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Also rename the class variables in MLPPCLogLogReg.
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@ -33,22 +33,22 @@ void MLPPCLogLogReg::gradient_descent(real_t learning_rate, int max_epoch, bool
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forward_pass();
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forward_pass();
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while (true) {
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while (true) {
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cost_prev = cost(y_hat, outputSet);
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cost_prev = cost(_y_hat, _output_set);
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std::vector<real_t> error = alg.subtraction(y_hat, outputSet);
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std::vector<real_t> error = alg.subtraction(_y_hat, _output_set);
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// Calculating the weight gradients
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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(inputSet), alg.hadamard_product(error, avn.cloglog(z, 1)))));
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_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate / _n, alg.mat_vec_mult(alg.transpose(_input_set), alg.hadamard_product(error, avn.cloglog(_z, true)))));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients
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// Calculating the bias gradients
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bias -= learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.cloglog(z, 1))) / n;
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bias -= learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.cloglog(_z, true))) / _n;
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forward_pass();
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forward_pass();
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if (ui) {
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputSet));
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(_y_hat, _output_set));
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MLPPUtilities::UI(weights, bias);
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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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epoch++;
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@ -70,21 +70,21 @@ void MLPPCLogLogReg::mle(real_t learning_rate, int max_epoch, bool ui) {
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forward_pass();
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forward_pass();
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while (true) {
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while (true) {
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cost_prev = cost(y_hat, outputSet);
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cost_prev = cost(_y_hat, _output_set);
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std::vector<real_t> error = alg.subtraction(y_hat, outputSet);
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std::vector<real_t> error = alg.subtraction(_y_hat, _output_set);
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weights = alg.addition(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(inputSet), alg.hadamard_product(error, avn.cloglog(z, 1)))));
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_weights = alg.addition(_weights, alg.scalarMultiply(learning_rate / _n, alg.mat_vec_mult(alg.transpose(_input_set), alg.hadamard_product(error, avn.cloglog(_z, true)))));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients
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// Calculating the bias gradients
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bias += learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.cloglog(z, 1))) / n;
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bias += learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.cloglog(_z, true))) / _n;
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forward_pass();
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forward_pass();
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if (ui) {
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputSet));
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(_y_hat, _output_set));
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MLPPUtilities::UI(weights, bias);
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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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epoch++;
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@ -107,27 +107,27 @@ void MLPPCLogLogReg::sgd(real_t learning_rate, int max_epoch, bool p_) {
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while (true) {
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while (true) {
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std::random_device rd;
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std::random_device rd;
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std::default_random_engine generator(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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std::uniform_int_distribution<int> distribution(0, int(_n - 1));
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int outputIndex = distribution(generator);
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int outputIndex = distribution(generator);
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real_t y_hat = evaluatev(inputSet[outputIndex]);
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real_t y_hat = evaluatev(_input_set[outputIndex]);
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real_t z = propagatev(inputSet[outputIndex]);
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real_t z = propagatev(_input_set[outputIndex]);
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cost_prev = cost({ y_hat }, { outputSet[outputIndex] });
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cost_prev = cost({ y_hat }, { _output_set[outputIndex] });
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real_t error = y_hat - outputSet[outputIndex];
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real_t error = y_hat - _output_set[outputIndex];
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// Weight Updation
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// Weight Updation
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate * error * exp(z - exp(z)), inputSet[outputIndex]));
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_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate * error * exp(z - exp(z)), _input_set[outputIndex]));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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// Bias updation
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// Bias updation
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bias -= learning_rate * error * exp(z - exp(z));
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bias -= learning_rate * error * exp(z - exp(z));
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y_hat = evaluatev(inputSet[outputIndex]);
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y_hat = evaluatev(_input_set[outputIndex]);
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if (p_) {
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if (p_) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost({ y_hat }, { outputSet[outputIndex] }));
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MLPPUtilities::CostInfo(epoch, cost_prev, cost({ y_hat }, { _output_set[outputIndex] }));
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MLPPUtilities::UI(weights, bias);
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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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epoch++;
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@ -148,8 +148,8 @@ void MLPPCLogLogReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_si
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int epoch = 1;
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int epoch = 1;
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// Creating the mini-batches
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// Creating the mini-batches
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int n_mini_batch = n / mini_batch_size;
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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 batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
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auto inputMiniBatches = std::get<0>(batches);
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auto inputMiniBatches = std::get<0>(batches);
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auto outputMiniBatches = std::get<1>(batches);
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auto outputMiniBatches = std::get<1>(batches);
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@ -162,11 +162,11 @@ void MLPPCLogLogReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_si
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std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
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std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
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// Calculating the weight gradients
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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]), alg.hadamard_product(error, avn.cloglog(z, 1)))));
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_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate / _n, alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), alg.hadamard_product(error, avn.cloglog(z, 1)))));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients
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// Calculating the bias gradients
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bias -= learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.cloglog(z, 1))) / n;
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bias -= learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.cloglog(z, 1))) / _n;
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forward_pass();
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forward_pass();
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@ -174,7 +174,7 @@ void MLPPCLogLogReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_si
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if (p_) {
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if (p_) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputMiniBatches[i]));
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputMiniBatches[i]));
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MLPPUtilities::UI(weights, bias);
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MLPPUtilities::UI(_weights, bias);
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}
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}
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}
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}
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@ -190,21 +190,21 @@ void MLPPCLogLogReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_si
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real_t MLPPCLogLogReg::score() {
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real_t MLPPCLogLogReg::score() {
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MLPPUtilities util;
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MLPPUtilities util;
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return util.performance(y_hat, outputSet);
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return util.performance(_y_hat, _output_set);
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}
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}
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MLPPCLogLogReg::MLPPCLogLogReg(std::vector<std::vector<real_t>> pinputSet, std::vector<real_t> poutputSet, std::string p_reg, real_t p_lambda, real_t p_alpha) {
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MLPPCLogLogReg::MLPPCLogLogReg(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set, std::string p_reg, real_t p_lambda, real_t p_alpha) {
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inputSet = pinputSet;
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_input_set = p_input_set;
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outputSet = poutputSet;
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_output_set = p_output_set;
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n = inputSet.size();
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_n = _input_set.size();
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k = inputSet[0].size();
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_k = _input_set[0].size();
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reg = p_reg;
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_reg = p_reg;
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lambda = p_lambda;
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_lambda = p_lambda;
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alpha = p_alpha;
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_alpha = p_alpha;
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y_hat.resize(n);
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_y_hat.resize(_n);
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weights = MLPPUtilities::weightInitialization(k);
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_weights = MLPPUtilities::weightInitialization(_k);
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bias = MLPPUtilities::biasInitialization();
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bias = MLPPUtilities::biasInitialization();
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}
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}
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@ -216,35 +216,35 @@ MLPPCLogLogReg::~MLPPCLogLogReg() {
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real_t MLPPCLogLogReg::cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
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real_t MLPPCLogLogReg::cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
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MLPPReg regularization;
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MLPPReg regularization;
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class MLPPCost cost;
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class MLPPCost cost;
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return cost.MSE(y_hat, y) + regularization.regTerm(weights, lambda, alpha, reg);
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return cost.MSE(y_hat, y) + regularization.regTerm(_weights, _lambda, _alpha, _reg);
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}
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}
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real_t MLPPCLogLogReg::evaluatev(std::vector<real_t> x) {
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real_t MLPPCLogLogReg::evaluatev(std::vector<real_t> x) {
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MLPPLinAlg alg;
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MLPPLinAlg alg;
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MLPPActivation avn;
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MLPPActivation avn;
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return avn.cloglog(alg.dot(weights, x) + bias);
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return avn.cloglog(alg.dot(_weights, x) + bias);
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}
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}
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real_t MLPPCLogLogReg::propagatev(std::vector<real_t> x) {
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real_t MLPPCLogLogReg::propagatev(std::vector<real_t> x) {
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MLPPLinAlg alg;
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MLPPLinAlg alg;
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return alg.dot(weights, x) + bias;
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return alg.dot(_weights, x) + bias;
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}
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}
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std::vector<real_t> MLPPCLogLogReg::evaluatem(std::vector<std::vector<real_t>> X) {
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std::vector<real_t> MLPPCLogLogReg::evaluatem(std::vector<std::vector<real_t>> X) {
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MLPPLinAlg alg;
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MLPPLinAlg alg;
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MLPPActivation avn;
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MLPPActivation avn;
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return avn.cloglog(alg.scalarAdd(bias, alg.mat_vec_mult(X, weights)));
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return avn.cloglog(alg.scalarAdd(bias, alg.mat_vec_mult(X, _weights)));
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}
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}
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std::vector<real_t> MLPPCLogLogReg::propagatem(std::vector<std::vector<real_t>> X) {
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std::vector<real_t> MLPPCLogLogReg::propagatem(std::vector<std::vector<real_t>> X) {
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MLPPLinAlg alg;
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MLPPLinAlg alg;
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return alg.scalarAdd(bias, alg.mat_vec_mult(X, weights));
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return alg.scalarAdd(bias, alg.mat_vec_mult(X, _weights));
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}
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}
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// cloglog ( wTx + b )
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// cloglog ( wTx + b )
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void MLPPCLogLogReg::forward_pass() {
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void MLPPCLogLogReg::forward_pass() {
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MLPPActivation avn;
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MLPPActivation avn;
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z = propagatem(inputSet);
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_z = propagatem(_input_set);
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y_hat = avn.cloglog(z);
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_y_hat = avn.cloglog(_z);
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}
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}
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@ -25,7 +25,7 @@ public:
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real_t score();
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real_t score();
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MLPPCLogLogReg(std::vector<std::vector<real_t>> pinputSet, std::vector<real_t> poutputSet, std::string p_reg = "None", real_t p_lambda = 0.5, real_t p_alpha = 0.5);
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MLPPCLogLogReg(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set, std::string p_reg = "None", real_t p_lambda = 0.5, real_t p_alpha = 0.5);
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MLPPCLogLogReg();
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MLPPCLogLogReg();
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~MLPPCLogLogReg();
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~MLPPCLogLogReg();
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@ -44,20 +44,20 @@ private:
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void forward_pass();
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void forward_pass();
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std::vector<std::vector<real_t>> inputSet;
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std::vector<std::vector<real_t>> _input_set;
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std::vector<real_t> outputSet;
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std::vector<real_t> _output_set;
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std::vector<real_t> y_hat;
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std::vector<real_t> _y_hat;
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std::vector<real_t> z;
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std::vector<real_t> _z;
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std::vector<real_t> weights;
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std::vector<real_t> _weights;
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real_t bias;
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real_t bias;
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int n;
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int _n;
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int k;
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int _k;
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// Regularization Params
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// Regularization Params
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std::string reg;
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std::string _reg;
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real_t lambda;
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real_t _lambda;
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real_t alpha; /* This is the controlling param for Elastic Net*/
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real_t _alpha; /* This is the controlling param for Elastic Net*/
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};
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};
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#endif /* CLogLogReg_hpp */
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#endif /* CLogLogReg_hpp */
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