def test_avg_case_insertion_sort():
data = gen_random_u_array()
sorted_data = SortedList(data)
insertion_sort(data)
assert np.array_equal(data, sorted_data)
def test_avg_case_quick_sort():
data = gen_random_u_array()
sorted_data = SortedList(data)
quick_sort(data, 0, len(data) - 1)
assert np.array_equal(data, sorted_data)
def test_postorder():
bst = BinarySearchTree()
rbt = RedBlackTree()
data = gen_random_u_array(10)
for d in data:
bst.insert(d)
rbt.insert(d)
assert np.array_equal(bst.postorder(), rbt.postorder()) is False
def test_maximum():
bst = BinarySearchTree()
rbt = RedBlackTree()
data = gen_random_u_array(10)
for d in data:
bst.insert(d)
rbt.insert(d)
assert bst.maximum().key is rbt.maximum().key
def test_tree_height():
bst = BinarySearchTree()
rbt = RedBlackTree()
data = gen_random_u_array()
for d in data:
bst.insert(d)
rbt.insert(d)
assert bst.tree_height() is not rbt.tree_height()
def test_insertion_rn():
data = gen_random_u_array()
sorted_data = SortedList(data)
rbt = RedBlackTree()
for d in data:
rbt.insert(d)
assert np.array_equal(rbt.inorder(), sorted_data)
def test_insertion_bst():
data = gen_random_u_array()
sorted_data = SortedList(data)
bst = BinarySearchTree()
for d in data:
bst.insert(d)
assert np.array_equal(bst.inorder(), sorted_data)