Class KDTreeNodeSplitter

    • Field Detail

      • m_Instances

        protected Instances m_Instances
        The instances that'll be used for tree construction.
      • m_EuclideanDistance

        protected EuclideanDistance m_EuclideanDistance
        The distance function used for building the tree.
      • m_InstList

        protected int[] m_InstList
        The master index array that'll be reshuffled as nodes are split and the tree is constructed.
      • m_NormalizeNodeWidth

        protected boolean m_NormalizeNodeWidth
        Stores whether if the width of a KDTree node is normalized or not.
      • MIN

        public static final int MIN
        Index of min value in an array of attributes' range.
        See Also:
        Constant Field Values
      • MAX

        public static final int MAX
        Index of max value in an array of attributes' range.
        See Also:
        Constant Field Values
      • WIDTH

        public static final int WIDTH
        Index of width value (max-min) in an array of attributes' range.
        See Also:
        Constant Field Values
    • Constructor Detail

      • KDTreeNodeSplitter

        public KDTreeNodeSplitter()
        default constructor.
      • KDTreeNodeSplitter

        public KDTreeNodeSplitter​(int[] instList,
                                  Instances insts,
                                  EuclideanDistance e)
        Creates a new instance of KDTreeNodeSplitter.
        Parameters:
        instList - Reference of the master index array.
        insts - The set of training instances on which the tree is built.
        e - The EuclideanDistance object that is used in tree contruction.
    • Method Detail

      • listOptions

        public Enumeration listOptions()
        Returns an enumeration describing the available options.
        Returns:
        an enumeration of all the available options.
      • setOptions

        public void setOptions​(String[] options)
                        throws Exception
        Parses a given list of options.
        Parameters:
        options - the list of options as an array of strings
        Throws:
        Exception - if an option is not supported
      • getOptions

        public String[] getOptions()
        Gets the current settings of the object.
        Returns:
        an array of strings suitable for passing to setOptions
      • correctlyInitialized

        protected void correctlyInitialized()
                                     throws Exception
        Checks whether an object of this class has been correctly initialized. Performs checks to see if all the necessary things (master index array, training instances, distance function) have been supplied or not.
        Throws:
        Exception - If the object has not been correctly initialized.
      • splitNode

        public abstract void splitNode​(KDTreeNode node,
                                       int numNodesCreated,
                                       double[][] nodeRanges,
                                       double[][] universe)
                                throws Exception
        Splits a node into two. After splitting two new nodes are created and correctly initialised. And, node.left and node.right are set appropriately.
        Parameters:
        node - The node to split.
        numNodesCreated - The number of nodes that so far have been created for the tree, so that the newly created nodes are assigned correct/meaningful node numbers/ids.
        nodeRanges - The attributes' range for the points inside the node that is to be split.
        universe - The attributes' range for the whole point-space.
        Throws:
        Exception - If there is some problem in splitting the given node.
      • setInstances

        public void setInstances​(Instances inst)
        Sets the training instances on which the tree is (or is to be) built.
        Parameters:
        inst - The training instances.
      • setInstanceList

        public void setInstanceList​(int[] instList)
        Sets the master index array containing indices of the training instances. This array will be rearranged as the tree is built, so that each node is assigned a portion in this array which contain the instances insides the node's region.
        Parameters:
        instList - The master index array.
      • setEuclideanDistanceFunction

        public void setEuclideanDistanceFunction​(EuclideanDistance func)
        Sets the EuclideanDistance object to use for splitting nodes.
        Parameters:
        func - The EuclideanDistance object.
      • setNodeWidthNormalization

        public void setNodeWidthNormalization​(boolean normalize)
        Sets whether if a nodes region is normalized or not. If set to true then, when selecting the widest attribute/dimension for splitting, the width of each attribute/dimension, of the points inside the node's region, is divided by the width of that attribute/dimension for the whole point-space. Thus, each attribute/dimension of that node is normalized.
        Parameters:
        normalize - Should be true if normalization is required.
      • widestDim

        protected int widestDim​(double[][] nodeRanges,
                                double[][] universe)
        Returns the widest dimension. The width of each dimension (for the points inside the node) is normalized, if m_NormalizeNodeWidth is set to true.
        Parameters:
        nodeRanges - The attributes' range of the points inside the node that is to be split.
        universe - The attributes' range for the whole point-space.
        Returns:
        The index of the attribute/dimension in which the points of the node have widest spread.