コード例 #1
0
        result_type result(Args const &args) const
        {
            float_type threshold = sum_of_weights(args)
                             * ( ( is_same<LeftRight, left>::value ) ? args[quantile_probability] : 1. - args[quantile_probability] );

            std::size_t n = 0;
            Weight sum = Weight(0);

            while (sum < threshold)
            {
                if (n < static_cast<std::size_t>(tail_weights(args).size()))
                {
                    sum += *(tail_weights(args).begin() + n);
                    n++;
                }
                else
                {
                    if (std::numeric_limits<result_type>::has_quiet_NaN)
                    {
                        return std::numeric_limits<result_type>::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 Sample(0);
                    }
                }
            }

            // Note that the cached samples of the left are sorted in ascending order,
            // whereas the samples of the right tail are sorted in descending order
            return *(boost::begin(tail(args)) + n - 1);
        }
コード例 #2
0
        result_type result(Args const &args) const
        {
            float_type threshold = sum_of_weights(args)
                             * ( ( is_same<LeftRight, left>::value ) ? args[quantile_probability] : 1. - args[quantile_probability] );

            std::size_t n = 0;
            Weight sum = Weight(0);

            while (sum < threshold)
            {
                if (n < static_cast<std::size_t>(tail_weights(args).size()))
                {
                    sum += *(tail_weights(args).begin() + n);
                    n++;
                }
                else
                {
                    if (std::numeric_limits<float_type>::has_quiet_NaN)
                    {
                        std::fill(
                            this->tail_means_.begin()
                          , this->tail_means_.end()
                          , std::numeric_limits<float_type>::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()));
                    }
                }
            }

            std::size_t num_variates = tail_variate(args).begin()->size();

            this->tail_means_.clear();
            this->tail_means_.resize(num_variates, Sample(0));

            this->tail_means_ = std::inner_product(
                tail_variate(args).begin()
              , tail_variate(args).begin() + n
              , tail_weights(args).begin()
              , this->tail_means_
              , numeric::functional::plus<array_type const, array_type const>()
              , numeric::functional::multiply_and_promote_to_double<VariateType const, Weight const>()
            );

            float_type factor = sum * ( (is_same<Impl, relative>::value) ? non_coherent_weighted_tail_mean(args) : 1. );

            std::transform(
                this->tail_means_.begin()
              , this->tail_means_.end()
              , this->tail_means_.begin()
              , std::bind2nd(numeric::functional::divides<typename array_type::value_type const, float_type const>(), factor)
            );

            return make_iterator_range(this->tail_means_);
        }
コード例 #3
0
        result_type result(Args const &args) const
        {
            float_type threshold = sum_of_weights(args)
                             * ( ( is_same<LeftRight, left>::value ) ? args[quantile_probability] : 1. - args[quantile_probability] );

            std::size_t n = 0;
            Weight sum = Weight(0);

            while (sum < threshold)
            {
                if (n < static_cast<std::size_t>(tail_weights(args).size()))
                {
                    sum += *(tail_weights(args).begin() + n);
                    n++;
                }
                else
                {
                    if (std::numeric_limits<result_type>::has_quiet_NaN)
                    {
                        return std::numeric_limits<result_type>::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::average(
                std::inner_product(
                    tail(args).begin()
                  , tail(args).begin() + n
                  , tail_weights(args).begin()
                  , weighted_sample(0)
                )
              , sum
            );
        }
コード例 #4
0
        result_type result(Args const &args) const
        {
            if (this->is_dirty_)
            {
                this->is_dirty_ = false;

                float_type threshold = sum_of_weights(args)
                             * ( ( is_same<LeftRight, left>::value ) ? this->threshold_probability_ : 1. - this->threshold_probability_ );

                std::size_t n = 0;
                Weight sum = Weight(0);

                while (sum < threshold)
                {
                    if (n < static_cast<std::size_t>(tail_weights(args).size()))
                    {
                        mu_ += *(tail_weights(args).begin() + n) * *(tail(args).begin() + n);
                        sigma2_ += *(tail_weights(args).begin() + n) * *(tail(args).begin() + n) * (*(tail(args).begin() + n));
                        sum += *(tail_weights(args).begin() + n);
                        n++;
                    }
                    else
                    {
                        if (std::numeric_limits<float_type>::has_quiet_NaN)
                        {
                            return boost::make_tuple(
                                std::numeric_limits<float_type>::quiet_NaN()
                              , std::numeric_limits<float_type>::quiet_NaN()
                              , std::numeric_limits<float_type>::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 boost::make_tuple(Sample(0), Sample(0), Sample(0));
                        }
                    }
                }

                float_type u = *(tail(args).begin() + n - 1) * this->sign_;


                this->mu_ = this->sign_ * numeric::average(this->mu_, sum);
                this->sigma2_ = numeric::average(this->sigma2_, sum);
                this->sigma2_ -= this->mu_ * this->mu_;

                if (is_same<LeftRight, left>::value)
                    this->threshold_probability_ = 1. - this->threshold_probability_;

                float_type tmp = numeric::average(( this->mu_ - u )*( this->mu_ - u ), this->sigma2_);
                float_type xi_hat = 0.5 * ( 1. - tmp );
                float_type beta_hat = 0.5 * ( this->mu_ - u ) * ( 1. + tmp );
                float_type beta_bar = beta_hat * std::pow(1. - threshold_probability_, xi_hat);
                float_type u_bar = u - beta_bar * ( std::pow(1. - threshold_probability_, -xi_hat) - 1.)/xi_hat;
                this->fit_parameters_ = boost::make_tuple(u_bar, beta_bar, xi_hat);

            }

            return this->fit_parameters_;
        }