Package weka.core

Class NormalizableDistance

java.lang.Object
weka.core.NormalizableDistance
All Implemented Interfaces:
Serializable, DistanceFunction, OptionHandler, RevisionHandler
Direct Known Subclasses:
ChebyshevDistance, EuclideanDistance, ManhattanDistance, MinkowskiDistance
public abstract class NormalizableDistance extends Object implements DistanceFunction, OptionHandler, Serializable, RevisionHandler
Represents the abstract ancestor for normalizable distance functions, like Euclidean or Manhattan distance.
Version:
$Revision: 14813 $
Author:
Fracpete (fracpete at waikato dot ac dot nz), Gabi Schmidberger (gabi@cs.waikato.ac.nz) -- original code from weka.core.EuclideanDistance, Ashraf M. Kibriya (amk14@cs.waikato.ac.nz) -- original code from weka.core.EuclideanDistance
See Also:

  • Field Details

  • Constructor Details

    • NormalizableDistance

      public NormalizableDistance()
      Invalidates the distance function, Instances must be still set.

    • NormalizableDistance

      public NormalizableDistance(Instances data)
      Initializes the distance function and automatically initializes the ranges.
      Parameters:
      data - the instances the distance function should work on

  • Method Details

    • globalInfo

      public abstract String globalInfo()
      Returns a string describing this object.
      Returns:
      a description of the evaluator suitable for displaying in the explorer/experimenter gui

    • listOptions

      public Enumeration<Option> listOptions()
      Returns an enumeration describing the available options.
      Specified by:
      listOptions in interface OptionHandler
      Returns:
      an enumeration of all the available options.

    • getOptions

      public String[] getOptions()
      Gets the current settings. Returns empty array.
      Specified by:
      getOptions in interface OptionHandler
      Returns:
      an array of strings suitable for passing to setOptions()

    • setOptions

      public void setOptions(String[] options) throws Exception
      Parses a given list of options.
      Specified by:
      setOptions in interface OptionHandler
      Parameters:
      options - the list of options as an array of strings
      Throws:
      Exception - if an option is not supported

    • dontNormalizeTipText

      public String dontNormalizeTipText()
      Returns the tip text for this property.
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • setDontNormalize

      public void setDontNormalize(boolean dontNormalize)
      Sets whether if the attribute values are to be normalized in distance calculation.
      Parameters:
      dontNormalize - if true the values are not normalized

    • getDontNormalize

      public boolean getDontNormalize()
      Gets whether if the attribute values are to be normazlied in distance calculation. (default false i.e. attribute values are normalized.)
      Returns:
      false if values get normalized

    • attributeIndicesTipText

      public String attributeIndicesTipText()
      Returns the tip text for this property.
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • setAttributeIndices

      public 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
      Specified by:
      setAttributeIndices in interface DistanceFunction
      Parameters:
      value - the new attribute index range

    • getAttributeIndices

      public String getAttributeIndices()
      Gets the range of attributes used in the calculation of the distance.
      Specified by:
      getAttributeIndices in interface DistanceFunction
      Returns:
      the attribute index range

    • invertSelectionTipText

      public String invertSelectionTipText()
      Returns the tip text for this property.
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • setInvertSelection

      public void setInvertSelection(boolean value)
      Sets whether the matching sense of attribute indices is inverted or not.
      Specified by:
      setInvertSelection in interface DistanceFunction
      Parameters:
      value - if true the matching sense is inverted

    • getInvertSelection

      public boolean getInvertSelection()
      Gets whether the matching sense of attribute indices is inverted or not.
      Specified by:
      getInvertSelection in interface DistanceFunction
      Returns:
      true if the matching sense is inverted

    • setInstances

      public void setInstances(Instances insts)
      Sets the instances.
      Specified by:
      setInstances in interface DistanceFunction
      Parameters:
      insts - the instances to use

    • getInstances

      public Instances getInstances()
      returns the instances currently set.
      Specified by:
      getInstances in interface DistanceFunction
      Returns:
      the current instances

    • postProcessDistances

      public void postProcessDistances(double[] distances)
      Does nothing, derived classes may override it though.
      Specified by:
      postProcessDistances in interface DistanceFunction
      Parameters:
      distances - the distances to post-process

    • update

      public void update(Instance ins)
      Update the distance function (if necessary) for the newly added instance.
      Specified by:
      update in interface DistanceFunction
      Parameters:
      ins - the instance to add

    • distance

      public double distance(Instance first, Instance second)
      Calculates the distance between two instances.
      Specified by:
      distance in interface DistanceFunction
      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 between two instances.
      Specified by:
      distance in interface DistanceFunction
      Parameters:
      first - the first instance
      second - the second instance
      stats - the performance stats object
      Returns:
      the distance between the two given instances

    • distance

      public double distance(Instance first, 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.
      Specified by:
      distance in interface DistanceFunction
      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.

    • distance

      public double distance(Instance first, Instance second, double cutOffValue, PerformanceStats stats)
      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.
      Specified by:
      distance in interface DistanceFunction
      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.
      stats - the performance stats object
      Returns:
      the distance between the two given instances or Double.POSITIVE_INFINITY if the distance being calculated becomes larger than cutOffValue.

    • initializeRanges

      public double[][] initializeRanges()
      Initializes the ranges using all instances of the dataset. Sets m_Ranges.
      Returns:
      the ranges

    • updateRangesFirst

      public void updateRangesFirst(Instance instance, int numAtt, double[][] ranges)
      Used to initialize the ranges. For this the values of the first instance is used to save time. Sets low and high to the values of the first instance and width to zero.
      Parameters:
      instance - the new instance
      numAtt - number of attributes in the model (ignored)
      ranges - low, high and width values for all attributes

    • updateRanges

      public void updateRanges(Instance instance, int numAtt, double[][] ranges)
      Updates the minimum and maximum and width values for all the attributes based on a new instance.
      Parameters:
      instance - the new instance
      numAtt - number of attributes in the model (ignored)
      ranges - low, high and width values for all attributes

    • initializeRangesEmpty

      public void initializeRangesEmpty(int numAtt, double[][] ranges)
      Used to initialize the ranges.
      Parameters:
      numAtt - number of attributes in the model
      ranges - low, high and width values for all attributes

    • updateRanges

      public double[][] updateRanges(Instance instance, double[][] ranges)
      Updates the ranges given a new instance.
      Parameters:
      instance - the new instance
      ranges - low, high and width values for all attributes
      Returns:
      the updated ranges

    • initializeRanges

      public double[][] initializeRanges(int[] instList) throws Exception
      Initializes the ranges of a subset of the instances of this dataset. Therefore m_Ranges is not set.
      Parameters:
      instList - list of indexes of the subset
      Returns:
      the ranges
      Throws:
      Exception - if something goes wrong

    • initializeRanges

      public double[][] initializeRanges(int[] instList, int startIdx, int endIdx) throws Exception
      Initializes the ranges of a subset of the instances of this dataset. Therefore m_Ranges is not set. The caller of this method should ensure that the supplied start and end indices are valid (start <= end, end<instList.length etc) and correct.
      Parameters:
      instList - list of indexes of the instances
      startIdx - start index of the subset of instances in the indices array
      endIdx - end index of the subset of instances in the indices array
      Returns:
      the ranges
      Throws:
      Exception - if something goes wrong

    • updateRanges

      public void updateRanges(Instance instance)
      Update the ranges with a new instance.
      Parameters:
      instance - the new instance

    • inRanges

      public boolean inRanges(Instance instance, double[][] ranges)
      Test if an instance is within the given ranges. Missing values are skipped. Inefficient when using sparse data.
      Parameters:
      instance - the instance
      ranges - the ranges the instance is tested to be in
      Returns:
      true if instance is within the ranges

    • rangesSet

      public boolean rangesSet()
      Check if ranges are set.
      Returns:
      true if ranges are set

    • getRanges

      public double[][] getRanges() throws Exception
      Method to get the ranges.
      Returns:
      the ranges
      Throws:
      Exception - if no randes are set yet

    • clean

      public void clean()
      Description copied from interface: DistanceFunction
      Free any references to training instances
      Specified by:
      clean in interface DistanceFunction

    • toString

      public String toString()
      Returns an empty string.
      Overrides:
      toString in class Object
      Returns:
      an empty string