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