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C++ Numerical Computing Runtime
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lhs.hpp
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// toolboxes/stats/include/nk/stats/sampling/lhs.hpp
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//
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// Latin Hypercube sampling.
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#pragma once
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#include <cstddef>
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#include <memory_resource>
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#include <nk/value/value.hpp>
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namespace
nk {
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namespace
ops {
class
RngContext
; } }
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namespace
nk::stats {
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enum class
LhsCriterion
{
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None
,
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Maximin
,
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Correlation
,
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};
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Value
lhsdesign
(
::nk::ops::RngContext
&rng, std::size_t n, std::size_t p,
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bool
smooth,
LhsCriterion
criterion, std::size_t iterations,
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std::pmr::memory_resource *mr =
nullptr
);
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Value
lhsdesign
(
::nk::ops::RngContext
&rng, std::size_t n, std::size_t p,
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std::pmr::memory_resource *mr =
nullptr
);
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Value
lhsnorm
(
::nk::ops::RngContext
&rng,
const
Value
&mu,
const
Value
&Sigma, std::size_t n,
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std::pmr::memory_resource *mr =
nullptr
);
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}
// namespace nk::stats
nk::Value
Core MATLAB-compatible multidimensional array and scalar value type.
Definition
value.hpp:61
nk::ops::RngContext
Definition
rng_context.hpp:29
nk::stats::lhsnorm
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)).
nk::stats::lhsdesign
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])).
nk::stats::LhsCriterion
LhsCriterion
Criterion for lhsdesign optimization.
Definition
lhs.hpp:22
nk::stats::LhsCriterion::Maximin
@ Maximin
Maximize the minimum pairwise Euclidean distance.
Definition
lhs.hpp:24
nk::stats::LhsCriterion::Correlation
@ Correlation
Minimize the maximum off-diagonal column correlation.
Definition
lhs.hpp:25
nk::stats::LhsCriterion::None
@ None
Single random LHS.
Definition
lhs.hpp:23
src
toolboxes
stats
include
nk
stats
sampling
lhs.hpp
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