leastSquares.cc

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/*
 * LSST Data Management System
 * Copyright 2008-2016  AURA/LSST.
 *
 * This product includes software developed by the
 * LSST Project (http://www.lsst.org/).
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the LSST License Statement and
 * the GNU General Public License along with this program.  If not,
 * see <https://www.lsstcorp.org/LegalNotices/>.
 */
 
#include <pybind11/pybind11.h>
//#include <pybind11/operators.h>
//#include <pybind11/stl.h>
 
#include "ndarray/pybind11.h"
//#include "ndarray/eigen.h"
 
#include "lsst/afw/math/LeastSquares.h"
 
namespace py = pybind11;
using namespace pybind11::literals;
 
using namespace lsst::afw::math;
 
template <typename T1, typename T2, int C1, int C2>
void declareLeastSquares(py::module &mod) {
    py::class_<LeastSquares> cls(mod, "LeastSquares");
    py::enum_<LeastSquares::Factorization>(cls, "Factorization")
            .value("NORMAL_EIGENSYSTEM", LeastSquares::Factorization::NORMAL_EIGENSYSTEM)
            .value("NORMAL_CHOLESKY", LeastSquares::Factorization::NORMAL_CHOLESKY)
            .value("DIRECT_SVD", LeastSquares::Factorization::DIRECT_SVD)
            .export_values();
    cls.def_static("fromDesignMatrix",
                   (LeastSquares(*)(ndarray::Array<T1, 2, C1> const &, ndarray::Array<T2, 1, C2> const &,
                                    LeastSquares::Factorization)) &
                           LeastSquares::fromDesignMatrix<T1, T2, C1, C2>,
                   "design"_a, "data"_a, "factorization"_a = LeastSquares::NORMAL_EIGENSYSTEM);
    cls.def_static("fromNormalEquations",
                   (LeastSquares(*)(ndarray::Array<T1, 2, C1> const &, ndarray::Array<T2, 1, C2> const &,
                                    LeastSquares::Factorization)) &
                           LeastSquares::fromNormalEquations<T1, T2, C1, C2>,
                   "fisher"_a, "rhs"_a, "factorization"_a = LeastSquares::NORMAL_EIGENSYSTEM);
    cls.def("getRank", &LeastSquares::getRank);
    cls.def("setDesignMatrix",
            (void (LeastSquares::*)(ndarray::Array<T1, 2, C1> const &, ndarray::Array<T2, 1, C2> const &)) &
                    LeastSquares::setDesignMatrix<T1, T2, C1, C2>);
    cls.def("getDimension", &LeastSquares::getDimension);
    cls.def("setNormalEquations",
            (void (LeastSquares::*)(ndarray::Array<T1, 2, C1> const &, ndarray::Array<T2, 1, C2> const &)) &
                    LeastSquares::setNormalEquations<T1, T2, C1, C2>);
    cls.def("getSolution", &LeastSquares::getSolution);
    cls.def("getFisherMatrix", &LeastSquares::getFisherMatrix);
    cls.def("getCovariance", &LeastSquares::getCovariance);
    cls.def("getFactorization", &LeastSquares::getFactorization);
    cls.def("getDiagnostic", &LeastSquares::getDiagnostic);
    cls.def("getThreshold", &LeastSquares::getThreshold);
    cls.def("setThreshold", &LeastSquares::setThreshold);
};
 
PYBIND11_MODULE(leastSquares, mod) {
    declareLeastSquares<double, double, 0, 0>(mod);
}