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<TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> void</CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/lazy/IBk.html#setKNN(int)">setKNN</A></B>(int k)</CODE><BR> Set the number of neighbours the learner is to use.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> void</CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/lazy/IBk.html#setMeanSquared(boolean)">setMeanSquared</A></B>(boolean newMeanSquared)</CODE><BR> Sets whether the mean squared error is used rather than mean absolute error when doing cross-validation.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> void</CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/lazy/IBk.html#setNoNormalization(boolean)">setNoNormalization</A></B>(boolean v)</CODE><BR> Set whether normalization is turned off.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> void</CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/lazy/IBk.html#setOptions(java.lang.String[])">setOptions</A></B>(java.lang.String[] options)</CODE><BR> Parses a given list of options.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> void</CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/lazy/IBk.html#setWindowSize(int)">setWindowSize</A></B>(int newWindowSize)</CODE><BR> Sets the maximum number of instances allowed in the training pool.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/lazy/IBk.html#toString()">toString</A></B>()</CODE><BR> Returns a description of this classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> void</CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/lazy/IBk.html#updateClassifier(weka.core.Instance)">updateClassifier</A></B>(<A HREF="../../../weka/core/Instance.html" title="class in weka.core">Instance</A> instance)</CODE><BR> Adds the supplied instance to the training set</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/lazy/IBk.html#windowSizeTipText()">windowSizeTipText</A></B>()</CODE><BR> Returns the tip text for this property</TD></TR></TABLE> <A NAME="methods_inherited_from_class_weka.classifiers.Classifier"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#EEEEFF" CLASS="TableSubHeadingColor"><TD><B>Methods inherited from class weka.classifiers.<A HREF="../../../weka/classifiers/Classifier.html" title="class in weka.classifiers">Classifier</A></B></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><CODE><A HREF="../../../weka/classifiers/Classifier.html#classifyInstance(weka.core.Instance)">classifyInstance</A>, <A HREF="../../../weka/classifiers/Classifier.html#debugTipText()">debugTipText</A>, <A HREF="../../../weka/classifiers/Classifier.html#forName(java.lang.String, java.lang.String[])">forName</A>, <A HREF="../../../weka/classifiers/Classifier.html#getDebug()">getDebug</A>, <A HREF="../../../weka/classifiers/Classifier.html#makeCopies(weka.classifiers.Classifier, int)">makeCopies</A>, <A HREF="../../../weka/classifiers/Classifier.html#makeCopy(weka.classifiers.Classifier)">makeCopy</A>, <A HREF="../../../weka/classifiers/Classifier.html#setDebug(boolean)">setDebug</A></CODE></TD></TR></TABLE> <A NAME="methods_inherited_from_class_java.lang.Object"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#EEEEFF" CLASS="TableSubHeadingColor"><TD><B>Methods inherited from class java.lang.Object</B></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><CODE>equals, getClass, hashCode, notify, notifyAll, wait, wait, wait</CODE></TD></TR></TABLE> <P><!-- ============ FIELD DETAIL =========== --><A NAME="field_detail"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TD COLSPAN=1><FONT SIZE="+2"><B>Field Detail</B></FONT></TD></TR></TABLE><A NAME="WEIGHT_NONE"><!-- --></A><H3>WEIGHT_NONE</H3><PRE>public static final int <B>WEIGHT_NONE</B></PRE><DL><DL><DT><B>See Also:</B><DD><A HREF="../../../constant-values.html#weka.classifiers.lazy.IBk.WEIGHT_NONE">Constant Field Values</A></DL></DL><HR><A NAME="WEIGHT_INVERSE"><!-- --></A><H3>WEIGHT_INVERSE</H3><PRE>public static final int <B>WEIGHT_INVERSE</B></PRE><DL><DL><DT><B>See Also:</B><DD><A HREF="../../../constant-values.html#weka.classifiers.lazy.IBk.WEIGHT_INVERSE">Constant Field Values</A></DL></DL><HR><A NAME="WEIGHT_SIMILARITY"><!-- --></A><H3>WEIGHT_SIMILARITY</H3><PRE>public static final int <B>WEIGHT_SIMILARITY</B></PRE><DL><DL><DT><B>See Also:</B><DD><A HREF="../../../constant-values.html#weka.classifiers.lazy.IBk.WEIGHT_SIMILARITY">Constant Field Values</A></DL></DL><HR><A NAME="TAGS_WEIGHTING"><!-- --></A><H3>TAGS_WEIGHTING</H3><PRE>public static final <A HREF="../../../weka/core/Tag.html" title="class in weka.core">Tag</A>[] <B>TAGS_WEIGHTING</B></PRE><DL><DL></DL></DL><!-- ========= CONSTRUCTOR DETAIL ======== --><A NAME="constructor_detail"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TD COLSPAN=1><FONT SIZE="+2"><B>Constructor Detail</B></FONT></TD></TR></TABLE><A NAME="IBk(int)"><!-- --></A><H3>IBk</H3><PRE>public <B>IBk</B>(int k)</PRE><DL><DD>IBk classifier. Simple instance-based learner that uses the class of the nearest k training instances for the class of the test instances.<P><DT><B>Parameters:</B><DD><CODE>k</CODE> - the number of nearest neighbors to use for prediction</DL><HR><A NAME="IBk()"><!-- --></A><H3>IBk</H3><PRE>public <B>IBk</B>()</PRE><DL><DD>IB1 classifer. Instance-based learner. Predicts the class of the single nearest training instance for each test instance.<P></DL><!-- ============ METHOD DETAIL ========== --><A NAME="method_detail"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TD COLSPAN=1><FONT SIZE="+2"><B>Method Detail</B></FONT></TD></TR></TABLE><A NAME="globalInfo()"><!-- --></A><H3>globalInfo</H3><PRE>public java.lang.String <B>globalInfo</B>()</PRE><DL><DD>Returns a string describing classifier<P><DD><DL></DL></DD><DD><DL><DT><B>Returns:</B><DD>a description suitable for displaying in the explorer/experimenter gui</DL></DD></DL><HR><A NAME="KNNTipText()"><!-- --></A><H3>KNNTipText</H3><PRE>public java.lang.String <B>KNNTipText</B>()</PRE><DL><DD>Returns the tip text for this property<P><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="setKNN(int)"><!-- --></A><H3>setKNN</H3><PRE>public void <B>setKNN</B>(int k)</PRE><DL><DD>Set the number of neighbours the learner is to use.<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>k</CODE> - the number of neighbours.</DL></DD></DL><HR><A NAME="getKNN()"><!-- --></A><H3>getKNN</H3><PRE>public int <B>getKNN</B>()</PRE><DL><DD>Gets the number of neighbours the learner will use.<P><DD><DL></DL></DD><DD><DL><DT><B>Returns:</B><DD>the number of neighbours.</DL></DD></DL><HR><A NAME="windowSizeTipText()"><!-- --></A><H3>windowSizeTipText</H3><PRE>public java.lang.String <B>windowSizeTipText</B>()</PRE><DL><DD>Returns the tip text for this property<P><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="getWindowSize()"><!-- --></A><H3>getWindowSize</H3><PRE>public int <B>getWindowSize</B>()</PRE><DL><DD>Gets the maximum number of instances allowed in the training pool. The addition of new instances above this value will result in old instances being removed. A value of 0 signifies no limit to the number of training instances.<P><DD><DL></DL></DD><DD><DL><DT><B>Returns:</B><DD>Value of WindowSize.</DL></DD></DL><HR><A NAME="setWindowSize(int)"><!-- --></A><H3>setWindowSize</H3><PRE>public void <B>setWindowSize</B>(int newWindowSize)</PRE><DL><DD>Sets the maximum number of instances allowed in the training pool. The addition of new instances above this value will result in old instances being removed. A value of 0 signifies no limit to the number of training instances.<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>newWindowSize</CODE> - Value to assign to WindowSize.</DL></DD></DL><HR><A NAME="distanceWeightingTipText()"><!-- --></A><H3>distanceWeightingTipText</H3><PRE>public java.lang.String <B>distanceWeightingTipText</B>()</PRE><DL><DD>Returns the tip text for this property<P><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="getDistanceWeighting()"><!-- --></A><H3>getDistanceWeighting</H3><PRE>public <A HREF="../../../weka/core/SelectedTag.html" title="class in weka.core">SelectedTag</A> <B>getDistanceWeighting</B>()</PRE><DL><DD>Gets the distance weighting method used. Will be one of WEIGHT_NONE, WEIGHT_INVERSE, or WEIGHT_SIMILARITY<P><DD><DL></DL></DD><DD><DL><DT><B>Returns:</B><DD>the distance weighting method used.</DL></DD></DL><HR><A NAME="setDistanceWeighting(weka.core.SelectedTag)"><!-- --></A><H3>setDistanceWeighting</H3><PRE>public void <B>setDistanceWeighting</B>(<A HREF="../../../weka/core/SelectedTag.html" title="class in weka.core">SelectedTag</A> newMethod)</PRE><DL><DD>Sets the distance weighting method used. Values other than WEIGHT_NONE, WEIGHT_INVERSE, or WEIGHT_SIMILARITY will be ignored.<P><DD><DL></DL></DD><DD><DL></DL></DD></DL><HR><A NAME="meanSquaredTipText()"><!-- --></A><H3>meanSquaredTipText</H3><PRE>public java.lang.String <B>meanSquaredTipText</B>()</PRE><DL><DD>Returns the tip text for this property<P><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="getMeanSquared()"><!-- --></A><H3>getMeanSquared</H3><PRE>public boolean <B>getMeanSquared</B>()</PRE><DL><DD>Gets whether the mean squared error is used rather than mean absolute error when doing cross-validation.<P><DD><DL></DL></DD><DD><DL><DT><B>Returns:</B><DD>true if so.</DL></DD></DL><HR><A NAME="setMeanSquared(boolean)"><!-- --></A><H3>setMeanSquared</H3><PRE>public void <B>setMeanSquared</B>(boolean newMeanSquared)</PRE><DL><DD>Sets whether the mean squared error is used rather than mean absolute error when doing cross-validation.⌨️ 快捷键说明
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