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Test cleanups.
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@ -536,21 +536,21 @@ void MLPPTests::test_mlp(bool ui) {
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MLPPMLP model_new(input_set, output_set, 2);
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model_new.gradient_descent(0.1, 10000, ui);
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String res = model_new.model_set_test(input_set)->to_string();
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res += "\nACCURACY (gradient_descent): " + String::num(100 * model_new.score()) + "%";
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res += "\nACCURACY (gradient_descent): " + String::num(100.0 * model_new.score()) + "%";
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PLOG_MSG(res);
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MLPPMLP model_new2(input_set, output_set, 2);
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model_new2.sgd(0.01, 10000, ui);
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res = model_new2.model_set_test(input_set)->to_string();
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res += "\nACCURACY (sgd): " + String::num(100 * model_new2.score()) + "%";
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res += "\nACCURACY (sgd): " + String::num(100.0 * model_new2.score()) + "%";
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PLOG_MSG(res);
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MLPPMLP model_new3(input_set, output_set, 2);
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model_new3.mbgd(0.01, 10000, 2, ui);
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res = model_new3.model_set_test(input_set)->to_string();
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res += "\nACCURACY (mbgd): " + String::num(100 * model_new3.score()) + "%";
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res += "\nACCURACY (mbgd): " + String::num(100.0 * model_new3.score()) + "%";
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PLOG_MSG(res);
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}
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@ -119,21 +119,6 @@ void MLPPTestsOld::test_support_vector_classification(bool ui) {
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}
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void MLPPTestsOld::test_mlp(bool ui) {
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MLPPLinAlgOld alg;
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// MLP
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std::vector<std::vector<real_t>> inputSet = {
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{ 0, 0 },
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{ 1, 1 },
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{ 0, 1 },
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{ 1, 0 }
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};
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std::vector<real_t> outputSet = { 0, 1, 1, 0 };
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MLPPMLPOld model(inputSet, outputSet, 2);
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model.gradientDescent(0.1, 10000, ui);
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alg.printVector(model.modelSetTest(inputSet));
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std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
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}
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void MLPPTestsOld::test_soft_max_network(bool ui) {
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MLPPLinAlgOld alg;
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