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Now MLPPCLogLogReg uses engine classes.
This commit is contained in:
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0a1d42f627
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@ -11,14 +11,13 @@
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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> MLPPCLogLogReg::model_set_test(std::vector<std::vector<real_t>> X) {
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Ref<MLPPVector> MLPPCLogLogReg::model_set_test(const Ref<MLPPMatrix> &X) {
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return evaluatem(X);
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}
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real_t MLPPCLogLogReg::model_test(std::vector<real_t> x) {
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real_t MLPPCLogLogReg::model_test(const Ref<MLPPVector> &x) {
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return evaluatev(x);
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}
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@ -35,20 +34,20 @@ void MLPPCLogLogReg::gradient_descent(real_t learning_rate, int max_epoch, bool
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while (true) {
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cost_prev = cost(_y_hat, _output_set);
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std::vector<real_t> error = alg.subtraction(_y_hat, _output_set);
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Ref<MLPPVector> error = alg.subtractionnv(_y_hat, _output_set);
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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(_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 = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multv(alg.transposem(_input_set), alg.hadamard_productnv(error, avn.cloglog_derivv(_z)))));
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_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
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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, true))) / _n;
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bias -= learning_rate * alg.sum_elementsv(alg.hadamard_productnv(error, avn.cloglog_derivv(_z))) / _n;
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forward_pass();
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if (ui) {
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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::cost_info(epoch, cost_prev, cost(_y_hat, _output_set));
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MLPPUtilities::print_ui_vb(_weights, bias);
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}
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epoch++;
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@ -72,19 +71,19 @@ void MLPPCLogLogReg::mle(real_t learning_rate, int max_epoch, bool ui) {
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while (true) {
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cost_prev = cost(_y_hat, _output_set);
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std::vector<real_t> error = alg.subtraction(_y_hat, _output_set);
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Ref<MLPPVector> error = alg.subtractionnv(_y_hat, _output_set);
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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 = alg.additionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multv(alg.transposem(_input_set), alg.hadamard_productnv(error, avn.cloglog_derivv(_z)))));
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_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
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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, true))) / _n;
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bias += learning_rate * alg.sum_elementsv(alg.hadamard_productnv(error, avn.cloglog_derivv(_z))) / _n;
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forward_pass();
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if (ui) {
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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::cost_info(epoch, cost_prev, cost(_y_hat, _output_set));
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MLPPUtilities::print_ui_vb(_weights, bias);
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}
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epoch++;
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@ -102,32 +101,52 @@ void MLPPCLogLogReg::sgd(real_t learning_rate, int max_epoch, bool p_) {
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real_t cost_prev = 0;
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int epoch = 1;
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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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forward_pass();
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Ref<MLPPVector> input_set_row_tmp;
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input_set_row_tmp.instance();
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input_set_row_tmp->resize(_input_set->size().x);
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Ref<MLPPVector> y_hat_row_tmp;
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y_hat_row_tmp.instance();
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y_hat_row_tmp->resize(1);
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Ref<MLPPVector> output_set_row_tmp;
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output_set_row_tmp.instance();
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output_set_row_tmp->resize(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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int output_index = distribution(generator);
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real_t y_hat = evaluatev(_input_set[outputIndex]);
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real_t z = propagatev(_input_set[outputIndex]);
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cost_prev = cost({ y_hat }, { _output_set[outputIndex] });
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_input_set->get_row_into_mlpp_vector(output_index, input_set_row_tmp);
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real_t output_set_element = _output_set->get_element(output_index);
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output_set_row_tmp->set_element(0, output_set_element);
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real_t error = y_hat - _output_set[outputIndex];
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real_t y_hat = evaluatev(input_set_row_tmp);
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y_hat_row_tmp->set_element(0, y_hat);
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real_t z = propagatev(input_set_row_tmp);
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cost_prev = cost(y_hat_row_tmp, output_set_row_tmp);
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real_t error = y_hat - output_set_element;
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// Weight Updation
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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 = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate * error * Math::exp(z - Math::exp(z)), input_set_row_tmp));
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_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
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// Bias updation
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bias -= learning_rate * error * exp(z - exp(z));
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y_hat = evaluatev(_input_set[outputIndex]);
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y_hat = evaluatev(input_set_row_tmp);
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if (p_) {
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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::cost_info(epoch, cost_prev, cost(y_hat_row_tmp, output_set_row_tmp));
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MLPPUtilities::print_ui_vb(_weights, bias);
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}
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epoch++;
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@ -149,32 +168,33 @@ void MLPPCLogLogReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_si
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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(_input_set, _output_set, 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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MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, 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<real_t> y_hat = evaluatem(inputMiniBatches[i]);
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std::vector<real_t> z = propagatem(inputMiniBatches[i]);
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cost_prev = cost(y_hat, outputMiniBatches[i]);
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Ref<MLPPMatrix> current_input_batch = batches.input_sets[i];
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Ref<MLPPVector> current_output_batch = batches.output_sets[i];
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std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
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Ref<MLPPVector> y_hat = evaluatem(current_input_batch);
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Ref<MLPPVector> z = propagatem(current_input_batch);
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cost_prev = cost(y_hat, current_output_batch);
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Ref<MLPPVector> error = alg.subtractionnv(y_hat, current_output_batch);
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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 = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multv(alg.transposem(current_input_batch), alg.hadamard_productnv(error, avn.cloglog_derivv(z)))));
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_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
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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_elementsv(alg.hadamard_productnv(error, avn.cloglog_derivv(z))) / _n;
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forward_pass();
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y_hat = evaluatem(inputMiniBatches[i]);
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y_hat = evaluatem(current_input_batch);
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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::UI(_weights, bias);
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MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, current_output_batch));
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MLPPUtilities::print_ui_vb(_weights, bias);
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}
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}
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@ -190,22 +210,29 @@ 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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MLPPUtilities util;
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return util.performance(_y_hat, _output_set);
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return util.performance_vec(_y_hat, _output_set);
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}
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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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MLPPCLogLogReg::MLPPCLogLogReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha) {
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_input_set = p_input_set;
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_output_set = p_output_set;
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_n = _input_set.size();
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_k = _input_set[0].size();
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_n = _input_set->size().y;
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_k = _input_set->size().x;
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_reg = p_reg;
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_lambda = p_lambda;
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_alpha = p_alpha;
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_y_hat.resize(_n);
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_y_hat.instance();
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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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MLPPUtilities utilities;
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_weights.instance();
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_weights->resize(_k);
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utilities.weight_initializationv(_weights);
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bias = utilities.bias_initializationr();
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}
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MLPPCLogLogReg::MLPPCLogLogReg() {
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@ -213,32 +240,37 @@ MLPPCLogLogReg::MLPPCLogLogReg() {
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MLPPCLogLogReg::~MLPPCLogLogReg() {
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}
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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(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
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MLPPReg regularization;
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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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MLPPCost mlpp_cost;
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return mlpp_cost.msev(y_hat, y) + regularization.reg_termv(_weights, _lambda, _alpha, _reg);
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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(const Ref<MLPPVector> &x) {
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MLPPLinAlg alg;
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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_normr(alg.dotv(_weights, x) + bias);
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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(const Ref<MLPPVector> &x) {
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MLPPLinAlg alg;
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return alg.dot(_weights, x) + bias;
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return alg.dotv(_weights, x) + bias;
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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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Ref<MLPPVector> MLPPCLogLogReg::evaluatem(const Ref<MLPPMatrix> &X) {
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MLPPLinAlg alg;
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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_normv(alg.scalar_addnv(bias, alg.mat_vec_multv(X, _weights)));
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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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Ref<MLPPVector> MLPPCLogLogReg::propagatem(const Ref<MLPPMatrix> &X) {
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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.scalar_addnv(bias, alg.mat_vec_multv(X, _weights));
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}
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// cloglog ( wTx + b )
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@ -246,7 +278,7 @@ void MLPPCLogLogReg::forward_pass() {
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MLPPActivation avn;
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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_normv(_z);
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}
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void MLPPCLogLogReg::_bind_methods() {
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@ -12,15 +12,17 @@
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#include "core/object/reference.h"
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#include <string>
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#include <vector>
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#include "../regularization/reg.h"
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#include "../lin_alg/mlpp_matrix.h"
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#include "../lin_alg/mlpp_vector.h"
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class MLPPCLogLogReg : public Reference {
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GDCLASS(MLPPCLogLogReg, Reference);
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public:
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std::vector<real_t> model_set_test(std::vector<std::vector<real_t>> X);
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real_t model_test(std::vector<real_t> x);
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Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
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real_t model_test(const Ref<MLPPVector> &x);
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void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
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void mle(real_t learning_rate, int max_epoch, bool ui = false);
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@ -29,7 +31,7 @@ public:
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real_t score();
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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(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_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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@ -38,30 +40,30 @@ protected:
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void weight_initialization(int k);
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void bias_initialization();
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real_t cost(std::vector<real_t> y_hat, std::vector<real_t> y);
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real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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real_t evaluatev(std::vector<real_t> x);
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real_t propagatev(std::vector<real_t> x);
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real_t evaluatev(const Ref<MLPPVector> &x);
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real_t propagatev(const Ref<MLPPVector> &x);
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std::vector<real_t> evaluatem(std::vector<std::vector<real_t>> X);
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std::vector<real_t> propagatem(std::vector<std::vector<real_t>> X);
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Ref<MLPPVector> evaluatem(const Ref<MLPPMatrix> &X);
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Ref<MLPPVector> propagatem(const Ref<MLPPMatrix> &X);
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void forward_pass();
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static void _bind_methods();
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std::vector<std::vector<real_t>> _input_set;
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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> _z;
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std::vector<real_t> _weights;
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Ref<MLPPMatrix> _input_set;
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Ref<MLPPVector> _output_set;
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Ref<MLPPVector> _y_hat;
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Ref<MLPPVector> _z;
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Ref<MLPPVector> _weights;
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real_t bias;
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int _n;
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int _k;
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// Regularization Params
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std::string _reg;
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MLPPReg::RegularizationType _reg;
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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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};
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@ -430,10 +430,18 @@ void MLPPTests::test_c_log_log_regression(bool ui) {
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alg.printVector(model_old.modelSetTest(alg.transpose(inputSet)));
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std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl;
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MLPPCLogLogReg model(alg.transpose(inputSet), outputSet);
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Ref<MLPPMatrix> input_set;
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input_set.instance();
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input_set->set_from_std_vectors(alg.transpose(inputSet));
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Ref<MLPPVector> output_set;
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output_set.instance();
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output_set->set_from_std_vector(outputSet);
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MLPPCLogLogReg model(alg.transposem(input_set), output_set);
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model.sgd(0.1, 10000, ui);
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alg.printVector(model.model_set_test(alg.transpose(inputSet)));
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std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
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PLOG_MSG(model.model_set_test(alg.transposem(input_set))->to_string());
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PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%");
|
||||
}
|
||||
void MLPPTests::test_exp_reg_regression(bool ui) {
|
||||
MLPPLinAlg alg;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user