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Packages that use SparseVector | |
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prea.data.splitter | |
prea.data.structure | |
prea.main | |
prea.recommender.baseline | |
prea.recommender.etc | |
prea.recommender.memory |
Uses of SparseVector in prea.data.splitter |
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Fields in prea.data.splitter declared as SparseVector | |
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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 | |
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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 |
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Fields in prea.data.structure declared as SparseVector | |
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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 | |
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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 | |
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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 |
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Fields in prea.main declared as SparseVector | |
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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 | |
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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 |
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Fields in prea.recommender.baseline declared as SparseVector | |
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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 | |
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ItemAverage(int uc,
int ic,
double max,
double min,
SparseVector ira)
Construct a constant model with the given data. |
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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 |
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Fields in prea.recommender.etc declared as SparseVector | |
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private SparseVector |
FastNPCA.mu
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Methods in prea.recommender.etc that return SparseVector | |
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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 | |
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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 |
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Fields in prea.recommender.memory declared as SparseVector | |
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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 | |
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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 | |
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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 | |
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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. |
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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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