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Diffstat (limited to '3rdparty/glm/source/glm/gtx/pca.hpp')
| -rw-r--r-- | 3rdparty/glm/source/glm/gtx/pca.hpp | 111 |
1 files changed, 0 insertions, 111 deletions
diff --git a/3rdparty/glm/source/glm/gtx/pca.hpp b/3rdparty/glm/source/glm/gtx/pca.hpp deleted file mode 100644 index 93da745..0000000 --- a/3rdparty/glm/source/glm/gtx/pca.hpp +++ /dev/null @@ -1,111 +0,0 @@ -/// @ref gtx_pca -/// @file glm/gtx/pca.hpp -/// -/// @see core (dependence) -/// @see ext_scalar_relational (dependence) -/// -/// @defgroup gtx_pca GLM_GTX_pca -/// @ingroup gtx -/// -/// Include <glm/gtx/pca.hpp> to use the features of this extension. -/// -/// Implements functions required for fundamental 'princple component analysis' in 2D, 3D, and 4D: -/// 1) Computing a covariance matrics from a list of _relative_ position vectors -/// 2) Compute the eigenvalues and eigenvectors of the covariance matrics -/// This is useful, e.g., to compute an object-aligned bounding box from vertices of an object. -/// https://en.wikipedia.org/wiki/Principal_component_analysis -/// -/// Example: -/// ``` -/// std::vector<glm::dvec3> ptData; -/// // ... fill ptData with some point data, e.g. vertices -/// -/// glm::dvec3 center = computeCenter(ptData); -/// -/// glm::dmat3 covarMat = glm::computeCovarianceMatrix(ptData.data(), ptData.size(), center); -/// -/// glm::dvec3 evals; -/// glm::dmat3 evecs; -/// int evcnt = glm::findEigenvaluesSymReal(covarMat, evals, evecs); -/// -/// if(evcnt != 3) -/// // ... error handling -/// -/// glm::sortEigenvalues(evals, evecs); -/// -/// // ... now evecs[0] points in the direction (symmetric) of the largest spatial distribuion within ptData -/// ``` - -#pragma once - -// Dependency: -#include "../glm.hpp" -#include "../ext/scalar_relational.hpp" - - -#if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED) -# ifndef GLM_ENABLE_EXPERIMENTAL -# pragma message("GLM: GLM_GTX_pca 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.") -# else -# pragma message("GLM: GLM_GTX_pca extension included") -# endif -#endif - -namespace glm { - /// @addtogroup gtx_pca - /// @{ - - /// Compute a covariance matrix form an array of relative coordinates `v` (e.g., relative to the center of gravity of the object) - /// @param v Points to a memory holding `n` times vectors - template<length_t D, typename T, qualifier Q> - GLM_INLINE mat<D, D, T, Q> computeCovarianceMatrix(vec<D, T, Q> const* v, size_t n); - - /// Compute a covariance matrix form an array of absolute coordinates `v` and a precomputed center of gravity `c` - /// @param v Points to a memory holding `n` times vectors - template<length_t D, typename T, qualifier Q> - GLM_INLINE mat<D, D, T, Q> computeCovarianceMatrix(vec<D, T, Q> const* v, size_t n, vec<D, T, Q> const& c); - - /// Compute a covariance matrix form a pair of iterators `b` (begin) and `e` (end) of a container with relative coordinates (e.g., relative to the center of gravity of the object) - /// Dereferencing an iterator of type I must yield a `vec<D, T, Q%gt;` - template<length_t D, typename T, qualifier Q, typename I> - GLM_FUNC_DECL mat<D, D, T, Q> computeCovarianceMatrix(I const& b, I const& e); - - /// Compute a covariance matrix form a pair of iterators `b` (begin) and `e` (end) of a container with absolute coordinates and a precomputed center of gravity `c` - /// Dereferencing an iterator of type I must yield a `vec<D, T, Q%gt;` - template<length_t D, typename T, qualifier Q, typename I> - GLM_FUNC_DECL mat<D, D, T, Q> computeCovarianceMatrix(I const& b, I const& e, vec<D, T, Q> const& c); - - /// Assuming the provided covariance matrix `covarMat` is symmetric and real-valued, this function find the `D` Eigenvalues of the matrix, and also provides the corresponding Eigenvectors. - /// Note: the data in `outEigenvalues` and `outEigenvectors` are in matching order, i.e. `outEigenvector[i]` is the Eigenvector of the Eigenvalue `outEigenvalue[i]`. - /// This is a numeric implementation to find the Eigenvalues, using 'QL decomposition` (variant of QR decomposition: https://en.wikipedia.org/wiki/QR_decomposition). - /// @param covarMat A symmetric, real-valued covariance matrix, e.g. computed from `computeCovarianceMatrix`. - /// @param outEigenvalues Vector to receive the found eigenvalues - /// @param outEigenvectors Matrix to receive the found eigenvectors corresponding to the found eigenvalues, as column vectors - /// @return The number of eigenvalues found, usually D if the precondition of the covariance matrix is met. - template<length_t D, typename T, qualifier Q> - GLM_FUNC_DECL unsigned int findEigenvaluesSymReal - ( - mat<D, D, T, Q> const& covarMat, - vec<D, T, Q>& outEigenvalues, - mat<D, D, T, Q>& outEigenvectors - ); - - /// Sorts a group of Eigenvalues&Eigenvectors, for largest Eigenvalue to smallest Eigenvalue. - /// The data in `outEigenvalues` and `outEigenvectors` are assumed to be matching order, i.e. `outEigenvector[i]` is the Eigenvector of the Eigenvalue `outEigenvalue[i]`. - template<typename T, qualifier Q> - GLM_INLINE void sortEigenvalues(vec<2, T, Q>& eigenvalues, mat<2, 2, T, Q>& eigenvectors); - - /// Sorts a group of Eigenvalues&Eigenvectors, for largest Eigenvalue to smallest Eigenvalue. - /// The data in `outEigenvalues` and `outEigenvectors` are assumed to be matching order, i.e. `outEigenvector[i]` is the Eigenvector of the Eigenvalue `outEigenvalue[i]`. - template<typename T, qualifier Q> - GLM_INLINE void sortEigenvalues(vec<3, T, Q>& eigenvalues, mat<3, 3, T, Q>& eigenvectors); - - /// Sorts a group of Eigenvalues&Eigenvectors, for largest Eigenvalue to smallest Eigenvalue. - /// The data in `outEigenvalues` and `outEigenvectors` are assumed to be matching order, i.e. `outEigenvector[i]` is the Eigenvector of the Eigenvalue `outEigenvalue[i]`. - template<typename T, qualifier Q> - GLM_INLINE void sortEigenvalues(vec<4, T, Q>& eigenvalues, mat<4, 4, T, Q>& eigenvectors); - - /// @} -}//namespace glm - -#include "pca.inl" |