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Fixed warnings in SVC.

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Relintai 2023-02-10 09:12:56 +01:00
parent 62492c8fde
commit 605a10d8f6
2 changed files with 22 additions and 18 deletions
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34
mlpp/svc/svc.cpp
View File

@ -14,14 +14,6 @@
#include <iostream>
#include <random>
MLPPSVC::MLPPSVC(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, real_t C) :
inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), C(C) {
y_hat.resize(n);
weights = MLPPUtilities::weightInitialization(k);
bias = MLPPUtilities::biasInitialization();
}
std::vector<real_t> MLPPSVC::modelSetTest(std::vector<std::vector<real_t>> X) {
return Evaluate(X);
}
@ -78,7 +70,7 @@ void MLPPSVC::SGD(real_t learning_rate, int max_epoch, bool UI) {
std::uniform_int_distribution<int> distribution(0, int(n - 1));
int outputIndex = distribution(generator);
real_t y_hat = Evaluate(inputSet[outputIndex]);
//real_t y_hat = Evaluate(inputSet[outputIndex]);
real_t z = propagate(inputSet[outputIndex]);
cost_prev = Cost({ z }, { outputSet[outputIndex] }, weights, C);
@ -91,12 +83,13 @@ void MLPPSVC::SGD(real_t learning_rate, int max_epoch, bool UI) {
// Bias updation
bias -= learning_rate * costDeriv;
y_hat = Evaluate({ inputSet[outputIndex] });
//y_hat = Evaluate({ inputSet[outputIndex] });
if (UI) {
MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ z }, { outputSet[outputIndex] }, weights, C));
MLPPUtilities::UI(weights, bias);
}
epoch++;
if (epoch > max_epoch) {
@ -116,7 +109,9 @@ void MLPPSVC::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, boo
// Creating the mini-batches
int n_mini_batch = n / mini_batch_size;
auto [inputMiniBatches, outputMiniBatches] = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
auto inputMiniBatches = std::get<0>(batches);
auto outputMiniBatches = std::get<1>(batches);
while (true) {
for (int i = 0; i < n_mini_batch; i++) {
@ -149,15 +144,27 @@ void MLPPSVC::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, boo
}
real_t MLPPSVC::score() {
MLPPUtilities util;
MLPPUtilities util;
return util.performance(y_hat, outputSet);
}
void MLPPSVC::save(std::string fileName) {
MLPPUtilities util;
MLPPUtilities util;
util.saveParameters(fileName, weights, bias);
}
MLPPSVC::MLPPSVC(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet, real_t p_C) {
inputSet = p_inputSet;
outputSet = p_outputSet;
n = inputSet.size();
k = inputSet[0].size();
C = p_C;
y_hat.resize(n);
weights = MLPPUtilities::weightInitialization(k);
bias = MLPPUtilities::biasInitialization();
}
real_t MLPPSVC::Cost(std::vector<real_t> z, std::vector<real_t> y, std::vector<real_t> weights, real_t C) {
class MLPPCost cost;
return cost.HingeLoss(z, y, weights, C);
@ -189,7 +196,6 @@ real_t MLPPSVC::propagate(std::vector<real_t> x) {
// sign ( wTx + b )
void MLPPSVC::forwardPass() {
MLPPLinAlg alg;
MLPPActivation avn;
z = propagate(inputSet);

6
mlpp/svc/svc.h
View File

@ -16,11 +16,8 @@
#include <string>
#include <vector>
class MLPPSVC {
public:
MLPPSVC(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, real_t C);
std::vector<real_t> modelSetTest(std::vector<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);
@ -29,6 +26,8 @@ public:
real_t score();
void save(std::string fileName);
MLPPSVC(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, real_t C);
private:
real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y, std::vector<real_t> weights, real_t C);
@ -53,5 +52,4 @@ private:
void UI(int epoch, real_t cost_prev);
};
#endif /* SVC_hpp */