Public

<init>

Set the number of clusters (-1 to select by CV).

setNumClusters

estimate the number of clusters by cross validation on the training data.

CVClusters

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

Initialize the global aggregated estimators and storage.

EM_Init

EM_Report

M_reEstimate

Generates a clusterer. Has to initialize all fields of the clusterer that are
not being set via opti

buildClusterer

distributionForInstance

doEM

calculate prior probabilites for the clusters

estimate_priors

Returns default capabilities of the clusterer (i.e., the ones of SimpleKMeans).

getCapabilities

Get the maximum number of clusters to consider when cross-validating

getMaximumNumberOfClusters

Get the minimum improvement in cross-validated log likelihood required to
consider increasing the nu

getMinLogLikelihoodImprovementCV

Get the minimum improvement in log likelihood necessary to perform another
iteration of the E and M 

getMinLogLikelihoodImprovementIterating

Get the minimum allowable standard deviation.

getMinStdDev

getNumClusters

getNumExecutionSlots

Get the number of folds to use when cross-validating to find the best number of
clusters.

getNumFolds

Making http requests using okhttp

java.awt.Menu

Allows reading from and writing to a file in a random-access manner. This is
different from the uni-

java.io.RandomAccessFile

Represents a Uniform Resource Identifier (URI) reference. Aside from some minor
deviations noted bel

java.net.URI

The Dictionary class is the abstract parent of any class, such as Hashtable,
which maps keys to valu

java.util.Dictionary

A NavigableSet implementation based on a TreeMap. The elements are ordered using
their Comparable, o

java.util.TreeSet

javax.naming.Reference

setRequestProperty

setContentView

runOnUiThread

/**
   * Perform the EM algorithm
   * 
   * @throws Exception if something goes wrong
   */

// throw away numbers to avoid problem of similar initial numbers

// cross validate to determine number of clusters?

/**
   * iterates the E and M steps until the log likelihood of the data converges.
   * 
   * @param inst the training instances.
   * @param report be verbose.
   * @return the log likelihood of the data
   * @throws Exception if something goes wrong
   */

// decrease in log likelihood - revert to the model from the

// System.err.println("Restarting after training failure");

// System.err.println("Reducing the number of clusters");

How to use startExecutorPoolmethodin weka.clusterers.EM

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

How to use
startExecutorPool
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weka.clusterers.EM