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

 1 /////////////////////////////////////////////////////////////////////////////// // weighted_tail_mean.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_TAIL_MEAN_HPP_DE_01_01_2006 #define BOOST_ACCUMULATORS_STATISTICS_WEIGHTED_TAIL_MEAN_HPP_DE_01_01_2006 #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #ifdef _MSC_VER # pragma warning(push) # pragma warning(disable: 4127) // conditional expression is constant #endif namespace boost { namespace accumulators { namespace impl { /////////////////////////////////////////////////////////////////////////////// // coherent_weighted_tail_mean_impl // // TODO /////////////////////////////////////////////////////////////////////////////// // non_coherent_weighted_tail_mean_impl // /** @brief Estimation of the (non-coherent) weighted tail mean based on order statistics (for both left and right tails) An estimation of the non-coherent, weighted tail mean \f$\widehat{NCTM}_{n,\alpha}(X)\f$ is given by the weighted mean of the \f[ \lambda = \inf\left\{ l \left| \frac{1}{\bar{w}_n}\sum_{i=1}^{l} w_i \geq \alpha \right. \right\} \f] smallest samples (left tail) or the weighted mean of the \f[ n + 1 - \rho = n + 1 - \sup\left\{ r \left| \frac{1}{\bar{w}_n}\sum_{i=r}^{n} w_i \geq (1 - \alpha) \right. \right\} \f] largest samples (right tail) above a quantile \f$\hat{q}_{\alpha}\f$ of level \f$\alpha\f$, \f$n\f$ being the total number of sample and \f$\bar{w}_n\f$ the sum of all \f$n\f$ weights: \f[ \widehat{NCTM}_{n,\alpha}^{\mathrm{left}}(X) = \frac{\sum_{i=1}^{\lambda} w_i X_{i:n}}{\sum_{i=1}^{\lambda} w_i}, \f] \f[ \widehat{NCTM}_{n,\alpha}^{\mathrm{right}}(X) = \frac{\sum_{i=\rho}^n w_i X_{i:n}}{\sum_{i=\rho}^n w_i}. \f] @param quantile_probability */ template struct non_coherent_weighted_tail_mean_impl : accumulator_base { typedef typename numeric::functional::multiplies::result_type weighted_sample; typedef typename numeric::functional::fdiv::result_type float_type; // for boost::result_of typedef typename numeric::functional::fdiv::result_type result_type; non_coherent_weighted_tail_mean_impl(dont_care) {} template result_type result(Args const &args) const { float_type threshold = sum_of_weights(args) * ( ( is_same::value ) ? args[quantile_probability] : 1. - args[quantile_probability] ); std::size_t n = 0; Weight sum = Weight(0); while (sum < threshold) { if (n < static_cast(tail_weights(args).size())) { sum += *(tail_weights(args).begin() + n); n++; } else { if (std::numeric_limits::has_quiet_NaN) { return std::numeric_limits::quiet_NaN(); } else { std::ostringstream msg; msg << "index n = " << n << " is not in valid range [0, " << tail(args).size() << ")"; boost::throw_exception(std::runtime_error(msg.str())); return result_type(0); } } } return numeric::fdiv( std::inner_product( tail(args).begin() , tail(args).begin() + n , tail_weights(args).begin() , weighted_sample(0) ) , sum ); } }; } // namespace impl /////////////////////////////////////////////////////////////////////////////// // tag::non_coherent_weighted_tail_mean<> // namespace tag { template struct non_coherent_weighted_tail_mean : depends_on > { typedef accumulators::impl::non_coherent_weighted_tail_mean_impl impl; }; } /////////////////////////////////////////////////////////////////////////////// // extract::non_coherent_weighted_tail_mean; // namespace extract { extractor const non_coherent_weighted_tail_mean = {}; BOOST_ACCUMULATORS_IGNORE_GLOBAL(non_coherent_weighted_tail_mean) } using extract::non_coherent_weighted_tail_mean; }} // namespace boost::accumulators #ifdef _MSC_VER # pragma warning(pop) #endif #endif