Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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<!-- Local TOC -->
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<li><a class="reference internal" href="#">parameters_estimator module</a></li>
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<div class="section" id="module-parameters_estimator">
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<span id="parameters-estimator-module"></span><h1>parameters_estimator module<a class="headerlink" href="#module-parameters_estimator" title="Permalink to this headline">¶</a></h1>
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<dl class="py class">
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<dt id="parameters_estimator.ParametersEstimator">
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<em class="property">class </em><code class="sig-prename descclassname">parameters_estimator.</code><code class="sig-name descname">ParametersEstimator</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">trajectories</span><span class="p">:</span> <span class="n"><a class="reference internal" href="trajectory.html#trajectory.Trajectory" title="trajectory.Trajectory">trajectory.Trajectory</a></span></em>, <em class="sig-param"><span class="n">net_graph</span><span class="p">:</span> <span class="n"><a class="reference internal" href="network_graph.html#network_graph.NetworkGraph" title="network_graph.NetworkGraph">network_graph.NetworkGraph</a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#parameters_estimator.ParametersEstimator" title="Permalink to this definition">¶</a></dt>
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<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
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<p>Has the task of computing the cims of particular node given the trajectories and the net structure
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in the graph <code class="docutils literal notranslate"><span class="pre">_net_graph</span></code>.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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<li><p><strong>trajectories</strong> (<a class="reference internal" href="trajectory.html#trajectory.Trajectory" title="trajectory.Trajectory"><em>Trajectory</em></a>) – the trajectories</p></li>
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<li><p><strong>net_graph</strong> (<a class="reference internal" href="network_graph.html#network_graph.NetworkGraph" title="network_graph.NetworkGraph"><em>NetworkGraph</em></a>) – the net structure</p></li>
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</ul>
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</dd>
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<dt class="field-even">_single_set_of_cims</dt>
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<dd class="field-even"><p>the set of cims object that will hold the cims of the node</p>
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</dd>
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</dl>
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<dl class="py method">
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<dt id="parameters_estimator.ParametersEstimator.compute_parameters_for_node">
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<code class="sig-name descname">compute_parameters_for_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → <a class="reference internal" href="set_of_cims.html#set_of_cims.SetOfCims" title="set_of_cims.SetOfCims">set_of_cims.SetOfCims</a><a class="headerlink" href="#parameters_estimator.ParametersEstimator.compute_parameters_for_node" title="Permalink to this definition">¶</a></dt>
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<dd><p>Compute the CIMS of the node identified by the label <code class="docutils literal notranslate"><span class="pre">node_id</span></code>.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p>
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</dd>
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<dt class="field-even">Returns</dt>
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<dd class="field-even"><p>A SetOfCims object filled with the computed CIMS</p>
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</dd>
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<dt class="field-odd">Return type</dt>
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<dd class="field-odd"><p><a class="reference internal" href="set_of_cims.html#set_of_cims.SetOfCims" title="set_of_cims.SetOfCims">SetOfCims</a></p>
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</dd>
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</dl>
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</dd></dl>
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<dl class="py method">
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<dt id="parameters_estimator.ParametersEstimator.compute_state_res_time_for_node">
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<code class="sig-name descname">compute_state_res_time_for_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">times</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">trajectory</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">cols_filter</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">scalar_indexes_struct</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">T</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#parameters_estimator.ParametersEstimator.compute_state_res_time_for_node" title="Permalink to this definition">¶</a></dt>
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<dd><p>Compute the state residence times for a node and fill the matrix <code class="docutils literal notranslate"><span class="pre">T</span></code> with the results</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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<li><p><strong>node_indx</strong> (<em>int</em>) – the index of the node</p></li>
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<li><p><strong>times</strong> (<em>numpy.array</em>) – the times deltas vector</p></li>
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<li><p><strong>trajectory</strong> (<em>numpy.ndArray</em>) – the trajectory</p></li>
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<li><p><strong>cols_filter</strong> (<em>numpy.array</em>) – the columns filtering structure</p></li>
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<li><p><strong>scalar_indexes_struct</strong> (<em>numpy.array</em>) – the indexing structure</p></li>
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<li><p><strong>T</strong> (<em>numpy.ndArray</em>) – the state residence times vectors</p></li>
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</ul>
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</dd>
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</dl>
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</dd></dl>
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<dl class="py method">
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<dt id="parameters_estimator.ParametersEstimator.compute_state_transitions_for_a_node">
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<code class="sig-name descname">compute_state_transitions_for_a_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">trajectory</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">cols_filter</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">scalar_indexing</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">M</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em><span class="sig-paren">)</span><a class="headerlink" href="#parameters_estimator.ParametersEstimator.compute_state_transitions_for_a_node" title="Permalink to this definition">¶</a></dt>
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<dd><p>Compute the state residence times for a node and fill the matrices <code class="docutils literal notranslate"><span class="pre">M</span></code> with the results.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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<li><p><strong>node_indx</strong> (<em>int</em>) – the index of the node</p></li>
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<li><p><strong>trajectory</strong> (<em>numpy.ndArray</em>) – the trajectory</p></li>
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<li><p><strong>cols_filter</strong> (<em>numpy.array</em>) – the columns filtering structure</p></li>
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<li><p><strong>scalar_indexing</strong> (<em>numpy.array</em>) – the indexing structure</p></li>
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<li><p><strong>M</strong> (<em>numpy.ndArray</em>) – the state transitions matrices</p></li>
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</ul>
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</dd>
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</dl>
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</dd></dl>
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<dl class="py method">
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<dt id="parameters_estimator.ParametersEstimator.fast_init">
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<code class="sig-name descname">fast_init</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#parameters_estimator.ParametersEstimator.fast_init" title="Permalink to this definition">¶</a></dt>
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<dd><p>Initializes all the necessary structures for the parameters estimation for the node <code class="docutils literal notranslate"><span class="pre">node_id</span></code>.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p>
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</dd>
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</dl>
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</dd></dl>
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<p>
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© Copyright 2020, Filippo Martini.
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