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## Uses of Class
prea.data.structure.SparseVector

Packages that use SparseVector
prea.data.splitter |
prea.data.structure |
prea.main |
prea.recommender.baseline |
prea.recommender.etc |
prea.recommender.memory |

Uses of SparseVector in prea.data.splitter

Fields in prea.data.splitter declared as SparseVector
`protected static SparseVector` | `DataSplitManager.itemRateAverage` Average of ratings for each item.
`protected static SparseVector` | `DataSplitManager.userRateAverage` Average of ratings for each user.

Methods in prea.data.splitter that return SparseVector
`SparseVector` | `DataSplitManager.getItemRateAverage()` Getter method for average of each item's rating.
`SparseVector` | `DataSplitManager.getUserRateAverage()` Getter method for average of each user's rating.

Uses of SparseVector in prea.data.structure

Fields in prea.data.structure declared as SparseVector
`private SparseVector[]` | `SparseMatrix.cols` The array of column references.
`private SparseVector[]` | `SparseMatrix.rows` The array of row references.

Methods in prea.data.structure that return SparseVector
`SparseVector` | `SparseVector.add(double alpha)` Scalar addition operator.
`SparseVector` | `SparseVector.commonMinus(SparseVector b)` Vector subtraction (a - b), for only existing values.
`SparseVector` | `SparseVector.copy()` Copy the whole sparse vector and make a clone.
`SparseVector` | `SparseMatrix.diagonal()` Return items in the diagonal in vector form.
`SparseVector` | `SparseVector.exp(double alpha)` Exponential of a given constant.
`SparseVector` | `SparseMatrix.getCol(int index)` Return a copy of a given column.
`SparseVector` | `SparseMatrix.getColRef(int index)` Return a reference of a given column.
`SparseVector` | `SparseMatrix.getRow(int index)` Return a copy of a given row.
`SparseVector` | `SparseMatrix.getRowRef(int index)` Return a reference of a given row.
`SparseVector` | `SparseVector.minus(SparseVector b)` Vector subtraction (a - b)
`SparseVector` | `SparseVector.partMinus(SparseVector b, int[] indexList)` Vector subtraction (a - b) for indices only in the given indices.
`SparseVector` | `SparseVector.partPlus(SparseVector b, int[] indexList)` Vector sum (a + b) for indices only in the given indices.
`SparseVector` | `SparseMatrix.partTimes(SparseVector x, int[] indexList)` Matrix-vector product (b = Ax) only with indices in indexList.
`SparseVector` | `SparseVector.plus(SparseVector b)` Vector sum (a + b)
`SparseVector` | `SparseVector.power(double alpha)` Scalar power operator.
`SparseVector` | `SparseVector.scale(double alpha)` Scalar multiplication operator.
`SparseVector` | `SparseVector.sub(double alpha)` Scalar subtraction operator.
`SparseVector` | `SparseMatrix.times(SparseVector x)` Matrix-vector product (b = Ax)
`SparseVector` | `DenseVector.toSparseVector()` Convert the vector into the sparse vector.

Methods in prea.data.structure with parameters of type SparseVector
`SparseVector` | `SparseVector.commonMinus(SparseVector b)` Vector subtraction (a - b), for only existing values.
`double` | `SparseVector.innerProduct(SparseVector b)` Inner product of two vectors.
`SparseVector` | `SparseVector.minus(SparseVector b)` Vector subtraction (a - b)
`SparseMatrix` | `SparseVector.outerProduct(SparseVector b)` Outer product of two vectors.
`double` | `SparseVector.partInnerProduct(SparseVector b, int[] indexList)` Inner-product for indices only in the given indices.
`SparseVector` | `SparseVector.partMinus(SparseVector b, int[] indexList)` Vector subtraction (a - b) for indices only in the given indices.
`SparseMatrix` | `SparseVector.partOuterProduct(SparseVector b, int[] indexList)` Outer-product for indices only in the given indices.
`SparseVector` | `SparseVector.partPlus(SparseVector b, int[] indexList)` Vector sum (a + b) for indices only in the given indices.
`SparseVector` | `SparseMatrix.partTimes(SparseVector x, int[] indexList)` Matrix-vector product (b = Ax) only with indices in indexList.
`SparseVector` | `SparseVector.plus(SparseVector b)` Vector sum (a + b)
`SparseVector` | `SparseMatrix.times(SparseVector x)` Matrix-vector product (b = Ax)

Uses of SparseVector in prea.main

Fields in prea.main declared as SparseVector
`static SparseVector` | `Splitter.itemRateAverage` Average rating for each item.
`static SparseVector` | `Prea.itemRateAverage` Average of ratings for each item.
`static SparseVector` | `Splitter.userRateAverage` Average rating for each user.
`static SparseVector` | `Prea.userRateAverage` Average of ratings for each user.

Methods in prea.main with parameters of type SparseVector
`private static double` | `Splitter.similarity(boolean rowOriented, SparseVector i1, SparseVector i2, double i1Avg, double i2Avg, int method)` Calculate similarity between two given vectors.

Uses of SparseVector in prea.recommender.baseline

Fields in prea.recommender.baseline declared as SparseVector
`SparseVector` | `ItemAverage.itemRateAverage` Average of ratings for each item.
`SparseVector` | `UserAverage.userRateAverage` Average of ratings for each user.

Constructors in prea.recommender.baseline with parameters of type SparseVector
`ItemAverage(int uc, int ic, double max, double min, SparseVector ira)` Construct a constant model with the given data.
`UserAverage(int uc, int ic, double max, double min, SparseVector ura)` Construct a constant model with the given data.

Uses of SparseVector in prea.recommender.etc

Fields in prea.recommender.etc declared as SparseVector
`private SparseVector` | `FastNPCA.mu`

Methods in prea.recommender.etc that return SparseVector
`private SparseVector` | `SlopeOne.getEstimation(int u, int[] testItems)` Estimate of ratings for a given user and a set of test items.
`private SparseVector` | `FastNPCA.getEstimation(int u, int[] testItems)` Estimate of ratings for a given user and a set of test items.

Methods in prea.recommender.etc with parameters of type SparseVector
`private void` | `RankBased.distanceOneToAllTest(int userId, int testId, SparseVector[] dist, int k, int[] indexK)` Compute the distance between the testing user with the testing item of every possible ratings and all the other training users with training items.
`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 SparseVector in prea.recommender.memory

Fields in prea.recommender.memory declared as SparseVector
`SparseVector` | `ItemBased.itemRateAverage` Average of ratings for each item.
`SparseVector` | `UserBased.userRateAverage` Average of ratings for each user.

Methods in prea.recommender.memory that return SparseVector
`private SparseVector` | `UserBased.predict(int userNo, int[] testItemIndex, int k, double[] userSim)` Predict ratings for a given user regarding given set of items, by user-based CF algorithm.
`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.

Methods in prea.recommender.memory with parameters of type SparseVector
`double` | `MemoryBasedRecommender.similarity(boolean rowOriented, SparseVector i1, SparseVector i2, double i1Avg, double i2Avg, int method)` Calculate similarity between two given vectors.

Constructors in prea.recommender.memory with parameters of type SparseVector
`ItemBased(int uc, int ic, int max, int min, int ns, int sim, boolean df, double dv, SparseVector ira, boolean isp, java.lang.String isfn)` Construct an item-based model with the given data.
`UserBased(int uc, int ic, int max, int min, int ns, int sim, boolean df, double dv, SparseVector ura, boolean usp, java.lang.String usfn)` Construct a user-based model with the given data.

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