Ticket #7917: fix_comment_typo.patch
File fix_comment_typo.patch, 10.9 KB (added by , 10 years ago) |
---|
-
boost/accumulators/statistics/tail_quantile.hpp
46 46 @brief Tail quantile estimation based on order statistics (for both left and right tails) 47 47 48 48 The estimation of a tail quantile \f$\hat{q}\f$ with level \f$\alpha\f$ based on order statistics requires the 49 c haching of at least the \f$\lceil n\alpha\rceil\f$ smallest or the \f$\lceil n(1-\alpha)\rceil\f$ largest samples,49 caching of at least the \f$\lceil n\alpha\rceil\f$ smallest or the \f$\lceil n(1-\alpha)\rceil\f$ largest samples, 50 50 \f$n\f$ being the total number of samples. The largest of the \f$\lceil n\alpha\rceil\f$ smallest samples or the 51 51 smallest of the \f$\lceil n(1-\alpha)\rceil\f$ largest samples provides an estimate for the quantile: 52 52 -
boost/accumulators/statistics/weighted_p_square_quantile.hpp
44 44 45 45 For further details, see 46 46 47 R. Jain and I. Chlamtac, The P^2 algorithm usfor dynamic calculation of quantiles and47 R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and 48 48 histograms without storing observations, Communications of the ACM, 49 49 Volume 28 (October), Number 10, 1985, p. 1076-1085. 50 50 … … 80 80 this->heights[cnt - 1] = args[sample]; 81 81 82 82 // In this initialization phase, actual_positions stores the weights of the 83 // init al samples that are needed at the end of the initialization phase to83 // initial samples that are needed at the end of the initialization phase to 84 84 // compute the correct initial positions of the markers. 85 85 this->actual_positions[cnt - 1] = args[weight]; 86 86 -
boost/accumulators/statistics/skewness.hpp
31 31 @brief Skewness estimation 32 32 33 33 The skewness of a sample distribution is defined as the ratio of the 3rd central moment and the \f$ 3/2 \f$-th power 34 of the 2nd central moment (the variance) of the samples s3. The skewness can also be expressed by the simple moments:34 of the 2nd central moment (the variance) of the samples 3. The skewness can also be expressed by the simple moments: 35 35 36 36 \f[ 37 37 \hat{g}_1 = -
boost/accumulators/statistics/p_square_cumul_dist.hpp
41 41 42 42 For further details, see 43 43 44 R. Jain and I. Chlamtac, The P^2 algorithm usfor dynamic calculation of quantiles and44 R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and 45 45 histograms without storing observations, Communications of the ACM, 46 46 Volume 28 (October), Number 10, 1985, p. 1076-1085. 47 47 -
boost/accumulators/statistics/weighted_tail_variate_means.hpp
37 37 namespace boost 38 38 { 39 39 // for _BinaryOperatrion2 in std::inner_product below 40 // mu tliplies two values and promotes the result to double40 // multiplies two values and promotes the result to double 41 41 namespace numeric { namespace functional 42 42 { 43 43 /////////////////////////////////////////////////////////////////////////////// -
boost/accumulators/statistics/weighted_extended_p_square.hpp
54 54 K. E. E. Raatikainen, Simultaneous estimation of several quantiles, Simulation, Volume 49, 55 55 Number 4 (October), 1986, p. 159-164. 56 56 57 The extended \f$ P^2 \f$ algorithm generalizes sthe \f$ P^2 \f$ algorithm of57 The extended \f$ P^2 \f$ algorithm generalizes the \f$ P^2 \f$ algorithm of 58 58 59 R. Jain and I. Chlamtac, The P^2 algorithm usfor dynamic calculation of quantiles and59 R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and 60 60 histograms without storing observations, Communications of the ACM, 61 61 Volume 28 (October), Number 10, 1985, p. 1076-1085. 62 62 -
boost/accumulators/statistics/p_square_quantile.hpp
44 44 45 45 For further details, see 46 46 47 R. Jain and I. Chlamtac, The P^2 algorithm us fordynamic calculation of quantiles and47 R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and 48 48 histograms without storing observations, Communications of the ACM, 49 49 Volume 28 (October), Number 10, 1985, p. 1076-1085. 50 50 … … 105 105 { 106 106 std::size_t sample_cell = 1; // k 107 107 108 // find cell k such that heights[k-1] <= args[sample] < heights[k] and a just extreme values108 // find cell k such that heights[k-1] <= args[sample] < heights[k] and adjust extreme values 109 109 if (args[sample] < this->heights[0]) 110 110 { 111 111 this->heights[0] = args[sample]; -
boost/accumulators/statistics/extended_p_square.hpp
55 55 K. E. E. Raatikainen, Simultaneous estimation of several quantiles, Simulation, Volume 49, 56 56 Number 4 (October), 1986, p. 159-164. 57 57 58 The extended \f$ P^2 \f$ algorithm generalizes sthe \f$ P^2 \f$ algorithm of58 The extended \f$ P^2 \f$ algorithm generalizes the \f$ P^2 \f$ algorithm of 59 59 60 R. Jain and I. Chlamtac, The P^2 algorithm usfor dynamic calculation of quantiles and60 R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and 61 61 histograms without storing observations, Communications of the ACM, 62 62 Volume 28 (October), Number 10, 1985, p. 1076-1085. 63 63 … … 256 256 typedef accumulators::impl::extended_p_square_impl<mpl::_1> impl; 257 257 258 258 #ifdef BOOST_ACCUMULATORS_DOXYGEN_INVOKED 259 /// tag::extended_p_square::probabilities named param ter259 /// tag::extended_p_square::probabilities named parameter 260 260 static boost::parameter::keyword<tag::probabilities> const probabilities; 261 261 #endif 262 262 }; -
boost/accumulators/statistics/weighted_variance.hpp
64 64 ,\quad n\ge2,\quad\hat{\sigma}_0^2 = 0. 65 65 \f] 66 66 where \f$\bar{w}_n\f$ is the sum of the \f$n\f$ weights \f$w_i\f$ and \f$\hat{\mu}_n\f$ 67 the estimate of the mean of the weighted s maples. Note that the sample variance is not defined for67 the estimate of the mean of the weighted samples. Note that the sample variance is not defined for 68 68 \f$n <= 1\f$. 69 69 */ 70 70 template<typename Sample, typename Weight, typename MeanFeature, typename Tag> -
boost/accumulators/statistics/weighted_p_square_cumul_dist.hpp
42 42 43 43 For further details, see 44 44 45 R. Jain and I. Chlamtac, The P^2 algorithm usfor dynamic calculation of quantiles and45 R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and 46 46 histograms without storing observations, Communications of the ACM, 47 47 Volume 28 (October), Number 10, 1985, p. 1076-1085. 48 48 -
boost/accumulators/framework/accumulators/droppable_accumulator.hpp
215 215 template<typename Args> 216 216 void on_drop(Args const &args) 217 217 { 218 // cache the result at the point this calcu ation was dropped218 // cache the result at the point this calculation was dropped 219 219 BOOST_ASSERT(!this->has_result()); 220 220 this->set(this->Accumulator::result(args)); 221 221 } -
libs/accumulators/test/weighted_p_square_cumul_dist.cpp
72 72 histogram_type histogram_upper = weighted_p_square_cumulative_distribution(acc_upper); 73 73 histogram_type histogram_lower = weighted_p_square_cumulative_distribution(acc_lower); 74 74 75 // Note that appl aying importance sampling results in a region of the distribution75 // Note that applying importance sampling results in a region of the distribution 76 76 // to be estimated more accurately and another region to be estimated less accurately 77 77 // than without importance sampling, i.e., with unweighted samples 78 78 -
libs/accumulators/example/main.cpp
123 123 // Demonstrate how to calculate weighted statistics. This example demonstrates 124 124 // both a simple weighted statistical calculation, and a more complicated 125 125 // calculation where the weight statistics are calculated and stored in an 126 // external weight accumulat aor.126 // external weight accumulator. 127 127 void example3() 128 128 { 129 129 // weight == double