ceph/src/boost/libs/accumulators/include/boost/accumulators/statistics/rolling_variance.hpp

   1 ///////////////////////////////////////////////////////////////////////////////
   2 // rolling_variance.hpp
   3 // Copyright (C) 2005 Eric Niebler
   4 // Copyright (C) 2014 Pieter Bastiaan Ober (Integricom).
   5 // Distributed under the Boost Software License, Version 1.0.
   6 // (See accompanying file LICENSE_1_0.txt or copy at
   7 // http://www.boost.org/LICENSE_1_0.txt)
   8
   9 #ifndef BOOST_ACCUMULATORS_STATISTICS_ROLLING_VARIANCE_HPP_EAN_15_11_2011
  10 #define BOOST_ACCUMULATORS_STATISTICS_ROLLING_VARIANCE_HPP_EAN_15_11_2011
  11
  12 #include <boost/accumulators/accumulators.hpp>
  13 #include <boost/accumulators/statistics/stats.hpp>
  14
  15 #include <boost/mpl/placeholders.hpp>
  16 #include <boost/accumulators/framework/accumulator_base.hpp>
  17 #include <boost/accumulators/framework/extractor.hpp>
  18 #include <boost/accumulators/numeric/functional.hpp>
  19 #include <boost/accumulators/framework/parameters/sample.hpp>
  20 #include <boost/accumulators/framework/depends_on.hpp>
  21 #include <boost/accumulators/statistics_fwd.hpp>
  22 #include <boost/accumulators/statistics/rolling_mean.hpp>
  23 #include <boost/accumulators/statistics/rolling_moment.hpp>
  24
  25 #include <boost/type_traits/is_arithmetic.hpp>
  26 #include <boost/utility/enable_if.hpp>
  27
  28 namespace boost { namespace accumulators
  29 {
  30 namespace impl
  31 {
  32     //! Immediate (lazy) calculation of the rolling variance.
  33     /*!
  34     Calculation of sample variance \f$\sigma_n^2\f$ is done as follows, see also
  35     http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance.
  36     For a rolling window of size \f$N\f$, when \f$n <= N\f$, the variance is computed according to the formula
  37     \f[
  38     \sigma_n^2 = \frac{1}{n-1} \sum_{i = 1}^n (x_i - \mu_n)^2.
  39     \f]
  40     When \f$n > N\f$, the sample variance over the window becomes:
  41     \f[
  42     \sigma_n^2 = \frac{1}{N-1} \sum_{i = n-N+1}^n (x_i - \mu_n)^2.
  43     \f]
  44     */
  45     ///////////////////////////////////////////////////////////////////////////////
  46     // lazy_rolling_variance_impl
  47     //
  48     template<typename Sample>
  49     struct lazy_rolling_variance_impl
  50         : accumulator_base
  51     {
  52         // for boost::result_of
  53         typedef typename numeric::functional::fdiv<Sample, std::size_t,void,void>::result_type result_type;
  54
  55         lazy_rolling_variance_impl(dont_care) {}
  56
  57         template<typename Args>
  58         result_type result(Args const &args) const
  59         {
  60             result_type mean = rolling_mean(args);
  61             size_t nr_samples = rolling_count(args);
  62             if (nr_samples < 2) return result_type();
  63             return nr_samples*(rolling_moment<2>(args) - mean*mean)/(nr_samples-1);
  64         }
  65     };
  66
  67     //! Iterative calculation of the rolling variance.
  68     /*!
  69     Iterative calculation of sample variance \f$\sigma_n^2\f$ is done as follows, see also
  70     http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance.
  71     For a rolling window of size \f$N\f$, for the first \f$N\f$ samples, the variance is computed according to the formula
  72     \f[
  73     \sigma_n^2 = \frac{1}{n-1} \sum_{i = 1}^n (x_i - \mu_n)^2 = \frac{1}{n-1}M_{2,n},
  74     \f]
  75     where the sum of squares \f$M_{2,n}\f$ can be recursively computed as:
  76     \f[
  77     M_{2,n} = \sum_{i = 1}^n (x_i - \mu_n)^2 = M_{2,n-1} + (x_n - \mu_n)(x_n - \mu_{n-1}),
  78     \f]
  79     and the estimate of the sample mean as:
  80     \f[
  81     \mu_n = \frac{1}{n} \sum_{i = 1}^n x_i = \mu_{n-1} + \frac{1}{n}(x_n - \mu_{n-1}).
  82     \f]
  83     For further samples, when the rolling window is fully filled with data, one has to take into account that the oldest
  84     sample \f$x_{n-N}\f$ is dropped from the window. The sample variance over the window now becomes:
  85     \f[
  86     \sigma_n^2 = \frac{1}{N-1} \sum_{i = n-N+1}^n (x_i - \mu_n)^2 = \frac{1}{n-1}M_{2,n},
  87     \f]
  88     where the sum of squares \f$M_{2,n}\f$ now equals:
  89     \f[
  90     M_{2,n} = \sum_{i = n-N+1}^n (x_i - \mu_n)^2 = M_{2,n-1} + (x_n - \mu_n)(x_n - \mu_{n-1}) - (x_{n-N} - \mu_n)(x_{n-N} - \mu_{n-1}),
  91     \f]
  92     and the estimated mean is:
  93     \f[
  94     \mu_n = \frac{1}{N} \sum_{i = n-N+1}^n x_i = \mu_{n-1} + \frac{1}{n}(x_n - x_{n-N}).
  95     \f]
  96
  97     Note that the sample variance is not defined for \f$n <= 1\f$.
  98
  99     */
 100     ///////////////////////////////////////////////////////////////////////////////
 101     // immediate_rolling_variance_impl
 102     //
 103     template<typename Sample>
 104     struct immediate_rolling_variance_impl
 105         : accumulator_base
 106     {
 107         // for boost::result_of
 108         typedef typename numeric::functional::fdiv<Sample, std::size_t>::result_type result_type;
 109
 110         template<typename Args>
 111         immediate_rolling_variance_impl(Args const &args)
 112             : previous_mean_(numeric::fdiv(args[sample | Sample()], numeric::one<std::size_t>::value))
 113             , sum_of_squares_(numeric::fdiv(args[sample | Sample()], numeric::one<std::size_t>::value))
 114         {
 115         }
 116
 117         template<typename Args>
 118         void operator()(Args const &args)
 119         {
 120             Sample added_sample = args[sample];
 121
 122             result_type mean = immediate_rolling_mean(args);
 123             sum_of_squares_ += (added_sample-mean)*(added_sample-previous_mean_);
 124
 125             if(is_rolling_window_plus1_full(args))
 126             {
 127                 Sample removed_sample = rolling_window_plus1(args).front();
 128                 sum_of_squares_ -= (removed_sample-mean)*(removed_sample-previous_mean_);
 129                 prevent_underflow(sum_of_squares_);
 130             }
 131             previous_mean_ = mean;
 132         }
 133
 134         template<typename Args>
 135         result_type result(Args const &args) const
 136         {
 137             size_t nr_samples = rolling_count(args);
 138             if (nr_samples < 2) return result_type();
 139             return numeric::fdiv(sum_of_squares_,(nr_samples-1));
 140         }
 141
 142     private:
 143
 144         result_type previous_mean_;
 145         result_type sum_of_squares_;
 146
 147         template<typename T>
 148         void prevent_underflow(T &non_negative_number,typename boost::enable_if<boost::is_arithmetic<T>,T>::type* = 0)
 149         {
 150             if (non_negative_number < T(0)) non_negative_number = T(0);
 151         }
 152         template<typename T>
 153         void prevent_underflow(T &non_arithmetic_quantity,typename boost::disable_if<boost::is_arithmetic<T>,T>::type* = 0)
 154         {
 155         }
 156     };
 157 } // namespace impl
 158
 159 ///////////////////////////////////////////////////////////////////////////////
 160 // tag:: lazy_rolling_variance
 161 // tag:: immediate_rolling_variance
 162 // tag:: rolling_variance
 163 //
 164 namespace tag
 165 {
 166     struct lazy_rolling_variance
 167         : depends_on< rolling_count, rolling_mean, rolling_moment<2> >
 168     {
 169         /// INTERNAL ONLY
 170         ///
 171         typedef accumulators::impl::lazy_rolling_variance_impl< mpl::_1 > impl;
 172
 173         #ifdef BOOST_ACCUMULATORS_DOXYGEN_INVOKED
 174         /// tag::rolling_window::window_size named parameter
 175         static boost::parameter::keyword<tag::rolling_window_size> const window_size;
 176         #endif
 177     };
 178
 179     struct immediate_rolling_variance
 180         : depends_on< rolling_window_plus1, rolling_count, immediate_rolling_mean>
 181     {
 182         /// INTERNAL ONLY
 183         ///
 184         typedef accumulators::impl::immediate_rolling_variance_impl< mpl::_1> impl;
 185
 186         #ifdef BOOST_ACCUMULATORS_DOXYGEN_INVOKED
 187         /// tag::rolling_window::window_size named parameter
 188         static boost::parameter::keyword<tag::rolling_window_size> const window_size;
 189         #endif
 190     };
 191
 192     // make immediate_rolling_variance the default implementation
 193     struct rolling_variance : immediate_rolling_variance {};
 194 } // namespace tag
 195
 196 ///////////////////////////////////////////////////////////////////////////////
 197 // extract::lazy_rolling_variance
 198 // extract::immediate_rolling_variance
 199 // extract::rolling_variance
 200 //
 201 namespace extract
 202 {
 203     extractor<tag::lazy_rolling_variance> const lazy_rolling_variance = {};
 204     extractor<tag::immediate_rolling_variance> const immediate_rolling_variance = {};
 205     extractor<tag::rolling_variance> const rolling_variance = {};
 206
 207     BOOST_ACCUMULATORS_IGNORE_GLOBAL(lazy_rolling_variance)
 208     BOOST_ACCUMULATORS_IGNORE_GLOBAL(immediate_rolling_variance)
 209     BOOST_ACCUMULATORS_IGNORE_GLOBAL(rolling_variance)
 210 }
 211
 212 using extract::lazy_rolling_variance;
 213 using extract::immediate_rolling_variance;
 214 using extract::rolling_variance;
 215
 216 // rolling_variance(lazy) -> lazy_rolling_variance
 217 template<>
 218 struct as_feature<tag::rolling_variance(lazy)>
 219 {
 220     typedef tag::lazy_rolling_variance type;
 221 };
 222
 223 // rolling_variance(immediate) -> immediate_rolling_variance
 224 template<>
 225 struct as_feature<tag::rolling_variance(immediate)>
 226 {
 227     typedef tag::immediate_rolling_variance type;
 228 };
 229
 230 // for the purposes of feature-based dependency resolution,
 231 // lazy_rolling_variance provides the same feature as rolling_variance
 232 template<>
 233 struct feature_of<tag::lazy_rolling_variance>
 234     : feature_of<tag::rolling_variance>
 235 {
 236 };
 237
 238 // for the purposes of feature-based dependency resolution,
 239 // immediate_rolling_variance provides the same feature as rolling_variance
 240 template<>
 241 struct feature_of<tag::immediate_rolling_variance>
 242   : feature_of<tag::rolling_variance>
 243 {
 244 };
 245 }} // namespace boost::accumulators
 246
 247 #endif