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<section id="getting-started">
<h1>Getting Started<a class="headerlink" href="#getting-started" title="Link to this heading">¶</a></h1>
<section id="installation">
<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">¶</a></h2>
<p>SPFlow requires <strong>Python 3.10+</strong> and PyTorch 2.0+.</p>
<section id="from-pypi">
<h3>From PyPI<a class="headerlink" href="#from-pypi" title="Link to this heading">¶</a></h3>
<p>The easiest way to install SPFlow is via pip:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">spflow</span>
</pre></div>
</div>
<p>Optional integrations can be installed via extras, for example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">spflow</span><span class="p">[</span><span class="n">sklearn</span><span class="p">]</span>
</pre></div>
</div>
</section>
<section id="development-from-source">
<h3>Development / From Source<a class="headerlink" href="#development-from-source" title="Link to this heading">¶</a></h3>
<p>Most users should install from PyPI. If you want to contribute to SPFlow or develop locally, see the repository’s
<code class="docutils literal notranslate"><span class="pre">CONTRIBUTING.md</span></code> for a source install with development dependencies.</p>
</section>
<section id="prerequisites">
<h3>Prerequisites<a class="headerlink" href="#prerequisites" title="Link to this heading">¶</a></h3>
<ul class="simple">
<li><p><strong>Python 3.10+</strong> with pip or uv</p></li>
<li><p><strong>PyTorch 2.0+</strong> (will be installed automatically with SPFlow)</p></li>
<li><p><strong>Graphviz</strong> (optional, for circuit visualization):</p>
<ul>
<li><p>macOS: <code class="docutils literal notranslate"><span class="pre">brew</span> <span class="pre">install</span> <span class="pre">graphviz</span></code></p></li>
<li><p>Ubuntu/Debian: <code class="docutils literal notranslate"><span class="pre">sudo</span> <span class="pre">apt-get</span> <span class="pre">install</span> <span class="pre">graphviz</span></code></p></li>
<li><p>Windows: Download from <a class="reference external" href="https://graphviz.org/download/">https://graphviz.org/download/</a></p></li>
</ul>
</li>
</ul>
</section>
</section>
<section id="quick-start">
<h2>Quick Start<a class="headerlink" href="#quick-start" title="Link to this heading">¶</a></h2>
<section id="basic-example-node-based-construction">
<h3>Basic Example: Node-Based Construction<a class="headerlink" href="#basic-example-node-based-construction" title="Link to this heading">¶</a></h3>
<p>Here’s a comprehensive example showing how to build a Probabilistic Circuit using explicit node-based composition:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">spflow.modules.sums</span><span class="w"> </span><span class="kn">import</span> <span class="n">Sum</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">spflow.modules.products</span><span class="w"> </span><span class="kn">import</span> <span class="n">Product</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">spflow.modules.leaves</span><span class="w"> </span><span class="kn">import</span> <span class="n">Normal</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">spflow.utils.visualization</span><span class="w"> </span><span class="kn">import</span> <span class="n">visualize</span>
<span class="c1"># Create leaf nodes: Normal distributions for two variables (X1 and X2)</span>
<span class="c1"># Each variable has multiple instantiations for mixture components</span>
<span class="n">x11</span> <span class="o">=</span> <span class="n">Normal</span><span class="p">(</span><span class="n">scope</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># X1 for component 1</span>
<span class="n">x12</span> <span class="o">=</span> <span class="n">Normal</span><span class="p">(</span><span class="n">scope</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># X1 for component 2</span>
<span class="n">x21</span> <span class="o">=</span> <span class="n">Normal</span><span class="p">(</span><span class="n">scope</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># X2 for component 1</span>
<span class="n">x22</span> <span class="o">=</span> <span class="n">Normal</span><span class="p">(</span><span class="n">scope</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># X2 for component 2</span>
<span class="c1"># Create product nodes combining different leaf instances</span>
<span class="n">prod1</span> <span class="o">=</span> <span class="n">Product</span><span class="p">([</span><span class="n">x11</span><span class="p">,</span> <span class="n">x21</span><span class="p">])</span>
<span class="n">prod2</span> <span class="o">=</span> <span class="n">Product</span><span class="p">([</span><span class="n">x12</span><span class="p">,</span> <span class="n">x21</span><span class="p">])</span>
<span class="n">prod3</span> <span class="o">=</span> <span class="n">Product</span><span class="p">([</span><span class="n">x11</span><span class="p">,</span> <span class="n">x22</span><span class="p">])</span>
<span class="n">prod4</span> <span class="o">=</span> <span class="n">Product</span><span class="p">([</span><span class="n">x12</span><span class="p">,</span> <span class="n">x22</span><span class="p">])</span>
<span class="c1"># Create a sum node (mixture) combining the products</span>
<span class="n">pc</span> <span class="o">=</span> <span class="n">Sum</span><span class="p">([</span><span class="n">prod1</span><span class="p">,</span> <span class="n">prod2</span><span class="p">,</span> <span class="n">prod3</span><span class="p">,</span> <span class="n">prod4</span><span class="p">],</span> <span class="n">weights</span><span class="o">=</span><span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">])</span>
<span class="c1"># Generate some test data</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="c1"># Compute log-likelihood</span>
<span class="n">log_likelihood</span> <span class="o">=</span> <span class="n">pc</span><span class="o">.</span><span class="n">log_likelihood</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="c1"># Sample from the model</span>
<span class="n">samples</span> <span class="o">=</span> <span class="n">pc</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">num_samples</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
<span class="c1"># Visualize the probabilistic circuit</span>
<span class="n">visualize</span><span class="p">(</span><span class="n">pc</span><span class="p">,</span> <span class="n">output_path</span><span class="o">=</span><span class="s2">"/tmp/node-based-structure"</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">"svg"</span><span class="p">)</span>
</pre></div>
</div>
<img src="_static/node-based-structure.svg" width="500px"/></section>
<section id="layered-module-composition">
<h3>Layered Module Composition<a class="headerlink" href="#layered-module-composition" title="Link to this heading">¶</a></h3>
<p>SPFlow also supports a more compact, layered approach to building circuits using module composition:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">spflow.modules.products</span><span class="w"> </span><span class="kn">import</span> <span class="n">OuterProduct</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">spflow.modules.sums</span><span class="w"> </span><span class="kn">import</span> <span class="n">Sum</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">spflow.modules.leaves</span><span class="w"> </span><span class="kn">import</span> <span class="n">Normal</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">spflow.modules.ops</span><span class="w"> </span><span class="kn">import</span> <span class="n">SplitConsecutive</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">spflow.utils.visualization</span><span class="w"> </span><span class="kn">import</span> <span class="n">visualize</span>
<span class="c1"># Create a single Normal layer covering both variables</span>
<span class="n">x_layered</span> <span class="o">=</span> <span class="n">Normal</span><span class="p">(</span><span class="n">scope</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="c1"># Use SplitConsecutive to automatically partition outputs, then OuterProduct to combine</span>
<span class="n">prod_layered</span> <span class="o">=</span> <span class="n">OuterProduct</span><span class="p">(</span><span class="n">SplitConsecutive</span><span class="p">(</span><span class="n">x_layered</span><span class="p">),</span> <span class="n">num_splits</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="c1"># Create a sum node over the outer products</span>
<span class="n">pc_layered</span> <span class="o">=</span> <span class="n">Sum</span><span class="p">(</span><span class="n">prod_layered</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">])</span>
<span class="c1"># Compute queries on sample data</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">pc_layered</span><span class="o">.</span><span class="n">log_likelihood</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="c1"># Visualize this circuit</span>
<span class="n">visualize</span><span class="p">(</span><span class="n">pc_layered</span><span class="p">,</span> <span class="n">output_path</span><span class="o">=</span><span class="s2">"/tmp/layered-structure"</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">"svg"</span><span class="p">)</span>
</pre></div>
</div>
<img src="_static/layered-structure.svg" width="200px"/><p>This layered approach is more concise and scales better for larger circuits, automatically handling module composition instead of explicit node construction.</p>
</section>
<section id="example-dsl-construction-for-demos">
<h3>Example DSL Construction (for demos)<a class="headerlink" href="#example-dsl-construction-for-demos" title="Link to this heading">¶</a></h3>
<p>SPFlow also includes a tiny, example-oriented DSL for writing small circuits in an algebraic form.
It is intentionally separate from the core <cite>spflow.modules</cite> API; call <code class="docutils literal notranslate"><span class="pre">.build()</span></code> to obtain an actual <code class="xref py py-class docutils literal notranslate"><span class="pre">spflow.modules.module.Module</span></code>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">spflow.modules.leaves</span><span class="w"> </span><span class="kn">import</span> <span class="n">Normal</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">spflow.dsl</span><span class="w"> </span><span class="kn">import</span> <span class="n">dsl</span>
<span class="k">with</span> <span class="n">dsl</span><span class="p">():</span>
<span class="n">terms</span> <span class="o">=</span> <span class="mf">0.4</span> <span class="o">*</span> <span class="n">Normal</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">Normal</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.6</span> <span class="o">*</span> <span class="n">Normal</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">Normal</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">pc</span> <span class="o">=</span> <span class="n">terms</span><span class="o">.</span><span class="n">build</span><span class="p">()</span>
<span class="n">ll</span> <span class="o">=</span> <span class="n">pc</span><span class="o">.</span><span class="n">log_likelihood</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">ll</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
</pre></div>
</div>
</section>
</section>
<section id="next-steps">
<h2>Next Steps<a class="headerlink" href="#next-steps" title="Link to this heading">¶</a></h2>
<ul class="simple">
<li><p>Read the <a class="reference internal" href="guides/user_guide.html"><span class="doc">User Guide</span></a> for comprehensive tutorials</p></li>
<li><p>Explore the <a class="reference internal" href="api/index.html"><span class="doc">API Documentation</span></a> for detailed API documentation</p></li>
<li><p>Browse <a class="reference internal" href="concepts.html"><span class="doc">Concepts</span></a> for core semantics (shapes, scopes, evidence, caching)</p></li>
</ul>
</section>
<section id="key-concepts">
<h2>Key Concepts<a class="headerlink" href="#key-concepts" title="Link to this heading">¶</a></h2>
<dl class="simple">
<dt>Probabilistic Circuits</dt><dd><p>A flexible class of probabilistic models that support efficient inference.</p>
</dd>
<dt>Leaves (Distributions)</dt><dd><p>Terminal nodes representing probability distributions (e.g., Normal, Binomial, Categorical).</p>
</dd>
<dt>Internal Nodes</dt><dd><p>Sum and Product nodes that combine distributions in a computational graph.</p>
</dd>
<dt>Structure Learning</dt><dd><p>Automatically learning the circuit structure from data.</p>
</dd>
<dt>Parameter Learning</dt><dd><p>Training the distribution parameters using gradient descent or EM.</p>
</dd>
</dl>
<p>For more information, see the <a class="reference internal" href="guides/user_guide.html"><span class="doc">User Guide</span></a> and <a class="reference internal" href="concepts.html"><span class="doc">Concepts</span></a>.</p>
</section>
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<ul>
<li><a class="reference internal" href="#">Getting Started</a><ul>
<li><a class="reference internal" href="#installation">Installation</a><ul>
<li><a class="reference internal" href="#from-pypi">From PyPI</a></li>
<li><a class="reference internal" href="#development-from-source">Development / From Source</a></li>
<li><a class="reference internal" href="#prerequisites">Prerequisites</a></li>
</ul>
</li>
<li><a class="reference internal" href="#quick-start">Quick Start</a><ul>
<li><a class="reference internal" href="#basic-example-node-based-construction">Basic Example: Node-Based Construction</a></li>
<li><a class="reference internal" href="#layered-module-composition">Layered Module Composition</a></li>
<li><a class="reference internal" href="#example-dsl-construction-for-demos">Example DSL Construction (for demos)</a></li>
</ul>
</li>
<li><a class="reference internal" href="#next-steps">Next Steps</a></li>
<li><a class="reference internal" href="#key-concepts">Key Concepts</a></li>
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