java.lang.Object
- weka.core.NormalizableDistance
- - weka.core.WeightedEuclideanDistanceRidge

All Implemented Interfaces:

Serializable, Cloneable, weka.core.DistanceFunction, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler
```
public class WeightedEuclideanDistanceRidge
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.
```
Author:

dale (dale at waikato dot ac dot nz)

See Also:

Serialized Form

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`
`protected double`	`m_Ridge`

Fields inherited from class weka.core.NormalizableDistance
m_ActiveIndices, m_AttributeIndices, m_Data, m_DontNormalize, m_Ranges, m_Validated, R_MAX, R_MIN, R_WIDTH

Constructor Summary

Constructors
Constructor	Description
`WeightedEuclideanDistanceRidge()`	Constructs an Euclidean Distance object, Instances must be still set.
`WeightedEuclideanDistanceRidge(weka.core.Instances data)`	Constructs an Euclidean Distance object and automatically initializes the ranges.

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[]`	`getOptions()`	Gets the current settings of the classifier.
`String`	`getRevision()`	Returns the revision string.
`double`	`getRidge()`	Get the value of Ridge.
`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.
`Enumeration<weka.core.Option>`	`listOptions()`	Returns an enumeration describing the available options.
`void`	`postProcessDistances(double[] distances)`	Does post processing of the distances (if necessary) returned by distance(distance(Instance first, Instance second, double cutOffValue).
`String`	`ridgeTipText()`	Returns the tip text for this property
`void`	`setOptions(String[] options)`	Parses a given list of options.
`void`	`setRidge(double newRidge)`	Set the value of Ridge.
`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, getRanges, initializeAttributeIndices, initializeRanges, initializeRanges, initializeRanges, initializeRangesEmpty, inRanges, invalidate, invertSelectionTipText, norm, rangesSet, setAttributeIndices, setDontNormalize, setInstances, setInvertSelection, toString, update, updateRanges, updateRanges, updateRanges, updateRangesFirst, validate

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - m_Coefficients
```
protected double[] m_Coefficients
```
    Array for storing coefficients of linear regression.
  - m_LR
```
protected LinearRegressionJ m_LR
```
  - m_norm
```
protected weka.filters.unsupervised.attribute.Normalize m_norm
```
  - m_Ridge
```
protected double m_Ridge
```
- Constructor Detail
  - WeightedEuclideanDistanceRidge
```
public WeightedEuclideanDistanceRidge()
```
    Constructs an Euclidean Distance object, Instances must be still set.
  - WeightedEuclideanDistanceRidge
```
public WeightedEuclideanDistanceRidge(weka.core.Instances data)
```
    Constructs an Euclidean Distance object and automatically initializes the ranges.
    
    Parameters:
    
    data - the instances the distance function should work on
- Method Detail
  - initialize
```
protected void initialize()
```
    initializes the ranges and the attributes being used.
    
    Overrides:
    
    initialize in class weka.core.NormalizableDistance
  - globalInfo
```
public String globalInfo()
```
    Returns a string describing this object.
    
    Specified by:
    
    globalInfo in class weka.core.NormalizableDistance
    
    Returns:
    
    a description of the evaluator suitable for displaying in the explorer/experimenter gui
  - 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 interface weka.core.TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - listOptions
```
public Enumeration<weka.core.Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.core.NormalizableDistance
    
    Returns:
    
    an enumeration of all the available options.
  - getOptions
```
public String[] getOptions()
```
    Gets the current settings of the classifier.
    
    Specified by:
    
    getOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.core.NormalizableDistance
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - setOptions
```
public void setOptions(String[] options)
                throws Exception
```
    Parses a given list of options.
    Valid options are:
    
    -S <number of selection method> Set the attribute selection method to use. 1 = None, 2 = Greedy. (default 0 = M5' method)
    
    -C Do not try to eliminate colinear attributes.
    
    -R <double> Set ridge parameter (default 1.0e-8).
    
    -minimal Conserve memory, don't keep dataset header and means/stdevs. Model cannot be printed out if this option is enabled. (default: keep data)
    
    -additional-stats Output additional statistics.
    
    -output-debug-info If set, classifier is run in debug mode and may output additional info to the console
    
    -do-not-check-capabilities If set, classifier capabilities are not checked before classifier is built (use with caution).
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.core.NormalizableDistance
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    Exception - if an option is not supported
  - ridgeTipText
```
public String ridgeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getRidge
```
public double getRidge()
```
    Get the value of Ridge.
    
    Returns:
    
    Value of Ridge.
  - setRidge
```
public void setRidge(double newRidge)
```
    Set the value of Ridge.
    
    Parameters:
    
    newRidge - Value to assign to Ridge.
  - distance
```
public double distance(weka.core.Instance first,
                       weka.core.Instance second)
```
    Calculates the distance between two instances.
    
    Specified by:
    
    distance in interface weka.core.DistanceFunction
    
    Overrides:
    
    distance in class weka.core.NormalizableDistance
    
    Parameters:
    
    first - the first instance
    
    second - the second instance
    
    Returns:
    
    the distance between the two given instances
  - 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 interface weka.core.DistanceFunction
    
    Overrides:
    
    distance in class weka.core.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.
  - 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 class weka.core.NormalizableDistance
    
    Parameters:
    
    currDist - the current distance calculated so far
    
    diff - the difference between two new attributes
    
    Returns:
    
    the update distance
    
    See Also:
    
    difference(int, double, double)
  - distance
```
public double distance(weka.core.Instance first,
                       weka.core.Instance second,
                       double cutOffValue,
                       weka.core.neighboursearch.PerformanceStats stats)
```
    Specified by:
    
    distance in interface weka.core.DistanceFunction
    
    Overrides:
    
    distance in class weka.core.NormalizableDistance
  - difference
```
protected double difference(int index,
                            double val1,
                            double val2)
```
    Computes the difference between two given attribute values.
    
    Overrides:
    
    difference in class weka.core.NormalizableDistance
    
    Parameters:
    
    index - the attribute index
    
    val1 - the first value
    
    val2 - the second value
    
    Returns:
    
    the difference
  - 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 weka.core.DistanceFunction
    
    Overrides:
    
    postProcessDistances in class weka.core.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
  - transform
```
protected weka.core.Instance transform(weka.core.Instance i)
```
  - 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 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 weka.core.RevisionHandler
    
    Returns:
    
    the revision

Class WeightedEuclideanDistanceRidge

Field Summary

Fields inherited from class weka.core.NormalizableDistance

Constructor Summary

Method Summary

Methods inherited from class weka.core.NormalizableDistance

Methods inherited from class java.lang.Object

Field Detail

m_Coefficients

m_LR

m_norm

m_Ridge

Constructor Detail

WeightedEuclideanDistanceRidge

WeightedEuclideanDistanceRidge

Method Detail

initialize

globalInfo

getTechnicalInformation

listOptions

getOptions

setOptions

ridgeTipText

getRidge

setRidge

distance

distance

updateDistance

distance

difference

postProcessDistances

sqDifference

getMiddle

transform

valueIsSmallerEqual

getRevision