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<PRE>
public void <B>addAndUpdate</B>(<A HREF="../../../weka/classifiers/rules/Rule.html" title="class in weka.classifiers.rules">Rule</A>&nbsp;lastRule)</PRE>
<DL>
<DD>Add a rule to the ruleset and update the stats
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
</DL>
</DD>
</DL>
<HR>

<A NAME="subsetDL(double, double, double)"><!-- --></A><H3>
subsetDL</H3>
<PRE>
public static double <B>subsetDL</B>(double&nbsp;t,
                              double&nbsp;k,
                              double&nbsp;p)</PRE>
<DL>
<DD>Subset description length: <br>
 S(t,k,p) = -k*log2(p)-(n-k)log2(1-p)

 Details see Quilan: "MDL and categorical theories (Continued)",ML95
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>t</CODE> - the number of elements in a known set<DD><CODE>k</CODE> - the number of elements in a subset<DD><CODE>p</CODE> - the expected proportion of subset known by recipient</DL>
</DD>
</DL>
<HR>

<A NAME="theoryDL(int)"><!-- --></A><H3>
theoryDL</H3>
<PRE>
public double <B>theoryDL</B>(int&nbsp;index)</PRE>
<DL>
<DD>The description length of the theory for a given rule.  Computed as:<br>
                 0.5* [||k||+ S(t, k, k/t)]<br>
 where k is the number of antecedents of the rule; t is the total
 possible antecedents that could appear in a rule; ||K|| is the 
 universal prior for k , log2*(k) and S(t,k,p) = -k*log2(p)-(n-k)log2(1-p)
 is the subset encoding length.<p>

 Details see Quilan: "MDL and categorical theories (Continued)",ML95
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>index</CODE> - the index of the given rule (assuming correct)
<DT><B>Returns:</B><DD>the theory DL, weighted if weight != 1.0
<DT><B>Throws:</B>
<DD><CODE>if</CODE> - index out of range or object not initialized yet</DL>
</DD>
</DL>
<HR>

<A NAME="dataDL(double, double, double, double, double)"><!-- --></A><H3>
dataDL</H3>
<PRE>
public static double <B>dataDL</B>(double&nbsp;expFPOverErr,
                            double&nbsp;cover,
                            double&nbsp;uncover,
                            double&nbsp;fp,
                            double&nbsp;fn)</PRE>
<DL>
<DD>The description length of data given the parameters of the data
 based on the ruleset. <p>
 Details see Quinlan: "MDL and categorical theories (Continued)",ML95<p>
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>expFPOverErr</CODE> - expected FP/(FP+FN)<DD><CODE>cover</CODE> - coverage<DD><CODE>uncover</CODE> - uncoverage<DD><CODE>fp</CODE> - False Positive<DD><CODE>fn</CODE> - False Negative</DL>
</DD>
</DL>
<HR>

<A NAME="potential(int, double, double[], double[], boolean)"><!-- --></A><H3>
potential</H3>
<PRE>
public double <B>potential</B>(int&nbsp;index,
                        double&nbsp;expFPOverErr,
                        double[]&nbsp;rulesetStat,
                        double[]&nbsp;ruleStat,
                        boolean&nbsp;checkErr)</PRE>
<DL>
<DD>Calculate the potential to decrease DL of the ruleset,
 i.e. the possible DL that could be decreased by deleting
 the rule whose index and simple statstics are given.  
 If there's no potentials (i.e. smOrEq 0 && error rate < 0.5),
 it returns NaN. <p>

 The way this procedure does is copied from original RIPPER
 implementation and is quite bizzare because it 
 does not update the following rules' stats recursively 
 any more when testing each rule, which means it assumes
 after deletion no data covered by the following rules (or
 regards the deleted rule as the last rule).  Reasonable 
 assumption?<p>
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>index</CODE> - the index of the rule in m_Ruleset to be deleted<DD><CODE>expFPOverErr</CODE> - expected FP/(FP+FN)<DD><CODE>rulesetStat</CODE> - the simple statistics of the ruleset, updated
                    if the rule should be deleted<DD><CODE>ruleStat</CODE> - the simple statistics of the rule to be deleted<DD><CODE>checkErr</CODE> - whether check if error rate >= 0.5
<DT><B>Returns:</B><DD>the potential DL that could be decreased</DL>
</DD>
</DL>
<HR>

<A NAME="minDataDLIfDeleted(int, double, boolean)"><!-- --></A><H3>
minDataDLIfDeleted</H3>
<PRE>
public double <B>minDataDLIfDeleted</B>(int&nbsp;index,
                                 double&nbsp;expFPRate,
                                 boolean&nbsp;checkErr)</PRE>
<DL>
<DD>Compute the minimal data description length of the ruleset
 if the rule in the given position is deleted.<br>
 The min_data_DL_if_deleted = data_DL_if_deleted - potential
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>index</CODE> - the index of the rule in question<DD><CODE>expFPRate</CODE> - expected FP/(FP+FN), used in dataDL calculation<DD><CODE>checkErr</CODE> - whether check if error rate >= 0.5</DL>
</DD>
</DL>
<HR>

<A NAME="minDataDLIfExists(int, double, boolean)"><!-- --></A><H3>
minDataDLIfExists</H3>
<PRE>
public double <B>minDataDLIfExists</B>(int&nbsp;index,
                                double&nbsp;expFPRate,
                                boolean&nbsp;checkErr)</PRE>
<DL>
<DD>Compute the minimal data description length of the ruleset
 if the rule in the given position is NOT deleted.<br>
 The min_data_DL_if_n_deleted = data_DL_if_n_deleted - potential
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>index</CODE> - the index of the rule in question<DD><CODE>expFPRate</CODE> - expected FP/(FP+FN), used in dataDL calculation<DD><CODE>checkErr</CODE> - whether check if error rate >= 0.5</DL>
</DD>
</DL>
<HR>

<A NAME="relativeDL(int, double, boolean)"><!-- --></A><H3>
relativeDL</H3>
<PRE>
public double <B>relativeDL</B>(int&nbsp;index,
                         double&nbsp;expFPRate,
                         boolean&nbsp;checkErr)</PRE>
<DL>
<DD>The description length (DL) of the ruleset relative to if the
 rule in the given position is deleted, which is obtained by: <br>
 MDL if the rule exists - MDL if the rule does not exist <br>
 Note the minimal possible DL of the ruleset is calculated(i.e. some
 other rules may also be deleted) instead of the DL of the current
 ruleset.<p>
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>index</CODE> - the given position of the rule in question 
              (assuming correct)<DD><CODE>expFPRate</CODE> - expected FP/(FP+FN), used in dataDL calculation<DD><CODE>checkErr</CODE> - whether check if error rate >= 0.5
<DT><B>Returns:</B><DD>the relative DL</DL>
</DD>
</DL>
<HR>

<A NAME="reduceDL(double, boolean)"><!-- --></A><H3>
reduceDL</H3>
<PRE>
public void <B>reduceDL</B>(double&nbsp;expFPRate,
                     boolean&nbsp;checkErr)</PRE>
<DL>
<DD>Try to reduce the DL of the ruleset by testing removing the rules
 one by one in reverse order and update all the stats
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>expFPRate</CODE> - expected FP/(FP+FN), used in dataDL calculation<DD><CODE>checkErr</CODE> - whether check if error rate >= 0.5</DL>
</DD>
</DL>
<HR>

<A NAME="removeLast()"><!-- --></A><H3>
removeLast</H3>
<PRE>
public void <B>removeLast</B>()</PRE>
<DL>
<DD>Remove the last rule in the ruleset as well as it's stats.
 It might be useful when the last rule was added for testing
 purpose and then the test failed
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
</DL>
</DD>
</DL>
<HR>

<A NAME="rmCoveredBySuccessives(weka.core.Instances, weka.core.FastVector, int)"><!-- --></A><H3>
rmCoveredBySuccessives</H3>
<PRE>
public static <A HREF="../../../weka/core/Instances.html" title="class in weka.core">Instances</A> <B>rmCoveredBySuccessives</B>(<A HREF="../../../weka/core/Instances.html" title="class in weka.core">Instances</A>&nbsp;data,
                                               <A HREF="../../../weka/core/FastVector.html" title="class in weka.core">FastVector</A>&nbsp;rules,
                                               int&nbsp;index)</PRE>
<DL>
<DD>Static utility function to count the data covered by the 
 rules after the given index in the given rules, and then
 remove them.  It returns the data not covered by the
 successive rules.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>data</CODE> - the data to be processed<DD><CODE>rules</CODE> - the ruleset<DD><CODE>index</CODE> - the given index
<DT><B>Returns:</B><DD>the data after processing</DL>
</DD>
</DL>
<HR>

<A NAME="stratify(weka.core.Instances, int, java.util.Random)"><!-- --></A><H3>
stratify</H3>
<PRE>
public static final <A HREF="../../../weka/core/Instances.html" title="class in weka.core">Instances</A> <B>stratify</B>(<A HREF="../../../weka/core/Instances.html" title="class in weka.core">Instances</A>&nbsp;data,
                                       int&nbsp;folds,
                                       java.util.Random&nbsp;rand)</PRE>
<DL>
<DD>Stratify the given data into the given number of bags based on the class
 values.  It differs from the <code>Instances.stratify(int fold)</code>
 that before stratification it sorts the instances according to the 
 class order in the header file.  It assumes no missing values in the class.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>data</CODE> - the given data<DD><CODE>folds</CODE> - the given number of folds<DD><CODE>rand</CODE> - the random object used to randomize the instances
<DT><B>Returns:</B><DD>the stratified instances</DL>
</DD>
</DL>
<HR>

<A NAME="combinedDL(double, double)"><!-- --></A><H3>
combinedDL</H3>
<PRE>
public double <B>combinedDL</B>(double&nbsp;expFPRate,
                         double&nbsp;predicted)</PRE>
<DL>
<DD>Compute the combined DL of the ruleset in this class, i.e. theory 
 DL and data DL.  Note this procedure computes the combined DL
 according to the current status of the ruleset in this class
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>expFPRate</CODE> - expected FP/(FP+FN), used in dataDL calculation<DD><CODE>predicted</CODE> - the default classification if ruleset covers null
<DT><B>Returns:</B><DD>the combined class</DL>
</DD>
</DL>
<HR>

<A NAME="partition(weka.core.Instances, int)"><!-- --></A><H3>
partition</H3>
<PRE>
public static final <A HREF="../../../weka/core/Instances.html" title="class in weka.core">Instances</A>[] <B>partition</B>(<A HREF="../../../weka/core/Instances.html" title="class in weka.core">Instances</A>&nbsp;data,
                                          int&nbsp;numFolds)</PRE>
<DL>
<DD>Patition the data into 2, first of which has (numFolds-1)/numFolds of
 the data and the second has 1/numFolds of the data
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>data</CODE> - the given data<DD><CODE>numFolds</CODE> - the given number of folds
<DT><B>Returns:</B><DD>the patitioned instances</DL>
</DD>
</DL>
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