Package | Description |
---|---|
librec.baseline | |
librec.data | |
librec.ext | |
librec.intf | |
librec.ranking | |
librec.rating |
Constructor and Description |
---|
ConstantGuess(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
GlobalAverage(SparseMatrix rm,
SparseMatrix tm,
int fold) |
ItemAverage(SparseMatrix rm,
SparseMatrix tm,
int fold) |
MostPopular(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
RandomGuess(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
UserAverage(SparseMatrix rm,
SparseMatrix tm,
int fold) |
Modifier and Type | Class and Description |
---|---|
class |
DiagMatrix
Data Structure: Diagonal Matrix
|
Modifier and Type | Method and Description |
---|---|
SparseMatrix |
SparseMatrix.clone()
Make a deep clone of current matrix
|
SparseMatrix[] |
DataSplitter.getDataView(java.lang.String view) |
SparseMatrix[] |
DataSplitter.getGiven(double ratio)
Split ratings into two parts: the training set consisting of user-item
ratings where
numGiven ratings are preserved for each user, and
the rest are used as the testing data |
SparseMatrix[] |
DataSplitter.getGiven(int numGiven)
Split ratings into two parts: the training set consisting of user-item
ratings where
numGiven ratings are preserved for each user, and
the rest are used as the testing data |
SparseMatrix[] |
DataSplitter.getKthFold(int k)
Return the k-th fold as test set (testMatrix), making all the others as
train set in rateMatrix.
|
SparseMatrix |
DataDAO.getRateMatrix() |
SparseMatrix[] |
DataSplitter.getRatio(double ratio)
Split ratings into two parts: (1-ratio) training, (ratio) testing data
|
SparseMatrix |
DataDAO.readData()
Default relevant columns {0: user column, 1: item column, 2: rate
column}; otherwise try
readData(int[] rels) |
SparseMatrix |
DataDAO.readData(boolean isCCSUsed) |
SparseMatrix |
DataDAO.readData(int[] cols,
boolean isCCSUsed)
Read data from the data file
|
SparseMatrix |
SparseMatrix.transpose() |
Modifier and Type | Method and Description |
---|---|
DenseMatrix |
DenseMatrix.add(SparseMatrix mat)
Do
A + B matrix operation |
DenseMatrix |
DenseMatrix.minus(SparseMatrix mat)
Do
A + B matrix operation |
DenseMatrix |
DenseMatrix.mult(SparseMatrix mat)
Matrix multiplication with a sparse matrix
|
static DenseMatrix |
DenseMatrix.mult(SparseMatrix sm,
DenseMatrix dm)
Matrix multiplication of a sparse matrix by a dense matrix
|
Constructor and Description |
---|
DataSplitter(SparseMatrix rateMatrix)
Construct a data splitter with data source of a given rate matrix
|
DataSplitter(SparseMatrix rateMatrix,
int kfold)
Construct a data splitter to split a given matrix into kfolds
|
SparseMatrix(SparseMatrix mat) |
SparseMatrix(SparseMatrix mat,
boolean deap)
Construct a sparse matrix from another sparse matrix
|
Constructor and Description |
---|
AR(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
Hybrid(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
NMF(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
PD(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
PRankD(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
SlopeOne(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
Modifier and Type | Field and Description |
---|---|
static SparseMatrix |
Recommender.rateMatrix |
Constructor and Description |
---|
IterativeRecommender(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
Recommender(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold)
Constructor for Recommender
|
SocialRecommender(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
Constructor and Description |
---|
BPRMF(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
CLiMF(SparseMatrix rm,
SparseMatrix tm,
int fold) |
RankALS(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
RankSGD(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
WRMF(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
Constructor and Description |
---|
BiasedMF(SparseMatrix rm,
SparseMatrix tm,
int fold) |
BPMF(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
ItemKNN(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
PMF(SparseMatrix rm,
SparseMatrix tm,
int fold) |
RegSVD(SparseMatrix rm,
SparseMatrix tm,
int fold) |
RSTE(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
SocialMF(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
SoRec(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
SoReg(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
SVDPlusPlus(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
TrustMF(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |
UserKNN(SparseMatrix trainMatrix,
SparseMatrix testMatrix,
int fold) |