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734 lines
22 KiB
C++
734 lines
22 KiB
C++
#define GLM_ENABLE_EXPERIMENTAL
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#include <glm/glm.hpp>
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#include <glm/gtx/pca.hpp>
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#include <glm/gtc/epsilon.hpp>
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#include <glm/gtx/string_cast.hpp>
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#include <cstdio>
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#include <vector>
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#if GLM_HAS_CXX11_STL == 1
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#include <random>
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#endif
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#if GLM_COMPILER & GLM_COMPILER_CLANG
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# pragma clang diagnostic push
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# pragma clang diagnostic ignored "-Wsign-conversion"
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#endif
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template<typename T>
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T myEpsilon();
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template<>
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GLM_INLINE GLM_CONSTEXPR float myEpsilon<float>() { return 0.00001f; }
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template<>
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GLM_INLINE GLM_CONSTEXPR double myEpsilon<double>() { return 0.000001; }
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template<glm::length_t D, typename T, glm::qualifier Q>
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static bool vectorEpsilonEqual(glm::vec<D, T, Q> const& a, glm::vec<D, T, Q> const& b, T epsilon)
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{
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for (int c = 0; c < D; ++c)
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if (!glm::epsilonEqual(a[c], b[c], epsilon))
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{
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fprintf(stderr, "failing vectorEpsilonEqual: [%d] %lf != %lf (~%lf)\n",
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c,
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static_cast<double>(a[c]),
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static_cast<double>(b[c]),
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static_cast<double>(epsilon)
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);
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return false;
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}
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return true;
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}
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template<glm::length_t D, typename T, glm::qualifier Q>
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static bool matrixEpsilonEqual(glm::mat<D, D, T, Q> const& a, glm::mat<D, D, T, Q> const& b, T epsilon)
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{
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for (int c = 0; c < D; ++c)
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for (int r = 0; r < D; ++r)
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if (!glm::epsilonEqual(a[c][r], b[c][r], epsilon))
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{
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fprintf(stderr, "failing vectorEpsilonEqual: [%d][%d] %lf != %lf (~%lf)\n",
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c, r,
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static_cast<double>(a[c][r]),
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static_cast<double>(b[c][r]),
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static_cast<double>(epsilon)
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);
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return false;
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}
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return true;
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}
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template<typename T>
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GLM_INLINE bool sameSign(T const& a, T const& b)
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{
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return ((a >= 0) && (b >= 0)) || ((a < 0) && (b < 0));
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}
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template<typename T>
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static T failReport(T line)
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{
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fprintf(stderr, "Failed in line %d\n", static_cast<int>(line));
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return line;
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}
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// Test data: 1AGA 'agarose double helix'
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// https://www.rcsb.org/structure/1aga
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// The fourth coordinate is randomized
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namespace agarose
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{
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// Fills `outTestData` with hard-coded atom positions from 1AGA
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// The fourth coordinate is randomized
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template<typename vec>
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static void fillTestData(std::vector<vec>& outTestData)
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{
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// x,y,z coordinates copied from RCSB PDB file of 1AGA
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// w coordinate randomized with standard normal distribution
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static const double _1aga[] = {
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3.219, -0.637, 19.462, 2.286,
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4.519, 0.024, 18.980, -0.828,
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4.163, 1.425, 18.481, -0.810,
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3.190, 1.341, 17.330, -0.170,
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1.962, 0.991, 18.165, 0.816,
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2.093, 1.952, 19.331, 0.276,
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5.119, -0.701, 17.908, -0.490,
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3.517, 2.147, 19.514, -0.207,
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2.970, 2.609, 16.719, 0.552,
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2.107, -0.398, 18.564, 0.403,
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2.847, 2.618, 15.335, 0.315,
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1.457, 3.124, 14.979, 0.683,
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1.316, 3.291, 13.473, 0.446,
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2.447, 4.155, 12.931, 1.324,
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3.795, 3.614, 13.394, 0.112,
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4.956, 4.494, 12.982, 0.253,
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0.483, 2.217, 15.479, 1.316,
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0.021, 3.962, 13.166, 1.522,
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2.311, 5.497, 13.395, 0.248,
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3.830, 3.522, 14.827, 0.591,
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5.150, 4.461, 11.576, 0.635,
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-1.057, 3.106, 13.132, 0.191,
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-2.280, 3.902, 12.650, 1.135,
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-3.316, 2.893, 12.151, 0.794,
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-2.756, 2.092, 11.000, 0.720,
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-1.839, 1.204, 11.835, -1.172,
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-2.737, 0.837, 13.001, -0.313,
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-1.952, 4.784, 11.578, 2.082,
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-3.617, 1.972, 13.184, 0.653,
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-3.744, 1.267, 10.389, -0.413,
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-0.709, 2.024, 12.234, -1.747,
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-3.690, 1.156, 9.005, -1.275,
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-3.434, -0.300, 8.649, 0.441,
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-3.508, -0.506, 7.143, 0.237,
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-4.822, 0.042, 6.601, -2.856,
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-5.027, 1.480, 7.064, 0.985,
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-6.370, 2.045, 6.652, 0.915,
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-2.162, -0.690, 9.149, 1.100,
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-3.442, -1.963, 6.836, -0.081,
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-5.916, -0.747, 7.065, -2.345,
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-4.965, 1.556, 8.497, 0.504,
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-6.439, 2.230, 5.246, 1.451,
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-2.161, -2.469, 6.802, -1.171,
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-2.239, -3.925, 6.320, -1.434,
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-0.847, -4.318, 5.821, 0.098,
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-0.434, -3.433, 4.670, -1.446,
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-0.123, -2.195, 5.505, 0.182,
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0.644, -2.789, 6.671, 0.865,
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-3.167, -4.083, 5.248, -0.098,
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0.101, -4.119, 6.854, -0.001,
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0.775, -3.876, 4.059, 1.061,
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-1.398, -1.625, 5.904, 0.230,
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0.844, -3.774, 2.675, 1.313,
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1.977, -2.824, 2.319, -0.112,
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2.192, -2.785, 0.813, -0.981,
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2.375, -4.197, 0.271, -0.355,
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1.232, -5.093, 0.734, 0.632,
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1.414, -6.539, 0.322, 0.576,
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1.678, -1.527, 2.819, -1.187,
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3.421, -1.999, 0.496, -1.770,
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3.605, -4.750, 0.735, 1.099,
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1.135, -5.078, 2.167, 0.854,
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1.289, -6.691, -1.084, -0.487,
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-1.057, 3.106, 22.602, -1.297,
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-2.280, 3.902, 22.120, 0.376,
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-3.316, 2.893, 21.621, 0.932,
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-2.756, 2.092, 20.470, 1.680,
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-1.839, 1.204, 21.305, 0.615,
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-2.737, 0.837, 22.471, 0.899,
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-1.952, 4.784, 21.048, -0.521,
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-3.617, 1.972, 22.654, 0.133,
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-3.744, 1.267, 19.859, 0.081,
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-0.709, 2.024, 21.704, 1.420,
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-3.690, 1.156, 18.475, -0.850,
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-3.434, -0.300, 18.119, -0.249,
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-3.508, -0.506, 16.613, 1.434,
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-4.822, 0.042, 16.071, -2.466,
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-5.027, 1.480, 16.534, -1.045,
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-6.370, 2.045, 16.122, 1.707,
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-2.162, -0.690, 18.619, -2.023,
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-3.442, -1.963, 16.336, -0.304,
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-5.916, -0.747, 16.535, 0.979,
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-4.965, 1.556, 17.967, -1.165,
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-6.439, 2.230, 14.716, 0.929,
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-2.161, -2.469, 16.302, -0.234,
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-2.239, -3.925, 15.820, -0.228,
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-0.847, -4.318, 15.321, 1.844,
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-0.434, -3.433, 14.170, 1.132,
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-0.123, -2.195, 15.005, 0.211,
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0.644, -2.789, 16.171, -0.632,
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-3.167, -4.083, 14.748, -0.519,
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0.101, -4.119, 16.354, 0.173,
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0.775, -3.876, 13.559, 1.243,
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-1.398, -1.625, 15.404, -0.187,
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0.844, -3.774, 12.175, -1.332,
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1.977, -2.824, 11.819, -1.616,
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2.192, -2.785, 10.313, 1.320,
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2.375, -4.197, 9.771, 0.237,
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1.232, -5.093, 10.234, 0.851,
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1.414, -6.539, 9.822, 1.816,
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1.678, -1.527, 12.319, -1.657,
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3.421, -1.999, 10.036, 1.559,
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3.605, -4.750, 10.235, 0.831,
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1.135, -5.078, 11.667, 0.060,
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1.289, -6.691, 8.416, 1.066,
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3.219, -0.637, 10.002, 2.111,
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4.519, 0.024, 9.520, -0.874,
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4.163, 1.425, 9.021, -1.012,
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3.190, 1.341, 7.870, -0.250,
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1.962, 0.991, 8.705, -1.359,
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2.093, 1.952, 9.871, -0.126,
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5.119, -0.701, 8.448, 0.995,
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3.517, 2.147, 10.054, 0.941,
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2.970, 2.609, 7.259, -0.562,
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2.107, -0.398, 9.104, -0.038,
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2.847, 2.618, 5.875, 0.398,
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1.457, 3.124, 5.519, 0.481,
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1.316, 3.291, 4.013, -0.187,
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2.447, 4.155, 3.471, -0.429,
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3.795, 3.614, 3.934, -0.432,
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4.956, 4.494, 3.522, -0.788,
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0.483, 2.217, 6.019, -0.923,
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0.021, 3.962, 3.636, -0.316,
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2.311, 5.497, 3.935, -1.917,
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3.830, 3.522, 5.367, -0.302,
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5.150, 4.461, 2.116, -1.615
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};
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static const glm::length_t _1agaSize = sizeof(_1aga) / (4 * sizeof(double));
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outTestData.resize(_1agaSize);
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for(glm::length_t i = 0; i < _1agaSize; ++i)
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for(glm::length_t d = 0; d < static_cast<glm::length_t>(vec::length()); ++d)
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outTestData[i][d] = static_cast<typename vec::value_type>(_1aga[i * 4 + d]);
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}
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// All reference values computed separately using symbolic precision
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// https://github.com/sgrottel/exp-pca-precision
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// This applies to all functions named: `agarose::expected*()`
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GLM_INLINE glm::dmat4 const& expectedCovarData()
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{
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static const glm::dmat4 covar4x4d(
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9.62434068027210898322, -0.00006657369614512471, -4.29321376568405099761, 0.01879374187452758846,
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-0.00006657369614512471, 9.62443937868480681175, 5.35113872637944076871, -0.11569259145880574080,
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-4.29321376568405099761, 5.35113872637944076871, 35.62848549634668415820, 0.90874239254220201545,
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0.01879374187452758846, -0.11569259145880574080, 0.90874239254220201545, 1.09705971856890904803
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);
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return covar4x4d;
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}
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template<glm::length_t D>
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GLM_INLINE glm::vec<D, double, glm::defaultp> const& expectedEigenvalues();
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template<>
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GLM_INLINE glm::dvec2 const& expectedEigenvalues<2>()
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{
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static const glm::dvec2 evals2(
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9.62447289926297399961763301774251330057894539467032275382255,
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9.62430715969394210015560961264297422776572580714373620309355
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);
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return evals2;
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}
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template<>
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GLM_INLINE glm::dvec3 const& expectedEigenvalues<3>()
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{
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static const glm::dvec3 evals3(
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37.3274494274683425233695502581182052836449738530676689472257,
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9.62431434161498823505729817436585077939509766554969096873168,
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7.92550178622027216422369326567668971675332732240052872097887
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);
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return evals3;
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}
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template<>
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GLM_INLINE glm::dvec4 const& expectedEigenvalues<4>()
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{
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static const glm::dvec4 evals4(
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37.3477389918792213596879452204499702406947817221901007885630,
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9.62470688921105696017807313860277172063600080413412567999700,
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7.94017075281634999342344275928070533134615133171969063657713,
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1.06170863996588365446060186982477896078741484440002343404155
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);
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return evals4;
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}
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template<glm::length_t D>
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GLM_INLINE glm::mat<D, D, double, glm::defaultp> const& expectedEigenvectors();
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template<>
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GLM_INLINE glm::dmat2 const& expectedEigenvectors<2>()
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{
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static const glm::dmat2 evecs2(
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glm::dvec2(
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-0.503510847492551904906870957742619139443409162857537237123308,
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1
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),
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glm::dvec2(
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1.98605453086051402895741763848787613048533838388005162794043,
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1
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)
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);
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return evecs2;
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}
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template<>
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GLM_INLINE glm::dmat3 const& expectedEigenvectors<3>()
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{
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static const glm::dmat3 evecs3(
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glm::dvec3(
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-0.154972738414395866005286433008304444294405085038689821864654,
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0.193161285869815165989799191097521722568079378840201629578695,
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1
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),
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glm::dvec3(
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-158565.112775416943154745839952575022429933119522746586149868,
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-127221.506282351944358932458687410410814983610301927832439675,
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1
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),
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glm::dvec3(
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2.52702248596556806145700361724323960543858113426446460406536,
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-3.14959802931313870497377546974185300816008580801457419079412,
|
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1
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|
)
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);
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return evecs3;
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}
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template<>
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GLM_INLINE glm::dmat4 const& expectedEigenvectors<4>()
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{
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static const glm::dmat4 evecs4(
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|
glm::dvec4(
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|
-6.35322390281037045217295803597357821705371650876122113027264,
|
|
7.91546394153385394517767054617789939529794642646629201212056,
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|
41.0301543819240679808549819457450130787045236815736490549663,
|
|
1
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|
),
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|
glm::dvec4(
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|
-114.622418941087829756565311692197154422302604224781253861297,
|
|
-92.2070185807065289900871215218752013659402949497379896153118,
|
|
0.0155846091025912430932734548933329458404665760587569100867246,
|
|
1
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|
),
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|
glm::dvec4(
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|
13.1771887761559019483954743159026938257325190511642952175789,
|
|
-16.3688257459634877666638419310116970616615816436949741766895,
|
|
5.17386502341472097227408249233288958059579189051394773143190,
|
|
1
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|
),
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|
glm::dvec4(
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|
-0.0192777078948229800494895064532553117703859768210647632969276,
|
|
0.0348034950916108873629241563077465542944938906271231198634442,
|
|
-0.0340715609308469289267379681032545422644143611273049912226126,
|
|
1
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|
)
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);
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return evecs4;
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|
}
|
|
|
|
} // namespace agarose
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|
|
|
// Compute center of gravity
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|
template<typename vec>
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|
static vec computeCenter(const std::vector<vec>& testData)
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|
{
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|
double c[4];
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|
std::fill(c, c + vec::length(), 0.0);
|
|
|
|
typename std::vector<vec>::const_iterator e = testData.end();
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|
for(typename std::vector<vec>::const_iterator i = testData.begin(); i != e; ++i)
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for(glm::length_t d = 0; d < static_cast<glm::length_t>(vec::length()); ++d)
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c[d] += static_cast<double>((*i)[d]);
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vec cVec(0);
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for(glm::length_t d = 0; d < static_cast<glm::length_t>(vec::length()); ++d)
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cVec[d] = static_cast<typename vec::value_type>(c[d] / static_cast<double>(testData.size()));
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return cVec;
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|
}
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|
|
|
// Test sorting of Eigenvalue&Eigenvector lists. Use exhaustive search.
|
|
template<glm::length_t D, typename T, glm::qualifier Q>
|
|
static int testEigenvalueSort()
|
|
{
|
|
// Test input data: four arbitrary values
|
|
static const glm::vec<D, T, Q> refVal(
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|
glm::vec<4, T, Q>(
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10, 8, 6, 4
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|
)
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|
);
|
|
// Test input data: four arbitrary vectors, which can be matched to the above values
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|
static const glm::mat<D, D, T, Q> refVec(
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glm::mat<4, 4, T, Q>(
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10, 20, 5, 40,
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8, 16, 4, 32,
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6, 12, 3, 24,
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|
4, 8, 2, 16
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|
)
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);
|
|
// Permutations of test input data for exhaustive check, based on `D` (1 <= D <= 4)
|
|
static const int permutationCount[] = {
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|
0,
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1,
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2,
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6,
|
|
24
|
|
};
|
|
// The permutations t perform, based on `D` (1 <= D <= 4)
|
|
static const glm::ivec4 permutation[] = {
|
|
glm::ivec4(0, 1, 2, 3),
|
|
glm::ivec4(1, 0, 2, 3), // last for D = 2
|
|
glm::ivec4(0, 2, 1, 3),
|
|
glm::ivec4(1, 2, 0, 3),
|
|
glm::ivec4(2, 0, 1, 3),
|
|
glm::ivec4(2, 1, 0, 3), // last for D = 3
|
|
glm::ivec4(0, 1, 3, 2),
|
|
glm::ivec4(1, 0, 3, 2),
|
|
glm::ivec4(0, 2, 3, 1),
|
|
glm::ivec4(1, 2, 3, 0),
|
|
glm::ivec4(2, 0, 3, 1),
|
|
glm::ivec4(2, 1, 3, 0),
|
|
glm::ivec4(0, 3, 1, 2),
|
|
glm::ivec4(1, 3, 0, 2),
|
|
glm::ivec4(0, 3, 2, 1),
|
|
glm::ivec4(1, 3, 2, 0),
|
|
glm::ivec4(2, 3, 0, 1),
|
|
glm::ivec4(2, 3, 1, 0),
|
|
glm::ivec4(3, 0, 1, 2),
|
|
glm::ivec4(3, 1, 0, 2),
|
|
glm::ivec4(3, 0, 2, 1),
|
|
glm::ivec4(3, 1, 2, 0),
|
|
glm::ivec4(3, 2, 0, 1),
|
|
glm::ivec4(3, 2, 1, 0) // last for D = 4
|
|
};
|
|
|
|
// initial sanity check
|
|
if(!vectorEpsilonEqual(refVal, refVal, myEpsilon<T>()))
|
|
return failReport(__LINE__);
|
|
if(!matrixEpsilonEqual(refVec, refVec, myEpsilon<T>()))
|
|
return failReport(__LINE__);
|
|
|
|
// Exhaustive search through all permutations
|
|
for(int p = 0; p < permutationCount[D]; ++p)
|
|
{
|
|
glm::vec<D, T, Q> testVal;
|
|
glm::mat<D, D, T, Q> testVec;
|
|
for(int i = 0; i < D; ++i)
|
|
{
|
|
testVal[i] = refVal[permutation[p][i]];
|
|
testVec[i] = refVec[permutation[p][i]];
|
|
}
|
|
|
|
glm::sortEigenvalues(testVal, testVec);
|
|
|
|
if (!vectorEpsilonEqual(testVal, refVal, myEpsilon<T>()))
|
|
return failReport(__LINE__);
|
|
if (!matrixEpsilonEqual(testVec, refVec, myEpsilon<T>()))
|
|
return failReport(__LINE__);
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
// Test covariance matrix creation functions
|
|
template<glm::length_t D, typename T, glm::qualifier Q>
|
|
static int testCovar(
|
|
#if GLM_HAS_CXX11_STL == 1
|
|
glm::length_t dataSize, unsigned int randomEngineSeed
|
|
#else // GLM_HAS_CXX11_STL == 1
|
|
glm::length_t, unsigned int
|
|
#endif // GLM_HAS_CXX11_STL == 1
|
|
)
|
|
{
|
|
typedef glm::vec<D, T, Q> vec;
|
|
typedef glm::mat<D, D, T, Q> mat;
|
|
|
|
// #1: test expected result with fixed data set
|
|
std::vector<vec> testData;
|
|
agarose::fillTestData(testData);
|
|
|
|
// compute center of gravity
|
|
vec center = computeCenter(testData);
|
|
|
|
mat covarMat = glm::computeCovarianceMatrix(testData.data(), testData.size(), center);
|
|
if(!matrixEpsilonEqual(covarMat, mat(agarose::expectedCovarData()), myEpsilon<T>()))
|
|
{
|
|
fprintf(stderr, "Reconstructed covarMat:\n%s\n", glm::to_string(covarMat).c_str());
|
|
return failReport(__LINE__);
|
|
}
|
|
|
|
// #2: test function variant consistency with random data
|
|
#if GLM_HAS_CXX11_STL == 1
|
|
std::default_random_engine rndEng(randomEngineSeed);
|
|
std::normal_distribution<T> normalDist;
|
|
testData.resize(dataSize);
|
|
// some common offset of all data
|
|
T offset[D];
|
|
for(glm::length_t d = 0; d < D; ++d)
|
|
offset[d] = normalDist(rndEng);
|
|
// init data
|
|
for(glm::length_t i = 0; i < dataSize; ++i)
|
|
for(glm::length_t d = 0; d < D; ++d)
|
|
testData[i][d] = offset[d] + normalDist(rndEng);
|
|
center = computeCenter(testData);
|
|
|
|
std::vector<vec> centeredTestData;
|
|
centeredTestData.reserve(testData.size());
|
|
typename std::vector<vec>::const_iterator e = testData.end();
|
|
for(typename std::vector<vec>::const_iterator i = testData.begin(); i != e; ++i)
|
|
centeredTestData.push_back((*i) - center);
|
|
|
|
mat c1 = glm::computeCovarianceMatrix(centeredTestData.data(), centeredTestData.size());
|
|
mat c2 = glm::computeCovarianceMatrix<D, T, Q>(centeredTestData.begin(), centeredTestData.end());
|
|
mat c3 = glm::computeCovarianceMatrix(testData.data(), testData.size(), center);
|
|
mat c4 = glm::computeCovarianceMatrix<D, T, Q>(testData.rbegin(), testData.rend(), center);
|
|
|
|
if(!matrixEpsilonEqual(c1, c2, myEpsilon<T>()))
|
|
return failReport(__LINE__);
|
|
if(!matrixEpsilonEqual(c1, c3, myEpsilon<T>()))
|
|
return failReport(__LINE__);
|
|
if(!matrixEpsilonEqual(c1, c4, myEpsilon<T>()))
|
|
return failReport(__LINE__);
|
|
#endif // GLM_HAS_CXX11_STL == 1
|
|
return 0;
|
|
}
|
|
|
|
// Computes eigenvalues and eigenvectors from well-known covariance matrix
|
|
template<glm::length_t D, typename T, glm::qualifier Q>
|
|
static int testEigenvectors(T epsilon)
|
|
{
|
|
typedef glm::vec<D, T, Q> vec;
|
|
typedef glm::mat<D, D, T, Q> mat;
|
|
|
|
// test expected result with fixed data set
|
|
std::vector<vec> testData;
|
|
mat covarMat(agarose::expectedCovarData());
|
|
|
|
vec eigenvalues;
|
|
mat eigenvectors;
|
|
unsigned int c = glm::findEigenvaluesSymReal(covarMat, eigenvalues, eigenvectors);
|
|
if(c != D)
|
|
return failReport(__LINE__);
|
|
glm::sortEigenvalues(eigenvalues, eigenvectors);
|
|
|
|
if (!vectorEpsilonEqual(eigenvalues, vec(agarose::expectedEigenvalues<D>()), epsilon))
|
|
return failReport(__LINE__);
|
|
|
|
for (int i = 0; i < D; ++i)
|
|
{
|
|
vec act = glm::normalize(eigenvectors[i]);
|
|
vec exp = glm::normalize(agarose::expectedEigenvectors<D>()[i]);
|
|
if (!sameSign(act[0], exp[0])) exp = -exp;
|
|
if (!vectorEpsilonEqual(act, exp, epsilon))
|
|
return failReport(__LINE__);
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
// A simple small smoke test:
|
|
// - a uniformly sampled block
|
|
// - reconstruct main axes
|
|
// - check order of eigenvalues equals order of extends of block in direction of main axes
|
|
static int smokeTest()
|
|
{
|
|
using glm::vec3;
|
|
using glm::mat3;
|
|
std::vector<vec3> pts;
|
|
pts.reserve(11 * 15 * 7);
|
|
|
|
for(int x = -5; x <= 5; ++x)
|
|
for(int y = -7; y <= 7; ++y)
|
|
for(int z = -3; z <= 3; ++z)
|
|
pts.push_back(vec3(x, y, z));
|
|
|
|
mat3 covar = glm::computeCovarianceMatrix(pts.data(), pts.size());
|
|
mat3 eVec;
|
|
vec3 eVal;
|
|
unsigned int eCnt = glm::findEigenvaluesSymReal(covar, eVal, eVec);
|
|
if(eCnt != 3u)
|
|
return failReport(__LINE__);
|
|
|
|
// sort eVec by descending eVal
|
|
if(eVal[0] < eVal[1])
|
|
{
|
|
std::swap(eVal[0], eVal[1]);
|
|
std::swap(eVec[0], eVec[1]);
|
|
}
|
|
if(eVal[0] < eVal[2])
|
|
{
|
|
std::swap(eVal[0], eVal[2]);
|
|
std::swap(eVec[0], eVec[2]);
|
|
}
|
|
if(eVal[1] < eVal[2])
|
|
{
|
|
std::swap(eVal[1], eVal[2]);
|
|
std::swap(eVec[1], eVec[2]);
|
|
}
|
|
|
|
if(!vectorEpsilonEqual(glm::abs(eVec[0]), vec3(0, 1, 0), myEpsilon<float>()))
|
|
return failReport(__LINE__);
|
|
if(!vectorEpsilonEqual(glm::abs(eVec[1]), vec3(1, 0, 0), myEpsilon<float>()))
|
|
return failReport(__LINE__);
|
|
if(!vectorEpsilonEqual(glm::abs(eVec[2]), vec3(0, 0, 1), myEpsilon<float>()))
|
|
return failReport(__LINE__);
|
|
|
|
return 0;
|
|
}
|
|
|
|
#if GLM_HAS_CXX11_STL == 1
|
|
static int rndTest(unsigned int randomEngineSeed)
|
|
{
|
|
std::default_random_engine rndEng(randomEngineSeed);
|
|
std::normal_distribution<double> normalDist;
|
|
|
|
// construct orthonormal system
|
|
glm::dvec3 x(normalDist(rndEng), normalDist(rndEng), normalDist(rndEng));
|
|
double l = glm::length(x);
|
|
while(l < myEpsilon<double>())
|
|
x = glm::dvec3(normalDist(rndEng), normalDist(rndEng), normalDist(rndEng));
|
|
x = glm::normalize(x);
|
|
glm::dvec3 y(normalDist(rndEng), normalDist(rndEng), normalDist(rndEng));
|
|
l = glm::length(y);
|
|
while(l < myEpsilon<double>())
|
|
y = glm::dvec3(normalDist(rndEng), normalDist(rndEng), normalDist(rndEng));
|
|
while(glm::abs(glm::dot(x, y)) < myEpsilon<double>())
|
|
{
|
|
y = glm::dvec3(normalDist(rndEng), normalDist(rndEng), normalDist(rndEng));
|
|
while(l < myEpsilon<double>())
|
|
y = glm::dvec3(normalDist(rndEng), normalDist(rndEng), normalDist(rndEng));
|
|
}
|
|
y = glm::normalize(y);
|
|
glm::dvec3 z = glm::normalize(glm::cross(x, y));
|
|
y = glm::normalize(glm::cross(z, x));
|
|
|
|
// generate input point data
|
|
std::vector<glm::dvec3> ptData;
|
|
static const int pattern[] = {
|
|
8, 0, 0,
|
|
4, 1, 2,
|
|
0, 2, 0,
|
|
0, 0, 4
|
|
};
|
|
glm::dvec3 offset(normalDist(rndEng), normalDist(rndEng), normalDist(rndEng));
|
|
for(int p = 0; p < 4; ++p)
|
|
for(int xs = 1; xs >= -1; xs -= 2)
|
|
for(int ys = 1; ys >= -1; ys -= 2)
|
|
for(int zs = 1; zs >= -1; zs -= 2)
|
|
ptData.push_back(
|
|
offset
|
|
+ x * static_cast<double>(pattern[p * 3 + 0] * xs)
|
|
+ y * static_cast<double>(pattern[p * 3 + 1] * ys)
|
|
+ z * static_cast<double>(pattern[p * 3 + 2] * zs));
|
|
|
|
// perform PCA:
|
|
glm::dvec3 center = computeCenter(ptData);
|
|
glm::dmat3 covarMat = glm::computeCovarianceMatrix(ptData.data(), ptData.size(), center);
|
|
glm::dvec3 evals;
|
|
glm::dmat3 evecs;
|
|
unsigned int evcnt = glm::findEigenvaluesSymReal(covarMat, evals, evecs);
|
|
if(evcnt != 3u)
|
|
return failReport(__LINE__);
|
|
glm::sortEigenvalues(evals, evecs);
|
|
|
|
if (!sameSign(evecs[0][0], x[0])) evecs[0] = -evecs[0];
|
|
if(!vectorEpsilonEqual(x, evecs[0], myEpsilon<double>()))
|
|
return failReport(__LINE__);
|
|
if (!sameSign(evecs[2][0], y[0])) evecs[2] = -evecs[2];
|
|
if (!vectorEpsilonEqual(y, evecs[2], myEpsilon<double>()))
|
|
return failReport(__LINE__);
|
|
if (!sameSign(evecs[1][0], z[0])) evecs[1] = -evecs[1];
|
|
if (!vectorEpsilonEqual(z, evecs[1], myEpsilon<double>()))
|
|
return failReport(__LINE__);
|
|
|
|
return 0;
|
|
}
|
|
#endif // GLM_HAS_CXX11_STL == 1
|
|
|
|
int main()
|
|
{
|
|
int error(0);
|
|
|
|
// A small smoke test to fail early with most problems
|
|
if(smokeTest())
|
|
return failReport(__LINE__);
|
|
|
|
// test sorting utility.
|
|
if(testEigenvalueSort<2, float, glm::defaultp>() != 0)
|
|
error = failReport(__LINE__);
|
|
if(testEigenvalueSort<2, double, glm::defaultp>() != 0)
|
|
error = failReport(__LINE__);
|
|
if(testEigenvalueSort<3, float, glm::defaultp>() != 0)
|
|
error = failReport(__LINE__);
|
|
if(testEigenvalueSort<3, double, glm::defaultp>() != 0)
|
|
error = failReport(__LINE__);
|
|
if(testEigenvalueSort<4, float, glm::defaultp>() != 0)
|
|
error = failReport(__LINE__);
|
|
if(testEigenvalueSort<4, double, glm::defaultp>() != 0)
|
|
error = failReport(__LINE__);
|
|
if (error != 0)
|
|
return error;
|
|
|
|
// Note: the random engine uses a fixed seed to create consistent and reproducible test data
|
|
// test covariance matrix computation from different data sources
|
|
if(testCovar<2, float, glm::defaultp>(100, 12345) != 0)
|
|
error = failReport(__LINE__);
|
|
if(testCovar<2, double, glm::defaultp>(100, 42) != 0)
|
|
error = failReport(__LINE__);
|
|
if(testCovar<3, float, glm::defaultp>(100, 2021) != 0)
|
|
error = failReport(__LINE__);
|
|
if(testCovar<3, double, glm::defaultp>(100, 815) != 0)
|
|
error = failReport(__LINE__);
|
|
if(testCovar<4, float, glm::defaultp>(100, 3141) != 0)
|
|
error = failReport(__LINE__);
|
|
if(testCovar<4, double, glm::defaultp>(100, 174) != 0)
|
|
error = failReport(__LINE__);
|
|
if (error != 0)
|
|
return error;
|
|
|
|
// test PCA eigen vector reconstruction
|
|
// Expected epsilon precision evaluated separately:
|
|
// https://github.com/sgrottel/exp-pca-precision
|
|
if(testEigenvectors<2, float, glm::defaultp>(0.002f) != 0)
|
|
error = failReport(__LINE__);
|
|
if(testEigenvectors<2, double, glm::defaultp>(0.00000000001) != 0)
|
|
error = failReport(__LINE__);
|
|
if(testEigenvectors<3, float, glm::defaultp>(0.00001f) != 0)
|
|
error = failReport(__LINE__);
|
|
if(testEigenvectors<3, double, glm::defaultp>(0.0000000001) != 0)
|
|
error = failReport(__LINE__);
|
|
if(testEigenvectors<4, float, glm::defaultp>(0.0001f) != 0)
|
|
error = failReport(__LINE__);
|
|
if(testEigenvectors<4, double, glm::defaultp>(0.0000001) != 0)
|
|
error = failReport(__LINE__);
|
|
if(error != 0)
|
|
return error;
|
|
|
|
// Final tests with randomized data
|
|
#if GLM_HAS_CXX11_STL == 1
|
|
if(rndTest(12345) != 0)
|
|
error = failReport(__LINE__);
|
|
if(rndTest(42) != 0)
|
|
error = failReport(__LINE__);
|
|
if (error != 0)
|
|
return error;
|
|
#endif // GLM_HAS_CXX11_STL == 1
|
|
|
|
return error;
|
|
}
|
|
|
|
#if GLM_COMPILER & GLM_COMPILER_CLANG
|
|
# pragma clang diagnostic pop
|
|
#endif
|