def test_repeated_row_col(self):
data = [1.0, 0.0, 2.0]
row = [1, 2, 1]
col = [2, 2, 2]
A = scipy.sparse.coo_matrix((data, (row, col)), shape=(3, 3))
data = [3.0, 0.0]
row = [1, 2]
col = [2, 2]
B = scipy.sparse.coo_matrix((data, (row, col)), shape=(3, 3))
# Our CondensedSparseSummation should not remove any
# structural nonzeros
sparse_sum = utils.CondensedSparseSummation([A, B])
C = sparse_sum.sum([A, B])
expected_data = np.asarray([6.0, 0.0], dtype=np.float64)
expected_row = np.asarray([1, 2], dtype=np.int64)
expected_col = np.asarray([2, 2], dtype=np.int64)
self.assertTrue(np.array_equal(expected_data, C.data))
self.assertTrue(np.array_equal(expected_row, C.row))
self.assertTrue(np.array_equal(expected_col, C.col))
def test_condensed_sparse_summation(self):
data = [1.0, 0.0]
row = [1, 2]
col = [2, 2]
A = scipy.sparse.coo_matrix((data, (row, col)), shape=(3, 3))
data = [3.0, 0.0]
B = scipy.sparse.coo_matrix((data, (row, col)), shape=(3, 3))
# By default, scipy will remove structural nonzeros that
# have zero values
C = A + B
self.assertEqual(C.nnz, 1)
# Our CondensedSparseSummation should not remove any
# structural nonzeros
sparse_sum = utils.CondensedSparseSummation([A, B])
C = sparse_sum.sum([A, B])
expected_data = np.asarray([4.0, 0.0], dtype=np.float64)
expected_row = np.asarray([1, 2], dtype=np.int64)
expected_col = np.asarray([2, 2], dtype=np.int64)
self.assertTrue(np.array_equal(expected_data, C.data))
self.assertTrue(np.array_equal(expected_row, C.row))
self.assertTrue(np.array_equal(expected_col, C.col))
B.data[1] = 5.0
C = sparse_sum.sum([A, B])
expected_data = np.asarray([4.0, 5.0], dtype=np.float64)
self.assertTrue(np.array_equal(expected_data, C.data))
self.assertTrue(np.array_equal(expected_row, C.row))
self.assertTrue(np.array_equal(expected_col, C.col))
B.data[1] = 0.0
C = sparse_sum.sum([A, B])
expected_data = np.asarray([4.0, 0.0], dtype=np.float64)
self.assertTrue(np.array_equal(expected_data, C.data))
self.assertTrue(np.array_equal(expected_row, C.row))
self.assertTrue(np.array_equal(expected_col, C.col))