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 multivariate linear regression tests.

Browse Source
This commit is contained in:
Relintai 2023-12-27 16:13:06 +01:00
parent 1ab7e52287
commit f01770ddba
3 changed files with 77 additions and 112 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

102
test/mlpp_tests.cpp
View File

@ -250,7 +250,7 @@ void MLPPTests::test_multivariate_linear_regression_sgd(bool ui) {
int rmse = (int)mlpp_cost.rmsev(ds->get_output(), res);
//Lose the bottom 15 bits (This should allow for 2^15 difference.)
//Lose the bottom X bits (This should allow for 2^X difference.)
rmse = rmse >> 15;
rmse = rmse << 15;
@ -264,8 +264,18 @@ void MLPPTests::test_multivariate_linear_regression_mbgd(bool ui) {
Ref<MLPPDataSimple> ds = data.load_california_housing(_california_housing_data_path);
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());
model.mbgd(0.00000001, 30, 2, ui);
Ref<MLPPVector> res = model.model_set_test(ds->get_input());
MLPPCost mlpp_cost;
int rmse = (int)mlpp_cost.rmsev(ds->get_output(), res);
//Lose the bottom X bits (This should allow for 2^X difference.)
rmse = rmse >> 10;
rmse = rmse << 10;
is_approx_equalsd(rmse, 230400, "test_multivariate_linear_regression_mbgd() RMSE");
}
void MLPPTests::test_multivariate_linear_regression_normal_equation(bool ui) {
@ -273,21 +283,48 @@ void MLPPTests::test_multivariate_linear_regression_normal_equation(bool ui) {
MLPPLinAlg alg;
Ref<MLPPDataSimple> ds = data.load_california_housing(_california_housing_data_path);
ds->get_input()->resize(Size2i(8, 10));
ds->get_output()->resize(10);
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());
Ref<MLPPVector> res = model.model_set_test(ds->get_input());
MLPPCost mlpp_cost;
int rmse = (int)mlpp_cost.rmsev(ds->get_output(), res);
//Lose the bottom X bits (This should allow for 2^X difference.)
rmse = rmse >> 10;
rmse = rmse << 10;
is_approx_equalsd(rmse, 319488, "test_multivariate_linear_regression_normal_equation() RMSE");
}
void MLPPTests::test_multivariate_linear_regression_adam() {
void MLPPTests::test_multivariate_linear_regression_adam(bool ui) {
MLPPData data;
MLPPLinAlg alg;
Ref<MLPPDataSimple> ds = data.load_california_housing(_california_housing_data_path);
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()) + "%");
MLPPLinReg model(ds->get_input(), ds->get_output());
model.adam(0.0001, 30, 10, 0.9, 0.999, 1e-8, ui);
//real_t score = 100 * model.score();
Ref<MLPPVector> res = model.model_set_test(ds->get_input());
MLPPCost mlpp_cost;
int rmse = (int)mlpp_cost.rmsev(ds->get_output(), res);
//Lose the bottom X bits (This should allow for 2^X difference.)
rmse = rmse >> 10;
rmse = rmse << 10;
is_approx_equalsd(rmse, 156672, "test_multivariate_linear_regression_adam() RMSE");
//is_approx_equalsd(score, 319488, "test_multivariate_linear_regression_adam() score");
}
void MLPPTests::test_multivariate_linear_regression_score_sgd_adam(bool ui) {
@ -297,23 +334,22 @@ void MLPPTests::test_multivariate_linear_regression_score_sgd_adam(bool ui) {
Ref<MLPPDataSimple> ds = data.load_california_housing(_california_housing_data_path);
const int TRIAL_NUM = 1000;
const int TRIAL_NUM = 10;
real_t scoreSGD = 0;
real_t scoreADAM = 0;
for (int i = 0; i < TRIAL_NUM; i++) {
MLPPLinReg modelf(algn.transposenm(ds->get_input()), ds->get_output());
MLPPLinReg modelf(ds->get_input(), ds->get_output());
modelf.mbgd(0.001, 5, 1, ui);
scoreSGD += modelf.score();
MLPPLinReg adamModelf(algn.transposenm(ds->get_input()), ds->get_output());
MLPPLinReg adamModelf(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;
is_approx_equalsd((int)(100 * scoreSGD / TRIAL_NUM), 0, "test_multivariate_linear_regression_score_sgd_adam() ACCURACY, AVG, SGD");
is_approx_equalsd((int)(100 * scoreADAM / TRIAL_NUM), 0, "test_multivariate_linear_regression_score_sgd_adam() ACCURACY, AVG, ADAM");
}
void MLPPTests::test_multivariate_linear_regression_epochs_gradient_descent(bool ui) {
@ -323,12 +359,20 @@ void MLPPTests::test_multivariate_linear_regression_epochs_gradient_descent(bool
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;
MLPPLinReg model(ds->get_input(), ds->get_output()); // Can use Lasso, Ridge, ElasticNet Reg
model.gradient_descent(0.0000001, 300, ui);
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());
Ref<MLPPVector> res = model.model_set_test(ds->get_input());
MLPPCost mlpp_cost;
int rmse = (int)mlpp_cost.rmsev(ds->get_output(), res);
//Lose the bottom X bits (This should allow for 2^X difference.)
rmse = rmse >> 16;
rmse = rmse << 16;
is_approx_equalsd(rmse, 131072, "test_multivariate_linear_regression_epochs_gradient_descent() RMSE");
}
void MLPPTests::test_multivariate_linear_regression_newton_raphson(bool ui) {
@ -338,13 +382,19 @@ void MLPPTests::test_multivariate_linear_regression_newton_raphson(bool ui) {
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;
MLPPLinReg model(ds->get_input(), ds->get_output());
model.newton_raphson(1.5, 300, ui);
Ref<MLPPVector> res = model.model_set_test(ds->get_input());
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());
MLPPCost mlpp_cost;
int rmse = (int)mlpp_cost.rmsev(ds->get_output(), res);
//Lose the bottom X bits (This should allow for 2^X difference.)
//rmse = rmse >> 15;
//rmse = rmse << 15;
//is_approx_equalsd(rmse, 98304, "test_multivariate_linear_regression_newton_raphson() RMSE");
}
void MLPPTests::test_logistic_regression(bool ui) {
@ -1389,7 +1439,7 @@ void MLPPTests::_bind_methods() {
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_adam"), &MLPPTests::test_multivariate_linear_regression_adam, false);
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);

2
test/mlpp_tests.h
View File

@ -27,7 +27,7 @@ public:
void test_multivariate_linear_regression_sgd(bool ui = false);
void test_multivariate_linear_regression_mbgd(bool ui = false);
void test_multivariate_linear_regression_normal_equation(bool ui = false);
void test_multivariate_linear_regression_adam();
void test_multivariate_linear_regression_adam(bool ui = false);
void test_multivariate_linear_regression_score_sgd_adam(bool ui = false);
void test_multivariate_linear_regression_epochs_gradient_descent(bool ui = false);
void test_multivariate_linear_regression_newton_raphson(bool ui = false);

85
test/mlpp_tests_old.cpp
View File

@ -80,112 +80,27 @@ void MLPPTestsOld::test_univariate_linear_regression() {
}
void MLPPTestsOld::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()));
}
void MLPPTestsOld::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()));
}
void MLPPTestsOld::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()));
}
void MLPPTestsOld::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()));
}
void MLPPTestsOld::test_multivariate_linear_regression_adam() {
MLPPData data;
MLPPLinAlgOld alg;
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;
}
void MLPPTestsOld::test_multivariate_linear_regression_score_sgd_adam(bool ui) {
MLPPData data;
MLPPLinAlgOld alg;
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();
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();
}
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 MLPPTestsOld::test_multivariate_linear_regression_epochs_gradient_descent(bool ui) {
MLPPData data;
MLPPLinAlgOld alg;
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()));
}
void MLPPTestsOld::test_multivariate_linear_regression_newton_raphson(bool ui) {
MLPPData data;
MLPPLinAlgOld alg;
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()));
}
void MLPPTestsOld::test_logistic_regression(bool ui) {