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# lyngby_km_loglikelihood

## (export/lyngby/lyngby_km_loglikelihood.m)

### Function Synopsis

l = lyngby_km_loglikelihood(X, Centers, Assign, varargin)

### Help text

lyngby_km_loglikelihood - Log likelihood of K-means model
l = lyngby_km_loglikelihood(X, Center, Assign)
Input: X Data matrix
Center Centers of estimated clusters
Assign Assignment vector
Property: SigmaType [ ML | {Unbiased} | Unbiased2 ] Estimation
type for the variance
Output: l Log likelihood
Calculate the logarithm of the likelihood to the K-means model
when it is regarded as a Gaussian mixture model with isotropic
variance and an equal assignment weight.
The within variance assumes an isotropic variance, ie, that
there has been no standardization (eg, 'std' or 'range')
during the K-means estimation.
Acknowledgment: Berkan Dulek, Kristoffer H. Madsen.
Example:
load iris.txt
for n = 1:50
[C,A] = lyngby_km_main(iris, 'type', 'mean', 'clusters', n, 'init', 'random');
l(n) = lyngby_km_loglikelihood(iris, C, A, 'sigmatype', 'unbiased2');
end
plot(l), xlabel('Clusters'), ylabel('Log likelihood')
See also LYNGBY, LYNGBY_KM_MAIN, LYNGBY_KM_WITHIN,
LYNGBY_KM_BIC, LYNGBY_IKM_MAIN, LYNGBY_IKM_BIC.
$Id: lyngby_km_loglikelihood.m,v 1.4 2005/09/27 21:56:13 fnielsen Exp $

### Cross-Reference Information

This function calls
This function is called by

Produced by mat2html on Wed Jul 29 15:43:40 2009

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