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Concatenates both check bipatrite graphs(bfs&dfs)#10708
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cclauss 5aec68d
fixes#8098
shivaparihar6119 97085bd
deleted: graphs/check_bipartite_graph_all.py
shivaparihar6119 bd440a0
renamed: graphs/check_bipatrite,py -> graphs/check_bipatrite.py
shivaparihar6119 f4d4644
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] dbb5d98
Add the new tests
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,179 @@ | ||
| from collections import defaultdict, deque | ||
| def is_bipartite_dfs(graph: defaultdict[int, list[int]]) -> bool: | ||
| """ | ||
| Check if a graph is bipartite using depth-first search (DFS). | ||
| Args: | ||
| graph: Adjacency list representing the graph. | ||
| Returns: | ||
| True if bipartite, False otherwise. | ||
| Checks if the graph can be divided into two sets of vertices, such that no two | ||
| vertices within the same set are connected by an edge. | ||
| Examples: | ||
| # FIXME: This test should pass. | ||
| >>> is_bipartite_dfs(defaultdict(list, {0: [1, 2], 1: [0, 3], 2: [0, 4]})) | ||
| Traceback (most recent call last): | ||
| ... | ||
| RuntimeError: dictionary changed size during iteration | ||
| >>> is_bipartite_dfs(defaultdict(list, {0: [1, 2], 1: [0, 3], 2: [0, 1]})) | ||
| False | ||
| >>> is_bipartite_dfs({}) | ||
| True | ||
| >>> is_bipartite_dfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}) | ||
| True | ||
| >>> is_bipartite_dfs({0: [1, 2, 3], 1: [0, 2], 2: [0, 1, 3], 3: [0, 2]}) | ||
| False | ||
| >>> is_bipartite_dfs({0: [4], 1: [], 2: [4], 3: [4], 4: [0, 2, 3]}) | ||
| True | ||
| >>> is_bipartite_dfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: [0]}) | ||
| False | ||
| >>> is_bipartite_dfs({7: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: [0]}) | ||
| Traceback (most recent call last): | ||
| ... | ||
| KeyError: 0 | ||
| # FIXME: This test should fails with KeyError: 4. | ||
| >>> is_bipartite_dfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 9: [0]}) | ||
| False | ||
| >>> is_bipartite_dfs({0: [-1, 3], 1: [0, -2]}) | ||
| Traceback (most recent call last): | ||
| ... | ||
| KeyError: -1 | ||
| >>> is_bipartite_dfs({-1: [0, 2], 0: [-1, 1], 1: [0, 2], 2: [-1, 1]}) | ||
| True | ||
| >>> is_bipartite_dfs({0.9: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}) | ||
| Traceback (most recent call last): | ||
| ... | ||
| KeyError: 0 | ||
| # FIXME: This test should fails with TypeError: list indices must be integers or... | ||
| >>> is_bipartite_dfs({0: [1.0, 3.0], 1.0: [0, 2.0], 2.0: [1.0, 3.0], 3.0: [0, 2.0]}) | ||
| True | ||
| >>> is_bipartite_dfs({"a": [1, 3], "b": [0, 2], "c": [1, 3], "d": [0, 2]}) | ||
| Traceback (most recent call last): | ||
| ... | ||
| KeyError: 1 | ||
| >>> is_bipartite_dfs({0: ["b", "d"], 1: ["a", "c"], 2: ["b", "d"], 3: ["a", "c"]}) | ||
| Traceback (most recent call last): | ||
| ... | ||
| KeyError: 'b' | ||
| """ | ||
| def depth_first_search(node: int, color: int) -> bool: | ||
| """ | ||
| Perform Depth-First Search (DFS) on the graph starting from a node. | ||
| Args: | ||
| node: The current node being visited. | ||
| color: The color assigned to the current node. | ||
| Returns: | ||
| True if the graph is bipartite starting from the current node, | ||
| False otherwise. | ||
| """ | ||
| if visited[node] == -1: | ||
| visited[node] = color | ||
| for neighbor in graph[node]: | ||
| if not depth_first_search(neighbor, 1 - color): | ||
| return False | ||
| return visited[node] == color | ||
| visited: defaultdict[int, int] = defaultdict(lambda: -1) | ||
| for node in graph: | ||
| if visited[node] == -1 and not depth_first_search(node, 0): | ||
| return False | ||
| return True | ||
| def is_bipartite_bfs(graph: defaultdict[int, list[int]]) -> bool: | ||
| """ | ||
| Check if a graph is bipartite using a breadth-first search (BFS). | ||
| Args: | ||
| graph: Adjacency list representing the graph. | ||
| Returns: | ||
| True if bipartite, False otherwise. | ||
| Check if the graph can be divided into two sets of vertices, such that no two | ||
| vertices within the same set are connected by an edge. | ||
| Examples: | ||
| # FIXME: This test should pass. | ||
| >>> is_bipartite_bfs(defaultdict(list, {0: [1, 2], 1: [0, 3], 2: [0, 4]})) | ||
| Traceback (most recent call last): | ||
| ... | ||
| RuntimeError: dictionary changed size during iteration | ||
| >>> is_bipartite_bfs(defaultdict(list, {0: [1, 2], 1: [0, 2], 2: [0, 1]})) | ||
| False | ||
| >>> is_bipartite_bfs({}) | ||
| True | ||
| >>> is_bipartite_bfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}) | ||
| True | ||
| >>> is_bipartite_bfs({0: [1, 2, 3], 1: [0, 2], 2: [0, 1, 3], 3: [0, 2]}) | ||
| False | ||
| >>> is_bipartite_bfs({0: [4], 1: [], 2: [4], 3: [4], 4: [0, 2, 3]}) | ||
| True | ||
| >>> is_bipartite_bfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: [0]}) | ||
| False | ||
| >>> is_bipartite_bfs({7: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: [0]}) | ||
| Traceback (most recent call last): | ||
| ... | ||
| KeyError: 0 | ||
| # FIXME: This test should fails with KeyError: 4. | ||
| >>> is_bipartite_bfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 9: [0]}) | ||
| False | ||
| >>> is_bipartite_bfs({0: [-1, 3], 1: [0, -2]}) | ||
| Traceback (most recent call last): | ||
| ... | ||
| KeyError: -1 | ||
| >>> is_bipartite_bfs({-1: [0, 2], 0: [-1, 1], 1: [0, 2], 2: [-1, 1]}) | ||
| True | ||
| >>> is_bipartite_bfs({0.9: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}) | ||
| Traceback (most recent call last): | ||
| ... | ||
| KeyError: 0 | ||
| # FIXME: This test should fails with TypeError: list indices must be integers or... | ||
| >>> is_bipartite_bfs({0: [1.0, 3.0], 1.0: [0, 2.0], 2.0: [1.0, 3.0], 3.0: [0, 2.0]}) | ||
| True | ||
| >>> is_bipartite_bfs({"a": [1, 3], "b": [0, 2], "c": [1, 3], "d": [0, 2]}) | ||
| Traceback (most recent call last): | ||
| ... | ||
| KeyError: 1 | ||
| >>> is_bipartite_bfs({0: ["b", "d"], 1: ["a", "c"], 2: ["b", "d"], 3: ["a", "c"]}) | ||
| Traceback (most recent call last): | ||
| ... | ||
| KeyError: 'b' | ||
| """ | ||
| visited: defaultdict[int, int] = defaultdict(lambda: -1) | ||
| for node in graph: | ||
| if visited[node] == -1: | ||
| queue: deque[int] = deque() | ||
| queue.append(node) | ||
| visited[node] = 0 | ||
| while queue: | ||
| curr_node = queue.popleft() | ||
| for neighbor in graph[curr_node]: | ||
| if visited[neighbor] == -1: | ||
| visited[neighbor] = 1 - visited[curr_node] | ||
| queue.append(neighbor) | ||
| elif visited[neighbor] == visited[curr_node]: | ||
| return False | ||
| return True | ||
| if __name__ == "__main": | ||
| import doctest | ||
| result = doctest.testmod() | ||
| if result.failed: | ||
| print(f"{result.failed} test(s) failed.") | ||
| else: | ||
| print("All tests passed!") | ||
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