type_gsupervisedlearner.html

来自「一个由Mike Gashler完成的机器学习方面的includes neural」· HTML 代码 · 共 46 行

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<html><head><title>Generated Documentation</title></head><body>	<image src="headerimage.png">	<br><br><table><tr><td><big><big><big style="font-family: arial;"><b>GSupervisedLearner</b></big></big></big><br><br></td><td></td></tr></table><br><br><big><big><i>Constructors (public)</i></big></big><br><div style="margin-left: 40px;"><big><b>GSupervisedLearner</b></big>(<a href="type_GArffRelation.html">GArffRelation</a>* pRelation)<br></div><br><big><big><i>Destructors</i></big></big><br><div style="margin-left: 40px;"><big><b>~GSupervisedLearner</b></big>()<br></div><br><big><big><i>Abstracts</i></big></big><br><div style="margin-left: 40px;">void <big><b>Eval</b></big>(double* pVector)<br><div style="margin-left: 80px;"><font color=brown> Evaluates the input values in the provided vector and deduce the output values</font></div><br>void <big><b>Reset</b></big>()<br><div style="margin-left: 80px;"><font color=brown> Discard any training (but not any settings) so it can be trained again</font></div><br>void <big><b>Train</b></big>(<a href="type_GArffData.html">GArffData</a>* pData)<br><div style="margin-left: 80px;"><font color=brown> Train with the provided data</font></div><br></div><br><big><big><i>Public</i></big></big><br><div style="margin-left: 40px;">double <big><b>CrossValidate</b></big>(<a href="type_GArffData.html">GArffData</a>* pData, int nFolds, bool bRegression)<br><div style="margin-left: 80px;"><font color=brown> Perform n-fold cross validation on pData. If bRegression is true, it will return the average mean squared error. If bRegression is false, it will return the average predictive accuracy.</font></div><br><a href="type_GArffRelation.html">GArffRelation</a>* <big><b>GetRelation</b></big>()<br><div style="margin-left: 80px;"><font color=brown> Returns the relation used to construct this learner</font></div><br>double <big><b>MeasureMeanSquaredError</b></big>(<a href="type_GArffData.html">GArffData</a>* pData)<br><div style="margin-left: 80px;"><font color=brown> Computes the mean squared error. If there are multiple output attributes, each one is considered independently. If there are discreet output attributes, a correct classification is considered to be a squared error of 0 and an incorrect classification is a squared error of 1.</font></div><br>double <big><b>MeasurePredictiveAccuracy</b></big>(<a href="type_GArffData.html">GArffData</a>* pData)<br><div style="margin-left: 80px;"><font color=brown> Computes predictive accuracy (the ratio of samples that are correctly classified to total samples). If there is more than one output attribute, each output attribute is evaluated independently. If there are continuous output values, it uses 1-1/(1+(squared error)) as an estimate so that a small squared error will be close to 1 (correct) and a large squared error will be close to 0 (incorrect).</font></div><br></div><br></body></html>

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