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"Vectorized" implementation of SGD for ProbitReg

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novak_99 2021-05-28 19:37:34 -07:00
parent 009fec444a
commit 13b0d76c5c
2 changed files with 15 additions and 23 deletions
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20
MLPP/ProbitReg/ProbitReg.cpp
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@ -94,6 +94,7 @@ namespace MLPP{
}
void ProbitReg::SGD(double learning_rate, int max_epoch, bool UI){
// NOTE: ∂y_hat/∂z is sparse
LinAlg alg;
Activation avn;
Reg regularization;
@ -111,24 +112,15 @@ namespace MLPP{
double z = propagate(inputSet[outputIndex]);
cost_prev = Cost({y_hat}, {outputSet[outputIndex]});
double error = y_hat - outputSet[outputIndex];
for(int i = 0; i < k; i++){
// Calculating the weight gradients
double w_gradient = (y_hat - outputSet[outputIndex]) * ((1 / sqrt(2 * M_PI)) * exp(-z * z / 2)) * inputSet[outputIndex][i];
std::cout << exp(-z * z / 2) << std::endl;
// Weight updation
weights[i] -= learning_rate * w_gradient;
}
// Weight Updation
weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate * error * ((1 / sqrt(2 * M_PI)) * exp(-z * z / 2)), inputSet[outputIndex]));
weights = regularization.regWeights(weights, lambda, alpha, reg);
// Calculating the bias gradients
double b_gradient = (y_hat - outputSet[outputIndex]);
// Bias updation
bias -= learning_rate * b_gradient * ((1 / sqrt(2 * M_PI)) * exp(-z * z / 2));
bias -= learning_rate * error * ((1 / sqrt(2 * M_PI)) * exp(-z * z / 2));
y_hat = Evaluate({inputSet[outputIndex]});
if(UI) {

18
main.cpp
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@ -142,22 +142,22 @@ int main() {
// alg.printVector(model.modelSetTest((alg.transpose(inputSet))));
// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
// LOGISTIC REGRESSION
std::vector<std::vector<double>> inputSet;
std::vector<double> outputSet;
data.setData(30, "/Users/marcmelikyan/Desktop/Data/BreastCancer.csv", inputSet, outputSet);
LogReg model(inputSet, outputSet);
model.SGD(0.001, 100000, 0);
// // LOGISTIC REGRESSION
// std::vector<std::vector<double>> inputSet;
// std::vector<double> outputSet;
// data.setData(30, "/Users/marcmelikyan/Desktop/Data/BreastCancer.csv", inputSet, outputSet);
// LogReg model(inputSet, outputSet);
// model.SGD(0.001, 100000, 0);
// model.MLE(0.1, 10000, 0);
alg.printVector(model.modelSetTest(inputSet));
std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
// alg.printVector(model.modelSetTest(inputSet));
// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
// // PROBIT REGRESSION
// std::vector<std::vector<double>> inputSet;
// std::vector<double> outputSet;
// data.setData(30, "/Users/marcmelikyan/Desktop/Data/BreastCancer.csv", inputSet, outputSet);
// ProbitReg model(inputSet, outputSet);
// model.gradientDescent(0.0001, 10000, 1);
// model.SGD(0.001, 10000, 1);
// alg.printVector(model.modelSetTest(inputSet));
// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;