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<div class="section" id="pyctbn-pyctbn-estimators-package"> |
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<h1>PyCTBN.PyCTBN.estimators package<a class="headerlink" href="#pyctbn-pyctbn-estimators-package" title="Permalink to this headline">¶</a></h1> |
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<div class="section" id="submodules"> |
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2> |
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</div> |
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<div class="section" id="module-PyCTBN.PyCTBN.estimators.fam_score_calculator"> |
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<span id="pyctbn-pyctbn-estimators-fam-score-calculator-module"></span><h2>PyCTBN.PyCTBN.estimators.fam_score_calculator module<a class="headerlink" href="#module-PyCTBN.PyCTBN.estimators.fam_score_calculator" title="Permalink to this headline">¶</a></h2> |
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<dl class="py class"> |
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<dt id="PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator"> |
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<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.estimators.fam_score_calculator.</code><code class="sig-name descname">FamScoreCalculator</code><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator" 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 calculating the FamScore of a node by using a Bayesian score function</p> |
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<dl class="py method"> |
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<dt id="PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.get_fam_score"> |
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<code class="sig-name descname">get_fam_score</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">cims</span><span class="p">:</span> <span class="n">numpy.array</span></em>, <em class="sig-param"><span class="n">tau_xu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">0.1</span></em>, <em class="sig-param"><span class="n">alpha_xu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">1</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.get_fam_score" title="Permalink to this definition">¶</a></dt> |
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<dd><p>Calculate the FamScore value of the node</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>cims</strong> (<em>np.array</em>) – np.array with all the node’s cims</p></li> |
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<li><p><strong>tau_xu</strong> (<em>float</em><em>, </em><em>optional</em>) – hyperparameter over the CTBN’s q parameters, default to 0.1</p></li> |
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<li><p><strong>alpha_xu</strong> (<em>float</em><em>, </em><em>optional</em>) – hyperparameter over the CTBN’s q parameters, default to 1</p></li> |
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</ul> |
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</dd> |
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<dt class="field-even">Returns</dt> |
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<dd class="field-even"><p>the FamScore value of the node</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>float</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="PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.marginal_likelihood_q"> |
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<code class="sig-name descname">marginal_likelihood_q</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">cims</span><span class="p">:</span> <span class="n">numpy.array</span></em>, <em class="sig-param"><span class="n">tau_xu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">0.1</span></em>, <em class="sig-param"><span class="n">alpha_xu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">1</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.marginal_likelihood_q" title="Permalink to this definition">¶</a></dt> |
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<dd><p>Calculate the value of the marginal likelihood over q of the node identified by the label node_id</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>cims</strong> (<em>np.array</em>) – np.array with all the node’s cims</p></li> |
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<li><p><strong>tau_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters</p></li> |
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<li><p><strong>alpha_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters</p></li> |
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</ul> |
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</dd> |
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<dt class="field-even">Returns</dt> |
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<dd class="field-even"><p>the value of the marginal likelihood over q</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>float</p> |
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</dd> |
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</dl> |
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</dd></dl> |
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|
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<dl class="py method"> |
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<dt id="PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.marginal_likelihood_theta"> |
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<code class="sig-name descname">marginal_likelihood_theta</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">cims</span><span class="p">:</span> <span class="n"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix">PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix</a></span></em>, <em class="sig-param"><span class="n">alpha_xu</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">alpha_xxu</span><span class="p">:</span> <span class="n">float</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.marginal_likelihood_theta" title="Permalink to this definition">¶</a></dt> |
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<dd><p>Calculate the FamScore value of the node identified by the label node_id</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>cims</strong> (<em>np.array</em>) – np.array with all the node’s cims</p></li> |
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<li><p><strong>alpha_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters, default to 0.1</p></li> |
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<li><p><strong>alpha_xxu</strong> (<em>float</em>) – distribuited hyperparameter over the CTBN’s theta parameters</p></li> |
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</ul> |
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</dd> |
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<dt class="field-even">Returns</dt> |
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<dd class="field-even"><p>the value of the marginal likelihood over theta</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>float</p> |
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</dd> |
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</dl> |
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</dd></dl> |
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|
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<dl class="py method"> |
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<dt id="PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.single_cim_xu_marginal_likelihood_q"> |
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<code class="sig-name descname">single_cim_xu_marginal_likelihood_q</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">M_xu_suff_stats</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">T_xu_suff_stats</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">tau_xu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">0.1</span></em>, <em class="sig-param"><span class="n">alpha_xu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">1</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.single_cim_xu_marginal_likelihood_q" title="Permalink to this definition">¶</a></dt> |
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<dd><p>Calculate the marginal likelihood on q of the node when assumes a specif value |
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and a specif parents’s assignment</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>M_xu_suff_stats</strong> – value of the suffucient statistic M[x|u]</p></li> |
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<li><p><strong>T_xu_suff_stats</strong> (<em>float</em>) – value of the suffucient statistic T[x|u]</p></li> |
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<li><p><strong>cim</strong> (<em>class:'ConditionalIntensityMatrix'</em>) – A conditional_intensity_matrix object with the sufficient statistics</p></li> |
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<li><p><strong>tau_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters</p></li> |
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<li><p><strong>alpha_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters</p></li> |
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</ul> |
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</dd> |
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<dt class="field-even">Returns</dt> |
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<dd class="field-even"><p>the value of the marginal likelihood of the node when assumes a specif value</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>float</p> |
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</dd> |
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</dl> |
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</dd></dl> |
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|
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<dl class="py method"> |
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<dt id="PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.single_cim_xu_marginal_likelihood_theta"> |
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<code class="sig-name descname">single_cim_xu_marginal_likelihood_theta</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">index</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">cim</span><span class="p">:</span> <span class="n"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix">PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix</a></span></em>, <em class="sig-param"><span class="n">alpha_xu</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">alpha_xxu</span><span class="p">:</span> <span class="n">float</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.single_cim_xu_marginal_likelihood_theta" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Calculate the marginal likelihood on q of the node when assumes a specif value |
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and a specif parents’s assignment</p> |
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<dl class="field-list simple"> |
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<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
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<li><p><strong>cim</strong> (<em>class:'ConditionalIntensityMatrix'</em>) – A conditional_intensity_matrix object with the sufficient statistics</p></li> |
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<li><p><strong>alpha_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters</p></li> |
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<li><p><strong>alpha_xxu</strong> (<em>float</em>) – distribuited hyperparameter over the CTBN’s theta parameters</p></li> |
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</ul> |
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</dd> |
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<dt class="field-even">Returns</dt> |
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<dd class="field-even"><p>the value of the marginal likelihood over theta when the node assumes a specif value</p> |
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</dd> |
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<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>float</p> |
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</dd> |
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</dl> |
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</dd></dl> |
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|
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<dl class="py method"> |
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<dt id="PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.single_internal_cim_xxu_marginal_likelihood_theta"> |
||||
<code class="sig-name descname">single_internal_cim_xxu_marginal_likelihood_theta</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">M_xxu_suff_stats</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">alpha_xxu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">1</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.single_internal_cim_xxu_marginal_likelihood_theta" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Calculate the second part of the marginal likelihood over theta formula</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>M_xxu_suff_stats</strong> (<em>float</em>) – value of the suffucient statistic M[xx’<a href="#id1"><span class="problematic" id="id2">|</span></a>u]</p></li> |
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<li><p><strong>alpha_xxu</strong> (<em>float</em>) – distribuited hyperparameter over the CTBN’s theta parameters</p></li> |
||||
</ul> |
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</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>the value of the marginal likelihood over theta when the node assumes a specif value</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>float</p> |
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</dd> |
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</dl> |
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</dd></dl> |
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|
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<dl class="py method"> |
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<dt id="PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.variable_cim_xu_marginal_likelihood_q"> |
||||
<code class="sig-name descname">variable_cim_xu_marginal_likelihood_q</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">cim</span><span class="p">:</span> <span class="n"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix">PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix</a></span></em>, <em class="sig-param"><span class="n">tau_xu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">0.1</span></em>, <em class="sig-param"><span class="n">alpha_xu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">1</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.variable_cim_xu_marginal_likelihood_q" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Calculate the value of the marginal likelihood over q given a cim</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>cim</strong> (<em>class:'ConditionalIntensityMatrix'</em>) – A conditional_intensity_matrix object with the sufficient statistics</p></li> |
||||
<li><p><strong>tau_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters</p></li> |
||||
<li><p><strong>alpha_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>the value of the marginal likelihood over q</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>float</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.variable_cim_xu_marginal_likelihood_theta"> |
||||
<code class="sig-name descname">variable_cim_xu_marginal_likelihood_theta</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">cim</span><span class="p">:</span> <span class="n"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix">PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix</a></span></em>, <em class="sig-param"><span class="n">alpha_xu</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">alpha_xxu</span><span class="p">:</span> <span class="n">float</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.variable_cim_xu_marginal_likelihood_theta" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Calculate the value of the marginal likelihood over theta given a cim</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>cim</strong> (<em>class:'ConditionalIntensityMatrix'</em>) – A conditional_intensity_matrix object with the sufficient statistics</p></li> |
||||
<li><p><strong>alpha_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters, default to 0.1</p></li> |
||||
<li><p><strong>alpha_xxu</strong> (<em>float</em>) – distribuited hyperparameter over the CTBN’s theta parameters</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>the value of the marginal likelihood over theta</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>float</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
</dd></dl> |
||||
|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.estimators.parameters_estimator"> |
||||
<span id="pyctbn-pyctbn-estimators-parameters-estimator-module"></span><h2>PyCTBN.PyCTBN.estimators.parameters_estimator module<a class="headerlink" href="#module-PyCTBN.PyCTBN.estimators.parameters_estimator" title="Permalink to this headline">¶</a></h2> |
||||
<dl class="py class"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.estimators.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="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory" title="PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory">PyCTBN.PyCTBN.structure_graph.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="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph" title="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph">PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph</a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>Has the task of computing the cims of particular node given the trajectories and the net structure |
||||
in the graph <code class="docutils literal notranslate"><span class="pre">_net_graph</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>trajectories</strong> (<a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory" title="PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory"><em>Trajectory</em></a>) – the trajectories</p></li> |
||||
<li><p><strong>net_graph</strong> (<a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph" title="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph"><em>NetworkGraph</em></a>) – the net structure</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">_single_set_of_cims</dt> |
||||
<dd class="field-even"><p>the set of cims object that will hold the cims of the node</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator.compute_parameters_for_node"> |
||||
<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="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims" title="PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims">PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims</a><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator.compute_parameters_for_node" title="Permalink to this definition">¶</a></dt> |
||||
<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> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>A SetOfCims object filled with the computed CIMS</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims" title="PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims">SetOfCims</a></p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator.compute_state_res_time_for_node"> |
||||
<em class="property">static </em><code class="sig-name descname">compute_state_res_time_for_node</code><span class="sig-paren">(</span><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="#PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator.compute_state_res_time_for_node" title="Permalink to this definition">¶</a></dt> |
||||
<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> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>node_indx</strong> (<em>int</em>) – the index of the node</p></li> |
||||
<li><p><strong>times</strong> (<em>numpy.array</em>) – the times deltas vector</p></li> |
||||
<li><p><strong>trajectory</strong> (<em>numpy.ndArray</em>) – the trajectory</p></li> |
||||
<li><p><strong>cols_filter</strong> (<em>numpy.array</em>) – the columns filtering structure</p></li> |
||||
<li><p><strong>scalar_indexes_struct</strong> (<em>numpy.array</em>) – the indexing structure</p></li> |
||||
<li><p><strong>T</strong> (<em>numpy.ndArray</em>) – the state residence times vectors</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator.compute_state_transitions_for_a_node"> |
||||
<em class="property">static </em><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> → None<a class="headerlink" href="#PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator.compute_state_transitions_for_a_node" title="Permalink to this definition">¶</a></dt> |
||||
<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> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>node_indx</strong> (<em>int</em>) – the index of the node</p></li> |
||||
<li><p><strong>trajectory</strong> (<em>numpy.ndArray</em>) – the trajectory</p></li> |
||||
<li><p><strong>cols_filter</strong> (<em>numpy.array</em>) – the columns filtering structure</p></li> |
||||
<li><p><strong>scalar_indexing</strong> (<em>numpy.array</em>) – the indexing structure</p></li> |
||||
<li><p><strong>M</strong> (<em>numpy.ndArray</em>) – the state transitions matrices</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator.fast_init"> |
||||
<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="#PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator.fast_init" title="Permalink to this definition">¶</a></dt> |
||||
<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> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
</dd></dl> |
||||
|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator"> |
||||
<span id="pyctbn-pyctbn-estimators-structure-constraint-based-estimator-module"></span><h2>PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator module<a class="headerlink" href="#module-PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator" title="Permalink to this headline">¶</a></h2> |
||||
<dl class="py class"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.</code><code class="sig-name descname">StructureConstraintBasedEstimator</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_path</span><span class="p">:</span> <span class="n"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath" title="PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath">PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath</a></span></em>, <em class="sig-param"><span class="n">exp_test_alfa</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">chi_test_alfa</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">known_edges</span><span class="p">:</span> <span class="n">List</span> <span class="o">=</span> <span class="default_value">[]</span></em>, <em class="sig-param"><span class="n">thumb_threshold</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">25</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <a class="reference internal" href="#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator" title="PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator</span></code></a></p> |
||||
<p>Has the task of estimating the network structure given the trajectories in samplepath by using a constraint-based approach.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>sample_path</strong> (<a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath" title="PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath"><em>SamplePath</em></a>) – the _sample_path object containing the trajectories and the real structure</p></li> |
||||
<li><p><strong>exp_test_alfa</strong> (<em>float</em>) – the significance level for the exponential Hp test</p></li> |
||||
<li><p><strong>chi_test_alfa</strong> (<em>float</em>) – the significance level for the chi Hp test</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">_nodes</dt> |
||||
<dd class="field-even"><p>the nodes labels</p> |
||||
</dd> |
||||
<dt class="field-odd">_nodes_vals</dt> |
||||
<dd class="field-odd"><p>the nodes cardinalities</p> |
||||
</dd> |
||||
<dt class="field-even">_nodes_indxs</dt> |
||||
<dd class="field-even"><p>the nodes indexes</p> |
||||
</dd> |
||||
<dt class="field-odd">_complete_graph</dt> |
||||
<dd class="field-odd"><p>the complete directed graph built using the nodes labels in <code class="docutils literal notranslate"><span class="pre">_nodes</span></code></p> |
||||
</dd> |
||||
<dt class="field-even">_cache</dt> |
||||
<dd class="field-even"><p>the Cache object</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.complete_test"> |
||||
<code class="sig-name descname">complete_test</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">test_parent</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">test_child</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">parent_set</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">child_states_numb</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">tot_vars_count</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">parent_indx</span></em>, <em class="sig-param"><span class="n">child_indx</span></em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.complete_test" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Performs a complete independence test on the directed graphs G1 = {test_child U parent_set} |
||||
G2 = {G1 U test_parent} (added as an additional parent of the test_child). |
||||
Generates all the necessary structures and datas to perform the tests.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>test_parent</strong> (<em>string</em>) – the node label of the test parent</p></li> |
||||
<li><p><strong>test_child</strong> (<em>string</em>) – the node label of the child</p></li> |
||||
<li><p><strong>parent_set</strong> (<em>List</em>) – the common parent set</p></li> |
||||
<li><p><strong>child_states_numb</strong> (<em>int</em>) – the cardinality of the <code class="docutils literal notranslate"><span class="pre">test_child</span></code></p></li> |
||||
<li><p><strong>tot_vars_count</strong> (<em>int</em>) – the total number of variables in the net</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>True iff test_child and test_parent are independent given the sep_set parent_set. False otherwise</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>bool</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.compute_thumb_value"> |
||||
<code class="sig-name descname">compute_thumb_value</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">parent_val</span></em>, <em class="sig-param"><span class="n">child_val</span></em>, <em class="sig-param"><span class="n">parent_set_vals</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.compute_thumb_value" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Compute the value to test against the thumb_threshold.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>parent_val</strong> (<em>int</em>) – test parent’s variable cardinality</p></li> |
||||
<li><p><strong>child_val</strong> (<em>int</em>) – test child’s variable cardinality</p></li> |
||||
<li><p><strong>parent_set_vals</strong> (<em>List</em>) – the cardinalities of the nodes in the current sep-set</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>the thumb value for the current independence test</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>int</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.ctpc_algorithm"> |
||||
<code class="sig-name descname">ctpc_algorithm</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">disable_multiprocessing</span><span class="p">:</span> <span class="n">bool</span> <span class="o">=</span> <span class="default_value">False</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.ctpc_algorithm" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Compute the CTPC algorithm over the entire net.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.estimate_structure"> |
||||
<code class="sig-name descname">estimate_structure</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">disable_multiprocessing</span><span class="p">:</span> <span class="n">bool</span> <span class="o">=</span> <span class="default_value">False</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.estimate_structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Abstract method to estimate the structure</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Returns</dt> |
||||
<dd class="field-odd"><p>List of estimated edges</p> |
||||
</dd> |
||||
<dt class="field-even">Return type</dt> |
||||
<dd class="field-even"><p>Typing.List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.independence_test"> |
||||
<code class="sig-name descname">independence_test</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">child_states_numb</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">cim1</span><span class="p">:</span> <span class="n"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix">PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix</a></span></em>, <em class="sig-param"><span class="n">cim2</span><span class="p">:</span> <span class="n"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix">PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix</a></span></em>, <em class="sig-param"><span class="n">thumb_value</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">parent_indx</span></em>, <em class="sig-param"><span class="n">child_indx</span></em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.independence_test" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Compute the actual independence test using two cims. |
||||
It is performed first the exponential test and if the null hypothesis is not rejected, |
||||
it is performed also the chi_test.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>child_states_numb</strong> (<em>int</em>) – the cardinality of the test child</p></li> |
||||
<li><p><strong>cim1</strong> (<a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix"><em>ConditionalIntensityMatrix</em></a>) – a cim belonging to the graph without test parent</p></li> |
||||
<li><p><strong>cim2</strong> (<a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix"><em>ConditionalIntensityMatrix</em></a>) – a cim belonging to the graph with test parent</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>True iff both tests do NOT reject the null hypothesis of independence. False otherwise.</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>bool</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.one_iteration_of_CTPC_algorithm"> |
||||
<code class="sig-name descname">one_iteration_of_CTPC_algorithm</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">var_id</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">tot_vars_count</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> → List<a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.one_iteration_of_CTPC_algorithm" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Performs an iteration of the CTPC algorithm using the node <code class="docutils literal notranslate"><span class="pre">var_id</span></code> as <code class="docutils literal notranslate"><span class="pre">test_child</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>var_id</strong> (<em>string</em>) – the node label of the test child</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
</dd></dl> |
||||
|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.estimators.structure_estimator"> |
||||
<span id="pyctbn-pyctbn-estimators-structure-estimator-module"></span><h2>PyCTBN.PyCTBN.estimators.structure_estimator module<a class="headerlink" href="#module-PyCTBN.PyCTBN.estimators.structure_estimator" title="Permalink to this headline">¶</a></h2> |
||||
<dl class="py class"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.estimators.structure_estimator.</code><code class="sig-name descname">StructureEstimator</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_path</span><span class="p">:</span> <span class="n"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath" title="PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath">PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath</a></span></em>, <em class="sig-param"><span class="n">known_edges</span><span class="p">:</span> <span class="n">List</span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>Has the task of estimating the network structure given the trajectories in <code class="docutils literal notranslate"><span class="pre">samplepath</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>sample_path</strong> (<a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath" title="PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath"><em>SamplePath</em></a>) – the _sample_path object containing the trajectories and the real structure</p> |
||||
</dd> |
||||
<dt class="field-even">_nodes</dt> |
||||
<dd class="field-even"><p>the nodes labels</p> |
||||
</dd> |
||||
<dt class="field-odd">_nodes_vals</dt> |
||||
<dd class="field-odd"><p>the nodes cardinalities</p> |
||||
</dd> |
||||
<dt class="field-even">_nodes_indxs</dt> |
||||
<dd class="field-even"><p>the nodes indexes</p> |
||||
</dd> |
||||
<dt class="field-odd">_complete_graph</dt> |
||||
<dd class="field-odd"><p>the complete directed graph built using the nodes labels in <code class="docutils literal notranslate"><span class="pre">_nodes</span></code></p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.adjacency_matrix"> |
||||
<code class="sig-name descname">adjacency_matrix</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → numpy.ndarray<a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.adjacency_matrix" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Converts the estimated structure <code class="docutils literal notranslate"><span class="pre">_complete_graph</span></code> to a boolean adjacency matrix representation.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Returns</dt> |
||||
<dd class="field-odd"><p>The adjacency matrix of the graph <code class="docutils literal notranslate"><span class="pre">_complete_graph</span></code></p> |
||||
</dd> |
||||
<dt class="field-even">Return type</dt> |
||||
<dd class="field-even"><p>numpy.ndArray</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.build_complete_graph"> |
||||
<em class="property">static </em><code class="sig-name descname">build_complete_graph</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_ids</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → networkx.classes.digraph.DiGraph<a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.build_complete_graph" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds a complete directed graph (no self loops) given the nodes labels in the list <code class="docutils literal notranslate"><span class="pre">node_ids</span></code>:</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node_ids</strong> (<em>List</em>) – the list of nodes labels</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>a complete Digraph Object</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>networkx.DiGraph</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.build_removable_edges_matrix"> |
||||
<code class="sig-name descname">build_removable_edges_matrix</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">known_edges</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.build_removable_edges_matrix" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds a boolean matrix who shows if a edge could be removed or not, based on prior knowledge given:</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>known_edges</strong> (<em>List</em>) – the list of nodes labels</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>a boolean matrix</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>np.ndarray</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.estimate_structure"> |
||||
<em class="property">abstract </em><code class="sig-name descname">estimate_structure</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → List<a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.estimate_structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Abstract method to estimate the structure</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Returns</dt> |
||||
<dd class="field-odd"><p>List of estimated edges</p> |
||||
</dd> |
||||
<dt class="field-even">Return type</dt> |
||||
<dd class="field-even"><p>Typing.List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.generate_possible_sub_sets_of_size"> |
||||
<em class="property">static </em><code class="sig-name descname">generate_possible_sub_sets_of_size</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">u</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">size</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">parent_label</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.generate_possible_sub_sets_of_size" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Creates a list containing all possible subsets of the list <code class="docutils literal notranslate"><span class="pre">u</span></code> of size <code class="docutils literal notranslate"><span class="pre">size</span></code>, |
||||
that do not contains a the node identified by <code class="docutils literal notranslate"><span class="pre">parent_label</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>u</strong> (<em>List</em>) – the list of nodes</p></li> |
||||
<li><p><strong>size</strong> (<em>int</em>) – the size of the subsets</p></li> |
||||
<li><p><strong>parent_label</strong> (<em>string</em>) – the node to exclude in the subsets generation</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>an Iterator Object containing a list of lists</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>Iterator</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.save_plot_estimated_structure_graph"> |
||||
<code class="sig-name descname">save_plot_estimated_structure_graph</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_path</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.save_plot_estimated_structure_graph" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Plot the estimated structure in a graphical model style, use .png extension. |
||||
Spurious edges are colored in red if a prior structure is present.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>file_path</strong> – path to save the file to</p> |
||||
</dd> |
||||
<dt class="field-even">Type</dt> |
||||
<dd class="field-even"><p>string</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.save_results"> |
||||
<code class="sig-name descname">save_results</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.save_results" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Save the estimated Structure to a .json file in the path where the data are loaded from. |
||||
The file is named as the input dataset but the <cite>results_</cite> word is appended to the results file.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.spurious_edges"> |
||||
<code class="sig-name descname">spurious_edges</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → List<a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.spurious_edges" title="Permalink to this definition">¶</a></dt> |
||||
<dd><dl class="simple"> |
||||
<dt>Return the spurious edges present in the estimated structure, if a prior net structure is present in</dt><dd><p><code class="docutils literal notranslate"><span class="pre">_sample_path.structure</span></code>.</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Returns</dt> |
||||
<dd class="field-odd"><p>A list containing the spurious edges</p> |
||||
</dd> |
||||
<dt class="field-even">Return type</dt> |
||||
<dd class="field-even"><p>List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
</dd></dl> |
||||
|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.estimators.structure_score_based_estimator"> |
||||
<span id="pyctbn-pyctbn-estimators-structure-score-based-estimator-module"></span><h2>PyCTBN.PyCTBN.estimators.structure_score_based_estimator module<a class="headerlink" href="#module-PyCTBN.PyCTBN.estimators.structure_score_based_estimator" title="Permalink to this headline">¶</a></h2> |
||||
<dl class="py class"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_score_based_estimator.StructureScoreBasedEstimator"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.estimators.structure_score_based_estimator.</code><code class="sig-name descname">StructureScoreBasedEstimator</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_path</span><span class="p">:</span> <span class="n"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath" title="PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath">PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath</a></span></em>, <em class="sig-param"><span class="n">tau_xu</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">0.1</span></em>, <em class="sig-param"><span class="n">alpha_xu</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">1</span></em>, <em class="sig-param"><span class="n">known_edges</span><span class="p">:</span> <span class="n">List</span> <span class="o">=</span> <span class="default_value">[]</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_score_based_estimator.StructureScoreBasedEstimator" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <a class="reference internal" href="#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator" title="PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator</span></code></a></p> |
||||
<p>Has the task of estimating the network structure given the trajectories in samplepath by |
||||
using a score based approach.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>sample_path</strong> (<a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath" title="PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath"><em>SamplePath</em></a>) – the _sample_path object containing the trajectories and the real structure</p></li> |
||||
<li><p><strong>tau_xu</strong> (<em>float</em><em>, </em><em>optional</em>) – hyperparameter over the CTBN’s q parameters, default to 0.1</p></li> |
||||
<li><p><strong>alpha_xu</strong> (<em>float</em><em>, </em><em>optional</em>) – hyperparameter over the CTBN’s q parameters, default to 1</p></li> |
||||
<li><p><strong>known_edges</strong> (<em>List</em><em>, </em><em>optional</em>) – List of known edges, default to []</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.estimate_parents"> |
||||
<code class="sig-name descname">estimate_parents</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>, <em class="sig-param"><span class="n">max_parents</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">iterations_number</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">40</span></em>, <em class="sig-param"><span class="n">patience</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">10</span></em>, <em class="sig-param"><span class="n">tabu_length</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">tabu_rules_duration</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">5</span></em>, <em class="sig-param"><span class="n">optimizer</span><span class="p">:</span> <span class="n">str</span> <span class="o">=</span> <span class="default_value">'hill'</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.estimate_parents" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Use the FamScore of a node in order to find the best parent nodes</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>node_id</strong> (<em>string</em>) – current node’s id</p></li> |
||||
<li><p><strong>max_parents</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum number of parents for each variable. If None, disabled, default to None</p></li> |
||||
<li><p><strong>iterations_number</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum number of optimization algorithm’s iteration, default to 40</p></li> |
||||
<li><p><strong>patience</strong> (<em>int</em><em>, </em><em>optional</em>) – number of iteration without any improvement before to stop the search.If None, disabled, default to None</p></li> |
||||
<li><p><strong>tabu_length</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum lenght of the data structures used in the optimization process, default to None</p></li> |
||||
<li><p><strong>tabu_rules_duration</strong> (<em>int</em><em>, </em><em>optional</em>) – number of iterations in which each rule keeps its value, default to None</p></li> |
||||
<li><p><strong>optimizer</strong> (<em>string</em><em>, </em><em>optional</em>) – name of the optimizer algorithm. Possible values: ‘hill’ (Hill climbing),’tabu’ (tabu search), defualt to ‘tabu’</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>A list of the best edges for the currente node</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.estimate_structure"> |
||||
<code class="sig-name descname">estimate_structure</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">max_parents</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">iterations_number</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">40</span></em>, <em class="sig-param"><span class="n">patience</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">tabu_length</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">tabu_rules_duration</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">optimizer</span><span class="p">:</span> <span class="n">str</span> <span class="o">=</span> <span class="default_value">'tabu'</span></em>, <em class="sig-param"><span class="n">disable_multiprocessing</span><span class="p">:</span> <span class="n">bool</span> <span class="o">=</span> <span class="default_value">False</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.estimate_structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Compute the score-based algorithm to find the optimal structure</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>max_parents</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum number of parents for each variable. If None, disabled, default to None</p></li> |
||||
<li><p><strong>iterations_number</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum number of optimization algorithm’s iteration, default to 40</p></li> |
||||
<li><p><strong>patience</strong> (<em>int</em><em>, </em><em>optional</em>) – number of iteration without any improvement before to stop the search.If None, disabled, default to None</p></li> |
||||
<li><p><strong>tabu_length</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum lenght of the data structures used in the optimization process, default to None</p></li> |
||||
<li><p><strong>tabu_rules_duration</strong> (<em>int</em><em>, </em><em>optional</em>) – number of iterations in which each rule keeps its value, default to None</p></li> |
||||
<li><p><strong>optimizer</strong> (<em>string</em><em>, </em><em>optional</em>) – name of the optimizer algorithm. Possible values: ‘hill’ (Hill climbing),’tabu’ (tabu search), defualt to ‘tabu’</p></li> |
||||
<li><p><strong>disable_multiprocessing</strong> (<em>Boolean</em><em>, </em><em>optional</em>) – true if you desire to disable the multiprocessing operations, default to False</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.get_score_from_graph"> |
||||
<code class="sig-name descname">get_score_from_graph</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">graph</span><span class="p">:</span> <span class="n"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph" title="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph">PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph</a></span></em>, <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="headerlink" href="#PyCTBN.PyCTBN.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.get_score_from_graph" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Get the FamScore of a node</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>node_id</strong> (<em>string</em>) – current node’s id</p></li> |
||||
<li><p><strong>graph</strong> (<em>class:'NetworkGraph'</em>) – current graph to be computed</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>The FamSCore for this graph structure</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>float</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
</dd></dl> |
||||
|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.estimators"> |
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-PyCTBN.PyCTBN.estimators" title="Permalink to this headline">¶</a></h2> |
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<div class="section" id="pyctbn-pyctbn-package"> |
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<h1>PyCTBN.PyCTBN package<a class="headerlink" href="#pyctbn-pyctbn-package" title="Permalink to this headline">¶</a></h1> |
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<div class="section" id="subpackages"> |
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<h2>Subpackages<a class="headerlink" href="#subpackages" title="Permalink to this headline">¶</a></h2> |
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<div class="toctree-wrapper compound"> |
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<ul> |
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<li class="toctree-l1"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html">PyCTBN.PyCTBN.estimators package</a><ul> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#submodules">Submodules</a></li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.fam_score_calculator">PyCTBN.PyCTBN.estimators.fam_score_calculator module</a></li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.parameters_estimator">PyCTBN.PyCTBN.estimators.parameters_estimator module</a></li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator">PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator module</a></li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_estimator">PyCTBN.PyCTBN.estimators.structure_estimator module</a></li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_score_based_estimator">PyCTBN.PyCTBN.estimators.structure_score_based_estimator module</a></li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators">Module contents</a></li> |
||||
</ul> |
||||
</li> |
||||
<li class="toctree-l1"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html">PyCTBN.PyCTBN.optimizers package</a><ul> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html#submodules">Submodules</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.constraint_based_optimizer">PyCTBN.PyCTBN.optimizers.constraint_based_optimizer module</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.hill_climbing_search">PyCTBN.PyCTBN.optimizers.hill_climbing_search module</a></li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.optimizer">PyCTBN.PyCTBN.optimizers.optimizer module</a></li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.tabu_search">PyCTBN.PyCTBN.optimizers.tabu_search module</a></li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers">Module contents</a></li> |
||||
</ul> |
||||
</li> |
||||
<li class="toctree-l1"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html">PyCTBN.PyCTBN.structure_graph package</a><ul> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#submodules">Submodules</a></li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix">PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix module</a></li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.network_graph">PyCTBN.PyCTBN.structure_graph.network_graph module</a></li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.sample_path">PyCTBN.PyCTBN.structure_graph.sample_path module</a></li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.set_of_cims">PyCTBN.PyCTBN.structure_graph.set_of_cims module</a></li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.structure">PyCTBN.PyCTBN.structure_graph.structure module</a></li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.trajectory">PyCTBN.PyCTBN.structure_graph.trajectory module</a></li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph">Module contents</a></li> |
||||
</ul> |
||||
</li> |
||||
<li class="toctree-l1"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html">PyCTBN.PyCTBN.utility package</a><ul> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html#submodules">Submodules</a></li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.abstract_importer">PyCTBN.PyCTBN.utility.abstract_importer module</a></li> |
||||
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.json_importer">PyCTBN.PyCTBN.utility.json_importer module</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.sample_importer">PyCTBN.PyCTBN.utility.sample_importer module</a></li> |
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<div class="section" id="pyctbn-pyctbn-optimizers-package"> |
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<h1>PyCTBN.PyCTBN.optimizers package<a class="headerlink" href="#pyctbn-pyctbn-optimizers-package" title="Permalink to this headline">¶</a></h1> |
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<div class="section" id="submodules"> |
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2> |
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</div> |
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<div class="section" id="module-PyCTBN.PyCTBN.optimizers.constraint_based_optimizer"> |
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<span id="pyctbn-pyctbn-optimizers-constraint-based-optimizer-module"></span><h2>PyCTBN.PyCTBN.optimizers.constraint_based_optimizer module<a class="headerlink" href="#module-PyCTBN.PyCTBN.optimizers.constraint_based_optimizer" title="Permalink to this headline">¶</a></h2> |
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<dl class="py class"> |
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<dt id="PyCTBN.PyCTBN.optimizers.constraint_based_optimizer.ConstraintBasedOptimizer"> |
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<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.optimizers.constraint_based_optimizer.</code><code class="sig-name descname">ConstraintBasedOptimizer</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>, <em class="sig-param"><span class="n">structure_estimator</span><span class="p">:</span> <span class="n"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator" title="PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator">PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator</a></span></em>, <em class="sig-param"><span class="n">tot_vars_count</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.optimizers.constraint_based_optimizer.ConstraintBasedOptimizer" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <a class="reference internal" href="#PyCTBN.PyCTBN.optimizers.optimizer.Optimizer" title="PyCTBN.PyCTBN.optimizers.optimizer.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">PyCTBN.PyCTBN.optimizers.optimizer.Optimizer</span></code></a></p> |
||||
<p>Optimizer class that implement a CTPC Algorithm</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>node_id</strong> (<em>string</em>) – current node’s id</p></li> |
||||
<li><p><strong>structure_estimator</strong> (<em>class:'StructureEstimator'</em>) – a structure estimator object with the information about the net</p></li> |
||||
<li><p><strong>tot_vars_count</strong> (<em>int</em>) – number of variables in the dataset</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
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<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.optimizers.constraint_based_optimizer.ConstraintBasedOptimizer.optimize_structure"> |
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<code class="sig-name descname">optimize_structure</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.optimizers.constraint_based_optimizer.ConstraintBasedOptimizer.optimize_structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Compute Optimization process for a structure_estimator by using a CTPC Algorithm</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Returns</dt> |
||||
<dd class="field-odd"><p>the estimated structure for the node</p> |
||||
</dd> |
||||
<dt class="field-even">Return type</dt> |
||||
<dd class="field-even"><p>List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
</dd></dl> |
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|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.optimizers.hill_climbing_search"> |
||||
<span id="pyctbn-pyctbn-optimizers-hill-climbing-search-module"></span><h2>PyCTBN.PyCTBN.optimizers.hill_climbing_search module<a class="headerlink" href="#module-PyCTBN.PyCTBN.optimizers.hill_climbing_search" title="Permalink to this headline">¶</a></h2> |
||||
<dl class="py class"> |
||||
<dt id="PyCTBN.PyCTBN.optimizers.hill_climbing_search.HillClimbing"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.optimizers.hill_climbing_search.</code><code class="sig-name descname">HillClimbing</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>, <em class="sig-param"><span class="n">structure_estimator</span><span class="p">:</span> <span class="n"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator" title="PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator">PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator</a></span></em>, <em class="sig-param"><span class="n">max_parents</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">iterations_number</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">40</span></em>, <em class="sig-param"><span class="n">patience</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.optimizers.hill_climbing_search.HillClimbing" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <a class="reference internal" href="#PyCTBN.PyCTBN.optimizers.optimizer.Optimizer" title="PyCTBN.PyCTBN.optimizers.optimizer.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">PyCTBN.PyCTBN.optimizers.optimizer.Optimizer</span></code></a></p> |
||||
<p>Optimizer class that implement Hill Climbing Search</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>node_id</strong> (<em>string</em>) – current node’s id</p></li> |
||||
<li><p><strong>structure_estimator</strong> (<em>class:'StructureEstimator'</em>) – a structure estimator object with the information about the net</p></li> |
||||
<li><p><strong>max_parents</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum number of parents for each variable. If None, disabled, default to None</p></li> |
||||
<li><p><strong>iterations_number</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum number of optimization algorithm’s iteration, default to 40</p></li> |
||||
<li><p><strong>patience</strong> (<em>int</em><em>, </em><em>optional</em>) – number of iteration without any improvement before to stop the search.If None, disabled, default to None</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.optimizers.hill_climbing_search.HillClimbing.optimize_structure"> |
||||
<code class="sig-name descname">optimize_structure</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → List<a class="headerlink" href="#PyCTBN.PyCTBN.optimizers.hill_climbing_search.HillClimbing.optimize_structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Compute Optimization process for a structure_estimator by using a Hill Climbing Algorithm</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Returns</dt> |
||||
<dd class="field-odd"><p>the estimated structure for the node</p> |
||||
</dd> |
||||
<dt class="field-even">Return type</dt> |
||||
<dd class="field-even"><p>List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
</dd></dl> |
||||
|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.optimizers.optimizer"> |
||||
<span id="pyctbn-pyctbn-optimizers-optimizer-module"></span><h2>PyCTBN.PyCTBN.optimizers.optimizer module<a class="headerlink" href="#module-PyCTBN.PyCTBN.optimizers.optimizer" title="Permalink to this headline">¶</a></h2> |
||||
<dl class="py class"> |
||||
<dt id="PyCTBN.PyCTBN.optimizers.optimizer.Optimizer"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.optimizers.optimizer.</code><code class="sig-name descname">Optimizer</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>, <em class="sig-param"><span class="n">structure_estimator</span><span class="p">:</span> <span class="n"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator" title="PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator">PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator</a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.optimizers.optimizer.Optimizer" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">abc.ABC</span></code></p> |
||||
<p>Interface class for all the optimizer’s child PyCTBN</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>node_id</strong> (<em>string</em>) – the node label</p></li> |
||||
<li><p><strong>structure_estimator</strong> (<em>class:'StructureEstimator'</em>) – A structureEstimator Object to predict the structure</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.optimizers.optimizer.Optimizer.optimize_structure"> |
||||
<em class="property">abstract </em><code class="sig-name descname">optimize_structure</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → List<a class="headerlink" href="#PyCTBN.PyCTBN.optimizers.optimizer.Optimizer.optimize_structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Compute Optimization process for a structure_estimator</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Returns</dt> |
||||
<dd class="field-odd"><p>the estimated structure for the node</p> |
||||
</dd> |
||||
<dt class="field-even">Return type</dt> |
||||
<dd class="field-even"><p>List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
</dd></dl> |
||||
|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.optimizers.tabu_search"> |
||||
<span id="pyctbn-pyctbn-optimizers-tabu-search-module"></span><h2>PyCTBN.PyCTBN.optimizers.tabu_search module<a class="headerlink" href="#module-PyCTBN.PyCTBN.optimizers.tabu_search" title="Permalink to this headline">¶</a></h2> |
||||
<dl class="py class"> |
||||
<dt id="PyCTBN.PyCTBN.optimizers.tabu_search.TabuSearch"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.optimizers.tabu_search.</code><code class="sig-name descname">TabuSearch</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>, <em class="sig-param"><span class="n">structure_estimator</span><span class="p">:</span> <span class="n"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator" title="PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator">PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator</a></span></em>, <em class="sig-param"><span class="n">max_parents</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">iterations_number</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">40</span></em>, <em class="sig-param"><span class="n">patience</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">tabu_length</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">tabu_rules_duration</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.optimizers.tabu_search.TabuSearch" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <a class="reference internal" href="#PyCTBN.PyCTBN.optimizers.optimizer.Optimizer" title="PyCTBN.PyCTBN.optimizers.optimizer.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">PyCTBN.PyCTBN.optimizers.optimizer.Optimizer</span></code></a></p> |
||||
<p>Optimizer class that implement Tabu Search</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>node_id</strong> (<em>string</em>) – current node’s id</p></li> |
||||
<li><p><strong>structure_estimator</strong> (<em>class:'StructureEstimator'</em>) – a structure estimator object with the information about the net</p></li> |
||||
<li><p><strong>max_parents</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum number of parents for each variable. If None, disabled, default to None</p></li> |
||||
<li><p><strong>iterations_number</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum number of optimization algorithm’s iteration, default to 40</p></li> |
||||
<li><p><strong>patience</strong> (<em>int</em><em>, </em><em>optional</em>) – number of iteration without any improvement before to stop the search.If None, disabled, default to None</p></li> |
||||
<li><p><strong>tabu_length</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum lenght of the data structures used in the optimization process, default to None</p></li> |
||||
<li><p><strong>tabu_rules_duration</strong> (<em>int</em><em>, </em><em>optional</em>) – number of iterations in which each rule keeps its value, default to None</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.optimizers.tabu_search.TabuSearch.optimize_structure"> |
||||
<code class="sig-name descname">optimize_structure</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → List<a class="headerlink" href="#PyCTBN.PyCTBN.optimizers.tabu_search.TabuSearch.optimize_structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Compute Optimization process for a structure_estimator by using a Hill Climbing Algorithm</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Returns</dt> |
||||
<dd class="field-odd"><p>the estimated structure for the node</p> |
||||
</dd> |
||||
<dt class="field-even">Return type</dt> |
||||
<dd class="field-even"><p>List</p> |
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</dd> |
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</dl> |
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</dd></dl> |
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</dd></dl> |
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<div class="section" id="module-PyCTBN.PyCTBN.optimizers"> |
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-PyCTBN.PyCTBN.optimizers" title="Permalink to this headline">¶</a></h2> |
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<div class="section" id="pyctbn-pyctbn-structure-graph-package"> |
||||
<h1>PyCTBN.PyCTBN.structure_graph package<a class="headerlink" href="#pyctbn-pyctbn-structure-graph-package" title="Permalink to this headline">¶</a></h1> |
||||
<div class="section" id="submodules"> |
||||
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2> |
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix"> |
||||
<span id="pyctbn-pyctbn-structure-graph-conditional-intensity-matrix-module"></span><h2>PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix module<a class="headerlink" href="#module-PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix" title="Permalink to this headline">¶</a></h2> |
||||
<dl class="py class"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.</code><code class="sig-name descname">ConditionalIntensityMatrix</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">state_residence_times</span><span class="p">:</span> <span class="n">numpy.array</span></em>, <em class="sig-param"><span class="n">state_transition_matrix</span><span class="p">:</span> <span class="n">numpy.array</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>Abstracts the Conditional Intesity matrix of a node as aggregation of the state residence times vector |
||||
and state transition matrix and the actual CIM matrix.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>state_residence_times</strong> (<em>numpy.array</em>) – state residence times vector</p></li> |
||||
<li><p><strong>state_transition_matrix</strong> (<em>numpy.ndArray</em>) – the transitions count matrix</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">_cim</dt> |
||||
<dd class="field-even"><p>the actual cim of the node</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.cim"> |
||||
<em class="property">property </em><code class="sig-name descname">cim</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.cim" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.compute_cim_coefficients"> |
||||
<code class="sig-name descname">compute_cim_coefficients</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.compute_cim_coefficients" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Compute the coefficients of the matrix _cim by using the following equality q_xx’ = M[x, x’] / T[x]. |
||||
The class member <code class="docutils literal notranslate"><span class="pre">_cim</span></code> will contain the computed cim</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.state_residence_times"> |
||||
<em class="property">property </em><code class="sig-name descname">state_residence_times</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.state_residence_times" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.state_transition_matrix"> |
||||
<em class="property">property </em><code class="sig-name descname">state_transition_matrix</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.state_transition_matrix" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
</dd></dl> |
||||
|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.structure_graph.network_graph"> |
||||
<span id="pyctbn-pyctbn-structure-graph-network-graph-module"></span><h2>PyCTBN.PyCTBN.structure_graph.network_graph module<a class="headerlink" href="#module-PyCTBN.PyCTBN.structure_graph.network_graph" title="Permalink to this headline">¶</a></h2> |
||||
<dl class="py class"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.structure_graph.network_graph.</code><code class="sig-name descname">NetworkGraph</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">graph_struct</span><span class="p">:</span> <span class="n"><a class="reference internal" href="#PyCTBN.PyCTBN.structure_graph.structure.Structure" title="PyCTBN.PyCTBN.structure_graph.structure.Structure">PyCTBN.PyCTBN.structure_graph.structure.Structure</a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>Abstracts the infos contained in the Structure class in the form of a directed graph. |
||||
Has the task of creating all the necessary filtering and indexing structures for parameters estimation</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>graph_struct</strong> (<a class="reference internal" href="#PyCTBN.PyCTBN.structure_graph.structure.Structure" title="PyCTBN.PyCTBN.structure_graph.structure.Structure"><em>Structure</em></a>) – the <code class="docutils literal notranslate"><span class="pre">Structure</span></code> object from which infos about the net will be extracted</p> |
||||
</dd> |
||||
<dt class="field-even">_graph</dt> |
||||
<dd class="field-even"><p>directed graph</p> |
||||
</dd> |
||||
<dt class="field-odd">_aggregated_info_about_nodes_parents</dt> |
||||
<dd class="field-odd"><p>a structure that contains all the necessary infos |
||||
about every parents of the node of which all the indexing and filtering structures will be constructed.</p> |
||||
</dd> |
||||
<dt class="field-even">_time_scalar_indexing_structure</dt> |
||||
<dd class="field-even"><p>the indexing structure for state res time estimation</p> |
||||
</dd> |
||||
<dt class="field-odd">_transition_scalar_indexing_structure</dt> |
||||
<dd class="field-odd"><p>the indexing structure for transition computation</p> |
||||
</dd> |
||||
<dt class="field-even">_time_filtering</dt> |
||||
<dd class="field-even"><p>the columns filtering structure used in the computation of the state res times</p> |
||||
</dd> |
||||
<dt class="field-odd">_transition_filtering</dt> |
||||
<dd class="field-odd"><p>the columns filtering structure used in the computation of the transition |
||||
from one state to another</p> |
||||
</dd> |
||||
<dt class="field-even">_p_combs_structure</dt> |
||||
<dd class="field-even"><p>all the possible parents states combination for the node of interest</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.add_edges"> |
||||
<code class="sig-name descname">add_edges</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">list_of_edges</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.add_edges" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Add the edges to the <code class="docutils literal notranslate"><span class="pre">_graph</span></code> contained in the list <code class="docutils literal notranslate"><span class="pre">list_of_edges</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>list_of_edges</strong> (<em>List</em>) – the list containing of tuples containing the edges</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.add_nodes"> |
||||
<code class="sig-name descname">add_nodes</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">list_of_nodes</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.add_nodes" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Adds the nodes to the <code class="docutils literal notranslate"><span class="pre">_graph</span></code> contained in the list of nodes <code class="docutils literal notranslate"><span class="pre">list_of_nodes</span></code>. |
||||
Sets all the properties that identify a nodes (index, positional index, cardinality)</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>list_of_nodes</strong> (<em>List</em>) – the nodes to add to <code class="docutils literal notranslate"><span class="pre">_graph</span></code></p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.build_p_comb_structure_for_a_node"> |
||||
<em class="property">static </em><code class="sig-name descname">build_p_comb_structure_for_a_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">parents_values</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → numpy.ndarray<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.build_p_comb_structure_for_a_node" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds the combinatorial structure that contains the combinations of all the values contained in |
||||
<code class="docutils literal notranslate"><span class="pre">parents_values</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>parents_values</strong> (<em>List</em>) – the cardinalities of the nodes</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>A numpy matrix containing a grid of the combinations</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>numpy.ndArray</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.build_time_columns_filtering_for_a_node"> |
||||
<em class="property">static </em><code class="sig-name descname">build_time_columns_filtering_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">p_indxs</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → numpy.ndarray<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.build_time_columns_filtering_for_a_node" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds the necessary structure to filter the desired columns indicated by <code class="docutils literal notranslate"><span class="pre">node_indx</span></code> and <code class="docutils literal notranslate"><span class="pre">p_indxs</span></code> |
||||
in the dataset. |
||||
This structute will be used in the computation of the state res times. |
||||
:param node_indx: the index of the node |
||||
:type node_indx: int |
||||
:param p_indxs: the indexes of the node’s parents |
||||
:type p_indxs: List |
||||
:return: The filtering structure for times estimation |
||||
:rtype: numpy.ndArray</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.build_time_scalar_indexing_structure_for_a_node"> |
||||
<em class="property">static </em><code class="sig-name descname">build_time_scalar_indexing_structure_for_a_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_states</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">parents_vals</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → numpy.ndarray<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.build_time_scalar_indexing_structure_for_a_node" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds an indexing structure for the computation of state residence times values.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>node_states</strong> (<em>int</em>) – the node cardinality</p></li> |
||||
<li><p><strong>parents_vals</strong> (<em>List</em>) – the caridinalites of the node’s parents</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>The time indexing structure</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>numpy.ndArray</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.build_transition_filtering_for_a_node"> |
||||
<em class="property">static </em><code class="sig-name descname">build_transition_filtering_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">p_indxs</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">nodes_number</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> → numpy.ndarray<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.build_transition_filtering_for_a_node" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds the necessary structure to filter the desired columns indicated by <code class="docutils literal notranslate"><span class="pre">node_indx</span></code> and <code class="docutils literal notranslate"><span class="pre">p_indxs</span></code> |
||||
in the dataset. |
||||
This structure will be used in the computation of the state transitions values. |
||||
:param node_indx: the index of the node |
||||
:type node_indx: int |
||||
:param p_indxs: the indexes of the node’s parents |
||||
:type p_indxs: List |
||||
:param nodes_number: the total number of nodes in the dataset |
||||
:type nodes_number: int |
||||
:return: The filtering structure for transitions estimation |
||||
:rtype: numpy.ndArray</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.build_transition_scalar_indexing_structure_for_a_node"> |
||||
<em class="property">static </em><code class="sig-name descname">build_transition_scalar_indexing_structure_for_a_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_states_number</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">parents_vals</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → numpy.ndarray<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.build_transition_scalar_indexing_structure_for_a_node" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds an indexing structure for the computation of state transitions values.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>node_states_number</strong> (<em>int</em>) – the node cardinality</p></li> |
||||
<li><p><strong>parents_vals</strong> (<em>List</em>) – the caridinalites of the node’s parents</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>The transition indexing structure</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>numpy.ndArray</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.clear_indexing_filtering_structures"> |
||||
<code class="sig-name descname">clear_indexing_filtering_structures</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.clear_indexing_filtering_structures" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Initialize all the filtering/indexing structures.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.edges"> |
||||
<em class="property">property </em><code class="sig-name descname">edges</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.edges" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.fast_init"> |
||||
<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="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.fast_init" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Initializes all the necessary structures for parameters estimation of the node identified by the label |
||||
node_id</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the label of the node</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.get_node_indx"> |
||||
<code class="sig-name descname">get_node_indx</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span></em><span class="sig-paren">)</span> → int<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.get_node_indx" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.get_ordered_by_indx_set_of_parents"> |
||||
<code class="sig-name descname">get_ordered_by_indx_set_of_parents</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → Tuple<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.get_ordered_by_indx_set_of_parents" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds the aggregated structure that holds all the infos relative to the parent set of the node, namely |
||||
(parents_labels, parents_indexes, parents_cardinalities).</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node</strong> (<em>string</em>) – the label of the node</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>a tuple containing all the parent set infos</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>Tuple</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.get_parents_by_id"> |
||||
<code class="sig-name descname">get_parents_by_id</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span></em><span class="sig-paren">)</span> → List<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.get_parents_by_id" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Returns a list of labels of the parents of the node <code class="docutils literal notranslate"><span class="pre">node_id</span></code></p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>a List of labels of the parents</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.get_positional_node_indx"> |
||||
<code class="sig-name descname">get_positional_node_indx</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span></em><span class="sig-paren">)</span> → int<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.get_positional_node_indx" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.get_states_number"> |
||||
<code class="sig-name descname">get_states_number</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span></em><span class="sig-paren">)</span> → int<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.get_states_number" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.has_edge"> |
||||
<code class="sig-name descname">has_edge</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">edge</span><span class="p">:</span> <span class="n">tuple</span></em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.has_edge" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Check if the graph contains a specific edge</p> |
||||
<dl class="simple"> |
||||
<dt>Parameters:</dt><dd><p>edge: a tuple that rappresents the edge</p> |
||||
</dd> |
||||
<dt>Returns:</dt><dd><p>bool</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.nodes"> |
||||
<em class="property">property </em><code class="sig-name descname">nodes</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.nodes" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.nodes_indexes"> |
||||
<em class="property">property </em><code class="sig-name descname">nodes_indexes</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.nodes_indexes" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.nodes_values"> |
||||
<em class="property">property </em><code class="sig-name descname">nodes_values</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.nodes_values" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.p_combs"> |
||||
<em class="property">property </em><code class="sig-name descname">p_combs</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.p_combs" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.remove_edges"> |
||||
<code class="sig-name descname">remove_edges</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">list_of_edges</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.remove_edges" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Remove the edges to the graph contained in the list list_of_edges.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>list_of_edges</strong> (<em>List</em>) – The edges to remove from the graph</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.remove_node"> |
||||
<code class="sig-name descname">remove_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> → None<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.remove_node" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Remove the node <code class="docutils literal notranslate"><span class="pre">node_id</span></code> from all the class members. |
||||
Initialize all the filtering/indexing structures.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.time_filtering"> |
||||
<em class="property">property </em><code class="sig-name descname">time_filtering</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.time_filtering" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.time_scalar_indexing_strucure"> |
||||
<em class="property">property </em><code class="sig-name descname">time_scalar_indexing_strucure</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.time_scalar_indexing_strucure" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.transition_filtering"> |
||||
<em class="property">property </em><code class="sig-name descname">transition_filtering</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.transition_filtering" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.transition_scalar_indexing_structure"> |
||||
<em class="property">property </em><code class="sig-name descname">transition_scalar_indexing_structure</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.transition_scalar_indexing_structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
</dd></dl> |
||||
|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.structure_graph.sample_path"> |
||||
<span id="pyctbn-pyctbn-structure-graph-sample-path-module"></span><h2>PyCTBN.PyCTBN.structure_graph.sample_path module<a class="headerlink" href="#module-PyCTBN.PyCTBN.structure_graph.sample_path" title="Permalink to this headline">¶</a></h2> |
||||
<dl class="py class"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.structure_graph.sample_path.</code><code class="sig-name descname">SamplePath</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">importer</span><span class="p">:</span> <span class="n"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter" title="PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter">PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter</a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>Aggregates all the informations about the trajectories, the real structure of the sampled net and variables |
||||
cardinalites. Has the task of creating the objects <code class="docutils literal notranslate"><span class="pre">Trajectory</span></code> and <code class="docutils literal notranslate"><span class="pre">Structure</span></code> that will |
||||
contain the mentioned data.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>importer</strong> (<a class="reference internal" href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter" title="PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter"><em>AbstractImporter</em></a>) – the Importer object which contains the imported and processed data</p> |
||||
</dd> |
||||
<dt class="field-even">_trajectories</dt> |
||||
<dd class="field-even"><p>the <code class="docutils literal notranslate"><span class="pre">Trajectory</span></code> object that will contain all the concatenated trajectories</p> |
||||
</dd> |
||||
<dt class="field-odd">_structure</dt> |
||||
<dd class="field-odd"><p>the <code class="docutils literal notranslate"><span class="pre">Structure</span></code> Object that will contain all the structural infos about the net</p> |
||||
</dd> |
||||
<dt class="field-even">_total_variables_count</dt> |
||||
<dd class="field-even"><p>the number of variables in the net</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.build_structure"> |
||||
<code class="sig-name descname">build_structure</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.build_structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds the <code class="docutils literal notranslate"><span class="pre">Structure</span></code> object that aggregates all the infos about the net.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.build_trajectories"> |
||||
<code class="sig-name descname">build_trajectories</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.build_trajectories" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds the Trajectory object that will contain all the trajectories. |
||||
Clears all the unused dataframes in <code class="docutils literal notranslate"><span class="pre">_importer</span></code> Object</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.clear_memory"> |
||||
<code class="sig-name descname">clear_memory</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.clear_memory" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.has_prior_net_structure"> |
||||
<em class="property">property </em><code class="sig-name descname">has_prior_net_structure</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.has_prior_net_structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.structure"> |
||||
<em class="property">property </em><code class="sig-name descname">structure</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.total_variables_count"> |
||||
<em class="property">property </em><code class="sig-name descname">total_variables_count</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.total_variables_count" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.trajectories"> |
||||
<em class="property">property </em><code class="sig-name descname">trajectories</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.trajectories" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
</dd></dl> |
||||
|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.structure_graph.set_of_cims"> |
||||
<span id="pyctbn-pyctbn-structure-graph-set-of-cims-module"></span><h2>PyCTBN.PyCTBN.structure_graph.set_of_cims module<a class="headerlink" href="#module-PyCTBN.PyCTBN.structure_graph.set_of_cims" title="Permalink to this headline">¶</a></h2> |
||||
<dl class="py class"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.structure_graph.set_of_cims.</code><code class="sig-name descname">SetOfCims</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>, <em class="sig-param"><span class="n">parents_states_number</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">node_states_number</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">p_combs</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>Aggregates all the CIMS of the node identified by the label _node_id.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>node_id</strong> – the node label</p></li> |
||||
<li><p><strong>parents_states_number</strong> (<em>List</em>) – the cardinalities of the parents</p></li> |
||||
<li><p><strong>node_states_number</strong> (<em>int</em>) – the caridinality of the node</p></li> |
||||
<li><p><strong>p_combs</strong> (<em>numpy.ndArray</em>) – the p_comb structure bound to this node</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">_state_residence_time</dt> |
||||
<dd class="field-even"><p>matrix containing all the state residence time vectors for the node</p> |
||||
</dd> |
||||
<dt class="field-odd">_transition_matrices</dt> |
||||
<dd class="field-odd"><p>matrix containing all the transition matrices for the node</p> |
||||
</dd> |
||||
<dt class="field-even">_actual_cims</dt> |
||||
<dd class="field-even"><p>the cims of the node</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.actual_cims"> |
||||
<em class="property">property </em><code class="sig-name descname">actual_cims</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.actual_cims" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.build_cims"> |
||||
<code class="sig-name descname">build_cims</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">state_res_times</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">transition_matrices</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.build_cims" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Build the <code class="docutils literal notranslate"><span class="pre">ConditionalIntensityMatrix</span></code> objects given the state residence times and transitions matrices. |
||||
Compute the cim coefficients.The class member <code class="docutils literal notranslate"><span class="pre">_actual_cims</span></code> will contain the computed cims.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>state_res_times</strong> (<em>numpy.ndArray</em>) – the state residence times matrix</p></li> |
||||
<li><p><strong>transition_matrices</strong> (<em>numpy.ndArray</em>) – the transition matrices</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.build_times_and_transitions_structures"> |
||||
<code class="sig-name descname">build_times_and_transitions_structures</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.build_times_and_transitions_structures" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Initializes at the correct dimensions the state residence times matrix and the state transition matrices.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.filter_cims_with_mask"> |
||||
<code class="sig-name descname">filter_cims_with_mask</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mask_arr</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">comb</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → numpy.ndarray<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.filter_cims_with_mask" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Filter the cims contained in the array <code class="docutils literal notranslate"><span class="pre">_actual_cims</span></code> given the boolean mask <code class="docutils literal notranslate"><span class="pre">mask_arr</span></code> and the index |
||||
<code class="docutils literal notranslate"><span class="pre">comb</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>mask_arr</strong> (<em>numpy.array</em>) – the boolean mask that indicates which parent to consider</p></li> |
||||
<li><p><strong>comb</strong> (<em>numpy.array</em>) – the state/s of the filtered parents</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>Array of <code class="docutils literal notranslate"><span class="pre">ConditionalIntensityMatrix</span></code> objects</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>numpy.array</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.get_cims_number"> |
||||
<code class="sig-name descname">get_cims_number</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.get_cims_number" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.p_combs"> |
||||
<em class="property">property </em><code class="sig-name descname">p_combs</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.p_combs" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
</dd></dl> |
||||
|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.structure_graph.structure"> |
||||
<span id="pyctbn-pyctbn-structure-graph-structure-module"></span><h2>PyCTBN.PyCTBN.structure_graph.structure module<a class="headerlink" href="#module-PyCTBN.PyCTBN.structure_graph.structure" title="Permalink to this headline">¶</a></h2> |
||||
<dl class="py class"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.structure.Structure"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.structure_graph.structure.</code><code class="sig-name descname">Structure</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">nodes_labels_list</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">nodes_indexes_arr</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">nodes_vals_arr</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">edges_list</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">total_variables_number</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.structure.Structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>Contains all the infos about the network structure(nodes labels, nodes caridinalites, edges, indexes)</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>nodes_labels_list</strong> (<em>List</em>) – the symbolic names of the variables</p></li> |
||||
<li><p><strong>nodes_indexes_arr</strong> (<em>numpy.ndArray</em>) – the indexes of the nodes</p></li> |
||||
<li><p><strong>nodes_vals_arr</strong> (<em>numpy.ndArray</em>) – the cardinalites of the nodes</p></li> |
||||
<li><p><strong>edges_list</strong> (<em>List</em>) – the edges of the network</p></li> |
||||
<li><p><strong>total_variables_number</strong> (<em>int</em>) – the total number of variables in the dataset</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.structure.Structure.add_edge"> |
||||
<code class="sig-name descname">add_edge</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">edge</span><span class="p">:</span> <span class="n">tuple</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.structure.Structure.add_edge" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.structure.Structure.clean_structure_edges"> |
||||
<code class="sig-name descname">clean_structure_edges</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.structure.Structure.clean_structure_edges" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.structure.Structure.contains_edge"> |
||||
<code class="sig-name descname">contains_edge</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">edge</span><span class="p">:</span> <span class="n">tuple</span></em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.structure.Structure.contains_edge" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.structure.Structure.edges"> |
||||
<em class="property">property </em><code class="sig-name descname">edges</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.structure.Structure.edges" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.structure.Structure.get_node_id"> |
||||
<code class="sig-name descname">get_node_id</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><span class="sig-paren">)</span> → str<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.structure.Structure.get_node_id" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Given the <code class="docutils literal notranslate"><span class="pre">node_index</span></code> returns the node label.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node_indx</strong> (<em>int</em>) – the node index</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>the node label</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>string</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.structure.Structure.get_node_indx"> |
||||
<code class="sig-name descname">get_node_indx</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> → int<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.structure.Structure.get_node_indx" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Given the <code class="docutils literal notranslate"><span class="pre">node_index</span></code> returns the node label.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>the node index</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>int</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.structure.Structure.get_positional_node_indx"> |
||||
<code class="sig-name descname">get_positional_node_indx</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> → int<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.structure.Structure.get_positional_node_indx" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.structure.Structure.get_states_number"> |
||||
<code class="sig-name descname">get_states_number</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → int<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.structure.Structure.get_states_number" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Given the node label <code class="docutils literal notranslate"><span class="pre">node</span></code> returns the cardinality of the node.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node</strong> (<em>string</em>) – the node label</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>the node cardinality</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>int</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.structure.Structure.nodes_indexes"> |
||||
<em class="property">property </em><code class="sig-name descname">nodes_indexes</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.structure.Structure.nodes_indexes" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.structure.Structure.nodes_labels"> |
||||
<em class="property">property </em><code class="sig-name descname">nodes_labels</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.structure.Structure.nodes_labels" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.structure.Structure.nodes_values"> |
||||
<em class="property">property </em><code class="sig-name descname">nodes_values</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.structure.Structure.nodes_values" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.structure.Structure.remove_edge"> |
||||
<code class="sig-name descname">remove_edge</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">edge</span><span class="p">:</span> <span class="n">tuple</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.structure.Structure.remove_edge" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.structure.Structure.remove_node"> |
||||
<code class="sig-name descname">remove_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> → None<a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.structure.Structure.remove_node" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Remove the node <code class="docutils literal notranslate"><span class="pre">node_id</span></code> from all the class members. |
||||
The class member <code class="docutils literal notranslate"><span class="pre">_total_variables_number</span></code> since it refers to the total number of variables in the dataset.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.structure.Structure.total_variables_number"> |
||||
<em class="property">property </em><code class="sig-name descname">total_variables_number</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.structure.Structure.total_variables_number" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
</dd></dl> |
||||
|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.structure_graph.trajectory"> |
||||
<span id="pyctbn-pyctbn-structure-graph-trajectory-module"></span><h2>PyCTBN.PyCTBN.structure_graph.trajectory module<a class="headerlink" href="#module-PyCTBN.PyCTBN.structure_graph.trajectory" title="Permalink to this headline">¶</a></h2> |
||||
<dl class="py class"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.structure_graph.trajectory.</code><code class="sig-name descname">Trajectory</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">list_of_columns</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">original_cols_number</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>Abstracts the infos about a complete set of trajectories, represented as a numpy array of doubles |
||||
(the time deltas) and a numpy matrix of ints (the changes of states).</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>list_of_columns</strong> (<em>List</em>) – the list containing the times array and values matrix</p></li> |
||||
<li><p><strong>original_cols_number</strong> (<em>int</em>) – total number of cols in the data</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">_actual_trajectory</dt> |
||||
<dd class="field-even"><p>the trajectory containing also the duplicated/shifted values</p> |
||||
</dd> |
||||
<dt class="field-odd">_times</dt> |
||||
<dd class="field-odd"><p>the array containing the time deltas</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory.complete_trajectory"> |
||||
<em class="property">property </em><code class="sig-name descname">complete_trajectory</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory.complete_trajectory" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory.size"> |
||||
<code class="sig-name descname">size</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory.size" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory.times"> |
||||
<em class="property">property </em><code class="sig-name descname">times</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory.times" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory.trajectory"> |
||||
<em class="property">property </em><code class="sig-name descname">trajectory</code><a class="headerlink" href="#PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory.trajectory" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
</dd></dl> |
||||
|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.structure_graph"> |
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-PyCTBN.PyCTBN.structure_graph" title="Permalink to this headline">¶</a></h2> |
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<div id="content" class="hfeed entry-container hentry"> |
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<div class="section" id="pyctbn-pyctbn-utility-package"> |
||||
<h1>PyCTBN.PyCTBN.utility package<a class="headerlink" href="#pyctbn-pyctbn-utility-package" title="Permalink to this headline">¶</a></h1> |
||||
<div class="section" id="submodules"> |
||||
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2> |
||||
</div> |
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<div class="section" id="module-PyCTBN.PyCTBN.utility.abstract_importer"> |
||||
<span id="pyctbn-pyctbn-utility-abstract-importer-module"></span><h2>PyCTBN.PyCTBN.utility.abstract_importer module<a class="headerlink" href="#module-PyCTBN.PyCTBN.utility.abstract_importer" title="Permalink to this headline">¶</a></h2> |
||||
<dl class="py class"> |
||||
<dt id="PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.utility.abstract_importer.</code><code class="sig-name descname">AbstractImporter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_path</span><span class="p">:</span> <span class="n">str</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">trajectory_list</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>pandas.core.frame.DataFrame<span class="p">, </span>numpy.ndarray<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">variables</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">prior_net_structure</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">abc.ABC</span></code></p> |
||||
<p>Abstract class that exposes all the necessary methods to process the trajectories and the net structure.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>file_path</strong> (<em>str</em>) – the file path, or dataset name if you import already processed data</p></li> |
||||
<li><p><strong>trajectory_list</strong> (<em>typing.Union</em><em>[</em><em>pandas.DataFrame</em><em>, </em><em>numpy.ndarray</em><em>]</em>) – Dataframe or numpy array containing the concatenation of all the processed trajectories</p></li> |
||||
<li><p><strong>variables</strong> (<em>pandas.DataFrame</em>) – Dataframe containing the nodes labels and cardinalities</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Prior_net_structure</dt> |
||||
<dd class="field-even"><p>Dataframe containing the structure of the network (edges)</p> |
||||
</dd> |
||||
<dt class="field-odd">_sorter</dt> |
||||
<dd class="field-odd"><p>A list containing the variables labels in the SAME order as the columns in <code class="docutils literal notranslate"><span class="pre">concatenated_samples</span></code></p> |
||||
</dd> |
||||
</dl> |
||||
<div class="admonition warning"> |
||||
<p class="admonition-title">Warning</p> |
||||
<p>The parameters <code class="docutils literal notranslate"><span class="pre">variables</span></code> and <code class="docutils literal notranslate"><span class="pre">prior_net_structure</span></code> HAVE to be properly constructed |
||||
as Pandas Dataframes with the following structure: |
||||
Header of _df_structure = [From_Node | To_Node] |
||||
Header of _df_variables = [Variable_Label | Variable_Cardinality] |
||||
See the tutorial on how to construct a correct <code class="docutils literal notranslate"><span class="pre">concatenated_samples</span></code> Dataframe/ndarray.</p> |
||||
</div> |
||||
<div class="admonition note"> |
||||
<p class="admonition-title">Note</p> |
||||
<p>See :class:<code class="docutils literal notranslate"><span class="pre">JsonImporter</span></code> for an example implementation</p> |
||||
</div> |
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.build_list_of_samples_array"> |
||||
<code class="sig-name descname">build_list_of_samples_array</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">concatenated_sample</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span></em><span class="sig-paren">)</span> → List<a class="headerlink" href="#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.build_list_of_samples_array" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds a List containing the the delta times numpy array, and the complete transitions matrix</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>concatenated_sample</strong> (<em>pandas.Dataframe</em>) – the dataframe/array from which the time, and transitions matrix have to be extracted |
||||
and converted</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>the resulting list of numpy arrays</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.build_sorter"> |
||||
<em class="property">abstract </em><code class="sig-name descname">build_sorter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">trajecory_header</span><span class="p">:</span> <span class="n">object</span></em><span class="sig-paren">)</span> → List<a class="headerlink" href="#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.build_sorter" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Initializes the <code class="docutils literal notranslate"><span class="pre">_sorter</span></code> class member from a trajectory dataframe, exctracting the header of the frame |
||||
and keeping ONLY the variables symbolic labels, cutting out the time label in the header.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>trajecory_header</strong> (<em>object</em>) – an object that will be used to define the header</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>A list containing the processed header.</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.clear_concatenated_frame"> |
||||
<code class="sig-name descname">clear_concatenated_frame</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.clear_concatenated_frame" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Removes all values in the dataframe concatenated_samples.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.compute_row_delta_in_all_samples_frames"> |
||||
<code class="sig-name descname">compute_row_delta_in_all_samples_frames</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">df_samples_list</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.compute_row_delta_in_all_samples_frames" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Calls the method <code class="docutils literal notranslate"><span class="pre">compute_row_delta_sigle_samples_frame</span></code> on every dataframe present in the list |
||||
<code class="docutils literal notranslate"><span class="pre">df_samples_list</span></code>. |
||||
Concatenates the result in the dataframe <code class="docutils literal notranslate"><span class="pre">concatanated_samples</span></code></p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>df_samples_list</strong> (<em>List</em>) – the datframe’s list to be processed and concatenated</p> |
||||
</dd> |
||||
</dl> |
||||
<div class="admonition warning"> |
||||
<p class="admonition-title">Warning</p> |
||||
<p>The Dataframe sample_frame has to follow the column structure of this header: |
||||
Header of sample_frame = [Time | Variable values] |
||||
The class member self._sorter HAS to be properly INITIALIZED (See class members definition doc)</p> |
||||
</div> |
||||
<div class="admonition note"> |
||||
<p class="admonition-title">Note</p> |
||||
<p>After the call of this method the class member <code class="docutils literal notranslate"><span class="pre">concatanated_samples</span></code> will contain all processed |
||||
and merged trajectories</p> |
||||
</div> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.compute_row_delta_sigle_samples_frame"> |
||||
<code class="sig-name descname">compute_row_delta_sigle_samples_frame</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_frame</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span></em>, <em class="sig-param"><span class="n">columns_header</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">shifted_cols_header</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → pandas.core.frame.DataFrame<a class="headerlink" href="#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.compute_row_delta_sigle_samples_frame" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Computes the difference between each value present in th time column. |
||||
Copies and shift by one position up all the values present in the remaining columns.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>sample_frame</strong> (<em>pandas.Dataframe</em>) – the traj to be processed</p></li> |
||||
<li><p><strong>columns_header</strong> (<em>List</em>) – the original header of sample_frame</p></li> |
||||
<li><p><strong>shifted_cols_header</strong> (<em>List</em>) – a copy of columns_header with changed names of the contents</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>The processed dataframe</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>pandas.Dataframe</p> |
||||
</dd> |
||||
</dl> |
||||
<div class="admonition warning"> |
||||
<p class="admonition-title">Warning</p> |
||||
<p>the Dataframe <code class="docutils literal notranslate"><span class="pre">sample_frame</span></code> has to follow the column structure of this header: |
||||
Header of sample_frame = [Time | Variable values]</p> |
||||
</div> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.concatenated_samples"> |
||||
<em class="property">property </em><code class="sig-name descname">concatenated_samples</code><a class="headerlink" href="#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.concatenated_samples" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.dataset_id"> |
||||
<em class="property">abstract </em><code class="sig-name descname">dataset_id</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → object<a class="headerlink" href="#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.dataset_id" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>If the original dataset contains multiple dataset, this method returns a unique id to identify the current |
||||
dataset</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.file_path"> |
||||
<em class="property">property </em><code class="sig-name descname">file_path</code><a class="headerlink" href="#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.file_path" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.sorter"> |
||||
<em class="property">property </em><code class="sig-name descname">sorter</code><a class="headerlink" href="#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.sorter" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.structure"> |
||||
<em class="property">property </em><code class="sig-name descname">structure</code><a class="headerlink" href="#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.variables"> |
||||
<em class="property">property </em><code class="sig-name descname">variables</code><a class="headerlink" href="#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.variables" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
</dd></dl> |
||||
|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.utility.cache"> |
||||
<span id="pyctbn-pyctbn-utility-cache-module"></span><h2>PyCTBN.PyCTBN.utility.cache module<a class="headerlink" href="#module-PyCTBN.PyCTBN.utility.cache" title="Permalink to this headline">¶</a></h2> |
||||
<dl class="py class"> |
||||
<dt id="PyCTBN.PyCTBN.utility.cache.Cache"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.utility.cache.</code><code class="sig-name descname">Cache</code><a class="headerlink" href="#PyCTBN.PyCTBN.utility.cache.Cache" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>This class acts as a cache of <code class="docutils literal notranslate"><span class="pre">SetOfCims</span></code> objects for a node.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">__list_of_sets_of_parents</dt> |
||||
<dd class="field-odd"><p>a list of <code class="docutils literal notranslate"><span class="pre">Sets</span></code> objects of the parents to which the cim in cache at SAME |
||||
index is related</p> |
||||
</dd> |
||||
<dt class="field-even">__actual_cache</dt> |
||||
<dd class="field-even"><p>a list of setOfCims objects</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.cache.Cache.clear"> |
||||
<code class="sig-name descname">clear</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.utility.cache.Cache.clear" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Clear the contents both of <code class="docutils literal notranslate"><span class="pre">__actual_cache</span></code> and <code class="docutils literal notranslate"><span class="pre">__list_of_sets_of_parents</span></code>.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.cache.Cache.find"> |
||||
<code class="sig-name descname">find</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">parents_comb</span><span class="p">:</span> <span class="n">Set</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.utility.cache.Cache.find" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Tries to find in cache given the symbolic parents combination <code class="docutils literal notranslate"><span class="pre">parents_comb</span></code> the <code class="docutils literal notranslate"><span class="pre">SetOfCims</span></code> |
||||
related to that <code class="docutils literal notranslate"><span class="pre">parents_comb</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>parents_comb</strong> (<em>Set</em>) – the parents related to that <code class="docutils literal notranslate"><span class="pre">SetOfCims</span></code></p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>A <code class="docutils literal notranslate"><span class="pre">SetOfCims</span></code> object if the <code class="docutils literal notranslate"><span class="pre">parents_comb</span></code> index is found in <code class="docutils literal notranslate"><span class="pre">__list_of_sets_of_parents</span></code>. |
||||
None otherwise.</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims" title="PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims">SetOfCims</a></p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.cache.Cache.put"> |
||||
<code class="sig-name descname">put</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">parents_comb</span><span class="p">:</span> <span class="n">Set</span></em>, <em class="sig-param"><span class="n">socim</span><span class="p">:</span> <span class="n"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims" title="PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims">PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims</a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.utility.cache.Cache.put" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Place in cache the <code class="docutils literal notranslate"><span class="pre">SetOfCims</span></code> object, and the related symbolic index <code class="docutils literal notranslate"><span class="pre">parents_comb</span></code> in |
||||
<code class="docutils literal notranslate"><span class="pre">__list_of_sets_of_parents</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>parents_comb</strong> (<em>Set</em>) – the symbolic set index</p></li> |
||||
<li><p><strong>socim</strong> (<a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims" title="PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims"><em>SetOfCims</em></a>) – the related SetOfCims object</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
</dd></dl> |
||||
|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.utility.json_importer"> |
||||
<span id="pyctbn-pyctbn-utility-json-importer-module"></span><h2>PyCTBN.PyCTBN.utility.json_importer module<a class="headerlink" href="#module-PyCTBN.PyCTBN.utility.json_importer" title="Permalink to this headline">¶</a></h2> |
||||
<dl class="py class"> |
||||
<dt id="PyCTBN.PyCTBN.utility.json_importer.JsonImporter"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.utility.json_importer.</code><code class="sig-name descname">JsonImporter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_path</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">samples_label</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">structure_label</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">variables_label</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">time_key</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">variables_key</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.utility.json_importer.JsonImporter" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <a class="reference internal" href="#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter" title="PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter"><code class="xref py py-class docutils literal notranslate"><span class="pre">PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter</span></code></a></p> |
||||
<p>Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare |
||||
the data in json extension.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>file_path</strong> (<em>string</em>) – the path of the file that contains tha data to be imported</p></li> |
||||
<li><p><strong>samples_label</strong> (<em>string</em>) – the reference key for the samples in the trajectories</p></li> |
||||
<li><p><strong>structure_label</strong> (<em>string</em>) – the reference key for the structure of the network data</p></li> |
||||
<li><p><strong>variables_label</strong> (<em>string</em>) – the reference key for the cardinalites of the nodes data</p></li> |
||||
<li><p><strong>time_key</strong> (<em>string</em>) – the key used to identify the timestamps in each trajectory</p></li> |
||||
<li><p><strong>variables_key</strong> (<em>string</em>) – the key used to identify the names of the variables in the net</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">_array_indx</dt> |
||||
<dd class="field-even"><p>the index of the outer JsonArray to extract the data from</p> |
||||
</dd> |
||||
<dt class="field-odd">_df_samples_list</dt> |
||||
<dd class="field-odd"><p>a Dataframe list in which every dataframe contains a trajectory</p> |
||||
</dd> |
||||
<dt class="field-even">_raw_data</dt> |
||||
<dd class="field-even"><p>The raw contents of the json file to import</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.json_importer.JsonImporter.build_sorter"> |
||||
<code class="sig-name descname">build_sorter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_frame</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span></em><span class="sig-paren">)</span> → List<a class="headerlink" href="#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.build_sorter" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Implements the abstract method build_sorter of the <code class="xref py py-class docutils literal notranslate"><span class="pre">AbstractImporter</span></code> for this dataset.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.json_importer.JsonImporter.clear_data_frame_list"> |
||||
<code class="sig-name descname">clear_data_frame_list</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.clear_data_frame_list" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Removes all values present in the dataframes in the list <code class="docutils literal notranslate"><span class="pre">_df_samples_list</span></code>.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.json_importer.JsonImporter.dataset_id"> |
||||
<code class="sig-name descname">dataset_id</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → object<a class="headerlink" href="#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.dataset_id" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>If the original dataset contains multiple dataset, this method returns a unique id to identify the current |
||||
dataset</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.json_importer.JsonImporter.import_data"> |
||||
<code class="sig-name descname">import_data</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">indx</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.import_data" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Implements the abstract method of <code class="xref py py-class docutils literal notranslate"><span class="pre">AbstractImporter</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>indx</strong> (<em>int</em>) – the index of the outer JsonArray to extract the data from</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.json_importer.JsonImporter.import_sampled_cims"> |
||||
<code class="sig-name descname">import_sampled_cims</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">cims_key</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → Dict<a class="headerlink" href="#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.import_sampled_cims" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Imports the synthetic CIMS in the dataset in a dictionary, using variables labels |
||||
as keys for the set of CIMS of a particular node.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p></li> |
||||
<li><p><strong>indx</strong> (<em>int</em>) – The index of the array from which the data have to be extracted</p></li> |
||||
<li><p><strong>cims_key</strong> (<em>string</em>) – the key where the json object cims are placed</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>a dictionary containing the sampled CIMS for all the variables in the net</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>Dictionary</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.json_importer.JsonImporter.import_structure"> |
||||
<code class="sig-name descname">import_structure</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → pandas.core.frame.DataFrame<a class="headerlink" href="#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.import_structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Imports in a dataframe the data in the list raw_data at the key <code class="docutils literal notranslate"><span class="pre">_structure_label</span></code></p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>Dataframe containg the starting node a ending node of every arc of the network</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>pandas.Dataframe</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.json_importer.JsonImporter.import_trajectories"> |
||||
<code class="sig-name descname">import_trajectories</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → List<a class="headerlink" href="#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.import_trajectories" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Imports the trajectories from the list of dicts <code class="docutils literal notranslate"><span class="pre">raw_data</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>List of dataframes containing all the trajectories</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.json_importer.JsonImporter.import_variables"> |
||||
<code class="sig-name descname">import_variables</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → pandas.core.frame.DataFrame<a class="headerlink" href="#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.import_variables" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Imports the data in <code class="docutils literal notranslate"><span class="pre">raw_data</span></code> at the key <code class="docutils literal notranslate"><span class="pre">_variables_label</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>Datframe containg the variables simbolic labels and their cardinalities</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>pandas.Dataframe</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.json_importer.JsonImporter.normalize_trajectories"> |
||||
<code class="sig-name descname">normalize_trajectories</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">trajectories_key</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → List<a class="headerlink" href="#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.normalize_trajectories" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Extracts the trajectories in <code class="docutils literal notranslate"><span class="pre">raw_data</span></code> at the index <code class="docutils literal notranslate"><span class="pre">index</span></code> at the key <code class="docutils literal notranslate"><span class="pre">trajectories</span> <span class="pre">key</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p></li> |
||||
<li><p><strong>indx</strong> (<em>int</em>) – The index of the array from which the data have to be extracted</p></li> |
||||
<li><p><strong>trajectories_key</strong> (<em>string</em>) – the key of the trajectories objects</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>A list of daframes containg the trajectories</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.json_importer.JsonImporter.one_level_normalizing"> |
||||
<code class="sig-name descname">one_level_normalizing</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">key</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → pandas.core.frame.DataFrame<a class="headerlink" href="#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.one_level_normalizing" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Extracts the one-level nested data in the list <code class="docutils literal notranslate"><span class="pre">raw_data</span></code> at the index <code class="docutils literal notranslate"><span class="pre">indx</span></code> at the key <code class="docutils literal notranslate"><span class="pre">key</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p></li> |
||||
<li><p><strong>indx</strong> (<em>int</em>) – The index of the array from which the data have to be extracted</p></li> |
||||
<li><p><strong>key</strong> (<em>string</em>) – the key for the Dicts from which exctract data</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>A normalized dataframe</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>pandas.Datframe</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.json_importer.JsonImporter.read_json_file"> |
||||
<code class="sig-name descname">read_json_file</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → List<a class="headerlink" href="#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.read_json_file" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Reads the JSON file in the path self.filePath.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Returns</dt> |
||||
<dd class="field-odd"><p>The contents of the json file</p> |
||||
</dd> |
||||
<dt class="field-even">Return type</dt> |
||||
<dd class="field-even"><p>List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
</dd></dl> |
||||
|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.utility.sample_importer"> |
||||
<span id="pyctbn-pyctbn-utility-sample-importer-module"></span><h2>PyCTBN.PyCTBN.utility.sample_importer module<a class="headerlink" href="#module-PyCTBN.PyCTBN.utility.sample_importer" title="Permalink to this headline">¶</a></h2> |
||||
<dl class="py class"> |
||||
<dt id="PyCTBN.PyCTBN.utility.sample_importer.SampleImporter"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">PyCTBN.PyCTBN.utility.sample_importer.</code><code class="sig-name descname">SampleImporter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">trajectory_list</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>pandas.core.frame.DataFrame<span class="p">, </span>numpy.ndarray<span class="p">, </span>List<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">variables</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>pandas.core.frame.DataFrame<span class="p">, </span>numpy.ndarray<span class="p">, </span>List<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">prior_net_structure</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>pandas.core.frame.DataFrame<span class="p">, </span>numpy.ndarray<span class="p">, </span>List<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.utility.sample_importer.SampleImporter" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <a class="reference internal" href="#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter" title="PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter"><code class="xref py py-class docutils literal notranslate"><span class="pre">PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter</span></code></a></p> |
||||
<p>Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare |
||||
the data loaded directly by using DataFrame</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>trajectory_list</strong> (<em>typing.Union</em><em>[</em><em>pd.DataFrame</em><em>, </em><em>np.ndarray</em><em>, </em><em>typing.List</em><em>]</em>) – the data that describes the trajectories</p></li> |
||||
<li><p><strong>variables</strong> (<em>typing.Union</em><em>[</em><em>pd.DataFrame</em><em>, </em><em>np.ndarray</em><em>, </em><em>typing.List</em><em>]</em>) – the data that describes the variables with name and cardinality</p></li> |
||||
<li><p><strong>prior_net_structure</strong> (<em>typing.Union</em><em>[</em><em>pd.DataFrame</em><em>, </em><em>np.ndarray</em><em>, </em><em>typing.List</em><em>]</em>) – the data of the real structure, if it exists</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">_df_samples_list</dt> |
||||
<dd class="field-even"><p>a Dataframe list in which every dataframe contains a trajectory</p> |
||||
</dd> |
||||
<dt class="field-odd">_raw_data</dt> |
||||
<dd class="field-odd"><p>The raw contents of the json file to import</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.sample_importer.SampleImporter.build_sorter"> |
||||
<code class="sig-name descname">build_sorter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_frame</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span></em><span class="sig-paren">)</span> → List<a class="headerlink" href="#PyCTBN.PyCTBN.utility.sample_importer.SampleImporter.build_sorter" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Implements the abstract method build_sorter of the <code class="xref py py-class docutils literal notranslate"><span class="pre">AbstractImporter</span></code> in order to get the ordered variables list.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.sample_importer.SampleImporter.dataset_id"> |
||||
<code class="sig-name descname">dataset_id</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → str<a class="headerlink" href="#PyCTBN.PyCTBN.utility.sample_importer.SampleImporter.dataset_id" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>If the original dataset contains multiple dataset, this method returns a unique id to identify the current |
||||
dataset</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="PyCTBN.PyCTBN.utility.sample_importer.SampleImporter.import_data"> |
||||
<code class="sig-name descname">import_data</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">header_column</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#PyCTBN.PyCTBN.utility.sample_importer.SampleImporter.import_data" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
</dd></dl> |
||||
|
||||
</div> |
||||
<div class="section" id="module-PyCTBN.PyCTBN.utility"> |
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-PyCTBN.PyCTBN.utility" title="Permalink to this headline">¶</a></h2> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.html">PyCTBN.PyCTBN package</a></li> |
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<div class="section" id="pyctbn-package"> |
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<h1>PyCTBN package<a class="headerlink" href="#pyctbn-package" title="Permalink to this headline">¶</a></h1> |
||||
<div class="section" id="subpackages"> |
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<h2>Subpackages<a class="headerlink" href="#subpackages" title="Permalink to this headline">¶</a></h2> |
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<ul> |
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<li class="toctree-l1"><a class="reference internal" href="PyCTBN.PyCTBN.html">PyCTBN.PyCTBN package</a><ul> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.html#subpackages">Subpackages</a><ul> |
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<li class="toctree-l3"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html">PyCTBN.PyCTBN.estimators package</a><ul> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#submodules">Submodules</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.fam_score_calculator">PyCTBN.PyCTBN.estimators.fam_score_calculator module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.parameters_estimator">PyCTBN.PyCTBN.estimators.parameters_estimator module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator">PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_estimator">PyCTBN.PyCTBN.estimators.structure_estimator module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_score_based_estimator">PyCTBN.PyCTBN.estimators.structure_score_based_estimator module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators">Module contents</a></li> |
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</ul> |
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</li> |
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<li class="toctree-l3"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html">PyCTBN.PyCTBN.optimizers package</a><ul> |
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<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html#submodules">Submodules</a></li> |
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<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.constraint_based_optimizer">PyCTBN.PyCTBN.optimizers.constraint_based_optimizer module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.hill_climbing_search">PyCTBN.PyCTBN.optimizers.hill_climbing_search module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.optimizer">PyCTBN.PyCTBN.optimizers.optimizer module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.tabu_search">PyCTBN.PyCTBN.optimizers.tabu_search module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers">Module contents</a></li> |
||||
</ul> |
||||
</li> |
||||
<li class="toctree-l3"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html">PyCTBN.PyCTBN.structure_graph package</a><ul> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#submodules">Submodules</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix">PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.network_graph">PyCTBN.PyCTBN.structure_graph.network_graph module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.sample_path">PyCTBN.PyCTBN.structure_graph.sample_path module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.set_of_cims">PyCTBN.PyCTBN.structure_graph.set_of_cims module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.structure">PyCTBN.PyCTBN.structure_graph.structure module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.trajectory">PyCTBN.PyCTBN.structure_graph.trajectory module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph">Module contents</a></li> |
||||
</ul> |
||||
</li> |
||||
<li class="toctree-l3"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html">PyCTBN.PyCTBN.utility package</a><ul> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html#submodules">Submodules</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.abstract_importer">PyCTBN.PyCTBN.utility.abstract_importer module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.cache">PyCTBN.PyCTBN.utility.cache module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.json_importer">PyCTBN.PyCTBN.utility.json_importer module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.sample_importer">PyCTBN.PyCTBN.utility.sample_importer module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility">Module contents</a></li> |
||||
</ul> |
||||
</li> |
||||
</ul> |
||||
</li> |
||||
<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.html#module-PyCTBN.PyCTBN">Module contents</a></li> |
||||
</ul> |
||||
</li> |
||||
</ul> |
||||
</div> |
||||
</div> |
||||
<div class="section" id="submodules"> |
||||
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2> |
||||
</div> |
||||
<div class="section" id="module-PyCTBN"> |
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-PyCTBN" title="Permalink to this headline">¶</a></h2> |
||||
</div> |
||||
</div> |
||||
|
||||
|
||||
</div> |
||||
<footer> |
||||
|
||||
|
||||
<hr/> |
||||
|
||||
<div role="contentinfo"> |
||||
<p> |
||||
© Copyright 2021, Bregoli Alessandro, Martini Filippo, Moretti Luca. |
||||
</p> |
||||
</div> |
||||
|
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</html> |
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@ -1,53 +0,0 @@ |
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PyCTBN.PyCTBN.estimators package |
||||
================================ |
||||
|
||||
Submodules |
||||
---------- |
||||
|
||||
PyCTBN.PyCTBN.estimators.fam\_score\_calculator module |
||||
------------------------------------------------------ |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.estimators.fam_score_calculator |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
PyCTBN.PyCTBN.estimators.parameters\_estimator module |
||||
----------------------------------------------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.estimators.parameters_estimator |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
PyCTBN.PyCTBN.estimators.structure\_constraint\_based\_estimator module |
||||
----------------------------------------------------------------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
PyCTBN.PyCTBN.estimators.structure\_estimator module |
||||
---------------------------------------------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.estimators.structure_estimator |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
PyCTBN.PyCTBN.estimators.structure\_score\_based\_estimator module |
||||
------------------------------------------------------------------ |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.estimators.structure_score_based_estimator |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
Module contents |
||||
--------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.estimators |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
@ -1,45 +0,0 @@ |
||||
PyCTBN.PyCTBN.optimizers package |
||||
================================ |
||||
|
||||
Submodules |
||||
---------- |
||||
|
||||
PyCTBN.PyCTBN.optimizers.constraint\_based\_optimizer module |
||||
------------------------------------------------------------ |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.optimizers.constraint_based_optimizer |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
PyCTBN.PyCTBN.optimizers.hill\_climbing\_search module |
||||
------------------------------------------------------ |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.optimizers.hill_climbing_search |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
PyCTBN.PyCTBN.optimizers.optimizer module |
||||
----------------------------------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.optimizers.optimizer |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
PyCTBN.PyCTBN.optimizers.tabu\_search module |
||||
-------------------------------------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.optimizers.tabu_search |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
Module contents |
||||
--------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.optimizers |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
@ -1,21 +0,0 @@ |
||||
PyCTBN.PyCTBN package |
||||
===================== |
||||
|
||||
Subpackages |
||||
----------- |
||||
|
||||
.. toctree:: |
||||
:maxdepth: 4 |
||||
|
||||
PyCTBN.PyCTBN.estimators |
||||
PyCTBN.PyCTBN.optimizers |
||||
PyCTBN.PyCTBN.structure_graph |
||||
PyCTBN.PyCTBN.utility |
||||
|
||||
Module contents |
||||
--------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
@ -1,61 +0,0 @@ |
||||
PyCTBN.PyCTBN.structure\_graph package |
||||
====================================== |
||||
|
||||
Submodules |
||||
---------- |
||||
|
||||
PyCTBN.PyCTBN.structure\_graph.conditional\_intensity\_matrix module |
||||
-------------------------------------------------------------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
PyCTBN.PyCTBN.structure\_graph.network\_graph module |
||||
---------------------------------------------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.structure_graph.network_graph |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
PyCTBN.PyCTBN.structure\_graph.sample\_path module |
||||
-------------------------------------------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.structure_graph.sample_path |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
PyCTBN.PyCTBN.structure\_graph.set\_of\_cims module |
||||
--------------------------------------------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.structure_graph.set_of_cims |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
PyCTBN.PyCTBN.structure\_graph.structure module |
||||
----------------------------------------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.structure_graph.structure |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
PyCTBN.PyCTBN.structure\_graph.trajectory module |
||||
------------------------------------------------ |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.structure_graph.trajectory |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
Module contents |
||||
--------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.structure_graph |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
@ -1,45 +0,0 @@ |
||||
PyCTBN.PyCTBN.utility package |
||||
============================= |
||||
|
||||
Submodules |
||||
---------- |
||||
|
||||
PyCTBN.PyCTBN.utility.abstract\_importer module |
||||
----------------------------------------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.utility.abstract_importer |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
PyCTBN.PyCTBN.utility.cache module |
||||
---------------------------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.utility.cache |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
PyCTBN.PyCTBN.utility.json\_importer module |
||||
------------------------------------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.utility.json_importer |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
PyCTBN.PyCTBN.utility.sample\_importer module |
||||
--------------------------------------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.utility.sample_importer |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
Module contents |
||||
--------------- |
||||
|
||||
.. automodule:: PyCTBN.PyCTBN.utility |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
@ -1,21 +0,0 @@ |
||||
PyCTBN package |
||||
============== |
||||
|
||||
Subpackages |
||||
----------- |
||||
|
||||
.. toctree:: |
||||
:maxdepth: 4 |
||||
|
||||
PyCTBN.PyCTBN |
||||
|
||||
Submodules |
||||
---------- |
||||
|
||||
Module contents |
||||
--------------- |
||||
|
||||
.. automodule:: PyCTBN |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
@ -1,121 +0,0 @@ |
||||
Examples |
||||
======== |
||||
|
||||
Installation/Usage |
||||
****************** |
||||
Download the release in .tar.gz or .whl format and simply use pip install to install it:: |
||||
|
||||
$pip install PyCTBN-1.0.tar.gz |
||||
|
||||
|
||||
Implementing your own data importer |
||||
*********************************** |
||||
.. code-block:: python |
||||
|
||||
"""This example demonstrates the implementation of a simple data importer the extends the class abstract importer to import data in csv format. |
||||
The net in exam has three ternary nodes and no prior net structure. |
||||
""" |
||||
|
||||
from PyCTBN import AbstractImporter |
||||
|
||||
class CSVImporter(AbstractImporter): |
||||
|
||||
def __init__(self, file_path): |
||||
self._df_samples_list = None |
||||
super(CSVImporter, self).__init__(file_path) |
||||
|
||||
def import_data(self): |
||||
self.read_csv_file() |
||||
self._sorter = self.build_sorter(self._df_samples_list[0]) |
||||
self.import_variables() |
||||
self.compute_row_delta_in_all_samples_frames(self._df_samples_list) |
||||
|
||||
def read_csv_file(self): |
||||
df = pd.read_csv(self._file_path) |
||||
df.drop(df.columns[[0]], axis=1, inplace=True) |
||||
self._df_samples_list = [df] |
||||
|
||||
def import_variables(self): |
||||
values_list = [3 for var in self._sorter] |
||||
# initialize dict of lists |
||||
data = {'Name':self._sorter, 'Value':values_list} |
||||
# Create the pandas DataFrame |
||||
self._df_variables = pd.DataFrame(data) |
||||
|
||||
def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List: |
||||
return list(sample_frame.columns)[1:] |
||||
|
||||
def dataset_id(self) -> object: |
||||
pass |
||||
|
||||
Parameters Estimation Example |
||||
***************************** |
||||
|
||||
.. code-block:: python |
||||
|
||||
from PyCTBN import JsonImporter |
||||
from PyCTBN import SamplePath |
||||
from PyCTBN import NetworkGraph |
||||
from PyCTBN import ParametersEstimator |
||||
|
||||
|
||||
def main(): |
||||
read_files = glob.glob(os.path.join('./data', "*.json")) #Take all json files in this dir |
||||
#import data |
||||
importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') |
||||
importer.import_data(0) |
||||
#Create a SamplePath Obj passing an already filled AbstractImporter object |
||||
s1 = SamplePath(importer) |
||||
#Build The trajectries and the structural infos |
||||
s1.build_trajectories() |
||||
s1.build_structure() |
||||
print(s1.structure.edges) |
||||
print(s1.structure.nodes_values) |
||||
#From The Structure Object build the Graph |
||||
g = NetworkGraph(s1.structure) |
||||
#Select a node you want to estimate the parameters |
||||
node = g.nodes[2] |
||||
print("Node", node) |
||||
#Init the _graph specifically for THIS node |
||||
g.fast_init(node) |
||||
#Use SamplePath and Grpah to create a ParametersEstimator Object |
||||
p1 = ParametersEstimator(s1.trajectories, g) |
||||
#Init the peEst specifically for THIS node |
||||
p1.fast_init(node) |
||||
#Compute the parameters |
||||
sofc1 = p1.compute_parameters_for_node(node) |
||||
#The est CIMS are inside the resultant SetOfCIms Obj |
||||
print(sofc1.actual_cims) |
||||
|
||||
Structure Estimation Example |
||||
**************************** |
||||
|
||||
.. code-block:: python |
||||
|
||||
from PyCTBN import JsonImporter |
||||
from PyCTBN import SamplePath |
||||
from PyCTBN import StructureEstimator |
||||
|
||||
def structure_estimation_example(): |
||||
|
||||
# read the json files in ./data path |
||||
read_files = glob.glob(os.path.join('./data', "*.json")) |
||||
# initialize a JsonImporter object for the first file |
||||
importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') |
||||
# import the data at index 0 of the outer json array |
||||
importer.import_data(0) |
||||
# construct a SamplePath Object passing a filled AbstractImporter |
||||
s1 = SamplePath(importer) |
||||
# build the trajectories |
||||
s1.build_trajectories() |
||||
# build the real structure |
||||
s1.build_structure() |
||||
# construct a StructureEstimator object |
||||
se1 = StructureEstimator(s1, 0.1, 0.1) |
||||
# call the ctpc algorithm |
||||
se1.ctpc_algorithm() |
||||
# the adjacency matrix of the estimated structure |
||||
print(se1.adjacency_matrix()) |
||||
# save results to a json file |
||||
se1.save_results() |
||||
|
||||
@ -1,22 +0,0 @@ |
||||
.. PyCTBN documentation master file, created by |
||||
sphinx-quickstart on Wed Mar 3 14:50:44 2021. |
||||
You can adapt this file completely to your liking, but it should at least |
||||
contain the root `toctree` directive. |
||||
|
||||
Welcome to PyCTBN's documentation! |
||||
================================== |
||||
|
||||
.. toctree:: |
||||
:maxdepth: 4 |
||||
:caption: Contents: |
||||
|
||||
modules |
||||
|
||||
|
||||
|
||||
Indices and tables |
||||
================== |
||||
|
||||
* :ref:`genindex` |
||||
* :ref:`modindex` |
||||
* :ref:`search` |
||||
@ -1,8 +0,0 @@ |
||||
PyCTBN |
||||
====== |
||||
|
||||
.. toctree:: |
||||
:maxdepth: 4 |
||||
|
||||
PyCTBN.PyCTBN |
||||
examples |
||||
@ -1,856 +0,0 @@ |
||||
/* |
||||
* basic.css |
||||
* ~~~~~~~~~ |
||||
* |
||||
* Sphinx stylesheet -- basic theme. |
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* |
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* :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. |
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* |
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*/ |
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/* -- main layout ----------------------------------------------------------- */ |
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|
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div.clearer { |
||||
clear: both; |
||||
} |
||||
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||||
display: block; |
||||
content: ''; |
||||
clear: left; |
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width: 100%; |
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||||
} |
||||
|
||||
div.related li.right { |
||||
float: right; |
||||
margin-right: 5px; |
||||
} |
||||
|
||||
/* -- sidebar --------------------------------------------------------------- */ |
||||
|
||||
div.sphinxsidebarwrapper { |
||||
padding: 10px 5px 0 10px; |
||||
} |
||||
|
||||
div.sphinxsidebar { |
||||
float: left; |
||||
width: 230px; |
||||
margin-left: -100%; |
||||
font-size: 90%; |
||||
word-wrap: break-word; |
||||
overflow-wrap : break-word; |
||||
} |
||||
|
||||
div.sphinxsidebar ul { |
||||
list-style: none; |
||||
} |
||||
|
||||
div.sphinxsidebar ul ul, |
||||
div.sphinxsidebar ul.want-points { |
||||
margin-left: 20px; |
||||
list-style: square; |
||||
} |
||||
|
||||
div.sphinxsidebar ul ul { |
||||
margin-top: 0; |
||||
margin-bottom: 0; |
||||
} |
||||
|
||||
div.sphinxsidebar form { |
||||
margin-top: 10px; |
||||
} |
||||
|
||||
div.sphinxsidebar input { |
||||
border: 1px solid #98dbcc; |
||||
font-family: sans-serif; |
||||
font-size: 1em; |
||||
} |
||||
|
||||
div.sphinxsidebar #searchbox form.search { |
||||
overflow: hidden; |
||||
} |
||||
|
||||
div.sphinxsidebar #searchbox input[type="text"] { |
||||
float: left; |
||||
width: 80%; |
||||
padding: 0.25em; |
||||
box-sizing: border-box; |
||||
} |
||||
|
||||
div.sphinxsidebar #searchbox input[type="submit"] { |
||||
float: left; |
||||
width: 20%; |
||||
border-left: none; |
||||
padding: 0.25em; |
||||
box-sizing: border-box; |
||||
} |
||||
|
||||
|
||||
img { |
||||
border: 0; |
||||
max-width: 100%; |
||||
} |
||||
|
||||
/* -- search page ----------------------------------------------------------- */ |
||||
|
||||
ul.search { |
||||
margin: 10px 0 0 20px; |
||||
padding: 0; |
||||
} |
||||
|
||||
ul.search li { |
||||
padding: 5px 0 5px 20px; |
||||
background-image: url(file.png); |
||||
background-repeat: no-repeat; |
||||
background-position: 0 7px; |
||||
} |
||||
|
||||
ul.search li a { |
||||
font-weight: bold; |
||||
} |
||||
|
||||
ul.search li div.context { |
||||
color: #888; |
||||
margin: 2px 0 0 30px; |
||||
text-align: left; |
||||
} |
||||
|
||||
ul.keywordmatches li.goodmatch a { |
||||
font-weight: bold; |
||||
} |
||||
|
||||
/* -- index page ------------------------------------------------------------ */ |
||||
|
||||
table.contentstable { |
||||
width: 90%; |
||||
margin-left: auto; |
||||
margin-right: auto; |
||||
} |
||||
|
||||
table.contentstable p.biglink { |
||||
line-height: 150%; |
||||
} |
||||
|
||||
a.biglink { |
||||
font-size: 1.3em; |
||||
} |
||||
|
||||
span.linkdescr { |
||||
font-style: italic; |
||||
padding-top: 5px; |
||||
font-size: 90%; |
||||
} |
||||
|
||||
/* -- general index --------------------------------------------------------- */ |
||||
|
||||
table.indextable { |
||||
width: 100%; |
||||
} |
||||
|
||||
table.indextable td { |
||||
text-align: left; |
||||
vertical-align: top; |
||||
} |
||||
|
||||
table.indextable ul { |
||||
margin-top: 0; |
||||
margin-bottom: 0; |
||||
list-style-type: none; |
||||
} |
||||
|
||||
table.indextable > tbody > tr > td > ul { |
||||
padding-left: 0em; |
||||
} |
||||
|
||||
table.indextable tr.pcap { |
||||
height: 10px; |
||||
} |
||||
|
||||
table.indextable tr.cap { |
||||
margin-top: 10px; |
||||
background-color: #f2f2f2; |
||||
} |
||||
|
||||
img.toggler { |
||||
margin-right: 3px; |
||||
margin-top: 3px; |
||||
cursor: pointer; |
||||
} |
||||
|
||||
div.modindex-jumpbox { |
||||
border-top: 1px solid #ddd; |
||||
border-bottom: 1px solid #ddd; |
||||
margin: 1em 0 1em 0; |
||||
padding: 0.4em; |
||||
} |
||||
|
||||
div.genindex-jumpbox { |
||||
border-top: 1px solid #ddd; |
||||
border-bottom: 1px solid #ddd; |
||||
margin: 1em 0 1em 0; |
||||
padding: 0.4em; |
||||
} |
||||
|
||||
/* -- domain module index --------------------------------------------------- */ |
||||
|
||||
table.modindextable td { |
||||
padding: 2px; |
||||
border-collapse: collapse; |
||||
} |
||||
|
||||
/* -- general body styles --------------------------------------------------- */ |
||||
|
||||
div.body { |
||||
min-width: 450px; |
||||
max-width: 800px; |
||||
} |
||||
|
||||
div.body p, div.body dd, div.body li, div.body blockquote { |
||||
-moz-hyphens: auto; |
||||
-ms-hyphens: auto; |
||||
-webkit-hyphens: auto; |
||||
hyphens: auto; |
||||
} |
||||
|
||||
a.headerlink { |
||||
visibility: hidden; |
||||
} |
||||
|
||||
a.brackets:before, |
||||
span.brackets > a:before{ |
||||
content: "["; |
||||
} |
||||
|
||||
a.brackets:after, |
||||
span.brackets > a:after { |
||||
content: "]"; |
||||
} |
||||
|
||||
h1:hover > a.headerlink, |
||||
h2:hover > a.headerlink, |
||||
h3:hover > a.headerlink, |
||||
h4:hover > a.headerlink, |
||||
h5:hover > a.headerlink, |
||||
h6:hover > a.headerlink, |
||||
dt:hover > a.headerlink, |
||||
caption:hover > a.headerlink, |
||||
p.caption:hover > a.headerlink, |
||||
div.code-block-caption:hover > a.headerlink { |
||||
visibility: visible; |
||||
} |
||||
|
||||
div.body p.caption { |
||||
text-align: inherit; |
||||
} |
||||
|
||||
div.body td { |
||||
text-align: left; |
||||
} |
||||
|
||||
.first { |
||||
margin-top: 0 !important; |
||||
} |
||||
|
||||
p.rubric { |
||||
margin-top: 30px; |
||||
font-weight: bold; |
||||
} |
||||
|
||||
img.align-left, .figure.align-left, object.align-left { |
||||
clear: left; |
||||
float: left; |
||||
margin-right: 1em; |
||||
} |
||||
|
||||
img.align-right, .figure.align-right, object.align-right { |
||||
clear: right; |
||||
float: right; |
||||
margin-left: 1em; |
||||
} |
||||
|
||||
img.align-center, .figure.align-center, object.align-center { |
||||
display: block; |
||||
margin-left: auto; |
||||
margin-right: auto; |
||||
} |
||||
|
||||
img.align-default, .figure.align-default { |
||||
display: block; |
||||
margin-left: auto; |
||||
margin-right: auto; |
||||
} |
||||
|
||||
.align-left { |
||||
text-align: left; |
||||
} |
||||
|
||||
.align-center { |
||||
text-align: center; |
||||
} |
||||
|
||||
.align-default { |
||||
text-align: center; |
||||
} |
||||
|
||||
.align-right { |
||||
text-align: right; |
||||
} |
||||
|
||||
/* -- sidebars -------------------------------------------------------------- */ |
||||
|
||||
div.sidebar { |
||||
margin: 0 0 0.5em 1em; |
||||
border: 1px solid #ddb; |
||||
padding: 7px; |
||||
background-color: #ffe; |
||||
width: 40%; |
||||
float: right; |
||||
clear: right; |
||||
overflow-x: auto; |
||||
} |
||||
|
||||
p.sidebar-title { |
||||
font-weight: bold; |
||||
} |
||||
|
||||
div.admonition, div.topic, blockquote { |
||||
clear: left; |
||||
} |
||||
|
||||
/* -- topics ---------------------------------------------------------------- */ |
||||
|
||||
div.topic { |
||||
border: 1px solid #ccc; |
||||
padding: 7px; |
||||
margin: 10px 0 10px 0; |
||||
} |
||||
|
||||
p.topic-title { |
||||
font-size: 1.1em; |
||||
font-weight: bold; |
||||
margin-top: 10px; |
||||
} |
||||
|
||||
/* -- admonitions ----------------------------------------------------------- */ |
||||
|
||||
div.admonition { |
||||
margin-top: 10px; |
||||
margin-bottom: 10px; |
||||
padding: 7px; |
||||
} |
||||
|
||||
div.admonition dt { |
||||
font-weight: bold; |
||||
} |
||||
|
||||
p.admonition-title { |
||||
margin: 0px 10px 5px 0px; |
||||
font-weight: bold; |
||||
} |
||||
|
||||
div.body p.centered { |
||||
text-align: center; |
||||
margin-top: 25px; |
||||
} |
||||
|
||||
/* -- content of sidebars/topics/admonitions -------------------------------- */ |
||||
|
||||
div.sidebar > :last-child, |
||||
div.topic > :last-child, |
||||
div.admonition > :last-child { |
||||
margin-bottom: 0; |
||||
} |
||||
|
||||
div.sidebar::after, |
||||
div.topic::after, |
||||
div.admonition::after, |
||||
blockquote::after { |
||||
display: block; |
||||
content: ''; |
||||
clear: both; |
||||
} |
||||
|
||||
/* -- tables ---------------------------------------------------------------- */ |
||||
|
||||
table.docutils { |
||||
margin-top: 10px; |
||||
margin-bottom: 10px; |
||||
border: 0; |
||||
border-collapse: collapse; |
||||
} |
||||
|
||||
table.align-center { |
||||
margin-left: auto; |
||||
margin-right: auto; |
||||
} |
||||
|
||||
table.align-default { |
||||
margin-left: auto; |
||||
margin-right: auto; |
||||
} |
||||
|
||||
table caption span.caption-number { |
||||
font-style: italic; |
||||
} |
||||
|
||||
table caption span.caption-text { |
||||
} |
||||
|
||||
table.docutils td, table.docutils th { |
||||
padding: 1px 8px 1px 5px; |
||||
border-top: 0; |
||||
border-left: 0; |
||||
border-right: 0; |
||||
border-bottom: 1px solid #aaa; |
||||
} |
||||
|
||||
table.footnote td, table.footnote th { |
||||
border: 0 !important; |
||||
} |
||||
|
||||
th { |
||||
text-align: left; |
||||
padding-right: 5px; |
||||
} |
||||
|
||||
table.citation { |
||||
border-left: solid 1px gray; |
||||
margin-left: 1px; |
||||
} |
||||
|
||||
table.citation td { |
||||
border-bottom: none; |
||||
} |
||||
|
||||
th > :first-child, |
||||
td > :first-child { |
||||
margin-top: 0px; |
||||
} |
||||
|
||||
th > :last-child, |
||||
td > :last-child { |
||||
margin-bottom: 0px; |
||||
} |
||||
|
||||
/* -- figures --------------------------------------------------------------- */ |
||||
|
||||
div.figure { |
||||
margin: 0.5em; |
||||
padding: 0.5em; |
||||
} |
||||
|
||||
div.figure p.caption { |
||||
padding: 0.3em; |
||||
} |
||||
|
||||
div.figure p.caption span.caption-number { |
||||
font-style: italic; |
||||
} |
||||
|
||||
div.figure p.caption span.caption-text { |
||||
} |
||||
|
||||
/* -- field list styles ----------------------------------------------------- */ |
||||
|
||||
table.field-list td, table.field-list th { |
||||
border: 0 !important; |
||||
} |
||||
|
||||
.field-list ul { |
||||
margin: 0; |
||||
padding-left: 1em; |
||||
} |
||||
|
||||
.field-list p { |
||||
margin: 0; |
||||
} |
||||
|
||||
.field-name { |
||||
-moz-hyphens: manual; |
||||
-ms-hyphens: manual; |
||||
-webkit-hyphens: manual; |
||||
hyphens: manual; |
||||
} |
||||
|
||||
/* -- hlist styles ---------------------------------------------------------- */ |
||||
|
||||
table.hlist { |
||||
margin: 1em 0; |
||||
} |
||||
|
||||
table.hlist td { |
||||
vertical-align: top; |
||||
} |
||||
|
||||
|
||||
/* -- other body styles ----------------------------------------------------- */ |
||||
|
||||
ol.arabic { |
||||
list-style: decimal; |
||||
} |
||||
|
||||
ol.loweralpha { |
||||
list-style: lower-alpha; |
||||
} |
||||
|
||||
ol.upperalpha { |
||||
list-style: upper-alpha; |
||||
} |
||||
|
||||
ol.lowerroman { |
||||
list-style: lower-roman; |
||||
} |
||||
|
||||
ol.upperroman { |
||||
list-style: upper-roman; |
||||
} |
||||
|
||||
:not(li) > ol > li:first-child > :first-child, |
||||
:not(li) > ul > li:first-child > :first-child { |
||||
margin-top: 0px; |
||||
} |
||||
|
||||
:not(li) > ol > li:last-child > :last-child, |
||||
:not(li) > ul > li:last-child > :last-child { |
||||
margin-bottom: 0px; |
||||
} |
||||
|
||||
ol.simple ol p, |
||||
ol.simple ul p, |
||||
ul.simple ol p, |
||||
ul.simple ul p { |
||||
margin-top: 0; |
||||
} |
||||
|
||||
ol.simple > li:not(:first-child) > p, |
||||
ul.simple > li:not(:first-child) > p { |
||||
margin-top: 0; |
||||
} |
||||
|
||||
ol.simple p, |
||||
ul.simple p { |
||||
margin-bottom: 0; |
||||
} |
||||
|
||||
dl.footnote > dt, |
||||
dl.citation > dt { |
||||
float: left; |
||||
margin-right: 0.5em; |
||||
} |
||||
|
||||
dl.footnote > dd, |
||||
dl.citation > dd { |
||||
margin-bottom: 0em; |
||||
} |
||||
|
||||
dl.footnote > dd:after, |
||||
dl.citation > dd:after { |
||||
content: ""; |
||||
clear: both; |
||||
} |
||||
|
||||
dl.field-list { |
||||
display: grid; |
||||
grid-template-columns: fit-content(30%) auto; |
||||
} |
||||
|
||||
dl.field-list > dt { |
||||
font-weight: bold; |
||||
word-break: break-word; |
||||
padding-left: 0.5em; |
||||
padding-right: 5px; |
||||
} |
||||
|
||||
dl.field-list > dt:after { |
||||
content: ":"; |
||||
} |
||||
|
||||
dl.field-list > dd { |
||||
padding-left: 0.5em; |
||||
margin-top: 0em; |
||||
margin-left: 0em; |
||||
margin-bottom: 0em; |
||||
} |
||||
|
||||
dl { |
||||
margin-bottom: 15px; |
||||
} |
||||
|
||||
dd > :first-child { |
||||
margin-top: 0px; |
||||
} |
||||
|
||||
dd ul, dd table { |
||||
margin-bottom: 10px; |
||||
} |
||||
|
||||
dd { |
||||
margin-top: 3px; |
||||
margin-bottom: 10px; |
||||
margin-left: 30px; |
||||
} |
||||
|
||||
dl > dd:last-child, |
||||
dl > dd:last-child > :last-child { |
||||
margin-bottom: 0; |
||||
} |
||||
|
||||
dt:target, span.highlighted { |
||||
background-color: #fbe54e; |
||||
} |
||||
|
||||
rect.highlighted { |
||||
fill: #fbe54e; |
||||
} |
||||
|
||||
dl.glossary dt { |
||||
font-weight: bold; |
||||
font-size: 1.1em; |
||||
} |
||||
|
||||
.optional { |
||||
font-size: 1.3em; |
||||
} |
||||
|
||||
.sig-paren { |
||||
font-size: larger; |
||||
} |
||||
|
||||
.versionmodified { |
||||
font-style: italic; |
||||
} |
||||
|
||||
.system-message { |
||||
background-color: #fda; |
||||
padding: 5px; |
||||
border: 3px solid red; |
||||
} |
||||
|
||||
.footnote:target { |
||||
background-color: #ffa; |
||||
} |
||||
|
||||
.line-block { |
||||
display: block; |
||||
margin-top: 1em; |
||||
margin-bottom: 1em; |
||||
} |
||||
|
||||
.line-block .line-block { |
||||
margin-top: 0; |
||||
margin-bottom: 0; |
||||
margin-left: 1.5em; |
||||
} |
||||
|
||||
.guilabel, .menuselection { |
||||
font-family: sans-serif; |
||||
} |
||||
|
||||
.accelerator { |
||||
text-decoration: underline; |
||||
} |
||||
|
||||
.classifier { |
||||
font-style: oblique; |
||||
} |
||||
|
||||
.classifier:before { |
||||
font-style: normal; |
||||
margin: 0.5em; |
||||
content: ":"; |
||||
} |
||||
|
||||
abbr, acronym { |
||||
border-bottom: dotted 1px; |
||||
cursor: help; |
||||
} |
||||
|
||||
/* -- code displays --------------------------------------------------------- */ |
||||
|
||||
pre { |
||||
overflow: auto; |
||||
overflow-y: hidden; /* fixes display issues on Chrome browsers */ |
||||
} |
||||
|
||||
pre, div[class*="highlight-"] { |
||||
clear: both; |
||||
} |
||||
|
||||
span.pre { |
||||
-moz-hyphens: none; |
||||
-ms-hyphens: none; |
||||
-webkit-hyphens: none; |
||||
hyphens: none; |
||||
} |
||||
|
||||
div[class*="highlight-"] { |
||||
margin: 1em 0; |
||||
} |
||||
|
||||
td.linenos pre { |
||||
border: 0; |
||||
background-color: transparent; |
||||
color: #aaa; |
||||
} |
||||
|
||||
table.highlighttable { |
||||
display: block; |
||||
} |
||||
|
||||
table.highlighttable tbody { |
||||
display: block; |
||||
} |
||||
|
||||
table.highlighttable tr { |
||||
display: flex; |
||||
} |
||||
|
||||
table.highlighttable td { |
||||
margin: 0; |
||||
padding: 0; |
||||
} |
||||
|
||||
table.highlighttable td.linenos { |
||||
padding-right: 0.5em; |
||||
} |
||||
|
||||
table.highlighttable td.code { |
||||
flex: 1; |
||||
overflow: hidden; |
||||
} |
||||
|
||||
.highlight .hll { |
||||
display: block; |
||||
} |
||||
|
||||
div.highlight pre, |
||||
table.highlighttable pre { |
||||
margin: 0; |
||||
} |
||||
|
||||
div.code-block-caption + div { |
||||
margin-top: 0; |
||||
} |
||||
|
||||
div.code-block-caption { |
||||
margin-top: 1em; |
||||
padding: 2px 5px; |
||||
font-size: small; |
||||
} |
||||
|
||||
div.code-block-caption code { |
||||
background-color: transparent; |
||||
} |
||||
|
||||
table.highlighttable td.linenos, |
||||
span.linenos, |
||||
div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */ |
||||
user-select: none; |
||||
} |
||||
|
||||
div.code-block-caption span.caption-number { |
||||
padding: 0.1em 0.3em; |
||||
font-style: italic; |
||||
} |
||||
|
||||
div.code-block-caption span.caption-text { |
||||
} |
||||
|
||||
div.literal-block-wrapper { |
||||
margin: 1em 0; |
||||
} |
||||
|
||||
code.descname { |
||||
background-color: transparent; |
||||
font-weight: bold; |
||||
font-size: 1.2em; |
||||
} |
||||
|
||||
code.descclassname { |
||||
background-color: transparent; |
||||
} |
||||
|
||||
code.xref, a code { |
||||
background-color: transparent; |
||||
font-weight: bold; |
||||
} |
||||
|
||||
h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { |
||||
background-color: transparent; |
||||
} |
||||
|
||||
.viewcode-link { |
||||
float: right; |
||||
} |
||||
|
||||
.viewcode-back { |
||||
float: right; |
||||
font-family: sans-serif; |
||||
} |
||||
|
||||
div.viewcode-block:target { |
||||
margin: -1px -10px; |
||||
padding: 0 10px; |
||||
} |
||||
|
||||
/* -- math display ---------------------------------------------------------- */ |
||||
|
||||
img.math { |
||||
vertical-align: middle; |
||||
} |
||||
|
||||
div.body div.math p { |
||||
text-align: center; |
||||
} |
||||
|
||||
span.eqno { |
||||
float: right; |
||||
} |
||||
|
||||
span.eqno a.headerlink { |
||||
position: absolute; |
||||
z-index: 1; |
||||
} |
||||
|
||||
div.math:hover a.headerlink { |
||||
visibility: visible; |
||||
} |
||||
|
||||
/* -- printout stylesheet --------------------------------------------------- */ |
||||
|
||||
@media print { |
||||
div.document, |
||||
div.documentwrapper, |
||||
div.bodywrapper { |
||||
margin: 0 !important; |
||||
width: 100%; |
||||
} |
||||
|
||||
div.sphinxsidebar, |
||||
div.related, |
||||
div.footer, |
||||
#top-link { |
||||
display: none; |
||||
} |
||||
} |
||||
@ -1,93 +0,0 @@ |
||||
body, .entry-container, |
||||
.wy-nav-side, |
||||
.wy-side-nav-search, |
||||
.fundo-claro, |
||||
.wy-menu-vertical li.current, |
||||
.rst-content dl:not(.docutils) dt, |
||||
code, .rst-content tt, |
||||
.wy-side-nav-search > a:hover, .wy-side-nav-search .wy-dropdown > a:hover, |
||||
.wy-nav-content{ |
||||
background-color: rgb(24, 26, 27) !important; |
||||
} |
||||
|
||||
h2 a, h2 a:visited, h2 a:hover { |
||||
color: rgb(209, 206, 199); |
||||
} |
||||
|
||||
body { |
||||
color: rgb(209, 206, 199); |
||||
} |
||||
|
||||
|
||||
a, a:hover, a:visited { |
||||
color: rgb(113, 178, 234); |
||||
} |
||||
|
||||
.wy-menu-vertical a { |
||||
color: #b3b3b3; |
||||
} |
||||
|
||||
code, .rst-content tt { |
||||
color: #fff8f8; |
||||
border: 0; |
||||
} |
||||
|
||||
codeblock, pre.literal-block, .rst-content .literal-block, .rst-content pre.literal-block, div[class^="highlight"] { |
||||
border: 1px solid #000; |
||||
} |
||||
|
||||
.rst-content dl:not(.docutils) dl dt { |
||||
background: rgb(24, 26, 27) !important; |
||||
} |
||||
|
||||
.wy-side-nav-search > a, .wy-side-nav-search .wy-dropdown > a { |
||||
color: #fcfcfc; |
||||
} |
||||
|
||||
|
||||
.wy-alert.wy-alert-info, .rst-content .note, .rst-content .wy-alert-info.attention, .rst-content .wy-alert-info.caution, .rst-content .wy-alert-info.danger, .rst-content .wy-alert-info.error, .rst-content .wy-alert-info.hint, .rst-content .wy-alert-info.important, .rst-content .wy-alert-info.tip, .rst-content .wy-alert-info.warning, .rst-content .seealso, .rst-content .wy-alert-info.admonition-todo, |
||||
.admonition.note code{ |
||||
background: #535050 !important; |
||||
} |
||||
|
||||
@media screen and (min-width: 768px) { |
||||
.wy-nav-side{ |
||||
width: 224px; |
||||
} |
||||
.wy-nav-content-wrap{ |
||||
margin-left: 200px; |
||||
background:rgb(24, 26, 27); |
||||
} |
||||
} |
||||
|
||||
.wy-nav-content-wrap{ |
||||
margin-left: 200px; |
||||
background: #343131; |
||||
} |
||||
|
||||
.hentry { |
||||
border-bottom: 2px solid rgb(24, 26, 27); |
||||
} |
||||
|
||||
.wy-alert.wy-alert-danger .wy-alert-title, .rst-content .wy-alert-danger.note .wy-alert-title, .rst-content .wy-alert-danger.attention .wy-alert-title, .rst-content .wy-alert-danger.caution .wy-alert-title, .rst-content .danger .wy-alert-title, .rst-content .error .wy-alert-title, .rst-content .wy-alert-danger.hint .wy-alert-title, .rst-content .wy-alert-danger.important .wy-alert-title, .rst-content .wy-alert-danger.tip .wy-alert-title, .rst-content .wy-alert-danger.warning .wy-alert-title, .rst-content .wy-alert-danger.seealso .wy-alert-title, .rst-content .wy-alert-danger.admonition-todo .wy-alert-title, .wy-alert.wy-alert-danger .rst-content .admonition-title, .rst-content .wy-alert.wy-alert-danger .admonition-title, .rst-content .wy-alert-danger.note .admonition-title, .rst-content .wy-alert-danger.attention .admonition-title, .rst-content .wy-alert-danger.caution .admonition-title, .rst-content .danger .admonition-title, .rst-content .error .admonition-title, .rst-content .wy-alert-danger.hint .admonition-title, .rst-content .wy-alert-danger.important .admonition-title, .rst-content .wy-alert-danger.tip .admonition-title, .rst-content .wy-alert-danger.warning .admonition-title, .rst-content .wy-alert-danger.seealso .admonition-title, .rst-content .wy-alert-danger.admonition-todo .admonition-title { |
||||
background: #db655a; |
||||
} |
||||
|
||||
.wy-alert.wy-alert-danger, .rst-content .wy-alert-danger.note, .rst-content .wy-alert-danger.attention, .rst-content .wy-alert-danger.caution, .rst-content .danger, .rst-content .error, .rst-content .wy-alert-danger.hint, .rst-content .wy-alert-danger.important, .rst-content .wy-alert-danger.tip, .rst-content .wy-alert-danger.warning, .rst-content .wy-alert-danger.seealso, .rst-content .wy-alert-danger.admonition-todo { |
||||
background: #e18279; |
||||
color: #fff; |
||||
} |
||||
|
||||
.wy-alert.wy-alert-warning, .rst-content .wy-alert-warning.note, .rst-content .attention, .rst-content .caution, .rst-content .wy-alert-warning.danger, .rst-content .wy-alert-warning.error, .rst-content .wy-alert-warning.hint, .rst-content .wy-alert-warning.important, .rst-content .wy-alert-warning.tip, .rst-content .warning, .rst-content .wy-alert-warning.seealso, .rst-content .admonition-todo { |
||||
background: #ca9f52; |
||||
color: #fff; |
||||
} |
||||
|
||||
.wy-alert.wy-alert-warning .wy-alert-title, .rst-content .wy-alert-warning.note .wy-alert-title, .rst-content .attention .wy-alert-title, .rst-content .caution .wy-alert-title, .rst-content .wy-alert-warning.danger .wy-alert-title, .rst-content .wy-alert-warning.error .wy-alert-title, .rst-content .wy-alert-warning.hint .wy-alert-title, .rst-content .wy-alert-warning.important .wy-alert-title, .rst-content .wy-alert-warning.tip .wy-alert-title, .rst-content .warning .wy-alert-title, .rst-content .wy-alert-warning.seealso .wy-alert-title, .rst-content .admonition-todo .wy-alert-title, .wy-alert.wy-alert-warning .rst-content .admonition-title, .rst-content .wy-alert.wy-alert-warning .admonition-title, .rst-content .wy-alert-warning.note .admonition-title, .rst-content .attention .admonition-title, .rst-content .caution .admonition-title, .rst-content .wy-alert-warning.danger .admonition-title, .rst-content .wy-alert-warning.error .admonition-title, .rst-content .wy-alert-warning.hint .admonition-title, .rst-content .wy-alert-warning.important .admonition-title, .rst-content .wy-alert-warning.tip .admonition-title, .rst-content .warning .admonition-title, .rst-content .wy-alert-warning.seealso .admonition-title, .rst-content .admonition-todo .admonition-title { |
||||
background: #ca7a35; |
||||
} |
||||
|
||||
|
||||
.wy-body-for-nav { |
||||
background-image: none !important; |
||||
} |
||||
@ -1,494 +0,0 @@ |
||||
@import url("theme.css"); |
||||
|
||||
.wy-side-nav-search{ |
||||
background-color: #595C5E; |
||||
} |
||||
|
||||
.wy-side-nav-search input[type=text] { |
||||
border-color: #595C5E; |
||||
} |
||||
|
||||
@media screen and (min-width: 768px) { |
||||
.wy-nav-side{ |
||||
width: 224px; |
||||
} |
||||
.wy-nav-content-wrap{ |
||||
margin-left: 200px; |
||||
background: #343131; |
||||
} |
||||
} |
||||
@media screen and (max-width: 768px) { |
||||
.wy-nav-content{ |
||||
background-color: #343131; |
||||
} |
||||
} |
||||
|
||||
.wy-nav-top{ |
||||
background-color: #595C5E; |
||||
} |
||||
.wy-nav-content{ |
||||
padding-top: 1%; |
||||
max-width: 100%; |
||||
background-color: #343131; |
||||
} |
||||
|
||||
.rst-content{ |
||||
margin-left: -3%; |
||||
} |
||||
|
||||
pre{ |
||||
font-size: 0.9em; |
||||
padding: 1%; |
||||
} |
||||
|
||||
pre > span, |
||||
pre > p{ |
||||
margin: 0; |
||||
font-size: 1em; |
||||
} |
||||
|
||||
pre > span{ |
||||
margin: 0; |
||||
} |
||||
|
||||
.hentry{ |
||||
padding-top: 2%; |
||||
padding-bottom: 5%; |
||||
padding-left: 3%; |
||||
padding-right: 3%; |
||||
border-bottom: 2px solid #343131; |
||||
} |
||||
|
||||
.entry-container{ |
||||
background-color: #fff; /*#EAEAEA; */ |
||||
border-radius: 3px; |
||||
/* -webkit-box-shadow: 0 0 10px 8px rgba(50, 50, 50, 0.75); */ |
||||
/* -moz-box-shadow: 0 0 10px 8px rgba(50, 50, 50, 0.75); */ |
||||
/* box-shadow: 0 0 10px 8px rgba(50, 50, 50, 0.75); */ |
||||
-webkit-box-shadow: 0 0 10px 8px rgba(2, 2, 2, 0.36); |
||||
-moz-box-shadow: 0 0 10px 8px rgba(2, 2, 2, 0.36); |
||||
box-shadow: 0 0 10px 8px rgba(2, 2, 2, 0.36); |
||||
position: relative; |
||||
z-index: 210; |
||||
} |
||||
|
||||
.entry-content{ |
||||
margin-right: 3%; |
||||
} |
||||
|
||||
.wy-side-nav-search{ |
||||
padding:0.639em |
||||
} |
||||
|
||||
|
||||
.wy-menu-vertical a:hover{ |
||||
-webkit-box-shadow: 0 0 5px 5px rgba(20, 20, 20, 0.30); |
||||
-moz-box-shadow: 0 0 5px 5px rgba(20, 20, 20, 0.30); |
||||
box-shadow: 0 0 5px 5px rgba(20, 20, 20, 0.30); |
||||
} |
||||
|
||||
.wy-menu-vertical a{ |
||||
width: 90% |
||||
} |
||||
|
||||
#comments, |
||||
#comment-form{ |
||||
padding: 20px; |
||||
} |
||||
|
||||
|
||||
@media screen and (max-width: 1330px) { |
||||
.fancybox img{ |
||||
width: 310px; /* 88px / 633px */ |
||||
height: 206px; |
||||
} |
||||
} |
||||
|
||||
@media screen and (max-width: 1241px) { |
||||
.fancybox img{ |
||||
width: 280px; |
||||
height: 186px; |
||||
} |
||||
} |
||||
|
||||
@media screen and (max-width: 1154px) { |
||||
.fancybox img{ |
||||
width: 260px; |
||||
height: 173px; |
||||
} |
||||
} |
||||
|
||||
.expander { |
||||
position: absolute; |
||||
top: 5px; |
||||
left: 5px; |
||||
width: 16px; |
||||
height: 16px; |
||||
padding: 4px; |
||||
background: white url(/static/blog/img/fsbtn.png) center center no-repeat; |
||||
z-index: 99999; |
||||
cursor: pointer; |
||||
} |
||||
|
||||
/* 340 */ |
||||
/* .rst-content img { */ |
||||
/* margin-top: 8px; */ |
||||
/* margin-left: 10px; */ |
||||
/* margin-bottom: 5px; */ |
||||
/* } */ |
||||
|
||||
/* 260 */ |
||||
.rst-content .row img { |
||||
margin-top: 8px; |
||||
margin-left: 5px; |
||||
border-radius: 3px; |
||||
-webkit-box-shadow: 0 0 10px 5px rgba(2, 2, 2, 0.36); |
||||
-moz-box-shadow: 0 0 10px 5px /*rgba(50, 50, 50, 0.75)*/ rgba(2, 2, 2, 0.36); |
||||
box-shadow: 0 0 10px 5px rgba(2, 2, 2, 0.36); |
||||
|
||||
} |
||||
|
||||
.rst-content{ |
||||
z-index: 210; |
||||
margin-top: -60px; |
||||
} |
||||
|
||||
h2 a, |
||||
h2 a:visited, |
||||
h2 a:hover{ |
||||
color: #404040; |
||||
} |
||||
|
||||
.wy-menu-vertical li.on a, .wy-menu-vertical li.current>a { |
||||
color: #b3b3b3; |
||||
background: #4e4a4a; |
||||
box-shadow: 0 0 5px 5px rgba(20, 20, 20, 0.30); |
||||
border: none; |
||||
} |
||||
|
||||
.wy-menu-vertical li.current { |
||||
background: #343131; |
||||
border: none; |
||||
} |
||||
|
||||
.wy-menu-vertical li.current a{ |
||||
border:none |
||||
} |
||||
|
||||
.wy-alert.wy-alert-info .wy-alert-title, .rst-content .note .wy-alert-title, .rst-content .wy-alert-info.attention .wy-alert-title, .rst-content .wy-alert-info.caution .wy-alert-title, .rst-content .wy-alert-info.danger .wy-alert-title, .rst-content .wy-alert-info.error .wy-alert-title, .rst-content .wy-alert-info.hint .wy-alert-title, .rst-content .wy-alert-info.important .wy-alert-title, .rst-content .wy-alert-info.tip .wy-alert-title, .rst-content .wy-alert-info.warning .wy-alert-title, .rst-content .seealso .wy-alert-title, .rst-content .wy-alert-info.admonition-todo .wy-alert-title, .wy-alert.wy-alert-info .rst-content .admonition-title, .rst-content .wy-alert.wy-alert-info .admonition-title, .rst-content .note .admonition-title, .rst-content .wy-alert-info.attention .admonition-title, .rst-content .wy-alert-info.caution .admonition-title, .rst-content .wy-alert-info.danger .admonition-title, .rst-content .wy-alert-info.error .admonition-title, .rst-content .wy-alert-info.hint .admonition-title, .rst-content .wy-alert-info.important .admonition-title, .rst-content .wy-alert-info.tip .admonition-title, .rst-content .wy-alert-info.warning .admonition-title, .rst-content .seealso .admonition-title, .rst-content .wy-alert-info.admonition-todo .admonition-title { |
||||
background: #47494a; |
||||
} |
||||
|
||||
.wy-alert.wy-alert-info, .rst-content .note, .rst-content .wy-alert-info.attention, .rst-content .wy-alert-info.caution, .rst-content .wy-alert-info.danger, .rst-content .wy-alert-info.error, .rst-content .wy-alert-info.hint, .rst-content .wy-alert-info.important, .rst-content .wy-alert-info.tip, .rst-content .wy-alert-info.warning, .rst-content .seealso, .rst-content .wy-alert-info.admonition-todo { |
||||
background: #ececec; |
||||
} |
||||
|
||||
.wy-alert.wy-alert-danger, .rst-content .wy-alert-danger.note, .rst-content .wy-alert-danger.attention, .rst-content .wy-alert-danger.caution, .rst-content .danger, .rst-content .error, .rst-content .wy-alert-danger.hint, .rst-content .wy-alert-danger.important, .rst-content .wy-alert-danger.tip, .rst-content .wy-alert-danger.warning, .rst-content .wy-alert-danger.seealso, .rst-content .wy-alert-danger.admonition-todo { |
||||
background: #f0d5d2; |
||||
} |
||||
|
||||
.wy-menu-vertical li.on a:hover, |
||||
.wy-menu-vertical li.current>a:hover { |
||||
background: #4e4a4a; |
||||
|
||||
} |
||||
|
||||
.wy-menu-vertical li.current a:hover { |
||||
background: #4e4a4a; |
||||
} |
||||
|
||||
wy-menu-vertical li.toctree-l2.current>a { |
||||
color: #343131 !important; |
||||
} |
||||
|
||||
.wy-menu-vertical li.toctree-l2.current>a { |
||||
background: #343131; |
||||
padding: 0.4045em 2.427em; |
||||
box-shadow: none; |
||||
} |
||||
|
||||
.highlight{ |
||||
background: #000 !important; |
||||
} |
||||
|
||||
.highlight pre, |
||||
.highlight .mi, |
||||
.highlight .go{ |
||||
color: #57de4e !important; |
||||
font-size: 0.9em !important; |
||||
} |
||||
|
||||
.highlight .nt{ |
||||
color: #8080ec; |
||||
} |
||||
|
||||
.highlight .n, .highlight .nn, |
||||
.highlight .p, .highlight .o, |
||||
.highlight .nv, |
||||
.highlight .gp{ |
||||
color: #57de4e; |
||||
} |
||||
|
||||
.highlight .kn{ |
||||
color: #47ffff !important; |
||||
} |
||||
|
||||
.highlight .nc, |
||||
.highlight .nd{ |
||||
color: #1c909e; |
||||
font-weight: normal; |
||||
} |
||||
|
||||
|
||||
.highlight .k, .highlight .bp{ |
||||
color: #47ffff !important; |
||||
} |
||||
|
||||
.highlight .kc { |
||||
color: #ae39ff; |
||||
font-weight: normal !important; |
||||
} |
||||
|
||||
.highlight .nf, |
||||
.highlight .nb{ |
||||
color: #87CEFA; |
||||
} |
||||
|
||||
.highlight .s1, .highlight .s2, .highlight .s, |
||||
.highlight .sd, .highlight .si, |
||||
.highlight .se{ |
||||
color: #FDF5E6; |
||||
} |
||||
|
||||
.highlight .ow{ |
||||
color: #47ffff; |
||||
} |
||||
|
||||
.highlight .c1, |
||||
.highlight .c{ |
||||
color: #d80e04; |
||||
} |
||||
|
||||
.fundo-claro{ |
||||
height: 88px; |
||||
background-color: #595C5E; |
||||
margin-left: -10%; |
||||
margin-top: -1.1%; |
||||
margin-right: -10%; |
||||
} |
||||
|
||||
|
||||
@media screen and (max-width: 980px) { |
||||
pre{ |
||||
overflow-x: scroll; |
||||
} |
||||
} |
||||
|
||||
|
||||
@media screen and (max-width: 768px){ |
||||
.fundo-claro{ |
||||
display:none; |
||||
} |
||||
|
||||
.rst-content{ |
||||
z-index: 210; |
||||
margin-top: -20px; |
||||
} |
||||
|
||||
} |
||||
|
||||
.page{ |
||||
width: 3%; |
||||
float:left; |
||||
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||||
|
||||
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||||
width: 10% |
||||
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||||
|
||||
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||||
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||||
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||||
padding-left: 20px; |
||||
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||||
|
||||
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||||
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||||
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||||
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||||
|
||||
blockquote, q { |
||||
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||||
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
|
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||||
|
||||
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||||
|
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|
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||||
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|
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.fa-home:before, .icon-home:before { |
||||
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||||
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||||
|
||||
|
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/* fundo preto */ |
||||
|
||||
.entry-container-escuro{ |
||||
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||||
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||||
padding: 15px; |
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||||
|
||||
.entry-container-escuro h2, |
||||
.entry-container-escuro h2 a, |
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File diff suppressed because one or more lines are too long
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||||
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||||
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||||
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$u = _.noConflict(); |
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/** |
||||
* make the code below compatible with browsers without |
||||
* an installed firebug like debugger |
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if (!window.console || !console.firebug) { |
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var names = ["log", "debug", "info", "warn", "error", "assert", "dir", |
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"dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", |
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"profile", "profileEnd"]; |
||||
window.console = {}; |
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for (var i = 0; i < names.length; ++i) |
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window.console[names[i]] = function() {}; |
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/** |
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* small helper function to urldecode strings |
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*/ |
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jQuery.urldecode = function(x) { |
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return decodeURIComponent(x).replace(/\+/g, ' '); |
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||||
* small helper function to urlencode strings |
||||
*/ |
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jQuery.urlencode = encodeURIComponent; |
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/** |
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* current request. Multiple values per key are supported, |
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*/ |
||||
jQuery.getQueryParameters = function(s) { |
||||
if (typeof s === 'undefined') |
||||
s = document.location.search; |
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var parts = s.substr(s.indexOf('?') + 1).split('&'); |
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var result = {}; |
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for (var i = 0; i < parts.length; i++) { |
||||
var tmp = parts[i].split('=', 2); |
||||
var key = jQuery.urldecode(tmp[0]); |
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var value = jQuery.urldecode(tmp[1]); |
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if (key in result) |
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result[key].push(value); |
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result[key] = [value]; |
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*/ |
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function highlight(node, addItems) { |
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var val = node.nodeValue; |
||||
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||||
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||||
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||||
node.nextSibling)); |
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||||
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jQuery.each(node.childNodes, function() { |
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highlight(this, addItems); |
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highlight(this, addItems); |
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for (var i = 0; i < addItems.length; ++i) { |
||||
jQuery(addItems[i].parent).before(addItems[i].target); |
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/* |
||||
* backward compatibility for jQuery.browser |
||||
* This will be supported until firefox bug is fixed. |
||||
*/ |
||||
if (!jQuery.browser) { |
||||
jQuery.uaMatch = function(ua) { |
||||
ua = ua.toLowerCase(); |
||||
|
||||
var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || |
||||
/(webkit)[ \/]([\w.]+)/.exec(ua) || |
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/(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || |
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/(msie) ([\w.]+)/.exec(ua) || |
||||
ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || |
||||
[]; |
||||
|
||||
return { |
||||
browser: match[ 1 ] || "", |
||||
version: match[ 2 ] || "0" |
||||
}; |
||||
}; |
||||
jQuery.browser = {}; |
||||
jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; |
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} |
||||
|
||||
/** |
||||
* Small JavaScript module for the documentation. |
||||
*/ |
||||
var Documentation = { |
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|
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this.highlightSearchWords(); |
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this.initIndexTable(); |
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if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { |
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this.initOnKeyListeners(); |
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/** |
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* i18n support |
||||
*/ |
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TRANSLATIONS : {}, |
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LOCALE : 'unknown', |
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|
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|
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gettext : function(string) { |
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var translated = Documentation.TRANSLATIONS[string]; |
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|
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ngettext : function(singular, plural, n) { |
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var translated = Documentation.TRANSLATIONS[singular]; |
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if (typeof translated === 'undefined') |
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return (n == 1) ? singular : plural; |
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}, |
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|
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addTranslations : function(catalog) { |
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for (var key in catalog.messages) |
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this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); |
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/** |
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* add context elements like header anchor links |
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*/ |
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addContextElements : function() { |
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$('div[id] > :header:first').each(function() { |
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$('<a class="headerlink">\u00B6</a>'). |
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attr('href', '#' + this.id). |
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attr('title', _('Permalink to this headline')). |
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appendTo(this); |
||||
}); |
||||
$('dt[id]').each(function() { |
||||
$('<a class="headerlink">\u00B6</a>'). |
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attr('href', '#' + this.id). |
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attr('title', _('Permalink to this definition')). |
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appendTo(this); |
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/** |
||||
* workaround a firefox stupidity |
||||
* see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
|
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*/ |
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fixFirefoxAnchorBug : function() { |
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if (document.location.hash && $.browser.mozilla) |
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window.setTimeout(function() { |
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document.location.href += ''; |
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}, 10); |
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highlightSearchWords : function() { |
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body = $('body'); |
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window.setTimeout(function() { |
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$('<p class="highlight-link"><a href="javascript:Documentation.' + |
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hideSearchWords : function() { |
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$('#searchbox .highlight-link').fadeOut(300); |
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||||
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getCurrentURL : function() { |
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||||
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||||
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$(document).keydown(function(event) { |
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|
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var prevHref = $('link[rel="prev"]').prop('href'); |
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if (prevHref) { |
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window.location.href = prevHref; |
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return false; |
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} |
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case 39: // right
|
||||
var nextHref = $('link[rel="next"]').prop('href'); |
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if (nextHref) { |
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window.location.href = nextHref; |
||||
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||||
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||||
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||||
}); |
||||
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||||
}; |
||||
|
||||
// quick alias for translations
|
||||
_ = Documentation.gettext; |
||||
|
||||
$(document).ready(function() { |
||||
Documentation.init(); |
||||
}); |
||||
@ -1,12 +0,0 @@ |
||||
var DOCUMENTATION_OPTIONS = { |
||||
URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), |
||||
VERSION: '2.0', |
||||
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||||
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||||
BUILDER: 'html', |
||||
FILE_SUFFIX: '.html', |
||||
LINK_SUFFIX: '.html', |
||||
HAS_SOURCE: true, |
||||
SOURCELINK_SUFFIX: '.txt', |
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Before Width: | Height: | Size: 286 B |
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|
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|
Before Width: | Height: | Size: 1.1 KiB |
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@ -1,13 +0,0 @@ |
||||
// setting highlight for async stuff in python
|
||||
var async_kw = ['async', 'await']; |
||||
jQuery.each(async_kw, function(i, kw){ |
||||
var elements = jQuery(".highlight-python .n:contains('" + kw + "')"); |
||||
|
||||
jQuery.each(elements, function(j, el){ |
||||
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||||
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||||
el.removeClass('n'); |
||||
el.addClass('kn'); |
||||
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||||
}); |
||||
@ -1,47 +0,0 @@ |
||||
$( document ).ready(function() { |
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// Shift nav in mobile when clicking the menu.
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$(document).on('click', "[data-toggle='wy-nav-top']", function() { |
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$("[data-toggle='wy-nav-shift']").toggleClass("shift"); |
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$("[data-toggle='rst-versions']").toggleClass("shift"); |
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// Close menu when you click a link.
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$(document).on('click', ".wy-menu-vertical .current ul li a", function() { |
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$("[data-toggle='rst-versions']").toggleClass("shift-up"); |
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$("table.docutils:not(.field-list)").wrap("<div class='wy-table-responsive'></div>"); |
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window.SphinxRtdTheme = (function (jquery) { |
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var navBar, |
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win, |
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applyStickNav = function () { |
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navBar.addClass(stickyNavCssClass); |
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navBar.removeClass(stickyNavCssClass); |
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applyStickNav(); |
||||
win.on('resize', applyStickNav); |
||||
}, |
||||
init = function () { |
||||
navBar = jquery('nav.wy-nav-side:first'); |
||||
win = jquery(window); |
||||
}; |
||||
jquery(init); |
||||
return { |
||||
enable : enable |
||||
}; |
||||
}()); |
||||
return { |
||||
StickyNav : stickyNav |
||||
}; |
||||
}($)); |
||||
@ -1,297 +0,0 @@ |
||||
/* |
||||
* language_data.js |
||||
* ~~~~~~~~~~~~~~~~ |
||||
* |
||||
* This script contains the language-specific data used by searchtools.js, |
||||
* namely the list of stopwords, stemmer, scorer and splitter. |
||||
* |
||||
* :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. |
||||
* :license: BSD, see LICENSE for details. |
||||
* |
||||
*/ |
||||
|
||||
var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"]; |
||||
|
||||
|
||||
/* Non-minified version JS is _stemmer.js if file is provided */
|
||||
/** |
||||
* Porter Stemmer |
||||
*/ |
||||
var Stemmer = function() { |
||||
|
||||
var step2list = { |
||||
ational: 'ate', |
||||
tional: 'tion', |
||||
enci: 'ence', |
||||
anci: 'ance', |
||||
izer: 'ize', |
||||
bli: 'ble', |
||||
alli: 'al', |
||||
entli: 'ent', |
||||
eli: 'e', |
||||
ousli: 'ous', |
||||
ization: 'ize', |
||||
ation: 'ate', |
||||
ator: 'ate', |
||||
alism: 'al', |
||||
iveness: 'ive', |
||||
fulness: 'ful', |
||||
ousness: 'ous', |
||||
aliti: 'al', |
||||
iviti: 'ive', |
||||
biliti: 'ble', |
||||
logi: 'log' |
||||
}; |
||||
|
||||
var step3list = { |
||||
icate: 'ic', |
||||
ative: '', |
||||
alize: 'al', |
||||
iciti: 'ic', |
||||
ical: 'ic', |
||||
ful: '', |
||||
ness: '' |
||||
}; |
||||
|
||||
var c = "[^aeiou]"; // consonant
|
||||
var v = "[aeiouy]"; // vowel
|
||||
var C = c + "[^aeiouy]*"; // consonant sequence
|
||||
var V = v + "[aeiou]*"; // vowel sequence
|
||||
|
||||
var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
|
||||
var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
|
||||
var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
|
||||
var s_v = "^(" + C + ")?" + v; // vowel in stem
|
||||
|
||||
this.stemWord = function (w) { |
||||
var stem; |
||||
var suffix; |
||||
var firstch; |
||||
var origword = w; |
||||
|
||||
if (w.length < 3) |
||||
return w; |
||||
|
||||
var re; |
||||
var re2; |
||||
var re3; |
||||
var re4; |
||||
|
||||
firstch = w.substr(0,1); |
||||
if (firstch == "y") |
||||
w = firstch.toUpperCase() + w.substr(1); |
||||
|
||||
// Step 1a
|
||||
re = /^(.+?)(ss|i)es$/; |
||||
re2 = /^(.+?)([^s])s$/; |
||||
|
||||
if (re.test(w)) |
||||
w = w.replace(re,"$1$2"); |
||||
else if (re2.test(w)) |
||||
w = w.replace(re2,"$1$2"); |
||||
|
||||
// Step 1b
|
||||
re = /^(.+?)eed$/; |
||||
re2 = /^(.+?)(ed|ing)$/; |
||||
if (re.test(w)) { |
||||
var fp = re.exec(w); |
||||
re = new RegExp(mgr0); |
||||
if (re.test(fp[1])) { |
||||
re = /.$/; |
||||
w = w.replace(re,""); |
||||
} |
||||
} |
||||
else if (re2.test(w)) { |
||||
var fp = re2.exec(w); |
||||
stem = fp[1]; |
||||
re2 = new RegExp(s_v); |
||||
if (re2.test(stem)) { |
||||
w = stem; |
||||
re2 = /(at|bl|iz)$/; |
||||
re3 = new RegExp("([^aeiouylsz])\\1$"); |
||||
re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); |
||||
if (re2.test(w)) |
||||
w = w + "e"; |
||||
else if (re3.test(w)) { |
||||
re = /.$/; |
||||
w = w.replace(re,""); |
||||
} |
||||
else if (re4.test(w)) |
||||
w = w + "e"; |
||||
} |
||||
} |
||||
|
||||
// Step 1c
|
||||
re = /^(.+?)y$/; |
||||
if (re.test(w)) { |
||||
var fp = re.exec(w); |
||||
stem = fp[1]; |
||||
re = new RegExp(s_v); |
||||
if (re.test(stem)) |
||||
w = stem + "i"; |
||||
} |
||||
|
||||
// Step 2
|
||||
re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; |
||||
if (re.test(w)) { |
||||
var fp = re.exec(w); |
||||
stem = fp[1]; |
||||
suffix = fp[2]; |
||||
re = new RegExp(mgr0); |
||||
if (re.test(stem)) |
||||
w = stem + step2list[suffix]; |
||||
} |
||||
|
||||
// Step 3
|
||||
re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; |
||||
if (re.test(w)) { |
||||
var fp = re.exec(w); |
||||
stem = fp[1]; |
||||
suffix = fp[2]; |
||||
re = new RegExp(mgr0); |
||||
if (re.test(stem)) |
||||
w = stem + step3list[suffix]; |
||||
} |
||||
|
||||
// Step 4
|
||||
re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; |
||||
re2 = /^(.+?)(s|t)(ion)$/; |
||||
if (re.test(w)) { |
||||
var fp = re.exec(w); |
||||
stem = fp[1]; |
||||
re = new RegExp(mgr1); |
||||
if (re.test(stem)) |
||||
w = stem; |
||||
} |
||||
else if (re2.test(w)) { |
||||
var fp = re2.exec(w); |
||||
stem = fp[1] + fp[2]; |
||||
re2 = new RegExp(mgr1); |
||||
if (re2.test(stem)) |
||||
w = stem; |
||||
} |
||||
|
||||
// Step 5
|
||||
re = /^(.+?)e$/; |
||||
if (re.test(w)) { |
||||
var fp = re.exec(w); |
||||
stem = fp[1]; |
||||
re = new RegExp(mgr1); |
||||
re2 = new RegExp(meq1); |
||||
re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); |
||||
if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) |
||||
w = stem; |
||||
} |
||||
re = /ll$/; |
||||
re2 = new RegExp(mgr1); |
||||
if (re.test(w) && re2.test(w)) { |
||||
re = /.$/; |
||||
w = w.replace(re,""); |
||||
} |
||||
|
||||
// and turn initial Y back to y
|
||||
if (firstch == "y") |
||||
w = firstch.toLowerCase() + w.substr(1); |
||||
return w; |
||||
} |
||||
} |
||||
|
||||
|
||||
|
||||
|
||||
|
||||
var splitChars = (function() { |
||||
var result = {}; |
||||
var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648, |
||||
1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702, |
||||
2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971, |
||||
2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345, |
||||
3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761, |
||||
3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823, |
||||
4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125, |
||||
8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695, |
||||
11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587, |
||||
43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141]; |
||||
var i, j, start, end; |
||||
for (i = 0; i < singles.length; i++) { |
||||
result[singles[i]] = true; |
||||
} |
||||
var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709], |
||||
[722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161], |
||||
[1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568], |
||||
[1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807], |
||||
[1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047], |
||||
[2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383], |
||||
[2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450], |
||||
[2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547], |
||||
[2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673], |
||||
[2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820], |
||||
[2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946], |
||||
[2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023], |
||||
[3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173], |
||||
[3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332], |
||||
[3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481], |
||||
[3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718], |
||||
[3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791], |
||||
[3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095], |
||||
[4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205], |
||||
[4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687], |
||||
[4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968], |
||||
[4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869], |
||||
[5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102], |
||||
[6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271], |
||||
[6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592], |
||||
[6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822], |
||||
[6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167], |
||||
[7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959], |
||||
[7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143], |
||||
[8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318], |
||||
[8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483], |
||||
[8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101], |
||||
[10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567], |
||||
[11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292], |
||||
[12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444], |
||||
[12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783], |
||||
[12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311], |
||||
[19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511], |
||||
[42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774], |
||||
[42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071], |
||||
[43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263], |
||||
[43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519], |
||||
[43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647], |
||||
[43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967], |
||||
[44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295], |
||||
[57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274], |
||||
[64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007], |
||||
[65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381], |
||||
[65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]]; |
||||
for (i = 0; i < ranges.length; i++) { |
||||
start = ranges[i][0]; |
||||
end = ranges[i][1]; |
||||
for (j = start; j <= end; j++) { |
||||
result[j] = true; |
||||
} |
||||
} |
||||
return result; |
||||
})(); |
||||
|
||||
function splitQuery(query) { |
||||
var result = []; |
||||
var start = -1; |
||||
for (var i = 0; i < query.length; i++) { |
||||
if (splitChars[query.charCodeAt(i)]) { |
||||
if (start !== -1) { |
||||
result.push(query.slice(start, i)); |
||||
start = -1; |
||||
} |
||||
} else if (start === -1) { |
||||
start = i; |
||||
} |
||||
} |
||||
if (start !== -1) { |
||||
result.push(query.slice(start)); |
||||
} |
||||
return result; |
||||
} |
||||
|
||||
|
||||
|
Before Width: | Height: | Size: 90 B |
|
Before Width: | Height: | Size: 90 B |
@ -1,7 +0,0 @@ |
||||
pre { line-height: 125%; } |
||||
td.linenos pre { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; } |
||||
span.linenos { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; } |
||||
td.linenos pre.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } |
||||
span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } |
||||
.highlight .hll { background-color: #ffffcc } |
||||
.highlight { background: #ffffff; } |
||||
@ -1,514 +0,0 @@ |
||||
/* |
||||
* searchtools.js |
||||
* ~~~~~~~~~~~~~~~~ |
||||
* |
||||
* Sphinx JavaScript utilities for the full-text search. |
||||
* |
||||
* :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. |
||||
* :license: BSD, see LICENSE for details. |
||||
* |
||||
*/ |
||||
|
||||
if (!Scorer) { |
||||
/** |
||||
* Simple result scoring code. |
||||
*/ |
||||
var Scorer = { |
||||
// Implement the following function to further tweak the score for each result
|
||||
// The function takes a result array [filename, title, anchor, descr, score]
|
||||
// and returns the new score.
|
||||
/* |
||||
score: function(result) { |
||||
return result[4]; |
||||
}, |
||||
*/ |
||||
|
||||
// query matches the full name of an object
|
||||
objNameMatch: 11, |
||||
// or matches in the last dotted part of the object name
|
||||
objPartialMatch: 6, |
||||
// Additive scores depending on the priority of the object
|
||||
objPrio: {0: 15, // used to be importantResults
|
||||
1: 5, // used to be objectResults
|
||||
2: -5}, // used to be unimportantResults
|
||||
// Used when the priority is not in the mapping.
|
||||
objPrioDefault: 0, |
||||
|
||||
// query found in title
|
||||
title: 15, |
||||
partialTitle: 7, |
||||
// query found in terms
|
||||
term: 5, |
||||
partialTerm: 2 |
||||
}; |
||||
} |
||||
|
||||
if (!splitQuery) { |
||||
function splitQuery(query) { |
||||
return query.split(/\s+/); |
||||
} |
||||
} |
||||
|
||||
/** |
||||
* Search Module |
||||
*/ |
||||
var Search = { |
||||
|
||||
_index : null, |
||||
_queued_query : null, |
||||
_pulse_status : -1, |
||||
|
||||
htmlToText : function(htmlString) { |
||||
var virtualDocument = document.implementation.createHTMLDocument('virtual'); |
||||
var htmlElement = $(htmlString, virtualDocument); |
||||
htmlElement.find('.headerlink').remove(); |
||||
docContent = htmlElement.find('[role=main]')[0]; |
||||
if(docContent === undefined) { |
||||
console.warn("Content block not found. Sphinx search tries to obtain it " + |
||||
"via '[role=main]'. Could you check your theme or template."); |
||||
return ""; |
||||
} |
||||
return docContent.textContent || docContent.innerText; |
||||
}, |
||||
|
||||
init : function() { |
||||
var params = $.getQueryParameters(); |
||||
if (params.q) { |
||||
var query = params.q[0]; |
||||
$('input[name="q"]')[0].value = query; |
||||
this.performSearch(query); |
||||
} |
||||
}, |
||||
|
||||
loadIndex : function(url) { |
||||
$.ajax({type: "GET", url: url, data: null, |
||||
dataType: "script", cache: true, |
||||
complete: function(jqxhr, textstatus) { |
||||
if (textstatus != "success") { |
||||
document.getElementById("searchindexloader").src = url; |
||||
} |
||||
}}); |
||||
}, |
||||
|
||||
setIndex : function(index) { |
||||
var q; |
||||
this._index = index; |
||||
if ((q = this._queued_query) !== null) { |
||||
this._queued_query = null; |
||||
Search.query(q); |
||||
} |
||||
}, |
||||
|
||||
hasIndex : function() { |
||||
return this._index !== null; |
||||
}, |
||||
|
||||
deferQuery : function(query) { |
||||
this._queued_query = query; |
||||
}, |
||||
|
||||
stopPulse : function() { |
||||
this._pulse_status = 0; |
||||
}, |
||||
|
||||
startPulse : function() { |
||||
if (this._pulse_status >= 0) |
||||
return; |
||||
function pulse() { |
||||
var i; |
||||
Search._pulse_status = (Search._pulse_status + 1) % 4; |
||||
var dotString = ''; |
||||
for (i = 0; i < Search._pulse_status; i++) |
||||
dotString += '.'; |
||||
Search.dots.text(dotString); |
||||
if (Search._pulse_status > -1) |
||||
window.setTimeout(pulse, 500); |
||||
} |
||||
pulse(); |
||||
}, |
||||
|
||||
/** |
||||
* perform a search for something (or wait until index is loaded) |
||||
*/ |
||||
performSearch : function(query) { |
||||
// create the required interface elements
|
||||
this.out = $('#search-results'); |
||||
this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out); |
||||
this.dots = $('<span></span>').appendTo(this.title); |
||||
this.status = $('<p class="search-summary"> </p>').appendTo(this.out); |
||||
this.output = $('<ul class="search"/>').appendTo(this.out); |
||||
|
||||
$('#search-progress').text(_('Preparing search...')); |
||||
this.startPulse(); |
||||
|
||||
// index already loaded, the browser was quick!
|
||||
if (this.hasIndex()) |
||||
this.query(query); |
||||
else |
||||
this.deferQuery(query); |
||||
}, |
||||
|
||||
/** |
||||
* execute search (requires search index to be loaded) |
||||
*/ |
||||
query : function(query) { |
||||
var i; |
||||
|
||||
// stem the searchterms and add them to the correct list
|
||||
var stemmer = new Stemmer(); |
||||
var searchterms = []; |
||||
var excluded = []; |
||||
var hlterms = []; |
||||
var tmp = splitQuery(query); |
||||
var objectterms = []; |
||||
for (i = 0; i < tmp.length; i++) { |
||||
if (tmp[i] !== "") { |
||||
objectterms.push(tmp[i].toLowerCase()); |
||||
} |
||||
|
||||
if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i] === "") { |
||||
// skip this "word"
|
||||
continue; |
||||
} |
||||
// stem the word
|
||||
var word = stemmer.stemWord(tmp[i].toLowerCase()); |
||||
// prevent stemmer from cutting word smaller than two chars
|
||||
if(word.length < 3 && tmp[i].length >= 3) { |
||||
word = tmp[i]; |
||||
} |
||||
var toAppend; |
||||
// select the correct list
|
||||
if (word[0] == '-') { |
||||
toAppend = excluded; |
||||
word = word.substr(1); |
||||
} |
||||
else { |
||||
toAppend = searchterms; |
||||
hlterms.push(tmp[i].toLowerCase()); |
||||
} |
||||
// only add if not already in the list
|
||||
if (!$u.contains(toAppend, word)) |
||||
toAppend.push(word); |
||||
} |
||||
var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" ")); |
||||
|
||||
// console.debug('SEARCH: searching for:');
|
||||
// console.info('required: ', searchterms);
|
||||
// console.info('excluded: ', excluded);
|
||||
|
||||
// prepare search
|
||||
var terms = this._index.terms; |
||||
var titleterms = this._index.titleterms; |
||||
|
||||
// array of [filename, title, anchor, descr, score]
|
||||
var results = []; |
||||
$('#search-progress').empty(); |
||||
|
||||
// lookup as object
|
||||
for (i = 0; i < objectterms.length; i++) { |
||||
var others = [].concat(objectterms.slice(0, i), |
||||
objectterms.slice(i+1, objectterms.length)); |
||||
results = results.concat(this.performObjectSearch(objectterms[i], others)); |
||||
} |
||||
|
||||
// lookup as search terms in fulltext
|
||||
results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms)); |
||||
|
||||
// let the scorer override scores with a custom scoring function
|
||||
if (Scorer.score) { |
||||
for (i = 0; i < results.length; i++) |
||||
results[i][4] = Scorer.score(results[i]); |
||||
} |
||||
|
||||
// now sort the results by score (in opposite order of appearance, since the
|
||||
// display function below uses pop() to retrieve items) and then
|
||||
// alphabetically
|
||||
results.sort(function(a, b) { |
||||
var left = a[4]; |
||||
var right = b[4]; |
||||
if (left > right) { |
||||
return 1; |
||||
} else if (left < right) { |
||||
return -1; |
||||
} else { |
||||
// same score: sort alphabetically
|
||||
left = a[1].toLowerCase(); |
||||
right = b[1].toLowerCase(); |
||||
return (left > right) ? -1 : ((left < right) ? 1 : 0); |
||||
} |
||||
}); |
||||
|
||||
// for debugging
|
||||
//Search.lastresults = results.slice(); // a copy
|
||||
//console.info('search results:', Search.lastresults);
|
||||
|
||||
// print the results
|
||||
var resultCount = results.length; |
||||
function displayNextItem() { |
||||
// results left, load the summary and display it
|
||||
if (results.length) { |
||||
var item = results.pop(); |
||||
var listItem = $('<li style="display:none"></li>'); |
||||
var requestUrl = ""; |
||||
var linkUrl = ""; |
||||
if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') { |
||||
// dirhtml builder
|
||||
var dirname = item[0] + '/'; |
||||
if (dirname.match(/\/index\/$/)) { |
||||
dirname = dirname.substring(0, dirname.length-6); |
||||
} else if (dirname == 'index/') { |
||||
dirname = ''; |
||||
} |
||||
requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname; |
||||
linkUrl = requestUrl; |
||||
|
||||
} else { |
||||
// normal html builders
|
||||
requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX; |
||||
linkUrl = item[0] + DOCUMENTATION_OPTIONS.LINK_SUFFIX; |
||||
} |
||||
listItem.append($('<a/>').attr('href', |
||||
linkUrl + |
||||
highlightstring + item[2]).html(item[1])); |
||||
if (item[3]) { |
||||
listItem.append($('<span> (' + item[3] + ')</span>')); |
||||
Search.output.append(listItem); |
||||
listItem.slideDown(5, function() { |
||||
displayNextItem(); |
||||
}); |
||||
} else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) { |
||||
$.ajax({url: requestUrl, |
||||
dataType: "text", |
||||
complete: function(jqxhr, textstatus) { |
||||
var data = jqxhr.responseText; |
||||
if (data !== '' && data !== undefined) { |
||||
listItem.append(Search.makeSearchSummary(data, searchterms, hlterms)); |
||||
} |
||||
Search.output.append(listItem); |
||||
listItem.slideDown(5, function() { |
||||
displayNextItem(); |
||||
}); |
||||
}}); |
||||
} else { |
||||
// no source available, just display title
|
||||
Search.output.append(listItem); |
||||
listItem.slideDown(5, function() { |
||||
displayNextItem(); |
||||
}); |
||||
} |
||||
} |
||||
// search finished, update title and status message
|
||||
else { |
||||
Search.stopPulse(); |
||||
Search.title.text(_('Search Results')); |
||||
if (!resultCount) |
||||
Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.')); |
||||
else |
||||
Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount)); |
||||
Search.status.fadeIn(500); |
||||
} |
||||
} |
||||
displayNextItem(); |
||||
}, |
||||
|
||||
/** |
||||
* search for object names |
||||
*/ |
||||
performObjectSearch : function(object, otherterms) { |
||||
var filenames = this._index.filenames; |
||||
var docnames = this._index.docnames; |
||||
var objects = this._index.objects; |
||||
var objnames = this._index.objnames; |
||||
var titles = this._index.titles; |
||||
|
||||
var i; |
||||
var results = []; |
||||
|
||||
for (var prefix in objects) { |
||||
for (var name in objects[prefix]) { |
||||
var fullname = (prefix ? prefix + '.' : '') + name; |
||||
var fullnameLower = fullname.toLowerCase() |
||||
if (fullnameLower.indexOf(object) > -1) { |
||||
var score = 0; |
||||
var parts = fullnameLower.split('.'); |
||||
// check for different match types: exact matches of full name or
|
||||
// "last name" (i.e. last dotted part)
|
||||
if (fullnameLower == object || parts[parts.length - 1] == object) { |
||||
score += Scorer.objNameMatch; |
||||
// matches in last name
|
||||
} else if (parts[parts.length - 1].indexOf(object) > -1) { |
||||
score += Scorer.objPartialMatch; |
||||
} |
||||
var match = objects[prefix][name]; |
||||
var objname = objnames[match[1]][2]; |
||||
var title = titles[match[0]]; |
||||
// If more than one term searched for, we require other words to be
|
||||
// found in the name/title/description
|
||||
if (otherterms.length > 0) { |
||||
var haystack = (prefix + ' ' + name + ' ' + |
||||
objname + ' ' + title).toLowerCase(); |
||||
var allfound = true; |
||||
for (i = 0; i < otherterms.length; i++) { |
||||
if (haystack.indexOf(otherterms[i]) == -1) { |
||||
allfound = false; |
||||
break; |
||||
} |
||||
} |
||||
if (!allfound) { |
||||
continue; |
||||
} |
||||
} |
||||
var descr = objname + _(', in ') + title; |
||||
|
||||
var anchor = match[3]; |
||||
if (anchor === '') |
||||
anchor = fullname; |
||||
else if (anchor == '-') |
||||
anchor = objnames[match[1]][1] + '-' + fullname; |
||||
// add custom score for some objects according to scorer
|
||||
if (Scorer.objPrio.hasOwnProperty(match[2])) { |
||||
score += Scorer.objPrio[match[2]]; |
||||
} else { |
||||
score += Scorer.objPrioDefault; |
||||
} |
||||
results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]); |
||||
} |
||||
} |
||||
} |
||||
|
||||
return results; |
||||
}, |
||||
|
||||
/** |
||||
* search for full-text terms in the index |
||||
*/ |
||||
performTermsSearch : function(searchterms, excluded, terms, titleterms) { |
||||
var docnames = this._index.docnames; |
||||
var filenames = this._index.filenames; |
||||
var titles = this._index.titles; |
||||
|
||||
var i, j, file; |
||||
var fileMap = {}; |
||||
var scoreMap = {}; |
||||
var results = []; |
||||
|
||||
// perform the search on the required terms
|
||||
for (i = 0; i < searchterms.length; i++) { |
||||
var word = searchterms[i]; |
||||
var files = []; |
||||
var _o = [ |
||||
{files: terms[word], score: Scorer.term}, |
||||
{files: titleterms[word], score: Scorer.title} |
||||
]; |
||||
// add support for partial matches
|
||||
if (word.length > 2) { |
||||
for (var w in terms) { |
||||
if (w.match(word) && !terms[word]) { |
||||
_o.push({files: terms[w], score: Scorer.partialTerm}) |
||||
} |
||||
} |
||||
for (var w in titleterms) { |
||||
if (w.match(word) && !titleterms[word]) { |
||||
_o.push({files: titleterms[w], score: Scorer.partialTitle}) |
||||
} |
||||
} |
||||
} |
||||
|
||||
// no match but word was a required one
|
||||
if ($u.every(_o, function(o){return o.files === undefined;})) { |
||||
break; |
||||
} |
||||
// found search word in contents
|
||||
$u.each(_o, function(o) { |
||||
var _files = o.files; |
||||
if (_files === undefined) |
||||
return |
||||
|
||||
if (_files.length === undefined) |
||||
_files = [_files]; |
||||
files = files.concat(_files); |
||||
|
||||
// set score for the word in each file to Scorer.term
|
||||
for (j = 0; j < _files.length; j++) { |
||||
file = _files[j]; |
||||
if (!(file in scoreMap)) |
||||
scoreMap[file] = {}; |
||||
scoreMap[file][word] = o.score; |
||||
} |
||||
}); |
||||
|
||||
// create the mapping
|
||||
for (j = 0; j < files.length; j++) { |
||||
file = files[j]; |
||||
if (file in fileMap && fileMap[file].indexOf(word) === -1) |
||||
fileMap[file].push(word); |
||||
else |
||||
fileMap[file] = [word]; |
||||
} |
||||
} |
||||
|
||||
// now check if the files don't contain excluded terms
|
||||
for (file in fileMap) { |
||||
var valid = true; |
||||
|
||||
// check if all requirements are matched
|
||||
var filteredTermCount = // as search terms with length < 3 are discarded: ignore
|
||||
searchterms.filter(function(term){return term.length > 2}).length |
||||
if ( |
||||
fileMap[file].length != searchterms.length && |
||||
fileMap[file].length != filteredTermCount |
||||
) continue; |
||||
|
||||
// ensure that none of the excluded terms is in the search result
|
||||
for (i = 0; i < excluded.length; i++) { |
||||
if (terms[excluded[i]] == file || |
||||
titleterms[excluded[i]] == file || |
||||
$u.contains(terms[excluded[i]] || [], file) || |
||||
$u.contains(titleterms[excluded[i]] || [], file)) { |
||||
valid = false; |
||||
break; |
||||
} |
||||
} |
||||
|
||||
// if we have still a valid result we can add it to the result list
|
||||
if (valid) { |
||||
// select one (max) score for the file.
|
||||
// for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
|
||||
var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]})); |
||||
results.push([docnames[file], titles[file], '', null, score, filenames[file]]); |
||||
} |
||||
} |
||||
return results; |
||||
}, |
||||
|
||||
/** |
||||
* helper function to return a node containing the |
||||
* search summary for a given text. keywords is a list |
||||
* of stemmed words, hlwords is the list of normal, unstemmed |
||||
* words. the first one is used to find the occurrence, the |
||||
* latter for highlighting it. |
||||
*/ |
||||
makeSearchSummary : function(htmlText, keywords, hlwords) { |
||||
var text = Search.htmlToText(htmlText); |
||||
var textLower = text.toLowerCase(); |
||||
var start = 0; |
||||
$.each(keywords, function() { |
||||
var i = textLower.indexOf(this.toLowerCase()); |
||||
if (i > -1) |
||||
start = i; |
||||
}); |
||||
start = Math.max(start - 120, 0); |
||||
var excerpt = ((start > 0) ? '...' : '') + |
||||
$.trim(text.substr(start, 240)) + |
||||
((start + 240 - text.length) ? '...' : ''); |
||||
var rv = $('<div class="context"></div>').text(excerpt); |
||||
$.each(hlwords, function() { |
||||
rv = rv.highlightText(this, 'highlighted'); |
||||
}); |
||||
return rv; |
||||
} |
||||
}; |
||||
|
||||
$(document).ready(function() { |
||||
Search.init(); |
||||
}); |
||||
@ -1,999 +0,0 @@ |
||||
// Underscore.js 1.3.1
|
||||
// (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
|
||||
// Underscore is freely distributable under the MIT license.
|
||||
// Portions of Underscore are inspired or borrowed from Prototype,
|
||||
// Oliver Steele's Functional, and John Resig's Micro-Templating.
|
||||
// For all details and documentation:
|
||||
// http://documentcloud.github.com/underscore
|
||||
|
||||
(function() { |
||||
|
||||
// Baseline setup
|
||||
// --------------
|
||||
|
||||
// Establish the root object, `window` in the browser, or `global` on the server.
|
||||
var root = this; |
||||
|
||||
// Save the previous value of the `_` variable.
|
||||
var previousUnderscore = root._; |
||||
|
||||
// Establish the object that gets returned to break out of a loop iteration.
|
||||
var breaker = {}; |
||||
|
||||
// Save bytes in the minified (but not gzipped) version:
|
||||
var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype; |
||||
|
||||
// Create quick reference variables for speed access to core prototypes.
|
||||
var slice = ArrayProto.slice, |
||||
unshift = ArrayProto.unshift, |
||||
toString = ObjProto.toString, |
||||
hasOwnProperty = ObjProto.hasOwnProperty; |
||||
|
||||
// All **ECMAScript 5** native function implementations that we hope to use
|
||||
// are declared here.
|
||||
var |
||||
nativeForEach = ArrayProto.forEach, |
||||
nativeMap = ArrayProto.map, |
||||
nativeReduce = ArrayProto.reduce, |
||||
nativeReduceRight = ArrayProto.reduceRight, |
||||
nativeFilter = ArrayProto.filter, |
||||
nativeEvery = ArrayProto.every, |
||||
nativeSome = ArrayProto.some, |
||||
nativeIndexOf = ArrayProto.indexOf, |
||||
nativeLastIndexOf = ArrayProto.lastIndexOf, |
||||
nativeIsArray = Array.isArray, |
||||
nativeKeys = Object.keys, |
||||
nativeBind = FuncProto.bind; |
||||
|
||||
// Create a safe reference to the Underscore object for use below.
|
||||
var _ = function(obj) { return new wrapper(obj); }; |
||||
|
||||
// Export the Underscore object for **Node.js**, with
|
||||
// backwards-compatibility for the old `require()` API. If we're in
|
||||
// the browser, add `_` as a global object via a string identifier,
|
||||
// for Closure Compiler "advanced" mode.
|
||||
if (typeof exports !== 'undefined') { |
||||
if (typeof module !== 'undefined' && module.exports) { |
||||
exports = module.exports = _; |
||||
} |
||||
exports._ = _; |
||||
} else { |
||||
root['_'] = _; |
||||
} |
||||
|
||||
// Current version.
|
||||
_.VERSION = '1.3.1'; |
||||
|
||||
// Collection Functions
|
||||
// --------------------
|
||||
|
||||
// The cornerstone, an `each` implementation, aka `forEach`.
|
||||
// Handles objects with the built-in `forEach`, arrays, and raw objects.
|
||||
// Delegates to **ECMAScript 5**'s native `forEach` if available.
|
||||
var each = _.each = _.forEach = function(obj, iterator, context) { |
||||
if (obj == null) return; |
||||
if (nativeForEach && obj.forEach === nativeForEach) { |
||||
obj.forEach(iterator, context); |
||||
} else if (obj.length === +obj.length) { |
||||
for (var i = 0, l = obj.length; i < l; i++) { |
||||
if (i in obj && iterator.call(context, obj[i], i, obj) === breaker) return; |
||||
} |
||||
} else { |
||||
for (var key in obj) { |
||||
if (_.has(obj, key)) { |
||||
if (iterator.call(context, obj[key], key, obj) === breaker) return; |
||||
} |
||||
} |
||||
} |
||||
}; |
||||
|
||||
// Return the results of applying the iterator to each element.
|
||||
// Delegates to **ECMAScript 5**'s native `map` if available.
|
||||
_.map = _.collect = function(obj, iterator, context) { |
||||
var results = []; |
||||
if (obj == null) return results; |
||||
if (nativeMap && obj.map === nativeMap) return obj.map(iterator, context); |
||||
each(obj, function(value, index, list) { |
||||
results[results.length] = iterator.call(context, value, index, list); |
||||
}); |
||||
if (obj.length === +obj.length) results.length = obj.length; |
||||
return results; |
||||
}; |
||||
|
||||
// **Reduce** builds up a single result from a list of values, aka `inject`,
|
||||
// or `foldl`. Delegates to **ECMAScript 5**'s native `reduce` if available.
|
||||
_.reduce = _.foldl = _.inject = function(obj, iterator, memo, context) { |
||||
var initial = arguments.length > 2; |
||||
if (obj == null) obj = []; |
||||
if (nativeReduce && obj.reduce === nativeReduce) { |
||||
if (context) iterator = _.bind(iterator, context); |
||||
return initial ? obj.reduce(iterator, memo) : obj.reduce(iterator); |
||||
} |
||||
each(obj, function(value, index, list) { |
||||
if (!initial) { |
||||
memo = value; |
||||
initial = true; |
||||
} else { |
||||
memo = iterator.call(context, memo, value, index, list); |
||||
} |
||||
}); |
||||
if (!initial) throw new TypeError('Reduce of empty array with no initial value'); |
||||
return memo; |
||||
}; |
||||
|
||||
// The right-associative version of reduce, also known as `foldr`.
|
||||
// Delegates to **ECMAScript 5**'s native `reduceRight` if available.
|
||||
_.reduceRight = _.foldr = function(obj, iterator, memo, context) { |
||||
var initial = arguments.length > 2; |
||||
if (obj == null) obj = []; |
||||
if (nativeReduceRight && obj.reduceRight === nativeReduceRight) { |
||||
if (context) iterator = _.bind(iterator, context); |
||||
return initial ? obj.reduceRight(iterator, memo) : obj.reduceRight(iterator); |
||||
} |
||||
var reversed = _.toArray(obj).reverse(); |
||||
if (context && !initial) iterator = _.bind(iterator, context); |
||||
return initial ? _.reduce(reversed, iterator, memo, context) : _.reduce(reversed, iterator); |
||||
}; |
||||
|
||||
// Return the first value which passes a truth test. Aliased as `detect`.
|
||||
_.find = _.detect = function(obj, iterator, context) { |
||||
var result; |
||||
any(obj, function(value, index, list) { |
||||
if (iterator.call(context, value, index, list)) { |
||||
result = value; |
||||
return true; |
||||
} |
||||
}); |
||||
return result; |
||||
}; |
||||
|
||||
// Return all the elements that pass a truth test.
|
||||
// Delegates to **ECMAScript 5**'s native `filter` if available.
|
||||
// Aliased as `select`.
|
||||
_.filter = _.select = function(obj, iterator, context) { |
||||
var results = []; |
||||
if (obj == null) return results; |
||||
if (nativeFilter && obj.filter === nativeFilter) return obj.filter(iterator, context); |
||||
each(obj, function(value, index, list) { |
||||
if (iterator.call(context, value, index, list)) results[results.length] = value; |
||||
}); |
||||
return results; |
||||
}; |
||||
|
||||
// Return all the elements for which a truth test fails.
|
||||
_.reject = function(obj, iterator, context) { |
||||
var results = []; |
||||
if (obj == null) return results; |
||||
each(obj, function(value, index, list) { |
||||
if (!iterator.call(context, value, index, list)) results[results.length] = value; |
||||
}); |
||||
return results; |
||||
}; |
||||
|
||||
// Determine whether all of the elements match a truth test.
|
||||
// Delegates to **ECMAScript 5**'s native `every` if available.
|
||||
// Aliased as `all`.
|
||||
_.every = _.all = function(obj, iterator, context) { |
||||
var result = true; |
||||
if (obj == null) return result; |
||||
if (nativeEvery && obj.every === nativeEvery) return obj.every(iterator, context); |
||||
each(obj, function(value, index, list) { |
||||
if (!(result = result && iterator.call(context, value, index, list))) return breaker; |
||||
}); |
||||
return result; |
||||
}; |
||||
|
||||
// Determine if at least one element in the object matches a truth test.
|
||||
// Delegates to **ECMAScript 5**'s native `some` if available.
|
||||
// Aliased as `any`.
|
||||
var any = _.some = _.any = function(obj, iterator, context) { |
||||
iterator || (iterator = _.identity); |
||||
var result = false; |
||||
if (obj == null) return result; |
||||
if (nativeSome && obj.some === nativeSome) return obj.some(iterator, context); |
||||
each(obj, function(value, index, list) { |
||||
if (result || (result = iterator.call(context, value, index, list))) return breaker; |
||||
}); |
||||
return !!result; |
||||
}; |
||||
|
||||
// Determine if a given value is included in the array or object using `===`.
|
||||
// Aliased as `contains`.
|
||||
_.include = _.contains = function(obj, target) { |
||||
var found = false; |
||||
if (obj == null) return found; |
||||
if (nativeIndexOf && obj.indexOf === nativeIndexOf) return obj.indexOf(target) != -1; |
||||
found = any(obj, function(value) { |
||||
return value === target; |
||||
}); |
||||
return found; |
||||
}; |
||||
|
||||
// Invoke a method (with arguments) on every item in a collection.
|
||||
_.invoke = function(obj, method) { |
||||
var args = slice.call(arguments, 2); |
||||
return _.map(obj, function(value) { |
||||
return (_.isFunction(method) ? method || value : value[method]).apply(value, args); |
||||
}); |
||||
}; |
||||
|
||||
// Convenience version of a common use case of `map`: fetching a property.
|
||||
_.pluck = function(obj, key) { |
||||
return _.map(obj, function(value){ return value[key]; }); |
||||
}; |
||||
|
||||
// Return the maximum element or (element-based computation).
|
||||
_.max = function(obj, iterator, context) { |
||||
if (!iterator && _.isArray(obj)) return Math.max.apply(Math, obj); |
||||
if (!iterator && _.isEmpty(obj)) return -Infinity; |
||||
var result = {computed : -Infinity}; |
||||
each(obj, function(value, index, list) { |
||||
var computed = iterator ? iterator.call(context, value, index, list) : value; |
||||
computed >= result.computed && (result = {value : value, computed : computed}); |
||||
}); |
||||
return result.value; |
||||
}; |
||||
|
||||
// Return the minimum element (or element-based computation).
|
||||
_.min = function(obj, iterator, context) { |
||||
if (!iterator && _.isArray(obj)) return Math.min.apply(Math, obj); |
||||
if (!iterator && _.isEmpty(obj)) return Infinity; |
||||
var result = {computed : Infinity}; |
||||
each(obj, function(value, index, list) { |
||||
var computed = iterator ? iterator.call(context, value, index, list) : value; |
||||
computed < result.computed && (result = {value : value, computed : computed}); |
||||
}); |
||||
return result.value; |
||||
}; |
||||
|
||||
// Shuffle an array.
|
||||
_.shuffle = function(obj) { |
||||
var shuffled = [], rand; |
||||
each(obj, function(value, index, list) { |
||||
if (index == 0) { |
||||
shuffled[0] = value; |
||||
} else { |
||||
rand = Math.floor(Math.random() * (index + 1)); |
||||
shuffled[index] = shuffled[rand]; |
||||
shuffled[rand] = value; |
||||
} |
||||
}); |
||||
return shuffled; |
||||
}; |
||||
|
||||
// Sort the object's values by a criterion produced by an iterator.
|
||||
_.sortBy = function(obj, iterator, context) { |
||||
return _.pluck(_.map(obj, function(value, index, list) { |
||||
return { |
||||
value : value, |
||||
criteria : iterator.call(context, value, index, list) |
||||
}; |
||||
}).sort(function(left, right) { |
||||
var a = left.criteria, b = right.criteria; |
||||
return a < b ? -1 : a > b ? 1 : 0; |
||||
}), 'value'); |
||||
}; |
||||
|
||||
// Groups the object's values by a criterion. Pass either a string attribute
|
||||
// to group by, or a function that returns the criterion.
|
||||
_.groupBy = function(obj, val) { |
||||
var result = {}; |
||||
var iterator = _.isFunction(val) ? val : function(obj) { return obj[val]; }; |
||||
each(obj, function(value, index) { |
||||
var key = iterator(value, index); |
||||
(result[key] || (result[key] = [])).push(value); |
||||
}); |
||||
return result; |
||||
}; |
||||
|
||||
// Use a comparator function to figure out at what index an object should
|
||||
// be inserted so as to maintain order. Uses binary search.
|
||||
_.sortedIndex = function(array, obj, iterator) { |
||||
iterator || (iterator = _.identity); |
||||
var low = 0, high = array.length; |
||||
while (low < high) { |
||||
var mid = (low + high) >> 1; |
||||
iterator(array[mid]) < iterator(obj) ? low = mid + 1 : high = mid; |
||||
} |
||||
return low; |
||||
}; |
||||
|
||||
// Safely convert anything iterable into a real, live array.
|
||||
_.toArray = function(iterable) { |
||||
if (!iterable) return []; |
||||
if (iterable.toArray) return iterable.toArray(); |
||||
if (_.isArray(iterable)) return slice.call(iterable); |
||||
if (_.isArguments(iterable)) return slice.call(iterable); |
||||
return _.values(iterable); |
||||
}; |
||||
|
||||
// Return the number of elements in an object.
|
||||
_.size = function(obj) { |
||||
return _.toArray(obj).length; |
||||
}; |
||||
|
||||
// Array Functions
|
||||
// ---------------
|
||||
|
||||
// Get the first element of an array. Passing **n** will return the first N
|
||||
// values in the array. Aliased as `head`. The **guard** check allows it to work
|
||||
// with `_.map`.
|
||||
_.first = _.head = function(array, n, guard) { |
||||
return (n != null) && !guard ? slice.call(array, 0, n) : array[0]; |
||||
}; |
||||
|
||||
// Returns everything but the last entry of the array. Especcialy useful on
|
||||
// the arguments object. Passing **n** will return all the values in
|
||||
// the array, excluding the last N. The **guard** check allows it to work with
|
||||
// `_.map`.
|
||||
_.initial = function(array, n, guard) { |
||||
return slice.call(array, 0, array.length - ((n == null) || guard ? 1 : n)); |
||||
}; |
||||
|
||||
// Get the last element of an array. Passing **n** will return the last N
|
||||
// values in the array. The **guard** check allows it to work with `_.map`.
|
||||
_.last = function(array, n, guard) { |
||||
if ((n != null) && !guard) { |
||||
return slice.call(array, Math.max(array.length - n, 0)); |
||||
} else { |
||||
return array[array.length - 1]; |
||||
} |
||||
}; |
||||
|
||||
// Returns everything but the first entry of the array. Aliased as `tail`.
|
||||
// Especially useful on the arguments object. Passing an **index** will return
|
||||
// the rest of the values in the array from that index onward. The **guard**
|
||||
// check allows it to work with `_.map`.
|
||||
_.rest = _.tail = function(array, index, guard) { |
||||
return slice.call(array, (index == null) || guard ? 1 : index); |
||||
}; |
||||
|
||||
// Trim out all falsy values from an array.
|
||||
_.compact = function(array) { |
||||
return _.filter(array, function(value){ return !!value; }); |
||||
}; |
||||
|
||||
// Return a completely flattened version of an array.
|
||||
_.flatten = function(array, shallow) { |
||||
return _.reduce(array, function(memo, value) { |
||||
if (_.isArray(value)) return memo.concat(shallow ? value : _.flatten(value)); |
||||
memo[memo.length] = value; |
||||
return memo; |
||||
}, []); |
||||
}; |
||||
|
||||
// Return a version of the array that does not contain the specified value(s).
|
||||
_.without = function(array) { |
||||
return _.difference(array, slice.call(arguments, 1)); |
||||
}; |
||||
|
||||
// Produce a duplicate-free version of the array. If the array has already
|
||||
// been sorted, you have the option of using a faster algorithm.
|
||||
// Aliased as `unique`.
|
||||
_.uniq = _.unique = function(array, isSorted, iterator) { |
||||
var initial = iterator ? _.map(array, iterator) : array; |
||||
var result = []; |
||||
_.reduce(initial, function(memo, el, i) { |
||||
if (0 == i || (isSorted === true ? _.last(memo) != el : !_.include(memo, el))) { |
||||
memo[memo.length] = el; |
||||
result[result.length] = array[i]; |
||||
} |
||||
return memo; |
||||
}, []); |
||||
return result; |
||||
}; |
||||
|
||||
// Produce an array that contains the union: each distinct element from all of
|
||||
// the passed-in arrays.
|
||||
_.union = function() { |
||||
return _.uniq(_.flatten(arguments, true)); |
||||
}; |
||||
|
||||
// Produce an array that contains every item shared between all the
|
||||
// passed-in arrays. (Aliased as "intersect" for back-compat.)
|
||||
_.intersection = _.intersect = function(array) { |
||||
var rest = slice.call(arguments, 1); |
||||
return _.filter(_.uniq(array), function(item) { |
||||
return _.every(rest, function(other) { |
||||
return _.indexOf(other, item) >= 0; |
||||
}); |
||||
}); |
||||
}; |
||||
|
||||
// Take the difference between one array and a number of other arrays.
|
||||
// Only the elements present in just the first array will remain.
|
||||
_.difference = function(array) { |
||||
var rest = _.flatten(slice.call(arguments, 1)); |
||||
return _.filter(array, function(value){ return !_.include(rest, value); }); |
||||
}; |
||||
|
||||
// Zip together multiple lists into a single array -- elements that share
|
||||
// an index go together.
|
||||
_.zip = function() { |
||||
var args = slice.call(arguments); |
||||
var length = _.max(_.pluck(args, 'length')); |
||||
var results = new Array(length); |
||||
for (var i = 0; i < length; i++) results[i] = _.pluck(args, "" + i); |
||||
return results; |
||||
}; |
||||
|
||||
// If the browser doesn't supply us with indexOf (I'm looking at you, **MSIE**),
|
||||
// we need this function. Return the position of the first occurrence of an
|
||||
// item in an array, or -1 if the item is not included in the array.
|
||||
// Delegates to **ECMAScript 5**'s native `indexOf` if available.
|
||||
// If the array is large and already in sort order, pass `true`
|
||||
// for **isSorted** to use binary search.
|
||||
_.indexOf = function(array, item, isSorted) { |
||||
if (array == null) return -1; |
||||
var i, l; |
||||
if (isSorted) { |
||||
i = _.sortedIndex(array, item); |
||||
return array[i] === item ? i : -1; |
||||
} |
||||
if (nativeIndexOf && array.indexOf === nativeIndexOf) return array.indexOf(item); |
||||
for (i = 0, l = array.length; i < l; i++) if (i in array && array[i] === item) return i; |
||||
return -1; |
||||
}; |
||||
|
||||
// Delegates to **ECMAScript 5**'s native `lastIndexOf` if available.
|
||||
_.lastIndexOf = function(array, item) { |
||||
if (array == null) return -1; |
||||
if (nativeLastIndexOf && array.lastIndexOf === nativeLastIndexOf) return array.lastIndexOf(item); |
||||
var i = array.length; |
||||
while (i--) if (i in array && array[i] === item) return i; |
||||
return -1; |
||||
}; |
||||
|
||||
// Generate an integer Array containing an arithmetic progression. A port of
|
||||
// the native Python `range()` function. See
|
||||
// [the Python documentation](http://docs.python.org/library/functions.html#range).
|
||||
_.range = function(start, stop, step) { |
||||
if (arguments.length <= 1) { |
||||
stop = start || 0; |
||||
start = 0; |
||||
} |
||||
step = arguments[2] || 1; |
||||
|
||||
var len = Math.max(Math.ceil((stop - start) / step), 0); |
||||
var idx = 0; |
||||
var range = new Array(len); |
||||
|
||||
while(idx < len) { |
||||
range[idx++] = start; |
||||
start += step; |
||||
} |
||||
|
||||
return range; |
||||
}; |
||||
|
||||
// Function (ahem) Functions
|
||||
// ------------------
|
||||
|
||||
// Reusable constructor function for prototype setting.
|
||||
var ctor = function(){}; |
||||
|
||||
// Create a function bound to a given object (assigning `this`, and arguments,
|
||||
// optionally). Binding with arguments is also known as `curry`.
|
||||
// Delegates to **ECMAScript 5**'s native `Function.bind` if available.
|
||||
// We check for `func.bind` first, to fail fast when `func` is undefined.
|
||||
_.bind = function bind(func, context) { |
||||
var bound, args; |
||||
if (func.bind === nativeBind && nativeBind) return nativeBind.apply(func, slice.call(arguments, 1)); |
||||
if (!_.isFunction(func)) throw new TypeError; |
||||
args = slice.call(arguments, 2); |
||||
return bound = function() { |
||||
if (!(this instanceof bound)) return func.apply(context, args.concat(slice.call(arguments))); |
||||
ctor.prototype = func.prototype; |
||||
var self = new ctor; |
||||
var result = func.apply(self, args.concat(slice.call(arguments))); |
||||
if (Object(result) === result) return result; |
||||
return self; |
||||
}; |
||||
}; |
||||
|
||||
// Bind all of an object's methods to that object. Useful for ensuring that
|
||||
// all callbacks defined on an object belong to it.
|
||||
_.bindAll = function(obj) { |
||||
var funcs = slice.call(arguments, 1); |
||||
if (funcs.length == 0) funcs = _.functions(obj); |
||||
each(funcs, function(f) { obj[f] = _.bind(obj[f], obj); }); |
||||
return obj; |
||||
}; |
||||
|
||||
// Memoize an expensive function by storing its results.
|
||||
_.memoize = function(func, hasher) { |
||||
var memo = {}; |
||||
hasher || (hasher = _.identity); |
||||
return function() { |
||||
var key = hasher.apply(this, arguments); |
||||
return _.has(memo, key) ? memo[key] : (memo[key] = func.apply(this, arguments)); |
||||
}; |
||||
}; |
||||
|
||||
// Delays a function for the given number of milliseconds, and then calls
|
||||
// it with the arguments supplied.
|
||||
_.delay = function(func, wait) { |
||||
var args = slice.call(arguments, 2); |
||||
return setTimeout(function(){ return func.apply(func, args); }, wait); |
||||
}; |
||||
|
||||
// Defers a function, scheduling it to run after the current call stack has
|
||||
// cleared.
|
||||
_.defer = function(func) { |
||||
return _.delay.apply(_, [func, 1].concat(slice.call(arguments, 1))); |
||||
}; |
||||
|
||||
// Returns a function, that, when invoked, will only be triggered at most once
|
||||
// during a given window of time.
|
||||
_.throttle = function(func, wait) { |
||||
var context, args, timeout, throttling, more; |
||||
var whenDone = _.debounce(function(){ more = throttling = false; }, wait); |
||||
return function() { |
||||
context = this; args = arguments; |
||||
var later = function() { |
||||
timeout = null; |
||||
if (more) func.apply(context, args); |
||||
whenDone(); |
||||
}; |
||||
if (!timeout) timeout = setTimeout(later, wait); |
||||
if (throttling) { |
||||
more = true; |
||||
} else { |
||||
func.apply(context, args); |
||||
} |
||||
whenDone(); |
||||
throttling = true; |
||||
}; |
||||
}; |
||||
|
||||
// Returns a function, that, as long as it continues to be invoked, will not
|
||||
// be triggered. The function will be called after it stops being called for
|
||||
// N milliseconds.
|
||||
_.debounce = function(func, wait) { |
||||
var timeout; |
||||
return function() { |
||||
var context = this, args = arguments; |
||||
var later = function() { |
||||
timeout = null; |
||||
func.apply(context, args); |
||||
}; |
||||
clearTimeout(timeout); |
||||
timeout = setTimeout(later, wait); |
||||
}; |
||||
}; |
||||
|
||||
// Returns a function that will be executed at most one time, no matter how
|
||||
// often you call it. Useful for lazy initialization.
|
||||
_.once = function(func) { |
||||
var ran = false, memo; |
||||
return function() { |
||||
if (ran) return memo; |
||||
ran = true; |
||||
return memo = func.apply(this, arguments); |
||||
}; |
||||
}; |
||||
|
||||
// Returns the first function passed as an argument to the second,
|
||||
// allowing you to adjust arguments, run code before and after, and
|
||||
// conditionally execute the original function.
|
||||
_.wrap = function(func, wrapper) { |
||||
return function() { |
||||
var args = [func].concat(slice.call(arguments, 0)); |
||||
return wrapper.apply(this, args); |
||||
}; |
||||
}; |
||||
|
||||
// Returns a function that is the composition of a list of functions, each
|
||||
// consuming the return value of the function that follows.
|
||||
_.compose = function() { |
||||
var funcs = arguments; |
||||
return function() { |
||||
var args = arguments; |
||||
for (var i = funcs.length - 1; i >= 0; i--) { |
||||
args = [funcs[i].apply(this, args)]; |
||||
} |
||||
return args[0]; |
||||
}; |
||||
}; |
||||
|
||||
// Returns a function that will only be executed after being called N times.
|
||||
_.after = function(times, func) { |
||||
if (times <= 0) return func(); |
||||
return function() { |
||||
if (--times < 1) { return func.apply(this, arguments); } |
||||
}; |
||||
}; |
||||
|
||||
// Object Functions
|
||||
// ----------------
|
||||
|
||||
// Retrieve the names of an object's properties.
|
||||
// Delegates to **ECMAScript 5**'s native `Object.keys`
|
||||
_.keys = nativeKeys || function(obj) { |
||||
if (obj !== Object(obj)) throw new TypeError('Invalid object'); |
||||
var keys = []; |
||||
for (var key in obj) if (_.has(obj, key)) keys[keys.length] = key; |
||||
return keys; |
||||
}; |
||||
|
||||
// Retrieve the values of an object's properties.
|
||||
_.values = function(obj) { |
||||
return _.map(obj, _.identity); |
||||
}; |
||||
|
||||
// Return a sorted list of the function names available on the object.
|
||||
// Aliased as `methods`
|
||||
_.functions = _.methods = function(obj) { |
||||
var names = []; |
||||
for (var key in obj) { |
||||
if (_.isFunction(obj[key])) names.push(key); |
||||
} |
||||
return names.sort(); |
||||
}; |
||||
|
||||
// Extend a given object with all the properties in passed-in object(s).
|
||||
_.extend = function(obj) { |
||||
each(slice.call(arguments, 1), function(source) { |
||||
for (var prop in source) { |
||||
obj[prop] = source[prop]; |
||||
} |
||||
}); |
||||
return obj; |
||||
}; |
||||
|
||||
// Fill in a given object with default properties.
|
||||
_.defaults = function(obj) { |
||||
each(slice.call(arguments, 1), function(source) { |
||||
for (var prop in source) { |
||||
if (obj[prop] == null) obj[prop] = source[prop]; |
||||
} |
||||
}); |
||||
return obj; |
||||
}; |
||||
|
||||
// Create a (shallow-cloned) duplicate of an object.
|
||||
_.clone = function(obj) { |
||||
if (!_.isObject(obj)) return obj; |
||||
return _.isArray(obj) ? obj.slice() : _.extend({}, obj); |
||||
}; |
||||
|
||||
// Invokes interceptor with the obj, and then returns obj.
|
||||
// The primary purpose of this method is to "tap into" a method chain, in
|
||||
// order to perform operations on intermediate results within the chain.
|
||||
_.tap = function(obj, interceptor) { |
||||
interceptor(obj); |
||||
return obj; |
||||
}; |
||||
|
||||
// Internal recursive comparison function.
|
||||
function eq(a, b, stack) { |
||||
// Identical objects are equal. `0 === -0`, but they aren't identical.
|
||||
// See the Harmony `egal` proposal: http://wiki.ecmascript.org/doku.php?id=harmony:egal.
|
||||
if (a === b) return a !== 0 || 1 / a == 1 / b; |
||||
// A strict comparison is necessary because `null == undefined`.
|
||||
if (a == null || b == null) return a === b; |
||||
// Unwrap any wrapped objects.
|
||||
if (a._chain) a = a._wrapped; |
||||
if (b._chain) b = b._wrapped; |
||||
// Invoke a custom `isEqual` method if one is provided.
|
||||
if (a.isEqual && _.isFunction(a.isEqual)) return a.isEqual(b); |
||||
if (b.isEqual && _.isFunction(b.isEqual)) return b.isEqual(a); |
||||
// Compare `[[Class]]` names.
|
||||
var className = toString.call(a); |
||||
if (className != toString.call(b)) return false; |
||||
switch (className) { |
||||
// Strings, numbers, dates, and booleans are compared by value.
|
||||
case '[object String]': |
||||
// Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
|
||||
// equivalent to `new String("5")`.
|
||||
return a == String(b); |
||||
case '[object Number]': |
||||
// `NaN`s are equivalent, but non-reflexive. An `egal` comparison is performed for
|
||||
// other numeric values.
|
||||
return a != +a ? b != +b : (a == 0 ? 1 / a == 1 / b : a == +b); |
||||
case '[object Date]': |
||||
case '[object Boolean]': |
||||
// Coerce dates and booleans to numeric primitive values. Dates are compared by their
|
||||
// millisecond representations. Note that invalid dates with millisecond representations
|
||||
// of `NaN` are not equivalent.
|
||||
return +a == +b; |
||||
// RegExps are compared by their source patterns and flags.
|
||||
case '[object RegExp]': |
||||
return a.source == b.source && |
||||
a.global == b.global && |
||||
a.multiline == b.multiline && |
||||
a.ignoreCase == b.ignoreCase; |
||||
} |
||||
if (typeof a != 'object' || typeof b != 'object') return false; |
||||
// Assume equality for cyclic structures. The algorithm for detecting cyclic
|
||||
// structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
|
||||
var length = stack.length; |
||||
while (length--) { |
||||
// Linear search. Performance is inversely proportional to the number of
|
||||
// unique nested structures.
|
||||
if (stack[length] == a) return true; |
||||
} |
||||
// Add the first object to the stack of traversed objects.
|
||||
stack.push(a); |
||||
var size = 0, result = true; |
||||
// Recursively compare objects and arrays.
|
||||
if (className == '[object Array]') { |
||||
// Compare array lengths to determine if a deep comparison is necessary.
|
||||
size = a.length; |
||||
result = size == b.length; |
||||
if (result) { |
||||
// Deep compare the contents, ignoring non-numeric properties.
|
||||
while (size--) { |
||||
// Ensure commutative equality for sparse arrays.
|
||||
if (!(result = size in a == size in b && eq(a[size], b[size], stack))) break; |
||||
} |
||||
} |
||||
} else { |
||||
// Objects with different constructors are not equivalent.
|
||||
if ('constructor' in a != 'constructor' in b || a.constructor != b.constructor) return false; |
||||
// Deep compare objects.
|
||||
for (var key in a) { |
||||
if (_.has(a, key)) { |
||||
// Count the expected number of properties.
|
||||
size++; |
||||
// Deep compare each member.
|
||||
if (!(result = _.has(b, key) && eq(a[key], b[key], stack))) break; |
||||
} |
||||
} |
||||
// Ensure that both objects contain the same number of properties.
|
||||
if (result) { |
||||
for (key in b) { |
||||
if (_.has(b, key) && !(size--)) break; |
||||
} |
||||
result = !size; |
||||
} |
||||
} |
||||
// Remove the first object from the stack of traversed objects.
|
||||
stack.pop(); |
||||
return result; |
||||
} |
||||
|
||||
// Perform a deep comparison to check if two objects are equal.
|
||||
_.isEqual = function(a, b) { |
||||
return eq(a, b, []); |
||||
}; |
||||
|
||||
// Is a given array, string, or object empty?
|
||||
// An "empty" object has no enumerable own-properties.
|
||||
_.isEmpty = function(obj) { |
||||
if (_.isArray(obj) || _.isString(obj)) return obj.length === 0; |
||||
for (var key in obj) if (_.has(obj, key)) return false; |
||||
return true; |
||||
}; |
||||
|
||||
// Is a given value a DOM element?
|
||||
_.isElement = function(obj) { |
||||
return !!(obj && obj.nodeType == 1); |
||||
}; |
||||
|
||||
// Is a given value an array?
|
||||
// Delegates to ECMA5's native Array.isArray
|
||||
_.isArray = nativeIsArray || function(obj) { |
||||
return toString.call(obj) == '[object Array]'; |
||||
}; |
||||
|
||||
// Is a given variable an object?
|
||||
_.isObject = function(obj) { |
||||
return obj === Object(obj); |
||||
}; |
||||
|
||||
// Is a given variable an arguments object?
|
||||
_.isArguments = function(obj) { |
||||
return toString.call(obj) == '[object Arguments]'; |
||||
}; |
||||
if (!_.isArguments(arguments)) { |
||||
_.isArguments = function(obj) { |
||||
return !!(obj && _.has(obj, 'callee')); |
||||
}; |
||||
} |
||||
|
||||
// Is a given value a function?
|
||||
_.isFunction = function(obj) { |
||||
return toString.call(obj) == '[object Function]'; |
||||
}; |
||||
|
||||
// Is a given value a string?
|
||||
_.isString = function(obj) { |
||||
return toString.call(obj) == '[object String]'; |
||||
}; |
||||
|
||||
// Is a given value a number?
|
||||
_.isNumber = function(obj) { |
||||
return toString.call(obj) == '[object Number]'; |
||||
}; |
||||
|
||||
// Is the given value `NaN`?
|
||||
_.isNaN = function(obj) { |
||||
// `NaN` is the only value for which `===` is not reflexive.
|
||||
return obj !== obj; |
||||
}; |
||||
|
||||
// Is a given value a boolean?
|
||||
_.isBoolean = function(obj) { |
||||
return obj === true || obj === false || toString.call(obj) == '[object Boolean]'; |
||||
}; |
||||
|
||||
// Is a given value a date?
|
||||
_.isDate = function(obj) { |
||||
return toString.call(obj) == '[object Date]'; |
||||
}; |
||||
|
||||
// Is the given value a regular expression?
|
||||
_.isRegExp = function(obj) { |
||||
return toString.call(obj) == '[object RegExp]'; |
||||
}; |
||||
|
||||
// Is a given value equal to null?
|
||||
_.isNull = function(obj) { |
||||
return obj === null; |
||||
}; |
||||
|
||||
// Is a given variable undefined?
|
||||
_.isUndefined = function(obj) { |
||||
return obj === void 0; |
||||
}; |
||||
|
||||
// Has own property?
|
||||
_.has = function(obj, key) { |
||||
return hasOwnProperty.call(obj, key); |
||||
}; |
||||
|
||||
// Utility Functions
|
||||
// -----------------
|
||||
|
||||
// Run Underscore.js in *noConflict* mode, returning the `_` variable to its
|
||||
// previous owner. Returns a reference to the Underscore object.
|
||||
_.noConflict = function() { |
||||
root._ = previousUnderscore; |
||||
return this; |
||||
}; |
||||
|
||||
// Keep the identity function around for default iterators.
|
||||
_.identity = function(value) { |
||||
return value; |
||||
}; |
||||
|
||||
// Run a function **n** times.
|
||||
_.times = function (n, iterator, context) { |
||||
for (var i = 0; i < n; i++) iterator.call(context, i); |
||||
}; |
||||
|
||||
// Escape a string for HTML interpolation.
|
||||
_.escape = function(string) { |
||||
return (''+string).replace(/&/g, '&').replace(/</g, '<').replace(/>/g, '>').replace(/"/g, '"').replace(/'/g, ''').replace(/\//g,'/'); |
||||
}; |
||||
|
||||
// Add your own custom functions to the Underscore object, ensuring that
|
||||
// they're correctly added to the OOP wrapper as well.
|
||||
_.mixin = function(obj) { |
||||
each(_.functions(obj), function(name){ |
||||
addToWrapper(name, _[name] = obj[name]); |
||||
}); |
||||
}; |
||||
|
||||
// Generate a unique integer id (unique within the entire client session).
|
||||
// Useful for temporary DOM ids.
|
||||
var idCounter = 0; |
||||
_.uniqueId = function(prefix) { |
||||
var id = idCounter++; |
||||
return prefix ? prefix + id : id; |
||||
}; |
||||
|
||||
// By default, Underscore uses ERB-style template delimiters, change the
|
||||
// following template settings to use alternative delimiters.
|
||||
_.templateSettings = { |
||||
evaluate : /<%([\s\S]+?)%>/g, |
||||
interpolate : /<%=([\s\S]+?)%>/g, |
||||
escape : /<%-([\s\S]+?)%>/g |
||||
}; |
||||
|
||||
// When customizing `templateSettings`, if you don't want to define an
|
||||
// interpolation, evaluation or escaping regex, we need one that is
|
||||
// guaranteed not to match.
|
||||
var noMatch = /.^/; |
||||
|
||||
// Within an interpolation, evaluation, or escaping, remove HTML escaping
|
||||
// that had been previously added.
|
||||
var unescape = function(code) { |
||||
return code.replace(/\\\\/g, '\\').replace(/\\'/g, "'"); |
||||
}; |
||||
|
||||
// JavaScript micro-templating, similar to John Resig's implementation.
|
||||
// Underscore templating handles arbitrary delimiters, preserves whitespace,
|
||||
// and correctly escapes quotes within interpolated code.
|
||||
_.template = function(str, data) { |
||||
var c = _.templateSettings; |
||||
var tmpl = 'var __p=[],print=function(){__p.push.apply(__p,arguments);};' + |
||||
'with(obj||{}){__p.push(\'' + |
||||
str.replace(/\\/g, '\\\\') |
||||
.replace(/'/g, "\\'") |
||||
.replace(c.escape || noMatch, function(match, code) { |
||||
return "',_.escape(" + unescape(code) + "),'"; |
||||
}) |
||||
.replace(c.interpolate || noMatch, function(match, code) { |
||||
return "'," + unescape(code) + ",'"; |
||||
}) |
||||
.replace(c.evaluate || noMatch, function(match, code) { |
||||
return "');" + unescape(code).replace(/[\r\n\t]/g, ' ') + ";__p.push('"; |
||||
}) |
||||
.replace(/\r/g, '\\r') |
||||
.replace(/\n/g, '\\n') |
||||
.replace(/\t/g, '\\t') |
||||
+ "');}return __p.join('');"; |
||||
var func = new Function('obj', '_', tmpl); |
||||
if (data) return func(data, _); |
||||
return function(data) { |
||||
return func.call(this, data, _); |
||||
}; |
||||
}; |
||||
|
||||
// Add a "chain" function, which will delegate to the wrapper.
|
||||
_.chain = function(obj) { |
||||
return _(obj).chain(); |
||||
}; |
||||
|
||||
// The OOP Wrapper
|
||||
// ---------------
|
||||
|
||||
// If Underscore is called as a function, it returns a wrapped object that
|
||||
// can be used OO-style. This wrapper holds altered versions of all the
|
||||
// underscore functions. Wrapped objects may be chained.
|
||||
var wrapper = function(obj) { this._wrapped = obj; }; |
||||
|
||||
// Expose `wrapper.prototype` as `_.prototype`
|
||||
_.prototype = wrapper.prototype; |
||||
|
||||
// Helper function to continue chaining intermediate results.
|
||||
var result = function(obj, chain) { |
||||
return chain ? _(obj).chain() : obj; |
||||
}; |
||||
|
||||
// A method to easily add functions to the OOP wrapper.
|
||||
var addToWrapper = function(name, func) { |
||||
wrapper.prototype[name] = function() { |
||||
var args = slice.call(arguments); |
||||
unshift.call(args, this._wrapped); |
||||
return result(func.apply(_, args), this._chain); |
||||
}; |
||||
}; |
||||
|
||||
// Add all of the Underscore functions to the wrapper object.
|
||||
_.mixin(_); |
||||
|
||||
// Add all mutator Array functions to the wrapper.
|
||||
each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) { |
||||
var method = ArrayProto[name]; |
||||
wrapper.prototype[name] = function() { |
||||
var wrapped = this._wrapped; |
||||
method.apply(wrapped, arguments); |
||||
var length = wrapped.length; |
||||
if ((name == 'shift' || name == 'splice') && length === 0) delete wrapped[0]; |
||||
return result(wrapped, this._chain); |
||||
}; |
||||
}); |
||||
|
||||
// Add all accessor Array functions to the wrapper.
|
||||
each(['concat', 'join', 'slice'], function(name) { |
||||
var method = ArrayProto[name]; |
||||
wrapper.prototype[name] = function() { |
||||
return result(method.apply(this._wrapped, arguments), this._chain); |
||||
}; |
||||
}); |
||||
|
||||
// Start chaining a wrapped Underscore object.
|
||||
wrapper.prototype.chain = function() { |
||||
this._chain = true; |
||||
return this; |
||||
}; |
||||
|
||||
// Extracts the result from a wrapped and chained object.
|
||||
wrapper.prototype.value = function() { |
||||
return this._wrapped; |
||||
}; |
||||
|
||||
}).call(this); |
||||
@ -1,31 +0,0 @@ |
||||
// Underscore.js 1.3.1
|
||||
// (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
|
||||
// Underscore is freely distributable under the MIT license.
|
||||
// Portions of Underscore are inspired or borrowed from Prototype,
|
||||
// Oliver Steele's Functional, and John Resig's Micro-Templating.
|
||||
// For all details and documentation:
|
||||
// http://documentcloud.github.com/underscore
|
||||
(function(){function q(a,c,d){if(a===c)return a!==0||1/a==1/c;if(a==null||c==null)return a===c;if(a._chain)a=a._wrapped;if(c._chain)c=c._wrapped;if(a.isEqual&&b.isFunction(a.isEqual))return a.isEqual(c);if(c.isEqual&&b.isFunction(c.isEqual))return c.isEqual(a);var e=l.call(a);if(e!=l.call(c))return false;switch(e){case "[object String]":return a==String(c);case "[object Number]":return a!=+a?c!=+c:a==0?1/a==1/c:a==+c;case "[object Date]":case "[object Boolean]":return+a==+c;case "[object RegExp]":return a.source== |
||||
c.source&&a.global==c.global&&a.multiline==c.multiline&&a.ignoreCase==c.ignoreCase}if(typeof a!="object"||typeof c!="object")return false;for(var f=d.length;f--;)if(d[f]==a)return true;d.push(a);var f=0,g=true;if(e=="[object Array]"){if(f=a.length,g=f==c.length)for(;f--;)if(!(g=f in a==f in c&&q(a[f],c[f],d)))break}else{if("constructor"in a!="constructor"in c||a.constructor!=c.constructor)return false;for(var h in a)if(b.has(a,h)&&(f++,!(g=b.has(c,h)&&q(a[h],c[h],d))))break;if(g){for(h in c)if(b.has(c, |
||||
h)&&!f--)break;g=!f}}d.pop();return g}var r=this,G=r._,n={},k=Array.prototype,o=Object.prototype,i=k.slice,H=k.unshift,l=o.toString,I=o.hasOwnProperty,w=k.forEach,x=k.map,y=k.reduce,z=k.reduceRight,A=k.filter,B=k.every,C=k.some,p=k.indexOf,D=k.lastIndexOf,o=Array.isArray,J=Object.keys,s=Function.prototype.bind,b=function(a){return new m(a)};if(typeof exports!=="undefined"){if(typeof module!=="undefined"&&module.exports)exports=module.exports=b;exports._=b}else r._=b;b.VERSION="1.3.1";var j=b.each= |
||||
b.forEach=function(a,c,d){if(a!=null)if(w&&a.forEach===w)a.forEach(c,d);else if(a.length===+a.length)for(var e=0,f=a.length;e<f;e++){if(e in a&&c.call(d,a[e],e,a)===n)break}else for(e in a)if(b.has(a,e)&&c.call(d,a[e],e,a)===n)break};b.map=b.collect=function(a,c,b){var e=[];if(a==null)return e;if(x&&a.map===x)return a.map(c,b);j(a,function(a,g,h){e[e.length]=c.call(b,a,g,h)});if(a.length===+a.length)e.length=a.length;return e};b.reduce=b.foldl=b.inject=function(a,c,d,e){var f=arguments.length>2;a== |
||||
null&&(a=[]);if(y&&a.reduce===y)return e&&(c=b.bind(c,e)),f?a.reduce(c,d):a.reduce(c);j(a,function(a,b,i){f?d=c.call(e,d,a,b,i):(d=a,f=true)});if(!f)throw new TypeError("Reduce of empty array with no initial value");return d};b.reduceRight=b.foldr=function(a,c,d,e){var f=arguments.length>2;a==null&&(a=[]);if(z&&a.reduceRight===z)return e&&(c=b.bind(c,e)),f?a.reduceRight(c,d):a.reduceRight(c);var g=b.toArray(a).reverse();e&&!f&&(c=b.bind(c,e));return f?b.reduce(g,c,d,e):b.reduce(g,c)};b.find=b.detect= |
||||
function(a,c,b){var e;E(a,function(a,g,h){if(c.call(b,a,g,h))return e=a,true});return e};b.filter=b.select=function(a,c,b){var e=[];if(a==null)return e;if(A&&a.filter===A)return a.filter(c,b);j(a,function(a,g,h){c.call(b,a,g,h)&&(e[e.length]=a)});return e};b.reject=function(a,c,b){var e=[];if(a==null)return e;j(a,function(a,g,h){c.call(b,a,g,h)||(e[e.length]=a)});return e};b.every=b.all=function(a,c,b){var e=true;if(a==null)return e;if(B&&a.every===B)return a.every(c,b);j(a,function(a,g,h){if(!(e= |
||||
e&&c.call(b,a,g,h)))return n});return e};var E=b.some=b.any=function(a,c,d){c||(c=b.identity);var e=false;if(a==null)return e;if(C&&a.some===C)return a.some(c,d);j(a,function(a,b,h){if(e||(e=c.call(d,a,b,h)))return n});return!!e};b.include=b.contains=function(a,c){var b=false;if(a==null)return b;return p&&a.indexOf===p?a.indexOf(c)!=-1:b=E(a,function(a){return a===c})};b.invoke=function(a,c){var d=i.call(arguments,2);return b.map(a,function(a){return(b.isFunction(c)?c||a:a[c]).apply(a,d)})};b.pluck= |
||||
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<li class="toctree-l2 current"><a class="current reference internal" href="#">Examples</a></li> |
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<div class="section" id="examples"> |
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<h1>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">¶</a></h1> |
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<div class="section" id="installation-usage"> |
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<h2>Installation/Usage<a class="headerlink" href="#installation-usage" title="Permalink to this headline">¶</a></h2> |
||||
<p>Download the release in .tar.gz or .whl format and simply use pip install to install it:</p> |
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$pip install PyCTBN-1.0.tar.gz |
||||
</pre></div> |
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</div> |
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</div> |
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<div class="section" id="implementing-your-own-data-importer"> |
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<h2>Implementing your own data importer<a class="headerlink" href="#implementing-your-own-data-importer" title="Permalink to this headline">¶</a></h2> |
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="sd">"""This example demonstrates the implementation of a simple data importer the extends the class abstract importer to import data in csv format.</span> |
||||
<span class="sd">The net in exam has three ternary nodes and no prior net structure.</span> |
||||
<span class="sd">"""</span> |
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|
||||
<span class="kn">from</span> <span class="nn">PyCTBN</span> <span class="kn">import</span> <span class="n">AbstractImporter</span> |
||||
|
||||
<span class="k">class</span> <span class="nc">CSVImporter</span><span class="p">(</span><span class="n">AbstractImporter</span><span class="p">):</span> |
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">file_path</span><span class="p">):</span> |
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_df_samples_list</span> <span class="o">=</span> <span class="kc">None</span> |
||||
<span class="nb">super</span><span class="p">(</span><span class="n">CSVImporter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span> |
||||
|
||||
<span class="k">def</span> <span class="nf">import_data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
||||
<span class="bp">self</span><span class="o">.</span><span class="n">read_csv_file</span><span class="p">()</span> |
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_sorter</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">build_sorter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_df_samples_list</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
||||
<span class="bp">self</span><span class="o">.</span><span class="n">import_variables</span><span class="p">()</span> |
||||
<span class="bp">self</span><span class="o">.</span><span class="n">compute_row_delta_in_all_samples_frames</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_df_samples_list</span><span class="p">)</span> |
||||
|
||||
<span class="k">def</span> <span class="nf">read_csv_file</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_file_path</span><span class="p">)</span> |
||||
<span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_df_samples_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">df</span><span class="p">]</span> |
||||
|
||||
<span class="k">def</span> <span class="nf">import_variables</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
||||
<span class="n">values_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sorter</span><span class="p">]</span> |
||||
<span class="c1"># initialize dict of lists</span> |
||||
<span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'Name'</span><span class="p">:</span><span class="bp">self</span><span class="o">.</span><span class="n">_sorter</span><span class="p">,</span> <span class="s1">'Value'</span><span class="p">:</span><span class="n">values_list</span><span class="p">}</span> |
||||
<span class="c1"># Create the pandas DataFrame</span> |
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_df_variables</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> |
||||
|
||||
<span class="k">def</span> <span class="nf">build_sorter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_frame</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-></span> <span class="n">typing</span><span class="o">.</span><span class="n">List</span><span class="p">:</span> |
||||
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="n">sample_frame</span><span class="o">.</span><span class="n">columns</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]</span> |
||||
|
||||
<span class="k">def</span> <span class="nf">dataset_id</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="nb">object</span><span class="p">:</span> |
||||
<span class="k">pass</span> |
||||
</pre></div> |
||||
</div> |
||||
</div> |
||||
<div class="section" id="parameters-estimation-example"> |
||||
<h2>Parameters Estimation Example<a class="headerlink" href="#parameters-estimation-example" title="Permalink to this headline">¶</a></h2> |
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">PyCTBN</span> <span class="kn">import</span> <span class="n">JsonImporter</span> |
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<span class="kn">from</span> <span class="nn">PyCTBN</span> <span class="kn">import</span> <span class="n">SamplePath</span> |
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<span class="kn">from</span> <span class="nn">PyCTBN</span> <span class="kn">import</span> <span class="n">NetworkGraph</span> |
||||
<span class="kn">from</span> <span class="nn">PyCTBN</span> <span class="kn">import</span> <span class="n">ParametersEstimator</span> |
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|
||||
|
||||
<span class="k">def</span> <span class="nf">main</span><span class="p">():</span> |
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<span class="n">read_files</span> <span class="o">=</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s1">'./data'</span><span class="p">,</span> <span class="s2">"*.json"</span><span class="p">))</span> <span class="c1">#Take all json files in this dir</span> |
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<span class="c1">#import data</span> |
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<span class="n">importer</span> <span class="o">=</span> <span class="n">JsonImporter</span><span class="p">(</span><span class="n">read_files</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">'samples'</span><span class="p">,</span> <span class="s1">'dyn.str'</span><span class="p">,</span> <span class="s1">'variables'</span><span class="p">,</span> <span class="s1">'Time'</span><span class="p">,</span> <span class="s1">'Name'</span><span class="p">)</span> |
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<span class="n">importer</span><span class="o">.</span><span class="n">import_data</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
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<span class="c1">#Create a SamplePath Obj passing an already filled AbstractImporter object</span> |
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<span class="n">s1</span> <span class="o">=</span> <span class="n">SamplePath</span><span class="p">(</span><span class="n">importer</span><span class="p">)</span> |
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<span class="c1">#Build The trajectries and the structural infos</span> |
||||
<span class="n">s1</span><span class="o">.</span><span class="n">build_trajectories</span><span class="p">()</span> |
||||
<span class="n">s1</span><span class="o">.</span><span class="n">build_structure</span><span class="p">()</span> |
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<span class="nb">print</span><span class="p">(</span><span class="n">s1</span><span class="o">.</span><span class="n">structure</span><span class="o">.</span><span class="n">edges</span><span class="p">)</span> |
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<span class="nb">print</span><span class="p">(</span><span class="n">s1</span><span class="o">.</span><span class="n">structure</span><span class="o">.</span><span class="n">nodes_values</span><span class="p">)</span> |
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<span class="c1">#From The Structure Object build the Graph</span> |
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<span class="n">g</span> <span class="o">=</span> <span class="n">NetworkGraph</span><span class="p">(</span><span class="n">s1</span><span class="o">.</span><span class="n">structure</span><span class="p">)</span> |
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<span class="c1">#Select a node you want to estimate the parameters</span> |
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<span class="n">node</span> <span class="o">=</span> <span class="n">g</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> |
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<span class="nb">print</span><span class="p">(</span><span class="s2">"Node"</span><span class="p">,</span> <span class="n">node</span><span class="p">)</span> |
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<span class="c1">#Init the _graph specifically for THIS node</span> |
||||
<span class="n">g</span><span class="o">.</span><span class="n">fast_init</span><span class="p">(</span><span class="n">node</span><span class="p">)</span> |
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<span class="c1">#Use SamplePath and Grpah to create a ParametersEstimator Object</span> |
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<span class="n">p1</span> <span class="o">=</span> <span class="n">ParametersEstimator</span><span class="p">(</span><span class="n">s1</span><span class="o">.</span><span class="n">trajectories</span><span class="p">,</span> <span class="n">g</span><span class="p">)</span> |
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<span class="c1">#Init the peEst specifically for THIS node</span> |
||||
<span class="n">p1</span><span class="o">.</span><span class="n">fast_init</span><span class="p">(</span><span class="n">node</span><span class="p">)</span> |
||||
<span class="c1">#Compute the parameters</span> |
||||
<span class="n">sofc1</span> <span class="o">=</span> <span class="n">p1</span><span class="o">.</span><span class="n">compute_parameters_for_node</span><span class="p">(</span><span class="n">node</span><span class="p">)</span> |
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<span class="c1">#The est CIMS are inside the resultant SetOfCIms Obj</span> |
||||
<span class="nb">print</span><span class="p">(</span><span class="n">sofc1</span><span class="o">.</span><span class="n">actual_cims</span><span class="p">)</span> |
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</pre></div> |
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</div> |
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</div> |
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<div class="section" id="structure-estimation-example"> |
||||
<h2>Structure Estimation Example<a class="headerlink" href="#structure-estimation-example" title="Permalink to this headline">¶</a></h2> |
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">PyCTBN</span> <span class="kn">import</span> <span class="n">JsonImporter</span> |
||||
<span class="kn">from</span> <span class="nn">PyCTBN</span> <span class="kn">import</span> <span class="n">SamplePath</span> |
||||
<span class="kn">from</span> <span class="nn">PyCTBN</span> <span class="kn">import</span> <span class="n">StructureEstimator</span> |
||||
|
||||
<span class="k">def</span> <span class="nf">structure_estimation_example</span><span class="p">():</span> |
||||
|
||||
<span class="c1"># read the json files in ./data path</span> |
||||
<span class="n">read_files</span> <span class="o">=</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s1">'./data'</span><span class="p">,</span> <span class="s2">"*.json"</span><span class="p">))</span> |
||||
<span class="c1"># initialize a JsonImporter object for the first file</span> |
||||
<span class="n">importer</span> <span class="o">=</span> <span class="n">JsonImporter</span><span class="p">(</span><span class="n">read_files</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">'samples'</span><span class="p">,</span> <span class="s1">'dyn.str'</span><span class="p">,</span> <span class="s1">'variables'</span><span class="p">,</span> <span class="s1">'Time'</span><span class="p">,</span> <span class="s1">'Name'</span><span class="p">)</span> |
||||
<span class="c1"># import the data at index 0 of the outer json array</span> |
||||
<span class="n">importer</span><span class="o">.</span><span class="n">import_data</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
||||
<span class="c1"># construct a SamplePath Object passing a filled AbstractImporter</span> |
||||
<span class="n">s1</span> <span class="o">=</span> <span class="n">SamplePath</span><span class="p">(</span><span class="n">importer</span><span class="p">)</span> |
||||
<span class="c1"># build the trajectories</span> |
||||
<span class="n">s1</span><span class="o">.</span><span class="n">build_trajectories</span><span class="p">()</span> |
||||
<span class="c1"># build the real structure</span> |
||||
<span class="n">s1</span><span class="o">.</span><span class="n">build_structure</span><span class="p">()</span> |
||||
<span class="c1"># construct a StructureEstimator object</span> |
||||
<span class="n">se1</span> <span class="o">=</span> <span class="n">StructureEstimator</span><span class="p">(</span><span class="n">s1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span> |
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<span class="c1"># call the ctpc algorithm</span> |
||||
<span class="n">se1</span><span class="o">.</span><span class="n">ctpc_algorithm</span><span class="p">()</span> |
||||
<span class="c1"># the adjacency matrix of the estimated structure</span> |
||||
<span class="nb">print</span><span class="p">(</span><span class="n">se1</span><span class="o">.</span><span class="n">adjacency_matrix</span><span class="p">())</span> |
||||
<span class="c1"># save results to a json file</span> |
||||
<span class="n">se1</span><span class="o">.</span><span class="n">save_results</span><span class="p">()</span> |
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</pre></div> |
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</div> |
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<h2 id="A">A</h2> |
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<table style="width: 100%" class="indextable genindextable"><tr> |
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<td style="width: 33%; vertical-align: top;"><ul> |
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<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter">AbstractImporter (class in PyCTBN.PyCTBN.utility.abstract_importer)</a> |
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</li> |
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<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.actual_cims">actual_cims() (PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims property)</a> |
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</li> |
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<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.structure.Structure.add_edge">add_edge() (PyCTBN.PyCTBN.structure_graph.structure.Structure method)</a> |
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</li> |
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</ul></td> |
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<td style="width: 33%; vertical-align: top;"><ul> |
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<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.add_edges">add_edges() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph method)</a> |
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</li> |
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<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.add_nodes">add_nodes() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph method)</a> |
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</li> |
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<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.adjacency_matrix">adjacency_matrix() (PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator method)</a> |
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</li> |
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</ul></td> |
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</tr></table> |
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|
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<h2 id="B">B</h2> |
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<table style="width: 100%" class="indextable genindextable"><tr> |
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<td style="width: 33%; vertical-align: top;"><ul> |
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<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.build_cims">build_cims() (PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims method)</a> |
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</li> |
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<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.build_complete_graph">build_complete_graph() (PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator static method)</a> |
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</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.build_list_of_samples_array">build_list_of_samples_array() (PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter method)</a> |
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</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.build_p_comb_structure_for_a_node">build_p_comb_structure_for_a_node() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph static method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.build_removable_edges_matrix">build_removable_edges_matrix() (PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator method)</a> |
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</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.build_sorter">build_sorter() (PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter method)</a> |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.build_sorter">(PyCTBN.PyCTBN.utility.json_importer.JsonImporter method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.sample_importer.SampleImporter.build_sorter">(PyCTBN.PyCTBN.utility.sample_importer.SampleImporter method)</a> |
||||
</li> |
||||
</ul></li> |
||||
</ul></td> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.build_structure">build_structure() (PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.build_time_columns_filtering_for_a_node">build_time_columns_filtering_for_a_node() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph static method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.build_time_scalar_indexing_structure_for_a_node">build_time_scalar_indexing_structure_for_a_node() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph static method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.build_times_and_transitions_structures">build_times_and_transitions_structures() (PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.build_trajectories">build_trajectories() (PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.build_transition_filtering_for_a_node">build_transition_filtering_for_a_node() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph static method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.build_transition_scalar_indexing_structure_for_a_node">build_transition_scalar_indexing_structure_for_a_node() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph static method)</a> |
||||
</li> |
||||
</ul></td> |
||||
</tr></table> |
||||
|
||||
<h2 id="C">C</h2> |
||||
<table style="width: 100%" class="indextable genindextable"><tr> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.cache.Cache">Cache (class in PyCTBN.PyCTBN.utility.cache)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.cim">cim() (PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix property)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.structure.Structure.clean_structure_edges">clean_structure_edges() (PyCTBN.PyCTBN.structure_graph.structure.Structure method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.cache.Cache.clear">clear() (PyCTBN.PyCTBN.utility.cache.Cache method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.clear_concatenated_frame">clear_concatenated_frame() (PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.clear_data_frame_list">clear_data_frame_list() (PyCTBN.PyCTBN.utility.json_importer.JsonImporter method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.clear_indexing_filtering_structures">clear_indexing_filtering_structures() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.clear_memory">clear_memory() (PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.complete_test">complete_test() (PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory.complete_trajectory">complete_trajectory() (PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory property)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.compute_cim_coefficients">compute_cim_coefficients() (PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix method)</a> |
||||
</li> |
||||
</ul></td> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator.compute_parameters_for_node">compute_parameters_for_node() (PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.compute_row_delta_in_all_samples_frames">compute_row_delta_in_all_samples_frames() (PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.compute_row_delta_sigle_samples_frame">compute_row_delta_sigle_samples_frame() (PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator.compute_state_res_time_for_node">compute_state_res_time_for_node() (PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator static method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator.compute_state_transitions_for_a_node">compute_state_transitions_for_a_node() (PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator static method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.compute_thumb_value">compute_thumb_value() (PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.concatenated_samples">concatenated_samples() (PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter property)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix">ConditionalIntensityMatrix (class in PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#PyCTBN.PyCTBN.optimizers.constraint_based_optimizer.ConstraintBasedOptimizer">ConstraintBasedOptimizer (class in PyCTBN.PyCTBN.optimizers.constraint_based_optimizer)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.structure.Structure.contains_edge">contains_edge() (PyCTBN.PyCTBN.structure_graph.structure.Structure method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.ctpc_algorithm">ctpc_algorithm() (PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator method)</a> |
||||
</li> |
||||
</ul></td> |
||||
</tr></table> |
||||
|
||||
<h2 id="D">D</h2> |
||||
<table style="width: 100%" class="indextable genindextable"><tr> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.dataset_id">dataset_id() (PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter method)</a> |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.dataset_id">(PyCTBN.PyCTBN.utility.json_importer.JsonImporter method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.sample_importer.SampleImporter.dataset_id">(PyCTBN.PyCTBN.utility.sample_importer.SampleImporter method)</a> |
||||
</li> |
||||
</ul></li> |
||||
</ul></td> |
||||
</tr></table> |
||||
|
||||
<h2 id="E">E</h2> |
||||
<table style="width: 100%" class="indextable genindextable"><tr> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.edges">edges() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph property)</a> |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.structure.Structure.edges">(PyCTBN.PyCTBN.structure_graph.structure.Structure property)</a> |
||||
</li> |
||||
</ul></li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.estimate_parents">estimate_parents() (PyCTBN.PyCTBN.estimators.structure_score_based_estimator.StructureScoreBasedEstimator method)</a> |
||||
</li> |
||||
</ul></td> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.estimate_structure">estimate_structure() (PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator method)</a> |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.estimate_structure">(PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.estimate_structure">(PyCTBN.PyCTBN.estimators.structure_score_based_estimator.StructureScoreBasedEstimator method)</a> |
||||
</li> |
||||
</ul></li> |
||||
</ul></td> |
||||
</tr></table> |
||||
|
||||
<h2 id="F">F</h2> |
||||
<table style="width: 100%" class="indextable genindextable"><tr> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator">FamScoreCalculator (class in PyCTBN.PyCTBN.estimators.fam_score_calculator)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator.fast_init">fast_init() (PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator method)</a> |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.fast_init">(PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph method)</a> |
||||
</li> |
||||
</ul></li> |
||||
</ul></td> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.file_path">file_path() (PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter property)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.filter_cims_with_mask">filter_cims_with_mask() (PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.cache.Cache.find">find() (PyCTBN.PyCTBN.utility.cache.Cache method)</a> |
||||
</li> |
||||
</ul></td> |
||||
</tr></table> |
||||
|
||||
<h2 id="G">G</h2> |
||||
<table style="width: 100%" class="indextable genindextable"><tr> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.generate_possible_sub_sets_of_size">generate_possible_sub_sets_of_size() (PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator static method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.get_cims_number">get_cims_number() (PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.get_fam_score">get_fam_score() (PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.structure.Structure.get_node_id">get_node_id() (PyCTBN.PyCTBN.structure_graph.structure.Structure method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.get_node_indx">get_node_indx() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph method)</a> |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.structure.Structure.get_node_indx">(PyCTBN.PyCTBN.structure_graph.structure.Structure method)</a> |
||||
</li> |
||||
</ul></li> |
||||
</ul></td> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.get_ordered_by_indx_set_of_parents">get_ordered_by_indx_set_of_parents() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.get_parents_by_id">get_parents_by_id() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.get_positional_node_indx">get_positional_node_indx() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph method)</a> |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.structure.Structure.get_positional_node_indx">(PyCTBN.PyCTBN.structure_graph.structure.Structure method)</a> |
||||
</li> |
||||
</ul></li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.get_score_from_graph">get_score_from_graph() (PyCTBN.PyCTBN.estimators.structure_score_based_estimator.StructureScoreBasedEstimator method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.get_states_number">get_states_number() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph method)</a> |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.structure.Structure.get_states_number">(PyCTBN.PyCTBN.structure_graph.structure.Structure method)</a> |
||||
</li> |
||||
</ul></li> |
||||
</ul></td> |
||||
</tr></table> |
||||
|
||||
<h2 id="H">H</h2> |
||||
<table style="width: 100%" class="indextable genindextable"><tr> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.has_edge">has_edge() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph method)</a> |
||||
</li> |
||||
</ul></td> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.has_prior_net_structure">has_prior_net_structure() (PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath property)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#PyCTBN.PyCTBN.optimizers.hill_climbing_search.HillClimbing">HillClimbing (class in PyCTBN.PyCTBN.optimizers.hill_climbing_search)</a> |
||||
</li> |
||||
</ul></td> |
||||
</tr></table> |
||||
|
||||
<h2 id="I">I</h2> |
||||
<table style="width: 100%" class="indextable genindextable"><tr> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.import_data">import_data() (PyCTBN.PyCTBN.utility.json_importer.JsonImporter method)</a> |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.sample_importer.SampleImporter.import_data">(PyCTBN.PyCTBN.utility.sample_importer.SampleImporter method)</a> |
||||
</li> |
||||
</ul></li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.import_sampled_cims">import_sampled_cims() (PyCTBN.PyCTBN.utility.json_importer.JsonImporter method)</a> |
||||
</li> |
||||
</ul></td> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.import_structure">import_structure() (PyCTBN.PyCTBN.utility.json_importer.JsonImporter method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.import_trajectories">import_trajectories() (PyCTBN.PyCTBN.utility.json_importer.JsonImporter method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.import_variables">import_variables() (PyCTBN.PyCTBN.utility.json_importer.JsonImporter method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.independence_test">independence_test() (PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator method)</a> |
||||
</li> |
||||
</ul></td> |
||||
</tr></table> |
||||
|
||||
<h2 id="J">J</h2> |
||||
<table style="width: 100%" class="indextable genindextable"><tr> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.json_importer.JsonImporter">JsonImporter (class in PyCTBN.PyCTBN.utility.json_importer)</a> |
||||
</li> |
||||
</ul></td> |
||||
</tr></table> |
||||
|
||||
<h2 id="M">M</h2> |
||||
<table style="width: 100%" class="indextable genindextable"><tr> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.marginal_likelihood_q">marginal_likelihood_q() (PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.marginal_likelihood_theta">marginal_likelihood_theta() (PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator method)</a> |
||||
</li> |
||||
<li> |
||||
module |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.html#module-PyCTBN">PyCTBN</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.html#module-PyCTBN.PyCTBN">PyCTBN.PyCTBN</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators">PyCTBN.PyCTBN.estimators</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.fam_score_calculator">PyCTBN.PyCTBN.estimators.fam_score_calculator</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.parameters_estimator">PyCTBN.PyCTBN.estimators.parameters_estimator</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator">PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_estimator">PyCTBN.PyCTBN.estimators.structure_estimator</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_score_based_estimator">PyCTBN.PyCTBN.estimators.structure_score_based_estimator</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers">PyCTBN.PyCTBN.optimizers</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.constraint_based_optimizer">PyCTBN.PyCTBN.optimizers.constraint_based_optimizer</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.hill_climbing_search">PyCTBN.PyCTBN.optimizers.hill_climbing_search</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.optimizer">PyCTBN.PyCTBN.optimizers.optimizer</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.tabu_search">PyCTBN.PyCTBN.optimizers.tabu_search</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph">PyCTBN.PyCTBN.structure_graph</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix">PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.network_graph">PyCTBN.PyCTBN.structure_graph.network_graph</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.sample_path">PyCTBN.PyCTBN.structure_graph.sample_path</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.set_of_cims">PyCTBN.PyCTBN.structure_graph.set_of_cims</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.structure">PyCTBN.PyCTBN.structure_graph.structure</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.trajectory">PyCTBN.PyCTBN.structure_graph.trajectory</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility">PyCTBN.PyCTBN.utility</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.abstract_importer">PyCTBN.PyCTBN.utility.abstract_importer</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.cache">PyCTBN.PyCTBN.utility.cache</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.json_importer">PyCTBN.PyCTBN.utility.json_importer</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.sample_importer">PyCTBN.PyCTBN.utility.sample_importer</a> |
||||
</li> |
||||
</ul></li> |
||||
</ul></td> |
||||
</tr></table> |
||||
|
||||
<h2 id="N">N</h2> |
||||
<table style="width: 100%" class="indextable genindextable"><tr> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph">NetworkGraph (class in PyCTBN.PyCTBN.structure_graph.network_graph)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.nodes">nodes() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph property)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.nodes_indexes">nodes_indexes() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph property)</a> |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.structure.Structure.nodes_indexes">(PyCTBN.PyCTBN.structure_graph.structure.Structure property)</a> |
||||
</li> |
||||
</ul></li> |
||||
</ul></td> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.structure.Structure.nodes_labels">nodes_labels() (PyCTBN.PyCTBN.structure_graph.structure.Structure property)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.nodes_values">nodes_values() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph property)</a> |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.structure.Structure.nodes_values">(PyCTBN.PyCTBN.structure_graph.structure.Structure property)</a> |
||||
</li> |
||||
</ul></li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.normalize_trajectories">normalize_trajectories() (PyCTBN.PyCTBN.utility.json_importer.JsonImporter method)</a> |
||||
</li> |
||||
</ul></td> |
||||
</tr></table> |
||||
|
||||
<h2 id="O">O</h2> |
||||
<table style="width: 100%" class="indextable genindextable"><tr> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.one_iteration_of_CTPC_algorithm">one_iteration_of_CTPC_algorithm() (PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.one_level_normalizing">one_level_normalizing() (PyCTBN.PyCTBN.utility.json_importer.JsonImporter method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#PyCTBN.PyCTBN.optimizers.constraint_based_optimizer.ConstraintBasedOptimizer.optimize_structure">optimize_structure() (PyCTBN.PyCTBN.optimizers.constraint_based_optimizer.ConstraintBasedOptimizer method)</a> |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#PyCTBN.PyCTBN.optimizers.hill_climbing_search.HillClimbing.optimize_structure">(PyCTBN.PyCTBN.optimizers.hill_climbing_search.HillClimbing method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#PyCTBN.PyCTBN.optimizers.optimizer.Optimizer.optimize_structure">(PyCTBN.PyCTBN.optimizers.optimizer.Optimizer method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#PyCTBN.PyCTBN.optimizers.tabu_search.TabuSearch.optimize_structure">(PyCTBN.PyCTBN.optimizers.tabu_search.TabuSearch method)</a> |
||||
</li> |
||||
</ul></li> |
||||
</ul></td> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#PyCTBN.PyCTBN.optimizers.optimizer.Optimizer">Optimizer (class in PyCTBN.PyCTBN.optimizers.optimizer)</a> |
||||
</li> |
||||
</ul></td> |
||||
</tr></table> |
||||
|
||||
<h2 id="P">P</h2> |
||||
<table style="width: 100%" class="indextable genindextable"><tr> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.p_combs">p_combs() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph property)</a> |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims.p_combs">(PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims property)</a> |
||||
</li> |
||||
</ul></li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.parameters_estimator.ParametersEstimator">ParametersEstimator (class in PyCTBN.PyCTBN.estimators.parameters_estimator)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.cache.Cache.put">put() (PyCTBN.PyCTBN.utility.cache.Cache method)</a> |
||||
</li> |
||||
<li> |
||||
PyCTBN |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.html#module-PyCTBN">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.html#module-PyCTBN.PyCTBN">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.estimators |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.estimators.fam_score_calculator |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.fam_score_calculator">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.estimators.parameters_estimator |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.parameters_estimator">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.estimators.structure_estimator |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_estimator">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.estimators.structure_score_based_estimator |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_score_based_estimator">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.optimizers |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.optimizers.constraint_based_optimizer |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.constraint_based_optimizer">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.optimizers.hill_climbing_search |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.hill_climbing_search">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.optimizers.optimizer |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.optimizer">module</a> |
||||
</li> |
||||
</ul></li> |
||||
</ul></td> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li> |
||||
PyCTBN.PyCTBN.optimizers.tabu_search |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.tabu_search">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.structure_graph |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.structure_graph.network_graph |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.network_graph">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.structure_graph.sample_path |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.sample_path">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.structure_graph.set_of_cims |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.set_of_cims">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.structure_graph.structure |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.structure">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.structure_graph.trajectory |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.trajectory">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.utility |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.utility.abstract_importer |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.abstract_importer">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.utility.cache |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.cache">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.utility.json_importer |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.json_importer">module</a> |
||||
</li> |
||||
</ul></li> |
||||
<li> |
||||
PyCTBN.PyCTBN.utility.sample_importer |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.sample_importer">module</a> |
||||
</li> |
||||
</ul></li> |
||||
</ul></td> |
||||
</tr></table> |
||||
|
||||
<h2 id="R">R</h2> |
||||
<table style="width: 100%" class="indextable genindextable"><tr> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.json_importer.JsonImporter.read_json_file">read_json_file() (PyCTBN.PyCTBN.utility.json_importer.JsonImporter method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.structure.Structure.remove_edge">remove_edge() (PyCTBN.PyCTBN.structure_graph.structure.Structure method)</a> |
||||
</li> |
||||
</ul></td> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.remove_edges">remove_edges() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.remove_node">remove_node() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph method)</a> |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.structure.Structure.remove_node">(PyCTBN.PyCTBN.structure_graph.structure.Structure method)</a> |
||||
</li> |
||||
</ul></li> |
||||
</ul></td> |
||||
</tr></table> |
||||
|
||||
<h2 id="S">S</h2> |
||||
<table style="width: 100%" class="indextable genindextable"><tr> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.sample_importer.SampleImporter">SampleImporter (class in PyCTBN.PyCTBN.utility.sample_importer)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath">SamplePath (class in PyCTBN.PyCTBN.structure_graph.sample_path)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.save_plot_estimated_structure_graph">save_plot_estimated_structure_graph() (PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.save_results">save_results() (PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims">SetOfCims (class in PyCTBN.PyCTBN.structure_graph.set_of_cims)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.single_cim_xu_marginal_likelihood_q">single_cim_xu_marginal_likelihood_q() (PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.single_cim_xu_marginal_likelihood_theta">single_cim_xu_marginal_likelihood_theta() (PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.single_internal_cim_xxu_marginal_likelihood_theta">single_internal_cim_xxu_marginal_likelihood_theta() (PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory.size">size() (PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory method)</a> |
||||
</li> |
||||
</ul></td> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.sorter">sorter() (PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter property)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator.spurious_edges">spurious_edges() (PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.state_residence_times">state_residence_times() (PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix property)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.state_transition_matrix">state_transition_matrix() (PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix property)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.structure.Structure">Structure (class in PyCTBN.PyCTBN.structure_graph.structure)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.structure">structure() (PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath property)</a> |
||||
|
||||
<ul> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.structure">(PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter property)</a> |
||||
</li> |
||||
</ul></li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator">StructureConstraintBasedEstimator (class in PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_estimator.StructureEstimator">StructureEstimator (class in PyCTBN.PyCTBN.estimators.structure_estimator)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.structure_score_based_estimator.StructureScoreBasedEstimator">StructureScoreBasedEstimator (class in PyCTBN.PyCTBN.estimators.structure_score_based_estimator)</a> |
||||
</li> |
||||
</ul></td> |
||||
</tr></table> |
||||
|
||||
<h2 id="T">T</h2> |
||||
<table style="width: 100%" class="indextable genindextable"><tr> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.optimizers.html#PyCTBN.PyCTBN.optimizers.tabu_search.TabuSearch">TabuSearch (class in PyCTBN.PyCTBN.optimizers.tabu_search)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.time_filtering">time_filtering() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph property)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.time_scalar_indexing_strucure">time_scalar_indexing_strucure() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph property)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory.times">times() (PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory property)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.total_variables_count">total_variables_count() (PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath property)</a> |
||||
</li> |
||||
</ul></td> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.structure.Structure.total_variables_number">total_variables_number() (PyCTBN.PyCTBN.structure_graph.structure.Structure property)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath.trajectories">trajectories() (PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath property)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory">Trajectory (class in PyCTBN.PyCTBN.structure_graph.trajectory)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory.trajectory">trajectory() (PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory property)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.transition_filtering">transition_filtering() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph property)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.structure_graph.html#PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph.transition_scalar_indexing_structure">transition_scalar_indexing_structure() (PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph property)</a> |
||||
</li> |
||||
</ul></td> |
||||
</tr></table> |
||||
|
||||
<h2 id="V">V</h2> |
||||
<table style="width: 100%" class="indextable genindextable"><tr> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.variable_cim_xu_marginal_likelihood_q">variable_cim_xu_marginal_likelihood_q() (PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator method)</a> |
||||
</li> |
||||
</ul></td> |
||||
<td style="width: 33%; vertical-align: top;"><ul> |
||||
<li><a href="PyCTBN.PyCTBN.estimators.html#PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator.variable_cim_xu_marginal_likelihood_theta">variable_cim_xu_marginal_likelihood_theta() (PyCTBN.PyCTBN.estimators.fam_score_calculator.FamScoreCalculator method)</a> |
||||
</li> |
||||
<li><a href="PyCTBN.PyCTBN.utility.html#PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter.variables">variables() (PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter property)</a> |
||||
</li> |
||||
</ul></td> |
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</tr></table> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.html">PyCTBN.PyCTBN package</a></li> |
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<h1>Welcome to PyCTBN’s documentation!<a class="headerlink" href="#welcome-to-pyctbn-s-documentation" title="Permalink to this headline">¶</a></h1> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.html">PyCTBN.PyCTBN package</a><ul> |
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<li class="toctree-l3"><a class="reference internal" href="PyCTBN.PyCTBN.html#subpackages">Subpackages</a><ul> |
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<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html">PyCTBN.PyCTBN.estimators package</a></li> |
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<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html">PyCTBN.PyCTBN.optimizers package</a></li> |
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<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html">PyCTBN.PyCTBN.structure_graph package</a></li> |
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<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html">PyCTBN.PyCTBN.utility package</a></li> |
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<li class="toctree-l3"><a class="reference internal" href="PyCTBN.PyCTBN.html#module-PyCTBN.PyCTBN">Module contents</a></li> |
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<div class="section" id="indices-and-tables"> |
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<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">¶</a></h1> |
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<div class="section" id="pyctbn"> |
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<h1>PyCTBN<a class="headerlink" href="#pyctbn" title="Permalink to this headline">¶</a></h1> |
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<div class="toctree-wrapper compound"> |
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<ul> |
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<li class="toctree-l1"><a class="reference internal" href="PyCTBN.PyCTBN.html">PyCTBN.PyCTBN package</a><ul> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.PyCTBN.html#subpackages">Subpackages</a><ul> |
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<li class="toctree-l3"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html">PyCTBN.PyCTBN.estimators package</a><ul> |
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<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#submodules">Submodules</a></li> |
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<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.fam_score_calculator">PyCTBN.PyCTBN.estimators.fam_score_calculator module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.parameters_estimator">PyCTBN.PyCTBN.estimators.parameters_estimator module</a></li> |
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<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator">PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_estimator">PyCTBN.PyCTBN.estimators.structure_estimator module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_score_based_estimator">PyCTBN.PyCTBN.estimators.structure_score_based_estimator module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators">Module contents</a></li> |
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</ul> |
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</li> |
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<li class="toctree-l3"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html">PyCTBN.PyCTBN.optimizers package</a><ul> |
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||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.constraint_based_optimizer">PyCTBN.PyCTBN.optimizers.constraint_based_optimizer module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.hill_climbing_search">PyCTBN.PyCTBN.optimizers.hill_climbing_search module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.optimizer">PyCTBN.PyCTBN.optimizers.optimizer module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.tabu_search">PyCTBN.PyCTBN.optimizers.tabu_search module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers">Module contents</a></li> |
||||
</ul> |
||||
</li> |
||||
<li class="toctree-l3"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html">PyCTBN.PyCTBN.structure_graph package</a><ul> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#submodules">Submodules</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix">PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.network_graph">PyCTBN.PyCTBN.structure_graph.network_graph module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.sample_path">PyCTBN.PyCTBN.structure_graph.sample_path module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.set_of_cims">PyCTBN.PyCTBN.structure_graph.set_of_cims module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.structure">PyCTBN.PyCTBN.structure_graph.structure module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.trajectory">PyCTBN.PyCTBN.structure_graph.trajectory module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph">Module contents</a></li> |
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</ul> |
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<li class="toctree-l3"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html">PyCTBN.PyCTBN.utility package</a><ul> |
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<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.abstract_importer">PyCTBN.PyCTBN.utility.abstract_importer module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.cache">PyCTBN.PyCTBN.utility.cache module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.json_importer">PyCTBN.PyCTBN.utility.json_importer module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.sample_importer">PyCTBN.PyCTBN.utility.sample_importer module</a></li> |
||||
<li class="toctree-l4"><a class="reference internal" href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility">Module contents</a></li> |
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<a href="PyCTBN.PyCTBN.html#module-PyCTBN.PyCTBN"><code class="xref">PyCTBN.PyCTBN</code></a></td><td> |
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<a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators"><code class="xref">PyCTBN.PyCTBN.estimators</code></a></td><td> |
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<a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.fam_score_calculator"><code class="xref">PyCTBN.PyCTBN.estimators.fam_score_calculator</code></a></td><td> |
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<em></em></td></tr> |
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<a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.parameters_estimator"><code class="xref">PyCTBN.PyCTBN.estimators.parameters_estimator</code></a></td><td> |
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<a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator"><code class="xref">PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator</code></a></td><td> |
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<a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_estimator"><code class="xref">PyCTBN.PyCTBN.estimators.structure_estimator</code></a></td><td> |
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<a href="PyCTBN.PyCTBN.estimators.html#module-PyCTBN.PyCTBN.estimators.structure_score_based_estimator"><code class="xref">PyCTBN.PyCTBN.estimators.structure_score_based_estimator</code></a></td><td> |
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<a href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers"><code class="xref">PyCTBN.PyCTBN.optimizers</code></a></td><td> |
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<a href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.constraint_based_optimizer"><code class="xref">PyCTBN.PyCTBN.optimizers.constraint_based_optimizer</code></a></td><td> |
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<em></em></td></tr> |
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<td>    |
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<a href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.hill_climbing_search"><code class="xref">PyCTBN.PyCTBN.optimizers.hill_climbing_search</code></a></td><td> |
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<a href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.optimizer"><code class="xref">PyCTBN.PyCTBN.optimizers.optimizer</code></a></td><td> |
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<em></em></td></tr> |
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<td>    |
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<a href="PyCTBN.PyCTBN.optimizers.html#module-PyCTBN.PyCTBN.optimizers.tabu_search"><code class="xref">PyCTBN.PyCTBN.optimizers.tabu_search</code></a></td><td> |
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<em></em></td></tr> |
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<tr class="cg-1"> |
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<td></td> |
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<td>    |
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<a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph"><code class="xref">PyCTBN.PyCTBN.structure_graph</code></a></td><td> |
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<em></em></td></tr> |
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<tr class="cg-1"> |
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<td></td> |
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<td>    |
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<a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix"><code class="xref">PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix</code></a></td><td> |
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<em></em></td></tr> |
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<a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.network_graph"><code class="xref">PyCTBN.PyCTBN.structure_graph.network_graph</code></a></td><td> |
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<em></em></td></tr> |
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<tr class="cg-1"> |
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<a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.sample_path"><code class="xref">PyCTBN.PyCTBN.structure_graph.sample_path</code></a></td><td> |
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<em></em></td></tr> |
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<a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.set_of_cims"><code class="xref">PyCTBN.PyCTBN.structure_graph.set_of_cims</code></a></td><td> |
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<em></em></td></tr> |
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<tr class="cg-1"> |
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<td>    |
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<a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.structure"><code class="xref">PyCTBN.PyCTBN.structure_graph.structure</code></a></td><td> |
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<em></em></td></tr> |
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<tr class="cg-1"> |
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<td>    |
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<a href="PyCTBN.PyCTBN.structure_graph.html#module-PyCTBN.PyCTBN.structure_graph.trajectory"><code class="xref">PyCTBN.PyCTBN.structure_graph.trajectory</code></a></td><td> |
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<em></em></td></tr> |
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<tr class="cg-1"> |
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<a href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility"><code class="xref">PyCTBN.PyCTBN.utility</code></a></td><td> |
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<em></em></td></tr> |
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<a href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.abstract_importer"><code class="xref">PyCTBN.PyCTBN.utility.abstract_importer</code></a></td><td> |
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<em></em></td></tr> |
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<tr class="cg-1"> |
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<td></td> |
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<td>    |
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<a href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.cache"><code class="xref">PyCTBN.PyCTBN.utility.cache</code></a></td><td> |
||||
<em></em></td></tr> |
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<tr class="cg-1"> |
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<a href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.json_importer"><code class="xref">PyCTBN.PyCTBN.utility.json_importer</code></a></td><td> |
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<em></em></td></tr> |
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<a href="PyCTBN.PyCTBN.utility.html#module-PyCTBN.PyCTBN.utility.sample_importer"><code class="xref">PyCTBN.PyCTBN.utility.sample_importer</code></a></td><td> |
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Reference in new issue