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Packages that use DenseMatrix | |
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prea.data.structure | |
prea.recommender.etc |
Uses of DenseMatrix in prea.data.structure |
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Methods in prea.data.structure that return DenseMatrix | |
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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 | |
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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 |
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Fields in prea.recommender.etc declared as DenseMatrix | |
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private DenseMatrix |
SlopeOne.diffMatrix
Prepared difference matrix |
private DenseMatrix |
SlopeOne.freqMatrix
Prepared frequency matrix |
Methods in prea.recommender.etc that return DenseMatrix | |
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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 | |
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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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