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Now MLPPLinReg uses engine classes.
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@ -993,6 +993,38 @@ std::vector<real_t> MLPPLinAlg::max(std::vector<real_t> a, std::vector<real_t> b
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return c;
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}
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Ref<MLPPVector> MLPPLinAlg::maxnvv(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b) {
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Ref<MLPPVector> ret;
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ret.instance();
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ERR_FAIL_COND_V(!a.is_valid() || !b.is_valid(), ret);
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int a_size = a->size();
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ERR_FAIL_COND_V(a_size != b->size(), ret);
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ret->resize(a_size);
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const real_t *aa = a->ptr();
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const real_t *ba = b->ptr();
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real_t *ret_ptr = ret->ptrw();
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real_t dist = 0;
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for (int i = 0; i < a_size; i++) {
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real_t aa_i = aa[i];
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real_t bb_i = ba[i];
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if (aa_i > bb_i) {
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ret_ptr[i] = aa_i;
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} else {
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ret_ptr[i] = bb_i;
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}
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}
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return ret;
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}
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real_t MLPPLinAlg::max(std::vector<std::vector<real_t>> A) {
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return max(flatten(A));
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}
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@ -2472,6 +2504,45 @@ real_t MLPPLinAlg::min(std::vector<real_t> a) {
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return min;
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}
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real_t MLPPLinAlg::maxvr(const Ref<MLPPVector> &a) {
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ERR_FAIL_COND_V(!a.is_valid(), -Math_INF);
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int a_size = a->size();
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const real_t *aa = a->ptr();
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real_t max_element = -Math_INF;
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for (int i = 0; i < a_size; i++) {
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real_t current_element = aa[i];
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if (current_element > max_element) {
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max_element = current_element;
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}
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}
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return max_element;
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}
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real_t MLPPLinAlg::minvr(const Ref<MLPPVector> &a) {
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ERR_FAIL_COND_V(!a.is_valid(), Math_INF);
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int a_size = a->size();
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const real_t *aa = a->ptr();
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real_t min_element = Math_INF;
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for (int i = 0; i < a_size; i++) {
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real_t current_element = aa[i];
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if (current_element > min_element) {
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min_element = current_element;
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}
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}
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return min_element;
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}
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std::vector<real_t> MLPPLinAlg::round(std::vector<real_t> a) {
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std::vector<real_t> b;
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b.resize(a.size());
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@ -268,10 +268,14 @@ public:
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std::vector<real_t> max(std::vector<real_t> a, std::vector<real_t> b);
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real_t max(std::vector<real_t> a);
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Ref<MLPPVector> maxnvv(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b);
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real_t max(std::vector<real_t> a);
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real_t min(std::vector<real_t> a);
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real_t maxvr(const Ref<MLPPVector> &a);
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real_t minvr(const Ref<MLPPVector> &a);
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std::vector<real_t> round(std::vector<real_t> a);
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real_t euclideanDistance(std::vector<real_t> a, std::vector<real_t> b);
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@ -63,13 +63,13 @@ void MLPPLinReg::set_alpha(const real_t val) {
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}
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*/
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std::vector<real_t> MLPPLinReg::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> MLPPLinReg::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 MLPPLinReg::model_test(std::vector<real_t> x) {
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real_t MLPPLinReg::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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@ -89,22 +89,22 @@ void MLPPLinReg::newton_raphson(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 (2nd derivative)
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std::vector<real_t> first_derivative = alg.mat_vec_mult(alg.transpose(_input_set), error);
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std::vector<std::vector<real_t>> second_derivative = alg.matmult(alg.transpose(_input_set), _input_set);
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_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate / _n, alg.mat_vec_mult(alg.transpose(alg.inverse(second_derivative)), first_derivative)));
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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Ref<MLPPVector> first_derivative = alg.mat_vec_multv(alg.transposem(_input_set), error);
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Ref<MLPPMatrix> second_derivative = alg.matmultm(alg.transposem(_input_set), _input_set);
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_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multv(alg.transposem(alg.inversem(second_derivative)), first_derivative)));
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_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients (2nd derivative)
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_bias -= learning_rate * alg.sum_elements(error) / _n; // We keep this the same. The 2nd derivative is just [1].
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_bias -= learning_rate * alg.sum_elementsv(error) / _n; // We keep this the same. The 2nd derivative is just [1].
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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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@ -129,20 +129,20 @@ void MLPPLinReg::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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@ -166,26 +166,44 @@ void MLPPLinReg::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_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> 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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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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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_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_tmp->set_element(0, output_set_element);
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cost_prev = cost(y_hat_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, _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_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;
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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 (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_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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@ -209,28 +227,29 @@ void MLPPLinReg::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 batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
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auto input_mini_batches = std::get<0>(batches);
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auto output_mini_batches = 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(input_mini_batches[i]);
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cost_prev = cost(y_hat, output_mini_batches[i]);
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Ref<MLPPMatrix> current_input_mini_batch = batches.input_sets[i];
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Ref<MLPPVector> current_output_mini_batch = batches.output_sets[i];
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std::vector<real_t> error = alg.subtraction(y_hat, output_mini_batches[i]);
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Ref<MLPPVector> y_hat = evaluatem(current_input_mini_batch);
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cost_prev = cost(y_hat, current_output_mini_batch);
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Ref<MLPPVector> error = alg.subtractionnv(y_hat, current_output_mini_batch);
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// Calculating the weight gradients
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_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate / output_mini_batches[i].size(), alg.mat_vec_mult(alg.transpose(input_mini_batches[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_output_mini_batch->size(), alg.mat_vec_multv(alg.transposem(current_input_mini_batch), 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) / output_mini_batches[i].size();
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y_hat = evaluatem(input_mini_batches[i]);
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_bias -= learning_rate * alg.sum_elementsv(error) / current_output_mini_batch->size();
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y_hat = evaluatem(current_input_mini_batch);
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, output_mini_batches[i]));
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MLPPUtilities::UI(_weights, _bias);
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MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, current_output_mini_batch));
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MLPPUtilities::print_ui_vb(_weights, _bias);
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}
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}
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@ -254,35 +273,37 @@ void MLPPLinReg::momentum(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 input_mini_batches = std::get<0>(batches);
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auto output_mini_batches = 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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// Initializing necessary components for Momentum.
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std::vector<real_t> v = alg.zerovec(_weights.size());
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Ref<MLPPVector> v = alg.zerovecv(_weights->size());
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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(input_mini_batches[i]);
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cost_prev = cost(y_hat, output_mini_batches[i]);
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Ref<MLPPMatrix> current_input_mini_batch = batches.input_sets[i];
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Ref<MLPPVector> current_output_mini_batch = batches.output_sets[i];
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std::vector<real_t> error = alg.subtraction(y_hat, output_mini_batches[i]);
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Ref<MLPPVector> y_hat = evaluatem(current_input_mini_batch);
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cost_prev = cost(y_hat, current_output_mini_batch);
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Ref<MLPPVector> error = alg.subtractionnv(y_hat, current_output_mini_batch);
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// Calculating the weight gradients
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std::vector<real_t> gradient = alg.scalarMultiply(1 / output_mini_batches[i].size(), alg.mat_vec_mult(alg.transpose(input_mini_batches[i]), error));
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std::vector<real_t> reg_deriv_term = regularization.regDerivTerm(_weights, _lambda, _alpha, _reg);
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std::vector<real_t> weight_grad = alg.addition(gradient, reg_deriv_term); // Weight_grad_final
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Ref<MLPPVector> gradient = alg.scalar_multiplynv(1 / current_output_mini_batch->size(), alg.mat_vec_multv(alg.transposem(current_input_mini_batch), error));
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Ref<MLPPVector> reg_deriv_term = regularization.reg_deriv_termv(_weights, _lambda, _alpha, _reg);
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Ref<MLPPVector> weight_grad = alg.additionnv(gradient, reg_deriv_term); // Weight_grad_final
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v = alg.addition(alg.scalarMultiply(gamma, v), alg.scalarMultiply(learning_rate, weight_grad));
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v = alg.additionnv(alg.scalar_multiplynv(gamma, v), alg.scalar_multiplynv(learning_rate, weight_grad));
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_weights = alg.subtraction(_weights, v);
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_weights = alg.subtractionnv(_weights, v);
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// Calculating the bias gradients
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_bias -= learning_rate * alg.sum_elements(error) / output_mini_batches[i].size(); // As normal
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y_hat = evaluatem(input_mini_batches[i]);
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_bias -= learning_rate * alg.sum_elementsv(error) / current_output_mini_batch->size(); // As normal
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y_hat = evaluatem(current_input_mini_batch);
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, output_mini_batches[i]));
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MLPPUtilities::UI(_weights, _bias);
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MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, current_output_mini_batch));
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MLPPUtilities::print_ui_vb(_weights, _bias);
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}
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}
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@ -306,37 +327,39 @@ void MLPPLinReg::nag(real_t learning_rate, int max_epoch, int mini_batch_size, r
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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 input_mini_batches = std::get<0>(batches);
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auto output_mini_batches = 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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// Initializing necessary components for Momentum.
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std::vector<real_t> v = alg.zerovec(_weights.size());
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Ref<MLPPVector> v = alg.zerovecv(_weights->size());
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while (true) {
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for (int i = 0; i < n_mini_batch; i++) {
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_weights = alg.subtraction(_weights, alg.scalarMultiply(gamma, v)); // "Aposterori" calculation
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Ref<MLPPMatrix> current_input_mini_batch = batches.input_sets[i];
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Ref<MLPPVector> current_output_mini_batch = batches.output_sets[i];
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std::vector<real_t> y_hat = evaluatem(input_mini_batches[i]);
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cost_prev = cost(y_hat, output_mini_batches[i]);
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_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(gamma, v)); // "Aposterori" calculation
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std::vector<real_t> error = alg.subtraction(y_hat, output_mini_batches[i]);
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Ref<MLPPVector> y_hat = evaluatem(current_input_mini_batch);
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cost_prev = cost(y_hat, current_output_mini_batch);
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Ref<MLPPVector> error = alg.subtractionnv(y_hat, current_output_mini_batch);
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// Calculating the weight gradients
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std::vector<real_t> gradient = alg.scalarMultiply(1 / output_mini_batches[i].size(), alg.mat_vec_mult(alg.transpose(input_mini_batches[i]), error));
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std::vector<real_t> reg_deriv_term = regularization.regDerivTerm(_weights, _lambda, _alpha, _reg);
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std::vector<real_t> weight_grad = alg.addition(gradient, reg_deriv_term); // Weight_grad_final
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Ref<MLPPVector> gradient = alg.scalar_multiplynv(1 / current_output_mini_batch->size(), alg.mat_vec_multv(alg.transposem(current_input_mini_batch), error));
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||||
Ref<MLPPVector> reg_deriv_term = regularization.reg_deriv_termv(_weights, _lambda, _alpha, _reg);
|
||||
Ref<MLPPVector> weight_grad = alg.additionnv(gradient, reg_deriv_term); // Weight_grad_final
|
||||
|
||||
v = alg.addition(alg.scalarMultiply(gamma, v), alg.scalarMultiply(learning_rate, weight_grad));
|
||||
v = alg.additionnv(alg.scalar_multiplynv(gamma, v), alg.scalar_multiplynv(learning_rate, weight_grad));
|
||||
|
||||
_weights = alg.subtraction(_weights, v);
|
||||
_weights = alg.subtractionnv(_weights, v);
|
||||
|
||||
// Calculating the bias gradients
|
||||
_bias -= learning_rate * alg.sum_elements(error) / output_mini_batches[i].size(); // As normal
|
||||
y_hat = evaluatem(input_mini_batches[i]);
|
||||
_bias -= learning_rate * alg.sum_elementsv(error) / current_output_mini_batch->size(); // As normal
|
||||
y_hat = evaluatem(current_input_mini_batch);
|
||||
|
||||
if (ui) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, output_mini_batches[i]));
|
||||
MLPPUtilities::UI(_weights, _bias);
|
||||
MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, current_output_mini_batch));
|
||||
MLPPUtilities::print_ui_vb(_weights, _bias);
|
||||
}
|
||||
}
|
||||
|
||||
@ -360,35 +383,37 @@ void MLPPLinReg::adagrad(real_t learning_rate, int max_epoch, int mini_batch_siz
|
||||
|
||||
// Creating the mini-batches
|
||||
int n_mini_batch = _n / mini_batch_size;
|
||||
auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
|
||||
auto input_mini_batches = std::get<0>(batches);
|
||||
auto output_mini_batches = std::get<1>(batches);
|
||||
MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
|
||||
|
||||
// Initializing necessary components for Adagrad.
|
||||
std::vector<real_t> v = alg.zerovec(_weights.size());
|
||||
Ref<MLPPVector> v = alg.zerovecv(_weights->size());
|
||||
|
||||
while (true) {
|
||||
for (int i = 0; i < n_mini_batch; i++) {
|
||||
std::vector<real_t> y_hat = evaluatem(input_mini_batches[i]);
|
||||
cost_prev = cost(y_hat, output_mini_batches[i]);
|
||||
Ref<MLPPMatrix> current_input_mini_batch = batches.input_sets[i];
|
||||
Ref<MLPPVector> current_output_mini_batch = batches.output_sets[i];
|
||||
|
||||
std::vector<real_t> error = alg.subtraction(y_hat, output_mini_batches[i]);
|
||||
Ref<MLPPVector> y_hat = evaluatem(current_input_mini_batch);
|
||||
cost_prev = cost(y_hat, current_output_mini_batch);
|
||||
|
||||
Ref<MLPPVector> error = alg.subtractionnv(y_hat, current_output_mini_batch);
|
||||
|
||||
// Calculating the weight gradients
|
||||
std::vector<real_t> gradient = alg.scalarMultiply(1 / output_mini_batches[i].size(), alg.mat_vec_mult(alg.transpose(input_mini_batches[i]), error));
|
||||
std::vector<real_t> reg_deriv_term = regularization.regDerivTerm(_weights, _lambda, _alpha, _reg);
|
||||
std::vector<real_t> weight_grad = alg.addition(gradient, reg_deriv_term); // Weight_grad_final
|
||||
Ref<MLPPVector> gradient = alg.scalar_multiplynv(1 / current_output_mini_batch->size(), alg.mat_vec_multv(alg.transposem(current_input_mini_batch), error));
|
||||
Ref<MLPPVector> reg_deriv_term = regularization.reg_deriv_termv(_weights, _lambda, _alpha, _reg);
|
||||
Ref<MLPPVector> weight_grad = alg.additionnv(gradient, reg_deriv_term); // Weight_grad_final
|
||||
|
||||
v = alg.hadamard_product(weight_grad, weight_grad);
|
||||
v = alg.hadamard_productnv(weight_grad, weight_grad);
|
||||
|
||||
_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(weight_grad, alg.sqrt(alg.scalarAdd(e, v)))));
|
||||
_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate, alg.element_wise_division(weight_grad, alg.sqrtv(alg.scalar_addnv(e, v)))));
|
||||
|
||||
// Calculating the bias gradients
|
||||
_bias -= learning_rate * alg.sum_elements(error) / output_mini_batches[i].size(); // As normal
|
||||
y_hat = evaluatem(input_mini_batches[i]);
|
||||
_bias -= learning_rate * alg.sum_elementsv(error) / current_output_mini_batch->size(); // As normal
|
||||
y_hat = evaluatem(current_input_mini_batch);
|
||||
|
||||
if (ui) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, output_mini_batches[i]));
|
||||
MLPPUtilities::UI(_weights, _bias);
|
||||
MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, current_output_mini_batch));
|
||||
MLPPUtilities::print_ui_vb(_weights, _bias);
|
||||
}
|
||||
}
|
||||
|
||||
@ -413,35 +438,37 @@ void MLPPLinReg::adadelta(real_t learning_rate, int max_epoch, int mini_batch_si
|
||||
|
||||
// Creating the mini-batches
|
||||
int n_mini_batch = _n / mini_batch_size;
|
||||
auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
|
||||
auto input_mini_batches = std::get<0>(batches);
|
||||
auto output_mini_batches = std::get<1>(batches);
|
||||
MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
|
||||
|
||||
// Initializing necessary components for Adagrad.
|
||||
std::vector<real_t> v = alg.zerovec(_weights.size());
|
||||
Ref<MLPPVector> v = alg.zerovecv(_weights->size());
|
||||
|
||||
while (true) {
|
||||
for (int i = 0; i < n_mini_batch; i++) {
|
||||
std::vector<real_t> y_hat = evaluatem(input_mini_batches[i]);
|
||||
cost_prev = cost(y_hat, output_mini_batches[i]);
|
||||
Ref<MLPPMatrix> current_input_mini_batch = batches.input_sets[i];
|
||||
Ref<MLPPVector> current_output_mini_batch = batches.output_sets[i];
|
||||
|
||||
std::vector<real_t> error = alg.subtraction(y_hat, output_mini_batches[i]);
|
||||
Ref<MLPPVector> y_hat = evaluatem(current_input_mini_batch);
|
||||
cost_prev = cost(y_hat, current_output_mini_batch);
|
||||
|
||||
Ref<MLPPVector> error = alg.subtractionnv(y_hat, current_output_mini_batch);
|
||||
|
||||
// Calculating the weight gradients
|
||||
std::vector<real_t> gradient = alg.scalarMultiply(1 / output_mini_batches[i].size(), alg.mat_vec_mult(alg.transpose(input_mini_batches[i]), error));
|
||||
std::vector<real_t> reg_deriv_term = regularization.regDerivTerm(_weights, _lambda, _alpha, _reg);
|
||||
std::vector<real_t> weight_grad = alg.addition(gradient, reg_deriv_term); // Weight_grad_final
|
||||
Ref<MLPPVector> gradient = alg.scalar_multiplynv(1 / current_output_mini_batch->size(), alg.mat_vec_multv(alg.transposem(current_input_mini_batch), error));
|
||||
Ref<MLPPVector> reg_deriv_term = regularization.reg_deriv_termv(_weights, _lambda, _alpha, _reg);
|
||||
Ref<MLPPVector> weight_grad = alg.additionnv(gradient, reg_deriv_term); // Weight_grad_final
|
||||
|
||||
v = alg.addition(alg.scalarMultiply(b1, v), alg.scalarMultiply(1 - b1, alg.hadamard_product(weight_grad, weight_grad)));
|
||||
v = alg.additionnv(alg.scalar_multiplynv(b1, v), alg.scalar_multiplynv(1 - b1, alg.hadamard_productnv(weight_grad, weight_grad)));
|
||||
|
||||
_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(weight_grad, alg.sqrt(alg.scalarAdd(e, v)))));
|
||||
_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate, alg.element_wise_division(weight_grad, alg.sqrtv(alg.scalar_addnv(e, v)))));
|
||||
|
||||
// Calculating the bias gradients
|
||||
_bias -= learning_rate * alg.sum_elements(error) / output_mini_batches[i].size(); // As normal
|
||||
y_hat = evaluatem(input_mini_batches[i]);
|
||||
_bias -= learning_rate * alg.sum_elementsv(error) / current_output_mini_batch->size(); // As normal
|
||||
y_hat = evaluatem(current_input_mini_batch);
|
||||
|
||||
if (ui) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, output_mini_batches[i]));
|
||||
MLPPUtilities::UI(_weights, _bias);
|
||||
MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, current_output_mini_batch));
|
||||
MLPPUtilities::print_ui_vb(_weights, _bias);
|
||||
}
|
||||
}
|
||||
|
||||
@ -465,41 +492,42 @@ void MLPPLinReg::adam(real_t learning_rate, int max_epoch, int mini_batch_size,
|
||||
|
||||
// Creating the mini-batches
|
||||
int n_mini_batch = _n / mini_batch_size;
|
||||
auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
|
||||
auto input_mini_batches = std::get<0>(batches);
|
||||
auto output_mini_batches = std::get<1>(batches);
|
||||
MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
|
||||
|
||||
// Initializing necessary components for Adam.
|
||||
std::vector<real_t> m = alg.zerovec(_weights.size());
|
||||
Ref<MLPPVector> m = alg.zerovecv(_weights->size());
|
||||
Ref<MLPPVector> v = alg.zerovecv(_weights->size());
|
||||
|
||||
std::vector<real_t> v = alg.zerovec(_weights.size());
|
||||
while (true) {
|
||||
for (int i = 0; i < n_mini_batch; i++) {
|
||||
std::vector<real_t> y_hat = evaluatem(input_mini_batches[i]);
|
||||
cost_prev = cost(y_hat, output_mini_batches[i]);
|
||||
Ref<MLPPMatrix> current_input_mini_batch = batches.input_sets[i];
|
||||
Ref<MLPPVector> current_output_mini_batch = batches.output_sets[i];
|
||||
|
||||
std::vector<real_t> error = alg.subtraction(y_hat, output_mini_batches[i]);
|
||||
Ref<MLPPVector> y_hat = evaluatem(current_input_mini_batch);
|
||||
cost_prev = cost(y_hat, current_output_mini_batch);
|
||||
|
||||
Ref<MLPPVector> error = alg.subtractionnv(y_hat, current_output_mini_batch);
|
||||
|
||||
// Calculating the weight gradients
|
||||
std::vector<real_t> gradient = alg.scalarMultiply(1 / output_mini_batches[i].size(), alg.mat_vec_mult(alg.transpose(input_mini_batches[i]), error));
|
||||
std::vector<real_t> reg_deriv_term = regularization.regDerivTerm(_weights, _lambda, _alpha, _reg);
|
||||
std::vector<real_t> weight_grad = alg.addition(gradient, reg_deriv_term); // Weight_grad_final
|
||||
Ref<MLPPVector> gradient = alg.scalar_multiplynv(1 / current_output_mini_batch->size(), alg.mat_vec_multv(alg.transposem(current_input_mini_batch), error));
|
||||
Ref<MLPPVector> reg_deriv_term = regularization.reg_deriv_termv(_weights, _lambda, _alpha, _reg);
|
||||
Ref<MLPPVector> weight_grad = alg.additionnv(gradient, reg_deriv_term); // 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 = alg.additionnv(alg.scalar_multiplynv(b1, m), alg.scalar_multiplynv(1 - b1, weight_grad));
|
||||
v = alg.additionnv(alg.scalar_multiplynv(b2, v), alg.scalar_multiplynv(1 - b2, alg.exponentiatev(weight_grad, 2)));
|
||||
|
||||
std::vector<real_t> m_hat = alg.scalarMultiply(1 / (1 - pow(b1, epoch)), m);
|
||||
std::vector<real_t> v_hat = alg.scalarMultiply(1 / (1 - pow(b2, epoch)), v);
|
||||
Ref<MLPPVector> m_hat = alg.scalar_multiplynv(1 / (1 - Math::pow(b1, epoch)), m);
|
||||
Ref<MLPPVector> v_hat = alg.scalar_multiplynv(1 / (1 - Math::pow(b2, epoch)), v);
|
||||
|
||||
_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(m_hat, alg.scalarAdd(e, alg.sqrt(v_hat)))));
|
||||
_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate, alg.element_wise_divisionm(m_hat, alg.scalar_addnv(e, alg.sqrtv(v_hat)))));
|
||||
|
||||
// Calculating the bias gradients
|
||||
_bias -= learning_rate * alg.sum_elements(error) / output_mini_batches[i].size(); // As normal
|
||||
y_hat = evaluatem(input_mini_batches[i]);
|
||||
_bias -= learning_rate * alg.sum_elementsv(error) / current_output_mini_batch->size(); // As normal
|
||||
y_hat = evaluatem(current_input_mini_batch);
|
||||
|
||||
if (ui) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, output_mini_batches[i]));
|
||||
MLPPUtilities::UI(_weights, _bias);
|
||||
MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, current_output_mini_batch));
|
||||
MLPPUtilities::print_ui_vb(_weights, _bias);
|
||||
}
|
||||
}
|
||||
|
||||
@ -523,39 +551,40 @@ void MLPPLinReg::adamax(real_t learning_rate, int max_epoch, int mini_batch_size
|
||||
|
||||
// Creating the mini-batches
|
||||
int n_mini_batch = _n / mini_batch_size;
|
||||
auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
|
||||
auto input_mini_batches = std::get<0>(batches);
|
||||
auto output_mini_batches = std::get<1>(batches);
|
||||
MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
|
||||
|
||||
std::vector<real_t> m = alg.zerovec(_weights.size());
|
||||
Ref<MLPPVector> m = alg.zerovecv(_weights->size());
|
||||
Ref<MLPPVector> u = alg.zerovecv(_weights->size());
|
||||
|
||||
std::vector<real_t> u = alg.zerovec(_weights.size());
|
||||
while (true) {
|
||||
for (int i = 0; i < n_mini_batch; i++) {
|
||||
std::vector<real_t> y_hat = evaluatem(input_mini_batches[i]);
|
||||
cost_prev = cost(y_hat, output_mini_batches[i]);
|
||||
Ref<MLPPMatrix> current_input_mini_batch = batches.input_sets[i];
|
||||
Ref<MLPPVector> current_output_mini_batch = batches.output_sets[i];
|
||||
|
||||
std::vector<real_t> error = alg.subtraction(y_hat, output_mini_batches[i]);
|
||||
Ref<MLPPVector> y_hat = evaluatem(current_input_mini_batch);
|
||||
cost_prev = cost(y_hat, current_output_mini_batch);
|
||||
|
||||
Ref<MLPPVector> error = alg.subtractionnv(y_hat, current_output_mini_batch);
|
||||
|
||||
// Calculating the weight gradients
|
||||
std::vector<real_t> gradient = alg.scalarMultiply(1 / output_mini_batches[i].size(), alg.mat_vec_mult(alg.transpose(input_mini_batches[i]), error));
|
||||
std::vector<real_t> reg_deriv_term = regularization.regDerivTerm(_weights, _lambda, _alpha, _reg);
|
||||
std::vector<real_t> weight_grad = alg.addition(gradient, reg_deriv_term); // Weight_grad_final
|
||||
Ref<MLPPVector> gradient = alg.scalar_multiplynv(1 / current_output_mini_batch->size(), alg.mat_vec_multv(alg.transposem(current_input_mini_batch), error));
|
||||
Ref<MLPPVector> reg_deriv_term = regularization.reg_deriv_termv(_weights, _lambda, _alpha, _reg);
|
||||
Ref<MLPPVector> weight_grad = alg.additionnv(gradient, reg_deriv_term); // 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));
|
||||
m = alg.additionnv(alg.scalar_multiplynv(b1, m), alg.scalar_multiplynv(1 - b1, weight_grad));
|
||||
u = alg.maxnvv(alg.scalar_multiplynv(b2, u), alg.absv(weight_grad));
|
||||
|
||||
std::vector<real_t> m_hat = alg.scalarMultiply(1 / (1 - pow(b1, epoch)), m);
|
||||
Ref<MLPPVector> m_hat = alg.scalar_multiplynv(1 / (1 - Math::pow(b1, epoch)), m);
|
||||
|
||||
_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(m_hat, u)));
|
||||
_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate, alg.element_wise_division(m_hat, u)));
|
||||
|
||||
// Calculating the bias gradients
|
||||
_bias -= learning_rate * alg.sum_elements(error) / output_mini_batches[i].size(); // As normal
|
||||
y_hat = evaluatem(input_mini_batches[i]);
|
||||
_bias -= learning_rate * alg.sum_elementsv(error) / current_output_mini_batch->size(); // As normal
|
||||
y_hat = evaluatem(current_input_mini_batch);
|
||||
|
||||
if (ui) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, output_mini_batches[i]));
|
||||
MLPPUtilities::UI(_weights, _bias);
|
||||
MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, current_output_mini_batch));
|
||||
MLPPUtilities::print_ui_vb(_weights, _bias);
|
||||
}
|
||||
}
|
||||
|
||||
@ -579,43 +608,44 @@ void MLPPLinReg::nadam(real_t learning_rate, int max_epoch, int mini_batch_size,
|
||||
|
||||
// Creating the mini-batches
|
||||
int n_mini_batch = _n / mini_batch_size;
|
||||
auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
|
||||
auto input_mini_batches = std::get<0>(batches);
|
||||
auto output_mini_batches = std::get<1>(batches);
|
||||
MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
|
||||
|
||||
// Initializing necessary components for Adam.
|
||||
std::vector<real_t> m = alg.zerovec(_weights.size());
|
||||
std::vector<real_t> v = alg.zerovec(_weights.size());
|
||||
std::vector<real_t> m_final = alg.zerovec(_weights.size());
|
||||
Ref<MLPPVector> m = alg.zerovecv(_weights->size());
|
||||
Ref<MLPPVector> v = alg.zerovecv(_weights->size());
|
||||
Ref<MLPPVector> m_final = alg.zerovecv(_weights->size());
|
||||
|
||||
while (true) {
|
||||
for (int i = 0; i < n_mini_batch; i++) {
|
||||
std::vector<real_t> y_hat = evaluatem(input_mini_batches[i]);
|
||||
cost_prev = cost(y_hat, output_mini_batches[i]);
|
||||
Ref<MLPPMatrix> current_input_mini_batch = batches.input_sets[i];
|
||||
Ref<MLPPVector> current_output_mini_batch = batches.output_sets[i];
|
||||
|
||||
std::vector<real_t> error = alg.subtraction(y_hat, output_mini_batches[i]);
|
||||
Ref<MLPPVector> y_hat = evaluatem(current_input_mini_batch);
|
||||
cost_prev = cost(y_hat, current_output_mini_batch);
|
||||
|
||||
Ref<MLPPVector> error = alg.subtractionnv(y_hat, current_output_mini_batch);
|
||||
|
||||
// Calculating the weight gradients
|
||||
std::vector<real_t> gradient = alg.scalarMultiply(1 / output_mini_batches[i].size(), alg.mat_vec_mult(alg.transpose(input_mini_batches[i]), error));
|
||||
std::vector<real_t> reg_deriv_term = regularization.regDerivTerm(_weights, _lambda, _alpha, _reg);
|
||||
std::vector<real_t> weight_grad = alg.addition(gradient, reg_deriv_term); // Weight_grad_final
|
||||
Ref<MLPPVector> gradient = alg.scalar_multiplynv(1 / current_output_mini_batch->size(), alg.mat_vec_multv(alg.transposem(current_input_mini_batch), error));
|
||||
Ref<MLPPVector> reg_deriv_term = regularization.reg_deriv_termv(_weights, _lambda, _alpha, _reg);
|
||||
Ref<MLPPVector> weight_grad = alg.additionnv(gradient, reg_deriv_term); // 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));
|
||||
m = alg.additionnv(alg.scalar_multiplynv(b1, m), alg.scalar_multiplynv(1 - b1, weight_grad));
|
||||
v = alg.additionnv(alg.scalar_multiplynv(b2, v), alg.scalar_multiplynv(1 - b2, alg.exponentiatev(weight_grad, 2)));
|
||||
m_final = alg.additionnv(alg.scalar_multiplynv(b1, m), alg.scalar_multiplynv((1 - b1) / (1 - Math::pow(b1, epoch)), weight_grad));
|
||||
|
||||
std::vector<real_t> m_hat = alg.scalarMultiply(1 / (1 - pow(b1, epoch)), m);
|
||||
std::vector<real_t> v_hat = alg.scalarMultiply(1 / (1 - pow(b2, epoch)), v);
|
||||
Ref<MLPPVector> m_hat = alg.scalar_multiplynv(1 / (1 - Math::pow(b1, epoch)), m);
|
||||
Ref<MLPPVector> v_hat = alg.scalar_multiplynv(1 / (1 - Math::pow(b2, epoch)), v);
|
||||
|
||||
_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(m_final, alg.scalarAdd(e, alg.sqrt(v_hat)))));
|
||||
_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate, alg.element_wise_division(m_final, alg.scalar_addnv(e, alg.sqrtv(v_hat)))));
|
||||
|
||||
// Calculating the bias gradients
|
||||
_bias -= learning_rate * alg.sum_elements(error) / output_mini_batches[i].size(); // As normal
|
||||
y_hat = evaluatem(input_mini_batches[i]);
|
||||
_bias -= learning_rate * alg.sum_elementsv(error) / current_output_mini_batch->size(); // As normal
|
||||
y_hat = evaluatem(current_input_mini_batch);
|
||||
|
||||
if (ui) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, output_mini_batches[i]));
|
||||
MLPPUtilities::UI(_weights, _bias);
|
||||
MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, current_output_mini_batch));
|
||||
MLPPUtilities::print_ui_vb(_weights, _bias);
|
||||
}
|
||||
}
|
||||
|
||||
@ -634,27 +664,36 @@ void MLPPLinReg::normal_equation() {
|
||||
|
||||
MLPPLinAlg alg;
|
||||
MLPPStat stat;
|
||||
std::vector<real_t> x_means;
|
||||
std::vector<std::vector<real_t>> _input_setT = alg.transpose(_input_set);
|
||||
|
||||
x_means.resize(_input_setT.size());
|
||||
for (uint32_t i = 0; i < _input_setT.size(); i++) {
|
||||
x_means[i] = (stat.mean(_input_setT[i]));
|
||||
Ref<MLPPMatrix> input_set_t = alg.transposem(_input_set);
|
||||
|
||||
Ref<MLPPVector> input_set_t_row_tmp;
|
||||
input_set_t_row_tmp.instance();
|
||||
input_set_t_row_tmp->resize(input_set_t->size().x);
|
||||
|
||||
Ref<MLPPVector> x_means;
|
||||
x_means.instance();
|
||||
x_means->resize(input_set_t->size().y);
|
||||
|
||||
for (int i = 0; i < input_set_t->size().y; i++) {
|
||||
input_set_t->get_row_into_mlpp_vector(i, input_set_t_row_tmp);
|
||||
|
||||
x_means->set_element(i, stat.meanv(input_set_t_row_tmp));
|
||||
}
|
||||
|
||||
std::vector<real_t> temp;
|
||||
temp.resize(_k);
|
||||
temp = alg.mat_vec_mult(alg.inverse(alg.matmult(alg.transpose(_input_set), _input_set)), alg.mat_vec_mult(alg.transpose(_input_set), _output_set));
|
||||
Ref<MLPPVector> temp;
|
||||
//temp.resize(_k);
|
||||
temp = alg.mat_vec_multv(alg.inversem(alg.matmultm(alg.transposem(_input_set), _input_set)), alg.mat_vec_multv(alg.transposem(_input_set), _output_set));
|
||||
|
||||
ERR_FAIL_COND_MSG(std::isnan(temp[0]), "ERR: Resulting matrix was noninvertible/degenerate, and so the normal equation could not be performed. Try utilizing gradient descent.");
|
||||
ERR_FAIL_COND_MSG(Math::is_nan(temp->get_element(0)), "ERR: Resulting matrix was noninvertible/degenerate, and so the normal equation could not be performed. Try utilizing gradient descent.");
|
||||
|
||||
if (_reg == "Ridge") {
|
||||
_weights = alg.mat_vec_mult(alg.inverse(alg.addition(alg.matmult(alg.transpose(_input_set), _input_set), alg.scalarMultiply(_lambda, alg.identity(_k)))), alg.mat_vec_mult(alg.transpose(_input_set), _output_set));
|
||||
if (_reg == MLPPReg::REGULARIZATION_TYPE_RIDGE) {
|
||||
_weights = alg.mat_vec_multv(alg.inversem(alg.additionm(alg.matmultm(alg.transposem(_input_set), _input_set), alg.scalar_multiplym(_lambda, alg.identitym(_k)))), alg.mat_vec_multv(alg.transposem(_input_set), _output_set));
|
||||
} else {
|
||||
_weights = alg.mat_vec_mult(alg.inverse(alg.matmult(alg.transpose(_input_set), _input_set)), alg.mat_vec_mult(alg.transpose(_input_set), _output_set));
|
||||
_weights = alg.mat_vec_multv(alg.inversem(alg.matmultm(alg.transposem(_input_set), _input_set)), alg.mat_vec_multv(alg.transposem(_input_set), _output_set));
|
||||
}
|
||||
|
||||
_bias = stat.mean(_output_set) - alg.dot(_weights, x_means);
|
||||
_bias = stat.meanv(_output_set) - alg.dotv(_weights, x_means);
|
||||
|
||||
forward_pass();
|
||||
}
|
||||
@ -664,15 +703,15 @@ real_t MLPPLinReg::score() {
|
||||
|
||||
MLPPUtilities util;
|
||||
|
||||
return util.performance(_y_hat, _output_set);
|
||||
return util.performance_vec(_y_hat, _output_set);
|
||||
}
|
||||
|
||||
void MLPPLinReg::save(std::string fileName) {
|
||||
void MLPPLinReg::save(const String &file_name) {
|
||||
ERR_FAIL_COND(!_initialized);
|
||||
|
||||
MLPPUtilities util;
|
||||
//MLPPUtilities util;
|
||||
|
||||
util.saveParameters(fileName, _weights, _bias);
|
||||
//util.saveParameters(fileName, _weights, _bias);
|
||||
}
|
||||
|
||||
bool MLPPLinReg::is_initialized() {
|
||||
@ -688,19 +727,25 @@ void MLPPLinReg::initialize() {
|
||||
_initialized = true;
|
||||
}
|
||||
|
||||
MLPPLinReg::MLPPLinReg(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) {
|
||||
MLPPLinReg::MLPPLinReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha) {
|
||||
_input_set = p_input_set;
|
||||
_output_set = p_output_set;
|
||||
_n = p_input_set.size();
|
||||
_k = p_input_set[0].size();
|
||||
_n = p_input_set->size().y;
|
||||
_k = p_input_set->size().x;
|
||||
_reg = p_reg;
|
||||
_lambda = p_lambda;
|
||||
_alpha = p_alpha;
|
||||
|
||||
_y_hat.resize(_n);
|
||||
_y_hat.instance();
|
||||
_y_hat->resize(_n);
|
||||
|
||||
_weights = MLPPUtilities::weightInitialization(_k);
|
||||
_bias = MLPPUtilities::biasInitialization();
|
||||
_weights.instance();
|
||||
_weights->resize(_k);
|
||||
|
||||
MLPPUtilities utils;
|
||||
|
||||
utils.weight_initializationv(_weights);
|
||||
_bias = utils.bias_initializationr();
|
||||
|
||||
_initialized = true;
|
||||
}
|
||||
@ -711,23 +756,23 @@ MLPPLinReg::MLPPLinReg() {
|
||||
MLPPLinReg::~MLPPLinReg() {
|
||||
}
|
||||
|
||||
real_t MLPPLinReg::cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
|
||||
real_t MLPPLinReg::cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
||||
MLPPReg regularization;
|
||||
MLPPCost mlpp_cost;
|
||||
|
||||
return mlpp_cost.MSE(y_hat, y) + regularization.regTerm(_weights, _lambda, _alpha, _reg);
|
||||
return mlpp_cost.msev(y_hat, y) + regularization.reg_termv(_weights, _lambda, _alpha, _reg);
|
||||
}
|
||||
|
||||
real_t MLPPLinReg::evaluatev(std::vector<real_t> x) {
|
||||
real_t MLPPLinReg::evaluatev(const Ref<MLPPVector> &x) {
|
||||
MLPPLinAlg alg;
|
||||
|
||||
return alg.dot(_weights, x) + _bias;
|
||||
return alg.dotv(_weights, x) + _bias;
|
||||
}
|
||||
|
||||
std::vector<real_t> MLPPLinReg::evaluatem(std::vector<std::vector<real_t>> X) {
|
||||
Ref<MLPPVector> MLPPLinReg::evaluatem(const Ref<MLPPMatrix> &X) {
|
||||
MLPPLinAlg alg;
|
||||
|
||||
return alg.scalarAdd(_bias, alg.mat_vec_mult(X, _weights));
|
||||
return alg.scalar_addnv(_bias, alg.mat_vec_multv(X, _weights));
|
||||
}
|
||||
|
||||
// wTx + b
|
||||
|
||||
@ -17,9 +17,6 @@
|
||||
|
||||
#include "../regularization/reg.h"
|
||||
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
class MLPPLinReg : public Reference {
|
||||
GDCLASS(MLPPLinReg, Reference);
|
||||
|
||||
@ -41,8 +38,8 @@ public:
|
||||
void set_alpha(const real_t val);
|
||||
*/
|
||||
|
||||
std::vector<real_t> model_set_test(std::vector<std::vector<real_t>> X);
|
||||
real_t model_test(std::vector<real_t> x);
|
||||
Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
|
||||
real_t model_test(const Ref<MLPPVector> &x);
|
||||
|
||||
void newton_raphson(real_t learning_rate, int max_epoch, bool ui = false);
|
||||
void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
|
||||
@ -60,37 +57,37 @@ public:
|
||||
|
||||
real_t score();
|
||||
|
||||
void save(std::string fileName);
|
||||
void save(const String &file_name);
|
||||
|
||||
bool is_initialized();
|
||||
void initialize();
|
||||
|
||||
MLPPLinReg(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);
|
||||
MLPPLinReg(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);
|
||||
|
||||
MLPPLinReg();
|
||||
~MLPPLinReg();
|
||||
|
||||
protected:
|
||||
real_t cost(std::vector<real_t> y_hat, std::vector<real_t> y);
|
||||
real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
||||
|
||||
real_t evaluatev(std::vector<real_t> x);
|
||||
std::vector<real_t> evaluatem(std::vector<std::vector<real_t>> X);
|
||||
real_t evaluatev(const Ref<MLPPVector> &x);
|
||||
Ref<MLPPVector> evaluatem(const Ref<MLPPMatrix> &X);
|
||||
|
||||
void forward_pass();
|
||||
|
||||
static void _bind_methods();
|
||||
|
||||
std::vector<std::vector<real_t>> _input_set;
|
||||
std::vector<real_t> _output_set;
|
||||
std::vector<real_t> _y_hat;
|
||||
std::vector<real_t> _weights;
|
||||
Ref<MLPPMatrix> _input_set;
|
||||
Ref<MLPPVector> _output_set;
|
||||
Ref<MLPPVector> _y_hat;
|
||||
Ref<MLPPVector> _weights;
|
||||
real_t _bias;
|
||||
|
||||
int _n;
|
||||
int _k;
|
||||
|
||||
// Regularization Params
|
||||
std::string _reg;
|
||||
MLPPReg::RegularizationType _reg;
|
||||
int _lambda;
|
||||
int _alpha; /* This is the controlling param for Elastic Net*/
|
||||
|
||||
|
||||
@ -248,9 +248,9 @@ void MLPPTests::test_multivariate_linear_regression_gradient_descent(bool ui) {
|
||||
model_old.gradientDescent(0.001, 30, ui);
|
||||
alg.printVector(model_old.modelSetTest(ds->get_input()->to_std_vector()));
|
||||
|
||||
MLPPLinReg model(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
MLPPLinReg model(ds->get_input(), ds->get_output()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
model.gradient_descent(0.001, 30, ui);
|
||||
alg.printVector(model.model_set_test(ds->get_input()->to_std_vector()));
|
||||
PLOG_MSG(model.model_set_test(ds->get_input())->to_string());
|
||||
}
|
||||
|
||||
void MLPPTests::test_multivariate_linear_regression_sgd(bool ui) {
|
||||
@ -263,9 +263,9 @@ void MLPPTests::test_multivariate_linear_regression_sgd(bool ui) {
|
||||
model_old.SGD(0.00000001, 300000, ui);
|
||||
alg.printVector(model_old.modelSetTest(ds->get_input()->to_std_vector()));
|
||||
|
||||
MLPPLinReg model(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
MLPPLinReg model(ds->get_input(), ds->get_output()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
model.sgd(0.00000001, 300000, ui);
|
||||
alg.printVector(model.model_set_test(ds->get_input()->to_std_vector()));
|
||||
PLOG_MSG(model.model_set_test(ds->get_input())->to_string());
|
||||
}
|
||||
|
||||
void MLPPTests::test_multivariate_linear_regression_mbgd(bool ui) {
|
||||
@ -278,9 +278,9 @@ void MLPPTests::test_multivariate_linear_regression_mbgd(bool ui) {
|
||||
model_old.MBGD(0.001, 10000, 2, ui);
|
||||
alg.printVector(model_old.modelSetTest(ds->get_input()->to_std_vector()));
|
||||
|
||||
MLPPLinReg model(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
MLPPLinReg model(ds->get_input(), ds->get_output()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
model.mbgd(0.001, 10000, 2, ui);
|
||||
alg.printVector(model.model_set_test(ds->get_input()->to_std_vector()));
|
||||
PLOG_MSG(model.model_set_test(ds->get_input())->to_string());
|
||||
}
|
||||
|
||||
void MLPPTests::test_multivariate_linear_regression_normal_equation(bool ui) {
|
||||
@ -293,9 +293,9 @@ void MLPPTests::test_multivariate_linear_regression_normal_equation(bool ui) {
|
||||
model_old.normalEquation();
|
||||
alg.printVector(model_old.modelSetTest(ds->get_input()->to_std_vector()));
|
||||
|
||||
MLPPLinReg model(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
MLPPLinReg model(ds->get_input(), ds->get_output()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
model.normal_equation();
|
||||
alg.printVector(model.model_set_test(ds->get_input()->to_std_vector()));
|
||||
PLOG_MSG(model.model_set_test(ds->get_input())->to_string());
|
||||
}
|
||||
|
||||
void MLPPTests::test_multivariate_linear_regression_adam() {
|
||||
@ -308,9 +308,9 @@ void MLPPTests::test_multivariate_linear_regression_adam() {
|
||||
alg.printVector(adamModelOld.modelSetTest(ds->get_input()->to_std_vector()));
|
||||
std::cout << "ACCURACY: " << 100 * adamModelOld.score() << "%" << std::endl;
|
||||
|
||||
MLPPLinReg adam_model(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector());
|
||||
alg.printVector(adam_model.model_set_test(ds->get_input()->to_std_vector()));
|
||||
std::cout << "ACCURACY: " << 100 * adam_model.score() << "%" << std::endl;
|
||||
MLPPLinReg adam_model(alg.transposem(ds->get_input()), ds->get_output());
|
||||
PLOG_MSG(adam_model.model_set_test(ds->get_input())->to_string());
|
||||
PLOG_MSG("ACCURACY: " + String::num(100 * adam_model.score()) + "%");
|
||||
}
|
||||
|
||||
void MLPPTests::test_multivariate_linear_regression_score_sgd_adam(bool ui) {
|
||||
@ -328,7 +328,7 @@ void MLPPTests::test_multivariate_linear_regression_score_sgd_adam(bool ui) {
|
||||
modelf_old.MBGD(0.001, 5, 1, ui);
|
||||
scoreSGD += modelf_old.score();
|
||||
|
||||
MLPPLinReg modelf(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector());
|
||||
MLPPLinReg modelf(alg.transposem(ds->get_input()), ds->get_output());
|
||||
modelf.mbgd(0.001, 5, 1, ui);
|
||||
scoreSGD += modelf.score();
|
||||
|
||||
@ -336,7 +336,7 @@ void MLPPTests::test_multivariate_linear_regression_score_sgd_adam(bool ui) {
|
||||
adamModelf_old.Adam(0.1, 5, 1, 0.9, 0.999, 1e-8, ui); // Change batch size = sgd, bgd
|
||||
scoreADAM += adamModelf_old.score();
|
||||
|
||||
MLPPLinReg adamModelf(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector());
|
||||
MLPPLinReg adamModelf(alg.transposem(ds->get_input()), ds->get_output());
|
||||
adamModelf.adam(0.1, 5, 1, 0.9, 0.999, 1e-8, ui); // Change batch size = sgd, bgd
|
||||
scoreADAM += adamModelf.score();
|
||||
}
|
||||
@ -359,9 +359,9 @@ void MLPPTests::test_multivariate_linear_regression_epochs_gradient_descent(bool
|
||||
model3_old.gradientDescent(0.001, 300, ui);
|
||||
alg.printVector(model3_old.modelSetTest(ds->get_input()->to_std_vector()));
|
||||
|
||||
MLPPLinReg model3(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
MLPPLinReg model3(alg.transposem(ds->get_input()), ds->get_output()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
model3.gradient_descent(0.001, 300, ui);
|
||||
alg.printVector(model3.model_set_test(ds->get_input()->to_std_vector()));
|
||||
PLOG_MSG(model3.model_set_test(ds->get_input())->to_string());
|
||||
}
|
||||
|
||||
void MLPPTests::test_multivariate_linear_regression_newton_raphson(bool ui) {
|
||||
@ -378,9 +378,9 @@ void MLPPTests::test_multivariate_linear_regression_newton_raphson(bool ui) {
|
||||
model2_old.NewtonRaphson(1.5, 300, ui);
|
||||
alg.printVector(model2_old.modelSetTest(ds->get_input()->to_std_vector()));
|
||||
|
||||
MLPPLinReg model2(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector());
|
||||
MLPPLinReg model2(alg.transposem(ds->get_input()), ds->get_output());
|
||||
model2.newton_raphson(1.5, 300, ui);
|
||||
alg.printVector(model2.model_set_test(ds->get_input()->to_std_vector()));
|
||||
PLOG_MSG(model2.model_set_test(ds->get_input())->to_string());
|
||||
}
|
||||
|
||||
void MLPPTests::test_logistic_regression(bool ui) {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user