de.unibi.techfak.jpredictor.clustering
Class MatrixDistance

java.lang.Object
  de.unibi.techfak.jpredictor.clustering.RelationMeasure
      de.unibi.techfak.jpredictor.clustering.MatrixRelationMeasure
          de.unibi.techfak.jpredictor.clustering.MatrixSimilarity
              de.unibi.techfak.jpredictor.clustering.MatrixDistance

All Implemented Interfaces:

Computable, java.lang.Comparable

public class MatrixDistance
extends MatrixSimilarity

Class for calculating the similarity between two matrices. The similarity is calculated by adding up the similarity between every two matrix arrays. Different vector similarity measures can be given. This class is used like this:

  MatrixSimilarity ms = new MatrixSimilarity( new EuclideanDistance() );
  ms.add( firstMatrix );
  ms.add( secondMatrix );
  ms.compute();
  // process result (first against second)
  ms.add( thirdMatrix );
  ms.compute();
  // process result (second against third)
  ...
 

.

See Also:

VectorRelationMeasure, MinkowskiDistance, LikelihoodMeasure

Field Summary

Fields inherited from class de.unibi.techfak.jpredictor.clustering.MatrixSimilarity

vrm

Fields inherited from class de.unibi.techfak.jpredictor.clustering.MatrixRelationMeasure

matrixList

Fields inherited from class de.unibi.techfak.jpredictor.clustering.RelationMeasure

computationResult

Fields inherited from interface de.unibi.techfak.jpredictor.clustering.Computable

COMPUTATION_FAILED, COMPUTATION_PENDING, COMPUTATION_SUCCESS, COMPUTATION_WAITING

Constructor Summary

MatrixDistance(VectorRelationMeasure vrm)
Constructs this class with the relation measure.

Method Summary

double	atomicCombine(double rel0, double rel1) Two relations measures are combined to one result, which is not stored but returned.
double	atomicNormalize(double rel, double norm) A relations measures is normalized by a certain amount.
RelationMeasure	newInstance() Creates a copy of this relation measure as if it was newly instantiated through a call to the constructor.
java.lang.String	toString() Creates a short string of this relation measure and maybe some results if available.
(package private) double	vectorResultsCombine(double resultSoFar, double nextResult) Operation to combine a result calculated so far and a next result.
(package private) double	vectorResultsInit() The neutral/initial value for combining the vectorial results.

Methods inherited from class de.unibi.techfak.jpredictor.clustering.MatrixSimilarity

compute, optimum

Methods inherited from class de.unibi.techfak.jpredictor.clustering.MatrixRelationMeasure

add, add, clear, length, lengthRow

Methods inherited from class de.unibi.techfak.jpredictor.clustering.RelationMeasure

atomicCombine, compareTo, optimum, result

Methods inherited from class java.lang.Object

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

Constructor Detail

MatrixDistance

public MatrixDistance(VectorRelationMeasure vrm)
               throws java.lang.NullPointerException

Constructs this class with the relation measure.

Parameters:

vrm - The relation measure between two vectors of the matrix.

Throws:

java.lang.NullPointerException - if vrm is null.

Method Detail

vectorResultsInit

double vectorResultsInit()

Description copied from class: MatrixSimilarity

The neutral/initial value for combining the vectorial results. For sums it would be zero, for products one.

Specified by:

vectorResultsInit in class MatrixSimilarity

Returns:

The init value/neutral element.

vectorResultsCombine

double vectorResultsCombine(double resultSoFar,
                            double nextResult)

Description copied from class: MatrixSimilarity

Operation to combine a result calculated so far and a next result. Might be an addition or a multiplication or a log.

Specified by:

vectorResultsCombine in class MatrixSimilarity

Returns:

The new result.

atomicCombine

public double atomicCombine(double rel0,
                            double rel1)

Description copied from class: RelationMeasure

Two relations measures are combined to one result, which is not stored but returned. This method provides a way to calculate the combined result, when the way how to do this is not known. For instance, distance relation measures normally sum up over single results while likelihood/probabilities are multiplied.

Note, that it is possible to mix up optimization of different relation measures, e.g. distances and likelihoods, since this method works on any numbers.

Specified by:

atomicCombine in class RelationMeasure

Parameters:

rel0 - The first relation measure to be compared to the second.

rel1 - The second relation measure to be compared to the first.

Returns:

The result of combining two relation measures, e.g. through adding them up, or multiplying them or such.

atomicNormalize

public double atomicNormalize(double rel,
                              double norm)

Description copied from class: RelationMeasure

A relations measures is normalized by a certain amount. This method provides a way to normalize a relation measure after numbers of them were combined. For instance, distance relation measures normally are normalized by dividing through the count, while likelihoods are rooted.

Note, that it is possible to mix up optimization of different relation measures, e.g. distances and likelihoods, since this method works on any numbers.

Specified by:

atomicNormalize in class RelationMeasure

Parameters:

rel - The relation measure to be normalized.

norm - The normalization value.

Returns:

The result of normalizing a relation measures. Or Double.NaN in case of error.

newInstance

public RelationMeasure newInstance()

Description copied from class: RelationMeasure

Creates a copy of this relation measure as if it was newly instantiated through a call to the constructor. Note: if a class relies on really unique relation measures that are never shared with other instances it should call this method in the constructor. This might be the case, if a class uses a relation measures to store results of partially results.

Specified by:

newInstance in class RelationMeasure

Returns:

A copy of the relation measure as if it was newly instantiated.

toString

public java.lang.String toString()

Description copied from class: RelationMeasure

Creates a short string of this relation measure and maybe some results if available.

Overrides:

toString in class MatrixSimilarity

Returns:

A string with a short description of the class.