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<BR> Get the number of training instances the classifier is currently using</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/IBk.html#getOptions()">getOptions</A></B>()</CODE><BR> Gets the current settings of IBk.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> int</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/IBk.html#getWindowSize()">getWindowSize</A></B>()</CODE><BR> Gets 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.util.Enumeration</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/IBk.html#listOptions()">listOptions</A></B>()</CODE><BR> Returns an enumeration describing the available options</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE>static void</CODE></FONT></TD><TD><CODE><B><A HREF="../../weka/classifiers/IBk.html#main(java.lang.String[])">main</A></B>(java.lang.String[] argv)</CODE><BR> Main method for testing this class.</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/IBk.html#setCrossValidate(boolean)">setCrossValidate</A></B>(boolean newCrossValidate)</CODE><BR> Sets whether hold-one-out cross-validation will be used to select the best k value</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/IBk.html#setDebug(boolean)">setDebug</A></B>(boolean newDebug)</CODE><BR> Set the value of Debug.</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/IBk.html#setDistanceWeighting(weka.core.SelectedTag)">setDistanceWeighting</A></B>(<A HREF="../../weka/core/SelectedTag.html">SelectedTag</A> newMethod)</CODE><BR> Sets the distance weighting method used.</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/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/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/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/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/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/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/IBk.html#updateClassifier(weka.core.Instance)">updateClassifier</A></B>(<A HREF="../../weka/core/Instance.html">Instance</A> instance)</CODE><BR> Adds the supplied instance to the training set</TD></TR></TABLE> <A NAME="methods_inherited_from_class_weka.classifiers.DistributionClassifier"><!-- --></A><TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%"><TR BGCOLOR="#EEEEFF" CLASS="TableSubHeadingColor"><TD><B>Methods inherited from class weka.classifiers.<A HREF="../../weka/classifiers/DistributionClassifier.html">DistributionClassifier</A></B></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><CODE><A HREF="../../weka/classifiers/DistributionClassifier.html#classifyInstance(weka.core.Instance)">classifyInstance</A></CODE></TD></TR></TABLE> <A NAME="methods_inherited_from_class_weka.classifiers.Classifier"><!-- --></A><TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%"><TR BGCOLOR="#EEEEFF" CLASS="TableSubHeadingColor"><TD><B>Methods inherited from class weka.classifiers.<A HREF="../../weka/classifiers/Classifier.html">Classifier</A></B></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><CODE><A HREF="../../weka/classifiers/Classifier.html#forName(java.lang.String, java.lang.String[])">forName</A>, <A HREF="../../weka/classifiers/Classifier.html#makeCopies(weka.classifiers.Classifier, int)">makeCopies</A></CODE></TD></TR></TABLE> <A NAME="methods_inherited_from_class_java.lang.Object"><!-- --></A><TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%"><TR BGCOLOR="#EEEEFF" CLASS="TableSubHeadingColor"><TD><B>Methods inherited from class java.lang.Object</B></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><CODE>clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait</CODE></TD></TR></TABLE> <P><!-- ============ FIELD DETAIL =========== --><A NAME="field_detail"><!-- --></A><TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%"><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TD COLSPAN=1><FONT SIZE="+2"><B>Field Detail</B></FONT></TD></TR></TABLE><A NAME="m_Train"><!-- --></A><H3>m_Train</H3><PRE>protected <A HREF="../../weka/core/Instances.html">Instances</A> <B>m_Train</B></PRE><DL><DD>The training instances used for classification.</DL><HR><A NAME="m_NumClasses"><!-- --></A><H3>m_NumClasses</H3><PRE>protected int <B>m_NumClasses</B></PRE><DL><DD>The number of class values (or 1 if predicting numeric)</DL><HR><A NAME="m_ClassType"><!-- --></A><H3>m_ClassType</H3><PRE>protected int <B>m_ClassType</B></PRE><DL><DD>The class attribute type</DL><HR><A NAME="m_Min"><!-- --></A><H3>m_Min</H3><PRE>protected double[] <B>m_Min</B></PRE><DL><DD>The minimum values for numeric attributes.</DL><HR><A NAME="m_Max"><!-- --></A><H3>m_Max</H3><PRE>protected double[] <B>m_Max</B></PRE><DL><DD>The maximum values for numeric attributes.</DL><HR><A NAME="m_kNN"><!-- --></A><H3>m_kNN</H3><PRE>protected int <B>m_kNN</B></PRE><DL><DD>The number of neighbours to use for classification (currently)</DL><HR><A NAME="m_kNNUpper"><!-- --></A><H3>m_kNNUpper</H3><PRE>protected int <B>m_kNNUpper</B></PRE><DL><DD>The value of kNN provided by the user. This may differ from m_kNN if cross-validation is being used</DL><HR><A NAME="m_kNNValid"><!-- --></A><H3>m_kNNValid</H3><PRE>protected boolean <B>m_kNNValid</B></PRE><DL><DD>Whether the value of k selected by cross validation has been invalidated by a change in the training instances</DL><HR><A NAME="m_WindowSize"><!-- --></A><H3>m_WindowSize</H3><PRE>protected int <B>m_WindowSize</B></PRE><DL><DD>The maximum number of training instances allowed. When this limit is reached, old training instances are removed, so the training data is "windowed". Set to 0 for unlimited numbers of instances.</DL><HR><A NAME="m_DistanceWeighting"><!-- --></A><H3>m_DistanceWeighting</H3><PRE>protected int <B>m_DistanceWeighting</B></PRE><DL><DD>Whether the neighbours should be distance-weighted</DL><HR><A NAME="m_CrossValidate"><!-- --></A><H3>m_CrossValidate</H3><PRE>protected boolean <B>m_CrossValidate</B></PRE><DL><DD>Whether to select k by cross validation</DL><HR><A NAME="m_MeanSquared"><!-- --></A><H3>m_MeanSquared</H3><PRE>protected boolean <B>m_MeanSquared</B></PRE><DL><DD>Whether to minimise mean squared error rather than mean absolute error when cross-validating on numeric prediction tasks</DL><HR><A NAME="m_DontNormalize"><!-- --></A><H3>m_DontNormalize</H3><PRE>protected boolean <B>m_DontNormalize</B></PRE><DL><DD>True if normalization is turned off</DL><HR><A NAME="WEIGHT_NONE"><!-- --></A><H3>WEIGHT_NONE</H3><PRE>public static final int <B>WEIGHT_NONE</B></PRE><DL></DL><HR><A NAME="WEIGHT_INVERSE"><!-- --></A><H3>WEIGHT_INVERSE</H3><PRE>public static final int <B>WEIGHT_INVERSE</B></PRE><DL></DL><HR><A NAME="WEIGHT_SIMILARITY"><!-- --></A><H3>WEIGHT_SIMILARITY</H3><PRE>public static final int <B>WEIGHT_SIMILARITY</B></PRE><DL></DL><HR><A NAME="TAGS_WEIGHTING"><!-- --></A><H3>TAGS_WEIGHTING</H3><PRE>public static final <A HREF="../../weka/core/Tag.html">Tag</A>[] <B>TAGS_WEIGHTING</B></PRE><DL></DL><HR><A NAME="m_NumAttributesUsed"><!-- --></A><H3>m_NumAttributesUsed</H3><PRE>protected double <B>m_NumAttributesUsed</B></PRE><DL><DD>The number of attributes the contribute to a prediction</DL><!-- ========= CONSTRUCTOR DETAIL ======== --><A NAME="constructor_detail"><!-- --></A><TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%"><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.<DD><DL><DT><B>Parameters:</B><DD><CODE>k</CODE> - the number of nearest neighbors to use for prediction</DL></DD></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.</DL><!-- ============ METHOD DETAIL ========== --><A NAME="method_detail"><!-- --></A><TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%"><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TD COLSPAN=1><FONT SIZE="+2"><B>Method Detail</B></FONT></TD></TR></TABLE><A NAME="getDebug()"><!-- --></A><H3>getDebug</H3><PRE>public boolean <B>getDebug</B>()</PRE><DL><DD>Get the value of Debug.<DD><DL></DL></DD><DD><DL><DT><B>Returns:</B><DD>Value of Debug.</DL></DD></DL><HR><A NAME="setDebug(boolean)"><!-- --></A><H3>setDebug</H3><PRE>public void <B>setDebug</B>(boolean newDebug)</PRE><DL><DD>Set the value of Debug.<DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>newDebug</CODE> - Value to assign to Debug.</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.<DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>k</CODE> - the number of neighbours.</DL></DD></DL><HR>⌨️ 快捷键说明
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