Added boost header

test/external/boost/random/discrete_distribution.hpp

@@ -0,0 +1,453 @@
+/* boost random/discrete_distribution.hpp header file
+ *
+ * Copyright Steven Watanabe 2009-2011
+ * 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: discrete_distribution.hpp 71018 2011-04-05 21:27:52Z steven_watanabe $
+ */
+
+#ifndef BOOST_RANDOM_DISCRETE_DISTRIBUTION_HPP_INCLUDED
+#define BOOST_RANDOM_DISCRETE_DISTRIBUTION_HPP_INCLUDED
+
+#include <vector>
+#include <limits>
+#include <numeric>
+#include <utility>
+#include <iterator>
+#include <boost/assert.hpp>
+#include <boost/random/uniform_01.hpp>
+#include <boost/random/uniform_int.hpp>
+#include <boost/random/detail/config.hpp>
+#include <boost/random/detail/operators.hpp>
+#include <boost/random/detail/vector_io.hpp>
+
+#ifndef BOOST_NO_INITIALIZER_LISTS
+#include <initializer_list>
+#endif
+
+#include <boost/range/begin.hpp>
+#include <boost/range/end.hpp>
+
+#include <boost/random/detail/disable_warnings.hpp>
+
+namespace boost {
+namespace random {
+
+/**
+ * The class @c discrete_distribution models a \random_distribution.
+ * It produces integers in the range [0, n) with the probability
+ * of producing each value is specified by the parameters of the
+ * distribution.
+ */
+template<class IntType = int, class WeightType = double>
+class discrete_distribution {
+public:
+    typedef WeightType input_type;
+    typedef IntType result_type;
+
+    class param_type {
+    public:
+
+        typedef discrete_distribution distribution_type;
+
+        /**
+         * Constructs a @c param_type object, representing a distribution
+         * with \f$p(0) = 1\f$ and \f$p(k|k>0) = 0\f$.
+         */
+        param_type() : _probabilities(1, static_cast<WeightType>(1)) {}
+        /**
+         * If @c first == @c last, equivalent to the default constructor.
+         * Otherwise, the values of the range represent weights for the
+         * possible values of the distribution.
+         */
+        template<class Iter>
+        param_type(Iter first, Iter last) : _probabilities(first, last)
+        {
+            normalize();
+        }
+#ifndef BOOST_NO_INITIALIZER_LISTS
+        /**
+         * If wl.size() == 0, equivalent to the default constructor.
+         * Otherwise, the values of the @c initializer_list represent
+         * weights for the possible values of the distribution.
+         */
+        param_type(const std::initializer_list<WeightType>& wl)
+          : _probabilities(wl)
+        {
+            normalize();
+        }
+#endif
+        /**
+         * If the range is empty, equivalent to the default constructor.
+         * Otherwise, the elements of the range represent
+         * weights for the possible values of the distribution.
+         */
+        template<class Range>
+        explicit param_type(const Range& range)
+          : _probabilities(boost::begin(range), boost::end(range))
+        {
+            normalize();
+        }
+
+        /**
+         * If nw is zero, equivalent to the default constructor.
+         * Otherwise, the range of the distribution is [0, nw),
+         * and the weights are found by  calling fw with values
+         * evenly distributed between \f$\mbox{xmin} + \delta/2\f$ and
+         * \f$\mbox{xmax} - \delta/2\f$, where
+         * \f$\delta = (\mbox{xmax} - \mbox{xmin})/\mbox{nw}\f$.
+         */
+        template<class Func>
+        param_type(std::size_t nw, double xmin, double xmax, Func fw)
+        {
+            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) {
+                _probabilities.push_back(fw(xmin + k*delta + delta/2));
+            }
+            normalize();
+        }
+
+        /**
+         * Returns a vector containing the probabilities of each possible
+         * value of the distribution.
+         */
+        std::vector<WeightType> probabilities() const
+        {
+            return _probabilities;
+        }
+
+        /** Writes the parameters to a @c std::ostream. */
+        BOOST_RANDOM_DETAIL_OSTREAM_OPERATOR(os, param_type, parm)
+        {
+            detail::print_vector(os, parm._probabilities);
+            return os;
+        }
+        
+        /** Reads the parameters from a @c std::istream. */
+        BOOST_RANDOM_DETAIL_ISTREAM_OPERATOR(is, param_type, parm)
+        {
+            std::vector<WeightType> temp;
+            detail::read_vector(is, temp);
+            if(is) {
+                parm._probabilities.swap(temp);
+            }
+            return is;
+        }
+
+        /** Returns true if the two sets of parameters are the same. */
+        BOOST_RANDOM_DETAIL_EQUALITY_OPERATOR(param_type, lhs, rhs)
+        {
+            return lhs._probabilities == rhs._probabilities;
+        }
+        /** Returns true if the two sets of parameters are different. */
+        BOOST_RANDOM_DETAIL_INEQUALITY_OPERATOR(param_type)
+    private:
+        /// @cond show_private
+        friend class discrete_distribution;
+        explicit param_type(const discrete_distribution& dist)
+          : _probabilities(dist.probabilities())
+        {}
+        void normalize()
+        {
+            WeightType sum =
+                std::accumulate(_probabilities.begin(), _probabilities.end(),
+                                static_cast<WeightType>(0));
+            for(typename std::vector<WeightType>::iterator
+                    iter = _probabilities.begin(),
+                    end = _probabilities.end();
+                    iter != end; ++iter)
+            {
+                *iter /= sum;
+            }
+        }
+        std::vector<WeightType> _probabilities;
+        /// @endcond
+    };
+
+    /**
+     * Creates a new @c discrete_distribution object that has
+     * \f$p(0) = 1\f$ and \f$p(i|i>0) = 0\f$.
+     */
+    discrete_distribution()
+    {
+        _alias_table.push_back(std::make_pair(static_cast<WeightType>(1),
+                                              static_cast<IntType>(0)));
+    }
+    /**
+     * Constructs a discrete_distribution from an iterator range.
+     * If @c first == @c last, equivalent to the default constructor.
+     * Otherwise, the values of the range represent weights for the
+     * possible values of the distribution.
+     */
+    template<class Iter>
+    discrete_distribution(Iter first, Iter last)
+    {
+        init(first, last);
+    }
+#ifndef BOOST_NO_INITIALIZER_LISTS
+    /**
+     * Constructs a @c discrete_distribution from a @c std::initializer_list.
+     * If the @c initializer_list is empty, equivalent to the default
+     * constructor.  Otherwise, the values of the @c initializer_list
+     * represent weights for the possible values of the distribution.
+     * For example, given the distribution
+     *
+     * @code
+     * discrete_distribution<> dist{1, 4, 5};
+     * @endcode
+     *
+     * The probability of a 0 is 1/10, the probability of a 1 is 2/5,
+     * the probability of a 2 is 1/2, and no other values are possible.
+     */
+    discrete_distribution(std::initializer_list<WeightType> wl)
+    {
+        init(wl.begin(), wl.end());
+    }
+#endif
+    /**
+     * Constructs a discrete_distribution from a Boost.Range range.
+     * If the range is empty, equivalent to the default constructor.
+     * Otherwise, the values of the range represent weights for the
+     * possible values of the distribution.
+     */
+    template<class Range>
+    explicit discrete_distribution(const Range& range)
+    {
+        init(boost::begin(range), boost::end(range));
+    }
+    /**
+     * Constructs a discrete_distribution that approximates a function.
+     * If nw is zero, equivalent to the default constructor.
+     * Otherwise, the range of the distribution is [0, nw),
+     * and the weights are found by  calling fw with values
+     * evenly distributed between \f$\mbox{xmin} + \delta/2\f$ and
+     * \f$\mbox{xmax} - \delta/2\f$, where
+     * \f$\delta = (\mbox{xmax} - \mbox{xmin})/\mbox{nw}\f$.
+     */
+    template<class Func>
+    discrete_distribution(std::size_t nw, double xmin, double xmax, Func fw)
+    {
+        std::size_t n = (nw == 0) ? 1 : nw;
+        double delta = (xmax - xmin) / n;
+        BOOST_ASSERT(delta > 0);
+        std::vector<WeightType> weights;
+        for(std::size_t k = 0; k < n; ++k) {
+            weights.push_back(fw(xmin + k*delta + delta/2));
+        }
+        init(weights.begin(), weights.end());
+    }
+    /**
+     * Constructs a discrete_distribution from its parameters.
+     */
+    explicit discrete_distribution(const param_type& parm)
+    {
+        param(parm);
+    }
+
+    /**
+     * Returns a value distributed according to the parameters of the
+     * discrete_distribution.
+     */
+    template<class URNG>
+    IntType operator()(URNG& urng) const
+    {
+        BOOST_ASSERT(!_alias_table.empty());
+        WeightType test = uniform_01<WeightType>()(urng);
+        IntType result = uniform_int<IntType>((min)(), (max)())(urng);
+        if(test < _alias_table[result].first) {
+            return result;
+        } else {
+            return(_alias_table[result].second);
+        }
+    }
+    
+    /**
+     * Returns a value distributed according to the parameters
+     * specified by param.
+     */
+    template<class URNG>
+    IntType operator()(URNG& urng, const param_type& parm) const
+    {
+        while(true) {
+            WeightType val = uniform_01<WeightType>()(urng);
+            WeightType sum = 0;
+            std::size_t result = 0;
+            for(typename std::vector<WeightType>::const_iterator
+                iter = parm._probabilities.begin(),
+                end = parm._probabilities.end();
+                iter != end; ++iter, ++result)
+            {
+                sum += *iter;
+                if(sum > val) {
+                    return result;
+                }
+            }
+        }
+    }
+    
+    /** Returns the smallest value that the distribution can produce. */
+    result_type min BOOST_PREVENT_MACRO_SUBSTITUTION () const { return 0; }
+    /** Returns the largest value that the distribution can produce. */
+    result_type max BOOST_PREVENT_MACRO_SUBSTITUTION () const
+    { return static_cast<result_type>(_alias_table.size() - 1); }
+
+    /**
+     * Returns a vector containing the probabilities of each
+     * value of the distribution.  For example, given
+     *
+     * @code
+     * discrete_distribution<> dist = { 1, 4, 5 };
+     * std::vector<double> p = dist.param();
+     * @endcode
+     *
+     * the vector, p will contain {0.1, 0.4, 0.5}.
+     */
+    std::vector<WeightType> probabilities() const
+    {
+        std::vector<WeightType> result(_alias_table.size());
+        const WeightType mean =
+            static_cast<WeightType>(1) / _alias_table.size();
+        std::size_t i = 0;
+        for(typename alias_table_t::const_iterator
+                iter = _alias_table.begin(),
+                end = _alias_table.end();
+                iter != end; ++iter, ++i)
+        {
+            WeightType val = iter->first * mean;
+            result[i] += val;
+            result[iter->second] += mean - val;
+        }
+        return(result);
+    }
+
+    /** Returns the parameters of the distribution. */
+    param_type param() const
+    {
+        return param_type(*this);
+    }
+    /** Sets the parameters of the distribution. */
+    void param(const param_type& parm)
+    {
+        init(parm._probabilities.begin(), parm._probabilities.end());
+    }
+    
+    /**
+     * Effects: Subsequent uses of the distribution do not depend
+     * on values produced by any engine prior to invoking reset.
+     */
+    void reset() {}
+
+    /** Writes a distribution to a @c std::ostream. */
+    BOOST_RANDOM_DETAIL_OSTREAM_OPERATOR(os, discrete_distribution, dd)
+    {
+        os << dd.param();
+        return os;
+    }
+
+    /** Reads a distribution from a @c std::istream */
+    BOOST_RANDOM_DETAIL_ISTREAM_OPERATOR(is, discrete_distribution, dd)
+    {
+        param_type parm;
+        if(is >> parm) {
+            dd.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(discrete_distribution, lhs, rhs)
+    {
+        return lhs._alias_table == rhs._alias_table;
+    }
+    /**
+     * Returns true if the two distributions may return different
+     * sequences of values, when passed equal generators.
+     */
+    BOOST_RANDOM_DETAIL_INEQUALITY_OPERATOR(discrete_distribution)
+
+private:
+
+    /// @cond show_private
+
+    template<class Iter>
+    void init(Iter first, Iter last, std::input_iterator_tag)
+    {
+        std::vector<WeightType> temp(first, last);
+        init(temp.begin(), temp.end());
+    }
+    template<class Iter>
+    void init(Iter first, Iter last, std::forward_iterator_tag)
+    {
+        std::vector<std::pair<WeightType, IntType> > below_average;
+        std::vector<std::pair<WeightType, IntType> > above_average;
+        std::size_t size = std::distance(first, last);
+        WeightType weight_sum =
+            std::accumulate(first, last, static_cast<WeightType>(0));
+        WeightType weight_average = weight_sum / size;
+        std::size_t i = 0;
+        for(; first != last; ++first, ++i) {
+            WeightType val = *first / weight_average;
+            std::pair<WeightType, IntType> elem(val, static_cast<IntType>(i));
+            if(val < static_cast<WeightType>(1)) {
+                below_average.push_back(elem);
+            } else {
+                above_average.push_back(elem);
+            }
+        }
+
+        _alias_table.resize(size);
+        typename alias_table_t::iterator
+            b_iter = below_average.begin(),
+            b_end = below_average.end(),
+            a_iter = above_average.begin(),
+            a_end = above_average.end()
+            ;
+        while(b_iter != b_end && a_iter != a_end) {
+            _alias_table[b_iter->second] =
+                std::make_pair(b_iter->first, a_iter->second);
+            a_iter->first -= (static_cast<WeightType>(1) - b_iter->first);
+            if(a_iter->first < static_cast<WeightType>(1)) {
+                *b_iter = *a_iter++;
+            } else {
+                ++b_iter;
+            }
+        }
+        for(; b_iter != b_end; ++b_iter) {
+            _alias_table[b_iter->second].first = static_cast<WeightType>(1);
+        }
+        for(; a_iter != a_end; ++a_iter) {
+            _alias_table[a_iter->second].first = static_cast<WeightType>(1);
+        }
+    }
+    template<class Iter>
+    void init(Iter first, Iter last)
+    {
+        if(first == last) {
+            _alias_table.clear();
+            _alias_table.push_back(std::make_pair(static_cast<WeightType>(1),
+                                                  static_cast<IntType>(0)));
+        } else {
+            typename std::iterator_traits<Iter>::iterator_category category;
+            init(first, last, category);
+        }
+    }
+    typedef std::vector<std::pair<WeightType, IntType> > alias_table_t;
+    alias_table_t _alias_table;
+    /// @endcond
+};
+
+}
+}
+
+#include <boost/random/detail/enable_warnings.hpp>
+
+#endif