157 lines
16 KiB
HTML
157 lines
16 KiB
HTML
|
|
|
|
<!doctype html>
|
|
|
|
<html xmlns="http://www.w3.org/1999/xhtml">
|
|
<head>
|
|
<meta http-equiv="X-UA-Compatible" content="IE=Edge" />
|
|
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
|
|
<title>pyFTS.benchmarks.knn — pyFTS 1.2.3 documentation</title>
|
|
<link rel="stylesheet" href="../../../_static/bizstyle.css" type="text/css" />
|
|
<link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
|
|
<script type="text/javascript" src="../../../_static/documentation_options.js"></script>
|
|
<script type="text/javascript" src="../../../_static/jquery.js"></script>
|
|
<script type="text/javascript" src="../../../_static/underscore.js"></script>
|
|
<script type="text/javascript" src="../../../_static/doctools.js"></script>
|
|
<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script>
|
|
<script type="text/javascript" src="../../../_static/bizstyle.js"></script>
|
|
<link rel="index" title="Index" href="../../../genindex.html" />
|
|
<link rel="search" title="Search" href="../../../search.html" />
|
|
<meta name="viewport" content="width=device-width,initial-scale=1.0">
|
|
<!--[if lt IE 9]>
|
|
<script type="text/javascript" src="_static/css3-mediaqueries.js"></script>
|
|
<![endif]-->
|
|
</head><body>
|
|
<div class="related" role="navigation" aria-label="related navigation">
|
|
<h3>Navigation</h3>
|
|
<ul>
|
|
<li class="right" style="margin-right: 10px">
|
|
<a href="../../../genindex.html" title="General Index"
|
|
accesskey="I">index</a></li>
|
|
<li class="right" >
|
|
<a href="../../../py-modindex.html" title="Python Module Index"
|
|
>modules</a> |</li>
|
|
<li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.2.3 documentation</a> »</li>
|
|
<li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> »</li>
|
|
</ul>
|
|
</div>
|
|
<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
|
|
<div class="sphinxsidebarwrapper">
|
|
<p class="logo"><a href="../../../index.html">
|
|
<img class="logo" src="../../../_static/logo_heading2.png" alt="Logo"/>
|
|
</a></p>
|
|
<div id="searchbox" style="display: none" role="search">
|
|
<h3>Quick search</h3>
|
|
<div class="searchformwrapper">
|
|
<form class="search" action="../../../search.html" method="get">
|
|
<input type="text" name="q" />
|
|
<input type="submit" value="Go" />
|
|
<input type="hidden" name="check_keywords" value="yes" />
|
|
<input type="hidden" name="area" value="default" />
|
|
</form>
|
|
</div>
|
|
</div>
|
|
<script type="text/javascript">$('#searchbox').show(0);</script>
|
|
</div>
|
|
</div>
|
|
|
|
<div class="document">
|
|
<div class="documentwrapper">
|
|
<div class="bodywrapper">
|
|
<div class="body" role="main">
|
|
|
|
<h1>Source code for pyFTS.benchmarks.knn</h1><div class="highlight"><pre>
|
|
<span></span><span class="ch">#!/usr/bin/python</span>
|
|
<span class="c1"># -*- coding: utf8 -*-</span>
|
|
|
|
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
|
<span class="kn">from</span> <span class="nn">statsmodels.tsa.tsatools</span> <span class="k">import</span> <span class="n">lagmat</span>
|
|
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">fts</span>
|
|
<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
|
|
|
|
|
|
<div class="viewcode-block" id="KNearestNeighbors"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors">[docs]</a><span class="k">class</span> <span class="nc">KNearestNeighbors</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="sd">"""</span>
|
|
<span class="sd"> K-Nearest Neighbors</span>
|
|
<span class="sd"> """</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">KNearestNeighbors</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"kNN"</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"kNN"</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">"K-Nearest Neighbors"</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="kc">True</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"alpha"</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">lag</span> <span class="o">=</span> <span class="kc">None</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">k</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"k"</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">uod</span> <span class="o">=</span> <span class="kc">None</span>
|
|
|
|
<div class="viewcode-block" id="KNearestNeighbors.train"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.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">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></div>
|
|
|
|
|
|
<div class="viewcode-block" id="KNearestNeighbors.knn"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.knn">[docs]</a> <span class="k">def</span> <span class="nf">knn</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="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
|
<span class="n">dist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">apply_along_axis</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">sample</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</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">data</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">argsort</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
|
|
<span class="k">else</span><span class="p">:</span>
|
|
<span class="n">dist</span> <span class="o">=</span> <span class="p">[]</span>
|
|
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">)):</span>
|
|
<span class="n">dist</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sum</span><span class="p">([</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">kk</span><span class="p">]</span> <span class="o">-</span> <span class="n">sample</span><span class="p">[</span><span class="n">kk</span><span class="p">])</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="nb">range</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="n">ix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dist</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="o">+</span> <span class="mi">1</span>
|
|
|
|
<span class="n">ix2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">ix</span><span class="p">[:</span><span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">],</span> <span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">ix2</span><span class="p">]</span></div>
|
|
|
|
<div class="viewcode-block" id="KNearestNeighbors.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
|
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
|
|
|
<span class="n">smooth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"smooth"</span><span class="p">,</span> <span class="s2">"KDE"</span><span class="p">)</span>
|
|
<span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"alpha"</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
|
|
|
<span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
|
|
|
|
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)):</span>
|
|
|
|
<span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
|
|
|
|
<span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
|
|
|
<span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">forecasts</span><span class="p">,</span>
|
|
<span class="n">name</span><span class="o">=</span><span class="s2">""</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">dist</span><span class="p">)</span>
|
|
|
|
<span class="k">return</span> <span class="n">ret</span></div></div>
|
|
|
|
|
|
</pre></div>
|
|
|
|
</div>
|
|
</div>
|
|
</div>
|
|
<div class="clearer"></div>
|
|
</div>
|
|
<div class="related" role="navigation" aria-label="related navigation">
|
|
<h3>Navigation</h3>
|
|
<ul>
|
|
<li class="right" style="margin-right: 10px">
|
|
<a href="../../../genindex.html" title="General Index"
|
|
>index</a></li>
|
|
<li class="right" >
|
|
<a href="../../../py-modindex.html" title="Python Module Index"
|
|
>modules</a> |</li>
|
|
<li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.2.3 documentation</a> »</li>
|
|
<li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> »</li>
|
|
</ul>
|
|
</div>
|
|
<div class="footer" role="contentinfo">
|
|
© Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
|
|
Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.7.2.
|
|
</div>
|
|
</body>
|
|
</html> |