Added boost header

test/external/boost/accumulators/statistics/p_square_quantile.hpp

@@ -0,0 +1,257 @@
+///////////////////////////////////////////////////////////////////////////////
+// p_square_quantile.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_P_SQUARE_QUANTILE_HPP_DE_01_01_2006
+#define BOOST_ACCUMULATORS_STATISTICS_P_SQUARE_QUANTILE_HPP_DE_01_01_2006
+
+#include <cmath>
+#include <functional>
+#include <boost/array.hpp>
+#include <boost/mpl/placeholders.hpp>
+#include <boost/type_traits/is_same.hpp>
+#include <boost/parameter/keyword.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/parameters/quantile_probability.hpp>
+
+namespace boost { namespace accumulators
+{
+
+namespace impl
+{
+    ///////////////////////////////////////////////////////////////////////////////
+    // p_square_quantile_impl
+    //  single quantile estimation
+    /**
+        @brief Single quantile estimation with the \f$P^2\f$ algorithm
+
+        The \f$P^2\f$ algorithm estimates a quantile dynamically without storing samples. Instead of
+        storing the whole sample cumulative distribution, only five points (markers) are stored. The heights
+        of these markers are the minimum and the maximum of the samples and the current estimates of the
+        \f$(p/2)\f$-, \f$p\f$- and \f$(1+p)/2\f$-quantiles. Their positions are equal to the number
+        of samples that are smaller or equal to the markers. Each time a new samples is recorded, the
+        positions of the markers are updated and if necessary their heights are adjusted using a piecewise-
+        parabolic formula.
+
+        For further details, see
+
+        R. Jain and I. Chlamtac, The P^2 algorithmus fordynamic calculation of quantiles and
+        histograms without storing observations, Communications of the ACM,
+        Volume 28 (October), Number 10, 1985, p. 1076-1085.
+
+        @param quantile_probability
+    */
+    template<typename Sample, typename Impl>
+    struct p_square_quantile_impl
+      : accumulator_base
+    {
+        typedef typename numeric::functional::average<Sample, std::size_t>::result_type float_type;
+        typedef array<float_type, 5> array_type;
+        // for boost::result_of
+        typedef float_type result_type;
+
+        template<typename Args>
+        p_square_quantile_impl(Args const &args)
+          : p(is_same<Impl, for_median>::value ? 0.5 : args[quantile_probability | 0.5])
+          , heights()
+          , actual_positions()
+          , desired_positions()
+          , positions_increments()
+        {
+            for(std::size_t i = 0; i < 5; ++i)
+            {
+                this->actual_positions[i] = i + 1;
+            }
+
+            this->desired_positions[0] = 1.;
+            this->desired_positions[1] = 1. + 2. * this->p;
+            this->desired_positions[2] = 1. + 4. * this->p;
+            this->desired_positions[3] = 3. + 2. * this->p;
+            this->desired_positions[4] = 5.;
+
+            this->positions_increments[0] = 0.;
+            this->positions_increments[1] = this->p / 2.;
+            this->positions_increments[2] = this->p;
+            this->positions_increments[3] = (1. + this->p) / 2.;
+            this->positions_increments[4] = 1.;
+        }
+
+        template<typename Args>
+        void operator ()(Args const &args)
+        {
+            std::size_t cnt = count(args);
+
+            // accumulate 5 first samples
+            if(cnt <= 5)
+            {
+                this->heights[cnt - 1] = args[sample];
+
+                // complete the initialization of heights by sorting
+                if(cnt == 5)
+                {
+                    std::sort(this->heights.begin(), this->heights.end());
+                }
+            }
+            else
+            {
+                std::size_t sample_cell = 1; // k
+
+                // find cell k such that heights[k-1] <= args[sample] < heights[k] and ajust extreme values
+                if (args[sample] < this->heights[0])
+                {
+                    this->heights[0] = args[sample];
+                    sample_cell = 1;
+                }
+                else if (this->heights[4] <= args[sample])
+                {
+                    this->heights[4] = args[sample];
+                    sample_cell = 4;
+                }
+                else
+                {
+                    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 positions of markers above sample_cell
+                for(std::size_t i = sample_cell; i < 5; ++i)
+                {
+                    ++this->actual_positions[i];
+                }
+
+                // update desired positions of all markers
+                for(std::size_t i = 0; i < 5; ++i)
+                {
+                    this->desired_positions[i] += this->positions_increments[i];
+                }
+
+                // adjust heights and actual positions of markers 1 to 3 if necessary
+                for(std::size_t i = 1; i <= 3; ++i)
+                {
+                    // offset to desired positions
+                    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));
+
+                        // try adjusting heights[i] using p-squared formula
+                        float_type h = this->heights[i] + sign_d / (dp - dm) * ((sign_d - dm) * hp
+                                     + (dp - sign_d) * hm);
+
+                        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
+        {
+            return this->heights[2];
+        }
+
+    private:
+        float_type p;                    // the quantile probability p
+        array_type heights;              // q_i
+        array_type actual_positions;     // n_i
+        array_type desired_positions;    // n'_i
+        array_type positions_increments; // dn'_i
+    };
+
+} // namespace detail
+
+///////////////////////////////////////////////////////////////////////////////
+// tag::p_square_quantile
+//
+namespace tag
+{
+    struct p_square_quantile
+      : depends_on<count>
+    {
+        /// INTERNAL ONLY
+        ///
+        typedef accumulators::impl::p_square_quantile_impl<mpl::_1, regular> impl;
+    };
+    struct p_square_quantile_for_median
+      : depends_on<count>
+    {
+        /// INTERNAL ONLY
+        ///
+        typedef accumulators::impl::p_square_quantile_impl<mpl::_1, for_median> impl;
+    };
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// extract::p_square_quantile
+// extract::p_square_quantile_for_median
+//
+namespace extract
+{
+    extractor<tag::p_square_quantile> const p_square_quantile = {};
+    extractor<tag::p_square_quantile_for_median> const p_square_quantile_for_median = {};
+
+    BOOST_ACCUMULATORS_IGNORE_GLOBAL(p_square_quantile)
+    BOOST_ACCUMULATORS_IGNORE_GLOBAL(p_square_quantile_for_median)
+}
+
+using extract::p_square_quantile;
+using extract::p_square_quantile_for_median;
+
+// So that p_square_quantile can be automatically substituted with
+// weighted_p_square_quantile when the weight parameter is non-void
+template<>
+struct as_weighted_feature<tag::p_square_quantile>
+{
+    typedef tag::weighted_p_square_quantile type;
+};
+
+template<>
+struct feature_of<tag::weighted_p_square_quantile>
+  : feature_of<tag::p_square_quantile>
+{
+};
+
+}} // namespace boost::accumulators
+
+#endif