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A C++ class library for scientific computing

long double RNGs takes longer, because filling the entire mantissa with
random bits requires several random integers.  The default parameter for
most generators is <CODE>float</CODE>.
<P>

<DT><CODE>IRNG</CODE>
<DD>is the underlying Integer RNG to use.  The default is MersenneTwister.
<P>

<DT><CODE>stateTag</CODE>
<DD><A NAME="IDX348"></A>
is either <CODE>sharedState</CODE> or <CODE>independentState</CODE>.  If
<CODE>sharedState</CODE>, the IRNG is shared with other generators.  If
<CODE>independentState</CODE>, the RNG contains its own IRNG.  The default is
sharedState.  
</DL>
<P>

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<H2> 9.6 Member functions </H2>
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<P>

<A NAME="IDX349"></A>
</P><P>

RNGs have these methods:
</P><P>

<TABLE><tr><td>&nbsp;</td><td class=example><pre>T random();
</pre></td></tr></table><A NAME="IDX350"></A>
Returns a random number.
</P><P>

<TABLE><tr><td>&nbsp;</td><td class=example><pre>void seed(unsigned int);
</pre></td></tr></table><A NAME="IDX351"></A>
Seeds the underlying IRNG.  See above for an example of seeding
with the system timer.
</P><P>

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<H2> 9.7 Detailed listing of RNGs </H2>
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<P>

To save space in the below list, template parameters have been omitted and
only constructors are listed.  The notation [a,b] means an interval which
includes the endpoints a and b; (a,b) is an interval which does not include
the endpoints.
</P><P>

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<H3> 9.7.1 <TT>`random/uniform.h'</TT> </H3>
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<P>

<TABLE><tr><td>&nbsp;</td><td class=example><pre>Uniform&#60;&#62;()
</pre></td></tr></table><A NAME="IDX352"></A>
Continuous uniform distribution on [0,1). 
</P><P>

<TABLE><tr><td>&nbsp;</td><td class=example><pre>UniformClosedOpen&#60;&#62;()
</pre></td></tr></table><A NAME="IDX353"></A>
Continuous uniform distribution on [0,1).  Same as <CODE>Uniform&#60;&#62;</CODE>.
</P><P>

<TABLE><tr><td>&nbsp;</td><td class=example><pre>UniformClosed&#60;&#62;()
</pre></td></tr></table><A NAME="IDX354"></A>
Continuous uniform distribution on [0,1].
</P><P>

<TABLE><tr><td>&nbsp;</td><td class=example><pre>UniformOpen&#60;&#62;()
</pre></td></tr></table><A NAME="IDX355"></A>
Continuous uniform distribution on (0,1).
</P><P>

<TABLE><tr><td>&nbsp;</td><td class=example><pre>UniformOpenClosed&#60;&#62;()
</pre></td></tr></table><A NAME="IDX356"></A>
Continuous uniform distribution on (0,1].
</P><P>

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<H3> 9.7.2 <TT>`random/normal.h'</TT> </H3>
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<P>

<TABLE><tr><td>&nbsp;</td><td class=example><pre>NormalUnit&#60;&#62;()
</pre></td></tr></table><A NAME="IDX357"></A>
Continuous normal distribution with mean 0 and variance 1.
</P><P>

<TABLE><tr><td>&nbsp;</td><td class=example><pre>Normal&#60;&#62;(T mean, T standardDeviation)
</pre></td></tr></table><A NAME="IDX358"></A>
Continuous normal distribution with specified mean and standard 
deviation.  
</P><P>

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<H3> 9.7.3 <TT>`random/exponential.h'</TT> </H3>
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<P>

<TABLE><tr><td>&nbsp;</td><td class=example><pre>ExponentialUnit&#60;&#62;()
</pre></td></tr></table><A NAME="IDX359"></A>
Continuous exponential distribution with mean 1.
</P><P>

<TABLE><tr><td>&nbsp;</td><td class=example><pre>Exponential&#60;&#62;(T mean)
</pre></td></tr></table><A NAME="IDX360"></A>
Continuous exponential distribution with specified mean.
</P><P>

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<H3> 9.7.4 <TT>`random/beta.h'</TT> </H3>
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<P>

<TABLE><tr><td>&nbsp;</td><td class=example><pre>Beta&#60;&#62;(T a, T b)
</pre></td></tr></table><A NAME="IDX361"></A>
Beta distribution with parameters a and b.  The mean of the distribution is
<EM>a/(a+b)</EM> and its variance is <EM>ab/((a+b)^2(a+b+1))</EM>.  Use the
method <CODE>setParameters(T a, T b)</CODE> to change the parameters.
</P><P>

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<H3> 9.7.5 <TT>`random/chisquare.h'</TT> </H3>
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<P>

<TABLE><tr><td>&nbsp;</td><td class=example><pre>ChiSquare&#60;&#62;(T df)
</pre></td></tr></table><A NAME="IDX362"></A>
<EM>\chi^2</EM> distribution with <CODE>df</CODE> degrees of freedom.  The parameter
df must be positive.  Use the method <CODE>setDF(T df)</CODE> to change the
degrees of freedom.
</P><P>

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<H3> 9.7.6 <TT>`random/gamma.h'</TT> </H3>
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<TABLE><tr><td>&nbsp;</td><td class=example><pre>Gamma&#60;&#62;(T mean)
</pre></td></tr></table><A NAME="IDX363"></A>
Gamma distribution with specified mean.  The mean must
be positive.  Use the method <CODE>setMean(T mean)</CODE> to
change the mean.
</P><P>

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<H3> 9.7.7 <TT>`random/F.h'</TT> </H3>
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<TABLE><tr><td>&nbsp;</td><td class=example><pre>F&#60;&#62;(T numeratorDF, T denominatorDF)
</pre></td></tr></table><A NAME="IDX364"></A>
F distribution with numerator and denominator degrees
of freedom specified.  Both these parameters must be
positive.  Use <CODE>setDF(T dfn, T dfd)</CODE> to change the
degrees of freedom. 
</P><P>

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<H3> 9.7.8 <TT>`random/discrete-uniform.h'</TT> </H3>
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<P>

<TABLE><tr><td>&nbsp;</td><td class=example><pre>DiscreteUniform&#60;&#62;(T n)
</pre></td></tr></table><A NAME="IDX365"></A>
Discrete uniform distribution over <EM>0, 1, \ldots, n-1</EM>.
</P><P>

<A NAME="Numeric properties"></A>
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