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Added ProbitRegOld.
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@ -58,6 +58,7 @@ sources = [
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"mlpp/pca/pca_old.cpp",
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"mlpp/uni_lin_reg/uni_lin_reg_old.cpp",
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"mlpp/outlier_finder/outlier_finder_old.cpp",
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"mlpp/probit_reg/probit_reg_old.cpp",
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"test/mlpp_tests.cpp",
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]
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245
mlpp/probit_reg/probit_reg_old.cpp
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245
mlpp/probit_reg/probit_reg_old.cpp
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//
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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_old.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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MLPPProbitRegOld::MLPPProbitRegOld(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, std::string reg, real_t lambda, real_t 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<real_t> MLPPProbitRegOld::modelSetTest(std::vector<std::vector<real_t>> X) {
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return Evaluate(X);
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}
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real_t MLPPProbitRegOld::modelTest(std::vector<real_t> x) {
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return Evaluate(x);
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}
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void MLPPProbitRegOld::gradientDescent(real_t 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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real_t 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<real_t> 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 MLPPProbitRegOld::MLE(real_t 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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real_t 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<real_t> 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 MLPPProbitRegOld::SGD(real_t 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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real_t 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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real_t y_hat = Evaluate(inputSet[outputIndex]);
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real_t z = propagate(inputSet[outputIndex]);
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cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
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real_t 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 MLPPProbitRegOld::MBGD(real_t 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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real_t 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 createMiniBatchesResult = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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auto inputMiniBatches = std::get<0>(createMiniBatchesResult);
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auto outputMiniBatches = std::get<1>(createMiniBatchesResult);
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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<real_t>> currentInputSet;
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std::vector<real_t> 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 (real_t(n) / real_t(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<real_t> y_hat = Evaluate(inputMiniBatches[i]);
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std::vector<real_t> z = propagate(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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std::vector<real_t> 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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real_t MLPPProbitRegOld::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 MLPPProbitRegOld::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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real_t MLPPProbitRegOld::Cost(std::vector<real_t> y_hat, std::vector<real_t> 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<real_t> MLPPProbitRegOld::Evaluate(std::vector<std::vector<real_t>> 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<real_t> MLPPProbitRegOld::propagate(std::vector<std::vector<real_t>> 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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real_t MLPPProbitRegOld::Evaluate(std::vector<real_t> 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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real_t MLPPProbitRegOld::propagate(std::vector<real_t> 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 MLPPProbitRegOld::forwardPass() {
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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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53
mlpp/probit_reg/probit_reg_old.h
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53
mlpp/probit_reg/probit_reg_old.h
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#ifndef MLPP_PROBIT_REG_OLD_H
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#define MLPP_PROBIT_REG_OLD_H
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//
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// ProbitReg.hpp
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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 "core/math/math_defs.h"
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#include <string>
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#include <vector>
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class MLPPProbitRegOld {
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public:
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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);
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std::vector<real_t> modelSetTest(std::vector<std::vector<real_t>> X);
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real_t modelTest(std::vector<real_t> x);
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void gradientDescent(real_t learning_rate, int max_epoch = 0, bool UI = false);
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void MLE(real_t learning_rate, int max_epoch = 0, bool UI = false);
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void SGD(real_t learning_rate, int max_epoch = 0, bool UI = false);
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void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false);
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real_t score();
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void save(std::string fileName);
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private:
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real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y);
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std::vector<real_t> Evaluate(std::vector<std::vector<real_t>> X);
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std::vector<real_t> propagate(std::vector<std::vector<real_t>> X);
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real_t Evaluate(std::vector<real_t> x);
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real_t propagate(std::vector<real_t> x);
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void forwardPass();
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std::vector<std::vector<real_t>> inputSet;
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std::vector<real_t> outputSet;
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std::vector<real_t> z;
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std::vector<real_t> y_hat;
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std::vector<real_t> weights;
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real_t bias;
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int n;
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int k;
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// Regularization Params
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std::string reg;
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real_t lambda;
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real_t alpha; /* This is the controlling param for Elastic Net*/
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};
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#endif /* ProbitReg_hpp */
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@ -52,6 +52,7 @@
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#include "../mlpp/pca/pca_old.h"
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#include "../mlpp/uni_lin_reg/uni_lin_reg_old.h"
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#include "../mlpp/wgan/wgan_old.h"
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#include "../mlpp/probit_reg/probit_reg_old.h"
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Vector<real_t> dstd_vec_to_vec(const std::vector<real_t> &in) {
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Vector<real_t> r;
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@ -348,10 +349,10 @@ void MLPPTests::test_probit_regression(bool ui) {
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// PROBIT REGRESSION
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Ref<MLPPDataSimple> dt = data.load_breast_cancer(_breast_cancer_data_path);
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MLPPProbitReg model(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector());
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model.SGD(0.001, 10000, ui);
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alg.printVector(model.modelSetTest(dt->get_input()->to_std_vector()));
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std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
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MLPPProbitRegOld model_old(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector());
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model_old.SGD(0.001, 10000, ui);
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alg.printVector(model_old.modelSetTest(dt->get_input()->to_std_vector()));
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std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl;
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}
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void MLPPTests::test_c_log_log_regression(bool ui) {
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MLPPLinAlg alg;
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