From a66308dc785bab8fa629ad418215544d2e07f0c8 Mon Sep 17 00:00:00 2001 From: novak_99 Date: Wed, 12 Jan 2022 18:25:49 -0800 Subject: [PATCH] added new optimizers. fixed isnan. --- MLPP/LinAlg/LinAlg.cpp | 24 +++ MLPP/LinAlg/LinAlg.hpp | 4 + MLPP/LinReg/LinReg.cpp | 326 +++++++++++++++++++++++++++++++++++- MLPP/LinReg/LinReg.hpp | 7 + MLPP/Regularization/Reg.cpp | 46 ++++- MLPP/Regularization/Reg.hpp | 3 + SharedLib/MLPP.so | Bin 1800832 -> 1916800 bytes a.out | Bin 2008816 -> 2044880 bytes main.cpp | 51 ++++-- 9 files changed, 437 insertions(+), 24 deletions(-) diff --git a/MLPP/LinAlg/LinAlg.cpp b/MLPP/LinAlg/LinAlg.cpp index f4cd7e0..66a514b 100644 --- a/MLPP/LinAlg/LinAlg.cpp +++ b/MLPP/LinAlg/LinAlg.cpp @@ -418,6 +418,18 @@ namespace MLPP{ return B; } + std::vector LinAlg::max(std::vector a, std::vector b){ + std::vector c; + c.resize(a.size()); + for(int i = 0; i < c.size(); i++){ + if(a[i] >= b[i]) { + c[i] = a[i]; + } + else { c[i] = b[i]; } + } + return c; + } + double LinAlg::max(std::vector> A){ return max(flatten(A)); } @@ -945,6 +957,18 @@ namespace MLPP{ return matmult(A, rotationMatrix); } + std::vector> LinAlg::max(std::vector> A, std::vector> B){ + std::vector> C; + C.resize(A.size()); + for(int i = 0; i < C.size(); i++){ + C[i].resize(A[0].size()); + } + for(int i = 0; i < A.size(); i++){ + C[i] = max(A[i], B[i]); + } + return C; + } + double LinAlg::max(std::vector a){ int max = a[0]; for(int i = 0; i < a.size(); i++){ diff --git a/MLPP/LinAlg/LinAlg.hpp b/MLPP/LinAlg/LinAlg.hpp index 055dd7d..75922a3 100644 --- a/MLPP/LinAlg/LinAlg.hpp +++ b/MLPP/LinAlg/LinAlg.hpp @@ -76,6 +76,8 @@ namespace MLPP{ std::vector> rotate(std::vector> A, double theta, int axis = -1); + std::vector> max(std::vector> A, std::vector> B); + double max(std::vector> A); double min(std::vector> A); @@ -162,6 +164,8 @@ namespace MLPP{ std::vector cos(std::vector a); + std::vector max(std::vector a, std::vector b); + double max(std::vector a); double min(std::vector a); diff --git a/MLPP/LinReg/LinReg.cpp b/MLPP/LinReg/LinReg.cpp index 8a42857..272410b 100644 --- a/MLPP/LinReg/LinReg.cpp +++ b/MLPP/LinReg/LinReg.cpp @@ -166,6 +166,327 @@ namespace MLPP{ forwardPass(); } + void LinReg::Momentum(double learning_rate, int max_epoch, int mini_batch_size, double gamma, bool UI){ + LinAlg alg; + Reg regularization; + double cost_prev = 0; + int epoch = 1; + + // Creating the mini-batches + int n_mini_batch = n/mini_batch_size; + auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); + + // Initializing necessary components for Momentum. + std::vector v = alg.zerovec(weights.size()); + while(true){ + for(int i = 0; i < n_mini_batch; i++){ + std::vector y_hat = Evaluate(inputMiniBatches[i]); + cost_prev = Cost(y_hat, outputMiniBatches[i]); + + std::vector error = alg.subtraction(y_hat, outputMiniBatches[i]); + + // Calculating the weight gradients + std::vector gradient = alg.scalarMultiply(1/outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)); + std::vector RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg); + std::vector weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final + + v = alg.addition(alg.scalarMultiply(gamma, v), alg.scalarMultiply(learning_rate, weight_grad)); + + weights = alg.subtraction(weights, v); + + // Calculating the bias gradients + bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal + y_hat = Evaluate(inputMiniBatches[i]); + + if(UI) { + Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i])); + Utilities::UI(weights, bias); + } + } + epoch++; + if(epoch > max_epoch) { break; } + } + forwardPass(); + } + + void LinReg::NAG(double learning_rate, int max_epoch, int mini_batch_size, double gamma, bool UI){ + LinAlg alg; + Reg regularization; + double cost_prev = 0; + int epoch = 1; + + // Creating the mini-batches + int n_mini_batch = n/mini_batch_size; + auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); + + // Initializing necessary components for Momentum. + std::vector v = alg.zerovec(weights.size()); + while(true){ + for(int i = 0; i < n_mini_batch; i++){ + weights = alg.subtraction(weights, alg.scalarMultiply(gamma, v)); // "Aposterori" calculation + + std::vector y_hat = Evaluate(inputMiniBatches[i]); + cost_prev = Cost(y_hat, outputMiniBatches[i]); + + std::vector error = alg.subtraction(y_hat, outputMiniBatches[i]); + + // Calculating the weight gradients + std::vector gradient = alg.scalarMultiply(1/outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)); + std::vector RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg); + std::vector weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final + + v = alg.addition(alg.scalarMultiply(gamma, v), alg.scalarMultiply(learning_rate, weight_grad)); + + weights = alg.subtraction(weights, v); + + // Calculating the bias gradients + bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal + y_hat = Evaluate(inputMiniBatches[i]); + + if(UI) { + Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i])); + Utilities::UI(weights, bias); + } + } + epoch++; + if(epoch > max_epoch) { break; } + } + forwardPass(); + } + + void LinReg::Adagrad(double learning_rate, int max_epoch, int mini_batch_size, double e, bool UI){ + LinAlg alg; + Reg regularization; + double cost_prev = 0; + int epoch = 1; + + // Creating the mini-batches + int n_mini_batch = n/mini_batch_size; + auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); + + // Initializing necessary components for Adagrad. + std::vector v = alg.zerovec(weights.size()); + while(true){ + for(int i = 0; i < n_mini_batch; i++){ + std::vector y_hat = Evaluate(inputMiniBatches[i]); + cost_prev = Cost(y_hat, outputMiniBatches[i]); + + std::vector error = alg.subtraction(y_hat, outputMiniBatches[i]); + + // Calculating the weight gradients + std::vector gradient = alg.scalarMultiply(1/outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)); + std::vector RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg); + std::vector weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final + + v = alg.hadamard_product(weight_grad, weight_grad); + + weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(weight_grad, alg.sqrt(alg.scalarAdd(e, v))))); + + // Calculating the bias gradients + bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal + y_hat = Evaluate(inputMiniBatches[i]); + + if(UI) { + Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i])); + Utilities::UI(weights, bias); + } + } + epoch++; + if(epoch > max_epoch) { break; } + } + forwardPass(); + } + + void LinReg::Adadelta(double learning_rate, int max_epoch, int mini_batch_size, double b1, double e, bool UI){ + // Adagrad upgrade. Momentum is applied. + LinAlg alg; + Reg regularization; + double cost_prev = 0; + int epoch = 1; + + // Creating the mini-batches + int n_mini_batch = n/mini_batch_size; + auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); + + // Initializing necessary components for Adagrad. + std::vector v = alg.zerovec(weights.size()); + while(true){ + for(int i = 0; i < n_mini_batch; i++){ + std::vector y_hat = Evaluate(inputMiniBatches[i]); + cost_prev = Cost(y_hat, outputMiniBatches[i]); + + std::vector error = alg.subtraction(y_hat, outputMiniBatches[i]); + + // Calculating the weight gradients + std::vector gradient = alg.scalarMultiply(1/outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)); + std::vector RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg); + std::vector weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final + + v = alg.addition(alg.scalarMultiply(b1, v), alg.scalarMultiply(1 - b1, alg.hadamard_product(weight_grad, weight_grad))); + + weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(weight_grad, alg.sqrt(alg.scalarAdd(e, v))))); + + // Calculating the bias gradients + bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal + y_hat = Evaluate(inputMiniBatches[i]); + + if(UI) { + Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i])); + Utilities::UI(weights, bias); + } + } + epoch++; + if(epoch > max_epoch) { break; } + } + forwardPass(); + } + + void LinReg::Adam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI){ + LinAlg alg; + Reg regularization; + double cost_prev = 0; + int epoch = 1; + + // Creating the mini-batches + int n_mini_batch = n/mini_batch_size; + auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); + + // Initializing necessary components for Adam. + std::vector m = alg.zerovec(weights.size()); + + std::vector v = alg.zerovec(weights.size()); + while(true){ + for(int i = 0; i < n_mini_batch; i++){ + std::vector y_hat = Evaluate(inputMiniBatches[i]); + cost_prev = Cost(y_hat, outputMiniBatches[i]); + + std::vector error = alg.subtraction(y_hat, outputMiniBatches[i]); + + // Calculating the weight gradients + std::vector gradient = alg.scalarMultiply(1/outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)); + std::vector RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg); + std::vector weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final + + m = alg.addition(alg.scalarMultiply(b1, m), alg.scalarMultiply(1 - b1, weight_grad)); + v = alg.addition(alg.scalarMultiply(b2, v), alg.scalarMultiply(1 - b2, alg.exponentiate(weight_grad, 2))); + + std::vector m_hat = alg.scalarMultiply(1/(1 - pow(b1, epoch)), m); + std::vector v_hat = alg.scalarMultiply(1/(1 - pow(b2, epoch)), v); + + weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(m_hat, alg.scalarAdd(e, alg.sqrt(v_hat))))); + + // Calculating the bias gradients + bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal + y_hat = Evaluate(inputMiniBatches[i]); + + if(UI) { + Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i])); + Utilities::UI(weights, bias); + } + } + epoch++; + if(epoch > max_epoch) { break; } + } + forwardPass(); + } + + void LinReg::Adamax(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI){ + LinAlg alg; + Reg regularization; + double cost_prev = 0; + int epoch = 1; + + // Creating the mini-batches + int n_mini_batch = n/mini_batch_size; + auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); + + std::vector m = alg.zerovec(weights.size()); + + std::vector u = alg.zerovec(weights.size()); + while(true){ + for(int i = 0; i < n_mini_batch; i++){ + std::vector y_hat = Evaluate(inputMiniBatches[i]); + cost_prev = Cost(y_hat, outputMiniBatches[i]); + + std::vector error = alg.subtraction(y_hat, outputMiniBatches[i]); + + // Calculating the weight gradients + std::vector gradient = alg.scalarMultiply(1/outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)); + std::vector RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg); + std::vector weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final + + m = alg.addition(alg.scalarMultiply(b1, m), alg.scalarMultiply(1 - b1, weight_grad)); + u = alg.max(alg.scalarMultiply(b2, u), alg.abs(weight_grad)); + + std::vector m_hat = alg.scalarMultiply(1/(1 - pow(b1, epoch)), m); + + weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(m_hat, u))); + + // Calculating the bias gradients + bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal + y_hat = Evaluate(inputMiniBatches[i]); + + if(UI) { + Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i])); + Utilities::UI(weights, bias); + } + } + epoch++; + if(epoch > max_epoch) { break; } + } + forwardPass(); + } + + void LinReg::Nadam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI){ + LinAlg alg; + Reg regularization; + double cost_prev = 0; + int epoch = 1; + + // Creating the mini-batches + int n_mini_batch = n/mini_batch_size; + auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); + + // Initializing necessary components for Adam. + std::vector m = alg.zerovec(weights.size()); + std::vector v = alg.zerovec(weights.size()); + std::vector m_final = alg.zerovec(weights.size()); + while(true){ + for(int i = 0; i < n_mini_batch; i++){ + std::vector y_hat = Evaluate(inputMiniBatches[i]); + cost_prev = Cost(y_hat, outputMiniBatches[i]); + + std::vector error = alg.subtraction(y_hat, outputMiniBatches[i]); + + // Calculating the weight gradients + std::vector gradient = alg.scalarMultiply(1/outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)); + std::vector RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg); + std::vector weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final + + m = alg.addition(alg.scalarMultiply(b1, m), alg.scalarMultiply(1 - b1, weight_grad)); + v = alg.addition(alg.scalarMultiply(b2, v), alg.scalarMultiply(1 - b2, alg.exponentiate(weight_grad, 2))); + m_final = alg.addition(alg.scalarMultiply(b1, m), alg.scalarMultiply((1 - b1)/(1 - pow(b1, epoch)), weight_grad)); + + std::vector m_hat = alg.scalarMultiply(1/(1 - pow(b1, epoch)), m); + std::vector v_hat = alg.scalarMultiply(1/(1 - pow(b2, epoch)), v); + + weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(m_final, alg.scalarAdd(e, alg.sqrt(v_hat))))); + + // Calculating the bias gradients + bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal + y_hat = Evaluate(inputMiniBatches[i]); + + if(UI) { + Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i])); + Utilities::UI(weights, bias); + } + } + epoch++; + if(epoch > max_epoch) { break; } + } + forwardPass(); + } + void LinReg::normalEquation(){ LinAlg alg; Stat stat; @@ -181,14 +502,14 @@ namespace MLPP{ std::vector temp; temp.resize(k); temp = alg.mat_vec_mult(alg.inverse(alg.matmult(alg.transpose(inputSet), inputSet)), alg.mat_vec_mult(alg.transpose(inputSet), outputSet)); - if(isnan(temp[0])){ + if(std::isnan(temp[0])){ throw 99; } else{ if(reg == "Ridge") { weights = alg.mat_vec_mult(alg.inverse(alg.addition(alg.matmult(alg.transpose(inputSet), inputSet), alg.scalarMultiply(lambda, alg.identity(k)))), alg.mat_vec_mult(alg.transpose(inputSet), outputSet)); } - else{ weights = alg.mat_vec_mult(alg.inverse(alg.matmult(alg.transpose(inputSet), inputSet)), alg.mat_vec_mult(alg.transpose(inputSet), outputSet)); } + else{ weights = alg.mat_vec_mult(alg.inverse(alg.matmult(alg.transpose(inputSet), inputSet)), alg.mat_vec_mult(alg.transpose(inputSet), outputSet)); } bias = stat.mean(outputSet) - alg.dot(weights, x_means); @@ -198,7 +519,6 @@ namespace MLPP{ catch(int err_num){ std::cout << "ERR " << err_num << ": Resulting matrix was noninvertible/degenerate, and so the normal equation could not be performed. Try utilizing gradient descent." << std::endl; } - } double LinReg::score(){ diff --git a/MLPP/LinReg/LinReg.hpp b/MLPP/LinReg/LinReg.hpp index 56b5ef8..bcc7a92 100644 --- a/MLPP/LinReg/LinReg.hpp +++ b/MLPP/LinReg/LinReg.hpp @@ -21,6 +21,13 @@ namespace MLPP{ void gradientDescent(double learning_rate, int max_epoch, bool UI = 1); void SGD(double learning_rate, int max_epoch, bool UI = 1); void MBGD(double learning_rate, int max_epoch, int mini_batch_size, bool UI = 1); + void Momentum(double learning_rate, int max_epoch, int mini_batch_size, double gamma, bool UI = 1); + void NAG(double learning_rate, int max_epoch, int mini_batch_size, double gamma, bool UI = 1); + void Adagrad(double learning_rate, int max_epoch, int mini_batch_size, double e, bool UI = 1); + void Adadelta(double learning_rate, int max_epoch, int mini_batch_size, double b1, double e, bool UI = 1); + void Adam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1); + void Adamax(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1); + void Nadam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI = 1); void normalEquation(); double score(); void save(std::string fileName); diff --git a/MLPP/Regularization/Reg.cpp b/MLPP/Regularization/Reg.cpp index 06f0c69..8f07eba 100644 --- a/MLPP/Regularization/Reg.cpp +++ b/MLPP/Regularization/Reg.cpp @@ -7,6 +7,7 @@ #include #include #include "Reg.hpp" +#include "LinAlg/LinAlg.hpp" #include "Activation/Activation.hpp" namespace MLPP{ @@ -70,19 +71,48 @@ namespace MLPP{ } std::vector Reg::regWeights(std::vector weights, double lambda, double alpha, std::string reg){ - for(int i = 0; i < weights.size(); i++){ - weights[i] -= regDerivTerm(weights, lambda, alpha, reg, i); - } - return weights; + LinAlg alg; + return alg.subtraction(weights, regDerivTerm(weights, lambda, alpha, reg)); + // for(int i = 0; i < weights.size(); i++){ + // weights[i] -= regDerivTerm(weights, lambda, alpha, reg, i); + // } + // return weights; } std::vector> Reg::regWeights(std::vector> weights, double lambda, double alpha, std::string reg){ - for(int i = 0; i < weights.size(); i++){ - for(int j = 0; j < weights[i].size(); j++){ - weights[i][j] -= regDerivTerm(weights, lambda, alpha, reg, i, j); + LinAlg alg; + return alg.subtraction(weights, regDerivTerm(weights, lambda, alpha, reg)); + // for(int i = 0; i < weights.size(); i++){ + // for(int j = 0; j < weights[i].size(); j++){ + // weights[i][j] -= regDerivTerm(weights, lambda, alpha, reg, i, j); + // } + // } + // return weights; + } + + std::vector Reg::regDerivTerm(std::vector weights, double lambda, double alpha, std::string reg){ + std::vector regDeriv; + regDeriv.resize(weights.size()); + + for(int i = 0; i < regDeriv.size(); i++){ + regDeriv[i] = regDerivTerm(weights, lambda, alpha, reg, i); + } + return regDeriv; + } + + std::vector> Reg::regDerivTerm(std::vector> weights, double lambda, double alpha, std::string reg){ + std::vector> regDeriv; + regDeriv.resize(weights.size()); + for(int i = 0; i < regDeriv.size(); i++){ + regDeriv[i].resize(weights[0].size()); + } + + for(int i = 0; i < regDeriv.size(); i++){ + for(int j = 0; j < regDeriv[i].size(); j++){ + regDeriv[i][j] = regDerivTerm(weights, lambda, alpha, reg, i, j); } } - return weights; + return regDeriv; } double Reg::regDerivTerm(std::vector weights, double lambda, double alpha, std::string reg, int j){ diff --git a/MLPP/Regularization/Reg.hpp b/MLPP/Regularization/Reg.hpp index cff6fb3..e9f4979 100644 --- a/MLPP/Regularization/Reg.hpp +++ b/MLPP/Regularization/Reg.hpp @@ -19,6 +19,9 @@ namespace MLPP{ std::vector regWeights(std::vector weights, double lambda, double alpha, std::string reg); std::vector> regWeights(std::vector> weights, double lambda, double alpha, std::string reg); + std::vector regDerivTerm(std::vector weights, double lambda, double alpha, std::string reg); + std::vector> regDerivTerm(std::vector>, double lambda, double alpha, std::string reg); + private: double regDerivTerm(std::vector weights, double lambda, double alpha, std::string reg, int j); double regDerivTerm(std::vector> weights, double lambda, double alpha, std::string reg, int i, int j); diff --git a/SharedLib/MLPP.so b/SharedLib/MLPP.so index faed978c1c21b68f5b23fdf96d7de260c035d4f3..532088e8cd2485a0aebbe57df93ee6d13686d4b7 100755 GIT binary patch delta 521258 zcmc${2~<^8*gwoU7ZjQ9MFl}bK}B%JOht3R6eSbG616nPQY*)bw81V8D7V|qVTXrI z3rmx-5_5o349kQ{3rmeMmC9<9nV6=gCExFP_PN}P`M&?}zt*?b=UQ_1+54IHGwtE* zb8gmOv$AR*UpcUGX{b-Vf2ETY~P)r$L7XZkf6 z|B0&ZV^P((7q@Bs!t%>ieRp3~PxroJV1p=8pv8n8IjCCaZd9#wuXF1aEvCufc}lKV z8mLy>i;jA~u+#HZy)9MM)4jsVvV=rW9CX)+dq#1KyI;u)&Zjg~Y_UX&@AbGiv#Ho@ zL6eUuIoA-!KAISzwP-VV;HZHfn#Cq!rP&|4x3I)nmdM6FjTOCQb@B0O42J#Nn$;DJ z>Z$tDM%Km}snXVr?ez5~YNE(!HXxGn&* z!aHz%$;T(*r^Y_{!9G6aHI03)9Q5(g0TV(0&o%M+C(*~pzfm)v1w9)u&2&ZAXG~`w 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model.gradientDescent(0.001, 30000, 0); - // model.SGD(0.001, 30000, 1); + //model.gradientDescent(0.001, 30, 0); + //model.SGD(0.001, 30000, 1); // model.MBGD(0.001, 10000, 2, 1); - // model.normalEquation(); + //model.normalEquation(); - // alg.printVector(model.modelSetTest((alg.transpose(inputSet)))); - // std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl; + + + LinReg adamModel(alg.transpose(inputSet), outputSet); + adamModel.Nadam(0.1, 5, 1, 0.9, 0.999, 1e-8, 0); // Change batch size = sgd, bgd + alg.printVector(adamModel.modelSetTest(alg.transpose(inputSet))); + std::cout << "ACCURACY: " << 100 * adamModel.score() << "%" << std::endl; + + + // const int TRIAL_NUM = 1000; + + // double scoreSGD = 0; + // double scoreADAM = 0; + // for(int i = 0; i < TRIAL_NUM; i++){ + // LinReg model(alg.transpose(inputSet), outputSet); + // model.MBGD(0.001, 5, 1, 0); + // scoreSGD += model.score(); + + // LinReg adamModel(alg.transpose(inputSet), outputSet); + // adamModel.Adam(0.1, 5, 1, 0.9, 0.999, 1e-8, 0); // Change batch size = sgd, bgd + // scoreADAM += adamModel.score(); + // } + + // std::cout << "ACCURACY, AVG, SGD: " << 100 * scoreSGD/TRIAL_NUM << "%" << std::endl; + + // std::cout << std::endl; + + // std::cout << "ACCURACY, AVG, ADAM: " << 100 * scoreADAM/TRIAL_NUM << "%" << std::endl; // std::cout << "Total epoch num: 300" << std::endl; @@ -646,12 +671,12 @@ int main() { // std::vector outputSet; // data.setData(30, "/Users/marcmelikyan/Desktop/Data/BreastCancerSVM.csv", inputSet, outputSet); - std::vector> inputSet; - std::vector outputSet; - data.setData(4, "/Users/marcmelikyan/Desktop/Data/IrisSVM.csv", inputSet, outputSet); + // std::vector> inputSet; + // std::vector outputSet; + // data.setData(4, "/Users/marcmelikyan/Desktop/Data/IrisSVM.csv", inputSet, outputSet); - DualSVC kernelSVM(inputSet, outputSet, 1000); - kernelSVM.gradientDescent(0.0001, 20, 1); + // DualSVC kernelSVM(inputSet, outputSet, 1000); + // kernelSVM.gradientDescent(0.0001, 20, 1); return 0;