduce this for computers, we can split the string using a space delimiter, <code>" "</code>, and then split the sentence into words or in this case, tokens. Let’s take a look at an example. Here we have a random sentence extracted from one of our documents.</p>
<pre class="r"><code>text <- c('A Song of Ice and Fire is a series of epic fantasy novels by the American novelist and screenwriter George R. R. Martin. He began the first volume of the series, A Game of Thrones, in 1991, and it was published in 1996.')</code></pre>
<p>We can can use the following code to tokenize this text.</p>
<pre class="r"><code>tokenize_text <- (unlist(lapply(text, function (x) strsplit(x, split = ' ' )))) #' ' is used as the space delimiter
tokenize_text</code></pre>
<pre><code>## [1] "A" "Song" "of" "Ice" "and"
## [6] "Fire" "is" "a" "series" "of"
## [11] "epic" "fantasy" "novels" "by" "the"
## [16] "American" "novelist" "and" "screenwriter" "George"
## [21] "R." "R." "Martin." "He" "began"
## [26] "the" "first" "volume" "of" "the"
## [31] "series," "A" "Game" "of" "Thrones,"
## [36] "in" "1991," "and" "it" "was"
## [41] "published" "in" "1996."</code></pre>
<p>Fortunately, tokenization is built into the <code>tidytext</code> package. First, we will do some text cleaning by removing some common HTML tags with the function <code>replace_html()</code>. Since HTML tags are not useful information for us, we can discard them. Then we will convert our large character vector into a data frame. We will follow this with <code>unnest_tokens()</code> to tokenize the documents.</p>
<pre class="r"><code>articles <- replace_html(articles, symbol = TRUE)
articles <- data.frame('articles' = articles)
articles$titles <- titles
tokenized <- articles %>% unnest_tokens(word, articles)
head(tokenized)</code></pre>
<div data-pagedtable="false">
<script data-pagedtable-source="" type="application/json">
{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["titles"],"name":[1],"type":["chr"],"align":["left"]},{"label":["word"],"name":[2],"type":["chr"],"align":["left"]}],"data":[{"1":"Integral","2":"integral","_rn_":"1"},{"1":"Integral","2":"wikipedia","_rn_":"2"},{"1":"Integral","2":"document.documentelement.classname","_rn_":"3"},{"1":"Integral","2":"client","_rn_":"4"},{"1":"Integral","2":"js","_rn_":"5"},{"1":"Integral","2":"rlconf","_rn_":"6"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
</script>
</div>
<p>If you haven’t noticed already, there are still some nonsense words that pertain to HTML code still. We can create a new database with additional HTML code to remove, but for now, this is okay as later we will weight the important words more than the non-important words, which should reduce the importance of the HTML code. Next, let’s count how many times each word appears in each document using the <code>count()</code> function.</p>
<pre class="r"><code>tokenized %>%
count(titles, word, sort = TRUE) %>%
head()</code></pre>
<div data-pagedtable="false">
<script data-pagedtable-source="" type="application/json">
{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["titles"],"name":[1],"type":["chr"],"align":["left"]},{"label":["word"],"name":[2],"type":["chr"],"align":["left"]},{"label":["n"],"name":[3],"type":["int"],"align":["right"]}],"data":[{"1":"The_Lord_of_the_Rings","2":"the","3":"1271","_rn_":"1"},{"1":"A_Song_of_Ice_and_Fire","2":"the","3":"1140","_rn_":"2"},{"1":"Football","2":"the","3":"997","_rn_":"3"},{"1":"Basketball","2":"the","3":"956","_rn_":"4"},{"1":"Soccer","2":"the","3":"845","_rn_":"5"},{"1":"A_Song_of_Ice_and_Fire","2":"of","3":"683","_rn_":"6"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
</script>
</div>
<p>As you can see, the top words are “the” and “of”, which are not important for understanding the hidden features of the documents. This set of words are called <em>stop words</em>. They are commonly used words in any language, where they are unimportant for NLP, and are removed to focus on the important words. We will load a generic <code>stop_words</code> dataset and filter out these words from our data frame. Please keep in the mind that depending on the context of the documents, you may need a different set of stop words.</p>
<pre class="r"><code>data(stop_words)
tokenized <- tokenized %>%
anti_join(stop_words) #this filters out the stop words</code></pre>
<pre><code>## Joining, by = "word"</code></pre>
<pre class="r"><code>document_words <- tokenized %>%
count(titles, word, sort = TRUE)
head(document_words)</code></pre>
<div data-pagedtable="false">
<script data-pagedtable-source="" type="application/json">
{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["titles"],"name":[1],"type":["chr"],"align":["left"]},{"label":["word"],"name":[2],"type":["chr"],"align":["left"]},{"label":["n"],"name":[3],"type":["int"],"align":["right"]}],"data":[{"1":"Football","2":"football","3":"539","_rn_":"1"},{"1":"A_Song_of_Ice_and_Fire","2":"93","3":"470","_rn_":"2"},{"1":"Basketball","2":"basketball","3":"340","_rn_":"3"},{"1":"Soccer","2":"football","3":"272","_rn_":"4"},{"1":"A_Song_of_Ice_and_Fire","2":"martin","3":"251","_rn_":"5"},{"1":"A_Song_of_Ice_and_Fire","2":"2012","3":"237","_rn_":"6"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
</script>
</div>
<p>Now we can see important words that make sense for each of our documents, like the word <em>football</em> appearing the most for the Wikipedia article “Football”. Let’s take a look at a list of the most common words in “Football”.</p>
<pre class="r"><code>document_words[document_words$titles == 'Football',] %>%
filter(n > 50) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(n, word)) +
geom_col() +
labs(y = NULL)</code></pre>
<img src="data:image/png;base64,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" width="672" />
<h2>
Term Frequency and Inverse Document Frequency
</h2>
<p>Let’s try to contextualize this data by ranking them based on how often they appear within the document, instead of looking at the absolute count. This is called <em>term frequency</em> (TF) and it gives the frequency of the word in each document. The TF is defined as <span class="math inline">\(tf(t,d)\)</span>: <span class="math display">\[tf(t,d) = \frac{f_{t,d}}{\Sigma_{k}f_{t,d}}\]</span> where <span class="math inline">\(f_{t,d}\)</span> is the raw count of each term. In addition, each document will have its own TF. Let’s calculate it by finding the total number of words that appears per document.</p>
<pre class="r"><code>total_words <- document_words %>%
group_by(titles) %>%
summarize(total = sum(n))
document_words <- left_join(document_words, total_words)</code></pre>
<pre><code>## Joining, by = "titles"</code></pre>
<pre class="r"><code>head(document_words)</code></pre>
<div data-pagedtable="false">
<script data-pagedtable-source="" type="application/json">
{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["titles"],"name":[1],"type":["chr"],"align":["left"]},{"label":["word"],"name":[2],"type":["chr"],"align":["left"]},{"label":["n"],"name":[3],"type":["int"],"align":["right"]},{"label":["total"],"name":[4],"type":["int"],"align":["right"]}],"data":[{"1":"Football","2":"football","3":"539","4":"11327","_rn_":"1"},{"1":"A_Song_of_Ice_and_Fire","2":"93","3":"470","4":"12513","_rn_":"2"},{"1":"Basketball","2":"basketball","3":"340","4":"11093","_rn_":"3"},{"1":"Soccer","2":"football","3":"272","4":"9452","_rn_":"4"},{"1":"A_Song_of_Ice_and_Fire","2":"martin","3":"251","4":"12513","_rn_":"5"},{"1":"A_Song_of_Ice_and_Fire","2":"2012","3":"237","4":"12513","_rn_":"6"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
</script>
</div>
<p>Now we can rank each word by dividing the raw count of the word by the total number of words in the document.</p>
<pre class="r"><code>freq_by_rank <- document_words %>%
group_by(titles) %>%
mutate(rank = row_number(),
`term frequency` = n/total) %>%
ungroup()
head(freq_by_rank)</code></pre>
<div data-pagedtable="false">
<script data-pagedtable-source="" type="application/json">
{"columns":[{"label":["titles"],"name":[1],"type":["chr"],"align":["left"]},{"label":["word"],"name":[2],"type":["chr"],"align":["left"]},{"label":["n"],"name":[3],"type":["int"],"align":["right"]},{"label":["total"],"name":[4],"type":["int"],"align":["right"]},{"label":["rank"],"name":[5],"type":["int"],"align":["right"]},{"label":["term frequency"],"name":[6],"type":["dbl"],"align":["right"]}],"data":[{"1":"Football","2":"football","3":"539","4":"11327","5":"1","6":"0.04758542"},{"1":"A_Song_of_Ice_and_Fire","2":"93","3":"470","4":"12513","5":"1","6":"0.03756094"},{"1":"Basketball","2":"basketball","3":"340","4":"11093","5":"1","6":"0.03064996"},{"1":"Soccer","2":"football","3":"272","4":"9452","5":"1","6":"0.02877698"},{"1":"A_Song_of_Ice_and_Fire","2":"martin","3":"251","4":"12513","5":"2","6":"0.02005914"},{"1":"A_Song_of_Ice_and_Fire","2":"2012","3":"237","4":"12513","5":"3","6":"0.01894030"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
</script>
</div>
<p>However, there are also rare words that may be important that may be lost by using TF, since TF is influenced by the length of the document. Here we introduce <em>inverse document frequency</em> (IDF), which weighs the rare words across all documents by dividing the total number of documents by the number of documents containing the term. We add a $ + 1$ term to prevent division by 0. <span class="math display">\[idf(t,D) = log \frac{N}{|d \in D: t \in d| + 1}\]</span> We can combine this with TF to produce the TF-IDF score. Now TF will be weighted based on if they show up in other documents. <span class="math display">\[tfidf(t,d,D) = tf(t,d) \times idf(t,D)\]</span></p>
<pre class="r"><code>document_tf_idf <- document_words %>%
bind_tf_idf(word, titles, n)
head(document_tf_idf)</code></pre>
<div data-pagedtable="false">
<script data-pagedtable-source="" type="application/json">
{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["titles"],"name":[1],"type":["chr"],"align":["left"]},{"label":["word"],"name":[2],"type":["chr"],"align":["left"]},{"label":["n"],"name":[3],"type":["int"],"align":["right"]},{"label":["total"],"name":[4],"type":["int"],"align":["right"]},{"label":["tf"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["idf"],"name":[6],"type":["dbl"],"align":["right"]},{"label":["tf_idf"],"name":[7],"type":["dbl"],"align":["right"]}],"data":[{"1":"Football","2":"football","3":"539","4":"11327","5":"0.04758542","6":"1.0986123","7":"0.052277922","_rn_":"1"},{"1":"A_Song_of_Ice_and_Fire","2":"93","3":"470","4":"12513","5":"0.03756094","6":"0.0000000","7":"0.000000000","_rn_":"2"},{"1":"Basketball","2":"basketball","3":"340","4":"11093","5":"0.03064996","6":"1.0986123","7":"0.033672422","_rn_":"3"},{"1":"Soccer","2":"football","3":"272","4":"9452","5":"0.02877698","6":"1.0986123","7":"0.031614742","_rn_":"4"},{"1":"A_Song_of_Ice_and_Fire","2":"martin","3":"251","4":"12513","5":"0.02005914","6":"0.8109302","7":"0.016266562","_rn_":"5"},{"1":"A_Song_of_Ice_and_Fire","2":"2012","3":"237","4":"12513","5":"0.01894030","6":"0.1177830","7":"0.002230846","_rn_":"6"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
</script>
</div>
<p>We can see that the word <code>93</code> in <code>A_Song_of_Ice_and_Fire</code> had a very high <em>term frequency</em>. However, when we weight it to produce the TF_IDF score, it is reduced to a value of 0. Now let’s take a look at the top 5 words per document.</p>
<pre class="r"><code>library(forcats)
document_tf_idf %>%
group_by(titles) %>%
slice_max(tf_idf, n = 5) %>%
ungroup() %>%
ggplot(aes(tf_idf, fct_reorder(word, tf_idf), fill = titles)) +
geom_col(show.legend = FALSE) +
facet_wrap(~titles, ncol = 2, scales = "free") +
labs(x = "tf-idf", y = NULL)</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<h2>
Latent Semantic Analysis
</h2>
<p>For the most part, the top words weighted using TF-IDF makes sense. Now that we have set up our data, we can start digging in deeper to find some relationships between the documents. We will take a look at <em>Latent Semantic Analysis</em> (LSA). It is an NLP technique that analyzes a set of documents and the terms within to find common or divergent concepts related to the documents and terms. We can extend this definition of LSA to be used as a method for classifying documents into different topics. To start, we need to convert our data into a term-document matrix, where the columns corresponds to documents and the rows correspond to terms. Document-term matrix is the transpose version.</p>
<pre class="r"><code>tf_idf_matrix <- document_tf_idf %>% select(titles, word, tf_idf)%>% pivot_wider(names_from = titles, values_from = tf_idf, values_fill = 0) %>% as.data.frame()
words <- tf_idf_matrix[[1]]
tf_idf_matrix <- tf_idf_matrix[,-1] #Removing the column containing the words
rownames(tf_idf_matrix) <- words</code></pre>
<p>LSA is a dimensional reduction technique similar to PCA. In fact, to perform it, we calculate the singular value decomposition (SVD) on the document-term matrix. See my other post to learn more about <a href="https://danhdtruong.com/Writing-the-code-for-PCA/">SVD</a>.</p>
<p><span class="math display">\[M = U \Sigma V^{T}\]</span> By using LSA, we will reduce our high-dimensional data into lower dimensional data to do one of three things:</p>
<ul>
<li>Reduce the data to save on computational work.</li>
<li>De-noise the data by weighting the more important terms.</li>
<li>Decrease the sparsity of the matrix by relating synonyms. This means that some dimensions become merged due to related terms. In addition, the effects of words with multiple means are mitigated.</li>
</ul>
<p>To further reduce the computational load, we will take the top 50 words weighed by TF-IDF from each document.</p>
<pre class="r"><code>top_words <- document_tf_idf %>%
group_by(titles) %>%
slice_max(tf_idf, n = 50) %>% pull(word) %>% unique()</code></pre>
<p>We perform LSA by passing our term-document matrix into the function<code>svd()</code>.</p>
<pre class="r"><code>LSA <- svd(tf_idf_matrix[top_words,], 4) #the rank was set to 4
rownames(LSA$u) <- rownames(tf_idf_matrix[top_words,])
rownames(LSA$v) <- colnames(tf_idf_matrix[top_words,])
colnames(LSA$v) <- paste0('LSA_', c(1:dim(LSA$v)[2])) #this renames each of the columns
colnames(LSA$u) <- paste0('LSA_', c(1:dim(LSA$u)[2]))</code></pre>
<p>We take the right-singular vector <span class="math inline">\(V\)</span>, which contains a set of orthonormal vectors that spans the row space of our term-document matrix. By plotting the first two columns of LSA, we should observe a separation between the documents due to the variance in the first two components.</p>
<pre class="r"><code>#install.packages("ggrepel")
library(ggrepel)
df <- LSA$v %>% as.data.frame() %>% select('LSA_1', 'LSA_2')
df$title <- rownames(df)
ggplot(df, aes(x = LSA_1, y = LSA_2, color = title )) +
geom_text_repel(aes(label = title), color = 'black') +
coord_fixed()</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<p>We observe that the math-related documents are in the top-right quadrant, while the fantasy-related documents occupy the bottom-right quadrant. The sports-related documents occupy the bottom-left quadrant but are more spread. We can take the top terms by ordering the terms in each column within the left-singular vector <span class="math inline">\(U\)</span>, which are orthnonormal vectors that span the column space of the term-document matrix.</p>
<pre class="r"><code>df <- LSA$u %>% as.data.frame()
df$word <- rownames(df)
df <- df %>% pivot_longer(cols = c(colnames(LSA$u)))
df %>%
group_by(name) %>%
slice_max(value, n = 5) %>%
ungroup() %>%
ggplot(aes(value, fct_reorder(word, value), fill = name)) +
geom_col(show.legend = FALSE) +
facet_wrap(~name, ncol = 2, scales = "free") +
labs(x = "Weight", y = NULL)</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAYAAAD0ZtPZAAAEGWlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9oU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvuuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTvYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8eUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiYMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpkhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thKbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpXzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477hLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549HQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgYhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjzhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VQNcC+8AAAA4ZVhJZk1NACoAAAAIAAGHaQAEAAAAAQAAABoAAAAAAAKgAgAEAAAAAQAABUCgAwAEAAAAAQAAA8AAAAAAthHDUQAAQABJREFUeAHs3QeYVNX5x/GXsvSlCygqRWwoIliwYgNFiUFjN1FRsAQQ/QdUFBT1IRqiolGIqFE02GMJNjQoKooFxV6pSpEiVToI+9/f0TvenZ2ZnbKzO/fO9zzPMDO3n89dlpf3nlKlqLgYBQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQCKFA1RDWiSohgAACCCCAAAIIIIAAAggggAACCCCAAAJOgAQoPwgIIIAAAggggAACCCCAAAIIIIAAAgggEFoBEqChvbVUDAEEEEAAAQQQQAABBBBAAAEEEEAAAQRIgPIzgAACCCCAAAIIIIAAAggggAACCCCAAAKhFSABGtpbS8UQQAABBBBAAAEEEEAAAQQQQAABBBBAgAQoPwMIIIAAAggggAACCCCAAAIIIIAAAgggEFoBEqChvbVUDAEEEEAAAQQQQAABBBBAAAEEEEAAAQRIgPIzgAACCCCAAAIIIIAAAggggAACCCCAAAKhFSABGtpbS8UQQAABBBBAAAEEEEAAAQQQQAABBBBAgAQoPwMIIIAAAggggAACCCCAAAIIIIAAAgggEFoBEqChvbVUDAEEEEAAAQQQQAABBBBAAAEEEEAAAQRIgPIzgAACCCCAAAIIIIAAAggggAACCCCAAAKhFSABGtpbS8UQQAABBBBAAAEEEEAAAQQQQAABBBBAgAQoPwMIIIAAAggggAACCCCAAAIIIIAAAgggEFqB6qGtGRVLSmDGjBm2YcOGpLZlIwQQQAABBBDIb4GOHTvmNwC1L3eB7777zlavXl3ux+WACCCAAAIIIBA+gUxiUVqAhu/ngRohgAACCCCAAAIIIIAAAggggAACCCCAwK8CJED5UUAAAQQQQAABBBBAAAEEEEAAAQQQQACB0AqQAA3traViCCCAAAIIIIAAAggggAACCCCAAAIIIEAClJ8BBBBAAAEEEEAAAQQQQAABBBBAAAEEEAitAAnQ0N5aKoYAAggggAACCCCAAAIIIIAAAggggAACJED5GUAAAQQQQAABBBBAAAEEEEAAAQQQQACB0AqQAA3traViCCCAAAIIIIAAAggggAACCCCAAAIIIEAClJ8BBBBAAAEEEEAAAQQQQAABBBBAAAEEEAitAAnQ0N5aKoYAAggggAACCCCAAAIIIIAAAggggAACJED5GUAAAQQQQAABBBBAAAEEEEAAAQQQQACB0AqQAA3traViCCCAAAIIIIAAAggggAACCCCAAAIIIEAClJ8BBBBAAAEEEEAAAQQQQAABBBBAAAEEEAitAAnQ0N5aKoYAAvkusGnTpnwnoP4IIIAAAggggAAClSRALFpJ8JwWAQRiClSPuZSFCCCAAAKVIvDll1/a6NGjrXbt2jZq1KiUrqGoqMgmTJhg06dPt9mzZ9vSpUutfv361rZtW2vXrp2deuqp1rRp0zKPOWvWLLvtttvcdr169bIePXqUuU+mG6xfv96uueYaa968uV199dWZHo79EUAAAQQQQAABBNIQyLdYdMqUKTZ16lRbsGCBbdu2zXbeeWfr0qWLHX300WnosQsCCOSyAAnQXL47XBsCCOSdgBKBM2fOtDp16qRU93Xr1tnIkSPtnXfecftVrVrVGjZsaFr+ySefuNfkyZPt+uuvt/bt2yc89ksvveSuQRs9+eSTWU+AKnF788032xdffGHVqlVLeG2sRAABBBBAAAEEEMieQL7Eops3b3YP3T/99FOHqUYDKorDX3vtNXvxxRdtxIgRrlGCW8EfCCAQeAESoIG/hVQAAQQQMLv11ltd8rNVq1bWr18/23fffSPJxHnz5tm4cePsrbfesr/85S9233332U477RSTTcGggj4lT3fccUeXlFQCVcfLRtmwYYONGTPG3n333WwcnmMigAACCCCAAAIIVIBA0GLRsWPHmpKfavGp3ke77rqrU/r222/dg3mtu/vuu13sXAF8nAIBBCpAgDFAKwCZUyCAAALZFFDS8oMPPrAqVarYTTfdZPvtt18k+anzKrC79tprbZ999rGff/7ZJULjXc/bb7/tWo0ecMABka4/zz//fLzNM1qurvp9+/a1l19+2dRilYIAAggggAACCCAQPIGgxaJ6AP/CCy+4+HP48OGR5Kfkd999d9djSp8nTpxo2paCAALhEOB/nOG4j9QCAQTyWEBddTZu3GiFhYVxx/hUgtEby1PJ0nhFyUgVJUC7du3qAkMlRVeuXBlvl7SWq5XpVVddZUuWLHHnGjhwYFrHYScEEEAAAQQQQACByhUIWiyqYZc03qd6O6n3VHRp3bq1i6k1TNOcOXOiV/MdAQQCKkACNKA3jstGAAEEPAEFaRo786effjIlK+OVY445xj3tvv3222NusnjxYvvoo4+soKDADjzwQNcNvlOnTrZ161b3BDzmTmkuVEJ1++23t8GDB7tWq+pyT0EAAQQQQAABBBAInkDQYtH999/fnnrqKReDxtJW7Ku4WoUYNZYQyxAIpgAJ0GDeN64aAQQQiAjUrVvXJSy1QF3gNQbTtGnTXKvQyEbFH5QkrVWrln9Ric+vvPKK+37IIYdYvXr13OfjjjvOvaubkJ6Ul1c58sgj7cEHH3StUtV1n4IAAggggAACCCAQTIGgxaKKPZXYbNGiRUzwV1991dStXxMj7bDDDjG3YSECCARPgEmQgnfPuGIEEECglMCwYcPsr3/9q5sISd3Y9VJLzj333NM6duxoXbp0sT322KPUft4CJTe9BKiX9NS6ww47zBTULl261N5//307+OCDvV0yem/atGlG+7MzAggggAACCCCAQO4IBC0WjSe3aNEiN2Go1vfp08eNsR9vW5YjgECwBGgBGqz7xdUigAACMQVq1qzpBmzXuJqasb169eq2ZcsW++yzz2z8+PE2YMAAu/DCC00zwscqH3/8sUtyNm7c2E2i5G1To0YNU2tNlWxNhuQOzh8IIIAAAggggAACgRUIQyy6YsUKN0b9qlWrTN3ke/bsGdj7wYUjgEBpARKgpU1YggACCARSQBMdde/e3XWBf/bZZ12L0NNOO83atGnj6jN37lzr37+/ayUaXUHNcqnSrVu3EjPIa5nXIlSTJ2mcUAoCCCCAAAIIIIAAAtECQY5F58+fb5qU84cffnC9pq699tro6vEdAQQCLkAX+IDfQC4fAQQQiCVQu3Zt1+1dXd8vvvhi++abb+y2224zJUFHjRrlurJ7Y2+uWbPGpk6d6g7z0ksvmWZo9xfNgKmi9xdffNF1B/Kv5zMCCCCAAAIIIIAAAn6BIMWin3/+uV133XWmmLhz5842fPhwNwSUvz58RgCB4AvQAjT495AaIIBAngtogqL777/fFi5cGFdC439q9neNC6puPTNmzIhsq4SnustrnbrOa+ZL/0vjg2ocUBW1FNW2FAQQQAABBBBAAAEEJBDkWPSNN96wK6+80iU/1ZNKE4p6cS93FwEEwiVAC9Bw3U9qgwACeSigoHPWrFmmbkfnn39+XAHN7N66dWubOXOmLVmyxHbffXe3rSZMUundu7edccYZ7nP0HxoT6cwzz3TJ07ffftuOOuqo6E34jgACCCCAAAIIIJCHAkGNRdXzST2jVM4991z3ysPbR5URyBsBWoDmza2mogggEFaBTp06uaq9+uqrtnz58rjVXLlypc2ePdslStW9R0WJU73UHf6YY46Ju68mR1J3ehUmQ4rLxAoEEEAAAQQQQCDvBIIYi06bNs31jlIMPGjQIJKfefdTS4XzUYAEaD7edeqMAAKhEjjllFOsfv36rlXnJZdcYu+9955t3ry5RB2/+uoru/zyy03d2RWkqjWoijf5kZY1bdq0xD7RX3r06OEWaWb577//Pno13xFAAAEEEEAAAQTyUCBosaji5LvuusuNb6/eU8cff3we3jWqjED+CdAFPv/uOTVGAIEACKxfv96OPfbYhFd65513ulkqlbgcPXq0abZKJSaHDRvmWnnuvPPOVqdOHbds3bp17lhq+an1Kgr+Jk+e7D5r9veyykEHHWQNGzZ03eDVCnTAgAFl7cJ6BBBAAAEEEEAAgQAKhDkWfeaZZ2zRokXurjz44IOmV7xy/fXX2yGHHBJvNcsRQCBAArQADdDN4lIRQCC/BNRaM9HLr7HDDju4J9knnniitW3b1qpVq2bfffedqeXnhg0brE2bNnbWWWfZzTffbIWFhW5Xzfyu2S5r1qxphx9+uP9wMT/rmF5SdtKkSbZx48aY27EQAQQQQAABBBBAIPgCieJQrfOXIMWimvXdK2XVsaioyNuUdwQQCLhAleK/0PyNDvhNzOTyNRO0kiMUBBAIl4BmcV+wYIGp5acSorVq1QpXBakNAghUikDHjh0r5bycNLwCeli3evXq8FaQmiGQpwLEonl646k2AlkWyCQWpQt8lm8Oh0cAAQQqQ0CtNVu1alUZp+acCCCAAAIIIIAAAnkuQCya5z8AVB+BHBSgC3wO3hQuCQEEEEAAAQQQQAABBBBAAAEEEEAAAQTKR4AWoOXjyFEQQACBvBLQTPP/+te/Uq5z//793Sz0Ke/IDggggAACCCCAAAII/CpALMqPAgIIpCpAAjRVMbZHAAEEELC6deum1cW+du3a6CGAAAIIIIAAAgggkJEAsWhGfOyMQF4KkADNy9tOpRFAAIHMBDp06GB6URBAAAEEEEAAAQQQqGgBYtGKFud8CARfgDFAg38PqQECCCCAAAIIIIAAAggggAACCCCAAAIIxBEgARoHhsUIIIAAAggggAACCCCAAAIIIIAAAgggEHwBEqDBv4fUAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTiCJAAjQPDYgQQQAABBBBAAAEEEEAAAQQQQAABBBAIvgAJ0ODfQ2qAAAIIIIAAAggggAACCCCAAAIIIIAAAnEESIDGgWExAggggAACCCCAAAIIIIAAAggggAACCARfgARo8O8hNUAAAQQQQAABBBBAAAEEEEAAAQQQQACBOALV4yxncZ4I1KlTx6pVq5YntaWaCCCAAAIIIIAAArkkUKtWLdu6dWsuXRLXggACCCCAAAIhFCABGsKbmkqVatasmfMJUCVpGzRo4Kq1evVqW79+fSpVDMy2uheNGze2pUuXhvY/AvXr17e6deu6e7J8+XLbvHlzYO5PKheqOhYWFtrixYtT2S1Q2+pnVT+zKkuWLLFt27YF6vqTvVj97ikoKLBly5Ylu0vgtmvWrJn7d0D3UPcyrKVJkybud+uqVatCWcUqVapYixYtXN30u1W/YykIBEFA/5YUFRWV+6VWr17dtttuO3fcDRs2WNj+7of1d5rqVaNGDXffFEdl42ej3H/Ykjygkv2NGjVy8WGY6lW7dm1r2LChU1izZo2tXbs2SZFgbNa8eXNXp3Xr1gXjgpO8StWratWqLjbS/z/DVPT/MP1chrFe9erVc7dqxYoVtmnTptDcNjXK0/9Jsl0vusCH5keGiiCAAAIIIIAAAggggAACCCCAAAIIIIBAtAAJ0GgRviOAAAIIIIAAAggggAACCCCAAAIIIIBAaARIgIbmVlIRBBBAAAEEEEAAAQQQQAABBBBAAAEEEIgWIAEaLcJ3BBBAAAEEEEAAAQQQQAABBBBAAAEEEAiNAAnQ0NxKKoIAAggggAACCCCAAAIIIIAAAggggAAC0QIkQKNF+I4AAggggAACCCCAAAIIIIAAAggggAACoREgARqaW0lFEEAAAQQQQAABBBBAAAEEEEAAAQQQQCBagARotAjfEUAAAQQQQAABBBBAAAEEEEAAAQQQQCA0AiRAQ3MrqQgCCCCAAAIIIIAAAggggAACCCCAAAIIRAtUj17AdwTKU6BwyOByOdzaX49Srfi9sFyOmJsHUT3r5OallctVFRUfxbuXNYs/6xXWsq64YmH+Wd1SXD+9VOr+8hbKP7cV12pT8SvM93KD786FuZ6bf61nmOuo36/1HnrEd0f5iAACA8YXgIAAAggggAACFSQwtNu8CjpT6qehBWjqZuyBAAIIIIAAAggggAACCCCAAAIIIIAAAgERIAEakBvFZSKAAAIIIIAAAggggAACCCCAAAIIIIBA6gIkQFM3Yw8EEEAAAQQQQAABBBBAAAEEEEAAAQQQCIgACdCA3CguEwEEEEAAAQQQQAABBBBAAAEEEEAAAQRSFyABmroZeyCAAAIIIIAAAggggAACCCCAAAIIIIBAQARIgAbkRnGZCCCAAAIIIIAAAggggAACCCCAAAIIIJC6AAnQ1M3YAwEEEEAAAQQQQAABBBBAAAEEEEAAAQQCIkACNCA3istEAAEEEEAAAQQQQAABBBBAAAEEEEAAgdQFSICmbsYeCCCAAAIIIIAAAggggAACCCCAAAIIIBAQARKgAblRXCYCCCCAAAIIIIAAAggggAACCCCAAAIIpC5AAjR1M/ZAAAEEEEAAAQQQQAABBBBAAAEEEEAAgYAIkAANyI3iMhFAAAEEEEAAAQQQQAABBBBAAAEEEEAgdQESoKmbsQcCCCCAAAIIIIAAAggggAACCCCAAAIIBESABGhAbhSXiQACCCCAAAIIIIAAAggggAACCCCAAAKpC5AATd2MPRBAAAEEEEAAAQQQQAABBBBAAAEEEEAgIAIkQANyo7hMBBBAAAEEEEAAAQQQQAABBBBAAAEEEEhdgARo6mbsgQACCCCAAAIIIIAAAggggAACCCCAAAIBESABGpAbxWUigAACCCCAAAIIIIAAAggggAACCCCAQOoCJEBTN2MPBBBAAAEEEEAAAQQQQAABBBBAAAEEEAiIAAnQgNwoLhMBBBBAAAEEEEAAAQQQQAABBBBAAAEEUhcgAZq6GXsggAACCCCAAAIIIIAAAggggAACCCCAQEAESIAG5EZxmQgggAACCCCAAAIIIIAAAggggAACCCCQugAJ0GKzSZMmWd++fW3cuHFJC951111un/feey/pfdgQAQQQQAABBBBAIL8EnnvuORczPv744xVa8co6b4VWkpMhgAACCCCAAAJJCpAALYZasWKFffvtt7Z48eIk2cwWLFjg9lmzZk3S+7AhAggggAACCCCAQH4JLFu2zMWMS5YsqdCKV9Z5K7SSnAwBBBBAAAEEEEhSoHqS24V6s912281OOeUUa9++fajrSeUQQAABBBBAAAEEEEAAAQQQQAABBBDINwESoMV3vFOnTu6Vbzef+iKAAAIIIIAAAggggAACCCCAAAIIIBB2gbxIgK5atcpWrlxpTZs2tVq1atn06dOtqKjI9ttvP6tRo4atXr3adYMvLCx020Tf9PXr19uHH37oFu+zzz7WsGHD6E1KfVfX+FmzZtn8+fNthx12sF133dUaNGhQaruyrs3bQd30v/76a1N3pubNm1vr1q2tRYsW3mreEUAAAQQQQAABBDIQ2LZtm82cOdN1V2/SpIntueee1rhx47hHVEz2/fff28KFC61+/fq24447WqtWraygoCDuPrFWJHNexbGKGXUeXVt0Ufd6xavbbbed1atXL3p15HsmxyEWjTDyAQEEEEAAAQQCKJAXCdDnn3/e7r33Xhs0aJBNnDjRvvrqK3erlMh8+umn7eWXX7bRo0fbCSecYFdffXXkNiqQvP766+2DDz6wn3/+ObL8kksuiXyO/rB161YbP368Pfjgg6bPXqlataqde+65dt5551n16r+xl3VtStDq2nSd/mvQcQ888EB3vUrsUhBAAAEEEEAAAQTSE9Dkli+99JKtXbs2cgDFYGeccYZdeOGFVqVKlcjypUuX2siRI23atGmRZd4HPfRWLLnvvvt6ixK+J3veZ5991k3Weeqpp9pll11W6pijRo2yd955x4YOHWo9evQotd5bkO5xiEU9Qd4RQAABBBBAIKgCv2XiglqDFK77iSeecJMXtWnTxjZt2mS77767awEa6xBqITpixAh79913bZdddrGTTz7Z6tSpY2+99ZaNHTs27tP96667zqZMmeJaiZ555pmupaZagT711FMuKTp37lx33Ohzxrs2zRiqdQqozz77bNf6c8aMGTZ58mQXeF9xxRUJZ69XPfzBfPR51erAH9RHr+c7AggggAACCARPgH/bk79nekj+5JNPWqNGjUwPuXfaaSf38HvSpEnuobZadJ5//vnugOrh07t3b9O7kpx6GN2yZUv76KOPXALyhx9+sGHDhrkH1zVr1kx4EamcN+GBsrwy01hUl6dYVDFprKIH/Nn4ec3GMWNdP8sQQAABBBBA4DeBdP799fbRu/f5tyOW36e8SoBq5vaBAwfaaaed5gSVBI1XlHRUslMTJI0ZM8Z1nde23bt3t3vuuccefvjhUru+9957Lvmp7lLjxo0r0W3q+OOPt759+9qbb77pEpcKmP0l3rW9+uqrbrMhQ4ZExik96KCDrGfPnqYEq7rZq7uWutjHKuoSdcQRR8Ra5ZYpqNUYqNkqv7WjyNYZOC4CCCCAAAII+AXUcpFhcvwi8T+//vrrLsmp2O2BBx6IdC/v2rWradijG2+80R599FEXc9WuXdtefPFFl/xs27at3XHHHVatWjV38KOPPtoNp3TSSSe5oZXef/990zHilVTPG+84FbE801hU16i4198zyn/dt956q5144on+RXxGAAEEEEAAgYAKZBKD6mF0NkteJUA1ZpK6Dnkl0ZP5qVOnus3UZV3jhvrLOeecYxMmTHABsH+5lxS94IILSiQ/tY3G/1TyVN3jlVyNToDGuzYvWNQYU/5EpbZXoK5xnrL9Q+KvI58RQAABBBBAAIGwCOjhtYp62USPrdmtWzf77LPPrG7duq4FoxKg7dq1c13iNY68l/z0LJRE3Xvvve3zzz8vFSN623jvqZ7X268y3olFK0OdcyKAAAIIIIBAeQvkVQJUXZqSbU6rlpUqevofXdQVvkOHDq6rk3/dd999575qvM+PP/7Yv8p99gJrJTOjS7xrU+sBXcttt93mxqZS60+99thjD9dFK/o40d/VbUvXGq8oCbx58+Z4q1mOAAIIIIAAAgET0PA20eOGl1cV1Lo0TEW9aFRijdmpmFHjx/vL/vvvb3p5RcnBRYsW2bx58+zLL780dYFX8ZKG3nbR76meN3r/ivyeaSyqa1U8He9nUhM7ZSMW1f1LdUKqinTlXAgggAACCIRRIJ1/071/sxUrKI5NVDKJRfMqAapxNJMp6jausYoEG2/Gd82y6S8aC0qzyav8/e9/968q9VmD5+uHwn/j4l2bxpnSZExqNapZ4PVS93pdl7q2a32iSZCUdNX4o/HKjz/+aMuXL4+3OuPlhRkfgQMggAACCCCAQCoCCh6z9W/79ttvn8ql5PS2ctLY7CrNmjVL+loXL17sxgydPn26S3z6E3vRrUJjHTTd88Y6VkUsyzQW1TVqyKV4RTPTZ+PnVZOORsfr8a6B5QgggAACCCBQPgLp/Juu+Emx2E8//eTm60l0JZnEonmVAPUnHBOBetvp6b2yz2rRGV38M7lHr9MA+onWR2+v7945o9cpE96/f387/fTTI+OHaqD9VatWuW74Gqf09ttvN41FRUEAAQQQQAABBBBIXsBrZVBWi03viN9++61deumltmHDBhfraUJN9crRhJmdO3c2zeruDaPk7RPrPdXzxjqGf9mWLVv8X9P+HOs4xKJpc7IjAggggAACCOSQQF4lQJN115iayj6rpeayZctitgrQ039/KSwsdON8qhXoAQcc4CZP8q/P9LOeYGv8Ur3UevSDDz4wDRqv63v++eftsssuy/QU7I8AAggggAACCOSNgB5Wa6B+dVtXzBerR80nn3ziHjq3b9/exYM33XSTS35qfFBNUBk9nry6w6t4Cc5YmOmc12tZGi9R63W9j3U+/7JMjkMs6pfkMwIIIIAAAggETaB008ag1SBL16un+SpvvPGGe/f/oS7pGuA+urRp08Yt0kzvsYoSlX/84x9txIgRsVaXWqau+AMGDLCzzjrLdcn3NlBr0UMPPdQt1zJvvFJvPe8IIIAAAggggAACZQu0bt3abfTuu+/G3Hjs2LF27bXXulhL3bLmzJnjtuvTp0+p5Kdahc6fP9+tj5eo9E6Synm1jya9VFmxYoV79/+h80Y/mPev939O9TjEon49PiOAAAIIIIBAkAVIgMa5ez179nQTJmnWdv+kRUVFRaZgWEFwdNHs7yqPPfaYqcWAvygwVbcoDZKf7JgFaoW6cOFCW7BgQcyxk7wZRNXlioIAAggggAACCCCQmsA555zjdtB46d54oN4RJk+e7CY20iQ9mvhIrT29FpQajshfNm3a5FqEel3Iy5oAIJXz6jw777yzO93bb79t3gRKWqBE6/Dhw8ucdMntXPxHqschFvXkeEcAAQQQQACBoAvQBT7OHTzkkENMY3nefffddtFFF1n37t1NEwpNmzbNvvjiC2vZsqVLTvp379Spk/Xq1cuNzTlw4EA78sgj3ZhQM2bMcF3W9YReXajOPvts/25xP2vMpX79+tmNN95oDz30kL3++uum61KQretQcrR58+bWo0ePuMdgBQIIIIAAAggggEBsgb333jsSu1144YWmru1K+k2ZMsVmz57tHoarBag3VvvRRx9tkyZNsjFjxriH3ZrdXNtpaCJ1o9eYoBonVJNMJiqpnlcJ2J122sm1MNXY8F26dLE6deq482riTn1///33E53SrUv1OMSiZZKyAQIIIIAAAggERIAEaIIbpUSlnuCrReeECRPclhrrc+jQoe7p+5NPPllq78GDB1vHjh1d4lQJS71UFDhr/E61Eq1du3ap/eItUOJV5YEHHnCtR9WCVEWtENQNXuNPxZup3m3IHwgggAACCCCAAAJxBRS77bnnnq6Hz4svvhjZTt3UNca6koZeGTRokBUUFNjEiRNdIlTJULUK3W+//eyWW24xzWiuBKUmqdRkSUogxiupnFfHUU+im2++2T0E94Zo0jVqXFI9GE8mAZrOcYhF491BliOAAAIIIIBAkASqFHfpLgrSBVfGtW7cuNF1g1cSU4FmomDWf32aEEkJSyVN1e09eqB8/7ZlfdZt0oRHGotJ4zepC1Os2enLOk70erVQ+Pnnn6MXl9v3wiGDy+1YHAgBBBBAAAEEEgvUe+gR9/B2+fLliTdMc22yw/ikefhK302xliYUUj016U+8smbNGtcTR8nPVq1aRVqIxtu+rOXJnlfH0Vj06q6v3kiZPARP9TjZikWVNFasXd5Fk03pHg4YX1Deh+Z4CCCAAAIIIBBHYGi3XxrtxVkdc7HiKfXA0Vjn6vGcqGQSi9ICNJHsr+tq1arlujQlsWmJTRo0aGAdOnQosSzdL0q6KohLFIyne2z2QwABBBBAAAEEEDA3E3ys2eCjbfRw25swM3pdOt91zmTOq2Or6/tee+2VzmlK7JPqcYhFS/DxBQEEEEAAAQQCJlA1YNfL5SKAAAIIIIAAAggggAACCCCAAAIIIIAAAkkLkABNmooNEUAAAQQQQAABBBBAAAEEEEAAAQQQQCBoAiRAg3bHuF4EEEAAAQQQQAABBBBAAAEEEEAAAQQQSFqABGjSVGyIAAIIIIAAAggggAACCCCAAAIIIIAAAkETIAEatDvG9SKAAAIIIIAAAggggAACCCCAAAIIIIBA0gIkQJOmYkMEEEAAAQQQQAABBBBAAAEEEEAAAQQQCJoACdCg3TGuFwEEEEAAAQQQQAABBBBAAAEEEEAAAQSSFiABmjQVGyKAAAIIIIAAAggggAACCCCAAAIIIIBA0ARIgAbtjnG9CCCAAAIIIIAAAggggAACCCCAAAIIIJC0AAnQpKnYEAEEEEAAAQQQQAABBBBAAAEEEEAAAQSCJkACNGh3jOtFAAEEEEAAAQQQQAABBBBAAAEEEEAAgaQFSIAmTcWGCCCAAAIIIIAAAggggAACCCCAAAIIIBA0ARKgQbtjXC8CCCCAAAIIIIAAAggggAACCCCAAAIIJC1AAjRpKjZEAAEEEEAAAQQQQAABBBBAAAEEEEAAgaAJkAAN2h3jehFAAAEEEEAAAQQQQAABBBBAAAEEEEAgaQESoElTsSECCCCAAAIIIIAAAggggAACCCCAAAIIBE2ABGjQ7hjXiwACCCCAAAIIIIAAAggggAACCCCAAAJJC5AATZqKDRFAAAEEEEAAAQQQQAABBBBAAAEEEEAgaAIkQIN2x7heBBBAAAEEEEAAAQQQQAABBBBAAAEEEEhagARo0lRsiAACCCCAAAIIIIAAAggggAACCCCAAAJBEyABGrQ7xvUigAACCCCAAAIIIIAAAggggAACCCCAQNIC1ZPekg0RSENgzd9uTWOvkrvUqVPHGjRo4BauXr3a1q9fX3KDkHyrWbOmNW7c2JYuXWpbt24NSa1KVqN+/fpWt25dt3D58uW2efPmkhuE5JvqWFhYaIsXLw5JjUpXQz+r+plVWbJkiW3btq30RiFYot89BQUFtmzZshDUJnYVmjVrZtWqVXP3UPcyrKVJkybud+uqVatCWcUqVapYixYtQlk3KoVAJgKjz9liGzZssLD93Q/r7zTVq0aNGu6WK44qKirK5Pbn1L61atWyRo0aufgwTPWqXbu2NWzY0FmvWbPG1q5dm1PumV5M8+bNXZ3WrVuX6aFyan/Vq2rVqi420v8/w1T0/zD9XIaxXvXq1XO3asWKFbZp06Yw3bYKqQstQCuEmZMggAACCCCAAAIIIIAAAggggAACCCCAQGUIkACtDHXOiQACCCCAAAIIIIAAAggggAACCCCAAAIVIkACtEKYOQkCCCCAAAIIIIAAAggggAACCCCAAAIIVIYACdDKUOecCCCAAAIIIIAAAggggAACCCCAAAIIIFAhAiRAK4SZkyCAAAIIIIAAAggggAACCCCAAAIIIIBAZQiQAK0Mdc6JAAIIIIAAAggggAACCCCAAAIIIIAAAhUiUL1CzsJJ8lagcMjgcqn72l+PUq34vbBcjpibB1E96+TmpZXLVRUVH8W7lzWLP+sV1rKuuGJh/lndUlw/vVTq/vIWyj+3FddqU/ErzPdyg+/Ohbmem3+tZ5jrqN+v9R56xHdH+YgAAgPGF4CAAAIIIFDBAkO7zavgM3I6BMoWoAVo2UZsgQACCCCAAAIIIIAAAggggAACCCCAAAIBFSABGtAbx2UjgAACCCCAAAIIIIAAAggggAACCCCAQNkCJEDLNmILBBBAAAEEEEAAAQQQQAABBBBAAAEEEAioAAnQgN44LhsBBBBAAAEEEEAAAQQQQAABBBBAAAEEyhYgAVq2EVsggAACCCCAAAIIIIAAAggggAACCCCAQEAFSIAG9MZx2QgggAACCCCAAAIIIIAAAggggAACCCBQtgAJ0LKN2AIBBBBAAAEEEEAAAQQQQAABBBBAAAEEAipAAjSgN47LRgABBBBAAAEEEEAAAQQQQAABBBBAAIGyBUiAlm3EFggggAACCCCAAAIIIIAAAggggAACCCAQUAESoAG9cVw2AggggAACCCCAAAIIIIAAAggggAACCJQtQAK0bCO2QAABBBBAAAEEEEAAAQQQQAABBBBAAIGACpAADeiN47IRQAABBBBAAAEEEEAAAQQQQAABBBBAoGwBEqBlG7EFAggggAACCCCAAAIIIIAAAggggAACCARUgARoQG8cl40AAggggAACCCCAAAIIIIAAAggggAACZQuQAC3biC0QQAABBBBAAAEEEEAAAQQQQAABBBBAIKACJEADeuO4bAQQQAABBBBAAAEEEEAAAQQQQAABBBAoW4AEaNlGbIEAAggggAACCCCAAAIIIIAAAggggAACARUgARrQG8dlI4AAAggggAACCCCAAAIIIIAAAggggEDZAiRAyzZiCwQQQAABBBBAAAEEEEAAAQQQQAABBBAIqAAJ0IDeOC4bAQQQQAABBBBAAAEEEEAAAQQQQAABBMoWIAFathFbIIAAAggggAACCCCAAAIIIIAAAggggEBABUiABvTGcdkIIIAAAggggAACCCCAAAIIIIAAAgggULYACdCyjSp8iwceeMD+8Ic/uNfUqVPjnr9v376m1/r16+NuwwoEEEAAAQQQQACB9AQmTZrkYq1x48ald4CovebPnx+1JNhfiUWDff+4egQQQAABBPJJgARojt3td955xxRk16pVy7Zt22Y33nijzZs3L+ZVfvvtt6bX1q1bY65nIQIIIIAAAggggED6AitWrHCx1uLFi9M/SPGeitXGjh1rffr0yeg4ubYzsWiu3RGuBwEEEEAAAQTiCZAAjSdTCct/+OEHGzFihFWpUsWuueYaGzx4sGvdOWTIEFu3bl0lXBGnRAABBBBAAAEE8ldgt912s1NOOcX222+/jBDWrl1rjzzyiG3ZsiWj47AzAggggAACCCCAQHoC1dPbjb2yIaCu7Ep2FhYW2t577+1Oceutt9qmTZts1apVVrdu3WyclmMigAACCCCAAAIIxBDo1KmT6UVBAAEEEEAAAQQQCLZAoBOg3333nVWtWtV23nlnKyoqcl3Fv/rqK6tfv77tvvvu1rRp07h3R0lFjcOkl57Kt2zZ0h0nep9ly5bZmjVr4h7HW7HddttZvXr1vK/uXcf95JNPTN2n2rRpY2pFULNmTXed6gqlZV6ZO3euVatWzQ477DBXJ295ly5dXAJUrUN1rTvttJO3incEEEAAAQQQQACBLAqsXr3axXF6OO3FiN4yfddy9dL5+uuvbcmSJbbLLru4V0FBQeSqFEsqjlNRvKqYT6VVq1YlYj4NfaRYb9asWa7L/K677upiU8WHiYq6oc+ZM8fFmHvttZc1b97cfvrpJ1u+fLk1atTIGjZs6HbXw/SVK1e6emiopenTp7vrUevWGjVqRE6RbIwc2YEPCCCAAAIIIIBAAAQCnQC96KKLrHbt2vavf/3LBg0aFAkoPfdzzjnHjbUUHTi+8MILdtddd5WaPEhdz08++WTr16+fCyJ1nH//+9/27LPPeoeM+z5s2DA77rjjIutffvllu/3220uco0mTJnbTTTfZlVde6ZKub7zxRmT7c889131+6aWXXDAdWVH8QYleDTK/ww472BNPPOFfxWcEEEAAAQQQQACBLAkonhs9erSdcMIJdvXVV7uzTJw40caMGeOGKtq4caP7rMSmV/SwWmO4t2vXzi166KGH7L///a/7rAfgXsz3yiuvWJ06ddxyJUVvuOEGmz17tncY966H5dddd13kWP6VSrxeddVV9umnn/oXW69evVziVLGu4sfzzjvPrX/++eft3nvvdTGz6qBGAypKkD799NMuCZpKjOx25g8EEEAAAQQQQCAgAoFOgMp4w4YN9uc//9k9wdbA8griPvroI3v99ddt/Pjxtv3229uJJ54YuR2aYEizrDdo0MB+//vf2z777OO6l2vyIQWQzzzzjLVo0cLOOusst89BBx3kto0coPiDEqV6Uv7++++7Fp7qmt6+ffvIJp9//rmNHDnSTWJ06qmnWseOHV2rgCeffNIuvfTSCp20SE/7hw8fHrm26A/9+/fPaqtSpmeKFuc7AggggAAC2RXQg1+v1V92z5TfR1fSUInLY445xvbdd1/78ccfTUlNteJUYlJxn+7FUUcdZeopdN9997kWnwMGDHBwXqtLtR7Vss2bN9sRRxzhXoo1FWcqAasH/nfffbfr3eSJK+GqJKti17Zt29pJJ53kekC9++67NmHCBNdAwNs2+l0P0xcsWOB6Iqm1p3pN6VpSjZGjj5vo+//93/+5uDjWNoq5FSuXd5EhBQEEEECgcgQqMw5RLwz1FK7Ma8iGevXqv6XvlINSY8CwFO/fbPWqzma9fhMMqJwSoOqCpKSmuvOoKAgcNWqUa7mpJ9leAlRBnhKcKmoxqoDUK2eccUZkHwWvXgL0kEMOMb2iy8cff2z333+/C2w1cZHXNV3d5dUa9Oeff3aBqf8cPXr0cEGsukFFt0qNPn55fVfLBAXP8Urv3r2z+gO2Nt6JWY4AAggggAACWRFQjJHN4DErFx3Agyr5qV49XpypKvTs2dMUWy1dutQ+/PBD01BGnTt3dt3ivQToaaedFqmtEpl33HGHS35ecMEFdv7550fWdevWze2n1qbaRklQrzz66KOmh/d77LGH69XkxcBKxmrIJbX+jFeU/Bw4cKB516H4OJ0YOd7xYy1XbK3Wr7GK6snPaywZliGAAALBFciF3+u5cA3ZuoMaWjGMxXs4nK26hWIWeHUl8gI/D6pr167u46JFi7xFpmSggks9SfcnJr0NvH3KGvNTXdKHDh3qkpx/+ctfbP/99/cOYV9++aUbK6pDhw6lzqFWpzo/BQEEEEAAAQQQQCDYAhqa6He/+12JSmiZ1/XdH4OW2Mj3RQ/U1RVdD/O9rvG+1a63kv4z8MUXX7gY01s3ZcoU9/Hiiy8uFQNr1nrvwby3vf9dQzKph5JX9J+o8oqRvWPyjgACCCCAAAII5JpA4FuAClSTIEWXZs2auUVqiekVJSA1xqe/KNk5b948N3i8nqSrxHtCrXWa0OiKK65wEyOplai60fvLjBkz3Nd4XXk00HxFFgXUzz33XNxTavB+ddnKVglPo+xsCXFcBBBAAAEEyldgy5Ytbnif8j3qL0dTV27KLwJKMnpdtvwmmoRIwyHpPpRV9FBdRUM2ffbZZ+5z9B8aB1QTHX3//femSY4Up2qiJBUN5RRd1AJYXfLVFT9WiXXd5REjxzqXt0xjoPrHSfWW613d+LIRi8qhcePG/lPxGQEEEECgggSy8Xs92UvXvyt6uKfcTZiK6uWNHa4JGTV0TliKhizQA9pk6pVJLBqKBKigoos3+2Z0sKWg8c033zR1jddA8/6/FGV1S9fTcXV1Wrx4sR1++OF2ySWXRJ/WZs6c6ZZ5CdjoDXStZZ0nep9MvmucCI3tFK/oF5M/SRxvO5YjgAACCCCAQDAEFPvwb3v271Ws+FNn9Y/RVdZVeElKJUzVLT1RWbhwoVutB/f6T4/GNovXVUxJ2HhFrVRjlUxi5FjH8y9Tt/x4RTPTK8amIIAAAgiER6Ay45Bt27Y5yMq8hmzcSa9eOrb+zQ5T/bwcWbbrFYoEqLLFyRT9h0Djdb766qtuc2WO1RV+l112cWMoKaOuCZViFd0ITSakJ/BKKGpGzljn9YLe9evXxzqMC1h1LO8Gx9woamEyLQiiduErAggggAACCCCAQBYFYsWB6Z6uU6dOduihhybc3UsilhVr6iAaIz9eiZU0zSRGjnceliOAAAIIIIAAArkkEIoEaLKgGi9JyU8lOv/2t7+57kH+fdUyVMWfWffW/+Mf/3CDzatlp/aNHnPU284bcyneuE9qPRqrKIjWeWNl8TVpEgUBBBBAAAEEEEAgXAJe3KgubZqQM5mi7vJ6kK5WoOrJFKubd7w4NN7xM4mR4x2T5QgggAACCCCAQC4JJNd0MpeuOINr+eSTT9zemtVdYyNFF2/8TrXQ9JfHH3/czSivWcRGjhzpBqr3r/d/9iZEmjx5ssVqBfr888/7N498rlevnvusbkDRZc6cOdGL+I4AAggggAACCCAQEAGvtWj00Ewa31Pl008/jTluq7qGa/JOTXb00UcfuW3VAtSLYzWkU3RRUnTq1KnRixN+TzdGTnhQViKAAAIIIIAAAjkkkFcJUG/AWAWZ0S0t3333XXvkkUfcrfEPJvv666/bmDFj3JP2v/71r5GZPePdQw1Gf8ABB7hJktTd3p8EnTRpkkukxtrXm8hp3LhxJVZrdtAnn3yyxDK+IIAAAggggAACCARHQJMxqOghu39iCHV9VzJz7dq1dsstt9imTZtKVGrs2LH29ddf29y5c0uM6d6nTx+33cMPP+x6KHk7rVq1ym688cZSx/HWx3tPJ0aOdyyWI4AAAggggAACuSiQV13gjznmGHvsscds6dKl7km6JjLSZEl66j19+nTbdddd3cRIetquQFRjJCmJqaLuRU888YT9+9//jjmr55577mmXXXaZ23bw4MFupvi33nrLTjrpJJc0VbCr7u/t27e3r776qtQYoJqd/osvvrA33njDzjvvPBcMa5ImLVOLVW+GencC/kAAAQQQQAABBBAIjIBiSk2atHz5crvgggtMkxRpSKWmTZvaX/7yFxswYICpG3rv3r3dWKBq5alWnJolXjPNDxkyxA3h5FW4Q4cO1rdvX/vXv/5lV111lbVs2dJNiqTJONU9vnXr1m5fb7xQb79476nGyF7PpXjHYzkCCCCAAAIIIJBrAnmVAG3btq1LaN5xxx2m7u5el3fNotmvXz/7wx/+4IJQJUMVhB599NFufCXdNCUw/U/so2+kusd7RbNr3nPPPXb//febZvWcNWuWC0SV5OzataudddZZ5j1p9/Y59thjXatUtTZVl3e9dMxevXq5WUEVmFIQQAABBBBAAAEEgikwbNgwF4cqCaqWmkpuKgGqbvDjx4+3UaNGuaSnHrh7RQ/n1QX+oIMO8hZF3vXAvF27dqbhlb755hv3oP3ggw+2Cy+80B1Px4+ONyM7R31INUY+4YQToo7AVwQQQAABBBBAILcFqhSPRVSU25dY/len7kdLliyxZcuWmZKVCj4rqmgWeT2x16D3jz76aMzTLly40HWdVzCaymzxMQ9WxkIldaOHAyhjl5RWFw4ZnNL2bIwAAggggAAC6QvUe+gR9/BWSbZsFE3AQ8lMQPdGLTMbNGhQ6kCKyRYsWOB6IslarUbTKWoVqt5D1113nXXv3j3pQ1RGjKzx79X7qryLjLfbbjsbML6gvA/N8RBAAAEEyhAY2m1eGVtkb3VhYaFrTKaev2EqqpfXA0PjfUcPmxPkuirvpQnHk6lXJrFoXrUA9X4ghKvEp17lXT777DP3dL9jx442dOjQUof/3//+55apy3y8om5MFAQQQAABBBBAAIHwCSRKaippp+7rZZVrrrnGDenUv39/0zii/qKH/N6kRhp6KZWSzRg5letgWwQQQAABBBBAoLwF8moSpPLGi3U8dWPSk4aXX37ZnnnmmcgkSJoMScnP//znP65VZ8+ePWPtzjIEEEAAAQQQQAABBBIK6CG+ehWNHj3aTZDkbazJkjS2qOLO/fff340N6q3jHQEEEEAAAQQQyGeBvGwBms0brmbJl19+ud122212++2325133um6u8+fP9/N/Kkn6zfccIN17tw5m5fBsRFAAAEEEEAAAQRCKtC7eLIkdXHXePbnnnuu6yqvUa3UdUxln332sZtuuimktadaCCCAAAIIIIBA6gIkQFM3K3MPzfy+2267udaeehK/evVq9xR+7733ti5dulii7u9lHpwNEEAAAQQQQAABBPJaQGOAaQb45557zk2ctGjRIjdL/GGHHWZ77bWXHXnkkW78s7xGovIIIIAAAggggIBPgASoD6M8P2rMpeHDh5fnITkWAggggAACCCCAAAJOQDO8n3nmme4FCQIIIIAAAggggEBiAcYATezDWgQQQAABBBBAAAEEEEAAAQQQQAABBBAIsAAJ0ADfPC4dAQQQQAABBBBAAAEEEEAAAQQQQAABBBILkABN7MNaBBBAAAEEEEAAAQQQQAABBBBAAAEEEAiwAAnQAN88Lh0BBBBAAAEEEEAAAQQQQAABBBBAAAEEEguQAE3sw1oEEEAAAQQQQAABBBBAAAEEEEAAAQQQCLAACdAA3zwuHQEEEEAAAQQQQAABBBBAAAEEEEAAAQQSC5AATezDWgQQQAABBBBAAAEEEEAAAQQQQAABBBAIsAAJ0ADfPC4dAQQQQAABBBBAAAEEEEAAAQQQQAABBBILkABN7MNaBBBAAAEEEEAAAQQQQAABBBBAAAEEEAiwAAnQAN88Lh0BBBBAAAEEEEAAAQQQQAABBBBAAAEEEguQAE3sw1oEEEAAAQQQQAABBBBAAAEEEEAAAQQQCLAACdAA3zwuHQEEEEAAAQQQQAABBBBAAAEEEEAAAQQSC5AATezDWgQQQAABBBBAAAEEEEAAAQQQQAABBBAIsAAJ0ADfPC4dAQQQQAABBBBAAAEEEEAAAQQQQAABBBILkABN7MNaBBBAAAEEEEAAAQQQQAABBBBAAAEEEAiwAAnQAN88Lh0BBBBAAAEEEEAAAQQQQAABBBBAAAEEEgtUT7yatQhkJrDmb7dmdoDivevUqWMNGjRwx1m9erWtX78+42Pm4gFq1qxpjRs3tqVLl9rWrVtz8RIzvqb69etb3bp13XGWL19umzdvzviYuXgA1bGwsNAWL16ci5dXLtekn1X9zKosWbLEtm3bVi7HzbWD6HdPQUGBLVu2LNcurdyup1mzZlatWjV3D3Uvw1qaNGnifreuWrUqlFWsUqWKtWjRIpR1o1IIZCIw+pwttmHDBgvb3/2w/k5TvWrUqOFuueKooqKiTG5/Tu1bq1Yta9SokYsPw1Sv2rVrW8OGDZ31mjVrbO3atTnlnunFNG/e3NVp3bp1mR4qp/ZXvapWrepiI/3/k4JAPgjQAjQf7jJ1RAABBBBAAAEEEEAAAQQQQAABBBBAIE8FSIDm6Y2n2ggggAACCCCAAAIIIIAAAggggAACCOSDAAnQfLjL1BEBBBBAAAEEEEAAAQQQQAABBBBAAIE8FSABmqc3nmojgAACCCCAAAIIIIAAAggggAACCCCQDwIkQPPhLlNHBBBAAAEEEEAAAQQQQAABBBBAAAEE8lSABGie3niqjQACCCCAAAIIIIAAAggggAACCCCAQD4IVM+HSlLHyhMoHDK4XE6+9tejVCt+LyyXI+bmQVTPOrl5aeVyVUXFR/HuZc3iz3qFtawrrliYf1a3FNdPL5W6v7yF8s9txbXaVPwK873c4LtzYa7n5l/rGeY66vdrvYce8d1RPiKAwIDxBSAggAACCFSAwNBu8yrgLJwCgfQFaAGavh17IoAAAggggAACCCCAAAIIIIAAAggggECOC5AAzfEbxOUhgAACCCCAAAIIIIAAAggggAACCCCAQPoCJEDTt2NPBBBAAAEEEEAAAQQQQAABBBBAAAEEEMhxARKgOX6DuDwEEEAAAQQQQAABBBBAAAEEEEAAAQQQSF+ABGj6duyJAAIIIIAAAggggAACCCCAAAIIIIAAAjkuQAI0x28Ql4cAAggggAACCCCAAAIIIIAAAggggAAC6QuQAE3fjj0RQAABBBBAAAEEEEAAAQQQQAABBBBAIMcFSIDm+A3i8hBAAAEEEEAAAQQQQAABBBBAAAEEEEAgfQESoOnbsScCCCCAAAIIIIAAAggggAACCCCAAAII5LgACdAcv0FcHgIIIIAAAggggAACCCCAAAIIIIAAAgikL0ACNH079kQAAQQQQAABBBBAAAEEEEAAAQQQQACBHBcgAZrjN4jLQwABBBBAAAEEEEAAAQQQQAABBBBAAIH0BUiApm/HnggggAACCCCAAAIIIIAAAggggAACCCCQ4wIkQHP8BnF5CCCAAAIIIIAAAggggAACCCCAAAIIIJC+AAnQ9O3YEwEEEEAAAQQQQAABBBBAAAEEEEAAAQRyXIAEaI7fIC4PAQQQQAABBBBAAAEEEEAAAQQQQAABBNIXIAGavh17IoAAAggggAACCCCAAAIIIIAAAggggECOC5AAzfEbxOUhgAACCCCAAAIIIIAAAggggAACCCCAQPoCJEDTt2NPBBBAAAEEEEAAAQQQQAABBBBAAAEEEMhxARKgOX6DuDwEEEAAAQQQQAABBBBAAAEEEEAAAQQQSF+ABGj6duyJAAIIIIAAAggggAACCCCAAAIIIIAAAjkuQAI0x28Ql4cAAggggAACCCCAAAIIIIAAAggggAAC6QuQAI1jN2/ePOvbt69dc801cbao/MVbt261H374ofIvhCtAAAEEEEAAAQQyFIgXe1166RLeL98AAEAASURBVKUuJlu+fHmGZ0htd8WBeq1fvz61HSth6/KICYNU30og5pQIIIAAAgggEHABEqBxbuCGDRvs22+/tblz58bZonIXf/PNN9anTx977bXXKvdCODsCCCCAAAIIIFAOAvFirxkzZriY7Oeffy6HsyR/CMWBeim5mMulvGLCoNQ3l+8F14YAAggggAACuStAAjR3703CK5s4caLNnj074TasRAABBBBAAAEEgi7Qq1cvO+WUU6xOnTpBr0pWrp+YMCusHBQBBBBAAAEEQiZQPWT1oToIIIAAAggggAACIRLo169fiGpDVRBAAAEEEEAAAQQqQ4AE6K/q6ur+9ddfW7t27WyXXXaJey+0XbVq1WznnXe2RYsW2aeffmr77LOP7bDDDiX2WbZsmc2aNcuWLl1qLVu2dMds2LBhiW30ZeXKlbZq1SqrX7++NWnSpNT6JUuWuLGntttuO6tXr57rhqUxsn766Se37YoVK1w3/QYNGljjxo1L7c8CBBBAAAEEEEAgFwWSjb2+//5727Ztm+20005WvXrJ0FVxkOI3xV3Nmze31q1bW4sWLUpV97vvvrOqVau6+G3Lli2ua/ucOXNc7NW5c2erXbt2qX3KWqBz6toWLlzo4rgdd9zRWrVqZQUFBZFdtc2aNWtcDKdYLlbR2KaK6xQnNmrUyG1SVFRkX331lc2fP999V710bP91qmt+vJhQXumcN9b1ect0PMW2uibFvbvuuqsp/qQggAACCCCAAAJBECgZRQbhisv5Gv/3v//Zvffea0o0ekVBpgaCj1UuuOACl2i87LLL7Nprr3UBubbr37+/nXnmmS7YvO+++2zChAmRdVqvoFvrNW5njRo1tMiVZ5991saNG2ennnqq6ZjRZdSoUfbOO+/Y0KFDrUePHi5APvfccyObPfXUU6bXGWecYQMGDIgs5wMCCCCAAAIIIJCLAqnGXhdddJF7GKx4R0lOr4wePdqefvppix4b9MADD7Srr77amjZt6m1qOoaSp3feeacNGjTIlDj1SmFhoZv08rDDDvMWJXzXw+2RI0fatGnTSm2nxKDOve+++7p1H374of31r391iddHHnmk1PZaoAk3ley87bbbTNeuJOPw4cNdctO/g5Kfl19+uZ1wwglusZKm8WJCPdBP9bz+c/k/K9E6fvx4e/DBB0uMh6rYVuc/77zzSiWm/fvzGQEEEEAAAQQQyAWBvE6Afvnll3bzzTe7ROVpp51mHTt2dE/xn3jiCbvhhhvi3h8N0q/AV4lMPf3WoPEHHXSQ6Wn9sGHD7KOPPnItCnRMtVZQq4P//Oc/9uijj7oAV8F3lSpV4h4/0QqNfzVw4EB744037LPPPrODDz7YDjjgANttt91i7rZ58+aYAbq3sVoTMKaWp8E7AggggAACwRdQjOF/2JpLNUo39oquw+OPP26K15RwPPvss11iVJMlTZ482cU9V1xxhXvA7N9v48aNphnl1cpS3eqVTH333XftsccesxtvvNE9ENdD8ERFrSB79+7tHngryamEpXr6KPbTA+sffvjBxYJKzNasWdOOPPJI08NstdRUvLj77ruXOLyWK/mpZO3+++/vEr2DBw82tQr9/e9/b126dDEvlps0aZKLW5WwPfzww138Fi8m1HlSOW+Ji4r6ct1119mUKVNcC1U9zJeRWoEqIa2kqFryjhgxImqvkl9lo1apsYr81BOqvIt6bFEQQAABBCpOIFdiD+/3f65cT3ndAa9eOp4e6ir/FJbi1S3b9crbBOj69etdq0q1GtBT9m7dukV+drp3725q6amu6bGKgl91r1LgrW7pmzZtckGuAkEFwArGx44dG+nG1LVrVzv++ONd64NPPvnEXnrpJevZs2esQ5e5TMG0EqsLFixwCdAOHTq47/F2VAsHtTqNV/QfiE6dOsVbnfHytRkfgQMggAACCCCAQCoC6oIda1idVI6RjW0zib2ir+fVV191i4YMGRKJY/QwWvGVknRqRTlz5kz3oNrbV13f1Q39nnvuMSURVZTE3H777V3rS7XEVEtHLwj39vO/v/jiiy752bZtW7vjjjsi2x599NGuVelJJ51kq1evtvfff98U/9WqVcu0Tvu98sorpRKgWqZy3HHHud5CihOV/NR1KYnrFcWpShIq9lQcqQRoWTFhKuf1zhP9/t5777nkp4ZZUo8l/3BLim3VY+rNN990SWclg+MVbaeWpLHKrbfeaieeeGKsVSxDAAEEEAiQQK7FHrl2PeV5K7Px4LA8ry/dY2W7Xnk7C7yewivA1Hif/uSnbpSC49NPPz3hPfvTn/7kkp/aSAGoirq9qyjI88ZwcguK/9AxlVRVUdKRggACCCCAAAII5JNAprGX38pLpmkMTn/Rf3YeeOABe+6550okP71t1F3bS356y5R8UxJUrRrVIjNRUdfyCy+80K688spI8tPbXsnBvffe233Vw3KveF3WX3vttRJJQLXc8BKg3jZevfQQXolUf1Ec+fDDDyfspeTf3jtmMuf17+f/rPOpeENA+ddp/E81GlBRYpaCAAIIIIAAAgjkskDetgBVywAVTWAUq+gptsYGjVc0CZK/qCWpN1B9vCfg3nK13lSAm6iFgf/YmXxW93Yla+MVtWBdt25dvNUsRwABBBBAAIGACSjGUHfvbJS6deumfdhMYy//idW6UsfTuJlqEanWn3rtsccebvgh/7b+z+pSHl0Uj+21115uckt1o2/Tpk30JpHv6qaul1dkrUkxlThV9351gVfxEpn6rFhTQyIpTtSYoN41qLWnxqBv3769GyNU22pCJhl/Vzx80llnneWGOtL2iiE1SZKGLkq2pHLeeMfUdahovM+PP/7Yffb/4bWuiU5E+7fRZ8WifhP/enWBz0Ysqmv2TxrlPyefEUAAAQTKXyAbv8vTuUr1hFFXag1dGKaiennd+hXnxft3NYh11vBNyl0lU69MYtG8T4A2a9Ys5s9HvJk6vY3Vzd1fFPDqB1CBVrwZMXVMBdlKlirgjT6G/3jl9VlNiDVZU7zy448/RmaUj7dNJst/6WCWyRHYFwEEEEAAAQRSEVA8oglyslEyCTq9BGi6sZe/PhqHU13q1fJQs8DrpS7aShIeccQRpvX+SZC0r/7T4O/C7T+ed026RnVHT1QWL15sTz75pE2fPt0lPv2TMMV7uK3u4nqwrgmgvAToyy+/7E6jdV6R7z//+U83kZJiS22vl/5joNalJ598cqTVpbdPovdkzxvrGGrF6rVC/fvf/x5rk8gyTQylsUq9/5hFVvz6QcMLxCsrV67Mys+r/vNLAjSeOssRQACB8hfIVuyR6pWqp4f+Pc6V60n1+uNtr3p5/84qBtJQjGEpul9KgCZTr0xi0bxNgHrd1v1Bq/+HR0FTouL94HnbeNvrPx3q0hRrkiOt00slXoDsHc9713hVFAQQQAABBBBAIOgCmcZe/vorzurfv78bssgbg1LjsKvruIYkeuutt+z22283jdXplUSTBXgtZssae0rd+DWRklqVKPbTZENqdaohldR686677rKpU6d6p4y89+jRw+677z53XdpXrRM1oaXiyWOOOSaynT7omjVxphKsmqRJ44mq9ejnn3/uXl988YX93//9X4l94n1J5bzxjqHll1xyCTO9JwJiHQIIIIAAAgjkvEDiLF/OX376F+jNwqmn+LFKvOWxttUyzSSqpKaefmviIa9LkH97tfr0ilooqHiJUC8x6q333r2uVN533hFAAAEEEEAAgSAKlHfsJQP1rjn11FPdSzHYBx98YJpUZ9myZfb888/bZZddFqHSQ2W1NozVCtSL0bxrjOwU9eGmm25yyU+NH68JmLykrreZusOrRM94rutUN3YlM/VSAlStHDRRUfSYpNpf8aG294ZPUjyopKiSu88++6ydf/75rrWrtk1UUj2v/1i6LvVqUivQAw44wHbbbTf/aj4jgAACCCCAAAKBEsjbSZD0tF5Fs1vGajqs4DSVokC1devWbhcNNh+reMv33HPPSMCsMThVlDSNLmohEC8Rq8BZJTrAjj4G3xFAAAEEEEAAgVwQKK/YS8nKAQMGuDEy165dG6maWlMeeuihbrkWel3uIxsUf3jnnXf8X91ndZFT61GVREk+bTdnzhy3XZ8+fSKxnFtQ/IfiNm88+FgPtr1JiaZMmRJpJerv/q7jaNKhc845x81G7x1X7xo26fLLL3cJSbVknTt3bmR1WTFhMueNHCzqgzceqlrZxipKMv/xj3+0ESNGxFrNMgQQQAABBBBAIGcE8jYBqmSlxlLSTPBjx4513da9u6KB3B977DHva9Lv3izv48ePLxV0f/PNN5FjqqWCV7zJlN5++22bOXOmt9h1lR8+fHiky3xkxa8fvBYH8RKk0dvzHQEEEEAAAQQQqEyB8oq9NF7nwoULTZNKPv7446WqpIfbKuqSHl00TqjX2lPrNBSSuq0reXnUUUclbFWp2MvrueMlTL3j62G6WoR6QxepNWp0Oeyww1xrT3VrVyJWvYXUstJf1AVfEw+ptWf02GWKJdUaU9ehiZO8UlZMmMx5vWNFv3uxreJiTdrkL4pBZacJoLbffnv/Kj4jgAACCCCAAAI5J5C3XeAVwKob00UXXWRPPfWUaUwndTNSlykNNq+B8zX4eypFM5IqeH799dft4osvdmM6adZPJVTV+lNB9sCBA+3YY4+NHFYziXozg2osKw2Mr8Ff1YVLrRr0PVZrVG8CpRdffNEN/K/gtm/fvpHj8gEBBBBAAAEEEMglgfKKvTT+Z79+/ezGG2+0hx56yMVdhxxyiOvRM23aNJcc1dBEGv8yuiiBqNab6sKuZKMSkYoB1Ttn6NCh0ZuX+K5Eo7qsT5o0ycaMGeMSgpppffbs2S5u00RA6kKv42mSyeiiFqo6r7qwq5x99tmRhKq3rWLE//73v+6h+B/+8Ac347yOqXE/vQSk9vOSntqvrJgwmfN6549+79Spk/Xq1ct1vVcMe+SRR7rxTmfMmOHqrMSxkrG6JgoCCCCAAAIIIJDLAnmbANVNadSokd1yyy02cuTIyMDyCqoV7Okp/umnn57yvVMwrsD1/vvvt4kTJ7r9NUi+AuuePXu6l/+gOp+ent98882moP2N4gHxVVoXt1BVglbLYiVA1Z3pww8/dIPpK/BW0pSCAAIIIIAAAgjkskB5xV7du3d31XzggQdcC0S1QlRRYlDd4BXHeeOtuxW//qHxQZW8fPrpp90SbaNu6Hpw7U8q+vfxfx40aJAVFBS4GE+JUL2U2N1vv/1cTKkxRvVAW5MwabIkxXn+oljQS4BGd3/XdrVq1TLNuO7NGK8JlbxJlVq0aOGSt9GJ3WRiwrLO67/G6M+DBw+2jh072t133+2SzXrQr6LEqno1qZUos61Hq/EdAQQQQAABBHJNoErxOEJFuXZRlXE96sajp/VKPMYajD6da1JrUnWz0sygCmjLKhoMX2M6tWzZMmbQHmt/dbVSN351o1JAnmpRndUyNVulcMjgbB2a4yKAAAIIIIBAlEC9hx5xEzIqNshGKc+uzuUReymM9eItjauuoYW8MTH99VfLSrVWfOaZZ9zESUpUKgZq165dzO39+8b6rF5C6oav5GerVq1cMjDWdtHL9NC6d+/ertXkPffcE726xHddr4x0Lo3FWVZ8migmTOW8JS4i6ota0CrZrGvRz0IySeOoQ5T6qnuxcePGUsszXaAGCJoEasD41OPjTM/N/ggggEA+Cgzt9svDyMquu/6N0oM59cwIU1G9/HPIxJrLJqj1VTylIY40N05Z9cokFs3rFqD+Hw49VderPIu60euVbFErzr322ivZzd12SnqW93WndAFsjAACCCCAAAIIpCFQHrGXWlgqyaVXKkUtUfVKt+g/Id6kTqkc44UXXnCb/+53vytzN/3nzZuEqMyNizdIFBOmct5E59Ks8B06dEi0CesQQAABBBBAAIGcFCABmpO3hYtCAAEEEEAAAQQQCIOAJmtSclJDF02YMMHN5O4fDz5bdays82arPhwXAQQQQAABBBDIRIAEaCZ67IsAAggggAACCCCAQAKBf/7zn25MUG2iFqtXX311uXQdT3BKt6qyzlvWdbEeAQQQQAABBBCoDAESoJWhzjkRQAABBBBAAAEEKkyga9eubmzU8hizMtWL7ty5s5vVXWO8q+u7JmmqiFJZ562IunEOBBBAAAEEEEAgVQESoKmKsT0CCCCAAAIIIIBAoASGDRtWadermdL1quhSWeet6HpyPgQQQAABBBBAIBmBqslsxDYIIIAAAggggAACCCCAAAIIIIAAAggggEAQBUiABvGucc0IIIAAAggggAACCCCAAAIIIIAAAgggkJQACdCkmNgIAQQQQAABBBBAAAEEEEAAAQQQQAABBIIoQAI0iHeNa0YAAQQQQAABBBBAAAEEEEAAAQQQQACBpARIgCbFxEYIIIAAAggggAACCCCAAAIIIIAAAgggEEQBEqBBvGtcMwIIIIAAAggggAACCCCAAAIIIIAAAggkJUACNCkmNkIAAQQQQAABBBBAAAEEEEAAAQQQQACBIAqQAA3iXeOaEUAAAQQQQAABBBBAAAEEEEAAAQQQQCApARKgSTGxEQIIIIAAAggggAACCCCAAAIIIIAAAggEUYAEaBDvGteMAAIIIIAAAggggAACCCCAAAIIIIAAAkkJkABNiomNEEAAAQQQQAABBBBAAAEEEEAAAQQQQCCIAiRAg3jXuGYEEEAAAQQQQAABBBBAAAEEEEAAAQQQSEqABGhSTGyEAAIIIIAAAggggAACCCCAAAIIIIAAAkEUIAEaxLvGNSOAAAIIIIAAAggggAACCCCAAAIIIIBAUgIkQJNiYiMEEEAAAQQQQAABBBBAAAEEEEAAAQQQCKIACdAg3jWuGQEEEEAAAQQQQAABBBBAAAEEEEAAAQSSEqie1FZshECaAmv+dmuae/62W506daxBgwZuwerVq239+vW/rQzRp5o1a1rjxo1t6dKltnXr1hDV7Leq1K9f3+rWresWLF++3DZv3vzbyhB9Uh0LCwtt8eLFIapVyaroZ1U/sypLliyxbdu2ldwgJN/0u6egoMCWLVsWkhqVrkazZs2sWrVq7h7qXoa1NGnSxP1uXbVqVSirWKVKFWvRokUo60alEMhEYPQ5W2zDhg0Wtr/7Yf2dpnrVqFHD3XLFUUVFRZnc/pzat1atWtaoUSMXH4apXrVr17aGDRs66zVr1tjatWtzyj3Ti2nevLmr07p16zI9VE7tr3pVrVrVxUb6/ycFgXwQoAVoPtxl6ogAAggggAACCCCAAAIIIIAAAggggECeCpAAzdMbT7URQAABBBBAAAEEEEAAAQQQQAABBBDIBwESoPlwl6kjAggggAACCCCAAAIIIIAAAggggAACeSpAAjRPbzzVRgABBBBAAAEEEEAAAQQQQAABBBBAIB8ESIDmw12mjggggAACCCCAAAIIIIAAAggggAACCOSpAAnQPL3xVBsBBBBAAAEEEEAAAQQQQAABBBBAAIF8EKieD5WkjpUnUDhkcLmcfO2vR6lW/F5YLkfMzYOonnVy89LK5aqKio/i3cuaxZ/1CmtZV1yxMP+sbimun14qdX95C+Wf24prtan4FeZ7ucF358Jcz82/1jPMddTv13oPPeK7o3xEAIEB4wtAQAABBPJCYGi3eXlRTyqJQLoCtABNV479EEAAAQQQQAABBBBAAAEEEEAAAQQQQCDnBUiA5vwt4gIRQAABBBBAAAEEEEAAAQQQQAABBBBAIF0BEqDpyrEfAggggAACCCCAAAIIIIAAAggggAACCOS8AAnQnL9FXCACCCCAAAIIIIAAAggggAACCCCAAAIIpCtAAjRdOfZDAAEEEEAAAQQQQAABBBBAAAEEEEAAgZwXIAGa87eIC0QAAQQQQAABBBBAAAEEEEAAAQQQQACBdAVIgKYrx34IIIAAAggggAACCCCAAAIIIIAAAgggkPMCJEBz/hZxgQgggAACCCCAAAIIIIAAAggggAACCCCQrgAJ0HTl2A8BBBBAAAEEEEAAAQQQQAABBBBAAAEEcl6ABGjO3yIuEAEEEEAAAQQQQAABBBBAAAEEEEAAAQTSFSABmq4c+yGAAAIIIIAAAggggAACCCCAAAIIIIBAzguQAM35W8QFIoAAAggggAACCCCAAAIIIIAAAggggEC6AiRA05VjPwQQQAABBBBAAAEEEEAAAQQQQAABBBDIeQESoDl/i7hABBBAAAEEEEAAAQQQQAABBBBAAAEEEEhXgARounLshwACCCCAAAIIIIAAAggggAACCCCAAAI5L0ACNOdvEReIAAIIIIAAAggggAACCCCAAAIIIIAAAukKkABNV479EEAAAQQQQAABBBBAAAEEEEAAAQQQQCDnBUiA5vwt4gIRQAABBBBAAAEEEEAAAQQQQAABBBBAIF0BEqDpyrEfAggggAACCCCAAAIIIIAAAggggAACCOS8AAnQnL9FXCACCCCAAAIIIIAAAggggAACCCCAAAIIpCtAAjRdOfZDAAEEEEAAAQQQQAABBBBAAAEEEEAAgZwXIAGa87eIC0QAAQQQQAABBBBAAAEEEEAAAQQQQACBdAVCkwB9+eWXrW/fvvbvf/87aYtJkya5fcaNG1diHx1Hr/Xr10eW//nPf3bLVq9eHVlW3h8mTpzoznHVVVeV96E5HgIIIIAAAgggkHcC8+bNc7HVNddck/W6X3rppe5cy5cvz/q5ok+wdetW++GHH0osjhfnltiILwgggAACCCCAQJ4IhCYBqmDz22+/tSVLliR961asWOH2Wbx4cYl9dBy9FEx6ZcaMGW7Zli1bvEXl/u5dz6xZs8r92BwQAQQQQAABBBDIN4ENGza4+G3u3LlZr7oXK/78889ZP5f/BN9884316dPHXnvtNf9i8+LK6Di3xEZ8QQABBBBAAAEE8kSgep7UM2Y1d9ttNzvllFOsffv2MdezEAEEEEAAAQQQQACBZAR69eplmzdvtjp16iSzebltox5Es2fPtmOOOabEMYlzS3DwBQEEEEAAAQTyXCCvE6CdOnUyvXKlNGzY0F1Ko0aNcuWSuA4EEEAAAQQQQACBJAT69euXxFYVt0muxbkVV3POhAACCCCAAAIIlBYIbQJU3X2++OILKygosD333NOaNWtWqvYaz1PdgwoLC61p06al1sdbsGnTJtedSuNKtWnTxnbddVerUaNGvM1NXaH0ZF7dr6pVq2bt2rWznXbayapXL8nfunVrd4xddtml1LF0nV9//bUtW7bMmjdvbtq2RYsWpbZjAQIIIIAAAgggkK8CirUULynWihVPRbts27bN5s+fbxp+SEMfKabbeeedXbwWva0Xx2n9okWL7NNPP7V99tnHdthhB7fp999/bzqeYjy9L1y40C1XrBirKJ7UuJ2KDXVMf9E6XZdea9eutZYtW7pt/PGqrlex6E8//eR2Vayoa2zQoIE1btzYouPcpUuX2rp160wP3OM9bNdQUhoDv0mTJla/fn3/JdmaNWuck65JdZaVzkVBAAEEEEAAAQSCIFAyAxeEKy7jGhVwjh492p544okSW3br1s2uvPJKq127dmS5Jk7StieccIJdffXVkeWJPiiRecstt5QYa1TB33XXXWcHHnhgqV3fe+89u/POO10A61+pQHfo0KElut97AXLbtm39m7prfPrpp10i1b9C59N1+4Nh/3o+I4AAAggggAAC+SDwv//9z+69994S8ZkeFmtSy3hFycIbbrjBPaT2b6N4THGdkqj+csEFF7jE4mWXXWbXXnutS3Jqff/+/e3MM8+0iy66yCUPn3rqKbfdgAEDXHJy7Nixttdee/kP5T4rDr311lvt4IMPtr///e+R9S+88ILdddddJSbj1MoqVarYySefbGppWrNmTXfsc889N7KfzqvXGWecYTp3dJw7efJkGzNmjHXp0sWdN7Ljrx8UQ1988cWmcfXvu+++SAJUidbx48fbgw8+WGJ8/KpVq5rOf95555V6qB99bL4jgAACCCCAAAKVLRC6BKjGQVKg1rNnTzvooINcEPf444/bq6++6p6yKwhVAJlu0Qztak2q4He77bazd955x1566SUbPHiw3XzzzXbooYdGDv3xxx/bFVdc4b4fffTRdsghh1hRUZFNmTLF3nrrLdPM8rfddpvtv//+bhuNGfXYY4+5oNk7iK5dyVw9aT/77LNd608Nsq8gdtq0ae740bPYe/vyjgACCCCAAAIIhF3gyy+/dDGYEninnXaadezY0bW+VPykBGesolaiShJqzM4jjjjCvRQfvv/++y5xqGTm3XffbbvvvnuJ3TWp0siRI13PH7WA1KSZijeji3ogHXvssS4hqdnY4yVAtZ8exHtFMd0DDzzgWlb+/ve/dy1MV61a5eJNtTh95plnXA+gs846y401OnDgQHvjjTfss88+c4nUAw44wDT2Z6xy3HHHmeLgDz/80FauXFmqFej06dNd3KwE8B577BE5hJLBil3VclSJ3tbFiWW1AlWyVUlRJZJHjBgR2Z4PCCCAAAIIIIBALgqELgGq5Oell15qp59+esRbg8JrdsyvvvrKBYlHHXVUZF2qH9T9XC0MvC4/CprVmlMB5T333OOCTz0RV/ehm266yR3eaxngnatHjx4uuFWQqwToQw89FOlCv+OOO3qbuXclblWGDBkSGa9UgbYSvApC1WVr5syZrhuS2zDqjx9//NH9ZyBqceSrWhhkcxKoDZEz8QEBBBBAAAEEKkJAybdYQ/9UxLkr+hyKt9SjRsMNDR8+3NTjxyvdu3c3tdpUAtFf9DD6jjvucMlPrT///PMjq7W/us6rpaS2URLUX9QNXEMQKblar149U1d1tcaMVRSrKUmo2dmVbPUPfaTu8RqqSd3MvYfnOpYSnCqDBg0yf7yqVp2jRo2yZ5991l555RVTAlTnVcJ3wYIFLgHaoUOHhDGfur3rYbwewiu+1L7+ouOq+BOy6smk5Ke61Ctu1btXjj/+eNfC9s0333QP5WP1hPK2VSyuGD1Wueaaa0pN4BRrO5YhgAACCCQWSOXffq9RmHIXqeyX+ApyY63qplcY6+UJe/ko73tY3rNdr9AlQDXukmZ29xc9sVbrSQWyChz9AaV/u2Q+K1COvikKIP/zn/+4J+B6Ar/vvvu6FgQah1TJ0egAU+dRlyF1TVLQOnXq1LjX5AWLGlfKP2GTxmZSCwEF3/HGcdJ5tL/GqYpX9B8GjT1FQQABBBBAAIFwCCjoz5d/29UCU122lbT0Jz91J9VTRw/E9eDaX9RDRw/FNYSQvwu5t41aXqoLuBKUal0a3XrzT3/6k4u/tH285KfWqQu9WonqQfUHH3zgHpJruYqXbFQrUSWsVTZu3OgSthrzM1as2rVrVxfHKgmbblFyUwlQDRngj0/VslWJTP3cqKWoVx5++GH30ev+7y3Xu+JhJZnVPV4J4UQJUMWiXkzrP4Y+K/GbLz+v0XXnOwIIIFCeAun8Lg1zzJCOR3nej2weK6x1y3a9QpcA7dy5c8wgSt2hVJRIzKSoa1F00QRIakWpgFLHVwL0u+++c5upe3usm6hWAEpoKiBMdE0KdtXKUy1F1dVerT/1UtckJXvLKvqFlig41xMftYSgIIAAAggggEA4BLL577riilwqipFUNBlRrKKkXHQC1IvRtt9+e9dyMtZ+6gau5KpitOgEaPSERbH295Yp4fiPf/zDJRw11qdXlIBU8be2VEJRY3z6i5Kdmuhozpw5rhu81sVLJPr3i/dZMaQenH/zzTfuuF5d1MpTCdjDDjusxIN1z0rxohLH0UUP5FUSxbJaX6tWrVJj2Wu5SjZj0Vz7ef2lxvyJAAIIZEcg1X//9Tsy1X2yc+UcNVkB79+1MN63ZH8ePYNkzfzbhS4BGm9mdHVdV9EMmXrK7Z8MyQ+S6LMSnf6uP/5tveOrRaeKFwgquI5XvFlDvX1ibde7d2/XnV5P1jVelV7qgqRWrep+r/WJJkHSdalVaryiLvJqqZqtUpitA3NcBBBAAAEEEIgpsGXLFtcqMubKDBcmimsyPHRau3sJ0Hjd3NQKNLpo/EqVzz//3DSGZqLizeTu38aL3/zL4n1WC091p9dDcnXX13jvalmq43otRP37KrmplpiaCEkTbypu9UqsB+reumTf9QBeLTw1xrzGJtUQUSpei1R/QlbJV80kr+KfpMktiPpDM8xrPFXFyrHKRx99FGuxW6bxSLMRi6quse5/3AthBQIIIBBwgVR+lypPoESSxs/W7/AwlcLCQpfvCWO91ANYRf92qgdFWIpiHMVyydQrk1g0dAlQ/QWOVfSfARV1M0rUIjLWvt6yRE/cveN7P5DeOE/qYh6veD+wiQJa/VLSGKLqwuWNsaQgUuNZTZgwwQXUt99+u0XPHB/vnCxHAAEEEEAAAQTCIuDFdPHiLS8ei1Vf9cTxxt+MtV7LYk0oFC/JF+sYGuPz8MMPt9dff92Npalx4GMlG7WvWnNoMiFv/Hcl79QVXt371fOnbt26bgLNWOdJZZmSnEqAqhWqEqDLli1zEyPp4bq/lar/mJdcckmJMUz96/6fvfsAk6q6+zh+kLZL7yIqzRgFRcQGdgVFY8OCPYm9ARoLiUSxvkZRDBLFBxMVooglYgQbKoLYFQXBFkVEREVBQDpI23d/R8949+7M7M7Mnd1773zP8ywzc+u5nzMznPnfU3iOAAIIIIAAAghEQSB2AVBFjJMldzdE0WJ1tckmKQCqu+G6o+BP7vhuEiP3mG78TbdPujE83XlUCe7bt6/90x12jSV1++2320rr008/bWeld9vyiAACCCCAAAIIFIKAm6Xd1an815xsuRtCSK0xNblQvpMCjgqAajIkjVOqRwVmNX6mN6kbuoKfCnQOGTLEDqnkXa8b4UqpbvZ7t033XN37NXSTxkFVzyL1FFLwVa1VvQFj1XfVLV+tQNPNLp/uXKxDAAEEEEAAAQTCIpBdJDAsuU+Sj3feeSfJUmOmTJlilye7k590hxQLNRumPynoOnPmTLtYg90ruRaZmuDItfS0K375RwPcu2NpzNBkaeHChXbWUM30qe1dUssDtVjQciXX/cut5xEBBBBAAAEEECgEAbWMVFKdKll9K1m9UAFApVmzZpWbIV7LNRbm+eefby644AKTruu2tq1MUvBQwxVNnz7d9tzRzXTNxq4Wl97k6pJal6xuOHv2bLu5v0eSu7GfSWDUdXVX0NXVkd0yb56clQu+etfpuW7Cn3766bblqn8drxFAAAEEEEAAgTAJxC4AqnGdNHOnN2kGz/Hjx9vJiDRmZi5p5MiRZWZVV5crzS6vSrfu5LvB5DWIvIKhmplUYz95K6va584777StSdu3b280UVKypDEQNEaUxghVVyV/cgFUTfxEQgABBBBAAAEECk1A9aidd97Z1rfuueeeMpM5aDz2Rx55pByJur4rwKiby0OHDi0XONVx1DLyyy+/NK6FabmDZLBAQx2p67uGS1L9TylZsFEtUpUUmPV36X/rrbfM2LFj7Xr1BPImNwxAstau3u28z3v16mXH65w4caJtCarrVFd7f9Ls70pydAFat43Od9ddd9nJlHIZj8sdj0cEEEAAAQQQQCCfArHrAq9uTQ8++KAdYN7Nsq5B3lXpPOOMM0y7du2y9lS3IN1lV4sAjeekCZFef/112wJTd/a13CVtd8UVV5jLLrvMPPnkk0ZB2O7du9tuSwpcamB7BUs1M6i3u5HbX48a/7Nfv37mxhtvNA888IDtPqVWAQq2Tps2zQZHNXixKtUkBBBAAAEEEECg0AQUXLz55ptti81x48bZmds187vGtdQYl6qfqcWlP11++eW2l41aQOrmuHrWqD6mnjua+Vx1sEGDBtnu6P59s3mtgOdDDz1k86X6o+qE/qSgpAKNmrTB1TU1dr0Cj2o9qhvrqj+qhaqCt27ceTcp07PPPmsDt7oJf+655/oPX+a19tVkmqojKyULyGq56tJ9+vSx485rwqiDDjrIBkrVGlXDMWliUXWnP+2007Q5CQEEEEAAAQQQCK1A7AKgxxxzjMUeNWpUotuSuhip0uYfaynTUlElVC1Ar7rqKvPUU0/Z3RXoVFBSlWT/WJ477bSTDcaqdYEqrq7rksbz1AycGlA+1azyLm8uz7qe+fPn2z+t091+VdZ1Xn8XKrcvjwgggAACCCCAQNwFVP9SXevWW2+1M7trdncFMBW8Uz1JE0n6k7p2jxkzxgwbNswGPR977LHEJgo0qgt8jx49EstyfaIb9F26dLH5Ux0w2c1vDZ+kSZDUs0h1RldvVD1PN8SPP/54o8Ct6pQK3LqgpR7fe+89271eAVLXkrSiPGs/BUBVv9XYpKnSwIEDTdeuXW0dWGOZ6k9JQzJpfHq1Ei0uLk61O8sRQAABBBBAAIFQCNQoHfS8JBQ5CTgT6nI+d+5ce+fe3RkP8hQa91MTHKmyWlRUVOGh1V1JXbFUQXQTJFW4k2cDFZNaM2hcUN21V+tRN+aTZ7OMn/7www/lulllfJA0OzQcNDDNWlYhgAACCCCAQJACDR4Ya1Tn0BA8+Uhh7+qsbtmq27Qv7RqfbNLKZCbqbq7hhtSqUtfXvHnzZJtV2TLVYVXfU71PdVi1Yq1MUm8nlbvyr6BmPpImRNINednKynW/z+VcqlOrVWvQSUFmNToYMCY/FkHnl+MhgAACuQpcfcj8Sh9CPUkVT9D/Oep5EKek/6MUd4njdbneH0uXLi03hE+Uy1A9ejQEZGWuK5e6aOxagLpCF6CbkMgtC/JRrQ38LT7THV93yXPJj1oyqBKnPxICCCCAAAIIIIBAeYHWrVsb/WWSFChTwDQsSXVYBT4zvYGvoGem157pNWtWeLVkJSGAAAIIIIAAAlETiN0kSFErAPKLAAIIIIAAAggggAACCCCAAAIIIIAAAvkTIACaP1uOjAACCCCAAAIIIIAAAggggAACCCCAAALVLEAAtJoLgNMjgAACCCCAAAIIIIAAAggggAACCCCAQP4ECIDmz5YjI4AAAggggAACCCCAAAIIIIAAAggggEA1CxAAreYC4PQIIIAAAggggAACCCCAAAIIIIAAAgggkD8BAqD5s+XICCCAAAIIIIAAAggggAACCCCAAAIIIFDNAgRAq7kAOD0CCCCAAAIIIIAAAggggAACCCCAAAII5E+AAGj+bDkyAggggAACCCCAAAIIIIAAAggggAACCFSzAAHQai4ATo8AAggggAACCCCAAAIIIIAAAggggAAC+RMgAJo/W46MAAIIIIAAAggggAACCCCAAAIIIIAAAtUsQAC0mguA0yOAAAIIIIAAAggggAACCCCAAAIIIIBA/gQIgObPliMjgAACCCCAAAIIIIAAAggggAACCCCAQDULEACt5gLg9AgggAACCCCAAAIIIIAAAggggAACCCCQPwECoPmz5cgIIIAAAggggAACCCCAAAIIIIAAAgggUM0CBECruQA4PQIIIIAAAggggAACCCCAAAIIIIAAAgjkT4AAaP5sOTICCCCAAAIIIIAAAggggAACCCCAAAIIVLMAAdBqLgBOjwACCCCAAAIIIIAAAggggAACCCCAAAL5EyAAmj9bjowAAggggAACCCCAAAIIIIAAAggggAAC1SxAALSaC4DTI4AAAggggAACCCCAAAIIIIAAAggggED+BAiA5s+WIyOAAAIIIIAAAggggAACCCCAAAIIIIBANQsQAK3mAuD0CCCAAAIIIIAAAggggAACCCCAAAIIIJA/gVr5OzRHRsCYlUNuz5mhXr16pnHjxvY4y5cvN2vWrMn5mGE8QN26dU2zZs3MokWLzKZNm8KYxZzz1KhRI1O/fn17nCVLlpj169fnfMwwHkDX2LBhQ/P999+HMXuB5EnvVb1nlRYuXGg2b94cyHHDdhB999SuXdssXrw4bFkLLD+tWrUyNWvWtGWosoxrat68uf1uXbZsWSwvsUaNGqZ169axvDYuCoFcBEb8YYNZu3atidtnP67fabquOnXq2CJXPaqkpCSX4g/VvkVFRaZp06a2fhin6youLjZNmjSx1itXrjSrVq0KlXuumdlyyy3tNa1evTrXQ7E/AghUswAtQKu5ADg9AggggAACCCCAAAIIIIAAAggggAACCORPgABo/mw5MgIIIIAAAggggAACCCCAAAIIIIAAAghUswAB0GouAE6PAAIIIIAAAggggAACCCCAAAIIIIAAAvkTIACaP1uOjAACCCCAAAIIIIAAAggggAACCCCAAALVLEAAtJoLgNMjgAACCCCAAAIIIIAAAggggAACCCCAQP4ECIDmz5YjI4AAAggggAACCCCAAAIIIIAAAggggEA1C9Sq5vNz+pgLNBw0MJArXPXLUWqWPjYM5IjhPIius144sxZIrkpKj+LKsm7pc/3FNa0uvbA4v1c3lF6f/pTq//wQy383l17VT6V/cS7LtZ6Si/N1rv/lOuN8jfp+bfDAWE+J8hQBBAaMqQ0CAghETODqQ+ZHLMdkFwEEoiBAC9AolBJ5RAABBBBAAAEEEEAAAQQQQAABBBBAAIGsBAiAZsXGTggggAACCCCAAAIIIIAAAggggAACCCAQBQECoFEoJfKIAAIIIIAAAggggAACCCCAAAIIIIAAAlkJEADNio2dEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAKAgRAo1BK5BEBBBBAAAEEEEAAAQQQQAABBBBAAAEEshIgAJoVGzshgAACCCCAAAIIIIAAAggggAACCCCAQBQECIBGoZTIIwIIIIAAAggggAACCCCAAAIIIIAAAghkJUAANCs2dkIAAQQQQAABBBBAAAEEEEAAAQQQQACBKAgQAI1CKZFHBBBAAAEEEEAAAQQQQAABBBBAAAEEEMhKgABoVmzshAACCCCAAAIIIIAAAggggAACCCCAAAJRECAAGoVSIo8IIIAAAggggAACCCCAAAIIIIAAAgggkJUAAdCs2NgJAQQQQAABBBBAAAEEEEAAAQQQQAABBKIgQAA0CqVEHhFAAAEEEEAAAQQQQAABBBBAAAEEEEAgKwECoFmxsRMCCCCAAAIIIIAAAggggAACCCCAAAIIREGAAGgUSok8IoAAAggggAACCCCAAAIIIIAAAggggEBWAgRAs2JjJwQQQAABBBBAAAEEEEAAAQQQQAABBBCIggAB0CiUEnlEAAEEEEAAAQQQQAABBBBAAAEEEEAAgawECIBmxcZOCCCAAAIIIIAAAggggAACCCCAAAIIIBAFAQKgUSgl8ogAAggggAACCCCAAAIIIIAAAggggAACWQkQAM2KjZ0QQAABBBBAAAEEEEAAAQQQQAABBBBAIAoCBECjUErkEQEEEEAAAQQQQAABBBBAAAEEEEAAAQSyEiAAmhVbuHbauHGj2bBhQ7gyRW4QQAABBBBAAAEECkKAumhBFDMXiQACCCCAQKQFQhUAff755825555rHnzwwUijBpn5p556ypo8+uij5Q47Y8YMc/HFF5vDDjvM9OzZ01x55ZXltmEBAggggAACCCCAQDwFvv7663IX1r9/f1t3/PHHH8utS7XgoosusvssX7481SZJl1MXTcrCQgQQQAABBBAIoUCtMOVpyZIl5rPPPjM77LBDmLJVrXlZvHixNenSpUuZfCxatMgMHjzYrFy50tSsWdO0bdvWtGvXrsw2vEAAAQQQQAABBBCIn8CmTZvMvffea/773/+aF198scwFfv7552bt2rVGrTIrm2bPnm3Wr1+fUY8i6qKV1WU7BBBAAAEEEAiDQKgCoGEAiUoePvroIxv8bNasmXnooYdMw4YNo5J18okAAggggAACCCCQg8CqVavM2LFjTa1a1VeVpy6aQwGyKwIIIIAAAghUuUCousBX+dVH+IQ//PCDzX23bt0Ifka4HMk6AggggAACCCAQRQHqolEsNfKMAAIIIIBA4QpU323jSph///33RneXa9eubTp16mRatWqVdi91B58zZ47ReEht2rQx22+/vWncuHHafdTFXPuoG8/WW29ttttuO9OkSZMy+8yfP9+oq5GOWbdu3TLrtL/O26hRI9O8efMy69T9SNdQXFxsWrdubTQW07Jly5Juqx0XLlxo1qxZY1q2bGkaNGhQ5ljuhdZrO1fpLCkpMV9++aWpUaOGad++vduMRwQQQAABBBBAAIE8CGiczKVLl5oWLVrYm9BqjTlr1izbhXzXXXc1TZs2TZxV3co//vhj880335htttnGdO3a1WyxRfL2Bz/99JOtw6oeq2OqXqohjnQeb1Ldc8GCBXaRqwfqhYZC8h9b9VfVE9XFXcfZaaedTP369b2HK/dc+dCQVKr/dujQwdan69Spk9iOumiCgicIIIAAAgggECGBUAZAN2/ebEaMGGEee+yxMpSHHHKI+ctf/mIDit4VqtyNGTPG/Pvf/7aBSrdOlcA//vGP5owzzijXRUhBS42dNGHCBKPzuaR9TjnlFHPOOecYV9kbPny4effdd82gQYPMkUce6Ta1j9dff72t9Hbv3t3cfvvtZdY9/fTT5q677jInn3yyGTBggHnyySfN6NGjTd++fc2f/vSnMtvqxbBhw8ybb75prr76anP44YeXW68FH3zwgfnzn/+cWDdlyhSjP+V18uTJieU8QQABBBBAAAEEEAheYOLEiebuu++2dVLdQNfknd665PHHH28uu+wy8/bbb5trr73WjsfpcqEb+rfeemuZIKnWPfPMM7bOqOCiN+kG93HHHWf69euXuAn/wAMPmPHjx9vNVAdWXVfphRdeMPXq1bPP9c+3335rJ8jUmKAu6XhnnXWW/XPLvI9ffPGFGTp0qL3Z7parMYGuY6+99rKLqIs6GR4RQAABBBBAIEoCoQyAqmKpCp2CjT169DCaHEmzoL/00kv2jvc999xjWzw6aFXKXn31VdtyU8FLtYTU3fNx48bZoKjufN90001uc6O75ZpASDNXqtXmiSeeaLbddlszb9488/jjj5uHH37YfPLJJ+bOO++059lvv/1sAHTatGllAqCqpKqFqpLu/Guwee9YTApmKu2///72MYh/dG2XXHKJmT59unnjjTeMJkc6+OCD7URIyY6vFgSqKKdKCrRW1LI21b4sRwABBBBAAIHwCWhyxFQ9ScKX2+jm6JFHHrFBxgMPPNDssssu5p133rF/mphIdcLnnnvO7L333jZwqN47uhH+v//9z9x3331lbmbr5vioUaNsr6VjjjnGHks9hlSPVP1Sx1NPolNPPdViqd6n3kK6ka8b97rJruRu3NsXpf9cccUVtm6seu5WW21lXn75ZfPhhx/ac2kM+T59+rhNE49XXnmlrRfqRr3OoTzoOgYOHGhuueUWs++++9p6diZ1UR185MiRZYLEiROWPlE9WS1Ng07+1rBBH5/jIYBA/gTC9n+Yvl91AylOyV2PHsPmnauzehDH9bqcTVFRke0p7V5H/dG9H/N9XaEMgCr4efHFF5uTTjopUY69evWyrTIVmJw6daoN+mml7q4r+KmKnCqQenTpd7/7nTn33HPNK6+8YhS8dHeun3jiCRv8VJd2BVNdV6UDDjjAaJ/zzz/fzJw501b4FIRVZe+OO+4w7733nq28uQrV+++/bwO1+kJct26dDZqqAqy0evVqewx1p995551dlnJ+VAVYFdkNGzbYAGjHjh3t61QHVgBUgdxUaZ999rHd/lOtz3X5qlwPwP4IIIAAAgggkJGAAqBMjpgRWVYb62a7Wma6wKR6+AwZMsQ8++yz5qmnnjKnn366ufDCCxPH/u1vf2tbUqru6pK6myvAqaSApYKbLqkHkXoHKXCq1p3uPLvttputu7kAqOqFyZLqoP/85z8TXei13b/+9S/ba0pBzWQB0C233NJu44aQUnBX3fBVX9axFNDNtC6qvKlHlOr3yZKO7+rPydazDAEECk8gbP+HKSijvzgmxTbC5h2Uc1yvSz7eHh9BeYXhOPm+ruSDEFXzlas15gknnFAmF6rEnXbaaXaZKoIuaQZ0pbPPPrtM8FPLVHk79NBD9bRMd3p1e1dScNQFP+2C0n90t1vHUlKrUyVVBjWe6IoVK8ynn35ql+kfdYtXOuqoo+yjWpS6pICrKnoKMOqHCAkBBBBAAAEEEEAgPgLqReSCku6qNAaoS65runut8TeVNI67Wogq6Qa66p26+e4NftqVpf/o5ryShm7KNJ155pmJ4KfbVwFNJY1RnywpLy746dYrcKprVY8qdX8nIYAAAggggAACURQIZQtQ3dlOFjTUwPFKX331VcJa3daVdOdCLTL9yU1M5PZRhVN37JVci1D/Pm65BqxXEFN5UTd4jaGkoGfnzp3tLnquwOyxxx5r796rW7oqm0qu+7uruNqF1fCP8qexnFIltZjV5Ez5SrXzdWCOiwACCCCAAAJJBVTXySZglvRgvoX+G8e+1QX1Uj2J/EkTHSmp/ulvxeB6KWkoJtUvNWySgo0a49ObVHaagGju3LmJ+mSq1pPe/fzPNSmSP7k8+Mcaddvtueee7mniUT2dVPd97bXXbB3cG+RNbFTBk9tuuy1lF/gddtghL3VR1d81SSkJAQSiJ5DP36eZauh7Wjer1GI/TklxAnU71hjWmtwvTkmTUOv/rjhel2uJrJ6+6hUcl6R4nj5rlbmuXOqioQyAqmtNsqSWmEqaeVMzrKuC797UqlilSxqkXjNx6o63KpH6UPjvcLv91QpUlSYdX2M2qYKrbvDqYq/xnTSpkparctqzZ087bpEKQbN86otRY06oe5PenHvssYc7bLU8Kg8aTypVUisEfaHnKxEAzZcsx0UAAQQQQCC5gH7M5PP/9uRnLbylGlfTn9wYVsnGU3Pr/PuoXqrhmjQRkiYhUj3XpWQNAty6ih5dvdm7ncuDgrD+pB+LLkDqX+eOpcYB2STXWyrZvgp05OP96h2XP9l5WYYAAuEVyMd3QrZXq5iB4gJhylO21+LdT/8P6P8EPcbt2hSP0V8cr8uVoWJbcQrKu/pOvq8rlAFQ70yaroD16CLcejPXrVs30X1I6zTGUmUqOm4bVTbdh177e5PW6U/JFYTuTiswqjFINb6nxgNVcgHO3Xff3U7SpMHlFVzV4PVq/al8Vja566vs9myHAAIIIIAAAgggUD0CChj6U7LAon8b72ttr4k6NdGnkuqa6gq/3XbbmR133NHUr1/fXHTRRd5dKv1crSkySa7um2wfV0dNFthNtj3LEEAAAQQQQACBsAmEMgCaqsm7G69Id9xVqdOgtrojo1ag6rKjweUrSrqDraCmIsu6w+66yHv3U+tOl9Q03CV1g9f4o+rq7gKgCnwqKRCqyqvWuQqnf/Z3F0xNVcFcsGCBOxWPCCCAAAIIIIAAAjEX0ESeqj8q0KkJlPzdy9UyVClV44AgeVQ/Vff7ZJNGuDq46+If5Hk5FgIIIIAAAgggUBUCmd0arooclZ5D3cyTpSlTptjF3kBnhw4d7DJXQfTv9/TTT9tZOHV3XUlByPbt29vnkydPto/+f9zyTp06lWnBqW7wSprgSOONqmu8G//JtQRVAPSNN96w59EESN7k7pp7uza59erS7yqXbhmPCCCAAAIIIIAAAvEVmDlzpr041Rn9wU+tmD17tl3vv3nubrZn2uLUHizNP94Z6t1mapjg8qlJQUkIIIAAAggggEAUBUIZANUkRffee28ZT42vOX78eBtYPPOXiYa0gZux/ZFHHklUztyOCijedddddqxO7zhNbp8xY8aYOXPmuM3to2Z517GU+vbtax/dP5qcSd3bJ02aZJYsWWJc60+tV8tSzV7tGhgzAABAAElEQVSv/TV+kyZs8g+83rZtW3uo119/3U6o5I6rSu11112X6HbvlvOIAAIIIIAAAgggEF8BN1HSrFmzygztpCt+6623zNixY+3Fq+eSN7khllSH1HjuQaWRI0ea7777LnE4jXs3fPhwO87YoYcealxdNrEBTxBAAAEEEEAAgYgIhLILvAKJDz74oPnoo49Mt27dbEVMQUeNP6QJiLyzWmp9nz59zIQJE8wll1xiDjroIDtuku6Ya5Z2tazUzJWnnXZaokg0NqfGV3r55ZfNBRdcYHr16mWDl5opXq0/VdnTsXr37p3YR0809miPHj3sfnrtDYDqtVqBuhnm/d3f3Xpdm7bp37+/6d69u50hVPnUbFd6nar1q/YnIYAAAggggAACCMRHQHVQ3XjXZJ2qk6r+qPqmWlyqV5FaXOrGuiZyUF3R9SbS+KMaxkk35HVjXzfi1YW+RYsWWeNonHy1LHX50IRIummvxgI6rpaTEEAAAQQQQACBqAqEMgDqZi0fNWqUmTFjhrXVWJwKSurusz8NHDjQtrjUXWsFNfWnpMqhWnGqYqiWm95044032hal999/v5k4caJdpYqfur0feeSR9s+7vXuubvDu+GoR6k0KgGqMUKVkAVDNsqYWqbfccovtRj916lS7rbrk33zzzXYZAVBLwj8IIIAAAggggEDsBTp27GgnQVIrS928d13eVe/t16+fOf74483ll19ug6EaL/SII45ImAwePNjuqyCoJt+cN29eTgFQBV5Vl77qqqvMU089Zc+jgKi65w8aNMg0bdo0cW6eIIAAAggggAACUROoUTp2UElYM61uPXPnzrUDw7uxNivKqyZEmj9/vh3AXd3eXRehdPstXrzYaOIjzbhZVFSUbtPA1q1Zs8Z8+eWXZuuttzbeiZYCO0ElD6RuU2rxmq/UcNDAfB2a4yKAAAIIIICAT6DBA2PtRI8KiuUjeYcUysfxC/WYqvOqLqo6qeq8mbTkVFnrJr4mBg0qadxPdYVXgDbfdWOdSy1cg04yadmypRkwpnbQh+Z4CCCQZ4GrD5mf5zNU/vBqYa8W+KtXr678ThHYUtelm1z6/0e9EOKUNJmfGsDF8bpcTxDNK/PTTz/Fptg0V0+rVq3sROUVXVcuddFQtgB1pSiETAdbV+WvS5cu7hCVelQlM5OKZqUOWsFGGvNpp512qmArViOAAAIIIIAAAgjEXUB1Xu/kmplcr7rCB53U2pMWn0GrcjwEEEAAAQQQqE6BUE6CVJ0gnBsBBBBAAAEEEEAAAQQQQAABBBBAAAEE4iNAADQ+ZcmVIIAAAggggAACCCCAAAIIIIAAAggggIBPgACoD4SXCCCAAAIIIIAAAggggAACCCCAAAIIIBAfAQKg8SlLrgQBBBBAAAEEEEAAAQQQQAABBBBAAAEEfAIEQH0gvEQAAQQQQAABBBBAAAEEEEAAAQQQQACB+AgQAI1PWXIlCCCAAAIIIIAAAggggAACCCCAAAIIIOATIADqA+ElAggggAACCCCAAAIIIIAAAggggAACCMRHgABofMqSK0EAAQQQQAABBBBAAAEEEEAAAQQQQAABnwABUB8ILxFAAAEEEEAAAQQQQAABBBBAAAEEEEAgPgIEQONTllwJAggggAACCCCAAAIIIIAAAggggAACCPgECID6QHiJAAIIIIAAAggggAACCCCAAAIIIIAAAvERIAAan7LkShBAAAEEEEAAAQQQQAABBBBAAAEEEEDAJ0AA1AfCSwQQQAABBBBAAAEEEEAAAQQQQAABBBCIjwAB0PiUJVeCAAIIIIAAAggggAACCCCAAAIIIIAAAj4BAqA+EF4igAACCCCAAAIIIIAAAggggAACCCCAQHwECIDGpyy5EgQQQAABBBBAAAEEEEAAAQQQQAABBBDwCRAA9YHwEgEEEEAAAQQQQAABBBBAAAEEEEAAAQTiI0AAND5lyZUggAACCCCAAAIIIIAAAggggAACCCCAgE+AAKgPhJcIIIAAAggggAACCCCAAAIIIIAAAgggEB8BAqDxKUuuBAEEEEAAAQQQQAABBBBAAAEEEEAAAQR8ArV8r3mJQKACK4fcnvPx6tWrZxo3bmyPs3z5crNmzZqcjxnGA9StW9c0a9bMLFq0yGzatCmMWcw5T40aNTL169e3x1myZIlZv359zscM4wF0jQ0bNjTff/99GLMXSJ70XtV7VmnhwoVm8+bNgRw3bAfRd0/t2rXN4sWLw5a1wPLTqlUrU7NmTVuGKsu4pubNm9vv1mXLlsXyEmvUqGFat24dy2vjohDIRWDEHzaYtWvXmrh99uP6nabrqlOnji1y1aNKSkpyKf5Q7VtUVGSaNm1q64dxuq7i4mLTpEkTa71y5UqzatWqULmTGQQQQMAJ0ALUSfCIAAIIIIAAAggggAACCCCAAAIIIIAAArETIAAauyLlghBAAAEEEEAAAQQQQAABBBBAAAEEEEDACRAAdRI8IoAAAggggAACCCCAAAIIIIAAAggggEDsBAiAxq5IuSAEEEAAAQQQQAABBBBAAAEEEEAAAQQQcAIEQJ0EjwgggAACCCCAAAIIIIAAAggggAACCCAQOwECoLErUi4IAQQQQAABBBBAAAEEEEAAAQQQQAABBJwAAVAnwSMCCCCAAAIIIIAAAggggAACCCCAAAIIxE6AAGjsipQLQgABBBBAAAEEEEAAAQQQQAABBBBAAAEnQADUSfCIAAIIIIAAAggggAACCCCAAAIIIIAAArETqFFSmmJ3VVxQpQVWrVplNm3aVOntq2PDL774wsyYMcOeerfddjPbbbdddWQj7+esUaOGqVmzptm4cWPez1VdJ1A5qjyVDjzwQNOqVavqykpez7vFFlsY/cW5LF955RWzaNEi63j00UeboqKivJpW18FVjvpshv17Mhefp59+2qxbt87Url3bHHvssbkcKtT76vtVKa5luX79ejNhwgR7jS1atDAHH3ywfR70P40bNw76kByvwAXWrFljNmzYELjCsmXLzKRJk+xx27Zta7p37x74OarzgHH9TpsyZYpZsmSJpe3Tp4+pU6dOdTIHeu641vW/+uorM23aNGu18847m06dOgXqVt0Hq1Wrltm8ebP9q+68BHn+J5980v5WKS4uNkcddVSQh672Y8X1t9gHH3xgPvvsM+u77777mjZt2lS7dZAZ0GdN9fSKQpS51EVrBZlhjhU9gQYNGoQ+05988okZOnSozec111xjFAQlRVNAQbNHHnnEZl4VpO233z6aF0KuzeOPP56o7OoHSi7/EcFZvQJ33323Wbp0qWnYsKE544wzqjcznD1rAQV73P+V3bp1i3UwO2skdgylQL169fKSrwULFiQ+E0cccYTp3bt3Xs7DQYMVePTRR83MmTPtQU888UTqF8Hy5uVoc+fOTXzWLr30UtOjR4+8nIeDBivwj3/8w+gGlBqknH766cEenKPlReDtt9829913nz12hw4dYnezIS9ovoPSBd4HwksEEEAAAQQQQAABBBBAAAEEEEAAAQQQiI8AAdD4lCVXggACCCCAAAIIIIAAAggggAACCCCAAAI+AQKgPhBeIoAAAggggAACCCCAAAIIIIAAAggggEB8BAiAxqcsuRIEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8AkQAPWB8BIBBBBAAAEEEEAAAQQQQAABBBBAAAEE4iPALPDxKcvYXolmqt96663t9UVh1vrYFkQAF9akSZNEWRYVFQVwRA5RXQItWrRIlGXNmjWrKxucNwCBNm3amOLiYsP3awCY1XgIfQ7d/5UtW7asxpxwagTCIVCnTp3EZ6JZs2bhyBS5qFBA31/uu4z6RYVcodigfv36iTJr2LBhKPJEJioWUP1v7dq1RnV6UjQEGjdunPisqe5OylygRklpynw39kAAAQQQQAABBBBAAAEEEEAAAQQQQAABBMIvQBf48JcROUQAAQQQQAABBBBAAAEEEEAAAQQQQACBLAUIgGYJx24IIIAAAggggAACCCCAAAIIIIAAAgggEH4BAqDhLyNyiAACCCCAAAIIIIAAAggggAACCCCAAAJZChAAzRKO3RBAAAEEEEAAAQQQQAABBBBAAAEEEEAg/AIEQMNfRuQQAQQQQAABBBBAAAEEEEAAAQQQQAABBLIUqJXlfuyGQFKB9957zzz//PNm3rx5pmbNmqZ9+/bm2GOPNZ06dUq6fb4Wfvrpp+bxxx83X331lalfv77p0qWL6dmzp+nYsWPSU7788svmgw8+SLpOC1u0aGFOP/30lOvjuOLHH380Tz31lPnss8/Mt99+a1q3bm169Ohhjj76aFOrVtV9dVCWwb67SkpKzI033mi+/vpr87e//c1sueWWwZ4gxdF++uknM27cOKPvCL23tt9+e7Prrruaww8/3H5XJNuNz2VZlSVLlpj//Oc/5osvvjALFy40rVq1Mh06dDAnn3yyadmyZdmN8/gq07Jcs2aNuffee9Pm6MgjjzS/+c1v0m4TtZWZOlX19WWTv2z2qerr4nzxFsi0TpBOg/dzOp3g1uXT+dlnnzUPPviguf7666v8t0ZwQuE8UpCftbDUX8IpHVyugv6svfTSS2bq1Knmm2++MVtttZX9PX3wwQfb58HlmiMF+Vnza77//vvm7rvvNt27dzfnnXeef3VBvq5R+mO4pCCvnIsOXGD48OHmiSeesMetW7eu0Vtr/fr1pkaNGuacc84xZ5xxRuDnTHZABVn+8Y9/2FUNGjSweVA+iouLzZAhQ8xuu+1WbrdLL73UTJ8+vdxyt0CB0wceeMC9jP3jxx9/bK655hrzww8/2PJr3ry5Wbx4sb1uBbPvuusuozLOd6Isgxd+9NFH7X+EOvLDDz9stt122+BP4jvismXLTL9+/WzQVauaNWtmli5darc64IADzHXXXWfq1Knj28sYPpe/kqgCevPNN5u1a9fagLE+k/pBsWnTJvvdduWVV5pevXr9ukOenmVTlrq51L9//7Q5uummm8yBBx6YdpsorczGqSqvL5v8ZbNPVV4T54q/QDZ1glQqvJ9TyQS7PJ/OH374obnkkkvMxo0bbb1UN1VJwQgE+VkLS/0lGJnwHiXIz5o+U4MGDTLvvPOOveCGDRuaVatW2d/2jRs3Nrfddpvp3LlzeDEilLMgP2v+y165cqU588wzzaJFi8whhxxif2/5tynE1wRAC7HU83DNaql17bXX2iDGn/70J/tDfPPmzWbixIlmxIgR9gvzjjvuMHvssUcezv7rIVUZ0g/t2rVr2w/5/vvvbytG48ePN3feeaddrqCPWjN6k1ofrVixwlakkgX29MWvO16FkOTQt29fG2hRyzK1fG3atKltTXv11VfbxxNOOMEGp/LpQVkGrztnzhx7908VG6WqCoBeccUVZtq0afbuowLrqjypVbHeT2rN2KdPHzNw4MByF8zn8mcSWZ111ln2M6lHfSb1PaU7/Wr5or+ioiIzatSovAe0sylL3RjTDbLdd9/dtsQvV9ClC/R/Q5s2bZKtiuSybJyq8kKzyV82+1TlNXGueAtkWydIpcL7OZVMsMvz5axWTbp5qh4lSroxTwA0mLIL8rMWpvpLMDrhPUqQnzW1GFSDCdXL9Lt6n332sZ+1J5980owZM8bWObVeN+NJ2QsE+VlLlgu1jJ88ebJdRQDUI6QWoCQEchX461//WrLffvuVlLZQKneoP//5z3bd//3f/5VbF/SC0i9/e67SQEC5Q5f+ALfrRo4cWWZdaVdSu7w0CFNmeaG+kJ3KUpalQewyDK+99ppd97vf/a6kNIhWZl3QLyjLYEXXrVtX8oc//KGk9D/AktLhIGw5zp8/P9iTJDlaaWtie65DDz20pLT1YpktSlswlpS2+rP5KQ28l1nH5/JXDn1n6TNZWpH5daHn2eDBg+360gqrZ2nwT7Mty1tuucXmb8KECcFnKoRHzNYp10spbbFfUtrTouSqq65Ke6hs8pfNPmkzwUoEMhTIpk6Q6hS8n1PJBLs8H86rV68uGTp0qP0/Rf8vlvYisc9LA6LBZr6AjxbkZy0s9Ze4F2eQn7XSm+slqrPr81XakKkc3amnnmrXlQ4/UW4dCzITCPKz5j/zCy+8YMvpqKOOso+pfkP49yuE10yC5AkG8zR7ge+++87uvNdee5U7iFphKukuYLKklqIaq1N3KF588UXz5Zdf2m6dybZNt0zjzKmVmdJhhx1WblO37JlnnrGtQt0Gn3/+uX26ww47uEUF+1gaJLPjNKoFrVrnafgCb9IdQLXwVZejDRs2eFfZ55RlOZLQLLjnnnvsZ+viiy829erVqzBfQZWluj4pqXuzWil6k7rC6ztDQ1Sotbg38bn8VUMtXZTcd+mva35+pnF9lNTCN1lSFxgdQ2P6agzW5cuXJ9uswmXZlqXLV6F8x2br5Aog2/JSy261qNb4vulSNvnLZp90eWAdApkIZFu/S3UO3s+pZIJdng/nc88915TeTLP1GPU80zjYpOAEgv6s5Vp/Ce7K4n2kID9ralWt4anUa0etBv1J65T+97//+VfxOgOBoD9r3lN///33ZtiwYWbrrbc2pQFr7yqelwrUQgGBIATU7UQ/cvUF7B+H7vXXX7en2GWXXcqdSsHOG264wf5o865UhUYVm0wmxdAXceldC9sFNFlXyh133NGoK7t+/Je2fEtMiOQPtOhHpLqBKzhTaEkWunaVp7q9+9MWW2xhu8f7l+s1ZZlMJRzL3n33XRvY3nvvvc0xxxxT4YQ0QZZl6V1pi+CCdH4RBUDfeustOwnZSSedlFjN5zJBYYfv0HifTZo0+XWh55kbT9W/XuODqqvSv//97zI3lfQ5/uMf/2jHZc5kQrNsylLfp3PnzrUTp7lJ6PQdoy78yYYb8VxWZJ9m46SLDbq8UgFmk79s9kl1fpYjkKlAtvW7VOfh/ZxKJtjl+XDWOIdq0KC5BTQpy9ixY4PNdIEfLejPmoYfy6b+UuDFkPHlB/lZ0+SopT2LUubB3dTeaaedUm7DiooFgv6suTOqAYvG1decAX//+9/tZMZuHY8/CxAA5Z0QiEDv3r2NZmIs7SJtKyOqnCgYqVZdCm6oxVlpc/oy59IHf8CAAbb1l1qH6U8tDjXgsmaSP//8801p1wlT2VZDroWpPwjgPanWqXWNWsi4H+Mu0KIf6pdddpltKaUfogqW7rnnnra1Y6GMceImOlLgWeX33HPP2RZjs2fPNttss40dw/HYY481CqB4E2Xp1QjXcwX8Ndu7xt3UgOYVpaouS/d59bda43P5a0kpUJjspo620PeWaz3rr4zqJtKrr75qA6ennHKKad++vf3u04DrCooq0K1KUmVTRd+xycpSrfvVWrxt27Z2zFmNH6UfQ/oO0bLSYRmM/v+IU8rGSdcfdHmlMs0mf9nsk+r8LEcgU4GK3n86XrL6XarzVHS8ZN9lqY7F8tQC+XAePXq0UYCGlB+BispMZ83ks5Zt/SU/Vxffo1ZUbkF8p2km+Kefftr+TtdcGuoVSMpeoKIy05Ez+ay5nGh+h1mzZtlGDvpd8Nlnn7lVPP4iQACUt0IgApoZXD+oNSC5utr+61//ssfVXQi1vFQrT+8PeAXXNCmGur6effbZdoIPlxE1t99uu+3sTNXaRkHQyqTScYHsZu5LPtk+jRo1sovdtnrh7mSppVTNmjVtfl2rpSlTptgAoGaVz6Q1arJzR2GZZolTql+/vikds9VMmjTJBqUVrFCr2TfffNO28h0yZEiiGzVlGe6SLR0rywacFAStqFVzdZRlss+kRPlcVu599c9//tOoUqpuLpo0yqW3337bBj9V5vrB6C370jF8jboRvvLKK3bYkGRDl7jjeB/d92aq79hkZekC2fr+uO+++0yLFi1st6p58+YZ/el7ZsaMGZUKznvzEubn2ThlU14KYKs1lEtuMhANZeK6Hbp12267rbXX62zyl80+7tw8IpCrQEXvPx0/2fdPqvNWdLxMjpXqHCzP7rumIjeCnxUJ5ba+os+Gjh7U5yNV/SW3KyjMvSsqt1zKTAE0TaajuqZSly5dTOmcH4n3QWGK537VFZWZzpBpuamsVNdW4zHN/k5KLlC2GVfybViKQKUEZs6cmRjnU3eG9ENXSeODumCGO5B+nH3yySd2G3XF9Cd1061Tp4756KOPjGvW79/G/1pjaSip5Waq1KBBA7tKPxCV9OWzYMEC+1ytVtWSSgFcfXk89NBDpnPnzrZLuL7oFRSNeyqdRMNe4uOPP240dEHpBFa2Na4Cobfeequd7U9l5wLc2piyDO+7Qq2yFeRSwMuN2ZMut0GXpW6AuM9aqs+l+0xqRnOX+Fw6ifSPmoFTf7pBUTrxTZkxVvX9paQbTN7gp5apNbBrkf/YY49pUYUp27J03/0qf83SqxagurGlR7W4V6t/vU+n/jJWbIUZCfkG2TplU1768ajxmN2fbkAq6f9ct8w96uaVUjb5y2YfezL+QSAggWzqd6lOzfs5lUywy3EO1rOqjhbkZy1dntPVX9Ltx7ryAvn+rGlscXWndnVJ9ex54403bE/B8rlhSWUFgv6s6XfUjTfeaBtzXXPNNXboqcrmpdC2owVooZV4FterVpquZYl/95YtWyZ+fKv7uwKG+tCpu7SSfvyqhY8m1Dn++OPtD14tV8sfJY3f88EHH9jn/n80DqjuZOiLVk24lQflxZ/UvV4/rtVqUSnZNm4fF2RxY88VFxfbbpnq+q1xL72T/qjFqr5ITj/9dKNWTJpgKerN/SsydBMb6UtZZXb44Yc7Onvt8ujfv7/573//awdV1p14yjJBVKVPKipLBfbVclk3IzRxVWVSPspSnzFVnNxnz58Pt1w3PFzic/mzhPtucy7eR92EUKt1BT81VpN/jGVXllqvwLY/uWE99P3qUkXvqWzKsnRWcjsutIKu3l4A+q7V/wnKp4KhCgAedNBBLiuRfZR3Nk7ZlNfOO+9sK7oOS58lDSGj980ee+zhFttH/V+rlE3+stnHnox/EAhIIJv6XapT835OJRPscpyD9ayqowX5WUuV54rqL6n2Y3lygXx/1tSI4ogjjrAn15Bo+i2onoAKgqqBECk7gaA/a3fffbftranffO3atcsuUwWyFwHQAinoXC5TrTBTBVA0Foi64Sn4qQ+yutm6lp86p7qN60tSrTwVNDv66KPtMjfe34cffmhbqqTLnxsjQ4FVjWnhT8cdd5y5/PLLE+fVBBupksb/VHJfOvpPQ10D9ZcsKcCnpv6aOVkTeUQ9AFqRoQLaSgooe4OfzkZBFpWpAtv6kw9l6XSq9jFdWerzqgqKWl/edtttifd7RTnMV1nquO6z58+DW+4+k1rP5/JnJffd5jXTTQp9p7744ou2lbzGjdT4yd4kUzfTu8o/XdKwF7pppAB0uveU+47NtCz1XaIhUlIlDXmiAKj+H1ErBpV91JP+D8zEKdvy+v3vf1+GSrN+nnjiifZ7Wf8Xp0qZ5k/HyWafVOdnOQKZCrh6ZSb1u3Tn4P2cTie4dTgHZ1lVRwr6s+bNd2XqL97teV55gXx+1ryNg37729/a3/q6ua3f/uql6R9/vvK5Luwtg/ysab4V1aV18/uEE04obNhKXD0B0EogFfom+kHqWkz6LfSlqElTlLp165YIQnq3U8sTBc7UglIBTO9Ymtpn33339W5e7rm+bJX0Az1ZPmrXrm3Xuy8SF1CxC33/uMpzshnOfZsmXrZq1co+18QdUU8VGboAqGstlOx6Ndaggp/6se1NlKVXI//P05WlykeVEn12FdTyJzdu4IUXXmi30aN3/MigyzJdMCibz6Sup5A+l6789N2mltlq0alxgRQI1Q2adEllW9mZ3tO9p3SOiirY2ZSlG89NQVgNfaCAadRTLk6ZlFe2TtnkL5t9ss0f+yHgF9D7Tymo+h3vZ79wfl7jnB/XfB416M+ay2s29Re3L48VC1TlZ02tCzWRsHpHqkUoAdCKyyfZFkF+1saPH29PoeEFNYygN7medhoSTQ3R1CPLDbvk3a6QnhMALaTSzvJa1TX8pZdeSrm3GxvTBSKTbegG8XXd012LS3XVO/nkk5PtUm7ZsGHDyi3zLnABEQVbdJfRnx+1iFq6dKkN+Gy//fZ2V3Wx1+QTyodaziRLbmIg160/2TZRWZaJ4aZNm8p0r3TX6IJn+s9PibJ0MlX7mK4s9b7WhF5K6X4wugG43dAH+SxLtaDu0aNHOSQtV/K2EuRzWY7JjkWs8Rw1FpO+i9Sy05WXf2sFEVXB0XfennvuadxNJP92/tfp3lPa1n3HZlKWGk9YN4/Uorx96Sz0/rRw4UK7SP9HxCH4qYvJ1Cnb8vJbVvZ1pvnL5poqmxe2Q6AyAu49m0n9Lt1x3fEy+S5LdzzWJRfAOblLmJe6Mgvqs6Zr1c3RytZfwmwT5ry5cgviO00NKPS7XzeoTznllKSX7X5jV/YGe9KDFPhCV2ZBfNb0m09/Cna6gKfj1QS3SorX6Deh+33o1hfiY/T7mhViqYXsml0wUWN5qgujP2nZp59+ahe71p8a31NJLUJdQM0u+OUfdd09//zzzQUXXGBnCPauS/Vc48tpxvlVq1bZcdD827388stGQT1to4Cnksa804RHmpzDOxae21cBUzcJUyHc4dKYcvpC1piN7rqdhR71xanWhfrydAErytIrFI7nmv1v6tSpKf/cLN4aQ1LbHXvssTbj+SjLXr162WMnu4mi74YpU6bY9brR4hKfSyfx86MqL4MGDbLBT5WtJmpLFfx0e7qy1B3fZEnDl2h845tuuinZ6qTLsilLlfvYsWPNAw88kPSYmmxNKU7fr9k45aO8koKXLswmf9nsk+r8LEcgU4Fs6nfpzsH7OZ1OcOtwDs6yqo4U9Gctm/pLVV1rnM4T5GdNv8HHjRtnWwm6Rk5eK91c1814pcreYPfuz/OfBYL8rGksVv2eS/bnhjHUe0TrXWvRQi4HAqCFXPoBXXvXrl3tXSK18hk6dGi5WeFGjRplvvnmGzsBhuuuqS62CngoWKl9/Hcr9ANfXes1Lpx+8Fc2nXrqqXbT0aNHl2n5placjzzyiF3nbempPCgYpP+g//3vf5eZ6V3/AWjmcwUD1U1fgdO4J93RO+200+xl3nDDDbbllrtmBY9HjBhhu6l27949MeM0ZemEov+Yj7JUq0+1/FNXmYkTJ5ZBUmBM3xvqTqP3lEt8Lp3Ez48KVmq8ZHWX0felWndWlDT7u5K+92bOnFlmcw1foZs+8+fPtxPRlVmZ5kU2ZXnwwQfbIyrQ7SrM7hTTp083aiGqdM4557jFkX/MximI8tLQLnp/DBw4MK1hNvnLZp+0mWAlAhkKZFq/0+G/++47M2nSJDN58uQyZ+P9XIYjby+yddbkKio3/QYgVb1AkJ+1bOovVX/F0T9jkJ81/VbXkGgKdOr3uLdxk36v67exHrWdawQVfcHquYIgP2vVcwXRPGuN0sDPz+1io5l/ch0SAY1Jd9lll9kWlmrlqW6XRUVFdvIg/XBXE3nNSO2dqVgVmwEDBtiuEerSqSCjtlPFRzPianzR66+/3vTs2bPSV6kg3UUXXWSDp7qzoh/funulVkgKtOgcukvinWhDExxpgg99FPQDsnfv3raF46uvvmoDt2qZox+Vbqy6SmcmohvqPzX9gFbQRMFhmWlM0DfffNNobJHtttvOBkIbNGiQuELKMkERiScaA0Ytrx9++OFyLQnzUZb6LGmyHn0+9ZlUhUnfCxq0W0H34cOHl/luECKfy5/fSvo8ajIkN5RBuq4r+u5Vi3aXbr/9djNhwgT7XXrQQQfZz67Ga3r33XftjZ3OnTtbe81aXtmUaVmqzPV/g5uJfvfddzf6U0tytcpX0ne2qwRWNh9h3y5TJ11PPsorlVM2+ctmn1TnZzkCmQpkU797/vnn7YQd+t5Uyxdv4v3s1cjf82ycTzrpJBu8Pu+88+wkqulyd+aZZ9qba7qp5+1Jkm4f1qUXCOqzlkv9JX0OWZtMIMjPmuroF198sa23N2/e3P421m9n9SpSoybdiL/33nszuomeLM+Fviyoz1o6R01CfccddxhNOnrdddel27Rg1hEALZiizv+FKnCiMeT8rY122203c8UVV5i2bduWy4S6mGsfBT29zewVIFEXeN3RyjTpP1x90DVLshvbUJVfBRHUpV6BWX9Sd28FaN2ETlqvoMABBxxgg6Ouy7x/v7i+1hfy/fffb2eUUytdJY1Tp7K89NJLk052RVlG592QLgCqq8hHWSoAppsP3smz1DJUwTG9r5IlPpfGDh+iH4GVSbo5oZbs3qRWNCNHjjQ//PBDYrEmO9Ig6Wp1mM24m5mWpVrTq7WvAu5uHGhlRt34NS5YNt/ziYsJ8ZNMnXQp+SivVETZ5C+bfVKdn+UIZCqQaf0uXQBU5+b9nGkJZLd9ps4EQLNzDnKvID5rGv4sl/pLkNdTKMcK8rOm8fj1G12NX1zS72l1pVYDpkwmFHb781heIIjPWvmj/rqEAOivFu4ZAVAnwWNgAmvWrLFdK9WCU0HPyrQuUvBTd5QUbFNrQ91tyjXpmOpyqZad+qFdv379Cg+p5v4ajFhBAe3jbSla4c4x3EB23377re32rqB0ZTwoy/i8EfJRlmqJrc+Yxppt3bp1pd5TfC5zf0/JUF3e9d2m79i6devmfNBMy1LvpwULFtjWx5pEzduKPOfMhPgAmTrpUvJRXqmIsslfNvukOj/LEchUIJv6Xbpz8H5OpxPcOpyDs6yqIwX9WauqfBf6eYL8rGlcftUf9Ttav+t1E50UvACfteBNUx2RAGgqGZYjgAACCCCAAAIIIIAAAggggAACCCCAQOQFmAQp8kXIBSCAAAIIIIAAAggggAACCCCAAAIIIIBAKgECoKlkWI4AAggggAACCCCAAAIIIIAAAggggAACkRcgABr5IuQCEEAAAQQQQAABBBBAAAEEEEAAAQQQQCCVAAHQVDIsRwABBBBAAAEEEEAAAQQQQAABBBBAAIHICxAAjXwRcgEIIIAAAggggAACCCCAAAIIIIAAAgggkEqAAGgqGZYjgAACCCCAAAIIIIAAAggggAACCCCAQOQFCIBGvgi5AAQQQAABBBBAAAEEEEAAAQQQQAABBBBIJUAANJUMyxFAAAEEEEAAAQQQQAABBBBAAAEEEEAg8gIEQCNfhFwAAggggAACCCCAAAIIIIAAAggggAACCKQSIACaSoblCCCAAAIIIIAAAggggAACCCCAAAIIIBB5AQKgkS9CLgABBBBAAAEEEEAAAQQQQAABBBBAAAEEUgkQAE0lw3IEEEAAAQQQQAABBBBAAAEEEEAAAQQQiLwAAdDIFyEXgAACCCCAAAIIIIAAAggggAACCCCAAAKpBAiAppJhOQIIIIAAAggggAACCCCAAAIIIIAAAghEXoAAaOSLkAtAAAEEEEAAAQQQQAABBBBAAAEEEEAAgVQCBEBTybAcAQQQQAABBBBAAAEEEEAAAQQQQAABBCIvQAA08kXIBSCAAAIIIIAAAggggAACCCCAAAIIIIBAKoFaqVawvDAE5syZY9atW1cYF8tVIoAAAggggEBOAjvvvHNO+7MzAn6B+fPnmxUrVvgX8xoBBBBAAAEEECgnkEtdlABoOc7CWrB582azadOmwrporhYBBBBAAAEEEEAgFALURUNRDGQCAQQQQACB2AvQBT72RcwFIoAAAggggAACCCCAAAIIIIAAAgggULgCBEALt+y5cgQQQAABBBBAAAEEEEAAAQQQQAABBGIvQAA09kXMBSKAAAIIIIAAAggggAACCCCAAAIIIFC4AgRAC7fsuXIEEEAAAQQQQAABBBBAAAEEEEAAAQRiL0AANPZFzAUigAACCCCAAAIIIIAAAggggAACCCBQuAIEQAu37LlyBBBAAAEEEEAAAQQQQAABBBBAAAEEYi9AADT2RcwFIoAAAggggAACCCCAAAIIIIAAAgggULgCBEALt+y5cgQQQAABBBBAAAEEEEAAAQQQQAABBGIvQAA09kXMBSKAAAIIIIAAAggggAACCCCAAAIIIFC4AgRAC7fsuXIEEEAAAQQQQAABBBBAAAEEEEAAAQRiL0AANPZFzAUigAACCCCAAAIIIIAAAggggAACCCBQuAIEQAu37LlyBBBAAAEEEEAAAQQQQAABBBBAAAEEYi9AADT2RcwFIoBAoQr89NNPhXrpXDcCCCCAAAIIIIBANQtQF63mAuD0CCBQRqBWmVe8QAABBBCoVoGPP/7YjBgxwhQXF5thw4ZllJeSkhIzYcIEM336dPPFF1+YRYsWmUaNGpmOHTua3/zmN6Zv376mRYsWFR5zzpw55u9//7vdrk+fPubwww+vcJ9sNli8eLH5z3/+Y3S+devWmU6dOpmuXbuaAw44IJvDsQ8CCCCAAAIIIIBAjgKFVBf1U6kufcstt5ivv/7aXH/99WbLLbf0b8JrBBCIsAAB0AgXHllHAIH4CaxZs8Z8/vnnpl69ehld3OrVq82tt95q3nzzTbvfFltsYZo0aWK0fObMmfZvypQptjLXuXPntMd+7rnnbB60kQKU+QiAvvvuu+aGG26wgU+dp2HDhmb27Nk2gKvzXXbZZaZmzZpaRUIAAQQQQAABBBCoIoFCqYsm4xw3bpxRfVlpw4YNyTZhGQIIRFiAAGiEC4+sI4AAAk7g9ttvt8HPdu3amX79+pldd901EUCcP3++GT16tHnttdfM5Zdfbu69916z7bbbul3LPK5fv95MnjzZBk+32WYb89FHH9ngqY4XVPrhhx/M3/72Nxv8PO6448zxxx9vWrVqZVuu3nPPPeb55583DRo0MBdeeGFQp+Q4CCCAAAIIIIAAAnkUiFJdNBnD3Llzzf33359sFcsQQCAmAowBGpOC5DIQQKBwBRS0VIvKGjVqmJtvvtnsvvvuieCnVNq2bWuuueYas8suu5iNGzfaQGgqrddff922Gt1zzz1Nz5497WZPP/10qs2zWj5p0iSzatUq06ZNG3PRRReZrbbayuZ3r732Mr///e/tMV999dWsjs1OCCCAAAIIIIAAAlUrELW6qF9H+VcdulatWqZOnTr+1bxGAIGYCBAAjUlBchkIIFC4AuoyrzE01Y081Rif6hLvurIrWJoqqfWlkgKgGotT+yko+uOPP6baJePlat25xx57mNNPP90e33uAffbZx77U+KVBntN7Dp4jgAACCCCAAAIIBCcQtbqo/8rVO2revHm295HG4SchgEA8BQiAxrNcuSoEECgggfbt29sWlCtWrLDBylSX3qtXL/PMM8+YO+64I+km33//vZkxY4apXbu2UWtMjSHarVs3s2nTJjNx4sSk+2Sz8JhjjjFDhgwxhx12WLndVflUUqvQpk2b2uf8gwACCCCAAAIIIBBegajVRb2Smjz0ySefNN27dzdHHXWUdxXPEUAgZgIEQGNWoFwOAggUnkD9+vVtwFJXru47GoNp2rRpiQmGnIgmFSoqKnIvyz2+8MILdplaYaqVppILUipwunnzZrssH/+o65HyrPwrKUhKQgABBBBAAAEEEAi/QFTrosuXLze33XabadSokRk4cGD4ockhAgjkJMAkSDnxsTMCCCAQDoHBgwfbiYU0C7y6setPLTk7depkunbtau9q77jjjikzq+CmC4C6oKc23m+//YwqteqS/s4775i999475TGyXTF8+HDz4osvGgVB1eX+yiuvNIceemi2h2M/BBBAAAEEEEAAgSoWiGJdVHXQJUuWmOuvv56eR1X8fuF0CFSHAC1Aq0OdcyKAAAIBC9StW9dW3hQ81IztGsR9w4YN5oMPPjBjxowxAwYMMOedd57RjPDJ0vvvv2+DnM2aNbOTKLltNBD8QQcdZF8GPRmSO8fMmTNN8+bNbZ4ViNUESAsXLnSreUQAAQQQQAABBBAIuUDU6qJqLPDaa6+Z3r172xv+IeclewggEIAAAdAAEDkEAgggEAYBtZ5Uy0l1gddYRn/729/MiSeeaDp06GCz9+WXX5r+/fsbtRL1JzfG5yGHHFJmBnlt51qEavIkjRMadBo9erQN0irAqlng33rrLXP22Webjz/+OOhTcTwEEEAAAQQQQACBPAlEpS66YMECc/fdd5stt9zS1o3zxMFhEUAgZAJ0gQ9ZgZAdBBBAIAgBzWCpwdz1d8EFF5hPP/3U/P3vfzcKgg4bNsx2Za9Ro4Y91cqVK80bb7xhnz/33HNm8uTJZbJQUlJiX+vx2WefNeecc06Z9bm+cPlQl/0zzzzTfPfddzYPDz30kLnllltyPTz7I4AAAggggAACCFSxQFjroprcU5Nxrlu3zjYW0FBPJAQQKAwBWoAWRjlzlQggEGMBTVB0//33m2+//TblVWr8T83+riDjsmXLzOzZsxPbKuCp7vJap67zqhh6/9Qt3VUO1VJU2+YzHXjggfbwn3/+eT5Pw7ERQAABBBBAAAEEAhCIUl30iy++MJ988onRDfgbb7zR9O3bt8yfJkZSuuSSS+xydZUnIYBAPARoARqPcuQqEECggAVU6ZwzZ46dQOiss85KKaGZ3du3b28UWNQYmzvssIPd1lXs1Pry5JNPTrr/0qVLzSmnnGKDp6+//ro5+OCDk25XmYVjx441X3/9te3m3qpVq3K7KBCrpFnrSQgggAACCCCAAALhFohSXVSBT1fHVC+oVGnVqlU2SJrvG/+pzs9yBBAIXoAWoMGbckQEEECgSgW6detmz/fSSy/ZmSxTnfzHH380uuut8Zl22203u5kCp/pTZbBXr16pdjWaHEnd6ZVynQxJY4kqrxp4PlnSpEhK22+/fbLVLEMAAQQQQAABBBAIkUCU6qKqX77wwgsp/xo3bmxlR40aZbc5+uijQyRNVhBAIBcBAqC56LEvAgggEAKBE044wTRq1Mi26rzwwgvN22+/bdavX18mZ+rqc+mllxp1Z1clVa1BldzkR1rWokWLMvv4Xxx++OF2kWaW/+qrr/yrK/3atR5VS1ANQu9Ns2bNMk888YRddMwxx3hX8RwBBBBAAAEEEEAghAJRq4uGkJAsIYBAFQjQBb4KkDkFAgggkKnAmjVrTO/evdPudueddxqN7anA5YgRI8w111xjA5ODBw+2rTzbtm1r6tWrZ5etXr3aHkstP7VeSUHSKVOm2Oea/b2i1KNHD9OkSRPbDV6tQAcMGFDRLknXK7A5bdo0884779hJj5Snrl272tapr7zyitFkSxqPaa+99kq6PwsRQAABBBBAAAEE8isQ57pofuU4OgIIhFWAFqBhLRnyhQACBS+g1prp/rxAbdq0MXfddZdRN52OHTvasY3mzZtnB3lfu3at6dChgzn11FPtrOoNGza0u2rmd419VLduXbP//vt7D5f0ucZLckHZSZMm2dkzk25YwUJ1t7/uuuts8FPjfb733nt2EqepU6eali1bmmuvvdaoJSsJAQQQQAABBBBAoPoE0tVDtc6bolQX9eab5wggUDgCNUpb2pQUzuVypX4BzQSt4AgJAQTiJaBZ3L/55hujlp8KiBYVFYXyAjdu3Ghnr9f4pGqxqrFGSQggEF4BtdYmIRCkgG7WuVmXgzwux0IAgeoViEpdtHqVODsCCGQqkEtdlC7wmWqzPQIIIBABAbXWbNeuXehzWqtWLZvPKOQ19JhkEAEEEEAAAQQQCIlAVOqiIeEiGwggUAUCdIGvAmROgQACCCCAAAIIIIAAAggggAACCCCAAALVI0AL0Opx56wIIIBApAU00/x9992X8TX079/fzkKf8Y7sgAACCCCAAAIIIIDALwLURXkrIIBApgIEQDMVY3sEEEAAAVO/fv2sutgXFxejhwACCCCAAAIIIIBATgLURXPiY2cEClKAAGhBFjsXjQACCOQm0KVLF6M/EgIIIIAAAggggAACVS1AXbSqxTkfAtEXYAzQ6JchV4AAAggggAACCCCAAAIIIIAAAggggAACKQQIgKaAYTECCCCAAAIIIIAAAggggAACCCCAAAIIRF+AAGj0y5ArQAABBBBAAAEEEEAAAQQQQAABBBBAAIEUAgRAU8CwGAEEEEAAAQQQQAABBBBAAAEEEEAAAQSiL0AANPplyBUggAACCCCAAAIIIIAAAggggAACCCCAQAoBAqApYFiMAAIIIIAAAggggAACCCCAAAIIIIAAAtEXIAAa/TLkChBAAAEEEEAAAQQQQAABBBBAAAEEEEAghUCtFMtZXCACLVu2NBs3bgz8amvVqmXq1atnj/vTTz8Z/RVqql27tikuLjYrVqwoVAJ73Q0aNDBbbLGFKSkpMStXrixoi4YNG9rPxPr16wvWoU6dOqaoqMhe/9q1a82GDRsK1qJu3bpG3xOrVq0qWANdeKNGjez1b9q0yaxevbpgLWrUqGH0HbFmzZq8/P9csLBceGgFmjVrZurXrx94/lTnUN1DSf/H6P+aKCf9n1mzZs3Ifz/G7f9/vcf0/or6bx39VlFdREn1dNXXo5ri9P+o6gO6ns2bN0e+nhiX38T6Hnb/Z+m33Lp166L6UbH51mdf7zHVOwshEQAthFJOc40KfuYjAKovBv2oV1KlIB/nSHNZoVqlL3tZFLKBCkQVXr0vVKH68ccfQ1VGVZ0ZWeg/zEJ+T+gz4b4jVHEoZAv9qNV7opAN9Bl074dC/z9DlVBZEACt6m9mzlddArrpkY/vP92Md98rCh7k4xxVaaY6lOqUUb8OlYkrFwWlo349KpN8vYer8v3l/bwsX77cXlNVnj/Ic7nfobqZGvX3lz4rqhfoOqJ+LXH5TezqaXrPxuGzr8+L/qL+/qrsdwhd4CsrxXYIIIAAAggggAACCCCAAAIIIIAAAgggEDkBAqCRKzIyjAACCCCAAAIIIIAAAggggAACCCCAAAKVFSAAWlkptkMAAQQQQAABBBBAAAEEEEAAAQQQQACByAkQAI1ckZFhBBBAAAEEEEAAAQQQQAABBBBAAAEEEKisAAHQykqxHQIIIIAAAggggAACCCCAAAIIIIAAAghEToAAaOSKjAwjgAACCCCAAAIIIIAAAggggAACCCCAQGUFCIBWVortEEAAAQQQQAABBBBAAAEEEEAAAQQQQCByAgRAI1dkZBgBBBBAAAEEEEAAAQQQQAABBBBAAAEEKitAALSyUmyHAAIIIIAAAggggAACCCCAAAIIIIAAApETqBW5HJPhSAm0fub5SOU3WWbf371rssUsQwABBBBAAAEEEAi5wItnKIPFv/yFPLOVyt5WldoqGhs1Kc2m/qKeGpRegP7iklrF5EKaRf46PkxcgcI2cfnsx+U6VDj1fvnT86in4Mqly5DvQotBC9DQFg0ZQwABBBBAAAEEEEAAAQQQQAABBBBAAIFcBQiA5irI/ggggAACCCCAAAIIIIAAAggggAACCCAQWgECoKEtGjKGAAIIIIAAAggggAACCCCAAAIIIIAAArkKEADNVZD9EUAAAQQQQAABBBBAAAEEEEAAAQQQQCC0AgRAQ1s0ZAwBBBBAAAEEEEAAAQQQQAABBBBAAAEEchUgAJqrIPsjgAACCCCAAAIIIIAAAggggAACCCCAQGgFCICGtmjIGAIIIIAAAggggAACCCCAAAIIIIAAAgjkKkAANFdB9kcAAQQQQAABBBBAAAEEEEAAAQQQQACB0AoQAA1t0ZAxBBBAAAEEEEAAAQQQQAABBBBAAAEEEMhVgABoroLsjwACCCCAAAIIIIAAAggggAACCCCAAAKhFSAAGtqiIWMIIIAAAggggAACCCCAAAIIIIAAAgggkKsAAdBcBdkfAQQQQAABBBBAAAEEEEAAAQQQQAABBEIrQAA0tEVDxhBAAAEEEEAAAQQQQAABBBBAAAEEEEAgVwECoLkKsj8CCCCAAAIIIIAAAggggAACCCCAAAIIhFaAAGhoi4aMIYAAAggggAACCCCAAAIIIIAAAggggECuAgRAcxVkfwQQQAABBBBAAAEEEEAAAQQQQAABBBAIrQAB0NAWDRlDAAEEEEAAAQQQQAABBBBAAAEEEEAAgVwFCIDmKsj+CCCAAAIIIIAAAggggAACCCCAAAIIIBBaAQKgoS0aMoYAAggggAACCCCAAAIIIIAAAggggAACuQoQAM1VkP0RQAABBBBAAAEEEEAAAQQQQAABBBBAILQCBEBDWzRkDAEEEEAAAQQQQAABBBBAAAEEEEAAAQRyFSAAmqsg+yOAAAIIIIAAAggUjMDGjRvNhg0bCuZ6uVAEEEAAAQQQQCAOAnkNgM6fP9+ce+655qqrrsq71cUXX2zPtWTJkryfy3+CTZs2mQULFpRZPGnSJJuf0aNHl1nOCwQQQAABBBBAAIHoCcyYMcOovnnYYYeZnj17miuvvLLKL+Lrr78ud86LLrrI1jmXL19ebl2QC1Sn19+aNWsSh022LLGSJwgggAACCCCAQIgEauUzL2vXrjWfffaZWb16dT5PY489e/ZsWyHTXfmqTJ9++qkZMmSI6dWrl/nDH/6QOPXSpUvttW+33XaJZTxBAAEEEEAAAQQQiJ7AokWLzODBg83KlStNzZo1Tdu2bU27du2q7EJ0s/3ee+81//3vf82LL75Y5ryqA69fvz7vrVJVp1dSXlxKtsyt4xEBBBBAAAEEEAiTQF4DoFV5oX369LGVv3r16lXlac3EiRPNF198YQOg3hP/9re/NSeccILp3LmzdzHPEUAAAQQQQAABBCIm8NFHH9ngZ7NmzcxDDz1kGjZsWKVXsGrVKjN27FhTq1Zsqu5V6sfJEEAAAQQQQACB2NSi+vXrF6rS7Natm9EfCQEEEEAAAQQQQCDaAj/88IO9ANXtqjr4GW05co8AAggggAACCIRDINAA6Jdffmn+97//md/85jemMl2/N2/ebDSW0Zw5c2x3mu233952KVLXIn/SsV2Xo++++87MmjXL7LLLLqZNmzZ206+++sroeNtuu619/Pbbb+3yDh06+A9lX//000923E53TO9GixcvNjqejtGoUSOzzTbb2G5OtWvXTmym7j8a43TFihV2mbq8K4+NGzc2ah2gcZi0TJXkFi1aGHWd0lAATZo0MU2bNk0cx/tk4cKFtht/8+bN7Xndukyc3D48IoAAAggggAACCOQmoPEuVT9zAdCSkhJb36tRo4Zp3759mYOr/qg6rep8W2+9ta0Lq96XLlVmH23jxpp359cx1QV/iy3KDuev+q26pauOqjqw6tZ16tRJmQVtr7q4/tTKVPlW937VXUkIIIAAAggggECcBAIJgGoson/961+2guhwVCnUwOipkoKFN9xwg+0+7t1GlbVrr73WBlG9y88++2wbWPzTn/5krrnmGhvk1Pr+/fubU045xZx//vk2eDhu3Di73YABA2xw8p577jE77bST91D2+fPPP29uv/12s/fee5vbbrvNLlOF9dZbbzXTpk0rt70CrX/961/Nrrvuatcp8PnHP/4xsZ3Oq7+TTz7Z6Nw6/ogRI8wRRxxh95syZYq5++67Tffu3e15Ezv+8kRBzgsuuMBoEieN8aTAq1KmTr8cjgcEEEAAAQQQQACBHAU++OAD8+c//zlxFNXn9Keg4uTJk+1yjQuqutuECRMS9VOtUHBSddRzzjmnXBAyk30eeOABM378eHsu3YB39c8XXnjBeId+0pBMQ4cOLVMf14151av32msvu7/3n2eeecbcddddZSY10noFd4877jij3lV169b17sJzBBBAAAEEEEAgsgI5B0A//vhjc8stt9gK34knnmi6du1qW04+9thjNsCZTEatRBUk1IDtBx54oP1TZeudd96xgUMFM0eOHGl22GGHMrtrUiUFKFXp7dfhxwAAQABJREFU1B1t3eHu0aNHmW30Qi01e/fubQOSmo09VQBU2ypAqaSK6JlnnmkfFeRURVF3wTXj55tvvmnvvGvw+yeeeMJWBlXhvOSSS8zUqVONKscKpO65555GY38mS5oxVMHY9957z/z444/lWoFOnz7dBj8VAN5xxx3tIbJ18p5fgVoX4PUud8/POOMMe53udVCPyVrxBnXsqj6OfjzkkpxFrsfJJQ9h2FefcZewqGGKiooKeiw37zh2xcXF9nvbvT8K7VEW+nwU+ufClbu+M7EwNrBF8Mm9K6rvsX3pDX3V91RPe+ONN0yXLl3MwQcfbHslKVdqkan6oeqL6sGjurB6I82bN888/vjj5uGHHzaffPKJufPOO+3nPJt9dL6WLVvaIKuCqqpDK/lbdmpW+latWhk1FtD2qr8+99xzZuDAgbauvu+++9r99M/o0aPNqFGj7GftmGOOsb2qli1bZvdRLytNttS6dWtz6qmnJvbJ5cl1111XZvIk77E0jn+yurp3m2yee+sd2ezPPggggAACCCCQuUCY6/E5BUDVLejqq682mnldFZtDDjkkoXPooYcatdpUZcqbVFEcPny4DX5q/VlnnZVYrf3VdV4tJbWNgqDepCClKmMKrjZo0MCo206qHwdHHnmkDYDq7rwqit4f2+rarsHs1crSVQafffZZG/zs2LGjPbcLWvXs2dN2ZT/22GNtt3YFaQ844AB7XlVyv/nmGxsAVYVYr1MldXvfZ599zGuvvWZeeumlctvqLr6SC8jm4uTNg8pIFfBUSRM1eVsPpNqukJcH5RPUcaJeFvpBgsXPP1z9P16jXrbZ5l8OWPwc8MrWME77KcDDd4RJWb+JU1lH4VpU71T9bsOGDTYAqnqit76nG+MKfqqnkG50u2GOVFf83e9+Z3sozZw50wYiVTdVynSf3XbbzdaP1cpUnw/v+b2GW265pe2R5X54qJGBurMrX//85z/tzXrtr/qzApxKV1xxhQ3ouuOoJ9OwYcPMk08+aVQ3DSoAqrqod/Z4dz49qneUGhGQEEAAAQQQQCD6AmGux5cdOChDa7XAVJdtBS29wU8dRneeTzrppHJHfP/99+2dcI0t5LrweDfSXWj9EFaAUq1L/en3v/+9DX5qeargp9ZpHFK1ElUA9t1339WiRHLBRrUSdeN6avvzzjvP/OUvf0nc1Xc7aEzPnXfe2b5UEDbb5IKbGjLAm9Sy9ZVXXrHnVUtRpVydvMfnOQIIIIAAAggggEDwAur2rqRhn1zw051FdWHd7Fd69NFH3WLbVV4vMtknsXOaJzqXC366zRQsVctUDamkHktK69ats/lSjyu1LvUnBW+Vcqnz+o/JawQQQAABBBBAoLoFcmoBqoHelTQZUbKkbuQaG9Sb1CVIaauttkpUxOwCzz/qBq7gqiYi8neJ0Z3syiYFHP/xj38YBRzVRd0lF4B0AUkt32OPPeyf20Z3qTXZkgaRVyDWDT6f6u612y/do7rrq3L86aef2uO6a3n11VdtZXS//fZLVJ5zdXL5UPB27Nix7mW5R3WV0uD6QScFsd04pkEfu6qPl6uPAvWaDCvX41T1dQd9Pr331bJarZt146SQkz6XuvGhv0JNGgJALfmVNPGGfpAXatJdUn1PaHiUQk5u0hX1KvH3HikkF7WSV8BKwSe11AtbcuUUtnxVR370XtXkQUrJxtj0LlePIdUh9X9gpvu4Xkn2RGn+SdaKUvWxzp072x5IqldrmCcFSTXGpzfp/aY679y5c203eK3Lpc7rPbaeP/jgg/5FidcKFOejjiQ3f1A6cVKeIIAAAggggEBeBPLxf7o3o7nURQMJgCqIliypQuNPrtL34Ycf2jGV/Ou9r91M7t5lbtZ377JUz9XCU93p1e1cXcH1I1MtS3Vc10LUu+/3339v/vOf/9hxnlQJVMXWpcpWPt32yR7VDV8tPNUKQGOTalB8Jdci1RuQzdXJnV8VXwV3UyXNaqpuXUEndbGKS8rVxw2/kOtx4uKp68Di5x+Wheyg7yaX9CO7kC1ckKGQDdx7QY8KEBWyhRu3UHWQQnbwvifC+lw3x/X51TjG/paXLs+qC6sOqfLUbPJ6zHSfytR99Z2qm2vJkrrGKykI65LyoN5HmghJkyctXbrUrSrXEyqxIocn6eqiuvmTj5tg+i4hIYAAAggggEDVCoS5/ppTANR1QfcGCr20LvDjXeaed+vWLTH+plvmf0w2oZD3R7N/e/9rtUDcf//9zcsvv2zUyvLwww9PGmzUfmpxevHFF9sWWcq3JmDSZETq3q+xlzRLpga/zzUpyKkAqFqhKgCq6LgmRmrSpEmZVqruPNk6uf15RAABBBBAAAEEEAhewNVzFUxUsM0Fr71n0jr9KXlvpmezj/e4/ufuHP7leu1+iLhW98rrTTfdZMek13oFadUVXnVe1X3r169vLrroIq0iIYAAAggggAACsRHIKQDqZmlXy8lkKdlyzYyppNaYGmg930kBRwVANRmSxinVoyqsmqTJm26++WYb/NQ2gwYNKje+qLrDK23evNm7W8bP1b1fXZE0I6hmedd4TKqIqrWqq0jroFXtlPGFsAMCCCCAAAIIIFDAAmpZqaDm+vXrbQtKDV3gT2r16ZJudquul+k+bv90jwqAqhu7htzxJ1cf32abbewqNQrQhJwKdA4ZMsR2i/fuo5ahSrnWeb3H5DkCCCCAAAIIIFDdAjn1U9ZdYqW333476ThVmjHdnxQAVJo1a1bSMb7UBUaDsl9wwQV2Vk3//pm+1nhIGiNg+vTptiu8KoeajV2VUJdWrFhhxzzSa7XKdC1b3XqN0+e6pPvvsLuu3plUEl1Xd1VAp0yZYk/jlrlzVrWTOy+PCCCAAAIIIIAAAhULKJDZvn17u6FusCdLbnmnTp1s/TKbfXRcV99M161b9XF/UvdyzUKvpMlBldxr1Yc1Jqg/zZ492y7y13n92/EaAQQQQAABBBCIkkBOAVBV+jQ7uiY0ueeee2xLRnfxGmj9kUcecS8Tj+rSrcqWJr0YOnRoucCpjqOWkZqt0rUwTeycxRNVNNX1Xd1/7rzzTnsEf7BRAU9tpzRjxgz76P7RBARqEeq6D+kuvze5YKm7u+5dl+p5r1697Ez3EydOtC1BdZ3qduRNVe3kPTfPEUAAAQQQQAABBCoWcLO8jxkzxrjJQd1emvTS1YX79u3rFidmhs9kH1ffVFBS47cnSyNHjrQTeLp1GqJq+PDhtq6tnk9u8k31wlJSYwT/MFZvvfVWYvJMf53XHZdHBBBAAAEEEEAgigI5dYFX0FBdx9Vic9y4cXYcTc2CqXEtNcalWl6qxaU/XX755WbAgAF2XM4zzzzTjgWqLkEaY3Ne6SzxGkNJQUd1zQkiKeD50EMP2XxpgPju3buXOawqlT179rQTE2nSJN0Z18z2GhT+3XffNYsWLbLBWI0T6q90uoHpn332WRu41Uzu5557bpnj+19oDKYDDzzQnk/r/AFZt31VO7nz8ogAAggggAACCCBQscABBxxgx8/UcEvqvaSb3BrGSA0B1PpTAcZLLrnEDnXkjpbNPhoDX13s1ehAQVd1v1f3dTcTqurRaiWqPGj8e9V3X3/9dRuU1TZa7pLyqMCs6rdu+9q1a9v6r3pMqaWo6sDqlaUGC27sULc/jwgggAACCCCAQBQFcgqA6oKbNm1qW3LeeuutRjO7608BTLVgVBDzpJNOKuei7t266z1s2DAb9HzssccS26jSpYBqjx49EstyfaKKaJcuXWzeNAu7d6xNd+wrrrjCqPKnVpmaoV1/CvDuvvvu9vrUhah///62G70mS3ID3St4qUmMNNO8Kovurro7bqpH7adz6JwadzRZqmqnZHlgGQIIIIAAAggggEBqgRtvvNGMHz/e3H///bYeqS1V11S39yOPPNL++ffOZp/BgwfbyYsUBF22bJltNOACoKpPqgXoVVddZZ566il7OgVE1c1d9XHV113q2LGjPY5ah6q7u+vyruGh+vXrZ44//nijm/AKhmq4plQ36t3xeEQAAQQQQAABBKIgUKN0LKGSoDKqbuBqIamu8ckGYU92Ht0Z/+abb+wd5q222sre3U62XVUtU4vVb7/91gY/27VrZ7uqV+bc6iKvCqnuzqsSGnTKl5PKy9/9KYi8q1WtWh+0fub5IA5Xrcd4f/euOZ2/uLjYjjnrJtLK6WAR3rlVq1b2c6WvnEyGjIjwJafMulruqFXN6tWrU24T9xVq4d+oUSN7mcuXLzdr1qyJ+yWnvD61rtLNM7XGKuSkOoCS/j9VT5JCTbrB2rp1a6Mbr2qBF7bkyils+QpLfvTe1cRHGtqoqKioUtnKdB/VNxVgbdy4cdLj672jOocCnenyoO70yqvOrx5NLpia9KB5XJiv97qMNMP9i2fkMfMcGgEEEEAAAQTKCHQZ8vME4mUWBvgil7pozi1AvdehCrv+MkmqnChgGpakwK2b3CmTPCnomem1Z3L8sDllkne2RQABBBBAAAEECkFAQcRMA4mZ7pNstnmvrVp7elt8etd5n6unkwKfbjgn7zqeI4AAAggggAACcRPIaRKkuGFwPQgggAACCCCAAAIIIIAAAggggAACCCAQLwECoPEqT64GAQQQQAABBBBAAAEEEEAAAQQQQAABBDwCBEA9GDxFAAEEEEAAAQQQQAABBBBAAAEEEEAAgXgJEACNV3lyNQgggAACCCCAAAIIIIAAAggggAACCCDgESAA6sHgKQIIIIAAAggggAACCCCAAAIIIIAAAgjES4AAaLzKk6tBAAEEEEAAAQQQQAABBBBAAAEEEEAAAY8AAVAPBk8RQAABBBBAAAEEEEAAAQQQQAABBBBAIF4CBEDjVZ5cDQIIIIAAAggggAACCCCAAAIIIIAAAgh4BAiAejB4igACCCCAAAIIIIAAAggggAACCCCAAALxEiAAGq/y5GoQQAABBBBAAAEEEEAAAQQQQAABBBBAwCNAANSDwVMEEEAAAQQQQAABBBBAAAEEEEAAAQQQiJcAAdB4lSdXgwACCCCAAAIIIIAAAggggAACCCCAAAIeAQKgHgyeIoAAAggggAACCCCAAAIIIIAAAggggEC8BAiAxqs8uRoEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8AgQAPVg8BQBBBBAAAEEEEAAAQQQQAABBBBAAAEE4iVAADRe5cnVIIAAAggggAACCCCAAAIIIIAAAggggIBHgACoB4OnCCCAAAIIIIAAAggggAACCCCAAAIIIBAvAQKg8SpPrgYBBBBAAAEEEEAAAQQQQAABBBBA4P/Zuw84qaq7/+M/BJSVLl0UFRuCoIhdsFAUS+wYlSgasAsasUb/mvgYRUWCoo8tUbHE8qhEBMEalCA2QI0Ne0Ep0ruA7n+/JzmT2dmZ2dmZO7P3znzO6wU7c+u573N39szvnoIAAnECBEDjMHiJAAIIIIAAAggggAACCCCAAAIIIIAAAsUlQAC0uMqTq0EAAQQQQAABBBBAAAEEEEAAAQQQQACBOIF6ca95iUDgAvOO6G8rV660FStWBH5sDogAAggggAACCCCAQDqBg8earVmzxpYuXZpus9Cva9q0qdWvX98WLlwY+rymy2DDhg2tSZMmbhOVicomyqlVq1a2du3ayH/Xad68uTVo0MAVxYIFC+znn3+ObLHUrVvXWrdubYsXL7affvopstehjLdt29bq1KljGzZssB9//DHS11JWVmbNmjWzuXPnRvo69DncsmVLdw2rV6+2ZcuWRfp6VCb6nVm0aFGkryPTzNMCNFMptkMAAQQQQAABBBBAAAEEEEAAAQQQQACByAkQAI1ckZFhBBBAAAEEEEAAAQQQQAABBBBAAAEEEMhUgABoplJshwACCCCAAAIIIIAAAggggAACCCCAAAKREyAAGrkiI8MIIIAAAggggAACCCCAAAIIIIAAAgggkKkAAdBMpdgOAQQQQAABBBBAAAEEEEAAAQQQQAABBCInQAA0ckVGhhFAAAEEEEAAAQQQQAABBBBAAAEEEEAgU4F6mW7IdghkI9B2wuRsdmMfBBBAAIGSE3iv5K44Hxc8q8cu+Tgsx0QgsgIvDFLWy/7zL7KXEZfxdnGvo/6yWcUF6F/V1HXE3KoLWYIAAggggEAOArQAzQGPXRFAAAEEEEAAAQQQQAABBBBAAAEEEEAg3AIEQMNdPuQOAQQQQAABBBBAAAEEEEAAAQQQQAABBHIQIACaAx67IoAAAggggAACCCCAAAIIIIAAAggggEC4BQiAhrt8yB0CCCCAAAIIIIAAAggggAACCCCAAAII5CBAADQHPHZFAAEEEEAAAQQQQAABBBBAAAEEEEAAgXALEAANd/mQOwQQQAABBBBAAAEEEEAAAQQQQAABBBDIQYAAaA547IoAAggggAACCCCAAAIIIIAAAggggAAC4RYgABru8iF3CCCAAAIIIIAAAggggAACCCCAAAIIIJCDAAHQHPDYFQEEEEAAAQQQQAABBBBAAAEEEEAAAQTCLUAANNzlQ+4QQAABBBBAAAEEEEAAAQQQQAABBBBAIAcBAqA54LErAggggAACCCCAAAIIIIAAAggggAACCIRbgABouMuH3CGAAAIIIIAAAggggAACCCCAAAIIIIBADgIEQHPAY1cEEEAAAQQQQAABBBBAAAEEEEAAAQQQCLcAAdBwlw+5QwABBBBAAAEEEEAAAQQQQAABBBBAAIEcBAiA5oDHrggggAACCCCAAAIIIIAAAggggAACCCAQbgECoOEuH3KHAAIIIIAAAggggAACCCCAAAIIIIAAAjkIEADNAY9dEUAAAQQQQAABBBBAAAEEEEAAAQQQQCDcAgRAw10+5A4BBBBAAAEEEEAAAQQQQAABBBBAAAEEchAgAJoDHrsigAACCCCAAAIIIIAAAggggAACCCCAQLgFCICGu3zIHQIIIIAAAggggAACCCCAAAIIIIAAAgjkIEAANAc8dkUAAQQQQAABBBBAAAEEEEAAAQQQQACBcAtELgC6Zs2acIuSOwQQQAABBBBAAAEEEEAAAQQQQAABBBAIjUAkAqAbNmywUaNG2QknnGAHH3yw9enTx95///2CIv7888/2ww8/VDnnOeecY0OGDLFly5ZVWRfkAp1D/1avXh07bLJlsZW8QAABBBBAAAEEEChagfHjx7u64WOPPVa018iFIYAAAggggAACQQnUC+pA+TzOPffcY+PGjXOnaNGihdWvX9/at2+fz1NWOvYnn3xiI0aMcIHXU045pdK6Tz/91NatW2fr16+vtDzoN7Nnz3aHVCDWp2TL/Dp+IoAAAggggAACCBSvwMKFC011wa5duxbvRXJlCCCAAAIIIIBAQAKRCIC+/vrr7nLPPvtsO/nkk61OnToBXX5mh5k0aZJ98cUXLgCa2R5shQACCCCAAAIIIIAAAggggAACCCCAAAJhEIhEF/gff/zRWfXq1avgwc8wFBJ5QAABBBBAAAEEEEAAAQQQQAABBBBAAIHsBELdAlRjbv7000+mMUCVFi9ebOoCvtlmm1nTpk1jV6z1aqH51VdfWd26dW277bazLbfc0urVS315meyjc3377be2fPny2Pl1Dp1beUhMyqu6ImmfbbbZxrbffnvbeOONEzeLvdf23333nfu3cuVK162/Q4cO1rJly9g2vEAAAQQQQAABBBBAoDoBX2/Vdqnqwb/88ot988037lCqqy5dutSWLFni6p4NGjSwGTNmWHl5ufXo0aNKHVZd7rXv999/b02aNLEtttjCttpqKzc0VXV5Yz0CCCCAAAIIIFDbAqkjhLWds4rz//GPf7SPPvoolpOhQ4e61+oKP3DgQPf6jTfesNtuu80FEWMbVrxQIPHKK6+0zp07xy+u0T4KfJ566qmx/Z988knTv1//+td2/vnnx5brhQKwN998s82fPz+2XIHSq6++2vbcc8/YMv9iwoQJNmbMmEqTGmmduvcfc8wxdu6559omm2ziN+cnAggggAACCCCAAAIpBTbaaCO7/PLL3aSdf/jDH5IO3fT222/bxRdfbF26dLG77rrLnn32WdNY+8OHDzcN+eTr3c2aNbOnnnrKBUEXLFhgN954o7311ltVzr355pvbFVdcYbvuumuVdSxAAAEEEEAAAQTCJBDqAKhmfVerzzvuuMO1/NSs55tuuql169bNGc6aNcsuueQS97p379627777uqfWr732mk2dOtU0Q/stt9xiu+++e8y8JvvoXMOGDbMpU6a4Wef32Wcf22OPPWyHHXaIHc+/uOyyy6x169Z2wQUXWKtWrUzjlj733HOuknnDDTfYfvvt5ze1+++/3+677z7XkvTII49016Mn8Nrnvffes6efftratm1rJ510UmyfbF9ocqaPP/445e6aVCofgdZ0rW9TZoYVCCCAAAIIIJCTgCaKJJWmgB6iH3roofbXv/7VXnjhhaQB0MmTJzscbRefHn/8cZszZ47rwaQeSjvuuKMLfq5YscJOO+00008FOfVQXxORzpw509Vb1VvrqquucsHSdPXJ999/P/50lV4r2Ko6d9BJvcJI0RWI0meZHj5EKb/J7gpdg0/6Hhf/3i+Pyk+fd30GRL1cvLk+36N+Lf4zOerXER/nKIbffd1bxXB/+d+V6n6GOgDap08fl/+7777bBUB/9atfxbqer1692q6//nq3/rzzzrMTTzwxdq39+/d3AUYFGhUAHTt2rKvE1XQfVeQGDBjgKoSquGmWTb1Pltq0aeOeoPuu+QcccIBrhaqn68q/gqf6BVGlUgFOJT1tP+igg2KHU8vSUaNGuRnvn3/++UACoIsWLUqZZ534scces+7du8fywAsEEEAAAQQQiK4Aw+hEt+yCyLkCm3rI/uabb7ru7Qou+qR6sBoJaHgmX8f26xT81EN/X89VfVVp4sSJLvjZsWNHGz16tBtqSsvV8ECNFI4++mhbtmyZO9/++++vVUmT6unqop8sjRw50lTHJyEQLxClzzIFRPIRxI/3KOTrZEO9FfL8QZ3Lfy8P6ni1eRwFD6P0O5HOqliuQ9eooWP0rxhSMZVLuvL476OedFuFcJ0qdvPmzXNBRl9Zi8+muq63a9fOBS+nTZvmVmWzT/wx073+7W9/W2lcUm2rfKmFpcYN9U++165da9r2zDPPrBT89Mf2lUc9aSchgAACCCCAAAIIIJCpgB7Ia/xOBRtfeeWVSrv94x//sHXr1pkmFW3UqFGldaqvHn/88bFlvjWnxtU/44wz7NJLL40FP/1GCpLsvPPO7i31Vq/CTwQQQAABBBAIq0CoW4CmQ/v666/danVv982p47fXkzi1bJw7d25ssPds9ok/ZrrX6hqfmPSEXWOQqju+Bo1X1yE9idIYn/FJlUZNnPTll1+67kRal+opefx+mbwuKyuzo446KuWmDRs2rDIOacqNa7BCZeIrzzXYjU0RQAABBBBAIAcBtfLLZyqmVk75dKrNYx922GH2zjvvmHoTHXvssbGspOr+rg00aZK6wCUm1bPjh5JS/VR1a9VbP/zwQzfeqPaprt6qIZ80AVOypCGk8nHf6npUDyZFUyAf90Q+JNT6S/e/hh2LctL3Nv+des2aNW5Yuahej//dV0v26j6bwn6N+gzT9ejzUw2popwUn1F8JCq/26ms1avXt/rUxNp6sBjlpDLRPeZ7fkThWnKpi0Y2AOpnsFQrz1RJA7MrqVuPUjb7uB2r+U83TaquAnoSr+TzoNf6IH711VdNEyFp8iR1IfLJ/+Hx73P9qYDrTTfdlPIwP/74o+u6lHKDLFfojygB0Czx2A0BBBBAAIEsBdQdOZ8pl0pnPvPFsf8roN5EesCtCY00Y7vG7NQkne+++67rQhkf0PR7+Tqzfx//Uz2unnjiCTdDvAKf+sLnU6b11hEjRvhdqvzULPT5uG/1ZZsAaBXuyCzIxz2Rj4vX90AFDqLeCrp58+axAKiuJcqBQ30u6Xd/1apVkQrqJLs//WeYAqBR+Z1Idh1apmvR70vUr0NjmPoAqIKfUb8eDZWj35koXUcuddHIBkBVqVGKr4S5BXH/+Si2r5xls0/c4VK+TPcHwj8N9F2NysvL7brrrrOXXnrJHU8TJmkc0G233dY6derkKqyavImEAAIIIIAAAggggEBNBfQAWmN8jh8/3rUC1dBLvvXnIYccEgtyxB9XX0qTpdmzZ9vQoUNNLcJUj9bkSKqvqt6622672ZgxY8wPNZVsf5YhgAACCCCAAAJhEYhsAHSLLbZwhuqGkyrpibWSnmgpZbOP27Ga/xQA1ZOyxo0bV9nS58GfW4PPK/ipJ/N6Gq5u8fFJLUOVUnUTit+W1wgggAACCCCAAAIIJAqoG7wCoFOmTHFjz2v8T6XE2d8T90t8rwlHFfzs27evXX755VV69/h6OPXWRDneI4AAAggggEDYBCI7CZJmo1TSU2ff0jMed+XKlfbGG2+4RT7ImM0+OoDGeVBKV7nz53Ib/uc/delRdyOl7bff3v307/fdd98qwU9t8Omnn7rt0rUqdRvwHwIIIIAAAggggAACSQS6dOliW221lZuIU+OBasgljUuvZZmm5cuXu/Hptf3gwYOrBD8VGP3uu+/c4ai3ZqrKdggggAACCCBQWwKRDYD27NnTBRUXLVpkd9xxR6VxStQt/rbbbnOtMrfeeuvY4O3Z7KOC8WNZ+tacyQrrzjvvdIPC+3XKw+jRo11wtl+/fm62eq3z4xW89957VbrvT58+3R555BF3iKgPpusd+IkAAggggAACCCBQeAG1AlUaNWqU+9m/f3/3M9P/VP/1w0jNnDmz0m5qfKAWoX6oJ+qtlXh4gwACCCCAAAIhFIhsF3i1yhw+fLj97ne/s3HjxrmZKPfaay/XSlOtMfWku0OHDnbrrbe6MYtkn80+2s8PDD9x4kT7+OOPTYHUIUOGaJVLGhNJxz7rrLOsV69ebkKkf/7zn/b555+7wea13CeNyfToo4/aggULYttrIF21DJ0xY4YL6irvmuVNrVj92KF+f34igAACCCCAAAIIIFCdgMb7vPvuu10rTdU11Y29JkkB0N69e9uLL77oGhuortqtWzdXx3777bddXVZjgmqcUE2qSUIAAQQQQAABBMIsENkWoEJV954HH3zQ9txzT1cZe+ihh1wLSnXZUaVPwc/E2dmz2UdP0DVRkZ6CKziZ+BRclUq1ANWM7xpv6YEHHnBdhtTN/b777nPL/U2gbviaBEmz16u7+1//+le766677JNPPrFzzz3XVVRVuVTSeKEkBBBAAAEEEEAAAQRqKtCiRQvbe++93W56eJ9srPrqjqnGBqoHq7u7AqG33HKLPfvss25m+bFjx9qwYcPcIaZOnWqa6JOEAAIIIIAAAgiEVaBORWWlKGor6nrzzTffWFlZWWyyo+rQa7qPuvmoy70qlAp6Jksa91MDwivQ2aBBg2SbuGUaK2n+/Pm2cOFC18K0ZcuWKbfN5wo9sVd3/aCTWg0o+Nx2wuSgD83xEEAAAQQQQCCFwKweu6RYE8xiPcAlRUfgyiuvdA/Ub7755lgwNJvca7LP77//3jUG0DiiqWaNz+bYqjur51PQST20WrVqZS8MCvrIHK8QAl1HpJ7othDnz/Qcusd0/+p3JMpJkwb7767qqRjlcX3VaKl169a2ePHipHOFRKmc2rZta3Xq1HHf16Pe0l5xmmbNmlUaNjBKZeHzqjiQj92sXr3ali1b5ldF8qfKRL8zinNFJeVSF41sF/jEwlFFzE80lLgu1fua7qObXR9C6ZL+ePhZ59Ntp5tMXet99/p027IOAQQQQAABBBBAAIGaCCiw+Prrr7tAgHpL5ZLUerRTp065HIJ9EUAAAQQQQACBWhUomgBorSpycgQQQAABBBBAAAEEallA48drKKhVq1a5cTvVy+e4445zY9XXctY4PQIIIIAAAgggUKsCBEBrlZ+TI4AAAggggAACCCAQjMC8efPs9NNPjx2sa9euNmDAgNh7XiCAAAIIIIAAAqUqQAC0VEue60YAAQQQQAABBBAoKgEN1aSu6hqPsEePHnbGGWekHLe+qC6ci0EAAQQQQAABBKoRIABaDRCrEUAAAQQQQAABBBCIgkCjRo3s3nvvjUJWySMCCCCAAAIIIFBQgY0KejZOhgACCCCAAAIIIIAAAggggAACCCCAAAIIFFCAAGgBsTkVAggggAACCCCAAAIIIIAAAggggAACCBRWgABoYb05GwIIIIAAAggggAACCCCAAAIIIIAAAggUUIAAaAGxORUCCCCAAAIIIIAAAggggAACCCCAAAIIFFaAAGhhvTkbAggggAACCCCAAAIIIIAAAggggAACCBRQgABoAbE5FQIIIIAAAggggAACCCCAAAIIIIAAAggUVoAAaGG9ORsCCCCAAAIIIIAAAggggAACCCCAAAIIFFCAAGgBsTkVAggggAACCCCAAAIIIIAAAggggAACCBRWgABoYb05GwIIIIAAAggggAACCCCAAAIIIIAAAggUUIAAaAGxORUCCCCAAAIIIIAAAggggAACCCCAAAIIFFaAAGhhvTkbAggggAACCCCAAAIIIIAAAggggAACCBRQgABoAbE5FQIIIIAAAggggAACCCCAAAIIIIAAAggUVoAAaGG9ORsCCCCAAAIIIIAAAggggAACCCCAAAIIFFCAAGgBsTkVAggggAACCCCAAAIIIIAAAggggAACCBRWgABoYb05GwIIIIAAAggggAACCCCAAAIIIIAAAggUUIAAaAGxORUCCCCAAAIIIIAAAggggAACCCCAAAIIFFagXmFPx9lKTWDeEf1t5cqVtmLFilK79Nj1lpWVWbNmzWzu3LmxZaX4onXr1la3bl0rLy+3efPmlSJB7JrbtGnjfi9WrVoVW1ZqLxo2bGhNmjRxl71s2TJbvXp1qRHErrdRo0a26aab2oIFC2LLSvFFu3bt3GWvX7/eFi5cWIoE7prr1Kljbdu2tSVLltjatWtL1oELRyAogYPHmq1Zs8aWLl0a1CFr5ThNmza1+vXrR/7zMf7vv8pEZUNCAAEEEECgEAK0AC2EMudAAAEEEEAAAQQQQAABBBBAAAEEEEAAgVoRIABaK+ycFAEEEEAAAQQQQAABBBBAAAEEEEAAAQQKIUAAtBDKnAMBBBBAAAEEEEAAAQQQQAABBBBAAAEEakWAAGitsHNSBBBAAAEEEEAAAQQQQAABBBBAAAEEECiEAAHQQihzDgQQQAABBBBAAAEEEEAAAQQQQAABBBCoFQECoLXCzkkRQAABBBBAAAEEEEAAAQQQQAABBBBAoBAC9QpxEs5RugJtJ0yO/MXP6rFL5K+BC0AAAQQQQAABBEpR4IVBuuqy//wrBoF2kb+Ig8dG/hK4AAQQQACBCArQAjSChUaWEUAAAQQQQAABBBBAAAEEEEAAAQQQQCAzAQKgmTmxFQIIIIAAAggggAACCCCAAAIIIIAAAghEUIAAaAQLjSwjgAACCCCAAAIIIIAAAggggAACCCCAQGYCBEAzc2IrBBBAAAEEEEAAAQQQQAABBBBAAAEEEIigAAHQCBYaWUYAAQQQQAABBBBAAAEEEEAAAQQQQACBzAQIgGbmxFYIIIAAAggggAACCCCAAAIIIIAAAgggEEEBAqARLDSyjAACCCCAAAIIIIAAAggggAACCCCAAAKZCRAAzcyJrRBAAAEEEEAAAQQQQAABBBBAAAEEEEAgggIEQCNYaGQZAQQQQAABBBBAAAEEEEAAAQQQQAABBDITIACamRNbIYAAAggggAACCCCAAAIIIIAAAggggEAEBQiARrDQyDICCCCAAAIIIIAAAggggAACCCCAAAIIZCZAADQzJ7ZCAAEEEEAAAQQQQAABBBBAAAEEEEAAgQgKEACNYKGRZQQQQAABBBBAAAEEEEAAAQQQQAABBBDITIAAaGZObIUAAggggAACCCCAAAIIIIAAAggggAACERQgABrBQiPLCCCAAAIIIIAAAggggAACCCCAAAIIIJCZAAHQzJzYCgEEEEAAAQQQQAABBBBAAAEEEEAAAQQiKEAANIKFRpYRQAABBBBAAAEEEEAAAQQQQAABBBBAIDMBAqCZObEVAggggAACCCCAAAIIIIAAAggggAACCERQgABoBAuNLCOAAAIIIIAAAggggAACCCCAAAIIIIBAZgIEQDNzYisEEEAAAQQQQAABBBBAAAEEEEAAAQQQiKAAAdAIFhpZRgABBBBAAAEEEEAAAQQQQAABBBBAAIHMBAiAZubEVggggAACCCCAAAIIIIAAAggggAACCCAQQQECoBEstMQsb9iwwdavX5+4mPcIIIAAAggggAACCCCAAAIIIIAAAgiUvAAB0CxvgZ9//tl++OGHSns///zzNmTIEBs7dmyl5eneTJ482e3z4IMPptss6bqZM2fa0KFD7ZBDDrHevXvbZZddlnQ7FiKAAAIIIIAAAgiEQ2D8+PGu7vfYY4/FMpRsWWwlLxBAAAEEEEAAAQRyFiAAmgXhJ598YoMHD7aXX3650t5Lliyx2bNn27x58yotT/dm0aJFbp/58+en26zKugULFthVV11l7777rikY26FDB9tqq62qbMcCBBBAAAEEEEAAgfAILFy4sErdL9my8OSYnCCAAAIIIIAAAtEXqBf9Syj8FUyaNMm++OIL69OnT+FP/p8zfvDBB7ZixQrbbLPN7OGHH7bGjRvXWl44MQIIIIAAAggggAACCCCAAAIIIIAAAmEVoAVoWEummnz9+OOPbovu3bsT/KzGitUIIIAAAggggAACCCCAAAIIIIAAAqUrELkWoMuWLbPFixdby5YtXeBv5cqV9t5779m6dets1113tebNm8dKU8s+/PBDmzNnjm2xxRa2yy672EYbJY/5/vTTT/bdd9+5fzpm+/btXbdynccndTX/9ttvbfny5W6R8vHVV19Z06ZNXUtMv53/uXTpUlN3eXWN32mnnVwX9Tp16vjVSX+q+7xad9avX9/t07p160rbrV692tRd3gdAy8vLXR503K233rrStrxBAAEEEEAAAQQQyEzg66+/dvVEDSukySU1rNGXX35pLVq0sN12283KysqqHEh1PNX3mjRp4rZL3EB1NtXdWrVqZY0aNUpcXe37+Dypzqf3H3/8savz7bjjjla3bt3YMXSuWbNm2SabbGKdO3e2Nm3axNYlvlCX+2+++ca+//57l3fVkzWUkuqf8Smx3r1q1Sp3fp1r2223df8S94nfn9cIIIAAAggggEBYBCIXAFX38zvuuMMuvfRS0ziYmjzol19+iXkee+yx9rvf/c7eeOMNu/rqq23NmjWxdQpC3njjjZWCpFo5YcIEGzNmjKugxjaueKGg4jHHHGPnnnuuq0wq8HnqqafGNnnyySdN/37961/b+eefH1uuCuqjjz5qd999txuf06/YfPPN7dZbb7W2bdv6RbGfuobbb7/dHn/88dgyvejbt6+7Vl/pfv/99+2SSy6JbfPKK6+Y/m288cZVxiSNbcQLBBBAAAEEEEAAgbQCZ555ptWrV89uu+02Gz58uHvg7nfQUEO///3vrWfPnn6R+zlu3Di7//777fjjj7cLLrig0jq9GTVqlL3++ut25ZVXWv/+/ausr26B8tSwYUNX973oootcwNLvo6DrnXfe6QKdqvPqPD6pXjhs2DA76qij/CL3U3Vn1YXfeuutSsv1RvXUK664wjUo8Ct9vfviiy+2tWvXunyonuvTlltuaddee61tt912fhE/EUAAAQQQQACBUApELgDqFRVg1FPrAw44wLp162Zvvvmm+/f000/bhg0b7LnnnrN99tnH9txzT9diUhVUPTH/y1/+UimAqErrfffd51pxHnnkke5YepKvSqRalup4CliedNJJtummm7rK5JQpU0yBSB1/jz32sB122MFny/188cUXXYtUv14tS1966SU3a7wqx5olvkGDBpX2UQVTLUwPP/xw23vvvU2TI2l2UL/fXXfd5QKyauWpCu2MGTNs2rRp1rVrVzvooIMqtQCIP7COc/rpp8cvqvT6hhtuqJL/Shtk+aa6lq5ZHrZWdotvBZxNBnyr41yPk825w7SPd1CeSt1Cvx/6QusfbISpnAqVl/j7Qa2i9PlaqkkW+lfqvxe+/BWAwsJcL5dsWgx6R35mJ6Ag39ChQ93Dcj0AVyvK6dOnuwfbCvTdc889rvVldkfPbi+1ulRe9DfjlFNOcXW+Z5991tUVFVjVePBqqar6XrNmzey1115z9UQ9dFc9uF27du7EGjv+tNNOc2PIq9eU1qnH08yZM12994cffnATbD711FPuwX98brVMvZ40/r32VU+k559/3vWcuuyyy+yJJ55IWRfVcdSgQPXcZOnCCy+0Xr16JVvFsiIX0Gec6kNRTmqFrd9NtbyOcopvTR7fozKK1+S/h+ohUfwDmyhei8+zyifqdSNf94/6dfj7S2WjmE7Ue0H43/2ol4v/XanuZ2QDoAoqqjKowKSSnryPGDHCJk6caOPHj7eBAwfa2WefHbt+BSn1dFwtQ31St3cFOJX0pF+BRJ/UqlNP7RU4VQVP59EftgEDBrgu9QqAKvio94lJXe+HDBligwYNiq367W9/ayeeeKILgmrmdgU545Mqhapwn3DCCbHFqmRqtvmPPvrIFHRV/hSM1TnVNUsB0I4dOybNgz+I78Ll3yf+VF6j/kubeE1Bvw/KJ6jjBH19hT6e/mhgYe6Lov+DU+gyCNv55ICF8XvxnxuTz4h/QygQTCq8gOpN6q6uXjx+gkkF/BREvOWWW1wr0Iceeqign1nqzeRbeyrAqfSrX/3KjjvuOPu6oku8urNrQkx11VfSA33VQzVhp4KberiupDqygqCqO44ePTp2Db1793atXY8++mhTl3c1Kth///3dPv4/BT/V+0rn9UnHPa0ioKpWpe+8847ttddeflWVnxpOIFUAVAFe6gVVyEpiQbF8zhVbPaZYfh+L5f7Sh0Ex1Y2K5f5SufhGDHod9eQD1FG/juryn3xAzOr2CsF6VfJ88NNnRxVUn+K7qmtZly5d3Co9sVYLUSU95VdgUt2L4oOfbmXFf77yp8piTZKi5/HBT+2rCqvGIFXSuEmJSV2IVJGNT9rn5JNPdosUiCUhgAACCCCAAAII5FdAdTgf/PRnUuBPQVA9gNd48IVOqtf64KfOrTHifcvOI444Ihb81Dp96e/UqZNemsaW90nd1M844wwXyEx86KRWpDvvvLPbNFm9V93jdZ74pGW+6/vcuXPjV/EaAQQQQAABBBAInUBkmxeo0pWYNIC7koKjid0pVbFTUjN4PYFW5VCTF6lLTnxSpU8VW3Ul8mMppXpiHb9f/GsNIp8s+TxoMPzEpMH1Eyuj2sYHTTVQfTZJLUb/9a9/pdxV3f3zUWlVa1l/vSlPHpEVufqoW4y+tOR6nIhwpcymvqzpHtfvYPwXspQ7FPEKdanUZGtq9VKqSV3e1KJJSS2Okn0uloqNHwJArahKOflgjlrgqUVbqSa18tDfbk2uowe1YUu+nMKWryDzk6wlo/5+6WG6/pZ/+umnts022wR5ymqPlareq/zoIXpi8nUw9fTxaffddzf980n1W+2veq8mDVUXeKVk9V6dQ/dmYtLfM9Uz9XubLmlYqVRdUfX3MB91JNX11ZqXFF4BfQ+Jn68hvDlNnTPdY/qsTvbgIPVe4Vujbu9+iDbVR5J9DoQv18lzpM9rfe/QhMXq8RnlpPqAPnvVgMtPghzV6ymW78Rqweq7i+v7i77HRDkpTqHfGQ2dGJWUS100sgHQZBftK2bJxszy6xILVR/ur776qpsISV2F9EHpk26EbFKqWTd9HpJVAPXhliz5YylfqiDog6OmSQPhp0o+T6nWsxwBBBBAAAEEECgVAdWZfPAw8Zr1hVrp888/t0MOOSRxdV7fZ1vvTazn6QGkxuvUWPIKfPpeUcp8unqv716feJGZdjFN1+UxMY+J5+A9AggggAACCCAQhEBkA6DJgnrJAovpkLT9dddd5yYa0nZ6gqeu8Ntuu63rOqQWSuecc066QyRdl834CfEz2ccf1D9RV8Ux6oNrx18XrxFAAAEEEEAAgbAJpKtL+ha5vvV6Jnn39bhMtk23Tbb13vjr0TicGm9eD9QVuNxxxx1dfVf1XvVEGjNmjBtfPlk+sqnbJjsOyxBAAAEEEEAAgdoSiGwANAgwzZKpWdYV6NQESvFjiOr4ahmqlCo46VYG9J+6uiVLvquwnvxT+UwmxDIEEEAAAQQQQCAYAQUsVSdL1grUj+GuwKFPvtVkqu6ivlu53742f15//fUu+Nm3b1+7/PLLqzxY993QC1HvrU0Hzo0AAggggAACpSkQ2UmQgiguzcautO+++1YJfmq5xnhSSqzU+kBkkBVEzbiZLL3yyitusWaxJyGAAAIIIIAAAgjkV8CPAR9/luXLl7sZ1bUsvk7mh12KH0LJ76eWlv5Btl9WWz+Vf41vrzR48OAqwU/lVRM8KSXWe91C/kMAAQQQQAABBCIuUNIBUD9RkgZmjx8DSWU6ffp0e+SRR1zxxg8grwW+K3qQlVpVOu+99153Pv+fBqT/+9//7sZkOu200/xifiKAAAIIIIAAAgjkSeD+++8339pTp1AdUd3DFSTUUEnxs7F36NDB5eKf//ynffbZZ7EcKYh4zTXXhCaYqLqrb606c+bMWD71QpOEqEWo766fWO+ttDFvEEAAAQQQQACBiAqUdBf4Pn362KOPPmqa6e6ss86yXr16mcbaVMtQDQ6//fbbmyZG0phPmqHSP+X3M3FOnDjRPv74Y+vZs6cNGTIkp1tAs2s++OCD9sEHH1j37t3dbJgvvviiq4wOGjTIUs0sn9NJ2RkBBBBAAAEEEECgkoBmdFUrSXUV13ifahGq8TN32mknu/LKKyttq1nVVYfTg+zzzjvPNIO8HrC//fbbru6o96l6+VQ6UJ7fKADau3dvU93yjjvucHXdbt26uXqu8qq6sLr26zqjPtNwnik5PAIIIIAAAghEVKCkA6AdO3Z0kyCNHj3adXf3Xd71ZP/cc8+1Y4891i666CIXDNV4oYcddpgrZv185513bOrUqa7i6FuS5nIPHHnkkW73++67L9bFSvkYNmyY9evXL5dDsy8CCCCAAAIIIIBAhgIjR450QcKnnnrK7aH62KGHHuoelvteQP5QmsFcrUNvuOEGe+utt2zKlClu1dZbb20ac1PLwhAAVaaGDx/uHvRPmjTJBUIVDFWr0B49etjNN9/sxj5VEFf1W02WxOzsrij5DwEEEEAAAQSKRKBOxeyQ5UVyLVlfhropqavTwoULTa07W7ZsmdGx1FVo0aJF1qJFC1ehzGinajZSXjRGkyZm8i1Nq9klp9V6yp/Y/T+nA/5nZ31B0AQCbSdMDuJwtXqMWT12yen8ZWVlrrucn1wgp4NFeOfWrVu7L1r6yAly+IgokrRp08a1DFq1alUUsx9InvUZ52dSVmur1atXB3LcKB5EvQv0IE0tsEo5abI/Jf1t1d/jUk0KOrVt29YFo/ys42Gy8OUUpjwFlZeDDz7YdXN/+umnrVWrVq4MVE/abrvtMpqIUp9jX331lbVv375SN/mg8hfUcVasWGHff/+9+5usHkbJZpgP6lyZHEeTTuXjXtdM9yrHFwZlkgu2KaTAwWP/fbalS5e637lCnjvoc+ke0/2r36sop+bNm1uDBg3cJag+EuWxgPVgR987NDazhviIclJ9QPUCfV+Peuv8YvlOrB7DPl6kv/v6HhPlpIe8+p1RXCsqKZe6aEm3APUFrAJXsLGmAUfd/PpQCjIpL+p6T0IAAQQQQAABBBCoPQEFBPQv06QHGV26dMl081rbrnHjxtapU6daOz8nRgABBBBAAAEEakOgpCdBqg1wzokAAggggAACCCCAAAIIIIAAAggggAAChRMgAFo4a86EAAIIIIAAAggggAACCCCAAAIIIIAAAgUWoAt8gcE5HQIIIIAAAggggED4BPbff39bt26dJU50FL6ckiMEEEAAAQQQQACBmgoQAK2pGNsjgAACCCCAAAIIFJ3AVVddVXTXxAUhgAACCCCAAAII/FuALvDcCQgggAACCCCAAAIIIIAAAggggAACCCBQtAIEQIu2aLkwBBBAAAEEEEAAAQQQQAABBBBAAAEEECAAyj2AAAIIIIAAAggggAACCCCAAAIIIIAAAkUrQAC0aIuWC0MAAQQQQAABBBBAAAEEEEAAAQQQQAABAqDcAwgggAACCCCAAAIIIIAAAggggAACCCBQtAIEQIu2aLkwBBBAAAEEEEAAAQQQQAABBBBAAAEEECAAyj2AAAIIIIAAAggggAACCCCAAAIIIIAAAkUrQAC0aIuWC0MAAQQQQAABBBBAAAEEEEAAAQQQQAABAqDcAwgggAACCCCAAAIIIIAAAggggAACCCBQtAIEQIu2aLkwBBBAAAEEEEAAAQQQQAABBBBAAAEEECAAyj2AAAIIIIAAAggggAACCCCAAAIIIIAAAkUrQAC0aIuWC0MAAQQQQAABBBBAAAEEEEAAAQQQQAABAqDcAwgggAACCCCAAAIIIIAAAggggAACCCBQtAIEQIu2aLkwBBBAAAEEEEAAAQQQQAABBBBAAAEEECAAyj2AAAIIIIAAAggggAACCCCAAAIIIIAAAkUrQAC0aIuWC0MAAQQQQAABBBBAAAEEEEAAAQQQQACBehAgkE+BeUf0t5UrV9qKFSvyeRqOjQACCCCAAAIIIIBAFYGDx5qtWbPGli5dWmVdlBY0bdrU6tevbwsXLoxStqvktWHDhhXLmlRZzgIEEEAAAQTyLUAL0HwLc3wEEEAAAQQQQAABBBBAAAEEEEAAAQQQqDUBAqC1Rs+JEUAAAQQQQAABBBBAAAEEEEAAAQQQQCDfAgRA8y3M8RFAAAEEEEAAAQQQQAABBBBAAAEEEECg1gQIgNYaPSdGAAEEEEAAAQQQQAABBBBAAAEEEEAAgXwLEADNtzDHRwABBBBAAAEEEEAAAQQQQAABBBBAAIFaEyAAWmv0nBgBBBBAAAEEEEAAAQQQQAABBBBAAAEE8i1QL98n4PilLdB2wuTSBuDqa0VgVo9dauW8nBQBBBBAAAEEwiXwwiDlp+w//8KVt+xy0y673UK5V7OKXOlf1FOjigvQv8xT1xFzM9+YLRFAAAEEAhGgBWggjBwEAQQQQAABBBBAAAEEEEAAAQQQQAABBMIoQAA0jKVCnhBAAAEEEEAAAQQQQAABBBBAAAEEEEAgEAECoIEwchAEEEAAAQQQQAABBBBAAAEEEEAAAQQQCKMAAdAwlgp5QgABBBBAAAEEEEAAAQQQQAABBBBAAIFABAiABsLIQRBAAAEEEEAAAQQQQAABBBBAAAEEEEAgjAIEQMNYKuQJAQQQQAABBBBAAAEEEEAAAQQQQAABBAIRIAAaCCMHQQABBBBAAAEEEEAAAQQQQAABBBBAAIEwChAADWOpkCcEEEAAAQQQQAABBBBAAAEEEEAAAQQQCESAAGggjBwEAQQQQAABBBBAAAEEEEAAAQQQQAABBMIoQAA0jKVCnhBAAAEEEEAAAQQQQAABBBBAAAEEEEAgEAECoIEwchAEEEAAAQQQQAABBBBAAAEEEEAAAQQQCKMAAdAwlgp5QgABBBBAAAEEEEAAAQQQQAABBBBAAIFABAiABsLIQRBAAAEEEEAAAQQQQAABBBBAAAEEEEAgjAIEQMNYKuQJAQQQQAABBBBAAAEEEEAAAQQQQAABBAIRIAAaCCMHQQABBBBAAAEEEEAAAQQQQAABBBBAAIEwChAADWOpkCcEEEAAAd7Rlm4AAEAASURBVAQQQAABBBBAAAEEEEAAAQQQCESAAGggjBwEAQQQQAABBBBAAAEEEEAAAQQQQAABBMIoQAA0jKVCnhBAAAEEEEAAAQQQQAABBBBAAAEEEEAgEAECoIEwchAEEEAAAQQQQAABBBBAAAEEEEAAAQQQCKMAAdAwlgp5QgABBBBAAAEEEEAAAQQQQAABBBBAAIFABAiABsLIQRBAAAEEEEAAAQQQQAABBBBAAAEEEEAgjAIEQMNYKuQJAQQQQAABBBBAAAEEEEAAAQQQQAABBAIRIAAaCGNmB5k8ebINGTLEHnzwwcx2YCsEEEAAAQQQQAABBAISoC4aECSHQQABBBBAAIHICRAALWCRLVq0yGbPnm3z588v4Fk5FQIIIIAAAggggAACZtRFuQsQQAABBBBAoFQFCICWaslz3QgggAACCCCAAAIIIIAAAggggAACCJSAAAHQEihkLhEBBBBAAAEEEEAAAQQQQAABBBBAAIFSFahXChe+dOlSW7JkibVs2dIaNGhgM2bMsPLycuvRo4dtvPHGbp22adKkibVo0aIKibqsr1692lq1amWNGjWqsl7d2r/88kvbZJNNrEuXLtamTRtbvny562bUvHlza9asWZV9tGDevHn2wQcfWP369W2nnXay1q1bV9pu4cKFtmLFCndOnTtZUlcmnUvn0LlICCCAAAIIIIAAAuESoC4arvIgNwgggAACCCBQegIlEQB99tln7Z577rHhw4fbpEmT7KOPPnIlraDhU089ZePGjbP777/fjj/+eLvggguq3AWjRo2y119/3a688krr379/bP2qVavssssus/feey+2TC+OOuoo69Chg40ZM8ZNejRo0KBK63/55Re7/fbb7fHHH6+0vG/fvnbppZdaWVmZW/7OO+/Yn/70J3esRx55pNK2/s3vf/97dz233HKL7bnnnn4xPxFAAAEEEEAAAQRCIkBdNCQFQTYQQAABBBBAoGQFSiIA6ktXAcc5c+bYNttsYz/99JPtuOOOrgWoX1+Tn2pBeu2117rgZ8eOHe3oo492LUinT59uzzzzTCyImeyYCsL+/PPPdvjhh9vee+/tWoo+9thj9tJLL9kPP/xgd911l9WpU8cOPPBAU/D122+/dZMnKb/xScsVzFXL1t133z1+Vey1Wq4qyJsq9erVKy8tR+vVK6lbKxUvy2tJYNNNN62lM2d+WrX8jkI+M7+imm2p6/dJLfFLOclCn/mlfD/El/9GG21U0ha6F5TUq0QWpOISKMW6qErw4Ycfdr2vkpWmemS1b98+2aqclvH7kxMfO+dZIGx/8+vWrRu7YjXGUYOdqCb/u6+/o/HXFdXrUb6LoW7k6/5hu/drel/E31OKeRTD9RTD/ZVpOZZUlErBz2HDhtmAAQOcj4Kg2aa//e1vrlVop06dXEtPda1X6tOnj+2www5uWapjK/g5dOhQO+GEE2KbaL/Bgwe7gOaUKVPsoIMOct31e/fubRMnTrTnn3/eBWxjO1S80DKlQw45JOWXJHWPv+6669x2yf5T4HXrrbdOtoplCERWoGnTpqHPuyqXvrV36DOb5wxi8W/gKNy3eb4V3OFVscTCIl+hLsS9EsVzlGJdVOV0/fXXu4f/ycps5MiR1rlz52SrWIZA0QqE+e9c48aNi8K9YcOGRXEduggFqMJ8z9QEuliuQ9esRhzF0pCjmMol3f1YUk0LNL6nurn7pKdC2abXXnvN7XrWWWe5QGX8cY477jjbcsst4xdVeq112iY+qTv+ySef7BapS75Phx12mHv58ssvV6o4qgWqD4D6bfw+/EQAAQQQQAABBBAInwB10fCVCTlCAAEEEEAAgdIQKKkWoAo8+q5luRSvWnB+/vnn7hDdunWrcii1Xtl1113tu+++q7JOC3bbbbekzfF32WUXt/0333wT20/HV751LI0Jutdee7l17777rmlyJj0x13ijqZImdvp//+//pVrtur8vW7Ys5fpsV6g5eDE9dcvWgf1qRyAf93SQV6In62qBvm7duiAPG6lj6WmpbwG7Zs2akrbQwzh5aNK7Uk7+ybP+xq5cubJkKVRP0d9uDWGzfv360Dn4cgpdxiKSoVKsi6porrrqqpRdarfddlvLx99ttZgqlpZsEbm9yWYNBPJxz9fg9FU2VTde30VZ9ZGod4HX777m69iwYUOVa43SAtUHVC9QeUS9nqj7S/dZ2O79mt4PivX4ibH1XU7fY6Kc9H1Mfy/1+xKVlEtdtKQCoJtvvnkgZaqxN3Wzq9VmqibPmgk+VWrbtm3SVX6fxYsXu18kHxw49NBD3SROL7zwQiwAOnnyZHcMrUuX9CHzm9/8JuUmP/74o/uSlXKDLFfoCz0B0Czx2C1nAQUOwpxUKVNgI+z5zKehKnP+M06fp6VsoUqHKoWlbKB7zVdmVMkvZQsfANVDkrVr1+bz1zCrY/tyympndrJSrIuq2H0vp2S3wJIlS/LyO6+H8QRAk4mzLAwCYfs7p+9uPgCqgI4eRkY1KUCl3339Hc1lyLswXL8CoErFUDdSvV+xibDd+zUtZ/2e+ACoAuxRvx4fz4rSdeRSFy2pLvC+cGt6kye2wPAT/KS7SdI9CUj1RM2fR79U8d3zNfO8vhBNnTrVBUb1QT6lYpxQXY/GDiUhgAACCCCAAAIIhF+Aumj4y4gcIoAAAggggEBxCpRUADRVEeopkVKqJ12amT0+tWvXznVhV6sltdZMlubOnZtssVumJ93J0rx589xiHV8tgnxq1aqV7bnnni74+eabb5r+Kfjas2dPnmx7JH4igAACCCCAAAIRFaAuGtGCI9sIIIAAAgggEBmB/0bZIpPl4DPqmzAnC2aqJacPTPozqwWoxvhUmjBhgl8c+6njTJs2LfY+8YUCmMnSK6+84hZrFvnE5Cc60uRL/tjVdX9PPAbvEUAAAQQQQAABBMInQF00fGVCjhBAAAEEEECguAQIgFaUp59E6J///Kd99tlnsRJWi9BrrrkmacvQwYMHu+0efvhhe/3112P7LF261K699tq0441oQqN77703to9efPjhh/b3v//dtSw97bTTKq3TG9/ac/r06e58mkV0jz32qLIdCxBAAAEEEEAAAQSiJUBdNFrlRW4RQAABBBBAIHoCJTUJUqri2X333WMzrZ933nluoiEN0Pv222+7mWg183piq82uXbvakCFD7C9/+Ytddtll1r59ezcpkgKo6sa09dZb29dff21+vND4c2sG0AcffNA++OAD6969u6m7/IsvvugmRRk0aJBttdVW8Zu71xozqm/fvjZu3Dj3XoPJ++5SVTZmAQIIIIAAAggggEBkBKiLRqaoyCgCCCCAAAIIRFSAFqAVBacJhsaMGeMCn5pxVRMMPffcc24W89tuu826deuWtHgVrBwxYoTtt99+bqZWdZXfZ599XOvOHXfc0e2jQGpiOvLII02B1o8//tj++te/xs519dVXu6Bq4vb+/eGHH+5fGt3fYxS8QAABBBBAAAEEIi1AXTTSxUfmEUAAAQQQQCACAiXRAvSUU04x/UuX1KV85MiRbnKhr776KtaiU/t06tTJTj311KS7K/ipf4lpxYoVbpEf00lvBg4c6P75bQcMGGBffvmlC7RuvvnmfnHKn741aefOnV0L05QbsgIBBBBAAAEEEEAgNALURUNTFGQEAQQQQAABBEpUoCQCoDUpW7XY7NKlS7W7/P73v7cFCxa4lpzqxh6f5s+fb++++65bpGBlqqQu7Ntvv32q1VWW+wmXjjjiiCrrWIAAAggggAACCCAQfQHqotEvQ64AAQQQQAABBMInQBf4LMtELTZnz55tt99+u6nFqE96rW7xq1evNo3npLFBc0lz5swxBVQnTpxozzzzjDVt2tQOPvjgXA7JvggggAACCCCAAAIRF6AuGvECJPsIIIAAAgggUFABWoBmya2Z2jX7+6effuq6x6sLfXl5uS1evNgdUeOGXn/99Vke/b+7/e///q9NnTrVLdD4UFdccYVtsskm/92AVwgggAACCCCAAAIlJ0BdtOSKnAtGAAEEEEAAgRwECIBmiaexPTUD/Pjx423atGluJveGDRtaz549XRf6Aw880MrKyrI8+n9322233Uwzy6slqbq+Jxtv9L9b8woBBBBAAAEEEECgFASoi5ZCKXONCCCAAAIIIBCUAAHQHCQ1RtOJJ57o/uVwmLS7Hn/88aZ/JAQQQAABBBBAAAEE4gWoi8Zr8BoBBBBAAAEEEEgtwBigqW1YgwACCCCAAAIIIIAAAggggAACCCCAAAIRFyAAGvECJPsIIIAAAggggAACCCCAAAIIIIAAAgggkFqAAGhqG9YggAACCCCAAAIIIIAAAggggAACCCCAQMQFCIBGvADJPgIIIIAAAggggAACCCCAAAIIIIAAAgikFiAAmtqGNQgggAACCCCAAAIIIIAAAggggAACCCAQcQECoBEvQLKPAAIIIIAAAggggAACCCCAAAIIIIAAAqkFCICmtmENAggggAACCCCAAAIIIIAAAggggAACCERcgABoxAuQ7COAAAIIIIAAAggggAACCCCAAAIIIIBAagECoKltWIMAAggggAACCCCAAAIIIIAAAggggAACERcgABrxAiT7CCCAAAIIIIAAAggggAACCCCAAAIIIJBagABoahvWIIAAAggggAACCCCAAAIIIIAAAggggEDEBQiARrwAyT4CCCCAAAIIIIAAAggggAACCCCAAAIIpBYgAJrahjUIIIAAAggggAACCCCAAAIIIIAAAgggEHEBAqARL0CyjwACCCCAAAIIIIAAAggggAACCCCAAAKpBQiAprZhDQIIIIAAAggggAACCCCAAAIIIIAAAghEXIAAaMQLkOwjgAACCCCAAAIIIIAAAggggAACCCCAQGqBeqlXsQaB3AXmHdHfVq5caStWrMj9YBE9QllZmTVr1szmzp0b0SsIJtutW7e2unXrWnl5uc2bNy+Yg3IUBBBAAAEEEEAgjcDBY83WrFljS5cuTbNV+Fc1bdrU6tevbwsXLgx/ZtPksGHDhtakSRO3hcpEZRPl1KpVK1u7dm1Jf9eJcvmRdwQQKC0BWoCWVnlztQgggAACCCCAAAIIIIAAAggggAACCJSUAAHQkipuLhYBBBBAAAEEEEAAAQQQQAABBBBAAIHSEiAAWlrlzdUigAACCCCAAAIIIIAAAggggAACCCBQUgIEQEuquLlYBBBAAAEEEEAAAQQQQAABBBBAAAEESkuAAGhplTdXiwACCCCAAAIIIIAAAggggAACCCCAQEkJEAAtqeLmYhFAAAEEEEAAAQQQQAABBBBAAAEEECgtgXqldblcbaEF2k6YXOhT1vr5ZvXYpdbzQAYQQAABBBBAAAEEzF4YJIWy//wrBpF2tXIRXUfMrZXzclIEEEAAAQSCEqAFaFCSHAcBBBBAAAEEEEAAAQQQQAABBBBAAAEEQidAADR0RUKGEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAoAQKgQUlyHAQQQAABBBBAAAEEEEAAAQQQQAABBBAInQAB0NAVCRlCAAEEEEAAAQQQQAABBBBAAAEEEEAAgaAECIAGJclxEEAAAQQQQAABBBBAAAEEEEAAAQQQQCB0AgRAQ1ckZAgBBBBAAAEEEEAAAQQQQAABBBBAAAEEghIgABqUJMdBAAEEEEAAAQQQQAABBBBAAAEEEEAAgdAJEAANXZGQIQQQQAABBBBAAAEEEEAAAQQQQAABBBAISoAAaFCSHAcBBBBAAAEEEEAAAQQQQAABBBBAAAEEQidAADR0RUKGEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAoAQKgQUlyHAQQQAABBBBAAAEEEEAAAQQQQAABBBAInQAB0NAVCRlCAAEEEEAAAQQQQAABBBBAAAEEEEAAgaAECIAGJclxEEAAAQQQQAABBBBAAAEEEEAAAQQQQCB0AgRAQ1ckZAgBBBBAAAEEEEAAAQQQQAABBBBAAAEEghIgABqUJMdBAAEEEEAAAQQQQAABBBBAAAEEEEAAgdAJEAANXZGQIQQQQAABBBBAAAEEEEAAAQQQQAABBBAISoAAaFCSHAcBBBBAAAEEEEAAAQQQQAABBBBAAAEEQidAADR0RUKGEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAoAQKgQUlyHAQQQAABBBBAAAEEEEAAAQQQQAABBBAInQAB0NAVCRlCAAEEEEAAAQQQQAABBBBAAAEEEEAAgaAECIAGJclxEEAAAQQQQAABBBBAAAEEEEAAAQQQQCB0AgRA81wkGzZssPXr12d8ljVr1mS8LRsigAACCCCAAAIIIIAAAggggAACCCCAQHqBeulXszZbgZkzZ9r9999vH330ka1bt8723Xdf69u3rz3++OO233772emnnx47tIKkt912m73xxhs2d+5c23jjje3Pf/6zdevWLbYNLxBAAAEEEEAAAQSiJfDzzz/b/PnzbfPNN89rxocOHWp6iH7jjTdaixYtsj7X+PHjTf9UZz3xxBOzPg47IoAAAggggAACYROgBWgeSmTBggV21VVX2bvvvmuq+Hbo0MG22morW7x4sc2ePdvmzZtX6az33HOPjRs3zgU/VWndbLPNrH379pW24Q0CCCCAAAIIIIBAdAQ++eQTGzx4sL388st5z/Snn37q6ph6qJ5LWrhwoTuOgrYkBBBAAAEEEECgmARoAZqH0vzggw9sxYoVLpD58MMPW+PGjd1ZZs2aZccdd5x17ty50llff/119/7ss8+2k08+2erUqVNpPW8QQAABBBBAAAEEoiUwadIk++KLL6xPnz55z/hRRx3lehxtuummeT8XJ0AAAQQQQAABBKIoQAA0D6X2448/uqN27949FvzUAr3Xv8Tkt+/VqxfBz0Qc3iOAAAIIIIAAAgikFTj33HPTrmclAggggAACCCBQ6gIEQAO8A1avXu3GefIBzfLycvvqq69cUHPrrbe2ZcuWuW7wahHasmVL++GHH+ynn34y311JXeTVZV5d4Js2bRrLmbb57rvv3L+VK1e67vHqVq9jkBBAAAEEEEAAAQQqC3z99de20UYbuWGIVB/T+48//thUH9txxx2tbt26sR3U3Vu9dDbZZBPXS6dNmzaxdYkvMqmTqS737bff2vLly93uqt+pPqi6nep48Uk9hj7//HNXx9M4odtvv32lOqDfdunSpbZkyRJX92vQoIHNmDHDdF09evRwY8d/88039ssvv9iWW25p9epVrd6ra7u2+f77761Jkya2xRZbuOGZ6tev709R7U9dhwx1LBnJsm3bttXuxwYIIIAAAggggEAYBKrWkMKQq4jm4f3337dLLrkklvtXXnnF9E+TGmn8p8mTJ9vtt99uhx12mF1xxRX2xz/+0U2S5HfQAPZK6go/cOBA93rChAk2ZswYU3A1Pqmb/DHHHGN64q8KOwkBBBBAAAEEEEDg3wJnnnmmNWzY0O644w676KKLXODP2ygAeOedd7og3tVXX21+KCKtV51t2LBhpi7liSnTOpkCn6eeemps9yeffNL079e//rWdf/75brmCpA899JA98MAD7uG331hBW+07aNCgSoHMZ5991jRm/PDhw01d6zXJplKzZs3sqaeeMl2v6oo6T3wAV+PSa2Kkt956y58i9lMBV9VHd91119iyVC9Uf9V5/EN7v92ee+7pjsFDeS/CTwQQQAABBBAIqwAB0ABLRk/CVWnWU/lp06ZZ165d7aCDDqrUyiD+dCeccIJrEarKuSrCQ4YMMY3d5Gd/1yzy9913n2sJcOSRR7rlagGgivp7771nTz/9tHvyftJJJ8UftsprtVZIldR6gIQAAggggAACCBSbwKpVq9yD4rKyMjvllFNcfUyBxEWLFtmVV17pWmN++eWXdvrpp7tA4muvvebqcLfeeqspsNeuXbsYSU3qZKrLqT44ZcoU08PxffbZx/bYYw/bYYcdYsdT4FXnUwBTs62rDqnePgpgKiiqFqPXXXddbHv/4vHHH7c5c+bYNtts43oRqTWrgrbJklqXnnbaaW5cegU5dU2aZHPmzJmuLqmeSJq0U4HNdA/TH3vsMdN5FTDVWPUKsGrSJT3kV2BVD//lky6tW7fOtVhNto1arpIQQAABBBBAAIF8CxAADVBY3YAGDBhg69evdwHQjh07uvepTuEHxb/77rtdAPRXv/pVrGuUgpYKcCrpab8CqT6pBcGoUaPczPHPP/+8pQuAasb5Aw44wO9a5acqtcnGJa2yIQsyFoj/whS/U6rl8duUwmu1XsbCXBdEtUIimXvIEz/sR6ma8Hvx75JXl1wszJo3b16qvwqBXfeaNWvcZ61aeyrQqKS6liakVJd4deXWZJUtWrRw6/SwWQ+jNXGRgoSHH364W17TOpmCiaoPKlCpAKgeiOu9T2+88YYLfqo7vAKH8d3iDz30UJeHV1991QUXFbSMTzqmgqv+eOkeck+cONEFP1UfHT16dOyBfO/evd0D+KOPPtoNz/Tmm2/a/vvvH3+aSq9feukl9/7yyy+P1Rn33ntv56Pgrbrwf/bZZ677fqUd494oAKuH/cnSyJEjXbkkW8ey8Ajk43NZv5f+dzM8V1rznDRq1Mj0r1hS69ati+JS4j9bo35BGtokH7+DteFSLNchOz3wLJbJB4upXNLd1xulW8m62hNYu3at/fa3v3VdmuKDnz5HvqKqp/skBBBAAAEEEEAAgaoC6k4eH2DRF3tfyT/iiCNiwU/tqS+YnTp1cgfRA2Sfgq6TKeiqpHpe4hd0PQzq16+fW69Wl4lJwdrjjz8+tjhdy83tttvOzjjjDLv00ktjwU+/o8678847u7fV1SV94FJjiMYn5UU9lcaPH582+Bm/D68RQAABBBBAAIHaEqAFaG3JV3NeVYA1xmd8UgVVg+qru5Yfr8pXSuO3i3+tljS+Mh+/3L9Wtym1WA06qZVfskH4gz5PGI+X6OktEpeHMe/5zJPuB1lo2IXEMcTyed4wHlsW6vJXyt3+NM6dn4REn2OlbiGPUv+98JOx8BlhJgvdD2EcpsaXUxg/W5PlSd22E5MmAJo7d66bMChxnQ9Iqsu2T0HVyfzx1PpUSb/3mnwpMfkWqYkBR22nSY70tzSTtPvuu5v++aTPWl236pIffvihm4xT66qrS+qhu1p53nLLLfbcc8+ZWn/qn+qXyk8maaeddkr5GaeWc/mqI0Xtfs3Esra2CaqM4v/+h/VzribGxVKnU51MZaMUVFnXxDHobcP8d7Qm11pM35+K5Tuxvw6Vo76/VPc3tCblXRvbxn8fq43zZ3POXP62EwDNRrxA++iXSV2gNOi+umNp9k2f/I3q36f6qUr0M888k2q1acZ6dQELOqlFgv8SEfSxw368RE+NPabWJ4nLw34dQedPrW78fVvqFho/TWPT6V+pJk1O4ocAWLlyZZWJ3krJRV/+1X2m1H8vfKs8fSEuZQtVrDWkjh56quVh2JIvp7DlK1V+kuXXBxCTdVn16/xPf9wg6mQ6lsp12bJl7rA33XSTP3zSn5rASIHY+DE+kwV0k+78n4VqyfrEE0+4sU0V+Ix/0OL/JqfbX+s0jqgmWFKLVM0Cr3/quq+6jYZZ0vrqJkHSOKOpkma3z8fvvAIHrVq1SnValtdQIKgySvz7r6Eqopx0j+mzurqW1GG/Rg270qBBA5dN/U5GOaijzzZ979CEdOmGCQl7mSh/qg8oqTyC+h10B6yF/4rlO7GCb/5vnn73/d/0WiAN5JT6W67fGY2PHpWUrG6Xad4JgGYqVeDt1OpDg9/7cZf0x1Vd4bfddlv3xF2Vh3POOafAueJ0CCCAAAIIIIBAdATig4c+15m0rI3fJl91srPPPrvGvWWSXY+/rsSfs2fPtqFDh5oCTAoGasIktdpUXXK33XazMWPGuDHrE/dLfK9g8HnnnWeavNOPTaoxUjUxpx6yT5061f785z+bxholIYAAAggggAACYRUgABrSktHMoAp+KtA5YsQI0+Dx8UkVUKVS7jYa78FrBBBAAAEEEEAgHwJB1skaN27sJn5Ti5HEmeGDzvv111/vgp99+/Y1TWCUOF6ousMrZVqX1MN4jT+qf2qZ+vbbb5smMFKrpGeffdYuuOCCoC+B4yGAAAIIIIAAAoEJMAlSYJTBHujdd991B9x3332rBD+14tNPP3Xro9w9wV0A/yGAAAIIIIAAAiEWyLZO5sfTSwwwbrPNNu5q/cPsxEtXMHHgwIGuJ1Diukzfq+unxoxXGjx4cJXgp1qFfvfdd259urrk/Pnz7fzzz7eTTjrJNFyJT2qJut9++7nlWqYxQkkIIIAAAggggECYBQiAhrR0NB6c0nvvvVdpvCYtmz59uj3yyCN66Z7Auxf8hwACCCCAAAIIIBC4QLZ1Mt/iMn5GeWVOs78rPfroo+aDq25BxX/aVl3TNV5nLmNc6dx+jE91V49PGhNPLUL9RCfxEz7Fb6fXGkfv+++/tzlz5thjjz2WuNreeOMNt0xd6kkIIIAAAggggECYBegCH9LS6dOnj6sYawD8s846y3r16uVmhVVFecaMGbb99tu7iZE08K6eyCcbyD+kl0a2EEAAAQQQQACByAhkWyfzExZNnDjRTRzUs2dPGzJkiHXv3t2OOuooN37msGHD7MADD3Tjcqp3j7qVq3Vm586d7eSTT87aSAHQ3r1724svvmh33HGHC7R269bN1R11DtUvNSaoxgnVhJipksb/PPfcc+3aa6+1sWPH2j/+8Q9T7yQFUd966y0XHNXEfv379091CJYjgAACCCCAAAKhECAAGopiqJoJDSSvSZBGjx7turv7Lu+apUsV0WOPPdYuuugiFwzV2FSHHXZY1YOwBAEEEEAAAQQQQCAngWzrZKqbvfPOO26SoC+++MJ8S1Jl5uKLL7ZddtnF7rzzThdUVGBRSV3LNcamWolqxtxc0vDhw93D80mTJrlAqIKhahXao0cPu/nmm00zPWtyI01ipMmSFOxMlvr16+cW33fffa5lqlqnKinIqm7wak2q+ikJAQQQQAABBBAIs0Cdipkty8OcwVLPm8Zl0vhLGmBeLQlatmwZKIme+m/YsCHQY+pgqhRvttlm1nbC5MCPHfYDzuqxS6Us6guMvhj4yQYqrSyhN+pGpy9e+shJ7A5YQgzuUtVaRi23V61aVWqXHrteTfDWpEkT916TgaxevTq2rtReqAW/AiNqkVXKyXf3Vbdc/c0r1aQgVNu2bV1wSr08wpZ8OYUtX4XIT7Z1Mt3TixYtshYtWriAZGJe9RmooKImSJKv6lBBphUrVriWmvobvNVWW7kgazbH199v/W6qXqrPrQ4dOpgf5zSb4/l9FIjNx72ume81cdMLg/yZ+JmLQNcR/540K5djaN/4v/9Lly51LZ5zPWZt7q97TPevfs+inJo3b24NGjRwl6D6SLqxgcN+nfqs0/eOxYsXu9bqYc9vuvypPqB6gb6vp2utn+4YYVlXLN+J69evH4vJ6PuL/oZHOSlOod8Z1VOiknKpi9ICNOSlrJtRgU/fjSrk2SV7CCCAAAIIIIBAUQpkWyfTlyV9iU2VmjZtal27dk21OuflCqx26tQp5+PoS7iCPfpHQgABBBBAAAEEoibAJEhRKzHyiwACCCCAAAIIIIAAAggggAACCCCAAAIZCxAAzZiKDRFAAAEEEEAAAQQQQAABBBBAAAEEEEAgagIEQKNWYuQXAQQQQAABBBBAAAEEEEAAAQQQQAABBDIWIACaMRUbIoAAAggggAACCCCAAAIIIIAAAggggEDUBAiARq3EyC8CCCCAAAIIIIAAAggggAACCCCAAAIIZCxAADRjKjZEAAEEEEAAAQQQQAABBBBAAAEEEEAAgagJEACNWomRXwQQQAABBBBAAAEEEEAAAQQQQAABBBDIWIAAaMZUbIgAAggggAACCCCAAAIIIIAAAggggAACURMgABq1EiO/CCCAAAIIIIAAAggggAACCCCAAAIIIJCxAAHQjKnYEAEEEEAAAQQQQAABBBBAAAEEEEAAAQSiJkAANGolRn4RQAABBBBAAAEEEEAAAQQQQAABBBBAIGMBAqAZU7EhAggggAACCCCAAAIIIIAAAggggAACCERNgABo1EqM/CKAAAIIIIAAAggggAACCCCAAAIIIIBAxgIEQDOmYkMEEEAAAQQQQAABBBBAAAEEEEAAAQQQiJoAAdColRj5RQABBBBAAAEEEEAAAQQQQAABBBBAAIGMBQiAZkzFhggggAACCCCAAAIIIIAAAggggAACCCAQNQECoFErMfKLAAIIIIAAAggggAACCCCAAAIIIIAAAhkLEADNmIoNEUAAAQQQQAABBBBAAAEEEEAAAQQQQCBqAgRAo1Zi5BcBBBBAAAEEEEAAAQQQQAABBBBAAAEEMhYgAJoxFRsigAACCCCAAAIIIIAAAggggAACCCCAQNQECIBGrcTILwIIIIAAAggggAACCCCAAAIIIIAAAghkLFAv4y3ZEIEsBOYd0d9WrlxpK1asyGJvdkEAAQQQQAABBBBAIHuBg8earVmzxpYuXZr9QUKwZ9OmTa1+/fq2cOHCEOSGLCCAAAIIIBA9AVqARq/MyDECCCCAAAIIIIAAAggggAACCCCAAAIIZChAADRDKDZDAAEEEEAAAQQQQAABBBBAAAEEEEAAgegJEACNXpmRYwQQQAABBBBAAAEEEEAAAQQQQAABBBDIUIAAaIZQbIYAAggggAACCCCAAAIIIIAAAggggAAC0RMgABq9MiPHCCCAAAIIIIAAAggggAACCCCAAAIIIJChAAHQDKHYDAEEEEAAAQQQQAABBBBAAAEEEEAAAQSiJ0AANHplRo4RQAABBBBAAAEEEEAAAQQQQAABBBBAIEMBAqAZQrEZAggggAACCCCAAAIIIIAAAggggAACCERPgABo9MqMHCOAAAIIIIAAAggggAACCCCAAAIIIIBAhgJ1yitShtuyGQIZC7z88st2wQUXuO2HDBliF154Ycb7smFxCvTr18/mzp1rG2+8sc2cObM4L5KryljgwQcftJtuusltf80119iAAQMy3pcNi09AVZGuXbu6C+vYsaONHz+++C6SK0IAgYIKfPLJJ3b88ce7c/bv399GjhxZ0PNzsuQCDzzwQKws/vCHP8TKKPnWLC2UwDnnnGNTp051p5s0aZJtueWWhTo150kj0L17d1u/fr21b9/enn/++TRbsqpQAvoee+qpp7rTHXvssXbttdcW6tScJwCBegEcg0MgUEXgl19+cR/WWvHzzz9XWc+C0hPQH2/9q1OnTuldPFdcRUCfC7oflPR5QSptAQVA/f3gf5a2CFePAAK5CsR/rlAXzVUzuP3jvyPw9z8411yPtGHDhtjf4VyPxf7BCahO5P8Fd1SOlItA/GcYf1tykaydfekCXzvunBUBBBBAAAEEEEAAAQQQQAABBBBAAAEECiBAALQAyJwCAQQQQAABBBBAAAEEEEAAAQQQQAABBGpHgABo7bhzVgQQQAABBBBAAAEEEEAAAQQQQAABBBAogAAB0AIgcwoEEEAAAQQQQAABBBBAAAEEEEAAAQQQqB0BAqC1485ZEUAAAQQQQAABBBBAAAEEEEAAAQQQQKAAAnUqZkgsL8B5OEWJCcyfP99mzZrlrrpjx462ww47lJgAl5so8Oqrr9qaNWusbt261q9fv8TVvC8xga+//to++eQTd9VdunSxLbfcssQEuNxEgcmTJ7tFjRo1sp49eyau5j0CCCBQI4EVK1bYtGnT3D7t2rWzXXbZpUb7s3F+BPj7nx/XXI86Y8YM+/HHH91h9t9/f9t0001zPST7ByDwwgsvmGYdLysrswMOOCCAI3KIXAWWLFlib775pjvMFltsYTvvvHOuh2T/AgoQAC0gNqdCAAEEEEAAAQQQQAABBBBAAAEEEEAAgcIK0AW+sN6cDQEEEEAAAQQQQAABBBBAAAEEEEAAAQQKKEAAtIDYnAoBBBBAAAEEEEAAAQQQQAABBBBAAAEECitAALSw3pwNAQQQQAABBBBAAAEEEEAAAQQQQAABBAooQAC0gNicCgEEEEAAAQQQQAABBBBAAAEEEEAAAQQKK1CvsKfjbGEX+Omnn+zJJ5+0d955xzTD2fbbb2+77rqr9e/f383eXdP8a5bn//u//7NvvvnGGjZsaF27drXevXubZoZPlbLZJ9WxWJ67QJDlkev9tXr1arv44outbdu2dvXVV+d+cRyhxgK5lmHiCbO5v/7xj3/Y1KlTbc6cOW5mzA4dOtg+++xj/fr1Szw87wsgkE0ZpspWNvfXwoUL7W9/+5t99tlntmbNGuvSpYt1797dDjzwwFSnYTkCCIRYIJvPgXSXk81nVNB5SJe/qKwL2qSm5aK//e+//35KrpYtW9rAgQNTri/WFTV1zNQh0zp30PdFpvkL+3a1WS4qu3vvvTct0eGHH27bbbdd2m2KbWXQ92o230fydV8UW1nl83qYBT6fuhE79tKlS+3cc8+17777zuV8s802s8WLF7vX+++/v11zzTW28cYbZ3xVCqTeeuutbvtGjRrZunXr3L+ysjIbMWKE7bbbblWOlc0+VQ7CgsAEgiyPXO+v8vJyu+KKK2zatGkuuHHbbbcFdp0cKDOBXMsw8Sw1vb9Ucbnkkkts1qxZ7lBNmjRxP5cvX+5+6mHNTTfdZPqMIRVGoKZlmC5X2dxfb775pl111VW2du1ad+jGjRvbihUr3OvDDjvMLr300qwe3qXLJ+sQQCB/Atl8DqTLTTafUUHnIV3+orIuaJNsyuXCCy+0GTNmpCRT44qxY8emXF+MK7JxzMQh0zp30PdFJnmLwja1XS56UHDeeeelpbruuuvsgAMOSLtNMa0M8l7N9vtIvu6LYiqnglxLxQccCQEncNFFF5X37NmzfPjw4eUVHxJuWUULq/JBgwa55TfffHPGUhUfvOW9evUqr2jtWf7qq6+W//LLL+UVAdDyJ554wh3roIMOKp87d26l42WzT6UD8CZQgaDLI5f7q+JJZvkNN9zg7h3do0OHDg30WjlYZgK5lGHiGbK5v2655RZ3D/zmN78pr3iCGjvkxx9/XH7SSSe5dTfeeGNsOS/yK5BNGabLUU3vr/nz55cfeuihrtxHjx5d/v3335dv2LChfPr06eW6R/RZMWbMmHSnZB0CCIRMoKafA+myn+1nVJB5SJe/KK0L0iTbcql4qOU+1/Vd4plnnqny75VXXokSac55zdaxuhPXpM4d5H1RXb6isj4M5VIRaHO/KxdccEGV3xP/u6M6UymlIO/VbL6P5Ou+KKUyDOpaGQO0IGHm8J/ko48+srfeesu1nNIToaZNm7pMt2/f3kaNGuVa0EyaNCnWsqa6K9IT2Iqb1Cq+hJpaj9apU8fq169vAwYMsOOPP97Wr19vf//73ysdJpt9Kh2AN4EKBFkeudxfGo7h1FNPtYkTJ9pGG/GRFWgh1+BguZRhstPU9P5Sd57x48e7e+Daa6+1HXfcMXbYTp062Z/+9Cf3fsKECaZtSfkXqGkZpstRNvfX5MmT3d8k/Z2qeChim2++uftbtffee7vPDJ1vypQp6U7LOgQQCJFANp8D6bKfzWdU0HlIl7+orAvaJJtyWbBggam3R4sWLdx3iSOPPNIS/1U0rogKaSD5zMaxuhPXpM4d9H1RXd6isr62y0VOGhJIScPOJf6e+PeqM5VKCvJezfb7SD7ui1Ipv6Cvk2hC0KIRPZ7/kqim8A0aNKh0FeoKv+eee7ru6wqCVpf0waBgqtIhhxxSZXO/TIGKitY6bn02+1Q5MAsCEwi6PLK9v1588UX73e9+Z/PmzbO99trLKp7eBXaNHKhmAtmWYbKzZHN//etf/7Kff/7ZttxyS9tmm22qHFbLWrVq5R68fPnll1XWsyBYgWzKMF0Osrm/1N1df5v0gCTx4UhFDwR3uopWom4863TnZh0CCIRDIJvPgVQ5z/YzKsg8pMpb1JYHaZJtufiATvzDz6g5BpnfbB3T5aGmde4g74t0+YrSujCUi7w+//xzx8bvy7/vniDv1Wy+j+Tjvvj3lfF/NgIEQLNRK8J9PvzwQ3dVCjIlS/qSqVTRfDvZ6krLKrqjuiCEAhXJni6ptZa+uC5btsy+/fZbt282+1Q6KW8CFQi6PLK9vzQGre6hyy+/3CqGYLDmzZsHep0cLHOBbMsw2Rmyub/0GaQWoLoPkiU9TNFnilKzZs2SbcKyAAWyKcN0p8/m/jrmmGOsohuSaazPxOSD4Pr84HMjUYf3CIRTIJvPgVRXku1nVJB5SJW3qC0P0iTbckkMgOpvvp+nIGqeQeQ3W8d0565pnTvI+yJdvqK0Lgzlot8N1YHq1asXm3RYrac1bmWppiDv1Wy+j+TjvijVsgziupkFPgjFIjhGxTgg7ipSBQ78cj9BUrpLru5Y2lfH00QVOp4GLc9mn3R5YF1uAkGXR3XHS3V/9enTx4477jj3Rzy3K2LvXAWyLcNk563uWNon8TNCw2ikC2S98MILrpW6hu9Ql2hSfgWyKcN0OarueKk+IxKPqQq+JsmqGBPUrVKQlIQAAtEQCOpzQFdb3bG0TeLfmUz2y/SzSMcqllSdZU1MqjuWzJKViw+AKrijnkH6nFevEDWo2GOPPWzYsGGue3yxmFd3Hdk6pjtuTevc1eWhJvdFunxFaV11JrqWZPd3umusabl88803bqi5Dh062N/+9jcbN26cLVq0yPWU0bJTTjnFDj744HSnLLp11ZVLTe7VbL6PVHd+gdf0vii6QirgBREALSB2mE+1atUqlz3/AZCYVz/bst8ucX38e79NqmNp28TjZbNP/Dl5HaxA0OVR3fES7wd/NS1btvQv+VnLAtmWYbJsV3cs7ZPqnkh2vB9++MHuvPNOt+rMM890Yw4n245lwQkEXYbVHS+T+2HkyJGmYVoqJtxzY4FeeeWV1r9//+AumiMhgEBeBYL4HPAZrO5Y2i7Z50p1+yXbx5+zWH8GaVLdsVKVi+/S+9BDD7nPd/Um8y3dKiY/Mo1deeutt9p2221XrMVQ6bqydax0kIQ3Na1zV5cHflcSgP/ztqYuNS0X/7BAvSz/8pe/mPbv0aOHff311+7f//zP/9jMmTNd77rkOSy+pYW6V1N9H6nu/BKv6X1RfKVUuCsiAFo469CeqWKGdlu7dq3Ln56kJkuNGjVyizNpPq9xLpRSHUvr/PH8ebPZR8ch5UcgyPII+v7KzxVz1HQCQZdhkPeXnmprbNilS5e68SA1uDsp/wJBlmFQ95cq9Kroa7IMfTHWmE+77rqrtW3bNv8gnAEBBHISCOpzwGcim8+ooPPg8xLln0GbZFMuCh4osKCkeQSGDx/uJm3Vey3/4x//aJrk5Prrr7d77rmnJHoNZeMor6BS0PdFUPmq7ePUdrno+v3DAn0P1++E6kFKmpxYrUHVQ0YTy2rCyAMPPNCtK+b/CnWvpvs+Eob7opjLuKbXRgC0pmJFuL0mjygrK7M1a9akHB/EBz433njjagUaNmzotlErnFTJH2+TTTZxm2SzT6pjszx3gSDLI+j7K/er4wg1FQi6DIO6v/R0++KLL7a5c+da586dTbPDkwojEFQZKrdB3V+PPPKIa/27fv16e/DBB+2BBx5wrYL+/Oc/W9euXQsDw1kQQCArgaA+B/zJs/mMCjoPPi9R/hm0STblou8o6sq7cOFCF8xRF1SfNM6z/vYPHDjQzXytSVj33Xdfv7pof2bjGCRG0PdFkHmrzWPVdrno2gcNGmTqNq8hoeLn4tDvzbHHHutagSoQ+vDDD5dEALQQ92p130fCcF/U5u9F2M7NJEhhK5Fayo9vXq9xOZMlv9z/Aifbxi/zx9KAy6lS4vGy2SfVsVmeu0DQ5eGP58s9MYd+eSb3V+K+vC+MQJBl6I9Vk8+IxKvUhGxnn322C37uvvvuNmrUKOP+SVTK3/sgyjA+d/54/rMgfp1e++Xpyth/Ka5fv74NHjzYjXGlh21jx45NPBzvEUAghAJBfA74y/LHqunfGb+f/8zxx/M//fJ0n0V+22L5GaSJP1ZNykUBDE2s2r1796RD3LRp0yb2kMtPgFcs9qmuIxvHVMfKdrnPg/+dSDyOX16Kvys1ub8T3XJ9r5afO+20U6XgZ/wx+/bt695+9dVXptaRpZDyea9m8n3En78274tSKOdMr5EAaKZSRb6d/8X0f6wSL9f/wqabhMTvU92xtF3i8bLZx5+Pn8ELBF0e1R0v8X4I/oo4Yq4CQZZhdcdSXtPdExrv68ILL3RBMXWH08zwpVTBzrUsg9g/1zJMzEN1x0t3PyQey78/6KCD3MvZs2f7Rf+/vfuAkaL6Azj+gwOkg6LIIV1BYqEXEUJVikAMIhoSJUIAS7AhERECagQCsZcoApKYUyEYJCrSpUuXU5pIy9ExUUCKdP78nnn7n92dvd0b5m73Zr+TLLvz5s2beZ8ZtvzuFZ4RQCCFBfx8H4hXljK4va/E289tnxQm9eXU/DSJV1as6xKvIpUrVzZZtBtqOiz55ZgXu3jnwP8Vd81ku+gfDHTRnpp2bEr3Mw1Oan7dq4n+Hol3fJVO9n0RnKsdvyYEQOMbpUUO+8Uh1l9Obbr+RSneYsvSGd61K2LkcuLECfn7779Nt8e6deuazV72iSyXdf8E/L4etjx7H0WeqU1P5P6K3Jf1ghHw8xrasvLyHmFr+f3338uYMWPMe0v//v1l1KhRaTHel61/qjxfyzV0q4Mtz74XROax6c73CG3Z+eabb8rRo0cjs5t1bQmqS7FijPZjIPgHgRQX8PI+EKtKtqy8fs7Y/ex7TmT5Nt35XhSZJ2jrfprYsvJyXfSPWPp+P3PmzJi0OvazLtWqVYuZJ0gbvDj6XX97Dvb/RGT5Nj0d/6/k5f6OdLvWdf1/8umnn5qu7m5l2e9MOulObvN1uO1bWNPy417Ny+8Re/xk3heF9drlx3kTAM0P1UJYpo4VosuiRYuizl6bx+tfOHSxAylHZXIk6HgjOjvjqVOnZO3atY4t/71csmSJXLp0yeQpXbq0SfSyT1TBJPgm4Pf18PP+8q2SFJQnAT+vodf7a82aNaa1p3Z1fuWVV2TAgAF5qgOZ/RPweg1jnYGX+0vHeps/f74sW7bMtdhNmzaZ9Hr16rluJxEBBFJLwMv7QKwaeH2P8vMcYp1bYUv308TLdTl27JiZzfrDDz+UnJycKD5tVLF161aTfuedd0ZtD2KCF0e/Hfy8L/w+t2SVlwrXRX/L65josYb/WblypeFJl/8rWlm/79W8/h5JhfsiWf8nUvG4BEBT8aok4Zx0JrhatWqZAcTnzp0bdgb6JqpdSmrWrCktW7YM27Zq1SpZuHCh6DgizqVv375mddq0aaGx2zRB/0L79ddfm219+vQxz/YfL/vYfXn2X8DL9dDJaPR+WLx4cdgJeb2/wgphJakCXq+hX+8ROpajTmajs1gOHDhQunfvnlQPDi6S7PcI+4VWv+QfPHgw7JJo8HPGjBkmrVevXmHbWEEAgdQUSPbnjKp4PYfUFPXnrLya+PX5r40vKlasaD7/dXK7ixcvhip29uxZmTBhgpnItXXr1qZxRWhjwF/4+RnshcrrfeHlWIVpn2RfFzv8jzZe2r17dxjdxo0bQy2pdaz0dFm83qtu72Fef494uS/S5foUdD2LXP0xeaWgD8rxUlNg+fLlMnr0aNM6U988tXv65s2bZfXq1aJdCd977z1p0KBB2Mk/8sgjZhKSQYMGSb9+/ULbtIXn008/Ldu3bzeDMGt5+oVF/yqlwVT9kjJu3DjTDd7u5GUfuy/P/gt4uR7z5s2TsWPHSkZGhixdujTspLzcX2EFXF3RMkaOHGkGwv/ggw8iN7OezwJerqFf7xH6hxjt0qOL3l+5Ldotuk2bNrllYZsPAsl+j9CvL8OHDzefUTpJhk6GpZNk7Nq1y/Ra0O2PPvqoDBkyxIfaUgQCCBSEQDI/Z2z9vJyD3Teoz15M/Pr8V9MNGzbI0KFDTRBU5yPo3Lmz+S6g53XgwAGpXbu26SFixzcM6nVw1svvz2Bn2fpabeN95/ZyX0QeJ2jryb4uevwXX3xRbC+Ypk2bij70u5H2wtRFf6PbgFzQ/GPVx8u96vYe5vX3iJf7IlZdSL82gYzXri7XVgR7B0VAW3g2bNjQvGFu2bJF9K9E+qVCW4bqmHtu3d91nBHt6q5vrLqvXfTHqH450WCnBlGzs7NN9xQdcLl3794ybNgwE1S1+fXZyz7O/Xntr4CX66EfritWrDDX8oknngg7IS/3V1gBV1e065P+RTMzM1O6desWuZn1fBbwcg39eo/Iysoy70daRQ1s5fbo2LGjabGezxxpX3yy3yN0KIR27dpJiRIlRD+z9u3bZz63tEeCjrekwyTol1cWBBAoPALJ/JyxSl7Owe4b1GcvJn59/qupdiFt0aKFadGm4+hpl3f9faFzDWhvAP3jeyITtQbp+vj9GRxpk8h3bi/3ReRxgrae7Ouix9eGR9pYYNu2bea7s/6m1+9G1atXN2Pnp+NvKC/3qtt7mNffI17ui6D930iV+tACNFWuRIqdhwYu9QuG/oisUqVKWEvNvJ6qtvzUJvgasNA33kRma/ayT17Pi/yJC/h9Pfy8vxKvBTn9FPDzGvp9f/lZT8pKTMDva5jX+0uPr3+w07HiatSoIZUqVUrsxMmFAAIpK5DX94HcKuL1PcrPc8jt/ArTNj9NvFwXnUxVf6PoBC76u0IDC+m+eHH028zP+8Lvc0tWecm+Lnr8Q4cOyfHjx6VOnTpStmzZZFGk1HGTfa8m+75IqYuRhJMhAJoEdA6JAAIIIIAAAggggAACCCCAAAIIIIAAAgUjwJ/MCsaZoyCAAAIIIIAAAggggAACCCCAAAIIIIBAEgQIgCYBnUMigAACCCCAAAIIIIAAAggggAACCCCAQMEIEAAtGGeOggACCCCAAAIIIIAAAggggAACCCCAAAJJECAAmgR0DokAAggggAACCCCAAAIIIIAAAggggAACBSNAALRgnDkKAggggAACCCCAAAIIIIAAAggggAACCCRBgABoEtA5JAIIIIAAAggggAACCCCAAAIIIIAAAggUjAAB0IJx5igIIIAAAggggAACCCCAAAIIIIAAAgggkAQBAqBJQOeQCCCAAAIIIIAAAggggAACCCCAAAIIIFAwAgRAC8aZoyCAAAIIIIAAAggggAACCCCAAAIIIIBAEgQIgCYBnUMigAACCCCAAAIIIIAAAggggAACCCCAQMEIEAAtGGeOggACCCCAAAIIIIAAAggggAACCCCAAAJJECAAmgR0DokAAggggAACCCCQmMCYMWOkSJEi5jFr1qxcd7py5YpkZmaG8nfp0iXX/Lpx6NChofxr1qyJmz+RDCdPngyV+eqrryayS8J5XnjhhVDZly9fTng/t4w///yzWzJpCCCAAAIIIIBA4AQIgAbuklIhBBBAAAEEEEAgOAKdOnUKVWblypWh124vfv31Vzly5Eho0/Lly+XMmTOhdbcXy5YtM8nXX3+9NG/e3C1L4NKOHj0qjz/+uDhtA1dJKoQAAggggAACCDgECIA6MHiJAAIIIIAAAgggkFoC99xzj5QpU8ac1KpVq3I9uXnz5pnt5cqVM89nz56VpUuXxtznn3/+kezsbLNdg4EZGRkx8+Zlg7ZYLVmypHkUK1YsL7sWSN7XXntNsrKyRFvMsiCAAAIIIIAAAukgQAA0Ha4ydUQAAQQQQAABBAqpQIkSJaRt27bm7Ddt2pRri865c+eafH379pXKlSub1zYo6lZ9bVFqu5EOxRDgAAANdklEQVR37tzZLYuntLJly8q///5rHm+88YanMtgJAQQQQAABBBBAwD8BAqD+WVISAggggAACCCCAQD4I3HfffabUCxcuyLp161yPoK057ZiWHTp0kPvvv9/ks0FRt520i7xd/AyA2jJ5RgABBBBAAAEEEEgNgdTrk5MaLpwFAggggAACCCCAQIoI2ACono52g2/fvn3UmS1evFguXrxoJgjq2LGjaLD0yy+/lF27dsnu3bvl1ltvjdrHjv9Zr149qVmzZtR2TdBu4jt37hQdX3Tr1q1StWpVadSokdx9991SqlQp1320VemGDRvMNs1frVo113wnTpyQH3/8Ufbs2WO6y7dq1UqaNGliXh84cEAOHTpkjqHHym3JyckxgeFt27ZJ/fr15d5775Xq1atH7aLlabl//vmn2aZ1swHlKlWqSI0aNaL2IQEBBBBAAAEEEAiCAAHQIFxF6oAAAggggAACCARYQAOA2qVdA3exJkKyXd0bN25s8toWoMqirUCHDBkSJnT69GnZuHGjSYvV+lODp4899pisXbs2bF9d0fOZOnWq9OjRI2qblt2yZUuTPmLECBk3blxUnrFjx8qECRNEZ4x3Lhow/fbbb2X27Nkyfvx40eDsjh07nFlCr7Wbvc4KP2XKlFCafaETOs2fP190cie7TJs2TUaNGmVX5fz586Hz1HLefffd0DZeIIAAAggggAACQRKgC3yQriZ1QQABBBBAAAEEAiigkwrZGctXr14dGrfTWVUbAO3SpYtJ1haNDRs2NK/tNmd+LUdbiepi93FunzVrlmnpqcFPPX6LFi2kf//+oq1Ry5cvb4KxPXv2lOHDhzt3S+j1k08+aQKRGvzUCZ60bjpuaZ06dUyrz3bt2smCBQviltW6dWsT/NTz07pqy1cb8Fy/fr2plw4NYJfMzExp1qyZ3HTTTSZJ99N1fbi1GLX78YwAAggggAACCBR2AQKghf0Kcv4IIIAAAggggEAaCNgAqHYb37JlS1iNt2/fLvv27TNpXbt2DW2zgc0lS5bIuXPnQun6wo7/Wbx48agu9ceOHZPBgweLtuTUwKDur4HQzz//XBYuXGi6rNsWphMnTjTd8sMKz2Xlhx9+kM8++8zk0KCqnvuiRYvkq6++Ml313377bdOV37ZOzW2mdu2W36dPHzl8+LCZzV6HAdi7d690797dlK9BUD1nuwwYMEA0rXfv3iZJJ5jSdX0MHTrUZuMZAQQQQAABBBAInAAB0MBdUiqEAAIIIIAAAggETyByHFBnDW0Lz3LlyomOo2kXGwA9c+ZMKOBpt9nxP3W8TJ213bmMHj1a/vrrLylatKhMnz5dtEWmc6lUqZIZu/P22283yc8995xrq1TnPva1nRVez3XOnDlRLS81EDls2DCbPddnPa+srCy5+eabQ/kqVKggkyZNkmLF/hvpasWKFaFtvEAAAQQQQAABBNJVgABoul556o0AAggggAACCBQiAZ2k6LbbbjNnrBMhORcbANUu4Nqi0y5t2rSR0qVLm1UdD9Mu2hrUjusZOf7npUuXZPLkySZrt27dzIRCdj/nswYYNfCpyy+//BLVKtWZ177WMTezs7PN6rPPPis33nij3RT2/PLLL4sGSOMtTz31lGgrzsjllltuCU3qZCc8iszDOgIIIIAAAgggkE4CBEDT6WpTVwQQQAABBBBAoBAL2FagzomQtHWnbc1pW3zaKmpw0M4Y79xHg5+2S3xkAFS70tttOq6mdoeP9dAJiuyiM8XHWzZv3hwad9TZUjVyPx3HM97M77qPbYEaub+u63ifujjHADUJ/IMAAggggAACCKShAAHQNLzoVBkBBBBAAAEEECiMAjYAmpOTIwcPHjRVWLp0aShg6Rz/09bPBkU3bdoUymfH/9QWmE2aNLFZzfMff/wRWtfZ22+44YaYDzsOqO6QSABUW4rapVatWval63Pt2rVd052JzgCsM11fa/d9XXIbQ9Rk4B8EEEAAAQQQQCANBAiApsFFpooIIIAAAggggEAQBDp06BAK7Nlu8Lb7e926dcUtaGgDoNr9XIOgutgAqAZUbaDQ+hw4cMC+zNPz/v374+Z35ilVqlSu+SPHJXXLHK8Mt31IQwABBBBAAAEE0lGAAGg6XnXqjAACCCCAAAIIFEIBbY1pW2zaMTx1VnZdbKAzslraTVzHD9VlzZo1omN86rMukd3fNa1atWr6ZJYvvvjCdCHXbuTxHu+8847dLeazM0DrDIa67RBvu9s+pCGAAAIIIIAAAgi4CxAAdXchFQEEEEAAAQQQQCAFBWw3+PXr18uRI0fk999/N2cZKwCqG+221atXi47DefLkSbOPWwBUW5LaZceOHWYyIp2QKN7juuuus7vFfHaWvWfPnpj5dEO87bnuzEYEEEAAAQQQQACBMAECoGEcrCCAAAIIIIAAAgikskCnTp3M6el4mjr+py462ZF2j4+12ECntvy0XefvuOMO0dnSIxcdm7NkyZImec6cOXL58uXILKH1mTNnik6U9OCDD4pzkqVQhogXjRo1Ep3gSJdJkyZFbP3/qnbRt4Hd/6f696pIkSKmMMYH9c+UkhBAAAEEEEAgtQUIgKb29eHsEEAAAQQQQAABBBwCbdq0MQHK06dPh4KImlamTBlHrvCXGjTNyMgQneH9m2++MRttUDQ853+TBz3//PMmOTs7Wz755JPILGb91KlTMnr0aPntt99kwYIFUr9+fdd8zkQd13PYsGEmad26dTJlyhTnZvP6+PHj8tJLL0Wl+5lgA7w6LqrWgwUBBBBAAAEEEAi6AAHQoF9h6ocAAggggAACCARIQIN3rVu3NjWyLUBtF/dY1axYsaK0bNky4X1GjRoVah2qwdCRI0eKBlx1OXfunMydO1ceeOCBUCvNZ555RnRG+UQWLc+OMzpo0CAZOHCgaEtTbdE6efJkM8bphg0bQkXZ1pqhBB9e2FaoWtTrr78uK1askG3btvlQMkUggAACCCCAAAKpKUAANDWvC2eFAAIIIIAAAgggEEPAjgNqN8cLgGo+Zx4dr7Nt27Z296hnbak5ffp0qV69upk0ady4cVK+fHnTylMDnRr81KChLg899JBMnDgxqoxYCdpSVbviN2vWzGSZOnWq9OjRQ5o2bSqDBw+WvXv3igZU7WRPtrVmrPK8pLdr105sYPWtt94yFiNGjPBSFPsggAACCCCAAAKFQoAAaKG4TJwkAggggAACCCCAgBVwBkAzMzPNOJx2W6xnZwBUu8yXLl06VlaTrnm2bNki2kpTg586FqhOimS7jOuM7tpic8aMGaZ7fa6FRWzUsUd1nM/x48eb8UN1vWrVqtKrVy9T3scffxwKUFaoUCFi72tf1eDvRx99ZI5pS9u+fbt9yTMCCCCAAAIIIBA4gSJXBz+/ErhaUSEEEEAAAQQQQAABBHwU2L9/v2zdutWMP1qnTh3Tjb1o0fxrS6Azxu/atUt69uwp3333nY81CS/q8OHDcubMGVOfRGayD9+bNQQQQAABBBBAoHAIFCscp8lZIoAAAggggAACCCCQPAHtDq+Pa1m03UG/fv1EW49269ZNWrVq5VpcTk6O6EOXu+66yzWPX4nagpYFAQQQQAABBBAIugAtQIN+hakfAggggAACCCCAQMoINGjQQDZv3myCqTt37hS3Vpc6Q/3ChQvNOf/000/SoUOHlDl/TgQBBBBAAAEEECiMAvnXb6cwanDOCCCAAAIIIIAAAgjko0DXrl1N6dql/uGHH5asrCw5evSonD9/3nSx11nhbfCzffv2ohMWsSCAAAIIIIAAAghcmwAtQK/Nj70RQAABBBBAAAEEEEhY4MKFC9KpU6fQLPK6o52R3Tk0f/PmzWX27NlhExUlfBAyIoAAAggggAACCIQJ0AI0jIMVBBBAAAEEEEAAAQTyT6B48eKyePFimTp1qmh3eF3XwKc+NBDauHFjef/992XlypUEP/PvMlAyAggggAACCKSZAC1A0+yCU10EEEAAAQQQQACB1BG4fPmyHDx40ARAq1SpIiVKlEidk+NMEEAAAQQQQACBgAgQAA3IhaQaCCCAAAIIIIAAAggggAACCCCAAAIIIBAtQBf4aBNSEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAgAgRAA3IhqQYCCCCAAAIIIIAAAggggAACCCCAAAIIRAsQAI02IQUBBBBAAAEEEEAAAQQQQAABBBBAAAEEAiJAADQgF5JqIIAAAggggAACCCCAAAIIIIAAAggggEC0AAHQaBNSEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAgAgRAA3IhqQYCCCCAAAIIIIAAAggggAACCCCAAAIIRAsQAI02IQUBBBBAAAEEEEAAAQQQQAABBBBAAAEEAiJAADQgF5JqIIAAAggggAACCCCAAAIIIIAAAggggEC0AAHQaBNSEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAgAgRAA3IhqQYCCCCAAAIIIIAAAggggAACCCCAAAIIRAsQAI02IQUBBBBAAAEEEEAAAQQQQAABBBBAAAEEAiJAADQgF5JqIIAAAggggAACCCCAAAIIIIAAAggggEC0wP8AXMARJVPf/WcAAAAASUVORK5CYII=" width="672" /></p>
<p><code>LSA_1</code> has words related to fantasy, while <code>LSA_2</code> has words related to math. We can see that <code>LSA_3</code> and <code>LSA_4</code> are comprised of football- and basketball-related terms and this may give reason to why the documents did not cluster as closely in the first two dimensions. Let’s make the same graph with <code>LSA_3</code> and <code>LSA_4</code>. We should observe a clear separation between these two topics.</p>
<pre class="r"><code>df <- LSA$v %>% as.data.frame() %>% select('LSA_3', 'LSA_4')
df$title <- rownames(df)
ggplot(df, aes(x = LSA_3, y = LSA_4, color = title )) +
geom_text_repel(aes(label = title), color = 'black') +
coord_fixed()</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<h2>
Non-negative Matrix Factorization
</h2>
<p>As expected, <code>basketball</code> is separated from the other sports-related documents along the <code>LSA_4</code> dimension. We can also use <em>Non-negative Matrix Factorization</em>, which decomposes a matrix into linear combination of vectors that are non-negative. This differs from SVD, which maintains orthogonality between columns.</p>
<pre class="r"><code>library(NMF)</code></pre>
<pre><code>## Loading required package: pkgmaker</code></pre>
<pre><code>## Loading required package: registry</code></pre>
<pre><code>## Loading required package: rngtools</code></pre>
<pre><code>## Loading required package: cluster</code></pre>
<pre><code>## NMF - BioConductor layer [OK] | Shared memory capabilities [NO: bigmemory] | Cores 3/4</code></pre>
<pre><code>## To enable shared memory capabilities, try: install.extras('
## NMF
## ')</code></pre>
<pre class="r"><code>nmf_results <- nmf(as.matrix(tf_idf_matrix[top_words,]), 4, seed = 23)
df <- basis(nmf_results) %>% as.data.frame()
colnames(df) <- paste0('Topic_', c(1:dim(df)[2]))
topics <- colnames(df)
df$word <- rownames(df)
df <- df %>% pivot_longer(cols = c(topics))</code></pre>
<pre><code>## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(topics)` instead of `topics` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.</code></pre>
<pre class="r"><code>df %>%
group_by(name) %>%
slice_max(value, n = 5) %>%
ungroup() %>%
ggplot(aes(value, fct_reorder(word, value), fill = name)) +
geom_col(show.legend = FALSE) +
facet_wrap(~name, ncol = 2, scales = "free") +
labs(x = "Weight", y = NULL)</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<p>Here we see a slightly different result. We first dimension <code>Topic_1</code> is composed of terms related to His_Dark_Materials" and A_Song_of_Ice_and_Fire“, but not”The_Lord_of_the_Rings“. On the other hand, <code>Topic_2</code> contains terms related to both”The_Lord_of_the_Rings". <code>Topic_3</code> and <code>Topic_4</code> are related to math and sports respectively. Now we chose the number of topics or factorization rank <em>a priori</em> and it may not have been the most optimal number. Too high a number, we risk overfitting, while too low of a number will provide a bad representation of the data. There are several techniques that we can use to find the optimal value that we won’t dig into here. In conclusion, we were able to clean and process the text data to produce meaningful relationships between the different articles.</p>
<p>See below for additional information:</p>
<p>-<a href="https://www.tidytextmining.com/index.html">https://www.tidytextmining.com/index.html</a></p>
<p>-<a href="https://mccormickml.com/2016/03/25/lsa-for-text-classification-tutorial/">https://mccormickml.com/2016/03/25/lsa-for-text-classification-tutorial/</a></p>
<p>-<a href="https://towardsdatascience.com/topic-modeling-articles-with-nmf-8c6b2a227a45">https://towardsdatascience.com/topic-modeling-articles-with-nmf-8c6b2a227a45</a></p>
<p>-<a href="https://www.kaggle.com/elenageminiani/nmf-and-image-compression">https://www.kaggle.com/elenageminiani/nmf-and-image-compression</a></p>
</div>
<script>
// add bootstrap table styles to pandoc tables
function bootstrapStylePandocTables() {
$('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
}
$(document).ready(function () {
bootstrapStylePandocTables();
});
</script>
<!-- tabsets -->
<script>
$(document).ready(function () {
window.buildTabsets("TOC");
});
$(document).ready(function () {
$('.tabset-dropdown > .nav-tabs > li').click(function () {
$(this).parent().toggleClass('nav-tabs-open');
});
});
</script>
<!-- code folding -->
<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
(function () {
var script = document.createElement("script");
script.type = "text/javascript";
script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
document.getElementsByTagName("head")[0].appendChild(script);
})();
</script>
</body>
</html>Danh TruongEntropy2021-04-06T00:00:00+00:002021-04-06T00:00:00+00:00https://danhdtruong.com/Entropy<h2 id="what-is-entropy">What is entropy?</h2>
<p>Entropy is a core concept in Information Theory. It is defined as the
average level of ‘information’, ‘surprise’, or ‘uncertainty’ inherent in
the possible outcome of a random variable. For example, a coin flip has
two possibilities so we can expect it to have lower entropy than a dice,
which has six possibilities. Below, we define the equation for Entropy,
sometimes called Shannon’s Entropy:</p>
\[H(X) = -\Sigma_{i=1}^{n} P(X_{i}) logP(X_{i})\]
<p>Here is a simple function for calculating entropy. Let’s use it to
compare between a coin and a dice.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">entropy</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">prob</span><span class="p">){</span><span class="w">
</span><span class="n">s</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">-</span><span class="nf">sum</span><span class="p">(</span><span class="n">prob</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">log2</span><span class="p">(</span><span class="n">prob</span><span class="p">))</span><span class="w">
</span><span class="nf">return</span><span class="p">(</span><span class="n">s</span><span class="p">)</span><span class="n">.</span><span class="w">
</span></code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">cat</span><span class="p">(</span><span class="s1">'\n The entropy of a fair coin toss is: '</span><span class="p">,</span><span class="n">entropy</span><span class="p">(</span><span class="nf">rep</span><span class="p">(</span><span class="m">1</span><span class="o">/</span><span class="m">2</span><span class="p">,</span><span class="m">2</span><span class="p">)))</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>##
## The entropy of a fair coin toss is: 1
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">cat</span><span class="p">(</span><span class="s1">'\n The entropy of a fair dice roll is: '</span><span class="p">,</span><span class="n">entropy</span><span class="p">(</span><span class="nf">rep</span><span class="p">(</span><span class="m">1</span><span class="o">/</span><span class="m">6</span><span class="p">,</span><span class="m">6</span><span class="p">)))</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>##
## The entropy of a fair dice roll is: 2.584963
</code></pre></div></div>
<p>As expected, the coin toss has lower entropy because there are fewer
possibilities compared to the dice roll.</p>
<p>Let’s expand our definition of entropy a bit more by conceptualizing its
relationship to information. In Information Theory, we can think of
information as being stored in or transmitted as variables. We can
obtain information from a variable just by observing its value. The
entropy of any variable is the “amount of information” contained within
the variable. However, the amount of information is not determined by
the number of different values. Instead, it is determined in proportion
to the average level of ‘uncertainty’.</p>
<p>For instance, we can take a book and see that a book with more pages
could have more information than a book with less pages. However, if you
have already ready the larger book, then there is no level of
‘uncertainty’ and therefore, no amount of information. Likewise, the
information in a variable is tied to the amount of uncertainy or
surprise. Another way of thinking of it is that lower probability events
have more information, amd higher probability events have less
information.</p>
<h2 id="exploring-entropy-in-mtcars">Exploring entropy in mtcars</h2>
<p>Let’s use the <code class="language-plaintext highlighter-rouge">mtcars</code> data set to test to explore Entropy.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">data</span><span class="p">(</span><span class="s2">"mtcars"</span><span class="p">)</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">mtcars</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
</code></pre></div></div>
<p>We can calulate the entropy of the engine type. Another way of stating
the question is asking how pure is this data set with respect to engine
type? If the data set only have 4 cylinder cars, then it is 100% pure.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">freq</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">table</span><span class="p">(</span><span class="n">mtcars</span><span class="o">$</span><span class="n">cyl</span><span class="p">)</span><span class="o">/</span><span class="nf">length</span><span class="p">(</span><span class="n">mtcars</span><span class="o">$</span><span class="n">cyl</span><span class="p">)</span><span class="w"> </span><span class="c1">#Find the frequency for each type of engine</span><span class="w">
</span><span class="n">cyl_prob</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">as.data.frame</span><span class="p">(</span><span class="n">freq</span><span class="p">)[,</span><span class="m">2</span><span class="p">]</span><span class="w">
</span><span class="n">e0</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">entropy</span><span class="p">(</span><span class="n">cyl_prob</span><span class="p">)</span><span class="w">
</span><span class="n">cat</span><span class="p">(</span><span class="s1">'\n The entropy is:'</span><span class="p">,</span><span class="w"> </span><span class="n">e0</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>##
## The entropy is: 1.530994
</code></pre></div></div>
<p>Since the entropy is non-zero, it is impure and not 100% for any type of
engine. Let’s subset the data and test it with only 4 cylinder cars.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">mtcars_subset</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mtcars</span><span class="p">[</span><span class="n">mtcars</span><span class="o">$</span><span class="n">cyl</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="s1">'4'</span><span class="p">,]</span><span class="w">
</span><span class="n">freq</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">table</span><span class="p">(</span><span class="n">mtcars_subset</span><span class="o">$</span><span class="n">cyl</span><span class="p">)</span><span class="o">/</span><span class="nf">length</span><span class="p">(</span><span class="n">mtcars_subset</span><span class="o">$</span><span class="n">cyl</span><span class="p">)</span><span class="w"> </span><span class="c1">#Find the frequency for each type of engine</span><span class="w">
</span><span class="n">cyl_prob</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">as.data.frame</span><span class="p">(</span><span class="n">freq</span><span class="p">)[,</span><span class="m">2</span><span class="p">]</span><span class="w">
</span><span class="n">cat</span><span class="p">(</span><span class="s1">'\n The entropy is:'</span><span class="p">,</span><span class="w"> </span><span class="n">entropy</span><span class="p">(</span><span class="n">cyl_prob</span><span class="p">))</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>##
## The entropy is: 0
</code></pre></div></div>
<p>The entropy is zero because we know within this subset that there are
only 4 cylinder cars. Suppose we want to know if we can reduce entropy
by segmenting the data. We can use <em>Information Gain</em> to measure how
much “information” a specified feature gives us about the data set.</p>
<h2 id="information-gain">Information gain</h2>
<p>Information gain is defined as the reduction of entropy due to a change
in the dataset. It is defined as follows:</p>
\[IS(X, Y)= H(X) - H(X|Y)\]
\[H(X|Y) =\Sigma_{i=1}^{n} P(Y_{i}) H(X|Y_{i})\]
<p>Let’s take a look at an example. Here we will split the data into four
bins and calculate the <code class="language-plaintext highlighter-rouge">min()</code> and <code class="language-plaintext highlighter-rouge">max()</code> for MPG.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">library</span><span class="p">(</span><span class="n">dplyr</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">data</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mtcars</span><span class="w">
</span><span class="n">data</span><span class="o">$</span><span class="n">bin</span><span class="o"><-</span><span class="n">cut</span><span class="p">(</span><span class="n">data</span><span class="p">[,</span><span class="s1">'mpg'</span><span class="p">],</span><span class="w"> </span><span class="n">breaks</span><span class="o">=</span><span class="m">4</span><span class="p">,</span><span class="w"> </span><span class="n">labels</span><span class="o">=</span><span class="nf">c</span><span class="p">(</span><span class="m">1</span><span class="o">:</span><span class="m">4</span><span class="p">))</span><span class="w">
</span><span class="n">summary_data</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="o">%>%</span><span class="w"> </span><span class="n">group_by</span><span class="p">(</span><span class="n">bin</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w"> </span><span class="n">summarise</span><span class="p">(</span><span class="w">
</span><span class="n">n</span><span class="o">=</span><span class="nf">length</span><span class="p">(</span><span class="n">cyl</span><span class="p">),</span><span class="w">
</span><span class="n">min</span><span class="o">=</span><span class="nf">min</span><span class="p">(</span><span class="n">mpg</span><span class="p">),</span><span class="w">
</span><span class="n">max</span><span class="o">=</span><span class="nf">max</span><span class="p">(</span><span class="n">mpg</span><span class="p">)</span><span class="n">.</span><span class="w">
</span><span class="n">summary_data</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## # A tibble: 4 × 4
## bin n min max
## <fct> <int> <dbl> <dbl>
## 1 1 10 10.4 15.8
## 2 2 13 16.4 21.5
## 3 3 5 22.8 27.3
## 4 4 4 30.4 33.9
</code></pre></div></div>
<p>Next, we will calculate the entropy for engine cylinder after
segmentation and we will also calculate the probability for choosing a
bin.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">cyl_prob</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="o">%>%</span><span class="w"> </span><span class="n">group_by</span><span class="p">(</span><span class="n">bin</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w"> </span><span class="n">count</span><span class="p">(</span><span class="n">cyl</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w"> </span><span class="n">summarise</span><span class="p">(</span><span class="n">e</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">entropy</span><span class="p">(</span><span class="n">n</span><span class="o">/</span><span class="nf">sum</span><span class="p">(</span><span class="n">n</span><span class="p">)))</span><span class="w"> </span><span class="c1">#Find the entropy of cylinder for each bin</span><span class="w">
</span><span class="n">summary_data</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">right_join</span><span class="p">(</span><span class="n">summary_data</span><span class="p">,</span><span class="w"> </span><span class="n">cyl_prob</span><span class="p">,</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="s1">'bin'</span><span class="p">))</span><span class="w">
</span><span class="n">summary_data</span><span class="o">$</span><span class="n">p</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">summary_data</span><span class="o">$</span><span class="n">n</span><span class="o">/</span><span class="n">nrow</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="w"> </span><span class="c1">#calculate probability for choosing a bin</span><span class="w">
</span><span class="n">summary_data</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## # A tibble: 4 × 6
## bin n min max e p
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 1 10 10.4 15.8 0 0.312
## 2 2 13 16.4 21.5 1.42 0.406
## 3 3 5 22.8 27.3 0 0.156
## 4 4 4 30.4 33.9 0 0.125
</code></pre></div></div>
<p>Finally, we find our information gain.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">IG</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">e0</span><span class="o">-</span><span class="nf">sum</span><span class="p">(</span><span class="n">summary_data</span><span class="o">$</span><span class="n">p</span><span class="o">*</span><span class="n">summary_data</span><span class="o">$</span><span class="n">e</span><span class="p">)</span><span class="w"> </span><span class="c1">#find the information gain</span><span class="w">
</span><span class="n">cat</span><span class="p">(</span><span class="s1">'\n The entropy is:'</span><span class="p">,</span><span class="w"> </span><span class="n">IG</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>##
## The entropy is: 0.954299
</code></pre></div></div>
<p>What does this all mean? To start, we were able to reduce entropy after
segmentation. We also learn that some of the bins have an entropy of
zero. This suggests that after segmentation, there is a decrease in
amount of uncertainty. This means that by selecting any one of the bins,
we may have a 100% chance to have cars with single type of engine. Let’s
have a look at the data again.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">data</span><span class="w"> </span><span class="o">%>%</span><span class="w"> </span><span class="n">group_by</span><span class="p">(</span><span class="n">bin</span><span class="p">,</span><span class="w"> </span><span class="n">cyl</span><span class="p">)</span><span class="w"> </span><span class="o">%>%</span><span class="w"> </span><span class="n">summarise</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="nf">length</span><span class="p">(</span><span class="n">cyl</span><span class="p">),</span><span class="w">
</span><span class="n">min</span><span class="o">=</span><span class="nf">min</span><span class="p">(</span><span class="n">mpg</span><span class="p">),</span><span class="w">
</span><span class="n">max</span><span class="o">=</span><span class="nf">max</span><span class="p">(</span><span class="n">mpg</span><span class="p">))</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## `summarise()` has grouped output by 'bin'. You can override using the `.groups` argument.
## # A tibble: 6 × 5
## # Groups: bin [4]
## bin cyl n min max
## <fct> <dbl> <int> <dbl> <dbl>
## 1 1 8 10 10.4 15.8
## 2 2 4 2 21.4 21.5
## 3 2 6 7 17.8 21.4
## 4 2 8 4 16.4 19.2
## 5 3 4 5 22.8 27.3
## 6 4 4 4 30.4 33.9
</code></pre></div></div>
<p>As you can see, bin 1, 3, and 4 only contain cars with a single type of
engine. So this means that we are improving entropy over the complete
data set by segmenting on MPG. In other words, if we were to draw a
random car and knew the MPG, we have a good idea of how many cylinders
the car has. So if given a car that had a MPG of 30.5, than we have a
100% chance of it being a 4 cylinder car.</p>
<h2 id="additional-information">Additional Information</h2>
<ul>
<li>
<p><a href="https://rstudio-pubs-static.s3.amazonaws.com/455435_30729e265f7a4d049400d03a18e218db.html">Entropy, Information Gain, and Data Exploration in
R</a></p>
</li>
<li>
<p><a href="https://medium.com/udacity/shannon-entropy-information-gain-and-picking-balls-from-buckets-5810d35d54b4">Shannon Entropy, Information Gain, and Picking Balls from
Buckets</a></p>
</li>
<li>
<p><a href="https://arxiv.org/pdf/1405.2061.pdf">Understanding Shannon’s Entropy metric for
Information</a></p>
</li>
</ul>Danh TruongWhat is entropy?Gradient Descents2021-04-05T00:00:00+00:002021-04-05T00:00:00+00:00https://danhdtruong.com/Gradient-Descents<h2 id="gradient-descents">Gradient Descents</h2>
<p>In this post, we are going to discuss <em>Gradient Descents</em>; what it is
and how to use it. Gradient Descent is used in many machine learning
algorithms to find the local minimum in differentiable functions where
there is a linear relationship. It goes hand in hand with optimization
problems. In this tutorial, we will focus on linear regressions. A
linear regression is used to model the relationship between one or more
variables. In our case, we have an input $x$ and an output $y$, but we do not know the slope $m$ or the intercept $b$.</p>
\[y = mx + b\]
<p>To build our model, we need to tune two parameters, namely $m$ and $b$
. To do so, we need to use the mean squared error (MSE) as our loss
function. This function measures the average squared difference between
the estimated values and the actual value. We expect this to be 0 if our
predicted values equal our actual value.</p>
\[MSE =\frac{1}{N}\Sigma_{i=1}^{n}(Y_{i}-\hat Y_{i})^{2}\]
<p>Therefore, we will need optimize the MSE for a linear regression using
the gradient descent. A gradient is defined as follows:</p>
\[\nabla J(\theta) = [\frac{\partial J}{\partial\theta_{1}},\frac{\partial J}{\partial\theta_{2}},...,\frac{\partial J}{\partial\theta_{p}}]\]
<p>The gradient descent is defined in the following equation. The algorithm
will iterate over and over using the gradient descent to find new values
of $m$ and $b$
. This will continue until MSE converges to zero, letting us know that
our predicted values equal to our actual values.</p>
\[\theta_{n + 1} =\theta_{n} -\eta\nabla J(\theta_{n})\]
<p>Let’s first solve for $\nabla J(\theta_{n})$ by plugging in the MSE equation and finding the partial derivatives with
respect to $m$ and $b$.</p>
\[MSE =\frac{1}{N}\Sigma_{i=1}^{n}(y_{i}-(mx_{i}+b))^{2}\]
\[f(m,b) =\frac{1}{N}\Sigma_{i=1}^{n}(y_{i}-(mx_{i}+b))^{2}\]
\[\frac{\partial f(m,b)}{\partial m} =\frac{1}{N}\Sigma_{i=1}^{n}-2(y_{i}-(mx_{i}+b))\frac{\partial f}{\partial m}(mx_{i}+b))\]
\[\frac{\partial f(m,b)}{\partial m} =\frac{1}{N}\Sigma_{i=1}^{n}-2x_{i}(y_{i}-(mx_{i}+b))\]
\[\frac{\partial f(m,b)}{\partial b} =\frac{1}{N}\Sigma_{i=1}^{n}-2(y_{i}-(mx_{i}+b))\frac{\partial f}{\partial b}(mx_{i}+b))\]
\[\frac{\partial f(m,b)}{\partial b} =\frac{1}{N}\Sigma_{i=1}^{n}-2(y_{i}-(mx_{i}+b))\]
<p>Now we have our two final equations. We can begin implementing them.</p>
\[\hat m = m -\eta(\frac{1}{N}\Sigma_{i=1}^{n}-2x_{i}(y_{i}-(mx_{i}+b)))\]
\[\hat b = b -\eta(\frac{1}{N}\Sigma_{i=1}^{n}-2(y_{i}-(mx_{i}+b)))\]
<p>We will input two vectors $x$ and $y$ and solve for $m$ and $b$. In addition, we set a learning rate, $\eta$ of 0.001. This controls the magnitude of the gradient. If it is too
large, we will miss the minima, but if it is too small, the number of
iterations will increase. For this function, we will iterate 10,000
times and break the loop if $MSE\le\epsilon$, $\epsilon = 0.001$.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">gradient_descent</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="w"> </span><span class="n">epsilon</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">0.001</span><span class="p">,</span><span class="w"> </span><span class="n">lr</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">0.001</span><span class="p">,</span><span class="w"> </span><span class="n">steps</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">10000</span><span class="p">){</span><span class="w">
</span><span class="n">epsilon</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">0.0001</span><span class="w">
</span><span class="n">m</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">runif</span><span class="p">(</span><span class="m">1</span><span class="p">)</span><span class="w"> </span><span class="c1">#Initialize a random number</span><span class="w">
</span><span class="n">b</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">runif</span><span class="p">(</span><span class="m">1</span><span class="p">)</span><span class="w"> </span><span class="c1">#Initialize a random number</span><span class="w">
</span><span class="n">N</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">length</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="w">
</span><span class="n">log</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">c</span><span class="p">()</span><span class="w">
</span><span class="n">mseloss</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">c</span><span class="p">()</span><span class="w">
</span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">step</span><span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="n">steps</span><span class="p">){</span><span class="w">
</span><span class="n">f</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="p">(</span><span class="n">m</span><span class="o">*</span><span class="n">x</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">b</span><span class="p">)</span><span class="w"> </span><span class="c1">#Subtracting the estimated value from the actual value</span><span class="w">
</span><span class="n">mseloss</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="n">mseloss</span><span class="p">,</span><span class="w"> </span><span class="nf">sum</span><span class="p">(</span><span class="n">f</span><span class="w"> </span><span class="o">**</span><span class="w"> </span><span class="m">2</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">N</span><span class="p">)</span><span class="w">
</span><span class="n">dm</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="m">-2</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">N</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="nf">sum</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">%*%</span><span class="w"> </span><span class="n">f</span><span class="p">)</span><span class="w">
</span><span class="n">db</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="m">-2</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">N</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="nf">sum</span><span class="p">(</span><span class="n">f</span><span class="p">)</span><span class="w">
</span><span class="n">m</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">m</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">lr</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">dm</span><span class="w">
</span><span class="n">b</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">b</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">lr</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">db</span><span class="w">
</span><span class="n">log</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">rbind</span><span class="p">(</span><span class="n">log</span><span class="p">,</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="n">m</span><span class="p">,</span><span class="n">b</span><span class="p">))</span><span class="w">
</span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="nf">sum</span><span class="p">(</span><span class="n">f</span><span class="w"> </span><span class="o">**</span><span class="w"> </span><span class="m">2</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">N</span><span class="w"> </span><span class="o"><=</span><span class="w"> </span><span class="n">epsilon</span><span class="p">){</span><span class="w"> </span><span class="c1">#stop the algorithm once we have converged to zero</span><span class="w">
</span><span class="k">break</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="n">list</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">list</span><span class="p">(</span><span class="s1">'m'</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">m</span><span class="p">,</span><span class="w"> </span><span class="s1">'b'</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">b</span><span class="p">,</span><span class="w"> </span><span class="s1">'steps'</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">step</span><span class="p">,</span><span class="w"> </span><span class="s1">'log'</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">log</span><span class="p">,</span><span class="w"> </span><span class="s1">'mseloss'</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mseloss</span><span class="p">)</span><span class="w">
</span><span class="nf">return</span><span class="p">(</span><span class="n">list</span><span class="p">)</span><span class="n">.</span><span class="w">
</span></code></pre></div></div>
<p>We can initialize a simple example by setting <code class="language-plaintext highlighter-rouge">x = c(1,2,3,4,5)</code> and
<code class="language-plaintext highlighter-rouge">y = 2 * x</code>. We expect <code class="language-plaintext highlighter-rouge">m = 2</code> and <code class="language-plaintext highlighter-rouge">b = 0</code>.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="m">1</span><span class="p">,</span><span class="m">2</span><span class="p">,</span><span class="m">3</span><span class="p">,</span><span class="m">4</span><span class="p">,</span><span class="m">5</span><span class="p">)</span><span class="w">
</span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">2</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="w">
</span><span class="n">ptm</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">proc.time</span><span class="p">()</span><span class="w">
</span><span class="n">res</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">gradient_descent</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span><span class="w">
</span><span class="nf">proc.time</span><span class="p">()</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">ptm</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## user system elapsed
## 0.723 0.454 1.185
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">res</span><span class="o">$</span><span class="n">m</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 1.992251
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">res</span><span class="o">$</span><span class="n">b</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 0.02797554
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">cat</span><span class="p">(</span><span class="s1">'The predicted values are:'</span><span class="p">,</span><span class="w"> </span><span class="n">head</span><span class="p">(</span><span class="n">res</span><span class="o">$</span><span class="n">m</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">res</span><span class="o">$</span><span class="n">b</span><span class="p">))</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## The predicted values are: 2.020227 4.012478 6.004729 7.99698 9.989232
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">cat</span><span class="p">(</span><span class="s1">'\nThe actual values are:'</span><span class="p">,</span><span class="w"> </span><span class="n">head</span><span class="p">(</span><span class="n">y</span><span class="p">))</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>##
## The actual values are: 2 4 6 8 10
</code></pre></div></div>
<p>We can observe convergence by plotting the MSE against the number of
iterations.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">library</span><span class="p">(</span><span class="n">ggplot2</span><span class="p">)</span><span class="w">
</span><span class="n">df</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">data.frame</span><span class="p">(</span><span class="s1">'iters'</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="n">res</span><span class="o">$</span><span class="n">steps</span><span class="p">,</span><span class="w"> </span><span class="s1">'mseloss'</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">res</span><span class="o">$</span><span class="n">mseloss</span><span class="p">)</span><span class="w">
</span><span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">iters</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mseloss</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">geom_point</span><span class="p">()</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">theme_minimal</span><span class="p">()</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">ylab</span><span class="p">(</span><span class="s1">'MSE Loss'</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/gradient_descent_files/unnamed-chunk-3-1.png" alt="" /></p>
<p>It appears to converge early but continues on to meet our requirement of $MSE\le\epsilon$
. Let’s repeat this with a larger data set. We would expect this to take
longer.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">runif</span><span class="p">(</span><span class="m">10000</span><span class="p">)</span><span class="w">
</span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">2</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="w">
</span><span class="n">ptm</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">proc.time</span><span class="p">()</span><span class="w">
</span><span class="n">res</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">gradient_descent</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span><span class="w">
</span><span class="nf">proc.time</span><span class="p">()</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">ptm</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## user system elapsed
## 2.542 1.425 4.022
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">res</span><span class="o">$</span><span class="n">m</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 1.577939
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">res</span><span class="o">$</span><span class="n">b</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 0.2254082
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">cat</span><span class="p">(</span><span class="s1">'The predicted values are:'</span><span class="p">,</span><span class="w"> </span><span class="n">head</span><span class="p">(</span><span class="n">res</span><span class="o">$</span><span class="n">m</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">res</span><span class="o">$</span><span class="n">b</span><span class="p">))</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## The predicted values are: 0.8785615 0.6359393 1.200908 0.628451 1.231726 0.5944415
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">cat</span><span class="p">(</span><span class="s1">'\nThe actual values are:'</span><span class="p">,</span><span class="w"> </span><span class="n">head</span><span class="p">(</span><span class="n">y</span><span class="p">))</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>##
## The actual values are: 0.8278561 0.5203383 1.236423 0.5108471 1.275484 0.4677408
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">df</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">data.frame</span><span class="p">(</span><span class="s1">'iters'</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="n">res</span><span class="o">$</span><span class="n">steps</span><span class="p">,</span><span class="w"> </span><span class="s1">'mseloss'</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">res</span><span class="o">$</span><span class="n">mseloss</span><span class="p">)</span><span class="w">
</span><span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">iters</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mseloss</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">geom_point</span><span class="p">()</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">theme_minimal</span><span class="p">()</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">ylab</span><span class="p">(</span><span class="s1">'MSE Loss'</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/gradient_descent_files/unnamed-chunk-5-1.png" alt="" /></p>
<p>As you can see, the predicted values don’t match the actual values. In
addition, the MSE loss does not converge. This suggests we may need more
iterations. Let’s try with 50,000 iterations.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">ptm</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">proc.time</span><span class="p">()</span><span class="w">
</span><span class="n">res</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">gradient_descent</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="w"> </span><span class="n">steps</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">50000</span><span class="p">)</span><span class="w">
</span><span class="nf">proc.time</span><span class="p">()</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">ptm</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## user system elapsed
## 9.538 5.721 15.327
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">res</span><span class="o">$</span><span class="n">m</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 1.965497
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">res</span><span class="o">$</span><span class="n">b</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 0.01842688
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">cat</span><span class="p">(</span><span class="s1">'The predicted values are:'</span><span class="p">,</span><span class="w"> </span><span class="n">head</span><span class="p">(</span><span class="n">res</span><span class="o">$</span><span class="n">m</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">res</span><span class="o">$</span><span class="n">b</span><span class="p">))</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## The predicted values are: 0.8320013 0.5297885 1.23352 0.5204611 1.271906 0.4780985
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">cat</span><span class="p">(</span><span class="s1">'\nThe actual values are:'</span><span class="p">,</span><span class="w"> </span><span class="n">head</span><span class="p">(</span><span class="n">y</span><span class="p">))</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>##
## The actual values are: 0.8278561 0.5203383 1.236423 0.5108471 1.275484 0.4677408
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">df</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">data.frame</span><span class="p">(</span><span class="s1">'iters'</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="n">res</span><span class="o">$</span><span class="n">steps</span><span class="p">,</span><span class="w"> </span><span class="s1">'mseloss'</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">res</span><span class="o">$</span><span class="n">mseloss</span><span class="p">)</span><span class="w">
</span><span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">iters</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mseloss</span><span class="p">))</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">geom_point</span><span class="p">()</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">theme_minimal</span><span class="p">()</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">ylab</span><span class="p">(</span><span class="s1">'MSE Loss'</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<p><img src="/images/gradient_descent_files/unnamed-chunk-7-1.png" alt="" /></p>
<h2 id="comparing-to-stochastic-gradient-descent">Comparing to Stochastic Gradient Descent</h2>
<p>Now it looks like we have converged and reached an optimal value.
However, the time it took was much longer. To wrap up, we were able to
determine a suitable $m$ and $b$
to satisfy our optimization problem. However, this algorithm can be slow
if we are working with large matrices. In that case, we can use
<em>Stochastic Gradient Descent</em> and use only a subset of the matrix with
one sample at a time for the optimization.</p>
<p>Let’s see what this would look like.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">s_gradient_descent</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="w"> </span><span class="n">epsilon</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">0.001</span><span class="p">,</span><span class="w"> </span><span class="n">lr</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">0.001</span><span class="p">,</span><span class="w"> </span><span class="n">steps</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">10000</span><span class="p">,</span><span class="w"> </span><span class="n">batch_size</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">1</span><span class="p">){</span><span class="w">
</span><span class="n">epsilon</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">0.0001</span><span class="w">
</span><span class="n">m</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">runif</span><span class="p">(</span><span class="m">1</span><span class="p">)</span><span class="w"> </span><span class="c1">#Initialize a random number</span><span class="w">
</span><span class="n">b</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">runif</span><span class="p">(</span><span class="m">1</span><span class="p">)</span><span class="w"> </span><span class="c1">#Initialize a random number</span><span class="w">
</span><span class="n">log</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">c</span><span class="p">()</span><span class="w">
</span><span class="n">mseloss</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">c</span><span class="p">()</span><span class="w">
</span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">step</span><span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="n">steps</span><span class="p">){</span><span class="w">
</span><span class="n">index</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">sample.int</span><span class="p">(</span><span class="nf">length</span><span class="p">(</span><span class="n">y</span><span class="p">),</span><span class="w"> </span><span class="n">batch_size</span><span class="p">)</span><span class="w">
</span><span class="n">N</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">length</span><span class="p">(</span><span class="n">index</span><span class="p">)</span><span class="w">
</span><span class="n">Xs</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">x</span><span class="p">[</span><span class="n">index</span><span class="p">]</span><span class="w">
</span><span class="n">Ys</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">y</span><span class="p">[</span><span class="n">index</span><span class="p">]</span><span class="w">
</span><span class="n">f</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Ys</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="p">(</span><span class="n">m</span><span class="o">*</span><span class="n">Xs</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">b</span><span class="p">)</span><span class="w"> </span><span class="c1">#Subtracting the estimated value from the actual value</span><span class="w">
</span><span class="n">mseloss</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="n">mseloss</span><span class="p">,</span><span class="w"> </span><span class="nf">sum</span><span class="p">(</span><span class="n">f</span><span class="w"> </span><span class="o">**</span><span class="w"> </span><span class="m">2</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">N</span><span class="p">)</span><span class="w">
</span><span class="n">dm</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="m">-2</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">N</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="nf">sum</span><span class="p">(</span><span class="n">Xs</span><span class="w"> </span><span class="o">%*%</span><span class="w"> </span><span class="n">f</span><span class="p">)</span><span class="w">
</span><span class="n">db</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="m">-2</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">N</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="nf">sum</span><span class="p">(</span><span class="n">f</span><span class="p">)</span><span class="w">
</span><span class="n">m</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">m</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">lr</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">dm</span><span class="w">
</span><span class="n">b</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">b</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">lr</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">db</span><span class="w">
</span><span class="n">log</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">rbind</span><span class="p">(</span><span class="n">log</span><span class="p">,</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="n">m</span><span class="p">,</span><span class="n">b</span><span class="p">))</span><span class="w">
</span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="nf">sum</span><span class="p">(</span><span class="n">f</span><span class="w"> </span><span class="o">**</span><span class="w"> </span><span class="m">2</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">N</span><span class="w"> </span><span class="o"><=</span><span class="w"> </span><span class="n">epsilon</span><span class="p">){</span><span class="w"> </span><span class="c1">#stop the algorithm once we have converged to zero</span><span class="w">
</span><span class="k">break</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="n">list</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">list</span><span class="p">(</span><span class="s1">'m'</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">m</span><span class="p">,</span><span class="w"> </span><span class="s1">'b'</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">b</span><span class="p">,</span><span class="w"> </span><span class="s1">'steps'</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">step</span><span class="p">,</span><span class="w"> </span><span class="s1">'log'</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">log</span><span class="p">,</span><span class="w"> </span><span class="s1">'mseloss'</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mseloss</span><span class="p">)</span><span class="w">
</span><span class="nf">return</span><span class="p">(</span><span class="n">list</span><span class="p">)</span><span class="n">.</span><span class="w">
</span></code></pre></div></div>
<p>With the above function, we will only be using a subset of our data for
optimization. In this case, I will set it to 100. Feel free to play
around with the parameters.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">ptm</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">proc.time</span><span class="p">()</span><span class="w">
</span><span class="n">sgd_res</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">s_gradient_descent</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="w"> </span><span class="n">steps</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">50000</span><span class="p">,</span><span class="w"> </span><span class="n">batch</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">100</span><span class="p">)</span><span class="w">
</span><span class="nf">proc.time</span><span class="p">()</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">ptm</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## user system elapsed
## 4.532 2.952 7.543
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">sgd_res</span><span class="o">$</span><span class="n">m</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 1.961864
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">sgd_res</span><span class="o">$</span><span class="n">b</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [1] 0.02037149
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">cat</span><span class="p">(</span><span class="s1">'The predicted values are:'</span><span class="p">,</span><span class="w"> </span><span class="n">head</span><span class="p">(</span><span class="n">sgd_res</span><span class="o">$</span><span class="n">m</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">x</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">sgd_res</span><span class="o">$</span><span class="n">b</span><span class="p">))</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## The predicted values are: 0.8324419 0.5307878 1.233218 0.5214777 1.271534 0.4791934
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">cat</span><span class="p">(</span><span class="s1">'\nThe actual values are:'</span><span class="p">,</span><span class="w"> </span><span class="n">head</span><span class="p">(</span><span class="n">y</span><span class="p">))</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>##
## The actual values are: 0.8278561 0.5203383 1.236423 0.5108471 1.275484 0.4677408
</code></pre></div></div>
<p>As you can see, we nearly cut out run time in half by just using
stochastic gradient descent.</p>
<h2 id="additional-resources">Additional Resources</h2>
<ul>
<li><a href="https://towardsdatascience.com/gradient-descent-from-scratch-e8b75fa986cc" target="\_blank">https://towardsdatascience.com/gradient-descent-from-scratch-e8b75fa986cc</a></li>
<li><a href="https://www.ocf.berkeley.edu/~janastas/stochastic-gradient-descent-in-r.html" target="\_blank">https://www.ocf.berkeley.edu/~janastas/stochastic-gradient-descent-in-r.html</a></li>
<li><a href="https://www.r-bloggers.com/2017/02/implementing-the-gradient-descent-algorithm-in-r/" target="\_blank">https://www.r-bloggers.com/2017/02/implementing-the-gradient-descent-algorithm-in-r/</a></li>
</ul>Danh TruongGradient DescentsNon-negative Matrix Factorization2021-04-03T00:00:00+00:002021-04-03T00:00:00+00:00https://danhdtruong.com/Non-negative-Matrix-Factorization<h2 id="non-negative-matrix-factorization">Non-negative Matrix Factorization</h2>
<p>In this post, I will be discussing Non-negative Matrix Factorization
(NMF). NMF is a low-rank approximation algorithm that discovers latent
features in your data. It is similar to PCA in the sense that they both
reduce high-dimensional data into lower dimensions for better
understanding of the data. The major difference is that PCA projects
data onto a subspace that maximizes variability for the discovered
features, while NMF discovers non-negative features that are additive in
nature.</p>
<p>NMF is formally defined as:</p>
\[V\approx{WH}\]
<p>where $V$ is a non-negative matrix and both $W$ and $H$ are unique and non-negative matrices. In other words, the matrix $V$ is factorized into two matrices $W$ and $H$, where $W$ is the features matrix or the basis and $H$ is the coefficient matrix. Typically, this means that $H$ represents a coordinate system that uses $W$ to reconstruct $V$. We can consider that $V$ is a linear combination of column vectors of $W$ using the coordinate system in $H$, $v_{i} = Wh_{i}$.</p>
<h2 id="solving-for-nmf">Solving for NMF</h2>
<p>Here, I will describe two algorithms to solve for NMF using iterative
updates of $W$ and $H$. First, we will consider the cost function. A cost function is a
function that quantifies or measures the error between the predicted
values and the expected values. The Mean Squared Error (MSE), or L2 loss
is one of the most popular cost functions in linear regressions. Given
an linear equation $y = mx + b$
, MSE is:</p>
\[MSE =\frac{1}{N}\Sigma_{i=1}^{n}(y_{i}-(mx_{i}+b))^{2}\]
<p>For the cost function in NMF, we use a similar function called the
Frobenius Norm. It is defined as:</p>
\[||A||_{F} =\sqrt{\Sigma_{i=1}^{m}\Sigma_{j=1}^{n} |a_{ij}|^{2}}\]
<p>In the case of NMF, we are using the square of the Forbenius norm to
measure how good of an approximation $WH$ is for $V$.</p>
\[||V - WH||_{F}^{2} =\Sigma_{i,j}(V - WH)^{2}_{ij}\]
<h2 id="optimization">Optimization</h2>
<p>We can see that as $WH$ approaches $V$
, then the equation will slowly converge to zero. Therefore, the
optimization can be defined as the following:</p>
<p>Minimize $||V - WH||_{F}^{2}$ with respect to $W$ and $H$, subject to the constraints $W,H\ge 0$
In the paper by Lee & Seung, they introduced the multiplicative update
rule to solve for NMF. Please see their original paper for details on
the proof. Essentially the update causes the function value to be
non-increasing to converge to zero.</p>
\[H_{ik}\leftarrow H_{ik}\frac{(W^{T}V)_{ik}}{(W^{T}WH)_{ik}}\]
\[W_{kj}\leftarrow W_{kj}\frac{(VH^{T})_{kj}}{(WHH^{T})_{kj}}\]
<h2 id="multiplicative-update-rule">Multiplicative Update Rule</h2>
<p>Here we will implement NMF using the multiplicative update rule. To get
started, make sure you have installed both
<a href="https://www.r-project.org/">R</a> and <a href="https://rstudio.com/">RStudio</a>. In
addition, we will also be using the package
<a href="https://cran.r-project.org/web/packages/NMF/index.html">NMF</a> to
benchmark our work.</p>
<p>Here I write a function to solve for NMF using the multiplicative update
rule. I added a <code class="language-plaintext highlighter-rouge">delta</code> variable to the denominator update rule to
prevent division by zero. <code class="language-plaintext highlighter-rouge">K</code> specifies the column length of $W$ and the row length of $H$. <code class="language-plaintext highlighter-rouge">K</code> can also be considered as the number of hidden features we are
discovering in $V$. <code class="language-plaintext highlighter-rouge">K</code> is less than $n$ in a $n\times m$ matrix. After iterating for <code class="language-plaintext highlighter-rouge">x</code> number of <code class="language-plaintext highlighter-rouge">steps</code>, the function returns
a <code class="language-plaintext highlighter-rouge">list</code> containing <code class="language-plaintext highlighter-rouge">W</code> and <code class="language-plaintext highlighter-rouge">H</code> for $W$ and $H$
respectively.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">nmf_mu</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">R</span><span class="p">,</span><span class="w"> </span><span class="n">K</span><span class="p">,</span><span class="w"> </span><span class="n">delta</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">0.001</span><span class="p">,</span><span class="w"> </span><span class="n">steps</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">5000</span><span class="p">){</span><span class="w">
</span><span class="n">N</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">dim</span><span class="p">(</span><span class="n">R</span><span class="p">)[</span><span class="m">1</span><span class="p">]</span><span class="w">
</span><span class="n">M</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">dim</span><span class="p">(</span><span class="n">R</span><span class="p">)[</span><span class="m">2</span><span class="p">]</span><span class="w">
</span><span class="n">K</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">N</span><span class="w">
</span><span class="n">W</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">rmatrix</span><span class="p">(</span><span class="n">N</span><span class="p">,</span><span class="n">K</span><span class="p">)</span><span class="w"> </span><span class="c1">#Random initialization of W</span><span class="w">
</span><span class="n">H</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">rmatrix</span><span class="p">(</span><span class="n">K</span><span class="p">,</span><span class="n">M</span><span class="p">)</span><span class="w"> </span><span class="c1">#Random initialization of H</span><span class="w">
</span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">step</span><span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="n">steps</span><span class="p">){</span><span class="w">
</span><span class="n">W_TA</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">t</span><span class="p">(</span><span class="n">W</span><span class="p">)</span><span class="w"> </span><span class="o">%*%</span><span class="w"> </span><span class="n">R</span><span class="w">
</span><span class="n">W_TWH</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">t</span><span class="p">(</span><span class="n">W</span><span class="p">)</span><span class="w"> </span><span class="o">%*%</span><span class="w"> </span><span class="n">W</span><span class="w"> </span><span class="o">%*%</span><span class="w"> </span><span class="n">H</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">delta</span><span class="w">
</span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">i</span><span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="nf">dim</span><span class="p">(</span><span class="n">H</span><span class="p">)[</span><span class="m">1</span><span class="p">]){</span><span class="w">
</span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">j</span><span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="nf">dim</span><span class="p">(</span><span class="n">H</span><span class="p">)[</span><span class="m">2</span><span class="p">]){</span><span class="w">
</span><span class="n">H</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">j</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">H</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">j</span><span class="p">]</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">W_TA</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">j</span><span class="p">]</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">W_TWH</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">j</span><span class="p">]</span><span class="w"> </span><span class="c1">#Updating H</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="n">RH_T</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">R</span><span class="w"> </span><span class="o">%*%</span><span class="w"> </span><span class="n">t</span><span class="p">(</span><span class="n">H</span><span class="p">)</span><span class="w">
</span><span class="n">WHH_T</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">W</span><span class="w"> </span><span class="o">%*%</span><span class="w"> </span><span class="n">H</span><span class="w"> </span><span class="o">%*%</span><span class="w"> </span><span class="n">t</span><span class="p">(</span><span class="n">H</span><span class="p">)</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">delta</span><span class="w">
</span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">i</span><span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="nf">dim</span><span class="p">(</span><span class="n">W</span><span class="p">)[</span><span class="m">1</span><span class="p">]){</span><span class="w">
</span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">j</span><span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="nf">dim</span><span class="p">(</span><span class="n">W</span><span class="p">)[</span><span class="m">2</span><span class="p">]){</span><span class="w">
</span><span class="n">W</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">j</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">W</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">j</span><span class="p">]</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">RH_T</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">j</span><span class="p">]</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">WHH_T</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">j</span><span class="p">]</span><span class="w"> </span><span class="c1">#Updating W</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="n">list</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">list</span><span class="p">(</span><span class="s1">'W'</span><span class="o">=</span><span class="n">W</span><span class="p">,</span><span class="w"> </span><span class="s1">'H'</span><span class="o">=</span><span class="n">H</span><span class="p">)</span><span class="w">
</span><span class="nf">return</span><span class="p">(</span><span class="n">list</span><span class="p">)</span><span class="n">.</span><span class="w">
</span></code></pre></div></div>
<p>Let’s initialize a random $n\times m$
matrix and test our function.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">require</span><span class="p">(</span><span class="n">NMF</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## Loading required package: NMF
## Loading required package: pkgmaker
## Loading required package: registry
## Loading required package: rngtools
## Loading required package: cluster
## NMF - BioConductor layer [OK] | Shared memory capabilities [NO: bigmemory] | Cores 11/12
## To enable shared memory capabilities, try: install.extras('
## NMF
## ')
##
## Attaching package: 'NMF'
## The following object is masked from 'package:rmarkdown':
##
## run
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">R</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">rmatrix</span><span class="p">(</span><span class="m">5</span><span class="p">,</span><span class="m">6</span><span class="p">)</span><span class="n">.</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.2572934 0.7850774 0.1964726 0.89460898 0.5124385 0.6538741
## [2,] 0.4548971 0.7619701 0.4504754 0.04016496 0.1973834 0.9347442
## [3,] 0.8251736 0.3744436 0.5294228 0.79247087 0.5080712 0.3198328
## [4,] 0.9600582 0.5075759 0.1684872 0.95316732 0.4352644 0.5006196
## [5,] 0.3160396 0.5518905 0.2720268 0.01742472 0.1015410 0.3987225
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">nmf_mu_results</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">nmf_mu</span><span class="p">(</span><span class="n">R</span><span class="p">)</span><span class="w">
</span><span class="n">cat</span><span class="p">(</span><span class="s1">'\fMatrix W is:\n'</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## Matrix W is:
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">print</span><span class="p">(</span><span class="n">nmf_mu_results</span><span class="o">$</span><span class="n">W</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.248983e-07 0.378515344 1.318960e-01 2.780618e-02 9.292941e-01
## [2,] 2.947557e-01 0.927749513 1.211089e-01 5.765705e-01 6.761024e-07
## [3,] 5.287462e-01 0.004320335 6.874418e-01 1.083155e-11 7.077161e-01
## [4,] 5.904350e-01 0.009073315 9.627698e-06 9.538376e-02 9.592665e-01
## [5,] 2.025896e-01 0.668434477 2.968120e-28 1.535730e-13 1.154320e-03
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">cat</span><span class="p">(</span><span class="s1">'Matrix H is:\n'</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## Matrix H is:
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">print</span><span class="p">(</span><span class="n">nmf_mu_results</span><span class="o">$</span><span class="n">H</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.237671420 1.418457e-02 2.791433e-01 7.841158e-02 5.989354e-08
## [2,] 0.096085379 8.172298e-01 3.225888e-01 5.855245e-09 1.527530e-01
## [3,] 0.002640661 2.122093e-04 5.518361e-01 1.179900e-01 2.745037e-01
## [4,] 0.000131951 1.638545e-06 2.627534e-03 1.515055e-03 3.591244e-02
## [5,] 0.237396251 5.120855e-01 1.347844e-08 9.453276e-01 4.488068e-01
## [,6]
## [1,] 1.323084e-02
## [2,] 5.933110e-01
## [3,] 3.825011e-08
## [4,] 6.572912e-01
## [5,] 4.411818e-01
</code></pre></div></div>
<p>Let’s see if we can reconstruct our original matrix and compare it to
the <code class="language-plaintext highlighter-rouge">nmf</code> function.</p>
<pre><code class="language-r.">
</code></pre>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.2572934 0.7850774 0.1964726 0.89460898 0.5124385 0.6538741
## [2,] 0.4548971 0.7619701 0.4504754 0.04016496 0.1973834 0.9347442
## [3,] 0.8251736 0.3744436 0.5294228 0.79247087 0.5080712 0.3198328
## [4,] 0.9600582 0.5075759 0.1684872 0.95316732 0.4352644 0.5006196
## [5,] 0.3160396 0.5518905 0.2720268 0.01742472 0.1015410 0.3987225
</code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">nmf_mu_results</span><span class="o">$</span><span class="n">W</span><span class="w"> </span><span class="o">%*%</span><span class="w"> </span><span class="n">nmf_mu_results</span><span class="o">$</span><span class="n">H</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.2573328 0.7852400 0.1949629 0.89409185 0.5120974 0.6528417
## [2,] 0.4543499 0.7623925 0.4499079 0.03827608 0.1956677 0.9333189
## [3,] 0.8246536 0.3735878 0.5283449 0.79159463 0.5069931 0.3217905
## [4,] 0.9593752 0.5070167 0.1679989 0.95326369 0.4353395 0.4991011
## [5,] 0.3152401 0.5497293 0.2721810 0.01697658 0.1026234 0.3997792
</code></pre></div></div>
<h2 id="comparing-to-nmf">Comparing to nmf()</h2>
<p>We get the same results using the <code class="language-plaintext highlighter-rouge">nmf</code> function with the <code class="language-plaintext highlighter-rouge">lee</code> method.</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">nmf</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">nmf</span><span class="p">(</span><span class="n">R</span><span class="p">,</span><span class="w"> </span><span class="nf">dim</span><span class="p">(</span><span class="n">R</span><span class="p">)[</span><span class="m">1</span><span class="p">],</span><span class="w"> </span><span class="n">method</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s1">'lee'</span><span class="p">)</span><span class="w">
</span><span class="n">basis</span><span class="p">(</span><span class="n">nmf</span><span class="p">)</span><span class="w"> </span><span class="o">%*%</span><span class="w"> </span><span class="n">coefficients</span><span class="p">(</span><span class="n">nmf</span><span class="p">)</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.2571071 0.7851170 0.1966876 0.89544068 0.5106587 0.6540914
## [2,] 0.4550240 0.7620104 0.4504028 0.03931272 0.1972511 0.9347751
## [3,] 0.8253166 0.3744389 0.5294810 0.79280535 0.5072254 0.3198603
## [4,] 0.9601864 0.5075281 0.1682418 0.95196013 0.4378507 0.5005094
## [5,] 0.3152430 0.5518350 0.2720400 0.02431452 0.1037512 0.3984364
</code></pre></div></div>
<h2 id="stochastic-gradient-descent-method">Stochastic Gradient Descent Method</h2>
<p>Now we will take a look at another method of implementing NMF. This one
is called Stochastic Gradient Descent (SGD). A gradient descent is a
first-order iterative optimization algorithm to finding a local minimum
for a function that is differentiable. In fact, we used the Block
Coordinate Descent in the multiplicative update rule. In SGD, we take
the derivative of the cost function like before. However, we will now be
focusing on taking the derivative of each variable, setting them to zero
or lower, solving for the feature variables, and finally updating each
feature. We also add a regularization term in the cost function to
control for over fitting.</p>
\[||V - WH||_{F}^{2} =\Sigma_{i,j}(V - WH)^{2}_{ij}\]
\[e_{ij}^{2} =\Sigma_{i,j}(v_{ij}-\hat v_{ij})^{2} = (v_{ij} -\Sigma_{k=1}^{K} w_{ik}h_{kj})^{2}\]
<p>\(e_{ij}^{2} = (v_{ij} -\Sigma_{k=1}^{K}w_{ik}h_{kj})^{2} +\lambda\Sigma_{k=1}^{K}(||W||^{2} + ||H||^{2})\)
$\lambda$ is used to control the magnitudes of $w$ and $h$ such that they would provide a good approximation of $v$. We will update each feature with each sample. We choose a small $\lambda$
, such as 0.01. The update is given by the equations below:</p>
\[w_{ik}\leftarrow w_{ik} -\eta\frac{\partial}{\partial w_{ik}}e_{ij}^{2}\]
<p>\(h_{kj}\leftarrow h_{kj} -\eta\frac{\partial}{\partial h_{kj}}e_{ij}^{2}\)
$\eta$ is the learning rate and modifies the magnitude that we update the
features. We first solve for $\frac{\partial}{\partial h_{kj}}e_{ij}^{2}$.</p>
<p>Using the chain rule, $\frac{\partial}{\partial h_{kj}}(v_{ij} -\Sigma_{k=1}^{K}w_{ik}h_{kj}) =\frac{\partial u^{2}}{\partial v}\frac{\partial u}{\partial v}$, where $u = (v_{ij} -\Sigma_{k=1}^{K}w_{ik}h_{kj}) and\frac{\partial u^{2}}{\partial v} = 2u$ \(\frac{\partial}{\partial h_{kj}}e_{ij}^{2} = 2(v_{ij} -\Sigma_{k=1}^{K}w_{ik}h_{kj})\frac{\partial}{\partial h_{kj}}(v_{ij} -\Sigma_{k=1}^{K}w_{ik}h_{kj}) + 2\lambda h_{kj}\)</p>
\[\frac{\partial}{\partial h_{kj}}e_{ij}^{2} = -2e_{ij}w_{ik} + 2\lambda h_{kj}\]
<p>The final update rules for both $W$ and $H$.</p>
\[\frac{\partial}{\partial h_{kj}}e_{ij}^{2} = -2e_{ij}w_{ik} + 2\lambda h_{kj}\]
\[\frac{\partial}{\partial w_{ik}}e_{ij}^{2} = -2e_{ij}h + 2\lambda w_{ik}\]
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">frob_norm</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">M</span><span class="p">){</span><span class="w">
</span><span class="n">norm</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">0</span><span class="w">
</span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">i</span><span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="nf">dim</span><span class="p">(</span><span class="n">M</span><span class="p">)[</span><span class="m">1</span><span class="p">]){</span><span class="w">
</span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">j</span><span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="nf">dim</span><span class="p">(</span><span class="n">M</span><span class="p">)[</span><span class="m">2</span><span class="p">]){</span><span class="w">
</span><span class="n">norm</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">norm</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">M</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="w"> </span><span class="o">**</span><span class="w"> </span><span class="m">2</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="nf">return</span><span class="p">(</span><span class="nf">sqrt</span><span class="p">(</span><span class="n">norm</span><span class="p">))</span><span class="n">.</span><span class="w">
</span><span class="n">nmf_sgd</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">A</span><span class="p">,</span><span class="n">steps</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">50000</span><span class="p">,</span><span class="w"> </span><span class="n">lam</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">1e-2</span><span class="p">,</span><span class="w"> </span><span class="n">lr</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">1e-3</span><span class="p">){</span><span class="w">
</span><span class="n">N</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">dim</span><span class="p">(</span><span class="n">A</span><span class="p">)[</span><span class="m">1</span><span class="p">]</span><span class="w">
</span><span class="n">M</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">dim</span><span class="p">(</span><span class="n">A</span><span class="p">)[</span><span class="m">2</span><span class="p">]</span><span class="w">
</span><span class="n">K</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">N</span><span class="w">
</span><span class="n">W</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">rmatrix</span><span class="p">(</span><span class="n">N</span><span class="p">,</span><span class="n">K</span><span class="p">)</span><span class="w">
</span><span class="n">H</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">rmatrix</span><span class="p">(</span><span class="n">K</span><span class="p">,</span><span class="n">M</span><span class="p">)</span><span class="w">
</span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">step</span><span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">1</span><span class="o">:</span><span class="n">steps</span><span class="p">){</span><span class="w">
</span><span class="n">R</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">A</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">W</span><span class="w"> </span><span class="o">%*%</span><span class="w"> </span><span class="n">H</span><span class="w">
</span><span class="n">dW</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">R</span><span class="w"> </span><span class="o">%*%</span><span class="w"> </span><span class="n">t</span><span class="p">(</span><span class="n">H</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">W</span><span class="o">*</span><span class="n">lam</span><span class="w">
</span><span class="n">dH</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">t</span><span class="p">(</span><span class="n">W</span><span class="p">)</span><span class="w"> </span><span class="o">%*%</span><span class="w"> </span><span class="n">R</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">H</span><span class="o">*</span><span class="n">lam</span><span class="w">
</span><span class="n">W</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">W</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">lr</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">dW</span><span class="w">
</span><span class="n">H</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">H</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">lr</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">dH</span><span class="w">
</span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">frob_norm</span><span class="p">(</span><span class="n">A</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">W</span><span class="w"> </span><span class="o">%*%</span><span class="w"> </span><span class="n">H</span><span class="p">)</span><span class="w"> </span><span class="o"><</span><span class="w"> </span><span class="m">0.01</span><span class="p">){</span><span class="w">
</span><span class="n">print</span><span class="p">(</span><span class="n">frob_norm</span><span class="p">(</span><span class="n">A</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">W</span><span class="w"> </span><span class="o">%*%</span><span class="w"> </span><span class="n">H</span><span class="p">))</span><span class="w">
</span><span class="k">break</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="n">list</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">list</span><span class="p">(</span><span class="n">W</span><span class="p">,</span><span class="w"> </span><span class="n">t</span><span class="p">(</span><span class="n">H</span><span class="p">))</span><span class="w">
</span><span class="nf">return</span><span class="p">(</span><span class="n">list</span><span class="p">)</span><span class="n">.</span><span class="w">
</span></code></pre></div></div>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">nmf_sgd_results</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">nmf_sgd</span><span class="p">(</span><span class="n">R</span><span class="p">)</span><span class="n">.</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.2572934 0.7850774 0.1964726 0.89460898 0.5124385 0.6538741
## [2,] 0.4548971 0.7619701 0.4504754 0.04016496 0.1973834 0.9347442
## [3,] 0.8251736 0.3744436 0.5294228 0.79247087 0.5080712 0.3198328
## [4,] 0.9600582 0.5075759 0.1684872 0.95316732 0.4352644 0.5006196
## [5,] 0.3160396 0.5518905 0.2720268 0.01742472 0.1015410 0.3987225
</code></pre></div></div>
<p>The reconstructed method using our NMF function looks like this:</p>
<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">nmf_sgd_results</span><span class="p">[[</span><span class="m">1</span><span class="p">]]</span><span class="w"> </span><span class="o">%*%</span><span class="w"> </span><span class="n">t</span><span class="p">(</span><span class="n">nmf_sgd_results</span><span class="p">[[</span><span class="m">2</span><span class="p">]])</span><span class="w">
</span></code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.2620366 0.7804305 0.1970252 0.88902520 0.5092941 0.6512767
## [2,] 0.4535915 0.7608638 0.4476562 0.04331351 0.1972235 0.9260586
## [3,] 0.8216679 0.3759426 0.5219099 0.78968437 0.5042836 0.3215420
## [4,] 0.9526964 0.5071181 0.1730190 0.94912249 0.4359087 0.4988104
## [5,] 0.3136085 0.5434768 0.2704516 0.02022789 0.1030468 0.4015469
</code></pre></div></div>
<p>As you can see, it is a near approximation of the original matrix. In
another post, I will go into detail the differences between each
dimensional reduction technique. See below for more information on NMF.</p>
<h2 id="additional-resources">Additional Resources</h2>
<p>-<a href="https://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf">Algorithms for non-negative matrix
factorization</a></p>
<p>-<a href="https://github.com/hpark3910/nonnegative-matrix-factorization">Non-negative matrix
factorization</a></p>
<p>-<a href="https://www.almoststochastic.com/2013/06/nonnegative-matrix-factorization.html">Non-negative matrix
factorization</a>
-<a href="https://github.com/carriexu24/NMF-from-scratch-using-SGD">NMF from scratch using
SGD</a> -<a href="https://albertauyeung.github.io/2017/04/23/python-matrix-factorization.html">Python
Matrix
Factorization</a></p>Danh TruongNon-negative Matrix Factorization