<?xml version="1.0" encoding="ascii"?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
<head>
<title>Bio.NeuralNetwork.BackPropagation.Network</title>
<link rel="stylesheet" href="epydoc.css" type="text/css" />
<script type="text/javascript" src="epydoc.js"></script>
</head>
<body bgcolor="white" text="black" link="blue" vlink="#204080"
alink="#204080">
<!-- ==================== NAVIGATION BAR ==================== -->
<table class="navbar" border="0" width="100%" cellpadding="0"
bgcolor="#a0c0ff" cellspacing="0">
<tr valign="middle">
<!-- Tree link -->
<th> <a
href="module-tree.html">Trees</a> </th>
<!-- Index link -->
<th> <a
href="identifier-index.html">Indices</a> </th>
<!-- Help link -->
<th> <a
href="help.html">Help</a> </th>
<th class="navbar" width="100%"></th>
</tr>
</table>
<table width="100%" cellpadding="0" cellspacing="0">
<tr valign="top">
<td width="100%">
<span class="breadcrumbs">
<a href="Bio-module.html">Package Bio</a> ::
<a href="Bio.NeuralNetwork-module.html">Package NeuralNetwork</a> ::
<a href="Bio.NeuralNetwork.BackPropagation-module.html">Package BackPropagation</a> ::
Module Network
</span>
</td>
<td>
<table cellpadding="0" cellspacing="0">
<!-- hide/show private -->
<tr><td align="right"><span class="options">[<a href="javascript:void(0);" class="privatelink"
onclick="toggle_private();">hide private</a>]</span></td></tr>
<tr><td align="right"><span class="options"
>[<a href="frames.html" target="_top">frames</a
>] | <a href="Bio.NeuralNetwork.BackPropagation.Network-pysrc.html"
target="_top">no frames</a>]</span></td></tr>
</table>
</td>
</tr>
</table>
<h1 class="epydoc">Source Code for <a href="Bio.NeuralNetwork.BackPropagation.Network-module.html">Module Bio.NeuralNetwork.BackPropagation.Network</a></h1>
<pre class="py-src">
<a name="L1"></a><tt class="py-lineno"> 1</tt> <tt class="py-line"><tt class="py-docstring">"""Represent Neural Networks.</tt> </tt>
<a name="L2"></a><tt class="py-lineno"> 2</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L3"></a><tt class="py-lineno"> 3</tt> <tt class="py-line"><tt class="py-docstring">This module contains classes to represent Generic Neural Networks that</tt> </tt>
<a name="L4"></a><tt class="py-lineno"> 4</tt> <tt class="py-line"><tt class="py-docstring">can be trained.</tt> </tt>
<a name="L5"></a><tt class="py-lineno"> 5</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L6"></a><tt class="py-lineno"> 6</tt> <tt class="py-line"><tt class="py-docstring">Many of the ideas in this and other modules were taken from</tt> </tt>
<a name="L7"></a><tt class="py-lineno"> 7</tt> <tt class="py-line"><tt class="py-docstring">Neil Schemenauer's bpnn.py, available from:</tt> </tt>
<a name="L8"></a><tt class="py-lineno"> 8</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L9"></a><tt class="py-lineno"> 9</tt> <tt class="py-line"><tt class="py-docstring">http://www.enme.ucalgary.ca/~nascheme/python/bpnn.py</tt> </tt>
<a name="L10"></a><tt class="py-lineno"> 10</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L11"></a><tt class="py-lineno"> 11</tt> <tt class="py-line"><tt class="py-docstring">My sincerest thanks to him for making this available for me to work from,</tt> </tt>
<a name="L12"></a><tt class="py-lineno"> 12</tt> <tt class="py-line"><tt class="py-docstring">and my apologies for anything I mangled.</tt> </tt>
<a name="L13"></a><tt class="py-lineno"> 13</tt> <tt class="py-line"><tt class="py-docstring">"""</tt> </tt>
<a name="L14"></a><tt class="py-lineno"> 14</tt> <tt class="py-line"><tt class="py-comment"># standard library</tt> </tt>
<a name="L15"></a><tt class="py-lineno"> 15</tt> <tt class="py-line"><tt class="py-comment"></tt><tt class="py-keyword">import</tt> <tt class="py-name">math</tt> </tt>
<a name="L16"></a><tt class="py-lineno"> 16</tt> <tt class="py-line"> </tt>
<a name="BasicNetwork"></a><div id="BasicNetwork-def"><a name="L17"></a><tt class="py-lineno"> 17</tt> <a class="py-toggle" href="#" id="BasicNetwork-toggle" onclick="return toggle('BasicNetwork');">-</a><tt class="py-line"><tt class="py-keyword">class</tt> <a class="py-def-name" href="Bio.NeuralNetwork.BackPropagation.Network.BasicNetwork-class.html">BasicNetwork</a><tt class="py-op">:</tt> </tt>
</div><div id="BasicNetwork-collapsed" style="display:none;" pad="+++" indent="++++"></div><div id="BasicNetwork-expanded"><a name="L18"></a><tt class="py-lineno"> 18</tt> <tt class="py-line"> <tt class="py-docstring">"""Represent a Basic Neural Network with three layers.</tt> </tt>
<a name="L19"></a><tt class="py-lineno"> 19</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L20"></a><tt class="py-lineno"> 20</tt> <tt class="py-line"><tt class="py-docstring"> This deals with a Neural Network containing three layers:</tt> </tt>
<a name="L21"></a><tt class="py-lineno"> 21</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L22"></a><tt class="py-lineno"> 22</tt> <tt class="py-line"><tt class="py-docstring"> o Input Layer</tt> </tt>
<a name="L23"></a><tt class="py-lineno"> 23</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L24"></a><tt class="py-lineno"> 24</tt> <tt class="py-line"><tt class="py-docstring"> o Hidden Layer</tt> </tt>
<a name="L25"></a><tt class="py-lineno"> 25</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L26"></a><tt class="py-lineno"> 26</tt> <tt class="py-line"><tt class="py-docstring"> o Output Layer</tt> </tt>
<a name="L27"></a><tt class="py-lineno"> 27</tt> <tt class="py-line"><tt class="py-docstring"> """</tt> </tt>
<a name="BasicNetwork.__init__"></a><div id="BasicNetwork.__init__-def"><a name="L28"></a><tt class="py-lineno"> 28</tt> <a class="py-toggle" href="#" id="BasicNetwork.__init__-toggle" onclick="return toggle('BasicNetwork.__init__');">-</a><tt class="py-line"> <tt class="py-keyword">def</tt> <a class="py-def-name" href="Bio.NeuralNetwork.BackPropagation.Network.BasicNetwork-class.html#__init__">__init__</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">input_layer</tt><tt class="py-op">,</tt> <tt class="py-param">hidden_layer</tt><tt class="py-op">,</tt> <tt class="py-param">output_layer</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="BasicNetwork.__init__-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="BasicNetwork.__init__-expanded"><a name="L29"></a><tt class="py-lineno"> 29</tt> <tt class="py-line"> <tt class="py-docstring">"""Initialize the network with the three layers.</tt> </tt>
<a name="L30"></a><tt class="py-lineno"> 30</tt> <tt class="py-line"><tt class="py-docstring"> """</tt> </tt>
<a name="L31"></a><tt class="py-lineno"> 31</tt> <tt class="py-line"> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_input</tt> <tt class="py-op">=</tt> <tt class="py-name">input_layer</tt> </tt>
<a name="L32"></a><tt class="py-lineno"> 32</tt> <tt class="py-line"> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_hidden</tt> <tt class="py-op">=</tt> <tt class="py-name">hidden_layer</tt> </tt>
<a name="L33"></a><tt class="py-lineno"> 33</tt> <tt class="py-line"> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_output</tt> <tt class="py-op">=</tt> <tt class="py-name">output_layer</tt> </tt>
</div><a name="L34"></a><tt class="py-lineno"> 34</tt> <tt class="py-line"> </tt>
<a name="BasicNetwork.train"></a><div id="BasicNetwork.train-def"><a name="L35"></a><tt class="py-lineno"> 35</tt> <a class="py-toggle" href="#" id="BasicNetwork.train-toggle" onclick="return toggle('BasicNetwork.train');">-</a><tt class="py-line"> <tt class="py-keyword">def</tt> <a class="py-def-name" href="Bio.NeuralNetwork.BackPropagation.Network.BasicNetwork-class.html#train">train</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">training_examples</tt><tt class="py-op">,</tt> <tt class="py-param">validation_examples</tt><tt class="py-op">,</tt> </tt>
<a name="L36"></a><tt class="py-lineno"> 36</tt> <tt class="py-line"> <tt class="py-param">stopping_criteria</tt><tt class="py-op">,</tt> <tt class="py-param">learning_rate</tt><tt class="py-op">,</tt> <tt class="py-param">momentum</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="BasicNetwork.train-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="BasicNetwork.train-expanded"><a name="L37"></a><tt class="py-lineno"> 37</tt> <tt class="py-line"> <tt class="py-docstring">"""Train the neural network to recognize particular examples.</tt> </tt>
<a name="L38"></a><tt class="py-lineno"> 38</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L39"></a><tt class="py-lineno"> 39</tt> <tt class="py-line"><tt class="py-docstring"> Arguments:</tt> </tt>
<a name="L40"></a><tt class="py-lineno"> 40</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L41"></a><tt class="py-lineno"> 41</tt> <tt class="py-line"><tt class="py-docstring"> o training_examples -- A list of TrainingExample classes that will</tt> </tt>
<a name="L42"></a><tt class="py-lineno"> 42</tt> <tt class="py-line"><tt class="py-docstring"> be used to train the network.</tt> </tt>
<a name="L43"></a><tt class="py-lineno"> 43</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L44"></a><tt class="py-lineno"> 44</tt> <tt class="py-line"><tt class="py-docstring"> o validation_examples -- A list of TrainingExample classes that</tt> </tt>
<a name="L45"></a><tt class="py-lineno"> 45</tt> <tt class="py-line"><tt class="py-docstring"> are used to validate the network as it is trained. These examples</tt> </tt>
<a name="L46"></a><tt class="py-lineno"> 46</tt> <tt class="py-line"><tt class="py-docstring"> are not used to train so the provide an independent method of</tt> </tt>
<a name="L47"></a><tt class="py-lineno"> 47</tt> <tt class="py-line"><tt class="py-docstring"> checking how the training is doing. Normally, when the error</tt> </tt>
<a name="L48"></a><tt class="py-lineno"> 48</tt> <tt class="py-line"><tt class="py-docstring"> from these examples starts to rise, then it's time to stop</tt> </tt>
<a name="L49"></a><tt class="py-lineno"> 49</tt> <tt class="py-line"><tt class="py-docstring"> training.</tt> </tt>
<a name="L50"></a><tt class="py-lineno"> 50</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L51"></a><tt class="py-lineno"> 51</tt> <tt class="py-line"><tt class="py-docstring"> o stopping_criteria -- A function, that when passed the number of</tt> </tt>
<a name="L52"></a><tt class="py-lineno"> 52</tt> <tt class="py-line"><tt class="py-docstring"> iterations, the training error, and the validation error, will</tt> </tt>
<a name="L53"></a><tt class="py-lineno"> 53</tt> <tt class="py-line"><tt class="py-docstring"> determine when to stop learning.</tt> </tt>
<a name="L54"></a><tt class="py-lineno"> 54</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L55"></a><tt class="py-lineno"> 55</tt> <tt class="py-line"><tt class="py-docstring"> o learning_rate -- The learning rate of the neural network.</tt> </tt>
<a name="L56"></a><tt class="py-lineno"> 56</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L57"></a><tt class="py-lineno"> 57</tt> <tt class="py-line"><tt class="py-docstring"> o momentum -- The momentum of the NN, which describes how much</tt> </tt>
<a name="L58"></a><tt class="py-lineno"> 58</tt> <tt class="py-line"><tt class="py-docstring"> of the prevoious weight change to use.</tt> </tt>
<a name="L59"></a><tt class="py-lineno"> 59</tt> <tt class="py-line"><tt class="py-docstring"> """</tt> </tt>
<a name="L60"></a><tt class="py-lineno"> 60</tt> <tt class="py-line"> <tt class="py-name">num_iterations</tt> <tt class="py-op">=</tt> <tt class="py-number">0</tt> </tt>
<a name="L61"></a><tt class="py-lineno"> 61</tt> <tt class="py-line"> <tt class="py-keyword">while</tt> <tt class="py-number">1</tt><tt class="py-op">:</tt> </tt>
<a name="L62"></a><tt class="py-lineno"> 62</tt> <tt class="py-line"> <tt class="py-name">num_iterations</tt> <tt class="py-op">+=</tt> <tt class="py-number">1</tt> </tt>
<a name="L63"></a><tt class="py-lineno"> 63</tt> <tt class="py-line"> <tt class="py-name">training_error</tt> <tt class="py-op">=</tt> <tt class="py-number">0.0</tt> </tt>
<a name="L64"></a><tt class="py-lineno"> 64</tt> <tt class="py-line"> <tt class="py-keyword">for</tt> <tt class="py-name">example</tt> <tt class="py-keyword">in</tt> <tt class="py-name">training_examples</tt><tt class="py-op">:</tt> </tt>
<a name="L65"></a><tt class="py-lineno"> 65</tt> <tt class="py-line"> <tt class="py-comment"># update the predicted values for all of the nodes</tt> </tt>
<a name="L66"></a><tt class="py-lineno"> 66</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-comment"># based on the current weights and the inputs</tt> </tt>
<a name="L67"></a><tt class="py-lineno"> 67</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-comment"># This propogates over the entire network from the input.</tt> </tt>
<a name="L68"></a><tt class="py-lineno"> 68</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_input</tt><tt class="py-op">.</tt><tt id="link-0" class="py-name" targets="Method Bio.GenBank.NCBIDictionary.update()=Bio.GenBank.NCBIDictionary-class.html#update,Method Bio.Index._InMemoryIndex.update()=Bio.Index._InMemoryIndex-class.html#update,Method Bio.NeuralNetwork.BackPropagation.Layer.HiddenLayer.update()=Bio.NeuralNetwork.BackPropagation.Layer.HiddenLayer-class.html#update,Method Bio.NeuralNetwork.BackPropagation.Layer.InputLayer.update()=Bio.NeuralNetwork.BackPropagation.Layer.InputLayer-class.html#update,Method Bio.NeuralNetwork.BackPropagation.Layer.OutputLayer.update()=Bio.NeuralNetwork.BackPropagation.Layer.OutputLayer-class.html#update,Method Bio.Prosite.ExPASyDictionary.update()=Bio.Prosite.ExPASyDictionary-class.html#update,Method Bio.Prosite.Prodoc.ExPASyDictionary.update()=Bio.Prosite.Prodoc.ExPASyDictionary-class.html#update,Method Bio.PubMed.Dictionary.update()=Bio.PubMed.Dictionary-class.html#update,Method Bio.Restriction._Update.Update.RebaseUpdate.update()=Bio.Restriction._Update.Update.RebaseUpdate-class.html#update,Method Bio.SwissProt.SProt.ExPASyDictionary.update()=Bio.SwissProt.SProt.ExPASyDictionary-class.html#update"><a title="Bio.GenBank.NCBIDictionary.update
Bio.Index._InMemoryIndex.update
Bio.NeuralNetwork.BackPropagation.Layer.HiddenLayer.update
Bio.NeuralNetwork.BackPropagation.Layer.InputLayer.update
Bio.NeuralNetwork.BackPropagation.Layer.OutputLayer.update
Bio.Prosite.ExPASyDictionary.update
Bio.Prosite.Prodoc.ExPASyDictionary.update
Bio.PubMed.Dictionary.update
Bio.Restriction._Update.Update.RebaseUpdate.update
Bio.SwissProt.SProt.ExPASyDictionary.update" class="py-name" href="#" onclick="return doclink('link-0', 'update', 'link-0');">update</a></tt><tt class="py-op">(</tt><tt class="py-name">example</tt><tt class="py-op">.</tt><tt class="py-name">inputs</tt><tt class="py-op">)</tt> </tt>
<a name="L69"></a><tt class="py-lineno"> 69</tt> <tt class="py-line"> </tt>
<a name="L70"></a><tt class="py-lineno"> 70</tt> <tt class="py-line"> <tt class="py-comment"># calculate the error via back propogation</tt> </tt>
<a name="L71"></a><tt class="py-lineno"> 71</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_input</tt><tt class="py-op">.</tt><tt id="link-1" class="py-name" targets="Method Bio.NeuralNetwork.BackPropagation.Layer.HiddenLayer.backpropagate()=Bio.NeuralNetwork.BackPropagation.Layer.HiddenLayer-class.html#backpropagate,Method Bio.NeuralNetwork.BackPropagation.Layer.InputLayer.backpropagate()=Bio.NeuralNetwork.BackPropagation.Layer.InputLayer-class.html#backpropagate,Method Bio.NeuralNetwork.BackPropagation.Layer.OutputLayer.backpropagate()=Bio.NeuralNetwork.BackPropagation.Layer.OutputLayer-class.html#backpropagate"><a title="Bio.NeuralNetwork.BackPropagation.Layer.HiddenLayer.backpropagate
Bio.NeuralNetwork.BackPropagation.Layer.InputLayer.backpropagate
Bio.NeuralNetwork.BackPropagation.Layer.OutputLayer.backpropagate" class="py-name" href="#" onclick="return doclink('link-1', 'backpropagate', 'link-1');">backpropagate</a></tt><tt class="py-op">(</tt><tt class="py-name">example</tt><tt class="py-op">.</tt><tt class="py-name">outputs</tt><tt class="py-op">,</tt> </tt>
<a name="L72"></a><tt class="py-lineno"> 72</tt> <tt class="py-line"> <tt class="py-name">learning_rate</tt><tt class="py-op">,</tt> <tt class="py-name">momentum</tt><tt class="py-op">)</tt> </tt>
<a name="L73"></a><tt class="py-lineno"> 73</tt> <tt class="py-line"> </tt>
<a name="L74"></a><tt class="py-lineno"> 74</tt> <tt class="py-line"> <tt class="py-comment"># get the errors in our predictions</tt> </tt>
<a name="L75"></a><tt class="py-lineno"> 75</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-keyword">for</tt> <tt id="link-2" class="py-name" targets="Method Bio.EUtils.POM.ElementNode.node()=Bio.EUtils.POM.ElementNode-class.html#node,Method Bio.Nexus.Trees.Tree.node()=Bio.Nexus.Trees.Tree-class.html#node"><a title="Bio.EUtils.POM.ElementNode.node
Bio.Nexus.Trees.Tree.node" class="py-name" href="#" onclick="return doclink('link-2', 'node', 'link-2');">node</a></tt> <tt class="py-keyword">in</tt> <tt class="py-name">range</tt><tt class="py-op">(</tt><tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">example</tt><tt class="py-op">.</tt><tt class="py-name">outputs</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L76"></a><tt class="py-lineno"> 76</tt> <tt class="py-line"> <tt class="py-name">training_error</tt> <tt class="py-op">+=</tt> \ </tt>
<a name="L77"></a><tt class="py-lineno"> 77</tt> <tt class="py-line"> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_output</tt><tt class="py-op">.</tt><tt id="link-3" class="py-name" targets="Method Bio.NeuralNetwork.BackPropagation.Layer.OutputLayer.get_error()=Bio.NeuralNetwork.BackPropagation.Layer.OutputLayer-class.html#get_error"><a title="Bio.NeuralNetwork.BackPropagation.Layer.OutputLayer.get_error" class="py-name" href="#" onclick="return doclink('link-3', 'get_error', 'link-3');">get_error</a></tt><tt class="py-op">(</tt><tt class="py-name">example</tt><tt class="py-op">.</tt><tt class="py-name">outputs</tt><tt class="py-op">[</tt><tt id="link-4" class="py-name"><a title="Bio.EUtils.POM.ElementNode.node
Bio.Nexus.Trees.Tree.node" class="py-name" href="#" onclick="return doclink('link-4', 'node', 'link-2');">node</a></tt><tt class="py-op">]</tt><tt class="py-op">,</tt> </tt>
<a name="L78"></a><tt class="py-lineno"> 78</tt> <tt class="py-line"> <tt id="link-5" class="py-name"><a title="Bio.EUtils.POM.ElementNode.node
Bio.Nexus.Trees.Tree.node" class="py-name" href="#" onclick="return doclink('link-5', 'node', 'link-2');">node</a></tt> <tt class="py-op">+</tt> <tt class="py-number">1</tt><tt class="py-op">)</tt> </tt>
<a name="L79"></a><tt class="py-lineno"> 79</tt> <tt class="py-line"> </tt>
<a name="L80"></a><tt class="py-lineno"> 80</tt> <tt class="py-line"> <tt class="py-comment"># get the current testing error for the validation examples</tt> </tt>
<a name="L81"></a><tt class="py-lineno"> 81</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-name">validation_error</tt> <tt class="py-op">=</tt> <tt class="py-number">0.0</tt> </tt>
<a name="L82"></a><tt class="py-lineno"> 82</tt> <tt class="py-line"> <tt class="py-keyword">for</tt> <tt class="py-name">example</tt> <tt class="py-keyword">in</tt> <tt class="py-name">validation_examples</tt><tt class="py-op">:</tt> </tt>
<a name="L83"></a><tt class="py-lineno"> 83</tt> <tt class="py-line"> <tt class="py-name">predictions</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt id="link-6" class="py-name" targets="Method Bio.NeuralNetwork.BackPropagation.Network.BasicNetwork.predict()=Bio.NeuralNetwork.BackPropagation.Network.BasicNetwork-class.html#predict"><a title="Bio.NeuralNetwork.BackPropagation.Network.BasicNetwork.predict" class="py-name" href="#" onclick="return doclink('link-6', 'predict', 'link-6');">predict</a></tt><tt class="py-op">(</tt><tt class="py-name">example</tt><tt class="py-op">.</tt><tt class="py-name">inputs</tt><tt class="py-op">)</tt> </tt>
<a name="L84"></a><tt class="py-lineno"> 84</tt> <tt class="py-line"> </tt>
<a name="L85"></a><tt class="py-lineno"> 85</tt> <tt class="py-line"> <tt class="py-keyword">for</tt> <tt class="py-name">prediction_num</tt> <tt class="py-keyword">in</tt> <tt class="py-name">range</tt><tt class="py-op">(</tt><tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">predictions</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L86"></a><tt class="py-lineno"> 86</tt> <tt class="py-line"> <tt class="py-name">real_value</tt> <tt class="py-op">=</tt> <tt class="py-name">example</tt><tt class="py-op">.</tt><tt class="py-name">outputs</tt><tt class="py-op">[</tt><tt class="py-name">prediction_num</tt><tt class="py-op">]</tt> </tt>
<a name="L87"></a><tt class="py-lineno"> 87</tt> <tt class="py-line"> <tt class="py-name">predicted_value</tt> <tt class="py-op">=</tt> <tt class="py-name">predictions</tt><tt class="py-op">[</tt><tt class="py-name">prediction_num</tt><tt class="py-op">]</tt> </tt>
<a name="L88"></a><tt class="py-lineno"> 88</tt> <tt class="py-line"> <tt class="py-name">validation_error</tt> <tt class="py-op">+=</tt> \ </tt>
<a name="L89"></a><tt class="py-lineno"> 89</tt> <tt class="py-line"> <tt class="py-number">0.5</tt> <tt class="py-op">*</tt> <tt class="py-name">math</tt><tt class="py-op">.</tt><tt class="py-name">pow</tt><tt class="py-op">(</tt><tt class="py-op">(</tt><tt class="py-name">real_value</tt> <tt class="py-op">-</tt> <tt class="py-name">predicted_value</tt><tt class="py-op">)</tt><tt class="py-op">,</tt> <tt class="py-number">2</tt><tt class="py-op">)</tt> </tt>
<a name="L90"></a><tt class="py-lineno"> 90</tt> <tt class="py-line"> </tt>
<a name="L91"></a><tt class="py-lineno"> 91</tt> <tt class="py-line"> <tt class="py-comment"># see if we have gone far enough to stop</tt> </tt>
<a name="L92"></a><tt class="py-lineno"> 92</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-keyword">if</tt> <tt id="link-7" class="py-name" targets="Method Bio.NeuralNetwork.StopTraining.ValidationIncreaseStop.stopping_criteria()=Bio.NeuralNetwork.StopTraining.ValidationIncreaseStop-class.html#stopping_criteria"><a title="Bio.NeuralNetwork.StopTraining.ValidationIncreaseStop.stopping_criteria" class="py-name" href="#" onclick="return doclink('link-7', 'stopping_criteria', 'link-7');">stopping_criteria</a></tt><tt class="py-op">(</tt><tt class="py-name">num_iterations</tt><tt class="py-op">,</tt> <tt class="py-name">training_error</tt><tt class="py-op">,</tt> </tt>
<a name="L93"></a><tt class="py-lineno"> 93</tt> <tt class="py-line"> <tt class="py-name">validation_error</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L94"></a><tt class="py-lineno"> 94</tt> <tt class="py-line"> <tt class="py-keyword">break</tt> </tt>
</div><a name="L95"></a><tt class="py-lineno"> 95</tt> <tt class="py-line"> </tt>
<a name="BasicNetwork.predict"></a><div id="BasicNetwork.predict-def"><a name="L96"></a><tt class="py-lineno"> 96</tt> <a class="py-toggle" href="#" id="BasicNetwork.predict-toggle" onclick="return toggle('BasicNetwork.predict');">-</a><tt class="py-line"> <tt class="py-keyword">def</tt> <a class="py-def-name" href="Bio.NeuralNetwork.BackPropagation.Network.BasicNetwork-class.html#predict">predict</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">inputs</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="BasicNetwork.predict-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="BasicNetwork.predict-expanded"><a name="L97"></a><tt class="py-lineno"> 97</tt> <tt class="py-line"> <tt class="py-docstring">"""Predict outputs from the neural network with the given inputs.</tt> </tt>
<a name="L98"></a><tt class="py-lineno"> 98</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L99"></a><tt class="py-lineno"> 99</tt> <tt class="py-line"><tt class="py-docstring"> This uses the current neural network to predict outputs, no</tt> </tt>
<a name="L100"></a><tt class="py-lineno">100</tt> <tt class="py-line"><tt class="py-docstring"> training of the neural network is done here.</tt> </tt>
<a name="L101"></a><tt class="py-lineno">101</tt> <tt class="py-line"><tt class="py-docstring"> """</tt> </tt>
<a name="L102"></a><tt class="py-lineno">102</tt> <tt class="py-line"> <tt class="py-comment"># update the predicted values for these inputs</tt> </tt>
<a name="L103"></a><tt class="py-lineno">103</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_input</tt><tt class="py-op">.</tt><tt id="link-8" class="py-name"><a title="Bio.GenBank.NCBIDictionary.update
Bio.Index._InMemoryIndex.update
Bio.NeuralNetwork.BackPropagation.Layer.HiddenLayer.update
Bio.NeuralNetwork.BackPropagation.Layer.InputLayer.update
Bio.NeuralNetwork.BackPropagation.Layer.OutputLayer.update
Bio.Prosite.ExPASyDictionary.update
Bio.Prosite.Prodoc.ExPASyDictionary.update
Bio.PubMed.Dictionary.update
Bio.Restriction._Update.Update.RebaseUpdate.update
Bio.SwissProt.SProt.ExPASyDictionary.update" class="py-name" href="#" onclick="return doclink('link-8', 'update', 'link-0');">update</a></tt><tt class="py-op">(</tt><tt class="py-name">inputs</tt><tt class="py-op">)</tt> </tt>
<a name="L104"></a><tt class="py-lineno">104</tt> <tt class="py-line"> </tt>
<a name="L105"></a><tt class="py-lineno">105</tt> <tt class="py-line"> <tt class="py-name">output_keys</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_output</tt><tt class="py-op">.</tt><tt id="link-9" class="py-name" targets="Method Bio.Crystal.Crystal.values()=Bio.Crystal.Crystal-class.html#values,Method Bio.EUtils.MultiDict._BaseMultiDict.values()=Bio.EUtils.MultiDict._BaseMultiDict-class.html#values,Method Bio.GenBank.NCBIDictionary.values()=Bio.GenBank.NCBIDictionary-class.html#values,Method Bio.Mindy.BaseDB.DictLookup.values()=Bio.Mindy.BaseDB.DictLookup-class.html#values,Method Bio.Prosite.ExPASyDictionary.values()=Bio.Prosite.ExPASyDictionary-class.html#values,Method Bio.Prosite.Prodoc.ExPASyDictionary.values()=Bio.Prosite.Prodoc.ExPASyDictionary-class.html#values,Method Bio.PubMed.Dictionary.values()=Bio.PubMed.Dictionary-class.html#values,Method Bio.SwissProt.SProt.ExPASyDictionary.values()=Bio.SwissProt.SProt.ExPASyDictionary-class.html#values,Method Bio.config.Registry.Registry.values()=Bio.config.Registry.Registry-class.html#values,Method BioSQL.BioSeqDatabase.BioSeqDatabase.values()=BioSQL.BioSeqDatabase.BioSeqDatabase-class.html#values,Method BioSQL.BioSeqDatabase.DBServer.values()=BioSQL.BioSeqDatabase.DBServer-class.html#values,Method Martel.Parser.MartelAttributeList.values()=Martel.Parser.MartelAttributeList-class.html#values"><a title="Bio.Crystal.Crystal.values
Bio.EUtils.MultiDict._BaseMultiDict.values
Bio.GenBank.NCBIDictionary.values
Bio.Mindy.BaseDB.DictLookup.values
Bio.Prosite.ExPASyDictionary.values
Bio.Prosite.Prodoc.ExPASyDictionary.values
Bio.PubMed.Dictionary.values
Bio.SwissProt.SProt.ExPASyDictionary.values
Bio.config.Registry.Registry.values
BioSQL.BioSeqDatabase.BioSeqDatabase.values
BioSQL.BioSeqDatabase.DBServer.values
Martel.Parser.MartelAttributeList.values" class="py-name" href="#" onclick="return doclink('link-9', 'values', 'link-9');">values</a></tt><tt class="py-op">.</tt><tt id="link-10" class="py-name" targets="Method Bio.Crystal.Crystal.keys()=Bio.Crystal.Crystal-class.html#keys,Method Bio.EUtils.MultiDict._BaseMultiDict.keys()=Bio.EUtils.MultiDict._BaseMultiDict-class.html#keys,Method Bio.GenBank.NCBIDictionary.keys()=Bio.GenBank.NCBIDictionary-class.html#keys,Method Bio.Mindy.BaseDB.DictLookup.keys()=Bio.Mindy.BaseDB.DictLookup-class.html#keys,Method Bio.Mindy.BaseDB.OpenDB.keys()=Bio.Mindy.BaseDB.OpenDB-class.html#keys,Method Bio.Mindy.BerkeleyDB.PrimaryNamespace.keys()=Bio.Mindy.BerkeleyDB.PrimaryNamespace-class.html#keys,Method Bio.Mindy.BerkeleyDB.SecondaryNamespace.keys()=Bio.Mindy.BerkeleyDB.SecondaryNamespace-class.html#keys,Method Bio.Mindy.FlatDB.PrimaryNamespace.keys()=Bio.Mindy.FlatDB.PrimaryNamespace-class.html#keys,Method Bio.Mindy.FlatDB.PrimaryTable.keys()=Bio.Mindy.FlatDB.PrimaryTable-class.html#keys,Method Bio.Mindy.FlatDB.SecondaryNamespace.keys()=Bio.Mindy.FlatDB.SecondaryNamespace-class.html#keys,Method Bio.Mindy.FlatDB.SecondaryTable.keys()=Bio.Mindy.FlatDB.SecondaryTable-class.html#keys,Method Bio.PDB.AbstractPropertyMap.AbstractPropertyMap.keys()=Bio.PDB.AbstractPropertyMap.AbstractPropertyMap-class.html#keys,Method Bio.Prosite.ExPASyDictionary.keys()=Bio.Prosite.ExPASyDictionary-class.html#keys,Method Bio.Prosite.Prodoc.ExPASyDictionary.keys()=Bio.Prosite.Prodoc.ExPASyDictionary-class.html#keys,Method Bio.PubMed.Dictionary.keys()=Bio.PubMed.Dictionary-class.html#keys,Method Bio.SwissProt.SProt.Dictionary.keys()=Bio.SwissProt.SProt.Dictionary-class.html#keys,Method Bio.SwissProt.SProt.ExPASyDictionary.keys()=Bio.SwissProt.SProt.ExPASyDictionary-class.html#keys,Method Bio.config.Registry.Registry.keys()=Bio.config.Registry.Registry-class.html#keys,Method BioSQL.BioSeqDatabase.BioSeqDatabase.keys()=BioSQL.BioSeqDatabase.BioSeqDatabase-class.html#keys,Method BioSQL.BioSeqDatabase.DBServer.keys()=BioSQL.BioSeqDatabase.DBServer-class.html#keys,Method Martel.Parser.MartelAttributeList.keys()=Martel.Parser.MartelAttributeList-class.html#keys"><a title="Bio.Crystal.Crystal.keys
Bio.EUtils.MultiDict._BaseMultiDict.keys
Bio.GenBank.NCBIDictionary.keys
Bio.Mindy.BaseDB.DictLookup.keys
Bio.Mindy.BaseDB.OpenDB.keys
Bio.Mindy.BerkeleyDB.PrimaryNamespace.keys
Bio.Mindy.BerkeleyDB.SecondaryNamespace.keys
Bio.Mindy.FlatDB.PrimaryNamespace.keys
Bio.Mindy.FlatDB.PrimaryTable.keys
Bio.Mindy.FlatDB.SecondaryNamespace.keys
Bio.Mindy.FlatDB.SecondaryTable.keys
Bio.PDB.AbstractPropertyMap.AbstractPropertyMap.keys
Bio.Prosite.ExPASyDictionary.keys
Bio.Prosite.Prodoc.ExPASyDictionary.keys
Bio.PubMed.Dictionary.keys
Bio.SwissProt.SProt.Dictionary.keys
Bio.SwissProt.SProt.ExPASyDictionary.keys
Bio.config.Registry.Registry.keys
BioSQL.BioSeqDatabase.BioSeqDatabase.keys
BioSQL.BioSeqDatabase.DBServer.keys
Martel.Parser.MartelAttributeList.keys" class="py-name" href="#" onclick="return doclink('link-10', 'keys', 'link-10');">keys</a></tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L106"></a><tt class="py-lineno">106</tt> <tt class="py-line"> <tt class="py-name">output_keys</tt><tt class="py-op">.</tt><tt id="link-11" class="py-name" targets="Method Bio.PDB.Residue.DisorderedResidue.sort()=Bio.PDB.Residue.DisorderedResidue-class.html#sort,Method Bio.PDB.Residue.Residue.sort()=Bio.PDB.Residue.Residue-class.html#sort,Method Bio.Sequencing.Ace.ACEFileRecord.sort()=Bio.Sequencing.Ace.ACEFileRecord-class.html#sort"><a title="Bio.PDB.Residue.DisorderedResidue.sort
Bio.PDB.Residue.Residue.sort
Bio.Sequencing.Ace.ACEFileRecord.sort" class="py-name" href="#" onclick="return doclink('link-11', 'sort', 'link-11');">sort</a></tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L107"></a><tt class="py-lineno">107</tt> <tt class="py-line"> </tt>
<a name="L108"></a><tt class="py-lineno">108</tt> <tt class="py-line"> <tt class="py-name">outputs</tt> <tt class="py-op">=</tt> <tt class="py-op">[</tt><tt class="py-op">]</tt> </tt>
<a name="L109"></a><tt class="py-lineno">109</tt> <tt class="py-line"> <tt class="py-keyword">for</tt> <tt class="py-name">output_key</tt> <tt class="py-keyword">in</tt> <tt class="py-name">output_keys</tt><tt class="py-op">:</tt> </tt>
<a name="L110"></a><tt class="py-lineno">110</tt> <tt class="py-line"> <tt class="py-name">outputs</tt><tt class="py-op">.</tt><tt id="link-12" class="py-name" targets="Method Bio.Crystal.Chain.append()=Bio.Crystal.Chain-class.html#append,Method Bio.EUtils.POM.ElementNode.append()=Bio.EUtils.POM.ElementNode-class.html#append,Method Bio.EUtils.sourcegen.SourceFile.append()=Bio.EUtils.sourcegen.SourceFile-class.html#append,Method Bio.SCOP.Raf.SeqMap.append()=Bio.SCOP.Raf.SeqMap-class.html#append,Method Bio.Seq.MutableSeq.append()=Bio.Seq.MutableSeq-class.html#append,Method Bio.Wise.psw.Alignment.append()=Bio.Wise.psw.Alignment-class.html#append,Method Bio.Wise.psw.AlignmentColumn.append()=Bio.Wise.psw.AlignmentColumn-class.html#append,Method Martel.msre_parse.SubPattern.append()=Martel.msre_parse.SubPattern-class.html#append"><a title="Bio.Crystal.Chain.append
Bio.EUtils.POM.ElementNode.append
Bio.EUtils.sourcegen.SourceFile.append
Bio.SCOP.Raf.SeqMap.append
Bio.Seq.MutableSeq.append
Bio.Wise.psw.Alignment.append
Bio.Wise.psw.AlignmentColumn.append
Martel.msre_parse.SubPattern.append" class="py-name" href="#" onclick="return doclink('link-12', 'append', 'link-12');">append</a></tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_output</tt><tt class="py-op">.</tt><tt id="link-13" class="py-name"><a title="Bio.Crystal.Crystal.values
Bio.EUtils.MultiDict._BaseMultiDict.values
Bio.GenBank.NCBIDictionary.values
Bio.Mindy.BaseDB.DictLookup.values
Bio.Prosite.ExPASyDictionary.values
Bio.Prosite.Prodoc.ExPASyDictionary.values
Bio.PubMed.Dictionary.values
Bio.SwissProt.SProt.ExPASyDictionary.values
Bio.config.Registry.Registry.values
BioSQL.BioSeqDatabase.BioSeqDatabase.values
BioSQL.BioSeqDatabase.DBServer.values
Martel.Parser.MartelAttributeList.values" class="py-name" href="#" onclick="return doclink('link-13', 'values', 'link-9');">values</a></tt><tt class="py-op">[</tt><tt class="py-name">output_key</tt><tt class="py-op">]</tt><tt class="py-op">)</tt> </tt>
<a name="L111"></a><tt class="py-lineno">111</tt> <tt class="py-line"> <tt class="py-keyword">return</tt> <tt class="py-name">outputs</tt> </tt>
</div></div><a name="L112"></a><tt class="py-lineno">112</tt> <tt class="py-line"> </tt><script type="text/javascript">
<!--
expandto(location.href);
// -->
</script>
</pre>
<br />
<!-- ==================== NAVIGATION BAR ==================== -->
<table class="navbar" border="0" width="100%" cellpadding="0"
bgcolor="#a0c0ff" cellspacing="0">
<tr valign="middle">
<!-- Tree link -->
<th> <a
href="module-tree.html">Trees</a> </th>
<!-- Index link -->
<th> <a
href="identifier-index.html">Indices</a> </th>
<!-- Help link -->
<th> <a
href="help.html">Help</a> </th>
<th class="navbar" width="100%"></th>
</tr>
</table>
<table border="0" cellpadding="0" cellspacing="0" width="100%%">
<tr>
<td align="left" class="footer">
Generated by Epydoc 3.0.1 on Mon Sep 15 09:28:09 2008
</td>
<td align="right" class="footer">
<a target="mainFrame" href="http://epydoc.sourceforge.net"
>http://epydoc.sourceforge.net</a>
</td>
</tr>
</table>
<script type="text/javascript">
<!--
// Private objects are initially displayed (because if
// javascript is turned off then we want them to be
// visible); but by default, we want to hide them. So hide
// them unless we have a cookie that says to show them.
checkCookie();
// -->
</script>
</body>
</html>
