mdsj
Class MDSJ
public class MDSJ
extends java.lang.Object
Multidimensional Scaling for Java.
Multidimensional Scaling converts proximity information into geometric coordinates.
This implementation is especially suitable for large sparse dissimilarity matrices
and can be used as a stand-alone tool or as a building block in larger Java projects.
Input is a set of n objects and some or all possible dissimilarities between them,
given as nonnegative doubles; the smaller dissimilarity i,j the more similar and
proximate are object i and object j. Output is a configuration of all n objects in a
low-dimensional space such that the Euclidean distance between two objects is similar
to the given dissimilarity between the two objects.
static double[][] | classicalScaling(double[][] d) - Performs classical multidimensional scaling on a given sparse dissimilarity
matrix.
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static double[][] | classicalScaling(double[][] d, int dim) - Performs classical multidimensional scaling on a given sparse dissimilarity
matrix.
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static void | main(String[] args) - Main program of MDSJ for command line interaction.
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static double[][] | stressMinimization(double[][] d) - Performs stress minimization on a given sparse dissimilarity matrix.
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static double[][] | stressMinimization(double[][] d, double[][] w) - Performs stress minimization on a given sparse dissimilarity matrix.
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static double[][] | stressMinimization(double[][] d, double[][] w, int dim) - Performs stress minimization on a given sparse dissimilarity matrix.
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static double[][] | stressMinimization(double[][] d, int dim) - Performs stress minimization on a given sparse dissimilarity matrix.
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MDSJ
public MDSJ(String[] args)
throws Exception
classicalScaling
public static double[][] classicalScaling(double[][] d)
Performs classical multidimensional scaling on a given sparse dissimilarity
matrix. The output is two-dimensional. This is a convenience method for the
most frequent uses of classical scaling.
d
- rectangular dissimilarity matrix
classicalScaling
public static double[][] classicalScaling(double[][] d,
int dim)
Performs classical multidimensional scaling on a given sparse dissimilarity
matrix. The output is two-dimensional. This is a convenience method for the
most frequent uses of classical scaling.
d
- rectangular dissimilarity matrixdim
- number of output dimensions
main
public static void main(String[] args)
Main program of MDSJ for command line interaction.
If no command line arguments are present, a brief documentation of all
available command line parameters is output. Otherwise, the command line
is parsed; if valid, it provides the necessary parameters for the MDS
to be executed on the input data, given as a matrix file.
args
- command line arguments
stressMinimization
public static double[][] stressMinimization(double[][] d)
Performs stress minimization on a given sparse dissimilarity matrix.
The computation is initialized with the output of classical scaling.
d
- rectangular dissimilarity matrix
stressMinimization
public static double[][] stressMinimization(double[][] d,
double[][] w)
Performs stress minimization on a given sparse dissimilarity matrix.
The computation is initialized with the output of classical scaling.
The output is two-dimensional.
d
- rectangular dissimilarity matrixw
- rectangular weight matrix
stressMinimization
public static double[][] stressMinimization(double[][] d,
double[][] w,
int dim)
Performs stress minimization on a given sparse dissimilarity matrix.
The computation is initialized with the output of classical scaling.
d
- rectangular dissimilarity matrixw
- rectangular weight matrixdim
- number of dimensions
stressMinimization
public static double[][] stressMinimization(double[][] d,
int dim)
Performs stress minimization on a given sparse dissimilarity matrix.
The computation is initialized with the output of classical scaling.
d
- rectangular dissimilarity matrixdim
- number of dimensions