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Made test a little more numerically robust.
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@ -116,18 +116,17 @@ namespace
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ovo_trainer trainer;
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typedef polynomial_kernel<sample_type> poly_kernel;
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typedef histogram_intersection_kernel<sample_type> hist_kernel;
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typedef radial_basis_kernel<sample_type> rbf_kernel;
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// make the binary trainers and set some parameters
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krr_trainer<rbf_kernel> rbf_trainer;
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svm_nu_trainer<poly_kernel> poly_trainer;
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poly_trainer.set_kernel(poly_kernel(0.1, 1, 2));
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svm_nu_trainer<hist_kernel> hist_trainer;
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rbf_trainer.set_kernel(rbf_kernel(0.1));
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trainer.set_trainer(rbf_trainer);
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trainer.set_trainer(poly_trainer, 1, 2);
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trainer.set_trainer(hist_trainer, 1, 2);
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randomize_samples(samples, labels);
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matrix<double> res = cross_validate_multiclass_trainer(trainer, samples, labels, 2);
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@ -143,8 +142,7 @@ namespace
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// test using a normalized_function with a one_vs_one_decision_function
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{
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poly_trainer.set_kernel(poly_kernel(1.1, 1, 2));
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trainer.set_trainer(poly_trainer, 1, 2);
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trainer.set_trainer(hist_trainer, 1, 2);
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vector_normalizer<sample_type> normalizer;
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normalizer.train(samples);
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for (unsigned long i = 0; i < samples.size(); ++i)
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@ -156,8 +154,7 @@ namespace
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DLIB_TEST(ndf(samples[40]) == labels[40]);
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DLIB_TEST(ndf(samples[90]) == labels[90]);
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DLIB_TEST(ndf(samples[120]) == labels[120]);
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poly_trainer.set_kernel(poly_kernel(0.1, 1, 2));
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trainer.set_trainer(poly_trainer, 1, 2);
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trainer.set_trainer(hist_trainer, 1, 2);
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print_spinner();
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}
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@ -173,7 +170,7 @@ namespace
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one_vs_one_decision_function<ovo_trainer,
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decision_function<poly_kernel>, // This is the output of the poly_trainer
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decision_function<hist_kernel>, // This is the output of the hist_trainer
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decision_function<rbf_kernel> // This is the output of the rbf_trainer
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> df2, df3;
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