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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>GKNN</b></big></big></big><br>extends <a href="type_GSupervisedLearner.html">GSupervisedLearner</a><br></td><td> Implements the K-Nearest Neighbor learning algorithm</td></tr></table><br><br><big><big><i>Constructors (public)</i></big></big><br><div style="margin-left: 40px;"><big><b>GKNN</b></big>(<a href="type_GArffRelation.html">GArffRelation</a>* pRelation, int nNeighbors)<br></div><br><big><big><i>Destructors</i></big></big><br><div style="margin-left: 40px;"><big><b>~GKNN</b></big>()<br></div><br><big><big><i>Virtual (public)</i></big></big><br><div style="margin-left: 40px;">void <big><b>Eval</b></big>(double* pRow)<br><div style="margin-left: 80px;"><font color=brown> Evaluates the input values in the provided row and deduce the output values</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 all the points in pData</font></div><br></div><br><big><big><i>Public</i></big></big><br><div style="margin-left: 40px;">void <big><b>AddVector</b></big>(double* pVector)<br><div style="margin-left: 80px;"><font color=brown> Makes a copy of the vector and adds it to the internal set</font></div><br>void <big><b>ComputeScaleFactors</b></big>(<a href="type_GArffData.html">GArffData</a>* pData)<br><div style="margin-left: 80px;"><font color=brown> Compute the amount to scale each dimension so that all dimensions have equal weight</font></div><br>double <big><b>DropRow</b></big>(int* nRow)<br><div style="margin-left: 80px;"><font color=brown> Drops a point from the collection</font></div><br>void <big><b>EvalEqualWeight</b></big>(double* pRow)<br><div style="margin-left: 80px;"><font color=brown> Evaluate with each neighbor having equal vote</font></div><br>void <big><b>EvalLinearWeight</b></big>(double* pRow)<br><div style="margin-left: 80px;"><font color=brown> Evaluate with each neighbor having a linear vote</font></div><br>int <big><b>FindLeastHelpfulRow</b></big>(<a href="type_GArffData.html">GArffData</a>* pTestSet)<br><div style="margin-left: 80px;"><font color=brown> Find the row that helps the lest with predictive accuracy (very expensive)</font></div><br></div><br><big><big><i>Protected</i></big></big><br><div style="margin-left: 40px;">void <big><b>FindNeighbors</b></big>(double* pRow)<br></div><br></body></html>⌨️ 快捷键说明
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