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  <div class="section" id="hep-ml-documentation">
<h1>hep_ml documentation<a class="headerlink" href="#hep-ml-documentation" title="Permalink to this headline">¶</a></h1>
<p><strong>hep_ml</strong> is machine learning library filled with methods used in high energy physics.
Classifiers and transformers from <strong>hep_ml</strong> are <strong>sklearn</strong>-compatible.</p>
<div style='height: 36px;'>
<iframe src="https://ghbtns.com/github-btn.html?user=arogozhnikov&repo=hep_ml&type=star&count=true" frameborder="0" scrolling="0" width="120px" height="27px"></iframe>
<iframe src="https://ghbtns.com/github-btn.html?user=arogozhnikov&repo=hep_ml&type=watch&count=true&v=2" frameborder="0" scrolling="0" width="120px" height="27px"></iframe>
<div style='clear: both' ></div>
</div><div class="section" id="installation">
<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h2>
<p>To install <strong>hep_ml</strong>, use <strong>pip</strong>. Type in terminal:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip install hep_ml
</pre></div>
</div>
</div>
<div class="section" id="installation-for-developers">
<h2>Installation for developers<a class="headerlink" href="#installation-for-developers" title="Permalink to this headline">¶</a></h2>
<p>After cloning repository type in bash:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span> hep_ml
pip install -e . -r requirements.txt
</pre></div>
</div>
</div>
<div class="section" id="contents">
<h2>Contents:<a class="headerlink" href="#contents" title="Permalink to this headline">¶</a></h2>
<div class="toctree-wrapper compound">
<ul class="current">
<li class="toctree-l1 current"><a class="current reference internal" href="#">hep_ml documentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="gb.html">Gradient boosting</a></li>
<li class="toctree-l1"><a class="reference internal" href="losses.html">Losses for Gradient Boosting</a><ul>
<li class="toctree-l2"><a class="reference internal" href="losses.html#examples">Examples</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="uboost.html">uBoost</a><ul>
<li class="toctree-l2"><a class="reference internal" href="uboost.html#examples">Examples</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="metrics.html">Metric functions</a><ul>
<li class="toctree-l2"><a class="reference internal" href="metrics.html#examples">Examples</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="nnet.html">Neural networks</a><ul>
<li class="toctree-l2"><a class="reference internal" href="nnet.html#examples">Examples</a></li>
<li class="toctree-l2"><a class="reference internal" href="nnet.html#interface">Interface</a></li>
<li class="toctree-l2"><a class="reference internal" href="nnet.html#recommended-networks">Recommended networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="nnet.html#custom-networks">Custom networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="nnet.html#loss-functions">Loss functions</a></li>
<li class="toctree-l2"><a class="reference internal" href="nnet.html#trainers">Trainers</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="preprocessing.html">Preprocessing data</a><ul>
<li class="toctree-l2"><a class="reference internal" href="preprocessing.html#examples">Examples</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="reweight.html">Reweighting algorithms</a><ul>
<li class="toctree-l2"><a class="reference internal" href="reweight.html#examples">Examples</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="speedup.html">Fast predictions</a><ul>
<li class="toctree-l2"><a class="reference internal" href="speedup.html#example">Example</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="splot.html">sPlot</a><ul>
<li class="toctree-l2"><a class="reference internal" href="splot.html#example">Example</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="notebooks.html">Code Examples</a></li>
</ul>
</div>
</div>
<div class="section" id="links">
<h2>Links<a class="headerlink" href="#links" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p><a class="reference external" href="https://arogozhnikov.github.io/hep_ml/">documentation</a></p></li>
<li><p><a class="reference external" href="https://github.com/arogozhnikov/hep_ml/tree/master/notebooks">notebook
examples</a></p></li>
<li><p><a class="reference external" href="https://github.com/arogozhnikov/hep_ml">repository</a></p></li>
<li><p><a class="reference external" href="https://github.com/arogozhnikov/hep_ml/issues">issue tracker</a></p></li>
</ul>
</div>
<div class="section" id="related-projects">
<h2>Related projects<a class="headerlink" href="#related-projects" title="Permalink to this headline">¶</a></h2>
<p>Libraries you’ll require to make your life easier.</p>
<ul class="simple">
<li><p><a class="reference external" href="http://ipython.org/notebook.html">IPython Notebook</a> — web-shell
for python</p></li>
<li><p><a class="reference external" href="http://scikit-learn.org/">scikit-learn</a> — general-purpose library
for machine learning in python</p></li>
<li><p><a class="reference external" href="https://github.com/yandex/REP">REP</a> — python wrappers around
different machine learning libraries (including TMVA) + goodies,
required to plot learning curves reports after classification</p></li>
<li><p><a class="reference external" href="http://www.numpy.org/">numpy</a> — ‘MATLAB in python’, vector
operation in python. Use it you need to perform any number crunching.</p></li>
<li><p><a class="reference external" href="http://deeplearning.net/software/theano/">theano</a> — optimized
vector analytical math engine in python</p></li>
<li><p><a class="reference external" href="https://root.cern.ch/">ROOT</a> — main data format in high energy
physics</p></li>
<li><p><a class="reference external" href="http://rootpy.github.io/root_numpy/">root_numpy</a> — python
library to deal with ROOT files (without pain)</p></li>
</ul>
</div>
</div>


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