contingencytables.html

< 数据挖掘--实用机器学习技术及java实现> 一书结合数据挖掘和机器学习的知识,作者陈述了自动挖掘模式的基础理论,并且以java语言实现了具有代表性的各类数据挖掘方法.例如:class

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

<!-- ============ METHOD DETAIL ========== -->

<A NAME="method_detail"><!-- --></A>
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<B>Method Detail</B></FONT></TD>
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<A NAME="chiSquared(double[][], boolean)"><!-- --></A><H3>
chiSquared</H3>
<PRE>
public static double <B>chiSquared</B>(double[][]&nbsp;matrix,
                                boolean&nbsp;yates)</PRE>
<DL>
<DD>Returns chi-squared probability for a given matrix.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>matrix</CODE> - the contigency table<DD><CODE>yates</CODE> - is Yates' correction to be used?<DT><B>Returns:</B><DD>the chi-squared probability</DL>
</DD>
</DL>
<HR>

<A NAME="chiVal(double[][], boolean)"><!-- --></A><H3>
chiVal</H3>
<PRE>
public static double <B>chiVal</B>(double[][]&nbsp;matrix,
                            boolean&nbsp;useYates)</PRE>
<DL>
<DD>Computes chi-squared statistic for a contingency table.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>matrix</CODE> - the contigency table<DD><CODE>yates</CODE> - is Yates' correction to be used?<DT><B>Returns:</B><DD>the value of the chi-squared statistic</DL>
</DD>
</DL>
<HR>

<A NAME="cochransCriterion(double[][])"><!-- --></A><H3>
cochransCriterion</H3>
<PRE>
public static boolean <B>cochransCriterion</B>(double[][]&nbsp;matrix)</PRE>
<DL>
<DD>Tests if Cochran's criterion is fullfilled for the given
 contingency table. Rows and columns with all zeros are not considered
 relevant.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>matrix</CODE> - the contigency table to be tested<DT><B>Returns:</B><DD>true if contingency table is ok, false if not</DL>
</DD>
</DL>
<HR>

<A NAME="CramersV(double[][])"><!-- --></A><H3>
CramersV</H3>
<PRE>
public static double <B>CramersV</B>(double[][]&nbsp;matrix)</PRE>
<DL>
<DD>Computes Cramer's V for a contingency table.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>matrix</CODE> - the contingency table<DT><B>Returns:</B><DD>Cramer's V</DL>
</DD>
</DL>
<HR>

<A NAME="entropy(double[])"><!-- --></A><H3>
entropy</H3>
<PRE>
public static double <B>entropy</B>(double[]&nbsp;array)</PRE>
<DL>
<DD>Computes the entropy of the given array.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>array</CODE> - the array<DT><B>Returns:</B><DD>the entropy</DL>
</DD>
</DL>
<HR>

<A NAME="entropyConditionedOnColumns(double[][])"><!-- --></A><H3>
entropyConditionedOnColumns</H3>
<PRE>
public static double <B>entropyConditionedOnColumns</B>(double[][]&nbsp;matrix)</PRE>
<DL>
<DD>Computes conditional entropy of the rows given
 the columns.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>matrix</CODE> - the contingency table<DT><B>Returns:</B><DD>the conditional entropy of the rows given the columns</DL>
</DD>
</DL>
<HR>

<A NAME="entropyConditionedOnRows(double[][])"><!-- --></A><H3>
entropyConditionedOnRows</H3>
<PRE>
public static double <B>entropyConditionedOnRows</B>(double[][]&nbsp;matrix)</PRE>
<DL>
<DD>Computes conditional entropy of the columns given
 the rows.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>matrix</CODE> - the contingency table<DT><B>Returns:</B><DD>the conditional entropy of the columns given the rows</DL>
</DD>
</DL>
<HR>

<A NAME="entropyConditionedOnRows(double[][], double[][], double)"><!-- --></A><H3>
entropyConditionedOnRows</H3>
<PRE>
public static double <B>entropyConditionedOnRows</B>(double[][]&nbsp;train,
                                              double[][]&nbsp;test,
                                              double&nbsp;numClasses)</PRE>
<DL>
<DD>Computes conditional entropy of the columns given the rows
 of the test matrix with respect to the train matrix. Uses a
 Laplace prior. Does NOT normalize the entropy.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>train</CODE> - the train matrix<DD><CODE>test</CODE> - the test matrix<DD><CODE>the</CODE> - number of symbols for Laplace<DT><B>Returns:</B><DD>the entropy</DL>
</DD>
</DL>
<HR>

<A NAME="entropyOverRows(double[][])"><!-- --></A><H3>
entropyOverRows</H3>
<PRE>
public static double <B>entropyOverRows</B>(double[][]&nbsp;matrix)</PRE>
<DL>
<DD>Computes the rows' entropy for the given contingency table.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>matrix</CODE> - the contingency table<DT><B>Returns:</B><DD>the rows' entropy</DL>
</DD>
</DL>
<HR>

<A NAME="entropyOverColumns(double[][])"><!-- --></A><H3>
entropyOverColumns</H3>
<PRE>
public static double <B>entropyOverColumns</B>(double[][]&nbsp;matrix)</PRE>
<DL>
<DD>Computes the columns' entropy for the given contingency table.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>matrix</CODE> - the contingency table<DT><B>Returns:</B><DD>the columns' entropy</DL>
</DD>
</DL>
<HR>

<A NAME="gainRatio(double[][])"><!-- --></A><H3>
gainRatio</H3>
<PRE>
public static double <B>gainRatio</B>(double[][]&nbsp;matrix)</PRE>
<DL>
<DD>Computes gain ratio for contingency table (split on rows).
 Returns Double.MAX_VALUE if the split entropy is 0.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>matrix</CODE> - the contingency table<DT><B>Returns:</B><DD>the gain ratio</DL>
</DD>
</DL>
<HR>

<A NAME="log2MultipleHypergeometric(double[][])"><!-- --></A><H3>
log2MultipleHypergeometric</H3>
<PRE>
public static double <B>log2MultipleHypergeometric</B>(double[][]&nbsp;matrix)</PRE>
<DL>
<DD>Returns negative base 2 logarithm of multiple hypergeometric
 probability for a contingency table.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>matrix</CODE> - the contingency table<DT><B>Returns:</B><DD>the log of the hypergeometric probability of the contingency table</DL>
</DD>
</DL>
<HR>

<A NAME="reduceMatrix(double[][])"><!-- --></A><H3>
reduceMatrix</H3>
<PRE>
public static double[][] <B>reduceMatrix</B>(double[][]&nbsp;matrix)</PRE>
<DL>
<DD>Reduces a matrix by deleting all zero rows and columns.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>matrix</CODE> - the matrix to be reduced<DD><CODE>the</CODE> - matrix with all zero rows and columns deleted</DL>
</DD>
</DL>
<HR>

<A NAME="symmetricalUncertainty(double[][])"><!-- --></A><H3>
symmetricalUncertainty</H3>
<PRE>
public static double <B>symmetricalUncertainty</B>(double[][]&nbsp;matrix)</PRE>
<DL>
<DD>Calculates the symmetrical uncertainty for base 2.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>matrix</CODE> - the contingency table<DT><B>Returns:</B><DD>the calculated symmetrical uncertainty</DL>
</DD>
</DL>
<HR>

<A NAME="tauVal(double[][])"><!-- --></A><H3>
tauVal</H3>
<PRE>
public static double <B>tauVal</B>(double[][]&nbsp;matrix)</PRE>
<DL>
<DD>Computes Goodman and Kruskal's tau-value for a contingency table.<DD><DL>
<DT><B>Parameters:</B><DD><CODE>matrix</CODE> - the contingency table<DD><CODE>Goodman</CODE> - and Kruskal's tau-value</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;ops)</PRE>
<DL>
<DD>Main method for testing this class.</DL>
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