ml::PCA< T > Class Template Reference

#include <pca.hpp>

Public Member Functions
	PCA ()=default
	Default constructor.
void	fit (const Matrix< T > &data, bool center=true, bool scale=false)
	Fit the PCA model to the data.
Matrix< T >	transform (const Matrix< T > &data, size_t n_components=0) const
	Transform data to the principal component space.
Matrix< T >	fit_transform (const Matrix< T > &data, size_t n_components=0, bool center=true, bool scale=false)
	Fit the model and transform the data in one step.
std::vector< T >	explained_variance_ratio () const
	Get the explained variance ratio for each component.
Matrix< T >	components () const
	Get the principal components (eigenvectors)
std::vector< T >	singular_values () const
	Get the singular values (square roots of eigenvalues)

Detailed Description

template<typename T>
class ml::PCA< T >

Definition at line 41 of file pca.hpp.

Constructor & Destructor Documentation

PCA()

template<typename T >

ml::PCA< T >::PCA ( )

default

Default constructor.

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/pca.hpp.

Member Function Documentation

components()

template<typename T >

Matrix< T > ml::PCA< T >::components

(

)

const

Get the principal components (eigenvectors)

Returns

Matrix where each column is a principal component

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/pca.hpp.

Definition at line 171 of file pca.cpp.

explained_variance_ratio()

template<typename T >

std::vector< T > ml::PCA< T >::explained_variance_ratio

(

)

const

Get the explained variance ratio for each component.

Returns

Vector of explained variance ratios

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/pca.hpp.

Definition at line 163 of file pca.cpp.

fit()

template<typename T >

void ml::PCA< T >::fit	(	const Matrix< T > &	data,
		bool	center = true,
		bool	scale = false
	)

Fit the PCA model to the data.

Parameters

data	Input data matrix where rows are samples and columns are features
center	Whether to center the data before computing PCA
scale	Whether to scale the data to unit variance before computing PCA

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/pca.hpp.

Definition at line 10 of file pca.cpp.

fit_transform()

template<typename T >

Matrix< T > ml::PCA< T >::fit_transform	(	const Matrix< T > &	data,
		size_t	n_components = 0,
		bool	center = true,
		bool	scale = false
	)

Fit the model and transform the data in one step.

Parameters

data	Input data matrix where rows are samples and columns are features
n_components	Number of components to keep (if 0, keep all components)
center	Whether to center the data before computing PCA
scale	Whether to scale the data to unit variance before computing PCA

Returns

Transformed data matrix

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/pca.hpp.

Definition at line 157 of file pca.cpp.

singular_values()

template<typename T >

std::vector< T > ml::PCA< T >::singular_values

(

)

const

Get the singular values (square roots of eigenvalues)

Returns

Vector of singular values

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/pca.hpp.

Definition at line 179 of file pca.cpp.

transform()

template<typename T >

Matrix< T > ml::PCA< T >::transform	(	const Matrix< T > &	data,
		size_t	n_components = 0
	)		const

Transform data to the principal component space.

Parameters

data	Input data matrix where rows are samples and columns are features
n_components	Number of components to keep (if 0, keep all components)

Returns

Transformed data matrix

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/pca.hpp.

Definition at line 117 of file pca.cpp.

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The documentation for this class was generated from the following files:

• include/ml/pca.hpp
• src/ml/pca.cpp