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  <h1>Source code for pyFTS.models.pwfts</h1><div class="highlight"><pre>
<span></span><span class="ch">#!/usr/bin/python</span>
<span class="c1"># -*- coding: utf8 -*-</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">from</span> <span class="nn">operator</span> <span class="k">import</span> <span class="n">itemgetter</span>
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">FuzzySet</span>
<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">ifts</span>
<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>


<div class="viewcode-block" id="ProbabilisticWeightedFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG">[docs]</a><span class="k">class</span> <span class="nc">ProbabilisticWeightedFLRG</span><span class="p">(</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFLRG</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;High Order Probabilistic Weighted Fuzzy Logical Relationship Group&quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">ProbabilisticWeightedFLRG</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">order</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">rhs_count</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">frequency_count</span> <span class="o">=</span> <span class="mf">0.0</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">Z</span> <span class="o">=</span> <span class="kc">None</span>

<div class="viewcode-block" id="ProbabilisticWeightedFLRG.get_membership"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_membership">[docs]</a>    <span class="k">def</span> <span class="nf">get_membership</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanprod</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">count</span><span class="p">])</span>
                               <span class="k">for</span> <span class="n">count</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">)])</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">sets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="n">mv</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;mv&#39;</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">frequency_count</span> <span class="o">+=</span> <span class="n">mv</span>
        <span class="k">if</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">rhs_count</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">+=</span> <span class="n">mv</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">c</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">rhs_count</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">mv</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFLRG.lhs_conditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability">[docs]</a>    <span class="k">def</span> <span class="nf">lhs_conditional_probability</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">norm</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="p">):</span>
        <span class="n">pk</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequency_count</span> <span class="o">/</span> <span class="n">norm</span>

        <span class="n">tmp</span> <span class="o">=</span> <span class="n">pk</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">sets</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">partition_function</span><span class="p">(</span><span class="n">sets</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="n">nbins</span><span class="p">))</span>

        <span class="k">return</span> <span class="n">tmp</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFLRG.rhs_unconditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_unconditional_probability">[docs]</a>    <span class="k">def</span> <span class="nf">rhs_unconditional_probability</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">rhs_count</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequency_count</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFLRG.rhs_conditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_conditional_probability">[docs]</a>    <span class="k">def</span> <span class="nf">rhs_conditional_probability</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="p">):</span>
        <span class="n">total</span> <span class="o">=</span> <span class="mf">0.0</span>
        <span class="k">for</span> <span class="n">rhs</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
            <span class="nb">set</span> <span class="o">=</span> <span class="n">sets</span><span class="p">[</span><span class="n">rhs</span><span class="p">]</span>
            <span class="n">wi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">rhs</span><span class="p">)</span>
            <span class="n">mv</span> <span class="o">=</span> <span class="nb">set</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="nb">set</span><span class="o">.</span><span class="n">partition_function</span><span class="p">(</span><span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="n">nbins</span><span class="p">)</span>
            <span class="n">total</span> <span class="o">+=</span> <span class="n">wi</span> <span class="o">*</span> <span class="n">mv</span>

        <span class="k">return</span> <span class="n">total</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFLRG.partition_function"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.partition_function">[docs]</a>    <span class="k">def</span> <span class="nf">partition_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">Z</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">Z</span> <span class="o">=</span> <span class="mf">0.0</span>
            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">uod</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">uod</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">nbins</span><span class="p">):</span>
                <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">:</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">Z</span> <span class="o">+=</span> <span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>

        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">Z</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFLRG.get_midpoint"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_midpoint">[docs]</a>    <span class="k">def</span> <span class="nf">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
        <span class="sd">&#39;&#39;&#39;Return the expectation of the PWFLRG, the weighted sum&#39;&#39;&#39;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">*</span> <span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
                                             <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">]))</span>

        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFLRG.get_upper"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_upper">[docs]</a>    <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">*</span> <span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">]))</span>

        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFLRG.get_lower"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_lower">[docs]</a>    <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">*</span> <span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">]))</span>

        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span></div>

    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">tmp2</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">):</span>
            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp2</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">&quot;, &quot;</span>
            <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">&quot;(&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rhs_count</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequency_count</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span> <span class="o">+</span> <span class="n">c</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span> <span class="o">+</span> <span class="n">tmp2</span></div>


<div class="viewcode-block" id="ProbabilisticWeightedFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS">[docs]</a><span class="k">class</span> <span class="nc">ProbabilisticWeightedFTS</span><span class="p">(</span><span class="n">ifts</span><span class="o">.</span><span class="n">IntervalFTS</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;High Order Probabilistic Weighted Fuzzy Time Series&quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">ProbabilisticWeightedFTS</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="o">**</span><span class="n">kwargs</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;PWFTS&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Probabilistic FTS&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Silva, P.; GuimarĂ£es, F.; Sadaei, H.&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">auto_update</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;update&#39;</span><span class="p">,</span><span class="kc">False</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
        <span class="n">parameters</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;parameters&#39;</span><span class="p">,</span><span class="s1">&#39;fuzzy&#39;</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">parameters</span> <span class="o">==</span> <span class="s1">&#39;monotonic&#39;</span><span class="p">:</span>
            <span class="n">tmpdata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;sets&#39;</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;maximum&#39;</span><span class="p">)</span>
            <span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.generate_lhs_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_lhs_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
        <span class="n">nsample</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;sets&quot;</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
                   <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">]</span>

        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">nsample</span><span class="p">,</span> <span class="n">explain</span><span class="p">)</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.generate_lhs_flrg_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg_fuzzyfied">[docs]</a>    <span class="k">def</span> <span class="nf">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
        <span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="n">flrgs</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">):</span>
            <span class="n">lhs</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="n">o</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
            <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">lhs</span> <span class="p">)</span>

            <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2"> (Lag </span><span class="si">{}</span><span class="s2">) </span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="n">sample</span><span class="p">[</span><span class="n">o</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">lhs</span><span class="p">))</span>

        <span class="c1"># Trace the possible paths</span>
        <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
            <span class="n">flrg</span> <span class="o">=</span> <span class="n">ProbabilisticWeightedFLRG</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>

            <span class="k">for</span> <span class="n">lhs</span> <span class="ow">in</span> <span class="n">path</span><span class="p">:</span>
                <span class="n">flrg</span><span class="o">.</span><span class="n">append_lhs</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span>

            <span class="n">flrgs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">flrgs</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.generate_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dump</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;FLR: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">))</span>

            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>

            <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>

            <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>

                <span class="n">lhs_mv</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>

                <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span><span class="p">;</span>

                <span class="n">fuzzyfied</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;fuzzy&#39;</span><span class="p">,</span>
                                                     <span class="n">alpha_cut</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>

                <span class="n">mvs</span> <span class="o">=</span> <span class="p">[]</span>
                <span class="k">for</span> <span class="nb">set</span><span class="p">,</span> <span class="n">mv</span> <span class="ow">in</span> <span class="n">fuzzyfied</span><span class="p">:</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="nb">set</span><span class="p">,</span> <span class="n">mv</span><span class="o">=</span><span class="n">lhs_mv</span> <span class="o">*</span> <span class="n">mv</span><span class="p">)</span>
                    <span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>

                <span class="n">tmp_fq</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">lhs_mv</span><span class="o">*</span><span class="n">kk</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">mvs</span> <span class="k">if</span> <span class="n">kk</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">])</span>

                <span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span> <span class="o">+=</span> <span class="n">tmp_fq</span></div>


<div class="viewcode-block" id="ProbabilisticWeightedFTS.update_model"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.update_model">[docs]</a>    <span class="k">def</span> <span class="nf">update_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">data</span><span class="p">):</span>
        <span class="k">pass</span></div>


<div class="viewcode-block" id="ProbabilisticWeightedFTS.add_new_PWFLGR"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.add_new_PWFLGR">[docs]</a>    <span class="k">def</span> <span class="nf">add_new_PWFLGR</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
            <span class="n">tmp</span> <span class="o">=</span> <span class="n">ProbabilisticWeightedFLRG</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
            <span class="k">for</span> <span class="n">fs</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">:</span> <span class="n">tmp</span><span class="o">.</span><span class="n">append_lhs</span><span class="p">(</span><span class="n">fs</span><span class="p">)</span>
            <span class="n">tmp</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">tmp</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">;</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span> <span class="o">+=</span> <span class="mi">1</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.flrg_lhs_unconditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_unconditional_probability">[docs]</a>    <span class="k">def</span> <span class="nf">flrg_lhs_unconditional_probability</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">frequency_count</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="mf">0.0</span></div>
            <span class="c1">#self.add_new_PWFLGR(flrg)</span>
            <span class="c1">#return self.flrg_lhs_unconditional_probability(flrg)</span>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.flrg_lhs_conditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability">[docs]</a>    <span class="k">def</span> <span class="nf">flrg_lhs_conditional_probability</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
        <span class="n">mv</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
        <span class="n">pb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_unconditional_probability</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">mv</span> <span class="o">*</span> <span class="n">pb</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.get_midpoint"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_midpoint">[docs]</a>    <span class="k">def</span> <span class="nf">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
            <span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span> <span class="c1">#sum(np.array([tmp.rhs_unconditional_probability(s) * self.setsDict[s].centroid for s in tmp.RHS]))</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">pi</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
                <span class="n">ret</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">pi</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">]))</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
        <span class="k">return</span> <span class="n">ret</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.flrg_rhs_conditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_rhs_conditional_probability">[docs]</a>    <span class="k">def</span> <span class="nf">flrg_rhs_conditional_probability</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>

        <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
            <span class="n">_flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
            <span class="n">cond</span> <span class="o">=</span> <span class="p">[]</span>
            <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">_flrg</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
                <span class="n">_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span>
                <span class="n">tmp</span> <span class="o">=</span> <span class="n">_flrg</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">_set</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="n">_set</span><span class="o">.</span><span class="n">partition_function</span><span class="p">(</span><span class="n">uod</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()))</span>
                <span class="n">cond</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
            <span class="n">ret</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">cond</span><span class="p">))</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">pi</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
            <span class="n">ret</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">pi</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">]))</span>
        <span class="k">return</span> <span class="n">ret</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.get_upper"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_upper">[docs]</a>    <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
            <span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">ret</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="k">return</span> <span class="n">ret</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.get_lower"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_lower">[docs]</a>    <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
            <span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">ret</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="k">return</span> <span class="n">ret</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="n">method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span><span class="s1">&#39;heuristic&#39;</span><span class="p">)</span>

        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>

        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>

            <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;heuristic&#39;</span><span class="p">:</span>
                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">point_heuristic</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
            <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;expected_value&#39;</span><span class="p">:</span>
                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">point_expected_value</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unknown point forecasting method!&quot;</span><span class="p">)</span>

            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">auto_update</span> <span class="ow">and</span> <span class="n">k</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_model</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">-</span> <span class="mi">1</span> <span class="p">:</span> <span class="n">k</span><span class="p">])</span>

        <span class="k">return</span> <span class="n">ret</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.point_heuristic"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_heuristic">[docs]</a>    <span class="k">def</span> <span class="nf">point_heuristic</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>

        <span class="n">explain</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;explain&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Fuzzyfication </span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>

        <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="p">)</span>

        <span class="n">mp</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">norms</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Rules:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>

        <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
            <span class="n">norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_conditional_probability</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">flrg</span><span class="p">)</span>

            <span class="k">if</span> <span class="n">norm</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_unconditional_probability</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>


            <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\t</span><span class="s2"> Midpoint: </span><span class="si">{}</span><span class="se">\t</span><span class="s2"> Norm: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]),</span>
                                                                  <span class="bp">self</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">flrg</span><span class="p">),</span> <span class="n">norm</span><span class="p">))</span>

            <span class="n">mp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">norm</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
            <span class="n">norms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">norm</span><span class="p">)</span>

        <span class="n">norm</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">norms</span><span class="p">)</span>

        <span class="n">final</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span> <span class="k">if</span> <span class="n">norm</span> <span class="o">!=</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>

        <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Deffuzyfied value: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">final</span><span class="p">))</span>
        <span class="k">return</span> <span class="n">final</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.point_expected_value"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_expected_value">[docs]</a>    <span class="k">def</span> <span class="nf">point_expected_value</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="n">explain</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;explain&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>

        <span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">sample</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>

        <span class="n">final</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">expected_value</span><span class="p">()</span>
        <span class="k">return</span> <span class="n">final</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>

        <span class="n">method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span><span class="s1">&#39;heuristic&#39;</span><span class="p">)</span>
        <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>

        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>

        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>

            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>

            <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;heuristic&#39;</span><span class="p">:</span>
                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interval_heuristic</span><span class="p">(</span><span class="n">sample</span><span class="p">))</span>
            <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;quantile&#39;</span><span class="p">:</span>
                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interval_quantile</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">alpha</span><span class="p">))</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unknown interval forecasting method!&quot;</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">ret</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.interval_quantile"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_quantile">[docs]</a>    <span class="k">def</span> <span class="nf">interval_quantile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
        <span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
        <span class="n">itvl</span> <span class="o">=</span> <span class="n">dist</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">([</span><span class="n">alpha</span><span class="p">,</span> <span class="mf">1.0</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">])</span>
        <span class="k">return</span> <span class="n">itvl</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.interval_heuristic"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_heuristic">[docs]</a>    <span class="k">def</span> <span class="nf">interval_heuristic</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>

        <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>

        <span class="n">up</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">lo</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">norms</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
            <span class="n">norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_conditional_probability</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">flrg</span><span class="p">)</span>
            <span class="k">if</span> <span class="n">norm</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_unconditional_probability</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
            <span class="n">up</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">norm</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
            <span class="n">lo</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">norm</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
            <span class="n">norms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">norm</span><span class="p">)</span>

            <span class="c1"># gerar o intervalo</span>
        <span class="n">norm</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">norms</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">norm</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="k">return</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">lo_</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">lo</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
            <span class="n">up_</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">up</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
            <span class="k">return</span> <span class="p">[</span><span class="n">lo_</span><span class="p">,</span> <span class="n">up_</span><span class="p">]</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>

        <span class="n">smooth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smooth&quot;</span><span class="p">,</span> <span class="s2">&quot;none&quot;</span><span class="p">)</span>

        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
        <span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>

        <span class="k">if</span> <span class="s1">&#39;bins&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
            <span class="n">_bins</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;bins&#39;</span><span class="p">)</span>
            <span class="n">nbins</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">_bins</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">nbins</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;num_bins&quot;</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
            <span class="n">_bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">uod</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">uod</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">nbins</span><span class="p">)</span>

        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>

            <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>

            <span class="k">if</span> <span class="s1">&#39;type&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
                <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;type&#39;</span><span class="p">)</span>

            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">_bins</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

            <span class="k">for</span> <span class="nb">bin</span> <span class="ow">in</span> <span class="n">_bins</span><span class="p">:</span>
                <span class="n">num</span> <span class="o">=</span> <span class="p">[]</span>
                <span class="n">den</span> <span class="o">=</span> <span class="p">[]</span>
                <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
                    <span class="k">if</span> <span class="n">s</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
                        <span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">s</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
                        <span class="n">pk</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">lhs_conditional_probability</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="p">)</span>
                        <span class="n">wi</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">rhs_conditional_probability</span><span class="p">(</span><span class="nb">bin</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="p">)</span>
                        <span class="n">num</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">wi</span> <span class="o">*</span> <span class="n">pk</span><span class="p">)</span>
                        <span class="n">den</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pk</span><span class="p">)</span>
                    <span class="k">else</span><span class="p">:</span>
                        <span class="n">num</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
                        <span class="n">den</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.000000001</span><span class="p">)</span>
                <span class="n">pf</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">num</span><span class="p">)</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">den</span><span class="p">)</span>

                <span class="n">dist</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="nb">bin</span><span class="p">,</span> <span class="n">pf</span><span class="p">)</span>

            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">ret</span></div>

    <span class="k">def</span> <span class="nf">__check_point_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">point</span><span class="p">):</span>
        <span class="n">lower_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">lower_set</span><span class="p">()</span>
        <span class="n">upper_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">upper_set</span><span class="p">()</span>
        <span class="k">return</span> <span class="n">point</span> <span class="o">&lt;=</span> <span class="n">lower_set</span><span class="o">.</span><span class="n">lower</span> <span class="ow">or</span> <span class="n">point</span> <span class="o">&gt;=</span> <span class="n">upper_set</span><span class="o">.</span><span class="n">upper</span>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast_ahead"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>

        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>

        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">)</span>

        <span class="n">ret</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">start</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>

        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">steps</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">):</span>

            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">__check_point_bounds</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="p">:</span>
                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">mp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mp</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>

        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:]</span></div>

    <span class="k">def</span> <span class="nf">__check_interval_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">interval</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
            <span class="n">lower_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">lower_set</span><span class="p">()</span>
            <span class="n">upper_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">upper_set</span><span class="p">()</span>
            <span class="k">return</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">lower_set</span><span class="o">.</span><span class="n">lower</span> <span class="ow">and</span> <span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">upper_set</span><span class="o">.</span><span class="n">upper</span>
        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="ow">and</span> <span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>

        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>

        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">)</span>

        <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">start</span><span class="p">]</span>

        <span class="n">ret</span> <span class="o">=</span> <span class="p">[[</span><span class="n">k</span><span class="p">,</span> <span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">]</span>
        
        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">sample</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>

        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">steps</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">):</span>

            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">__check_interval_bounds</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">lower</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">ret</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">k</span><span class="p">)],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
                <span class="n">upper</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">ret</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">k</span><span class="p">)],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">lower</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">upper</span><span class="p">)])</span>

        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:]</span></div>

<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>

        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="n">smooth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smooth&quot;</span><span class="p">,</span> <span class="s2">&quot;none&quot;</span><span class="p">)</span>

        <span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>

        <span class="k">if</span> <span class="s1">&#39;bins&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
            <span class="n">_bins</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;bins&#39;</span><span class="p">)</span>
            <span class="n">nbins</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">_bins</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">nbins</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;num_bins&quot;</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
            <span class="n">_bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">uod</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">uod</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">nbins</span><span class="p">)</span>

        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">)</span>

        <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">start</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">start</span><span class="p">]</span>

        <span class="k">for</span> <span class="n">dat</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">:</span>
            <span class="k">if</span> <span class="s1">&#39;type&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
                <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;type&#39;</span><span class="p">)</span>
            <span class="n">tmp</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">_bins</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
            <span class="n">tmp</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">dat</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>

        <span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">_bins</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>

        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>

        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">steps</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">_bins</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

            <span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>

            <span class="c1"># Find all bins of past distributions with probability greater than zero</span>

            <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">):</span>
                <span class="n">dd</span> <span class="o">=</span> <span class="n">ret</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">lag</span><span class="p">]</span>
                <span class="n">vals</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">dd</span><span class="o">.</span><span class="n">bins</span> <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">dd</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">v</span><span class="p">),</span> <span class="mi">4</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">]</span>
                <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">vals</span><span class="p">)</span> <span class="p">)</span>


            <span class="c1"># Trace all possible combinations between the bins of past distributions</span>

            <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>

                <span class="c1"># get the combined probabilities for this path</span>
                <span class="n">pk</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">([</span><span class="n">ret</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="n">lag</span><span class="p">)]</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">path</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span>
                              <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">)])</span>


                <span class="n">d</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">path</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>

                <span class="k">for</span> <span class="nb">bin</span> <span class="ow">in</span> <span class="n">_bins</span><span class="p">:</span>
                    <span class="n">dist</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="nb">bin</span><span class="p">,</span> <span class="n">dist</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="nb">bin</span><span class="p">)</span> <span class="o">+</span> <span class="n">pk</span> <span class="o">*</span> <span class="n">d</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="nb">bin</span><span class="p">))</span>

            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:]</span></div>

    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;:</span><span class="se">\n</span><span class="s2">&quot;</span>
        <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">):</span>
            <span class="n">p</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">.</span><span class="n">frequency_count</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
            <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span> <span class="o">+</span> <span class="s2">&quot;(&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;) &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">r</span><span class="p">])</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span>
        <span class="k">return</span> <span class="n">tmp</span></div>


<div class="viewcode-block" id="visualize_distributions"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.visualize_distributions">[docs]</a><span class="k">def</span> <span class="nf">visualize_distributions</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
    <span class="kn">from</span> <span class="nn">matplotlib</span> <span class="k">import</span> <span class="n">gridspec</span>
    <span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>

    <span class="n">ordered_sets</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
    <span class="n">ftpg_keys</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>

    <span class="n">lhs_probs</span> <span class="o">=</span> <span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">flrg_lhs_unconditional_probability</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
                 <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">ftpg_keys</span><span class="p">]</span>

    <span class="n">mat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">ftpg_keys</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">ordered_sets</span><span class="p">)))</span>
    <span class="k">for</span> <span class="n">row</span><span class="p">,</span> <span class="n">w</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ftpg_keys</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">col</span><span class="p">,</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ordered_sets</span><span class="p">):</span>
            <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">w</span><span class="p">]</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
                <span class="n">mat</span><span class="p">[</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">w</span><span class="p">]</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>

    <span class="n">size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;size&#39;</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>

    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>

    <span class="n">gs</span> <span class="o">=</span> <span class="n">gridspec</span><span class="o">.</span><span class="n">GridSpec</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">width_ratios</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
    <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
    <span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;darkblue&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="n">ftpg_keys</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">:</span> <span class="n">lhs_probs</span><span class="p">},</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
    <span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;LHS Probabilities&quot;</span><span class="p">)</span>

    <span class="n">ind_sets</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ordered_sets</span><span class="p">))</span>
    <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
    <span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">mat</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;Blues&#39;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">yticklabels</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;RHS probabilities&quot;</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">ind_sets</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">ordered_sets</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_tick_params</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span></div>
</pre></div>

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