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clang-format
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@yannrichet
yannrichet committed Sep 9, 2022
1 parent e2d91d0 commit 8bf6ec4
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Showing 9 changed files with 154 additions and 161 deletions.
26 changes: 13 additions & 13 deletions bindings/Octave/NoiseKriging_binding.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -13,11 +13,11 @@ static NoiseKriging::Parameters makeParameters(std::optional<Params*> dict) {
if (dict) {
const Params& params = *dict.value();
return NoiseKriging::Parameters{params.get<arma::mat>("sigma2"), // should be converted as arma::vec by execution
params.get<bool>("is_sigma2_estim").value_or(true),
params.get<arma::mat>("theta"),
params.get<bool>("is_theta_estim").value_or(true),
params.get<arma::mat>("beta"), // should be converted as arma::colvec by execution
params.get<bool>("is_beta_estim").value_or(true)};
params.get<bool>("is_sigma2_estim").value_or(true),
params.get<arma::mat>("theta"),
params.get<bool>("is_theta_estim").value_or(true),
params.get<arma::mat>("beta"), // should be converted as arma::colvec by execution
params.get<bool>("is_beta_estim").value_or(true)};
} else {
return NoiseKriging::Parameters{};
}
Expand All @@ -35,14 +35,14 @@ void build(int nlhs, mxArray** plhs, int nrhs, const mxArray** prhs) {
const auto objective = input.getOptional<std::string>(7, "objective").value_or("LL");
const auto parameters = makeParameters(input.getOptionalObject<Params>(8, "parameters"));
auto km = buildObject<NoiseKriging>(input.get<arma::vec>(0, "vector"),
input.get<arma::vec>(1, "vector"),
input.get<arma::mat>(2, "matrix"),
input.get<std::string>(3, "kernel"),
regmodel,
normalize,
optim,
objective,
parameters);
input.get<arma::vec>(1, "vector"),
input.get<arma::mat>(2, "matrix"),
input.get<std::string>(3, "kernel"),
regmodel,
normalize,
optim,
objective,
parameters);
output.set(0, km, "new object reference");
}

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2 changes: 1 addition & 1 deletion bindings/Octave/mLibKriging.cpp
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
#include "Kriging_binding.hpp"
#include "LinearRegression_binding.hpp"
#include "NuggetKriging_binding.hpp"
#include "NoiseKriging_binding.hpp"
#include "NuggetKriging_binding.hpp"
#include "Params_binding.hpp"
#include "mex.h" // cf https://fr.mathworks.com/help/
#include "tools/MxException.hpp"
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80 changes: 41 additions & 39 deletions bindings/Python/src/_pylibkriging/NoiseKriging_binding.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -13,62 +13,62 @@
PyNoiseKriging::PyNoiseKriging(const std::string& kernel) : m_internal{new NoiseKriging{kernel}} {}

PyNoiseKriging::PyNoiseKriging(const py::array_t<double>& y,
const py::array_t<double>& noise,
const py::array_t<double>& X,
const std::string& covType,
const Trend::RegressionModel& regmodel,
bool normalize,
const std::string& optim,
const std::string& objective,
const NoiseKriging::Parameters& parameters) {
const py::array_t<double>& noise,
const py::array_t<double>& X,
const std::string& covType,
const Trend::RegressionModel& regmodel,
bool normalize,
const std::string& optim,
const std::string& objective,
const NoiseKriging::Parameters& parameters) {
arma::colvec mat_y = carma::arr_to_col_view<double>(y);
arma::colvec mat_noise = carma::arr_to_col_view<double>(noise);
arma::mat mat_X = carma::arr_to_mat_view<double>(X);
m_internal
= std::make_unique<NoiseKriging>(mat_y,mat_noise, mat_X, covType, regmodel, normalize, optim, objective, parameters);
m_internal = std::make_unique<NoiseKriging>(
mat_y, mat_noise, mat_X, covType, regmodel, normalize, optim, objective, parameters);
}

PyNoiseKriging::PyNoiseKriging(const py::array_t<double>& y,
const py::array_t<double>& noise,
const py::array_t<double>& X,
const std::string& covType,
const Trend::RegressionModel& regmodel,
bool normalize,
const std::string& optim,
const std::string& objective,
const py::dict& dict) {
const py::array_t<double>& noise,
const py::array_t<double>& X,
const std::string& covType,
const Trend::RegressionModel& regmodel,
bool normalize,
const std::string& optim,
const std::string& objective,
const py::dict& dict) {
arma::colvec mat_y = carma::arr_to_col_view<double>(y);
arma::colvec mat_noise = carma::arr_to_col_view<double>(noise);
arma::mat mat_X = carma::arr_to_mat_view<double>(X);
NoiseKriging::Parameters parameters{get_entry<arma::vec>(dict, "sigma2"),
get_entry<bool>(dict, "is_sigma2_estim").value_or(true),
get_entry<arma::mat>(dict, "theta"),
get_entry<bool>(dict, "is_theta_estim").value_or(true),
get_entry<arma::colvec>(dict, "beta"),
get_entry<bool>(dict, "is_beta_estim").value_or(true)};
m_internal
= std::make_unique<NoiseKriging>(mat_y, mat_noise, mat_X, covType, regmodel, normalize, optim, objective, parameters);
get_entry<bool>(dict, "is_sigma2_estim").value_or(true),
get_entry<arma::mat>(dict, "theta"),
get_entry<bool>(dict, "is_theta_estim").value_or(true),
get_entry<arma::colvec>(dict, "beta"),
get_entry<bool>(dict, "is_beta_estim").value_or(true)};
m_internal = std::make_unique<NoiseKriging>(
mat_y, mat_noise, mat_X, covType, regmodel, normalize, optim, objective, parameters);
}

PyNoiseKriging::~PyNoiseKriging() {}

void PyNoiseKriging::fit(const py::array_t<double>& y,
const py::array_t<double>& noise,
const py::array_t<double>& X,
const Trend::RegressionModel& regmodel,
bool normalize,
const std::string& optim,
const std::string& objective,
const py::dict& dict) {
const py::array_t<double>& noise,
const py::array_t<double>& X,
const Trend::RegressionModel& regmodel,
bool normalize,
const std::string& optim,
const std::string& objective,
const py::dict& dict) {
arma::mat mat_y = carma::arr_to_col_view<double>(y);
arma::mat mat_noise = carma::arr_to_col_view<double>(noise);
arma::mat mat_X = carma::arr_to_mat_view<double>(X);
NoiseKriging::Parameters parameters{get_entry<arma::vec>(dict, "sigma2"),
get_entry<bool>(dict, "is_sigma2_estim").value_or(true),
get_entry<arma::mat>(dict, "theta"),
get_entry<bool>(dict, "is_theta_estim").value_or(true),
get_entry<arma::colvec>(dict, "beta"),
get_entry<bool>(dict, "is_beta_estim").value_or(true)};
get_entry<bool>(dict, "is_sigma2_estim").value_or(true),
get_entry<arma::mat>(dict, "theta"),
get_entry<bool>(dict, "is_theta_estim").value_or(true),
get_entry<arma::colvec>(dict, "beta"),
get_entry<bool>(dict, "is_beta_estim").value_or(true)};
m_internal->fit(mat_y, mat_noise, mat_X, regmodel, normalize, optim, objective, parameters);
}

Expand All @@ -90,7 +90,9 @@ py::array_t<double> PyNoiseKriging::simulate(const int nsim, const int seed, con
return carma::mat_to_arr(result, true);
}

void PyNoiseKriging::update(const py::array_t<double>& newy, const py::array_t<double>& newnoise, const py::array_t<double>& newX) {
void PyNoiseKriging::update(const py::array_t<double>& newy,
const py::array_t<double>& newnoise,
const py::array_t<double>& newX) {
arma::mat mat_y = carma::arr_to_col<double>(newy);
arma::mat mat_noise = carma::arr_to_col<double>(newnoise);
arma::mat mat_X = carma::arr_to_mat<double>(newX);
Expand All @@ -102,7 +104,7 @@ std::string PyNoiseKriging::summary() const {
}

std::tuple<double, py::array_t<double>> PyNoiseKriging::logLikelihoodFun(const py::array_t<double>& theta_sigma2,
const bool want_grad) {
const bool want_grad) {
arma::vec vec_theta_sigma2 = carma::arr_to_col<double>(theta_sigma2);
auto [llo, grad] = m_internal->logLikelihoodFun(vec_theta_sigma2, want_grad);
return {llo, carma::col_to_arr(grad)};
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32 changes: 16 additions & 16 deletions bindings/Python/src/_pylibkriging/NoiseKriging_binding.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -16,23 +16,23 @@ class PyNoiseKriging {
public:
PyNoiseKriging(const std::string& kernel);
PyNoiseKriging(const py::array_t<double>& y,
const py::array_t<double>& noise,
const py::array_t<double>& X,
const std::string& covType,
const Trend::RegressionModel& regmodel,
bool normalize,
const std::string& optim,
const std::string& objective,
const NoiseKriging::Parameters& parameters);
const py::array_t<double>& noise,
const py::array_t<double>& X,
const std::string& covType,
const Trend::RegressionModel& regmodel,
bool normalize,
const std::string& optim,
const std::string& objective,
const NoiseKriging::Parameters& parameters);
PyNoiseKriging(const py::array_t<double>& y,
const py::array_t<double>& noise,
const py::array_t<double>& X,
const std::string& covType,
const Trend::RegressionModel& regmodel,
bool normalize,
const std::string& optim,
const std::string& objective,
const py::dict& dict);
const py::array_t<double>& noise,
const py::array_t<double>& X,
const std::string& covType,
const Trend::RegressionModel& regmodel,
bool normalize,
const std::string& optim,
const std::string& objective,
const py::dict& dict);
~PyNoiseKriging();

void fit(const py::array_t<double>& y,
Expand Down
2 changes: 1 addition & 1 deletion bindings/Python/src/_pylibkriging/pylibkriging.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -13,8 +13,8 @@

#include "Kriging_binding.hpp"
#include "LinearRegression_binding.hpp"
#include "NuggetKriging_binding.hpp"
#include "NoiseKriging_binding.hpp"
#include "NuggetKriging_binding.hpp"
#include "RandomGenerator.hpp"

// To compare string at compile time (before latest C++)
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16 changes: 8 additions & 8 deletions bindings/R/rlibkriging/src/noisekriging_binding.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -16,14 +16,14 @@

// [[Rcpp::export]]
Rcpp::List new_NoiseKriging(arma::vec y,
arma::vec noise,
arma::mat X,
std::string kernel,
std::string regmodel = "constant",
bool normalize = false,
std::string optim = "BFGS",
std::string objective = "LL",
Rcpp::Nullable<Rcpp::List> parameters = R_NilValue) {
arma::vec noise,
arma::mat X,
std::string kernel,
std::string regmodel = "constant",
bool normalize = false,
std::string optim = "BFGS",
std::string objective = "LL",
Rcpp::Nullable<Rcpp::List> parameters = R_NilValue) {
NoiseKriging* ok = new NoiseKriging(kernel);

Rcpp::List _parameters;
Expand Down

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