Uses of Class
prea.datastructure.SparseVector

Packages that use SparseVector
prea.datastructure   
prea.main   
prea.recommender.baseline   
prea.recommender.etc   
prea.recommender.memory   
 

Uses of SparseVector in prea.datastructure
 

Fields in prea.datastructure declared as SparseVector
private  SparseVector[] SparseMatrix.cols
          The array of column references.
private  SparseVector[] SparseMatrix.rows
          The array of row references.
 

Methods in prea.datastructure 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.datastructure 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 Prea.itemRateAverage
          Average of ratings for each item.
static SparseVector Prea.userRateAverage
          Average of ratings for each user.
 

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, int max, int min, SparseVector ira)
          Construct a constant model with the given data.
UserAverage(int uc, int ic, int max, int 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.