type_gnaiveinstance.html

来自「一个由Mike Gashler完成的机器学习方面的includes neural」· HTML 代码 · 共 43 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>GNaiveInstance</b></big></big></big><br>extends <a href="type_GSupervisedLearner.html">GSupervisedLearner</a><br></td><td> This is a cross between a KNN instance learner and a Naive Bayes learner.
 It's not yet well tested, but in theory it should be able to regress with
 precision like KNN, but be tolerant to the curse of dimensionality like
 Naive Bayes.
</td></tr></table><br><br><big><big><i>Constructors (public)</i></big></big><br><div style="margin-left: 40px;"><big><b>GNaiveInstance</b></big>(<a href="type_GArffRelation.html">GArffRelation</a>* pRelation, int nNeighbors)<br><div style="margin-left: 80px;"><font color=brown> nNeighbors is the number of neighbors (in every dimension)
 that will contribute to the output value. dDiscernment tells
 it how much to emphasize low-variance patterns over high-variance
 patterns. (0=none, 1=proportional to variance ratio)
</font></div><br></div><br><big><big><i>Destructors</i></big></big><br><div style="margin-left: 40px;"><big><b>~GNaiveInstance</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* pVector)<br><div style="margin-left: 80px;"><font color=brown> Deduce the output values from the input 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 using all the samples in a collection
</font></div><br></div><br><big><big><i>Public</i></big></big><br><div style="margin-left: 40px;">void <big><b>AddInstance</b></big>(double* pVector)<br><div style="margin-left: 80px;"><font color=brown> Incrementally train with a single instance
</font></div><br></div><br><big><big><i>Protected</i></big></big><br><div style="margin-left: 40px;">void <big><b>EvalInput</b></big>(int nInputDim, double dInput)<br></div><br></body></html>

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