Classifier for SFA demos

Description

Train or apply a Gaussian classifier..

Usage

1
gaussClassifier(gauss, y, realC, method = "train")

Arguments

gauss

List created by gaussCreate. Contains also the elements:

aligned

=0: do not align the Gaussian classifiers with axes, use full covariance matrix
=1 (default): set the off-diagonals in covariance matrix to 0, i.e. the Gaussian classifier is forced to be aligned with the axes. This is more robust in the case where the data deviate largely from a multivariate normal distribution.

epsD

[defaults to 0.04] replace diagonal elements of COV smaller than epsD with epsD to avoid too small Gaussians

y

K x M matrix where K is the total number of patterns and M is the number of variables used for classification. I.e. each row of y contains the data for one pattern.

realC

1 x K matrix with NCLASS distinct real class labels needed only for method='train'. In case of method="apply" realC is not used and can have any value

method

either "train" (default) or "apply"

Value

list gauss containing

gauss$predC

1 x K matrix: the predicted class

gauss$prob

K x NCLASS matrix: prob(k,n) is the estimated probability that pattern k belongs to class m

See Also

gaussCreate

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