Alphabetical Index

C D E F G I L M N P R S W

C

The classical version of Multidimensional Scaling.
classicalScaling(double[][]) - static method in class mdsj.MDSJ
Performs classical multidimensional scaling on a given sparse dissimilarity matrix.
classicalScaling(double[][],int) - static method in class mdsj.MDSJ
Performs classical multidimensional scaling on a given sparse dissimilarity matrix.

D

Data - class mdsj.Data
Convenience class for data handling.
distance(double[][],int,int) - static method in class mdsj.Data
Gives the Euclidean distances of two data points in a data matrix.
distanceMatrix(double[][]) - static method in class mdsj.Data
Gives the complete matrix of Euclidean distances in a configuration of data points in D-dimensional Euclidean space, where D=matrix[0].length.
doubleCenter(double[][]) - static method in class mdsj.Data
Double-centers the matrix so that each rows and each columns sums to zero, by subtracting the row mean in each row, subtracting the column mean in each column, and adding the overall mean for each entry.

E

Computes the dominant eigenvectors of a square symmetric matrix.

F

format(double[][]) - static method in class mdsj.Data
Print all entries of a matrix.
fullmds(double[][],double[][]) - static method in class mdsj.ClassicalScaling
Computes a classical multidimensional scaling of a square matrix of distances.

G

Returns the matrix of dissimilarities.
Computes the weighted stress of the current positions.
getPositions() - method in class mdsj.StressMinimization
Returns the matrix of positions.
getStress() - method in class mdsj.StressMinimization
Computes the weighted stress of the current positions.
getWeights() - method in class mdsj.StressMinimization
Returns the matrix of weights.

I

IO - class mdsj.IO
Functionality for reading and writing files.
iterate() - method in class mdsj.StressMinimization
Performs a single step of the iterative stress minimization.
iterate(int) - method in class mdsj.StressMinimization
Performs a given number of steps of the iterative stress minimization.
Performs some steps of the iterative stress minimization.

L

landmarkIndices(double[][]) - static method in class mdsj.Data
From a rectangular k x n matrix of dissimilarities, an index array of length k is computed.
landmarkMatrix(double[][]) - static method in class mdsj.Data
From a rectangular k x n matrix of dissimilarities, a square k x k dissimilarity matrix is computed which contains just the dissimilarities among the k objects described by the k rows.
largestEigenvalue(double[][]) - static method in class mdsj.ClassicalScaling
Computes the largest eigenvalue of a given square symmetric matrix.
lmds(double[][],double[][]) - static method in class mdsj.ClassicalScaling
Computes an approximation of classical multidimensional scaling for a given matrix of dissimilarities.

M

main(String[]) - static method in class mdsj.MDSJ
Main program of MDSJ for command line interaction.
Majorization method for distance scaling.
Majorization method for distance scaling.
maxminPivotMatrix(double[][],int) - static method in class mdsj.Data
Gives a pivot matrix for a configuration of data points in Euclidean space.
MDSJ - class mdsj.MDSJ
Multidimensional Scaling for Java.
MDSJ(String[]) - constructor for class mdsj.MDSJ
multiply(double[][],double) - static method in class mdsj.Data
Scales each entry in a matrix by a factor.

N

normalize(double[]) - static method in class mdsj.Data
Normalizes a vector to have unit length
normalize(double[][]) - static method in class mdsj.Data
Scales every column of a matrix to have length one.
Computes the weighted normalized stress of a configuration corresponding to a given matrix of distances.

P

Computes an approximation of classical multidimensional scaling for a given matrix of dissimilarities.
pivotRows(double[][],int) - static method in class mdsj.Data
Given a set of dissimilarity rows, a subsample of rows is constructed which should be as representative as possible, using a greedy farthest-minimal dissimilarity approach.
prod(double[],double[]) - static method in class mdsj.Data
Computes inner product of two vectors of the same length, the sum of entry-wise products x[0]*y[0]+...+x[n-1]*y[n-1], where n is the minimum length of the vectors

R

randomize(double[][]) - static method in class mdsj.Data
Fills a matrix with pseudo-random entries in the range -0.5 to 0.5 with uniformly probability distribution.
randomPivotMatrix(double[][],int) - static method in class mdsj.Data
Gives a pivot matrix for a configuration of data points in Euclidean space.
read(String) - static method in class mdsj.IO
Reads a data matrix from a file into a two-dimensional array.

S

scale(double[][],double[][]) - static method in class mdsj.Data
Scales a configuration such that the sum of the corresponding distances equals the sum of the input dissimilarities
selfprod(double[][],double[][]) - static method in class mdsj.Data
Computes the self product of a matrix d with its transpose d'.
Sets the dissimilarity to a given matrix.
Sets the positions to a given matrix.
Sets the weights to a given matrix.
smallestEigenvalue(double[][]) - static method in class mdsj.ClassicalScaling
Computes the smallest eigenvalue of a given square symmetric matrix.
squareDoubleCenter(double[][]) - static method in class mdsj.Data
Squares each entry of a matrix and then double-centers it.
squareEntries(double[][]) - static method in class mdsj.Data
Squares each entry in a matrix.
Computes the weighted stress of a configuration corresponding to a given matrix of distances.
Stress Minimization.
stressMinimization(double[][]) - static method in class mdsj.MDSJ
Performs stress minimization on a given sparse dissimilarity matrix.
stressMinimization(double[][],double[][]) - static method in class mdsj.MDSJ
Performs stress minimization on a given sparse dissimilarity matrix.
Creates a new stress minimizer with the given parameters.
Creates a new stress minimizer with the given parameters.
Performs stress minimization on a given sparse dissimilarity matrix.
stressMinimization(double[][],int) - static method in class mdsj.MDSJ
Performs stress minimization on a given sparse dissimilarity matrix.

W

Gives a weight matrix derived from a given matrix of distances.
write(double[][],String) - static method in class mdsj.IO
Writes a data matrix to a file.
writeStandardOut(double[][]) - static method in class mdsj.IO
Writes a data matrix to a the standard output