- actionPerformed(ActionEvent) - Method in class MainMenu
-
method listens for actions taking place on text only menu items
- actual_error(Vector, Vector) - Method in class AlgorithmKF
-
Compute the actual error from the given data points and the estimated
values.
- actual_error(Vector, Vector) - Method in class AlgorithmLP
-
Compute the actual error from the given data points and the estimated
values.
- actual_error(Vector, Vector) - Method in class AlgorithmPF
-
Compute the actual error from the given data points and the estimated
values.
- actual_error(Vector, Vector) - Method in class AlgorithmUKF
-
Compute the actual error from the given data points and the estimated
values.
- add_components() - Method in class InputPanel
-
add components to the input panel
- add_components() - Method in class MainMenu
-
adds components to control panel
- add_components() - Method in class OutputPanel
-
Adds components to the output panel
- addInput(Vector<MyPoint>, int, Color) - Method in class InputPanel
-
Adds points 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
- addToColumn(double[], int, int) - Method in class Matrix
-
add the array to a specific column of matrix
- addToRow(double[], int, int) - Method in class Matrix
-
add the array to a specific row of matrix
- 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
-
- AlgorithmHLDA - Class in <Unnamed>
-
Implements the Heteroscedastic Linear Discriminant Analysis Algorithm
- AlgorithmHLDA() - Constructor for class AlgorithmHLDA
-
- 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>
-
This class implements the particle filtering algorithm
- AlgorithmPF() - Constructor for class AlgorithmPF
-
- AlgorithmRVM - Class in <Unnamed>
-
This interface is designed to be the base for all algorithms
.....
- AlgorithmRVM() - Constructor for class AlgorithmRVM
-
- AlgorithmSVM - Class in <Unnamed>
-
Algorithm Support Vector Machines
- AlgorithmSVM() - Constructor for class AlgorithmSVM
-
- AlgorithmUKF - Class in <Unnamed>
-
Class that handles the Unscented Kalman filter algorithm
- AlgorithmUKF() - Constructor for class AlgorithmUKF
-
- 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 AlgorithmKF
-
Actaully computes the autocorrelation coefficients
- autocorrelate(Vector, double[]) - Method in class AlgorithmLP
-
Actaully computes the autocorrelation coefficients
- autocorrelate(Vector, double[]) - Method in class AlgorithmPF
-
Actaully computes the autocorrelation coefficients
- autocorrelate(Vector, double[]) - Method in class AlgorithmUKF
-
Actaully computes the autocorrelation coefficients
- autoCorrelation() - Method in class AlgorithmKF
-
Computes the autocorrelation coeffient from the data sets
- autoCorrelation() - Method in class AlgorithmLP
-
Computes the autocorrelation coeffient from the data sets
- autoCorrelation() - Method in class AlgorithmPF
-
Computes the autocorrelation coeffient from the data sets
- autoCorrelation() - Method in class AlgorithmUKF
-
Computes the autocorrelation coeffient from the data sets
- calculate_lpc(double[], double[], double[]) - Method in class AlgorithmKF
-
Actually calculate the LP coefficient and the Residual Error
Energy, and Reflection Coefficients
- calculate_lpc(double[], double[], double[]) - Method in class AlgorithmLP
-
Actually calculate the LP coefficient and the Residual Error
Energy, and Reflection Coefficients
- calculate_lpc(double[], double[], double[]) - Method in class AlgorithmPF
-
Actually calculate the LP coefficient and the Residual Error
Energy, and Reflection Coefficients
- calculate_lpc(double[], double[], double[]) - Method in class AlgorithmUKF
-
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 Kalman 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
- checkdata_UKF(Vector<MyPoint>) - Method in class AlgorithmUKF
-
Validates the class entered by user for unscented Kalman 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 InputPanel
-
redraw the input panel screen.
- clear() - Method in class OutputPanel
-
Clears the output 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 AlgorithmHLDA
-
Computes the line of discrimination for class independent HLDA
- 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
-
- 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 AlgorithmHLDA
-
Counts the data points in each set in error and displays
them on the text message window
- 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
-
- computeErrors() - Method in class AlgorithmSVM
-
computes errors
display two matrices
- computeHLDA(int, int, Matrix) - Method in class AlgorithmHLDA
-
- computeLikelihood() - Method in class AlgorithmRVM
-
- 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
- computeMeans(Vector) - Method in class AlgorithmLBG
-
Computes the binary deviates 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
-
- computeSupportVectors() - Method in class AlgorithmSVM
-
method computes the all the support vectors
- computeVarianceCholesky() - Method in class AlgorithmRVM
-
- 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 InputPanel
-
Duplicates the InputPanel Object
- copy() - Method in class OutputPanel
-
Duplicates the OutputPanel Object
- copyColumns(Matrix, int, int) - Method in class Matrix
-
copy specified number of columns into another matrix
- copyLowerMatrix(Matrix) - Method in class Matrix
-
this routine copies the lower triangle 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.
- copyRows(Matrix, int, int) - Method in class Matrix
-
copy row blocks into another matrix
- 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
-
- 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 InputPanel
-
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
-
- dimenMatrix() - Method in class Matrix
-
Prints the matrix dimensions to STDOUT
- 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 AlgorithmHLDA
-
Display two matrices - covariance matrix and the transformation
matrix in the text message window
- 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, 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
- gausRandGen(Matrix) - Method in class Matrix
-
this routine is a gaussian random generator
Matrix of row, col dimension, mean, std deviation will be passed
input will be a matrix with (means, std deviation)
- 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
- getColumn(double[], int, int) - Method in class Matrix
-
retrieve a column into an array
- 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
-
- getRow(double[], int, int) - Method in class Matrix
-
retrieve a row into an array
- 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
-
- 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
-
- initialize() - Method in class Algorithm
-
Initializes algorithm.
- initialize() - Method in class AlgorithmED
-
Overrides the initialize() method in the base class.
- initialize() - Method in class AlgorithmHLDA
-
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
-
- initialize() - Method in class AlgorithmSVM
-
Overrides the initialize() method in the base class.
- initialize() - Method in class AlgorithmUKF
-
Implements 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
- InputPanel - Class in <Unnamed>
-
class acts as a top level container that emcompasses the input plot
hierarchy: JPanel->SubPanel->InputPanel
- 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>, Vector<MyPoint>) - Method in class AlgorithmUKF
-
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
-
- isDataValid() - Method in class DataPoints
-
method returns true if valid data is present else it returns false
- 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 InputPanel
-
redraw the input panel screen.
- refresh() - Method in class OutputPanel
-
Redraws the output 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 AlgorithmHLDA
-
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
-
- run() - Method in class AlgorithmSVM
-
Implementation of the run function from the Runnable interface.
- run() - Method in class AlgorithmUKF
-
Implementation of the run function from the Runnable interface.
- Rx - Static variable in class OutputPanel
-
- Ry - Static variable in class OutputPanel
-
- scalarMultMatrix(double) - Method in class Matrix
-
This routine multiplies 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
- setColumn(int, double) - Method in class Matrix
-
sets the value given column index to the double value passed
works for square matrix only
- setColumn(double[], int, int) - Method in class Matrix
-
set a column from an array
- 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
- setDiagonal(double) - Method in class Matrix
-
sets the value of diagonal index to the double value passed
matrix assumed to be square
- setDisplayScale(DisplayScale) - Method in class DisplayArea
-
Sets the display scale of the current display area.
- setDisplayScale(DisplayScale) - Method in class SelectionArea
-
Sets the DisplayScale
- setDrawFlag(boolean) - Method in class InputPanel
-
set the value of the draw flag for the input canvas
- 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
- setInputPanel(InputPanel) - Method in class Algorithm
-
Initializes the input panel.
- setLorentzSignal(DisplayScale) - Method in class DataPoints
-
Draws a Lorentz generated signal.
- 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.
- setRandomSignal(DisplayScale) - Method in class DataPoints
-
Draws a randomly generated signal.
- setRotatedEllipses(DisplayScale) - Method in class DataPoints
-
create a set of point that correspond to two rotated ellipses to
be displayed on the input canvas
- setRow(int, double) - Method in class Matrix
-
sets the value given row index to the double value passed
- setRow(double[], int, int) - Method in class Matrix
-
retrieve a row into an array
- 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
- spline(double[], double[], double[], int) - Method in class AlgorithmUKF
-
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
- splint(MyPoint, MyPoint, MyPoint, double[], int) - Method in class AlgorithmUKF
-
Interpolates for a point between the two known points
using Cubic Interpolation
- spofa(double[], int, int) - Method in class BiNormal
-
linpack.
- sqrtMatrix(Matrix) - Method in class Matrix
-
finds square root of a matrix using
Denman-Beavers square root iteration,
- step2_display() - Method in class AlgorithmLP
-
Displays LP order, Error Energy and Reflection Coefficients
- subFromColumn(double[], int, int) - Method in class Matrix
-
subtract the matrix with input array at the specific column
- subFromRow(double[], int, int) - Method in class Matrix
-
subtract the matrix with input array to a specific row of matrix
- subtractMatrix(Matrix, Matrix) - Method in class Matrix
-
This routine subtracts two matrices
- sutractScalarMatrix(Matrix, double) - Method in class Matrix
-
this routine subtracts a scalar value from each element of a matrix
- SYMMETRIC - Static variable in class Matrix
-