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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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