This notebook was prepared by Donne Martin. Source and license info is on GitHub.

Challenge Notebook¶

Problem: Implement depth-first search on a graph.¶

Constraints¶

Is the graph directed?
- Yes
Can we assume we already have Graph and Node classes?
- Yes
Can we assume this is a connected graph?
- Yes
Can we assume the inputs are valid?
- Yes
Can we assume this fits memory?
- Yes

Test Cases¶

Input:

add_edge(source, destination, weight)

graph.add_edge(0, 1, 5)
graph.add_edge(0, 4, 3)
graph.add_edge(0, 5, 2)
graph.add_edge(1, 3, 5)
graph.add_edge(1, 4, 4)
graph.add_edge(2, 1, 6)
graph.add_edge(3, 2, 7)
graph.add_edge(3, 4, 8)

Result:

Order of nodes visited: [0, 1, 3, 2, 4, 5]

Algorithm¶

Refer to the Solution Notebook. If you are stuck and need a hint, the solution notebook's algorithm discussion might be a good place to start.

Code¶

In [ ]:

%run ../graph/graph.py
%load ../graph/graph.py

In [ ]:

class GraphDfs(Graph):

    def dfs(self, root, visit_func):
        # TODO: Implement me
        pass

Unit Test¶

The following unit test is expected to fail until you solve the challenge.

In [ ]:

%run ../utils/results.py

In [ ]:

# %load test_dfs.py
import unittest


class TestDfs(unittest.TestCase):

    def __init__(self, *args, **kwargs):
        super(TestDfs, self).__init__()
        self.results = Results()

    def test_dfs(self):
        nodes = []
        graph = GraphDfs()
        for id in range(0, 6):
            nodes.append(graph.add_node(id))
        graph.add_edge(0, 1, 5)
        graph.add_edge(0, 4, 3)
        graph.add_edge(0, 5, 2)
        graph.add_edge(1, 3, 5)
        graph.add_edge(1, 4, 4)
        graph.add_edge(2, 1, 6)
        graph.add_edge(3, 2, 7)
        graph.add_edge(3, 4, 8)
        graph.dfs(nodes[0], self.results.add_result)
        self.assertEqual(str(self.results), "[0, 1, 3, 2, 4, 5]")

        print('Success: test_dfs')


def main():
    test = TestDfs()
    test.test_dfs()


if __name__ == '__main__':
    main()

Solution Notebook¶

Review the Solution Notebook for a discussion on algorithms and code solutions.