Package-level declarations
Types
Basic linear algebra operations implemented with broadcasting. For more information: https://pytorch.org/docs/stable/notes/broadcasting.html
Represents Tensor over a MutableBuffer intended to be used through DoubleTensor and IntTensor
Default BufferedTensor implementation for Double values.
Implementation of basic operations over double tensors and basic algebra operations on them.
Default BufferedTensor implementation for Int values
Implementation of basic operations over double tensors and basic algebra operations on them.
Default BufferedTensor implementation for Int values
Functions
Transforms StructureND of Double to DoubleTensor. Zero copy if possible, but is not guaranteed
A zero-copy cast to 1D structure. Changes in resulting structure are reflected on original tensor.
A zero-copy cast to 2D structure. Changes in resulting structure are reflected on original tensor.
QR decomposition.
Create a mutable copy of given StructureND.
Returns the covariance matrix M of given vectors.
Computes the determinant of a square matrix input, or of each square matrix in a batched input using LU factorization algorithm.
Computes the multiplicative inverse matrix of a square matrix input, or of each square matrix in a batched input using LU factorization algorithm. Given a square matrix a, return the matrix aInv satisfying a dot aInv == aInv dot a == eye(a.shape[0]).
LUP decomposition.
Computes the LU factorization of a matrix or batches of matrices input. Returns a tuple containing the LU factorization and pivots of input.
Unpacks the data and pivots from a LU factorization of a tensor. Given a tensor luTensor, return tensors Triple(P, L, U) satisfying P dot luTensor = L dot U, with P being a permutation matrix or batch of matrices, L being a lower triangular matrix or batch of matrices, U being an upper triangular matrix or batch of matrices.
Map only operable content of the offset buffer
map in place
Returns a tensor of random numbers drawn from normal distributions with 0.0 mean and 1.0 standard deviation.
Returns a tensor with the same shape as input of random numbers drawn from normal distributions with 0.0 mean and 1.0 standard deviation.
Singular Value Decomposition.
Returns eigenvalues and eigenvectors of a real symmetric matrix input or a batch of real symmetric matrices, represented by a pair eigenvalues to eigenvectors.
Compute a value using broadcast double tensor algebra