additiveregression.html

< 数据挖掘--实用机器学习技术及java实现> 一书结合数据挖掘和机器学习的知识,作者陈述了自动挖掘模式的基础理论,并且以java语言实现了具有代表性的各类数据挖掘方法.例如:class

</DL>
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<A NAME="getDebug()"><!-- --></A><H3>
getDebug</H3>
<PRE>
public boolean <B>getDebug</B>()</PRE>
<DL>
<DD>Gets whether debugging has been turned on<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>true if debugging has been turned on</DL>
</DD>
</DL>
<HR>

<A NAME="classifierTipText()"><!-- --></A><H3>
classifierTipText</H3>
<PRE>
public java.lang.String <B>classifierTipText</B>()</PRE>
<DL>
<DD>Returns the tip text for this property<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>tip text for this property suitable for
 displaying in the explorer/experimenter gui</DL>
</DD>
</DL>
<HR>

<A NAME="setClassifier(weka.classifiers.Classifier)"><!-- --></A><H3>
setClassifier</H3>
<PRE>
public void <B>setClassifier</B>(<A HREF="../../weka/classifiers/Classifier.html">Classifier</A>&nbsp;classifier)</PRE>
<DL>
<DD>Sets the classifier<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>classifier</CODE> - the classifier with all options set.</DL>
</DD>
</DL>
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<A NAME="getClassifier()"><!-- --></A><H3>
getClassifier</H3>
<PRE>
public <A HREF="../../weka/classifiers/Classifier.html">Classifier</A> <B>getClassifier</B>()</PRE>
<DL>
<DD>Gets the classifier used.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>the classifier</DL>
</DD>
</DL>
<HR>

<A NAME="getClassifierSpec()"><!-- --></A><H3>
getClassifierSpec</H3>
<PRE>
protected java.lang.String <B>getClassifierSpec</B>()</PRE>
<DL>
<DD>Gets the classifier specification string, which contains the class name of
 the classifier and any options to the classifier<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>the classifier string.</DL>
</DD>
</DL>
<HR>

<A NAME="maxModelsTipText()"><!-- --></A><H3>
maxModelsTipText</H3>
<PRE>
public java.lang.String <B>maxModelsTipText</B>()</PRE>
<DL>
<DD>Returns the tip text for this property<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>tip text for this property suitable for
 displaying in the explorer/experimenter gui</DL>
</DD>
</DL>
<HR>

<A NAME="setMaxModels(int)"><!-- --></A><H3>
setMaxModels</H3>
<PRE>
public void <B>setMaxModels</B>(int&nbsp;maxM)</PRE>
<DL>
<DD>Set the maximum number of models to generate<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>maxM</CODE> - the maximum number of models</DL>
</DD>
</DL>
<HR>

<A NAME="getMaxModels()"><!-- --></A><H3>
getMaxModels</H3>
<PRE>
public int <B>getMaxModels</B>()</PRE>
<DL>
<DD>Get the max number of models to generate<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>the max number of models to generate</DL>
</DD>
</DL>
<HR>

<A NAME="shrinkageTipText()"><!-- --></A><H3>
shrinkageTipText</H3>
<PRE>
public java.lang.String <B>shrinkageTipText</B>()</PRE>
<DL>
<DD>Returns the tip text for this property<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>tip text for this property suitable for
 displaying in the explorer/experimenter gui</DL>
</DD>
</DL>
<HR>

<A NAME="setShrinkage(double)"><!-- --></A><H3>
setShrinkage</H3>
<PRE>
public void <B>setShrinkage</B>(double&nbsp;l)</PRE>
<DL>
<DD>Set the shrinkage parameter<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>l</CODE> - the shrinkage rate.</DL>
</DD>
</DL>
<HR>

<A NAME="getShrinkage()"><!-- --></A><H3>
getShrinkage</H3>
<PRE>
public double <B>getShrinkage</B>()</PRE>
<DL>
<DD>Get the shrinkage rate.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>the value of the learning rate</DL>
</DD>
</DL>
<HR>

<A NAME="buildClassifier(weka.core.Instances)"><!-- --></A><H3>
buildClassifier</H3>
<PRE>
public void <B>buildClassifier</B>(<A HREF="../../weka/core/Instances.html">Instances</A>&nbsp;data)
                     throws java.lang.Exception</PRE>
<DL>
<DD>Build the classifier on the supplied data<DD><DL>
<DT><B>Overrides:</B><DD><CODE><A HREF="../../weka/classifiers/Classifier.html#buildClassifier(weka.core.Instances)">buildClassifier</A></CODE> in class <CODE><A HREF="../../weka/classifiers/Classifier.html">Classifier</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>data</CODE> - the training data<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if the classifier could not be built successfully</DL>
</DD>
</DL>
<HR>

<A NAME="classifyInstance(weka.core.Instance)"><!-- --></A><H3>
classifyInstance</H3>
<PRE>
public double <B>classifyInstance</B>(<A HREF="../../weka/core/Instance.html">Instance</A>&nbsp;inst)
                        throws java.lang.Exception</PRE>
<DL>
<DD>Classify an instance.<DD><DL>
<DT><B>Overrides:</B><DD><CODE><A HREF="../../weka/classifiers/Classifier.html#classifyInstance(weka.core.Instance)">classifyInstance</A></CODE> in class <CODE><A HREF="../../weka/classifiers/Classifier.html">Classifier</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>inst</CODE> - the instance to predict<DT><B>Returns:</B><DD>a prediction for the instance<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if an error occurs</DL>
</DD>
</DL>
<HR>

<A NAME="enumerateMeasures()"><!-- --></A><H3>
enumerateMeasures</H3>
<PRE>
public java.util.Enumeration <B>enumerateMeasures</B>()</PRE>
<DL>
<DD>Returns an enumeration of the additional measure names<DD><DL>
<DT><B>Specified by: </B><DD><CODE><A HREF="../../weka/core/AdditionalMeasureProducer.html#enumerateMeasures()">enumerateMeasures</A></CODE> in interface <CODE><A HREF="../../weka/core/AdditionalMeasureProducer.html">AdditionalMeasureProducer</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>an enumeration of the measure names</DL>
</DD>
</DL>
<HR>

<A NAME="getMeasure(java.lang.String)"><!-- --></A><H3>
getMeasure</H3>
<PRE>
public double <B>getMeasure</B>(java.lang.String&nbsp;additionalMeasureName)</PRE>
<DL>
<DD>Returns the value of the named measure<DD><DL>
<DT><B>Specified by: </B><DD><CODE><A HREF="../../weka/core/AdditionalMeasureProducer.html#getMeasure(java.lang.String)">getMeasure</A></CODE> in interface <CODE><A HREF="../../weka/core/AdditionalMeasureProducer.html">AdditionalMeasureProducer</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>measureName</CODE> - the name of the measure to query for its value<DT><B>Returns:</B><DD>the value of the named measure<DT><B>Throws:</B><DD><CODE>java.lang.IllegalArgumentException</CODE> - if the named measure is not supported</DL>
</DD>
</DL>
<HR>

<A NAME="measureNumIterations()"><!-- --></A><H3>
measureNumIterations</H3>
<PRE>
public double <B>measureNumIterations</B>()</PRE>
<DL>
<DD>return the number of iterations (base classifiers) completed<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>the number of iterations (same as number of base classifier
 models)</DL>
</DD>
</DL>
<HR>

<A NAME="toString()"><!-- --></A><H3>
toString</H3>
<PRE>
public java.lang.String <B>toString</B>()</PRE>
<DL>
<DD>Returns textual description of the classifier.<DD><DL>
<DT><B>Overrides:</B><DD><CODE>toString</CODE> in class <CODE>java.lang.Object</CODE></DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>a description of the classifier as a string</DL>
</DD>
</DL>
<HR>

<A NAME="main(java.lang.String[])"><!-- --></A><H3>
main</H3>
<PRE>
public static void <B>main</B>(java.lang.String[]&nbsp;argv)</PRE>
<DL>
<DD>Main method for testing this class.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>argv</CODE> - should contain the following arguments:
 -t training file [-T test file] [-c class index]</DL>
</DD>
</DL>
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