Relintai/MLPP
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Minor optimization to LinReg.normalEquation

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novak_99 2021-06-03 21:43:28 -07:00
parent 086b8bea31
commit 6dfa1cafe3
3 changed files with 9 additions and 6 deletions
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7
MLPP/LinReg/LinReg.cpp
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@ -141,10 +141,11 @@ namespace MLPP{
LinAlg alg; LinAlg alg;
Stat stat; Stat stat;
std::vector<double> x_means; std::vector<double> x_means;
std::vector<std::vector<double>> inputSetT = alg.transpose(inputSet);
x_means.resize(alg.transpose(inputSet).size()); x_means.resize(inputSetT.size());
for(int i = 0; i < alg.transpose(inputSet).size(); i++){ for(int i = 0; i < inputSetT.size(); i++){
x_means[i] = (stat.mean(alg.transpose(inputSet)[i])); x_means[i] = (stat.mean(inputSetT[i]));
} }
try{ try{

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SharedLib/MLPP.so
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6
main.cpp
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@ -39,6 +39,7 @@
#include "MLPP/Stat/Stat.hpp" #include "MLPP/Stat/Stat.hpp"
#include "MLPP/LinAlg/LinAlg.hpp" #include "MLPP/LinAlg/LinAlg.hpp"
#include "MLPP/Activation/Activation.hpp" #include "MLPP/Activation/Activation.hpp"
#include "MLPP/Cost/Cost.hpp"
#include "MLPP/Data/Data.hpp" #include "MLPP/Data/Data.hpp"
#include "MLPP/Convolutions/Convolutions.hpp" #include "MLPP/Convolutions/Convolutions.hpp"
@ -51,6 +52,7 @@ int main() {
Stat stat; Stat stat;
LinAlg alg; LinAlg alg;
Activation avn; Activation avn;
Cost cost;
Data data; Data data;
Convolutions conv; Convolutions conv;
@ -142,12 +144,12 @@ int main() {
// alg.printVector(model.modelSetTest((alg.transpose(inputSet)))); // alg.printVector(model.modelSetTest((alg.transpose(inputSet))));
// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl; // std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
// // LOGISTIC REGRESSION // LOGISTIC REGRESSION
// std::vector<std::vector<double>> inputSet; // std::vector<std::vector<double>> inputSet;
// std::vector<double> outputSet; // std::vector<double> outputSet;
// data.setData(30, "/Users/marcmelikyan/Desktop/Data/BreastCancer.csv", inputSet, outputSet); // data.setData(30, "/Users/marcmelikyan/Desktop/Data/BreastCancer.csv", inputSet, outputSet);
// LogReg model(inputSet, outputSet); // LogReg model(inputSet, outputSet);
// model.SGD(0.001, 100000, 0); // model.gradientDescent(0.0001, 100000, 0);
// model.MLE(0.1, 10000, 0); // model.MLE(0.1, 10000, 0);
// alg.printVector(model.modelSetTest(inputSet)); // alg.printVector(model.modelSetTest(inputSet));
// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl; // std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;