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Petrônio Cândido 2019-04-09 17:14:51 -03:00
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@ -112,7 +112,6 @@
<li><a href="pyFTS/data/rossler.html">pyFTS.data.rossler</a></li> <li><a href="pyFTS/data/rossler.html">pyFTS.data.rossler</a></li>
<li><a href="pyFTS/data/sunspots.html">pyFTS.data.sunspots</a></li> <li><a href="pyFTS/data/sunspots.html">pyFTS.data.sunspots</a></li>
<li><a href="pyFTS/distributed/spark.html">pyFTS.distributed.spark</a></li> <li><a href="pyFTS/distributed/spark.html">pyFTS.distributed.spark</a></li>
<li><a href="pyFTS/hyperparam/GridSearch.html">pyFTS.hyperparam.GridSearch</a></li>
<li><a href="pyFTS/hyperparam/Util.html">pyFTS.hyperparam.Util</a></li> <li><a href="pyFTS/hyperparam/Util.html">pyFTS.hyperparam.Util</a></li>
<li><a href="pyFTS/models/chen.html">pyFTS.models.chen</a></li> <li><a href="pyFTS/models/chen.html">pyFTS.models.chen</a></li>
<li><a href="pyFTS/models/cheng.html">pyFTS.models.cheng</a></li> <li><a href="pyFTS/models/cheng.html">pyFTS.models.cheng</a></li>

250
docs/build/html/_modules/pyFTS/hyperparam/GridSearch.html vendored
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<title>pyFTS.hyperparam.GridSearch &#8212; pyFTS 1.4 documentation</title>
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<h1>Source code for pyFTS.hyperparam.GridSearch</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">Util</span><span class="p">,</span> <span class="n">Membership</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.partitioners</span> <span class="k">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span>
<span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="k">import</span> <span class="n">Measures</span>
<span class="kn">from</span> <span class="nn">pyFTS.hyperparam</span> <span class="k">import</span> <span class="n">Util</span> <span class="k">as</span> <span class="n">hUtil</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">dispy</span>
<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>
<div class="viewcode-block" id="dict_individual"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.dict_individual">[docs]</a><span class="k">def</span> <span class="nf">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">lags</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">):</span>
<span class="k">return</span> <span class="p">{</span>
<span class="s1">&#39;mf&#39;</span><span class="p">:</span> <span class="n">mf</span><span class="p">,</span>
<span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="n">partitioner</span><span class="p">,</span>
<span class="s1">&#39;npart&#39;</span><span class="p">:</span> <span class="n">partitions</span><span class="p">,</span>
<span class="s1">&#39;alpha&#39;</span><span class="p">:</span> <span class="n">alpha_cut</span><span class="p">,</span>
<span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">order</span><span class="p">,</span>
<span class="s1">&#39;lags&#39;</span><span class="p">:</span> <span class="n">lags</span>
<span class="p">}</span></div>
<div class="viewcode-block" id="cluster_method"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.cluster_method">[docs]</a><span class="k">def</span> <span class="nf">cluster_method</span><span class="p">(</span><span class="n">individual</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">):</span>
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Util</span><span class="p">,</span> <span class="n">Membership</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.partitioners</span> <span class="k">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span>
<span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="k">import</span> <span class="n">Measures</span>
<span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span>
<span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span>
<span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span> <span class="ow">and</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</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="k">else</span><span class="p">:</span>
<span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span>
<span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">partitioner</span> <span class="o">=</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">],</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">npart</span> <span class="o">=</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">10</span> <span class="k">else</span> <span class="mi">10</span>
<span class="n">partitioner</span> <span class="o">=</span> <span class="n">Entropy</span><span class="o">.</span><span class="n">EntropyPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">npart</span><span class="p">,</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="n">partitioner</span><span class="p">,</span>
<span class="n">lags</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">],</span>
<span class="n">alpha_cut</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">],</span>
<span class="n">order</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">])</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">)</span>
<span class="n">rmse</span><span class="p">,</span> <span class="n">mape</span><span class="p">,</span> <span class="n">u</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_point_statistics</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
<span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="k">return</span> <span class="n">individual</span><span class="p">,</span> <span class="n">rmse</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">mape</span><span class="p">,</span> <span class="n">u</span></div>
<div class="viewcode-block" id="process_jobs"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.process_jobs">[docs]</a><span class="k">def</span> <span class="nf">process_jobs</span><span class="p">(</span><span class="n">jobs</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
<span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="p">:</span>
<span class="n">result</span><span class="p">,</span> <span class="n">rmse</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">mape</span><span class="p">,</span> <span class="n">u</span> <span class="o">=</span> <span class="n">job</span><span class="p">()</span>
<span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">result</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Processing result of </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">result</span><span class="p">))</span>
<span class="n">metrics</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;rmse&#39;</span><span class="p">:</span> <span class="n">rmse</span><span class="p">,</span> <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="n">size</span><span class="p">,</span> <span class="s1">&#39;mape&#39;</span><span class="p">:</span> <span class="n">mape</span><span class="p">,</span> <span class="s1">&#39;u&#39;</span><span class="p">:</span> <span class="n">u</span> <span class="p">}</span>
<span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">metrics</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
<span class="n">record</span> <span class="o">=</span> <span class="p">(</span><span class="n">datasetname</span><span class="p">,</span> <span class="s1">&#39;GridSearch&#39;</span><span class="p">,</span> <span class="s1">&#39;WHOFTS&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">],</span>
<span class="n">result</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">],</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">],</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">],</span>
<span class="n">result</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">],</span> <span class="nb">str</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">]),</span> <span class="n">metric</span><span class="p">,</span> <span class="n">metrics</span><span class="p">[</span><span class="n">metric</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">record</span><span class="p">)</span>
<span class="n">hUtil</span><span class="o">.</span><span class="n">insert_hyperparam</span><span class="p">(</span><span class="n">record</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span></div>
<div class="viewcode-block" id="execute"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.execute">[docs]</a><span class="k">def</span> <span class="nf">execute</span><span class="p">(</span><span class="n">hyperparams</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">nodes</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;nodes&#39;</span><span class="p">,[</span><span class="s1">&#39;127.0.0.1&#39;</span><span class="p">])</span>
<span class="n">individuals</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">if</span> <span class="s1">&#39;lags&#39;</span> <span class="ow">in</span> <span class="n">hyperparams</span><span class="p">:</span>
<span class="n">lags</span> <span class="o">=</span> <span class="n">hyperparams</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;lags&#39;</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</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">50</span><span class="p">)]</span>
<span class="n">keys_sorted</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</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">hyperparams</span><span class="o">.</span><span class="n">keys</span><span class="p">())]</span>
<span class="n">index</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">keys_sorted</span><span class="p">)):</span>
<span class="n">index</span><span class="p">[</span><span class="n">keys_sorted</span><span class="p">[</span><span class="n">k</span><span class="p">]]</span> <span class="o">=</span> <span class="n">k</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Evaluation order: </span><span class="se">\n</span><span class="s2"> </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">index</span><span class="p">))</span>
<span class="n">hp_values</span> <span class="o">=</span> <span class="p">[</span>
<span class="p">[</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">hyperparams</span><span class="p">[</span><span class="n">hp</span><span class="p">]]</span>
<span class="k">for</span> <span class="n">hp</span> <span class="ow">in</span> <span class="n">keys_sorted</span>
<span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Evaluation values: </span><span class="se">\n</span><span class="s2"> </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">hp_values</span><span class="p">))</span>
<span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span> <span class="o">=</span> <span class="n">Util</span><span class="o">.</span><span class="n">start_dispy_cluster</span><span class="p">(</span><span class="n">cluster_method</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="n">nodes</span><span class="p">)</span>
<span class="n">conn</span> <span class="o">=</span> <span class="n">hUtil</span><span class="o">.</span><span class="n">open_hyperparam_db</span><span class="p">(</span><span class="s1">&#39;hyperparam.db&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">hp_values</span><span class="p">):</span>
<span class="n">partitions</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">&#39;partitions&#39;</span><span class="p">]]</span>
<span class="n">partitioner</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]]</span>
<span class="n">mf</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]]</span>
<span class="n">alpha_cut</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">]]</span>
<span class="n">order</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">]]</span>
<span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">lag1</span> <span class="ow">in</span> <span class="n">lags</span><span class="p">:</span> <span class="c1"># o é o lag1</span>
<span class="n">_lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">lag1</span><span class="p">]</span>
<span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">order</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">for</span> <span class="n">lag2</span> <span class="ow">in</span> <span class="n">lags</span><span class="p">:</span> <span class="c1"># o é o lag1</span>
<span class="n">_lags2</span> <span class="o">=</span> <span class="p">[</span><span class="n">lag1</span><span class="p">,</span> <span class="n">lag1</span><span class="o">+</span><span class="n">lag2</span><span class="p">]</span>
<span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">order</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
<span class="k">for</span> <span class="n">lag3</span> <span class="ow">in</span> <span class="n">lags</span><span class="p">:</span> <span class="c1"># o é o lag1</span>
<span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">_lags3</span> <span class="o">=</span> <span class="p">[</span><span class="n">lag1</span><span class="p">,</span> <span class="n">lag1</span> <span class="o">+</span> <span class="n">lag2</span><span class="p">,</span> <span class="n">lag1</span> <span class="o">+</span> <span class="n">lag2</span><span class="o">+</span><span class="n">lag3</span> <span class="p">]</span>
<span class="n">individuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">_lags3</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">individuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
<span class="n">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">_lags2</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">individuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">_lags</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">))</span>
<span class="k">if</span> <span class="n">count</span> <span class="o">&gt;</span> <span class="mi">50</span><span class="p">:</span>
<span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">ind</span> <span class="ow">in</span> <span class="n">individuals</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Testing individual </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">ind</span><span class="p">))</span>
<span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span>
<span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
<span class="n">process_jobs</span><span class="p">(</span><span class="n">jobs</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
<span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">individuals</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">Util</span><span class="o">.</span><span class="n">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">)</span></div>
</pre></div>
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2
docs/build/html/modules.html vendored
View File

@ -164,7 +164,7 @@
<li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam">Module contents</a></li> <li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam">Module contents</a></li>
<li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#submodules">Submodules</a></li> <li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#submodules">Submodules</a></li>
<li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.Util">pyFTS.hyperparam.Util module</a></li> <li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.Util">pyFTS.hyperparam.Util module</a></li>
<li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.GridSearch">pyFTS.hyperparam.GridSearch module</a></li> <li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#pyfts-hyperparam-gridsearch-module">pyFTS.hyperparam.GridSearch module</a></li>
<li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#pyfts-hyperparam-evolutionary-module">pyFTS.hyperparam.Evolutionary module</a></li> <li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#pyfts-hyperparam-evolutionary-module">pyFTS.hyperparam.Evolutionary module</a></li>
</ul> </ul>
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2
docs/build/html/searchindex.js vendored
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File diff suppressed because one or more lines are too long

3
pyFTS/common/FuzzySet.py
View File

@ -121,13 +121,12 @@ def fuzzyfy(data, partitioner, **kwargs):
:param data: input value to be fuzzyfied :param data: input value to be fuzzyfied
:param partitioner: a trained pyFTS.partitioners.Partitioner object :param partitioner: a trained pyFTS.partitioners.Partitioner object
:param kwargs: dict, optional arguments :param kwargs: dict, optional arguments
:keyword alpha_cut: the minimal membership value to be considered on fuzzyfication (only for mode='sets') :keyword alpha_cut: the minimal membership value to be considered on fuzzyfication (only for mode='sets')
:keyword method: the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership) :keyword method: the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership)
:keyword mode: the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership :keyword mode: the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership
values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) ) 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 :returns a list with the fuzzyfied values, depending on the mode
""" """
alpha_cut = kwargs.get('alpha_cut', 0.) alpha_cut = kwargs.get('alpha_cut', 0.)
mode = kwargs.get('mode', 'sets') mode = kwargs.get('mode', 'sets')

10
pyFTS/partitioners/partitioner.py
View File

@ -127,7 +127,7 @@ class Partitioner(object):
def fuzzyfy(self, data, **kwargs): def fuzzyfy(self, data, **kwargs):
""" """
A general method for fuzzyfication. Fuzzyfy the input data according to this partitioner fuzzy sets.
:param data: input value to be fuzzyfied :param data: input value to be fuzzyfied
:keyword alpha_cut: the minimal membership value to be considered on fuzzyfication (only for mode='sets') :keyword alpha_cut: the minimal membership value to be considered on fuzzyfication (only for mode='sets')
@ -178,6 +178,14 @@ class Partitioner(object):
return sets return sets
def check_bounds(self, data): def check_bounds(self, data):
'''
Check if the input data is outside the known Universe of Discourse and, if it is, round it to the closest
fuzzy set.
:param data: input data to be verified
:return: the index of the closest fuzzy set when data is outside de universe of discourse or None if
the data is inside the UoD.
'''
if data < self.min: if data < self.min:
return 0 return 0
elif data > self.max: elif data > self.max:

6
pyFTS/probabilistic/Mixture.py
View File

@ -7,7 +7,7 @@ from pyFTS.probabilistic import ProbabilityDistribution
class Mixture(ProbabilityDistribution.ProbabilityDistribution): class Mixture(ProbabilityDistribution.ProbabilityDistribution):
""" """
Mix two or more Probability Distributions smoothing them with weights.
""" """
def __init__(self, type="mixture", **kwargs): def __init__(self, type="mixture", **kwargs):
self.models = [] self.models = []
@ -21,7 +21,7 @@ class Mixture(ProbabilityDistribution.ProbabilityDistribution):
if not isinstance(values, list): if not isinstance(values, list):
values = [values] values = [values]
for ct, m in enumerate(self.models): #for ct, m in enumerate(self.models):
probs = [m.density(values) ] #probs = [m.density(values) ]