weka.core

Class EuclideanDistance

java.lang.Object

weka.core.NormalizableDistance

weka.core.EuclideanDistance

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, DistanceFunction, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class EuclideanDistance
extends NormalizableDistance
implements java.lang.Cloneable, TechnicalInformationHandler

Implementing Euclidean distance (or similarity) function.

One object defines not one distance but the data model in which the distances between objects of that data model can be computed.

Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low.

For more information, see:

Wikipedia. Euclidean distance. URL http://en.wikipedia.org/wiki/Euclidean_distance.

BibTeX:

 @misc{missing_id,
    author = {Wikipedia},
    title = {Euclidean distance},
    URL = {http://en.wikipedia.org/wiki/Euclidean_distance}
 }
 

Valid options are:

 -D
  Turns off the normalization of attribute 
  values in distance calculation.

 -R <col1,col2-col4,...>
  Specifies list of columns to used in the calculation of the 
  distance. 'first' and 'last' are valid indices.
  (default: first-last)

 -V
  Invert matching sense of column indices.

Version:

$Revision: 1.13 $

Author:

Gabi Schmidberger (gabi@cs.waikato.ac.nz), Ashraf M. Kibriya (amk14@cs.waikato.ac.nz), FracPete (fracpete at waikato dot ac dot nz)

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.core.NormalizableDistance

R_MAX, R_MIN, R_WIDTH

Constructor Summary

Constructors

Constructor and Description
EuclideanDistance() Constructs an Euclidean Distance object, Instances must be still set.
EuclideanDistance(Instances data) Constructs an Euclidean Distance object and automatically initializes the ranges.

Method Summary

Modifier and Type	Method and Description
int	closestPoint(Instance instance, Instances allPoints, int[] pointList) Returns the index of the closest point to the current instance.
double	distance(Instance first, Instance second) Calculates the distance between two instances.
double	distance(Instance first, Instance second, PerformanceStats stats) Calculates the distance (or similarity) between two instances.
double	getMiddle(double[] ranges) Returns value in the middle of the two parameter values.
java.lang.String	getRevision() Returns the revision string.
TechnicalInformation	getTechnicalInformation() Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
java.lang.String	globalInfo() Returns a string describing this object.
void	postProcessDistances(double[] distances) Does post processing of the distances (if necessary) returned by distance(distance(Instance first, Instance second, double cutOffValue).
double	sqDifference(int index, double val1, double val2) Returns the squared difference of two values of an attribute.
boolean	valueIsSmallerEqual(Instance instance, int dim, double value) Returns true if the value of the given dimension is smaller or equal the value to be compared with.

Methods inherited from class weka.core.NormalizableDistance

attributeIndicesTipText, clean, distance, distance, dontNormalizeTipText, getAttributeIndices, getDontNormalize, getInstances, getInvertSelection, getOptions, getRanges, initializeRanges, initializeRanges, initializeRanges, initializeRangesEmpty, inRanges, invertSelectionTipText, listOptions, rangesSet, setAttributeIndices, setDontNormalize, setInstances, setInvertSelection, setOptions, toString, update, updateRanges, updateRanges, updateRanges, updateRangesFirst

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

EuclideanDistance

public EuclideanDistance()

Constructs an Euclidean Distance object, Instances must be still set.

EuclideanDistance

public EuclideanDistance(Instances data)

Constructs an Euclidean Distance object and automatically initializes the ranges.

Parameters:

data - the instances the distance function should work on

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this object.

Specified by:

globalInfo in class NormalizableDistance

Returns:

a description of the evaluator suitable for displaying in the explorer/experimenter gui

getTechnicalInformation

public TechnicalInformation getTechnicalInformation()

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

Specified by:

getTechnicalInformation in interface TechnicalInformationHandler

Returns:

the technical information about this class

distance

public double distance(Instance first,
                       Instance second)

Calculates the distance between two instances.

Specified by:

distance in interface DistanceFunction

Overrides:

distance in class NormalizableDistance

Parameters:

first - the first instance

second - the second instance

Returns:

the distance between the two given instances

distance

public double distance(Instance first,
                       Instance second,
                       PerformanceStats stats)

Calculates the distance (or similarity) between two instances. Need to pass this returned distance later on to postprocess method to set it on correct scale.
P.S.: Please don't mix the use of this function with distance(Instance first, Instance second), as that already does post processing. Please consider passing Double.POSITIVE_INFINITY as the cutOffValue to this function and then later on do the post processing on all the distances.

Specified by:

distance in interface DistanceFunction

Overrides:

distance in class NormalizableDistance

Parameters:

first - the first instance

second - the second instance

stats - the structure for storing performance statistics.

Returns:

the distance between the two given instances or Double.POSITIVE_INFINITY.

postProcessDistances

public void postProcessDistances(double[] distances)

Does post processing of the distances (if necessary) returned by distance(distance(Instance first, Instance second, double cutOffValue). It is necessary to do so to get the correct distances if distance(distance(Instance first, Instance second, double cutOffValue) is used. This is because that function actually returns the squared distance to avoid inaccuracies arising from floating point comparison.

Specified by:

postProcessDistances in interface DistanceFunction

Overrides:

postProcessDistances in class NormalizableDistance

Parameters:

distances - the distances to post-process

sqDifference

public double sqDifference(int index,
                           double val1,
                           double val2)

Returns the squared difference of two values of an attribute.

Parameters:

index - the attribute index

val1 - the first value

val2 - the second value

Returns:

the squared difference

getMiddle

public double getMiddle(double[] ranges)

Returns value in the middle of the two parameter values.

Parameters:

ranges - the ranges to this dimension

Returns:

the middle value

closestPoint

public int closestPoint(Instance instance,
                        Instances allPoints,
                        int[] pointList)
                 throws java.lang.Exception

Returns the index of the closest point to the current instance. Index is index in Instances object that is the second parameter.

Parameters:

instance - the instance to assign a cluster to

allPoints - all points

pointList - the list of points

Returns:

the index of the closest point

Throws:

java.lang.Exception - if something goes wrong

valueIsSmallerEqual

public boolean valueIsSmallerEqual(Instance instance,
                                   int dim,
                                   double value)

Returns true if the value of the given dimension is smaller or equal the value to be compared with.

Parameters:

instance - the instance where the value should be taken of

dim - the dimension of the value

value - the value to compare with

Returns:

true if value of instance is smaller or equal value

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Returns:

the revision