org.metaqtl
Class EMResult

java.lang.Object
  org.metaqtl.EMResult

public class EMResult
extends java.lang.Object

Field Summary

double[]	ccov The complete information (just for means).
double	clog The complete loglikelihood.
EMCriteria	criteria The model choice criteria.
double[][]	dm The matrix of convergence rate for the EM-Algorithm (just for means).
double	edist The euclidean distance between the current and the previous vector of parameters (mu + pi)
int	k The number of clusters.
double[]	mu The means of the clusters.
int	n The number of data points.
double[][]	ocov The observed information (just for means).
double	olog The observed loglikelihood.
double[]	pi The mixing proportions.
double	rate The quadratric rate of convergence of the algorithm.
double[][]	z The cluster membership probabilties.

Constructor Summary

EMResult()

EMResult(int n, int k)

Method Summary

void	computeCriteria() This methos computes the following criteria : AIC AIC3 ICOMP MIR BIC AWE
static void	copy(EMResult dest, EMResult src)
double	getCriterion(int critIdx)
double[]	getEuclidean()
int	getK()
double[]	getMahalanobis(double[] sd)
double	getMu(int i)
double	getPi(int i)
double	getSD(int i)
double[]	getXBestPred()
double[]	getXPred()
int	getXPredIdx(int i)
double[]	getZ(int i)
void	sortCluster() Sort the components in increasing order.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

k

public int k

The number of clusters.

n

public int n

The number of data points.

rate

public double rate

The quadratric rate of convergence of the algorithm.

edist

public double edist

The euclidean distance between the current and the previous vector of parameters (mu + pi)

mu

public double[] mu

The means of the clusters.

pi

public double[] pi

The mixing proportions.

z

public double[][] z

The cluster membership probabilties.

olog

public double olog

The observed loglikelihood.

clog

public double clog

The complete loglikelihood.

dm

public double[][] dm

The matrix of convergence rate for the EM-Algorithm (just for means).

ocov

public double[][] ocov

The observed information (just for means).

ccov

public double[] ccov

The complete information (just for means).

criteria

public EMCriteria criteria

The model choice criteria.

Constructor Detail

EMResult

public EMResult()

EMResult

public EMResult(int n,
                int k)

Parameters:

n - the number of data points.

k - the number of components.

Method Detail

sortCluster

public void sortCluster()

Sort the components in increasing order.

copy

public static void copy(EMResult dest,
                        EMResult src)

Parameters:

theta -

spoint -

computeCriteria

public void computeCriteria()

This methos computes the following criteria :

• AIC
• AIC3
• ICOMP
• MIR
• BIC
• AWE

getCriterion

public double getCriterion(int critIdx)

Parameters:

critIdx -

getMahalanobis

public double[] getMahalanobis(double[] sd)

Parameters:

sd -

getXPred

public double[] getXPred()

Returns:

getSD

public double getSD(int i)

Parameters:

i -

Returns:

getMu

public double getMu(int i)

Parameters:

i -

Returns:

getK

public int getK()

Returns:

getZ

public double[] getZ(int i)

getEuclidean

public double[] getEuclidean()

Parameters:

sd -

Returns:

getPi

public double getPi(int i)

Parameters:

i -

Returns:

getXPredIdx

public int getXPredIdx(int i)

Parameters:

i -

Returns:

getXBestPred

public double[] getXBestPred()

Returns: