heat.sparse.arithmetics

Arithmetic functions for Dcsr_matrices

Module Contents

add(t1: heat.sparse.dcsr_matrix.DCSR_matrix, t2: heat.sparse.dcsr_matrix.DCSR_matrix) → heat.sparse.dcsr_matrix.DCSR_matrix

Element-wise addition of values from two operands, commutative. Takes the first and second operand (scalar or DCSR_matrix) whose elements are to be added as argument and returns a DCSR_matrix containing the results of element-wise addition of t1 and t2.

Parameters:

  • t1 (DCSR_matrix) – The first operand involved in the addition

  • t2 (DCSR_matrix) – The second operand involved in the addition

Examples

>>> heat_sparse_csr
(indptr: tensor([0, 2, 3]), indices: tensor([0, 2, 2]), data: tensor([1., 2., 3.]), dtype=ht.float32, device=cpu:0, split=0)
>>> heat_sparse_csr.todense()
DNDarray([[1., 0., 2.],
          [0., 0., 3.]], dtype=ht.float32, device=cpu:0, split=0)
>>> sum_sparse = heat_sparse_csr + heat_sparse_csr
    (or)
>>> sum_sparse = ht.sparse.sparse_add(heat_sparse_csr, heat_sparse_csr)
>>> sum_sparse
(indptr: tensor([0, 2, 3], dtype=torch.int32), indices: tensor([0, 2, 2], dtype=torch.int32), data: tensor([2., 4., 6.]), dtype=ht.float32, device=cpu:0, split=0)
>>> sum_sparse.todense()
DNDarray([[2., 0., 4.],
          [0., 0., 6.]], dtype=ht.float32, device=cpu:0, split=0)
mul(t1: heat.sparse.dcsr_matrix.DCSR_matrix, t2: heat.sparse.dcsr_matrix.DCSR_matrix) → heat.sparse.dcsr_matrix.DCSR_matrix

Element-wise multiplication (NOT matrix multiplication) of values from two operands, commutative. Takes the first and second operand (scalar or DCSR_matrix) whose elements are to be multiplied as argument.

Parameters:

  • t1 (DCSR_matrix) – The first operand involved in the multiplication

  • t2 (DCSR_matrix) – The second operand involved in the multiplication

Examples

>>> heat_sparse_csr
(indptr: tensor([0, 2, 3]), indices: tensor([0, 2, 2]), data: tensor([1., 2., 3.]), dtype=ht.float32, device=cpu:0, split=0)
>>> heat_sparse_csr.todense()
DNDarray([[1., 0., 2.],
          [0., 0., 3.]], dtype=ht.float32, device=cpu:0, split=0)
>>> pdt_sparse = heat_sparse_csr * heat_sparse_csr
    (or)
>>> pdt_sparse = ht.sparse.sparse_mul(heat_sparse_csr, heat_sparse_csr)
>>> pdt_sparse
(indptr: tensor([0, 2, 3]), indices: tensor([0, 2, 2]), data: tensor([1., 4., 9.]), dtype=ht.float32, device=cpu:0, split=0)
>>> pdt_sparse.todense()
DNDarray([[1., 0., 4.],
          [0., 0., 9.]], dtype=ht.float32, device=cpu:0, split=0)