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Returns the tip text for this property</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/mi/MINND.html#numTrainingNoisesTipText()">numTrainingNoisesTipText</A></B>()</CODE><BR> Returns the tip text for this property</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> <A HREF="../../../weka/core/Instance.html" title="class in weka.core">Instance</A></CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/mi/MINND.html#preprocess(weka.core.Instances, int)">preprocess</A></B>(<A HREF="../../../weka/core/Instances.html" title="class in weka.core">Instances</A> data, int pos)</CODE><BR> Pre-process the given exemplar according to the other exemplars in the given exemplars.</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/mi/MINND.html#setNumNeighbours(int)">setNumNeighbours</A></B>(int numNeighbour)</CODE><BR> Sets the number of nearest neighbours to estimate the class prediction of tests bags</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/mi/MINND.html#setNumTestingNoises(int)">setNumTestingNoises</A></B>(int numTesting)</CODE><BR> Sets The number of nearest neighbour exemplars in the selection of noises in the test data</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/mi/MINND.html#setNumTrainingNoises(int)">setNumTrainingNoises</A></B>(int numTraining)</CODE><BR> Sets the number of nearest neighbour instances in the selection of noises in the training data</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/mi/MINND.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> double</CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/mi/MINND.html#target(double[], double[][], int, double[])">target</A></B>(double[] x, double[][] X, int rowpos, double[] Y)</CODE><BR> Compute the target function to minimize in gradient descent The formula is:<br/> 1/2*sum[i=1..p](f(X, Xi)-var(Y, Yi))^2 <p/> where p is the number of exemplars and Y is the class label.</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"><TH ALIGN="left"><B>Methods inherited from class weka.classifiers.<A HREF="../../../weka/classifiers/Classifier.html" title="class in weka.classifiers">Classifier</A></B></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><CODE><A HREF="../../../weka/classifiers/Classifier.html#debugTipText()">debugTipText</A>, <A HREF="../../../weka/classifiers/Classifier.html#distributionForInstance(weka.core.Instance)">distributionForInstance</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"><TH ALIGN="left"><B>Methods inherited from class java.lang.Object</B></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><CODE>equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait</CODE></TD></TR></TABLE> <P><!-- ========= CONSTRUCTOR DETAIL ======== --><A NAME="constructor_detail"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TH ALIGN="left" COLSPAN="1"><FONT SIZE="+2"><B>Constructor Detail</B></FONT></TH></TR></TABLE><A NAME="MINND()"><!-- --></A><H3>MINND</H3><PRE>public <B>MINND</B>()</PRE><DL></DL><!-- ============ METHOD DETAIL ========== --><A NAME="method_detail"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TH ALIGN="left" COLSPAN="1"><FONT SIZE="+2"><B>Method Detail</B></FONT></TH></TR></TABLE><A NAME="globalInfo()"><!-- --></A><H3>globalInfo</H3><PRE>public java.lang.String <B>globalInfo</B>()</PRE><DL><DD>Returns a string describing this filter<P><DD><DL></DL></DD><DD><DL><DT><B>Returns:</B><DD>a description of the filter suitable for displaying in the explorer/experimenter gui</DL></DD></DL><HR><A NAME="getTechnicalInformation()"><!-- --></A><H3>getTechnicalInformation</H3><PRE>public <A HREF="../../../weka/core/TechnicalInformation.html" title="class in weka.core">TechnicalInformation</A> <B>getTechnicalInformation</B>()</PRE><DL><DD>Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.<P><DD><DL><DT><B>Specified by:</B><DD><CODE><A HREF="../../../weka/core/TechnicalInformationHandler.html#getTechnicalInformation()">getTechnicalInformation</A></CODE> in interface <CODE><A HREF="../../../weka/core/TechnicalInformationHandler.html" title="interface in weka.core">TechnicalInformationHandler</A></CODE></DL></DD><DD><DL><DT><B>Returns:</B><DD>the technical information about this class</DL></DD></DL><HR><A NAME="getCapabilities()"><!-- --></A><H3>getCapabilities</H3><PRE>public <A HREF="../../../weka/core/Capabilities.html" title="class in weka.core">Capabilities</A> <B>getCapabilities</B>()</PRE><DL><DD>Returns default capabilities of the classifier.<P><DD><DL><DT><B>Specified by:</B><DD><CODE><A HREF="../../../weka/core/CapabilitiesHandler.html#getCapabilities()">getCapabilities</A></CODE> in interface <CODE><A HREF="../../../weka/core/CapabilitiesHandler.html" title="interface in weka.core">CapabilitiesHandler</A></CODE><DT><B>Overrides:</B><DD><CODE><A HREF="../../../weka/classifiers/Classifier.html#getCapabilities()">getCapabilities</A></CODE> in class <CODE><A HREF="../../../weka/classifiers/Classifier.html" title="class in weka.classifiers">Classifier</A></CODE></DL></DD><DD><DL><DT><B>Returns:</B><DD>the capabilities of this classifier<DT><B>See Also:</B><DD><A HREF="../../../weka/core/Capabilities.html" title="class in weka.core"><CODE>Capabilities</CODE></A></DL></DD></DL><HR><A NAME="getMultiInstanceCapabilities()"><!-- --></A><H3>getMultiInstanceCapabilities</H3><PRE>public <A HREF="../../../weka/core/Capabilities.html" title="class in weka.core">Capabilities</A> <B>getMultiInstanceCapabilities</B>()</PRE><DL><DD>Returns the capabilities of this multi-instance classifier for the relational data.<P><DD><DL><DT><B>Specified by:</B><DD><CODE><A HREF="../../../weka/core/MultiInstanceCapabilitiesHandler.html#getMultiInstanceCapabilities()">getMultiInstanceCapabilities</A></CODE> in interface <CODE><A HREF="../../../weka/core/MultiInstanceCapabilitiesHandler.html" title="interface in weka.core">MultiInstanceCapabilitiesHandler</A></CODE></DL></DD><DD><DL><DT><B>Returns:</B><DD>the capabilities of this object<DT><B>See Also:</B><DD><A HREF="../../../weka/core/Capabilities.html" title="class in weka.core"><CODE>Capabilities</CODE></A></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" title="class in weka.core">Instances</A> exs) throws java.lang.Exception</PRE><DL><DD>As normal Nearest Neighbour algorithm does, it's lazy and simply records the exemplar information (i.e. mean and variance for each dimension of each exemplar and their classes) when building the model. There is actually no need to store the exemplars themselves.<P><DD><DL><DT><B>Specified by:</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" title="class in weka.classifiers">Classifier</A></CODE></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>exs</CODE> - the training exemplars<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if the model cannot be built properly</DL></DD></DL><HR><A NAME="preprocess(weka.core.Instances, int)"><!-- --></A><H3>preprocess</H3><PRE>public <A HREF="../../../weka/core/Instance.html" title="class in weka.core">Instance</A> <B>preprocess</B>(<A HREF="../../../weka/core/Instances.html" title="class in weka.core">Instances</A> data, int pos) throws java.lang.Exception</PRE><DL><DD>Pre-process the given exemplar according to the other exemplars in the given exemplars. It also updates noise data statistics.<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>data</CODE> - the whole exemplars<DD><CODE>pos</CODE> - the position of given exemplar in data<DT><B>Returns:</B><DD>the processed exemplar<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if the returned exemplar is wrong</DL></DD></DL><HR><A NAME="findWeights(int, double[][])"><!-- --></A><H3>findWeights</H3><PRE>public void <B>findWeights</B>(int row, double[][] mean)</PRE><DL><DD>Use gradient descent to distort the MU parameter for the exemplar. The exemplar can be in the specified row in the given matrix, which has numExemplar rows and numDimension columns; or not in the matrix.<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>row</CODE> - the given row index<DD><CODE>mean</CODE> - </DL></DD></DL><HR><A NAME="target(double[], double[][], int, double[])"><!-- --></A><H3>target</H3><PRE>public double <B>target</B>(double[] x, double[][] X, int rowpos, double[] Y)</PRE><DL><DD>Compute the target function to minimize in gradient descent The formula is:<br/> 1/2*sum[i=1..p](f(X, Xi)-var(Y, Yi))^2 <p/> where p is the number of exemplars and Y is the class label. In the case of X=MU, f() is the Euclidean distance between two exemplars together with the related weights and var() is sqrt(numDimension)*(Y-Yi) where Y-Yi is either 0 (when Y==Yi) or 1 (Y!=Yi)<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>x</CODE> - the weights of the exemplar in question<DD><CODE>rowpos</CODE> - row index of x in X<DD><CODE>Y</CODE> - the observed class label<DT><B>Returns:</B><DD>the result of the target function</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" title="class in weka.core">Instance</A> ex) throws java.lang.Exception</PRE><DL><DD>Use Kullback Leibler distance to find the nearest neighbours of the given exemplar. It also uses K-Nearest Neighbour algorithm to classify the test exemplar<P><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" title="class in weka.classifiers">Classifier</A></CODE></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>ex</CODE> - the given test exemplar<DT><B>Returns:</B><DD>the classification<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if the exemplar could not be classified successfully</DL></DD></DL><HR><A NAME="cleanse(weka.core.Instance)"><!-- --></A><H3>cleanse</H3><PRE>public <A HREF="../../../weka/core/Instance.html" title="class in weka.core">Instance</A> <B>cleanse</B>(<A HREF="../../../weka/core/Instance.html" title="class in weka.core">Instance</A> before) throws java.lang.Exception</PRE><DL><DD>Cleanse the given exemplar according to the valid and noise data statistics<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>before</CODE> - the given exemplar<DT><B>Returns:</B><DD>the processed exemplar<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if the returned exemplar is wrong</DL></DD></DL><HR><A NAME="kullback(double[], double[], double[], double[], int)"><!-- --></A><H3>kullback</H3><PRE>public double <B>kullback</B>(double[] mu1, double[] mu2, double[] var1, double[] var2, int pos)</PRE>⌨️ 快捷键说明
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