Multiple Formats¶

Import¶

Let's set the backend and import sparse.

In [1]:

import sparse

import numpy as np

import sparse import numpy as np

Perform Operations¶

Let's create two arrays.

In [2]:

rng = np.random.default_rng(42)  # Seed for reproducibility
a = sparse.random((3, 3), density=1 / 6, random_state=rng)
b = sparse.random((3, 3), density=1 / 6, random_state=rng)

rng = np.random.default_rng(42) # Seed for reproducibility a = sparse.random((3, 3), density=1 / 6, random_state=rng) b = sparse.random((3, 3), density=1 / 6, random_state=rng)

Now let's matrix multiply them.

In [3]:

c = a @ b

c = a @ b

And view the result as a (dense) NumPy array.

In [4]:

c_dense = c.todense()

c_dense = c.todense()

Now let's do the same for other formats, and compare the results.

In [5]:

for format in ["coo", "csr", "csc"]:
    af = sparse.asarray(a, format=format)
    bf = sparse.asarray(b, format=format)
    cf = af @ bf
    np.testing.assert_array_equal(c_dense, cf.todense())

for format in ["coo", "csr", "csc"]: af = sparse.asarray(a, format=format) bf = sparse.asarray(b, format=format) cf = af @ bf np.testing.assert_array_equal(c_dense, cf.todense())