LSST Applications 27.0.0,g0265f82a02+469cd937ee,g02d81e74bb+21ad69e7e1,g1470d8bcf6+cbe83ee85a,g2079a07aa2+e67c6346a6,g212a7c68fe+04a9158687,g2305ad1205+94392ce272,g295015adf3+81dd352a9d,g2bbee38e9b+469cd937ee,g337abbeb29+469cd937ee,g3939d97d7f+72a9f7b576,g487adcacf7+71499e7cba,g50ff169b8f+5929b3527e,g52b1c1532d+a6fc98d2e7,g591dd9f2cf+df404f777f,g5a732f18d5+be83d3ecdb,g64a986408d+21ad69e7e1,g858d7b2824+21ad69e7e1,g8a8a8dda67+a6fc98d2e7,g99cad8db69+f62e5b0af5,g9ddcbc5298+d4bad12328,ga1e77700b3+9c366c4306,ga8c6da7877+71e4819109,gb0e22166c9+25ba2f69a1,gb6a65358fc+469cd937ee,gbb8dafda3b+69d3c0e320,gc07e1c2157+a98bf949bb,gc120e1dc64+615ec43309,gc28159a63d+469cd937ee,gcf0d15dbbd+72a9f7b576,gdaeeff99f8+a38ce5ea23,ge6526c86ff+3a7c1ac5f1,ge79ae78c31+469cd937ee,gee10cc3b42+a6fc98d2e7,gf1cff7945b+21ad69e7e1,gfbcc870c63+9a11dc8c8f
LSST Data Management Base Package
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_leastSquares.cc
Go to the documentation of this file.
1/*
2 * LSST Data Management System
3 * Copyright 2008-2016 AURA/LSST.
4 *
5 * This product includes software developed by the
6 * LSST Project (http://www.lsst.org/).
7 *
8 * This program is free software: you can redistribute it and/or modify
9 * it under the terms of the GNU General Public License as published by
10 * the Free Software Foundation, either version 3 of the License, or
11 * (at your option) any later version.
12 *
13 * This program is distributed in the hope that it will be useful,
14 * but WITHOUT ANY WARRANTY; without even the implied warranty of
15 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
16 * GNU General Public License for more details.
17 *
18 * You should have received a copy of the LSST License Statement and
19 * the GNU General Public License along with this program. If not,
20 * see <https://www.lsstcorp.org/LegalNotices/>.
21 */
22
23#include <pybind11/pybind11.h>
24#include <lsst/utils/python.h>
25
26#include "ndarray/pybind11.h"
27
29
30namespace py = pybind11;
31using namespace pybind11::literals;
32
33using namespace lsst::afw::math;
34namespace lsst {
35namespace afw {
36namespace math {
37namespace {
38template <typename T1, typename T2, int C1, int C2>
39void declareLeastSquares(lsst::utils::python::WrapperCollection &wrappers) {
40 auto clsLeastSquares = wrappers.wrapType(
41 py::class_<LeastSquares>(wrappers.module, "LeastSquares"), [](auto &mod, auto &cls) {
42 cls.def_static(
43 "fromDesignMatrix",
44 (LeastSquares(*)(ndarray::Array<T1, 2, C1> const &, ndarray::Array<T2, 1, C2> const &,
45 LeastSquares::Factorization)) &
46 LeastSquares::fromDesignMatrix<T1, T2, C1, C2>,
47 "design"_a, "data"_a, "factorization"_a = LeastSquares::NORMAL_EIGENSYSTEM);
48 cls.def_static(
49 "fromNormalEquations",
50 (LeastSquares(*)(ndarray::Array<T1, 2, C1> const &, ndarray::Array<T2, 1, C2> const &,
51 LeastSquares::Factorization)) &
52 LeastSquares::fromNormalEquations<T1, T2, C1, C2>,
53 "fisher"_a, "rhs"_a, "factorization"_a = LeastSquares::NORMAL_EIGENSYSTEM);
54 cls.def("getRank", &LeastSquares::getRank);
55 cls.def("setDesignMatrix", (void (LeastSquares::*)(ndarray::Array<T1, 2, C1> const &,
56 ndarray::Array<T2, 1, C2> const &)) &
57 LeastSquares::setDesignMatrix<T1, T2, C1, C2>);
58 cls.def("getDimension", &LeastSquares::getDimension);
59 cls.def("setNormalEquations", (void (LeastSquares::*)(ndarray::Array<T1, 2, C1> const &,
60 ndarray::Array<T2, 1, C2> const &)) &
61 LeastSquares::setNormalEquations<T1, T2, C1, C2>);
62 cls.def("getSolution", &LeastSquares::getSolution);
63 cls.def("getFisherMatrix", &LeastSquares::getFisherMatrix);
64 cls.def("getCovariance", &LeastSquares::getCovariance);
65 cls.def("getFactorization", &LeastSquares::getFactorization);
66 cls.def("getDiagnostic", &LeastSquares::getDiagnostic);
67 cls.def("getThreshold", &LeastSquares::getThreshold);
68 cls.def("setThreshold", &LeastSquares::setThreshold);
69 });
70 wrappers.wrapType(py::enum_<LeastSquares::Factorization>(clsLeastSquares, "Factorization"),
71 [](auto &mod, auto &enm) {
72 enm.value("NORMAL_EIGENSYSTEM", LeastSquares::Factorization::NORMAL_EIGENSYSTEM);
73 enm.value("NORMAL_CHOLESKY", LeastSquares::Factorization::NORMAL_CHOLESKY);
74 enm.value("DIRECT_SVD", LeastSquares::Factorization::DIRECT_SVD);
75 enm.export_values();
76 });
77};
78} // namespace
79
80void wrapLeastSquares(lsst::utils::python::WrapperCollection &wrappers) {
81 declareLeastSquares<double, double, 0, 0>(wrappers);
82}
83} // namespace math
84} // namespace afw
85} // namespace lsst
@ NORMAL_EIGENSYSTEM
Use the normal equations with a symmetric Eigensystem decomposition.
@ NORMAL_CHOLESKY
Use the normal equations with a Cholesky decomposition.
@ DIRECT_SVD
Use a thin singular value decomposition of the design matrix.
void wrapLeastSquares(lsst::utils::python::WrapperCollection &wrappers)