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Petrônio Cândido 2019-04-22 10:01:58 -03:00
parent c3457a3569
commit 7c11b7e5de
37 changed files with 665 additions and 104 deletions
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@ -127,6 +127,8 @@
<li><a href="pyFTS/models/multivariate/cmvfts.html">pyFTS.models.multivariate.cmvfts</a></li>
<li><a href="pyFTS/models/multivariate/common.html">pyFTS.models.multivariate.common</a></li>
<li><a href="pyFTS/models/multivariate/flrg.html">pyFTS.models.multivariate.flrg</a></li>
<li><a href="pyFTS/models/multivariate/granular.html">pyFTS.models.multivariate.granular</a></li>
<li><a href="pyFTS/models/multivariate/grid.html">pyFTS.models.multivariate.grid</a></li>
<li><a href="pyFTS/models/multivariate/mvfts.html">pyFTS.models.multivariate.mvfts</a></li>
<li><a href="pyFTS/models/multivariate/variable.html">pyFTS.models.multivariate.variable</a></li>
<li><a href="pyFTS/models/multivariate/wmvfts.html">pyFTS.models.multivariate.wmvfts</a></li>

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docs/build/html/_modules/pyFTS/common/FuzzySet.html vendored
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@ -81,6 +81,7 @@
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fuzzy Set</span>
<span class="sd"> &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">name</span><span class="p">,</span> <span class="n">mf</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">centroid</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Create a Fuzzy Set</span>
@ -97,15 +98,15 @@
<span class="sd">&quot;&quot;&quot;The alpha cut value&quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">type</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;type&#39;</span><span class="p">,</span> <span class="s1">&#39;common&#39;</span><span class="p">)</span>
<span class="sd">&quot;&quot;&quot;The fuzzy set type (common, composite, nonstationary, etc)&quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">variable</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;variable&#39;</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">variable</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;variable&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="sd">&quot;&quot;&quot;In multivariate time series, indicate for which variable this fuzzy set belogs&quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">Z</span> <span class="o">=</span> <span class="kc">None</span>
<span class="sd">&quot;&quot;&quot;Partition function in respect to the membership function&quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">parameters</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mf</span> <span class="o">==</span> <span class="n">Membership</span><span class="o">.</span><span class="n">gaussmf</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">parameters</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">parameters</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">3</span>
<span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">parameters</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">3</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">parameters</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="mi">3</span>
<span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">parameters</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="mi">3</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mf</span> <span class="o">==</span> <span class="n">Membership</span><span class="o">.</span><span class="n">sigmf</span><span class="p">:</span>
<span class="n">k</span> <span class="o">=</span> <span class="p">(</span><span class="n">parameters</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">parameters</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">k</span>
@ -135,7 +136,7 @@
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">mf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transform</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">parameters</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span></div>
<div class="viewcode-block" id="FuzzySet.partition_function"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet.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">uod</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
<div class="viewcode-block" id="FuzzySet.partition_function"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet.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">uod</span><span class="o">=</span><span class="kc">None</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="sd">&quot;&quot;&quot;</span>
<span class="sd"> Calculate the partition function over the membership function.</span>
@ -175,7 +176,7 @@
<span class="n">fs2</span> <span class="o">=</span> <span class="n">ordered_sets</span><span class="p">[</span><span class="n">midpoint</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="n">midpoint</span> <span class="o">&lt;</span> <span class="n">max_len</span> <span class="k">else</span> <span class="n">ordered_sets</span><span class="p">[</span><span class="n">max_len</span><span class="p">]</span>
<span class="k">if</span> <span class="n">fuzzy_sets</span><span class="p">[</span><span class="n">fs1</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="o">&lt;=</span> <span class="n">fuzzy_sets</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">fuzzy_sets</span><span class="p">[</span><span class="n">fs2</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span><span class="p">:</span>
<span class="k">return</span> <span class="p">(</span><span class="n">midpoint</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">midpoint</span><span class="p">,</span> <span class="n">midpoint</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">midpoint</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">midpoint</span><span class="p">,</span> <span class="n">midpoint</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">midpoint</span> <span class="o">&lt;=</span> <span class="mi">1</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="k">elif</span> <span class="n">midpoint</span> <span class="o">&gt;=</span> <span class="n">max_len</span><span class="p">:</span>
@ -194,13 +195,12 @@
<span class="sd"> :param data: input value to be fuzzyfied</span>
<span class="sd"> :param partitioner: a trained pyFTS.partitioners.Partitioner object</span>
<span class="sd"> :param kwargs: dict, optional arguments</span>
<span class="sd"> :keyword alpha_cut: the minimal membership value to be considered on fuzzyfication (only for mode=&#39;sets&#39;)</span>
<span class="sd"> :keyword method: the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership)</span>
<span class="sd"> :keyword mode: the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership</span>
<span class="sd"> values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )</span>
<span class="sd"> </span>
<span class="sd"> :returns a list with the fuzzyfied values, depending on the mode</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">alpha_cut</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_cut&#39;</span><span class="p">,</span> <span class="mf">0.</span><span class="p">)</span>
<span class="n">mode</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;mode&#39;</span><span class="p">,</span> <span class="s1">&#39;sets&#39;</span><span class="p">)</span>
@ -369,6 +369,7 @@
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">data</span></div>
<div class="viewcode-block" id="check_bounds"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FuzzySet.check_bounds">[docs]</a><span class="k">def</span> <span class="nf">check_bounds</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">fuzzy_sets</span><span class="p">,</span> <span class="n">ordered_sets</span><span class="p">):</span>
<span class="k">if</span> <span class="n">data</span> <span class="o">&lt;</span> <span class="n">fuzzy_sets</span><span class="p">[</span><span class="n">ordered_sets</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">lower</span><span class="p">:</span>
<span class="k">return</span> <span class="n">fuzzy_sets</span><span class="p">[</span><span class="n">ordered_sets</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>

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@ -145,6 +145,8 @@
<span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
<span class="sd">&quot;&quot;&quot;A integer indicating the largest lag used by the model. This value also indicates the minimum number of past lags </span>
<span class="sd"> needed to forecast a single step ahead&quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([],</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Datetime&quot;</span><span class="p">,</span><span class="s2">&quot;Operation&quot;</span><span class="p">,</span><span class="s2">&quot;Value&quot;</span><span class="p">])</span>
<span class="sd">&quot;&quot;&quot;&quot;&quot;&quot;</span>
<div class="viewcode-block" id="FTS.fuzzy"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.fuzzy">[docs]</a> <span class="k">def</span> <span class="nf">fuzzy</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="sd">&quot;&quot;&quot;</span>
@ -222,7 +224,7 @@
<span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;distribution&#39;</span><span class="p">:</span>
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead_distribution</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps_ahead</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="nb">type</span> <span class="o">==</span> <span class="s1">&#39;multivariate&#39;</span><span class="p">:</span>
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead_multivariate</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">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead_multivariate</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="p">[</span><span class="s1">&#39;point&#39;</span><span class="p">,</span> <span class="s1">&#39;interval&#39;</span><span class="p">,</span> <span class="s1">&#39;distribution&#39;</span><span class="p">,</span> <span class="s1">&#39;multivariate&#39;</span><span class="p">]</span><span class="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="nb">type</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The argument </span><span class="se">\&#39;</span><span class="s1">type</span><span class="se">\&#39;</span><span class="s1"> has an unknown value.&#39;</span><span class="p">)</span>
@ -630,7 +632,10 @@
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="bp">self</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="p">]</span><span class="o">.</span><span class="n">reset_calculated_values</span><span class="p">()</span></div></div>
<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="p">]</span><span class="o">.</span><span class="n">reset_calculated_values</span><span class="p">()</span></div>
<div class="viewcode-block" id="FTS.append_log"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.append_log">[docs]</a> <span class="k">def</span> <span class="nf">append_log</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">operation</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
<span class="k">pass</span></div></div>

2
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@ -124,7 +124,7 @@
<div class="viewcode-block" id="ConventionalFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFTS.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="n">tmpdata</span> <span class="o">=</span> <span class="n">FuzzySet</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">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</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">mode</span><span class="o">=</span><span class="s1">&#39;sets&#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">method</span><span class="o">=</span><span class="s1">&#39;maximum&#39;</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">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_non_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></div>

21
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@ -200,6 +200,9 @@
<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">if</span> <span class="n">explain</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">&quot;Fuzzyfication&quot;</span><span class="p">,</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">nsample</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="HighOrderFTS.generate_lhs_flrg_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.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>
@ -211,7 +214,7 @@
<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="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">&quot;Ordering Lags&quot;</span><span class="p">,</span> <span class="s2">&quot;Lag </span><span class="si">{}</span><span class="s2"> Value </span><span class="si">{}</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">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>
@ -291,17 +294,11 @@
<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="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="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="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</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="k">else</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_fuzzyfied</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="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="n">midpoints</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">memberships</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>
@ -314,7 +311,7 @@
<span class="n">memberships</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="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"> -&gt; </span><span class="si">{}</span><span class="s2"> (Naïve)</span><span class="se">\t</span><span class="s2"> Midpoint: </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="n">flrg</span><span class="o">.</span><span class="n">LHS</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">append_log</span><span class="p">(</span><span class="s2">&quot;Rule Matching&quot;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2"> (Naïve) Midpoint: </span><span class="si">{}</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="n">flrg</span><span class="o">.</span><span class="n">LHS</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="n">mp</span><span class="p">))</span>
<span class="k">else</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>
@ -324,19 +321,17 @@
<span class="n">memberships</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="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">\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="n">flrg</span><span class="p">),</span> <span class="n">mp</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"> Membership: </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="n">flrg</span><span class="p">),</span> <span class="n">mv</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">&quot;Rule Matching&quot;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">, Midpoint: </span><span class="si">{}</span><span class="s2"> Membership: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</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">mp</span><span class="p">,</span> <span class="n">mv</span><span class="p">))</span>
<span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;mean&quot;</span> <span class="ow">or</span> <span class="n">fuzzyfied</span><span class="p">:</span>
<span class="n">final</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">midpoints</span><span class="p">)</span>
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">&quot;Deffuzyfication&quot;</span><span class="p">,</span> <span class="s2">&quot;By Mean: </span><span class="si">{}</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">else</span><span class="p">:</span>
<span class="n">final</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">midpoints</span><span class="p">,</span> <span class="n">memberships</span><span class="p">)</span>
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">&quot;Deffuzyfication&quot;</span><span class="p">,</span> <span class="s2">&quot;By Memberships: </span><span class="si">{}</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="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">final</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;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">ret</span></div></div>

2
docs/build/html/_modules/pyFTS/models/ismailefendi.html vendored
View File

@ -137,7 +137,7 @@
<div class="viewcode-block" id="ImprovedWeightedFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">tmpdata</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</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">mode</span><span class="o">=</span><span class="s1">&#39;sets&#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">ndata</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">mode</span><span class="o">=</span><span class="s1">&#39;sets&#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></div>

61
docs/build/html/_modules/pyFTS/models/multivariate/cmvfts.html vendored
View File

@ -78,11 +78,12 @@
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">flrg</span>
<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="k">import</span> <span class="n">mvfts</span><span class="p">,</span> <span class="n">grid</span><span class="p">,</span> <span class="n">common</span>
<span class="kn">from</span> <span class="nn">types</span> <span class="k">import</span> <span class="n">LambdaType</span>
<div class="viewcode-block" id="ClusteredMVFTS"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS">[docs]</a><span class="k">class</span> <span class="nc">ClusteredMVFTS</span><span class="p">(</span><span class="n">mvfts</span><span class="o">.</span><span class="n">MVFTS</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Meta model for multivariate, high order, clustered multivariate FTS</span>
<span class="sd"> Meta model for high order, clustered multivariate FTS</span>
<span class="sd"> &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">ClusteredMVFTS</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>
@ -112,16 +113,15 @@
<span class="n">ndata</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
<span class="n">data_point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
<span class="n">ndata</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">common</span><span class="o">.</span><span class="n">fuzzyfy_instance_clustered</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</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">ndata</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">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data_point</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="k">return</span> <span class="n">ndata</span></div>
<div class="viewcode-block" id="ClusteredMVFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.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">fts_params</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
<span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">is_high_order</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
@ -147,18 +147,53 @@
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</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">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span> <span class="o">!=</span> <span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span> <span class="o">=</span> <span class="n">var</span>
<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">change_target_variable</span><span class="p">(</span><span class="n">var</span><span class="p">)</span>
<span class="bp">self</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="bp">self</span><span class="o">.</span><span class="n">partitioner</span>
<span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">reset_calculated_values</span><span class="p">()</span>
<span class="n">generators</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;generators&#39;</span><span class="p">,</span> <span class="p">{})</span>
<span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</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">pre_fuzzyfy</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="n">already_processed_cols</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">ret</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">ret</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</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">pre_fuzzyfy</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="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">:</span>
<span class="k">if</span> <span class="n">var</span><span class="o">.</span><span class="n">data_label</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">already_processed_cols</span><span class="p">:</span>
<span class="k">if</span> <span class="n">var</span><span class="o">.</span><span class="n">data_label</span> <span class="ow">in</span> <span class="n">generators</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">],</span> <span class="n">LambdaType</span><span class="p">):</span>
<span class="n">fx</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span>
<span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">fx</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">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</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">else</span><span class="p">:</span>
<span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">fx</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])]</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">],</span> <span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
<span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span> <span class="o">!=</span> <span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span> <span class="o">=</span> <span class="n">var</span>
<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">change_target_variable</span><span class="p">(</span><span class="n">var</span><span class="p">)</span>
<span class="bp">self</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="bp">self</span><span class="o">.</span><span class="n">partitioner</span>
<span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">reset_calculated_values</span><span class="p">()</span>
<span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</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">pre_fuzzyfy</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="n">already_processed_cols</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">)</span>
<span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">ret</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span></div>
<div class="viewcode-block" id="ClusteredMVFTS.forecast_ahead_multivariate"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_multivariate">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_multivariate</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">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</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="n">ndata</span><span class="o">.</span><span class="n">iloc</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">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="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
<span class="n">sample</span> <span class="o">=</span> <span class="n">ret</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">k</span><span class="p">:</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="n">k</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">forecast_multivariate</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">ret</span> <span class="o">=</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">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">return</span> <span class="n">ret</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="sd">&quot;&quot;&quot;String representation of the model&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>

4
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@ -117,7 +117,7 @@
<div class="viewcode-block" id="fuzzyfy_instance"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.common.fuzzyfy_instance">[docs]</a><span class="k">def</span> <span class="nf">fuzzyfy_instance</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">tuples</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="n">fsets</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="n">var</span><span class="o">.</span><span class="n">partitioner</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;fuzzy&#39;</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="n">var</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
<span class="n">fsets</span> <span class="o">=</span> <span class="n">var</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_point</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;fuzzy&#39;</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="n">var</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
<span class="k">if</span> <span class="n">tuples</span><span class="p">:</span>
<span class="k">return</span> <span class="p">[(</span><span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">fs</span><span class="p">)</span> <span class="k">for</span> <span class="n">fs</span> <span class="ow">in</span> <span class="n">fsets</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
@ -128,7 +128,7 @@
<span class="n">alpha_cut</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_cut&#39;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
<span class="n">mode</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;mode&#39;</span><span class="p">,</span> <span class="s1">&#39;sets&#39;</span><span class="p">)</span>
<span class="n">fsets</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">fset</span> <span class="ow">in</span> <span class="n">cluster</span><span class="o">.</span><span class="n">knn</span><span class="p">(</span><span class="n">data_point</span><span class="p">):</span>
<span class="k">for</span> <span class="n">fset</span> <span class="ow">in</span> <span class="n">cluster</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s1">&#39;name&#39;</span><span class="p">):</span>
<span class="k">if</span> <span class="n">cluster</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data_point</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">alpha_cut</span><span class="p">:</span>
<span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;sets&#39;</span><span class="p">:</span>
<span class="n">fsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fset</span><span class="p">)</span>

1
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@ -119,7 +119,6 @@
<div class="viewcode-block" id="MVFTS.format_data"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.format_data">[docs]</a> <span class="k">def</span> <span class="nf">format_data</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">ndata</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">:</span>
<span class="c1">#ndata[var.name] = data[var.data_label]</span>
<span class="n">ndata</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">extractor</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">])</span>
<span class="k">return</span> <span class="n">ndata</span></div>

2
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@ -141,7 +141,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
<div class="viewcode-block" id="ExponentialyWeightedFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFTS.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="n">tmpdata</span> <span class="o">=</span> <span class="n">FuzzySet</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">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</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">mode</span><span class="o">=</span><span class="s1">&#39;sets&#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">method</span><span class="o">=</span><span class="s1">&#39;maximum&#39;</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">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="bp">self</span><span class="o">.</span><span class="n">c</span><span class="p">)</span></div>

6
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View File

@ -93,9 +93,9 @@
<span class="n">day_of_month</span> <span class="o">=</span> <span class="mi">30</span>
<span class="n">day_of_year</span> <span class="o">=</span> <span class="mi">364</span>
<span class="n">day_of_week</span> <span class="o">=</span> <span class="mi">7</span>
<span class="n">hour</span> <span class="o">=</span> <span class="mi">6</span>
<span class="n">minute</span> <span class="o">=</span> <span class="mi">7</span>
<span class="n">second</span> <span class="o">=</span> <span class="mi">8</span>
<span class="n">hour</span> <span class="o">=</span> <span class="mi">24</span>
<span class="n">minute</span> <span class="o">=</span> <span class="mi">60</span>
<span class="n">second</span> <span class="o">=</span> <span class="mi">60</span>
<span class="n">hour_of_day</span> <span class="o">=</span> <span class="mi">24</span>
<span class="n">hour_of_week</span> <span class="o">=</span> <span class="mi">168</span>
<span class="n">hour_of_month</span> <span class="o">=</span> <span class="mi">744</span>

60
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@ -78,6 +78,7 @@
<span class="kn">from</span> <span class="nn">pyFTS.models.seasonal.common</span> <span class="k">import</span> <span class="n">DateTime</span><span class="p">,</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">strip_datepart</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">matplotlib.pylab</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">scipy.spatial</span> <span class="k">import</span> <span class="n">KDTree</span>
<div class="viewcode-block" id="TimeGridPartitioner"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner">[docs]</a><span class="k">class</span> <span class="nc">TimeGridPartitioner</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">Partitioner</span><span class="p">):</span>
@ -130,6 +131,8 @@
<span class="n">partlen</span> <span class="o">=</span> <span class="n">dlen</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">season</span> <span class="o">==</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">day_of_week</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max</span><span class="p">,</span> <span class="n">partlen</span><span class="p">,</span> <span class="n">pl2</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">season</span> <span class="o">==</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">minute</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max</span><span class="p">,</span> <span class="n">partlen</span><span class="p">,</span> <span class="n">pl2</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">season</span> <span class="o">==</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">hour</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max</span><span class="p">,</span> <span class="n">partlen</span><span class="p">,</span> <span class="n">pl2</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">24</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">season</span> <span class="o">==</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">month</span><span class="p">:</span>
@ -148,7 +151,7 @@
<span class="n">tmp</span> <span class="o">=</span> <span class="n">Composite</span><span class="p">(</span><span class="n">set_name</span><span class="p">,</span> <span class="n">superset</span><span class="o">=</span><span class="kc">True</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">append_set</span><span class="p">(</span><span class="n">FuzzySet</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">season</span><span class="p">,</span> <span class="n">set_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">,</span>
<span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">season</span><span class="o">.</span><span class="n">value</span> <span class="o">-</span> <span class="n">pl2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">season</span><span class="o">.</span><span class="n">value</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">season</span><span class="o">.</span><span class="n">value</span> <span class="o">+</span> <span class="mf">0.0000001</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">season</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=.</span><span class="mi">5</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">season</span><span class="o">.</span><span class="n">value</span> <span class="o">+</span> <span class="n">pl2</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">season</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</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">append_set</span><span class="p">(</span><span class="n">FuzzySet</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">season</span><span class="p">,</span> <span class="n">set_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">,</span>
<span class="p">[</span><span class="n">c</span> <span class="o">-</span> <span class="n">partlen</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">c</span> <span class="o">+</span> <span class="n">partlen</span><span class="p">],</span> <span class="n">c</span><span class="p">,</span>
@ -158,8 +161,8 @@
<span class="k">elif</span> <span class="n">c</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">max</span> <span class="o">-</span> <span class="n">partlen</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">Composite</span><span class="p">(</span><span class="n">set_name</span><span class="p">,</span> <span class="n">superset</span><span class="o">=</span><span class="kc">True</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">append_set</span><span class="p">(</span><span class="n">FuzzySet</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">season</span><span class="p">,</span> <span class="n">set_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">,</span>
<span class="p">[</span><span class="mf">0.0000001</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span>
<span class="n">pl2</span><span class="p">],</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=.</span><span class="mi">5</span><span class="p">,</span>
<span class="p">[</span><span class="o">-</span><span class="n">pl2</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span>
<span class="n">pl2</span><span class="p">],</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</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">append_set</span><span class="p">(</span><span class="n">FuzzySet</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">season</span><span class="p">,</span> <span class="n">set_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">,</span>
<span class="p">[</span><span class="n">c</span> <span class="o">-</span> <span class="n">partlen</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">c</span> <span class="o">+</span> <span class="n">partlen</span><span class="p">],</span> <span class="n">c</span><span class="p">,</span>
@ -196,6 +199,57 @@
<span class="k">return</span> <span class="n">sets</span></div>
<div class="viewcode-block" id="TimeGridPartitioner.build_index"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.build_index">[docs]</a> <span class="k">def</span> <span class="nf">build_index</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">points</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">fset</span> <span class="o">=</span> <span class="bp">self</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">ordered_sets</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
<span class="n">points</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">fset</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">fset</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span><span class="p">,</span> <span class="n">fset</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span><span class="p">])</span>
<span class="k">for</span> <span class="n">ct</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">ordered_sets</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
<span class="n">fset</span> <span class="o">=</span> <span class="bp">self</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="n">points</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">fset</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">fset</span><span class="o">.</span><span class="n">centroid</span><span class="p">,</span> <span class="n">fset</span><span class="o">.</span><span class="n">upper</span><span class="p">])</span>
<span class="n">fset</span> <span class="o">=</span> <span class="bp">self</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">ordered_sets</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
<span class="n">points</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">fset</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">fset</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span><span class="p">,</span> <span class="n">fset</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span><span class="p">])</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="n">sys</span><span class="o">.</span><span class="n">setrecursionlimit</span><span class="p">(</span><span class="mi">100000</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="n">KDTree</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
<span class="n">sys</span><span class="o">.</span><span class="n">setrecursionlimit</span><span class="p">(</span><span class="mi">1000</span><span class="p">)</span></div>
<div class="viewcode-block" id="TimeGridPartitioner.search"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.search">[docs]</a> <span class="k">def</span> <span class="nf">search</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="sd">&#39;&#39;&#39;</span>
<span class="sd"> Perform a search for the nearest fuzzy sets of the point &#39;data&#39;. This function were designed to work with several</span>
<span class="sd"> overlapped fuzzy sets.</span>
<span class="sd"> :param data: the value to search for the nearest fuzzy sets</span>
<span class="sd"> :param type: the return type: &#39;index&#39; for the fuzzy set indexes or &#39;name&#39; for fuzzy set names.</span>
<span class="sd"> :param results: the number of nearest fuzzy sets to return</span>
<span class="sd"> :return: a list with the nearest fuzzy sets</span>
<span class="sd"> &#39;&#39;&#39;</span>
<span class="nb">type</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;type&#39;</span><span class="p">,</span><span class="s1">&#39;index&#39;</span><span class="p">)</span>
<span class="n">results</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;results&#39;</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</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">build_index</span><span class="p">()</span>
<span class="n">_</span><span class="p">,</span> <span class="n">ix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">query</span><span class="p">([</span><span class="n">data</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">data</span><span class="p">],</span> <span class="n">results</span><span class="p">)</span>
<span class="n">ix</span> <span class="o">=</span> <span class="n">ix</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
<span class="k">if</span> <span class="mi">0</span> <span class="ow">in</span> <span class="n">ix</span><span class="p">:</span>
<span class="n">ix</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span><span class="o">-</span><span class="mi">1</span> <span class="ow">in</span> <span class="n">ix</span><span class="p">:</span>
<span class="n">ix</span><span class="o">.</span><span class="n">insert</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">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;name&#39;</span><span class="p">:</span>
<span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">ordered_sets</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="nb">sorted</span><span class="p">(</span><span class="n">ix</span><span class="p">)]</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">ix</span><span class="p">)</span></div>
<div class="viewcode-block" id="TimeGridPartitioner.plot"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.plot">[docs]</a> <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>

2
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@ -124,7 +124,7 @@
<div class="viewcode-block" id="ConventionalFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.song.ConventionalFTS.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="n">tmpdata</span> <span class="o">=</span> <span class="n">FuzzySet</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">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</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">mode</span><span class="o">=</span><span class="s1">&#39;sets&#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">method</span><span class="o">=</span><span class="s1">&#39;maximum&#39;</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">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_non_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">operation_matrix</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>

2
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View File

@ -133,7 +133,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
<div class="viewcode-block" id="WeightedFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.yu.WeightedFTS.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">tmpdata</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</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">mode</span><span class="o">=</span><span class="s1">&#39;sets&#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">ndata</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">mode</span><span class="o">=</span><span class="s1">&#39;sets&#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></div>

140
docs/build/html/_modules/pyFTS/partitioners/partitioner.html vendored
View File

@ -74,6 +74,7 @@
<h1>Source code for pyFTS.partitioners.partitioner</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">Membership</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy.spatial</span> <span class="k">import</span> <span class="n">KDTree</span>
<span class="kn">import</span> <span class="nn">matplotlib.pylab</span> <span class="k">as</span> <span class="nn">plt</span>
@ -108,6 +109,8 @@
<span class="sd">&quot;&quot;&quot;Anonymous function used to extract a single primitive type from an object instance&quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">ordered_sets</span> <span class="o">=</span> <span class="kc">None</span>
<span class="sd">&quot;&quot;&quot;A ordered list of the fuzzy sets names, sorted by their middle point&quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="kc">None</span>
<span class="sd">&quot;&quot;&quot;A spatial index to help in fuzzyfication&quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;preprocess&#39;</span><span class="p">,</span><span class="kc">True</span><span class="p">):</span>
@ -179,8 +182,112 @@
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</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">ordered_sets</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span></div>
<div class="viewcode-block" id="Partitioner.build_index"><a class="viewcode-back" href="../../../pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.build_index">[docs]</a> <span class="k">def</span> <span class="nf">build_index</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">points</span> <span class="o">=</span> <span class="p">[]</span>
<span class="c1">#self.index = {}</span>
<span class="k">for</span> <span class="n">ct</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">ordered_sets</span><span class="p">):</span>
<span class="n">fset</span> <span class="o">=</span> <span class="bp">self</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="n">points</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">fset</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">fset</span><span class="o">.</span><span class="n">centroid</span><span class="p">,</span> <span class="n">fset</span><span class="o">.</span><span class="n">upper</span><span class="p">])</span>
<span class="c1">#self.index[ct] = fset.name</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="n">sys</span><span class="o">.</span><span class="n">setrecursionlimit</span><span class="p">(</span><span class="mi">100000</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="n">KDTree</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
<span class="n">sys</span><span class="o">.</span><span class="n">setrecursionlimit</span><span class="p">(</span><span class="mi">1000</span><span class="p">)</span></div>
<div class="viewcode-block" id="Partitioner.fuzzyfy"><a class="viewcode-back" href="../../../pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.fuzzyfy">[docs]</a> <span class="k">def</span> <span class="nf">fuzzyfy</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="k">return</span> <span class="n">FuzzySet</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="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fuzzyfy the input data according to this partitioner fuzzy sets.</span>
<span class="sd"> :param data: input value to be fuzzyfied</span>
<span class="sd"> :keyword alpha_cut: the minimal membership value to be considered on fuzzyfication (only for mode=&#39;sets&#39;)</span>
<span class="sd"> :keyword method: the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership)</span>
<span class="sd"> :keyword mode: the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership</span>
<span class="sd"> values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )</span>
<span class="sd"> :returns a list with the fuzzyfied values, depending on the mode</span>
<span class="sd"> &quot;&quot;&quot;</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="nb">list</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="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">inst</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
<span class="n">mv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">inst</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">mv</span><span class="p">)</span>
<span class="k">return</span> <span class="n">ret</span>
<span class="n">alpha_cut</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_cut&#39;</span><span class="p">,</span> <span class="mf">0.</span><span class="p">)</span>
<span class="n">mode</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;mode&#39;</span><span class="p">,</span> <span class="s1">&#39;sets&#39;</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;fuzzy&#39;</span><span class="p">)</span>
<span class="n">nearest</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s1">&#39;index&#39;</span><span class="p">)</span>
<span class="n">mv</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="bp">self</span><span class="o">.</span><span class="n">partitions</span><span class="p">)</span>
<span class="k">for</span> <span class="n">ix</span> <span class="ow">in</span> <span class="n">nearest</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="p">[</span><span class="n">ix</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">mv</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span> <span class="k">if</span> <span class="n">tmp</span> <span class="o">&gt;=</span> <span class="n">alpha_cut</span> <span class="k">else</span> <span class="mf">0.</span>
<span class="n">ix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">mv</span> <span class="o">&gt;</span> <span class="mf">0.</span><span class="p">))</span>
<span class="k">if</span> <span class="n">ix</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">mv</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">check_bounds</span><span class="p">(</span><span class="n">data</span><span class="p">)]</span> <span class="o">=</span> <span class="mf">1.</span>
<span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;fuzzy&#39;</span> <span class="ow">and</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;vector&#39;</span><span class="p">:</span>
<span class="k">return</span> <span class="n">mv</span>
<span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;fuzzy&#39;</span> <span class="ow">and</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;sets&#39;</span><span class="p">:</span>
<span class="n">ix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">mv</span> <span class="o">&gt;</span> <span class="mf">0.</span><span class="p">))</span>
<span class="n">sets</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ix</span><span class="p">]</span>
<span class="k">return</span> <span class="n">sets</span>
<span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;maximum&#39;</span> <span class="ow">and</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;sets&#39;</span><span class="p">:</span>
<span class="n">mx</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
<span class="n">ix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">mv</span> <span class="o">==</span> <span class="n">mx</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
<span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;both&#39;</span><span class="p">:</span>
<span class="n">ix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">mv</span> <span class="o">&gt;</span> <span class="mf">0.</span><span class="p">))</span>
<span class="n">sets</span> <span class="o">=</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mv</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ix</span><span class="p">]</span>
<span class="k">return</span> <span class="n">sets</span></div>
<div class="viewcode-block" id="Partitioner.check_bounds"><a class="viewcode-back" href="../../../pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.check_bounds">[docs]</a> <span class="k">def</span> <span class="nf">check_bounds</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="sd">&#39;&#39;&#39;</span>
<span class="sd"> Check if the input data is outside the known Universe of Discourse and, if it is, round it to the closest</span>
<span class="sd"> fuzzy set.</span>
<span class="sd"> :param data: input data to be verified</span>
<span class="sd"> :return: the index of the closest fuzzy set when data is outside de universe of discourse or None if</span>
<span class="sd"> the data is inside the UoD.</span>
<span class="sd"> &#39;&#39;&#39;</span>
<span class="k">if</span> <span class="n">data</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">min</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">0</span>
<span class="k">elif</span> <span class="n">data</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span><span class="o">-</span><span class="mi">1</span></div>
<div class="viewcode-block" id="Partitioner.search"><a class="viewcode-back" href="../../../pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.search">[docs]</a> <span class="k">def</span> <span class="nf">search</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="sd">&#39;&#39;&#39;</span>
<span class="sd"> Perform a search for the nearest fuzzy sets of the point &#39;data&#39;. This function were designed to work with several</span>
<span class="sd"> overlapped fuzzy sets.</span>
<span class="sd"> :param data: the value to search for the nearest fuzzy sets</span>
<span class="sd"> :param type: the return type: &#39;index&#39; for the fuzzy set indexes or &#39;name&#39; for fuzzy set names.</span>
<span class="sd"> :param results: the number of nearest fuzzy sets to return</span>
<span class="sd"> :return: a list with the nearest fuzzy sets</span>
<span class="sd"> &#39;&#39;&#39;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</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">build_index</span><span class="p">()</span>
<span class="nb">type</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;type&#39;</span><span class="p">,</span><span class="s1">&#39;index&#39;</span><span class="p">)</span>
<span class="n">results</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;results&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="n">ix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">query</span><span class="p">([</span><span class="n">data</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">data</span><span class="p">],</span> <span class="n">results</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;name&#39;</span><span class="p">:</span>
<span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">ordered_sets</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="nb">sorted</span><span class="p">(</span><span class="n">ix</span><span class="p">)]</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">ix</span><span class="p">)</span></div>
<div class="viewcode-block" id="Partitioner.plot"><a class="viewcode-back" href="../../../pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.plot">[docs]</a> <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">rounding</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
@ -240,7 +347,36 @@
<span class="sd"> :return: number of partitions</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span></div>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span>
<span class="k">def</span> <span class="nf">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">item</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return a fuzzy set by its order or its name.</span>
<span class="sd"> :param item: If item is an integer then it represents the fuzzy set index (order), if it was a string then</span>
<span class="sd"> it represents the fuzzy set name.</span>
<span class="sd"> :return: the fuzzy set</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">item</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">int</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">int8</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">int16</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)):</span>
<span class="k">if</span> <span class="n">item</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">item</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The fuzzy set index must be between 0 and </span><span class="si">{}</span><span class="s2">.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitions</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</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">ordered_sets</span><span class="p">[</span><span class="n">item</span><span class="p">]]</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">item</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="k">if</span> <span class="n">item</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The fuzzy set with name </span><span class="si">{}</span><span class="s2"> does not exist.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">item</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">item</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;The parameter &#39;item&#39; must be an integer or a string and the value informed was </span><span class="si">{}</span><span class="s2"> of type </span><span class="si">{}</span><span class="s2">!&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">item</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">item</span><span class="p">)))</span>
<span class="k">def</span> <span class="nf">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Iterate over the fuzzy sets, ordered by its midpoints.</span>
<span class="sd"> :return: An iterator over the fuzzy sets.</span>
<span class="sd"> &quot;&quot;&quot;</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">ordered_sets</span><span class="p">:</span>
<span class="k">yield</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span></div>
</pre></div>
</div>

24
docs/build/html/_sources/pyFTS.models.multivariate.rst.txt vendored
View File

@ -45,6 +45,22 @@ pyFTS.models.multivariate.flrg module
:undoc-members:
:show-inheritance:
pyFTS.models.multivariate.partitioner module
---------------------------------------------
.. automodule:: pyFTS.models.multivariate.partitioner
:members:
:undoc-members:
:show-inheritance:
pyFTS.models.multivariate.grid module
------------------------------------------
.. automodule:: pyFTS.models.multivariate.grid
:members:
:undoc-members:
:show-inheritance:
pyFTS.models.multivariate.mvfts module
--------------------------------------
@ -69,3 +85,11 @@ pyFTS.models.multivariate.cmvfts module
:undoc-members:
:show-inheritance:
pyFTS.models.multivariate.granular module
---------------------------------------------
.. automodule:: pyFTS.models.multivariate.granular
:members:
:undoc-members:
:show-inheritance:

76
docs/build/html/genindex.html vendored
View File

@ -154,6 +154,8 @@
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.append_lhs">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG method)</a>
</li>
</ul></li>
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.append_log">append_log() (pyFTS.common.fts.FTS method)</a>
</li>
<li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.append_model">append_model() (pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
</li>
<li><a href="pyFTS.common.html#pyFTS.common.flrg.FLRG.append_rhs">append_rhs() (pyFTS.common.flrg.FLRG method)</a>
@ -278,11 +280,11 @@
</li>
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.brier_score">brier_score() (in module pyFTS.benchmarks.Measures)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.build">build() (pyFTS.models.multivariate.variable.Variable method)</a>
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.grid.GridCluster.build">build() (pyFTS.models.multivariate.grid.GridCluster method)</a>
<ul>
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.build">(pyFTS.models.multivariate.variable.Variable method)</a>
</li>
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner.build">(pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner method)</a>
</li>
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.partitioners.SimpleNonStationaryPartitioner.build">(pyFTS.models.nonstationary.partitioners.SimpleNonStationaryPartitioner method)</a>
@ -304,8 +306,16 @@
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.build">(pyFTS.partitioners.partitioner.Partitioner method)</a>
</li>
</ul></li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.build_cdf_qtl">build_cdf_qtl() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
</li>
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.build_index">build_index() (pyFTS.models.seasonal.partitioner.TimeGridPartitioner method)</a>
<ul>
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.build_index">(pyFTS.partitioners.partitioner.Partitioner method)</a>
</li>
</ul></li>
<li><a href="pyFTS.common.html#pyFTS.common.tree.build_tree_without_order">build_tree_without_order() (in module pyFTS.common.tree)</a>
</li>
</ul></td>
@ -330,6 +340,8 @@
<ul>
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.check_bounds">(in module pyFTS.models.nonstationary.common)</a>
</li>
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.check_bounds">(pyFTS.partitioners.partitioner.Partitioner method)</a>
</li>
</ul></li>
<li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.check_bounds_index">check_bounds_index() (in module pyFTS.common.FuzzySet)</a>
@ -633,7 +645,11 @@
</li>
</ul></li>
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_multivariate">forecast_ahead_multivariate() (pyFTS.common.fts.FTS method)</a>
<ul>
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_multivariate">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
</li>
</ul></li>
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_distribution">forecast_distribution() (pyFTS.benchmarks.arima.ARIMA method)</a>
<ul>
@ -710,6 +726,8 @@
<ul>
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fuzzyfy">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
</li>
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.grid.IncrementalGridCluster.fuzzyfy">(pyFTS.models.multivariate.grid.IncrementalGridCluster method)</a>
</li>
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.fuzzyfy">(pyFTS.partitioners.partitioner.Partitioner method)</a>
</li>
@ -1091,6 +1109,10 @@
<li><a href="pyFTS.common.html#pyFTS.common.tree.FLRGTreeNode.getStr">getStr() (pyFTS.common.tree.FLRGTreeNode method)</a>
</li>
<li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.grant_bounds">grant_bounds() (in module pyFTS.common.FuzzySet)</a>
</li>
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.granular.GranularWMVFTS">GranularWMVFTS (class in pyFTS.models.multivariate.granular)</a>
</li>
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.grid.GridCluster">GridCluster (class in pyFTS.models.multivariate.grid)</a>
</li>
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Grid.GridPartitioner">GridPartitioner (class in pyFTS.partitioners.Grid)</a>
</li>
@ -1132,7 +1154,7 @@
</li>
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS">HighOrderNonStationaryFTS (class in pyFTS.models.nonstationary.honsfts)</a>
</li>
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.hour_of_day">hour_of_day (pyFTS.models.seasonal.common.DateTime attribute)</a>
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.hour">hour (pyFTS.models.seasonal.common.DateTime attribute)</a>
</li>
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.hour_of_month">hour_of_month (pyFTS.models.seasonal.common.DateTime attribute)</a>
</li>
@ -1153,8 +1175,12 @@
<li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS">ImprovedWeightedFTS (class in pyFTS.models.ismailefendi)</a>
</li>
<li><a href="pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.incremental_gaussian">incremental_gaussian() (pyFTS.data.artificial.SignalEmulator method)</a>
</li>
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.grid.IncrementalGridCluster.incremental_search">incremental_search() (pyFTS.models.multivariate.grid.IncrementalGridCluster method)</a>
</li>
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS">IncrementalEnsembleFTS (class in pyFTS.models.incremental.IncrementalEnsemble)</a>
</li>
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.grid.IncrementalGridCluster">IncrementalGridCluster (class in pyFTS.models.multivariate.grid)</a>
</li>
<li><a href="pyFTS.common.html#pyFTS.common.FLR.IndexedFLR.index">index (pyFTS.common.FLR.IndexedFLR attribute)</a>
</li>
@ -1175,11 +1201,11 @@
<li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.insert_right">insert_right() (pyFTS.common.SortedCollection.SortedCollection method)</a>
</li>
<li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.inside">inside() (pyFTS.common.SortedCollection.SortedCollection method)</a>
</li>
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.interval_dataframe_analytic_columns">interval_dataframe_analytic_columns() (in module pyFTS.benchmarks.Util)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.interval_dataframe_analytic_columns">interval_dataframe_analytic_columns() (in module pyFTS.benchmarks.Util)</a>
</li>
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.interval_dataframe_synthetic_columns">interval_dataframe_synthetic_columns() (in module pyFTS.benchmarks.Util)</a>
</li>
<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_heuristic">interval_heuristic() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
@ -1218,6 +1244,8 @@
<h2 id="K">K</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.kdtree">kdtree (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
</li>
<li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.kde.KernelSmoothing.kernel">kernel (pyFTS.probabilistic.kde.KernelSmoothing attribute)</a>
</li>
<li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.kde.KernelSmoothing.kernel_function">kernel_function() (pyFTS.probabilistic.kde.KernelSmoothing method)</a>
@ -1269,6 +1297,8 @@
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.FuzzySet.location">location (pyFTS.models.nonstationary.common.FuzzySet attribute)</a>
</li>
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.FuzzySet.location_params">location_params (pyFTS.models.nonstationary.common.FuzzySet attribute)</a>
</li>
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.log">log (pyFTS.common.fts.FTS attribute)</a>
</li>
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.lower_set">lower_set() (pyFTS.partitioners.partitioner.Partitioner method)</a>
</li>
@ -1308,15 +1338,15 @@
</li>
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.merge">merge() (pyFTS.common.fts.FTS method)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet.mf">mf (pyFTS.common.FuzzySet.FuzzySet attribute)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.min_order">min_order (pyFTS.common.fts.FTS attribute)</a>
</li>
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.minute_of_day">minute_of_day (pyFTS.models.seasonal.common.DateTime attribute)</a>
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.minute">minute (pyFTS.models.seasonal.common.DateTime attribute)</a>
</li>
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.minute_of_hour">minute_of_hour (pyFTS.models.seasonal.common.DateTime attribute)</a>
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.minute_of_day">minute_of_day (pyFTS.models.seasonal.common.DateTime attribute)</a>
</li>
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.minute_of_month">minute_of_month (pyFTS.models.seasonal.common.DateTime attribute)</a>
</li>
@ -1328,6 +1358,8 @@
<ul>
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.model">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS attribute)</a>
</li>
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.granular.GranularWMVFTS.model">(pyFTS.models.multivariate.granular.GranularWMVFTS attribute)</a>
</li>
</ul></li>
<li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.models">models (pyFTS.models.ensemble.ensemble.EnsembleFTS attribute)</a>
@ -1575,6 +1607,8 @@
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_probabilistic_jobs">process_probabilistic_jobs() (in module pyFTS.benchmarks.benchmarks)</a>
</li>
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.product_dict">product_dict() (in module pyFTS.models.multivariate.mvfts)</a>
</li>
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.grid.IncrementalGridCluster.prune">prune() (pyFTS.models.multivariate.grid.IncrementalGridCluster method)</a>
</li>
<li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.pseudologlikelihood">pseudologlikelihood() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
</li>
@ -1600,10 +1634,10 @@
</li>
<li><a href="pyFTS.common.html#module-pyFTS.common">pyFTS.common (module)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="pyFTS.common.html#module-pyFTS.common.Composite">pyFTS.common.Composite (module)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="pyFTS.common.html#module-pyFTS.common.FLR">pyFTS.common.FLR (module)</a>
</li>
<li><a href="pyFTS.common.html#module-pyFTS.common.flrg">pyFTS.common.flrg (module)</a>
@ -1713,6 +1747,10 @@
<li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.FLR">pyFTS.models.multivariate.FLR (module)</a>
</li>
<li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.flrg">pyFTS.models.multivariate.flrg (module)</a>
</li>
<li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.granular">pyFTS.models.multivariate.granular (module)</a>
</li>
<li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.grid">pyFTS.models.multivariate.grid (module)</a>
</li>
<li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.mvfts">pyFTS.models.multivariate.mvfts (module)</a>
</li>
@ -1874,6 +1912,12 @@
</li>
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner.scale_up">scale_up() (pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner method)</a>
</li>
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.search">search() (pyFTS.models.seasonal.partitioner.TimeGridPartitioner method)</a>
<ul>
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.search">(pyFTS.partitioners.partitioner.Partitioner method)</a>
</li>
</ul></li>
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.season">season (pyFTS.models.seasonal.partitioner.TimeGridPartitioner attribute)</a>
</li>
<li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS">SeasonalEnsembleFTS (class in pyFTS.models.ensemble.multiseasonal)</a>
@ -1883,8 +1927,6 @@
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFTS">SeasonalFTS (class in pyFTS.models.seasonal.sfts)</a>
</li>
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.SeasonalIndexer.SeasonalIndexer">SeasonalIndexer (class in pyFTS.models.seasonal.SeasonalIndexer)</a>
</li>
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.second">second (pyFTS.models.seasonal.common.DateTime attribute)</a>
</li>
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.second_of_day">second_of_day (pyFTS.models.seasonal.common.DateTime attribute)</a>
</li>
@ -1915,11 +1957,11 @@
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.common.MultivariateFuzzySet.set_target_variable">set_target_variable() (pyFTS.models.multivariate.common.MultivariateFuzzySet method)</a>
</li>
<li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.set_transformations">set_transformations() (pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS method)</a>
</li>
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.setnames">setnames (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.setnames">setnames (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
</li>
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.sets">sets (pyFTS.common.fts.FTS attribute)</a>
</li>
<li><a href="pyFTS.distributed.html#pyFTS.distributed.spark.share_parameters">share_parameters() (in module pyFTS.distributed.spark)</a>
@ -2020,6 +2062,8 @@
<li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.train">(pyFTS.models.ismailefendi.ImprovedWeightedFTS method)</a>
</li>
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.train">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
</li>
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.granular.GranularWMVFTS.train">(pyFTS.models.multivariate.granular.GranularWMVFTS method)</a>
</li>
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.train">(pyFTS.models.multivariate.mvfts.MVFTS method)</a>
</li>

BIN
docs/build/html/objects.inv vendored
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10
docs/build/html/py-modindex.html vendored
View File

@ -420,6 +420,16 @@
<td>&#160;&#160;&#160;
<a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.flrg"><code class="xref">pyFTS.models.multivariate.flrg</code></a></td><td>
<em></em></td></tr>
<tr class="cg-1">
<td></td>
<td>&#160;&#160;&#160;
<a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.granular"><code class="xref">pyFTS.models.multivariate.granular</code></a></td><td>
<em></em></td></tr>
<tr class="cg-1">
<td></td>
<td>&#160;&#160;&#160;
<a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.grid"><code class="xref">pyFTS.models.multivariate.grid</code></a></td><td>
<em></em></td></tr>
<tr class="cg-1">
<td></td>
<td>&#160;&#160;&#160;

14
docs/build/html/pyFTS.common.html vendored
View File

@ -452,8 +452,8 @@
</tr>
</tbody>
</table>
<p>values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )</p>
<p>:returns a list with the fuzzyfied values, depending on the mode</p>
<p>values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )
:returns a list with the fuzzyfied values, depending on the mode</p>
</dd></dl>
<dl class="function">
@ -1554,6 +1554,11 @@ when the LHS pattern is identified on time t.</p>
<dd><p>A float with the minimal membership to be considered on fuzzyfication process</p>
</dd></dl>
<dl class="method">
<dt id="pyFTS.common.fts.FTS.append_log">
<code class="descname">append_log</code><span class="sig-paren">(</span><em>operation</em>, <em>value</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.append_log"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.append_log" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="pyFTS.common.fts.FTS.append_rule">
<code class="descname">append_rule</code><span class="sig-paren">(</span><em>flrg</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.append_rule"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.append_rule" title="Permalink to this definition"></a></dt>
@ -1951,6 +1956,11 @@ a monovariate method, default: False</p>
</table>
</dd></dl>
<dl class="attribute">
<dt id="pyFTS.common.fts.FTS.log">
<code class="descname">log</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.common.fts.FTS.log" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="attribute">
<dt id="pyFTS.common.fts.FTS.max_lag">
<code class="descname">max_lag</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.common.fts.FTS.max_lag" title="Permalink to this definition"></a></dt>

2
docs/build/html/pyFTS.html vendored
View File

@ -204,9 +204,11 @@
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.common">pyFTS.models.multivariate.common module</a></li>
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.variable">pyFTS.models.multivariate.variable module</a></li>
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.flrg">pyFTS.models.multivariate.flrg module</a></li>
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#pyfts-models-multivariate-partitioner-module">pyFTS.models.multivariate.partitioner module</a></li>
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.mvfts">pyFTS.models.multivariate.mvfts module</a></li>
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.wmvfts">pyFTS.models.multivariate.wmvfts module</a></li>
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.cmvfts">pyFTS.models.multivariate.cmvfts module</a></li>
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.granular">pyFTS.models.multivariate.granular module</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="pyFTS.models.nonstationary.html">pyFTS.models.nonstationary package</a><ul>

2
docs/build/html/pyFTS.models.html vendored
View File

@ -144,9 +144,11 @@
<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.common">pyFTS.models.multivariate.common module</a></li>
<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.variable">pyFTS.models.multivariate.variable module</a></li>
<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.flrg">pyFTS.models.multivariate.flrg module</a></li>
<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#pyfts-models-multivariate-partitioner-module">pyFTS.models.multivariate.partitioner module</a></li>
<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.mvfts">pyFTS.models.multivariate.mvfts module</a></li>
<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.wmvfts">pyFTS.models.multivariate.wmvfts module</a></li>
<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.cmvfts">pyFTS.models.multivariate.cmvfts module</a></li>
<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.granular">pyFTS.models.multivariate.granular module</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="pyFTS.models.nonstationary.html">pyFTS.models.nonstationary package</a><ul>

127
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@ -70,9 +70,11 @@
<li><a class="reference internal" href="#module-pyFTS.models.multivariate.common">pyFTS.models.multivariate.common module</a></li>
<li><a class="reference internal" href="#module-pyFTS.models.multivariate.variable">pyFTS.models.multivariate.variable module</a></li>
<li><a class="reference internal" href="#module-pyFTS.models.multivariate.flrg">pyFTS.models.multivariate.flrg module</a></li>
<li><a class="reference internal" href="#pyfts-models-multivariate-partitioner-module">pyFTS.models.multivariate.partitioner module</a></li>
<li><a class="reference internal" href="#module-pyFTS.models.multivariate.mvfts">pyFTS.models.multivariate.mvfts module</a></li>
<li><a class="reference internal" href="#module-pyFTS.models.multivariate.wmvfts">pyFTS.models.multivariate.wmvfts module</a></li>
<li><a class="reference internal" href="#module-pyFTS.models.multivariate.cmvfts">pyFTS.models.multivariate.cmvfts module</a></li>
<li><a class="reference internal" href="#module-pyFTS.models.multivariate.granular">pyFTS.models.multivariate.granular module</a></li>
</ul>
</li>
</ul>
@ -354,6 +356,73 @@ transformations and partitioners.</p>
</dd></dl>
</div>
<div class="section" id="pyfts-models-multivariate-partitioner-module">
<h2>pyFTS.models.multivariate.partitioner module<a class="headerlink" href="#pyfts-models-multivariate-partitioner-module" title="Permalink to this headline"></a></h2>
<hr class="docutils" />
<span class="target" id="module-pyFTS.models.multivariate.grid"></span><dl class="class">
<dt id="pyFTS.models.multivariate.grid.GridCluster">
<em class="property">class </em><code class="descclassname">pyFTS.models.multivariate.grid.</code><code class="descname">GridCluster</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/grid.html#GridCluster"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.grid.GridCluster" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.multivariate.partitioner.MultivariatePartitioner</span></code></p>
<p>A cartesian product of all fuzzy sets of all variables</p>
<dl class="method">
<dt id="pyFTS.models.multivariate.grid.GridCluster.build">
<code class="descname">build</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/grid.html#GridCluster.build"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.grid.GridCluster.build" title="Permalink to this definition"></a></dt>
<dd><p>Perform the partitioning of the Universe of Discourse</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>data</strong> – training data</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="pyFTS.models.multivariate.grid.IncrementalGridCluster">
<em class="property">class </em><code class="descclassname">pyFTS.models.multivariate.grid.</code><code class="descname">IncrementalGridCluster</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/grid.html#IncrementalGridCluster"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.grid.IncrementalGridCluster" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.multivariate.partitioner.MultivariatePartitioner</span></code></p>
<p>Create combinations of fuzzy sets of the variables on demand, incrementally increasing the
multivariate fuzzy set base.</p>
<dl class="method">
<dt id="pyFTS.models.multivariate.grid.IncrementalGridCluster.fuzzyfy">
<code class="descname">fuzzyfy</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/grid.html#IncrementalGridCluster.fuzzyfy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.grid.IncrementalGridCluster.fuzzyfy" title="Permalink to this definition"></a></dt>
<dd><p>Fuzzyfy the input data according to this partitioner fuzzy sets.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data</strong> – input value to be fuzzyfied</li>
<li><strong>alpha_cut</strong> – the minimal membership value to be considered on fuzzyfication (only for mode=’sets’)</li>
<li><strong>method</strong> – the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership)</li>
<li><strong>mode</strong> – the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )</p>
<p>:returns a list with the fuzzyfied values, depending on the mode</p>
</dd></dl>
<dl class="method">
<dt id="pyFTS.models.multivariate.grid.IncrementalGridCluster.incremental_search">
<code class="descname">incremental_search</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/grid.html#IncrementalGridCluster.incremental_search"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.grid.IncrementalGridCluster.incremental_search" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="pyFTS.models.multivariate.grid.IncrementalGridCluster.prune">
<code class="descname">prune</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/grid.html#IncrementalGridCluster.prune"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.grid.IncrementalGridCluster.prune" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<div class="section" id="module-pyFTS.models.multivariate.mvfts">
<span id="pyfts-models-multivariate-mvfts-module"></span><h2>pyFTS.models.multivariate.mvfts module<a class="headerlink" href="#module-pyFTS.models.multivariate.mvfts" title="Permalink to this headline"></a></h2>
@ -614,7 +683,7 @@ transformations and partitioners.</p>
<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS">
<em class="property">class </em><code class="descclassname">pyFTS.models.multivariate.cmvfts.</code><code class="descname">ClusteredMVFTS</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.multivariate.mvfts.MVFTS" title="pyFTS.models.multivariate.mvfts.MVFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.multivariate.mvfts.MVFTS</span></code></a></p>
<p>Meta model for multivariate, high order, clustered multivariate FTS</p>
<p>Meta model for high order, clustered multivariate FTS</p>
<dl class="method">
<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.check_data">
<code class="descname">check_data</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS.check_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.check_data" title="Permalink to this definition"></a></dt>
@ -641,6 +710,28 @@ transformations and partitioners.</p>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_multivariate">
<code class="descname">forecast_ahead_multivariate</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS.forecast_ahead_multivariate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_multivariate" title="Permalink to this definition"></a></dt>
<dd><p>Multivariate forecast n step ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model</li>
<li><strong>steps</strong> – the number of steps ahead to forecast</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a Pandas Dataframe object representing the forecasted values for each variable</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_multivariate">
<code class="descname">forecast_multivariate</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS.forecast_multivariate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_multivariate" title="Permalink to this definition"></a></dt>
@ -705,6 +796,40 @@ transformations and partitioners.</p>
</dd></dl>
</div>
<div class="section" id="module-pyFTS.models.multivariate.granular">
<span id="pyfts-models-multivariate-granular-module"></span><h2>pyFTS.models.multivariate.granular module<a class="headerlink" href="#module-pyFTS.models.multivariate.granular" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt id="pyFTS.models.multivariate.granular.GranularWMVFTS">
<em class="property">class </em><code class="descclassname">pyFTS.models.multivariate.granular.</code><code class="descname">GranularWMVFTS</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/granular.html#GranularWMVFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.granular.GranularWMVFTS" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS" title="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.multivariate.cmvfts.ClusteredMVFTS</span></code></a></p>
<p>Granular multivariate weighted high order FTS</p>
<dl class="attribute">
<dt id="pyFTS.models.multivariate.granular.GranularWMVFTS.model">
<code class="descname">model</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.multivariate.granular.GranularWMVFTS.model" title="Permalink to this definition"></a></dt>
<dd><p>The most recent trained model</p>
</dd></dl>
<dl class="method">
<dt id="pyFTS.models.multivariate.granular.GranularWMVFTS.train">
<code class="descname">train</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/granular.html#GranularWMVFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.granular.GranularWMVFTS.train" title="Permalink to this definition"></a></dt>
<dd><p>Method specific parameter fitting</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data</strong> – training time series data</li>
<li><strong>kwargs</strong> – Method specific parameters</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
</div>

45
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View File

@ -381,8 +381,8 @@
<dd></dd></dl>
<dl class="attribute">
<dt id="pyFTS.models.seasonal.common.DateTime.hour_of_day">
<code class="descname">hour_of_day</code><em class="property"> = 24</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.hour_of_day" title="Permalink to this definition"></a></dt>
<dt id="pyFTS.models.seasonal.common.DateTime.hour">
<code class="descname">hour</code><em class="property"> = 24</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.hour" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="attribute">
@ -401,13 +401,13 @@
<dd></dd></dl>
<dl class="attribute">
<dt id="pyFTS.models.seasonal.common.DateTime.minute_of_day">
<code class="descname">minute_of_day</code><em class="property"> = 1440</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.minute_of_day" title="Permalink to this definition"></a></dt>
<dt id="pyFTS.models.seasonal.common.DateTime.minute">
<code class="descname">minute</code><em class="property"> = 60</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.minute" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="attribute">
<dt id="pyFTS.models.seasonal.common.DateTime.minute_of_hour">
<code class="descname">minute_of_hour</code><em class="property"> = 60</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.minute_of_hour" title="Permalink to this definition"></a></dt>
<dt id="pyFTS.models.seasonal.common.DateTime.minute_of_day">
<code class="descname">minute_of_day</code><em class="property"> = 1440</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.minute_of_day" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="attribute">
@ -435,11 +435,6 @@
<code class="descname">quarter</code><em class="property"> = 4</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.quarter" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="attribute">
<dt id="pyFTS.models.seasonal.common.DateTime.second">
<code class="descname">second</code><em class="property"> = 8</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.second" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="attribute">
<dt id="pyFTS.models.seasonal.common.DateTime.second_of_day">
<code class="descname">second_of_day</code><em class="property"> = 86400</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.second_of_day" title="Permalink to this definition"></a></dt>
@ -600,6 +595,11 @@
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.models.seasonal.partitioner.TimeGridPartitioner.build_index">
<code class="descname">build_index</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/partitioner.html#TimeGridPartitioner.build_index"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.build_index" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="attribute">
<dt id="pyFTS.models.seasonal.partitioner.TimeGridPartitioner.mask">
<code class="descname">mask</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.mask" title="Permalink to this definition"></a></dt>
@ -614,6 +614,29 @@
:return:</p>
</dd></dl>
<dl class="method">
<dt id="pyFTS.models.seasonal.partitioner.TimeGridPartitioner.search">
<code class="descname">search</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/partitioner.html#TimeGridPartitioner.search"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.search" title="Permalink to this definition"></a></dt>
<dd><p>Perform a search for the nearest fuzzy sets of the point ‘data’. This function were designed to work with several
overlapped fuzzy sets.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – the value to search for the nearest fuzzy sets</li>
<li><strong>type</strong> – the return type: ‘index’ for the fuzzy set indexes or ‘name’ for fuzzy set names.</li>
<li><strong>results</strong> – the number of nearest fuzzy sets to return</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the nearest fuzzy sets</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="attribute">
<dt id="pyFTS.models.seasonal.partitioner.TimeGridPartitioner.season">
<code class="descname">season</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.season" title="Permalink to this definition"></a></dt>

71
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@ -144,6 +144,29 @@
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.partitioners.partitioner.Partitioner.build_index">
<code class="descname">build_index</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/partitioner.html#Partitioner.build_index"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.partitioners.partitioner.Partitioner.build_index" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="pyFTS.partitioners.partitioner.Partitioner.check_bounds">
<code class="descname">check_bounds</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/partitioner.html#Partitioner.check_bounds"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.partitioners.partitioner.Partitioner.check_bounds" title="Permalink to this definition"></a></dt>
<dd><p>Check if the input data is outside the known Universe of Discourse and, if it is, round it to the closest
fuzzy set.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>data</strong> – input data to be verified</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">the index of the closest fuzzy set when data is outside de universe of discourse or None if</td>
</tr>
</tbody>
</table>
<p>the data is inside the UoD.</p>
</dd></dl>
<dl class="attribute">
<dt id="pyFTS.partitioners.partitioner.Partitioner.extractor">
<code class="descname">extractor</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.partitioners.partitioner.Partitioner.extractor" title="Permalink to this definition"></a></dt>
@ -153,7 +176,24 @@
<dl class="method">
<dt id="pyFTS.partitioners.partitioner.Partitioner.fuzzyfy">
<code class="descname">fuzzyfy</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/partitioner.html#Partitioner.fuzzyfy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.partitioners.partitioner.Partitioner.fuzzyfy" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dd><p>Fuzzyfy the input data according to this partitioner fuzzy sets.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data</strong> – input value to be fuzzyfied</li>
<li><strong>alpha_cut</strong> – the minimal membership value to be considered on fuzzyfication (only for mode=’sets’)</li>
<li><strong>method</strong> – the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership)</li>
<li><strong>mode</strong> – the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )</p>
<p>:returns a list with the fuzzyfied values, depending on the mode</p>
</dd></dl>
<dl class="method">
<dt id="pyFTS.partitioners.partitioner.Partitioner.get_name">
@ -171,6 +211,12 @@
</table>
</dd></dl>
<dl class="attribute">
<dt id="pyFTS.partitioners.partitioner.Partitioner.kdtree">
<code class="descname">kdtree</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.partitioners.partitioner.Partitioner.kdtree" title="Permalink to this definition"></a></dt>
<dd><p>A spatial index to help in fuzzyfication</p>
</dd></dl>
<dl class="method">
<dt id="pyFTS.partitioners.partitioner.Partitioner.lower_set">
<code class="descname">lower_set</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/partitioner.html#Partitioner.lower_set"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.partitioners.partitioner.Partitioner.lower_set" title="Permalink to this definition"></a></dt>
@ -247,6 +293,29 @@
<dd><p>prefix of auto generated partition names</p>
</dd></dl>
<dl class="method">
<dt id="pyFTS.partitioners.partitioner.Partitioner.search">
<code class="descname">search</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/partitioner.html#Partitioner.search"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.partitioners.partitioner.Partitioner.search" title="Permalink to this definition"></a></dt>
<dd><p>Perform a search for the nearest fuzzy sets of the point ‘data’. This function were designed to work with several
overlapped fuzzy sets.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – the value to search for the nearest fuzzy sets</li>
<li><strong>type</strong> – the return type: ‘index’ for the fuzzy set indexes or ‘name’ for fuzzy set names.</li>
<li><strong>results</strong> – the number of nearest fuzzy sets to return</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the nearest fuzzy sets</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="attribute">
<dt id="pyFTS.partitioners.partitioner.Partitioner.setnames">
<code class="descname">setnames</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.partitioners.partitioner.Partitioner.setnames" title="Permalink to this definition"></a></dt>

2
docs/build/html/searchindex.js vendored
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24
docs/pyFTS.models.multivariate.rst
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@ -45,6 +45,22 @@ pyFTS.models.multivariate.flrg module
:undoc-members:
:show-inheritance:
pyFTS.models.multivariate.partitioner module
---------------------------------------------
.. automodule:: pyFTS.models.multivariate.partitioner
:members:
:undoc-members:
:show-inheritance:
pyFTS.models.multivariate.grid module
------------------------------------------
.. automodule:: pyFTS.models.multivariate.grid
:members:
:undoc-members:
:show-inheritance:
pyFTS.models.multivariate.mvfts module
--------------------------------------
@ -69,3 +85,11 @@ pyFTS.models.multivariate.cmvfts module
:undoc-members:
:show-inheritance:
pyFTS.models.multivariate.granular module
---------------------------------------------
.. automodule:: pyFTS.models.multivariate.granular
:members:
:undoc-members:
:show-inheritance:

5
pyFTS/common/fts.py
View File

@ -71,6 +71,8 @@ class FTS(object):
self.max_lag = self.order
"""A integer indicating the largest lag used by the model. This value also indicates the minimum number of past lags
needed to forecast a single step ahead"""
self.log = pd.DataFrame([],columns=["Datetime","Operation","Value"])
""""""
def fuzzy(self, data):
"""
@ -558,6 +560,9 @@ class FTS(object):
for flrg in self.flrgs.keys():
self.flrgs[flrg].reset_calculated_values()
def append_log(self,operation, value):
pass

21
pyFTS/models/hofts.py
View File

@ -126,6 +126,9 @@ class HighOrderFTS(fts.FTS):
nsample = [self.partitioner.fuzzyfy(k, mode="sets", alpha_cut=self.alpha_cut)
for k in sample]
if explain:
self.append_log("Fuzzyfication","{} -> {}".format(sample, nsample))
return self.generate_lhs_flrg_fuzzyfied(nsample, explain)
def generate_lhs_flrg_fuzzyfied(self, sample, explain=False):
@ -137,7 +140,7 @@ class HighOrderFTS(fts.FTS):
lags.append(lhs)
if explain:
print("\t (Lag {}) {} -> {} \n".format(o, sample[o-1], lhs))
self.append_log("Ordering Lags", "Lag {} Value {}".format(o, lhs))
# Trace the possible paths
for path in product(*lags):
@ -217,17 +220,11 @@ class HighOrderFTS(fts.FTS):
sample = ndata[k - self.max_lag: k]
if explain:
print("Fuzzyfication \n")
if not fuzzyfied:
flrgs = self.generate_lhs_flrg(sample, explain)
else:
flrgs = self.generate_lhs_flrg_fuzzyfied(sample, explain)
if explain:
print("Rules:\n")
midpoints = []
memberships = []
for flrg in flrgs:
@ -240,7 +237,7 @@ class HighOrderFTS(fts.FTS):
memberships.append(mv)
if explain:
print("\t {} -> {} (Naïve)\t Midpoint: {}\n".format(str(flrg.LHS), flrg.LHS[-1],
self.append_log("Rule Matching", "{} -> {} (Naïve) Midpoint: {}".format(str(flrg.LHS), flrg.LHS[-1],
mp))
else:
flrg = self.flrgs[flrg.get_key()]
@ -250,19 +247,17 @@ class HighOrderFTS(fts.FTS):
memberships.append(mv)
if explain:
print("\t {} \t Midpoint: {}\n".format(str(flrg), mp))
print("\t {} \t Membership: {}\n".format(str(flrg), mv))
self.append_log("Rule Matching", "{}, Midpoint: {} Membership: {}".format(flrg.get_key(), mp, mv))
if mode == "mean" or fuzzyfied:
final = np.nanmean(midpoints)
if explain: self.append_log("Deffuzyfication", "By Mean: {}".format(final))
else:
final = np.dot(midpoints, memberships)
if explain: self.append_log("Deffuzyfication", "By Memberships: {}".format(final))
ret.append(final)
if explain:
print("Deffuzyfied value: {} \n".format(final))
return ret

4
pyFTS/models/multivariate/grid.py
View File

@ -33,6 +33,10 @@ class GridCluster(partitioner.MultivariatePartitioner):
class IncrementalGridCluster(partitioner.MultivariatePartitioner):
"""
Create combinations of fuzzy sets of the variables on demand, incrementally increasing the
multivariate fuzzy set base.
"""
def __init__(self, **kwargs):
super(IncrementalGridCluster, self).__init__(**kwargs)
self.name="IncrementalGridCluster"

5
pyFTS/models/multivariate/mvfts.py
View File

@ -9,11 +9,11 @@ import pandas as pd
def product_dict(**kwargs):
'''
"""
Code by Seth Johnson
:param kwargs:
:return:
'''
"""
keys = kwargs.keys()
vals = kwargs.values()
for instance in product(*vals):
@ -244,7 +244,6 @@ class MVFTS(fts.FTS):
params=data[self.target_variable.data_label].values)
return ret
def clone_parameters(self, model):
super(MVFTS, self).clone_parameters(model)

8
pyFTS/models/multivariate/partitioner.py
View File

@ -53,14 +53,15 @@ class MultivariatePartitioner(partitioner.Partitioner):
self.build_index()
def search(self, data, **kwargs):
'''
"""
Perform a search for the nearest fuzzy sets of the point 'data'. This function were designed to work with several
overlapped fuzzy sets.
:param data: the value to search for the nearest fuzzy sets
:param type: the return type: 'index' for the fuzzy set indexes or 'name' for fuzzy set names.
:return: a list with the nearest fuzzy sets
'''
"""
if self.kdtree is None:
self.build_index()
@ -83,9 +84,6 @@ class MultivariatePartitioner(partitioner.Partitioner):
elif type == 'index':
return sorted(ix)
def fuzzyfy(self, data, **kwargs):
return fuzzyfy_instance_clustered(data, self, **kwargs)