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Pre-create and move commented code from main to test methods.
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@ -297,6 +297,492 @@ void MLPPTests::test_multivariate_linear_regression_newton_raphson(bool ui) {
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alg.printVector(model2.modelSetTest(ds->input));
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}
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void MLPPTests::test_logistic_regression(bool ui) {
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//MLPPStat stat;
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// MLPPLinAlg alg;
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//MLPPActivation avn;
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// MLPPCost cost;
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// MLPPData data;
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// MLPPConvolutions conv;
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// // LOGISTIC REGRESSION
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// auto [inputSet, outputSet] = data.load rastCancer();
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// LogReg model(inputSet, outputSet);
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// model.SGD(0.001, 100000, 0);
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// alg.printVector(model.modelSetTest(inputSet));
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// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
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}
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void MLPPTests::test_probit_regression(bool ui) {
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// // PROBIT REGRESSION
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// std::vector<std::vector<double>> inputSet;
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// std::vector<double> outputSet;
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// data.setData(30, "/Users/marcmelikyan/Desktop/Data/BreastCancer.csv", inputSet, outputSet);
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// ProbitReg model(inputSet, outputSet);
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// model.SGD(0.001, 10000, 1);
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// alg.printVector(model.modelSetTest(inputSet));
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// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
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}
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void MLPPTests::test_c_log_log_regression(bool ui) {
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// // CLOGLOG REGRESSION
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// std::vector<std::vector<double>> inputSet = {{1,2,3,4,5,6,7,8}, {0,0,0,0,1,1,1,1}};
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// std::vector<double> outputSet = {0,0,0,0,1,1,1,1};
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// CLogLogReg model(alg.transpose(inputSet), outputSet);
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// model.SGD(0.1, 10000, 0);
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// alg.printVector(model.modelSetTest(alg.transpose(inputSet)));
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// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
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}
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void MLPPTests::test_exp_reg_regression(bool ui) {
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// // EXPREG REGRESSION
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// std::vector<std::vector<double>> inputSet = {{0,1,2,3,4}};
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// std::vector<double> outputSet = {1,2,4,8,16};
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// ExpReg model(alg.transpose(inputSet), outputSet);
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// model.SGD(0.001, 10000, 0);
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// alg.printVector(model.modelSetTest(alg.transpose(inputSet)));
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// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
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}
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void MLPPTests::test_tanh_regression(bool ui) {
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// // TANH REGRESSION
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// std::vector<std::vector<double>> inputSet = {{4,3,0,-3,-4}, {0,0,0,1,1}};
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// std::vector<double> outputSet = {1,1,0,-1,-1};
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// TanhReg model(alg.transpose(inputSet), outputSet);
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// model.SGD(0.1, 10000, 0);
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// alg.printVector(model.modelSetTest(alg.transpose(inputSet)));
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// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
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}
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void MLPPTests::test_softmax_regression(bool ui) {
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// // SOFTMAX REGRESSION
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// auto [inputSet, outputSet] = data.loadIris();
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// SoftmaxReg model(inputSet, outputSet);
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// model.SGD(0.1, 10000, 1);
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// alg.printMatrix(model.modelSetTest(inputSet));
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// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
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}
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void MLPPTests::test_support_vector_classification(bool ui) {
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// // SUPPORT VECTOR CLASSIFICATION
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// auto [inputSet, outputSet] = data.loadBreastCancerSVC();
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// SVC model(inputSet, outputSet, 1);
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// model.SGD(0.00001, 100000, 1);
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// alg.printVector(model.modelSetTest(inputSet));
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// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
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// SoftmaxReg model(inputSet, outputSet);
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// model.SGD(0.001, 20000, 0);
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// alg.printMatrix(model.modelSetTest(inputSet));
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}
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void MLPPTests::test_mlp(bool ui) {
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// // MLP
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// std::vector<std::vector<double>> inputSet = {{0,0,1,1}, {0,1,0,1}};
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// inputSet = alg.transpose(inputSet);
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// std::vector<double> outputSet = {0,1,1,0};
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// MLP model(inputSet, outputSet, 2);
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// model.gradientDescent(0.1, 10000, 0);
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// alg.printVector(model.modelSetTest(inputSet));
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// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
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}
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void MLPPTests::test_soft_max_network(bool ui) {
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// // SOFTMAX NETWORK
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// auto [inputSet, outputSet] = data.loadWine();
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// SoftmaxNet model(inputSet, outputSet, 1);
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// model.gradientDescent(0.01, 100000, 1);
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// alg.printMatrix(model.modelSetTest(inputSet));
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// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
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}
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void MLPPTests::test_autoencoder(bool ui) {
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// // AUTOENCODER
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// 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}};
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// AutoEncoder model(alg.transpose(inputSet), 5);
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// model.SGD(0.001, 300000, 0);
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// alg.printMatrix(model.modelSetTest(alg.transpose(inputSet)));
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// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
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}
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void MLPPTests::test_dynamically_sized_ann(bool ui) {
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// DYNAMICALLY SIZED ANN
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// Possible Weight Init Methods: Default, Uniform, HeNormal, HeUniform, XavierNormal, XavierUniform
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// Possible Activations: Linear, Sigmoid, Swish, Softplus, Softsign, CLogLog, Ar{Sinh, Cosh, Tanh, Csch, Sech, Coth}, GaussianCDF, GELU, UnitStep
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// Possible Loss Functions: MSE, RMSE, MBE, LogLoss, CrossEntropy, HingeLoss
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// std::vector<std::vector<double>> inputSet = {{0,0,1,1}, {0,1,0,1}};
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// std::vector<double> outputSet = {0,1,1,0};
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// ANN ann(alg.transpose(inputSet), outputSet);
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// ann.addLayer(2, "Cosh");
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// ann.addOutputLayer("Sigmoid", "LogLoss");
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// ann.AMSGrad(0.1, 10000, 1, 0.9, 0.999, 0.000001, 1);
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// ann.Adadelta(1, 1000, 2, 0.9, 0.000001, 1);
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// ann.Momentum(0.1, 8000, 2, 0.9, true, 1);
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//ann.setLearningRateScheduler("Step", 0.5, 1000);
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// ann.gradientDescent(0.01, 30000);
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// alg.printVector(ann.modelSetTest(alg.transpose(inputSet)));
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// std::cout << "ACCURACY: " << 100 * ann.score() << "%" << std::endl;
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}
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void MLPPTests::test_wgan(bool ui) {
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/*
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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},
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{2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40}};
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WGAN gan(2, alg.transpose(outputSet)); // our gan is a wasserstein gan (wgan)
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gan.addLayer(5, "Sigmoid");
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gan.addLayer(2, "RELU");
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gan.addLayer(5, "Sigmoid");
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gan.addOutputLayer(); // User can specify weight init- if necessary.
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gan.gradientDescent(0.1, 55000, 0);
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std::cout << "GENERATED INPUT: (Gaussian-sampled noise):" << std::endl;
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alg.printMatrix(gan.generateExample(100));
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*/
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}
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void MLPPTests::test_ann(bool ui) {
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// typedef std::vector<std::vector<double>> Matrix;
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// typedef std::vector<double> Vector;
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// Matrix inputSet = {{0,0}, {0,1}, {1,0}, {1,1}}; // XOR
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// Vector outputSet = {0,1,1,0};
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// ANN ann(inputSet, outputSet);
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// ann.addLayer(5, "Sigmoid");
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// ann.addLayer(8, "Sigmoid"); // Add more layers as needed.
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// ann.addOutputLayer("Sigmoid", "LogLoss");
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// ann.gradientDescent(1, 20000, 1);
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// Vector predictions = ann.modelSetTest(inputSet);
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// alg.printVector(predictions); // Testing out the model's preds for train set.
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// std::cout << "ACCURACY: " << 100 * ann.score() << "%" << std::endl; // Accuracy.
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}
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void MLPPTests::test_dynamically_sized_mann(bool ui) {
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// // DYNAMICALLY SIZED MANN (Multidimensional Output ANN)
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// std::vector<std::vector<double>> inputSet = {{1,2,3},{2,4,6},{3,6,9},{4,8,12}};
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// std::vector<std::vector<double>> outputSet = {{1,5}, {2,10}, {3,15}, {4,20}};
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// MANN mann(inputSet, outputSet);
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// mann.addOutputLayer("Linear", "MSE");
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// mann.gradientDescent(0.001, 80000, 0);
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// alg.printMatrix(mann.modelSetTest(inputSet));
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// std::cout << "ACCURACY: " << 100 * mann.score() << "%" << std::endl;
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// std::vector<std::vector<double>> inputSet;
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// std::vector<double> tempOutputSet;
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// data.setData(4, "/Users/marcmelikyan/Desktop/Data/Iris.csv", inputSet, tempOutputSet);
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// std::vector<std::vector<double>> outputSet = data.oneHotRep(tempOutputSet, 3);
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}
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void MLPPTests::test_train_test_split_mann(bool ui) {
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// TRAIN TEST SPLIT CHECK
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// 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}};
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// std::vector<std::vector<double>> outputSet1 = {{2,4,6,8,10,12,14,16,18,20}};
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// auto [inputSet, outputSet, inputTestSet, outputTestSet] = data.trainTestSplit(alg.transpose(inputSet1), alg.transpose(outputSet1), 0.2);
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// alg.printMatrix(inputSet);
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// alg.printMatrix(outputSet);
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// alg.printMatrix(inputTestSet);
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// alg.printMatrix(outputTestSet);
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// alg.printMatrix(inputSet);
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// alg.printMatrix(outputSet);
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// MANN mann(inputSet, outputSet);
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// mann.addLayer(100, "RELU", "XavierNormal");
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// mann.addOutputLayer("Softmax", "CrossEntropy", "XavierNormal");
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// mann.gradientDescent(0.1, 80000, 1);
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// alg.printMatrix(mann.modelSetTest(inputSet));
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// std::cout << "ACCURACY: " << 100 * mann.score() << "%" << std::endl;
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}
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void MLPPTests::test_naive_bayes(bool ui) {
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// // NAIVE BAYES
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// std::vector<std::vector<double>> inputSet = {{1,1,1,1,1}, {0,0,1,1,1}, {0,0,1,0,1}};
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// std::vector<double> outputSet = {0,1,0,1,1};
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// MultinomialNB MNB(alg.transpose(inputSet), outputSet, 2);
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// alg.printVector(MNB.modelSetTest(alg.transpose(inputSet)));
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// BernoulliNB BNB(alg.transpose(inputSet), outputSet);
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// alg.printVector(BNB.modelSetTest(alg.transpose(inputSet)));
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// GaussianNB GNB(alg.transpose(inputSet), outputSet, 2);
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// alg.printVector(GNB.modelSetTest(alg.transpose(inputSet)));
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}
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void MLPPTests::test_k_means(bool ui) {
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// // KMeans
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// std::vector<std::vector<double>> inputSet = {{32, 0, 7}, {2, 28, 17}, {0, 9, 23}};
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// KMeans kmeans(inputSet, 3, "KMeans++");
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// kmeans.train(3, 1);
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// std::cout << std::endl;
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// alg.printMatrix(kmeans.modelSetTest(inputSet)); // Returns the assigned centroids to each of the respective training examples
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// std::cout << std::endl;
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// alg.printVector(kmeans.silhouette_scores());
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}
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void MLPPTests::test_knn(bool ui) {
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// // kNN
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// std::vector<std::vector<double>> inputSet = {{1,2,3,4,5,6,7,8}, {0,0,0,0,1,1,1,1}};
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// std::vector<double> outputSet = {0,0,0,0,1,1,1,1};
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// kNN knn(alg.transpose(inputSet), outputSet, 8);
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// alg.printVector(knn.modelSetTest(alg.transpose(inputSet)));
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// std::cout << "ACCURACY: " << 100 * knn.score() << "%" << std::endl;
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}
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void MLPPTests::test_convolution_tensors_etc() {
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// // CONVOLUTION, POOLING, ETC..
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// std::vector<std::vector<double>> input = {
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// {1},
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// };
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// std::vector<std::vector<std::vector<double>>> tensorSet;
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// tensorSet.push_back(input);
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// tensorSet.push_back(input);
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// tensorSet.push_back(input);
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// alg.printTensor(data.rgb2xyz(tensorSet));
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// std::vector<std::vector<double>> input = {
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// {62,55,55,54,49,48,47,55},
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// {62,57,54,52,48,47,48,53},
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// {61,60,52,49,48,47,49,54},
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// {63,61,60,60,63,65,68,65},
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// {67,67,70,74,79,85,91,92},
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// {82,95,101,106,114,115,112,117},
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// {96,111,115,119,128,128,130,127},
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// {109,121,127,133,139,141,140,133},
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// };
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// Transforms trans;
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// alg.printMatrix(trans.discreteCosineTransform(input));
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// alg.printMatrix(conv.convolve(input, conv.getPrewittVertical(), 1)); // Can use padding
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// alg.printMatrix(conv.pool(input, 4, 4, "Max")); // Can use Max, Min, or Average pooling.
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// std::vector<std::vector<std::vector<double>>> tensorSet;
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// tensorSet.push_back(input);
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// tensorSet.push_back(input);
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// alg.printVector(conv.globalPool(tensorSet, "Average")); // Can use Max, Min, or Average global pooling.
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// std::vector<std::vector<double>> laplacian = {{1, 1, 1}, {1, -4, 1}, {1, 1, 1}};
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// alg.printMatrix(conv.convolve(conv.gaussianFilter2D(5, 1), laplacian, 1));
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}
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void MLPPTests::test_pca_svd_eigenvalues_eigenvectors(bool ui) {
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// // PCA, SVD, eigenvalues & eigenvectors
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// std::vector<std::vector<double>> inputSet = {{1,1}, {1,1}};
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// auto [Eigenvectors, Eigenvalues] = alg.eig(inputSet);
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// std::cout << "Eigenvectors:" << std::endl;
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// alg.printMatrix(Eigenvectors);
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// std::cout << std::endl;
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// std::cout << "Eigenvalues:" << std::endl;
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// alg.printMatrix(Eigenvalues);
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// auto [U, S, Vt] = alg.SVD(inputSet);
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// // PCA done using Jacobi's method to approximate eigenvalues and eigenvectors.
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// PCA dr(inputSet, 1); // 1 dimensional representation.
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// std::cout << std::endl;
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// std::cout << "Dimensionally reduced representation:" << std::endl;
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// alg.printMatrix(dr.principalComponents());
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// std::cout << "SCORE: " << dr.score() << std::endl;
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}
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void MLPPTests::test_nlp_and_data(bool ui) {
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// // NLP/DATA
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// std::string verbText = "I am appearing and thinking, as well as conducting.";
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// std::cout << "Stemming Example:" << std::endl;
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// std::cout << data.stemming(verbText) << std::endl;
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// std::cout << std::endl;
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// std::vector<std::string> sentences = {"He is a good boy", "She is a good girl", "The boy and girl are good"};
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// std::cout << "Bag of Words Example:" << std::endl;
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// alg.printMatrix(data.BOW(sentences, "Default"));
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// std::cout << std::endl;
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// std::cout << "TFIDF Example:" << std::endl;
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// alg.printMatrix(data.TFIDF(sentences));
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// std::cout << std::endl;
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// std::cout << "Tokenization:" << std::endl;
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// alg.printVector(data.tokenize(verbText));
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// std::cout << std::endl;
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// std::cout << "Word2Vec:" << std::endl;
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// std::string textArchive = {"He is a good boy. She is a good girl. The boy and girl are good."};
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// std::vector<std::string> corpus = data.splitSentences(textArchive);
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// auto [wordEmbeddings, wordList] = data.word2Vec(corpus, "CBOW", 2, 2, 0.1, 10000); // Can use either CBOW or Skip-n-gram.
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// alg.printMatrix(wordEmbeddings);
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// std::cout << std::endl;
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// std::vector<std::string> textArchive = {"pizza", "pizza hamburger cookie", "hamburger", "ramen", "sushi", "ramen sushi"};
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// alg.printMatrix(data.LSA(textArchive, 2));
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// //alg.printMatrix(data.BOW(textArchive, "Default"));
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// std::cout << std::endl;
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// std::vector<std::vector<double>> inputSet = {{1,2},{2,3},{3,4},{4,5},{5,6}};
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// std::cout << "Feature Scaling Example:" << std::endl;
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// alg.printMatrix(data.featureScaling(inputSet));
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// std::cout << std::endl;
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// std::cout << "Mean Centering Example:" << std::endl;
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// alg.printMatrix(data.meanCentering(inputSet));
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// std::cout << std::endl;
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// std::cout << "Mean Normalization Example:" << std::endl;
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// alg.printMatrix(data.meanNormalization(inputSet));
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// std::cout << std::endl;
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}
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void MLPPTests::test_outlier_finder(bool ui) {
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// // Outlier Finder
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// std::vector<double> inputSet = {1,2,3,4,5,6,7,8,9,23554332523523};
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// OutlierFinder outlierFinder(2); // Any datapoint outside of 2 stds from the mean is marked as an outlier.
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// alg.printVector(outlierFinder.modelTest(inputSet));
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}
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void MLPPTests::test_new_math_functions() {
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// // Testing new Functions
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// double z_s = 0.001;
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// std::cout << avn.logit(z_s) << std::endl;
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// std::cout << avn.logit(z_s, 1) << std::endl;
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// std::vector<double> z_v = {0.001};
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// alg.printVector(avn.logit(z_v));
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// alg.printVector(avn.logit(z_v, 1));
|
||||
|
||||
// std::vector<std::vector<double>> Z_m = {{0.001}};
|
||||
// alg.printMatrix(avn.logit(Z_m));
|
||||
// alg.printMatrix(avn.logit(Z_m, 1));
|
||||
|
||||
// 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<double>> matrixOfCubes = {{1,2,64,27}};
|
||||
// std::vector<double> 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<double> chicken;
|
||||
// data.getImage("../../Data/apple.jpeg", chicken);
|
||||
// alg.printVector(chicken);
|
||||
|
||||
// std::vector<std::vector<double>> P = {{12, -51, 4}, {6, 167, -68}, {-4, 24, -41}};
|
||||
// alg.printMatrix(P);
|
||||
|
||||
// alg.printMatrix(alg.gramSchmidtProcess(P));
|
||||
|
||||
// auto [Q, R] = alg.QRD(P); // It works!
|
||||
|
||||
// alg.printMatrix(Q);
|
||||
|
||||
// alg.printMatrix(R);
|
||||
}
|
||||
void MLPPTests::test_positive_definiteness_checker() {
|
||||
// // Checking positive-definiteness checker. For Cholesky Decomp.
|
||||
// std::vector<std::vector<double>> 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;
|
||||
// auto [L, Lt] = alg.chol(A); // works.
|
||||
// alg.printMatrix(L);
|
||||
// alg.printMatrix(Lt);
|
||||
}
|
||||
void MLPPTests::test_numerical_analysis() {
|
||||
// Checks for numerical analysis class.
|
||||
//NumericalAnalysis 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<double>>> 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;
|
||||
|
||||
// alg.printMatrix(conv.dx(A));
|
||||
// alg.printMatrix(conv.dy(A));
|
||||
|
||||
// alg.printMatrix(conv.gradOrientation(A));
|
||||
|
||||
// std::vector<std::vector<double>> A =
|
||||
// {
|
||||
// {1,0,0,0},
|
||||
// {0,0,0,0},
|
||||
// {0,0,0,0},
|
||||
// {0,0,0,1}
|
||||
// };
|
||||
|
||||
// std::vector<std::vector<std::string>> h = conv.harrisCornerDetection(A);
|
||||
|
||||
// for(int i = 0; i < h.size(); i++){
|
||||
// for(int j = 0; j < h[i].size(); j++){
|
||||
// std::cout << h[i][j] << " ";
|
||||
// }
|
||||
// std::cout << std::endl;
|
||||
// } // Harris detector works. Life is good!
|
||||
|
||||
// std::vector<double> a = {3,4,4};
|
||||
// std::vector<double> b = {4,4,4};
|
||||
// alg.printVector(alg.cross(a,b));
|
||||
}
|
||||
void MLPPTests::test_support_vector_classification_kernel(bool ui) {
|
||||
//SUPPORT VECTOR CLASSIFICATION (kernel method)
|
||||
// std::vector<std::vector<double>> inputSet;
|
||||
// std::vector<double> outputSet;
|
||||
// data.setData(30, "/Users/marcmelikyan/Desktop/Data/BreastCancerSVM.csv", inputSet, outputSet);
|
||||
|
||||
// std::vector<std::vector<double>> inputSet;
|
||||
// std::vector<double> outputSet;
|
||||
// data.setData(4, "/Users/marcmelikyan/Desktop/Data/IrisSVM.csv", inputSet, outputSet);
|
||||
|
||||
// DualSVC kernelSVM(inputSet, outputSet, 1000);
|
||||
// kernelSVM.gradientDescent(0.0001, 20, 1);
|
||||
|
||||
// std::vector<std::vector<double>> linearlyIndependentMat =
|
||||
|
||||
// {
|
||||
// {1,2,3,4},
|
||||
// {234538495,4444,6111,55}
|
||||
// };
|
||||
|
||||
// std::cout << "True of false: linearly independent?: " << std::boolalpha << alg.linearIndependenceChecker(linearlyIndependentMat) << std::endl;
|
||||
}
|
||||
|
||||
void MLPPTests::is_approx_equalsd(double a, double b, const String &str) {
|
||||
if (!Math::is_equal_approx(a, b)) {
|
||||
ERR_PRINT("TEST FAILED: " + str + " Got: " + String::num(a) + " Should be: " + String::num(b));
|
||||
@ -417,4 +903,38 @@ void MLPPTests::_bind_methods() {
|
||||
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_logistic_regression", "ui"), &MLPPTests::test_logistic_regression, 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", "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", "ui"), &MLPPTests::test_naive_bayes, false);
|
||||
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);
|
||||
}
|
||||
|
||||
@ -27,6 +27,37 @@ public:
|
||||
void test_multivariate_linear_regression_epochs_gradient_descent(bool ui = false);
|
||||
void test_multivariate_linear_regression_newton_raphson(bool ui = false);
|
||||
|
||||
void test_logistic_regression(bool ui = false);
|
||||
void test_probit_regression(bool ui = false);
|
||||
void test_c_log_log_regression(bool ui = false);
|
||||
void test_exp_reg_regression(bool ui = false);
|
||||
void test_tanh_regression(bool ui = false);
|
||||
void test_softmax_regression(bool ui = false);
|
||||
void test_support_vector_classification(bool ui = false);
|
||||
|
||||
void test_mlp(bool ui = false);
|
||||
void test_soft_max_network(bool ui = false);
|
||||
void test_autoencoder(bool ui = false);
|
||||
void test_dynamically_sized_ann(bool ui = false);
|
||||
void test_wgan(bool ui = false);
|
||||
void test_ann(bool ui = false);
|
||||
void test_dynamically_sized_mann(bool ui = false);
|
||||
void test_train_test_split_mann(bool ui = false);
|
||||
|
||||
void test_naive_bayes(bool ui = false);
|
||||
void test_k_means(bool ui = false);
|
||||
void test_knn(bool ui = false);
|
||||
|
||||
void test_convolution_tensors_etc();
|
||||
void test_pca_svd_eigenvalues_eigenvectors(bool ui = false);
|
||||
|
||||
void test_nlp_and_data(bool ui = false);
|
||||
void test_outlier_finder(bool ui = false);
|
||||
void test_new_math_functions();
|
||||
void test_positive_definiteness_checker();
|
||||
void test_numerical_analysis();
|
||||
void test_support_vector_classification_kernel(bool ui = false);
|
||||
|
||||
void is_approx_equalsd(double a, double b, const String &str);
|
||||
void is_approx_equals_dvec(const Vector<double> &a, const Vector<double> &b, const String &str);
|
||||
void is_approx_equals_dmat(const Vector<Vector<double>> &a, const Vector<Vector<double>> &b, const String &str);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user