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

Reading from database using SQL prepared statement

Wraps an existing Writer and buffers the output. Expensive interaction with the
underlying reader is

java.io.BufferedWriter

Signals that an I/O exception of some sort has occurred. This class is the
general class of exceptio

java.io.IOException

A PrintStream adds functionality to another output stream, namely the ability to
print representatio

java.io.PrintStream

Formats and parses dates in a locale-sensitive manner. Formatting turns a Date
into a String, and pa

java.text.SimpleDateFormat

General IO stream manipulation utilities. This class provides static utility
methods for input/outpu

org.apache.commons.io.IOUtils

org.hibernate.mapping.Join

addToBackStack

getApplicationContext

putExtra

/**
   * Computes the log of the conditional density (per cluster) for a given
   * instance.
   * 
   * @param inst the instance to compute the density for
   * @return an array containing the estimated densities
   * @throws Exception if the density could not be computed successfully
   */

/*
           * System.err.println(logNormalDens(inst.value(j),
           * m_modelNormal[i][j][0], m_modelNormal[i][j][1]) + " ");
           */

How to use logNormalDensmethodin weka.clusterers.EM

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How to use
logNormalDens
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