A B C D E F G I L M N O P R S T U V W X Y

A

actionPerformed(ActionEvent) - Method in class MainMenu
method listens for actions taking place on text only menu items
actual_error(Vector, Vector) - Method in class AlgorithmLP
Compute the actual error from the given data points and the estimated values.
add_components() - Method in class MainMenu
adds components to control panel
add_components() - Method in class OutputPanel
Adds components to the input panel
addMatrix(Matrix) - Method in class Matrix
This routine adds two matrices
addMatrixElements(double) - Method in class Matrix
This routine adds a double value to every element in a matrix
addOutput(Vector<MyPoint>, int, Color) - Method in class OutputPanel
Adds points to the output panel
addPoint(MyPoint) - Method in class DataPoints
add a point to the data set that is determined by the index (setNum)
addPoint(MyPoint, String) - Method in class DecisionRegion
add a point to the deciosion region
addRegion(Vector<MyPoint>) - Method in class DecisionRegion
add a set a decision region points
addRegion(Vector<MyPoint>, String) - Method in class DecisionRegion
add a set a decision region points
Algorithm - Class in <Unnamed>
Base class for all pattern recognition algorithms
Algorithm() - Constructor for class Algorithm
 
AlgorithmED - Class in <Unnamed>
implements the euclidean distance algorithm.
AlgorithmED() - Constructor for class AlgorithmED
 
AlgorithmKF - Class in <Unnamed>
Class that handles the Kalman Filter algorithm
AlgorithmKF() - Constructor for class AlgorithmKF
 
AlgorithmKMeans - Class in <Unnamed>
implements the K nearest neighbor algorithms
AlgorithmKMeans() - Constructor for class AlgorithmKMeans
 
AlgorithmLBG - Class in <Unnamed>
implements the LBG algorithm
AlgorithmLBG() - Constructor for class AlgorithmLBG
 
AlgorithmLDA - Class in <Unnamed>
Implements the Linear Discriminant Analysis Algorithm
AlgorithmLDA() - Constructor for class AlgorithmLDA
 
AlgorithmLDA2 - Class in <Unnamed>
Implements the class dependent Linear Discriminant Algorithm
AlgorithmLDA2() - Constructor for class AlgorithmLDA2
 
AlgorithmLDAPCA - Class in <Unnamed>
Implements the combined LDA and PCA algorithm.
AlgorithmLDAPCA() - Constructor for class AlgorithmLDAPCA
 
AlgorithmLP - Class in <Unnamed>
 
AlgorithmLP() - Constructor for class AlgorithmLP
 
AlgorithmNN - Class in <Unnamed>
 
AlgorithmNN() - Constructor for class AlgorithmNN
 
AlgorithmPCA - Class in <Unnamed>
Operation of the PCA Class-Independent Algorithm
AlgorithmPCA() - Constructor for class AlgorithmPCA
 
AlgorithmPCA2 - Class in <Unnamed>
Operation of the PCA Class-Independent Algorithm
AlgorithmPCA2() - Constructor for class AlgorithmPCA2
 
AlgorithmPF - Class in <Unnamed>
 
AlgorithmPF() - Constructor for class AlgorithmPF
 
AlgorithmRVM - Class in <Unnamed>
Algorithm Relevance Vector Machines
AlgorithmRVM() - Constructor for class AlgorithmRVM
 
AlgorithmSVM - Class in <Unnamed>
Algorithm Support Vector Machines
AlgorithmSVM() - Constructor for class AlgorithmSVM
 
almostEqual(Vector, Vector) - Static method in class MathUtil
method sees if input vectors are "almost" equal
almostEqual(Vector, double) - Static method in class MathUtil
method sees if input vectors are "almost" equal
almostEqual(double, double) - Static method in class MathUtil
method sees if input doubles are "almost" equal
autocorrelate(Vector, double[]) - Method in class AlgorithmLP
Actaully computes the autocorrelation coefficients
autoCorrelation() - Method in class AlgorithmLP
Computes the autocorrelation coeffient from the data sets

B

betweenClass(Matrix) - Method in class AlgorithmLDA
Determines the between class scatter matrix for the class independent linear discrimination algorithm
betweenClass(Matrix) - Method in class AlgorithmLDAPCA
Determines the between class scatter matrix for the class independent linear discrimination algorithm
betweenClass(DataPoints, Matrix, double, double) - Static method in class MathUtil
this method determines the between class scatter matrix for the class independent linear discrimination algorithm
betweenClass1(Matrix, Matrix, Matrix, Matrix) - Method in class AlgorithmLDA2
Determines the between class scatter matrix for the class dependent linear discrimination algorithm
BiNormal - Class in <Unnamed>
Handles random generation of Gaussian uses
BiNormal() - Constructor for class BiNormal
 

C

calculate_lpc(double[], double[], double[]) - Method in class AlgorithmLP
Actually calculate the LP coefficient and the Residual Error Energy, and Reflection Coefficients
checkdata_KF(Vector<MyPoint>) - Method in class AlgorithmKF
Validates the class entered by user for Partical Filtering
checkdata_LP(Vector) - Method in class AlgorithmLP
Validates the class entered by user for Linear Prediction
checkdata_PF(Vector<MyPoint>) - Method in class AlgorithmPF
Validates the class entered by user for Partical Filtering
choleskySolve(Matrix, Matrix, Matrix) - Method in class Matrix
This method solves the linear equation l * l' * x = b, where L is the cholesky decomposition matrix and b is an input vector.
classify(DecisionRegion) - Method in class AlgorithmKMeans
Classifies the data sets based on the k-means iterative algorithm
classify(Vector) - Method in class AlgorithmLBG
Classifies the data sets based on the k-means iterative algorithm
Classify - Class in <Unnamed>
Classify is the main driver program that extend JApplet and is the class called when the applet is loaded, which inturn initialize all other objects and components need to run the applet hierarchy: JApplet->Classify
Classify() - Constructor for class Classify
 
clear() - Method in class DisplayArea
Clears the Display Area
clear() - Method in class OutputPanel
Clears the input panel screen
clear() - Method in class SelectionArea
Clears data sets
clearAllRegions() - Method in class DecisionRegion
clears all decision regions
clearAllSets() - Method in class DataPoints
clear out all point form the data sets
clearRegion(String) - Method in class DecisionRegion
clears the specified decision region
clusterDeviation(Vector, MyPoint) - Method in class AlgorithmLBG
Calculates the standard deviation of the cluster
col - Variable in class Matrix
 
computeBinaryDeviates(Vector) - Method in class AlgorithmLBG
Computes the binary deviates after each iteraion
computeClusterMean(Vector) - Static method in class MathUtil
methods computes and returns the mean of the given cluster
computeCovariance(double[], double[]) - Method in class Covariance
computes the covariance matrix given two discrete random variables
computeDecisionRegions() - Method in class AlgorithmED
Computes the line of discrimination
computeDecisionRegions() - Method in class AlgorithmLDA
Computes the line of discrimination for class independent LDA
computeDecisionRegions() - Method in class AlgorithmLDA2
method computes the line of discrimination for class dependent LDA
computeDecisionRegions() - Method in class AlgorithmLDAPCA
Computes the line of discrimination for the classification algorithms when the corresponding flags have been initialized
computeDecisionRegions() - Method in class AlgorithmNN
Computes the line of discrimination for nearest neighbor
computeDecisionRegions() - Method in class AlgorithmPCA
Computes the line of discrimination for the classification algorithms when the corresponding flags have been initialized
computeDecisionRegions() - Method in class AlgorithmPCA2
Computes the line of discrimination for the classification algorithms when the corresponding flags have been initialized
computeDecisionRegions() - Method in class AlgorithmRVM
Computes the line of discrimination for the classification algorithms when the corresponding flags have been initialized
computeDecisionRegions() - Method in class AlgorithmSVM
method computes the line of discrimination for the classification algorithms when the corresponding flags have been initialized
computeErrors() - Method in class AlgorithmED
Computes and displays the classification errors for each set
computeErrors() - Method in class AlgorithmLDA
Counts the data points in each set in error and displays them on the text message window
computeErrors() - Method in class AlgorithmLDA2
Counts the data points in each set in error and displays them on the text message window
computeErrors() - Method in class AlgorithmLDAPCA
Counts the data points in each set in error and displays them on the text message window
computeErrors() - Method in class AlgorithmNN
Computes and displays the classification errors for each set
computeErrors() - Method in class AlgorithmPCA
Computes the number of data points in classification error
computeErrors() - Method in class AlgorithmPCA2
Computes the classification error for the data points given
computeErrors() - Method in class AlgorithmRVM
Computes the classification error for the data points
computeErrors() - Method in class AlgorithmSVM
computes errors display two matrices
computeLikelihood() - Method in class AlgorithmRVM
Computes the log likelihood of the weights given the data.
computeMeans() - Method in class Algorithm
Computes the means for each existing data set
computeMeans(DecisionRegion) - Method in class AlgorithmKMeans
Computes the means of the data sets after each iteraion
computeMyPointMean(Vector) - Static method in class MathUtil
methods computes and returns the mean of the given cluster
computePointMean(Vector) - Static method in class MathUtil
methods computes and returns the mean of the given cluster
computeSigma() - Method in class AlgorithmRVM
Computes the sigma valued defined on pp.
computeSupportVectors() - Method in class AlgorithmSVM
method computes the all the support vectors
computeVarianceCholesky() - Method in class AlgorithmRVM
Computes the diagonal elements of the inverse from the cholesky decomposition
convertMyPoint(MyPoint, int, int, DisplayScale) - Static method in class DataPoints
Converts an instance of MyPoint to an instance of Point
convertMyPoints(Vector<MyPoint>, int, int, DisplayScale) - Static method in class DataPoints
Converts a Vector of MyPoints to a Vector of points that correspond to Java coordinates.
convertPoint(Point, int, int, DisplayScale) - Static method in class DataPoints
Converts an instance of Point to an instance of MyPoint containing the cartesian x-y values
convertPoints(Vector<Point>, int, int, DisplayScale) - Static method in class DataPoints
Converts a vector of Points to a vector of MyPoints containing the cartesian x-y values
copy() - Method in class DecisionRegion
 
copy() - Method in class OutputPanel
Duplicates the OutputPanel Object
copyLowerMatrix(Matrix) - Method in class Matrix
this routine computes the inverse of a matrix
copyMatrix(Matrix) - Method in class Matrix
This routine takes a matrix as an argument and copies it to the the matrix object that invoked it
copyMatrixRows(Matrix, boolean[]) - Method in class Matrix
This routine takes a matrix as an argument and copies its rows to the the matrix object that invoked it only for columns that the flags are true.
copyVector(Vector<Double>, Vector<Double>, int, int, int) - Static method in class MathUtil
Copies vector given specific parameters
Covariance - Class in <Unnamed>
Covariance as the name sggests computes the covariance matrix given two random vectors i.e., the X = (x1, x2, x3 ...) and Y = (y1, y2, y3 ...)
Covariance() - Constructor for class Covariance
 

D

DataPoints - Class in <Unnamed>
class holds the input DataPoints classes that are to be classified as well as the classification algorithms needed to compute the decision regions
DecisionRegion - Class in <Unnamed>
class: DecisionRegion class that stores the point of the various decision regions computed
decisionRegion - Variable in class DecisionRegion
 
decompositionCholesky(Matrix) - Method in class Matrix
this method constructs the Cholesky decomposition of an input matrix: W.
DetermineDimensions() - Method in class Classify
determines the dimensions of the selection area
DetermineDimensions() - Method in class DisplayArea
determine the dimensions of the selection area
DetermineDimensions() - Method in class SelectionArea
Determines the dimensions of the selection area
DIAGONAL - Static variable in class Matrix
 
disableControl() - Method in class Algorithm
Disables the next and previous keys before beggining to execute a step.
display_result(double[], double[], double[], double, double, int, int) - Method in class AlgorithmLP
Display the results in the process box
DisplayArea - Class in <Unnamed>
class implements the graph plot that displays the output points and the comouted decision region based on the algorithm used
DisplayArea() - Constructor for class DisplayArea
constructor initializes the samples to be plotted in the signal panel
displayClusterError(int, Vector, int) - Method in class AlgorithmKMeans
determines the number of points in error, i.e, not classified by finding the distance of the datapoints from the closest of the vector set
displayClusterError(int, Vector, int) - Method in class AlgorithmLBG
Finds the datapoints in error, for all datasets
displayMatrices() - Method in class AlgorithmLDA
Display two matrices - covariance matrix and the transformation matrix in the text message window
displayMatrices() - Method in class AlgorithmLDAPCA
Display two matrices - covariance matrix and the transformation matrix in the text message window
distance(double, double, double, double) - Static method in class MathUtil
calculate the euclidean distance between the two points
doubleValue(Vector<Double>, int) - Static method in class MathUtil
gets the double value of the input vector at the index passed
drawGaussian(double, double, int) - Method in class AlgorithmPF
Draws Gaussian points around each data point
drawGaussian(double, double, DisplayScale) - Method in class DataPoints
Draws Gaussian distribution by calling setGaussian()
DrawGrid(Graphics) - Method in class DisplayArea
draw the selection area grid lines
DrawGrid(Graphics) - Method in class SelectionArea
Draws the selection area grid lines
drawPoint(Graphics, Point, int, Color) - Static method in class Classify
determine the dimensions of the selection area actually this draws a line of width 3 pixels in either directions in way, this draws a + sign..
drawPoints(Graphics, Vector, int, Color) - Static method in class Classify
determine the dimensions of the selection area

E

Elem - Variable in class Matrix
 
enableControl() - Method in class Algorithm
Enables the next and previous keys after the execution of a step.
estimate(Vector<MyPoint>, Vector<MyPoint>, double, double[]) - Method in class AlgorithmLP
Estimates the amplitude based on the LP coeficients.
expMatrix() - Method in class Matrix
This routine takes every element to its exponent with Math.exp()

F

final_estimate() - Method in class AlgorithmLP
Calculates the estimated points for the data inputs
FULL - Static variable in class Matrix
 

G

gaussian(int, double, double, double[], double[], double, double, double, double) - Method in class BiNormal
Generates binormal gaussian random deviates
gaussj(double[][], int, double[][], int) - Method in class Matrix
This routine computes the inverse matrix using the gauss jordan method see numerical recipes, the are of scientific computing, second edition, cambridge university press.
generateMeans(int) - Method in class AlgorithmKMeans
Generates random initial guesses (means) for the data set
generatePool() - Method in class AlgorithmKMeans
Collects all the data points of all the data sets
generatePool() - Method in class AlgorithmLBG
Collects all the data points together
genmn(double[], double[], double[]) - Method in class BiNormal
methods generates the multivariate normal deviates using the procedure: 1) generate p independent standard normal deviates - ei ~ n(0,1) 2) using cholesky decomposition find a s.t.
getCanvasHeight() - Method in class SelectionArea
Gets the canvas height
getCanvasWidth() - Method in class SelectionArea
Gets the canvas width
getClosestSet(MyPoint) - Method in class AlgorithmKMeans
Determines the closest data sets to the cluster
getClosestSet(MyPoint) - Method in class AlgorithmLBG
Determines the closest data sets to the cluster
getColSumSquare(int) - Method in class Matrix
this routine computes the sum of a column
getCurrAlgo() - Static method in class Classify
determine the current algorithm and returns an object for it
getDecisionRegion(Vector<MyPoint>) - Method in class AlgorithmKMeans
Computes the k-mean decision region - nearest neighbor algorithm
getDecisionRegion(Vector<MyPoint>) - Method in class AlgorithmLBG
Computes the k-mean decision region - nearest neighbor algorithm
getDiagonalVector(Vector<Double>) - Method in class Matrix
Puts the diagonal of a matrix in the vector passed
getDisplayHeight() - Method in class OutputPanel
Gets the height on the output canvas
getDisplayScale() - Method in class DisplayArea
gets the current DisplayScale
getDisplayScale() - Method in class SelectionArea
Gets the DisplayScale
getDisplayWidth() - Method in class OutputPanel
Gets the width on the output canvas method: getDisplayWidth
getGuesses() - Method in class DecisionRegion
method retrieves the initial guesses - means of the data sets
getHeight() - Method in class DisplayArea
gets height of the output canvas
getNumColumns() - Method in class Matrix
Gets the number of Columns
getNumRegions() - Method in class DecisionRegion
get the number of data sets currently stored
getNumRows() - Method in class Matrix
Gets the number of Rows
getRegion(String) - Method in class DecisionRegion
get the specified decision region
getRegion(int) - Method in class DecisionRegion
method retrieves a given data set based on the index
getRegions() - Method in class DecisionRegion
 
getValue(int, int) - Method in class Matrix
gets value at indexed row and column
getWidth() - Method in class DisplayArea
gets width of the output canvas
getXPrecision() - Method in class DisplayArea
get the x value
getXRatio() - Method in class OutputPanel
Gets the X direction Ratio
getYPrecision() - Method in class DisplayArea
get the y value
getYRatio() - Method in class OutputPanel
Gets the Y direction Ratio
grand(double, double) - Static method in class MathUtil
method generates a random distribution centered about the mean with a variance that is specified by the standard deviation.
guesses - Variable in class DecisionRegion
 

I

identityMatrix() - Method in class Matrix
This routine makes an identity matrix
IFAlgorithm - Interface in <Unnamed>
This interface is designed to be the base for all algorithms
init() - Method in class Classify
this method is called when the applet is started
initDiagonalMatrix(Vector<Double>) - Method in class Matrix
Creates a diagonal matrix of values specified by the input vector
initDoubleVector(Vector<Double>, double) - Static method in class MathUtil
initializes components of vector to double value passed
initFullTrain() - Method in class AlgorithmRVM
initializes the data structures in preparation for a full training pass
initialize() - Method in class Algorithm
Initializes algorithm.
initialize() - Method in class AlgorithmED
Overrides the initialize() method in the base class.
initialize() - Method in class AlgorithmKF
Implements the initialize() method in the base class.
initialize() - Method in class AlgorithmKMeans
Overrides the initialize() method in the base class.
initialize() - Method in class AlgorithmLBG
Overrides the initialize() method in the base class.
initialize() - Method in class AlgorithmLDA
Overrides the initialize() method in the base class.
initialize() - Method in class AlgorithmLDA2
Overrides the initialize() method in the base class.
initialize() - Method in class AlgorithmLDAPCA
Overrides the initialize() method in the base class.
initialize() - Method in class AlgorithmLP
Implements the initialize() method in the base class.
initialize() - Method in class AlgorithmNN
Implements the initialize() method in the base class.
initialize() - Method in class AlgorithmPCA
Overrides the initialize() method in the base class.
initialize() - Method in class AlgorithmPCA2
Overrides the initialize() method in the base class.
initialize() - Method in class AlgorithmPF
Implements the initialize() method in the base class.
initialize() - Method in class AlgorithmRVM
Overrides the initialize() method in the base class.
initialize() - Method in class AlgorithmSVM
Overrides the initialize() method in the base class.
initialize() - Method in class DataPoints
initializes dset1-dset4
initialize() - Method in interface IFAlgorithm
Initializes member data and prepares for execution of first step.
initializeAlgo(Algorithm) - Static method in class Classify
determine the dimensions of the selection area
initializeKmeans() - Method in class AlgorithmKMeans
Initializes the kmean array with the original data sets
initMatrix(double[][], int, int) - Method in class Matrix
this method initializes matrix to be the values of the 2-d array passed
initMatrix(Vector<Double>) - Method in class Matrix
Creates a 1 row matrix of values specified by the input vector
initMatrixValue(int, int, double, int) - Method in class Matrix
this method initialize the matrix to all r x n elements being the double value passed
interpol(Vector<MyPoint>, Vector<MyPoint>) - Method in class AlgorithmKF
Calculates the interpolated points for the data inputs
interpol(Vector<MyPoint>, Vector<MyPoint>) - Method in class AlgorithmLP
Calculates the interpolated points for the data inputs
interpol(Vector<MyPoint>, Vector<MyPoint>) - Method in class AlgorithmPF
Calculates the interpolated points for the data inputs
interpol(Vector<MyPoint>) - Method in class interpolate
Interpolates
interpolate - Class in <Unnamed>
Interpolate class
interpolate() - Constructor for class interpolate
 
inverseMatrixElements() - Method in class Matrix
This routine inverses every element of the matrix
invertMatrix(Matrix) - Method in class Matrix
this routine computes the inverse of a matrix
irlsTrain() - Method in class AlgorithmRVM
Completes one pass of IRLS training to update the weights given the currently assigne hyperparamters.
isDataValid() - Method in class DataPoints
method returns true if valid data is present else it returns false

L

linearKernel(Vector, Vector) - Static method in class MathUtil
this method evaluates the linear Kernel on the input vectors K(x,y) = (x .
LOWER_TRIANGULAR - Static variable in class Matrix
 
lpcCoefficient() - Method in class AlgorithmLP
Computes the Linear Prediction coefficient from the data sets

M

main(String[]) - Static method in class Classify
main method, the entrance of the program
MainMenu - Class in <Unnamed>
implement the menu driven system that drives the applet hierarchy: JPanel->SubPanel->MainMenu
MathUtil - Class in <Unnamed>
class that round off floating point numbers to the specified number of decimal places givem
MathUtil() - Constructor for class MathUtil
Default constructor.
Matrix - Class in <Unnamed>
this class represents a matrix object and performs matrix operations that are needed to compute the eigenvectors and eigenvalues
Matrix() - Constructor for class Matrix
 
mean(Vector<MyPoint>, Vector<MyPoint>) - Method in class AlgorithmKF
Calculates the mean and the zero-mean data points
mean(Vector<MyPoint>, Vector<MyPoint>) - Method in class AlgorithmLP
Calculates the mean and the zero-mean data points
mean(Vector<MyPoint>, Vector<MyPoint>) - Method in class AlgorithmPF
Calculates the mean and the zero-mean data points
multMatrix(Matrix, Matrix) - Method in class Matrix
This routine multiplys two matrices

N

nextStep() - Method in class Algorithm
Determines and executes next step of the algorithm in a new thread of execution.
nextStep() - Method in interface IFAlgorithm
Directs algorithm to next step.
normalizeCovariance(double[][]) - Method in class Covariance
The method normalizes the covariance matrix based on the the range of the values within the covariance matrix
normMatrix() - Method in class Matrix
This routine takes the square root of the matrix object

O

outputDecisionRegion() - Method in class AlgorithmKMeans
displays the decision region on the output panel
outputDecisionRegion() - Method in class AlgorithmLBG
Displays the decision regoin on output panel
OutputPanel - Class in <Unnamed>
OutputPanel acts as the top level container that encompasses the output plot that displays the input classes as well as the decision regions heirarchy: JPanel->SubPanel->OutputPanel

P

paintComponent(Graphics) - Method in class DisplayArea
method paints the selection area
paintComponent(Graphics) - Method in class OutputPanel
Paints the current data points and if needed the decision regions
paintComponent(Graphics) - Method in class SelectionArea
Method paints the selection area
polynomialKernel(Vector, Vector) - Static method in class MathUtil
this method evaluates the second degree polynomial Kernel on the input vectors K(x,y) = (x .
preInit() - Method in class Classify
Initializes variables
prevStep() - Method in class Algorithm
Determines and executes previous step of the algorithm in a new thread of execution.
prevStep() - Method in interface IFAlgorithm
Directs algorithm to previous step.
printDoubleVector(Vector<Double>) - Static method in class Matrix
Puts the DoubleVector contents in a vector and calls printMatrix()
printMatrices() - Method in class AlgorithmLDA2
Display two matrices - covariance matrix and the transformation matrix in the text message window
printMatrices() - Method in class AlgorithmLDAPCA
Displays the caovariance and LDA transform data matrix
printMatrices() - Method in class AlgorithmPCA
Appends messages to the pro_box_d variable
printMatrices() - Method in class AlgorithmPCA2
Appends messages to the pro_box_d variable
printMatrix() - Method in class Matrix
Prints the matrix contents to STDOUT
pruneAndUpdate() - Method in class AlgorithmRVM
prunes off vectors whose hyperparameters have gone to infinity and updates working data sets
pruneWeights() - Method in class AlgorithmRVM
Prunes off vectors which attain a zero weight during training.
PTYPE_INPUT - Static variable in class Classify
 
PTYPE_LINE - Static variable in class Classify
 
PTYPE_OUTPUT - Static variable in class Classify
 
PTYPE_OUTPUT_LARGE - Static variable in class Classify
 
PTYPE_SUPPORT_VECTOR - Static variable in class Classify
 
putLine(Graphics, Point, Point, Color) - Static method in class Classify
Method to draw a line between two points.
putLines(Graphics, Vector, int, Color) - Static method in class Classify
Method : putlines Method to draw a line between a series of points.

R

RAND_SEED - Static variable in class AlgorithmKMeans
The random number generator
RAND_SEED - Static variable in class MathUtil
 
random - Static variable in class MathUtil
 
ranf() - Method in class BiNormal
generates a uniform distribution over 0 - 1
rbfKernel(Vector, Vector) - Static method in class MathUtil
this method evaluates the redial basis function Kernel on the input vectors with standard deviation sigma K(x,y) = exp(-gamma * ((a.a)-2*(a.b)+(b.b)))
rbfKernel(Vector, Vector, double) - Static method in class MathUtil
this method evaluates the redial basis function Kernel on the input vectors with standard deviation sigma K(x,y) = exp(-gamma * ((a.a)-2*(a.b)+(b.b)))
refresh() - Method in class OutputPanel
Redraws the input panel screen
removeOutput() - Method in class OutputPanel
Removes last vector of points from output
resetMatrix() - Method in class Matrix
This routine takes a matrix as an argument and copies it to the the matrix object that invoked it
row - Variable in class Matrix
 
run() - Method in class Algorithm
Implementation of run from the Runnable interface.
run() - Method in class AlgorithmED
Implementation of the run function from the Runnable interface.
run() - Method in class AlgorithmKF
Implementation of the run function from the Runnable interface.
run() - Method in class AlgorithmKMeans
Implementation of the run function from the Runnable interface.
run() - Method in class AlgorithmLBG
Implementation of the run function from the Runnable interface.
run() - Method in class AlgorithmLDA
Implementation of the run function from the Runnable interface.
run() - Method in class AlgorithmLDA2
Implementation of the run function from the Runnable interface.
run() - Method in class AlgorithmLDAPCA
Implementation of the run function from the Runnable interface.
run() - Method in class AlgorithmLP
Implementation of the run function from the Runnable interface.
run() - Method in class AlgorithmNN
Implements the run function from the Runnable interface.
run() - Method in class AlgorithmPCA
Implementation of the run function from the Runnable interface.
run() - Method in class AlgorithmPCA2
Implementation of the run function from the Runnable interface.
run() - Method in class AlgorithmPF
Implementation of the run function from the Runnable interface.
run() - Method in class AlgorithmRVM
Implementation of the run function from the Runnable interface.
run() - Method in class AlgorithmSVM
Implementation of the run function from the Runnable interface.
Rx - Static variable in class OutputPanel
 
Ry - Static variable in class OutputPanel
 

S

scalarMultMatrix(double) - Method in class Matrix
This routine multiplys a matrix with a scalar
scaleToFitData() - Method in class Algorithm
Scales the data axes to fit the data.
sdot(int, double[], int, int, double[], int, int) - Method in class BiNormal
linpack.
SelectionArea - Class in <Unnamed>
SelectionArea extend a JPanle and is used to implements the plotting area for the data input classes hierarchy: JPanel->SelectionArea
SelectionArea(DataPoints) - Constructor for class SelectionArea
constructor initializes the input samples to be plotted
setColors(Color, Color, Color, Color) - Method in class DataPoints
sets the gaussian values
setDataPoints(DataPoints) - Method in class Algorithm
Initializes the data points.
setDataPoints(DataPoints) - Method in interface IFAlgorithm
Sets DataPoints given DataPoints type variable.
SetDecimal(double, int) - Static method in class MathUtil
method to round off floating point numbers to the specified number of decimal places given
setDecimal(double, int) - Static method in class MathUtil
method takes in a decimal number and rounds to the given number of decimal places passed
setDisplayScale(DisplayScale) - Method in class DisplayArea
Sets the display scale of the current display area.
setDisplayScale(DisplayScale) - Method in class SelectionArea
Sets the DisplayScale
setDrawGaussValues(int, double, double, double, double) - Method in class DataPoints
sets the gaussian values
setFourEllipses(DisplayScale) - Method in class DataPoints
create a set of point that correspond to four ellipses to be displayed on the input canvas
setFourGaussian(DisplayScale) - Method in class DataPoints
create a set of point that correspond to four gaussians to be displayed on the input canvas method: setFourGaussian
setGaussian(int, double, double, double, double, double, double, DisplayScale) - Method in class DataPoints
creates a set of point that correspond to a gaussians distribution
setgmn(double[], double[], int, double[]) - Method in class BiNormal
methods sets up the parameters needed to generate the multivariate normal deviates form the inputs given
setGuesses(Vector<MyPoint>) - Method in class DecisionRegion
method sets the initial guesses - means of the data sets
setOutputPanel(OutputPanel) - Method in class Algorithm
Initializes the output panel.
setOutputPanel(OutputPanel) - Method in interface IFAlgorithm
Sets the output panel.
setOverGaussian(DisplayScale) - Method in class DataPoints
create a set of point that correspond to two overlapped gaussians to be displayed on the input canvas
setProcessBox(ProcessBox) - Method in class Algorithm
Initializes the process box.
setProcessBox(ProcessBox) - Method in interface IFAlgorithm
Sets the ProcessBox.
setRotatedEllipses(DisplayScale) - Method in class DataPoints
create a set of point that correspond to two rotated ellipses to be displayed on the input canvas
setToroidal(DisplayScale) - Method in class DataPoints
create a set of point that correspond a toroidal to be displayed on the input canvas
setTwoEllipses(DisplayScale) - Method in class DataPoints
create a set of point that correspond to two ellipses to be displayed on the input canvas
setTwoGaussian(DisplayScale) - Method in class DataPoints
create a set of point that correspond to two gaussians to be displayed on the input canvas
setValue(int, int, double) - Method in class Matrix
sets the value given row and column index to the double value passed
setYinYang(DisplayScale) - Method in class DataPoints
create a set of point that correspond a yin and yang symbol to be displayed on the input canvas
slope(MyPoint, MyPoint) - Method in class interpolate
Sets the slope variable m and c
snorm() - Method in class BiNormal
ahrens, j.h.
snorm100() - Method in class BiNormal
linpack.
snorm110() - Method in class BiNormal
linpack.
snorm120() - Method in class BiNormal
linpack.
snorm140() - Method in class BiNormal
linpack.
snorm150() - Method in class BiNormal
linpack.
snorm160() - Method in class BiNormal
linpack.
snorm40() - Method in class BiNormal
linpack.
snorm50() - Method in class BiNormal
linpack.
snorm60() - Method in class BiNormal
linpack.
snorm70() - Method in class BiNormal
linpack.
snorm80() - Method in class BiNormal
linpack.
SPARSE - Static variable in class Matrix
 
spline(double[], double[], double[], int) - Method in class AlgorithmKF
Actually interpolates the points
spline(double[], double[], double[], int) - Method in class AlgorithmLP
Actually interpolates the points
spline(double[], double[], double[], int) - Method in class AlgorithmPF
Actually interpolates the points
splint(MyPoint, MyPoint, MyPoint, double[], int) - Method in class AlgorithmKF
Interpolates for a point between the two known points using Cubic Interpolation
splint(MyPoint, MyPoint, MyPoint, double[], int) - Method in class AlgorithmLP
Interpolates for a point between the two known points using Cubic Interpolation
splint(MyPoint, MyPoint, MyPoint, double[], int) - Method in class AlgorithmPF
Interpolates for a point between the two known points using Cubic Interpolation
spofa(double[], int, int) - Method in class BiNormal
linpack.
step2_display() - Method in class AlgorithmLP
Displays LP order, Error Energy and Reflection Coefficients
subtractMatrix(Matrix, Matrix) - Method in class Matrix
This routine subtracts two matrices
SYMMETRIC - Static variable in class Matrix
 

T

toDoubleVector(Vector<Double>) - Method in class Matrix
Given a column or row matrix the values can be put into a vector
trainFull() - Method in class AlgorithmRVM
this method trains an RVM probabilistic classifier on the input data and targets provided.
transformLDA(DataPoints, Matrix) - Method in class AlgorithmLDA
Transforms a given set of points to a new space using the class independent linear discrimination analysis algorithm
transformLDA(DataPoints, Matrix) - Method in class AlgorithmLDAPCA
Transforms a given set of points to a new space using the class independent linear discrimination analysis algorithm
transformLDA1(Matrix, Matrix, Matrix, Matrix) - Method in class AlgorithmLDA2
Transforms a given set of points to a new space using the class dependent linear discrimination analysis algorithm
transformPCA() - Method in class AlgorithmLDAPCA
Transforms a given set of points to a new space using the class independent principal component analysis algorithm
transformPCA() - Method in class AlgorithmPCA
transforms a given set of points to a new space using the class independent principal component analysis algorithm
transformPCA(int, int) - Method in class AlgorithmPF
Transforms a given set of points to a new space using the class independent principal component analysis algorithm
transformPCA2() - Method in class AlgorithmPCA2
Transforms a given set of points to a new space using the class dependent principal component analysis algorithm
transposeMatrix(Matrix) - Method in class Matrix
This routine performs the transpose of a matrix
type_d - Variable in class Matrix
 

U

updateHyperparametersFull() - Method in class AlgorithmRVM
Updates the hyperparameter values
UPPER_TRIANGULAR - Static variable in class Matrix
 

V

vectorProduct(Vector, Vector) - Static method in class MathUtil
this method evaluates the vector dot product

W

withinClass(Matrix) - Method in class AlgorithmLDA
Determines the within class scatter matrix
withinClass(Matrix) - Method in class AlgorithmLDA2
Determines the within class scatter matrix
withinClass(Matrix) - Method in class AlgorithmLDAPCA
Determines the within class scatter matrix
withinClass(DataPoints, double, double) - Static method in class MathUtil
Within Class

X

X_MAX - Static variable in class Classify
 
Xmax - Static variable in class OutputPanel
 
Xmin - Static variable in class OutputPanel
 

Y

Y_MAX - Static variable in class Classify
 
Ymax - Static variable in class OutputPanel
 
Ymin - Static variable in class OutputPanel
 

A B C D E F G I L M N O P R S T U V W X Y