BayesClassification: BayesClassification
In AdaptGauss: Gaussian Mixture Models (GMM)

Description Usage Arguments Value Author(s)

Bayes Klassifikation den Daten zuordnen

1 2	BayesClassification(Data, Means, SDs, Weights, IsLogDistribution = Means * 0, ClassLabels = c(1:length(Means)))

`Data`	vector of Data
`Means`	vector[1:L] of Means of Gaussians (of GMM)
`SDs`	vector of standard deviations, estimated Gaussian Kernels, has to be the same length as Means
`Weights`	vector of relative number of points in Gaussians (prior probabilities), has to be the same length as Means
`IsLogDistribution`	Optional, ==1 if distribution(i) is a LogNormal, default vector of zeros of length 1:L
`ClassLabels`	Optional numbered class labels that are assigned to the classes. default (1:L), L number of different components of gaussian mixture model

Cls(1:n,1:d) classiffication of Data, such that 1= first component of gaussian mixture model, 2= second component of gaussian mixture model and so on. For Every datapoint a number is returned.

Michael Thrun

AdaptGauss documentation built on March 26, 2020, 7:57 p.m.