Statistics
| Revision:

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

 1 ///////////////////////////////////////////////////////////////////////////////  // weighted_p_square_cumul_dist.hpp  //  // Copyright 2006 Daniel Egloff, Olivier Gygi. 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_CUMUL_DIST_HPP_DE_01_01_2006  #define BOOST_ACCUMULATORS_STATISTICS_WEIGHTED_P_SQUARE_CUMUL_DIST_HPP_DE_01_01_2006  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include  #include // for named parameter p_square_cumulative_distribution_num_cells  namespace boost { namespace accumulators  {  namespace impl  {   ///////////////////////////////////////////////////////////////////////////////   // weighted_p_square_cumulative_distribution_impl   // cumulative distribution calculation (as histogram)   /**   @brief Histogram calculation of the cumulative distribution with the \f$P^2\f$ algorithm for weighted samples     A histogram of the sample cumulative distribution is computed dynamically without storing samples   based on the \f$P^2 \f$ algorithm for weighted samples. The returned histogram has a specifiable   amount (num_cells) equiprobable (and not equal-sized) cells.     Note that applying importance sampling results in regions to be more and other regions to be less   accurately estimated than without importance sampling, i.e., with unweighted samples.     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 p_square_cumulative_distribution_num_cells   */   template   struct weighted_p_square_cumulative_distribution_impl   : accumulator_base   {   typedef typename numeric::functional::multiplies::result_type weighted_sample;   typedef typename numeric::functional::fdiv::result_type float_type;   typedef std::vector > histogram_type;   typedef std::vector array_type;   // for boost::result_of   typedef iterator_range result_type;   template   weighted_p_square_cumulative_distribution_impl(Args const &args)   : num_cells(args[p_square_cumulative_distribution_num_cells])   , heights(num_cells + 1)   , actual_positions(num_cells + 1)   , desired_positions(num_cells + 1)   , histogram(num_cells + 1)   , is_dirty(true)   {   }   template   void operator ()(Args const &args)   {   this->is_dirty = true;   std::size_t cnt = count(args);   std::size_t sample_cell = 1; // k   std::size_t b = this->num_cells;   // accumulate num_cells + 1 first samples   if (cnt <= b + 1)   {   this->heights[cnt - 1] = args[sample];   this->actual_positions[cnt - 1] = args[weight];   // complete the initialization of heights by sorting   if (cnt == b + 1)   {   //std::sort(this->heights.begin(), this->heights.end());   // 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 < b; ++i)   {   this->actual_positions[i] += this->actual_positions[i - 1];   }   }   }   else   {   // 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[b] <= args[sample])   {   this->heights[b] = args[sample];   sample_cell = b;   }   else   {   typename array_type::iterator it;   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 < b + 1; ++i)   {   this->actual_positions[i] += args[weight];   }   // determine desired marker positions   for (std::size_t i = 1; i < b + 1; ++i)   {   this->desired_positions[i] = this->actual_positions[0]   + numeric::fdiv((i-1) * (sum_of_weights(args) - this->actual_positions[0]), b);   }   // adjust heights of markers 2 to num_cells if necessary   for (std::size_t i = 1; i < b; ++i)   {   // offset to desire 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(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;   }   }   }   }   template   result_type result(Args const &args) const   {   if (this->is_dirty)   {   this->is_dirty = false;   // creates a vector of std::pair where each pair i holds   // the values heights[i] (x-axis of histogram) and   // actual_positions[i] / sum_of_weights (y-axis of histogram)   for (std::size_t i = 0; i < this->histogram.size(); ++i)   {   this->histogram[i] = std::make_pair(this->heights[i], numeric::fdiv(this->actual_positions[i], sum_of_weights(args)));   }   }   return make_iterator_range(this->histogram);   }   private:   std::size_t num_cells; // number of cells b   array_type heights; // q_i   array_type actual_positions; // n_i   array_type desired_positions; // n'_i   mutable histogram_type histogram; // histogram   mutable bool is_dirty;   };  } // namespace detail  ///////////////////////////////////////////////////////////////////////////////  // tag::weighted_p_square_cumulative_distribution  //  namespace tag  {   struct weighted_p_square_cumulative_distribution   : depends_on   , p_square_cumulative_distribution_num_cells   {   typedef accumulators::impl::weighted_p_square_cumulative_distribution_impl impl;   };  }  ///////////////////////////////////////////////////////////////////////////////  // extract::weighted_p_square_cumulative_distribution  //  namespace extract  {   extractor const weighted_p_square_cumulative_distribution = {};   BOOST_ACCUMULATORS_IGNORE_GLOBAL(weighted_p_square_cumulative_distribution)  }  using extract::weighted_p_square_cumulative_distribution;  }} // namespace boost::accumulators  #endif