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<B>Constructor Detail</B></FONT></TH>
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<A NAME="SOM.JSomMath(int)"><!-- --></A><H3>
SOM.JSomMath</H3>
<PRE>
public <B>SOM.JSomMath</B>(int vectorSize)</PRE>
<DL>
<DD>Constructor.
<P>
<DL>
<DT><B>Parameters:</B><DD><CODE>int</CODE> - vectorSize - Size of a weight/input vector.</DL>
</DL>
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<B>Method Detail</B></FONT></TH>
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<A NAME="expLRP(int, double, int)"><!-- --></A><H3>
expLRP</H3>
<PRE>
public double <B>expLRP</B>(int n,
double a,
int A)</PRE>
<DL>
<DD>Calculates the exponential learning-rate parameter value.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>int</CODE> - n - current step (time).<DD><CODE>double</CODE> - a - initial value for learning-rate parameter (should be close to 0.1).<DD><CODE>int</CODE> - A - time constant (usually the number of iterations in the learning process).
<DT><B>Returns:</B><DD>double - exponential learning-rate parameter value.</DL>
</DD>
</DL>
<HR>
<A NAME="linLRP(int, double, int)"><!-- --></A><H3>
linLRP</H3>
<PRE>
public double <B>linLRP</B>(int n,
double a,
int A)</PRE>
<DL>
<DD>Calculates the linear learning-rate parameter value.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>int</CODE> - n - current step (time).<DD><CODE>double</CODE> - a - initial value for learning-rate parameter (should be close to 0.1).<DD><CODE>int</CODE> - A - another constant (usually the number of iterations in the learning process).
<DT><B>Returns:</B><DD>double - linear learning-rate parameter value.</DL>
</DD>
</DL>
<HR>
<A NAME="invLRP(int, double, double, double)"><!-- --></A><H3>
invLRP</H3>
<PRE>
public double <B>invLRP</B>(int n,
double a,
double A,
double B)</PRE>
<DL>
<DD>Calculates the inverse time learning-rate parameter value.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>int</CODE> - n - current step (time).<DD><CODE>double</CODE> - a - initial value for learning-rate parameter (should be close to 0.1).<DD><CODE>double</CODE> - A - another constant.<DD><CODE>double</CODE> - B - another constant.
<DT><B>Returns:</B><DD>double - inverse time learning-rate parameter value.</DL>
</DD>
</DL>
<HR>
<A NAME="gaussianWidth(double, int, int)"><!-- --></A><H3>
gaussianWidth</H3>
<PRE>
public double <B>gaussianWidth</B>(double g,
int n,
int t)</PRE>
<DL>
<DD>Calculates the gaussian neighbourhood width value.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>double</CODE> - g - initial width value of the neighbourhood.<DD><CODE>int</CODE> - n - current step (time).<DD><CODE>int</CODE> - t - time constant (usually the number of iterations in the learning process).
<DT><B>Returns:</B><DD>double - adapted gaussian neighbourhood function value.</DL>
</DD>
</DL>
<HR>
<A NAME="bubbleAdaptation(double[], double[], double[], double[], double, double)"><!-- --></A><H3>
bubbleAdaptation</H3>
<PRE>
public double[] <B>bubbleAdaptation</B>(double[] x,
double[] w,
double[] i,
double[] j,
double g,
double lrp)</PRE>
<DL>
<DD>Calculates the new adapted values for a weight vector, based on Bubble neighbourhood.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>double[]</CODE> - x - input vector.<DD><CODE>double[]</CODE> - w - weight vector.<DD><CODE>double[]</CODE> - i - winning neuron location in the lattice.<DD><CODE>double[]</CODE> - j - excited neuron location in the lattice.<DD><CODE>double</CODE> - g - adapted width value of the neighbourhood.<DD><CODE>double</CODE> - lrp - adapted learning-rate parameter value.
<DT><B>Returns:</B><DD>double[] - Returns the adapted neuron values.</DL>
</DD>
</DL>
<HR>
<A NAME="gaussianAdaptation(double[], double[], double[], double[], double, double)"><!-- --></A><H3>
gaussianAdaptation</H3>
<PRE>
public double[] <B>gaussianAdaptation</B>(double[] x,
double[] w,
double[] i,
double[] j,
double width,
double lrp)</PRE>
<DL>
<DD>Calculates the new adapted values for a weight vector, based on Gaussian neighbourhood.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>double[]</CODE> - x - input vector.<DD><CODE>double[]</CODE> - w - weight vector.<DD><CODE>double[]</CODE> - i - winning neuron location in the lattice.<DD><CODE>double[]</CODE> - j - excited neuron location in the lattice.<DD><CODE>double</CODE> - width - adapted width value of the neighbourhood.<DD><CODE>double</CODE> - lrp - adapted learning-rate parameter value.
<DT><B>Returns:</B><DD>double[] - Returns the adapted neuron values.</DL>
</DD>
</DL>
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