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pmlpp/test/mlpp_tests.cpp

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#include "mlpp_tests.h"
#include "core/math/math_funcs.h"
#include "core/log/logger.h"
//TODO remove
#include <cmath>
#include <ctime>
#include <iostream>
#include <vector>
#include "../mlpp/lin_alg/mlpp_matrix.h"
#include "../mlpp/lin_alg/mlpp_vector.h"
#include "../mlpp/activation/activation.h"
#include "../mlpp/ann/ann.h"
#include "../mlpp/auto_encoder/auto_encoder.h"
#include "../mlpp/bernoulli_nb/bernoulli_nb.h"
#include "../mlpp/c_log_log_reg/c_log_log_reg.h"
#include "../mlpp/convolutions/convolutions.h"
#include "../mlpp/cost/cost.h"
#include "../mlpp/data/data.h"
#include "../mlpp/dual_svc/dual_svc.h"
#include "../mlpp/exp_reg/exp_reg.h"
#include "../mlpp/gan/gan.h"
#include "../mlpp/gaussian_nb/gaussian_nb.h"
#include "../mlpp/kmeans/kmeans.h"
#include "../mlpp/knn/knn.h"
#include "../mlpp/lin_alg/lin_alg.h"
#include "../mlpp/lin_reg/lin_reg.h"
#include "../mlpp/log_reg/log_reg.h"
#include "../mlpp/mann/mann.h"
#include "../mlpp/mlp/mlp.h"
#include "../mlpp/multinomial_nb/multinomial_nb.h"
#include "../mlpp/numerical_analysis/numerical_analysis.h"
#include "../mlpp/outlier_finder/outlier_finder.h"
#include "../mlpp/pca/pca.h"
#include "../mlpp/probit_reg/probit_reg.h"
#include "../mlpp/softmax_net/softmax_net.h"
#include "../mlpp/softmax_reg/softmax_reg.h"
#include "../mlpp/stat/stat.h"
#include "../mlpp/svc/svc.h"
#include "../mlpp/tanh_reg/tanh_reg.h"
#include "../mlpp/transforms/transforms.h"
#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<real_t> dstd_vec_to_vec(const std::vector<real_t> &in) {
Vector<real_t> r;
r.resize(static_cast<int>(in.size()));
real_t *darr = r.ptrw();
for (uint32_t i = 0; i < in.size(); ++i) {
darr[i] = in[i];
}
return r;
}
Vector<Vector<real_t>> dstd_mat_to_mat(const std::vector<std::vector<real_t>> &in) {
Vector<Vector<real_t>> r;
for (uint32_t i = 0; i < in.size(); ++i) {
r.push_back(dstd_vec_to_vec(in[i]));
}
return r;
}
void MLPPTests::test_statistics() {
ERR_PRINT("MLPPTests::test_statistics() Started!");
MLPPStatOld stat;
MLPPConvolutions conv;
// STATISTICS
std::vector<real_t> x = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };
std::vector<real_t> y = { 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 };
std::vector<real_t> 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");
is_approx_equals_dvec(dstd_vec_to_vec(stat.mode(x)), dstd_vec_to_vec(x), "stat.mode(x)");
is_approx_equalsd(stat.range(x), 9, "Range");
is_approx_equalsd(stat.midrange(x), 4.5, "Midrange");
is_approx_equalsd(stat.absAvgDeviation(x), 2.5, "Absolute Average Deviation");
is_approx_equalsd(stat.standardDeviation(x), 3.02765, "Standard Deviation");
is_approx_equalsd(stat.variance(x), 9.16667, "Variance");
is_approx_equalsd(stat.covariance(x, y), -9.16667, "Covariance");
is_approx_equalsd(stat.correlation(x, y), -1, "Correlation");
is_approx_equalsd(stat.R2(x, y), 1, "R^2");
// Returns 1 - (1/k^2)
is_approx_equalsd(stat.chebyshevIneq(2), 0.75, "Chebyshev Inequality");
is_approx_equalsd(stat.weightedMean(x, w), 5.5, "Weighted Mean");
is_approx_equalsd(stat.geometricMean(x), 4.52873, "Geometric Mean");
is_approx_equalsd(stat.harmonicMean(x), 3.41417, "Harmonic Mean");
is_approx_equalsd(stat.RMS(x), 6.20484, "Root Mean Square (Quadratic mean)");
is_approx_equalsd(stat.powerMean(x, 5), 7.39281, "Power Mean (p = 5)");
is_approx_equalsd(stat.lehmerMean(x, 5), 8.71689, "Lehmer Mean (p = 5)");
is_approx_equalsd(stat.weightedLehmerMean(x, w, 5), 8.71689, "Weighted Lehmer Mean (p = 5)");
is_approx_equalsd(stat.contraHarmonicMean(x), 7, "Contraharmonic Mean");
is_approx_equalsd(stat.heronianMean(1, 10), 4.72076, "Hernonian Mean");
is_approx_equalsd(stat.heinzMean(1, 10, 1), 5.5, "Heinz Mean (x = 1)");
is_approx_equalsd(stat.neumanSandorMean(1, 10), 3.36061, "Neuman-Sandor Mean");
is_approx_equalsd(stat.stolarskyMean(1, 10, 5), 6.86587, "Stolarsky Mean (p = 5)");
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<std::vector<real_t>> square = { { 1, 1 }, { -1, 1 }, { 1, -1 }, { -1, -1 } };
std::vector<std::vector<real_t>> 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<std::vector<real_t>> A = {
{ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 },
{ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 },
};
std::vector<real_t> a = { 4, 3, 1, 3 };
std::vector<real_t> b = { 3, 5, 6, 1 };
std::vector<std::vector<real_t>> 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<std::vector<real_t>> 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<std::vector<real_t>> 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() {
// Univariate, simple linear regression, case where k = 1
MLPPData data;
Ref<MLPPDataESimple> 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<real_t> 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<real_t> slr_res_n = {
24.109467, 28.482935, 29.808228, 26.097408, 27.290173, 61.085152, 30.470875, 25.037172, 25.567291,
35.904579, 54.458687, 18.808294, 23.446819, 18.543236, 19.205883, 21.193821, 23.049232, 18.808294,
25.434761, 35.904579, 37.759987, 40.278046, 63.868271, 68.50679, 40.410576, 46.77198, 32.061226,
23.314291, 44.784042, 44.518982, 27.82029, 20.663704, 22.519115, 53.796036, 38.952751,
30.868464, 20.398645
};
Ref<MLPPVector> slr_res_v;
slr_res_v.instance();
slr_res_v->set_from_std_vector(slr_res_n);
Ref<MLPPVector> res = model.model_set_test(ds->get_input());
if (!slr_res_v->is_equal_approx(res)) {
ERR_PRINT("!slr_res_v->is_equal_approx(res)");
ERR_PRINT(res->to_string());
ERR_PRINT(slr_res_v->to_string());
}
}
void MLPPTests::test_multivariate_linear_regression_gradient_descent(bool ui) {
MLPPData data;
MLPPLinAlgOld alg;
Ref<MLPPDataSimple> 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());
}
void MLPPTests::test_multivariate_linear_regression_sgd(bool ui) {
MLPPData data;
MLPPLinAlgOld alg;
Ref<MLPPDataSimple> 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());
}
void MLPPTests::test_multivariate_linear_regression_mbgd(bool ui) {
MLPPData data;
MLPPLinAlgOld alg;
Ref<MLPPDataSimple> 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());
}
void MLPPTests::test_multivariate_linear_regression_normal_equation(bool ui) {
MLPPData data;
MLPPLinAlgOld alg;
Ref<MLPPDataSimple> 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());
}
void MLPPTests::test_multivariate_linear_regression_adam() {
MLPPData data;
MLPPLinAlgOld alg;
MLPPLinAlg algn;
Ref<MLPPDataSimple> 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());
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 algn;
Ref<MLPPDataSimple> ds = data.load_california_housing(_california_housing_data_path);
const int TRIAL_NUM = 1000;
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();
}
std::cout << "ACCURACY, AVG, SGD: " << 100 * scoreSGD / TRIAL_NUM << "%" << std::endl;
std::cout << std::endl;
std::cout << "ACCURACY, AVG, ADAM: " << 100 * scoreADAM / TRIAL_NUM << "%" << std::endl;
}
void MLPPTests::test_multivariate_linear_regression_epochs_gradient_descent(bool ui) {
MLPPData data;
MLPPLinAlgOld alg;
MLPPLinAlg algn;
Ref<MLPPDataSimple> ds = data.load_california_housing(_california_housing_data_path);
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());
}
void MLPPTests::test_multivariate_linear_regression_newton_raphson(bool ui) {
MLPPData data;
MLPPLinAlgOld alg;
MLPPLinAlg algn;
Ref<MLPPDataSimple> ds = data.load_california_housing(_california_housing_data_path);
std::cout << "--------------------------------------------" << std::endl;
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;
MLPPData data;
Ref<MLPPDataSimple> 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;
MLPPData data;
// PROBIT REGRESSION
Ref<MLPPDataSimple> 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 algn;
// CLOGLOG REGRESSION
std::vector<std::vector<real_t>> inputSet = { { 1, 2, 3, 4, 5, 6, 7, 8 }, { 0, 0, 0, 0, 1, 1, 1, 1 } };
std::vector<real_t> 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<MLPPMatrix> input_set;
input_set.instance();
input_set->set_from_std_vectors(alg.transpose(inputSet));
Ref<MLPPVector> output_set;
output_set.instance();
output_set->set_from_std_vector(outputSet);
MLPPCLogLogReg model(algn.transposenm(input_set), output_set);
model.sgd(0.1, 10000, ui);
PLOG_MSG(model.model_set_test(algn.transposenm(input_set))->to_string());
PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%");
}
void MLPPTests::test_exp_reg_regression(bool ui) {
MLPPLinAlgOld alg;
MLPPLinAlg algn;
// EXPREG REGRESSION
std::vector<std::vector<real_t>> inputSet = { { 0, 1, 2, 3, 4 } };
std::vector<real_t> 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<MLPPMatrix> input_set;
input_set.instance();
input_set->set_from_std_vectors(inputSet);
Ref<MLPPVector> output_set;
output_set.instance();
output_set->set_from_std_vector(outputSet);
MLPPExpReg model(algn.transposenm(input_set), output_set);
model.sgd(0.001, 10000, ui);
PLOG_MSG(model.model_set_test(algn.transposenm(input_set))->to_string());
PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%");
}
void MLPPTests::test_tanh_regression(bool ui) {
MLPPLinAlgOld alg;
// TANH REGRESSION
std::vector<std::vector<real_t>> inputSet = { { 4, 3, 0, -3, -4 }, { 0, 0, 0, 1, 1 } };
std::vector<real_t> 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;
MLPPData data;
Ref<MLPPDataComplex> 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());
PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%");
}
void MLPPTests::test_support_vector_classification(bool ui) {
//MLPPStat stat;
MLPPLinAlgOld alg;
//MLPPActivation avn;
//MLPPCost cost;
MLPPData data;
//MLPPConvolutions conv;
// SUPPORT VECTOR CLASSIFICATION
Ref<MLPPDataSimple> 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()));
PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%");
}
void MLPPTests::test_mlp(bool ui) {
MLPPLinAlgOld alg;
// MLP
std::vector<std::vector<real_t>> inputSet = {
{ 0, 0 },
{ 1, 1 },
{ 0, 1 },
{ 1, 0 }
};
std::vector<real_t> 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<MLPPMatrix> input_set;
input_set.instance();
input_set->set_from_std_vectors(inputSet);
Ref<MLPPVector> output_set;
output_set.instance();
output_set->set_from_std_vector(outputSet);
MLPPMLP model_new(input_set, output_set, 2);
model_new.gradient_descent(0.1, 10000, ui);
String res = model_new.model_set_test(input_set)->to_string();
res += "\nACCURACY (gradient_descent): " + String::num(100 * model_new.score()) + "%";
PLOG_MSG(res);
MLPPMLP model_new2(input_set, output_set, 2);
model_new2.sgd(0.01, 10000, ui);
res = model_new2.model_set_test(input_set)->to_string();
res += "\nACCURACY (sgd): " + String::num(100 * model_new2.score()) + "%";
PLOG_MSG(res);
MLPPMLP model_new3(input_set, output_set, 2);
model_new3.mbgd(0.01, 10000, 2, ui);
res = model_new3.model_set_test(input_set)->to_string();
res += "\nACCURACY (mbgd): " + String::num(100 * model_new3.score()) + "%";
PLOG_MSG(res);
}
void MLPPTests::test_soft_max_network(bool ui) {
MLPPLinAlgOld alg;
MLPPData data;
// SOFTMAX NETWORK
Ref<MLPPDataComplex> 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 algn;
std::vector<std::vector<real_t>> 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<MLPPMatrix> input_set;
input_set.instance();
input_set->set_from_std_vectors(inputSet);
MLPPAutoEncoder model(algn.transposenm(input_set), 5);
model.sgd(0.001, 300000, ui);
PLOG_MSG(model.model_set_test(algn.transposenm(input_set))->to_string());
PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%");
}
void MLPPTests::test_dynamically_sized_ann(bool ui) {
MLPPLinAlgOld alg;
MLPPLinAlg algn;
// DYNAMICALLY SIZED ANN
// Possible Weight Init Methods: Default, Uniform, HeNormal, HeUniform, XavierNormal, XavierUniform
// Possible Activations: Linear, Sigmoid, Swish, Softplus, Softsign, CLogLog, Ar{Sinh, Cosh, Tanh, Csch, Sech, Coth}, GaussianCDF, GELU, UnitStep
// Possible Loss Functions: MSE, RMSE, MBE, LogLoss, CrossEntropy, HingeLoss
std::vector<std::vector<real_t>> inputSet = { { 0, 0, 1, 1 }, { 0, 1, 0, 1 } };
std::vector<real_t> 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<MLPPMatrix> input_set;
input_set.instance();
input_set->set_from_std_vectors(inputSet);
Ref<MLPPVector> output_set;
output_set.instance();
output_set->set_from_std_vector(outputSet);
MLPPANN ann(algn.transposenm(input_set), output_set);
ann.add_layer(2, MLPPActivation::ACTIVATION_FUNCTION_COSH);
ann.add_output_layer(MLPPActivation::ACTIVATION_FUNCTION_SIGMOID, MLPPCost::COST_TYPE_LOGISTIC_LOSS);
ann.amsgrad(0.1, 10000, 1, 0.9, 0.999, 0.000001, ui);
ann.adadelta(1, 1000, 2, 0.9, 0.000001, ui);
ann.momentum(0.1, 8000, 2, 0.9, true, ui);
ann.set_learning_rate_scheduler_drop(MLPPANN::SCHEDULER_TYPE_STEP, 0.5, 1000);
ann.gradient_descent(0.01, 30000);
PLOG_MSG(ann.model_set_test(algn.transposenm(input_set))->to_string());
PLOG_MSG("ACCURACY: " + String::num(100 * ann.score()) + "%");
}
void MLPPTests::test_wgan_old(bool ui) {
//MLPPStat stat;
MLPPLinAlgOld alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
std::vector<std::vector<real_t>> outputSet = {
{ 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;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
std::vector<std::vector<real_t>> outputSet = {
{ 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 }
};
Ref<MLPPMatrix> output_set;
output_set.instance();
output_set->set_from_std_vectors(alg.transpose(outputSet));
MLPPWGAN gan(2, output_set); // our gan is a wasserstein gan (wgan)
gan.add_layer(5, MLPPActivation::ACTIVATION_FUNCTION_SIGMOID);
gan.add_layer(2, MLPPActivation::ACTIVATION_FUNCTION_RELU);
gan.add_layer(5, MLPPActivation::ACTIVATION_FUNCTION_SIGMOID);
gan.add_output_layer(); // User can specify weight init- if necessary.
gan.gradient_descent(0.1, 55000, ui);
String str = "GENERATED INPUT: (Gaussian-sampled noise):\n";
str += gan.generate_example(100)->to_string();
PLOG_MSG(str);
}
void MLPPTests::test_ann(bool ui) {
MLPPLinAlgOld alg;
std::vector<std::vector<real_t>> inputSet = { { 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 } }; // XOR
std::vector<real_t> 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<real_t> 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<MLPPMatrix> input_set;
input_set.instance();
input_set->set_from_std_vectors(inputSet);
Ref<MLPPVector> output_set;
output_set.instance();
output_set->set_from_std_vector(outputSet);
MLPPANN ann(input_set, output_set);
ann.add_layer(5, MLPPActivation::ACTIVATION_FUNCTION_SIGMOID);
ann.add_layer(8, MLPPActivation::ACTIVATION_FUNCTION_SIGMOID); // Add more layers as needed.
ann.add_output_layer(MLPPActivation::ACTIVATION_FUNCTION_SIGMOID, MLPPCost::COST_TYPE_LOGISTIC_LOSS);
ann.gradient_descent(1, 20000, ui);
Ref<MLPPVector> predictions = ann.model_set_test(input_set);
PLOG_MSG(predictions->to_string()); // Testing out the model's preds for train set.
PLOG_MSG("ACCURACY: " + String::num(100 * ann.score()) + "%"); // Accuracy.
}
void MLPPTests::test_dynamically_sized_mann(bool ui) {
MLPPLinAlgOld alg;
MLPPData data;
// DYNAMICALLY SIZED MANN (Multidimensional Output ANN)
std::vector<std::vector<real_t>> inputSet = { { 1, 2, 3 }, { 2, 4, 6 }, { 3, 6, 9 }, { 4, 8, 12 } };
std::vector<std::vector<real_t>> 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<MLPPMatrix> input_set;
input_set.instance();
input_set->set_from_std_vectors(inputSet);
Ref<MLPPMatrix> output_set;
output_set.instance();
output_set->set_from_std_vectors(outputSet);
MLPPMANN mann(input_set, output_set);
mann.add_output_layer(MLPPActivation::ACTIVATION_FUNCTION_LINEAR, MLPPCost::COST_TYPE_MSE);
mann.gradient_descent(0.001, 80000, false);
PLOG_MSG(mann.model_set_test(input_set)->to_string());
PLOG_MSG("ACCURACY: " + String::num(100 * mann.score()) + "%");
}
void MLPPTests::test_train_test_split_mann(bool ui) {
MLPPLinAlgOld alg;
MLPPLinAlg algn;
MLPPData data;
// TRAIN TEST SPLIT CHECK
std::vector<std::vector<real_t>> inputSet1 = { { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }, { 3, 5, 9, 12, 15, 18, 21, 24, 27, 30 } };
std::vector<std::vector<real_t>> outputSet1 = { { 2, 4, 6, 8, 10, 12, 14, 16, 18, 20 } };
Ref<MLPPMatrix> input_set_1;
input_set_1.instance();
input_set_1->set_from_std_vectors(inputSet1);
Ref<MLPPMatrix> output_set_1;
output_set_1.instance();
output_set_1->set_from_std_vectors(outputSet1);
Ref<MLPPDataComplex> d;
d.instance();
d->set_input(algn.transposenm(input_set_1));
d->set_output(algn.transposenm(output_set_1));
MLPPData::SplitComplexData split_data = data.train_test_split(d, 0.2);
PLOG_MSG(split_data.train->get_input()->to_string());
PLOG_MSG(split_data.train->get_output()->to_string());
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);
mann.gradient_descent(0.1, 80000, ui);
PLOG_MSG(mann.model_set_test(split_data.test->get_input())->to_string());
PLOG_MSG("ACCURACY: " + String::num(100 * mann.score()) + "%");
}
void MLPPTests::test_naive_bayes() {
MLPPLinAlgOld alg;
MLPPLinAlg algn;
// NAIVE BAYES
std::vector<std::vector<real_t>> inputSet = { { 1, 1, 1, 1, 1 }, { 0, 0, 1, 1, 1 }, { 0, 0, 1, 0, 1 } };
std::vector<real_t> outputSet = { 0, 1, 0, 1, 1 };
MLPPMultinomialNBOld MNB_old(alg.transpose(inputSet), outputSet, 2);
alg.printVector(MNB_old.modelSetTest(alg.transpose(inputSet)));
Ref<MLPPMatrix> input_set;
input_set.instance();
input_set->set_from_std_vectors(alg.transpose(inputSet));
Ref<MLPPVector> output_set;
output_set.instance();
output_set->set_from_std_vector(outputSet);
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());
}
void MLPPTests::test_k_means(bool ui) {
// KMeans
std::vector<std::vector<real_t>> inputSet = { { 32, 0, 7 }, { 2, 28, 17 }, { 0, 9, 23 } };
Ref<MLPPMatrix> input_set;
input_set.instance();
input_set->set_from_std_vectors(inputSet);
Ref<MLPPKMeans> kmeans;
kmeans.instance();
kmeans->set_input_set(input_set);
kmeans->set_k(3);
kmeans->set_mean_type(MLPPKMeans::MEAN_TYPE_KMEANSPP);
kmeans->train(3, ui);
PLOG_MSG(kmeans->model_set_test(input_set)->to_string());
PLOG_MSG(kmeans->silhouette_scores()->to_string());
}
void MLPPTests::test_knn(bool ui) {
MLPPLinAlgOld alg;
// kNN
std::vector<std::vector<real_t>> inputSet = {
{ 1, 2, 3, 4, 5, 6, 7, 8 },
{ 0, 0, 0, 0, 1, 1, 1, 1 }
};
std::vector<real_t> outputSet = { 0, 0, 0, 0, 1, 1, 1, 1 };
Ref<MLPPMatrix> ism;
ism.instance();
ism->set_from_std_vectors(alg.transpose(inputSet));
//ERR_PRINT(ism->to_string());
Ref<MLPPVector> osm;
osm.instance();
osm->set_from_std_vector(outputSet);
//ERR_PRINT(osm->to_string());
Ref<MLPPKNN> knn;
knn.instance();
knn->set_k(7);
knn->set_input_set(ism);
knn->set_output_set(osm);
PoolIntArray res = knn->model_set_test(ism);
ERR_PRINT(String(Variant(res)));
ERR_PRINT("ACCURACY: " + itos(100 * knn->score()) + "%");
//(alg.transpose(inputSet), outputSet, 8);
//alg.printVector(knn.modelSetTest(alg.transpose(inputSet)));
//std::cout << "ACCURACY: " << 100 * knn.score() << "%" << std::endl;
}
void MLPPTests::test_convolution_tensors_etc() {
MLPPLinAlgOld alg;
MLPPLinAlg algn;
MLPPData data;
MLPPConvolutionsOld conv;
// CONVOLUTION, POOLING, ETC..
std::vector<std::vector<real_t>> input = {
{ 1 },
};
std::vector<std::vector<std::vector<real_t>>> tensorSet;
tensorSet.push_back(input);
tensorSet.push_back(input);
tensorSet.push_back(input);
alg.printTensor(data.rgb2xyz(tensorSet));
std::vector<std::vector<real_t>> input2 = {
{ 62, 55, 55, 54, 49, 48, 47, 55 },
{ 62, 57, 54, 52, 48, 47, 48, 53 },
{ 61, 60, 52, 49, 48, 47, 49, 54 },
{ 63, 61, 60, 60, 63, 65, 68, 65 },
{ 67, 67, 70, 74, 79, 85, 91, 92 },
{ 82, 95, 101, 106, 114, 115, 112, 117 },
{ 96, 111, 115, 119, 128, 128, 130, 127 },
{ 109, 121, 127, 133, 139, 141, 140, 133 },
};
MLPPTransformsOld trans;
alg.printMatrix(trans.discreteCosineTransform(input2));
alg.printMatrix(conv.convolve_2d(input2, conv.get_prewitt_vertical(), 1)); // Can use padding
alg.printMatrix(conv.pool_2d(input2, 4, 4, "Max")); // Can use Max, Min, or Average pooling.
std::vector<std::vector<std::vector<real_t>>> tensorSet2;
tensorSet2.push_back(input2);
tensorSet2.push_back(input2);
alg.printVector(conv.global_pool_3d(tensorSet2, "Average")); // Can use Max, Min, or Average global pooling.
std::vector<std::vector<real_t>> 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;
// PCA, SVD, eigenvalues & eigenvectors
std::vector<std::vector<real_t>> inputSet = { { 1, 1 }, { 1, 1 } };
MLPPLinAlgOld::EigenResultOld eigen = alg.eigen_old(inputSet);
std::cout << "Eigenvectors:" << std::endl;
alg.printMatrix(eigen.eigen_vectors);
std::cout << std::endl;
std::cout << "Eigenvalues:" << std::endl;
alg.printMatrix(eigen.eigen_values);
std::cout << "SVD OLD START" << std::endl;
MLPPLinAlgOld::SVDResultOld svd_old = alg.SVD(inputSet);
std::cout << "U:" << std::endl;
alg.printMatrix(svd_old.U);
std::cout << "S:" << std::endl;
alg.printMatrix(svd_old.S);
std::cout << "Vt:" << std::endl;
alg.printMatrix(svd_old.Vt);
std::cout << "SVD OLD FIN" << std::endl;
Ref<MLPPMatrix> input_set;
input_set.instance();
input_set->set_from_std_vectors(inputSet);
/*
String str_svd = "SVD\n";
MLPPLinAlgOld::SVDResult svd = alg.svd(input_set);
str_svd += "U:\n";
str_svd += svd.U->to_string();
str_svd += "\nS:\n";
str_svd += svd.S->to_string();
str_svd += "\nVt:\n";
str_svd += svd.Vt->to_string();
str_svd += "\n";
PLOG_MSG(str_svd);
*/
std::cout << "PCA" << std::endl;
// PCA done using Jacobi's method to approximate eigenvalues and eigenvectors.
MLPPPCAOld dr_old(inputSet, 1); // 1 dimensional representation.
std::cout << std::endl;
std::cout << "OLD Dimensionally reduced representation:" << std::endl;
alg.printMatrix(dr_old.principalComponents());
std::cout << "SCORE: " << dr_old.score() << std::endl;
// PCA done using Jacobi's method to approximate eigenvalues and eigenvectors.
MLPPPCA dr(input_set, 1); // 1 dimensional representation.
String str = "\nDimensionally reduced representation:\n";
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;
MLPPData data;
// NLP/DATA
std::string verbText = "I am appearing and thinking, as well as conducting.";
std::cout << "Stemming Example:" << std::endl;
std::cout << data.stemming(verbText) << std::endl;
std::cout << std::endl;
std::vector<std::string> sentences = { "He is a good boy", "She is a good girl", "The boy and girl are good" };
std::cout << "Bag of Words Example:" << std::endl;
alg.printMatrix(data.BOW(sentences, "Default"));
std::cout << std::endl;
std::cout << "TFIDF Example:" << std::endl;
alg.printMatrix(data.TFIDF(sentences));
std::cout << std::endl;
std::cout << "Tokenization:" << std::endl;
alg.printVector(data.tokenize(verbText));
std::cout << std::endl;
std::cout << "Word2Vec:" << std::endl;
std::string textArchive = { "He is a good boy. She is a good girl. The boy and girl are good." };
std::vector<std::string> corpus = data.splitSentences(textArchive);
MLPPData::WordsToVecResult wtvres = data.word_to_vec(corpus, "CBOW", 2, 2, 0.1, 10000); // Can use either CBOW or Skip-n-gram.
alg.printMatrix(wtvres.word_embeddings);
std::cout << std::endl;
std::vector<std::string> textArchive2 = { "pizza", "pizza hamburger cookie", "hamburger", "ramen", "sushi", "ramen sushi" };
alg.printMatrix(data.LSA(textArchive2, 2));
//alg.printMatrix(data.BOW(textArchive, "Default"));
std::cout << std::endl;
std::vector<std::vector<real_t>> inputSet = { { 1, 2 }, { 2, 3 }, { 3, 4 }, { 4, 5 }, { 5, 6 } };
std::cout << "Feature Scaling Example:" << std::endl;
alg.printMatrix(data.featureScaling(inputSet));
std::cout << std::endl;
std::cout << "Mean Centering Example:" << std::endl;
alg.printMatrix(data.meanCentering(inputSet));
std::cout << std::endl;
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;
// Outlier Finder
//std::vector<real_t> inputSet = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 23554332523523 };
std::vector<real_t> 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<MLPPVector> input_set;
input_set.instance();
input_set->set_from_std_vector(inputSet);
MLPPOutlierFinder outlier_finder(2); // Any datapoint outside of 2 stds from the mean is marked as an outlier.
PLOG_MSG(Variant(outlier_finder.model_test(input_set)));
}
void MLPPTests::test_new_math_functions() {
MLPPLinAlgOld alg;
MLPPActivationOld avn;
MLPPData data;
// Testing new Functions
real_t z_s = 0.001;
std::cout << avn.logit(z_s) << std::endl;
std::cout << avn.logit(z_s, true) << std::endl;
std::vector<real_t> z_v = { 0.001 };
alg.printVector(avn.logit(z_v));
alg.printVector(avn.logit(z_v, true));
std::vector<std::vector<real_t>> Z_m = { { 0.001 } };
alg.printMatrix(avn.logit(Z_m));
alg.printMatrix(avn.logit(Z_m, true));
std::cout << alg.trace({ { 1, 2 }, { 3, 4 } }) << std::endl;
alg.printMatrix(alg.pinverse({ { 1, 2 }, { 3, 4 } }));
alg.printMatrix(alg.diag({ 1, 2, 3, 4, 5 }));
alg.printMatrix(alg.kronecker_product({ { 1, 2, 3, 4, 5 } }, { { 6, 7, 8, 9, 10 } }));
alg.printMatrix(alg.matrixPower({ { 5, 5 }, { 5, 5 } }, 2));
alg.printVector(alg.solve({ { 1, 1 }, { 1.5, 4.0 } }, { 2200, 5050 }));
std::vector<std::vector<real_t>> matrixOfCubes = { { 1, 2, 64, 27 } };
std::vector<real_t> vectorOfCubes = { 1, 2, 64, 27 };
alg.printMatrix(alg.cbrt(matrixOfCubes));
alg.printVector(alg.cbrt(vectorOfCubes));
std::cout << alg.max({ { 1, 2, 3, 4, 5 }, { 6, 5, 3, 4, 1 }, { 9, 9, 9, 9, 9 } }) << std::endl;
std::cout << alg.min({ { 1, 2, 3, 4, 5 }, { 6, 5, 3, 4, 1 }, { 9, 9, 9, 9, 9 } }) << std::endl;
//std::vector<real_t> chicken;
//data.getImage("../../Data/apple.jpeg", chicken);
//alg.printVector(chicken);
std::vector<std::vector<real_t>> P = { { 12, -51, 4 }, { 6, 167, -68 }, { -4, 24, -41 } };
alg.printMatrix(P);
alg.printMatrix(alg.gramSchmidtProcess(P));
//MLPPLinAlgOld::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;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// Checking positive-definiteness checker. For Cholesky Decomp.
std::vector<std::vector<real_t>> A = {
{ 1, -1, -1, -1 },
{ -1, 2, 2, 2 },
{ -1, 2, 3, 1 },
{ -1, 2, 1, 4 }
};
std::cout << std::boolalpha << alg.positiveDefiniteChecker(A) << std::endl;
MLPPLinAlgOld::CholeskyResult chres = alg.cholesky(A); // works.
alg.printMatrix(chres.L);
alg.printMatrix(chres.Lt);
}
// real_t f(real_t x){
// return x*x*x + 2*x - 2;
// }
real_t f(real_t x) {
return sin(x);
}
real_t f_prime(real_t x) {
return 2 * x;
}
real_t f_prime_2var(std::vector<real_t> x) {
return 2 * x[0] + x[1];
}
/*
y = x^3 + 2x - 2
y' = 3x^2 + 2
y'' = 6x
y''(2) = 12
*/
// real_t f_mv(std::vector<real_t> x){
// return x[0] * x[0] + x[0] * x[1] * x[1] + x[1] + 5;
// }
/*
Where x, y = x[0], x[1], this function is defined as:
f(x, y) = x^2 + xy^2 + y + 5
∂f/∂x = 2x + 2y
∂^2f/∂x∂y = 2
*/
real_t f_mv(std::vector<real_t> x) {
return x[0] * x[0] * x[0] + x[0] + x[1] * x[1] * x[1] * x[0] + x[2] * x[2] * x[1];
}
/*
Where x, y = x[0], x[1], this function is defined as:
f(x, y) = x^3 + x + xy^3 + yz^2
fy = 3xy^2 + 2yz
fyy = 6xy + 2z
fyyz = 2
∂^2f/∂y^2 = 6xy + 2z
∂^3f/∂y^3 = 6x
∂f/∂z = 2zy
∂^2f/∂z^2 = 2y
∂^3f/∂z^3 = 0
∂f/∂x = 3x^2 + 1 + y^3
∂^2f/∂x^2 = 6x
∂^3f/∂x^3 = 6
∂f/∂z = 2zy
∂^2f/∂z^2 = 2z
∂f/∂y = 3xy^2
∂^2f/∂y∂x = 3y^2
*/
void MLPPTests::test_numerical_analysis() {
MLPPLinAlgOld alg;
MLPPConvolutionsOld conv;
// Checks for numerical analysis class.
MLPPNumericalAnalysisOld numAn;
std::cout << numAn.quadraticApproximation(f, 0, 1) << std::endl;
std::cout << numAn.cubicApproximation(f, 0, 1.001) << std::endl;
std::cout << f(1.001) << std::endl;
std::cout << numAn.quadraticApproximation(f_mv, { 0, 0, 0 }, { 1, 1, 1 }) << std::endl;
std::cout << numAn.numDiff(&f, 1) << std::endl;
std::cout << numAn.newtonRaphsonMethod(&f, 1, 1000) << std::endl;
std::cout << numAn.invQuadraticInterpolation(&f, { 100, 2, 1.5 }, 10) << std::endl;
std::cout << numAn.numDiff(&f_mv, { 1, 1 }, 1) << std::endl; // Derivative w.r.t. x.
alg.printVector(numAn.jacobian(&f_mv, { 1, 1 }));
std::cout << numAn.numDiff_2(&f, 2) << std::endl;
std::cout << numAn.numDiff_3(&f, 2) << std::endl;
std::cout << numAn.numDiff_2(&f_mv, { 2, 2, 500 }, 2, 2) << std::endl;
std::cout << numAn.numDiff_3(&f_mv, { 2, 1000, 130 }, 0, 0, 0) << std::endl;
alg.printTensor(numAn.thirdOrderTensor(&f_mv, { 1, 1, 1 }));
std::cout << "Our Hessian." << std::endl;
alg.printMatrix(numAn.hessian(&f_mv, { 2, 2, 500 }));
std::cout << numAn.laplacian(f_mv, { 1, 1, 1 }) << std::endl;
std::vector<std::vector<std::vector<real_t>>> tensor;
tensor.push_back({ { 1, 2 }, { 1, 2 }, { 1, 2 } });
tensor.push_back({ { 1, 2 }, { 1, 2 }, { 1, 2 } });
alg.printTensor(tensor);
alg.printMatrix(alg.tensor_vec_mult(tensor, { 1, 2 }));
std::cout << numAn.cubicApproximation(f_mv, { 0, 0, 0 }, { 1, 1, 1 }) << std::endl;
std::cout << numAn.eulerianMethod(f_prime, { 1, 1 }, 1.5, 0.000001) << std::endl;
std::cout << numAn.eulerianMethod(f_prime_2var, { 2, 3 }, 2.5, 0.00000001) << std::endl;
std::vector<std::vector<real_t>> A = {
{ 1, 0, 0, 0 },
{ 0, 0, 0, 0 },
{ 0, 0, 0, 0 },
{ 0, 0, 0, 1 }
};
alg.printMatrix(conv.dx(A));
alg.printMatrix(conv.dy(A));
alg.printMatrix(conv.grad_orientation(A));
std::vector<std::vector<std::string>> h = conv.harris_corner_detection(A);
for (uint32_t i = 0; i < h.size(); i++) {
for (uint32_t j = 0; j < h[i].size(); j++) {
std::cout << h[i][j] << " ";
}
std::cout << std::endl;
} // Harris detector works. Life is good!
std::vector<real_t> a = { 3, 4, 4 };
std::vector<real_t> b = { 4, 4, 4 };
alg.printVector(alg.cross(a, b));
}
void MLPPTests::test_support_vector_classification_kernel(bool ui) {
MLPPLinAlgOld alg;
MLPPData data;
//SUPPORT VECTOR CLASSIFICATION (kernel method)
Ref<MLPPDataSimple> 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<std::vector<real_t>> 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() {
std::vector<real_t> a = { 4, 3, 1, 3 };
Ref<MLPPVector> rv;
rv.instance();
rv->set_from_std_vector(a);
Ref<MLPPVector> rv2;
rv2.instance();
rv2->set_from_std_vector(a);
is_approx_equals_vec(rv, rv2, "set_from_std_vectors test.");
rv2->set_from_std_vector(a);
is_approx_equals_vec(rv, rv2, "re-set_from_std_vectors test.");
}
void MLPPTests::is_approx_equalsd(real_t a, real_t b, const String &str) {
if (!Math::is_equal_approx(a, b)) {
PLOG_ERR("TEST FAILED: " + str + " Got: " + String::num(a) + " Should be: " + String::num(b));
} else {
PLOG_MSG("TEST PASSED: " + str);
}
}
void MLPPTests::is_approx_equals_dvec(const Vector<real_t> &a, const Vector<real_t> &b, const String &str) {
if (a.size() != b.size()) {
goto IAEDVEC_FAILED;
}
for (int i = 0; i < a.size(); ++i) {
if (!Math::is_equal_approx(a[i], b[i])) {
goto IAEDVEC_FAILED;
}
}
PLOG_MSG("TEST PASSED: " + str);
return;
IAEDVEC_FAILED:
String fail_str = "TEST FAILED: ";
fail_str += str;
fail_str += " Got: [ ";
for (int i = 0; i < a.size(); ++i) {
fail_str += String::num(a[i]);
fail_str += " ";
}
fail_str += "] Should be: [ ";
for (int i = 0; i < b.size(); ++i) {
fail_str += String::num(b[i]);
fail_str += " ";
}
fail_str += "].";
PLOG_ERR(fail_str);
}
String vmat_to_str(const Vector<Vector<real_t>> &a) {
String str;
str += "[ \n";
for (int i = 0; i < a.size(); ++i) {
str += " [ ";
const Vector<real_t> &aa = a[i];
for (int j = 0; j < aa.size(); ++j) {
str += String::num(aa[j]);
str += " ";
}
str += "]\n";
}
str += "]\n";
return str;
}
void MLPPTests::is_approx_equals_dmat(const Vector<Vector<real_t>> &a, const Vector<Vector<real_t>> &b, const String &str) {
if (a.size() != b.size()) {
goto IAEDMAT_FAILED;
}
for (int i = 0; i < a.size(); ++i) {
const Vector<real_t> &aa = a[i];
const Vector<real_t> &bb = b[i];
if (aa.size() != bb.size()) {
goto IAEDMAT_FAILED;
}
for (int j = 0; j < aa.size(); ++j) {
if (!Math::is_equal_approx(aa[j], bb[j])) {
goto IAEDMAT_FAILED;
}
}
}
PLOG_MSG("TEST PASSED: " + str);
return;
IAEDMAT_FAILED:
String fail_str = "TEST FAILED: ";
fail_str += str;
fail_str += "\nGot:\n";
fail_str += vmat_to_str(a);
fail_str += "Should be:\n";
fail_str += vmat_to_str(b);
PLOG_ERR(fail_str);
}
void MLPPTests::is_approx_equals_mat(Ref<MLPPMatrix> a, Ref<MLPPMatrix> b, const String &str) {
ERR_FAIL_COND(!a.is_valid());
ERR_FAIL_COND(!b.is_valid());
int ds = a->data_size();
const real_t *aa = a->ptr();
const real_t *bb = b->ptr();
if (a->size() != b->size()) {
goto IAEMAT_FAILED;
}
ERR_FAIL_COND(!aa);
ERR_FAIL_COND(!bb);
for (int i = 0; i < ds; ++i) {
if (!Math::is_equal_approx(aa[i], bb[i])) {
goto IAEMAT_FAILED;
}
}
PLOG_MSG("TEST PASSED: " + str);
return;
IAEMAT_FAILED:
String fail_str = "TEST FAILED: ";
fail_str += str;
fail_str += "\nGot:\n";
fail_str += a->to_string();
fail_str += "\nShould be:\n";
fail_str += b->to_string();
PLOG_ERR(fail_str);
}
void MLPPTests::is_approx_equals_vec(Ref<MLPPVector> a, Ref<MLPPVector> b, const String &str) {
ERR_FAIL_COND(!a.is_valid());
ERR_FAIL_COND(!b.is_valid());
if (a->size() != b->size()) {
goto IAEDVEC_FAILED;
}
for (int i = 0; i < a->size(); ++i) {
if (!Math::is_equal_approx(a->get_element(i), b->get_element(i))) {
goto IAEDVEC_FAILED;
}
}
PLOG_MSG("TEST PASSED: " + str);
return;
IAEDVEC_FAILED:
String fail_str = "TEST FAILED: ";
fail_str += str;
fail_str += "\nGot:\n";
fail_str += a->to_string();
fail_str += "\nShould be:\n";
fail_str += b->to_string();
fail_str += "\n.";
PLOG_ERR(fail_str);
}
MLPPTests::MLPPTests() {
_breast_cancer_data_path = "res://datasets/BreastCancer.csv";
_breast_cancer_svm_data_path = "res://datasets/BreastCancerSVM.csv";
_california_housing_data_path = "res://datasets/CaliforniaHousing.csv";
_fires_and_crime_data_path = "res://datasets/FiresAndCrime.csv";
_iris_data_path = "res://datasets/Iris.csv";
_mnist_test_data_path = "res://datasets/MnistTest.csv";
_mnist_train_data_path = "res://datasets/MnistTrain.csv";
_wine_data_path = "res://datasets/Wine.csv";
}
MLPPTests::~MLPPTests() {
}
void MLPPTests::_bind_methods() {
ClassDB::bind_method(D_METHOD("test_statistics"), &MLPPTests::test_statistics);
ClassDB::bind_method(D_METHOD("test_linear_algebra"), &MLPPTests::test_linear_algebra);
ClassDB::bind_method(D_METHOD("test_univariate_linear_regression"), &MLPPTests::test_univariate_linear_regression);
ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_gradient_descent", "ui"), &MLPPTests::test_multivariate_linear_regression_gradient_descent, false);
ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_sgd", "ui"), &MLPPTests::test_multivariate_linear_regression_sgd, false);
ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_mbgd", "ui"), &MLPPTests::test_multivariate_linear_regression_mbgd, false);
ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_normal_equation", "ui"), &MLPPTests::test_multivariate_linear_regression_normal_equation, false);
ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_adam"), &MLPPTests::test_multivariate_linear_regression_adam);
ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_score_sgd_adam", "ui"), &MLPPTests::test_multivariate_linear_regression_score_sgd_adam, false);
ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_epochs_gradient_descent", "ui"), &MLPPTests::test_multivariate_linear_regression_epochs_gradient_descent, false);
ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_newton_raphson", "ui"), &MLPPTests::test_multivariate_linear_regression_newton_raphson, false);
ClassDB::bind_method(D_METHOD("test_logistic_regression", "ui"), &MLPPTests::test_logistic_regression, false);
ClassDB::bind_method(D_METHOD("test_probit_regression", "ui"), &MLPPTests::test_probit_regression, false);
ClassDB::bind_method(D_METHOD("test_c_log_log_regression", "ui"), &MLPPTests::test_c_log_log_regression, false);
ClassDB::bind_method(D_METHOD("test_exp_reg_regression", "ui"), &MLPPTests::test_exp_reg_regression, false);
ClassDB::bind_method(D_METHOD("test_tanh_regression", "ui"), &MLPPTests::test_tanh_regression, false);
ClassDB::bind_method(D_METHOD("test_softmax_regression", "ui"), &MLPPTests::test_softmax_regression, false);
ClassDB::bind_method(D_METHOD("test_support_vector_classification", "ui"), &MLPPTests::test_support_vector_classification, false);
ClassDB::bind_method(D_METHOD("test_mlp", "ui"), &MLPPTests::test_mlp, false);
ClassDB::bind_method(D_METHOD("test_soft_max_network", "ui"), &MLPPTests::test_soft_max_network, false);
ClassDB::bind_method(D_METHOD("test_autoencoder", "ui"), &MLPPTests::test_autoencoder, false);
ClassDB::bind_method(D_METHOD("test_dynamically_sized_ann", "ui"), &MLPPTests::test_dynamically_sized_ann, false);
ClassDB::bind_method(D_METHOD("test_wgan_old", "ui"), &MLPPTests::test_wgan_old, false);
ClassDB::bind_method(D_METHOD("test_wgan", "ui"), &MLPPTests::test_wgan, false);
ClassDB::bind_method(D_METHOD("test_ann", "ui"), &MLPPTests::test_ann, false);
ClassDB::bind_method(D_METHOD("test_dynamically_sized_mann", "ui"), &MLPPTests::test_dynamically_sized_mann, false);
ClassDB::bind_method(D_METHOD("test_train_test_split_mann", "ui"), &MLPPTests::test_train_test_split_mann, false);
ClassDB::bind_method(D_METHOD("test_naive_bayes"), &MLPPTests::test_naive_bayes);
ClassDB::bind_method(D_METHOD("test_k_means", "ui"), &MLPPTests::test_k_means, false);
ClassDB::bind_method(D_METHOD("test_knn", "ui"), &MLPPTests::test_knn, false);
ClassDB::bind_method(D_METHOD("test_convolution_tensors_etc"), &MLPPTests::test_convolution_tensors_etc);
ClassDB::bind_method(D_METHOD("test_pca_svd_eigenvalues_eigenvectors", "ui"), &MLPPTests::test_pca_svd_eigenvalues_eigenvectors, false);
ClassDB::bind_method(D_METHOD("test_nlp_and_data", "ui"), &MLPPTests::test_nlp_and_data, false);
ClassDB::bind_method(D_METHOD("test_outlier_finder", "ui"), &MLPPTests::test_outlier_finder, false);
ClassDB::bind_method(D_METHOD("test_new_math_functions"), &MLPPTests::test_new_math_functions);
ClassDB::bind_method(D_METHOD("test_positive_definiteness_checker"), &MLPPTests::test_positive_definiteness_checker);
ClassDB::bind_method(D_METHOD("test_numerical_analysis"), &MLPPTests::test_numerical_analysis);
ClassDB::bind_method(D_METHOD("test_support_vector_classification_kernel", "ui"), &MLPPTests::test_support_vector_classification_kernel, false);
ClassDB::bind_method(D_METHOD("test_mlpp_vector"), &MLPPTests::test_mlpp_vector);
}
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