pmlpp/mlpp/svc/svc_old.cpp
2023-02-10 09:16:49 +01:00

204 lines
5.5 KiB
C++

//
// SVC.cpp
//
// Created by Marc Melikyan on 10/2/20.
//
#include "svc_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>
std::vector<real_t> MLPPSVCOld::modelSetTest(std::vector<std::vector<real_t>> X) {
return Evaluate(X);
}
real_t MLPPSVCOld::modelTest(std::vector<real_t> x) {
return Evaluate(x);
}
void MLPPSVCOld::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
class MLPPCost cost;
MLPPActivation avn;
MLPPLinAlg alg;
MLPPReg regularization;
real_t cost_prev = 0;
int epoch = 1;
forwardPass();
while (true) {
cost_prev = Cost(y_hat, outputSet, weights, C);
weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(inputSet), cost.HingeLossDeriv(z, outputSet, C))));
weights = regularization.regWeights(weights, learning_rate / n, 0, "Ridge");
// Calculating the bias gradients
bias += learning_rate * alg.sum_elements(cost.HingeLossDeriv(y_hat, outputSet, C)) / n;
forwardPass();
// UI PORTION
if (UI) {
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet, weights, C));
MLPPUtilities::UI(weights, bias);
}
epoch++;
if (epoch > max_epoch) {
break;
}
}
}
void MLPPSVCOld::SGD(real_t learning_rate, int max_epoch, bool UI) {
class MLPPCost cost;
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({ z }, { outputSet[outputIndex] }, weights, C);
real_t costDeriv = cost.HingeLossDeriv(std::vector<real_t>({ z }), std::vector<real_t>({ outputSet[outputIndex] }), C)[0]; // Explicit conversion to avoid ambiguity with overloaded function. Error occured on Ubuntu.
// Weight Updation
weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate * costDeriv, inputSet[outputIndex]));
weights = regularization.regWeights(weights, learning_rate, 0, "Ridge");
// Bias updation
bias -= learning_rate * costDeriv;
//y_hat = Evaluate({ inputSet[outputIndex] });
if (UI) {
MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ z }, { outputSet[outputIndex] }, weights, C));
MLPPUtilities::UI(weights, bias);
}
epoch++;
if (epoch > max_epoch) {
break;
}
}
forwardPass();
}
void MLPPSVCOld::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
class MLPPCost cost;
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 batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
auto inputMiniBatches = std::get<0>(batches);
auto outputMiniBatches = std::get<1>(batches);
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(z, outputMiniBatches[i], weights, C);
// Calculating the weight gradients
weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), cost.HingeLossDeriv(z, outputMiniBatches[i], C))));
weights = regularization.regWeights(weights, learning_rate / n, 0, "Ridge");
// Calculating the bias gradients
bias -= learning_rate * alg.sum_elements(cost.HingeLossDeriv(y_hat, outputMiniBatches[i], C)) / n;
forwardPass();
y_hat = Evaluate(inputMiniBatches[i]);
if (UI) {
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(z, outputMiniBatches[i], weights, C));
MLPPUtilities::UI(weights, bias);
}
}
epoch++;
if (epoch > max_epoch) {
break;
}
}
forwardPass();
}
real_t MLPPSVCOld::score() {
MLPPUtilities util;
return util.performance(y_hat, outputSet);
}
void MLPPSVCOld::save(std::string fileName) {
MLPPUtilities util;
util.saveParameters(fileName, weights, bias);
}
MLPPSVCOld::MLPPSVCOld(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet, real_t p_C) {
inputSet = p_inputSet;
outputSet = p_outputSet;
n = inputSet.size();
k = inputSet[0].size();
C = p_C;
y_hat.resize(n);
weights = MLPPUtilities::weightInitialization(k);
bias = MLPPUtilities::biasInitialization();
}
real_t MLPPSVCOld::Cost(std::vector<real_t> z, std::vector<real_t> y, std::vector<real_t> weights, real_t C) {
class MLPPCost cost;
return cost.HingeLoss(z, y, weights, C);
}
std::vector<real_t> MLPPSVCOld::Evaluate(std::vector<std::vector<real_t>> X) {
MLPPLinAlg alg;
MLPPActivation avn;
return avn.sign(alg.scalarAdd(bias, alg.mat_vec_mult(X, weights)));
}
std::vector<real_t> MLPPSVCOld::propagate(std::vector<std::vector<real_t>> X) {
MLPPLinAlg alg;
MLPPActivation avn;
return alg.scalarAdd(bias, alg.mat_vec_mult(X, weights));
}
real_t MLPPSVCOld::Evaluate(std::vector<real_t> x) {
MLPPLinAlg alg;
MLPPActivation avn;
return avn.sign(alg.dot(weights, x) + bias);
}
real_t MLPPSVCOld::propagate(std::vector<real_t> x) {
MLPPLinAlg alg;
MLPPActivation avn;
return alg.dot(weights, x) + bias;
}
// sign ( wTx + b )
void MLPPSVCOld::forwardPass() {
MLPPActivation avn;
z = propagate(inputSet);
y_hat = avn.sign(z);
}