/usr/include/boost/accumulators/statistics
Edit: /usr/include/boost/accumulators/statistics/extended_p_square.hpp (11633B)
///////////////////////////////////////////////////////////////////////////////
// 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_EXTENDED_SINGLE_HPP_DE_01_01_2006
#define BOOST_ACCUMULATORS_STATISTICS_EXTENDED_SINGLE_HPP_DE_01_01_2006
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namespace boost { namespace accumulators
{
///////////////////////////////////////////////////////////////////////////////
// probabilities named parameter
//
BOOST_PARAMETER_NESTED_KEYWORD(tag, extended_p_square_probabilities, probabilities)
BOOST_ACCUMULATORS_IGNORE_GLOBAL(extended_p_square_probabilities)
namespace impl
{
///////////////////////////////////////////////////////////////////////////////
// extended_p_square_impl
// multiple quantile estimation
/**
@brief Multiple quantile estimation with the extended \f$P^2\f$ algorithm
Extended \f$P^2\f$ algorithm for estimation of 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 these central markers are the current estimates of the quantiles
and 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
struct extended_p_square_impl
: accumulator_base
{
typedef typename numeric::functional::fdiv::result_type float_type;
typedef std::vector 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
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())
, positions_increments(heights.size())
{
std::size_t num_quantiles = this->probabilities.size();
std::size_t num_markers = this->heights.size();
for(std::size_t i = 0; i < num_markers; ++i)
{
this->actual_positions[i] = i + 1;
}
this->positions_increments[0] = 0.;
this->positions_increments[num_markers - 1] = 1.;
for(std::size_t i = 0; i < num_quantiles; ++i)
{
this->positions_increments[2 * i + 2] = probabilities[i];
}
for(std::size_t i = 0; i <= num_quantiles; ++i)
{
this->positions_increments[2 * i + 1] =
0.5 * (this->positions_increments[2 * i] + this->positions_increments[2 * i + 2]);
}
for(std::size_t i = 0; i < num_markers; ++i)
{
this->desired_positions[i] = 1. + 2. * (num_quantiles + 1.) * this->positions_increments[i];
}
}
template
void operator ()(Args const &args)
{
std::size_t cnt = count(args);
// m+2 principal markers and m+1 middle markers
std::size_t num_markers = 2 * this->probabilities.size() + 3;
// first accumulate num_markers samples
if(cnt <= num_markers)
{
this->heights[cnt - 1] = args[sample];
// complete the initialization of heights by sorting
if(cnt == num_markers)
{
std::sort(this->heights.begin(), this->heights.end());
}
}
else
{
std::size_t sample_cell = 1;
// find cell k = sample_cell such that heights[k-1] <= sample < heights[k]
if(args[sample] < this->heights[0])
{
this->heights[0] = args[sample];
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
{
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 positions of all markers above sample_cell index
for(std::size_t i = sample_cell; i < num_markers; ++i)
{
++this->actual_positions[i];
}
// update desired positions of all markers
for(std::size_t i = 0; i < num_markers; ++i)
{
this->desired_positions[i] += this->positions_increments[i];
}
// 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(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)
);
}
public:
// make this accumulator serializeable
// TODO: do we need to split to load/save and verify that the parameters did not change?
template
void serialize(Archive & ar, const unsigned int file_version)
{
ar & probabilities;
ar & heights;
ar & actual_positions;
ar & desired_positions;
ar & positions_increments;
}
private:
array_type probabilities; // the quantile probabilities
array_type heights; // q_i
array_type actual_positions; // n_i
array_type desired_positions; // d_i
array_type positions_increments; // f_i
};
} // namespace impl
///////////////////////////////////////////////////////////////////////////////
// tag::extended_p_square
//
namespace tag
{
struct extended_p_square
: depends_on
, extended_p_square_probabilities
{
typedef accumulators::impl::extended_p_square_impl impl;
#ifdef BOOST_ACCUMULATORS_DOXYGEN_INVOKED
/// tag::extended_p_square::probabilities named parameter
static boost::parameter::keyword const probabilities;
#endif
};
}
///////////////////////////////////////////////////////////////////////////////
// extract::extended_p_square
//
namespace extract
{
extractor const extended_p_square = {};
BOOST_ACCUMULATORS_IGNORE_GLOBAL(extended_p_square)
}
using extract::extended_p_square;
// So that extended_p_square can be automatically substituted with
// weighted_extended_p_square when the weight parameter is non-void
template<>
struct as_weighted_feature
{
typedef tag::weighted_extended_p_square type;
};
template<>
struct feature_of
: feature_of
{
};
}} // namespace boost::accumulators
#endif