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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
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