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https://github.com/Relintai/pmlpp.git
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Initial cleanup pass on MLPPCLogLogReg.
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parent
cdb0e47d16
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@ -14,31 +14,26 @@
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#include <iostream>
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#include <random>
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MLPPCLogLogReg::MLPPCLogLogReg(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, std::string reg, real_t lambda, real_t alpha) :
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inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), reg(reg), lambda(lambda), alpha(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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std::vector<real_t> MLPPCLogLogReg::model_set_test(std::vector<std::vector<real_t>> X) {
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return evaluatem(X);
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}
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std::vector<real_t> MLPPCLogLogReg::modelSetTest(std::vector<std::vector<real_t>> X) {
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return Evaluate(X);
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real_t MLPPCLogLogReg::model_test(std::vector<real_t> x) {
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return evaluatev(x);
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}
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real_t MLPPCLogLogReg::modelTest(std::vector<real_t> x) {
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return Evaluate(x);
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}
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void MLPPCLogLogReg::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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void MLPPCLogLogReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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MLPPActivation avn;
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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forwardPass();
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forward_pass();
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while (true) {
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cost_prev = Cost(y_hat, outputSet);
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cost_prev = cost(y_hat, outputSet);
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std::vector<real_t> error = alg.subtraction(y_hat, outputSet);
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@ -49,12 +44,13 @@ void MLPPCLogLogReg::gradientDescent(real_t learning_rate, int max_epoch, bool U
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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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forwardPass();
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forward_pass();
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputSet));
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MLPPUtilities::UI(weights, bias);
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}
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epoch++;
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if (epoch > max_epoch) {
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@ -63,16 +59,18 @@ void MLPPCLogLogReg::gradientDescent(real_t learning_rate, int max_epoch, bool U
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}
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}
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void MLPPCLogLogReg::MLE(real_t learning_rate, int max_epoch, bool UI) {
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void MLPPCLogLogReg::mle(real_t learning_rate, int max_epoch, bool ui) {
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MLPPActivation avn;
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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forwardPass();
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forward_pass();
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while (true) {
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cost_prev = Cost(y_hat, outputSet);
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cost_prev = cost(y_hat, outputSet);
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std::vector<real_t> error = alg.subtraction(y_hat, outputSet);
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@ -81,12 +79,14 @@ void MLPPCLogLogReg::MLE(real_t learning_rate, int max_epoch, bool UI) {
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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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forwardPass();
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
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forward_pass();
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputSet));
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MLPPUtilities::UI(weights, bias);
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}
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epoch++;
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if (epoch > max_epoch) {
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@ -95,12 +95,14 @@ void MLPPCLogLogReg::MLE(real_t learning_rate, int max_epoch, bool UI) {
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}
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}
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void MLPPCLogLogReg::SGD(real_t learning_rate, int max_epoch, bool UI) {
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void MLPPCLogLogReg::sgd(real_t learning_rate, int max_epoch, bool p_) {
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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forwardPass();
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forward_pass();
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while (true) {
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std::random_device rd;
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@ -108,9 +110,9 @@ void MLPPCLogLogReg::SGD(real_t learning_rate, int max_epoch, bool UI) {
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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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real_t y_hat = Evaluate(inputSet[outputIndex]);
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real_t z = propagate(inputSet[outputIndex]);
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cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
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real_t y_hat = evaluatev(inputSet[outputIndex]);
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real_t z = propagatev(inputSet[outputIndex]);
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cost_prev = cost({ y_hat }, { outputSet[outputIndex] });
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real_t error = y_hat - outputSet[outputIndex];
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@ -121,22 +123,24 @@ void MLPPCLogLogReg::SGD(real_t learning_rate, int max_epoch, bool UI) {
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// Bias updation
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bias -= learning_rate * error * exp(z - exp(z));
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y_hat = Evaluate({ inputSet[outputIndex] });
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y_hat = evaluatev(inputSet[outputIndex]);
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ y_hat }, { outputSet[outputIndex] }));
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if (p_) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost({ y_hat }, { outputSet[outputIndex] }));
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MLPPUtilities::UI(weights, bias);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forwardPass();
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forward_pass();
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}
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void MLPPCLogLogReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
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void MLPPCLogLogReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool p_) {
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MLPPActivation avn;
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MLPPLinAlg alg;
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MLPPReg regularization;
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@ -151,9 +155,9 @@ void MLPPCLogLogReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_si
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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 = Evaluate(inputMiniBatches[i]);
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std::vector<real_t> z = propagate(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[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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std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
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@ -164,21 +168,24 @@ void MLPPCLogLogReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_si
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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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forwardPass();
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forward_pass();
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y_hat = Evaluate(inputMiniBatches[i]);
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y_hat = evaluatem(inputMiniBatches[i]);
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
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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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}
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forwardPass();
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forward_pass();
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}
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real_t MLPPCLogLogReg::score() {
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@ -186,38 +193,58 @@ real_t MLPPCLogLogReg::score() {
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return util.performance(y_hat, outputSet);
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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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MLPPCLogLogReg::MLPPCLogLogReg(std::vector<std::vector<real_t>> pinputSet, std::vector<real_t> poutputSet, std::string p_reg, real_t p_lambda, real_t p_alpha) {
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inputSet = pinputSet;
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outputSet = poutputSet;
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n = inputSet.size();
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k = inputSet[0].size();
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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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}
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MLPPCLogLogReg::MLPPCLogLogReg() {
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}
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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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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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}
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std::vector<real_t> MLPPCLogLogReg::Evaluate(std::vector<std::vector<real_t>> 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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}
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std::vector<real_t> MLPPCLogLogReg::propagate(std::vector<std::vector<real_t>> 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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}
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real_t MLPPCLogLogReg::Evaluate(std::vector<real_t> x) {
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real_t MLPPCLogLogReg::evaluatev(std::vector<real_t> x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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return avn.cloglog(alg.dot(weights, x) + bias);
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}
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real_t MLPPCLogLogReg::propagate(std::vector<real_t> x) {
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real_t MLPPCLogLogReg::propagatev(std::vector<real_t> x) {
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MLPPLinAlg alg;
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return alg.dot(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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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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}
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std::vector<real_t> MLPPCLogLogReg::propagatem(std::vector<std::vector<real_t>> X) {
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MLPPLinAlg alg;
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return alg.scalarAdd(bias, alg.mat_vec_mult(X, weights));
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}
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// cloglog ( wTx + b )
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void MLPPCLogLogReg::forwardPass() {
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void MLPPCLogLogReg::forward_pass() {
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MLPPActivation avn;
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z = propagate(inputSet);
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z = propagatem(inputSet);
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y_hat = avn.cloglog(z);
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}
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@ -15,25 +15,34 @@
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class MLPPCLogLogReg {
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public:
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MLPPCLogLogReg(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
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std::vector<real_t> modelSetTest(std::vector<std::vector<real_t>> X);
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real_t modelTest(std::vector<real_t> x);
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void gradientDescent(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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void SGD(real_t learning_rate, int max_epoch, bool UI = false);
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void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false);
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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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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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void sgd(real_t learning_rate, int max_epoch, bool ui = false);
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void mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
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real_t score();
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private:
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void weightInitialization(int k);
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void biasInitialization();
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real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y);
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MLPPCLogLogReg(std::vector<std::vector<real_t>> pinputSet, std::vector<real_t> poutputSet, std::string p_reg = "None", real_t p_lambda = 0.5, real_t p_alpha = 0.5);
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std::vector<real_t> Evaluate(std::vector<std::vector<real_t>> X);
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std::vector<real_t> propagate(std::vector<std::vector<real_t>> X);
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real_t Evaluate(std::vector<real_t> x);
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real_t propagate(std::vector<real_t> x);
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void forwardPass();
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MLPPCLogLogReg();
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~MLPPCLogLogReg();
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private:
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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 evaluatev(std::vector<real_t> x);
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real_t propagatev(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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std::vector<real_t> propagatem(std::vector<std::vector<real_t>> X);
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void forward_pass();
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std::vector<std::vector<real_t>> inputSet;
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std::vector<real_t> outputSet;
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@ -1,6 +1,6 @@
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#ifndef MLPP_C_LOG_LOG_REG_H
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#define MLPP_C_LOG_LOG_REG_H
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#ifndef MLPP_C_LOG_LOG_REG_OLD_H
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#define MLPP_C_LOG_LOG_REG_OLD_H
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//
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// CLogLogReg.hpp
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@ -423,9 +423,15 @@ void MLPPTests::test_c_log_log_regression(bool ui) {
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// CLOGLOG REGRESSION
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std::vector<std::vector<real_t>> inputSet = { { 1, 2, 3, 4, 5, 6, 7, 8 }, { 0, 0, 0, 0, 1, 1, 1, 1 } };
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std::vector<real_t> outputSet = { 0, 0, 0, 0, 1, 1, 1, 1 };
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MLPPCLogLogRegOld model_old(alg.transpose(inputSet), outputSet);
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model_old.SGD(0.1, 10000, 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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model.SGD(0.1, 10000, ui);
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alg.printVector(model.modelSetTest(alg.transpose(inputSet)));
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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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}
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void MLPPTests::test_exp_reg_regression(bool ui) {
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