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

# Challenge Notebook¶

## Constraints¶

• Is the graph directed?
• Yes
• Is the graph weighted?
• No
• Can we assume we already have Graph and Node classes?
• Yes
• Are the inputs two Nodes?
• Yes
• Is the output a list of Node keys that make up the shortest path?
• Yes
• If there is no path, should we return None?
• 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)
graph.add_edge(3, 4)

Result:

• search_path(start=0, end=2) -> [0, 1, 3, 2]
• search_path(start=0, end=0) -> [0]
• search_path(start=4, end=5) -> None

## 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

In [ ]:
class GraphShortestPath(Graph):

def shortest_path(self, source_key, dest_key):
# TODO: Implement me
pass


## Unit Test¶

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

In [ ]:
# %load test_shortest_path.py
import unittest

class TestShortestPath(unittest.TestCase):

def test_shortest_path(self):
nodes = []
graph = GraphShortestPath()
for id in range(0, 6):

self.assertEqual(graph.shortest_path(nodes[0].key, nodes[2].key), [0, 1, 3, 2])
self.assertEqual(graph.shortest_path(nodes[0].key, nodes[0].key), [0])
self.assertEqual(graph.shortest_path(nodes[4].key, nodes[5].key), None)

print('Success: test_shortest_path')

def main():
test = TestShortestPath()
test.test_shortest_path()

if __name__ == '__main__':
main()


## Solution Notebook¶

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