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author | LaG1924 <12997935+LaG1924@users.noreply.github.com> | 2021-06-22 02:38:30 +0200 |
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committer | GitHub <noreply@github.com> | 2021-06-22 02:38:30 +0200 |
commit | ca7bcf9f7dc5eb47ccbec1049b323f0604dabadd (patch) | |
tree | 33d7d2673ba0da8a92323b07d061f655c719ae40 /external/include/glm/gtx/matrix_factorisation.inl | |
parent | Merge pull request #44 from LaG1924/fix/protocol_support (diff) | |
parent | Updated build instructions and added MacOS #49 (diff) | |
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Diffstat (limited to 'external/include/glm/gtx/matrix_factorisation.inl')
-rw-r--r-- | external/include/glm/gtx/matrix_factorisation.inl | 85 |
1 files changed, 0 insertions, 85 deletions
diff --git a/external/include/glm/gtx/matrix_factorisation.inl b/external/include/glm/gtx/matrix_factorisation.inl deleted file mode 100644 index f0d9560..0000000 --- a/external/include/glm/gtx/matrix_factorisation.inl +++ /dev/null @@ -1,85 +0,0 @@ -/// @ref gtx_matrix_factorisation -/// @file glm/gtx/matrix_factorisation.inl - -namespace glm -{ - template <length_t C, length_t R, typename T, qualifier Q> - GLM_FUNC_QUALIFIER mat<C, R, T, Q> flipud(mat<C, R, T, Q> const& in) - { - mat<R, C, T, Q> tin = transpose(in); - tin = fliplr(tin); - mat<C, R, T, Q> out = transpose(tin); - - return out; - } - - template <length_t C, length_t R, typename T, qualifier Q> - GLM_FUNC_QUALIFIER mat<C, R, T, Q> fliplr(mat<C, R, T, Q> const& in) - { - mat<C, R, T, Q> out; - for (length_t i = 0; i < C; i++) - { - out[i] = in[(C - i) - 1]; - } - - return out; - } - - template <length_t C, length_t R, typename T, qualifier Q> - GLM_FUNC_QUALIFIER void qr_decompose(mat<C, R, T, Q> const& in, mat<(C < R ? C : R), R, T, Q>& q, mat<C, (C < R ? C : R), T, Q>& 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++) - { - //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++) - { - q[i] -= dot(q[i], q[j])*q[j]; - r[j][i] = 0; - } - - //Now, Q i-th column is orthogonal to all the previous columns. Normalize it. - q[i] = normalize(q[i]); - - //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++) - { - r[j][i] = dot(in[j], q[i]); - } - } - } - - template <length_t C, length_t R, typename T, qualifier Q> - GLM_FUNC_QUALIFIER void rq_decompose(mat<C, R, T, Q> const& in, mat<(C < R ? C : R), R, T, Q>& r, mat<C, (C < R ? C : R), T, Q>& 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. - // RQ decomposition is Gram–Schmidt orthogonalization of rows of A, started from the last row. - - mat<R, C, T, Q> tin = transpose(in); - tin = fliplr(tin); - - mat<R, (C < R ? C : R), T, Q> tr; - mat<(C < R ? C : R), C, T, Q> tq; - qr_decompose(tin, tq, tr); - - tr = fliplr(tr); - r = transpose(tr); - r = fliplr(r); - - tq = fliplr(tq); - q = transpose(tq); - } -} //namespace glm |