:mod:`heat.sparse.arithmetics`
==============================
.. py:module:: heat.sparse.arithmetics

.. autoapi-nested-parse::

   Arithmetic functions for Dcsr_matrices



Module Contents
---------------


.. function:: add(t1: heat.sparse.dcsx_matrix.DCSR_matrix, t2: heat.sparse.dcsx_matrix.DCSR_matrix, orientation: str = 'row') -> heat.sparse.dcsx_matrix.DCSR_matrix

   Element-wise addition of values from two operands, commutative.
   Takes the first and second operand (scalar or :class:`~heat.sparse.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``.

   :param t1: The first operand involved in the addition
   :type t1: DCSR_matrix
   :param t2: The second operand involved in the addition
   :type t2: DCSR_matrix
   :param orientation: The orientation of the operation. Options: 'row' or 'col'
                       Default: 'row'
   :type orientation: str, optional

   .. rubric:: 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)


.. function:: mul(t1: heat.sparse.dcsx_matrix.DCSR_matrix, t2: heat.sparse.dcsx_matrix.DCSR_matrix, orientation: str = 'row') -> heat.sparse.dcsx_matrix.DCSR_matrix

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

   :param t1: The first operand involved in the multiplication
   :type t1: DCSR_matrix
   :param t2: The second operand involved in the multiplication
   :type t2: DCSR_matrix
   :param orientation: The orientation of the operation. Options: 'row' or 'col'
                       Default: 'row'
   :type orientation: str, optional

   .. rubric:: 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)