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<B>Constructor Detail</B></FONT></TD>
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<A NAME="LBR()"><!-- --></A><H3>
LBR</H3>
<PRE>
public <B>LBR</B>()</PRE>
<DL>
</DL>

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<A NAME="globalInfo()"><!-- --></A><H3>
globalInfo</H3>
<PRE>
public java.lang.String <B>globalInfo</B>()</PRE>
<DL>
<DD><DL>

<DT><B>Returns:</B><DD>a description of the classifier suitable for
 displaying in the explorer/experimenter gui</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>&nbsp;instances)
                     throws java.lang.Exception</PRE>
<DL>
<DD>For lazy learning, building classifier is only to prepare their inputs
 until classification time.
<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>instances</CODE> - set of instances serving as training data
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if the preparation has not been generated.</DL>
</DD>
</DL>
<HR>

<A NAME="distributionForInstance(weka.core.Instance)"><!-- --></A><H3>
distributionForInstance</H3>
<PRE>
public double[] <B>distributionForInstance</B>(<A HREF="../../../weka/core/Instance.html" title="class in weka.core">Instance</A>&nbsp;testInstance)
                                 throws java.lang.Exception</PRE>
<DL>
<DD>Calculates the class membership probabilities
 for the given test instance.
 This is the most important method for Lazy Bayesian Rule algorithm.
<P>
<DD><DL>
<DT><B>Overrides:</B><DD><CODE><A HREF="../../../weka/classifiers/Classifier.html#distributionForInstance(weka.core.Instance)">distributionForInstance</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>testInstance</CODE> - the instance to be classified
<DT><B>Returns:</B><DD>predicted class probability distribution
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if distribution can't be computed</DL>
</DD>
</DL>
<HR>

<A NAME="toString()"><!-- --></A><H3>
toString</H3>
<PRE>
public java.lang.String <B>toString</B>()</PRE>
<DL>
<DD>Returns a description of the classifier.
<P>
<DD><DL>

<DT><B>Returns:</B><DD>a description of the classifier as a string.</DL>
</DD>
</DL>
<HR>

<A NAME="leaveOneOut(weka.classifiers.lazy.LBR.Indexes, int[][][], int[], boolean[])"><!-- --></A><H3>
leaveOneOut</H3>
<PRE>
public int <B>leaveOneOut</B>(<A HREF="../../../weka/classifiers/lazy/LBR.Indexes.html" title="class in weka.classifiers.lazy">LBR.Indexes</A>&nbsp;instanceIndex,
                       int[][][]&nbsp;counts,
                       int[]&nbsp;priors,
                       boolean[]&nbsp;errorFlags)
                throws java.lang.Exception</PRE>
<DL>
<DD>Leave-one-out strategy. For a given sample data set with n instances,
 using (n - 1) instances by leaving one out and tested on the single
 remaining case.
 This is repeated n times in turn.
 The final "Error" is the sum of the instances to be classified
 incorrectly.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>counts</CODE> - serving as all the counts of training data.<DD><CODE>priors</CODE> - serving as the number of instances in each class.
<DT><B>Returns:</B><DD>error flag array about each instance.
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE></DL>
</DD>
</DL>
<HR>

<A NAME="localNaiveBayes(weka.classifiers.lazy.LBR.Indexes)"><!-- --></A><H3>
localNaiveBayes</H3>
<PRE>
public void <B>localNaiveBayes</B>(<A HREF="../../../weka/classifiers/lazy/LBR.Indexes.html" title="class in weka.classifiers.lazy">LBR.Indexes</A>&nbsp;instanceIndex)
                     throws java.lang.Exception</PRE>
<DL>
<DD>Class for building and using a simple Naive Bayes classifier.
 For more information, see<p>

 Richard Duda and Peter Hart (1973).<i>Pattern
 Classification and Scene Analysis</i>. Wiley, New York.

 This method only get m_Counts and m_Priors.
<P>
<DD><DL>

<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if m_Counts and m_Priors have not been
  generated successfully</DL>
</DD>
</DL>
<HR>

<A NAME="localDistributionForInstance(weka.core.Instance, weka.classifiers.lazy.LBR.Indexes)"><!-- --></A><H3>
localDistributionForInstance</H3>
<PRE>
public double[] <B>localDistributionForInstance</B>(<A HREF="../../../weka/core/Instance.html" title="class in weka.core">Instance</A>&nbsp;instance,
                                             <A HREF="../../../weka/classifiers/lazy/LBR.Indexes.html" title="class in weka.classifiers.lazy">LBR.Indexes</A>&nbsp;instanceIndex)
                                      throws java.lang.Exception</PRE>
<DL>
<DD>Calculates the class membership probabilities.
 for the given test instance.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>instance</CODE> - the instance to be classified
<DT><B>Returns:</B><DD>predicted class probability distribution
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if distribution can't be computed</DL>
</DD>
</DL>
<HR>

<A NAME="binomP(double, double, double)"><!-- --></A><H3>
binomP</H3>
<PRE>
public double <B>binomP</B>(double&nbsp;r,
                     double&nbsp;n,
                     double&nbsp;p)
              throws java.lang.Exception</PRE>
<DL>
<DD>Significance test
 binomp:
<P>
<DD><DL>

<DT><B>Returns:</B><DD>returns the probability of obtaining r or fewer out of n
 if the probability of an event is p.
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE></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.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>argv</CODE> - the options</DL>
</DD>
</DL>
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