nk 1.0.0
C++ Numerical Computing Runtime
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lhs.hpp
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1
3// toolboxes/stats/include/nk/stats/sampling/lhs.hpp
4//
5// Latin Hypercube sampling.
6
7#pragma once
8
9#include <cstddef>
10#include <memory_resource>
11#include <nk/value/value.hpp>
12
13namespace nk {
14
17 namespace ops { class RngContext; } }
18
19namespace nk::stats {
20
27
58Value lhsdesign(::nk::ops::RngContext &rng, std::size_t n, std::size_t p,
59 bool smooth, LhsCriterion criterion, std::size_t iterations,
60 std::pmr::memory_resource *mr = nullptr);
61
64Value lhsdesign(::nk::ops::RngContext &rng, std::size_t n, std::size_t p,
65 std::pmr::memory_resource *mr = nullptr);
66
84Value lhsnorm(::nk::ops::RngContext &rng, const Value &mu, const Value &Sigma, std::size_t n,
85 std::pmr::memory_resource *mr = nullptr);
86
87
89} // namespace nk::stats
Core MATLAB-compatible multidimensional array and scalar value type.
Definition value.hpp:61
Value lhsnorm(::nk::ops::RngContext &rng, const Value &mu, const Value &Sigma, std::size_t n, std::pmr::memory_resource *mr=nullptr)
Latin Hypercube sample mapped to a Gaussian (Y = lhsnorm(mu, Sigma, n)).
Value lhsdesign(::nk::ops::RngContext &rng, std::size_t n, std::size_t p, bool smooth, LhsCriterion criterion, std::size_t iterations, std::pmr::memory_resource *mr=nullptr)
Latin Hypercube design (X = lhsdesign(n, p[, options])).
LhsCriterion
Criterion for lhsdesign optimization.
Definition lhs.hpp:22
@ Maximin
Maximize the minimum pairwise Euclidean distance.
Definition lhs.hpp:24
@ Correlation
Minimize the maximum off-diagonal column correlation.
Definition lhs.hpp:25
@ None
Single random LHS.
Definition lhs.hpp:23