📄 globalscoresearchalgorithm.html
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This will return a string describing the search algorithm.</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/bayes/net/search/global/GlobalScoreSearchAlgorithm.html#kFoldCV(weka.classifiers.bayes.BayesNet, int)">kFoldCV</A></B>(<A HREF="../../../../../../weka/classifiers/bayes/BayesNet.html" title="class in weka.classifiers.bayes">BayesNet</A> bayesNet, int nNrOfFolds)</CODE><BR> kFoldCV uses k-fold cross validation to measure the accuracy of a Bayes network classifier.</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/bayes/net/search/global/GlobalScoreSearchAlgorithm.html#leaveOneOutCV(weka.classifiers.bayes.BayesNet)">leaveOneOutCV</A></B>(<A HREF="../../../../../../weka/classifiers/bayes/BayesNet.html" title="class in weka.classifiers.bayes">BayesNet</A> bayesNet)</CODE><BR> LeaveOneOutCV returns the accuracy calculated using Leave One Out cross validation.</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/bayes/net/search/global/GlobalScoreSearchAlgorithm.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> java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../../weka/classifiers/bayes/net/search/global/GlobalScoreSearchAlgorithm.html#markovBlanketClassifierTipText()">markovBlanketClassifierTipText</A></B>()</CODE><BR> </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/bayes/net/search/global/GlobalScoreSearchAlgorithm.html#setCVType(weka.core.SelectedTag)">setCVType</A></B>(<A HREF="../../../../../../weka/core/SelectedTag.html" title="class in weka.core">SelectedTag</A> newCVType)</CODE><BR> set cross validation strategy to be used in searching for networks.</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/bayes/net/search/global/GlobalScoreSearchAlgorithm.html#setMarkovBlanketClassifier(boolean)">setMarkovBlanketClassifier</A></B>(boolean bMarkovBlanketClassifier)</CODE><BR> </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/bayes/net/search/global/GlobalScoreSearchAlgorithm.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/bayes/net/search/global/GlobalScoreSearchAlgorithm.html#setUseProb(boolean)">setUseProb</A></B>(boolean useProb)</CODE><BR> </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/bayes/net/search/global/GlobalScoreSearchAlgorithm.html#useProbTipText()">useProbTipText</A></B>()</CODE><BR> </TD></TR></TABLE> <A NAME="methods_inherited_from_class_weka.classifiers.bayes.net.search.SearchAlgorithm"><!-- --></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.bayes.net.search.<A HREF="../../../../../../weka/classifiers/bayes/net/search/SearchAlgorithm.html" title="class in weka.classifiers.bayes.net.search">SearchAlgorithm</A></B></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><CODE><A HREF="../../../../../../weka/classifiers/bayes/net/search/SearchAlgorithm.html#buildStructure(weka.classifiers.bayes.BayesNet, weka.core.Instances)">buildStructure</A>, <A HREF="../../../../../../weka/classifiers/bayes/net/search/SearchAlgorithm.html#initAsNaiveBayesTipText()">initAsNaiveBayesTipText</A>, <A HREF="../../../../../../weka/classifiers/bayes/net/search/SearchAlgorithm.html#maxNrOfParentsTipText()">maxNrOfParentsTipText</A>, <A HREF="../../../../../../weka/classifiers/bayes/net/search/SearchAlgorithm.html#toString()">toString</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, 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"><TH ALIGN="left" COLSPAN="1"><FONT SIZE="+2"><B>Field Detail</B></FONT></TH></TR></TABLE><A NAME="TAGS_CV_TYPE"><!-- --></A><H3>TAGS_CV_TYPE</H3><PRE>public static final <A HREF="../../../../../../weka/core/Tag.html" title="class in weka.core">Tag</A>[] <B>TAGS_CV_TYPE</B></PRE><DL><DD>the score types<P><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"><TH ALIGN="left" COLSPAN="1"><FONT SIZE="+2"><B>Constructor Detail</B></FONT></TH></TR></TABLE><A NAME="GlobalScoreSearchAlgorithm()"><!-- --></A><H3>GlobalScoreSearchAlgorithm</H3><PRE>public <B>GlobalScoreSearchAlgorithm</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="calcScore(weka.classifiers.bayes.BayesNet)"><!-- --></A><H3>calcScore</H3><PRE>public double <B>calcScore</B>(<A HREF="../../../../../../weka/classifiers/bayes/BayesNet.html" title="class in weka.classifiers.bayes">BayesNet</A> bayesNet) throws java.lang.Exception</PRE><DL><DD>performCV returns the accuracy calculated using cross validation. The dataset used is m_Instances associated with the Bayes Network.<P><DD><DL><DT><B>Parameters:</B><DD><CODE>bayesNet</CODE> - : Bayes Network containing structure to evaluate<DT><B>Returns:</B><DD>accuracy (in interval 0..1) measured using cv.<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - whn m_nCVType is invalided + exceptions passed on by updateClassifier</DL></DD></DL><HR><A NAME="calcScoreWithExtraParent(int, int)"><!-- --></A><H3>calcScoreWithExtraParent</H3><PRE>public double <B>calcScoreWithExtraParent</B>(int nNode, int nCandidateParent) throws java.lang.Exception</PRE><DL><DD>Calc Node Score With Added Parent<P><DD><DL><DT><B>Parameters:</B><DD><CODE>nNode</CODE> - node for which the score is calculate<DD><CODE>nCandidateParent</CODE> - candidate parent to add to the existing parent set<DT><B>Returns:</B><DD>log score<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL></DD></DL><HR><A NAME="calcScoreWithMissingParent(int, int)"><!-- --></A><H3>calcScoreWithMissingParent</H3><PRE>public double <B>calcScoreWithMissingParent</B>(int nNode, int nCandidateParent) throws java.lang.Exception</PRE><DL><DD>Calc Node Score With Parent Deleted<P><DD><DL><DT><B>Parameters:</B><DD><CODE>nNode</CODE> - node for which the score is calculate<DD><CODE>nCandidateParent</CODE> - candidate parent to delete from the existing parent set<DT><B>Returns:</B><DD>log score<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL></DD></DL><HR><A NAME="calcScoreWithReversedParent(int, int)"><!-- --></A><H3>calcScoreWithReversedParent</H3><PRE>public double <B>calcScoreWithReversedParent</B>(int nNode, int nCandidateParent) throws java.lang.Exception</PRE><DL><DD>Calc Node Score With Arrow reversed<P><DD><DL><DT><B>Parameters:</B><DD><CODE>nNode</CODE> - node for which the score is calculate<DD><CODE>nCandidateParent</CODE> - candidate parent to delete from the existing parent set<DT><B>Returns:</B><DD>log score<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL></DD></DL><HR><A NAME="leaveOneOutCV(weka.classifiers.bayes.BayesNet)"><!-- --></A><H3>leaveOneOutCV</H3><PRE>public double <B>leaveOneOutCV</B>(<A HREF="../../../../../../weka/classifiers/bayes/BayesNet.html" title="class in weka.classifiers.bayes">BayesNet</A> bayesNet) throws java.lang.Exception</PRE><DL><DD>LeaveOneOutCV returns the accuracy calculated using Leave One Out cross validation. The dataset used is m_Instances associated with the Bayes Network.<P><DD><DL><DT><B>Parameters:</B><DD><CODE>bayesNet</CODE> - : Bayes Network containing structure to evaluate<DT><B>Returns:</B><DD>accuracy (in interval 0..1) measured using leave one out cv.<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - passed on by updateClassifier</DL></DD></DL><HR><A NAME="cumulativeCV(weka.classifiers.bayes.BayesNet)"><!-- --></A><H3>cumulativeCV</H3><PRE>public double <B>cumulativeCV</B>(<A HREF="../../../../../../weka/classifiers/bayes/BayesNet.html" title="class in weka.classifiers.bayes">BayesNet</A> bayesNet) throws java.lang.Exception</PRE><DL><DD>CumulativeCV returns the accuracy calculated using cumulative cross validation. The idea is to run through the data set and try to classify each of the instances based on the previously seen data. The data set used is m_Instances associated with the Bayes Network.<P><DD><DL><DT><B>Parameters:</B><DD><CODE>bayesNet</CODE> - : Bayes Network containing structure to evaluate<DT><B>Returns:</B><DD>accuracy (in interval 0..1) measured using leave one out cv.<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - passed on by updateClassifier</DL></DD></DL><HR><A NAME="kFoldCV(weka.classifiers.bayes.BayesNet, int)"><!-- --></A><H3>kFoldCV</H3><PRE>public double <B>kFoldCV</B>(<A HREF="../../../../../../weka/classifiers/bayes/BayesNet.html" title="class in weka.classifiers.bayes">BayesNet</A> bayesNet, int nNrOfFolds) throws java.lang.Exception</PRE><DL><DD>kFoldCV uses k-fold cross validation to measure the accuracy of a Bayes network classifier.<P><DD><DL><DT><B>Parameters:</B><DD><CODE>bayesNet</CODE> - : Bayes Network containing structure to evaluate<DD><CODE>nNrOfFolds</CODE> - : the number of folds k to perform k-fold cv<DT><B>Returns:</B><DD>accuracy (in interval 0..1) measured using leave one out cv.<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - passed on by updateClassifier</DL></DD></DL><HR><A NAME="getUseProb()"><!-- --></A><H3>getUseProb</H3>⌨️ 快捷键说明
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