Bivariate statistics

Functions
template<std::floating_point T, nan_policy Policy = nan_policy::propagate, std::random_access_iterator I, std::random_access_iterator J, typename F1 = std::identity, typename F2 = std::identity> requires concepts::arithmetic_projection<F1, std::iter_value_t<I>>
and concepts::arithmetic_projection< F2, std::iter_value_t< J > > auto	accumulate (I first1, I last1, J first2, F1 &&f1=F1 {}, F2 &&f2=F2 {}) noexcept -> std::conditional_t< Policy==nan_policy::omit, std::pair< bivariate_statistics, std::size_t >, bivariate_statistics >
template<std::floating_point T, nan_policy Policy = nan_policy::propagate, std::random_access_iterator I, std::random_access_iterator J, std::random_access_iterator K, typename F1 = std::identity, typename F2 = std::identity> requires concepts::arithmetic_projection<F1, std::iter_value_t<I>>
and concepts::arithmetic_projection< F2, std::iter_value_t< J > > and std::is_arithmetic_v< std::iter_value_t< K > > auto	accumulate (I first1, I last1, J first2, K first3, F1 &&f1=F1 {}, F2 &&f2=F2 {}) noexcept -> std::conditional_t< Policy==nan_policy::omit, std::pair< bivariate_statistics, std::size_t >, bivariate_statistics >

Detailed Description

Methods for bivariate statistics.

Function Documentation

accumulate()

template<std::floating_point T, nan_policy Policy = nan_policy::propagate, std::random_access_iterator I, std::random_access_iterator J, typename F1 = std::identity, typename F2 = std::identity>
requires concepts::arithmetic_projection<F1, std::iter_value_t<I>>

and concepts::arithmetic_projection< F2, std::iter_value_t< J > > auto accumulate ( I  first1, I  last1, J  first2, F1 &&  f1 = F1 {}, F2 &&  f2 = F2 {}  ) -> std::conditional_t<Policy == nan_policy::omit, std::pair<bivariate_statistics, std::size_t>, bivariate_statistics>

inlinenoexcept

Compute bivariate statistics from two sequences of values. The values can be provided directly or via a projection method.

Template Parameters

T	The scalar value type underlying the eve::wide<T> SIMD type used to compute the stats.
Policy	nan_policy::propagate (default): a non-finite value poisons the whole result. nan_policy::omit: positions where either raw input is non-finite are skipped (zero-weighted) instead, and the count of skipped pairs is additionally returned.

Parameters

first1	The begin iterator for the first sequence
last1	The end iterator for the first sequence
first2	The begin iterator for the second sequence
f1	A projection mapping std::iter_value_t<I> to a scalar value
f2	A projection mapping std::iter_value_t<J> to a scalar value

Returns

nan_policy::propagate: the accumulated bivariate statistics. nan_policy::omit: the accumulated bivariate statistics over finite pairs, and the count of skipped (non-finite) pairs.

Example

 float x[] = { 1., 1., 2., 6. };
 float y[] = { 2., 4., 3., 1. };
 auto stats = bivariate::accumulate<float>(std::begin(x), std::end(x),
std::begin(y)); std::cout << stats << "\n";
 // results
 count:                  4
 sum_x:                  10
 ssr_x:                  17
 mean_x:                 2.5
 variance_x:             4.25
 sample variance_x:      5.66667
 sum_y:                  10
 ssr_y:                  5
 mean_y:                 2.5
 variance_y:             1.25
 sample variance_y:      1.66667
 correlation:            -0.759257
 covariance:             -1.75
 sample covariance:      -2.33333