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## Uses of Class
prea.data.structure.DenseMatrix

Packages that use DenseMatrix
prea.data.structure |
prea.recommender.etc |

Uses of DenseMatrix in prea.data.structure

Methods in prea.data.structure 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.data.structure 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.

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