Relintai/MLPP
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novak_99 2022-01-21 19:11:23 -08:00
parent 90f29f8a89
commit 00004817d9
4 changed files with 19 additions and 19 deletions
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1
MLPP/NumericalAnalysis/NumericalAnalysis.hpp
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@ -7,6 +7,7 @@
#define NumericalAnalysis_hpp
#include <vector>
#include <string>
namespace MLPP{
class NumericalAnalysis{

2
MLPP/Stat/Stat.cpp
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@ -31,7 +31,6 @@ namespace MLPP{
double Stat::median(std::vector<double> x){
double center = double(x.size())/double(2);
std::vector<double> original_vec = x;
sort(x.begin(), x.end());
if(x.size() % 2 == 0){
return mean({x[center - 1], x[center]});
@ -39,7 +38,6 @@ namespace MLPP{
else{
return x[center - 1 + 0.5];
}
x = original_vec;
}
std::vector<double> Stat::mode(std::vector<double> x){

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a.out
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35
main.cpp
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@ -148,13 +148,14 @@ int main() {
// std::vector<std::vector<double>> inputSet = {{0,0,1,1}, {0,1,0,1}};
// std::vector<double> outputSet = {0,1,1,0};
// // STATISTICS
// std::vector<double> x = {1,2,3,4,5,6,7,8,9,10};
// std::vector<double> y = {10,9,8,7,6,5,4,3,2,1};
// std::vector<double> w = {0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1};
// STATISTICS
std::vector<double> x = {1,2,3,4,5,6,5,8,9,10,1};
std::vector<double> y = {10,9,8,7,6,5,4,3,2,1};
std::vector<double> w = {0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1};
// std::cout << "Arithmetic Mean: " << stat.mean(x) << std::endl;
// std::cout << "Median: " << stat.median(x) << std::endl;
std::cout << "Median: " << stat.median(x) << std::endl;
alg.printVector(x);
// alg.printVector(stat.mode(x));
// std::cout << "Range: " << stat.range(x) << std::endl;
// std::cout << "Midrange: " << stat.midrange(x) << std::endl;
@ -361,18 +362,18 @@ int main() {
// Possible Weight Init Methods: Default, Uniform, HeNormal, HeUniform, XavierNormal, XavierUniform
// Possible Activations: Linear, Sigmoid, Swish, Softplus, Softsign, CLogLog, Ar{Sinh, Cosh, Tanh, Csch, Sech, Coth}, GaussianCDF, GELU, UnitStep
// Possible Loss Functions: MSE, RMSE, MBE, LogLoss, CrossEntropy, HingeLoss
std::vector<std::vector<double>> inputSet = {{0,0,1,1}, {0,1,0,1}};
std::vector<double> outputSet = {0,1,1,0};
ANN ann(alg.transpose(inputSet), outputSet);
//ann.addLayer(10, "RELU");
ann.addLayer(10, "Sigmoid");
ann.addOutputLayer("Sigmoid", "LogLoss");
//ann.AMSGrad(0.1, 10000, 1, 0.9, 0.999, 0.000001, 1);
//ann.Adadelta(1, 1000, 2, 0.9, 0.000001, 1);
ann.Momentum(0.1, 8000, 2, 0.9, true, 1);
//ann.MBGD(0.1, 1000, 2, 1);
alg.printVector(ann.modelSetTest(alg.transpose(inputSet)));
std::cout << "ACCURACY: " << 100 * ann.score() << "%" << std::endl;
// std::vector<std::vector<double>> inputSet = {{0,0,1,1}, {0,1,0,1}};
// std::vector<double> outputSet = {0,1,1,0};
// ANN ann(alg.transpose(inputSet), outputSet);
// //ann.addLayer(10, "RELU");
// ann.addLayer(10, "Sigmoid");
// ann.addOutputLayer("Sigmoid", "LogLoss");
// //ann.AMSGrad(0.1, 10000, 1, 0.9, 0.999, 0.000001, 1);
// //ann.Adadelta(1, 1000, 2, 0.9, 0.000001, 1);
// ann.Momentum(0.1, 8000, 2, 0.9, true, 1);
// //ann.MBGD(0.1, 1000, 2, 1);
// alg.printVector(ann.modelSetTest(alg.transpose(inputSet)));
// std::cout << "ACCURACY: " << 100 * ann.score() << "%" << std::endl;
// typedef std::vector<std::vector<double>> Matrix;
// typedef std::vector<double> Vector;