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[4d6936]: doc / tutorial / src / mog.cpp Maximize Restore History

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144``` ```#include #include #include #include #include using std::cout; using std::endl; using std::fixed; using std::setprecision; using namespace itpp; int main() { bool print_progress = false; // // first, let's generate some synthetic data int N = 100000; // number of vectors int D = 3; // number of dimensions int K = 5; // number of Gaussians Array X(N); for(int n=0;n mu(K); mu(0) = "-6, -4, -2"; mu(1) = "-4, -2, 0"; mu(2) = "-2, 0, 2"; mu(3) = " 0, +2, +4"; mu(4) = "+2, +4, +6"; // the diagonal variances Array var(K); var(0) = "0.1, 0.2, 0.3"; var(1) = "0.2, 0.3, 0.1"; var(2) = "0.3, 0.1, 0.2"; var(3) = "0.1, 0.2, 0.3"; var(4) = "0.2, 0.3, 0.1"; cout << fixed << setprecision(3); cout << "user configured means and variances:" << endl; cout << "mu = " << mu << endl; cout << "var = " << var << endl; // randomise the order of Gaussians "generating" the vectors I_Uniform_RNG rnd_uniform(0, K-1); ivec gaus_id = rnd_uniform(N); ivec gaus_count(K); gaus_count = 0; Array mu_test(K); for(int k=0;k var_test(K); for(int k=0;k