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📁 Java方面的数值算法
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<CODE>&nbsp;double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../../../../../net/sf/javaml/clustering/mcl/MarkovClustering.html#inflate(net.sf.javaml.clustering.mcl.SparseMatrix, double, double)">inflate</A></B>(<A HREF="../../../../../net/sf/javaml/clustering/mcl/SparseMatrix.html" title="class in net.sf.javaml.clustering.mcl">SparseMatrix</A>&nbsp;m,
        double&nbsp;p,
        double&nbsp;zeromax)</CODE>

<BR>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;inflate stochastic matrix by Hadamard (elementwise) exponentiation, pruning and normalisation :  result = Gamma ( m, p ) = normalise ( prune ( m .^ p ) ).</TD>
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<CODE>&nbsp;<A HREF="../../../../../net/sf/javaml/clustering/mcl/SparseMatrix.html" title="class in net.sf.javaml.clustering.mcl">SparseMatrix</A></CODE></FONT></TD>
<TD><CODE><B><A HREF="../../../../../net/sf/javaml/clustering/mcl/MarkovClustering.html#run(net.sf.javaml.clustering.mcl.SparseMatrix, double, double, double, double)">run</A></B>(<A HREF="../../../../../net/sf/javaml/clustering/mcl/SparseMatrix.html" title="class in net.sf.javaml.clustering.mcl">SparseMatrix</A>&nbsp;a,
    double&nbsp;maxResidual,
    double&nbsp;pGamma,
    double&nbsp;loopGain,
    double&nbsp;maxZero)</CODE>

<BR>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;run the MCL process.</TD>
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<A NAME="MarkovClustering()"><!-- --></A><H3>
MarkovClustering</H3>
<PRE>
public <B>MarkovClustering</B>()</PRE>
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<A NAME="run(net.sf.javaml.clustering.mcl.SparseMatrix, double, double, double, double)"><!-- --></A><H3>
run</H3>
<PRE>
public <A HREF="../../../../../net/sf/javaml/clustering/mcl/SparseMatrix.html" title="class in net.sf.javaml.clustering.mcl">SparseMatrix</A> <B>run</B>(<A HREF="../../../../../net/sf/javaml/clustering/mcl/SparseMatrix.html" title="class in net.sf.javaml.clustering.mcl">SparseMatrix</A>&nbsp;a,
                        double&nbsp;maxResidual,
                        double&nbsp;pGamma,
                        double&nbsp;loopGain,
                        double&nbsp;maxZero)</PRE>
<DL>
<DD>run the MCL process.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>a</CODE> - matrix<DD><CODE>maxResidual</CODE> - maximum difference between row elements and row square sum            (measure of idempotence)<DD><CODE>pGamma</CODE> - inflation exponent for Gamma operator<DD><CODE>loopGain</CODE> - values for cycles<DD><CODE>maxZero</CODE> - maximum value considered zero for pruning operations
<DT><B>Returns:</B><DD>the resulting matrix</DL>
</DD>
</DL>
<HR>

<A NAME="inflate(net.sf.javaml.clustering.mcl.SparseMatrix, double, double)"><!-- --></A><H3>
inflate</H3>
<PRE>
public double <B>inflate</B>(<A HREF="../../../../../net/sf/javaml/clustering/mcl/SparseMatrix.html" title="class in net.sf.javaml.clustering.mcl">SparseMatrix</A>&nbsp;m,
                      double&nbsp;p,
                      double&nbsp;zeromax)</PRE>
<DL>
<DD>inflate stochastic matrix by Hadamard (elementwise) exponentiation, pruning and normalisation : <p> result = Gamma ( m, p ) = normalise ( prune ( m .^ p ) ). <p> By convention, normalisation is done along rows (SparseMatrix has row-major representation)
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>m</CODE> - matrix (mutable)<DD><CODE>p</CODE> - exponent as a double<DD><CODE>zeromax</CODE> - below which elements are pruned from the sparse matrix
<DT><B>Returns:</B><DD>residuum value, m is modified.</DL>
</DD>
</DL>
<HR>

<A NAME="expand(net.sf.javaml.clustering.mcl.SparseMatrix)"><!-- --></A><H3>
expand</H3>
<PRE>
public <A HREF="../../../../../net/sf/javaml/clustering/mcl/SparseMatrix.html" title="class in net.sf.javaml.clustering.mcl">SparseMatrix</A> <B>expand</B>(<A HREF="../../../../../net/sf/javaml/clustering/mcl/SparseMatrix.html" title="class in net.sf.javaml.clustering.mcl">SparseMatrix</A>&nbsp;m)</PRE>
<DL>
<DD>expand stochastic quadratic matrix by sqaring it with itself: result = m * m. Here normalisation is rowwise.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>matrix</CODE> - 
<DT><B>Returns:</B><DD>new matrix (pointer != argument)</DL>
</DD>
</DL>
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