def test_rectangular_matrix_3():
rows, cols = 2000, 300
mask = numpy.random.rand(rows, cols) < sparsity
A = numpy.random.rand(rows, cols)
A[mask] = 0.0
B = numpy.random.rand(cols, 64)
indexes = A.nonzero()
values = A[indexes]
sp_A = (indexes, values)
sp_res = sp_matmul(sp_A, B, A.shape[0])
res = np.matmul(A, B)
diff = distance(sp_res, res)
print(diff)
assert diff < tolerance
def test_square_sparse_matrix_fail():
rows = 2000
mask = numpy.random.rand(rows, rows) < sparsity
A = numpy.random.rand(rows, rows)
A[mask] = 0.0
B = numpy.random.rand(rows, 1)
indexes = A.nonzero()
values = A[indexes]
sp_A = (indexes, values)
sp_res = sp_matmul(sp_A, B, A.shape[0])
res = np.matmul(A.T, B)
diff = distance(sp_res, res)
print(diff)
assert diff > tolerance
def matmul(A, B, shape):
if sparse:
return sp_matmul(A, B, shape)
else:
return np.matmul(A, B)