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## D

dataFileName - Static variable in class prea.main.Prea
  The name of data file used for test.
DataMap<Key extends java.lang.Comparable<Key>,Val> - Class in prea.data.structure
  This is a class implementing HashMap-based data map.
DataMap() - Constructor for class prea.data.structure.DataMap
  Basic constructor without specifying the capacity.
DataMap(int) - Constructor for class prea.data.structure.DataMap
  A constructor specifying the capacity.
DataSplitManager - Class in prea.data.splitter
  This class implements data split functions, which are common in individual model selection methods.
DataSplitManager(SparseMatrix, int, int) - Constructor for class prea.data.splitter.DataSplitManager
  Construct a data set manager.
defaultValue - Variable in class prea.recommender.memory.MemoryBasedRecommender
  The default voting value, if used.
defaultVote - Variable in class prea.recommender.memory.MemoryBasedRecommender
  Indicating whether to use default vote value.
DenseMatrix - Class in prea.data.structure
  This class implements dense matrix.
DenseMatrix(int, int) - Constructor for class prea.data.structure.DenseMatrix
  Construct an empty dense matrix, with a given size.
DenseMatrix(Matrix) - Constructor for class prea.data.structure.DenseMatrix
  Construct an empty dense matrix, with data copied from UJMP matrix.
DenseVector - Class in prea.data.structure
  This class implements dense vector with array-based implementation.
DenseVector() - Constructor for class prea.data.structure.DenseVector
  Construct an empty dense vector, with capacity 0.
DenseVector(int) - Constructor for class prea.data.structure.DenseVector
  Construct a new dense vector with size n.
DenseVector(Matrix) - Constructor for class prea.data.structure.DenseVector
  Construct a new dense vector, having same data with the given UJMP matrix.
diagonal() - Method in class prea.data.structure.DenseMatrix
  Return items in the diagonal in vector form.
diagonal() - Method in class prea.data.structure.SparseMatrix
  Return items in the diagonal in vector form.
diffMatrix - Variable in class prea.recommender.etc.SlopeOne
  Prepared difference matrix
Distance - Class in prea.util
  This is a class implementing various distance measures of two vectors.
Distance() - Constructor for class prea.util.Distance
distanceKendall(int[], double[], int[], double[], int) - Static method in class prea.util.Distance
  Return the Kendall's Tau distance for two rankings.
distanceKendallParsed(int[], double[], int[], double[], int) - Static method in class prea.util.Distance
  Return intermediate Kendall's Tau distance for two rankings parsed by prb
distanceNDCG(int[], double[], int[], double[]) - Static method in class prea.util.Distance
  Return NDCG score for a ranked list given the scores and relevance of items in the list.
distanceOneToAllTest(int, int, SparseVector[], int, int[]) - Method in class prea.recommender.etc.RankBased
  Compute the distance between the testing user with the testing item of every possible ratings and all the other training users with training items.
distanceOneToAllTrain(int, double[], int, int[]) - Method in class prea.recommender.etc.RankBased
  Compute the distance between the testing user and all the other training users with training items.
distancePairWise(DenseMatrix, DenseMatrix) - Method in class prea.recommender.etc.NonlinearPMF
  Compute the squared Euclidean distance between the row vectors of one matrix and the row vectors of another matrix.
distancePairWise(DenseMatrix, DenseVector) - Method in class prea.recommender.etc.NonlinearPMF
  Compute the squared euclidean distance between the row vectors of one matrix and a vector.
distanceSpearman(int[], double[], int[], double[], int) - Static method in class prea.util.Distance
  Return Spearman distance for two rankings.
distanceSpearmanParsed(int[], double[], int[], double[], int) - Static method in class prea.util.Distance
  Return the Spearman distance for two rankings parsed by probability
Distribution - Class in prea.util
  This class implements several statistical distributions.
Distribution() - Constructor for class prea.util.Distribution
divideFolds(int) - Method in class prea.data.splitter.KfoldCrossValidation
  Divide the original rating matrix into k-fold.

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