algorithmlbg.html

包含了模式识别中常用的一些分类器设计算法

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<A NAME="AlgorithmLBG()"><!-- --></A><H3>
AlgorithmLBG</H3>
<PRE>
public <B>AlgorithmLBG</B>()</PRE>
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<A NAME="initialize()"><!-- --></A><H3>
initialize</H3>
<PRE>
public boolean <B>initialize</B>()</PRE>
<DL>
<DD>Overrides the initialize() method in the base class. Initializes
 member data and prepares for execution of first step. This method
 "resets" the algorithm.
<P>
<DD><DL>
<DT><B>Specified by:</B><DD><CODE><A HREF="Algorithm.html#initialize()">initialize</A></CODE> in class <CODE><A HREF="Algorithm.html" title="class in &lt;Unnamed&gt;">Algorithm</A></CODE></DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>Returns true</DL>
</DD>
</DL>
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<A NAME="run()"><!-- --></A><H3>
run</H3>
<PRE>
public void <B>run</B>()</PRE>
<DL>
<DD>Implementation of the run function from the Runnable interface.
 Determines what the current step is and calls the appropriate method.
<P>
<DD><DL>
<DT><B>Specified by:</B><DD><CODE>run</CODE> in interface <CODE>java.lang.Runnable</CODE><DT><B>Specified by:</B><DD><CODE><A HREF="Algorithm.html#run()">run</A></CODE> in class <CODE><A HREF="Algorithm.html" title="class in &lt;Unnamed&gt;">Algorithm</A></CODE></DL>
</DD>
<DD><DL>
</DL>
</DD>
</DL>
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<A NAME="generatePool()"><!-- --></A><H3>
generatePool</H3>
<PRE>
public void <B>generatePool</B>()</PRE>
<DL>
<DD>Collects all the data points together
<P>
<DD><DL>
</DL>
</DD>
</DL>
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<A NAME="getClosestSet(MyPoint)"><!-- --></A><H3>
getClosestSet</H3>
<PRE>
public int <B>getClosestSet</B>(<A HREF="MyPoint.html" title="class in &lt;Unnamed&gt;">MyPoint</A>&nbsp;mean)</PRE>
<DL>
<DD>Determines the closest data sets to the cluster
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>mean</CODE> - mean point of the cluster
<DT><B>Returns:</B><DD>closest data set to the cluster</DL>
</DD>
</DL>
<HR>

<A NAME="displayClusterError(int, java.util.Vector, int)"><!-- --></A><H3>
displayClusterError</H3>
<PRE>
public int <B>displayClusterError</B>(int&nbsp;closest,
                               java.util.Vector&nbsp;cluster,
                               int&nbsp;id)</PRE>
<DL>
<DD>Finds the datapoints in error, for all datasets
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>closest</CODE> - Variable can be int values 1-4.  Marks which
 set of data is closest<DD><CODE>cluster</CODE> - Stores the points of a cluster<DD><CODE>id</CODE> - ID number
<DT><B>Returns:</B><DD>Returns the error number of the misclassified samples</DL>
</DD>
</DL>
<HR>

<A NAME="computeBinaryDeviates(java.util.Vector)"><!-- --></A><H3>
computeBinaryDeviates</H3>
<PRE>
public void <B>computeBinaryDeviates</B>(java.util.Vector&nbsp;decisionRegions)</PRE>
<DL>
<DD>Computes the binary deviates after each iteraion
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>decisionRegions</CODE> - region: classified data sets</DL>
</DD>
</DL>
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<A NAME="clusterDeviation(java.util.Vector, MyPoint)"><!-- --></A><H3>
clusterDeviation</H3>
<PRE>
public <A HREF="MyPoint.html" title="class in &lt;Unnamed&gt;">MyPoint</A> <B>clusterDeviation</B>(java.util.Vector&nbsp;cluster,
                                <A HREF="MyPoint.html" title="class in &lt;Unnamed&gt;">MyPoint</A>&nbsp;mean)</PRE>
<DL>
<DD>Calculates the standard deviation of the cluster
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>cluster</CODE> - cluster of data points<DD><CODE>mean</CODE> - mean of the cluster
<DT><B>Returns:</B><DD>MYPoint standard deviation of the cluster along
         x direction and y direction.<DT><B>See Also:</B><DD><A HREF="MyPoint.html" title="class in &lt;Unnamed&gt;"><CODE>MyPoint</CODE></A></DL>
</DD>
</DL>
<HR>

<A NAME="classify(java.util.Vector)"><!-- --></A><H3>
classify</H3>
<PRE>
public void <B>classify</B>(java.util.Vector&nbsp;guesses)</PRE>
<DL>
<DD>Classifies the data sets based on the k-means iterative algorithm
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>guesses</CODE> - stored data sets from the classification</DL>
</DD>
</DL>
<HR>

<A NAME="getDecisionRegion(java.util.Vector)"><!-- --></A><H3>
getDecisionRegion</H3>
<PRE>
public java.util.Vector&lt;<A HREF="MyPoint.html" title="class in &lt;Unnamed&gt;">MyPoint</A>&gt; <B>getDecisionRegion</B>(java.util.Vector&lt;<A HREF="MyPoint.html" title="class in &lt;Unnamed&gt;">MyPoint</A>&gt;&nbsp;vec)</PRE>
<DL>
<DD>Computes the k-mean decision region - nearest neighbor algorithm
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>vec</CODE> - vector of initial guesses
<DT><B>Returns:</B><DD>vector of desision region points</DL>
</DD>
</DL>
<HR>

<A NAME="outputDecisionRegion()"><!-- --></A><H3>
outputDecisionRegion</H3>
<PRE>
public void <B>outputDecisionRegion</B>()</PRE>
<DL>
<DD>Displays the decision regoin on output panel
<P>
<DD><DL>
<DT><B>See Also:</B><DD><A HREF="OutputPanel.html" title="class in &lt;Unnamed&gt;"><CODE>OutputPanel</CODE></A></DL>
</DD>
</DL>
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