| /////////////////////////////////////////////////////////////////////////////// |
| // 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> |
| |
| 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 generalizess the \f$ P^2 \f$ algorithm of |
| |
| R. Jain and I. Chlamtac, The P^2 algorithmus 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::average<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) |
| ); |
| } |
| |
| 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 |