Modifier and Type | Method and Description |
---|---|
SparseMatrix |
DataConvertor.getDatetimeMatrix()
Returns a
SparseMatrix object which stores time data. |
SparseMatrix |
DataConvertor.getPreferenceMatrix()
Returns a
SparseMatrix object which stores rate data. |
SparseMatrix |
DataSplitter.getTestData()
Get test data.
|
SparseMatrix |
DataSplitter.getTrainData()
Get train data.
|
SparseMatrix |
DataSplitter.getValidData()
Get valid data.
|
Modifier and Type | Field and Description |
---|---|
protected SparseMatrix |
AbstractDataConvertor.datetimeMatrix
store time data as {user, item, rate} matrix
|
SparseMatrix |
ArffDataConvertor.oneHotFeatureMatrix |
protected SparseMatrix |
AbstractDataConvertor.preferenceMatrix
store rate data as {user, item, rate} matrix
|
Modifier and Type | Method and Description |
---|---|
SparseMatrix |
AbstractDataConvertor.getDatetimeMatrix()
Return the date matrix.
|
SparseMatrix |
AbstractDataConvertor.getPreferenceMatrix()
Return the rate matrix.
|
Modifier and Type | Method and Description |
---|---|
SparseMatrix |
SocialDataAppender.getItemAppender()
Get item appender.
|
SparseMatrix |
SocialDataAppender.getUserAppender()
Get user appender.
|
Modifier and Type | Field and Description |
---|---|
protected SparseMatrix |
AbstractDataSplitter.testMatrix
testMatrix
|
protected SparseMatrix |
AbstractDataSplitter.trainMatrix
trainMatrix
|
protected SparseMatrix |
AbstractDataSplitter.validationMatrix
validationMatrix
|
Modifier and Type | Method and Description |
---|---|
SparseMatrix |
AbstractDataSplitter.getTestData()
(non-Javadoc)
|
SparseMatrix |
AbstractDataSplitter.getTrainData()
(non-Javadoc)
|
SparseMatrix |
AbstractDataSplitter.getValidData()
(non-Javadoc)
|
Modifier and Type | Method and Description |
---|---|
abstract double |
AbstractRecommenderEvaluator.evaluate(SparseMatrix testMatrix,
RecommendedList recommendedList)
Evaluate on the test set with the the list of recommended items.
|
Modifier and Type | Method and Description |
---|---|
double |
ReciprocalRankEvaluator.evaluate(SparseMatrix testMatrix,
RecommendedList recommendedList)
Evaluate on the test set with the the list of recommended items.
|
double |
RecallEvaluator.evaluate(SparseMatrix testMatrix,
RecommendedList recommendedList)
Evaluate on the test set with the the list of recommended items.
|
double |
PrecisionEvaluator.evaluate(SparseMatrix testMatrix,
RecommendedList recommendedList)
Evaluate on the test set with the the list of recommended items.
|
double |
NormalizedDCGEvaluator.evaluate(SparseMatrix testMatrix,
RecommendedList recommendedList)
Evaluate on the test set with the the list of recommended items.
|
double |
IdealDCGEvaluator.evaluate(SparseMatrix testMatrix,
RecommendedList recommendedList)
Evaluate on the test set with the the list of recommended items.
|
double |
HitRateEvaluator.evaluate(SparseMatrix testMatrix,
RecommendedList recommendedList)
Evaluate on the test set with the the list of recommended items.
|
double |
DiversityEvaluator.evaluate(SparseMatrix testMatrix,
RecommendedList recommendedList)
Evaluate on the test set with the the list of recommended items.
|
double |
AverageReciprocalHitRankEvaluator.evaluate(SparseMatrix testMatrix,
RecommendedList recommendedList)
Evaluate on the test set with the the list of recommended items.
|
double |
AveragePrecisionEvaluator.evaluate(SparseMatrix testMatrix,
RecommendedList recommendedList)
Evaluate on the test set with the the list of recommended items.
|
double |
AUCEvaluator.evaluate(SparseMatrix testMatrix,
RecommendedList recommendedList)
Evaluate on the test set with the the list of recommended items.
|
Modifier and Type | Method and Description |
---|---|
double |
RMSEEvaluator.evaluate(SparseMatrix testMatrix,
RecommendedList recommendedList) |
double |
MSEEvaluator.evaluate(SparseMatrix testMatrix,
RecommendedList recommendedList) |
double |
MPEEvaluator.evaluate(SparseMatrix testMatrix,
RecommendedList recommendedList) |
double |
MAEEvaluator.evaluate(SparseMatrix testMatrix,
RecommendedList recommendedList) |
Constructor and Description |
---|
Shuffle(SparseMatrix sparseMatrix)
Construct a shuffle for SparseMatrix.
|
Modifier and Type | Class and Description |
---|---|
class |
DiagMatrix |
Modifier and Type | Method and Description |
---|---|
SparseMatrix |
SparseMatrix.clone()
Make a deep clone of current matrix
|
SparseMatrix |
SparseTensor.matricization(int n)
Re-ordering entries of a tensor into a matrix
|
SparseMatrix |
SparseTensor.rateMatrix()
retrieve a rating matrix from the tensor.
|
SparseMatrix |
SparseMatrix.reshape(int rows,
int cols)
Return a new matrix with shape (rows, cols) with data from the current matrix.
|
SparseMatrix |
SparseTensor.slice(int rowDim,
int colDim,
int... otherKeys)
Slice is a two-dimensional sub-array of a tensor, defined by fixing all but two indices.
|
SparseMatrix |
SparseMatrix.transpose() |
Modifier and Type | Method and Description |
---|---|
DenseMatrix |
DenseMatrix.add(SparseMatrix mat)
Do
A + B matrix operation |
DenseMatrix |
DenseMatrix.addEqual(SparseMatrix mat)
Do
A + B matrix operation |
DenseMatrix |
DenseMatrix.minus(SparseMatrix mat)
Do
A - B matrix operation |
DenseMatrix |
DenseMatrix.minusEqual(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
|
static void |
SparseMatrix.reshape(SparseMatrix mat)
remove zero entries of the given matrix
|
Constructor and Description |
---|
SparseMatrix(SparseMatrix mat)
Construct a sparse matrix from another sparse matrix
|
Modifier and Type | Field and Description |
---|---|
protected SparseMatrix |
SocialRecommender.socialMatrix
socialMatrix: social rate matrix, indicating a user is connecting to a number of other users
|
protected SparseMatrix |
AbstractRecommender.testMatrix
testMatrix
|
protected SparseMatrix |
AbstractRecommender.trainMatrix
trainMatrix
|
protected SparseMatrix |
AbstractRecommender.validMatrix
validMatrix
|
Modifier and Type | Field and Description |
---|---|
protected SparseMatrix |
WRMFRecommender.confindenceMinusIdentityMatrix
confindence Minus Identity Matrix{ui} = confidenceMatrix_{ui} - 1 =alpha * r_{ui} or log(1+10^alpha * r_{ui})
|
protected SparseMatrix |
WRMFRecommender.preferenceMatrix
preferenceMatrix_{ui} = 1 if
r_{ui}>0 or preferenceMatrix_{ui} = 0 |
Constructor and Description |
---|
LLORMAUpdater(int threadIDParam,
int numFactorsParam,
int numUsersParam,
int numItemsParam,
int anchorUserParam,
int anchorItemParam,
double learnRateParam,
double localRegUserParam,
double localRegItemParam,
int localIterationParam,
DenseVector userWeightsParam,
DenseVector itemWeightsParam,
SparseMatrix trainMatrixParam)
Construct a local model for singleton LLORMA.
|
Modifier and Type | Field and Description |
---|---|
protected SparseMatrix |
HFTRecommender.trainMatrix |
protected SparseMatrix |
EFMRecommender.trainMatrix |
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