mirror of https://github.com/Relintai/pmlpp.git
245 lines
7.0 KiB
C++
245 lines
7.0 KiB
C++
//
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// DualSVC.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 "dual_svc_old.h"
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#include "../activation/activation_old.h"
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#include "../cost/cost_old.h"
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#include "../lin_alg/lin_alg_old.h"
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#include "../regularization/reg_old.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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MLPPDualSVCOld::MLPPDualSVCOld(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet, real_t p_C, std::string p_kernel) {
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inputSet = p_inputSet;
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outputSet = p_outputSet;
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n = p_inputSet.size();
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k = p_inputSet[0].size();
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C = p_C;
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kernel = p_kernel;
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y_hat.resize(n);
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bias = MLPPUtilities::biasInitialization();
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alpha = MLPPUtilities::weightInitialization(n); // One alpha for all training examples, as per the lagrangian multipliers.
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K = kernelFunction(inputSet, inputSet, kernel); // For now this is unused. When non-linear kernels are added, the K will be manipulated.
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}
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std::vector<real_t> MLPPDualSVCOld::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 MLPPDualSVCOld::modelTest(std::vector<real_t> x) {
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return Evaluate(x);
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}
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void MLPPDualSVCOld::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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class MLPPCostOld cost;
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MLPPLinAlgOld alg;
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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(alpha, inputSet, outputSet);
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alpha = alg.subtraction(alpha, alg.scalarMultiply(learning_rate, cost.dualFormSVMDeriv(alpha, inputSet, outputSet)));
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alphaProjection();
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// Calculating the bias
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real_t biasGradient = 0;
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for (uint32_t i = 0; i < alpha.size(); i++) {
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real_t sum = 0;
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if (alpha[i] < C && alpha[i] > 0) {
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for (uint32_t j = 0; j < alpha.size(); j++) {
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if (alpha[j] > 0) {
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sum += alpha[j] * outputSet[j] * alg.dot(inputSet[j], inputSet[i]); // TO DO: DON'T forget to add non-linear kernelizations.
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}
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}
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}
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biasGradient = (1 - outputSet[i] * sum) / outputSet[i];
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break;
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}
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bias -= biasGradient * learning_rate;
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forwardPass();
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// UI PORTION
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(alpha, inputSet, outputSet));
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MLPPUtilities::UI(alpha, bias);
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std::cout << score() << std::endl; // TO DO: DELETE THIS.
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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 MLPPDualSVCOld::SGD(real_t learning_rate, int max_epoch, bool UI){
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// class MLPPCostOld cost;
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// MLPPActivationOld avn;
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// MLPPLinAlgOld alg;
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// MLPPRegOld 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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// cost_prev = Cost(alpha, inputSet[outputIndex], outputSet[outputIndex]);
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// // Bias updation
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// bias -= learning_rate * costDeriv;
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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(alpha));
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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) { break; }
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// }
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// forwardPass();
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// }
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// void MLPPDualSVCOld::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI){
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// class MLPPCostOld cost;
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// MLPPActivationOld avn;
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// MLPPLinAlgOld alg;
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// MLPPRegOld 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 [inputMiniBatches, outputMiniBatches] = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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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(z, outputMiniBatches[i], weights, C);
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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(inputMiniBatches[i]), cost.HingeLossDeriv(z, outputMiniBatches[i], C))));
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// weights = regularization.regWeights(weights, learning_rate/n, 0, "Ridge");
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// // Calculating the bias gradients
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// bias -= learning_rate * alg.sum_elements(cost.HingeLossDeriv(y_hat, outputMiniBatches[i], C)) / n;
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// forwardPass();
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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(z, outputMiniBatches[i], weights, C));
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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) { break; }
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// }
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// forwardPass();
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// }
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real_t MLPPDualSVCOld::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 MLPPDualSVCOld::save(std::string fileName) {
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MLPPUtilities util;
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util.saveParameters(fileName, alpha, bias);
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}
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real_t MLPPDualSVCOld::Cost(std::vector<real_t> alpha, std::vector<std::vector<real_t>> X, std::vector<real_t> y) {
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class MLPPCostOld cost;
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return cost.dualFormSVM(alpha, X, y);
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}
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std::vector<real_t> MLPPDualSVCOld::Evaluate(std::vector<std::vector<real_t>> X) {
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MLPPActivationOld avn;
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return avn.sign(propagate(X));
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}
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std::vector<real_t> MLPPDualSVCOld::propagate(std::vector<std::vector<real_t>> X) {
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MLPPLinAlgOld alg;
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std::vector<real_t> z;
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for (uint32_t i = 0; i < X.size(); i++) {
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real_t sum = 0;
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for (uint32_t j = 0; j < alpha.size(); j++) {
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if (alpha[j] != 0) {
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sum += alpha[j] * outputSet[j] * alg.dot(inputSet[j], X[i]); // TO DO: DON'T forget to add non-linear kernelizations.
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}
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}
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sum += bias;
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z.push_back(sum);
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}
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return z;
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}
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real_t MLPPDualSVCOld::Evaluate(std::vector<real_t> x) {
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MLPPActivationOld avn;
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return avn.sign(propagate(x));
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}
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real_t MLPPDualSVCOld::propagate(std::vector<real_t> x) {
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MLPPLinAlgOld alg;
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real_t z = 0;
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for (uint32_t j = 0; j < alpha.size(); j++) {
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if (alpha[j] != 0) {
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z += alpha[j] * outputSet[j] * alg.dot(inputSet[j], x); // TO DO: DON'T forget to add non-linear kernelizations.
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}
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}
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z += bias;
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return z;
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}
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void MLPPDualSVCOld::forwardPass() {
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MLPPActivationOld avn;
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z = propagate(inputSet);
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y_hat = avn.sign(z);
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}
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void MLPPDualSVCOld::alphaProjection() {
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for (uint32_t i = 0; i < alpha.size(); i++) {
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if (alpha[i] > C) {
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alpha[i] = C;
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} else if (alpha[i] < 0) {
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alpha[i] = 0;
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}
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}
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}
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real_t MLPPDualSVCOld::kernelFunction(std::vector<real_t> u, std::vector<real_t> v, std::string kernel) {
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MLPPLinAlgOld alg;
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if (kernel == "Linear") {
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return alg.dot(u, v);
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}
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return 0;
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}
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std::vector<std::vector<real_t>> MLPPDualSVCOld::kernelFunction(std::vector<std::vector<real_t>> A, std::vector<std::vector<real_t>> B, std::string kernel) {
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MLPPLinAlgOld alg;
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if (kernel == "Linear") {
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return alg.matmult(inputSet, alg.transpose(inputSet));
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}
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return std::vector<std::vector<real_t>>();
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}
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