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datamicroscopes</a>
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<h1>datamicroscopes: Bayesian nonparametric models in Python<a class="headerlink" href="#datamicroscopes-bayesian-nonparametric-models-in-python" title="Permalink to this headline">¶</a></h1>
<p>datamicroscopes is a library for discovering structure in your data. It implements several Bayesian nonparametric models for clustering such as the <a class="reference internal" href="gauss2d.html#gauss2d"><span>Dirichlet Process Mixture Model (DPMM)</span></a> , <a class="reference internal" href="enron_email.html#enron-email"><span>the Infinite Relational Model (IRM)</span></a> , and the <a class="reference internal" href="hdp.html#hdp"><span>Hierarchichal Dirichlet Process (HDP)</span></a> . These models rely on the <a class="reference internal" href="intro.html#intro"><span>Dirichlet Process</span></a>, which allow for the automatic learning of the number of clusters in a datset. Additionally, our <a class="reference internal" href="api.html#api"><span>API</span></a> provides users with a flexible set of likelihood models for various types of data, such as binary, ordinal, categorical, and real-valued variables( <a class="reference internal" href="docs.html#docs"><span>datatypes</span></a>) .</p>
<p>Please read our <a class="reference internal" href="intro.html#intro"><span>introduction</span></a> for an overview of clustering and structure discovery.</p>
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<h2>Tutorials</h2><div class="toctree-wrapper compound">
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<li class="toctree-l1"><a class="reference internal" href="intro.html">Discovering structure in your data: an overview of clustering</a></li>
<li class="toctree-l1"><a class="reference internal" href="ncluster.html">Finding the number of clusters with the Dirichlet Process</a></li>
<li class="toctree-l1"><a class="reference internal" href="enron_blog.html">Network Modeling with the Infinite Relational Model</a></li>
<li class="toctree-l1"><a class="reference internal" href="topic.html">Bayesian Nonparametric Topic Modeling with the Daily Kos</a></li>
</ul>
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<h2>Datatypes and Likelihood Models</h2><div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="datatypes.html">Datatypes and Bayesian Nonparametric Models</a></li>
<li class="toctree-l1"><a class="reference internal" href="bb.html">Binary Data with the Beta Bernouli Distribution</a></li>
<li class="toctree-l1"><a class="reference internal" href="dd.html">Categorical Data and the Dirichlet Discrete Distribution</a></li>
<li class="toctree-l1"><a class="reference internal" href="niw.html">Real Valued Data and the Normal Inverse-Wishart Distribution</a></li>
<li class="toctree-l1"><a class="reference internal" href="nic.html">Univariate Data with the Normal Inverse Chi-Square Distribution</a></li>
<li class="toctree-l1"><a class="reference internal" href="gamma_poisson.html">Count Data and Ordinal Data with the Gamma-Poisson Distribution</a></li>
</ul>
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<h2>Examples</h2><div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="gauss2d.html">Inferring Gaussians with the Dirichlet Process Mixture Model</a></li>
<li class="toctree-l1"><a class="reference internal" href="mnist_predictions.html">Digit recognition with the MNIST dataset</a></li>
<li class="toctree-l1"><a class="reference internal" href="enron_email.html">Clustering the Enron e-mail corpus using the Infinite Relational Model</a></li>
<li class="toctree-l1"><a class="reference internal" href="hdp.html">Learning Topics in The Daily Kos with the Hierarchical Dirichlet Process</a></li>
</ul>
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<h1>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h1>
<p>First, install <a class="reference external" href="https://store.continuum.io/cshop/anaconda/">Anaconda</a>. Then in the terminal type:</p>
<div class="highlight-bash"><div class="highlight"><pre><span class="nv">$ </span>conda config --add channels distributions
<span class="nv">$ </span>conda config --add channels datamicroscopes
<span class="nv">$ </span>conda install microscopes-common
<span class="nv">$ </span>conda install microscopes-<span class="o">{</span>mixturemodel, irm, lda<span class="o">}</span>
</pre></div>
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<center> Datamicroscopes is developed by <a href="http://www.qadium.com">Qadium</a>, with funding from the <a href="http://www.darpa.mil">DARPA</a> <a href="http://www.darpa.mil/program/xdata">XDATA</a> program. Copyright Qadium 2015. </center>
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