pmlpp/mlpp/lin_reg/lin_reg.cpp

//
//  LinReg.cpp
//
//  Created by Marc Melikyan on 10/2/20.
//

#include "lin_reg.h"
#include "../cost/cost.h"
#include "../lin_alg/lin_alg.h"
#include "../regularization/reg.h"
#include "../stat/stat.h"
#include "../utilities/utilities.h"

#include <cmath>
#include <iostream>
#include <random>



LinReg::LinReg(std::vector<std::vector<double>> inputSet, std::vector<double> outputSet, std::string reg, double lambda, double alpha) :
		inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha) {
	y_hat.resize(n);

	weights = Utilities::weightInitialization(k);
	bias = Utilities::biasInitialization();
}

std::vector<double> LinReg::modelSetTest(std::vector<std::vector<double>> X) {
	return Evaluate(X);
}

double LinReg::modelTest(std::vector<double> x) {
	return Evaluate(x);
}

void LinReg::NewtonRaphson(double learning_rate, int max_epoch, bool UI) {
	MLPPLinAlg alg;
	Reg regularization;
	double cost_prev = 0;
	int epoch = 1;
	forwardPass();
	while (true) {
		cost_prev = Cost(y_hat, outputSet);

		std::vector<double> error = alg.subtraction(y_hat, outputSet);

		// Calculating the weight gradients (2nd derivative)
		std::vector<double> first_derivative = alg.mat_vec_mult(alg.transpose(inputSet), error);
		std::vector<std::vector<double>> second_derivative = alg.matmult(alg.transpose(inputSet), inputSet);
		weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(alg.inverse(second_derivative)), first_derivative)));
		weights = regularization.regWeights(weights, lambda, alpha, reg);

		// Calculating the bias gradients (2nd derivative)
		bias -= learning_rate * alg.sum_elements(error) / n; // We keep this the same. The 2nd derivative is just [1].
		forwardPass();

		if (UI) {
			Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
			Utilities::UI(weights, bias);
		}
		epoch++;
		if (epoch > max_epoch) {
			break;
		}
	}
}

void LinReg::gradientDescent(double learning_rate, int max_epoch, bool UI) {
	MLPPLinAlg alg;
	Reg regularization;
	double cost_prev = 0;
	int epoch = 1;
	forwardPass();

	while (true) {
		cost_prev = Cost(y_hat, outputSet);

		std::vector<double> 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), error)));
		weights = regularization.regWeights(weights, lambda, alpha, reg);

		// Calculating the bias gradients
		bias -= learning_rate * alg.sum_elements(error) / n;
		forwardPass();

		if (UI) {
			Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
			Utilities::UI(weights, bias);
		}
		epoch++;
		if (epoch > max_epoch) {
			break;
		}
	}
}

void LinReg::SGD(double learning_rate, int max_epoch, bool UI) {
	MLPPLinAlg alg;
	Reg regularization;
	double 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);

		double y_hat = Evaluate(inputSet[outputIndex]);
		cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });

		double error = y_hat - outputSet[outputIndex];

		// Weight updation
		weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate * error, inputSet[outputIndex]));
		weights = regularization.regWeights(weights, lambda, alpha, reg);

		// Bias updation
		bias -= learning_rate * error;

		y_hat = Evaluate({ inputSet[outputIndex] });

		if (UI) {
			Utilities::CostInfo(epoch, cost_prev, Cost({ y_hat }, { outputSet[outputIndex] }));
			Utilities::UI(weights, bias);
		}
		epoch++;

		if (epoch > max_epoch) {
			break;
		}
	}
	forwardPass();
}

void LinReg::MBGD(double learning_rate, int max_epoch, int mini_batch_size, bool UI) {
	MLPPLinAlg alg;
	Reg regularization;
	double cost_prev = 0;
	int epoch = 1;

	// Creating the mini-batches
	int n_mini_batch = n / mini_batch_size;
	auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch);

	while (true) {
		for (int i = 0; i < n_mini_batch; i++) {
			std::vector<double> y_hat = Evaluate(inputMiniBatches[i]);
			cost_prev = Cost(y_hat, outputMiniBatches[i]);

			std::vector<double> error = alg.subtraction(y_hat, outputMiniBatches[i]);

			// Calculating the weight gradients
			weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)));
			weights = regularization.regWeights(weights, lambda, alpha, reg);

			// Calculating the bias gradients
			bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size();
			y_hat = Evaluate(inputMiniBatches[i]);

			if (UI) {
				Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
				Utilities::UI(weights, bias);
			}
		}
		epoch++;
		if (epoch > max_epoch) {
			break;
		}
	}
	forwardPass();
}

void LinReg::normalEquation() {
	MLPPLinAlg alg;
	Stat stat;
	std::vector<double> x_means;
	std::vector<std::vector<double>> inputSetT = alg.transpose(inputSet);

	x_means.resize(inputSetT.size());
	for (int i = 0; i < inputSetT.size(); i++) {
		x_means[i] = (stat.mean(inputSetT[i]));
	}

	//try {
		std::vector<double> temp;
		temp.resize(k);
		temp = alg.mat_vec_mult(alg.inverse(alg.matmult(alg.transpose(inputSet), inputSet)), alg.mat_vec_mult(alg.transpose(inputSet), outputSet));
		if (std::isnan(temp[0])) {
			//throw 99;
			//TODO ERR_FAIL_COND
		} else {
			if (reg == "Ridge") {
				weights = alg.mat_vec_mult(alg.inverse(alg.addition(alg.matmult(alg.transpose(inputSet), inputSet), alg.scalarMultiply(lambda, alg.identity(k)))), alg.mat_vec_mult(alg.transpose(inputSet), outputSet));
			} else {
				weights = alg.mat_vec_mult(alg.inverse(alg.matmult(alg.transpose(inputSet), inputSet)), alg.mat_vec_mult(alg.transpose(inputSet), outputSet));
			}

			bias = stat.mean(outputSet) - alg.dot(weights, x_means);

			forwardPass();
		}
	//} catch (int err_num) {
	//	std::cout << "ERR " << err_num << ": Resulting matrix was noninvertible/degenerate, and so the normal equation could not be performed. Try utilizing gradient descent." << std::endl;
	//}
}

double LinReg::score() {
	Utilities util;
	return util.performance(y_hat, outputSet);
}

void LinReg::save(std::string fileName) {
	Utilities util;
	util.saveParameters(fileName, weights, bias);
}

double LinReg::Cost(std::vector<double> y_hat, std::vector<double> y) {
	Reg regularization;
	class MLPPCost cost;
	return cost.MSE(y_hat, y) + regularization.regTerm(weights, lambda, alpha, reg);
}

std::vector<double> LinReg::Evaluate(std::vector<std::vector<double>> X) {
	MLPPLinAlg alg;
	return alg.scalarAdd(bias, alg.mat_vec_mult(X, weights));
}

double LinReg::Evaluate(std::vector<double> x) {
	MLPPLinAlg alg;
	return alg.dot(weights, x) + bias;
}

// wTx + b
void LinReg::forwardPass() {
	y_hat = Evaluate(inputSet);
}