weka.core

Class MinkowskiDistance

java.lang.Object

weka.core.NormalizableDistance

weka.core.MinkowskiDistance

All Implemented Interfaces:

Serializable, Cloneable, DistanceFunction, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class MinkowskiDistance
extends NormalizableDistance
implements Cloneable, TechnicalInformationHandler

Implementing Minkowski 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. Minkowski distance. URL http://en.wikipedia.org/wiki/Minkowski_distance.

BibTeX:

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

Valid options are:

 -P <order>
  The order 'p'. With '1' being the Manhattan distance and '2'
  the Euclidean distance.
  (default: 2)

 -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: 8034 $

Author:

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
MinkowskiDistance() Constructs an Minkowski Distance object, Instances must be still set.
MinkowskiDistance(Instances data) Constructs an Minkowski Distance object and automatically initializes the ranges.

Method Summary

Methods

Modifier and Type	Method and Description
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	getOrder() Gets the order.
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.
String	globalInfo() Returns a string describing this object.
Enumeration	listOptions() Returns an enumeration describing the available options.
String	orderTipText() Returns the tip text for this property.
void	postProcessDistances(double[] distances) Does post processing of the distances (if necessary) returned by distance(distance(Instance first, Instance second, double cutOffValue).
void	setOrder(double value) Sets the order.

Methods inherited from class weka.core.NormalizableDistance

attributeIndicesTipText, distance, distance, dontNormalizeTipText, getAttributeIndices, getDontNormalize, getInstances, getInvertSelection, getOptions, getRanges, initializeRanges, initializeRanges, initializeRanges, initializeRangesEmpty, inRanges, invertSelectionTipText, 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

MinkowskiDistance

public MinkowskiDistance()

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

MinkowskiDistance

public MinkowskiDistance(Instances data)

Constructs an Minkowski Distance object and automatically initializes the ranges.

Parameters:

data - the instances the distance function should work on

Method Detail

globalInfo

public 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

listOptions

public Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class NormalizableDistance

Returns:

an enumeration of all the available options.

orderTipText

public String orderTipText()

Returns the tip text for this property.

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setOrder

public void setOrder(double value)

Sets the order.

Parameters:

value - the new order

getOrder

public double getOrder()

Gets the order.

Returns:

the order

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

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Returns:

the revision