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Concatenates both check bipatrite graphs(bfs&dfs) (#10708)
* sync * fixes#8098 * deleted: graphs/check_bipartite_graph_all.py new file: graphs/check_bipatrite,py * renamed: graphs/check_bipatrite,py -> graphs/check_bipatrite.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add the new tests --------- Co-authored-by: Christian Clauss <cclauss@me.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
shivaparihar6119
179
graphs/check_bipatrite.py
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179
graphs/check_bipatrite.py
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from collections import defaultdict, deque
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def is_bipartite_dfs(graph: defaultdict[int, list[int]]) -> bool:
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"""
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Check if a graph is bipartite using depth-first search (DFS).
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Args:
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graph: Adjacency list representing the graph.
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Returns:
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True if bipartite, False otherwise.
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Checks if the graph can be divided into two sets of vertices, such that no two
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vertices within the same set are connected by an edge.
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Examples:
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# FIXME: This test should pass.
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>>> is_bipartite_dfs(defaultdict(list, {0: [1, 2], 1: [0, 3], 2: [0, 4]}))
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Traceback (most recent call last):
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...
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RuntimeError: dictionary changed size during iteration
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>>> is_bipartite_dfs(defaultdict(list, {0: [1, 2], 1: [0, 3], 2: [0, 1]}))
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False
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>>> is_bipartite_dfs({})
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True
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>>> is_bipartite_dfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]})
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True
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>>> is_bipartite_dfs({0: [1, 2, 3], 1: [0, 2], 2: [0, 1, 3], 3: [0, 2]})
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False
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>>> is_bipartite_dfs({0: [4], 1: [], 2: [4], 3: [4], 4: [0, 2, 3]})
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True
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>>> is_bipartite_dfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: [0]})
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False
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>>> is_bipartite_dfs({7: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: [0]})
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Traceback (most recent call last):
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...
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KeyError: 0
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# FIXME: This test should fails with KeyError: 4.
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>>> is_bipartite_dfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 9: [0]})
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False
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>>> is_bipartite_dfs({0: [-1, 3], 1: [0, -2]})
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Traceback (most recent call last):
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...
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KeyError: -1
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>>> is_bipartite_dfs({-1: [0, 2], 0: [-1, 1], 1: [0, 2], 2: [-1, 1]})
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True
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>>> is_bipartite_dfs({0.9: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]})
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Traceback (most recent call last):
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...
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KeyError: 0
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# FIXME: This test should fails with TypeError: list indices must be integers or...
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>>> is_bipartite_dfs({0: [1.0, 3.0], 1.0: [0, 2.0], 2.0: [1.0, 3.0], 3.0: [0, 2.0]})
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True
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>>> is_bipartite_dfs({"a": [1, 3], "b": [0, 2], "c": [1, 3], "d": [0, 2]})
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Traceback (most recent call last):
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...
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KeyError: 1
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>>> is_bipartite_dfs({0: ["b", "d"], 1: ["a", "c"], 2: ["b", "d"], 3: ["a", "c"]})
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Traceback (most recent call last):
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...
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KeyError: 'b'
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"""
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def depth_first_search(node: int, color: int) -> bool:
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"""
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Perform Depth-First Search (DFS) on the graph starting from a node.
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Args:
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node: The current node being visited.
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color: The color assigned to the current node.
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Returns:
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True if the graph is bipartite starting from the current node,
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False otherwise.
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"""
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if visited[node] == -1:
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visited[node] = color
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for neighbor in graph[node]:
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if not depth_first_search(neighbor, 1 - color):
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return False
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return visited[node] == color
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visited: defaultdict[int, int] = defaultdict(lambda: -1)
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for node in graph:
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if visited[node] == -1 and not depth_first_search(node, 0):
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return False
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return True
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def is_bipartite_bfs(graph: defaultdict[int, list[int]]) -> bool:
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"""
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Check if a graph is bipartite using a breadth-first search (BFS).
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Args:
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graph: Adjacency list representing the graph.
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Returns:
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True if bipartite, False otherwise.
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Check if the graph can be divided into two sets of vertices, such that no two
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vertices within the same set are connected by an edge.
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Examples:
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# FIXME: This test should pass.
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>>> is_bipartite_bfs(defaultdict(list, {0: [1, 2], 1: [0, 3], 2: [0, 4]}))
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Traceback (most recent call last):
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...
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RuntimeError: dictionary changed size during iteration
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>>> is_bipartite_bfs(defaultdict(list, {0: [1, 2], 1: [0, 2], 2: [0, 1]}))
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False
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>>> is_bipartite_bfs({})
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True
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>>> is_bipartite_bfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]})
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True
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>>> is_bipartite_bfs({0: [1, 2, 3], 1: [0, 2], 2: [0, 1, 3], 3: [0, 2]})
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False
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>>> is_bipartite_bfs({0: [4], 1: [], 2: [4], 3: [4], 4: [0, 2, 3]})
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True
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>>> is_bipartite_bfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: [0]})
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False
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>>> is_bipartite_bfs({7: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: [0]})
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Traceback (most recent call last):
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...
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KeyError: 0
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# FIXME: This test should fails with KeyError: 4.
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>>> is_bipartite_bfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 9: [0]})
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False
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>>> is_bipartite_bfs({0: [-1, 3], 1: [0, -2]})
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Traceback (most recent call last):
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...
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KeyError: -1
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>>> is_bipartite_bfs({-1: [0, 2], 0: [-1, 1], 1: [0, 2], 2: [-1, 1]})
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True
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>>> is_bipartite_bfs({0.9: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]})
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Traceback (most recent call last):
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...
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KeyError: 0
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# FIXME: This test should fails with TypeError: list indices must be integers or...
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>>> is_bipartite_bfs({0: [1.0, 3.0], 1.0: [0, 2.0], 2.0: [1.0, 3.0], 3.0: [0, 2.0]})
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True
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>>> is_bipartite_bfs({"a": [1, 3], "b": [0, 2], "c": [1, 3], "d": [0, 2]})
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Traceback (most recent call last):
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...
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KeyError: 1
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>>> is_bipartite_bfs({0: ["b", "d"], 1: ["a", "c"], 2: ["b", "d"], 3: ["a", "c"]})
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Traceback (most recent call last):
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...
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KeyError: 'b'
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"""
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visited: defaultdict[int, int] = defaultdict(lambda: -1)
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for node in graph:
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if visited[node] == -1:
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queue: deque[int] = deque()
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queue.append(node)
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visited[node] = 0
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while queue:
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curr_node = queue.popleft()
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for neighbor in graph[curr_node]:
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if visited[neighbor] == -1:
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visited[neighbor] = 1 - visited[curr_node]
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queue.append(neighbor)
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elif visited[neighbor] == visited[curr_node]:
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return False
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return True
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if __name__ == "__main":
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import doctest
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result = doctest.testmod()
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if result.failed:
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print(f"{result.failed} test(s) failed.")
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else:
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print("All tests passed!")
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