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241 lines
8.1 KiB
C++
241 lines
8.1 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 "DualSVC.hpp"
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#include "Activation/Activation.hpp"
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#include "LinAlg/LinAlg.hpp"
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#include "Regularization/Reg.hpp"
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#include "Utilities/Utilities.hpp"
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#include "Cost/Cost.hpp"
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#include <iostream>
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#include <random>
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namespace MLPP{
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DualSVC::DualSVC(std::vector<std::vector<double>> inputSet, std::vector<double> outputSet, double C, std::string kernel)
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: inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), C(C), kernel(kernel)
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{
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y_hat.resize(n);
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bias = Utilities::biasInitialization();
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alpha = Utilities::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<double> DualSVC::modelSetTest(std::vector<std::vector<double>> X){
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return Evaluate(X);
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}
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double DualSVC::modelTest(std::vector<double> x){
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return Evaluate(x);
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}
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void DualSVC::gradientDescent(double learning_rate, int max_epoch, bool UI){
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class Cost cost;
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Activation avn;
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LinAlg alg;
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Reg 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(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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double biasGradient = 0;
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for(int i = 0; i < alpha.size(); i++){
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double sum = 0;
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if(alpha[i] < C && alpha[i] > 0){
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for(int 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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Utilities::CostInfo(epoch, cost_prev, Cost(alpha, inputSet, outputSet));
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Utilities::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) { break; }
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}
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}
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// void DualSVC::SGD(double learning_rate, int max_epoch, bool UI){
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// class Cost cost;
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// Activation avn;
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// LinAlg alg;
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// Reg 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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// 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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// Utilities::CostInfo(epoch, cost_prev, Cost(alpha));
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// Utilities::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 DualSVC::MBGD(double learning_rate, int max_epoch, int mini_batch_size, bool UI){
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// class Cost cost;
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// Activation avn;
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// LinAlg alg;
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// Reg 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] = Utilities::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<double> y_hat = Evaluate(inputMiniBatches[i]);
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// std::vector<double> 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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// Utilities::CostInfo(epoch, cost_prev, Cost(z, outputMiniBatches[i], weights, C));
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// Utilities::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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double DualSVC::score(){
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Utilities util;
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return util.performance(y_hat, outputSet);
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}
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void DualSVC::save(std::string fileName){
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Utilities util;
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util.saveParameters(fileName, alpha, bias);
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}
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double DualSVC::Cost(std::vector<double> alpha, std::vector<std::vector<double>> X, std::vector<double> y){
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class Cost cost;
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return cost.dualFormSVM(alpha, X, y);
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}
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std::vector<double> DualSVC::Evaluate(std::vector<std::vector<double>> X){
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Activation avn;
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return avn.sign(propagate(X));
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}
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std::vector<double> DualSVC::propagate(std::vector<std::vector<double>> X){
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LinAlg alg;
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std::vector<double> z;
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for(int i = 0; i < X.size(); i++){
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double sum = 0;
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for(int 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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double DualSVC::Evaluate(std::vector<double> x){
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Activation avn;
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return avn.sign(propagate(x));
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}
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double DualSVC::propagate(std::vector<double> x){
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LinAlg alg;
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double z = 0;
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for(int 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 DualSVC::forwardPass(){
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LinAlg alg;
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Activation 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 DualSVC::alphaProjection(){
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for(int 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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}
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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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double DualSVC::kernelFunction(std::vector<double> u, std::vector<double> v, std::string kernel){
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LinAlg alg;
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if(kernel == "Linear"){
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return alg.dot(u, v);
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} // warning: non-void function does not return a value in all control paths [-Wreturn-type]
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}
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std::vector<std::vector<double>> DualSVC::kernelFunction(std::vector<std::vector<double>> A, std::vector<std::vector<double>> B, std::string kernel){
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LinAlg alg;
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if(kernel == "Linear"){
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return alg.matmult(inputSet, alg.transpose(inputSet));
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} // warning: non-void function does not return a value in all control paths [-Wreturn-type]
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}
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} |