Relintai/pmlpp
1
0
Fork 0
mirror of https://github.com/Relintai/pmlpp.git synced 2024-12-22 15:06:47 +01:00
Code Issues Packages Projects Releases Wiki Activity

Cleaned up more tests.

Browse Source
This commit is contained in:
Relintai 2023-12-27 18:15:58 +01:00
parent 681118c9d2
commit 7229bc7242
2 changed files with 16 additions and 87 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

20
test/mlpp_tests.cpp
View File

@ -446,7 +446,6 @@ void MLPPTests::test_c_log_log_regression(bool ui) {
} }
void MLPPTests::test_exp_reg_regression(bool ui) { void MLPPTests::test_exp_reg_regression(bool ui) {
MLPPLinAlg alg; MLPPLinAlg alg;
MLPPLinAlg algn;
// EXPREG REGRESSION // EXPREG REGRESSION
std::vector<std::vector<real_t>> inputSet = { { 0, 1, 2, 3, 4 } }; std::vector<std::vector<real_t>> inputSet = { { 0, 1, 2, 3, 4 } };
@ -460,9 +459,9 @@ void MLPPTests::test_exp_reg_regression(bool ui) {
output_set.instance(); output_set.instance();
output_set->set_from_std_vector(outputSet); output_set->set_from_std_vector(outputSet);
MLPPExpReg model(algn.transposenm(input_set), output_set); MLPPExpReg model(alg.transposenm(input_set), output_set);
model.sgd(0.001, 10000, ui); model.sgd(0.001, 10000, ui);
PLOG_MSG(model.model_set_test(algn.transposenm(input_set))->to_string()); PLOG_MSG(model.model_set_test(alg.transposenm(input_set))->to_string());
PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%"); PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%");
} }
void MLPPTests::test_tanh_regression(bool ui) { void MLPPTests::test_tanh_regression(bool ui) {
@ -471,6 +470,19 @@ void MLPPTests::test_tanh_regression(bool ui) {
// TANH REGRESSION // TANH REGRESSION
std::vector<std::vector<real_t>> inputSet = { { 4, 3, 0, -3, -4 }, { 0, 0, 0, 1, 1 } }; 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 }; std::vector<real_t> outputSet = { 1, 1, 0, -1, -1 };
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);
MLPPTanhReg model(alg.transposenm(input_set), output_set);
model.train_sgd(0.1, 10000, ui);
//PLOG_MSG(model.model_set_test(alg.transposenm(input_set))->to_string());
PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%");
} }
void MLPPTests::test_softmax_regression(bool ui) { void MLPPTests::test_softmax_regression(bool ui) {
MLPPLinAlg alg; MLPPLinAlg alg;
@ -481,7 +493,7 @@ void MLPPTests::test_softmax_regression(bool ui) {
// SOFTMAX REGRESSION // SOFTMAX REGRESSION
MLPPSoftmaxReg model(dt->get_input(), dt->get_output()); MLPPSoftmaxReg model(dt->get_input(), dt->get_output());
model.train_sgd(0.1, 10000, ui); model.train_sgd(0.1, 10000, ui);
PLOG_MSG(model.model_set_test(dt->get_input())->to_string()); //PLOG_MSG(model.model_set_test(dt->get_input())->to_string());
PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%"); PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%");
} }
void MLPPTests::test_support_vector_classification(bool ui) { void MLPPTests::test_support_vector_classification(bool ui) {

83
test/mlpp_tests_old.cpp
View File

@ -104,101 +104,18 @@ void MLPPTestsOld::test_multivariate_linear_regression_newton_raphson(bool ui) {
} }
void MLPPTestsOld::test_logistic_regression(bool ui) { void MLPPTestsOld::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;
} }
void MLPPTestsOld::test_probit_regression(bool ui) { void MLPPTestsOld::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;
} }
void MLPPTestsOld::test_c_log_log_regression(bool ui) { void MLPPTestsOld::test_c_log_log_regression(bool ui) {
MLPPLinAlgOld alg;
// 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;
} }
void MLPPTestsOld::test_exp_reg_regression(bool ui) { void MLPPTestsOld::test_exp_reg_regression(bool ui) {
MLPPLinAlgOld alg;
// 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);
} }
void MLPPTestsOld::test_tanh_regression(bool ui) { void MLPPTestsOld::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 MLPPTestsOld::test_softmax_regression(bool ui) { void MLPPTestsOld::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;
} }
void MLPPTestsOld::test_support_vector_classification(bool ui) { void MLPPTestsOld::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;
} }
void MLPPTestsOld::test_mlp(bool ui) { void MLPPTestsOld::test_mlp(bool ui) {