Package weka.core
Class WeightedEuclideanDistance
- java.lang.Object
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- weka.core.NormalizableDistance
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- weka.core.WeightedEuclideanDistance
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- All Implemented Interfaces:
Serializable
,Cloneable
,weka.core.DistanceFunction
,weka.core.OptionHandler
,weka.core.RevisionHandler
,weka.core.TechnicalInformationHandler
public class WeightedEuclideanDistance extends weka.core.NormalizableDistance implements Cloneable, weka.core.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:
- $Revision$
- Author:
- dale (dale at waikato dot ac dot nz)
- See Also:
- Serialized Form
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Field Summary
Fields Modifier and Type Field Description protected double[]
m_Coefficients
Array for storing coefficients of linear regression.protected LinearRegressionJ
m_LR
protected weka.filters.unsupervised.attribute.Normalize
m_norm
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Constructor Summary
Constructors Constructor Description WeightedEuclideanDistance()
Constructs an Euclidean Distance object, Instances must be still set.WeightedEuclideanDistance(weka.core.Instances data)
Constructs an Euclidean Distance object and automatically initializes the ranges.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description protected double
difference(int index, double val1, double val2)
Computes the difference between two given attribute values.double
distance(weka.core.Instance first, weka.core.Instance second)
Calculates the distance between two instances.double
distance(weka.core.Instance first, weka.core.Instance second, double cutOffValue, weka.core.neighboursearch.PerformanceStats stats)
double
distance(weka.core.Instance first, weka.core.Instance second, weka.core.neighboursearch.PerformanceStats stats)
Calculates the distance (or similarity) between two instances.double
getMiddle(double[] ranges)
Returns value in the middle of the two parameter values.String
getRevision()
Returns the revision string.weka.core.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.protected void
initialize()
initializes the ranges and the attributes being used.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.protected weka.core.Instance
transform(weka.core.Instance i)
protected double
updateDistance(double currDist, double diff)
Updates the current distance calculated so far with the new difference between two attributes.boolean
valueIsSmallerEqual(weka.core.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, dontNormalizeTipText, getAttributeIndices, getDontNormalize, getInstances, getInvertSelection, getOptions, getRanges, initializeAttributeIndices, initializeRanges, initializeRanges, initializeRanges, initializeRangesEmpty, inRanges, invalidate, invertSelectionTipText, listOptions, norm, rangesSet, setAttributeIndices, setDontNormalize, setInstances, setInvertSelection, setOptions, toString, update, updateRanges, updateRanges, updateRanges, updateRangesFirst, validate
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Field Detail
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m_Coefficients
protected double[] m_Coefficients
Array for storing coefficients of linear regression.
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m_LR
protected LinearRegressionJ m_LR
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m_norm
protected weka.filters.unsupervised.attribute.Normalize m_norm
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Constructor Detail
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WeightedEuclideanDistance
public WeightedEuclideanDistance()
Constructs an Euclidean Distance object, Instances must be still set.
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WeightedEuclideanDistance
public WeightedEuclideanDistance(weka.core.Instances data)
Constructs an Euclidean Distance object and automatically initializes the ranges.- Parameters:
data
- the instances the distance function should work on
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Method Detail
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initialize
protected void initialize()
initializes the ranges and the attributes being used.- Overrides:
initialize
in classweka.core.NormalizableDistance
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globalInfo
public String globalInfo()
Returns a string describing this object.- Specified by:
globalInfo
in classweka.core.NormalizableDistance
- Returns:
- a description of the evaluator suitable for displaying in the explorer/experimenter gui
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getTechnicalInformation
public weka.core.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 interfaceweka.core.TechnicalInformationHandler
- Returns:
- the technical information about this class
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distance
public double distance(weka.core.Instance first, weka.core.Instance second)
Calculates the distance between two instances.- Specified by:
distance
in interfaceweka.core.DistanceFunction
- Overrides:
distance
in classweka.core.NormalizableDistance
- Parameters:
first
- the first instancesecond
- the second instance- Returns:
- the distance between the two given instances
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distance
public double distance(weka.core.Instance first, weka.core.Instance second, weka.core.neighboursearch.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 interfaceweka.core.DistanceFunction
- Overrides:
distance
in classweka.core.NormalizableDistance
- Parameters:
first
- the first instancesecond
- the second instancestats
- the structure for storing performance statistics.- Returns:
- the distance between the two given instances or Double.POSITIVE_INFINITY.
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updateDistance
protected double updateDistance(double currDist, double diff)
Updates the current distance calculated so far with the new difference between two attributes. The difference between the attributes was calculated with the difference(int,double,double) method.- Specified by:
updateDistance
in classweka.core.NormalizableDistance
- Parameters:
currDist
- the current distance calculated so fardiff
- the difference between two new attributes- Returns:
- the update distance
- See Also:
difference(int, double, double)
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distance
public double distance(weka.core.Instance first, weka.core.Instance second, double cutOffValue, weka.core.neighboursearch.PerformanceStats stats)
- Specified by:
distance
in interfaceweka.core.DistanceFunction
- Overrides:
distance
in classweka.core.NormalizableDistance
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difference
protected double difference(int index, double val1, double val2)
Computes the difference between two given attribute values.- Overrides:
difference
in classweka.core.NormalizableDistance
- Parameters:
index
- the attribute indexval1
- the first valueval2
- the second value- Returns:
- the difference
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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 interfaceweka.core.DistanceFunction
- Overrides:
postProcessDistances
in classweka.core.NormalizableDistance
- Parameters:
distances
- the distances to post-process
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sqDifference
public double sqDifference(int index, double val1, double val2)
Returns the squared difference of two values of an attribute.- Parameters:
index
- the attribute indexval1
- the first valueval2
- the second value- Returns:
- the squared difference
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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
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transform
protected weka.core.Instance transform(weka.core.Instance i)
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valueIsSmallerEqual
public boolean valueIsSmallerEqual(weka.core.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 ofdim
- the dimension of the valuevalue
- the value to compare with- Returns:
- true if value of instance is smaller or equal value
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getRevision
public String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceweka.core.RevisionHandler
- Returns:
- the revision
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