# Changeset 83305

Ignore:
Timestamp:
Mar 5, 2013, 6:22:17 AM (6 years ago)
Message:

fix comment typos, refs #7917

Location:
trunk
Files:
13 edited

Unmodified
Removed
• ## trunk/boost/accumulators/framework/accumulators/droppable_accumulator.hpp

 r77437 void on_drop(Args const &args) { // cache the result at the point this calcuation was dropped // cache the result at the point this calculation was dropped BOOST_ASSERT(!this->has_result()); this->set(this->Accumulator::result(args));
• ## trunk/boost/accumulators/statistics/extended_p_square.hpp

 r55050 Number 4 (October), 1986, p. 159-164. The extended \f$P^2 \f$ algorithm generalizess the \f$P^2 \f$ algorithm of R. Jain and I. Chlamtac, The P^2 algorithmus for dynamic calculation of quantiles and 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. #ifdef BOOST_ACCUMULATORS_DOXYGEN_INVOKED /// tag::extended_p_square::probabilities named paramter /// tag::extended_p_square::probabilities named parameter static boost::parameter::keyword const probabilities; #endif
• ## trunk/boost/accumulators/statistics/p_square_cumul_dist.hpp

 r77422 For further details, see R. Jain and I. Chlamtac, The P^2 algorithmus for dynamic calculation of quantiles and 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.
• ## trunk/boost/accumulators/statistics/p_square_quantile.hpp

 r55050 For further details, see R. Jain and I. Chlamtac, The P^2 algorithmus fordynamic calculation of quantiles and 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. std::size_t sample_cell = 1; // k // find cell k such that heights[k-1] <= args[sample] < heights[k] and ajust extreme values // find cell k such that heights[k-1] <= args[sample] < heights[k] and adjust extreme values if (args[sample] < this->heights[0]) {
• ## trunk/boost/accumulators/statistics/skewness.hpp

 r55076 The skewness of a sample distribution is defined as the ratio of the 3rd central moment and the \f$3/2 \f$-th power of the 2nd central moment (the variance) of the sampless 3. The skewness can also be expressed by the simple moments: of the 2nd central moment (the variance) of the samples 3. The skewness can also be expressed by the simple moments: \f[
• ## trunk/boost/accumulators/statistics/tail_quantile.hpp

 r55050 The estimation of a tail quantile \f$\hat{q}\f$ with level \f$\alpha\f$ based on order statistics requires the chaching of at least the \f$\lceil n\alpha\rceil\f$ smallest or the \f$\lceil n(1-\alpha)\rceil\f$ largest samples, caching of at least the \f$\lceil n\alpha\rceil\f$ smallest or the \f$\lceil n(1-\alpha)\rceil\f$ largest samples, \f$n\f$ being the total number of samples. The largest of the \f$\lceil n\alpha\rceil\f$ smallest samples or the smallest of the \f$\lceil n(1-\alpha)\rceil\f$ largest samples provides an estimate for the quantile:
• ## trunk/boost/accumulators/statistics/weighted_extended_p_square.hpp

 r55050 Number 4 (October), 1986, p. 159-164. The extended \f$P^2 \f$ algorithm generalizess the \f$P^2 \f$ algorithm of R. Jain and I. Chlamtac, The P^2 algorithmus for dynamic calculation of quantiles and 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.
• ## trunk/boost/accumulators/statistics/weighted_p_square_cumul_dist.hpp

 r77587 For further details, see R. Jain and I. Chlamtac, The P^2 algorithmus for dynamic calculation of quantiles and 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.
• ## trunk/boost/accumulators/statistics/weighted_p_square_quantile.hpp

 r74366 For further details, see R. Jain and I. Chlamtac, The P^2 algorithmus for dynamic calculation of quantiles and 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. // In this initialization phase, actual_positions stores the weights of the // inital samples that are needed at the end of the initialization phase to // initial samples that are needed at the end of the initialization phase to // compute the correct initial positions of the markers. this->actual_positions[cnt - 1] = args[weight];
• ## trunk/boost/accumulators/statistics/weighted_tail_variate_means.hpp

 r55050 { // for _BinaryOperatrion2 in std::inner_product below // mutliplies two values and promotes the result to double // multiplies two values and promotes the result to double namespace numeric { namespace functional {
• ## trunk/boost/accumulators/statistics/weighted_variance.hpp

 r55076 \f] where \f$\bar{w}_n\f$ is the sum of the \f$n\f$ weights \f$w_i\f$ and \f$\hat{\mu}_n\f$ the estimate of the mean of the weighted smaples. Note that the sample variance is not defined for the estimate of the mean of the weighted samples. Note that the sample variance is not defined for \f$n <= 1\f$. */
• ## trunk/libs/accumulators/example/main.cpp

 r55076 //  both a simple weighted statistical calculation, and a more complicated //  calculation where the weight statistics are calculated and stored in an //  external weight accumulataor. //  external weight accumulator. void example3() {
• ## trunk/libs/accumulators/test/weighted_p_square_cumul_dist.cpp

 r77422 histogram_type histogram_lower = weighted_p_square_cumulative_distribution(acc_lower); // Note that applaying importance sampling results in a region of the distribution // Note that applying importance sampling results in a region of the distribution // to be estimated more accurately and another region to be estimated less accurately // than without importance sampling, i.e., with unweighted samples
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