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## root / tmp / org.txm.statsengine.r.core.win32 / res / win32 / library / BH / include / boost / accumulators / statistics / weighted_p_square_quantile.hpp @ 2486

 1 ///////////////////////////////////////////////////////////////////////////////  // weighted_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_WEIGHTED_P_SQUARE_QUANTILE_HPP_DE_01_01_2006  #define BOOST_ACCUMULATORS_STATISTICS_WEIGHTED_P_SQUARE_QUANTILE_HPP_DE_01_01_2006  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  namespace boost { namespace accumulators  {  namespace impl {   ///////////////////////////////////////////////////////////////////////////////   // weighted_p_square_quantile_impl   // single quantile estimation with weighted samples   /**   @brief Single quantile estimation with the \f$P^2\f$ algorithm for weighted samples     This version of the \f$P^2\f$ algorithm extends the \f$P^2\f$ algorithm to support weighted samples.   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 sample is added, 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 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 quantile_probability   */   template   struct weighted_p_square_quantile_impl   : accumulator_base   {   typedef typename numeric::functional::multiplies::result_type weighted_sample;   typedef typename numeric::functional::fdiv::result_type float_type;   typedef array array_type;   // for boost::result_of   typedef float_type result_type;   template   weighted_p_square_quantile_impl(Args const &args)   : p(is_same::value ? 0.5 : args[quantile_probability | 0.5])   , heights()   , actual_positions()   , desired_positions()   {   }   template   void operator ()(Args const &args)   {   std::size_t cnt = count(args);   // accumulate 5 first samples   if (cnt <= 5)   {   this->heights[cnt - 1] = args[sample];   // In this initialization phase, actual_positions stores the weights of the   // initial samples that are needed at the end of the initialization phase to   // compute the correct initial positions of the markers.   this->actual_positions[cnt - 1] = args[weight];   // complete the initialization of heights and actual_positions by sorting   if (cnt == 5)   {   // 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 < 5; ++i)   {   this->actual_positions[i] += this->actual_positions[i - 1];   }   }   }   else   {   std::size_t sample_cell = 1; // k   // find cell k such that heights[k-1] <= args[sample] < heights[k] and adjust extreme values   if (args[sample] < this->heights[0])   {   this->heights[0] = args[sample];   this->actual_positions[0] = args[weight];   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);   }   // increment positions of markers above sample_cell   for (std::size_t i = sample_cell; i < 5; ++i)   {   this->actual_positions[i] += args[weight];   }   // update desired positions for all markers   this->desired_positions[0] = this->actual_positions[0];   this->desired_positions[1] = (sum_of_weights(args) - this->actual_positions[0])   * this->p/2. + this->actual_positions[0];   this->desired_positions[2] = (sum_of_weights(args) - this->actual_positions[0])   * this->p + this->actual_positions[0];   this->desired_positions[3] = (sum_of_weights(args) - this->actual_positions[0])   * (1. + this->p)/2. + this->actual_positions[0];   this->desired_positions[4] = sum_of_weights(args);   // adjust height 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(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   };  } // namespace impl  ///////////////////////////////////////////////////////////////////////////////  // tag::weighted_p_square_quantile  //  namespace tag  {   struct weighted_p_square_quantile   : depends_on   {   typedef accumulators::impl::weighted_p_square_quantile_impl impl;   };   struct weighted_p_square_quantile_for_median   : depends_on   {   typedef accumulators::impl::weighted_p_square_quantile_impl impl;   };  }  ///////////////////////////////////////////////////////////////////////////////  // extract::weighted_p_square_quantile  // extract::weighted_p_square_quantile_for_median  //  namespace extract  {   extractor const weighted_p_square_quantile = {};   extractor const weighted_p_square_quantile_for_median = {};   BOOST_ACCUMULATORS_IGNORE_GLOBAL(weighted_p_square_quantile)   BOOST_ACCUMULATORS_IGNORE_GLOBAL(weighted_p_square_quantile_for_median)  }  using extract::weighted_p_square_quantile;  using extract::weighted_p_square_quantile_for_median;  }} // namespace boost::accumulators  #endif