PCA Class Reference

Performs Pricipal Component Analysis on samples with N variables. More...

Public Member Functions
	PCA (int nrvars)
virtual	~PCA ()
template<class IDXABL >
void	addSample (const IDXABL &sample)
bool	calculate ()
void	clearAllSamples ()
float	getEigenValue (int idx) const
template<class IDXABL >
void	getEigenVector (int idx, IDXABL &vec) const

Protected Member Functions
bool	tqli (float[], float[], int, ObjectSet< float > &)
void	tred2 (ObjectSet< float > &, int, float[], float[])

Protected Attributes
Array2DImpl< float >	covariancematrix_
float *	eigenvalues_
int *	eigenvecindexes_
const int	nrvars_
ObjectSet< TypeSet< float > >	samples_
TypeSet< float >	samplesums_
ObjectSet< SequentialTask >	tasks_
TypeSet< Threads::Work >	workload_

Detailed Description

Performs Pricipal Component Analysis on samples with N variables.

Example of usage:

//The samples will have three variables
PCA pca( 3 );         
 
//Samples can be added by any object that has a readable [] operator
const float sample0[] = { 0, 1, 2 };
pca.addSample( sample0 );
 
const float sample1[] = { 4.343, 9.8, 2.72 };
pca.addSample( sample1 );
 
const float sample2[] = { 23.15, 210, -15 };
pca.addSample( sample2 );
 
const float sample3[] = { -0.36, 0.68, 3 };
pca.addSample( sample3 );
 
const float sample4[] = { 4.4, 9,6, 11 };
pca.addSample( sample4 );
 
TypeSet<float> sample5; sample5 += 34.1; sample5 += 8.37; sample5 += -44;
pca.addSample( sample5 ); 
 
pca.calculate();
 
 
//Any object that has a writable [] operator can be used to fetch
//the resulting vectors:
TypeSet<float> eigenvec0(3,0);
float eigenval0 = pca.getEigenValue(0);
pca.getEigenVector( 0, eigenvec0 );
 
float[3] eigenvec1;
float eigenval1 = pca.getEigenValue(1);
pca.getEigenVector( 1, eigenvec1 );
 
float[3] eigenvec2;
float eigenval2 = pca.getEigenValue(2);
pca.getEigenVector( 2, eigenvec2 );

<>

Constructor & Destructor Documentation

PCA()

PCA::PCA

(

int

nrvars

)

Parameters

nrvars

The number of variables that the samples have.

~PCA()

virtual PCA::~PCA ( )

virtual

Member Function Documentation

addSample()

template<class IDXABL >

void PCA::addSample ( const IDXABL &  sample )

inline

Adds a sample to the analysis.

Parameters

sample

The sample that should be added. The sample can be of any type that have [] operators.

calculate()

bool PCA::calculate

(

)

Computes the pca for all added samples.

clearAllSamples()

void PCA::clearAllSamples

(

)

Removes all samples so a new analysis can be made (by adding new samples)

getEigenValue()

float PCA::getEigenValue

(

int

idx

)

const

Returns

an eigenvalue.

Parameters

idx	Determines which eigenvalue to return. The eigenvalues are sorted in descending order, so idx==0 gives the largest eigenvalue.

getEigenVector()

template<class IDXABL >

void PCA::getEigenVector ( int  idx, IDXABL &  vec  ) const

inline

Returns the eigenvector corresponding to the eigenvalue idx.

Parameters

idx	Determines which eigenvector to return. The eigenvectors are sorted in order of descending eigenvalue, so idx==0 gives the eigenvector corresponding to the largest eigenvalue.
vec	The object where the to store the eigenvector. Any object that has a writable [] operator can be used, for example float* and TypeSet<T>.

tqli()

bool PCA::tqli ( float  [], float  [], int  , ObjectSet< float > &    )

protected

tred2()

void PCA::tred2 ( ObjectSet< float > &  , int  , float  [], float  []  )

protected

Member Data Documentation

covariancematrix_

Array2DImpl<float> PCA::covariancematrix_

protected

eigenvalues_

float* PCA::eigenvalues_

protected

The negation of the eigenval, to get the sorting right.

eigenvecindexes_

int* PCA::eigenvecindexes_

protected

nrvars_

const int PCA::nrvars_

protected

samples_

ObjectSet<TypeSet<float> > PCA::samples_

protected

samplesums_

TypeSet<float> PCA::samplesums_

protected

tasks_

ObjectSet<SequentialTask> PCA::tasks_

protected

workload_

TypeSet<Threads::Work> PCA::workload_

protected