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## 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.

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