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

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

java.io.RandomAccessFile

A controller for the selection of SelectableChannel objects. Selectable channels
can be registered w

java.nio.channels.Selector

A reentrant mutual exclusion Lock with the same basic behavior and semantics as
the implicit monitor

java.util.concurrent.locks.ReentrantLock

A Handler object accepts a logging request and exports the desired messages to a
target, for example

java.util.logging.Handler

Encapsulate an XML parse error or warning.> This module, both source code and documentation, is in t

org.xml.sax.SAXParseException

scala.Option

getContentResolver

startActivity

setRequestProperty

/**
   * 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?

/**
   * estimate the number of clusters by cross validation on the training data.
   * 
   * @throws Exception if something goes wrong
   */

// catch any problems - i.e. empty clusters occurring

// System.err.println("Restarting after CV training failure ("+num_clusters+" clusters");

// System.err.println("Restarting after CV testing failure ("+num_clusters+" 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 getSeedmethodin weka.clusterers.EM

Best Java code snippets using weka.clusterers.EM.getSeed (Showing top 6 results out of 315)

How to use
getSeed
method
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weka.clusterers.EM