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https://github.com/Relintai/pmlpp.git
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Now MLPPLogReg uses engine classes.
This commit is contained in:
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7fb1827630
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33cd501094
@ -62,13 +62,13 @@ void MLPPLogReg::set_alpha(const real_t val) {
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}
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*/
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std::vector<real_t> MLPPLogReg::model_set_test(std::vector<std::vector<real_t>> X) {
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ERR_FAIL_COND_V(!_initialized, std::vector<real_t>());
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Ref<MLPPVector> MLPPLogReg::model_set_test(const Ref<MLPPMatrix> &X) {
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ERR_FAIL_COND_V(!_initialized, Ref<MLPPVector>());
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return evaluatem(X);
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}
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real_t MLPPLogReg::model_test(std::vector<real_t> x) {
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real_t MLPPLogReg::model_test(const Ref<MLPPVector> &x) {
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ERR_FAIL_COND_V(!_initialized, 0);
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return evaluatev(x);
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@ -88,19 +88,20 @@ void MLPPLogReg::gradient_descent(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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// 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), error)));
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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), error)));
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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(error) / _n;
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_bias -= learning_rate * alg.sum_elementsv(error) / _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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@ -125,20 +126,20 @@ void MLPPLogReg::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(_output_set, _y_hat);
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Ref<MLPPVector> error = alg.subtractionnv(_output_set, _y_hat);
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// Calculating the weight gradients
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_weights = alg.addition(_weights, alg.scalarMultiply(learning_rate / _n, alg.mat_vec_mult(alg.transpose(_input_set), error)));
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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), error)));
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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(error) / _n;
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_bias += learning_rate * alg.sum_elementsv(error) / _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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@ -161,26 +162,44 @@ void MLPPLogReg::sgd(real_t learning_rate, int max_epoch, bool ui) {
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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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Ref<MLPPVector> input_row_tmp;
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input_row_tmp.instance();
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input_row_tmp->resize(_input_set->size().x);
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Ref<MLPPVector> y_hat_tmp;
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y_hat_tmp.instance();
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y_hat_tmp->resize(1);
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Ref<MLPPVector> output_set_element_tmp;
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output_set_element_tmp.instance();
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output_set_element_tmp->resize(1);
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while (true) {
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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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cost_prev = cost({ y_hat }, { _output_set[outputIndex] });
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_input_set->get_row_into_mlpp_vector(output_index, input_row_tmp);
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real_t output_set_element = _output_set->get_element(output_index);
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output_set_element_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_row_tmp);
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y_hat_tmp->set_element(0, y_hat);
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cost_prev = cost(y_hat_tmp, output_set_element_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, _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, input_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;
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y_hat = evaluatev(_input_set[outputIndex]);
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y_hat = evaluatev(input_row_tmp);
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if (ui) {
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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_tmp, output_set_element_tmp));
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MLPPUtilities::print_ui_vb(_weights, _bias);
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}
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epoch++;
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@ -203,28 +222,29 @@ void MLPPLogReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size,
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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 bacthes = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
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auto inputMiniBatches = std::get<0>(bacthes);
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auto outputMiniBatches = std::get<1>(bacthes);
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MLPPUtilities::CreateMiniBatchMVBatch bacthes = 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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cost_prev = cost(y_hat, outputMiniBatches[i]);
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Ref<MLPPMatrix> current_mini_batch_input_entry = bacthes.input_sets[i];
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Ref<MLPPVector> current_mini_batch_output_entry = bacthes.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_mini_batch_input_entry);
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cost_prev = cost(y_hat, current_mini_batch_output_entry);
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Ref<MLPPVector> error = alg.subtractionnv(y_hat, current_mini_batch_output_entry);
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// Calculating the weight gradients
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_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)));
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / current_mini_batch_output_entry->size(), alg.mat_vec_multv(alg.transposem(current_mini_batch_input_entry), error)));
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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(error) / outputMiniBatches[i].size();
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y_hat = evaluatem(inputMiniBatches[i]);
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_bias -= learning_rate * alg.sum_elementsv(error) / current_mini_batch_output_entry->size();
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y_hat = evaluatem(current_mini_batch_input_entry);
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if (UI) {
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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_mini_batch_output_entry));
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MLPPUtilities::print_ui_vb(_weights, _bias);
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}
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}
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@ -242,14 +262,14 @@ real_t MLPPLogReg::score() {
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ERR_FAIL_COND_V(!_initialized, 0);
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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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void MLPPLogReg::save(std::string file_name) {
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ERR_FAIL_COND(!_initialized);
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//ERR_FAIL_COND(!_initialized);
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MLPPUtilities util;
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util.saveParameters(file_name, _weights, _bias);
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//MLPPUtilities util;
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//util.saveParameters(file_name, _weights, _bias);
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}
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bool MLPPLogReg::is_initialized() {
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@ -265,18 +285,25 @@ void MLPPLogReg::initialize() {
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_initialized = true;
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}
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MLPPLogReg::MLPPLogReg(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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MLPPLogReg::MLPPLogReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &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 = p_input_set.size();
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_k = p_input_set[0].size();
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_n = p_input_set->size().y;
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_k = p_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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_weights = MLPPUtilities::weightInitialization(_k);
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_bias = MLPPUtilities::biasInitialization();
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_y_hat.instance();
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_y_hat->resize(_n);
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_weights.instance();
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_weights->resize(_k);
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MLPPUtilities utils;
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utils.weight_initializationv(_weights);
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_bias = utils.bias_initializationr();
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_initialized = true;
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}
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@ -287,25 +314,25 @@ MLPPLogReg::MLPPLogReg() {
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MLPPLogReg::~MLPPLogReg() {
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}
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real_t MLPPLogReg::cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
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real_t MLPPLogReg::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.LogLoss(y_hat, y) + regularization.regTerm(_weights, _lambda, _alpha, _reg);
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return cost.log_lossv(y_hat, y) + regularization.reg_termv(_weights, _lambda, _alpha, _reg);
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}
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real_t MLPPLogReg::evaluatev(std::vector<real_t> x) {
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real_t MLPPLogReg::evaluatev(const Ref<MLPPVector> &x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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return avn.sigmoid(alg.dot(_weights, x) + _bias);
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return avn.sigmoid_normr(alg.dotv(_weights, x) + _bias);
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}
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std::vector<real_t> MLPPLogReg::evaluatem(std::vector<std::vector<real_t>> X) {
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Ref<MLPPVector> MLPPLogReg::evaluatem(const Ref<MLPPMatrix> &X) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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return avn.sigmoid(alg.scalarAdd(_bias, alg.mat_vec_mult(X, _weights)));
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return avn.sigmoid_normv(alg.scalar_addnv(_bias, alg.mat_vec_multv(X, _weights)));
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}
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// sigmoid ( wTx + b )
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@ -41,8 +41,8 @@ public:
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void set_alpha(const real_t val);
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*/
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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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@ -58,25 +58,25 @@ public:
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//MLPPSoftmaxReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &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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MLPPLogReg(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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MLPPLogReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &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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MLPPLogReg();
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~MLPPLogReg();
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protected:
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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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std::vector<real_t> evaluatem(std::vector<std::vector<real_t>> X);
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real_t evaluatev(const Ref<MLPPVector> &x);
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Ref<MLPPVector> evaluatem(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> _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> _weights;
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real_t _bias;
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int _n;
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@ -84,7 +84,7 @@ protected:
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real_t _learning_rate;
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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; /* Regularization Parameter */
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real_t _alpha; /* This is the controlling param for Elastic Net*/
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@ -396,9 +396,9 @@ void MLPPTests::test_logistic_regression(bool ui) {
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alg.printVector(model_old.modelSetTest(dt->get_input()->to_std_vector()));
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std::cout << "ACCURACY (Old): " << 100 * model_old.score() << "%" << std::endl;
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MLPPLogReg model(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector());
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MLPPLogReg model(dt->get_input(), dt->get_output());
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model.sgd(0.001, 100000, ui);
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alg.printVector(model.model_set_test(dt->get_input()->to_std_vector()));
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PLOG_MSG(model.model_set_test(dt->get_input())->to_string());
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std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
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}
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void MLPPTests::test_probit_regression(bool ui) {
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@ -1034,11 +1034,11 @@ void MLPPTests::test_new_math_functions() {
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std::vector<real_t> z_v = { 0.001 };
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alg.printVector(avn.logit(z_v));
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alg.printVector(avn.logit(z_v, 1));
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alg.printVector(avn.logit(z_v, true));
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std::vector<std::vector<real_t>> Z_m = { { 0.001 } };
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alg.printMatrix(avn.logit(Z_m));
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alg.printMatrix(avn.logit(Z_m, 1));
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alg.printMatrix(avn.logit(Z_m, true));
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std::cout << alg.trace({ { 1, 2 }, { 3, 4 } }) << std::endl;
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alg.printMatrix(alg.pinverse({ { 1, 2 }, { 3, 4 } }));
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