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2bbdc3bfe7
* Implement connected components algorithm for graphs
* fixup! Format Python code with psf/black push
* Add parameters and return values annotations with Python type hints
* updating DIRECTORY.md
* Add doctests and typehints
* Remove unnecessary comments, change variable names
* fixup! Format Python code with psf/black push
Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
74 lines
1.5 KiB
Python
74 lines
1.5 KiB
Python
"""
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https://en.wikipedia.org/wiki/Component_(graph_theory)
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Finding connected components in graph
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"""
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test_graph_1 = {
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0: [1, 2],
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1: [0, 3],
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2: [0],
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3: [1],
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4: [5, 6],
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5: [4, 6],
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6: [4, 5],
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}
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test_graph_2 = {
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0: [1, 2, 3],
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1: [0, 3],
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2: [0],
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3: [0, 1],
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4: [],
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5: [],
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}
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def dfs(graph: dict, vert: int, visited: list) -> list:
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"""
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Use depth first search to find all vertexes
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being in the same component as initial vertex
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>>> dfs(test_graph_1, 0, 5 * [False])
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[0, 1, 3, 2]
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>>> dfs(test_graph_2, 0, 6 * [False])
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[0, 1, 3, 2]
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"""
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visited[vert] = True
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connected_verts = []
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for neighbour in graph[vert]:
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if not visited[neighbour]:
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connected_verts += dfs(graph, neighbour, visited)
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return [vert] + connected_verts
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def connected_components(graph: dict) -> list:
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"""
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This function takes graph as a parameter
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and then returns the list of connected components
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>>> connected_components(test_graph_1)
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[[0, 1, 3, 2], [4, 5, 6]]
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>>> connected_components(test_graph_2)
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[[0, 1, 3, 2], [4], [5]]
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"""
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graph_size = len(graph)
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visited = graph_size * [False]
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components_list = []
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for i in range(graph_size):
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if not visited[i]:
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i_connected = dfs(graph, i, visited)
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components_list.append(i_connected)
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return components_list
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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