Package weka.core.neighboursearch

Class PCANNSearch

  • All Implemented Interfaces:
    Serializable, weka.core.AdditionalMeasureProducer, weka.core.OptionHandler, weka.core.RevisionHandler
    Direct Known Subclasses:
    PCANNSearchWithIndices
    public class PCANNSearch
extends NewNNSearch
    Class implementing the brute force search algorithm for nearest neighbour search, filtered using PLS.

    Valid options are: 

     -S
  Skip identical instances (distances equal to zero).
    Version:
    $Revision$
    Author:
    Dale
    See Also:
    Serialized Form

    • Nested Class Summary

      • Nested classes/interfaces inherited from class weka.core.neighboursearch.NearestNeighbourSearch

        weka.core.neighboursearch.NearestNeighbourSearch.MyHeap, weka.core.neighboursearch.NearestNeighbourSearch.MyHeapElement, weka.core.neighboursearch.NearestNeighbourSearch.NeighborList, weka.core.neighboursearch.NearestNeighbourSearch.NeighborNode

    • Field Summary

      Fields 
      Modifier and Type Field Description
      protected double m_CoverVariance
      the amount of varaince to cover in the original data when retaining the best n PC's.
      protected int m_MaxAttrsInName
      maximum number of attributes in the transformed attribute name.
      protected weka.core.Instances m_myInstances  
      protected PrincipalComponentsJ m_pca
      The neighbourhood of instances to find neighbours in.
      protected int m_Preprocessing
      the type of preprocessing

      • Fields inherited from class weka.core.neighboursearch.NearestNeighbourSearch

        m_DistanceFunction, m_Instances, m_kNN, m_MeasurePerformance, m_Stats

    • Constructor Summary

      Constructors 
      Constructor Description
      PCANNSearch()
      Constructor.
      PCANNSearch​(weka.core.Instances insts)
      Constructor that uses the supplied set of instances.

    • Field Detail

      • m_CoverVariance

        protected double m_CoverVariance
        the amount of varaince to cover in the original data when retaining the best n PC's.

      • m_MaxAttrsInName

        protected int m_MaxAttrsInName
        maximum number of attributes in the transformed attribute name.

      • m_myInstances

        protected weka.core.Instances m_myInstances

      • m_Preprocessing

        protected int m_Preprocessing
        the type of preprocessing

    • Constructor Detail

      • PCANNSearch

        public PCANNSearch()
        Constructor. Needs setInstances(Instances) to be called before the class is usable.

      • PCANNSearch

        public PCANNSearch​(weka.core.Instances insts)
        Constructor that uses the supplied set of instances.
        Parameters:
        insts - the instances to use

    • Method Detail

      • setVarianceCovered

        public void setVarianceCovered​(double value)
        Sets the amount of variance to account for when retaining principal components.
        Parameters:
        value - the proportion of total variance to account for

      • getVarianceCovered

        public double getVarianceCovered()
        Gets the proportion of total variance to account for when retaining principal components.
        Returns:
        the proportion of variance to account for

      • setMaximumAttributeNames

        public void setMaximumAttributeNames​(int value)
        Sets maximum number of attributes to include in transformed attribute names.
        Parameters:
        value - the maximum number of attributes

      • getMaximumAttributeNames

        public int getMaximumAttributeNames()
        Gets maximum number of attributes to include in transformed attribute names.
        Returns:
        the maximum number of attributes

      • transformInstances

        protected weka.core.Instances transformInstances​(weka.core.Instances in)
                                          throws Exception
        Throws:
        Exception

      • transformInstance

        protected weka.core.Instance transformInstance​(weka.core.Instance in)
                                        throws Exception
        Throws:
        Exception

      • kNearestNeighbours

        public weka.core.Instances kNearestNeighbours​(weka.core.Instance target,
                                              int kNN)
                                       throws Exception
        Returns k nearest instances in the current neighbourhood to the supplied instance.
        Overrides:
        kNearestNeighbours in class NewNNSearch
        Parameters:
        target - The instance to find the k nearest neighbours for.
        kNN - The number of nearest neighbours to find.
        Returns:
        the k nearest neighbors
        Throws:
        Exception - if the neighbours could not be found.

      • setInstances

        public void setInstances​(weka.core.Instances insts)
                  throws Exception
        Sets the instances comprising the current neighbourhood.
        Overrides:
        setInstances in class NewNNSearch
        Parameters:
        insts - The set of instances on which the nearest neighbour search is carried out. Usually this set is the training set.
        Throws:
        Exception - if setting of instances fails

      • update

        public void update​(weka.core.Instance ins)
            throws Exception
        Updates the LinearNNSearch to cater for the new added instance. This implementation only updates the ranges of the DistanceFunction class, since our set of instances is passed by reference and should already have the newly added instance.
        Overrides:
        update in class NewNNSearch
        Parameters:
        ins - The instance to add. Usually this is the instance that is added to our neighbourhood i.e. the training instances.
        Throws:
        Exception - if the given instances are null

      • addInstanceInfo

        public void addInstanceInfo​(weka.core.Instance ins)
        Adds the given instance info. This implementation updates the range datastructures of the DistanceFunction class.
        Overrides:
        addInstanceInfo in class NewNNSearch
        Parameters:
        ins - The instance to add the information of. Usually this is the test instance supplied to update the range of attributes in the distance function.

      • listOptions

        public Enumeration listOptions()
        Description copied from class: NewNNSearch
        Returns an enumeration describing the available options.
        Specified by:
        listOptions in interface weka.core.OptionHandler
        Overrides:
        listOptions in class NewNNSearch
        Returns:
        an enumeration of all the available options.

      • setPreprocessing

        public void setPreprocessing​(weka.core.SelectedTag value)
        Sets the type of preprocessing to use
        Parameters:
        value - the preprocessing type

      • setOptions

        public void setOptions​(String[] options)
                throws Exception
        Description copied from class: NewNNSearch
        Parses a given list of options.

        Valid options are: 

         -S
  Skip identical instances (distances equal to zero).
        Specified by:
        setOptions in interface weka.core.OptionHandler
        Overrides:
        setOptions in class NewNNSearch
        Parameters:
        options - the list of options as an array of strings
        Throws:
        Exception - if an option is not supported

      • getOptions

        public String[] getOptions()
        Description copied from class: NewNNSearch
        Gets the current settings.
        Specified by:
        getOptions in interface weka.core.OptionHandler
        Overrides:
        getOptions in class NewNNSearch
        Returns:
        an array of strings suitable for passing to setOptions()