int main(int argc, char *argv[]) { testSequence(); testUnivariateStats(); testBivariateStats(); testUtility(); testVectorAlgebra(); testMatrixDeterminant(); testMatrixInverse(); testLUFactorisation(); testLUSolver(); testGaussianElimination(); testInterpolation(); testIntegration(); testRoot(); testTimeStep(); testRSignificanceTest(); testChiSquared(); testMultipleRegression(); test_golden_fit(); testSimplex(); testSimplex2(); testSimplex3(); test_simplex_fit(); return (EXIT_SUCCESS); }
int main() { testSequence2(); return 0; testSequenceCMAES(); return 0; testSequence(); return 0; testLogs(); return 0; //Plot real target function Leph::Plot plot; for (double x=0.0;x<=1.0;x+=0.03) { for (double y=0.0;y<=1.0;y+=0.03) { plot.add(Leph::VectorLabel( "x", x, "y", y, "real", function(x, y))); } } //Initialize Gaussian Process libgp::GaussianProcess gp(2, "CovSum ( CovSEiso, CovNoise)"); //Adding noised data point to model for (size_t k=0;k<100;k++) { double x = 0.5*uniform(generator) + 0.5; double y = 0.5*uniform(generator) + 0.5; double f = function(x, y) + noise(1.0); plot.add(Leph::VectorLabel( "x", x, "y", y, "target", f)); double input[] = {x, y}; gp.add_pattern(input, f); } //Optimize hyper parameter libgp::RProp rprop; rprop.init(); rprop.maximize(&gp, 50, true); //Predict fitted model for (double x=0.0;x<=1.0;x+=0.04) { for (double y=0.0;y<=1.0;y+=0.04) { double input[] = {x, y}; plot.add(Leph::VectorLabel( "x", x, "y", y, "fitted", gp.f(input))); } } //Display plot plot.plot("x", "y", "all").render(); return 0; }
int main(int argc, char** argv) { int period; int sequence[4] = {1, 2, 0, 3}; // cf. Table 1 period = sizeof(sequence) / sizeof(sequence[0]); testSequence(period, 0, sequence); return 0; }
int main() { const int repeats = 8; const int steps = 350; const size_t kb = 1024; const size_t mb = 1024 * kb; size_t init_size = 16 * kb / sizeof(Node); size_t small_init_size = 1 * kb / sizeof(Node); size_t size = 1 * kb / sizeof(Node); Node * nodes = initNodeArray(size); for(int j = 0; j < steps; j++) { double rand_sum = 0; double seq_sum = 0; for(int i = 0; i < repeats; i++) { nodes = initNodeArray(size); seq_sum += testSequence(nodes, size); rand_sum += testRandom(nodes, size); } std::cout.setf(std::ios::fixed); std::cout.precision( 3 ); std::cout << size / sizeof(Node) << "kb :" << seq_sum * 1000 /(size * (double)repeats) << " --- " << rand_sum * 1000 /(size * (double)repeats) << std::endl; if(size >= init_size * 3) size += init_size; else size += small_init_size; } delete[] nodes; return 0; }
main(int argc, char *argv[], char *envp[]) { testSequence(); }
void SkaarhojUHB::testSequence() { testSequence(20); }
void SkaarhojBroadcastButtons::testSequence() { testSequence(20); }