heat.sparse.manipulations

Manipulation operations for (potentially distributed) DCSR_matrix.

Module Contents

to_sparse(array: heat.core.dndarray.DNDarray) → heat.sparse.dcsr_matrix.DCSR_matrix

Convert the distributed array to a sparse DCSR_matrix representation.

Parameters:

array (DNDarray) – The distributed array to be converted to a sparse DCSR_matrix.

Returns:

A sparse DCSR_matrix representation of the input DNDarray.

Return type:

DCSR_matrix

Notes

This method allows for the conversion of a DNDarray into a sparse DCSR_matrix representation, which is useful for handling large and sparse datasets efficiently.

Examples

>>> dense_array = ht.array([[1, 0, 0], [0, 0, 2], [0, 3, 0]])
>>> sparse_matrix = dense_array.to_sparse()
to_dense(sparse_matrix: heat.sparse.dcsr_matrix.DCSR_matrix, order='C', out: heat.core.dndarray.DNDarray = None) → heat.core.dndarray.DNDarray

Convert DCSR_matrix to a dense DNDarray. Output follows the same distribution among processes as the input

Parameters:

  • sparse_matrix (DCSR_matrix) – The sparse csr matrix which is to be converted to a dense array

  • order (str, optional) – Options: 'C' or 'F'. Specifies the memory layout of the newly created DNDarray. Default is order='C', meaning the array will be stored in row-major order (C-like). If order=‘F’, the array will be stored in column-major order (Fortran-like).

  • out (DNDarray) – Output buffer in which the values of the dense format is stored. If not specified, a new DNDarray is created.

Raises:

  • ValueError – If shape of output buffer does not match that of the input.

  • ValueError – If split axis of output buffer does not match that of the input.

Examples

>>> indptr = torch.tensor([0, 2, 3, 6])
>>> indices = torch.tensor([0, 2, 2, 0, 1, 2])
>>> data = torch.tensor([1, 2, 3, 4, 5, 6], dtype=torch.float)
>>> torch_sparse_csr = torch.sparse_csr_tensor(indptr, indices, data)
>>> heat_sparse_csr = ht.sparse.sparse_csr_matrix(torch_sparse_csr, split=0)
>>> heat_sparse_csr
(indptr: tensor([0, 2, 3, 6]), indices: tensor([0, 2, 2, 0, 1, 2]), data: tensor([1., 2., 3., 4., 5., 6.]), dtype=ht.float32, device=cpu:0, split=0)
>>> heat_sparse_csr.todense()
DNDarray([[1., 0., 2.],
          [0., 0., 3.],
          [4., 5., 6.]], dtype=ht.float32, device=cpu:0, split=0)