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Work on cleaning up tests,

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Relintai 2023-01-26 01:17:37 +01:00
parent 478859374a
commit b398337558
1 changed files with 184 additions and 25 deletions
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test/mlpp_tests.cpp
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@ -297,14 +297,30 @@ void MLPPTests::test_multivariate_linear_regression_newton_raphson(bool ui) {
alg.printVector(model2.modelSetTest(ds->input));
}
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
void MLPPTests::test_logistic_regression(bool ui) {
MLPPLinAlg alg;
MLPPData data;
// LOGISTIC REGRESSION
Ref<MLPPDataSimple> dt = data.load_breast_cancer(_breast_cancer_data_path);
MLPPLogReg model(dt->input, dt->output);
model.SGD(0.001, 100000, ui);
alg.printVector(model.modelSetTest(dt->input));
std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
}
void MLPPTests::test_probit_regression(bool ui) {
MLPPLinAlg alg;
MLPPData data;
// PROBIT REGRESSION
Ref<MLPPDataSimple> dt = data.load_breast_cancer(_breast_cancer_data_path);
MLPPProbitReg model(dt->input, dt->output);
model.SGD(0.001, 10000, ui);
alg.printVector(model.modelSetTest(dt->input));
std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
}
void MLPPTests::test_c_log_log_regression(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
@ -312,24 +328,6 @@ void MLPPTests::test_logistic_regression(bool ui) {
//MLPPData data;
//MLPPConvolutions conv;
// LOGISTIC REGRESSION
// auto [inputSet, outputSet] = data.load rastCancer();
// LogReg model(inputSet, outputSet);
// model.SGD(0.001, 100000, 0);
// alg.printVector(model.modelSetTest(inputSet));
// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
}
void MLPPTests::test_probit_regression(bool ui) {
// // PROBIT REGRESSION
// std::vector<std::vector<double>> inputSet;
// std::vector<double> outputSet;
// data.setData(30, "/Users/marcmelikyan/Desktop/Data/BreastCancer.csv", inputSet, outputSet);
// ProbitReg model(inputSet, outputSet);
// model.SGD(0.001, 10000, 1);
// alg.printVector(model.modelSetTest(inputSet));
// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
}
void MLPPTests::test_c_log_log_regression(bool ui) {
// // CLOGLOG REGRESSION
// std::vector<std::vector<double>> inputSet = {{1,2,3,4,5,6,7,8}, {0,0,0,0,1,1,1,1}};
// std::vector<double> outputSet = {0,0,0,0,1,1,1,1};
@ -339,6 +337,13 @@ void MLPPTests::test_c_log_log_regression(bool ui) {
// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
}
void MLPPTests::test_exp_reg_regression(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// // EXPREG REGRESSION
// std::vector<std::vector<double>> inputSet = {{0,1,2,3,4}};
// std::vector<double> outputSet = {1,2,4,8,16};
@ -348,6 +353,13 @@ void MLPPTests::test_exp_reg_regression(bool ui) {
// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
}
void MLPPTests::test_tanh_regression(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// // TANH REGRESSION
// std::vector<std::vector<double>> inputSet = {{4,3,0,-3,-4}, {0,0,0,1,1}};
// std::vector<double> outputSet = {1,1,0,-1,-1};
@ -357,6 +369,13 @@ void MLPPTests::test_tanh_regression(bool ui) {
// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
}
void MLPPTests::test_softmax_regression(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// // SOFTMAX REGRESSION
// auto [inputSet, outputSet] = data.loadIris();
// SoftmaxReg model(inputSet, outputSet);
@ -365,6 +384,13 @@ void MLPPTests::test_softmax_regression(bool ui) {
// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
}
void MLPPTests::test_support_vector_classification(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// // SUPPORT VECTOR CLASSIFICATION
// auto [inputSet, outputSet] = data.loadBreastCancerSVC();
// SVC model(inputSet, outputSet, 1);
@ -378,6 +404,13 @@ void MLPPTests::test_support_vector_classification(bool ui) {
}
void MLPPTests::test_mlp(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// // MLP
// std::vector<std::vector<double>> inputSet = {{0,0,1,1}, {0,1,0,1}};
// inputSet = alg.transpose(inputSet);
@ -389,6 +422,13 @@ void MLPPTests::test_mlp(bool ui) {
// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
}
void MLPPTests::test_soft_max_network(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// // SOFTMAX NETWORK
// auto [inputSet, outputSet] = data.loadWine();
// SoftmaxNet model(inputSet, outputSet, 1);
@ -397,6 +437,13 @@ void MLPPTests::test_soft_max_network(bool ui) {
// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
}
void MLPPTests::test_autoencoder(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// // AUTOENCODER
// std::vector<std::vector<double>> inputSet = {{1,2,3,4,5,6,7,8,9,10}, {3,5,9,12,15,18,21,24,27,30}};
// AutoEncoder model(alg.transpose(inputSet), 5);
@ -405,6 +452,13 @@ void MLPPTests::test_autoencoder(bool ui) {
// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
}
void MLPPTests::test_dynamically_sized_ann(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// 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
@ -425,6 +479,13 @@ void MLPPTests::test_dynamically_sized_ann(bool ui) {
// std::cout << "ACCURACY: " << 100 * ann.score() << "%" << std::endl;
}
void MLPPTests::test_wgan(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
/*
std::vector<std::vector<double>> 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}};
@ -440,6 +501,13 @@ void MLPPTests::test_wgan(bool ui) {
*/
}
void MLPPTests::test_ann(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// typedef std::vector<std::vector<double>> Matrix;
// typedef std::vector<double> Vector;
@ -457,6 +525,13 @@ void MLPPTests::test_ann(bool ui) {
// std::cout << "ACCURACY: " << 100 * ann.score() << "%" << std::endl; // Accuracy.
}
void MLPPTests::test_dynamically_sized_mann(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// // DYNAMICALLY SIZED MANN (Multidimensional Output ANN)
// std::vector<std::vector<double>> inputSet = {{1,2,3},{2,4,6},{3,6,9},{4,8,12}};
// std::vector<std::vector<double>> outputSet = {{1,5}, {2,10}, {3,15}, {4,20}};
@ -473,6 +548,13 @@ void MLPPTests::test_dynamically_sized_mann(bool ui) {
// std::vector<std::vector<double>> outputSet = data.oneHotRep(tempOutputSet, 3);
}
void MLPPTests::test_train_test_split_mann(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// TRAIN TEST SPLIT CHECK
// std::vector<std::vector<double>> 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<double>> outputSet1 = {{2,4,6,8,10,12,14,16,18,20}};
@ -494,6 +576,13 @@ void MLPPTests::test_train_test_split_mann(bool ui) {
}
void MLPPTests::test_naive_bayes(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// // NAIVE BAYES
// std::vector<std::vector<double>> inputSet = {{1,1,1,1,1}, {0,0,1,1,1}, {0,0,1,0,1}};
// std::vector<double> outputSet = {0,1,0,1,1};
@ -508,6 +597,13 @@ void MLPPTests::test_naive_bayes(bool ui) {
// alg.printVector(GNB.modelSetTest(alg.transpose(inputSet)));
}
void MLPPTests::test_k_means(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// // KMeans
// std::vector<std::vector<double>> inputSet = {{32, 0, 7}, {2, 28, 17}, {0, 9, 23}};
// KMeans kmeans(inputSet, 3, "KMeans++");
@ -518,6 +614,13 @@ void MLPPTests::test_k_means(bool ui) {
// alg.printVector(kmeans.silhouette_scores());
}
void MLPPTests::test_knn(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// // kNN
// std::vector<std::vector<double>> inputSet = {{1,2,3,4,5,6,7,8}, {0,0,0,0,1,1,1,1}};
// std::vector<double> outputSet = {0,0,0,0,1,1,1,1};
@ -527,6 +630,13 @@ void MLPPTests::test_knn(bool ui) {
}
void MLPPTests::test_convolution_tensors_etc() {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// // CONVOLUTION, POOLING, ETC..
// std::vector<std::vector<double>> input = {
// {1},
@ -566,6 +676,13 @@ void MLPPTests::test_convolution_tensors_etc() {
// alg.printMatrix(conv.convolve(conv.gaussianFilter2D(5, 1), laplacian, 1));
}
void MLPPTests::test_pca_svd_eigenvalues_eigenvectors(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// // PCA, SVD, eigenvalues & eigenvectors
// std::vector<std::vector<double>> inputSet = {{1,1}, {1,1}};
// auto [Eigenvectors, Eigenvalues] = alg.eig(inputSet);
@ -586,6 +703,13 @@ void MLPPTests::test_pca_svd_eigenvalues_eigenvectors(bool ui) {
}
void MLPPTests::test_nlp_and_data(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// // NLP/DATA
// std::string verbText = "I am appearing and thinking, as well as conducting.";
// std::cout << "Stemming Example:" << std::endl;
@ -631,12 +755,26 @@ void MLPPTests::test_nlp_and_data(bool ui) {
// std::cout << std::endl;
}
void MLPPTests::test_outlier_finder(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// // Outlier Finder
// std::vector<double> inputSet = {1,2,3,4,5,6,7,8,9,23554332523523};
// OutlierFinder outlierFinder(2); // Any datapoint outside of 2 stds from the mean is marked as an outlier.
// alg.printVector(outlierFinder.modelTest(inputSet));
}
void MLPPTests::test_new_math_functions() {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// // Testing new Functions
// double z_s = 0.001;
// std::cout << avn.logit(z_s) << std::endl;
@ -680,6 +818,13 @@ void MLPPTests::test_new_math_functions() {
// alg.printMatrix(R);
}
void MLPPTests::test_positive_definiteness_checker() {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// // Checking positive-definiteness checker. For Cholesky Decomp.
// std::vector<std::vector<double>> A =
// {
@ -695,6 +840,13 @@ void MLPPTests::test_positive_definiteness_checker() {
// alg.printMatrix(Lt);
}
void MLPPTests::test_numerical_analysis() {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
// Checks for numerical analysis class.
//NumericalAnalysis numAn;
@ -768,6 +920,13 @@ void MLPPTests::test_numerical_analysis() {
// alg.printVector(alg.cross(a,b));
}
void MLPPTests::test_support_vector_classification_kernel(bool ui) {
//MLPPStat stat;
//MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
//SUPPORT VECTOR CLASSIFICATION (kernel method)
// std::vector<std::vector<double>> inputSet;
// std::vector<double> outputSet;