Class AlgorithmLBG

java.lang.Object
  Algorithm
      AlgorithmLBG

All Implemented Interfaces:

java.lang.Runnable

public class AlgorithmLBG
extends Algorithm

implements the LBG algorithm

Constructor Summary

AlgorithmLBG()

Method Summary

void	classify(java.util.Vector guesses) Classifies the data sets based on the k-means iterative algorithm
MyPoint	clusterDeviation(java.util.Vector cluster, MyPoint mean) Calculates the standard deviation of the cluster
void	computeBinaryDeviates(java.util.Vector decisionRegions) Computes the binary deviates after each iteraion
int	displayClusterError(int closest, java.util.Vector cluster, int id) Finds the datapoints in error, for all datasets
void	generatePool() Collects all the data points together
int	getClosestSet(MyPoint mean) Determines the closest data sets to the cluster
java.util.Vector<MyPoint>	getDecisionRegion(java.util.Vector<MyPoint> vec) Computes the k-mean decision region - nearest neighbor algorithm
boolean	initialize() Overrides the initialize() method in the base class.
void	outputDecisionRegion() Displays the decision regoin on output panel
void	run() Implementation of the run function from the Runnable interface.

Methods inherited from class Algorithm

computeMeans, disableControl, enableControl, nextStep, prevStep, scaleToFitData, setDataPoints, setOutputPanel, setProcessBox

Methods inherited from class java.lang.Object

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

Constructor Detail

AlgorithmLBG

public AlgorithmLBG()

Method Detail

initialize

public boolean initialize()

Overrides the initialize() method in the base class. Initializes member data and prepares for execution of first step. This method "resets" the algorithm.

Specified by:

initialize in class Algorithm

Returns:

Returns true

run

public void run()

Implementation of the run function from the Runnable interface. Determines what the current step is and calls the appropriate method.

Specified by:

run in interface java.lang.Runnable

Specified by:

run in class Algorithm

generatePool

public void generatePool()

Collects all the data points together

getClosestSet

public int getClosestSet(MyPoint mean)

Determines the closest data sets to the cluster

Parameters:

mean - mean point of the cluster

Returns:

closest data set to the cluster

displayClusterError

public int displayClusterError(int closest,
                               java.util.Vector cluster,
                               int id)

Finds the datapoints in error, for all datasets

Parameters:

closest - Variable can be int values 1-4. Marks which set of data is closest

cluster - Stores the points of a cluster

id - ID number

Returns:

Returns the error number of the misclassified samples

computeBinaryDeviates

public void computeBinaryDeviates(java.util.Vector decisionRegions)

Computes the binary deviates after each iteraion

Parameters:

decisionRegions - region: classified data sets

clusterDeviation

public MyPoint clusterDeviation(java.util.Vector cluster,
                                MyPoint mean)

Calculates the standard deviation of the cluster

Parameters:

cluster - cluster of data points

mean - mean of the cluster

Returns:

MYPoint standard deviation of the cluster along x direction and y direction.

See Also:

MyPoint

classify

public void classify(java.util.Vector guesses)

Classifies the data sets based on the k-means iterative algorithm

Parameters:

guesses - stored data sets from the classification

getDecisionRegion

public java.util.Vector<MyPoint> getDecisionRegion(java.util.Vector<MyPoint> vec)

Computes the k-mean decision region - nearest neighbor algorithm

Parameters:

vec - vector of initial guesses

Returns:

vector of desision region points

outputDecisionRegion

public void outputDecisionRegion()

Displays the decision regoin on output panel

See Also:

OutputPanel