Uses of Class
ciir.umass.edu.learning.DataPoint

Packages that use DataPoint

Package	Description
ciir.umass.edu.learning
ciir.umass.edu.learning.boosting
ciir.umass.edu.learning.neuralnet
ciir.umass.edu.learning.tree

Uses of DataPoint in ciir.umass.edu.learning

Subclasses of DataPoint in ciir.umass.edu.learning

Modifier and Type	Class	Description
class	DenseDataPoint
class	SparseDataPoint	Implements a sparse data point using a compressed sparse row data structure

Fields in ciir.umass.edu.learning declared as DataPoint

Modifier and Type	Field	Description
protected DataPoint[]	RankList.rl

Methods in ciir.umass.edu.learning that return DataPoint

Modifier and Type	Method	Description
DataPoint	RankList.get(int k)

Methods in ciir.umass.edu.learning with parameters of type DataPoint

Modifier and Type	Method	Description
double	CoorAscent.eval(DataPoint p)
double	LinearRegRank.eval(DataPoint p)
double	Ranker.eval(DataPoint p)
void	RankList.set(int k, DataPoint p)

Constructor parameters in ciir.umass.edu.learning with type arguments of type DataPoint

Constructor	Description
RankList(java.util.List<DataPoint> rl)

Uses of DataPoint in ciir.umass.edu.learning.boosting

Methods in ciir.umass.edu.learning.boosting with parameters of type DataPoint

Modifier and Type	Method	Description
double	AdaRank.eval(DataPoint p)
double	RankBoost.eval(DataPoint p)
int	RBWeakRanker.score(DataPoint p)

Uses of DataPoint in ciir.umass.edu.learning.neuralnet

Methods in ciir.umass.edu.learning.neuralnet with parameters of type DataPoint

Modifier and Type	Method	Description
protected void	RankNet.addInput(DataPoint p)	Auxiliary functions for pair-wise preference network learning.
double	ListNet.eval(DataPoint p)
double	RankNet.eval(DataPoint p)

Uses of DataPoint in ciir.umass.edu.learning.tree

Fields in ciir.umass.edu.learning.tree declared as DataPoint

Modifier and Type	Field	Description
protected DataPoint[]	LambdaMART.martSamples
protected DataPoint[]	RegressionTree.trainingSamples

Methods in ciir.umass.edu.learning.tree with parameters of type DataPoint

Modifier and Type	Method	Description
protected void	FeatureHistogram.construct(DataPoint[] samples, double[] labels, int[][] sampleSortedIdx, float[][] thresholds, int start, int end)
void	FeatureHistogram.construct(DataPoint[] samples, double[] labels, int[][] sampleSortedIdx, int[] features, float[][] thresholds)
float	Ensemble.eval(DataPoint dp)
double	LambdaMART.eval(DataPoint dp)
double	RegressionTree.eval(DataPoint dp)	Get the tree output for the input sample
double	RFRanker.eval(DataPoint dp)
double	Split.eval(DataPoint dp)
protected int[]	LambdaMART.sortSamplesByFeature(DataPoint[] samples, int fid)

Constructors in ciir.umass.edu.learning.tree with parameters of type DataPoint

Constructor	Description
RegressionTree(int nLeaves, DataPoint[] trainingSamples, double[] labels, FeatureHistogram hist, int minLeafSupport)