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<a href="Bio-module.html">Package Bio</a> ::
Module NaiveBayes
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<h1 class="epydoc">Source Code for <a href="Bio.NaiveBayes-module.html">Module Bio.NaiveBayes</a></h1>
<pre class="py-src">
<a name="L1"></a><tt class="py-lineno"> 1</tt> <tt class="py-line"><tt class="py-comment"># Copyright 2000 by Jeffrey Chang. All rights reserved.</tt> </tt>
<a name="L2"></a><tt class="py-lineno"> 2</tt> <tt class="py-line"><tt class="py-comment"></tt><tt class="py-comment"># This code is part of the Biopython distribution and governed by its</tt> </tt>
<a name="L3"></a><tt class="py-lineno"> 3</tt> <tt class="py-line"><tt class="py-comment"></tt><tt class="py-comment"># license. Please see the LICENSE file that should have been included</tt> </tt>
<a name="L4"></a><tt class="py-lineno"> 4</tt> <tt class="py-line"><tt class="py-comment"></tt><tt class="py-comment"># as part of this package.</tt> </tt>
<a name="L5"></a><tt class="py-lineno"> 5</tt> <tt class="py-line"><tt class="py-comment"></tt> </tt>
<a name="L6"></a><tt class="py-lineno"> 6</tt> <tt class="py-line"><tt class="py-docstring">"""This provides code for a general Naive Bayes learner.</tt> </tt>
<a name="L7"></a><tt class="py-lineno"> 7</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L8"></a><tt class="py-lineno"> 8</tt> <tt class="py-line"><tt class="py-docstring">Naive Bayes is a supervised classification algorithm that uses Bayes</tt> </tt>
<a name="L9"></a><tt class="py-lineno"> 9</tt> <tt class="py-line"><tt class="py-docstring">rule to compute the fit between a new observation and some previously</tt> </tt>
<a name="L10"></a><tt class="py-lineno"> 10</tt> <tt class="py-line"><tt class="py-docstring">observed data. The observations are discrete feature vectors, with</tt> </tt>
<a name="L11"></a><tt class="py-lineno"> 11</tt> <tt class="py-line"><tt class="py-docstring">the Bayes assumption that the features are independent. Although this</tt> </tt>
<a name="L12"></a><tt class="py-lineno"> 12</tt> <tt class="py-line"><tt class="py-docstring">is hardly ever true, the classifier works well enough in practice.</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-docstring">Glossary:</tt> </tt>
<a name="L15"></a><tt class="py-lineno"> 15</tt> <tt class="py-line"><tt class="py-docstring">observation A feature vector of discrete data.</tt> </tt>
<a name="L16"></a><tt class="py-lineno"> 16</tt> <tt class="py-line"><tt class="py-docstring">class A possible classification for an observation.</tt> </tt>
<a name="L17"></a><tt class="py-lineno"> 17</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L18"></a><tt class="py-lineno"> 18</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L19"></a><tt class="py-lineno"> 19</tt> <tt class="py-line"><tt class="py-docstring">Classes:</tt> </tt>
<a name="L20"></a><tt class="py-lineno"> 20</tt> <tt class="py-line"><tt class="py-docstring">NaiveBayes Holds information for a naive Bayes classifier.</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">Functions:</tt> </tt>
<a name="L23"></a><tt class="py-lineno"> 23</tt> <tt class="py-line"><tt class="py-docstring">train Train a new naive Bayes classifier.</tt> </tt>
<a name="L24"></a><tt class="py-lineno"> 24</tt> <tt class="py-line"><tt class="py-docstring">calculate Calculate the probabilities of each class, given an observation.</tt> </tt>
<a name="L25"></a><tt class="py-lineno"> 25</tt> <tt class="py-line"><tt class="py-docstring">classify Classify an observation into a class.</tt> </tt>
<a name="L26"></a><tt class="py-lineno"> 26</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L27"></a><tt class="py-lineno"> 27</tt> <tt class="py-line"><tt class="py-docstring">"""</tt> </tt>
<a name="L28"></a><tt class="py-lineno"> 28</tt> <tt class="py-line"><tt class="py-comment"># To Do:</tt> </tt>
<a name="L29"></a><tt class="py-lineno"> 29</tt> <tt class="py-line"><tt class="py-comment"></tt><tt class="py-comment"># add code to help discretize data</tt> </tt>
<a name="L30"></a><tt class="py-lineno"> 30</tt> <tt class="py-line"><tt class="py-comment"></tt><tt class="py-comment"># use objects</tt> </tt>
<a name="L31"></a><tt class="py-lineno"> 31</tt> <tt class="py-line"><tt class="py-comment"></tt> </tt>
<a name="L32"></a><tt class="py-lineno"> 32</tt> <tt class="py-line"><tt class="py-keyword">try</tt><tt class="py-op">:</tt> </tt>
<a name="L33"></a><tt class="py-lineno"> 33</tt> <tt class="py-line"> <tt class="py-keyword">from</tt> <tt class="py-name">Numeric</tt> <tt class="py-keyword">import</tt> <tt class="py-op">*</tt> </tt>
<a name="L34"></a><tt class="py-lineno"> 34</tt> <tt class="py-line"><tt class="py-keyword">except</tt> <tt class="py-name">ImportError</tt><tt class="py-op">,</tt> <tt id="link-0" class="py-name" targets="Variable Bio.MarkovModel.x=Bio.MarkovModel-module.html#x,Variable Bio.Statistics.lowess.x=Bio.Statistics.lowess-module.html#x"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-0', 'x', 'link-0');">x</a></tt><tt class="py-op">:</tt> </tt>
<a name="L35"></a><tt class="py-lineno"> 35</tt> <tt class="py-line"> <tt class="py-keyword">raise</tt> <tt class="py-name">ImportError</tt><tt class="py-op">,</tt> <tt class="py-string">"This module requires Numeric (precursor to NumPy)"</tt> </tt>
<a name="L36"></a><tt class="py-lineno"> 36</tt> <tt class="py-line"> </tt>
<a name="L37"></a><tt class="py-lineno"> 37</tt> <tt class="py-line"><tt class="py-keyword">from</tt> <tt id="link-1" class="py-name" targets="Package Bio=Bio-module.html"><a title="Bio" class="py-name" href="#" onclick="return doclink('link-1', 'Bio', 'link-1');">Bio</a></tt> <tt class="py-keyword">import</tt> <tt id="link-2" class="py-name" targets="Module Bio.mathfns=Bio.mathfns-module.html"><a title="Bio.mathfns" class="py-name" href="#" onclick="return doclink('link-2', 'mathfns', 'link-2');">mathfns</a></tt><tt class="py-op">,</tt> <tt id="link-3" class="py-name" targets="Module Bio.listfns=Bio.listfns-module.html"><a title="Bio.listfns" class="py-name" href="#" onclick="return doclink('link-3', 'listfns', 'link-3');">listfns</a></tt> </tt>
<a name="L38"></a><tt class="py-lineno"> 38</tt> <tt class="py-line"> </tt>
<a name="NaiveBayes"></a><div id="NaiveBayes-def"><a name="L39"></a><tt class="py-lineno"> 39</tt> <a class="py-toggle" href="#" id="NaiveBayes-toggle" onclick="return toggle('NaiveBayes');">-</a><tt class="py-line"><tt class="py-keyword">class</tt> <a class="py-def-name" href="Bio.NaiveBayes.NaiveBayes-class.html">NaiveBayes</a><tt class="py-op">:</tt> </tt>
</div><div id="NaiveBayes-collapsed" style="display:none;" pad="+++" indent="++++"></div><div id="NaiveBayes-expanded"><a name="L40"></a><tt class="py-lineno"> 40</tt> <tt class="py-line"> <tt class="py-docstring">"""Holds information for a NaiveBayes classifier.</tt> </tt>
<a name="L41"></a><tt class="py-lineno"> 41</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L42"></a><tt class="py-lineno"> 42</tt> <tt class="py-line"><tt class="py-docstring"> Members:</tt> </tt>
<a name="L43"></a><tt class="py-lineno"> 43</tt> <tt class="py-line"><tt class="py-docstring"> classes List of the possible classes of data.</tt> </tt>
<a name="L44"></a><tt class="py-lineno"> 44</tt> <tt class="py-line"><tt class="py-docstring"> p_conditional CLASS x DIM array of dicts of value -> P(value|class,dim)</tt> </tt>
<a name="L45"></a><tt class="py-lineno"> 45</tt> <tt class="py-line"><tt class="py-docstring"> p_prior List of the prior probabilities for every class.</tt> </tt>
<a name="L46"></a><tt class="py-lineno"> 46</tt> <tt class="py-line"><tt class="py-docstring"> dimensionality Dimensionality of the data.</tt> </tt>
<a name="L47"></a><tt class="py-lineno"> 47</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L48"></a><tt class="py-lineno"> 48</tt> <tt class="py-line"><tt class="py-docstring"> """</tt> </tt>
<a name="NaiveBayes.__init__"></a><div id="NaiveBayes.__init__-def"><a name="L49"></a><tt class="py-lineno"> 49</tt> <a class="py-toggle" href="#" id="NaiveBayes.__init__-toggle" onclick="return toggle('NaiveBayes.__init__');">-</a><tt class="py-line"> <tt class="py-keyword">def</tt> <a class="py-def-name" href="Bio.NaiveBayes.NaiveBayes-class.html#__init__">__init__</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="NaiveBayes.__init__-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="NaiveBayes.__init__-expanded"><a name="L50"></a><tt class="py-lineno"> 50</tt> <tt class="py-line"> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">classes</tt> <tt class="py-op">=</tt> <tt class="py-op">[</tt><tt class="py-op">]</tt> </tt>
<a name="L51"></a><tt class="py-lineno"> 51</tt> <tt class="py-line"> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">p_conditional</tt> <tt class="py-op">=</tt> <tt class="py-name">None</tt> </tt>
<a name="L52"></a><tt class="py-lineno"> 52</tt> <tt class="py-line"> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">p_prior</tt> <tt class="py-op">=</tt> <tt class="py-op">[</tt><tt class="py-op">]</tt> </tt>
<a name="L53"></a><tt class="py-lineno"> 53</tt> <tt class="py-line"> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">dimensionality</tt> <tt class="py-op">=</tt> <tt class="py-name">None</tt> </tt>
</div></div><a name="L54"></a><tt class="py-lineno"> 54</tt> <tt class="py-line"> </tt>
<a name="calculate"></a><div id="calculate-def"><a name="L55"></a><tt class="py-lineno"> 55</tt> <a class="py-toggle" href="#" id="calculate-toggle" onclick="return toggle('calculate');">-</a><tt class="py-line"><tt class="py-keyword">def</tt> <a class="py-def-name" href="Bio.NaiveBayes-module.html#calculate">calculate</a><tt class="py-op">(</tt><tt class="py-param">nb</tt><tt class="py-op">,</tt> <tt class="py-param">observation</tt><tt class="py-op">,</tt> <tt class="py-param">scale</tt><tt class="py-op">=</tt><tt class="py-number">0</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="calculate-collapsed" style="display:none;" pad="+++" indent="++++"></div><div id="calculate-expanded"><a name="L56"></a><tt class="py-lineno"> 56</tt> <tt class="py-line"> <tt class="py-docstring">"""calculate(nb, observation[, scale]) -> probability dict</tt> </tt>
<a name="L57"></a><tt class="py-lineno"> 57</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L58"></a><tt class="py-lineno"> 58</tt> <tt class="py-line"><tt class="py-docstring"> Calculate log P(class|observation) for each class. nb is a NaiveBayes</tt> </tt>
<a name="L59"></a><tt class="py-lineno"> 59</tt> <tt class="py-line"><tt class="py-docstring"> classifier that has been trained. observation is a list representing</tt> </tt>
<a name="L60"></a><tt class="py-lineno"> 60</tt> <tt class="py-line"><tt class="py-docstring"> the observed data. scale is whether the probability should be</tt> </tt>
<a name="L61"></a><tt class="py-lineno"> 61</tt> <tt class="py-line"><tt class="py-docstring"> scaled by P(observation). By default, no scaling is done. The return</tt> </tt>
<a name="L62"></a><tt class="py-lineno"> 62</tt> <tt class="py-line"><tt class="py-docstring"> value is a dictionary where the keys is the class and the value is the</tt> </tt>
<a name="L63"></a><tt class="py-lineno"> 63</tt> <tt class="py-line"><tt class="py-docstring"> log probability of the class.</tt> </tt>
<a name="L64"></a><tt class="py-lineno"> 64</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L65"></a><tt class="py-lineno"> 65</tt> <tt class="py-line"><tt class="py-docstring"> """</tt> </tt>
<a name="L66"></a><tt class="py-lineno"> 66</tt> <tt class="py-line"> <tt class="py-comment"># P(class|observation) = P(observation|class)*P(class)/P(observation)</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"># Taking the log:</tt> </tt>
<a name="L68"></a><tt class="py-lineno"> 68</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-comment"># lP(class|observation) = lP(observation|class)+lP(class)-lP(observation)</tt> </tt>
<a name="L69"></a><tt class="py-lineno"> 69</tt> <tt class="py-line"><tt class="py-comment"></tt> </tt>
<a name="L70"></a><tt class="py-lineno"> 70</tt> <tt class="py-line"> <tt class="py-comment"># Make sure the observation has the right dimensionality.</tt> </tt>
<a name="L71"></a><tt class="py-lineno"> 71</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-keyword">if</tt> <tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">observation</tt><tt class="py-op">)</tt> <tt class="py-op">!=</tt> <tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">dimensionality</tt><tt class="py-op">:</tt> </tt>
<a name="L72"></a><tt class="py-lineno"> 72</tt> <tt class="py-line"> <tt class="py-keyword">raise</tt> <tt class="py-name">ValueError</tt><tt class="py-op">,</tt> <tt class="py-string">"observation in %d dimension, but classifier in %d"</tt> \ </tt>
<a name="L73"></a><tt class="py-lineno"> 73</tt> <tt class="py-line"> <tt class="py-op">%</tt> <tt class="py-op">(</tt><tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">observation</tt><tt class="py-op">)</tt><tt class="py-op">,</tt> <tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">dimensionality</tt><tt class="py-op">)</tt> </tt>
<a name="L74"></a><tt class="py-lineno"> 74</tt> <tt class="py-line"> </tt>
<a name="L75"></a><tt class="py-lineno"> 75</tt> <tt class="py-line"> <tt class="py-comment"># Calculate log P(observation|class) for every class.</tt> </tt>
<a name="L76"></a><tt class="py-lineno"> 76</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-name">lp_observation_class</tt> <tt class="py-op">=</tt> <tt class="py-op">[</tt><tt class="py-op">]</tt> <tt class="py-comment"># list of log P(observation|class)</tt> </tt>
<a name="L77"></a><tt class="py-lineno"> 77</tt> <tt class="py-line"> <tt class="py-keyword">for</tt> <tt id="link-4" class="py-name" targets="Variable Bio.PDB.Polypeptide.i=Bio.PDB.Polypeptide-module.html#i"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-4', 'i', 'link-4');">i</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">nb</tt><tt class="py-op">.</tt><tt class="py-name">classes</tt><tt class="py-op">)</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 class="py-comment"># log P(observation|class) = SUM_i log P(observation_i|class)</tt> </tt>
<a name="L79"></a><tt class="py-lineno"> 79</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-name">probs</tt> <tt class="py-op">=</tt> <tt class="py-op">[</tt><tt class="py-name">None</tt><tt class="py-op">]</tt> <tt class="py-op">*</tt> <tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">observation</tt><tt class="py-op">)</tt> </tt>
<a name="L80"></a><tt class="py-lineno"> 80</tt> <tt class="py-line"> <tt class="py-keyword">for</tt> <tt class="py-name">j</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">observation</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L81"></a><tt class="py-lineno"> 81</tt> <tt class="py-line"> <tt class="py-name">probs</tt><tt class="py-op">[</tt><tt class="py-name">j</tt><tt class="py-op">]</tt> <tt class="py-op">=</tt> <tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">p_conditional</tt><tt class="py-op">[</tt><tt id="link-5" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-5', 'i', 'link-4');">i</a></tt><tt class="py-op">]</tt><tt class="py-op">[</tt><tt class="py-name">j</tt><tt class="py-op">]</tt><tt class="py-op">.</tt><tt id="link-6" class="py-name" targets="Method Bio.Crystal.Crystal.get()=Bio.Crystal.Crystal-class.html#get,Method Bio.Data.CodonTable.AmbiguousForwardTable.get()=Bio.Data.CodonTable.AmbiguousForwardTable-class.html#get,Method Bio.EUtils.MultiDict._BaseMultiDict.get()=Bio.EUtils.MultiDict._BaseMultiDict-class.html#get,Method Bio.EUtils.POM.ElementNode.get()=Bio.EUtils.POM.ElementNode-class.html#get,Method Bio.GenBank.NCBIDictionary.get()=Bio.GenBank.NCBIDictionary-class.html#get,Method Bio.Mindy.BaseDB.DictLookup.get()=Bio.Mindy.BaseDB.DictLookup-class.html#get,Method Bio.Prosite.ExPASyDictionary.get()=Bio.Prosite.ExPASyDictionary-class.html#get,Method Bio.Prosite.Prodoc.ExPASyDictionary.get()=Bio.Prosite.Prodoc.ExPASyDictionary-class.html#get,Method Bio.PubMed.Dictionary.get()=Bio.PubMed.Dictionary-class.html#get,Method Bio.Restriction.Restriction.RestrictionBatch.get()=Bio.Restriction.Restriction.RestrictionBatch-class.html#get,Method Bio.Restriction._Update.RestrictionCompiler.DictionaryBuilder.get()=Bio.Restriction._Update.RestrictionCompiler.DictionaryBuilder-class.html#get,Method Bio.SeqUtils.MissingTable.get()=Bio.SeqUtils.MissingTable-class.html#get,Method Bio.SwissProt.SProt.ExPASyDictionary.get()=Bio.SwissProt.SProt.ExPASyDictionary-class.html#get,Method Bio.config.DBRegistry.DBGroup.get()=Bio.config.DBRegistry.DBGroup-class.html#get,Method Bio.config.DBRegistry.DBObject.get()=Bio.config.DBRegistry.DBObject-class.html#get,Method Bio.config.Registry.Registry.get()=Bio.config.Registry.Registry-class.html#get,Method Martel.Parser.MartelAttributeList.get()=Martel.Parser.MartelAttributeList-class.html#get,Method Martel.msre_parse.Tokenizer.get()=Martel.msre_parse.Tokenizer-class.html#get"><a title="Bio.Crystal.Crystal.get
Bio.Data.CodonTable.AmbiguousForwardTable.get
Bio.EUtils.MultiDict._BaseMultiDict.get
Bio.EUtils.POM.ElementNode.get
Bio.GenBank.NCBIDictionary.get
Bio.Mindy.BaseDB.DictLookup.get
Bio.Prosite.ExPASyDictionary.get
Bio.Prosite.Prodoc.ExPASyDictionary.get
Bio.PubMed.Dictionary.get
Bio.Restriction.Restriction.RestrictionBatch.get
Bio.Restriction._Update.RestrictionCompiler.DictionaryBuilder.get
Bio.SeqUtils.MissingTable.get
Bio.SwissProt.SProt.ExPASyDictionary.get
Bio.config.DBRegistry.DBGroup.get
Bio.config.DBRegistry.DBObject.get
Bio.config.Registry.Registry.get
Martel.Parser.MartelAttributeList.get
Martel.msre_parse.Tokenizer.get" class="py-name" href="#" onclick="return doclink('link-6', 'get', 'link-6');">get</a></tt><tt class="py-op">(</tt><tt class="py-name">observation</tt><tt class="py-op">[</tt><tt class="py-name">j</tt><tt class="py-op">]</tt><tt class="py-op">,</tt> <tt class="py-number">0</tt><tt class="py-op">)</tt> </tt>
<a name="L82"></a><tt class="py-lineno"> 82</tt> <tt class="py-line"> <tt class="py-name">lprobs</tt> <tt class="py-op">=</tt> <tt class="py-op">[</tt><tt id="link-7" class="py-name"><a title="Bio.mathfns" class="py-name" href="#" onclick="return doclink('link-7', 'mathfns', 'link-2');">mathfns</a></tt><tt class="py-op">.</tt><tt id="link-8" class="py-name" targets="Function Bio.mathfns.safe_log()=Bio.mathfns-module.html#safe_log"><a title="Bio.mathfns.safe_log" class="py-name" href="#" onclick="return doclink('link-8', 'safe_log', 'link-8');">safe_log</a></tt><tt class="py-op">(</tt><tt id="link-9" class="py-name"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-9', 'x', 'link-0');">x</a></tt><tt class="py-op">,</tt> <tt class="py-op">-</tt><tt class="py-number">10000</tt><tt class="py-op">)</tt> <tt class="py-keyword">for</tt> <tt id="link-10" class="py-name"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-10', 'x', 'link-0');">x</a></tt> <tt class="py-keyword">in</tt> <tt class="py-name">probs</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">lprob</tt> <tt class="py-op">=</tt> <tt id="link-11" class="py-name" targets="Method Bio.Nexus.Nexus.StepMatrix.sum()=Bio.Nexus.Nexus.StepMatrix-class.html#sum,Function Bio.utils.sum()=Bio.utils-module.html#sum"><a title="Bio.Nexus.Nexus.StepMatrix.sum
Bio.utils.sum" class="py-name" href="#" onclick="return doclink('link-11', 'sum', 'link-11');">sum</a></tt><tt class="py-op">(</tt><tt class="py-name">lprobs</tt><tt class="py-op">)</tt> </tt>
<a name="L84"></a><tt class="py-lineno"> 84</tt> <tt class="py-line"> <tt class="py-name">lp_observation_class</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">lprob</tt><tt class="py-op">)</tt> </tt>
<a name="L85"></a><tt class="py-lineno"> 85</tt> <tt class="py-line"> </tt>
<a name="L86"></a><tt class="py-lineno"> 86</tt> <tt class="py-line"> <tt class="py-comment"># Calculate log P(class).</tt> </tt>
<a name="L87"></a><tt class="py-lineno"> 87</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-name">lp_prior</tt> <tt class="py-op">=</tt> <tt id="link-13" class="py-name" targets="Method Bio.GFF.FeatureAggregate.map()=Bio.GFF.FeatureAggregate-class.html#map"><a title="Bio.GFF.FeatureAggregate.map" class="py-name" href="#" onclick="return doclink('link-13', 'map', 'link-13');">map</a></tt><tt class="py-op">(</tt><tt class="py-name">math</tt><tt class="py-op">.</tt><tt id="link-14" class="py-name" targets="Variable Bio.Affy.CelFile.log=Bio.Affy.CelFile-module.html#log,Variable Bio.LogisticRegression.log=Bio.LogisticRegression-module.html#log,Variable Bio.MarkovModel.log=Bio.MarkovModel-module.html#log,Variable Bio.MaxEntropy.log=Bio.MaxEntropy-module.html#log,Variable Bio.NaiveBayes.log=Bio.NaiveBayes-module.html#log,Variable Bio.Statistics.lowess.log=Bio.Statistics.lowess-module.html#log,Variable Bio.distance.log=Bio.distance-module.html#log,Variable Bio.kNN.log=Bio.kNN-module.html#log"><a title="Bio.Affy.CelFile.log
Bio.LogisticRegression.log
Bio.MarkovModel.log
Bio.MaxEntropy.log
Bio.NaiveBayes.log
Bio.Statistics.lowess.log
Bio.distance.log
Bio.kNN.log" class="py-name" href="#" onclick="return doclink('link-14', 'log', 'link-14');">log</a></tt><tt class="py-op">,</tt> <tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">p_prior</tt><tt class="py-op">)</tt> </tt>
<a name="L88"></a><tt class="py-lineno"> 88</tt> <tt class="py-line"> </tt>
<a name="L89"></a><tt class="py-lineno"> 89</tt> <tt class="py-line"> <tt class="py-comment"># Calculate log P(observation).</tt> </tt>
<a name="L90"></a><tt class="py-lineno"> 90</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-name">lp_observation</tt> <tt class="py-op">=</tt> <tt class="py-number">0.0</tt> <tt class="py-comment"># P(observation)</tt> </tt>
<a name="L91"></a><tt class="py-lineno"> 91</tt> <tt class="py-line"> <tt class="py-keyword">if</tt> <tt class="py-name">scale</tt><tt class="py-op">:</tt> <tt class="py-comment"># Only calculate this if requested.</tt> </tt>
<a name="L92"></a><tt class="py-lineno"> 92</tt> <tt class="py-line"> <tt class="py-comment"># log P(observation) = log SUM_i P(observation|class_i)P(class_i)</tt> </tt>
<a name="L93"></a><tt class="py-lineno"> 93</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-name">obs</tt> <tt class="py-op">=</tt> <tt class="py-name">zeros</tt><tt class="py-op">(</tt><tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">classes</tt><tt class="py-op">)</tt><tt class="py-op">,</tt> <tt id="link-15" class="py-name" targets="Variable Bio.Affy.CelFile.Float32=Bio.Affy.CelFile-module.html#Float32,Variable Bio.LogisticRegression.Float32=Bio.LogisticRegression-module.html#Float32,Variable Bio.MarkovModel.Float32=Bio.MarkovModel-module.html#Float32,Variable Bio.MaxEntropy.Float32=Bio.MaxEntropy-module.html#Float32,Variable Bio.NaiveBayes.Float32=Bio.NaiveBayes-module.html#Float32,Variable Bio.Statistics.lowess.Float32=Bio.Statistics.lowess-module.html#Float32,Variable Bio.distance.Float32=Bio.distance-module.html#Float32,Variable Bio.kNN.Float32=Bio.kNN-module.html#Float32"><a title="Bio.Affy.CelFile.Float32
Bio.LogisticRegression.Float32
Bio.MarkovModel.Float32
Bio.MaxEntropy.Float32
Bio.NaiveBayes.Float32
Bio.Statistics.lowess.Float32
Bio.distance.Float32
Bio.kNN.Float32" class="py-name" href="#" onclick="return doclink('link-15', 'Float32', 'link-15');">Float32</a></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">for</tt> <tt id="link-16" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-16', 'i', 'link-4');">i</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">nb</tt><tt class="py-op">.</tt><tt class="py-name">classes</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L95"></a><tt class="py-lineno"> 95</tt> <tt class="py-line"> <tt class="py-name">obs</tt><tt class="py-op">[</tt><tt id="link-17" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-17', 'i', 'link-4');">i</a></tt><tt class="py-op">]</tt> <tt class="py-op">=</tt> <tt id="link-18" class="py-name"><a title="Bio.mathfns" class="py-name" href="#" onclick="return doclink('link-18', 'mathfns', 'link-2');">mathfns</a></tt><tt class="py-op">.</tt><tt id="link-19" class="py-name" targets="Function Bio.mathfns.safe_exp()=Bio.mathfns-module.html#safe_exp"><a title="Bio.mathfns.safe_exp" class="py-name" href="#" onclick="return doclink('link-19', 'safe_exp', 'link-19');">safe_exp</a></tt><tt class="py-op">(</tt><tt class="py-name">lp_prior</tt><tt class="py-op">[</tt><tt id="link-20" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-20', 'i', 'link-4');">i</a></tt><tt class="py-op">]</tt><tt class="py-op">+</tt><tt class="py-name">lp_observation_class</tt><tt class="py-op">[</tt><tt id="link-21" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-21', 'i', 'link-4');">i</a></tt><tt class="py-op">]</tt><tt class="py-op">,</tt> </tt>
<a name="L96"></a><tt class="py-lineno"> 96</tt> <tt class="py-line"> <tt class="py-name">under</tt><tt class="py-op">=</tt><tt class="py-number">1E-300</tt><tt class="py-op">)</tt> </tt>
<a name="L97"></a><tt class="py-lineno"> 97</tt> <tt class="py-line"> <tt class="py-name">lp_observation</tt> <tt class="py-op">=</tt> <tt class="py-name">math</tt><tt class="py-op">.</tt><tt id="link-22" class="py-name"><a title="Bio.Affy.CelFile.log
Bio.LogisticRegression.log
Bio.MarkovModel.log
Bio.MaxEntropy.log
Bio.NaiveBayes.log
Bio.Statistics.lowess.log
Bio.distance.log
Bio.kNN.log" class="py-name" href="#" onclick="return doclink('link-22', 'log', 'link-14');">log</a></tt><tt class="py-op">(</tt><tt id="link-23" class="py-name"><a title="Bio.Nexus.Nexus.StepMatrix.sum
Bio.utils.sum" class="py-name" href="#" onclick="return doclink('link-23', 'sum', 'link-11');">sum</a></tt><tt class="py-op">(</tt><tt class="py-name">obs</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
<a name="L98"></a><tt class="py-lineno"> 98</tt> <tt class="py-line"> </tt>
<a name="L99"></a><tt class="py-lineno"> 99</tt> <tt class="py-line"> <tt class="py-comment"># Calculate log P(class|observation).</tt> </tt>
<a name="L100"></a><tt class="py-lineno">100</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-name">lp_class_observation</tt> <tt class="py-op">=</tt> <tt class="py-op">{</tt><tt class="py-op">}</tt> <tt class="py-comment"># Dict of class : log P(class|observation)</tt> </tt>
<a name="L101"></a><tt class="py-lineno">101</tt> <tt class="py-line"> <tt class="py-keyword">for</tt> <tt id="link-24" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-24', 'i', 'link-4');">i</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">nb</tt><tt class="py-op">.</tt><tt class="py-name">classes</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L102"></a><tt class="py-lineno">102</tt> <tt class="py-line"> <tt class="py-name">lp_class_observation</tt><tt class="py-op">[</tt><tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">classes</tt><tt class="py-op">[</tt><tt id="link-25" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-25', 'i', 'link-4');">i</a></tt><tt class="py-op">]</tt><tt class="py-op">]</tt> <tt class="py-op">=</tt> \ </tt>
<a name="L103"></a><tt class="py-lineno">103</tt> <tt class="py-line"> <tt class="py-name">lp_observation_class</tt><tt class="py-op">[</tt><tt id="link-26" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-26', 'i', 'link-4');">i</a></tt><tt class="py-op">]</tt> <tt class="py-op">+</tt> <tt class="py-name">lp_prior</tt><tt class="py-op">[</tt><tt id="link-27" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-27', 'i', 'link-4');">i</a></tt><tt class="py-op">]</tt> <tt class="py-op">-</tt> <tt class="py-name">lp_observation</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-keyword">return</tt> <tt class="py-name">lp_class_observation</tt> </tt>
</div><a name="L106"></a><tt class="py-lineno">106</tt> <tt class="py-line"> </tt>
<a name="classify"></a><div id="classify-def"><a name="L107"></a><tt class="py-lineno">107</tt> <a class="py-toggle" href="#" id="classify-toggle" onclick="return toggle('classify');">-</a><tt class="py-line"><tt class="py-keyword">def</tt> <a class="py-def-name" href="Bio.NaiveBayes-module.html#classify">classify</a><tt class="py-op">(</tt><tt class="py-param">nb</tt><tt class="py-op">,</tt> <tt class="py-param">observation</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="classify-collapsed" style="display:none;" pad="+++" indent="++++"></div><div id="classify-expanded"><a name="L108"></a><tt class="py-lineno">108</tt> <tt class="py-line"> <tt class="py-docstring">"""classify(nb, observation) -> class</tt> </tt>
<a name="L109"></a><tt class="py-lineno">109</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L110"></a><tt class="py-lineno">110</tt> <tt class="py-line"><tt class="py-docstring"> Classify an observation into a class.</tt> </tt>
<a name="L111"></a><tt class="py-lineno">111</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L112"></a><tt class="py-lineno">112</tt> <tt class="py-line"><tt class="py-docstring"> """</tt> </tt>
<a name="L113"></a><tt class="py-lineno">113</tt> <tt class="py-line"> <tt class="py-comment"># The class is the one with the highest probability.</tt> </tt>
<a name="L114"></a><tt class="py-lineno">114</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-name">probs</tt> <tt class="py-op">=</tt> <tt id="link-28" class="py-name" targets="Function Bio.LogisticRegression.calculate()=Bio.LogisticRegression-module.html#calculate,Function Bio.MaxEntropy.calculate()=Bio.MaxEntropy-module.html#calculate,Function Bio.NaiveBayes.calculate()=Bio.NaiveBayes-module.html#calculate,Function Bio.kNN.calculate()=Bio.kNN-module.html#calculate"><a title="Bio.LogisticRegression.calculate
Bio.MaxEntropy.calculate
Bio.NaiveBayes.calculate
Bio.kNN.calculate" class="py-name" href="#" onclick="return doclink('link-28', 'calculate', 'link-28');">calculate</a></tt><tt class="py-op">(</tt><tt class="py-name">nb</tt><tt class="py-op">,</tt> <tt class="py-name">observation</tt><tt class="py-op">,</tt> <tt class="py-name">scale</tt><tt class="py-op">=</tt><tt class="py-number">0</tt><tt class="py-op">)</tt> </tt>
<a name="L115"></a><tt class="py-lineno">115</tt> <tt class="py-line"> <tt class="py-name">max_prob</tt> <tt class="py-op">=</tt> <tt class="py-name">max_class</tt> <tt class="py-op">=</tt> <tt class="py-name">None</tt> </tt>
<a name="L116"></a><tt class="py-lineno">116</tt> <tt class="py-line"> <tt class="py-keyword">for</tt> <tt class="py-name">klass</tt> <tt class="py-keyword">in</tt> <tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">classes</tt><tt class="py-op">:</tt> </tt>
<a name="L117"></a><tt class="py-lineno">117</tt> <tt class="py-line"> <tt class="py-keyword">if</tt> <tt class="py-name">max_prob</tt> <tt class="py-keyword">is</tt> <tt class="py-name">None</tt> <tt class="py-keyword">or</tt> <tt class="py-name">probs</tt><tt class="py-op">[</tt><tt class="py-name">klass</tt><tt class="py-op">]</tt> <tt class="py-op">></tt> <tt class="py-name">max_prob</tt><tt class="py-op">:</tt> </tt>
<a name="L118"></a><tt class="py-lineno">118</tt> <tt class="py-line"> <tt class="py-name">max_prob</tt><tt class="py-op">,</tt> <tt class="py-name">max_class</tt> <tt class="py-op">=</tt> <tt class="py-name">probs</tt><tt class="py-op">[</tt><tt class="py-name">klass</tt><tt class="py-op">]</tt><tt class="py-op">,</tt> <tt class="py-name">klass</tt> </tt>
<a name="L119"></a><tt class="py-lineno">119</tt> <tt class="py-line"> <tt class="py-keyword">return</tt> <tt class="py-name">max_class</tt> </tt>
</div><a name="L120"></a><tt class="py-lineno">120</tt> <tt class="py-line"> </tt>
<a name="train"></a><div id="train-def"><a name="L121"></a><tt class="py-lineno">121</tt> <a class="py-toggle" href="#" id="train-toggle" onclick="return toggle('train');">-</a><tt class="py-line"><tt class="py-keyword">def</tt> <a class="py-def-name" href="Bio.NaiveBayes-module.html#train">train</a><tt class="py-op">(</tt><tt class="py-param">training_set</tt><tt class="py-op">,</tt> <tt class="py-param">results</tt><tt class="py-op">,</tt> <tt class="py-param">priors</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">,</tt> <tt class="py-param">typecode</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="train-collapsed" style="display:none;" pad="+++" indent="++++"></div><div id="train-expanded"><a name="L122"></a><tt class="py-lineno">122</tt> <tt class="py-line"> <tt class="py-docstring">"""train(training_set, results[, priors]) -> NaiveBayes</tt> </tt>
<a name="L123"></a><tt class="py-lineno">123</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L124"></a><tt class="py-lineno">124</tt> <tt class="py-line"><tt class="py-docstring"> Train a naive bayes classifier on a training set. training_set is a</tt> </tt>
<a name="L125"></a><tt class="py-lineno">125</tt> <tt class="py-line"><tt class="py-docstring"> list of observations. results is a list of the class assignments</tt> </tt>
<a name="L126"></a><tt class="py-lineno">126</tt> <tt class="py-line"><tt class="py-docstring"> for each observation. Thus, training_set and results must be the same</tt> </tt>
<a name="L127"></a><tt class="py-lineno">127</tt> <tt class="py-line"><tt class="py-docstring"> length. priors is an optional dictionary specifying the prior</tt> </tt>
<a name="L128"></a><tt class="py-lineno">128</tt> <tt class="py-line"><tt class="py-docstring"> probabilities for each type of result. If not specified, the priors</tt> </tt>
<a name="L129"></a><tt class="py-lineno">129</tt> <tt class="py-line"><tt class="py-docstring"> will be estimated from the training results.</tt> </tt>
<a name="L130"></a><tt class="py-lineno">130</tt> <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L131"></a><tt class="py-lineno">131</tt> <tt class="py-line"><tt class="py-docstring"> """</tt> </tt>
<a name="L132"></a><tt class="py-lineno">132</tt> <tt class="py-line"> <tt class="py-keyword">if</tt> <tt class="py-keyword">not</tt> <tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">training_set</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L133"></a><tt class="py-lineno">133</tt> <tt class="py-line"> <tt class="py-keyword">raise</tt> <tt class="py-name">ValueError</tt><tt class="py-op">,</tt> <tt class="py-string">"No data in the training set."</tt> </tt>
<a name="L134"></a><tt class="py-lineno">134</tt> <tt class="py-line"> <tt class="py-keyword">if</tt> <tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">training_set</tt><tt class="py-op">)</tt> <tt class="py-op">!=</tt> <tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">results</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L135"></a><tt class="py-lineno">135</tt> <tt class="py-line"> <tt class="py-keyword">raise</tt> <tt class="py-name">ValueError</tt><tt class="py-op">,</tt> <tt class="py-string">"training_set and results should be parallel lists."</tt> </tt>
<a name="L136"></a><tt class="py-lineno">136</tt> <tt class="py-line"> </tt>
<a name="L137"></a><tt class="py-lineno">137</tt> <tt class="py-line"> <tt class="py-comment"># If no typecode is specified, try to pick a reasonable one. If</tt> </tt>
<a name="L138"></a><tt class="py-lineno">138</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-comment"># training_set is a Numeric array, then use that typecode.</tt> </tt>
<a name="L139"></a><tt class="py-lineno">139</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-comment"># Otherwise, choose a reasonable default.</tt> </tt>
<a name="L140"></a><tt class="py-lineno">140</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-comment"># XXX NOT IMPLEMENTED</tt> </tt>
<a name="L141"></a><tt class="py-lineno">141</tt> <tt class="py-line"><tt class="py-comment"></tt> </tt>
<a name="L142"></a><tt class="py-lineno">142</tt> <tt class="py-line"> <tt class="py-comment"># Check to make sure each vector in the training set has the same</tt> </tt>
<a name="L143"></a><tt class="py-lineno">143</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-comment"># dimensionality.</tt> </tt>
<a name="L144"></a><tt class="py-lineno">144</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-name">dimensions</tt> <tt class="py-op">=</tt> <tt class="py-op">[</tt><tt class="py-name">len</tt><tt class="py-op">(</tt><tt id="link-29" class="py-name"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-29', 'x', 'link-0');">x</a></tt><tt class="py-op">)</tt> <tt class="py-keyword">for</tt> <tt id="link-30" class="py-name"><a title="Bio.MarkovModel.x
Bio.Statistics.lowess.x" class="py-name" href="#" onclick="return doclink('link-30', 'x', 'link-0');">x</a></tt> <tt class="py-keyword">in</tt> <tt class="py-name">training_set</tt><tt class="py-op">]</tt> </tt>
<a name="L145"></a><tt class="py-lineno">145</tt> <tt class="py-line"> <tt class="py-keyword">if</tt> <tt class="py-name">min</tt><tt class="py-op">(</tt><tt class="py-name">dimensions</tt><tt class="py-op">)</tt> <tt class="py-op">!=</tt> <tt class="py-name">max</tt><tt class="py-op">(</tt><tt class="py-name">dimensions</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L146"></a><tt class="py-lineno">146</tt> <tt class="py-line"> <tt class="py-keyword">raise</tt> <tt class="py-name">ValueError</tt><tt class="py-op">,</tt> <tt class="py-string">"observations have different dimensionality"</tt> </tt>
<a name="L147"></a><tt class="py-lineno">147</tt> <tt class="py-line"> </tt>
<a name="L148"></a><tt class="py-lineno">148</tt> <tt class="py-line"> <tt class="py-name">nb</tt> <tt class="py-op">=</tt> <tt id="link-31" class="py-name" targets="Module Bio.NaiveBayes=Bio.NaiveBayes-module.html,Class Bio.NaiveBayes.NaiveBayes=Bio.NaiveBayes.NaiveBayes-class.html"><a title="Bio.NaiveBayes
Bio.NaiveBayes.NaiveBayes" class="py-name" href="#" onclick="return doclink('link-31', 'NaiveBayes', 'link-31');">NaiveBayes</a></tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L149"></a><tt class="py-lineno">149</tt> <tt class="py-line"> <tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">dimensionality</tt> <tt class="py-op">=</tt> <tt class="py-name">dimensions</tt><tt class="py-op">[</tt><tt class="py-number">0</tt><tt class="py-op">]</tt> </tt>
<a name="L150"></a><tt class="py-lineno">150</tt> <tt class="py-line"> </tt>
<a name="L151"></a><tt class="py-lineno">151</tt> <tt class="py-line"> <tt class="py-comment"># Get a list of all the classes.</tt> </tt>
<a name="L152"></a><tt class="py-lineno">152</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">classes</tt> <tt class="py-op">=</tt> <tt id="link-32" class="py-name"><a title="Bio.listfns" class="py-name" href="#" onclick="return doclink('link-32', 'listfns', 'link-3');">listfns</a></tt><tt class="py-op">.</tt><tt id="link-33" class="py-name" targets="Method Bio.Crystal.Crystal.items()=Bio.Crystal.Crystal-class.html#items,Method Bio.EUtils.MultiDict._BaseMultiDict.items()=Bio.EUtils.MultiDict._BaseMultiDict-class.html#items,Variable Bio.Entrez.SerialSet.items=Bio.Entrez.SerialSet-module.html#items,Method Bio.GenBank.NCBIDictionary.items()=Bio.GenBank.NCBIDictionary-class.html#items,Method Bio.Mindy.BaseDB.DictLookup.items()=Bio.Mindy.BaseDB.DictLookup-class.html#items,Method Bio.Prosite.ExPASyDictionary.items()=Bio.Prosite.ExPASyDictionary-class.html#items,Method Bio.Prosite.Prodoc.ExPASyDictionary.items()=Bio.Prosite.Prodoc.ExPASyDictionary-class.html#items,Method Bio.PubMed.Dictionary.items()=Bio.PubMed.Dictionary-class.html#items,Method Bio.SwissProt.SProt.ExPASyDictionary.items()=Bio.SwissProt.SProt.ExPASyDictionary-class.html#items,Method Bio.config.Registry.Registry.items()=Bio.config.Registry.Registry-class.html#items,Function Bio.listfns.items()=Bio.listfns-module.html#items,Method BioSQL.BioSeqDatabase.BioSeqDatabase.items()=BioSQL.BioSeqDatabase.BioSeqDatabase-class.html#items,Method BioSQL.BioSeqDatabase.DBServer.items()=BioSQL.BioSeqDatabase.DBServer-class.html#items,Method Martel.Parser.MartelAttributeList.items()=Martel.Parser.MartelAttributeList-class.html#items"><a title="Bio.Crystal.Crystal.items
Bio.EUtils.MultiDict._BaseMultiDict.items
Bio.Entrez.SerialSet.items
Bio.GenBank.NCBIDictionary.items
Bio.Mindy.BaseDB.DictLookup.items
Bio.Prosite.ExPASyDictionary.items
Bio.Prosite.Prodoc.ExPASyDictionary.items
Bio.PubMed.Dictionary.items
Bio.SwissProt.SProt.ExPASyDictionary.items
Bio.config.Registry.Registry.items
Bio.listfns.items
BioSQL.BioSeqDatabase.BioSeqDatabase.items
BioSQL.BioSeqDatabase.DBServer.items
Martel.Parser.MartelAttributeList.items" class="py-name" href="#" onclick="return doclink('link-33', 'items', 'link-33');">items</a></tt><tt class="py-op">(</tt><tt class="py-name">results</tt><tt class="py-op">)</tt> </tt>
<a name="L153"></a><tt class="py-lineno">153</tt> <tt class="py-line"> <tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">classes</tt><tt class="py-op">.</tt><tt id="link-34" 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-34', 'sort', 'link-34');">sort</a></tt><tt class="py-op">(</tt><tt class="py-op">)</tt> <tt class="py-comment"># keep it tidy</tt> </tt>
<a name="L154"></a><tt class="py-lineno">154</tt> <tt class="py-line"> </tt>
<a name="L155"></a><tt class="py-lineno">155</tt> <tt class="py-line"> <tt class="py-comment"># Estimate the prior probabilities for the classes.</tt> </tt>
<a name="L156"></a><tt class="py-lineno">156</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-keyword">if</tt> <tt class="py-name">priors</tt> <tt class="py-keyword">is</tt> <tt class="py-keyword">not</tt> <tt class="py-name">None</tt><tt class="py-op">:</tt> </tt>
<a name="L157"></a><tt class="py-lineno">157</tt> <tt class="py-line"> <tt class="py-name">percs</tt> <tt class="py-op">=</tt> <tt class="py-name">priors</tt> </tt>
<a name="L158"></a><tt class="py-lineno">158</tt> <tt class="py-line"> <tt class="py-keyword">else</tt><tt class="py-op">:</tt> </tt>
<a name="L159"></a><tt class="py-lineno">159</tt> <tt class="py-line"> <tt class="py-name">percs</tt> <tt class="py-op">=</tt> <tt id="link-35" class="py-name"><a title="Bio.listfns" class="py-name" href="#" onclick="return doclink('link-35', 'listfns', 'link-3');">listfns</a></tt><tt class="py-op">.</tt><tt id="link-36" class="py-name" targets="Function Bio.listfns.contents()=Bio.listfns-module.html#contents"><a title="Bio.listfns.contents" class="py-name" href="#" onclick="return doclink('link-36', 'contents', 'link-36');">contents</a></tt><tt class="py-op">(</tt><tt class="py-name">results</tt><tt class="py-op">)</tt> </tt>
<a name="L160"></a><tt class="py-lineno">160</tt> <tt class="py-line"> <tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">p_prior</tt> <tt class="py-op">=</tt> <tt class="py-name">zeros</tt><tt class="py-op">(</tt><tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">classes</tt><tt class="py-op">)</tt><tt class="py-op">,</tt> <tt id="link-37" class="py-name" targets="Variable Bio.Affy.CelFile.Float16=Bio.Affy.CelFile-module.html#Float16,Variable Bio.LogisticRegression.Float16=Bio.LogisticRegression-module.html#Float16,Variable Bio.MarkovModel.Float16=Bio.MarkovModel-module.html#Float16,Variable Bio.MaxEntropy.Float16=Bio.MaxEntropy-module.html#Float16,Variable Bio.NaiveBayes.Float16=Bio.NaiveBayes-module.html#Float16,Variable Bio.Statistics.lowess.Float16=Bio.Statistics.lowess-module.html#Float16,Variable Bio.distance.Float16=Bio.distance-module.html#Float16,Variable Bio.kNN.Float16=Bio.kNN-module.html#Float16"><a title="Bio.Affy.CelFile.Float16
Bio.LogisticRegression.Float16
Bio.MarkovModel.Float16
Bio.MaxEntropy.Float16
Bio.NaiveBayes.Float16
Bio.Statistics.lowess.Float16
Bio.distance.Float16
Bio.kNN.Float16" class="py-name" href="#" onclick="return doclink('link-37', 'Float16', 'link-37');">Float16</a></tt><tt class="py-op">)</tt> </tt>
<a name="L161"></a><tt class="py-lineno">161</tt> <tt class="py-line"> <tt class="py-keyword">for</tt> <tt id="link-38" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-38', 'i', 'link-4');">i</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">nb</tt><tt class="py-op">.</tt><tt class="py-name">classes</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L162"></a><tt class="py-lineno">162</tt> <tt class="py-line"> <tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">p_prior</tt><tt class="py-op">[</tt><tt id="link-39" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-39', 'i', 'link-4');">i</a></tt><tt class="py-op">]</tt> <tt class="py-op">=</tt> <tt class="py-name">percs</tt><tt class="py-op">[</tt><tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">classes</tt><tt class="py-op">[</tt><tt id="link-40" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-40', 'i', 'link-4');">i</a></tt><tt class="py-op">]</tt><tt class="py-op">]</tt> </tt>
<a name="L163"></a><tt class="py-lineno">163</tt> <tt class="py-line"> </tt>
<a name="L164"></a><tt class="py-lineno">164</tt> <tt class="py-line"> <tt class="py-comment"># Collect all the observations in class. For each class, make a</tt> </tt>
<a name="L165"></a><tt class="py-lineno">165</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-comment"># matrix of training instances versus dimensions. I might be able</tt> </tt>
<a name="L166"></a><tt class="py-lineno">166</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-comment"># to optimize this with Numeric, if the training_set parameter</tt> </tt>
<a name="L167"></a><tt class="py-lineno">167</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-comment"># were guaranteed to be a matrix. However, this may not be the</tt> </tt>
<a name="L168"></a><tt class="py-lineno">168</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-comment"># case, because the client may be hacking up a sparse matrix or</tt> </tt>
<a name="L169"></a><tt class="py-lineno">169</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-comment"># something.</tt> </tt>
<a name="L170"></a><tt class="py-lineno">170</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-name">c2i</tt> <tt class="py-op">=</tt> <tt id="link-41" class="py-name"><a title="Bio.listfns" class="py-name" href="#" onclick="return doclink('link-41', 'listfns', 'link-3');">listfns</a></tt><tt class="py-op">.</tt><tt id="link-42" class="py-name" targets="Function Bio.listfns.itemindex()=Bio.listfns-module.html#itemindex"><a title="Bio.listfns.itemindex" class="py-name" href="#" onclick="return doclink('link-42', 'itemindex', 'link-42');">itemindex</a></tt><tt class="py-op">(</tt><tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">classes</tt><tt class="py-op">)</tt> <tt class="py-comment"># class to index of class</tt> </tt>
<a name="L171"></a><tt class="py-lineno">171</tt> <tt class="py-line"> <tt class="py-name">observations</tt> <tt class="py-op">=</tt> <tt class="py-op">[</tt><tt class="py-op">[</tt><tt class="py-op">]</tt> <tt class="py-keyword">for</tt> <tt class="py-name">c</tt> <tt class="py-keyword">in</tt> <tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">classes</tt><tt class="py-op">]</tt> <tt class="py-comment"># separate observations by class</tt> </tt>
<a name="L172"></a><tt class="py-lineno">172</tt> <tt class="py-line"> <tt class="py-keyword">for</tt> <tt id="link-43" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-43', 'i', 'link-4');">i</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">results</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L173"></a><tt class="py-lineno">173</tt> <tt class="py-line"> <tt class="py-name">klass</tt><tt class="py-op">,</tt> <tt class="py-name">obs</tt> <tt class="py-op">=</tt> <tt class="py-name">results</tt><tt class="py-op">[</tt><tt id="link-44" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-44', 'i', 'link-4');">i</a></tt><tt class="py-op">]</tt><tt class="py-op">,</tt> <tt class="py-name">training_set</tt><tt class="py-op">[</tt><tt id="link-45" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-45', 'i', 'link-4');">i</a></tt><tt class="py-op">]</tt> </tt>
<a name="L174"></a><tt class="py-lineno">174</tt> <tt class="py-line"> <tt class="py-name">observations</tt><tt class="py-op">[</tt><tt class="py-name">c2i</tt><tt class="py-op">[</tt><tt class="py-name">klass</tt><tt class="py-op">]</tt><tt class="py-op">]</tt><tt class="py-op">.</tt><tt id="link-46" class="py-name"><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-46', 'append', 'link-12');">append</a></tt><tt class="py-op">(</tt><tt class="py-name">obs</tt><tt class="py-op">)</tt> </tt>
<a name="L175"></a><tt class="py-lineno">175</tt> <tt class="py-line"> <tt class="py-comment"># Now make the observations Numeric matrics.</tt> </tt>
<a name="L176"></a><tt class="py-lineno">176</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-keyword">for</tt> <tt id="link-47" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-47', 'i', 'link-4');">i</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">observations</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L177"></a><tt class="py-lineno">177</tt> <tt class="py-line"> <tt class="py-comment"># XXX typecode must be specified!</tt> </tt>
<a name="L178"></a><tt class="py-lineno">178</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-name">observations</tt><tt class="py-op">[</tt><tt id="link-48" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-48', 'i', 'link-4');">i</a></tt><tt class="py-op">]</tt> <tt class="py-op">=</tt> <tt class="py-name">asarray</tt><tt class="py-op">(</tt><tt class="py-name">observations</tt><tt class="py-op">[</tt><tt id="link-49" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-49', 'i', 'link-4');">i</a></tt><tt class="py-op">]</tt><tt class="py-op">,</tt> <tt class="py-name">typecode</tt><tt class="py-op">)</tt> </tt>
<a name="L179"></a><tt class="py-lineno">179</tt> <tt class="py-line"> </tt>
<a name="L180"></a><tt class="py-lineno">180</tt> <tt class="py-line"> <tt class="py-comment"># Calculate P(value|class,dim) for every class.</tt> </tt>
<a name="L181"></a><tt class="py-lineno">181</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-comment"># This is a good loop to optimize.</tt> </tt>
<a name="L182"></a><tt class="py-lineno">182</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">p_conditional</tt> <tt class="py-op">=</tt> <tt class="py-op">[</tt><tt class="py-op">]</tt> </tt>
<a name="L183"></a><tt class="py-lineno">183</tt> <tt class="py-line"> <tt class="py-keyword">for</tt> <tt id="link-50" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-50', 'i', 'link-4');">i</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">nb</tt><tt class="py-op">.</tt><tt class="py-name">classes</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L184"></a><tt class="py-lineno">184</tt> <tt class="py-line"> <tt class="py-name">class_observations</tt> <tt class="py-op">=</tt> <tt class="py-name">observations</tt><tt class="py-op">[</tt><tt id="link-51" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-51', 'i', 'link-4');">i</a></tt><tt class="py-op">]</tt> <tt class="py-comment"># observations for this class</tt> </tt>
<a name="L185"></a><tt class="py-lineno">185</tt> <tt class="py-line"> <tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">p_conditional</tt><tt class="py-op">.</tt><tt id="link-52" class="py-name"><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-52', 'append', 'link-12');">append</a></tt><tt class="py-op">(</tt><tt class="py-op">[</tt><tt class="py-name">None</tt><tt class="py-op">]</tt> <tt class="py-op">*</tt> <tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">dimensionality</tt><tt class="py-op">)</tt> </tt>
<a name="L186"></a><tt class="py-lineno">186</tt> <tt class="py-line"> <tt class="py-keyword">for</tt> <tt class="py-name">j</tt> <tt class="py-keyword">in</tt> <tt class="py-name">range</tt><tt class="py-op">(</tt><tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">dimensionality</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L187"></a><tt class="py-lineno">187</tt> <tt class="py-line"> <tt class="py-comment"># Collect all the values in this dimension.</tt> </tt>
<a name="L188"></a><tt class="py-lineno">188</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt id="link-53" 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-53', 'values', 'link-53');">values</a></tt> <tt class="py-op">=</tt> <tt class="py-name">class_observations</tt><tt class="py-op">[</tt><tt class="py-op">:</tt><tt class="py-op">,</tt> <tt class="py-name">j</tt><tt class="py-op">]</tt> </tt>
<a name="L189"></a><tt class="py-lineno">189</tt> <tt class="py-line"> </tt>
<a name="L190"></a><tt class="py-lineno">190</tt> <tt class="py-line"> <tt class="py-comment"># Add pseudocounts here. This needs to be parameterized.</tt> </tt>
<a name="L191"></a><tt class="py-lineno">191</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-comment">#values = list(values) + range(len(nb.classes)) # XXX add 1</tt> </tt>
<a name="L192"></a><tt class="py-lineno">192</tt> <tt class="py-line"><tt class="py-comment"></tt> </tt>
<a name="L193"></a><tt class="py-lineno">193</tt> <tt class="py-line"> <tt class="py-comment"># Estimate P(value|class,dim)</tt> </tt>
<a name="L194"></a><tt class="py-lineno">194</tt> <tt class="py-line"><tt class="py-comment"></tt> <tt class="py-name">nb</tt><tt class="py-op">.</tt><tt class="py-name">p_conditional</tt><tt class="py-op">[</tt><tt id="link-54" class="py-name"><a title="Bio.PDB.Polypeptide.i" class="py-name" href="#" onclick="return doclink('link-54', 'i', 'link-4');">i</a></tt><tt class="py-op">]</tt><tt class="py-op">[</tt><tt class="py-name">j</tt><tt class="py-op">]</tt> <tt class="py-op">=</tt> <tt id="link-55" class="py-name"><a title="Bio.listfns" class="py-name" href="#" onclick="return doclink('link-55', 'listfns', 'link-3');">listfns</a></tt><tt class="py-op">.</tt><tt id="link-56" class="py-name"><a title="Bio.listfns.contents" class="py-name" href="#" onclick="return doclink('link-56', 'contents', 'link-36');">contents</a></tt><tt class="py-op">(</tt><tt id="link-57" 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-57', 'values', 'link-53');">values</a></tt><tt class="py-op">)</tt> </tt>
<a name="L195"></a><tt class="py-lineno">195</tt> <tt class="py-line"> <tt class="py-keyword">return</tt> <tt class="py-name">nb</tt> </tt>
</div><a name="L196"></a><tt class="py-lineno">196</tt> <tt class="py-line"> </tt><script type="text/javascript">
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