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<title>Boost Random Number Library Distributions</title>
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<h1>Random Number Library Distributions</h1>
<ul>
<li><a href="#intro">Introduction</a></li>
<li><a href="#synopsis">Synopsis</a></li>
<li><a href="#uniform_smallint">Class template
<code>uniform_smallint</code></a></li>
<li><a href="#uniform_int">Class template
<code>uniform_int</code></a></li>
<li><a href="#uniform_01">Class template <code>uniform_01</code></a></li>
<li><a href="#uniform_real">Class template
<code>uniform_real</code></a></li>
<li><a href="#bernoulli_distribution">Class template
<code>bernoulli_distribution</code></a></li>
<li><a href="#geometric_distribution">Class template
<code>geometric_distribution</code></a></li>
<li><a href="#triangle_distribution">Class template
<code>triangle_distribution</code></a></li>
<li><a href="#exponential_distribution">Class template
<code>exponential_distribution</code></a></li>
<li><a href="#normal_distribution">Class template
<code>normal_distribution</code></a></li>
<li><a href="#lognormal_distribution">Class template
<code>lognormal_distribution</code></a></li>
<li><a href="#uniform_on_sphere">Class template
<code>uniform_on_sphere</code></a></li>
</ul>
<h2><a name="intro" id="intro">Introduction</a></h2>
<p>In addition to the <a href="random-generators.html">random number
generators</a>, this library provides distribution functions which map one
distribution (often a uniform distribution provided by some generator) to
another.</p>
<p>Usually, there are several possible implementations of any given
mapping. Often, there is a choice between using more space, more
invocations of the underlying source of random numbers, or more
time-consuming arithmetic such as trigonometric functions. This interface
description does not mandate any specific implementation. However,
implementations which cannot reach certain values of the specified
distribution or otherwise do not converge statistically to it are not
acceptable.</p>
<table border="1" summary="">
<tr>
<th>distribution</th>
<th>explanation</th>
<th>example</th>
</tr>
<tr>
<td><code><a href="#uniform_smallint">uniform_smallint</a></code></td>
<td>discrete uniform distribution on a small set of integers (much
smaller than the range of the underlying generator)</td>
<td>drawing from an urn</td>
</tr>
<tr>
<td><code><a href="#uniform_int">uniform_int</a></code></td>
<td>discrete uniform distribution on a set of integers; the underlying
generator may be called several times to gather enough randomness for
the output</td>
<td>drawing from an urn</td>
</tr>
<tr>
<td><code><a href="#uniform_01">uniform_01</a></code></td>
<td>continuous uniform distribution on the range [0,1); important basis
for other distributions</td>
<td>-</td>
</tr>
<tr>
<td><code><a href="#uniform_real">uniform_real</a></code></td>
<td>continuous uniform distribution on some range [min, max) of real
numbers</td>
<td>for the range [0, 2pi): randomly dropping a stick and measuring its
angle in radiants (assuming the angle is uniformly distributed)</td>
</tr>
<tr>
<td><code><a href=
"#bernoulli_distribution">bernoulli_distribution</a></code></td>
<td>Bernoulli experiment: discrete boolean valued distribution with
configurable probability</td>
<td>tossing a coin (p=0.5)</td>
</tr>
<tr>
<td><code><a href=
"#geometric_distribution">geometric_distribution</a></code></td>
<td>measures distance between outcomes of repeated Bernoulli
experiments</td>
<td>throwing a die several times and counting the number of tries until
a "6" appears for the first time</td>
</tr>
<tr>
<td><code><a href=
"#triangle_distribution">triangle_distribution</a></code></td>
<td>?</td>
<td>?</td>
</tr>
<tr>
<td><code><a href=
"#exponential_distribution">exponential_distribution</a></code></td>
<td>exponential distribution</td>
<td>measuring the inter-arrival time of alpha particles emitted by
radioactive matter</td>
</tr>
<tr>
<td><code><a href=
"#normal_distribution">normal_distribution</a></code></td>
<td>counts outcomes of (infinitely) repeated Bernoulli experiments</td>
<td>tossing a coin 10000 times and counting how many front sides are
shown</td>
</tr>
<tr>
<td><code><a href=
"#lognormal_distribution">lognormal_distribution</a></code></td>
<td>lognormal distribution (sometimes used in simulations)</td>
<td>measuring the job completion time of an assembly line worker</td>
</tr>
<tr>
<td><code><a href=
"#uniform_on_sphere">uniform_on_sphere</a></code></td>
<td>uniform distribution on a unit sphere of arbitrary dimension</td>
<td>choosing a random point on Earth (assumed to be a sphere) where to
spend the next vacations</td>
</tr>
</table>
<p>The template parameters of the distribution functions are always in the
order</p>
<ul>
<li>Underlying source of random numbers</li>
<li>If applicable, return type, with a default to a reasonable type.</li>
</ul>
<p><em>The distribution functions no longer satisfy the input iterator
requirements (std:24.1.1 [lib.input.iterators]), because this is redundant
given the Generator interface and imposes a run-time overhead on all users.
Moreover, a Generator interface appeals to random number generation as
being more "natural". Use an <a href=
"../utility/iterator_adaptors.htm">iterator adaptor</a> if you need to wrap
any of the generators in an input iterator interface.</em></p>
<p>All of the distribution functions described below store a non-const
reference to the underlying source of random numbers. Therefore, the
distribution functions are not Assignable. However, they are
CopyConstructible. Copying a distribution function will copy the parameter
values. Furthermore, both the copy and the original will refer to the same
underlying source of random numbers. Therefore, both the copy and the
original will obtain their underlying random numbers from a single
sequence.</p>
<p>In this description, I have refrained from documenting those members in
detail which are already defined in the <a href=
"random-concepts.html">concept documentation</a>.</p>
<h2><a name="synopsis" id="synopsis">Synopsis of the distributions</a>
available from header <code>&lt;boost/random.hpp&gt;</code></h2>
<pre>
namespace boost {
template&lt;class IntType = int&gt;
class uniform_smallint;
template&lt;class IntType = int&gt;
class uniform_int;
template&lt;class RealType = double&gt;
class uniform_01;
template&lt;class RealType = double&gt;
class uniform_real;
// discrete distributions
template&lt;class RealType = double&gt;
class bernoulli_distribution;
template&lt;class IntType = int&gt;
class geometric_distribution;
// continuous distributions
template&lt;class RealType = double&gt;
class triangle_distribution;
template&lt;class RealType = double&gt;
class exponential_distribution;
template&lt;class RealType = double&gt;
class normal_distribution;
template&lt;class RealType = double&gt;
class lognormal_distribution;
template&lt;class RealType = double,
class Cont = std::vector&lt;RealType&gt; &gt;
class uniform_on_sphere;
}
</pre>
<h2><a name="uniform_smallint" id="uniform_smallint">Class template
<code>uniform_smallint</code></a></h2>
<h3>Synopsis</h3>
<pre>
#include &lt;<a href=
"../../boost/random/uniform_smallint.hpp">boost/random/uniform_smallint.hpp</a>&gt;
template&lt;class IntType = int&gt;
class uniform_smallint
{
public:
typedef IntType input_type;
typedef IntType result_type;
static const bool has_fixed_range = false;
uniform_smallint(IntType min, IntType max);
result_type min() const;
result_type max() const;
void reset();
template&lt;class UniformRandomNumberGenerator&gt;
result_type operator()(UniformRandomNumberGenerator&amp; urng);
};
</pre>
<h3>Description</h3>
<p>The distribution function <code>uniform_smallint</code> models a
<a href="random-concepts.html#random-dist">random distribution</a>. On each
invocation, it returns a random integer value uniformly distributed in the
set of integer numbers {min, min+1, min+2, ..., max}. It assumes that the
desired range (max-min+1) is small compared to the range of the underlying
source of random numbers and thus makes no attempt to limit quantization
errors.</p>
<p>Let r<sub>out</sub>=(max-min+1) the desired range of integer numbers,
and let r<sub>base</sub> be the range of the underlying source of random
numbers. Then, for the uniform distribution, the theoretical probability
for any number i in the range r<sub>out</sub> will be p<sub>out</sub>(i) =
1/r<sub>out</sub>. Likewise, assume a uniform distribution on
r<sub>base</sub> for the underlying source of random numbers, i.e.
p<sub>base</sub>(i) = 1/r<sub>base</sub>. Let p<sub>out_s</sub>(i) denote
the random distribution generated by <code>uniform_smallint</code>. Then
the sum over all i in r<sub>out</sub> of
(p<sub>out_s</sub>(i)/p<sub>out</sub>(i) -1)<sup>2</sup> shall not exceed
r<sub>out</sub>/r<sub>base</sub><sup>2</sup> (r<sub>base</sub> mod
r<sub>out</sub>)(r<sub>out</sub> - r<sub>base</sub> mod
r<sub>out</sub>).</p>
<p>The template parameter <code>IntType</code> shall denote an integer-like
value type.</p>
<p><em>Note:</em> The property above is the square sum of the relative
differences in probabilities between the desired uniform distribution
p<sub>out</sub>(i) and the generated distribution p<sub>out_s</sub>(i). The
property can be fulfilled with the calculation (base_rng mod
r<sub>out</sub>), as follows: Let r = r<sub>base</sub> mod r<sub>out</sub>.
The base distribution on r<sub>base</sub> is folded onto the range
r<sub>out</sub>. The numbers i &lt; r have assigned (r<sub>base</sub> div
r<sub>out</sub>)+1 numbers of the base distribution, the rest has only
(r<sub>base</sub> div r<sub>out</sub>). Therefore, p<sub>out_s</sub>(i) =
((r<sub>base</sub> div r<sub>out</sub>)+1) / r<sub>base</sub> for i &lt; r
and p<sub>out_s</sub>(i) = (r<sub>base</sub> div
r<sub>out</sub>)/r<sub>base</sub> otherwise. Substituting this in the above
sum formula leads to the desired result.</p>
<p><em>Note:</em> The upper bound for (r<sub>base</sub> mod
r<sub>out</sub>)(r<sub>out</sub> - r<sub>base</sub> mod r<sub>out</sub>) is
r<sub>out</sub><sup>2</sup>/4. Regarding the upper bound for the square sum
of the relative quantization error of
r<sub>out</sub><sup>3</sup>/(4*r<sub>base</sub><sup>2</sup>), it seems wise
to either choose r<sub>base</sub> so that r<sub>base</sub> &gt;
10*r<sub>out</sub><sup>2</sup> or ensure that r<sub>base</sub> is divisible
by r<sub>out</sub>.</p>
<h3>Members</h3>
<pre>
uniform_smallint(IntType min, IntType max)
</pre>
<p><strong>Effects:</strong> Constructs a <code>uniform_smallint</code>
functor. <code>min</code> and <code>max</code> are the lower and upper
bounds of the output range, respectively.</p>
<h2><a name="uniform_int" id="uniform_int">Class template
<code>uniform_int</code></a></h2>
<h3>Synopsis</h3>
<pre>
#include &lt;<a href=
"../../boost/random/uniform_int.hpp">boost/random/uniform_int.hpp</a>&gt;
template&lt;class IntType = int&gt;
class uniform_int
{
public:
typedef IntType input_type;
typedef IntType result_type;
static const bool has_fixed_range = false;
explicit uniform_int(IntType min = 0, IntType max = 9);
result_type min() const;
result_type max() const;
void reset();
template&lt;class UniformRandomNumberGenerator&gt;
result_type operator()(UniformRandomNumberGenerator&amp; urng);
template&lt;class UniformRandomNumberGenerator&gt;
result_type operator()(UniformRandomNumberGenerator&amp; urng, result_type n);
};
</pre>
<h3>Description</h3>
<p>The distribution function <code>uniform_int</code> models a <a href=
"random-concepts.html#random-dist">random distribution</a>. On each
invocation, it returns a random integer value uniformly distributed in the
set of integer numbers {min, min+1, min+2, ..., max}.</p>
<p>The template parameter <code>IntType</code> shall denote an integer-like
value type.</p>
<h3>Members</h3>
<pre>
uniform_int(IntType min = 0, IntType max = 9)
</pre>
<p><strong>Requires:</strong> min &lt;= max<br>
<strong>Effects:</strong> Constructs a <code>uniform_int</code> object.
<code>min</code> and <code>max</code> are the parameters of the
distribution.</p>
<pre>
result_type min() const
</pre>
<p><strong>Returns:</strong> The "min" parameter of the distribution.</p>
<pre>
result_type max() const
</pre>
<p><strong>Returns:</strong> The "max" parameter of the distribution.</p>
<pre>
result_type operator()(UniformRandomNumberGenerator&amp; urng, result_type
n)
</pre>
<p><strong>Returns:</strong> A uniform random number x in the range 0 &lt;=
x &lt; n. <em>[Note: This allows a <code>variate_generator</code> object
with a <code>uniform_int</code> distribution to be used with
std::random_shuffe, see [lib.alg.random.shuffle]. ]</em></p>
<h2><a name="uniform_01" id="uniform_01">Class template
<code>uniform_01</code></a></h2>
<h3>Synopsis</h3>
<pre>
#include &lt;<a href=
"../../boost/random/uniform_01.hpp">boost/random/uniform_01.hpp</a>&gt;
template&lt;class UniformRandomNumberGenerator, class RealType = double&gt;
class uniform_01
{
public:
typedef UniformRandomNumberGenerator base_type;
typedef RealType result_type;
static const bool has_fixed_range = false;
explicit uniform_01(base_type rng);
result_type operator()();
result_type min() const;
result_type max() const;
};
</pre>
<h3>Description</h3>
<p>The distribution function <code>uniform_01</code> models a <a href=
"random-concepts.html#random-dist">random distribution</a>. On each
invocation, it returns a random floating-point value uniformly distributed
in the range [0..1). The value is computed using
<code>std::numeric_limits&lt;RealType&gt;::digits</code> random binary
digits, i.e. the mantissa of the floating-point value is completely filled
with random bits. [<em>Note:</em> Should this be configurable?]</p>
<p><em>WARNING:</em> As an exception / historic accident, this class
takes a UniformRandomNumberGenerator as its constructor parameter,
and BY VALUE. Usually, you want reference semantics so that the
state of the passed-in generator is changed in-place and not copied.
In that case, explicitly supply a reference type for the template
parameter UniformRandomNumberGenerator.</p>
<p>The template parameter <code>RealType</code> shall denote a float-like
value type with support for binary operators +, -, and /. It must be large
enough to hold floating-point numbers of value
<code>rng.max()-rng.min()+1</code>.</p>
<p><code>base_type::result_type</code> must be a number-like value type, it
must support <code>static_cast&lt;&gt;</code> to <code>RealType</code> and
binary operator -.</p>
<p><em>Note:</em> The current implementation is buggy, because it may not
fill all of the mantissa with random bits. I'm unsure how to fill a
(to-be-invented) <code>boost::bigfloat</code> class with random bits
efficiently. It's probably time for a traits class.</p>
<h3>Members</h3>
<pre>
explicit uniform_01(base_type rng)
</pre>
<p><strong>Effects:</strong> Constructs a <code>uniform_01</code> functor
with the given uniform random number generator as the underlying source of
random numbers.</p>
<h2><a name="uniform_real" id="uniform_real">Class template
<code>uniform_real</code></a></h2>
<h3>Synopsis</h3>
<pre>
#include &lt;<a href=
"../../boost/random/uniform_real.hpp">boost/random/uniform_real.hpp</a>&gt;
template&lt;class RealType = double&gt;
class uniform_real
{
public:
typedef RealType input_type;
typedef RealType result_type;
static const bool has_fixed_range = false;
uniform_real(RealType min = RealType(0), RealType max = RealType(1));
result_type min() const;
result_type max() const;
void reset();
template&lt;class UniformRandomNumberGenerator&gt;
result_type operator()(UniformRandomNumberGenerator&amp; urng);
};
</pre>
<h3>Description</h3>
<p>The distribution function <code>uniform_real</code> models a <a href=
"random-concepts.html#random-dist">random distribution</a>. On each
invocation, it returns a random floating-point value uniformly distributed
in the range [min..max). The value is computed using
<code>std::numeric_limits&lt;RealType&gt;::digits</code> random binary
digits, i.e. the mantissa of the floating-point value is completely filled
with random bits.</p>
<p><em>Note:</em> The current implementation is buggy, because it may not
fill all of the mantissa with random bits.</p>
<h3>Members</h3>
<pre>
uniform_real(RealType min = RealType(0), RealType max = RealType(1))
</pre>
<p><strong>Requires:</strong> min &lt;= max<br>
<strong>Effects:</strong> Constructs a <code>uniform_real</code> object;
<code>min</code> and <code>max</code> are the parameters of the
distribution.</p>
<pre>
result_type min() const
</pre>
<p><strong>Returns:</strong> The "min" parameter of the distribution.</p>
<pre>
result_type max() const
</pre>
<p><strong>Returns:</strong> The "max" parameter of the distribution.</p>
<h2><a name="bernoulli_distribution" id="bernoulli_distribution">Class
template <code>bernoulli_distribution</code></a></h2>
<h3>Synopsis</h3>
<pre>
#include &lt;<a href=
"../../boost/random/bernoulli_distribution.hpp">boost/random/bernoulli_distribution.hpp</a>&gt;
template&lt;class RealType = double&gt;
class bernoulli_distribution
{
public:
typedef int input_type;
typedef bool result_type;
explicit bernoulli_distribution(const RealType&amp; p = RealType(0.5));
RealType p() const;
void reset();
template&lt;class UniformRandomNumberGenerator&gt;
result_type operator()(UniformRandomNumberGenerator&amp; urng);
};
</pre>
<h3>Description</h3>
<p>Instantiations of class template <code>bernoulli_distribution</code>
model a <a href="random-concepts.html#random-dist">random distribution</a>.
Such a random distribution produces <code>bool</code> values distributed
with probabilities P(true) = p and P(false) = 1-p. p is the parameter of
the distribution.</p>
<h3>Members</h3>
<pre>
bernoulli_distribution(const RealType&amp; p = RealType(0.5))
</pre>
<p><strong>Requires:</strong> 0 &lt;= p &lt;= 1<br>
<strong>Effects:</strong> Constructs a <code>bernoulli_distribution</code>
object. <code>p</code> is the parameter of the distribution.</p>
<pre>
RealType p() const
</pre>
<p><strong>Returns:</strong> The "p" parameter of the distribution.</p>
<h2><a name="geometric_distribution" id="geometric_distribution">Class
template <code>geometric_distribution</code></a></h2>
<h3>Synopsis</h3>
<pre>
#include &lt;<a href=
"../../boost/random/geometric_distribution.hpp">boost/random/geometric_distribution.hpp</a>&gt;
template&lt;class UniformRandomNumberGenerator, class IntType = int&gt;
class geometric_distribution
{
public:
typedef RealType input_type;
typedef IntType result_type;
explicit geometric_distribution(const RealType&amp; p = RealType(0.5));
RealType p() const;
void reset();
template&lt;class UniformRandomNumberGenerator&gt;
result_type operator()(UniformRandomNumberGenerator&amp; urng);
};
</pre>
<h3>Description</h3>
<p>Instantiations of class template <code>geometric_distribution</code>
model a <a href="random-concepts.html#random-dist">random distribution</a>.
A <code>geometric_distribution</code> random distribution produces integer
values <em>i</em> &gt;= 1 with p(i) = (1-p) * p<sup>i-1</sup>. p is the
parameter of the distribution.
Each invocation of the UniformRandomNumberGenerator shall result in a
floating-point value in the range [0,1).</p>
<h3>Members</h3>
<pre>
geometric_distribution(const RealType&amp; p = RealType(0.5))
</pre>
<p><strong>Requires:</strong> 0 &lt; p &lt; 1<br>
<strong>Effects:</strong> Constructs a <code>geometric_distribution</code>
object; <code>p</code> is the parameter of the distribution.</p>
<pre>
RealType p() const
</pre>
<p><strong>Returns:</strong> The "p" parameter of the distribution.</p>
<h2><a name="triangle_distribution" id="triangle_distribution">Class
template <code>triangle_distribution</code></a></h2>
<h3>Synopsis</h3>
<pre>
#include &lt;<a href=
"../../boost/random/triangle_distribution.hpp">boost/random/triangle_distribution.hpp</a>&gt;
template&lt;class RealType = double&gt;
class triangle_distribution
{
public:
typedef RealType input_type;
typedef RealType result_type;
triangle_distribution(result_type a, result_type b, result_type c);
result_type a() const;
result_type b() const;
result_type c() const;
void reset();
template&lt;class UniformRandomNumberGenerator&gt;
result_type operator()(UniformRandomNumberGenerator&amp; urng);
};
</pre>
<h3>Description</h3>
<p>Instantiations of class template <code>triangle_distribution</code>
model a <a href="random-concepts.html#random-dist">random distribution</a>.
The returned floating-point values <code>x</code> satisfy <code>a &lt;= x
&lt;= c</code>; <code>x</code> has a triangle distribution, where
<code>b</code> is the most probable value for <code>x</code>.
Each invocation of the UniformRandomNumberGenerator shall result in a
floating-point value in the range [0,1). </p>
<h3>Members</h3>
<pre>
triangle_distribution(result_type a, result_type b, result_type c)
</pre>
<p><strong>Effects:</strong> Constructs a
<code>triangle_distribution</code> functor. <code>a, b, c</code> are the
parameters for the distribution.</p>
<h2><a name="exponential_distribution" id="exponential_distribution">Class
template <code>exponential_distribution</code></a></h2>
<h3>Synopsis</h3>
<pre>
#include &lt;<a href=
"../../boost/random/exponential_distribution.hpp">boost/random/exponential_distribution.hpp</a>&gt;
template&lt;class RealType = double&gt;
class exponential_distribution
{
public:
typedef RealType input_type;
typedef RealType result_type;
explicit exponential_distribution(const result_type&amp; lambda);
RealType lambda() const;
void reset();
template&lt;class UniformRandomNumberGenerator&gt;
result_type operator()(UniformRandomNumberGenerator&amp; urng);
};
</pre>
<h3>Description</h3>
<p>Instantiations of class template <code>exponential_distribution</code>
model a <a href="random-concepts.html#random-dist">random distribution</a>.
Such a distribution produces random numbers x &gt; 0 distributed with
probability density function p(x) = lambda * exp(-lambda * x), where lambda
is the parameter of the distribution.
Each invocation of the UniformRandomNumberGenerator shall result in a
floating-point value in the range [0,1). </p>
<h3>Members</h3>
<pre>
exponential_distribution(const result_type&amp; lambda = result_type(1))
</pre>
<p><strong>Requires:</strong> lambda &gt; 0<br>
<strong>Effects:</strong> Constructs an
<code>exponential_distribution</code> object with <code>rng</code> as the
reference to the underlying source of random numbers. <code>lambda</code>
is the parameter for the distribution.</p>
<pre>
RealType lambda() const
</pre>
<p><strong>Returns:</strong> The "lambda" parameter of the
distribution.</p>
<h2><a name="normal_distribution" id="normal_distribution">Class template
<code>normal_distribution</code></a></h2>
<h3>Synopsis</h3>
<pre>
#include &lt;<a href=
"../../boost/random/normal_distribution.hpp">boost/random/normal_distribution.hpp</a>&gt;
template&lt;class RealType = double&gt;
class normal_distribution
{
public:
typedef RealType input_type;
typedef RealType result_type;
explicit normal_distribution(const result_type&amp; mean = 0,
const result_type&amp; sigma = 1);
RealType mean() const;
RealType sigma() const;
void reset();
template&lt;class UniformRandomNumberGenerator&gt;
result_type operator()(UniformRandomNumberGenerator&amp; urng);
};
</pre>
<h3>Description</h3>
<p>Instantiations of class template <code>normal_distribution</code> model
a <a href="random-concepts.html#random-dist">random distribution</a>. Such
a distribution produces random numbers x distributed with probability
density function p(x) = 1/sqrt(2*pi*sigma) * exp(- (x-mean)<sup>2</sup> /
(2*sigma<sup>2</sup>) ), where mean and sigma are the parameters of the
distribution. Each invocation of the UniformRandomNumberGenerator shall
result in a floating-point value in the range [0,1).</p>
<h3>Members</h3>
<pre>
explicit normal_distribution(const result_type&amp; mean = 0,
const result_type&amp; sigma = 1);
</pre>
<p><strong>Requires:</strong> sigma &gt; 0<br>
<strong>Effects:</strong> Constructs a <code>normal_distribution</code>
object; <code>mean</code> and <code>sigma</code> are the parameters for the
distribution.</p>
<pre>
RealType mean() const
</pre>
<p><strong>Returns:</strong> The "mean" parameter of the distribution.</p>
<pre>
RealType sigma() const
</pre>
<p><strong>Returns:</strong> The "sigma" parameter of the distribution.</p>
<h2><a name="lognormal_distribution" id="lognormal_distribution">Class
template <code>lognormal_distribution</code></a></h2>
<h3>Synopsis</h3>
<pre>
#include &lt;<a href=
"../../boost/random/lognormal_distribution.hpp">boost/random/lognormal_distribution.hpp</a>&gt;
template&lt;class RealType = double&gt;
class lognormal_distribution
{
public:
typedef typename normal_distribution&lt;RealType&gt;::input_type
typedef RealType result_type;
explicit lognormal_distribution(const result_type&amp; mean = 1.0,
const result_type&amp; sigma = 1.0);
RealType&amp; mean() const;
RealType&amp; sigma() const;
void reset();
template&lt;class UniformRandomNumberGenerator&gt;
result_type operator()(UniformRandomNumberGenerator&amp; urng);
};
</pre>
<h3>Description</h3>
<p>Instantiations of class template <code>lognormal_distribution</code>
model a <a href="random-concepts.html#random-dist">random distribution</a>.
Such a distribution produces random numbers with p(x) = 1/(x * normal_sigma
* sqrt(2*pi)) * exp( -(log(x)-normal_mean)<sup>2</sup> /
(2*normal_sigma<sup>2</sup>) ) for x &gt; 0, where normal_mean =
log(mean<sup>2</sup>/sqrt(sigma<sup>2</sup> + mean<sup>2</sup>)) and
normal_sigma = sqrt(log(1 + sigma<sup>2</sup>/mean<sup>2</sup>)).
Each invocation of the UniformRandomNumberGenerator shall result in a
floating-point value in the range [0,1). </p>
<h3>Members</h3>
<pre>
lognormal_distribution(const result_type&amp; mean,
const result_type&amp; sigma)
</pre>
<p><strong>Effects:</strong> Constructs a
<code>lognormal_distribution</code> functor. <code>mean</code> and
<code>sigma</code> are the mean and standard deviation of the lognormal
distribution.</p>
<h2><a name="uniform_on_sphere" id="uniform_on_sphere">Class template
<code>uniform_on_sphere</code></a></h2>
<h3>Synopsis</h3>
<pre>
#include &lt;<a href=
"../../boost/random/uniform_on_sphere.hpp">boost/random/uniform_on_sphere.hpp</a>&gt;
template&lt;class RealType = double,
class Cont = std::vector&lt;RealType&gt; &gt;
class uniform_on_sphere
{
public:
typedef RealType input_type;
typedef Cont result_type;
explicit uniform_on_sphere(int dim = 2);
void reset();
template&lt;class UniformRandomNumberGenerator&gt;
const result_type &amp; operator()(UniformRandomNumberGenerator&amp; urng);
};
</pre>
<h3>Description</h3>
<p>Instantiations of class template <code>uniform_on_sphere</code> model a
<a href="random-concepts.html#random-dist">random distribution</a>. Such a
distribution produces random numbers uniformly distributed on the unit
sphere of arbitrary dimension <code>dim</code>. The <code>Cont</code>
template parameter must be a STL-like container type with
<code>begin</code> and <code>end</code> operations returning non-const
ForwardIterators of type <code>Cont::iterator</code>.
Each invocation of the UniformRandomNumberGenerator shall result in a
floating-point value in the range [0,1). </p>
<h3>Members</h3>
<pre>
explicit uniform_on_sphere(int dim = 2)
</pre>
<p><strong>Effects:</strong> Constructs a <code>uniform_on_sphere</code>
functor. <code>dim</code> is the dimension of the sphere.</p>
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<p>Revised
<!--webbot bot="Timestamp" s-type="EDITED" s-format="%d %B, %Y" startspan -->05
December, 2006<!--webbot bot="Timestamp" endspan i-checksum="38516" --></p>
<p><i>Copyright &copy; 2000-2007 <a href=
"http://www.boost.org/people/jens_maurer.htm">Jens Maurer</a></i></p>
<p><i>Distributed under the Boost Software License, Version 1.0. (See
accompanying file <a href="../../LICENSE_1_0.txt">LICENSE_1_0.txt</a> or
copy at <a href=
"http://www.boost.org/LICENSE_1_0.txt">http://www.boost.org/LICENSE_1_0.txt</a>)</i></p>
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