pca

Principal component analysis of raw data. Template: correlation = Flag to use correlation matrix instead of covariance

PcaResult!T pca(Slice!(const(T)*, 2, kind) data, DeviationEstimator devEst, MeanEstimator meanEst, Flag!"fixEigenvectorDirections" fixEigenvectorDirections)
@safe pure @nogc
PcaResult!T
pca
(
SliceKind kind
T
)
(
Slice!(const(T)*, 2, kind) data
,
DeviationEstimator devEst = DeviationEstimator.sample
,
MeanEstimator meanEst = MeanEstimator.average
,
Flag!"fixEigenvectorDirections" fixEigenvectorDirections = Yes.fixEigenvectorDirections
)
PcaResult!T pca(Slice!(RCI!T, 2, kind) data, DeviationEstimator devEst, MeanEstimator meanEst, Flag!"fixEigenvectorDirections" fixEigenvectorDirections)

Parameters

data Slice!(const(T)*, 2, kind): input M x N matrix, where 'M (rows)>= N(cols)'
devEst DeviationEstimator
meanEst MeanEstimator
fixEigenvectorDirections Flag!"fixEigenvectorDirections"

Return Value

Type: PcaResult!T

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See Implementation

kaleidic lubeck2 enums

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