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Initial cleanup pass on MLPPCLogLogReg.

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Relintai 2023-02-12 13:04:33 +01:00
parent cdb0e47d16
commit 8d9651b65a
4 changed files with 119 additions and 77 deletions
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141
mlpp/c_log_log_reg/c_log_log_reg.cpp
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@ -14,31 +14,26 @@
#include <iostream> #include <iostream>
#include <random> #include <random>
MLPPCLogLogReg::MLPPCLogLogReg(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, std::string reg, real_t lambda, real_t alpha) : std::vector<real_t> MLPPCLogLogReg::model_set_test(std::vector<std::vector<real_t>> X) {
inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha) { return evaluatem(X);
y_hat.resize(n);
weights = MLPPUtilities::weightInitialization(k);
bias = MLPPUtilities::biasInitialization();
} }
std::vector<real_t> MLPPCLogLogReg::modelSetTest(std::vector<std::vector<real_t>> X) { real_t MLPPCLogLogReg::model_test(std::vector<real_t> x) {
return Evaluate(X); return evaluatev(x);
} }
real_t MLPPCLogLogReg::modelTest(std::vector<real_t> x) { void MLPPCLogLogReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
return Evaluate(x);
}
void MLPPCLogLogReg::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
MLPPActivation avn; MLPPActivation avn;
MLPPLinAlg alg; MLPPLinAlg alg;
MLPPReg regularization; MLPPReg regularization;
real_t cost_prev = 0; real_t cost_prev = 0;
int epoch = 1; int epoch = 1;
forwardPass();
forward_pass();
while (true) { while (true) {
cost_prev = Cost(y_hat, outputSet); cost_prev = cost(y_hat, outputSet);
std::vector<real_t> error = alg.subtraction(y_hat, outputSet); std::vector<real_t> error = alg.subtraction(y_hat, outputSet);
@ -49,12 +44,13 @@ void MLPPCLogLogReg::gradientDescent(real_t learning_rate, int max_epoch, bool U
// Calculating the bias gradients // Calculating the bias gradients
bias -= learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.cloglog(z, 1))) / n; bias -= learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.cloglog(z, 1))) / n;
forwardPass(); forward_pass();
if (UI) { if (ui) {
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet)); MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputSet));
MLPPUtilities::UI(weights, bias); MLPPUtilities::UI(weights, bias);
} }
epoch++; epoch++;
if (epoch > max_epoch) { if (epoch > max_epoch) {
@ -63,16 +59,18 @@ void MLPPCLogLogReg::gradientDescent(real_t learning_rate, int max_epoch, bool U
} }
} }
void MLPPCLogLogReg::MLE(real_t learning_rate, int max_epoch, bool UI) { void MLPPCLogLogReg::mle(real_t learning_rate, int max_epoch, bool ui) {
MLPPActivation avn; MLPPActivation avn;
MLPPLinAlg alg; MLPPLinAlg alg;
MLPPReg regularization; MLPPReg regularization;
real_t cost_prev = 0; real_t cost_prev = 0;
int epoch = 1; int epoch = 1;
forwardPass();
forward_pass();
while (true) { while (true) {
cost_prev = Cost(y_hat, outputSet); cost_prev = cost(y_hat, outputSet);
std::vector<real_t> error = alg.subtraction(y_hat, outputSet); std::vector<real_t> error = alg.subtraction(y_hat, outputSet);
@ -81,12 +79,14 @@ void MLPPCLogLogReg::MLE(real_t learning_rate, int max_epoch, bool UI) {
// Calculating the bias gradients // Calculating the bias gradients
bias += learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.cloglog(z, 1))) / n; bias += learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.cloglog(z, 1))) / n;
forwardPass();
if (UI) { forward_pass();
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
if (ui) {
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputSet));
MLPPUtilities::UI(weights, bias); MLPPUtilities::UI(weights, bias);
} }
epoch++; epoch++;
if (epoch > max_epoch) { if (epoch > max_epoch) {
@ -95,12 +95,14 @@ void MLPPCLogLogReg::MLE(real_t learning_rate, int max_epoch, bool UI) {
} }
} }
void MLPPCLogLogReg::SGD(real_t learning_rate, int max_epoch, bool UI) { void MLPPCLogLogReg::sgd(real_t learning_rate, int max_epoch, bool p_) {
MLPPLinAlg alg; MLPPLinAlg alg;
MLPPReg regularization; MLPPReg regularization;
real_t cost_prev = 0; real_t cost_prev = 0;
int epoch = 1; int epoch = 1;
forwardPass();
forward_pass();
while (true) { while (true) {
std::random_device rd; std::random_device rd;
@ -108,9 +110,9 @@ void MLPPCLogLogReg::SGD(real_t learning_rate, int max_epoch, bool UI) {
std::uniform_int_distribution<int> distribution(0, int(n - 1)); std::uniform_int_distribution<int> distribution(0, int(n - 1));
int outputIndex = distribution(generator); int outputIndex = distribution(generator);
real_t y_hat = Evaluate(inputSet[outputIndex]); real_t y_hat = evaluatev(inputSet[outputIndex]);
real_t z = propagate(inputSet[outputIndex]); real_t z = propagatev(inputSet[outputIndex]);
cost_prev = Cost({ y_hat }, { outputSet[outputIndex] }); cost_prev = cost({ y_hat }, { outputSet[outputIndex] });
real_t error = y_hat - outputSet[outputIndex]; real_t error = y_hat - outputSet[outputIndex];
@ -121,22 +123,24 @@ void MLPPCLogLogReg::SGD(real_t learning_rate, int max_epoch, bool UI) {
// Bias updation // Bias updation
bias -= learning_rate * error * exp(z - exp(z)); bias -= learning_rate * error * exp(z - exp(z));
y_hat = Evaluate({ inputSet[outputIndex] }); y_hat = evaluatev(inputSet[outputIndex]);
if (UI) { if (p_) {
MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ y_hat }, { outputSet[outputIndex] })); MLPPUtilities::CostInfo(epoch, cost_prev, cost({ y_hat }, { outputSet[outputIndex] }));
MLPPUtilities::UI(weights, bias); MLPPUtilities::UI(weights, bias);
} }
epoch++; epoch++;
if (epoch > max_epoch) { if (epoch > max_epoch) {
break; break;
} }
} }
forwardPass();
forward_pass();
} }
void MLPPCLogLogReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) { void MLPPCLogLogReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool p_) {
MLPPActivation avn; MLPPActivation avn;
MLPPLinAlg alg; MLPPLinAlg alg;
MLPPReg regularization; MLPPReg regularization;
@ -151,9 +155,9 @@ void MLPPCLogLogReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_si
while (true) { while (true) {
for (int i = 0; i < n_mini_batch; i++) { for (int i = 0; i < n_mini_batch; i++) {
std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]); std::vector<real_t> y_hat = evaluatem(inputMiniBatches[i]);
std::vector<real_t> z = propagate(inputMiniBatches[i]); std::vector<real_t> z = propagatem(inputMiniBatches[i]);
cost_prev = Cost(y_hat, outputMiniBatches[i]); cost_prev = cost(y_hat, outputMiniBatches[i]);
std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]); std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
@ -164,21 +168,24 @@ void MLPPCLogLogReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_si
// Calculating the bias gradients // Calculating the bias gradients
bias -= learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.cloglog(z, 1))) / n; bias -= learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.cloglog(z, 1))) / n;
forwardPass(); forward_pass();
y_hat = Evaluate(inputMiniBatches[i]); y_hat = evaluatem(inputMiniBatches[i]);
if (UI) { if (p_) {
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i])); MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputMiniBatches[i]));
MLPPUtilities::UI(weights, bias); MLPPUtilities::UI(weights, bias);
} }
} }
epoch++; epoch++;
if (epoch > max_epoch) { if (epoch > max_epoch) {
break; break;
} }
} }
forwardPass();
forward_pass();
} }
real_t MLPPCLogLogReg::score() { real_t MLPPCLogLogReg::score() {
@ -186,38 +193,58 @@ real_t MLPPCLogLogReg::score() {
return util.performance(y_hat, outputSet); return util.performance(y_hat, outputSet);
} }
real_t MLPPCLogLogReg::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) { 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) {
inputSet = pinputSet;
outputSet = poutputSet;
n = inputSet.size();
k = inputSet[0].size();
reg = p_reg;
lambda = p_lambda;
alpha = p_alpha;
y_hat.resize(n);
weights = MLPPUtilities::weightInitialization(k);
bias = MLPPUtilities::biasInitialization();
}
MLPPCLogLogReg::MLPPCLogLogReg() {
}
MLPPCLogLogReg::~MLPPCLogLogReg() {
}
real_t MLPPCLogLogReg::cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
MLPPReg regularization; MLPPReg regularization;
class MLPPCost cost; class MLPPCost cost;
return cost.MSE(y_hat, y) + regularization.regTerm(weights, lambda, alpha, reg); return cost.MSE(y_hat, y) + regularization.regTerm(weights, lambda, alpha, reg);
} }
std::vector<real_t> MLPPCLogLogReg::Evaluate(std::vector<std::vector<real_t>> X) { real_t MLPPCLogLogReg::evaluatev(std::vector<real_t> x) {
MLPPLinAlg alg;
MLPPActivation avn;
return avn.cloglog(alg.scalarAdd(bias, alg.mat_vec_mult(X, weights)));
}
std::vector<real_t> MLPPCLogLogReg::propagate(std::vector<std::vector<real_t>> X) {
MLPPLinAlg alg;
return alg.scalarAdd(bias, alg.mat_vec_mult(X, weights));
}
real_t MLPPCLogLogReg::Evaluate(std::vector<real_t> x) {
MLPPLinAlg alg; MLPPLinAlg alg;
MLPPActivation avn; MLPPActivation avn;
return avn.cloglog(alg.dot(weights, x) + bias); return avn.cloglog(alg.dot(weights, x) + bias);
} }
real_t MLPPCLogLogReg::propagate(std::vector<real_t> x) { real_t MLPPCLogLogReg::propagatev(std::vector<real_t> x) {
MLPPLinAlg alg; MLPPLinAlg alg;
return alg.dot(weights, x) + bias; return alg.dot(weights, x) + bias;
} }
std::vector<real_t> MLPPCLogLogReg::evaluatem(std::vector<std::vector<real_t>> X) {
MLPPLinAlg alg;
MLPPActivation avn;
return avn.cloglog(alg.scalarAdd(bias, alg.mat_vec_mult(X, weights)));
}
std::vector<real_t> MLPPCLogLogReg::propagatem(std::vector<std::vector<real_t>> X) {
MLPPLinAlg alg;
return alg.scalarAdd(bias, alg.mat_vec_mult(X, weights));
}
// cloglog ( wTx + b ) // cloglog ( wTx + b )
void MLPPCLogLogReg::forwardPass() { void MLPPCLogLogReg::forward_pass() {
MLPPActivation avn; MLPPActivation avn;
z = propagate(inputSet); z = propagatem(inputSet);
y_hat = avn.cloglog(z); y_hat = avn.cloglog(z);
} }

41
mlpp/c_log_log_reg/c_log_log_reg.h
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@ -15,25 +15,34 @@
class MLPPCLogLogReg { class MLPPCLogLogReg {
public: public:
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); std::vector<real_t> model_set_test(std::vector<std::vector<real_t>> X);
std::vector<real_t> modelSetTest(std::vector<std::vector<real_t>> X); real_t model_test(std::vector<real_t> x);
real_t modelTest(std::vector<real_t> x);
void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false); void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
void MLE(real_t learning_rate, int max_epoch, bool UI = false); void mle(real_t learning_rate, int max_epoch, bool ui = false);
void SGD(real_t learning_rate, int max_epoch, bool UI = false); void sgd(real_t learning_rate, int max_epoch, bool ui = false);
void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false); void mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
real_t score(); real_t score();
private: 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);
void weightInitialization(int k);
void biasInitialization();
real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y);
std::vector<real_t> Evaluate(std::vector<std::vector<real_t>> X); MLPPCLogLogReg();
std::vector<real_t> propagate(std::vector<std::vector<real_t>> X); ~MLPPCLogLogReg();
real_t Evaluate(std::vector<real_t> x);
real_t propagate(std::vector<real_t> x); private:
void forwardPass(); void weight_initialization(int k);
void bias_initialization();
real_t cost(std::vector<real_t> y_hat, std::vector<real_t> y);
real_t evaluatev(std::vector<real_t> x);
real_t propagatev(std::vector<real_t> x);
std::vector<real_t> evaluatem(std::vector<std::vector<real_t>> X);
std::vector<real_t> propagatem(std::vector<std::vector<real_t>> X);
void forward_pass();
std::vector<std::vector<real_t>> inputSet; std::vector<std::vector<real_t>> inputSet;
std::vector<real_t> outputSet; std::vector<real_t> outputSet;

4
mlpp/c_log_log_reg/c_log_log_reg_old.h
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@ -1,6 +1,6 @@
#ifndef MLPP_C_LOG_LOG_REG_H #ifndef MLPP_C_LOG_LOG_REG_OLD_H
#define MLPP_C_LOG_LOG_REG_H #define MLPP_C_LOG_LOG_REG_OLD_H
// //
// CLogLogReg.hpp // CLogLogReg.hpp

10
test/mlpp_tests.cpp
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@ -423,9 +423,15 @@ void MLPPTests::test_c_log_log_regression(bool ui) {
// CLOGLOG REGRESSION // CLOGLOG REGRESSION
std::vector<std::vector<real_t>> inputSet = { { 1, 2, 3, 4, 5, 6, 7, 8 }, { 0, 0, 0, 0, 1, 1, 1, 1 } }; std::vector<std::vector<real_t>> inputSet = { { 1, 2, 3, 4, 5, 6, 7, 8 }, { 0, 0, 0, 0, 1, 1, 1, 1 } };
std::vector<real_t> outputSet = { 0, 0, 0, 0, 1, 1, 1, 1 }; std::vector<real_t> outputSet = { 0, 0, 0, 0, 1, 1, 1, 1 };
MLPPCLogLogRegOld model_old(alg.transpose(inputSet), outputSet);
model_old.SGD(0.1, 10000, ui);
alg.printVector(model_old.modelSetTest(alg.transpose(inputSet)));
std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl;
MLPPCLogLogReg model(alg.transpose(inputSet), outputSet); MLPPCLogLogReg model(alg.transpose(inputSet), outputSet);
model.SGD(0.1, 10000, ui); model.sgd(0.1, 10000, ui);
alg.printVector(model.modelSetTest(alg.transpose(inputSet))); alg.printVector(model.model_set_test(alg.transpose(inputSet)));
std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl; std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
} }
void MLPPTests::test_exp_reg_regression(bool ui) { void MLPPTests::test_exp_reg_regression(bool ui) {