Uses of Class
prea.datastructure.SparseMatrix

Packages that use SparseMatrix
prea.datastructure   
prea.main   
prea.recommender   
prea.recommender.baseline   
prea.recommender.etc   
prea.recommender.matrix   
prea.recommender.memory   
prea.util   
 

Uses of SparseMatrix in prea.datastructure
 

Methods in prea.datastructure that return SparseMatrix
 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.datastructure with parameters of type SparseMatrix
 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.datastructure with parameters of type SparseMatrix
SparseMatrix(SparseMatrix sm)
          Construct an empty sparse matrix, with data copied from another sparse matrix.
 

Uses of SparseMatrix in prea.main
 

Fields in prea.main declared as SparseMatrix
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
 

Methods in prea.recommender with parameters of type SparseMatrix
 void Recommender.buildModel(SparseMatrix rm)
          Interface of learning method.
 EvaluationMetrics Recommender.evaluate(SparseMatrix tm)
          Interface of evaluation method.
 

Uses of SparseMatrix in prea.recommender.baseline
 

Fields in prea.recommender.baseline declared as SparseMatrix
protected  SparseMatrix BaselineRecommender.rateMatrix
          Rating matrix for each user (row) and item (column)
 

Methods in prea.recommender.baseline with parameters of type SparseMatrix
 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
 

Fields in prea.recommender.etc declared as SparseMatrix
 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
 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
 

Fields in prea.recommender.matrix declared as SparseMatrix
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
 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
 

Fields in prea.recommender.memory declared as SparseMatrix
 SparseMatrix MemoryBasedRecommender.rateMatrix
          Rating matrix for each user (row) and item (column)
 

Methods in prea.recommender.memory that return SparseMatrix
private  SparseMatrix ItemBased.readItemSimData(int[] validationItemSet)
          Read the pre-calculated item similarity data file.
 

Methods in prea.recommender.memory with parameters of type SparseMatrix
 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
 

Fields in prea.util declared as SparseMatrix
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
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
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
EvaluationMetrics(SparseMatrix tm, SparseMatrix p, int max, int min)
          Standard constructor for EvaluationMetrics class.