moa.classifiers.lazy.neighboursearch

Interface DistanceFunction

All Known Implementing Classes:

EuclideanDistance, NormalizableDistance

public interface DistanceFunction

Interface for any class that can compute and return distances between two instances.

Version:

$Revision: 8034 $

Author:

Ashraf M. Kibriya (amk14@cs.waikato.ac.nz)

Method Summary

Methods

Modifier and Type	Method and Description
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) Calculates the distance between two instances.
String	getAttributeIndices() Gets the range of attributes used in the calculation of the distance.
weka.core.Instances	getInstances() returns the instances currently set.
boolean	getInvertSelection() Gets whether the matching sense of attribute indices is inverted or not.
void	postProcessDistances(double[] distances) Does post processing of the distances (if necessary) returned by distance(distance(Instance first, Instance second, double cutOffValue).
void	setAttributeIndices(String value) Sets the range of attributes to use in the calculation of the distance.
void	setInstances(weka.core.Instances insts) Sets the instances.
void	setInvertSelection(boolean value) Sets whether the matching sense of attribute indices is inverted or not.
void	update(weka.core.Instance ins) Update the distance function (if necessary) for the newly added instance.

Method Detail

setInstances

void setInstances(weka.core.Instances insts)

Sets the instances.

Parameters:

insts - the instances to use

getInstances

weka.core.Instances getInstances()

returns the instances currently set.

Returns:

the current instances

setAttributeIndices

void setAttributeIndices(String value)

Sets the range of attributes to use in the calculation of the distance. The indices start from 1, 'first' and 'last' are valid as well. E.g.: first-3,5,6-last

Parameters:

value - the new attribute index range

getAttributeIndices

String getAttributeIndices()

Gets the range of attributes used in the calculation of the distance.

Returns:

the attribute index range

setInvertSelection

void setInvertSelection(boolean value)

Sets whether the matching sense of attribute indices is inverted or not.

Parameters:

value - if true the matching sense is inverted

getInvertSelection

boolean getInvertSelection()

Gets whether the matching sense of attribute indices is inverted or not.

Returns:

true if the matching sense is inverted

distance

double distance(weka.core.Instance first,
              weka.core.Instance second)

Calculates the distance between two instances.

Parameters:

first - the first instance

second - the second instance

Returns:

the distance between the two given instances

distance

double distance(weka.core.Instance first,
              weka.core.Instance second,
              double cutOffValue)

Calculates the distance between two instances. Offers speed up (if the distance function class in use supports it) in nearest neighbour search by taking into account the cutOff or maximum distance. Depending on the distance function class, post processing of the distances by postProcessDistances(double []) may be required if this function is used.

Parameters:

first - the first instance

second - the second instance

cutOffValue - If the distance being calculated becomes larger than cutOffValue then the rest of the calculation is discarded.

Returns:

the distance between the two given instances or Double.POSITIVE_INFINITY if the distance being calculated becomes larger than cutOffValue.

postProcessDistances

void postProcessDistances(double[] distances)

Does post processing of the distances (if necessary) returned by distance(distance(Instance first, Instance second, double cutOffValue). It may be necessary, depending on the distance function, to do post processing to set the distances on the correct scale. Some distance function classes may not return correct distances using the cutOffValue distance function to minimize the inaccuracies resulting from floating point comparison and manipulation.

Parameters:

distances - the distances to post-process

update

void update(weka.core.Instance ins)

Update the distance function (if necessary) for the newly added instance.

Parameters:

ins - the instance to add