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## weka.core.neighboursearch.kdtrees
Class KDTreeNodeSplitter

    java.lang.Object
      weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter

All Implemented Interfaces:
  java.io.Serializable, OptionHandler, RevisionHandler

Direct Known Subclasses:
  KMeansInpiredMethod, MedianOfWidestDimension, MidPointOfWidestDimension, SlidingMidPointOfWidestSide

    public abstract class KDTreeNodeSplitter

extends java.lang.Object
implements java.io.Serializable, OptionHandler, RevisionHandler

Class that splits up a KDTreeNode.

Version:
  $Revision: 5953 $
Author:
  Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz)
See Also:
  Serialized Form

Field Summary
`static int` | `MAX` Index of max value in an array of attributes' range.
`static int` | `MIN` Index of min value in an array of attributes' range.
`static int` | `WIDTH` Index of width value (max-min) in an array of attributes' range.

Constructor Summary
`KDTreeNodeSplitter()` default constructor.
`KDTreeNodeSplitter(int[] instList, Instances insts, EuclideanDistance e)` Creates a new instance of KDTreeNodeSplitter.

Method Summary
`java.lang.String[]` | `getOptions()` Gets the current settings of the object.
`java.lang.String` | `getRevision()` Returns the revision string.
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`void` | `setEuclideanDistanceFunction(EuclideanDistance func)` Sets the EuclideanDistance object to use for splitting nodes.
`void` | `setInstanceList(int[] instList)` Sets the master index array containing indices of the training instances.
`void` | `setInstances(Instances inst)` Sets the training instances on which the tree is (or is to be) built.
`void` | `setNodeWidthNormalization(boolean normalize)` Sets whether if a nodes region is normalized or not.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`abstract void` | `splitNode(KDTreeNode node, int numNodesCreated, double[][] nodeRanges, double[][] universe)` Splits a node into two.

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Field Detail

### 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 java.util.Enumeration listOptions()

  Returns an enumeration describing the available options.

  Specified by:
    `listOptions` in interface `OptionHandler`
  Returns:
    an enumeration of all the available options.

### setOptions

    public void setOptions(java.lang.String[] options)
                    throws java.lang.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:
    `java.lang.Exception` - if an option is not supported

### getOptions

    public java.lang.String[] getOptions()

  Gets the current settings of the object.

  Specified by:
    `getOptions` in interface `OptionHandler`
  Returns:
    an array of strings suitable for passing to setOptions

### splitNode

    public abstract void splitNode(KDTreeNode node,
                                   int numNodesCreated,
                                   double[][] nodeRanges,
                                   double[][] universe)
                            throws java.lang.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:
    `java.lang.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.

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Returns:
    the revision

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