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Packages that use DenseVector | |
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prea.datastructure | |
prea.recommender.etc |
Uses of DenseVector in prea.datastructure |
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Methods in prea.datastructure that return DenseVector | |
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DenseVector |
DenseVector.add(double alpha)
Scalar addition operator. |
DenseVector |
DenseVector.commonMinus(DenseVector b)
Vector subtraction (a - b), for only existing values. |
DenseVector |
DenseVector.copy()
Copy the whole dense vector and make a clone. |
DenseVector |
DenseMatrix.diagonal()
Return items in the diagonal in vector form. |
DenseVector |
DenseVector.exp(double alpha)
Exponential of a given constant. |
DenseVector |
DenseMatrix.getCol(int index)
Return a copy of a given column. |
DenseVector |
DenseMatrix.getColRef(int index)
Return a reference of a given column. |
DenseVector |
DenseMatrix.getRow(int index)
Return a copy of a given row. |
DenseVector |
DenseMatrix.getRowRef(int index)
Return a reference of a given row. |
DenseVector |
DenseVector.minus(DenseVector b)
Vector subtraction (a - b) |
DenseVector |
DenseVector.partMinus(DenseVector b,
int[] indexList)
Vector subtraction (a - b) for indices only in the given indices. |
DenseVector |
DenseVector.partPlus(DenseVector b,
int[] indexList)
Vector sum (a + b) for indices only in the given indices. |
DenseVector |
DenseMatrix.partTimes(DenseVector x,
int[] indexList)
Matrix-vector product (b = Ax) only with indices in indexList. |
DenseVector |
DenseVector.plus(DenseVector b)
Vector sum (a + b) |
DenseVector |
DenseVector.power(double alpha)
Scalar power operator. |
DenseVector |
DenseVector.scale(double alpha)
Scalar multiplication operator. |
DenseVector |
DenseVector.sub(double alpha)
Scalar subtraction operator. |
DenseVector |
DenseMatrix.times(DenseVector x)
Matrix-vector product (b = Ax) |
DenseVector |
SparseVector.toDenseSubset(int[] indexList)
Convert the vector into the array-based dense representation, but only with the selected indices. |
DenseVector |
DenseVector.toDenseSubset(int[] indexList)
Convert the vector into a sparse vector, but only with the selected indices. |
DenseVector |
SparseVector.toDenseVector()
Convert the vector into the array-based dense representation. |
Methods in prea.datastructure with parameters of type DenseVector | |
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DenseVector |
DenseVector.commonMinus(DenseVector b)
Vector subtraction (a - b), for only existing values. |
double |
DenseVector.innerProduct(DenseVector b)
Inner product of two vectors. |
DenseVector |
DenseVector.minus(DenseVector b)
Vector subtraction (a - b) |
DenseMatrix |
DenseVector.outerProduct(DenseVector b)
Outer product of two vectors. |
double |
DenseVector.partInnerProduct(DenseVector b,
int[] indexList)
Inner-product for indices only in the given indices. |
DenseVector |
DenseVector.partMinus(DenseVector b,
int[] indexList)
Vector subtraction (a - b) for indices only in the given indices. |
DenseMatrix |
DenseVector.partOuterProduct(DenseVector b,
int[] indexList)
Outer-product for indices only in the given indices. |
DenseVector |
DenseVector.partPlus(DenseVector b,
int[] indexList)
Vector sum (a + b) for indices only in the given indices. |
DenseVector |
DenseMatrix.partTimes(DenseVector x,
int[] indexList)
Matrix-vector product (b = Ax) only with indices in indexList. |
DenseVector |
DenseVector.plus(DenseVector b)
Vector sum (a + b) |
DenseVector |
DenseMatrix.times(DenseVector x)
Matrix-vector product (b = Ax) |
Uses of DenseVector in prea.recommender.etc |
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Fields in prea.recommender.etc declared as DenseVector | |
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DenseVector |
NonlinearPMF.kernParam
kernel parameter value |
Methods in prea.recommender.etc that return DenseVector | |
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private DenseVector |
NonlinearPMF.collabLogLikeGradients(int user,
DenseVector kernParam,
double momentum,
double learningRate)
Compute the gradients of the model (latent factors and kernel parameters) given one users ratings. |
private DenseVector |
NonlinearPMF.distancePairWise(DenseMatrix X1,
DenseVector x)
Compute the squared euclidean distance between the row vectors of one matrix and a vector. |
private DenseVector |
NonlinearPMF.expTransform(DenseVector x,
int transformType)
Transform a vector by log function, exponential function or linear function. |
private DenseVector |
NonlinearPMF.getEstimation(int u,
int[] testItemsRaw)
Estimate of ratings for a given user and a set of test items. |
Methods in prea.recommender.etc with parameters of type DenseVector | |
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private DenseVector |
NonlinearPMF.collabLogLikeGradients(int user,
DenseVector kernParam,
double momentum,
double learningRate)
Compute the gradients of the model (latent factors and kernel parameters) given one users ratings. |
private DenseVector |
NonlinearPMF.distancePairWise(DenseMatrix X1,
DenseVector x)
Compute the squared euclidean distance between the row vectors of one matrix and a vector. |
private DenseVector |
NonlinearPMF.expTransform(DenseVector x,
int transformType)
Transform a vector by log function, exponential function or linear function. |
private DenseMatrix |
NonlinearPMF.rbfKernGradXpoint(DenseVector x,
DenseMatrix X2,
double kernInverseWidth,
double kernVarianceRbf)
Compute the gradient of RBF kernel with respect to input locations. |
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