public class MDS
extends java.lang.Object
PCA
,
SammonMapping
Modifier and Type  Field and Description 

double[][] 
coordinates
The principal coordinates.

double[] 
proportion
The proportion of variance contained in each principal component.

double[] 
scores
The component scores.

Constructor and Description 

MDS(double[] scores,
double[] proportion,
double[][] coordinates)
Constructor.

Modifier and Type  Method and Description 

static MDS 
of(double[][] proximity)
Fits the classical multidimensional scaling.

static MDS 
of(double[][] proximity,
int k)
Fits the classical multidimensional scaling.

static MDS 
of(double[][] proximity,
int k,
boolean positive)
Fits the classical multidimensional scaling.

static MDS 
of(double[][] proximity,
java.util.Properties prop)
Fits the classical multidimensional scaling.

public final double[] scores
public final double[][] coordinates
public final double[] proportion
public MDS(double[] scores, double[] proportion, double[][] coordinates)
scores
 the component scores.proportion
 the proportion of variance contained in each principal component.coordinates
 the principal coordinatespublic static MDS of(double[][] proximity)
proximity
 the nonnegative proximity matrix of dissimilarities. The
diagonal should be zero and all other elements should be positive and
symmetric. For pairwise distances matrix, it should be just the plain
distance, not squared.public static MDS of(double[][] proximity, int k)
proximity
 the nonnegative proximity matrix of dissimilarities. The
diagonal should be zero and all other elements should be positive and
symmetric. For pairwise distances matrix, it should be just the plain
distance, not squared.k
 the dimension of the projection.public static MDS of(double[][] proximity, java.util.Properties prop)
proximity
 the nonnegative proximity matrix of dissimilarities. The
diagonal should be zero and all other elements should be positive and
symmetric. For pairwise distances matrix, it should be just the plain
distance, not squared.public static MDS of(double[][] proximity, int k, boolean positive)
proximity
 the nonnegative proximity matrix of dissimilarities. The
diagonal should be zero and all other elements should be positive and
symmetric. For pairwise distances matrix, it should be just the plain
distance, not squared.k
 the dimension of the projection.positive
 if true, estimate an appropriate constant to be added
to all the dissimilarities, apart from the selfdissimilarities, that
makes the learning matrix positive semidefinite. The other formulation of
the additive constant problem is as follows. If the proximity is
measured in an interval scale, where there is no natural origin, then there
is not a sympathy of the dissimilarities to the distances in the Euclidean
space used to represent the objects. In this case, we can estimate a constant c
such that proximity + c may be taken as ratio data, and also possibly
to minimize the dimensionality of the Euclidean space required for
representing the objects.