randomvariates.html

数据挖掘的最常用工具。由于开源

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<A NAME="RandomVariates()"><!-- --></A><H3>
RandomVariates</H3>
<PRE>
public <B>RandomVariates</B>()</PRE>
<DL>
<DD>Simply the constructor of super class
<P>
</DL>
<HR>

<A NAME="RandomVariates(long)"><!-- --></A><H3>
RandomVariates</H3>
<PRE>
public <B>RandomVariates</B>(long&nbsp;seed)</PRE>
<DL>
<DD>Simply the constructor of super class
<P>
<DL>
<DT><B>Parameters:</B><DD><CODE>seed</CODE> - the seed in this random object</DL>
</DL>

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<A NAME="nextExponential()"><!-- --></A><H3>
nextExponential</H3>
<PRE>
public double <B>nextExponential</B>()</PRE>
<DL>
<DD>Generate a value of a variate following standard exponential
 distribution using simple inverse method.<p>

 Variates related to standard Exponential can be generated using simple
 transformations.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>a value of the variate</DL>
</DD>
</DL>
<HR>

<A NAME="nextErlang(int)"><!-- --></A><H3>
nextErlang</H3>
<PRE>
public double <B>nextErlang</B>(int&nbsp;a)
                  throws java.lang.Exception</PRE>
<DL>
<DD>Generate a value of a variate following standard Erlang distribution.
 It can be used when the shape parameter is an integer and not too large,
 say, <100.  When the parameter is not an integer (which is often named
 Gamma distribution) or is large, use <code>nextGamma(double a)</code>
 instead.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>a</CODE> - the shape parameter, must be no less than 1
<DT><B>Returns:</B><DD>a value of the variate
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if parameter less than 1</DL>
</DD>
</DL>
<HR>

<A NAME="nextGamma(double)"><!-- --></A><H3>
nextGamma</H3>
<PRE>
public double <B>nextGamma</B>(double&nbsp;a)
                 throws java.lang.Exception</PRE>
<DL>
<DD>Generate a value of a variate following standard Gamma distribution 
 with shape parameter a.<p>
 If a>1, it uses a rejection method developed by Minh(1988)"Generating
 Gamma Variates", ACM Trans. on Math. Software, Vol.14, No.3, pp261-266.
 <br>
 If a<1, it uses the algorithm "GS" developed by Ahrens and Dieter(1974)
 "COMPUTER METHODS FOR SAMPLING FROM GAMMA, BETA, POISSON AND BINOMIAL
 DISTRIBUTIONS", COMPUTING, 12 (1974), pp223-246, and further implemented
 in Fortran by Ahrens, Kohrt and Dieter(1983) "Algorithm 599: sampling
 from Gamma and Poisson distributions", ACM Trans. on Math. Software, 
 Vol.9 No.2, pp255-257.<p> 
 
 Variates related to standard Gamma can be generated using simple
 transformations.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>a</CODE> - the shape parameter, must be greater than 1
<DT><B>Returns:</B><DD>a value of the variate
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if parameter not greater than 1</DL>
</DD>
</DL>
<HR>

<A NAME="getRevision()"><!-- --></A><H3>
getRevision</H3>
<PRE>
public java.lang.String <B>getRevision</B>()</PRE>
<DL>
<DD>Returns the revision string.
<P>
<DD><DL>
<DT><B>Specified by:</B><DD><CODE><A HREF="../../weka/core/RevisionHandler.html#getRevision()">getRevision</A></CODE> in interface <CODE><A HREF="../../weka/core/RevisionHandler.html" title="interface in weka.core">RevisionHandler</A></CODE></DL>
</DD>
<DD><DL>

<DT><B>Returns:</B><DD>the revision</DL>
</DD>
</DL>
<HR>

<A NAME="main(java.lang.String[])"><!-- --></A><H3>
main</H3>
<PRE>
public static void <B>main</B>(java.lang.String[]&nbsp;ops)</PRE>
<DL>
<DD>Main method for testing this class.
<P>
<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>ops</CODE> - # of variates/seed, default is 10/45</DL>
</DD>
</DL>
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