Changeset 83305


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

fix comment typos, refs #7917

Location:
trunk
Files:
13 edited

Legend:

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

    r77437 r83305  
    216216        void on_drop(Args const &args)
    217217        {
    218             // cache the result at the point this calcuation was dropped
     218            // cache the result at the point this calculation was dropped
    219219            BOOST_ASSERT(!this->has_result());
    220220            this->set(this->Accumulator::result(args));
  • trunk/boost/accumulators/statistics/extended_p_square.hpp

    r55050 r83305  
    5656        Number 4 (October), 1986, p. 159-164.
    5757
    58         The extended \f$ P^2 \f$ algorithm generalizess the \f$ P^2 \f$ algorithm of
    59 
    60         R. Jain and I. Chlamtac, The P^2 algorithmus for dynamic calculation of quantiles and
     58        The extended \f$ P^2 \f$ algorithm generalizes the \f$ P^2 \f$ algorithm of
     59
     60        R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and
    6161        histograms without storing observations, Communications of the ACM,
    6262        Volume 28 (October), Number 10, 1985, p. 1076-1085.
     
    257257
    258258        #ifdef BOOST_ACCUMULATORS_DOXYGEN_INVOKED
    259         /// tag::extended_p_square::probabilities named paramter
     259        /// tag::extended_p_square::probabilities named parameter
    260260        static boost::parameter::keyword<tag::probabilities> const probabilities;
    261261        #endif
  • trunk/boost/accumulators/statistics/p_square_cumul_dist.hpp

    r77422 r83305  
    4242        For further details, see
    4343
    44         R. Jain and I. Chlamtac, The P^2 algorithmus for dynamic calculation of quantiles and
     44        R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and
    4545        histograms without storing observations, Communications of the ACM,
    4646        Volume 28 (October), Number 10, 1985, p. 1076-1085.
  • trunk/boost/accumulators/statistics/p_square_quantile.hpp

    r55050 r83305  
    4545        For further details, see
    4646
    47         R. Jain and I. Chlamtac, The P^2 algorithmus fordynamic calculation of quantiles and
     47        R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and
    4848        histograms without storing observations, Communications of the ACM,
    4949        Volume 28 (October), Number 10, 1985, p. 1076-1085.
     
    106106                std::size_t sample_cell = 1; // k
    107107
    108                 // find cell k such that heights[k-1] <= args[sample] < heights[k] and ajust extreme values
     108                // find cell k such that heights[k-1] <= args[sample] < heights[k] and adjust extreme values
    109109                if (args[sample] < this->heights[0])
    110110                {
  • trunk/boost/accumulators/statistics/skewness.hpp

    r55076 r83305  
    3232
    3333        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 sampless 3. 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:
    3535
    3636        \f[
  • trunk/boost/accumulators/statistics/tail_quantile.hpp

    r55050 r83305  
    4747
    4848        The estimation of a tail quantile \f$\hat{q}\f$ with level \f$\alpha\f$ based on order statistics requires the
    49         chaching 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,
    5050        \f$n\f$ being the total number of samples. The largest of the \f$\lceil n\alpha\rceil\f$ smallest samples or the
    5151        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 r83305  
    5555        Number 4 (October), 1986, p. 159-164.
    5656
    57         The extended \f$ P^2 \f$ algorithm generalizess the \f$ P^2 \f$ algorithm of
    58 
    59         R. Jain and I. Chlamtac, The P^2 algorithmus for dynamic calculation of quantiles and
     57        The extended \f$ P^2 \f$ algorithm generalizes the \f$ P^2 \f$ algorithm of
     58
     59        R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and
    6060        histograms without storing observations, Communications of the ACM,
    6161        Volume 28 (October), Number 10, 1985, p. 1076-1085.
  • trunk/boost/accumulators/statistics/weighted_p_square_cumul_dist.hpp

    r77587 r83305  
    4343        For further details, see
    4444
    45         R. Jain and I. Chlamtac, The P^2 algorithmus for dynamic calculation of quantiles and
     45        R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and
    4646        histograms without storing observations, Communications of the ACM,
    4747        Volume 28 (October), Number 10, 1985, p. 1076-1085.
  • trunk/boost/accumulators/statistics/weighted_p_square_quantile.hpp

    r74366 r83305  
    4545        For further details, see
    4646
    47         R. Jain and I. Chlamtac, The P^2 algorithmus for dynamic calculation of quantiles and
     47        R. Jain and I. Chlamtac, The P^2 algorithm for dynamic calculation of quantiles and
    4848        histograms without storing observations, Communications of the ACM,
    4949        Volume 28 (October), Number 10, 1985, p. 1076-1085.
     
    8181
    8282                // In this initialization phase, actual_positions stores the weights of the
    83                 // inital samples that are needed at the end of the initialization phase to
     83                // initial samples that are needed at the end of the initialization phase to
    8484                // compute the correct initial positions of the markers.
    8585                this->actual_positions[cnt - 1] = args[weight];
  • trunk/boost/accumulators/statistics/weighted_tail_variate_means.hpp

    r55050 r83305  
    3838{
    3939    // for _BinaryOperatrion2 in std::inner_product below
    40     // mutliplies two values and promotes the result to double
     40    // multiplies two values and promotes the result to double
    4141    namespace numeric { namespace functional
    4242    {
  • trunk/boost/accumulators/statistics/weighted_variance.hpp

    r55076 r83305  
    6565        \f]
    6666        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 smaples. Note that the sample variance is not defined for
     67        the estimate of the mean of the weighted samples. Note that the sample variance is not defined for
    6868        \f$n <= 1\f$.
    6969    */
  • trunk/libs/accumulators/example/main.cpp

    r55076 r83305  
    124124//  both a simple weighted statistical calculation, and a more complicated
    125125//  calculation where the weight statistics are calculated and stored in an
    126 //  external weight accumulataor.
     126//  external weight accumulator.
    127127void example3()
    128128{
  • trunk/libs/accumulators/test/weighted_p_square_cumul_dist.cpp

    r77422 r83305  
    7373    histogram_type histogram_lower = weighted_p_square_cumulative_distribution(acc_lower);
    7474
    75     // Note that applaying importance sampling results in a region of the distribution
     75    // Note that applying importance sampling results in a region of the distribution
    7676    // to be estimated more accurately and another region to be estimated less accurately
    7777    // than without importance sampling, i.e., with unweighted samples
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