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Added SVCOld.
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@ -59,6 +59,7 @@ sources = [
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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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"mlpp/svc/svc_old.cpp",
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"test/mlpp_tests.cpp",
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]
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203
mlpp/svc/svc_old.cpp
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203
mlpp/svc/svc_old.cpp
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//
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// SVC.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 "svc_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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std::vector<real_t> MLPPSVCOld::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 MLPPSVCOld::modelTest(std::vector<real_t> x) {
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return Evaluate(x);
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}
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void MLPPSVCOld::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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class MLPPCost cost;
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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, weights, C);
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(inputSet), cost.HingeLossDeriv(z, outputSet, 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, outputSet, C)) / n;
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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(y_hat, outputSet, weights, C));
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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 MLPPSVCOld::SGD(real_t learning_rate, int max_epoch, bool UI) {
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class MLPPCost cost;
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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({ z }, { outputSet[outputIndex] }, weights, C);
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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.
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// Weight Updation
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate * costDeriv, inputSet[outputIndex]));
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weights = regularization.regWeights(weights, learning_rate, 0, "Ridge");
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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({ z }, { outputSet[outputIndex] }, weights, C));
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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 MLPPSVCOld::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
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class MLPPCost cost;
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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 batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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auto inputMiniBatches = std::get<0>(batches);
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auto outputMiniBatches = std::get<1>(batches);
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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) {
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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 MLPPSVCOld::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 MLPPSVCOld::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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MLPPSVCOld::MLPPSVCOld(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet, real_t p_C) {
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inputSet = p_inputSet;
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outputSet = p_outputSet;
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n = inputSet.size();
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k = inputSet[0].size();
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C = p_C;
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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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real_t MLPPSVCOld::Cost(std::vector<real_t> z, std::vector<real_t> y, std::vector<real_t> weights, real_t C) {
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class MLPPCost cost;
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return cost.HingeLoss(z, y, weights, C);
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}
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std::vector<real_t> MLPPSVCOld::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.sign(alg.scalarAdd(bias, alg.mat_vec_mult(X, weights)));
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}
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std::vector<real_t> MLPPSVCOld::propagate(std::vector<std::vector<real_t>> X) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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return alg.scalarAdd(bias, alg.mat_vec_mult(X, weights));
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}
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real_t MLPPSVCOld::Evaluate(std::vector<real_t> x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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return avn.sign(alg.dot(weights, x) + bias);
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}
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real_t MLPPSVCOld::propagate(std::vector<real_t> x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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return alg.dot(weights, x) + bias;
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}
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// sign ( wTx + b )
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void MLPPSVCOld::forwardPass() {
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MLPPActivation 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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55
mlpp/svc/svc_old.h
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55
mlpp/svc/svc_old.h
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#ifndef MLPP_SVC_OLD_H
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#define MLPP_SVC_OLD_H
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//
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// SVC.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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// https://towardsdatascience.com/svm-implementation-from-scratch-python-2db2fc52e5c2
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// Illustratd a practical definition of the Hinge Loss function and its gradient when optimizing with SGD.
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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 MLPPSVCOld {
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public:
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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, bool UI = false);
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void SGD(real_t learning_rate, int max_epoch, 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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MLPPSVCOld(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, real_t C);
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private:
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real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y, std::vector<real_t> weights, real_t C);
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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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real_t C;
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int n;
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int k;
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// UI Portion
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void UI(int epoch, real_t cost_prev);
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};
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#endif /* SVC_hpp */
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@ -53,6 +53,7 @@
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#include "../mlpp/probit_reg/probit_reg_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/svc/svc_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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@ -413,10 +414,10 @@ void MLPPTests::test_support_vector_classification(bool ui) {
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// SUPPORT VECTOR CLASSIFICATION
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Ref<MLPPDataSimple> dt = data.load_breast_cancer_svc(_breast_cancer_svm_data_path);
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MLPPSVC model(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector(), ui);
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model.SGD(0.00001, 100000, 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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MLPPSVCOld model_old(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector(), ui);
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model_old.SGD(0.00001, 100000, 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_mlp(bool ui) {
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