Relintai/pmlpp
1
0
Fork 0
mirror of https://github.com/Relintai/pmlpp.git synced 2024-11-08 13:12:09 +01:00
Code Issues Packages Projects Releases Wiki Activity

Cleaned up MLPPTestsOld.

Browse Source
This commit is contained in:
Relintai 2023-04-27 19:16:35 +02:00
parent 9be48155c0
commit dfd8b21e21
1 changed files with 2 additions and 351 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

353
test/mlpp_tests_old.cpp
View File

@ -11,41 +11,7 @@
#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"
@ -108,7 +74,6 @@ void MLPPTestsOld::test_statistics() {
ERR_PRINT("MLPPTestsOld::test_statistics() Started!");
MLPPStatOld stat;
MLPPConvolutions conv;
// STATISTICS
std::vector<real_t> x = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };
@ -221,28 +186,6 @@ void MLPPTestsOld::test_univariate_linear_regression() {
};
is_approx_equals_dvec(dstd_vec_to_vec_old(model_old.modelSetTest(ds->get_input()->to_std_vector())), dstd_vec_to_vec_old(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 MLPPTestsOld::test_multivariate_linear_regression_gradient_descent(bool ui) {
@ -254,10 +197,6 @@ void MLPPTestsOld::test_multivariate_linear_regression_gradient_descent(bool ui)
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 MLPPTestsOld::test_multivariate_linear_regression_sgd(bool ui) {
@ -269,10 +208,6 @@ void MLPPTestsOld::test_multivariate_linear_regression_sgd(bool ui) {
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 MLPPTestsOld::test_multivariate_linear_regression_mbgd(bool ui) {
@ -284,10 +219,6 @@ void MLPPTestsOld::test_multivariate_linear_regression_mbgd(bool ui) {
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 MLPPTestsOld::test_multivariate_linear_regression_normal_equation(bool ui) {
@ -299,32 +230,22 @@ void MLPPTestsOld::test_multivariate_linear_regression_normal_equation(bool ui)
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 MLPPTestsOld::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 MLPPTestsOld::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);
@ -337,17 +258,9 @@ void MLPPTestsOld::test_multivariate_linear_regression_score_sgd_adam(bool ui) {
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;
@ -358,7 +271,6 @@ void MLPPTestsOld::test_multivariate_linear_regression_score_sgd_adam(bool ui) {
void MLPPTestsOld::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);
@ -368,16 +280,11 @@ void MLPPTestsOld::test_multivariate_linear_regression_epochs_gradient_descent(b
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 MLPPTestsOld::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);
@ -388,10 +295,6 @@ void MLPPTestsOld::test_multivariate_linear_regression_newton_raphson(bool ui) {
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 MLPPTestsOld::test_logistic_regression(bool ui) {
@ -406,11 +309,6 @@ void MLPPTestsOld::test_logistic_regression(bool ui) {
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 MLPPTestsOld::test_probit_regression(bool ui) {
MLPPLinAlgOld alg;
@ -423,16 +321,9 @@ void MLPPTestsOld::test_probit_regression(bool ui) {
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 MLPPTestsOld::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 };
@ -441,23 +332,9 @@ void MLPPTestsOld::test_c_log_log_regression(bool ui) {
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 MLPPTestsOld::test_exp_reg_regression(bool ui) {
MLPPLinAlgOld alg;
MLPPLinAlg algn;
// EXPREG REGRESSION
std::vector<std::vector<real_t>> inputSet = { { 0, 1, 2, 3, 4 } };
@ -475,11 +352,6 @@ void MLPPTestsOld::test_exp_reg_regression(bool ui) {
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 MLPPTestsOld::test_tanh_regression(bool ui) {
MLPPLinAlgOld alg;
@ -505,11 +377,6 @@ void MLPPTestsOld::test_softmax_regression(bool ui) {
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 MLPPTestsOld::test_support_vector_classification(bool ui) {
//MLPPStat stat;
@ -526,11 +393,6 @@ void MLPPTestsOld::test_support_vector_classification(bool 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 MLPPTestsOld::test_mlp(bool ui) {
@ -549,35 +411,6 @@ void MLPPTestsOld::test_mlp(bool ui) {
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 MLPPTestsOld::test_soft_max_network(bool ui) {
MLPPLinAlgOld alg;
@ -590,15 +423,9 @@ void MLPPTestsOld::test_soft_max_network(bool ui) {
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 MLPPTestsOld::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 } };
@ -611,15 +438,9 @@ void MLPPTestsOld::test_autoencoder(bool ui) {
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 MLPPTestsOld::test_dynamically_sized_ann(bool ui) {
MLPPLinAlgOld alg;
MLPPLinAlg algn;
// DYNAMICALLY SIZED ANN
// Possible Weight Init Methods: Default, Uniform, HeNormal, HeUniform, XavierNormal, XavierUniform
@ -640,27 +461,6 @@ void MLPPTestsOld::test_dynamically_sized_ann(bool ui) {
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 MLPPTestsOld::test_wgan_old(bool ui) {
//MLPPStat stat;
@ -685,32 +485,6 @@ void MLPPTestsOld::test_wgan_old(bool ui) {
alg.printMatrix(gan_old.generateExample(100));
}
void MLPPTestsOld::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 MLPPTestsOld::test_ann(bool ui) {
MLPPLinAlgOld alg;
@ -727,24 +501,6 @@ void MLPPTestsOld::test_ann(bool 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 MLPPTestsOld::test_dynamically_sized_mann(bool ui) {
MLPPLinAlgOld alg;
@ -759,24 +515,9 @@ void MLPPTestsOld::test_dynamically_sized_mann(bool ui) {
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 MLPPTestsOld::test_train_test_split_mann(bool ui) {
MLPPLinAlgOld alg;
MLPPLinAlg algn;
MLPPData data;
// TRAIN TEST SPLIT CHECK
@ -794,8 +535,8 @@ void MLPPTestsOld::test_train_test_split_mann(bool ui) {
Ref<MLPPDataComplex> d;
d.instance();
d->set_input(algn.transposenm(input_set_1));
d->set_output(algn.transposenm(output_set_1));
d->set_input(input_set_1->transposen());
d->set_output(output_set_1->transposen());
MLPPData::SplitComplexData split_data = data.train_test_split(d, 0.2);
@ -810,18 +551,10 @@ void MLPPTestsOld::test_train_test_split_mann(bool ui) {
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 MLPPTestsOld::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 } };
@ -838,82 +571,19 @@ void MLPPTestsOld::test_naive_bayes() {
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 MLPPTestsOld::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 MLPPTestsOld::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 MLPPTestsOld::test_convolution_tensors_etc() {
MLPPLinAlgOld alg;
MLPPLinAlg algn;
MLPPData data;
MLPPConvolutionsOld conv;
@ -1010,14 +680,6 @@ void MLPPTestsOld::test_pca_svd_eigenvalues_eigenvectors(bool ui) {
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 MLPPTestsOld::test_nlp_and_data(bool ui) {
@ -1078,13 +740,6 @@ void MLPPTestsOld::test_outlier_finder(bool ui) {
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 MLPPTestsOld::test_new_math_functions() {
MLPPLinAlgOld alg;
@ -1301,10 +956,6 @@ void MLPPTestsOld::test_support_vector_classification_kernel(bool ui) {
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 }