kstarnominalattribute.html

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<CODE>&nbsp;double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../../../weka/classifiers/kstar/KStarNominalAttribute.html#transProb()">transProb</A></B>()</CODE>

<BR>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Calculates the probability of the indexed nominal attribute of the test
 instance transforming into the indexed nominal attribute of the training 
 instance.</TD>
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<TD><B>Methods inherited from class java.lang.Object</B></TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD><CODE>clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait</CODE></TD>
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<!-- ============ FIELD DETAIL =========== -->

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<B>Field Detail</B></FONT></TD>
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<A NAME="m_TrainSet"><!-- --></A><H3>
m_TrainSet</H3>
<PRE>
protected <A HREF="../../../weka/core/Instances.html">Instances</A> <B>m_TrainSet</B></PRE>
<DL>
<DD>The training instances used for classification.</DL>
<HR>

<A NAME="m_Test"><!-- --></A><H3>
m_Test</H3>
<PRE>
protected <A HREF="../../../weka/core/Instance.html">Instance</A> <B>m_Test</B></PRE>
<DL>
<DD>The test instance</DL>
<HR>

<A NAME="m_Train"><!-- --></A><H3>
m_Train</H3>
<PRE>
protected <A HREF="../../../weka/core/Instance.html">Instance</A> <B>m_Train</B></PRE>
<DL>
<DD>The train instance</DL>
<HR>

<A NAME="m_AttrIndex"><!-- --></A><H3>
m_AttrIndex</H3>
<PRE>
protected int <B>m_AttrIndex</B></PRE>
<DL>
<DD>The index of the nominal attribute in the test and train instances</DL>
<HR>

<A NAME="m_Stop"><!-- --></A><H3>
m_Stop</H3>
<PRE>
protected double <B>m_Stop</B></PRE>
<DL>
<DD>The stop parameter</DL>
<HR>

<A NAME="m_MissingProb"><!-- --></A><H3>
m_MissingProb</H3>
<PRE>
protected double <B>m_MissingProb</B></PRE>
<DL>
<DD>Probability of test attribute transforming into train attribute 
with missing value</DL>
<HR>

<A NAME="m_AverageProb"><!-- --></A><H3>
m_AverageProb</H3>
<PRE>
protected double <B>m_AverageProb</B></PRE>
<DL>
<DD>Average probability of test attribute transforming into train 
attribute</DL>
<HR>

<A NAME="m_SmallestProb"><!-- --></A><H3>
m_SmallestProb</H3>
<PRE>
protected double <B>m_SmallestProb</B></PRE>
<DL>
<DD>Smallest probability of test attribute transforming into 
train attribute</DL>
<HR>

<A NAME="m_TotalCount"><!-- --></A><H3>
m_TotalCount</H3>
<PRE>
protected int <B>m_TotalCount</B></PRE>
<DL>
<DD>Number of trai instances with no missing attribute values</DL>
<HR>

<A NAME="m_Distribution"><!-- --></A><H3>
m_Distribution</H3>
<PRE>
protected int[] <B>m_Distribution</B></PRE>
<DL>
<DD>Distribution of the attribute value in the train dataset</DL>
<HR>

<A NAME="m_RandClassCols"><!-- --></A><H3>
m_RandClassCols</H3>
<PRE>
protected int[][] <B>m_RandClassCols</B></PRE>
<DL>
<DD>Set of colomns: each colomn representing a randomised version 
of the train dataset class colomn</DL>
<HR>

<A NAME="m_Cache"><!-- --></A><H3>
m_Cache</H3>
<PRE>
protected <A HREF="../../../weka/classifiers/kstar/KStarCache.html">KStarCache</A> <B>m_Cache</B></PRE>
<DL>
<DD>A cache for storing attribute values and their corresponding 
stop parameters</DL>
<HR>

<A NAME="m_NumInstances"><!-- --></A><H3>
m_NumInstances</H3>
<PRE>
protected int <B>m_NumInstances</B></PRE>
<DL>
<DD>The number of instances in the dataset</DL>
<HR>

<A NAME="m_NumClasses"><!-- --></A><H3>
m_NumClasses</H3>
<PRE>
protected int <B>m_NumClasses</B></PRE>
<DL>
<DD>The number of class values</DL>
<HR>

<A NAME="m_NumAttributes"><!-- --></A><H3>
m_NumAttributes</H3>
<PRE>
protected int <B>m_NumAttributes</B></PRE>
<DL>
<DD>The number of attributes</DL>
<HR>

<A NAME="m_ClassType"><!-- --></A><H3>
m_ClassType</H3>
<PRE>
protected int <B>m_ClassType</B></PRE>
<DL>
<DD>The class attribute type</DL>
<HR>

<A NAME="m_MissingMode"><!-- --></A><H3>
m_MissingMode</H3>
<PRE>
protected int <B>m_MissingMode</B></PRE>
<DL>
<DD>missing value treatment</DL>
<HR>

<A NAME="m_BlendMethod"><!-- --></A><H3>
m_BlendMethod</H3>
<PRE>
protected int <B>m_BlendMethod</B></PRE>
<DL>
<DD>B_SPHERE = use specified blend, B_ENTROPY = entropic blend setting</DL>
<HR>

<A NAME="m_BlendFactor"><!-- --></A><H3>
m_BlendFactor</H3>
<PRE>
protected int <B>m_BlendFactor</B></PRE>
<DL>
<DD>default sphere of influence blend setting</DL>

<!-- ========= CONSTRUCTOR DETAIL ======== -->

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<A NAME="KStarNominalAttribute(weka.core.Instance, weka.core.Instance, int, weka.core.Instances, int[][], weka.classifiers.kstar.KStarCache)"><!-- --></A><H3>
KStarNominalAttribute</H3>
<PRE>
public <B>KStarNominalAttribute</B>(<A HREF="../../../weka/core/Instance.html">Instance</A>&nbsp;test,
                             <A HREF="../../../weka/core/Instance.html">Instance</A>&nbsp;train,
                             int&nbsp;attrIndex,
                             <A HREF="../../../weka/core/Instances.html">Instances</A>&nbsp;trainSet,
                             int[][]&nbsp;randClassCol,
                             <A HREF="../../../weka/classifiers/kstar/KStarCache.html">KStarCache</A>&nbsp;cache)</PRE>
<DL>
<DD>Constructor</DL>

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<A NAME="transProb()"><!-- --></A><H3>
transProb</H3>
<PRE>
public double <B>transProb</B>()</PRE>
<DL>
<DD>Calculates the probability of the indexed nominal attribute of the test
 instance transforming into the indexed nominal attribute of the training 
 instance.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>the value of the transformation probability.</DL>
</DD>
</DL>
<HR>

<A NAME="setOptions(int, int, int)"><!-- --></A><H3>
setOptions</H3>
<PRE>
public void <B>setOptions</B>(int&nbsp;missingmode,
                       int&nbsp;blendmethod,
                       int&nbsp;blendfactor)</PRE>
<DL>
<DD>Sets the options.<DD><DL>
</DL>
</DD>
</DL>
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