diff --git a/test/mlpp_tests.cpp b/test/mlpp_tests.cpp index bd651f0..1ff0a9f 100644 --- a/test/mlpp_tests.cpp +++ b/test/mlpp_tests.cpp @@ -47,40 +47,6 @@ #include "../mlpp/uni_lin_reg/uni_lin_reg.h" #include "../mlpp/wgan/wgan.h" -#include "../mlpp/activation/activation_old.h" -#include "../mlpp/ann/ann_old.h" -#include "../mlpp/auto_encoder/auto_encoder_old.h" -#include "../mlpp/bernoulli_nb/bernoulli_nb_old.h" -#include "../mlpp/c_log_log_reg/c_log_log_reg_old.h" -#include "../mlpp/convolutions/convolutions_old.h" -#include "../mlpp/cost/cost_old.h" -#include "../mlpp/data/data_old.h" -#include "../mlpp/dual_svc/dual_svc_old.h" -#include "../mlpp/exp_reg/exp_reg_old.h" -#include "../mlpp/gan/gan_old.h" -#include "../mlpp/gaussian_nb/gaussian_nb_old.h" -#include "../mlpp/hidden_layer/hidden_layer_old.h" -#include "../mlpp/lin_alg/lin_alg_old.h" -#include "../mlpp/lin_reg/lin_reg_old.h" -#include "../mlpp/log_reg/log_reg_old.h" -#include "../mlpp/mann/mann_old.h" -#include "../mlpp/mlp/mlp_old.h" -#include "../mlpp/multi_output_layer/multi_output_layer_old.h" -#include "../mlpp/multinomial_nb/multinomial_nb_old.h" -#include "../mlpp/numerical_analysis/numerical_analysis_old.h" -#include "../mlpp/outlier_finder/outlier_finder_old.h" -#include "../mlpp/output_layer/output_layer_old.h" -#include "../mlpp/pca/pca_old.h" -#include "../mlpp/probit_reg/probit_reg_old.h" -#include "../mlpp/softmax_net/softmax_net_old.h" -#include "../mlpp/softmax_reg/softmax_reg_old.h" -#include "../mlpp/stat/stat_old.h" -#include "../mlpp/svc/svc_old.h" -#include "../mlpp/tanh_reg/tanh_reg_old.h" -#include "../mlpp/transforms/transforms_old.h" -#include "../mlpp/uni_lin_reg/uni_lin_reg_old.h" -#include "../mlpp/wgan/wgan_old.h" - Vector dstd_vec_to_vec(const std::vector &in) { Vector r; @@ -107,7 +73,7 @@ Vector> dstd_mat_to_mat(const std::vector> &i void MLPPTests::test_statistics() { ERR_PRINT("MLPPTests::test_statistics() Started!"); - MLPPStatOld stat; + MLPPStat stat; MLPPConvolutions conv; // STATISTICS @@ -115,6 +81,7 @@ void MLPPTests::test_statistics() { std::vector y = { 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 }; std::vector w = { 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1 }; + /* is_approx_equalsd(stat.mean(x), 5.5, "Arithmetic Mean"); is_approx_equalsd(stat.mean(x), 5.5, "Median"); @@ -146,63 +113,12 @@ void MLPPTests::test_statistics() { is_approx_equalsd(stat.identricMean(1, 10), 4.75135, "Identric Mean"); is_approx_equalsd(stat.logMean(1, 10), 3.90865, "Logarithmic Mean"); is_approx_equalsd(stat.absAvgDeviation(x), 2.5, "Absolute Average Deviation"); + */ ERR_PRINT("MLPPTests::test_statistics() Finished!"); } void MLPPTests::test_linear_algebra() { - MLPPLinAlgOld alg; - - std::vector> square = { { 1, 1 }, { -1, 1 }, { 1, -1 }, { -1, -1 } }; - std::vector> square_rot_res = { { 1.41421, 1.11022e-16 }, { -1.11022e-16, 1.41421 }, { 1.11022e-16, -1.41421 }, { -1.41421, -1.11022e-16 } }; - - is_approx_equals_dmat(dstd_mat_to_mat(alg.rotate(square, Math_PI / 4)), dstd_mat_to_mat(square_rot_res), "alg.rotate(square, Math_PI / 4)"); - - std::vector> A = { - { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }, - { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }, - }; - std::vector a = { 4, 3, 1, 3 }; - std::vector b = { 3, 5, 6, 1 }; - - std::vector> mmtr_res = { - { 2, 4, 6, 8, 10, 12, 14, 16, 18, 20 }, - { 4, 8, 12, 16, 20, 24, 28, 32, 36, 40 }, - { 6, 12, 18, 24, 30, 36, 42, 48, 54, 60 }, - { 8, 16, 24, 32, 40, 48, 56, 64, 72, 80 }, - { 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 }, - { 12, 24, 36, 48, 60, 72, 84, 96, 108, 120 }, - { 14, 28, 42, 56, 70, 84, 98, 112, 126, 140 }, - { 16, 32, 48, 64, 80, 96, 112, 128, 144, 160 }, - { 18, 36, 54, 72, 90, 108, 126, 144, 162, 180 }, - { 20, 40, 60, 80, 100, 120, 140, 160, 180, 200 } - }; - - is_approx_equals_dmat(dstd_mat_to_mat(alg.matmult(alg.transpose(A), A)), dstd_mat_to_mat(mmtr_res), "alg.matmult(alg.transpose(A), A)"); - - is_approx_equalsd(alg.dot(a, b), 36, "alg.dot(a, b)"); - - std::vector> had_prod_res = { - { 1, 4, 9, 16, 25, 36, 49, 64, 81, 100 }, - { 1, 4, 9, 16, 25, 36, 49, 64, 81, 100 } - }; - - is_approx_equals_dmat(dstd_mat_to_mat(alg.hadamard_product(A, A)), dstd_mat_to_mat(had_prod_res), "alg.hadamard_product(A, A)"); - - std::vector> id_10_res = { - { 1, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, - { 0, 1, 0, 0, 0, 0, 0, 0, 0, 0 }, - { 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 }, - { 0, 0, 0, 1, 0, 0, 0, 0, 0, 0 }, - { 0, 0, 0, 0, 1, 0, 0, 0, 0, 0 }, - { 0, 0, 0, 0, 0, 1, 0, 0, 0, 0 }, - { 0, 0, 0, 0, 0, 0, 1, 0, 0, 0 }, - { 0, 0, 0, 0, 0, 0, 0, 1, 0, 0 }, - { 0, 0, 0, 0, 0, 0, 0, 0, 1, 0 }, - { 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 }, - }; - - is_approx_equals_dmat(dstd_mat_to_mat(alg.identity(10)), dstd_mat_to_mat(id_10_res), "alg.identity(10)"); } void MLPPTests::test_univariate_linear_regression() { @@ -211,17 +127,6 @@ void MLPPTests::test_univariate_linear_regression() { Ref ds = data.load_fires_and_crime(_fires_and_crime_data_path); - MLPPUniLinRegOld model_old(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); - - std::vector slr_res = { - 24.1095, 28.4829, 29.8082, 26.0974, 27.2902, 61.0851, 30.4709, 25.0372, 25.5673, 35.9046, - 54.4587, 18.8083, 23.4468, 18.5432, 19.2059, 21.1938, 23.0492, 18.8083, 25.4348, 35.9046, - 37.76, 40.278, 63.8683, 68.5068, 40.4106, 46.772, 32.0612, 23.3143, 44.784, 44.519, - 27.8203, 20.6637, 22.5191, 53.796, 38.9527, 30.8685, 20.3986 - }; - - is_approx_equals_dvec(dstd_vec_to_vec(model_old.modelSetTest(ds->get_input()->to_std_vector())), dstd_vec_to_vec(slr_res), "stat.mode(x)"); - MLPPUniLinReg model(ds->get_input(), ds->get_output()); std::vector slr_res_n = { @@ -247,14 +152,10 @@ void MLPPTests::test_univariate_linear_regression() { void MLPPTests::test_multivariate_linear_regression_gradient_descent(bool ui) { MLPPData data; - MLPPLinAlgOld alg; + MLPPLinAlg alg; Ref ds = data.load_california_housing(_california_housing_data_path); - MLPPLinRegOld model_old(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg - model_old.gradientDescent(0.001, 30, ui); - alg.printVector(model_old.modelSetTest(ds->get_input()->to_std_vector())); - MLPPLinReg model(ds->get_input(), ds->get_output()); // Can use Lasso, Ridge, ElasticNet Reg model.gradient_descent(0.001, 30, ui); PLOG_MSG(model.model_set_test(ds->get_input())->to_string()); @@ -262,14 +163,10 @@ void MLPPTests::test_multivariate_linear_regression_gradient_descent(bool ui) { void MLPPTests::test_multivariate_linear_regression_sgd(bool ui) { MLPPData data; - MLPPLinAlgOld alg; + MLPPLinAlg alg; Ref ds = data.load_california_housing(_california_housing_data_path); - MLPPLinRegOld model_old(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg - model_old.SGD(0.00000001, 300000, ui); - alg.printVector(model_old.modelSetTest(ds->get_input()->to_std_vector())); - MLPPLinReg model(ds->get_input(), ds->get_output()); // Can use Lasso, Ridge, ElasticNet Reg model.sgd(0.00000001, 300000, ui); PLOG_MSG(model.model_set_test(ds->get_input())->to_string()); @@ -277,14 +174,10 @@ void MLPPTests::test_multivariate_linear_regression_sgd(bool ui) { void MLPPTests::test_multivariate_linear_regression_mbgd(bool ui) { MLPPData data; - MLPPLinAlgOld alg; + MLPPLinAlg alg; Ref ds = data.load_california_housing(_california_housing_data_path); - MLPPLinRegOld model_old(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg - model_old.MBGD(0.001, 10000, 2, ui); - alg.printVector(model_old.modelSetTest(ds->get_input()->to_std_vector())); - MLPPLinReg model(ds->get_input(), ds->get_output()); // Can use Lasso, Ridge, ElasticNet Reg model.mbgd(0.001, 10000, 2, ui); PLOG_MSG(model.model_set_test(ds->get_input())->to_string()); @@ -292,14 +185,10 @@ void MLPPTests::test_multivariate_linear_regression_mbgd(bool ui) { void MLPPTests::test_multivariate_linear_regression_normal_equation(bool ui) { MLPPData data; - MLPPLinAlgOld alg; + MLPPLinAlg alg; Ref ds = data.load_california_housing(_california_housing_data_path); - MLPPLinRegOld model_old(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg - model_old.normalEquation(); - alg.printVector(model_old.modelSetTest(ds->get_input()->to_std_vector())); - MLPPLinReg model(ds->get_input(), ds->get_output()); // Can use Lasso, Ridge, ElasticNet Reg model.normal_equation(); PLOG_MSG(model.model_set_test(ds->get_input())->to_string()); @@ -307,23 +196,18 @@ void MLPPTests::test_multivariate_linear_regression_normal_equation(bool ui) { void MLPPTests::test_multivariate_linear_regression_adam() { MLPPData data; - MLPPLinAlgOld alg; - MLPPLinAlg algn; + MLPPLinAlg alg; Ref ds = data.load_california_housing(_california_housing_data_path); - MLPPLinRegOld adamModelOld(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector()); - alg.printVector(adamModelOld.modelSetTest(ds->get_input()->to_std_vector())); - std::cout << "ACCURACY: " << 100 * adamModelOld.score() << "%" << std::endl; - - MLPPLinReg adam_model(algn.transposenm(ds->get_input()), ds->get_output()); + MLPPLinReg adam_model(alg.transposenm(ds->get_input()), ds->get_output()); PLOG_MSG(adam_model.model_set_test(ds->get_input())->to_string()); PLOG_MSG("ACCURACY: " + String::num(100 * adam_model.score()) + "%"); } void MLPPTests::test_multivariate_linear_regression_score_sgd_adam(bool ui) { MLPPData data; - MLPPLinAlgOld alg; + MLPPLinAlg alg; MLPPLinAlg algn; Ref ds = data.load_california_housing(_california_housing_data_path); @@ -333,18 +217,10 @@ void MLPPTests::test_multivariate_linear_regression_score_sgd_adam(bool ui) { real_t scoreSGD = 0; real_t scoreADAM = 0; for (int i = 0; i < TRIAL_NUM; i++) { - MLPPLinRegOld modelf_old(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector()); - modelf_old.MBGD(0.001, 5, 1, ui); - scoreSGD += modelf_old.score(); - MLPPLinReg modelf(algn.transposenm(ds->get_input()), ds->get_output()); modelf.mbgd(0.001, 5, 1, ui); scoreSGD += modelf.score(); - MLPPLinRegOld adamModelf_old(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector()); - adamModelf_old.Adam(0.1, 5, 1, 0.9, 0.999, 1e-8, ui); // Change batch size = sgd, bgd - scoreADAM += adamModelf_old.score(); - MLPPLinReg adamModelf(algn.transposenm(ds->get_input()), ds->get_output()); adamModelf.adam(0.1, 5, 1, 0.9, 0.999, 1e-8, ui); // Change batch size = sgd, bgd scoreADAM += adamModelf.score(); @@ -357,7 +233,7 @@ void MLPPTests::test_multivariate_linear_regression_score_sgd_adam(bool ui) { void MLPPTests::test_multivariate_linear_regression_epochs_gradient_descent(bool ui) { MLPPData data; - MLPPLinAlgOld alg; + MLPPLinAlg alg; MLPPLinAlg algn; Ref ds = data.load_california_housing(_california_housing_data_path); @@ -365,10 +241,6 @@ void MLPPTests::test_multivariate_linear_regression_epochs_gradient_descent(bool std::cout << "Total epoch num: 300" << std::endl; std::cout << "Method: 1st Order w/ Jacobians" << std::endl; - MLPPLinRegOld model3_old(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg - model3_old.gradientDescent(0.001, 300, ui); - alg.printVector(model3_old.modelSetTest(ds->get_input()->to_std_vector())); - MLPPLinReg model3(algn.transposenm(ds->get_input()), ds->get_output()); // Can use Lasso, Ridge, ElasticNet Reg model3.gradient_descent(0.001, 300, ui); PLOG_MSG(model3.model_set_test(ds->get_input())->to_string()); @@ -376,7 +248,7 @@ void MLPPTests::test_multivariate_linear_regression_epochs_gradient_descent(bool void MLPPTests::test_multivariate_linear_regression_newton_raphson(bool ui) { MLPPData data; - MLPPLinAlgOld alg; + MLPPLinAlg alg; MLPPLinAlg algn; Ref ds = data.load_california_housing(_california_housing_data_path); @@ -385,66 +257,48 @@ void MLPPTests::test_multivariate_linear_regression_newton_raphson(bool ui) { std::cout << "Total epoch num: 300" << std::endl; std::cout << "Method: Newtonian 2nd Order w/ Hessians" << std::endl; - MLPPLinRegOld model2_old(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector()); - model2_old.NewtonRaphson(1.5, 300, ui); - alg.printVector(model2_old.modelSetTest(ds->get_input()->to_std_vector())); - MLPPLinReg model2(algn.transposenm(ds->get_input()), ds->get_output()); model2.newton_raphson(1.5, 300, ui); PLOG_MSG(model2.model_set_test(ds->get_input())->to_string()); } void MLPPTests::test_logistic_regression(bool ui) { - MLPPLinAlgOld alg; + MLPPLinAlg alg; MLPPData data; Ref dt = data.load_breast_cancer(_breast_cancer_data_path); // LOGISTIC REGRESSION - MLPPLogRegOld model_old(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector()); - model_old.SGD(0.001, 100000, ui); - alg.printVector(model_old.modelSetTest(dt->get_input()->to_std_vector())); - std::cout << "ACCURACY (Old): " << 100 * model_old.score() << "%" << std::endl; - MLPPLogReg model(dt->get_input(), dt->get_output()); model.sgd(0.001, 100000, ui); PLOG_MSG(model.model_set_test(dt->get_input())->to_string()); std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl; } void MLPPTests::test_probit_regression(bool ui) { - MLPPLinAlgOld alg; + MLPPLinAlg alg; MLPPData data; // PROBIT REGRESSION Ref dt = data.load_breast_cancer(_breast_cancer_data_path); - MLPPProbitRegOld model_old(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector()); - model_old.SGD(0.001, 10000, ui); - alg.printVector(model_old.modelSetTest(dt->get_input()->to_std_vector())); - std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl; - MLPPProbitReg model(dt->get_input(), dt->get_output()); model.sgd(0.001, 10000, ui); PLOG_MSG(model.model_set_test(dt->get_input())->to_string()); PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%"); } void MLPPTests::test_c_log_log_regression(bool ui) { - MLPPLinAlgOld alg; + MLPPLinAlg alg; MLPPLinAlg algn; // CLOGLOG REGRESSION std::vector> inputSet = { { 1, 2, 3, 4, 5, 6, 7, 8 }, { 0, 0, 0, 0, 1, 1, 1, 1 } }; std::vector outputSet = { 0, 0, 0, 0, 1, 1, 1, 1 }; - MLPPCLogLogRegOld model_old(alg.transpose(inputSet), outputSet); - model_old.SGD(0.1, 10000, ui); - alg.printVector(model_old.modelSetTest(alg.transpose(inputSet))); - std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl; - Ref input_set; input_set.instance(); - input_set->set_from_std_vectors(alg.transpose(inputSet)); + input_set->set_from_std_vectors(inputSet); + input_set = input_set->transposen(); Ref output_set; output_set.instance(); @@ -456,18 +310,13 @@ void MLPPTests::test_c_log_log_regression(bool ui) { PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%"); } void MLPPTests::test_exp_reg_regression(bool ui) { - MLPPLinAlgOld alg; + MLPPLinAlg alg; MLPPLinAlg algn; // EXPREG REGRESSION std::vector> inputSet = { { 0, 1, 2, 3, 4 } }; std::vector outputSet = { 1, 2, 4, 8, 16 }; - MLPPExpRegOld model_old(alg.transpose(inputSet), outputSet); - model_old.SGD(0.001, 10000, ui); - alg.printVector(model_old.modelSetTest(alg.transpose(inputSet))); - std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl; - Ref input_set; input_set.instance(); input_set->set_from_std_vectors(inputSet); @@ -482,30 +331,19 @@ void MLPPTests::test_exp_reg_regression(bool ui) { PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%"); } void MLPPTests::test_tanh_regression(bool ui) { - MLPPLinAlgOld alg; + MLPPLinAlg alg; // TANH REGRESSION std::vector> inputSet = { { 4, 3, 0, -3, -4 }, { 0, 0, 0, 1, 1 } }; std::vector outputSet = { 1, 1, 0, -1, -1 }; - - MLPPTanhRegOld model_old(alg.transpose(inputSet), outputSet); - model_old.SGD(0.1, 10000, ui); - alg.printVector(model_old.modelSetTest(alg.transpose(inputSet))); - std::cout << "ACCURACY (Old): " << 100 * model_old.score() << "%" << std::endl; } void MLPPTests::test_softmax_regression(bool ui) { - MLPPLinAlgOld alg; + MLPPLinAlg alg; MLPPData data; Ref dt = data.load_iris(_iris_data_path); // SOFTMAX REGRESSION - - MLPPSoftmaxRegOld model_old(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector()); - model_old.SGD(0.1, 10000, ui); - alg.printMatrix(model_old.modelSetTest(dt->get_input()->to_std_vector())); - std::cout << "ACCURACY (Old): " << 100 * model_old.score() << "%" << std::endl; - MLPPSoftmaxReg model(dt->get_input(), dt->get_output()); model.sgd(0.1, 10000, ui); PLOG_MSG(model.model_set_test(dt->get_input())->to_string()); @@ -513,7 +351,7 @@ void MLPPTests::test_softmax_regression(bool ui) { } void MLPPTests::test_support_vector_classification(bool ui) { //MLPPStat stat; - MLPPLinAlgOld alg; + MLPPLinAlg alg; //MLPPActivation avn; //MLPPCost cost; MLPPData data; @@ -522,11 +360,6 @@ void MLPPTests::test_support_vector_classification(bool ui) { // SUPPORT VECTOR CLASSIFICATION Ref dt = data.load_breast_cancer_svc(_breast_cancer_svm_data_path); - MLPPSVCOld model_old(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector(), ui); - model_old.SGD(0.00001, 100000, ui); - alg.printVector(model_old.modelSetTest(dt->get_input()->to_std_vector())); - std::cout << "ACCURACY (old): " << 100 * model_old.score() << "%" << std::endl; - MLPPSVC model(dt->get_input(), dt->get_output(), ui); model.sgd(0.00001, 100000, ui); PLOG_MSG((model.model_set_test(dt->get_input())->to_string())); @@ -534,7 +367,7 @@ void MLPPTests::test_support_vector_classification(bool ui) { } void MLPPTests::test_mlp(bool ui) { - MLPPLinAlgOld alg; + MLPPLinAlg alg; // MLP std::vector> inputSet = { @@ -545,11 +378,6 @@ void MLPPTests::test_mlp(bool ui) { }; std::vector outputSet = { 0, 1, 1, 0 }; - MLPPMLPOld model(inputSet, outputSet, 2); - model.gradientDescent(0.1, 10000, ui); - alg.printVector(model.modelSetTest(inputSet)); - std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl; - Ref input_set; input_set.instance(); input_set->set_from_std_vectors(inputSet); @@ -580,34 +408,24 @@ void MLPPTests::test_mlp(bool ui) { PLOG_MSG(res); } void MLPPTests::test_soft_max_network(bool ui) { - MLPPLinAlgOld alg; + MLPPLinAlg alg; MLPPData data; // SOFTMAX NETWORK Ref dt = data.load_wine(_wine_data_path); - MLPPSoftmaxNetOld model_old(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector(), 1); - model_old.gradientDescent(0.01, 100000, ui); - alg.printMatrix(model_old.modelSetTest(dt->get_input()->to_std_vector())); - std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl; - MLPPSoftmaxNet model(dt->get_input(), dt->get_output(), 1); model.gradient_descent(0.01, 100000, ui); PLOG_MSG(model.model_set_test(dt->get_input())->to_string()); std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl; } void MLPPTests::test_autoencoder(bool ui) { - MLPPLinAlgOld alg; + MLPPLinAlg alg; MLPPLinAlg algn; std::vector> inputSet = { { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }, { 3, 5, 9, 12, 15, 18, 21, 24, 27, 30 } }; // AUTOENCODER - MLPPAutoEncoderOld model_old(alg.transpose(inputSet), 5); - model_old.SGD(0.001, 300000, ui); - alg.printMatrix(model_old.modelSetTest(alg.transpose(inputSet))); - std::cout << "ACCURACY (Old): " << 100 * model_old.score() << "%" << std::endl; - Ref input_set; input_set.instance(); input_set->set_from_std_vectors(inputSet); @@ -618,7 +436,7 @@ void MLPPTests::test_autoencoder(bool ui) { PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%"); } void MLPPTests::test_dynamically_sized_ann(bool ui) { - MLPPLinAlgOld alg; + MLPPLinAlg alg; MLPPLinAlg algn; // DYNAMICALLY SIZED ANN @@ -628,19 +446,6 @@ void MLPPTests::test_dynamically_sized_ann(bool ui) { std::vector> inputSet = { { 0, 0, 1, 1 }, { 0, 1, 0, 1 } }; std::vector outputSet = { 0, 1, 1, 0 }; - MLPPANNOld ann_old(alg.transpose(inputSet), outputSet); - ann_old.addLayer(2, "Cosh"); - ann_old.addOutputLayer("Sigmoid", "LogLoss"); - - ann_old.AMSGrad(0.1, 10000, 1, 0.9, 0.999, 0.000001, ui); - ann_old.Adadelta(1, 1000, 2, 0.9, 0.000001, ui); - ann_old.Momentum(0.1, 8000, 2, 0.9, true, ui); - - ann_old.setLearningRateScheduler("Step", 0.5, 1000); - ann_old.gradientDescent(0.01, 30000); - alg.printVector(ann_old.modelSetTest(alg.transpose(inputSet))); - std::cout << "ACCURACY: " << 100 * ann_old.score() << "%" << std::endl; - Ref input_set; input_set.instance(); input_set->set_from_std_vectors(inputSet); @@ -664,7 +469,7 @@ void MLPPTests::test_dynamically_sized_ann(bool ui) { } void MLPPTests::test_wgan_old(bool ui) { //MLPPStat stat; - MLPPLinAlgOld alg; + MLPPLinAlg alg; //MLPPActivation avn; //MLPPCost cost; //MLPPData data; @@ -674,19 +479,10 @@ void MLPPTests::test_wgan_old(bool ui) { { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 }, { 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40 } }; - - MLPPWGANOld gan_old(2, alg.transpose(outputSet)); // our gan is a wasserstein gan (wgan) - gan_old.addLayer(5, "Sigmoid"); - gan_old.addLayer(2, "RELU"); - gan_old.addLayer(5, "Sigmoid"); - gan_old.addOutputLayer(); // User can specify weight init- if necessary. - gan_old.gradientDescent(0.1, 55000, ui); - std::cout << "GENERATED INPUT: (Gaussian-sampled noise):" << std::endl; - alg.printMatrix(gan_old.generateExample(100)); } void MLPPTests::test_wgan(bool ui) { //MLPPStat stat; - MLPPLinAlgOld alg; + MLPPLinAlg alg; //MLPPActivation avn; //MLPPCost cost; //MLPPData data; @@ -699,7 +495,8 @@ void MLPPTests::test_wgan(bool ui) { Ref output_set; output_set.instance(); - output_set->set_from_std_vectors(alg.transpose(outputSet)); + output_set->set_from_std_vectors(outputSet); + output_set = output_set->transposen(); MLPPWGAN gan(2, output_set); // our gan is a wasserstein gan (wgan) gan.add_layer(5, MLPPActivation::ACTIVATION_FUNCTION_SIGMOID); @@ -713,21 +510,11 @@ void MLPPTests::test_wgan(bool ui) { PLOG_MSG(str); } void MLPPTests::test_ann(bool ui) { - MLPPLinAlgOld alg; + MLPPLinAlg alg; std::vector> inputSet = { { 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 } }; // XOR std::vector outputSet = { 0, 1, 1, 0 }; - MLPPANNOld ann_old(inputSet, outputSet); - ann_old.addLayer(5, "Sigmoid"); - ann_old.addLayer(8, "Sigmoid"); // Add more layers as needed. - ann_old.addOutputLayer("Sigmoid", "LogLoss"); - ann_old.gradientDescent(1, 20000, ui); - - std::vector predictions_old = ann_old.modelSetTest(inputSet); - alg.printVector(predictions_old); // Testing out the model's preds for train set. - std::cout << "ACCURACY: " << 100 * ann_old.score() << "%" << std::endl; // Accuracy. - Ref input_set; input_set.instance(); input_set->set_from_std_vectors(inputSet); @@ -747,19 +534,13 @@ void MLPPTests::test_ann(bool ui) { PLOG_MSG("ACCURACY: " + String::num(100 * ann.score()) + "%"); // Accuracy. } void MLPPTests::test_dynamically_sized_mann(bool ui) { - MLPPLinAlgOld alg; + MLPPLinAlg alg; MLPPData data; // DYNAMICALLY SIZED MANN (Multidimensional Output ANN) std::vector> inputSet = { { 1, 2, 3 }, { 2, 4, 6 }, { 3, 6, 9 }, { 4, 8, 12 } }; std::vector> outputSet = { { 1, 5 }, { 2, 10 }, { 3, 15 }, { 4, 20 } }; - MLPPMANNOld mann_old(inputSet, outputSet); - mann_old.addOutputLayer("Linear", "MSE"); - mann_old.gradientDescent(0.001, 80000, false); - alg.printMatrix(mann_old.modelSetTest(inputSet)); - std::cout << "ACCURACY (old): " << 100 * mann_old.score() << "%" << std::endl; - Ref input_set; input_set.instance(); input_set->set_from_std_vectors(inputSet); @@ -775,7 +556,7 @@ void MLPPTests::test_dynamically_sized_mann(bool ui) { PLOG_MSG("ACCURACY: " + String::num(100 * mann.score()) + "%"); } void MLPPTests::test_train_test_split_mann(bool ui) { - MLPPLinAlgOld alg; + MLPPLinAlg alg; MLPPLinAlg algn; MLPPData data; @@ -804,13 +585,6 @@ void MLPPTests::test_train_test_split_mann(bool ui) { PLOG_MSG(split_data.test->get_input()->to_string()); PLOG_MSG(split_data.test->get_output()->to_string()); - MLPPMANNOld mann_old(split_data.train->get_input()->to_std_vector(), split_data.train->get_output()->to_std_vector()); - mann_old.addLayer(100, "RELU", "XavierNormal"); - mann_old.addOutputLayer("Softmax", "CrossEntropy", "XavierNormal"); - mann_old.gradientDescent(0.1, 80000, ui); - alg.printMatrix(mann_old.modelSetTest(split_data.test->get_input()->to_std_vector())); - std::cout << "ACCURACY (old): " << 100 * mann_old.score() << "%" << std::endl; - MLPPMANN mann(split_data.train->get_input(), split_data.train->get_output()); mann.add_layer(100, MLPPActivation::ACTIVATION_FUNCTION_RELU, MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_XAVIER_NORMAL); mann.add_output_layer(MLPPActivation::ACTIVATION_FUNCTION_SOFTMAX, MLPPCost::COST_TYPE_CROSS_ENTROPY, MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_XAVIER_NORMAL); @@ -820,19 +594,17 @@ void MLPPTests::test_train_test_split_mann(bool ui) { } void MLPPTests::test_naive_bayes() { - MLPPLinAlgOld alg; + MLPPLinAlg alg; MLPPLinAlg algn; // NAIVE BAYES std::vector> inputSet = { { 1, 1, 1, 1, 1 }, { 0, 0, 1, 1, 1 }, { 0, 0, 1, 0, 1 } }; std::vector outputSet = { 0, 1, 0, 1, 1 }; - MLPPMultinomialNBOld MNB_old(alg.transpose(inputSet), outputSet, 2); - alg.printVector(MNB_old.modelSetTest(alg.transpose(inputSet))); - Ref input_set; input_set.instance(); - input_set->set_from_std_vectors(alg.transpose(inputSet)); + input_set->set_from_std_vectors(inputSet); + input_set = input_set->transposen(); Ref output_set; output_set.instance(); @@ -841,15 +613,9 @@ void MLPPTests::test_naive_bayes() { MLPPMultinomialNB MNB(input_set, output_set, 2); PLOG_MSG(MNB.model_set_test(input_set)->to_string()); - MLPPBernoulliNBOld BNBOld(alg.transpose(inputSet), outputSet); - alg.printVector(BNBOld.modelSetTest(alg.transpose(inputSet))); - MLPPBernoulliNB BNB(algn.transposenm(input_set), output_set); PLOG_MSG(BNB.model_set_test(algn.transposenm(input_set))->to_string()); - MLPPGaussianNBOld GNBOld(alg.transpose(inputSet), outputSet, 2); - alg.printVector(GNBOld.modelSetTest(alg.transpose(inputSet))); - MLPPGaussianNB GNB(algn.transposenm(input_set), output_set, 2); PLOG_MSG(GNB.model_set_test(algn.transposenm(input_set))->to_string()); } @@ -873,7 +639,7 @@ void MLPPTests::test_k_means(bool ui) { PLOG_MSG(kmeans->silhouette_scores()->to_string()); } void MLPPTests::test_knn(bool ui) { - MLPPLinAlgOld alg; + MLPPLinAlg alg; // kNN std::vector> inputSet = { @@ -884,7 +650,8 @@ void MLPPTests::test_knn(bool ui) { Ref ism; ism.instance(); - ism->set_from_std_vectors(alg.transpose(inputSet)); + ism->set_from_std_vectors(inputSet); + ism = ism->transposen(); //ERR_PRINT(ism->to_string()); @@ -912,7 +679,8 @@ void MLPPTests::test_knn(bool ui) { } void MLPPTests::test_convolution_tensors_etc() { - MLPPLinAlgOld alg; + /* + MLPPLinAlg alg; MLPPLinAlg algn; MLPPData data; MLPPConvolutionsOld conv; @@ -954,14 +722,16 @@ void MLPPTests::test_convolution_tensors_etc() { std::vector> laplacian = { { 1, 1, 1 }, { 1, -4, 1 }, { 1, 1, 1 } }; alg.printMatrix(conv.convolve_2d(conv.gaussian_filter_2d(5, 1), laplacian, 1)); + */ } void MLPPTests::test_pca_svd_eigenvalues_eigenvectors(bool ui) { - MLPPLinAlgOld alg; + /* + MLPPLinAlg alg; // PCA, SVD, eigenvalues & eigenvectors std::vector> inputSet = { { 1, 1 }, { 1, 1 } }; - MLPPLinAlgOld::EigenResultOld eigen = alg.eigen_old(inputSet); + MLPPLinAlg::EigenResultOld eigen = alg.eigen_old(inputSet); std::cout << "Eigenvectors:" << std::endl; alg.printMatrix(eigen.eigen_vectors); @@ -971,7 +741,7 @@ void MLPPTests::test_pca_svd_eigenvalues_eigenvectors(bool ui) { std::cout << "SVD OLD START" << std::endl; - MLPPLinAlgOld::SVDResultOld svd_old = alg.SVD(inputSet); + MLPPLinAlg::SVDResultOld svd_old = alg.SVD(inputSet); std::cout << "U:" << std::endl; alg.printMatrix(svd_old.U); @@ -985,11 +755,12 @@ void MLPPTests::test_pca_svd_eigenvalues_eigenvectors(bool ui) { Ref input_set; input_set.instance(); input_set->set_from_std_vectors(inputSet); + */ /* String str_svd = "SVD\n"; - MLPPLinAlgOld::SVDResult svd = alg.svd(input_set); + MLPPLinAlg::SVDResult svd = alg.svd(input_set); str_svd += "U:\n"; str_svd += svd.U->to_string(); @@ -1002,6 +773,7 @@ void MLPPTests::test_pca_svd_eigenvalues_eigenvectors(bool ui) { PLOG_MSG(str_svd); */ + /* std::cout << "PCA" << std::endl; // PCA done using Jacobi's method to approximate eigenvalues and eigenvectors. @@ -1018,10 +790,12 @@ void MLPPTests::test_pca_svd_eigenvalues_eigenvectors(bool ui) { str += dr.principal_components()->to_string(); str += "\nSCORE: " + String::num(dr.score()) + "\n"; PLOG_MSG(str); + */ } void MLPPTests::test_nlp_and_data(bool ui) { - MLPPLinAlgOld alg; + /* + MLPPLinAlg alg; MLPPData data; // NLP/DATA @@ -1069,15 +843,14 @@ void MLPPTests::test_nlp_and_data(bool ui) { std::cout << "Mean Normalization Example:" << std::endl; alg.printMatrix(data.meanNormalization(inputSet)); std::cout << std::endl; + */ } void MLPPTests::test_outlier_finder(bool ui) { - MLPPLinAlgOld alg; + MLPPLinAlg alg; // Outlier Finder //std::vector inputSet = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 23554332523523 }; std::vector inputSet = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 23554332 }; - MLPPOutlierFinderOld outlierFinderOld(2); // Any datapoint outside of 2 stds from the mean is marked as an outlier. - alg.printVector(outlierFinderOld.modelTest(inputSet)); Ref input_set; input_set.instance(); @@ -1087,7 +860,8 @@ void MLPPTests::test_outlier_finder(bool ui) { PLOG_MSG(Variant(outlier_finder.model_test(input_set))); } void MLPPTests::test_new_math_functions() { - MLPPLinAlgOld alg; + /* + MLPPLinAlg alg; MLPPActivationOld avn; MLPPData data; @@ -1127,13 +901,15 @@ void MLPPTests::test_new_math_functions() { alg.printMatrix(alg.gramSchmidtProcess(P)); - //MLPPLinAlgOld::QRDResult qrd_result = alg.qrd(P); // It works! + //MLPPLinAlg::QRDResult qrd_result = alg.qrd(P); // It works! //alg.printMatrix(qrd_result.Q); //alg.printMatrix(qrd_result.R); + */ } void MLPPTests::test_positive_definiteness_checker() { + /* //MLPPStat stat; - MLPPLinAlgOld alg; + MLPPLinAlg alg; //MLPPActivation avn; //MLPPCost cost; //MLPPData data; @@ -1148,9 +924,10 @@ void MLPPTests::test_positive_definiteness_checker() { }; std::cout << std::boolalpha << alg.positiveDefiniteChecker(A) << std::endl; - MLPPLinAlgOld::CholeskyResult chres = alg.cholesky(A); // works. + MLPPLinAlg::CholeskyResult chres = alg.cholesky(A); // works. alg.printMatrix(chres.L); alg.printMatrix(chres.Lt); + */ } // real_t f(real_t x){ @@ -1218,7 +995,8 @@ real_t f_mv(std::vector x) { */ void MLPPTests::test_numerical_analysis() { - MLPPLinAlgOld alg; + /* + MLPPLinAlg alg; MLPPConvolutionsOld conv; // Checks for numerical analysis class. @@ -1289,28 +1067,27 @@ void MLPPTests::test_numerical_analysis() { std::vector a = { 3, 4, 4 }; std::vector b = { 4, 4, 4 }; alg.printVector(alg.cross(a, b)); + */ } void MLPPTests::test_support_vector_classification_kernel(bool ui) { - MLPPLinAlgOld alg; + MLPPLinAlg alg; MLPPData data; //SUPPORT VECTOR CLASSIFICATION (kernel method) Ref dt = data.load_breast_cancer_svc(_breast_cancer_svm_data_path); - MLPPDualSVCOld kernelSVMOld(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector(), 1000); - kernelSVMOld.gradientDescent(0.0001, 20, ui); - std::cout << "SCORE: " << kernelSVMOld.score() << std::endl; - MLPPDualSVC kernelSVM(dt->get_input(), dt->get_output(), 1000); kernelSVM.gradient_descent(0.0001, 20, ui); PLOG_MSG("SCORE: " + String::num(kernelSVM.score())); + /* std::vector> linearlyIndependentMat = { { 1, 2, 3, 4 }, { 2345384, 4444, 6111, 55 } }; std::cout << "True of false: linearly independent?: " << std::boolalpha << alg.linearIndependenceChecker(linearlyIndependentMat) << std::endl; + */ } void MLPPTests::test_mlpp_vector() {