#include <principalcomponentanalysis.h>

Inheritance diagram for Cosan::PrincipalComponentAnalysis< NumericType >:

Public Member Functions
	PrincipalComponentAnalysis ()=delete

	PrincipalComponentAnalysis (CosanRawData< NumericType > &RD, gsl::index ncom=3)

	PrincipalComponentAnalysis (CosanData< NumericType > &RD, gsl::index ncom=3)

	PrincipalComponentAnalysis (const CosanMatrix< NumericType > &RD, gsl::index ncom=3)

CosanMatrix< NumericType > &	GetPC ()

Public Member Functions inherited from Cosan::Preprocessor< NumericType >
	Preprocessor ()

virtual	~Preprocessor ()=default

virtual void	fit (const CosanMatrix< NumericType > &X)

virtual CosanMatrix< NumericType >	transform (const CosanMatrix< NumericType > &X)

Public Member Functions inherited from Cosan::CosanBO
	CosanBO ()
	Default constructor. More...

virtual const std::string	GetName () const
	Get the name of the objects. More...

Private Member Functions
void	fit (const CosanMatrix< NumericType > &X, gsl::index ncom)

Private Attributes
CosanMatrix< NumericType >	PrincipalComponent

CosanMatrix< NumericType >	FullComponent

Detailed Description

template<Numeric NumericType>
class Cosan::PrincipalComponentAnalysis< NumericType >

https://en.wikipedia.org/wiki/Principal_component_analysis

Definition at line 16 of file principalcomponentanalysis.h.

Constructor & Destructor Documentation

◆ PrincipalComponentAnalysis() [1/4]

template<Numeric NumericType>

Cosan::PrincipalComponentAnalysis< NumericType >::PrincipalComponentAnalysis ( )

delete

◆ PrincipalComponentAnalysis() [2/4]

template<Numeric NumericType>

Cosan::PrincipalComponentAnalysis< NumericType >::PrincipalComponentAnalysis	(	CosanRawData< NumericType > &	RD,
		gsl::index	ncom = `3`
	)

inline

Definition at line 19 of file principalcomponentanalysis.h.

                                                                                        :Preprocessor<NumericType>(){
                 ncom = std::min(ncom,RD.GetcolsX());
                 this->fit(RD.GetInput(),ncom);
             }

◆ PrincipalComponentAnalysis() [3/4]

template<Numeric NumericType>

Cosan::PrincipalComponentAnalysis< NumericType >::PrincipalComponentAnalysis	(	CosanData< NumericType > &	RD,
		gsl::index	ncom = `3`
	)

inline

Definition at line 23 of file principalcomponentanalysis.h.

                                                                                     :Preprocessor<NumericType>(){
                 ncom = std::min(ncom,RD.GetcolsX());
                 this->fit(RD.GetInput(),ncom);
             }

◆ PrincipalComponentAnalysis() [4/4]

template<Numeric NumericType>

Cosan::PrincipalComponentAnalysis< NumericType >::PrincipalComponentAnalysis	(	const CosanMatrix< NumericType > &	RD,
		gsl::index	ncom = `3`
	)

inline

Definition at line 28 of file principalcomponentanalysis.h.

                                                                                             :Preprocessor<NumericType>(){
                 ncom = std::min(ncom,RD.cols());
                 this->fit(RD,ncom);
             }

Member Function Documentation

◆ fit()

template<Numeric NumericType>

void Cosan::PrincipalComponentAnalysis< NumericType >::fit	(	const CosanMatrix< NumericType > &	X,
		gsl::index	ncom
	)

inlineprivate

Definition at line 37 of file principalcomponentanalysis.h.

                                                                      {
                 fmt::print("*********************************\n");
                 fmt::print("Begin PCA on Input Data X. Select the first {:} principal components\n",ncom);
                 CosanMatrix<NumericType> centered = X.rowwise() - X.colwise().mean();
                 CosanMatrix<NumericType> cov = centered.adjoint() * centered;
  
                 Eigen::SelfAdjointEigenSolver<CosanMatrix<NumericType>> eigensolver(cov);
                 if (eigensolver.info() != Eigen::Success) {
                     throw std::invalid_argument("Cannot solve eigenvalue decomposition.");}
  
                 std::vector<NumericType> vec(eigensolver.eigenvalues().data(), eigensolver.eigenvalues().data() + eigensolver.eigenvalues().size());
                 std::reverse(vec.begin(),vec.end());
                 std::vector<NumericType> runningSum(vec.size());
                 std::partial_sum(vec.begin(), vec.end(), runningSum.begin());
                 for (gsl::index i =0;i<runningSum.size();i++){
                     fmt::print("The first {:} principal components explains {:f}% of the total variance.\n",i,runningSum[i]/runningSum.back()*100);
                     if (runningSum[i]/runningSum.back()>0.98 and i>=8){
                         break;
                     }
                 }
 //                std::cout<<eigensolver.eigenvalues()<<std::endl;
 //                std::cout<<eigensolver.eigenvectors()<<std::endl;
 //                std::cout<<eigensolver.eigenvectors().rightCols(1)<<std::endl;
                 fmt::print("Finsh PCA on Input Data X. The first {:} principal components explains {:f}% of the total variance.\n",ncom,runningSum[ncom]/runningSum.back()*100);
                 fmt::print("Uer .GetPC() function to get the principal components.\n" );
                 fmt::print("*********************************\n");
                 PrincipalComponent = eigensolver.eigenvectors().rightCols(ncom);
             }