iskra.sparse

SparseTensor(tensor, *[, layout, dtype, ...])

Sane PyTorch sparse tensor.

alias(x)	Creates an alias (view) of a tensor.
append(x, indices, values)	Appends new values to an existing COO tensor.
cat(xs[, dim])	Concatenates sparse COO matrices along a dimension.
coo_tensor(indices, values[, size, dtype, ...])	Constructs a COO SparseTensor from indices and values.
csr_tensor(crow_indices, col_indices, values)	Constructs a CSR SparseTensor from compressed-row data.
diag(values)	Constructs a sparse COO diagonal matrix from vector of diagonal elements.
eye(n[, dtype, device])	Constructs a sparse COO identity matrix.
fill_slice(x, fill_value, *indices)	Sets all nonzero entries in a slice of a sparse COO tensor to a chosen value.
from_scipy(x[, device])	Converts SciPy sparse COO/CSR arrays into PyTorch sparse tensors.
get_diag(mat)	Extracts diagonal from sparse COO matrix.
get_slice(x, *indices)	Slices a sparse tensor.
index_complement(n, idx)	Computes the complement of a set of linear indices.
inv_diag(mat)	Inverts elements of a diagonal COO matrix.
is_sparse_any(x)	Checks if tensor is either in sparse COO or CSR format.
isect_indices(a_idx, b_idx)	Checks which indices in two tensors apepar in both.
matmul(...)	Matrix-matrix product with support for sparse COO tensors.
mul(...)	Elementwise product of two tensors with support for sparse COO tensors.
mul_sparse_sparse(...)	Elementwise product of two sparse COO tensors.
ravel_indices(indices, shape)	Converts a COO indices into a linear index corresponding to a strided layout.
repdiag(x, n_reps)	Repeats a sparse COO matrix along a diagonal to make a block-diagonal matrix.
reshape(x, *shape)	Returns a sparse COO tensor with the same data, but with the specified shape.
square(x)	Elementwise square of sparse tensors.
to_scipy(x)	Converts PyTorch sprase COO/CSR tensors into SciPy sparse arrays.
unravel_index(linear, shape)	Converts a linear index into COO indices.
zero_slice(x, *indices)