weka.core
Class EuclideanDistance

java.lang.Object
  extended by weka.core.NormalizableDistance
      extended by weka.core.EuclideanDistance
All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, DistanceFunction, OptionHandler, 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: 1.12 $
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
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
 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.
 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, 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