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</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/rules/RuleStats.html#setData(weka.core.Instances)">setData</A></B>(<A HREF="../../../weka/core/Instances.html" title="class in weka.core">Instances</A> data)</CODE><BR> Set the data of the stats, overwriting the old one if any</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/rules/RuleStats.html#setMDLTheoryWeight(double)">setMDLTheoryWeight</A></B>(double weight)</CODE><BR> Set the weight of theory in MDL calcualtion</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/rules/RuleStats.html#setNumAllConds(double)">setNumAllConds</A></B>(double total)</CODE><BR> Set the number of all conditions that could appear in a rule in this RuleStats object, if the number set is smaller than 0 (typically -1), then it calcualtes based on the data store</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/rules/RuleStats.html#setRuleset(weka.core.FastVector)">setRuleset</A></B>(<A HREF="../../../weka/core/FastVector.html" title="class in weka.core">FastVector</A> rules)</CODE><BR> Set the ruleset of the stats, overwriting the old one if any</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE>static <A HREF="../../../weka/core/Instances.html" title="class in weka.core">Instances</A></CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/rules/RuleStats.html#stratify(weka.core.Instances, int, java.util.Random)">stratify</A></B>(<A HREF="../../../weka/core/Instances.html" title="class in weka.core">Instances</A> data, int folds, java.util.Random rand)</CODE><BR> Stratify the given data into the given number of bags based on the class values.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE>static double</CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/rules/RuleStats.html#subsetDL(double, double, double)">subsetDL</A></B>(double t, double k, double p)</CODE><BR> 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</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/rules/RuleStats.html#theoryDL(int)">theoryDL</A></B>(int index)</CODE><BR> The description length of the theory for a given rule.</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"><TD><B>Methods inherited from class java.lang.Object</B></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><CODE>equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait</CODE></TD></TR></TABLE> <P><!-- ============ FIELD DETAIL =========== --><!-- ========= CONSTRUCTOR DETAIL ======== --><A NAME="constructor_detail"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TD COLSPAN=1><FONT SIZE="+2"><B>Constructor Detail</B></FONT></TD></TR></TABLE><A NAME="RuleStats()"><!-- --></A><H3>RuleStats</H3><PRE>public <B>RuleStats</B>()</PRE><DL><DD>Default constructor<P></DL><HR><A NAME="RuleStats(weka.core.Instances, weka.core.FastVector)"><!-- --></A><H3>RuleStats</H3><PRE>public <B>RuleStats</B>(<A HREF="../../../weka/core/Instances.html" title="class in weka.core">Instances</A> data, <A HREF="../../../weka/core/FastVector.html" title="class in weka.core">FastVector</A> rules)</PRE><DL><DD>Constructor that provides ruleset and data<P><DT><B>Parameters:</B><DD><CODE>data</CODE> - the data<DD><CODE>rules</CODE> - the ruleset</DL><!-- ============ METHOD DETAIL ========== --><A NAME="method_detail"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TD COLSPAN=1><FONT SIZE="+2"><B>Method Detail</B></FONT></TD></TR></TABLE><A NAME="setNumAllConds(double)"><!-- --></A><H3>setNumAllConds</H3><PRE>public void <B>setNumAllConds</B>(double total)</PRE><DL><DD>Set the number of all conditions that could appear in a rule in this RuleStats object, if the number set is smaller than 0 (typically -1), then it calcualtes based on the data store<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>total</CODE> - the set number</DL></DD></DL><HR><A NAME="setData(weka.core.Instances)"><!-- --></A><H3>setData</H3><PRE>public void <B>setData</B>(<A HREF="../../../weka/core/Instances.html" title="class in weka.core">Instances</A> data)</PRE><DL><DD>Set the data of the stats, overwriting the old one if any<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>data</CODE> - the data to be set</DL></DD></DL><HR><A NAME="getData()"><!-- --></A><H3>getData</H3><PRE>public <A HREF="../../../weka/core/Instances.html" title="class in weka.core">Instances</A> <B>getData</B>()</PRE><DL><DD>Get the data of the stats<P><DD><DL></DL></DD><DD><DL><DT><B>Returns:</B><DD>the data</DL></DD></DL><HR><A NAME="setRuleset(weka.core.FastVector)"><!-- --></A><H3>setRuleset</H3><PRE>public void <B>setRuleset</B>(<A HREF="../../../weka/core/FastVector.html" title="class in weka.core">FastVector</A> rules)</PRE><DL><DD>Set the ruleset of the stats, overwriting the old one if any<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>rules</CODE> - the set of rules to be set</DL></DD></DL><HR><A NAME="getRuleset()"><!-- --></A><H3>getRuleset</H3><PRE>public <A HREF="../../../weka/core/FastVector.html" title="class in weka.core">FastVector</A> <B>getRuleset</B>()</PRE><DL><DD>Get the ruleset of the stats<P><DD><DL></DL></DD><DD><DL><DT><B>Returns:</B><DD>the set of rules</DL></DD></DL><HR><A NAME="getRulesetSize()"><!-- --></A><H3>getRulesetSize</H3><PRE>public int <B>getRulesetSize</B>()</PRE><DL><DD>Get the size of the ruleset in the stats<P><DD><DL></DL></DD><DD><DL><DT><B>Returns:</B><DD>the size of ruleset</DL></DD></DL><HR><A NAME="getSimpleStats(int)"><!-- --></A><H3>getSimpleStats</H3><PRE>public double[] <B>getSimpleStats</B>(int index)</PRE><DL><DD>Get the simple stats of one rule, including 6 parameters: 0: coverage; 1:uncoverage; 2: true positive; 3: true negatives; 4: false positives; 5: false negatives<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>index</CODE> - the index of the rule<DT><B>Returns:</B><DD>the stats</DL></DD></DL><HR><A NAME="getFiltered(int)"><!-- --></A><H3>getFiltered</H3><PRE>public <A HREF="../../../weka/core/Instances.html" title="class in weka.core">Instances</A>[] <B>getFiltered</B>(int index)</PRE><DL><DD>Get the data after filtering the given rule<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>index</CODE> - the index of the rule<DT><B>Returns:</B><DD>the data covered and uncovered by the rule</DL></DD></DL><HR><A NAME="getDistributions(int)"><!-- --></A><H3>getDistributions</H3><PRE>public double[] <B>getDistributions</B>(int index)</PRE><DL><DD>Get the class distribution predicted by the rule in given position<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>index</CODE> - the position index of the rule<DT><B>Returns:</B><DD>the class distributions</DL></DD></DL><HR><A NAME="setMDLTheoryWeight(double)"><!-- --></A><H3>setMDLTheoryWeight</H3><PRE>public void <B>setMDLTheoryWeight</B>(double weight)</PRE><DL><DD>Set the weight of theory in MDL calcualtion<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>weight</CODE> - the weight to be set</DL></DD></DL><HR><A NAME="numAllConditions(weka.core.Instances)"><!-- --></A><H3>numAllConditions</H3><PRE>public static double <B>numAllConditions</B>(<A HREF="../../../weka/core/Instances.html" title="class in weka.core">Instances</A> data)</PRE><DL><DD>Compute the number of all possible conditions that could appear in a rule of a given data. For nominal attributes, it's the number of values that could appear; for numeric attributes, it's the number of values * 2, i.e. <= and >= are counted as different possible conditions.<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>data</CODE> - the given data<DT><B>Returns:</B><DD>number of all conditions of the data</DL></DD></DL><HR><A NAME="countData()"><!-- --></A><H3>countData</H3><PRE>public void <B>countData</B>()</PRE><DL><DD>Filter the data according to the ruleset and compute the basic stats: coverage/uncoverage, true/false positive/negatives of each rule<P><DD><DL></DL></DD><DD><DL></DL></DD></DL><HR><A NAME="countData(int, weka.core.Instances, double[][])"><!-- --></A><H3>countData</H3><PRE>public void <B>countData</B>(int index, <A HREF="../../../weka/core/Instances.html" title="class in weka.core">Instances</A> uncovered, double[][] prevRuleStats)</PRE><DL><DD>Count data from the position index in the ruleset assuming that given data are not covered by the rules in position 0...(index-1), and the statistics of these rules are provided.<br> This procedure is typically useful when a temporary object of RuleStats is constructed in order to efficiently calculate the relative DL of rule in position index, thus all other stuff is not needed.<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>index</CODE> - the given position<DD><CODE>uncovered</CODE> - the data not covered by rules before index<DD><CODE>prevRuleStats</CODE> - the provided stats of previous rules</DL></DD></DL><HR><A NAME="addAndUpdate(weka.classifiers.rules.Rule)"><!-- --></A><H3>addAndUpdate</H3>⌨️ 快捷键说明
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