Added ProbitRegOld.

SCsub

@@ -58,6 +58,7 @@ sources = [
     "mlpp/pca/pca_old.cpp",
     "mlpp/uni_lin_reg/uni_lin_reg_old.cpp",
     "mlpp/outlier_finder/outlier_finder_old.cpp",
+    "mlpp/probit_reg/probit_reg_old.cpp",
 
     "test/mlpp_tests.cpp",
 ]

mlpp/probit_reg/probit_reg_old.cpp

@@ -0,0 +1,245 @@
+//
+//  ProbitReg.cpp
+//
+//  Created by Marc Melikyan on 10/2/20.
+//
+
+#include "probit_reg_old.h"
+#include "../activation/activation.h"
+#include "../cost/cost.h"
+#include "../lin_alg/lin_alg.h"
+#include "../regularization/reg.h"
+#include "../utilities/utilities.h"
+
+#include <iostream>
+#include <random>
+
+MLPPProbitRegOld::MLPPProbitRegOld(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, std::string reg, real_t lambda, real_t alpha) :
+		inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha) {
+	y_hat.resize(n);
+	weights = MLPPUtilities::weightInitialization(k);
+	bias = MLPPUtilities::biasInitialization();
+}
+
+std::vector<real_t> MLPPProbitRegOld::modelSetTest(std::vector<std::vector<real_t>> X) {
+	return Evaluate(X);
+}
+
+real_t MLPPProbitRegOld::modelTest(std::vector<real_t> x) {
+	return Evaluate(x);
+}
+
+void MLPPProbitRegOld::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
+	MLPPActivation avn;
+	MLPPLinAlg alg;
+	MLPPReg regularization;
+	real_t cost_prev = 0;
+	int epoch = 1;
+	forwardPass();
+
+	while (true) {
+		cost_prev = Cost(y_hat, outputSet);
+
+		std::vector<real_t> error = alg.subtraction(y_hat, outputSet);
+
+		// Calculating the weight gradients
+		weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(inputSet), alg.hadamard_product(error, avn.gaussianCDF(z, 1)))));
+		weights = regularization.regWeights(weights, lambda, alpha, reg);
+
+		// Calculating the bias gradients
+		bias -= learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.gaussianCDF(z, 1))) / n;
+		forwardPass();
+
+		if (UI) {
+			MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
+			MLPPUtilities::UI(weights, bias);
+		}
+		epoch++;
+
+		if (epoch > max_epoch) {
+			break;
+		}
+	}
+}
+
+void MLPPProbitRegOld::MLE(real_t learning_rate, int max_epoch, bool UI) {
+	MLPPActivation avn;
+	MLPPLinAlg alg;
+	MLPPReg regularization;
+	real_t cost_prev = 0;
+	int epoch = 1;
+	forwardPass();
+
+	while (true) {
+		cost_prev = Cost(y_hat, outputSet);
+
+		std::vector<real_t> error = alg.subtraction(outputSet, y_hat);
+
+		// Calculating the weight gradients
+		weights = alg.addition(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(inputSet), alg.hadamard_product(error, avn.gaussianCDF(z, 1)))));
+		weights = regularization.regWeights(weights, lambda, alpha, reg);
+
+		// Calculating the bias gradients
+		bias += learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.gaussianCDF(z, 1))) / n;
+		forwardPass();
+
+		if (UI) {
+			MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
+			MLPPUtilities::UI(weights, bias);
+		}
+		epoch++;
+
+		if (epoch > max_epoch) {
+			break;
+		}
+	}
+}
+
+void MLPPProbitRegOld::SGD(real_t learning_rate, int max_epoch, bool UI) {
+	// NOTE: ∂y_hat/∂z is sparse
+	MLPPActivation avn;
+	MLPPLinAlg alg;
+	MLPPReg regularization;
+	real_t cost_prev = 0;
+	int epoch = 1;
+
+	while (true) {
+		std::random_device rd;
+		std::default_random_engine generator(rd());
+		std::uniform_int_distribution<int> distribution(0, int(n - 1));
+		int outputIndex = distribution(generator);
+
+		real_t y_hat = Evaluate(inputSet[outputIndex]);
+		real_t z = propagate(inputSet[outputIndex]);
+		cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
+
+		real_t error = y_hat - outputSet[outputIndex];
+
+		// Weight Updation
+		weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate * error * ((1 / sqrt(2 * M_PI)) * exp(-z * z / 2)), inputSet[outputIndex]));
+		weights = regularization.regWeights(weights, lambda, alpha, reg);
+
+		// Bias updation
+		bias -= learning_rate * error * ((1 / sqrt(2 * M_PI)) * exp(-z * z / 2));
+
+		y_hat = Evaluate({ inputSet[outputIndex] });
+
+		if (UI) {
+			MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ y_hat }, { outputSet[outputIndex] }));
+			MLPPUtilities::UI(weights, bias);
+		}
+		epoch++;
+
+		if (epoch > max_epoch) {
+			break;
+		}
+	}
+	forwardPass();
+}
+
+void MLPPProbitRegOld::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
+	MLPPActivation avn;
+	MLPPLinAlg alg;
+	MLPPReg regularization;
+	real_t cost_prev = 0;
+	int epoch = 1;
+
+	// Creating the mini-batches
+	int n_mini_batch = n / mini_batch_size;
+	auto createMiniBatchesResult = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
+	auto inputMiniBatches = std::get<0>(createMiniBatchesResult);
+	auto outputMiniBatches = std::get<1>(createMiniBatchesResult);
+
+	// Creating the mini-batches
+	for (int i = 0; i < n_mini_batch; i++) {
+		std::vector<std::vector<real_t>> currentInputSet;
+		std::vector<real_t> currentOutputSet;
+		for (int j = 0; j < n / n_mini_batch; j++) {
+			currentInputSet.push_back(inputSet[n / n_mini_batch * i + j]);
+			currentOutputSet.push_back(outputSet[n / n_mini_batch * i + j]);
+		}
+		inputMiniBatches.push_back(currentInputSet);
+		outputMiniBatches.push_back(currentOutputSet);
+	}
+
+	if (real_t(n) / real_t(n_mini_batch) - int(n / n_mini_batch) != 0) {
+		for (int i = 0; i < n - n / n_mini_batch * n_mini_batch; i++) {
+			inputMiniBatches[n_mini_batch - 1].push_back(inputSet[n / n_mini_batch * n_mini_batch + i]);
+			outputMiniBatches[n_mini_batch - 1].push_back(outputSet[n / n_mini_batch * n_mini_batch + i]);
+		}
+	}
+
+	while (true) {
+		for (int i = 0; i < n_mini_batch; i++) {
+			std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]);
+			std::vector<real_t> z = propagate(inputMiniBatches[i]);
+			cost_prev = Cost(y_hat, outputMiniBatches[i]);
+
+			std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
+
+			// Calculating the weight gradients
+			weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / outputMiniBatches.size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), alg.hadamard_product(error, avn.gaussianCDF(z, 1)))));
+			weights = regularization.regWeights(weights, lambda, alpha, reg);
+
+			// Calculating the bias gradients
+			bias -= learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.gaussianCDF(z, 1))) / outputMiniBatches.size();
+			y_hat = Evaluate(inputMiniBatches[i]);
+
+			if (UI) {
+				MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
+				MLPPUtilities::UI(weights, bias);
+			}
+		}
+		epoch++;
+		if (epoch > max_epoch) {
+			break;
+		}
+	}
+	forwardPass();
+}
+
+real_t MLPPProbitRegOld::score() {
+	MLPPUtilities util;
+	return util.performance(y_hat, outputSet);
+}
+
+void MLPPProbitRegOld::save(std::string fileName) {
+	MLPPUtilities util;
+	util.saveParameters(fileName, weights, bias);
+}
+
+real_t MLPPProbitRegOld::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
+	MLPPReg regularization;
+	class MLPPCost cost;
+	return cost.MSE(y_hat, y) + regularization.regTerm(weights, lambda, alpha, reg);
+}
+
+std::vector<real_t> MLPPProbitRegOld::Evaluate(std::vector<std::vector<real_t>> X) {
+	MLPPLinAlg alg;
+	MLPPActivation avn;
+	return avn.gaussianCDF(alg.scalarAdd(bias, alg.mat_vec_mult(X, weights)));
+}
+
+std::vector<real_t> MLPPProbitRegOld::propagate(std::vector<std::vector<real_t>> X) {
+	MLPPLinAlg alg;
+	return alg.scalarAdd(bias, alg.mat_vec_mult(X, weights));
+}
+
+real_t MLPPProbitRegOld::Evaluate(std::vector<real_t> x) {
+	MLPPLinAlg alg;
+	MLPPActivation avn;
+	return avn.gaussianCDF(alg.dot(weights, x) + bias);
+}
+
+real_t MLPPProbitRegOld::propagate(std::vector<real_t> x) {
+	MLPPLinAlg alg;
+	return alg.dot(weights, x) + bias;
+}
+
+// gaussianCDF ( wTx + b )
+void MLPPProbitRegOld::forwardPass() {
+	MLPPActivation avn;
+
+	z = propagate(inputSet);
+	y_hat = avn.gaussianCDF(z);
+}

mlpp/probit_reg/probit_reg_old.h

@@ -0,0 +1,53 @@
+
+#ifndef MLPP_PROBIT_REG_OLD_H
+#define MLPP_PROBIT_REG_OLD_H
+
+//
+//  ProbitReg.hpp
+//
+//  Created by Marc Melikyan on 10/2/20.
+//
+
+#include "core/math/math_defs.h"
+
+#include <string>
+#include <vector>
+
+class MLPPProbitRegOld {
+public:
+	MLPPProbitRegOld(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
+	std::vector<real_t> modelSetTest(std::vector<std::vector<real_t>> X);
+	real_t modelTest(std::vector<real_t> x);
+	void gradientDescent(real_t learning_rate, int max_epoch = 0, bool UI = false);
+	void MLE(real_t learning_rate, int max_epoch = 0, bool UI = false);
+	void SGD(real_t learning_rate, int max_epoch = 0, bool UI = false);
+	void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false);
+	real_t score();
+	void save(std::string fileName);
+
+private:
+	real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y);
+
+	std::vector<real_t> Evaluate(std::vector<std::vector<real_t>> X);
+	std::vector<real_t> propagate(std::vector<std::vector<real_t>> X);
+	real_t Evaluate(std::vector<real_t> x);
+	real_t propagate(std::vector<real_t> x);
+	void forwardPass();
+
+	std::vector<std::vector<real_t>> inputSet;
+	std::vector<real_t> outputSet;
+	std::vector<real_t> z;
+	std::vector<real_t> y_hat;
+	std::vector<real_t> weights;
+	real_t bias;
+
+	int n;
+	int k;
+
+	// Regularization Params
+	std::string reg;
+	real_t lambda;
+	real_t alpha; /* This is the controlling param for Elastic Net*/
+};
+
+#endif /* ProbitReg_hpp */

test/mlpp_tests.cpp

@@ -52,6 +52,7 @@
 #include "../mlpp/pca/pca_old.h"
 #include "../mlpp/uni_lin_reg/uni_lin_reg_old.h"
 #include "../mlpp/wgan/wgan_old.h"
+#include "../mlpp/probit_reg/probit_reg_old.h"
 
 Vector<real_t> dstd_vec_to_vec(const std::vector<real_t> &in) {
 	Vector<real_t> r;
@@ -348,10 +349,10 @@ void MLPPTests::test_probit_regression(bool ui) {
 	// PROBIT REGRESSION
 	Ref<MLPPDataSimple> dt = data.load_breast_cancer(_breast_cancer_data_path);
 
-	MLPPProbitReg model(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector());
+	MLPPProbitRegOld model_old(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector());
-	model.SGD(0.001, 10000, ui);
+	model_old.SGD(0.001, 10000, ui);
-	alg.printVector(model.modelSetTest(dt->get_input()->to_std_vector()));
+	alg.printVector(model_old.modelSetTest(dt->get_input()->to_std_vector()));
-	std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
+	std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl;
 }
 void MLPPTests::test_c_log_log_regression(bool ui) {
 	MLPPLinAlg alg;