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Updated GTX_matrix_factorisation to be more consistency with the rest of the codebase #654

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This commit is contained in:
Christophe Riccio 2017-07-07 09:34:34 +04:30
parent 1ce38b4fad
commit 64cfbc0451
2 changed files with 24 additions and 14 deletions
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11
glm/gtx/matrix_factorisation.hpp
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@ -29,26 +29,27 @@ Suggestions:
- Implement other types of matrix factorisation, such as: QL and LQ, L(D)U, eigendecompositions, etc...
*/
namespace glm{
namespace glm
{
/// @addtogroup gtx_matrix_factorisation
/// @{
/// Flips the matrix rows up and down.
/// From GLM_GTX_matrix_factorisation extension.
template <length_t C, length_t R, typename T, precision P, template<length_t, length_t, typename, precision> class matType>
GLM_FUNC_DECL matType<C, R, T, P> flipud(const matType<C, R, T, P>& in);
GLM_FUNC_DECL matType<C, R, T, P> flipud(matType<C, R, T, P> const& in);
/// Flips the matrix columns right and left.
/// From GLM_GTX_matrix_factorisation extension.
template <length_t C, length_t R, typename T, precision P, template<length_t, length_t, typename, precision> class matType>
GLM_FUNC_DECL matType<C, R, T, P> fliplr(const matType<C, R, T, P>& in);
GLM_FUNC_DECL matType<C, R, T, P> fliplr(matType<C, R, T, P> const& in);
/// Performs QR factorisation of a matrix.
/// Returns 2 matrices, q and r, such that the columns of q are orthonormal and span the same subspace than those of the input matrix, r is an upper triangular matrix, and q*r=in.
/// Given an n-by-m input matrix, q has dimensions min(n,m)-by-m, and r has dimensions n-by-min(n,m).
/// From GLM_GTX_matrix_factorisation extension.
template <length_t C, length_t R, typename T, precision P, template<length_t, length_t, typename, precision> class matType>
GLM_FUNC_DECL void qr_decompose(matType<(C < R ? C : R), R, T, P>& q, matType<C, (C < R ? C : R), T, P>& r, const matType<C, R, T, P>& in);
GLM_FUNC_DECL void qr_decompose(matType<C, R, T, P> const& in, matType<(C < R ? C : R), R, T, P>& q, matType<C, (C < R ? C : R), T, P>& r);
/// Performs RQ factorisation of a matrix.
/// Returns 2 matrices, r and q, such that r is an upper triangular matrix, the rows of q are orthonormal and span the same subspace than those of the input matrix, and r*q=in.
@ -56,7 +57,7 @@ namespace glm{
/// Given an n-by-m input matrix, r has dimensions min(n,m)-by-m, and q has dimensions n-by-min(n,m).
/// From GLM_GTX_matrix_factorisation extension.
template <length_t C, length_t R, typename T, precision P, template<length_t, length_t, typename, precision> class matType>
GLM_FUNC_DECL void rq_decompose(matType<(C < R ? C : R), R, T, P>& r, matType<C, (C < R ? C : R), T, P>& q, const matType<C, R, T, P>& in);
GLM_FUNC_DECL void rq_decompose(matType<C, R, T, P> const& in, matType<(C < R ? C : R), R, T, P>& r, matType<C, (C < R ? C : R), T, P>& q);
/// @}
}

27
glm/gtx/matrix_factorisation.inl
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@ -1,9 +1,11 @@
/// @ref gtx_matrix_factorisation
/// @file glm/gtx/matrix_factorisation.inl
namespace glm {
namespace glm
{
template <length_t C, length_t R, typename T, precision P, template<length_t, length_t, typename, precision> class matType>
GLM_FUNC_QUALIFIER matType<C, R, T, P> flipud(const matType<C, R, T, P>& in) {
GLM_FUNC_QUALIFIER matType<C, R, T, P> flipud(matType<C, R, T, P> const& in)
{
matType<R, C, T, P> tin = transpose(in);
tin = fliplr(tin);
matType<C, R, T, P> out = transpose(tin);
@ -12,9 +14,11 @@ namespace glm {
}
template <length_t C, length_t R, typename T, precision P, template<length_t, length_t, typename, precision> class matType>
GLM_FUNC_QUALIFIER matType<C, R, T, P> fliplr(const matType<C, R, T, P>& in) {
GLM_FUNC_QUALIFIER matType<C, R, T, P> fliplr(matType<C, R, T, P> const& in)
{
matType<C, R, T, P> out;
for (length_t i = 0; i < C; i++) {
for (length_t i = 0; i < C; i++)
{
out[i] = in[(C - i) - 1];
}
@ -22,21 +26,24 @@ namespace glm {
}
template <length_t C, length_t R, typename T, precision P, template<length_t, length_t, typename, precision> class matType>
GLM_FUNC_QUALIFIER void qr_decompose(matType<(C < R ? C : R), R, T, P>& q, matType<C, (C < R ? C : R), T, P>& r, const matType<C, R, T, P>& in) {
GLM_FUNC_QUALIFIER void qr_decompose(matType<C, R, T, P> const& in, matType<(C < R ? C : R), R, T, P>& q, matType<C, (C < R ? C : R), T, P>& r)
{
// Uses modified Gram-Schmidt method
// Source: https://en.wikipedia.org/wiki/Gram–Schmidt_process
// And https://en.wikipedia.org/wiki/QR_decomposition
//For all the linearly independs columns of the input...
// (there can be no more linearly independents columns than there are rows.)
for (length_t i = 0; i < (C < R ? C : R); i++) {
for (length_t i = 0; i < (C < R ? C : R); i++)
{
//Copy in Q the input's i-th column.
q[i] = in[i];
//j = [0,i[
// Make that column orthogonal to all the previous ones by substracting to it the non-orthogonal projection of all the previous columns.
// Also: Fill the zero elements of R
for (length_t j = 0; j < i; j++) {
for (length_t j = 0; j < i; j++)
{
q[i] -= dot(q[i], q[j])*q[j];
r[j][i] = 0;
}
@ -46,14 +53,16 @@ namespace glm {
//j = [i,C[
//Finally, compute the corresponding coefficients of R by computing the projection of the resulting column on the other columns of the input.
for (length_t j = i; j < C; j++) {
for (length_t j = i; j < C; j++)
{
r[j][i] = dot(in[j], q[i]);
}
}
}
template <length_t C, length_t R, typename T, precision P, template<length_t, length_t, typename, precision> class matType>
GLM_FUNC_QUALIFIER void rq_decompose(matType<(C < R ? C : R), R, T, P>& r, matType<C, (C < R ? C : R), T, P>& q, const matType<C, R, T, P>& in) {
GLM_FUNC_QUALIFIER void rq_decompose(matType<C, R, T, P> const& in, matType<(C < R ? C : R), R, T, P>& r, matType<C, (C < R ? C : R), T, P>& q)
{
// From https://en.wikipedia.org/wiki/QR_decomposition:
// 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.
// QR decomposition is Gram–Schmidt orthogonalization of columns of A, started from the first column.