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/* boost random/piecewise_constant_distribution.hpp header file
*
* Copyright Steven Watanabe 2011
* Distributed under the Boost Software License, Version 1.0. (See
* accompanying file LICENSE_1_0.txt or copy at
* http://www.boost.org/LICENSE_1_0.txt)
*
* See http://www.boost.org for most recent version including documentation.
*
* $Id$
*/
#ifndef BOOST_RANDOM_PIECEWISE_CONSTANT_DISTRIBUTION_HPP_INCLUDED
#define BOOST_RANDOM_PIECEWISE_CONSTANT_DISTRIBUTION_HPP_INCLUDED
#include <vector>
#include <numeric>
#include <boost/assert.hpp>
#include <boost/random/uniform_real.hpp>
#include <boost/random/discrete_distribution.hpp>
#include <boost/random/detail/config.hpp>
#include <boost/random/detail/operators.hpp>
#include <boost/random/detail/vector_io.hpp>
#ifndef BOOST_NO_CXX11_HDR_INITIALIZER_LIST
#include <initializer_list>
#endif
#include <boost/range/begin.hpp>
#include <boost/range/end.hpp>
namespace boost {
namespace random {
/**
* The class @c piecewise_constant_distribution models a \random_distribution.
*/
template<class RealType = double, class WeightType = double>
class piecewise_constant_distribution {
public:
typedef std::size_t input_type;
typedef RealType result_type;
class param_type {
public:
typedef piecewise_constant_distribution distribution_type;
/**
* Constructs a @c param_type object, representing a distribution
* that produces values uniformly distributed in the range [0, 1).
*/
param_type()
{
_weights.push_back(WeightType(1));
_intervals.push_back(RealType(0));
_intervals.push_back(RealType(1));
}
/**
* Constructs a @c param_type object from two iterator ranges
* containing the interval boundaries and the interval weights.
* If there are less than two boundaries, then this is equivalent to
* the default constructor and creates a single interval, [0, 1).
*
* The values of the interval boundaries must be strictly
* increasing, and the number of weights must be one less than
* the number of interval boundaries. If there are extra
* weights, they are ignored.
*/
template<class IntervalIter, class WeightIter>
param_type(IntervalIter intervals_first, IntervalIter intervals_last,
WeightIter weight_first)
: _intervals(intervals_first, intervals_last)
{
if(_intervals.size() < 2) {
_intervals.clear();
_intervals.push_back(RealType(0));
_intervals.push_back(RealType(1));
_weights.push_back(WeightType(1));
} else {
_weights.reserve(_intervals.size() - 1);
for(std::size_t i = 0; i < _intervals.size() - 1; ++i) {
_weights.push_back(*weight_first++);
}
}
}
#ifndef BOOST_NO_CXX11_HDR_INITIALIZER_LIST
/**
* Constructs a @c param_type object from an
* initializer_list containing the interval boundaries
* and a unary function specifying the weights. Each
* weight is determined by calling the function at the
* midpoint of the corresponding interval.
*
* If the initializer_list contains less than two elements,
* this is equivalent to the default constructor and the
* distribution will produce values uniformly distributed
* in the range [0, 1).
*/
template<class T, class F>
param_type(const std::initializer_list<T>& il, F f)
: _intervals(il.begin(), il.end())
{
if(_intervals.size() < 2) {
_intervals.clear();
_intervals.push_back(RealType(0));
_intervals.push_back(RealType(1));
_weights.push_back(WeightType(1));
} else {
_weights.reserve(_intervals.size() - 1);
for(std::size_t i = 0; i < _intervals.size() - 1; ++i) {
RealType midpoint = (_intervals[i] + _intervals[i + 1]) / 2;
_weights.push_back(f(midpoint));
}
}
}
#endif
/**
* Constructs a @c param_type object from Boost.Range
* ranges holding the interval boundaries and the weights. If
* there are less than two interval boundaries, this is equivalent
* to the default constructor and the distribution will produce
* values uniformly distributed in the range [0, 1). The
* number of weights must be one less than the number of
* interval boundaries.
*/
template<class IntervalRange, class WeightRange>
param_type(const IntervalRange& intervals_arg,
const WeightRange& weights_arg)
: _intervals(boost::begin(intervals_arg), boost::end(intervals_arg)),
_weights(boost::begin(weights_arg), boost::end(weights_arg))
{
if(_intervals.size() < 2) {
_intervals.clear();
_intervals.push_back(RealType(0));
_intervals.push_back(RealType(1));
_weights.push_back(WeightType(1));
}
}
/**
* Constructs the parameters for a distribution that approximates a
* function. The range of the distribution is [xmin, xmax). This
* range is divided into nw equally sized intervals and the weights
* are found by calling the unary function f on the midpoints of the
* intervals.
*/
template<class F>
param_type(std::size_t nw, RealType xmin, RealType xmax, F f)
{
std::size_t n = (nw == 0) ? 1 : nw;
double delta = (xmax - xmin) / n;
BOOST_ASSERT(delta > 0);
for(std::size_t k = 0; k < n; ++k) {
_weights.push_back(f(xmin + k*delta + delta/2));
_intervals.push_back(xmin + k*delta);
}
_intervals.push_back(xmax);
}
/** Returns a vector containing the interval boundaries. */
std::vector<RealType> intervals() const { return _intervals; }
/**
* Returns a vector containing the probability densities
* over all the intervals of the distribution.
*/
std::vector<RealType> densities() const
{
RealType sum = std::accumulate(_weights.begin(), _weights.end(),
static_cast<RealType>(0));
std::vector<RealType> result;
result.reserve(_weights.size());
for(std::size_t i = 0; i < _weights.size(); ++i) {
RealType width = _intervals[i + 1] - _intervals[i];
result.push_back(_weights[i] / (sum * width));
}
return result;
}
/** Writes the parameters to a @c std::ostream. */
BOOST_RANDOM_DETAIL_OSTREAM_OPERATOR(os, param_type, parm)
{
detail::print_vector(os, parm._intervals);
detail::print_vector(os, parm._weights);
return os;
}
/** Reads the parameters from a @c std::istream. */
BOOST_RANDOM_DETAIL_ISTREAM_OPERATOR(is, param_type, parm)
{
std::vector<RealType> new_intervals;
std::vector<WeightType> new_weights;
detail::read_vector(is, new_intervals);
detail::read_vector(is, new_weights);
if(is) {
parm._intervals.swap(new_intervals);
parm._weights.swap(new_weights);
}
return is;
}
/** Returns true if the two sets of parameters are the same. */
BOOST_RANDOM_DETAIL_EQUALITY_OPERATOR(param_type, lhs, rhs)
{
return lhs._intervals == rhs._intervals
&& lhs._weights == rhs._weights;
}
/** Returns true if the two sets of parameters are different. */
BOOST_RANDOM_DETAIL_INEQUALITY_OPERATOR(param_type)
private:
friend class piecewise_constant_distribution;
std::vector<RealType> _intervals;
std::vector<WeightType> _weights;
};
/**
* Creates a new @c piecewise_constant_distribution with
* a single interval, [0, 1).
*/
piecewise_constant_distribution()
{
_intervals.push_back(RealType(0));
_intervals.push_back(RealType(1));
}
/**
* Constructs a piecewise_constant_distribution from two iterator ranges
* containing the interval boundaries and the interval weights.
* If there are less than two boundaries, then this is equivalent to
* the default constructor and creates a single interval, [0, 1).
*
* The values of the interval boundaries must be strictly
* increasing, and the number of weights must be one less than
* the number of interval boundaries. If there are extra
* weights, they are ignored.
*
* For example,
*
* @code
* double intervals[] = { 0.0, 1.0, 4.0 };
* double weights[] = { 1.0, 1.0 };
* piecewise_constant_distribution<> dist(
* &intervals[0], &intervals[0] + 3, &weights[0]);
* @endcode
*
* The distribution has a 50% chance of producing a
* value between 0 and 1 and a 50% chance of producing
* a value between 1 and 4.
*/
template<class IntervalIter, class WeightIter>
piecewise_constant_distribution(IntervalIter first_interval,
IntervalIter last_interval,
WeightIter first_weight)
: _intervals(first_interval, last_interval)
{
if(_intervals.size() < 2) {
_intervals.clear();
_intervals.push_back(RealType(0));
_intervals.push_back(RealType(1));
} else {
std::vector<WeightType> actual_weights;
actual_weights.reserve(_intervals.size() - 1);
for(std::size_t i = 0; i < _intervals.size() - 1; ++i) {
actual_weights.push_back(*first_weight++);
}
typedef discrete_distribution<std::size_t, WeightType> bins_type;
typename bins_type::param_type bins_param(actual_weights);
_bins.param(bins_param);
}
}
#ifndef BOOST_NO_CXX11_HDR_INITIALIZER_LIST
/**
* Constructs a piecewise_constant_distribution from an
* initializer_list containing the interval boundaries
* and a unary function specifying the weights. Each
* weight is determined by calling the function at the
* midpoint of the corresponding interval.
*
* If the initializer_list contains less than two elements,
* this is equivalent to the default constructor and the
* distribution will produce values uniformly distributed
* in the range [0, 1).
*/
template<class T, class F>
piecewise_constant_distribution(std::initializer_list<T> il, F f)
: _intervals(il.begin(), il.end())
{
if(_intervals.size() < 2) {
_intervals.clear();
_intervals.push_back(RealType(0));
_intervals.push_back(RealType(1));
} else {
std::vector<WeightType> actual_weights;
actual_weights.reserve(_intervals.size() - 1);
for(std::size_t i = 0; i < _intervals.size() - 1; ++i) {
RealType midpoint = (_intervals[i] + _intervals[i + 1]) / 2;
actual_weights.push_back(f(midpoint));
}
typedef discrete_distribution<std::size_t, WeightType> bins_type;
typename bins_type::param_type bins_param(actual_weights);
_bins.param(bins_param);
}
}
#endif
/**
* Constructs a piecewise_constant_distribution from Boost.Range
* ranges holding the interval boundaries and the weights. If
* there are less than two interval boundaries, this is equivalent
* to the default constructor and the distribution will produce
* values uniformly distributed in the range [0, 1). The
* number of weights must be one less than the number of
* interval boundaries.
*/
template<class IntervalsRange, class WeightsRange>
piecewise_constant_distribution(const IntervalsRange& intervals_arg,
const WeightsRange& weights_arg)
: _bins(weights_arg),
_intervals(boost::begin(intervals_arg), boost::end(intervals_arg))
{
if(_intervals.size() < 2) {
_intervals.clear();
_intervals.push_back(RealType(0));
_intervals.push_back(RealType(1));
}
}
/**
* Constructs a piecewise_constant_distribution that approximates a
* function. The range of the distribution is [xmin, xmax). This
* range is divided into nw equally sized intervals and the weights
* are found by calling the unary function f on the midpoints of the
* intervals.
*/
template<class F>
piecewise_constant_distribution(std::size_t nw,
RealType xmin,
RealType xmax,
F f)
: _bins(nw, xmin, xmax, f)
{
if(nw == 0) { nw = 1; }
RealType delta = (xmax - xmin) / nw;
_intervals.reserve(nw + 1);
for(std::size_t i = 0; i < nw; ++i) {
_intervals.push_back(xmin + i * delta);
}
_intervals.push_back(xmax);
}
/**
* Constructs a piecewise_constant_distribution from its parameters.
*/
explicit piecewise_constant_distribution(const param_type& parm)
: _bins(parm._weights),
_intervals(parm._intervals)
{
}
/**
* Returns a value distributed according to the parameters of the
* piecewist_constant_distribution.
*/
template<class URNG>
RealType operator()(URNG& urng) const
{
std::size_t i = _bins(urng);
return uniform_real<RealType>(_intervals[i], _intervals[i+1])(urng);
}
/**
* Returns a value distributed according to the parameters
* specified by param.
*/
template<class URNG>
RealType operator()(URNG& urng, const param_type& parm) const
{
return piecewise_constant_distribution(parm)(urng);
}
/** Returns the smallest value that the distribution can produce. */
result_type min BOOST_PREVENT_MACRO_SUBSTITUTION () const
{ return _intervals.front(); }
/** Returns the largest value that the distribution can produce. */
result_type max BOOST_PREVENT_MACRO_SUBSTITUTION () const
{ return _intervals.back(); }
/**
* Returns a vector containing the probability density
* over each interval.
*/
std::vector<RealType> densities() const
{
std::vector<RealType> result(_bins.probabilities());
for(std::size_t i = 0; i < result.size(); ++i) {
result[i] /= (_intervals[i+1] - _intervals[i]);
}
return(result);
}
/** Returns a vector containing the interval boundaries. */
std::vector<RealType> intervals() const { return _intervals; }
/** Returns the parameters of the distribution. */
param_type param() const
{
return param_type(_intervals, _bins.probabilities());
}
/** Sets the parameters of the distribution. */
void param(const param_type& parm)
{
std::vector<RealType> new_intervals(parm._intervals);
typedef discrete_distribution<std::size_t, WeightType> bins_type;
typename bins_type::param_type bins_param(parm._weights);
_bins.param(bins_param);
_intervals.swap(new_intervals);
}
/**
* Effects: Subsequent uses of the distribution do not depend
* on values produced by any engine prior to invoking reset.
*/
void reset() { _bins.reset(); }
/** Writes a distribution to a @c std::ostream. */
BOOST_RANDOM_DETAIL_OSTREAM_OPERATOR(
os, piecewise_constant_distribution, pcd)
{
os << pcd.param();
return os;
}
/** Reads a distribution from a @c std::istream */
BOOST_RANDOM_DETAIL_ISTREAM_OPERATOR(
is, piecewise_constant_distribution, pcd)
{
param_type parm;
if(is >> parm) {
pcd.param(parm);
}
return is;
}
/**
* Returns true if the two distributions will return the
* same sequence of values, when passed equal generators.
*/
BOOST_RANDOM_DETAIL_EQUALITY_OPERATOR(
piecewise_constant_distribution, lhs, rhs)
{
return lhs._bins == rhs._bins && lhs._intervals == rhs._intervals;
}
/**
* Returns true if the two distributions may return different
* sequences of values, when passed equal generators.
*/
BOOST_RANDOM_DETAIL_INEQUALITY_OPERATOR(piecewise_constant_distribution)
private:
discrete_distribution<std::size_t, WeightType> _bins;
std::vector<RealType> _intervals;
};
}
}
#endif