Uses of Class
prea.datastructure.DenseVector

Packages that use DenseVector
prea.datastructure   
prea.recommender.etc   
 

Uses of DenseVector in prea.datastructure
 

Methods in prea.datastructure that return DenseVector
 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
 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
 

Fields in prea.recommender.etc declared as DenseVector
 DenseVector NonlinearPMF.kernParam
          kernel parameter value
 

Methods in prea.recommender.etc that return DenseVector
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
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.