Package weka.core

# Class EuclideanDistance

java.lang.Object
weka.core.NormalizableDistance
weka.core.EuclideanDistance

All Implemented Interfaces:
  `Serializable`, `Cloneable`, `DistanceFunction`, `OptionHandler`, `RevisionHandler`, `TechnicalInformationHandler`

public class EuclideanDistance extends NormalizableDistance implements 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: 8034 $
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
  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
  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.
  `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.
  `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 Details

  - ### 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 Details

  - ### 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

  - ### 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 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:
      `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 String getRevision()
    Returns the revision string.
    Specified by:
      `getRevision` in interface `RevisionHandler`
    Returns:
      the revision