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Uses of SparseMatrix in prea.data.splitter |
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Fields in prea.data.splitter declared as SparseMatrix | |
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private SparseMatrix |
KfoldCrossValidation.assign
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protected SparseMatrix |
DataSplitManager.rateMatrix
Rating matrix for each user (row) and item (column) |
protected SparseMatrix |
DataSplitManager.testMatrix
Rating matrix for test items. |
Methods in prea.data.splitter that return SparseMatrix | |
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SparseMatrix |
KfoldCrossValidation.getKthFold(int k)
Return the k-th fold as test set (testMatrix), making all the others as train set in rateMatrix. |
SparseMatrix |
DataSplitManager.getTestMatrix()
Getter method for rating matrix with test data. |
Constructors in prea.data.splitter with parameters of type SparseMatrix | |
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DataSplitManager(SparseMatrix originalMatrix,
int max,
int min)
Construct a data set manager. |
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KfoldCrossValidation(SparseMatrix originalMatrix,
int k,
int max,
int min)
Construct an instance for K-fold cross-validation. |
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PredefinedSplit(SparseMatrix originalMatrix,
java.lang.String splitFileName,
int max,
int min)
Construct an instance for splitter with predefined split file. |
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SimpleSplit(SparseMatrix originalMatrix,
double testRatio,
int max,
int min)
Construct an instance for simple splitter. |
Uses of SparseMatrix in prea.data.structure |
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Methods in prea.data.structure that return SparseMatrix | |
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SparseMatrix |
SparseMatrix.add(double alpha)
Scalar addition. |
SparseMatrix |
SparseMatrix.cholesky()
Calculate Cholesky decomposition of the matrix. |
SparseMatrix |
SparseMatrix.covariance()
Generate a covariance matrix of the current matrix. |
SparseMatrix |
SparseMatrix.exp(double alpha)
Exponential of a given constant. |
SparseMatrix |
SparseMatrix.inverse()
Calculate inverse matrix. |
static SparseMatrix |
SparseMatrix.makeIdentity(int n)
Generate an identity matrix with the given size. |
SparseMatrix |
SparseVector.outerProduct(SparseVector b)
Outer product of two vectors. |
SparseMatrix |
SparseMatrix.partInverse(int[] indexList)
Inverse of matrix only with indices in indexList. |
SparseMatrix |
SparseMatrix.partMinus(SparseMatrix B,
int[] indexList)
Matrix subtraction (A = A - B) only with indices in indexList. |
SparseMatrix |
SparseVector.partOuterProduct(SparseVector b,
int[] indexList)
Outer-product for indices only in the given indices. |
SparseMatrix |
SparseMatrix.partPlus(SparseMatrix B,
int[] indexList)
Matrix summation (A = A + B) only with indices in indexList. |
SparseMatrix |
SparseMatrix.partScale(double alpha,
int[] indexList)
Scalar Multiplication only with indices in indexList. |
SparseMatrix |
SparseMatrix.plus(SparseMatrix B)
Matrix-matrix sum (C = A + B) |
SparseMatrix |
SparseMatrix.scale(double alpha)
Scalar subtraction (aX). |
SparseMatrix |
SparseMatrix.times(SparseMatrix B)
Matrix-matrix product (C = AB) |
SparseMatrix |
DenseMatrix.toSparseMatrix()
Convert the matrix into sparse matrix. |
SparseMatrix |
SparseMatrix.transpose()
The transpose of the matrix. |
Methods in prea.data.structure with parameters of type SparseMatrix | |
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SparseMatrix |
SparseMatrix.partMinus(SparseMatrix B,
int[] indexList)
Matrix subtraction (A = A - B) only with indices in indexList. |
SparseMatrix |
SparseMatrix.partPlus(SparseMatrix B,
int[] indexList)
Matrix summation (A = A + B) only with indices in indexList. |
SparseMatrix |
SparseMatrix.plus(SparseMatrix B)
Matrix-matrix sum (C = A + B) |
void |
SparseMatrix.selfTimes(SparseMatrix B)
Matrix-matrix product (A = AB), without using extra memory. |
SparseMatrix |
SparseMatrix.times(SparseMatrix B)
Matrix-matrix product (C = AB) |
Constructors in prea.data.structure with parameters of type SparseMatrix | |
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SparseMatrix(SparseMatrix sm)
Construct an empty sparse matrix, with data copied from another sparse matrix. |
Uses of SparseMatrix in prea.main |
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Fields in prea.main declared as SparseMatrix | |
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static SparseMatrix |
Splitter.rateMatrix
Rating matrix for train dataset. |
static SparseMatrix |
Prea.rateMatrix
Rating matrix for each user (row) and item (column) |
static SparseMatrix |
Prea.testMatrix
Rating matrix for test items. |
Uses of SparseMatrix in prea.recommender |
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Fields in prea.recommender declared as SparseMatrix | |
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private SparseMatrix |
UnitTest.preservedRateMatrix
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private SparseMatrix |
UnitTest.preservedTestMatrix
|
SparseMatrix |
CustomRecommender.rateMatrix
Rating matrix for each user (row) and item (column) |
private SparseMatrix |
UnitTest.usedRateMatrix
|
private SparseMatrix |
UnitTest.usedTestMatrix
|
Methods in prea.recommender with parameters of type SparseMatrix | |
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void |
Recommender.buildModel(SparseMatrix rm)
Interface of learning method. |
void |
CustomRecommender.buildModel(SparseMatrix rm)
Build a model with the given data and algorithm. |
EvaluationMetrics |
Recommender.evaluate(SparseMatrix tm)
Interface of evaluation method. |
EvaluationMetrics |
CustomRecommender.evaluate(SparseMatrix testMatrix)
Evaluate the designated algorithm with the given test data. |
Constructors in prea.recommender with parameters of type SparseMatrix | |
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UnitTest(Recommender r,
SparseMatrix rm,
SparseMatrix tm)
Construct an instance of unit test module. |
Uses of SparseMatrix in prea.recommender.baseline |
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Fields in prea.recommender.baseline declared as SparseMatrix | |
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protected SparseMatrix |
BaselineRecommender.rateMatrix
Rating matrix for each user (row) and item (column) |
Methods in prea.recommender.baseline with parameters of type SparseMatrix | |
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void |
BaselineRecommender.buildModel(SparseMatrix rm)
Build a model with given training set. |
void |
Average.buildModel(SparseMatrix rm)
Build a model with given training set. |
EvaluationMetrics |
BaselineRecommender.evaluate(SparseMatrix testMatrix)
Evaluate the designated algorithm with the given test data. |
Uses of SparseMatrix in prea.recommender.etc |
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Fields in prea.recommender.etc declared as SparseMatrix | |
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SparseMatrix |
NonlinearPMF.itemFeatures
Item profile in low-rank matrix form. |
SparseMatrix |
NonlinearPMF.itemFeaturesChange
Change of Item profile in low-rank matrix form. |
private SparseMatrix |
FastNPCA.K
|
SparseMatrix[] |
RankBased.predictedArray
The probability array of each rating for testing items and users. |
SparseMatrix |
SlopeOne.rateMatrix
Rating matrix for each user (row) and item (column) |
SparseMatrix |
RankBased.rateMatrix
Rating matrix for each user (row) and item (column) |
SparseMatrix |
NonlinearPMF.rateMatrix
Rating matrix for each user (row) and item (column) |
SparseMatrix |
FastNPCA.rateMatrix
Rating matrix for each user (row) and item (column) |
private SparseMatrix |
FastNPCA.validationMatrix
Rating matrix for items which will be used during the validation phase. |
Methods in prea.recommender.etc with parameters of type SparseMatrix | |
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void |
SlopeOne.buildModel(SparseMatrix rm)
Build a model with the given data and algorithm. |
void |
RankBased.buildModel(SparseMatrix rm)
Predict the probabilities using the rank-based algorithm with the given test data. |
void |
NonlinearPMF.buildModel(SparseMatrix rm)
Build a model with the given data and algorithm. |
void |
FastNPCA.buildModel(SparseMatrix rm)
Build a model with the given data and algorithm. |
EvaluationMetrics |
SlopeOne.evaluate(SparseMatrix testMatrix)
Evaluate the designated algorithm with the given test data. |
EvaluationMetrics |
RankBased.evaluate(SparseMatrix testMatrix)
Evaluate the rank-based CF algorithm with the given probabilites and loss function. |
EvaluationMetrics |
NonlinearPMF.evaluate(SparseMatrix testMatrix)
Evaluate the designated algorithm with the given test data. |
EvaluationMetrics |
FastNPCA.evaluate(SparseMatrix testMatrix)
Evaluate the designated algorithm with the given test data. |
private void |
RankBased.getEstimation(SparseMatrix testMatrix)
Estimate ratings for (user, item) pairs in test data matrix. |
private void |
RankBased.rankBasedPerUser(int userId,
int testItemId,
double ker,
int k,
int[] index,
double[] distL0,
SparseVector[] distL,
SparseMatrix[] predictedArray)
Predict ratings for a given user and a given test item, by rank-based CF algorithm. |
Uses of SparseMatrix in prea.recommender.matrix |
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Fields in prea.recommender.matrix declared as SparseMatrix | |
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protected SparseMatrix |
MatrixFactorizationRecommender.itemFeatures
Item profile in low-rank matrix form. |
protected SparseMatrix |
MatrixFactorizationRecommender.userFeatures
User profile in low-rank matrix form. |
private SparseMatrix |
NMF.validationMatrix
Rating matrix for items which will be used during the validation phase. |
Methods in prea.recommender.matrix with parameters of type SparseMatrix | |
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void |
RegularizedSVD.buildModel(SparseMatrix rateMatrix)
Build a model with given training set. |
void |
PMF.buildModel(SparseMatrix rateMatrix)
Build a model with given training set. |
void |
NMF.buildModel(SparseMatrix rateMatrix)
Build a model with given training set. |
void |
MatrixFactorizationRecommender.buildModel(SparseMatrix rateMatrix)
Build a model with given training set. |
void |
BayesianPMF.buildModel(SparseMatrix rateMatrix)
Build a model with given training set. |
EvaluationMetrics |
MatrixFactorizationRecommender.evaluate(SparseMatrix testMatrix)
Evaluate the designated algorithm with the given test data. |
private void |
NMF.makeValidationSet(SparseMatrix rateMatrix,
double validationRatio)
Items which will be used for validation purpose are moved from rateMatrix to validationMatrix. |
private void |
NMF.restoreValidationSet(SparseMatrix rateMatrix)
Items in validationMatrix are moved to original rateMatrix. |
Uses of SparseMatrix in prea.recommender.memory |
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Fields in prea.recommender.memory declared as SparseMatrix | |
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SparseMatrix |
MemoryBasedRecommender.rateMatrix
Rating matrix for each user (row) and item (column) |
Methods in prea.recommender.memory that return SparseMatrix | |
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private SparseMatrix |
ItemBased.readItemSimData(int[] validationItemSet)
Read the pre-calculated item similarity data file. |
Methods in prea.recommender.memory with parameters of type SparseMatrix | |
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void |
MemoryBasedRecommender.buildModel(SparseMatrix rm)
Build a model with given training set. |
EvaluationMetrics |
UserBased.evaluate(SparseMatrix testMatrix)
Evaluate the designated algorithm with the given test data. |
EvaluationMetrics |
ItemBased.evaluate(SparseMatrix testMatrix)
Evaluate the designated algorithm with the given test data. |
private SparseVector |
ItemBased.predict(int userNo,
int[] testItemIndex,
int k,
SparseMatrix itemSim)
Predict ratings for a given user regarding given set of items, by user-based CF algorithm. |
Uses of SparseMatrix in prea.util |
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Fields in prea.util declared as SparseMatrix | |
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private SparseMatrix |
EvaluationMetrics.predicted
Predicted ratings by CF algorithms for test items. |
private SparseMatrix |
EvaluationMetrics.testMatrix
Real ratings for test items. |
Methods in prea.util that return SparseMatrix | |
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static SparseMatrix |
Distribution.wishartRandom(SparseMatrix scale,
double df)
Randomly sample a matrix from Wishart Distribution with the given parameters. |
Methods in prea.util with parameters of type SparseMatrix | |
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static SparseMatrix |
Distribution.wishartRandom(SparseMatrix scale,
double df)
Randomly sample a matrix from Wishart Distribution with the given parameters. |
Constructors in prea.util with parameters of type SparseMatrix | |
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EvaluationMetrics(SparseMatrix tm,
SparseMatrix p,
double max,
double min)
Standard constructor for EvaluationMetrics class. |
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