Implementation.cpp File Reference

#include <iostream>
#include <cosan/selection/randomgridsearch.h>
#include <cosan/selection/gridsearch.h>
#include <cosan/selection/kfold.h>
#include <cosan/selection/timeseriessplit.h>
#include <cosan/selection/randomkfold.h>
#include <cosan/utils/utils.h>
#include <cosan/data/CosanData.h>
#include <cosan/model/CosanPCRRidge.h>
#include <cosan/model/CosanPrincipalComponentRegression.h>
#include <cosan/model/CosanRidgeRegression.h>
#include <cosan/model/CosanLinearRegression.h>
#include <cosan/preprocessing/customtransform.h>
#include <cosan/preprocessing/encoder.h>
#include <cosan/preprocessing/minmaxscaler.h>
#include <cosan/preprocessing/missingvalues.h>
#include <cosan/preprocessing/normalizer.h>
#include <cosan/preprocessing/onehotEncoder.h>
#include <cosan/preprocessing/ordinalEncoder.h>
#include <cosan/preprocessing/overunderflow.h>
#include <cosan/preprocessing/polynomialfeatures.h>
#include <cosan/preprocessing/preprocessor.h>
#include <cosan/preprocessing/principalcomponentanalysis.h>
#include <cosan/preprocessing/standardScaler.h>

Go to the source code of this file.

Typedefs
typedef double	db

Functions
int	main ()

Typedef Documentation

db

typedef double db

Definition at line 35 of file Implementation.cpp.

Function Documentation

main()

int main

(

)

Definition at line 36 of file Implementation.cpp.

           {
    fmt::print("fmt library can be used", 42);
//  Data Reading
    constexpr gsl::index nrows = 3;
    constexpr gsl::index ncols = 3;
 
 
    Cosan::CosanMatrix<db> CM;
    CM.resize(nrows,ncols);
    CM<< 1,2,3,
        4,5,6,
        7,8,9;
    Cosan::CosanData<db>  CD0(CM);
    std::vector<db> inputX({1,2,3,4,5,6});
    Cosan::CosanData<db>  CD1(inputX,inputX,6,"rowfirst");
    std::cout<<CD1.GetInput()<<std::endl;
    db lb=0,ub=1;
    Cosan::CosanData<db> CD2(3,4,lb,ub);
    std::cout<<CD2.GetInput()<<std::endl;
 
//  Data Preprocessing
    Cosan::CosanRawData<db> CRD("./example_data/toy2/X_.csv","./example_data/toy2/Y_.csv");
    std::cout<<CRD.GetSummaryMessageX()<<std::endl;
    std::cout<<CRD.GetSummaryMessageY()<<std::endl;    
    Cosan::OverUnderFlow  OUF(CRD); 
    Cosan::MissingValues  MSV(CRD);     
    Cosan::StandardScaler SS(CRD);
    Cosan::Normalizer NM(CRD,2);
    Cosan::Encoder ED(CRD,true);
    lb = 2;
    ub=6;
    Cosan::MinmaxScaler MMS(CRD,lb,ub); 
    Cosan::PolynomialFeatures PF(CRD,{{1,0},{1,1}});
    Cosan::PrincipalComponentAnalysis PCA(CRD);
    std::cout<<PCA.GetPC()<<std::endl;
//  Model fitting
    Cosan::CosanRawData<db> CD("./example_data/toy/X.csv","./example_data/toy/y.csv");
    Cosan::CosanLinearRegression<db> CLRwbias(true);
    CLRwbias.fit(CD.GetInput(),CD.GetTarget());
    std::cout<<CLRwbias.GetBeta()<<std::endl;
    std::cout<<(CLRwbias.predict(CD.GetInput())-CD.GetTarget()).norm()<<std::endl;   
    Cosan::CosanLinearRegression<db> CLRwobias(false);
    CLRwobias.fit(CD.GetInput(),CD.GetTarget());
    std::cout<<CLRwobias.GetBeta()<<std::endl;  
    std::cout<<(CLRwobias.predict(CD.GetInput())-CD.GetTarget()).norm()<<std::endl;   
 
    db RegularizationTerm = 1;
    Cosan::CosanRidgeRegression<db> CRRwBias(RegularizationTerm,true);
    CRRwBias.fit(CD.GetInput(),CD.GetTarget());
    std::cout<<CRRwBias.GetBeta()<<std::endl;
    std::cout<<(CRRwBias.predict(CD.GetInput())-CD.GetTarget()).norm()<<std::endl;   
 
    gsl::index ncomp = 4;
    Cosan::CosanPrincipalComponentRegression<db> CPCR(ncomp);
    CPCR.fit(CD.GetInput(),CD.GetTarget());
    std::cout<<CPCR.GetBeta()<<std::endl;
    std::cout<<(CPCR.predict(CD.GetInput())-CD.GetTarget()).norm()<<std::endl;
 
 
    db regularier = 0.01;
    Cosan::CosanPCRRidge<db> CPCRR({ncomp,regularier});
    CPCRR.fit(CD.GetInput(),CD.GetTarget());
    std::cout<<CPCRR.GetBeta()<<std::endl;
    std::cout<<(CPCRR.predict(CD.GetInput())-CD.GetTarget()).norm()<<std::endl;
    std::cout<<CD.GetcolsX()<<std::endl;
 
 
    constexpr gsl::index nrows1 = 10000;
    constexpr gsl::index ncols1 = 10;
 
    Cosan::CosanMatrix<db> X_input;
    Cosan::CosanMatrix<db> Y_input;
    X_input.resize(nrows1,ncols1);
    Y_input.resize(nrows1,1);
    X_input = Eigen::Matrix<decltype(X_input)::Scalar,nrows1,ncols1>::Random();
    Y_input = Eigen::Matrix<decltype(X_input)::Scalar,nrows1,1>::Random();
    Cosan::CosanData CD_search(X_input,Y_input);
//  Cosan::CosanRidgeRegression<db> CRRwBias(RegularizationTerm,true);
    Cosan::MeanSquareError<decltype(X_input)::Scalar> mse;
    db a = 0.05;
    std::vector<db> v(10);
    std::generate(v.begin(), v.end(), [n = 1, &a]() mutable { return n++ * a; });
    Cosan::KFold kf(5);
    Cosan::GridSearch GDS(CD_search,CRRwBias,mse,kf,v);
    fmt::print("The best params selected is\n ");
    std::cout<<GDS.GetBestParams()<<std::endl;
//  Grid Search
 
    return 0;
}