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PCA

Performs the PCA on provided data, create the feature vector and normalization coefficients (mean and standard deviation of variables)

Syntax

void avl::PCA
(
	const avl::Matrix& inMatrix,
	const int inDimensions,
	atl::Optional<float> inVarianceToLeave,
	avl::PCAModel& outPCAModel,
	avl::Matrix& outTransformedMatrix,
	avl::Matrix& diagCovarianceMatrix,
	avl::Matrix& diagNormalizedData
)

Parameters

	Name	Type	Range	Default	Description
	inMatrix	const Matrix&			Input data, where variables are in column, and examples are in rows.
	inDimensions	const int	1 -		How many data dimensions (variables) to be left in transformed data.
	inVarianceToLeave	Optional<float>	0.0 - 1.0	0.95f	How many of input data variance should be left in transformed data; overrides inDimensions input.
	outPCAModel	PCAModel&			Resulting PCA model.
	outTransformedMatrix	Matrix&			Transformed inMatrix with reduced dimensionality.
	diagCovarianceMatrix	Matrix&			Covariance matrix of input data.
	diagNormalizedData	Matrix&			Input data after normalization: scaling and centering.

Errors