Overview | Package | Class | Use | Tree | Deprecated | Index | Help
PREV LETTER NEXT LETTER | FRAMES NO FRAMES

A B C D E F G H I K L M N O P Q R S T U V W X

## M

M - Variable in class prea.data.structure.DenseMatrix
  The number of rows.
M - Variable in class prea.data.structure.SparseMatrix
  The number of rows.
mae - Variable in class prea.util.EvaluationMetrics
  Mean Absoulte Error (MAE)
main(String[]) - Static method in class prea.main.Prea
  Test examples for every algorithm.
main(String[]) - Static method in class prea.main.Splitter
  Main method for reading the arff file, writing split and similarity results.
makeIdentity(int) - Static method in class prea.data.structure.DenseMatrix
  Generate an identity matrix with the given size.
makeIdentity(int) - Static method in class prea.data.structure.SparseMatrix
  Generate an identity matrix with the given size.
makeValidationSet(double) - Method in class prea.recommender.etc.FastNPCA
  Items which will be used for validation purpose are moved from rateMatrix to validationMatrix.
makeValidationSet(SparseMatrix, double) - Method in class prea.recommender.matrix.NMF
  Items which will be used for validation purpose are moved from rateMatrix to validationMatrix.
map - Variable in class prea.data.structure.DataMap
  Key-value mapping structure
map - Variable in class prea.data.structure.DenseMatrix
  The UJMP matrix to store data.
map - Variable in class prea.data.structure.DenseVector
  The UJMP matrix to store data.
map - Variable in class prea.data.structure.SparseVector
  Data map for pairs.
MatrixFactorizationRecommender - Class in prea.recommender.matrix
  This is an abstract class implementing four matrix-factorization-based methods including Regularized SVD, NMF, PMF, and Bayesian PMF.
MatrixFactorizationRecommender(int, int, double, double, int, double, double, double, int, boolean) - Constructor for class prea.recommender.matrix.MatrixFactorizationRecommender
  Construct a matrix-factorization-based model with the given data.
max() - Method in class prea.data.structure.SparseMatrix
  The value of maximum element in the matrix.
max() - Method in class prea.data.structure.SparseVector
  The value of maximum element in the vector.
maxIter - Variable in class prea.recommender.etc.FastNPCA
  Maximum number of iteration.
maxIter - Variable in class prea.recommender.etc.NonlinearPMF
  Maximum number of iteration.
maxIter - Variable in class prea.recommender.matrix.MatrixFactorizationRecommender
  Maximum number of iteration.
maxValue - Variable in class prea.data.splitter.DataSplitManager
  Maximum value of rating, existing in the dataset.
maxValue - Static variable in class prea.main.Prea
  Maximum value of rating, existing in the dataset.
maxValue - Variable in class prea.recommender.baseline.BaselineRecommender
  Maximum value of rating, existing in the dataset.
maxValue - Variable in class prea.recommender.CustomRecommender
  Maximum value of rating, existing in the dataset.
maxValue - Variable in class prea.recommender.etc.FastNPCA
  Maximum value of rating, existing in the dataset.
maxValue - Variable in class prea.recommender.etc.NonlinearPMF
  Maximum value of rating, existing in the dataset.
maxValue - Variable in class prea.recommender.etc.RankBased
  Maximum value of rating, existing in the dataset.
maxValue - Variable in class prea.recommender.etc.SlopeOne
  Maximum value of rating, existing in the dataset.
maxValue - Variable in class prea.recommender.matrix.MatrixFactorizationRecommender
  Maximum value of rating, existing in the dataset.
maxValue - Variable in class prea.recommender.memory.MemoryBasedRecommender
  Maximum value of rating, existing in the dataset.
maxValue - Variable in class prea.util.EvaluationMetrics
  Maximum value of rating, existing in the dataset.
MEAN_ABS_DIFF - Static variable in class prea.main.Splitter
  Similarity measure code for Mean Absoulte Difference.
MEAN_ABS_DIFF - Static variable in class prea.recommender.memory.MemoryBasedRecommender
  Similarity Measure Code for Mean Absolute Difference (MAD)
MEAN_LOSS - Static variable in class prea.recommender.etc.RankBased
  Mean loss function
MEAN_SQUARE_DIFF - Static variable in class prea.main.Splitter
  Similarity measure code for Mean Squared Difference.
MEAN_SQUARE_DIFF - Static variable in class prea.recommender.memory.MemoryBasedRecommender
  Similarity Measure Code for Mean Squared Difference (MSD)
MemoryBasedRecommender - Class in prea.recommender.memory
  The class implementing two memory-based (neighborhood-based) methods, predicting by referring to rating matrix for each query.
MemoryBasedRecommender(int, int, int, int, int, int, boolean, double) - Constructor for class prea.recommender.memory.MemoryBasedRecommender
  Construct a memory-based model with the given data.
min() - Method in class prea.data.structure.SparseMatrix
  The value of minimum element in the matrix.
min() - Method in class prea.data.structure.SparseVector
  The value of minimum element in the vector.
minus(DenseVector) - Method in class prea.data.structure.DenseVector
  Vector subtraction (a - b)
minus(SparseVector) - Method in class prea.data.structure.SparseVector
  Vector subtraction (a - b)
minValue - Variable in class prea.data.splitter.DataSplitManager
  Minimum value of rating, existing in the dataset.
minValue - Static variable in class prea.main.Prea
  Minimum value of rating, existing in the dataset.
minValue - Variable in class prea.recommender.baseline.BaselineRecommender
  Minimum value of rating, existing in the dataset.
minValue - Variable in class prea.recommender.CustomRecommender
  Minimum value of rating, existing in the dataset.
minValue - Variable in class prea.recommender.etc.FastNPCA
  Minimum value of rating, existing in the dataset.
minValue - Variable in class prea.recommender.etc.NonlinearPMF
  Minimum value of rating, existing in the dataset.
minValue - Variable in class prea.recommender.etc.RankBased
  Minimum value of rating, existing in the dataset.
minValue - Variable in class prea.recommender.etc.SlopeOne
  Minimum value of rating, existing in the dataset.
minValue - Variable in class prea.recommender.matrix.MatrixFactorizationRecommender
  Minimum value of rating, existing in the dataset.
minValue - Variable in class prea.recommender.memory.MemoryBasedRecommender
  Minimum value of rating, existing in the dataset.
minValue - Variable in class prea.util.EvaluationMetrics
  Minimum value of rating, existing in the dataset.
momentum - Variable in class prea.recommender.etc.NonlinearPMF
  Momentum parameter.
momentum - Variable in class prea.recommender.matrix.MatrixFactorizationRecommender
  Momentum parameter.
mse - Variable in class prea.util.EvaluationMetrics
  Mean Squared Error (MSE)
mu - Variable in class prea.recommender.etc.FastNPCA

Overview | Package | Class | Use | Tree | Deprecated | Index | Help
PREV LETTER NEXT LETTER | FRAMES NO FRAMES

A B C D E F G H I K L M N O P Q R S T U V W X