serialized-form.html

这是一个由java实现的数据挖掘

<H3>
m_numToSelect</H3>
<PRE>
int <B>m_numToSelect</B></PRE>
<DL>
<DD>The number of attributes to retain if a ranking is requested. -1
indicates that all attributes are to be retained. Has precedence over
m_threshold</DL>
<HR>

<H3>
m_calculatedNumToSelect</H3>
<PRE>
int <B>m_calculatedNumToSelect</B></PRE>
<DL>
</DL>
<HR>

<H3>
m_threshold</H3>
<PRE>
double <B>m_threshold</B></PRE>
<DL>
<DD>the threshold for removing attributes if ranking is requested</DL>

<P>
<A NAME="weka.attributeSelection.RandomSearch"><!-- --></A>
<TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%">
<TR BGCOLOR="#CCCCFF" CLASS="TableSubHeadingColor">
<TD COLSPAN=2><FONT SIZE="+2">
<B>Class <A HREF="weka/attributeSelection/RandomSearch.html">weka.attributeSelection.RandomSearch</A> implements Serializable</B></FONT></TD>
</TR>
</TABLE>

<P>
<A NAME="serializedForm"><!-- --></A>
<TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%">
<TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor">
<TD COLSPAN=1><FONT SIZE="+2">
<B>Serialized Fields</B></FONT></TD>
</TR>
</TABLE>

<H3>
m_starting</H3>
<PRE>
int[] <B>m_starting</B></PRE>
<DL>
<DD>holds a starting set as an array of attributes.</DL>
<HR>

<H3>
m_startRange</H3>
<PRE>
<A HREF="weka/core/Range.html">Range</A> <B>m_startRange</B></PRE>
<DL>
<DD>holds the start set as a range</DL>
<HR>

<H3>
m_bestGroup</H3>
<PRE>
java.util.BitSet <B>m_bestGroup</B></PRE>
<DL>
<DD>the best feature set found during the search</DL>
<HR>

<H3>
m_bestMerit</H3>
<PRE>
double <B>m_bestMerit</B></PRE>
<DL>
<DD>the merit of the best subset found</DL>
<HR>

<H3>
m_onlyConsiderBetterAndSmaller</H3>
<PRE>
boolean <B>m_onlyConsiderBetterAndSmaller</B></PRE>
<DL>
<DD>only accept a feature set as being "better" than the best if its
 merit is better or equal to the best, and it contains fewer
 features than the best (this allows LVF to be implimented).</DL>
<HR>

<H3>
m_hasClass</H3>
<PRE>
boolean <B>m_hasClass</B></PRE>
<DL>
<DD>does the data have a class</DL>
<HR>

<H3>
m_classIndex</H3>
<PRE>
int <B>m_classIndex</B></PRE>
<DL>
<DD>holds the class index</DL>
<HR>

<H3>
m_numAttribs</H3>
<PRE>
int <B>m_numAttribs</B></PRE>
<DL>
<DD>number of attributes in the data</DL>
<HR>

<H3>
m_seed</H3>
<PRE>
int <B>m_seed</B></PRE>
<DL>
<DD>seed for random number generation</DL>
<HR>

<H3>
m_searchSize</H3>
<PRE>
double <B>m_searchSize</B></PRE>
<DL>
<DD>percentage of the search space to consider</DL>
<HR>

<H3>
m_iterations</H3>
<PRE>
int <B>m_iterations</B></PRE>
<DL>
<DD>the number of iterations performed</DL>
<HR>

<H3>
m_random</H3>
<PRE>
java.util.Random <B>m_random</B></PRE>
<DL>
<DD>random number object</DL>
<HR>

<H3>
m_verbose</H3>
<PRE>
boolean <B>m_verbose</B></PRE>
<DL>
<DD>output new best subsets as the search progresses</DL>

<P>
<A NAME="weka.attributeSelection.Ranker"><!-- --></A>
<TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%">
<TR BGCOLOR="#CCCCFF" CLASS="TableSubHeadingColor">
<TD COLSPAN=2><FONT SIZE="+2">
<B>Class <A HREF="weka/attributeSelection/Ranker.html">weka.attributeSelection.Ranker</A> implements Serializable</B></FONT></TD>
</TR>
</TABLE>

<P>
<A NAME="serializedForm"><!-- --></A>
<TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%">
<TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor">
<TD COLSPAN=1><FONT SIZE="+2">
<B>Serialized Fields</B></FONT></TD>
</TR>
</TABLE>

<H3>
m_starting</H3>
<PRE>
int[] <B>m_starting</B></PRE>
<DL>
<DD>Holds the starting set as an array of attributes</DL>
<HR>

<H3>
m_startRange</H3>
<PRE>
<A HREF="weka/core/Range.html">Range</A> <B>m_startRange</B></PRE>
<DL>
<DD>Holds the start set for the search as a range</DL>
<HR>

<H3>
m_attributeList</H3>
<PRE>
int[] <B>m_attributeList</B></PRE>
<DL>
<DD>Holds the ordered list of attributes</DL>
<HR>

<H3>
m_attributeMerit</H3>
<PRE>
double[] <B>m_attributeMerit</B></PRE>
<DL>
<DD>Holds the list of attribute merit scores</DL>
<HR>

<H3>
m_hasClass</H3>
<PRE>
boolean <B>m_hasClass</B></PRE>
<DL>
<DD>Data has class attribute---if unsupervised evaluator then no class</DL>
<HR>

<H3>
m_classIndex</H3>
<PRE>
int <B>m_classIndex</B></PRE>
<DL>
<DD>Class index of the data if supervised evaluator</DL>
<HR>

<H3>
m_numAttribs</H3>
<PRE>
int <B>m_numAttribs</B></PRE>
<DL>
<DD>The number of attribtes</DL>
<HR>

<H3>
m_threshold</H3>
<PRE>
double <B>m_threshold</B></PRE>
<DL>
<DD>A threshold by which to discard attributes---used by the
 AttributeSelection module</DL>
<HR>

<H3>
m_numToSelect</H3>
<PRE>
int <B>m_numToSelect</B></PRE>
<DL>
<DD>The number of attributes to select. -1 indicates that all attributes
are to be retained. Has precedence over m_threshold</DL>
<HR>

<H3>
m_calculatedNumToSelect</H3>
<PRE>
int <B>m_calculatedNumToSelect</B></PRE>
<DL>
<DD>Used to compute the number to select</DL>

<P>
<A NAME="weka.attributeSelection.RankSearch"><!-- --></A>
<TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%">
<TR BGCOLOR="#CCCCFF" CLASS="TableSubHeadingColor">
<TD COLSPAN=2><FONT SIZE="+2">
<B>Class <A HREF="weka/attributeSelection/RankSearch.html">weka.attributeSelection.RankSearch</A> implements Serializable</B></FONT></TD>
</TR>
</TABLE>

<P>
<A NAME="serializedForm"><!-- --></A>
<TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%">
<TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor">
<TD COLSPAN=1><FONT SIZE="+2">
<B>Serialized Fields</B></FONT></TD>
</TR>
</TABLE>

<H3>
m_hasClass</H3>
<PRE>
boolean <B>m_hasClass</B></PRE>
<DL>
<DD>does the data have a class</DL>
<HR>

<H3>
m_classIndex</H3>
<PRE>
int <B>m_classIndex</B></PRE>
<DL>
<DD>holds the class index</DL>
<HR>

<H3>
m_numAttribs</H3>
<PRE>
int <B>m_numAttribs</B></PRE>
<DL>
<DD>number of attributes in the data</DL>
<HR>

<H3>
m_best_group</H3>
<PRE>
java.util.BitSet <B>m_best_group</B></PRE>
<DL>
<DD>the best subset found</DL>
<HR>

<H3>
m_ASEval</H3>
<PRE>
<A HREF="weka/attributeSelection/ASEvaluation.html">ASEvaluation</A> <B>m_ASEval</B></PRE>
<DL>
<DD>the attribute evaluator to use for generating the ranking</DL>
<HR>

<H3>
m_SubsetEval</H3>
<PRE>
<A HREF="weka/attributeSelection/ASEvaluation.html">ASEvaluation</A> <B>m_SubsetEval</B></PRE>
<DL>
<DD>the subset evaluator with which to evaluate the ranking</DL>
<HR>

<H3>
m_Instances</H3>
<PRE>
<A HREF="weka/core/Instances.html">Instances</A> <B>m_Instances</B></PRE>
<DL>
<DD>the training instances</DL>
<HR>

<H3>
m_bestMerit</H3>
<PRE>
double <B>m_bestMerit</B></PRE>
<DL>
<DD>the merit of the best subset found</DL>
<HR>

<H3>
m_Ranking</H3>
<PRE>
int[] <B>m_Ranking</B></PRE>
<DL>
<DD>will hold the attribute ranking</DL>

<P>
<A NAME="weka.attributeSelection.ReliefFAttributeEval"><!-- --></A>
<TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%">
<TR BGCOLOR="#CCCCFF" CLASS="TableSubHeadingColor">
<TD COLSPAN=2><FONT SIZE="+2">
<B>Class <A HREF="weka/attributeSelection/ReliefFAttributeEval.html">weka.attributeSelection.ReliefFAttributeEval</A> implements Serializable</B></FONT></TD>
</TR>
</TABLE>

<P>
<A NAME="serializedForm"><!-- --></A>
<TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%">
<TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor">
<TD COLSPAN=1><FONT SIZE="+2">
<B>Serialized Fields</B></FONT></TD>
</TR>
</TABLE>

<H3>
m_trainInstances</H3>
<PRE>
<A HREF="weka/core/Instances.html">Instances</A> <B>m_trainInstances</B></PRE>
<DL>
<DD>The training instances</DL>
<HR>

<H3>
m_classIndex</H3>
<PRE>
int <B>m_classIndex</B></PRE>
<DL>
<DD>The class index</DL>
<HR>

<H3>
m_numAttribs</H3>
<PRE>
int <B>m_numAttribs</B></PRE>
<DL>
<DD>The number of attributes</DL>
<HR>

<H3>
m_numInstances</H3>
<PRE>
int <B>m_numInstances</B></PRE>
<DL>
<DD>The number of instances</DL>
<HR>

<H3>
m_numericClass</H3>
<PRE>
boolean <B>m_numericClass</B></PRE>
<DL>
<DD>Numeric class</DL>
<HR>

<H3>
m_numClasses</H3>
<PRE>
int <B>m_numClasses</B></PRE>
<DL>
<DD>The number of classes if class is nominal</DL>
<HR>

<H3>
m_ndc</H3>
<PRE>
double <B>m_ndc</B></PRE>
<DL>
<DD>Used to hold the probability of a different class val given nearest
 instances (numeric class)</DL>
<HR>

<H3>
m_nda</H3>
<PRE>
double[] <B>m_nda</B></PRE>
<DL>
<DD>Used to hold the prob of different value of an attribute given
 nearest instances (numeric class case)</DL>
<HR>

<H3>
m_ndcda</H3>
<PRE>
double[] <B>m_ndcda</B></PRE>
<DL>
<DD>Used to hold the prob of a different class val and different att
 val given nearest instances (numeric class case)</DL>
<HR>

<H3>
m_weights</H3>
<PRE>
double[] <B>m_weights</B></PRE>
<DL>
<DD>Holds the weights that relief assigns to attributes</DL>
<HR>

<H3>
m_classProbs</H3>
<PRE>
double[] <B>m_classProbs</B></PRE>
<DL>
<DD>Prior class probabilities (discrete class case)</DL>
<HR>

<H3>
m_sampleM</H3>
<PRE>
int <B>m_sampleM</B></PRE>
<DL>
<DD>The number of instances to sample when estimating attributes
 default == -1, use all instances</DL>
<HR>

<H3>
m_Knn</H3>
<PRE>
int <B>m_Knn</B></PRE>
<DL>
<DD>The number of nearest hits/misses</DL>
<HR>

<H3>
m_karray</H3>
<PRE>
double[][][] <B>m_karray</B></PRE>
<DL>
<DD>k nearest scores + instance indexes for n classes</DL>
<HR>

<H3>
m_maxArray</H3>
<PRE>
double[] <B>m_maxArray</B></PRE>
<DL>
<DD>Upper bound for numeric attributes</DL>
<HR>

<H3>
m_minArray</H3>
<PRE>
double[] <B>m_minArray</B></PRE>
<DL>
<DD>Lower bound for numeric attributes</DL>
<HR>

<H3>
m_worst</H3>
<PRE>
double[] <B>m_worst</B></PRE>
<DL>
<DD>Keep track of the farthest instance for each class</DL>
<HR>

<H3>
m_index</H3>
<PRE>
int[] <B>m_index</B></PRE>
<DL>
<DD>Index in the m_karray of the farthest instance for each class</DL>
<HR>

<H3>
m_stored</H3>
<PRE>
int[] <B>m_stored</B></PRE>
<DL>
<DD>Number of nearest neighbours stored of each class</DL>
<HR>

<H3>
m_seed</H3>
<PRE>
int <B>m_seed</B></PRE>
<DL>
<DD>Random number seed used for sampling instances</DL>
<HR>

<H3>
m_weightsByRank</H3>
<PRE>
double[] <B>m_weightsByRank</B></PRE>
<DL>
<DD>used to (optionally) weight nearest neighbours by their distance
  from the instance in question. Each entry holds 
  exp(-((rank(r_i, i_j)/sigma)^2)) where rank(r_i,i_j) is the rank of
  instance i_j in a sequence of instances ordered by the distance
  from r_i. sigma is a user defined parameter, default=20</DL>
<HR>

<H3>
m_sigma</H3>
<PRE>
int <B>m_sigma</B></PRE>
<DL>
</DL>
<HR>

<H3>
m_weightByDistance</H3>
<PRE>
boolean <B>m_weightByDistance</B></PRE>
<DL>
<DD>Weight by distance rather than equal weights</DL>

<P>
<A NAME="weka.attributeSelection.SubsetEvaluator"><!-- --></A>
<TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%">
<TR BGCOLOR="#CCCCFF" CLASS="TableSubHeadingColor">
<TD COLSPAN=2><FONT SIZE="+2">
<B>Class <A HREF="weka/attributeSelection/SubsetEvaluator.html">weka.attributeSelection.SubsetEvaluator</A> implements Serializable</B></FONT></TD>
</TR>
</TABLE>

<P>

<P>
<A NAME="weka.attributeSelection.SymmetricalUncertAttributeEval"><!-- --></A>
<TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%">
<TR BGCOLOR="#CCCCFF" CLASS="TableSubHeadingColor">
<TD COLSPAN=2><FONT SIZE="+2">
<B>Class <A HREF="weka/attributeSelection/SymmetricalUncertAttributeEval.html">weka.attributeSelection.SymmetricalUncertAttributeEval</A> implements Serializable</B></FONT></TD>
</TR>
</TABLE>

<P>
<A NAME="serializedForm"><!-- --></A>
<TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%">
<TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor">
<TD COLSPAN=1><FONT SIZE="+2">
<B>Serialized Fields</B></FONT></TD>
</TR>
</TABLE>

<H3>
m_trainInstances</H3>
<PRE>
<A HREF="weka/core/Instances.html">Instances</A> <B>m_trainInstances</B></PRE>
<DL>
<DD>The training instances</DL>
<HR>

<H3>
m_classIndex</H3>
<PRE>
int <B>m_classIndex</B></PRE>
<DL>
<DD>The class index</DL>
<HR>

<H3>
m_numAttribs</H3>
<PRE>
int <B>m_numAttribs</B></PRE>
<DL>
<DD>The number of attributes</DL>
<HR>

<H3>
m_numInstances</H3>
<PRE>
int <B>m_numInstances</B></PRE>
<DL>
<DD>The number of instances</DL>
<HR>

<H3>
m_numClasses</H3>
<PRE>
int <B>m_numClasses</B></PRE>
<DL>
<DD>The number of classes</DL>
<HR>

<H3>
m_missing_merge</H3>
<PRE>
boolean <B>m_missing_merge</B></PRE>
<DL>
<DD>Treat missing values as a seperate value</DL>

<P>
<A NAME="weka.attributeSelection.UnsupervisedAttributeEvaluator"><!-- --></A>
<TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%">