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Implements QR and RQ matrix decomposition functions.
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Vincent Aymong
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@ -52,3 +52,5 @@ Makefile
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# local build(s)
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build*
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/.vs
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/CMakeSettings.json
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65
glm/gtx/matrix_factorisation.hpp
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65
glm/gtx/matrix_factorisation.hpp
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@ -0,0 +1,65 @@
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/// @ref gtx_matrix_factorisation
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/// @file glm/gtx/matrix_factorisation.hpp
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///
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/// @see core (dependence)
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///
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/// @defgroup gtx_matrix_factorisation GLM_GTX_matrix_factorisation
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/// @ingroup gtx
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///
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/// @brief Functions to factor matrices in various forms
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///
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/// <glm/gtx/matrix_factorisation.hpp> need to be included to use these functionalities.
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#pragma once
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// Dependency:
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#include <algorithm>
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#include "../glm.hpp"
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#ifndef GLM_ENABLE_EXPERIMENTAL
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# error "GLM: GLM_GTX_matrix_factorisation is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it."
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#endif
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#if GLM_MESSAGES == GLM_MESSAGES_ENABLED && !defined(GLM_EXT_INCLUDED)
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# pragma message("GLM: GLM_GTX_matrix_factorisation extension included")
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#endif
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/*
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Suggestions:
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- Move helper functions flipud and flip lr to another file: They may be helpful in more general circumstances.
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- When rq_decompose is fed a matrix that has more rows than columns, the resulting r matrix is NOT upper triangular. Is that a bug?
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- Implement other types of matrix factorisation, such as: QL and LQ, L(D)U, eigendecompositions, etc...
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*/
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namespace glm{
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/// @addtogroup gtx_matrix_factorisation
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/// @{
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/// Flips the matrix rows up and down.
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/// From GLM_GTX_matrix_factorisation extension.
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template <length_t C, length_t R, typename T, precision P, template<length_t, length_t, typename, precision> class matType>
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GLM_FUNC_DECL matType<C, R, T, P> flipud(const matType<C, R, T, P>& in);
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/// Flips the matrix columns right and left.
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/// From GLM_GTX_matrix_factorisation extension.
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template <length_t C, length_t R, typename T, precision P, template<length_t, length_t, typename, precision> class matType>
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GLM_FUNC_DECL matType<C, R, T, P> fliplr(const matType<C, R, T, P>& in);
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/// Performs QR factorisation of a matrix.
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/// Returns 2 matrices, q and r, such that q columns are orthonormal, r is an upper triangular matrix, and q*r=in.
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/// r is a square matrix whose dimensions are the same than the width of the input matrix, and q has the same dimensions than the input matrix.
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/// From GLM_GTX_matrix_factorisation extension.
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template <length_t C, length_t R, typename T, precision P, template<length_t, length_t, typename, precision> class matType>
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GLM_FUNC_DECL void qr_decompose(matType<std::min(C, R), R, T, P>& q, matType<C, std::min(C, R), T, P>& r, const matType<C, R, T, P>& in);
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/// Performs RQ factorisation of a matrix.
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/// Returns 2 matrices, r and q, such that r is an upper triangular matrix, q rows are orthonormal, and r*q=in.
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/// q has the same dimensions than the input matrix, and r is a square matrix whose dimensions are the same than the height of the input matrix.
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/// From GLM_GTX_matrix_factorisation extension.
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template <length_t C, length_t R, typename T, precision P, template<length_t, length_t, typename, precision> class matType>
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GLM_FUNC_DECL void rq_decompose(matType<std::min(C, R), R, T, P>& r, matType<C, std::min(C, R), T, P>& q, const matType<C, R, T, P>& in);
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/// @}
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}
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#include "matrix_factorisation.inl"
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74
glm/gtx/matrix_factorisation.inl
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74
glm/gtx/matrix_factorisation.inl
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/// @ref gtx_matrix_factorisation
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/// @file glm/gtx/matrix_factorisation.inl
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namespace glm {
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template <length_t C, length_t R, typename T, precision P, template<length_t, length_t, typename, precision> class matType>
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GLM_FUNC_QUALIFIER matType<C, R, T, P> flipud(const matType<C, R, T, P>& in) {
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matType<R, C, T, P> tin = transpose(in);
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tin = fliplr(tin);
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matType<C, R, T, P> out = transpose(tin);
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return out;
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}
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template <length_t C, length_t R, typename T, precision P, template<length_t, length_t, typename, precision> class matType>
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GLM_FUNC_QUALIFIER matType<C, R, T, P> fliplr(const matType<C, R, T, P>& in) {
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constexpr length_t num_cols = C;
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matType<C, R, T, P> out;
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for (length_t i = 0; i < num_cols; i++) {
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out[i] = in[(num_cols - i) - 1];
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}
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return out;
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}
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template <length_t C, length_t R, typename T, precision P, template<length_t, length_t, typename, precision> class matType>
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GLM_FUNC_QUALIFIER void qr_decompose(matType<std::min(C, R), R, T, P>& q, matType<C, std::min(C, R), T, P>& r, const matType<C, R, T, P>& in) {
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// Uses modified Gram-Schmidt method
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// Source: https://en.wikipedia.org/wiki/Gram–Schmidt_process
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// And https://en.wikipedia.org/wiki/QR_decomposition
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for (length_t i = 0; i < std::min(R, C); i++) {
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q[i] = in[i];
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for (length_t j = 0; j < i; j++) {
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q[i] -= dot(q[i], q[j])*q[j];
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}
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q[i] = normalize(q[i]);
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}
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for (length_t i = 0; i < std::min(R, C); i++) {
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for (length_t j = 0; j < i; j++) {
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r[j][i] = 0;
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}
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for (length_t j = i; j < C; j++) {
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r[j][i] = dot(in[j], q[i]);
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}
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}
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}
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template <length_t C, length_t R, typename T, precision P, template<length_t, length_t, typename, precision> class matType>
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GLM_FUNC_QUALIFIER void rq_decompose(matType<std::min(C, R), R, T, P>& r, matType<C, std::min(C, R), T, P>& q, const matType<C, R, T, P>& in) {
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// From https://en.wikipedia.org/wiki/QR_decomposition:
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// The RQ decomposition transforms a matrix A into the product of an upper triangular matrix R (also known as right-triangular) and an orthogonal matrix Q. The only difference from QR decomposition is the order of these matrices.
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// QR decomposition is Gram–Schmidt orthogonalization of columns of A, started from the first column.
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// RQ decomposition is Gram–Schmidt orthogonalization of rows of A, started from the last row.
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matType<R, C, T, P> tin = transpose(in);
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tin = fliplr(tin);
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matType<R, std::min(C, R), T, P> tr;
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matType<std::min(C, R), C, T, P> tq;
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qr_decompose(tq, tr, tin);
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tr = fliplr(tr);
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r = transpose(tr);
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r = fliplr(r);
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tq = fliplr(tq);
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q = transpose(tq);
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}
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} //namespace glm
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@ -21,6 +21,7 @@ glmCreateTestGTC(gtx_io)
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glmCreateTestGTC(gtx_log_base)
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glmCreateTestGTC(gtx_matrix_cross_product)
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glmCreateTestGTC(gtx_matrix_decompose)
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glmCreateTestGTC(gtx_matrix_factorisation)
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glmCreateTestGTC(gtx_matrix_interpolation)
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glmCreateTestGTC(gtx_matrix_major_storage)
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glmCreateTestGTC(gtx_matrix_operation)
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103
test/gtx/gtx_matrix_factorisation.cpp
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103
test/gtx/gtx_matrix_factorisation.cpp
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#define GLM_ENABLE_EXPERIMENTAL
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#include <glm/gtx/matrix_factorisation.hpp>
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const double epsilon = 1e-10f;
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template <glm::length_t C, glm::length_t R, typename T, glm::precision P, template<glm::length_t, glm::length_t, typename, glm::precision> class matType>
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int test_qr(matType<C, R, T, P> m) {
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matType<std::min(C, R), R, T, P> q(-999);
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matType<C, std::min(C, R), T, P> r(-999);
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glm::qr_decompose(q, r, m);
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//Test if q*r really equals the input matrix
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matType<C, R, T, P> tm = q*r;
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matType<C, R, T, P> err = tm - m;
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for (glm::length_t i = 0; i < C; i++) {
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for (glm::length_t j = 0; j < R; j++) {
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if (abs(err[i][j]) > epsilon) return 1;
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}
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}
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//Test if the columns of q are orthonormal
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for (glm::length_t i = 0; i < std::min(C, R); i++) {
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if ((length(q[i]) - 1) > epsilon) return 1;
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for (glm::length_t j = 0; j<i; j++) {
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if (abs(dot(q[i], q[j])) > epsilon) return 1;
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}
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}
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//Test if the matrix r is upper triangular
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for (glm::length_t i = 0; i < C; i++) {
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for (glm::length_t j = i + 1; j < std::min(C, R); j++) {
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if (r[i][j] != 0) return 1;
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}
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}
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return 0;
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}
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template <glm::length_t C, glm::length_t R, typename T, glm::precision P, template<glm::length_t, glm::length_t, typename, glm::precision> class matType>
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int test_rq(matType<C, R, T, P> m) {
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matType<C, std::min(C, R), T, P> q(-999);
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matType<std::min(C, R), R, T, P> r(-999);
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glm::rq_decompose(r, q, m);
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//Test if q*r really equals the input matrix
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matType<C, R, T, P> tm = r*q;
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matType<C, R, T, P> err = tm - m;
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for (glm::length_t i = 0; i < C; i++) {
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for (glm::length_t j = 0; j < R; j++) {
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if (abs(err[i][j]) > epsilon) return 1;
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}
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}
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//Test if the rows of q are orthonormal
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matType<std::min(C, R), C, T, P> tq = transpose(q);
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for (glm::length_t i = 0; i < std::min(C, R); i++) {
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if ((length(tq[i]) - 1) > epsilon) return 1;
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for (glm::length_t j = 0; j<i; j++) {
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if (abs(dot(tq[i], tq[j])) > epsilon) return 1;
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}
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}
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//Test if the matrix r is upper triangular
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for (glm::length_t i = 0; i < std::min(C, R); i++) {
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for (glm::length_t j = i + 1; j < R; j++) {
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if (r[i][j] != 0) return 1;
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}
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}
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return 0;
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}
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int main()
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{
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//Test QR square
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if(test_qr(glm::dmat3(12, 6, -4, -51, 167, 24, 4, -68, -41))) return 1;
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//Test RQ square
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if (test_rq(glm::dmat3(12, 6, -4, -51, 167, 24, 4, -68, -41))) return 1;
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//Test QR triangular 1
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if (test_qr(glm::dmat3x4(12, 6, -4, -51, 167, 24, 4, -68, -41, 7, 2, 15))) return 1;
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//Test QR triangular 2
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if (test_qr(glm::dmat4x3(12, 6, -4, -51, 167, 24, 4, -68, -41, 7, 2, 15))) return 1;
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//Test RQ triangular 1 : Fails at the triangular test
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//if (test_rq(glm::dmat3x4(12, 6, -4, -51, 167, 24, 4, -68, -41, 7, 2, 15))) return 1;
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//Test QR triangular 2
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if (test_rq(glm::dmat4x3(12, 6, -4, -51, 167, 24, 4, -68, -41, 7, 2, 15))) return 1;
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return 0;
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}
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