FM / statistics

/usr/include/boost/accumulators/statistics/

/usr/include/boost/accumulators/statistics

Name	Size	Mode	Actions
parameters/	-	0755	rm
variates/	-	0755	rm
count.hpp	2013	0644	edit dl rm
covariance.hpp	7137	0644	edit dl rm
density.hpp	10007	0644	edit dl rm
error_of.hpp	2588	0644	edit dl rm
error_of_mean.hpp	2193	0644	edit dl rm
extended_p_square.hpp	11633	0644	edit dl rm
extended_p_square_quantile.hpp	12522	0644	edit dl rm
kurtosis.hpp	3864	0644	edit dl rm
max.hpp	2294	0644	edit dl rm
mean.hpp	9334	0644	edit dl rm
median.hpp	10242	0644	edit dl rm
min.hpp	2294	0644	edit dl rm
moment.hpp	3436	0644	edit dl rm
peaks_over_threshold.hpp	17525	0644	edit dl rm
pot_quantile.hpp	7336	0644	edit dl rm
pot_tail_mean.hpp	7802	0644	edit dl rm
p_square_cumulative_distribution.hpp	955	0644	edit dl rm
p_square_cumul_dist.hpp	10365	0644	edit dl rm
p_square_quantile.hpp	10086	0644	edit dl rm
rolling_count.hpp	2574	0644	edit dl rm
rolling_mean.hpp	6753	0644	edit dl rm
rolling_moment.hpp	3741	0644	edit dl rm
rolling_sum.hpp	2871	0644	edit dl rm
rolling_variance.hpp	9486	0644	edit dl rm
rolling_window.hpp	7254	0644	edit dl rm
skewness.hpp	3691	0644	edit dl rm
stats.hpp	972	0644	edit dl rm
sum.hpp	3884	0644	edit dl rm
sum_kahan.hpp	5104	0644	edit dl rm
tail.hpp	11012	0644	edit dl rm
tail_mean.hpp	9158	0644	edit dl rm
tail_quantile.hpp	5535	0644	edit dl rm
tail_variate.hpp	4504	0644	edit dl rm
tail_variate_means.hpp	10463	0644	edit dl rm
times2_iterator.hpp	1965	0644	edit dl rm
variance.hpp	7522	0644	edit dl rm
weighted_covariance.hpp	5244	0644	edit dl rm
weighted_density.hpp	9518	0644	edit dl rm
weighted_extended_p_square.hpp	12842	0644	edit dl rm
weighted_kurtosis.hpp	4147	0644	edit dl rm
weighted_mean.hpp	6637	0644	edit dl rm
weighted_median.hpp	8667	0644	edit dl rm
weighted_moment.hpp	3384	0644	edit dl rm
weighted_peaks_over_threshold.hpp	12352	0644	edit dl rm
weighted_p_square_cumulative_distribution.hpp	1009	0644	edit dl rm
weighted_p_square_cumul_dist.hpp	11058	0644	edit dl rm
weighted_p_square_quantile.hpp	11110	0644	edit dl rm
weighted_skewness.hpp	3844	0644	edit dl rm
weighted_sum.hpp	3639	0644	edit dl rm
weighted_sum_kahan.hpp	4559	0644	edit dl rm
weighted_tail_mean.hpp	5788	0644	edit dl rm
weighted_tail_quantile.hpp	5135	0644	edit dl rm
weighted_tail_variate_means.hpp	10052	0644	edit dl rm
weighted_variance.hpp	6774	0644	edit dl rm
with_error.hpp	1277	0644	edit dl rm

Edit: /usr/include/boost/accumulators/statistics/weighted_extended_p_square.hpp (12842B)

///////////////////////////////////////////////////////////////////////////////
// weighted_extended_p_square.hpp
//
//  Copyright 2005 Daniel Egloff. 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)

#ifndef BOOST_ACCUMULATORS_STATISTICS_WEIGHTED_EXTENDED_P_SQUARE_HPP_DE_01_01_2006
#define BOOST_ACCUMULATORS_STATISTICS_WEIGHTED_EXTENDED_P_SQUARE_HPP_DE_01_01_2006

#include <vector>
#include <functional>
#include <boost/range/begin.hpp>
#include <boost/range/end.hpp>
#include <boost/range/iterator_range.hpp>
#include <boost/iterator/transform_iterator.hpp>
#include <boost/iterator/counting_iterator.hpp>
#include <boost/iterator/permutation_iterator.hpp>
#include <boost/parameter/keyword.hpp>
#include <boost/mpl/placeholders.hpp>
#include <boost/accumulators/framework/accumulator_base.hpp>
#include <boost/accumulators/framework/extractor.hpp>
#include <boost/accumulators/numeric/functional.hpp>
#include <boost/accumulators/framework/parameters/sample.hpp>
#include <boost/accumulators/framework/depends_on.hpp>
#include <boost/accumulators/statistics_fwd.hpp>
#include <boost/accumulators/statistics/count.hpp>
#include <boost/accumulators/statistics/sum.hpp>
#include <boost/accumulators/statistics/times2_iterator.hpp>
#include <boost/accumulators/statistics/extended_p_square.hpp>
#include <boost/serialization/vector.hpp>

namespace boost { namespace accumulators
{

namespace impl
{
    ///////////////////////////////////////////////////////////////////////////////
    // weighted_extended_p_square_impl
    //  multiple quantile estimation with weighted samples
    /**
        @brief Multiple quantile estimation with the extended \f$P^2\f$ algorithm for weighted samples

This version of the extended \f$P^2\f$ algorithm extends the extended \f$P^2\f$ algorithm to
        support weighted samples. The extended \f$P^2\f$ algorithm dynamically estimates several
        quantiles without storing samples. Assume that \f$m\f$ quantiles
        \f$\xi_{p_1}, \ldots, \xi_{p_m}\f$ are to be estimated. Instead of storing the whole sample
        cumulative distribution, the algorithm maintains only \f$m+2\f$ principal markers and
        \f$m+1\f$ middle markers, whose positions are updated with each sample and whose heights
        are adjusted (if necessary) using a piecewise-parablic formula. The heights of the principal
        markers are the current estimates of the quantiles and are returned as an iterator range.

For further details, see

K. E. E. Raatikainen, Simultaneous estimation of several quantiles, Simulation, Volume 49,
        Number 4 (October), 1986, p. 159-164.

The extended \f$ P^2 \f$ algorithm generalizes the \f$ P^2 \f$ algorithm of

R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and
        histograms without storing observations, Communications of the ACM,
        Volume 28 (October), Number 10, 1985, p. 1076-1085.

@param extended_p_square_probabilities A vector of quantile probabilities.
    */
    template<typename Sample, typename Weight>
    struct weighted_extended_p_square_impl
      : accumulator_base
    {
        typedef typename numeric::functional::multiplies<Sample, Weight>::result_type weighted_sample;
        typedef typename numeric::functional::fdiv<weighted_sample, std::size_t>::result_type float_type;
        typedef std::vector<float_type> array_type;
        // for boost::result_of
        typedef iterator_range<
            detail::lvalue_index_iterator<
                permutation_iterator<
                    typename array_type::const_iterator
                  , detail::times2_iterator
                >
            >
        > result_type;

template<typename Args>
        weighted_extended_p_square_impl(Args const &args)
          : probabilities(
                boost::begin(args[extended_p_square_probabilities])
              , boost::end(args[extended_p_square_probabilities])
            )
          , heights(2 * probabilities.size() + 3)
          , actual_positions(heights.size())
          , desired_positions(heights.size())
        {
        }

template<typename Args>
        void operator ()(Args const &args)
        {
            std::size_t cnt = count(args);
            std::size_t sample_cell = 1; // k
            std::size_t num_quantiles = this->probabilities.size();

// m+2 principal markers and m+1 middle markers
            std::size_t num_markers = 2 * num_quantiles + 3;

// first accumulate num_markers samples
            if(cnt <= num_markers)
            {
                this->heights[cnt - 1] = args[sample];
                this->actual_positions[cnt - 1] = args[weight];

// complete the initialization of heights (and actual_positions) by sorting
                if(cnt == num_markers)
                {
                    // TODO: we need to sort the initial samples (in heights) in ascending order and
                    // sort their weights (in actual_positions) the same way. The following lines do
                    // it, but there must be a better and more efficient way of doing this.
                    typename array_type::iterator it_begin, it_end, it_min;

it_begin = this->heights.begin();
                    it_end   = this->heights.end();

std::size_t pos = 0;

while (it_begin != it_end)
                    {
                        it_min = std::min_element(it_begin, it_end);
                        std::size_t d = std::distance(it_begin, it_min);
                        std::swap(*it_begin, *it_min);
                        std::swap(this->actual_positions[pos], this->actual_positions[pos + d]);
                        ++it_begin;
                        ++pos;
                    }

// calculate correct initial actual positions
                    for (std::size_t i = 1; i < num_markers; ++i)
                    {
                        actual_positions[i] += actual_positions[i - 1];
                    }
                }
            }
            else
            {
                if(args[sample] < this->heights[0])
                {
                    this->heights[0] = args[sample];
                    this->actual_positions[0] = args[weight];
                    sample_cell = 1;
                }
                else if(args[sample] >= this->heights[num_markers - 1])
                {
                    this->heights[num_markers - 1] = args[sample];
                    sample_cell = num_markers - 1;
                }
                else
                {
                    // find cell k = sample_cell such that heights[k-1] <= sample < heights[k]

typedef typename array_type::iterator iterator;
                    iterator it = std::upper_bound(
                        this->heights.begin()
                      , this->heights.end()
                      , args[sample]
                    );

sample_cell = std::distance(this->heights.begin(), it);
                }

// update actual position of all markers above sample_cell
                for(std::size_t i = sample_cell; i < num_markers; ++i)
                {
                    this->actual_positions[i] += args[weight];
                }

// compute desired positions
                {
                    this->desired_positions[0] = this->actual_positions[0];
                    this->desired_positions[num_markers - 1] = sum_of_weights(args);
                    this->desired_positions[1] = (sum_of_weights(args) - this->actual_positions[0]) * probabilities[0]
                                              / 2. + this->actual_positions[0];
                    this->desired_positions[num_markers - 2] = (sum_of_weights(args) - this->actual_positions[0])
                                                            * (probabilities[num_quantiles - 1] + 1.)
                                                            / 2. + this->actual_positions[0];

for (std::size_t i = 0; i < num_quantiles; ++i)
                    {
                        this->desired_positions[2 * i + 2] = (sum_of_weights(args) - this->actual_positions[0])
                                                          * probabilities[i] + this->actual_positions[0];
                    }

for (std::size_t i = 1; i < num_quantiles; ++i)
                    {
                        this->desired_positions[2 * i + 1] = (sum_of_weights(args) - this->actual_positions[0])
                                                      * (probabilities[i - 1] + probabilities[i])
                                                      / 2. + this->actual_positions[0];
                    }
                }

// adjust heights and actual_positions of markers 1 to num_markers - 2 if necessary
                for (std::size_t i = 1; i <= num_markers - 2; ++i)
                {
                    // offset to desired position
                    float_type d = this->desired_positions[i] - this->actual_positions[i];

// offset to next position
                    float_type dp = this->actual_positions[i + 1] - this->actual_positions[i];

// offset to previous position
                    float_type dm = this->actual_positions[i - 1] - this->actual_positions[i];

// height ds
                    float_type hp = (this->heights[i + 1] - this->heights[i]) / dp;
                    float_type hm = (this->heights[i - 1] - this->heights[i]) / dm;

if((d >= 1 && dp > 1) || (d <= -1 && dm < -1))
                    {
                        short sign_d = static_cast<short>(d / std::abs(d));

float_type h = this->heights[i] + sign_d / (dp - dm) * ((sign_d - dm)*hp + (dp - sign_d) * hm);

// try adjusting heights[i] using p-squared formula
                        if(this->heights[i - 1] < h && h < this->heights[i + 1])
                        {
                            this->heights[i] = h;
                        }
                        else
                        {
                            // use linear formula
                            if(d > 0)
                            {
                                this->heights[i] += hp;
                            }
                            if(d < 0)
                            {
                                this->heights[i] -= hm;
                            }
                        }
                        this->actual_positions[i] += sign_d;
                    }
                }
            }
        }

result_type result(dont_care) const
        {
            // for i in [1,probabilities.size()], return heights[i * 2]
            detail::times2_iterator idx_begin = detail::make_times2_iterator(1);
            detail::times2_iterator idx_end = detail::make_times2_iterator(this->probabilities.size() + 1);

return result_type(
                make_permutation_iterator(this->heights.begin(), idx_begin)
              , make_permutation_iterator(this->heights.begin(), idx_end)
            );
        }

// make this accumulator serializeable
        // TODO: do we need to split to load/save and verify that the parameters did not change?
        template<class Archive>
        void serialize(Archive & ar, const unsigned int file_version)
        { 
            ar & probabilities;
            ar & heights;
            ar & actual_positions;
            ar & desired_positions;
        }

private:
        array_type probabilities;         // the quantile probabilities
        array_type heights;               // q_i
        array_type actual_positions;      // n_i
        array_type desired_positions;     // d_i
    };

} // namespace impl

///////////////////////////////////////////////////////////////////////////////
// tag::weighted_extended_p_square
//
namespace tag
{
    struct weighted_extended_p_square
      : depends_on<count, sum_of_weights>
      , extended_p_square_probabilities
    {
        typedef accumulators::impl::weighted_extended_p_square_impl<mpl::_1, mpl::_2> impl;
    };
}

///////////////////////////////////////////////////////////////////////////////
// extract::weighted_extended_p_square
//
namespace extract
{
    extractor<tag::weighted_extended_p_square> const weighted_extended_p_square = {};

BOOST_ACCUMULATORS_IGNORE_GLOBAL(weighted_extended_p_square)
}

using extract::weighted_extended_p_square;

}} // namespace boost::accumulators

#endif

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