CreatePCATransform


Performs the Principal Component Analysis (PCA) on provided data, creates the feature vector and normalization coefficients (mean and standard deviation of variables).

Syntax

C++
C#
Python
 
def CreatePCATransform(
	inMatrix: Matrix,
	outPCAModel: PCAModel,
	outTransformedMatrix: Matrix,
	/,
	*,
	inDimensions: int = 1,
	inVarianceToLeave: float | None = 0.95,
	diagCovarianceMatrix: Matrix | None = None,
	diagNormalizedData: Matrix | None = None
)
-> diagUsedFeatureIndices: list[int]

Parameters

Name Type Range Default Description
Input value inMatrix Matrix Input data, where variables are in column, and examples are in rows.
Input value inDimensions int 1 - 1 How many data dimensions (variables) to be left in transformed data.
Input value inVarianceToLeave float | None 0.0 - 1.0 0.95 How many of input data variance should be left in transformed data; overrides inDimensions input.
Output value outPCAModel PCAModel Resulting PCA model.
Output value outTransformedMatrix Matrix Transformed inMatrix with reduced dimensionality.
Diagnostic input diagCovarianceMatrix Matrix | None None Covariance matrix of input data.
Diagnostic input diagNormalizedData Matrix | None None Input data after normalization: scaling and centering.
Diagnostic input diagUsedFeatureIndices list[int] Indices of columns in inMatrix, which were used as Principal Components.