org.knime.base.node.mine.decisiontree2.learner
Class SplitNominalNormal

java.lang.Object
  org.knime.base.node.mine.decisiontree2.learner.Split
      org.knime.base.node.mine.decisiontree2.learner.SplitNominal
          org.knime.base.node.mine.decisiontree2.learner.SplitNominalNormal

public class SplitNominalNormal
extends SplitNominal

This class determines the best split for a nominal attribute. The split is performed by creating one partition for each nominal value, i.e. the branching degree of the tree.

Author:

Christoph Sieb, University of Konstanz

Field Summary

Fields inherited from class org.knime.base.node.mine.decisiontree2.learner.Split

m_splitQualityMeasure

Constructor Summary

SplitNominalNormal(InMemoryTable table, int attributeIndex, SplitQualityMeasure splitQualityMeasure, double minObjectsCount)
Constructs the best split for the given nominal attribute.

Method Summary

boolean	canBeFurtherUsed() For normal nominal splits it makes no sense to be used in deeper levels.
int	getNumberPartitions() The number of partitions of a normal nominal split corresponds to the number of different nominal values of the attribute.
int	getPartitionForRow(DataRowWeighted row) Returns the partition the given row belongs to according to this split.
double[]	getPartitionWeights() Returns the partition weights.

Methods inherited from class org.knime.base.node.mine.decisiontree2.learner.SplitNominal

getSplitValues

Methods inherited from class org.knime.base.node.mine.decisiontree2.learner.Split

getAttributeIndex, getBestQualityMeasure, getQualityMeasureName, getSplitAttributeName, getTable, isValidSplit, setBestQualityMeasure, toString

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

SplitNominalNormal

public SplitNominalNormal(InMemoryTable table,
                          int attributeIndex,
                          SplitQualityMeasure splitQualityMeasure,
                          double minObjectsCount)

Constructs the best split for the given nominal attribute. The results can be retrieved from getter methods.

Parameters:

table - the attribute list for which to create the split

attributeIndex - the index of the attribute for which to calculate the split

splitQualityMeasure - the split quality measure (e.g. gini or gain ratio)

minObjectsCount - the minimumn number of objects in at least two partitions

Method Detail

getNumberPartitions

public int getNumberPartitions()

The number of partitions of a normal nominal split corresponds to the number of different nominal values of the attribute. Return the number of partitions resulting from this split.

Specified by:

getNumberPartitions in class Split

Returns:

the number of partitions resulting from this split

canBeFurtherUsed

public boolean canBeFurtherUsed()

For normal nominal splits it makes no sense to be used in deeper levels. Returns true if it makes sense to use this split's attribute further in deeper levels, false if not.

Specified by:

canBeFurtherUsed in class Split

Returns:

true if it makes sense to use this split's attribute further in deeper levels, false if not

getPartitionForRow

public int getPartitionForRow(DataRowWeighted row)

Returns the partition the given row belongs to according to this split. If the value of the split attribute is missing (i.e. NaN) -1 is returned.

Specified by:

getPartitionForRow in class Split

Parameters:

row - the row for which to get the partition index

Returns:

the partition the given row belongs to according to this split; if the value of the split attribute is missing (i.e. NaN) -1 is returned

getPartitionWeights

public double[] getPartitionWeights()

Returns the partition weights. The weights represent the relative frequency of valid rows per partition. The weights are normally used to adapt the weight of rows whose split value is missing. Such a row is then assigned to each parition with the adapted weight.

Specified by:

getPartitionWeights in class Split

Returns:

the partition weights