Uses of Class
prea.datastructure.DenseMatrix

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

Uses of DenseMatrix in prea.datastructure
 

Methods in prea.datastructure that return DenseMatrix
 DenseMatrix DenseMatrix.add(double alpha)
          Scalar addition.
 DenseMatrix DenseMatrix.cholesky()
          Calculate Cholesky decomposition of the matrix.
 DenseMatrix DenseMatrix.covariance()
          Generate a covariance matrix of the current matrix.
 DenseMatrix DenseMatrix.exp(double alpha)
          Exponential of a given constant.
 DenseMatrix DenseMatrix.inverse()
          Calculate inverse matrix.
static DenseMatrix DenseMatrix.makeIdentity(int n)
          Generate an identity matrix with the given size.
 DenseMatrix DenseVector.outerProduct(DenseVector b)
          Outer product of two vectors.
 DenseMatrix DenseMatrix.partInverse(int[] indexList)
          Inverse of matrix only with indices in indexList.
 DenseMatrix DenseMatrix.partMinus(DenseMatrix B, int[] indexList)
          Matrix subtraction (A = A - B) only with indices in indexList.
 DenseMatrix DenseVector.partOuterProduct(DenseVector b, int[] indexList)
          Outer-product for indices only in the given indices.
 DenseMatrix DenseMatrix.partPlus(DenseMatrix B, int[] indexList)
          Matrix summation (A = A + B) only with indices in indexList.
 DenseMatrix DenseMatrix.partScale(double alpha, int[] indexList)
          Scalar Multiplication only with indices in indexList.
 DenseMatrix DenseMatrix.plus(DenseMatrix B)
          Matrix-matrix sum (C = A + B)
 DenseMatrix DenseMatrix.scale(double alpha)
          Scalar multiplication (aX).
 DenseMatrix DenseMatrix.times(DenseMatrix B)
          Matrix-matrix product (C = AB)
 DenseMatrix SparseMatrix.toDenseMatrix()
          Convert the matrix into the array-based dense representation.
 DenseMatrix SparseMatrix.toDenseSubset(int[] indexList)
          Convert the matrix into the array-based dense representation, but only with the selected indices.
 DenseMatrix DenseMatrix.toDenseSubset(int[] indexList)
          Condense the matrix only with given indices.
 DenseMatrix SparseMatrix.toDenseSubset(int[] rowList, int[] colList)
          Convert the matrix into the array-based dense representation, but only with the selected indices, both rows and columns separately.
 DenseMatrix DenseMatrix.toDenseSubset(int[] rowList, int[] colList)
          Condense the matrix only with given indices, both rows and columns separately.
 DenseMatrix DenseMatrix.transpose()
          The transpose of the matrix.
 

Methods in prea.datastructure with parameters of type DenseMatrix
 DenseMatrix DenseMatrix.partMinus(DenseMatrix B, int[] indexList)
          Matrix subtraction (A = A - B) only with indices in indexList.
 DenseMatrix DenseMatrix.partPlus(DenseMatrix B, int[] indexList)
          Matrix summation (A = A + B) only with indices in indexList.
 DenseMatrix DenseMatrix.plus(DenseMatrix B)
          Matrix-matrix sum (C = A + B)
 DenseMatrix DenseMatrix.times(DenseMatrix B)
          Matrix-matrix product (C = AB)
 

Uses of DenseMatrix in prea.recommender.etc
 

Fields in prea.recommender.etc declared as DenseMatrix
private  DenseMatrix SlopeOne.diffMatrix
          Prepared difference matrix
private  DenseMatrix SlopeOne.freqMatrix
          Prepared frequency matrix
 

Methods in prea.recommender.etc that return DenseMatrix
private  DenseMatrix NonlinearPMF.distancePairWise(DenseMatrix X1, DenseMatrix X2)
          Compute the squared Euclidean distance between the row vectors of one matrix and the row vectors of another matrix.
private  DenseMatrix NonlinearPMF.kernCompute(DenseMatrix X1, DenseMatrix X2, boolean whiteNoiseFlag)
          Compute kernel parameters for vectors in matrix X1 and vectors in matrix X2.
private  DenseMatrix NonlinearPMF.rbfKernCompute(DenseMatrix distance, double kernInverseWidth, double kernVarianceRbf)
          Compute RBF(radial basis function) kernel parameters for given distances.
private  DenseMatrix NonlinearPMF.rbfKernGradXpoint(DenseVector x, DenseMatrix X2, double kernInverseWidth, double kernVarianceRbf)
          Compute the gradient of RBF kernel with respect to input locations.
 

Methods in prea.recommender.etc with parameters of type DenseMatrix
private  DenseMatrix NonlinearPMF.distancePairWise(DenseMatrix X1, DenseMatrix X2)
          Compute the squared Euclidean distance between the row vectors of one matrix and the row vectors of another matrix.
private  DenseVector NonlinearPMF.distancePairWise(DenseMatrix X1, DenseVector x)
          Compute the squared euclidean distance between the row vectors of one matrix and a vector.
private  DenseMatrix NonlinearPMF.kernCompute(DenseMatrix X1, DenseMatrix X2, boolean whiteNoiseFlag)
          Compute kernel parameters for vectors in matrix X1 and vectors in matrix X2.
private  DenseMatrix NonlinearPMF.rbfKernCompute(DenseMatrix distance, double kernInverseWidth, double kernVarianceRbf)
          Compute RBF(radial basis function) kernel parameters for given distances.
private  DenseMatrix NonlinearPMF.rbfKernGradXpoint(DenseVector x, DenseMatrix X2, double kernInverseWidth, double kernVarianceRbf)
          Compute the gradient of RBF kernel with respect to input locations.