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## N

N - Variable in class prea.data.structure.DenseMatrix
  The number of columns.
N - Variable in class prea.data.structure.DenseVector
  The length (maximum number of items to be stored) of sparse vector.
N - Variable in class prea.data.structure.SparseMatrix
  The number of columns.
N - Variable in class prea.data.structure.SparseVector
  The length (maximum number of items to be stored) of sparse vector.
ndcg - Variable in class prea.util.EvaluationMetrics
  Rank-based Normalized Discounted Cumulative Gain (NDCG)
neighborSize - Variable in class prea.recommender.memory.MemoryBasedRecommender
  The number of neighbors, used for estimation.
NMF - Class in prea.recommender.matrix
  This is a class implementing Non-negative Matrix Factorization.
NMF(int, int, double, double, int, double, double, double, int, double, boolean) - Constructor for class prea.recommender.matrix.NMF
  Construct a matrix-factorization model with the given data.
NonlinearPMF - Class in prea.recommender.etc
  This is a class implementing Non-linear Probabilistic Matrix Factorization.
NonlinearPMF(int, int, double, double, int, double, double, int, double, double, double, double) - Constructor for class prea.recommender.etc.NonlinearPMF
  Construct a matrix-factorization model with the given data.
norm() - Method in class prea.data.structure.DenseVector
  2-norm of the vector.
norm() - Method in class prea.data.structure.SparseVector
  2-norm of the vector.
normalDistribution(double, double, int) - Static method in class prea.util.Distribution
  Randomly sample several points from Normal Distribution with the given mean and standard deviation.
normalRandom(double, double) - Static method in class prea.util.Distribution
  Randomly sample 1 point from Normal Distribution with the given mean and standard deviation.
numTieCount - Variable in class prea.recommender.etc.RankBased
  The number of tie structure for each user with size m testing users by l raing levels.

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