marf.Storage
Class TrainingSet.TrainingSample

java.lang.Object
  |
  +--marf.Storage.TrainingSet.TrainingSample

All Implemented Interfaces:

java.io.Serializable

Enclosing class:

TrainingSet

public class TrainingSet.TrainingSample

extends java.lang.Object

implements java.io.Serializable

TrainingSample contains one item in the training set. Each training sample consists of the feature vector plus information describing that feature vector.

See Also:

Serialized Form

Constructor Summary

TrainingSet.TrainingSample()

Method Summary

boolean	addFilename(java.lang.String pstrFilename) A filename to the training set.
void	dump(java.io.BufferedWriter bw) Write one training sample to a text file.
boolean	existsFilename(java.lang.String pstrFilename) Check existance of the file in the training set.
int	getMeanCount() Retrieves current mean count
double[]	getMeanVector() Retrieves the mean vector
int	getSubjectID() Retrieves Subject ID of a particular training sample
int	incMeanCount() Increases mean count by one
void	restore(java.io.BufferedReader br) Retrieve one training sample from a text file.
void	setMeanVector(double[] padMeanVector) Sets new mean vector
void	setSubjectID(int piSubjectID) Sets new Subject ID

Methods inherited from class java.lang.Object

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

Constructor Detail

TrainingSet.TrainingSample

public TrainingSet.TrainingSample()

Method Detail

addFilename

public final boolean addFilename(java.lang.String pstrFilename)

A filename to the training set.

Parameters:

pstrFilename - filename to check

Returns:

true if the filename is there; false if not

See Also:

existsFilename

existsFilename

public final boolean existsFilename(java.lang.String pstrFilename)

Check existance of the file in the training set. Servers as an indication that we already trained on the given file.

Parameters:

pstrFilename - filename to check

Returns:

true if the filename is there; false if not

getSubjectID

public final int getSubjectID()

Retrieves Subject ID of a particular training sample

Returns:

int ID

getMeanCount

public final int getMeanCount()

Retrieves current mean count

Returns:

mean count

incMeanCount

public final int incMeanCount()

Increases mean count by one

Returns:

new mean count

getMeanVector

public final double[] getMeanVector()

Retrieves the mean vector

Returns:

array of doubles representing the mean for that cluster

setSubjectID

public final void setSubjectID(int piSubjectID)

Sets new Subject ID

Parameters:

piSubjectID - integer ID

setMeanVector

public final void setMeanVector(double[] padMeanVector)

Sets new mean vector

Parameters:

padMeanVector - double array representing the mean vector

dump

public final void dump(java.io.BufferedWriter bw)
                throws java.io.IOException

Write one training sample to a text file.

Parameters:

bw - BufferedWriter

restore

public final void restore(java.io.BufferedReader br)
                   throws java.io.IOException

Retrieve one training sample from a text file.

Parameters:

br - BufferedReader

Throws:

java.io.IOException -