smile.mds

Class IsotonicMDS

java.lang.Object

smile.mds.IsotonicMDS

public class IsotonicMDS
extends java.lang.Object

Kruskal's nonmetric MDS. In non-metric MDS, only the rank order of entries in the proximity matrix (not the actual dissimilarities) is assumed to contain the significant information. Hence, the distances of the final configuration should as far as possible be in the same rank order as the original data. Note that a perfect ordinal re-scaling of the data into distances is usually not possible. The relationship is typically found using isotonic regression.

Field Summary

Fields

Modifier and Type	Field and Description
double[][]	coordinates The coordinates.
double	stress The final stress achieved.

Constructor Summary

Constructors

Constructor and Description
IsotonicMDS(double stress, double[][] coordinates) Constructor.

Method Summary

Modifier and Type	Method and Description
static IsotonicMDS	of(double[][] proximity) Fits Kruskal's non-metric MDS with default k = 2, tolerance = 1E-4 and maxIter = 200.
static IsotonicMDS	of(double[][] proximity, double[][] init, double tol, int maxIter) Fits Kruskal's non-metric MDS.
static IsotonicMDS	of(double[][] proximity, int k) Fits Kruskal's non-metric MDS.
static IsotonicMDS	of(double[][] proximity, int k, double tol, int maxIter) Fits Kruskal's non-metric MDS.
static IsotonicMDS	of(double[][] proximity, java.util.Properties prop) Fits Kruskal's non-metric MDS.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

stress

public final double stress

The final stress achieved.

coordinates

public final double[][] coordinates

The coordinates.

Constructor Detail

IsotonicMDS

public IsotonicMDS(double stress,
                   double[][] coordinates)

Constructor.

Parameters:

stress - the objective function value.

coordinates - the principal coordinates

Method Detail

of

public static IsotonicMDS of(double[][] proximity)

Fits Kruskal's non-metric MDS with default k = 2, tolerance = 1E-4 and maxIter = 200.

Parameters:

proximity - the nonnegative proximity matrix of dissimilarities. The diagonal should be zero and all other elements should be positive and symmetric.

of

public static IsotonicMDS of(double[][] proximity,
                             int k)

Fits Kruskal's non-metric MDS.

Parameters:

proximity - the nonnegative proximity matrix of dissimilarities. The diagonal should be zero and all other elements should be positive and symmetric.

k - the dimension of the projection.

of

public static IsotonicMDS of(double[][] proximity,
                             java.util.Properties prop)

Fits Kruskal's non-metric MDS.

Parameters:

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.

of

public static IsotonicMDS of(double[][] proximity,
                             int k,
                             double tol,
                             int maxIter)

Fits Kruskal's non-metric MDS.

Parameters:

proximity - the nonnegative proximity matrix of dissimilarities. The diagonal should be zero and all other elements should be positive and symmetric.

k - the dimension of the projection.

tol - the tolerance for stopping iterations.

maxIter - maximum number of iterations.

of

public static IsotonicMDS of(double[][] proximity,
                             double[][] init,
                             double tol,
                             int maxIter)

Fits Kruskal's non-metric MDS.

Parameters:

proximity - the nonnegative proximity matrix of dissimilarities. The diagonal should be zero and all other elements should be positive and symmetric.

init - the initial projected coordinates, of which the column size is the projection dimension.

tol - the tolerance for stopping iterations.

maxIter - maximum number of iterations.