How to use
E
method
in
weka.clusterers.EM

Best Java code snippets using weka.clusterers.EM.E (Showing top 4 results out of 315)

private void myMethod () {
 EM e =new EM()
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}

return E(inst, true);

 tll = E(cvTest, false);
} catch (Exception ex) {

 tll = E(cvTest, false);
} catch (Exception ex) {

return E(inst, true);

The E step of the EM algorithm. Estimate cluster membership probabilities.

<init>
Constructor.
setNumClusters
Set the number of clusters (-1 to select by CV).
CVClusters
estimate the number of clusters by cross validation on the training data.
EM_Init
Initialize the global aggregated estimators and storage.
EM_Report
verbose output for debugging
M
The M step of the EM algorithm.
M_reEstimate
buildClusterer
Generates a clusterer. Has to initialize all fields of the clusterer that are not being set via opti
distributionForInstance
doEM
Perform the EM algorithm
estimate_priors
calculate prior probabilites for the clusters
getCapabilities
Returns default capabilities of the clusterer (i.e., the ones of SimpleKMeans).

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