|
| 1 | +import pytest |
| 2 | +from search import * |
| 3 | + |
| 4 | +class Graph(Problem): |
| 5 | + |
| 6 | + """ |
| 7 | + Graph class to test uninformed search algorithms which work on graphs with path costs. |
| 8 | + """ |
| 9 | + |
| 10 | + def __init__(self, initial, goal=None, paths={}, bidirectional = False): |
| 11 | + """ |
| 12 | + The constructor takes as input the initial state, list of goal states and a dictionary representing a list of tuples which contiains the action and path cost |
| 13 | + """ |
| 14 | + #Make a dictionary of actions |
| 15 | + action_dict = {} |
| 16 | + for state in paths.keys(): |
| 17 | + action_dict[state] = {} |
| 18 | + for next_state, path_cost in paths[state]: |
| 19 | + action_dict[state][next_state] = path_cost |
| 20 | + if bidirectional: |
| 21 | + if next_state not in action_dict.keys(): |
| 22 | + action_dict[next_state]={} |
| 23 | + action_dict[next_state][state] = path_cost |
| 24 | + |
| 25 | + update(self, initial=initial, goal=goal, action_dict=action_dict) |
| 26 | + |
| 27 | + def actions(self, state): |
| 28 | + """ |
| 29 | + returns the possible actions to take as a list of strings representing the state that action leads to |
| 30 | + """ |
| 31 | + return [ action for action in self.action_dict[state] ] |
| 32 | + |
| 33 | + def result(self, state, action): |
| 34 | + """ |
| 35 | + Return the state that results from executing the given action |
| 36 | + """ |
| 37 | + #Make sure the action is in actions(state) |
| 38 | + assert is_in(action, self.actions(state)) |
| 39 | + return action |
| 40 | + |
| 41 | + def goal_test(self, state): |
| 42 | + """ |
| 43 | + Return True if the state is a goal. |
| 44 | + """ |
| 45 | + return is_in(state, self.goal) |
| 46 | + |
| 47 | + def path_cost(self, c, state1, action, state2): |
| 48 | + """Return the cost of a solution path that arrives at state2 from |
| 49 | + state1 via action, assuming cost c to get up to state1. If the problem |
| 50 | + is such that the path doesn't matter, this function will only look at |
| 51 | + state2. If the path does matter, it will consider c and maybe state1 |
| 52 | + and action. The default method costs 1 for every step in the path.""" |
| 53 | + return c + self.action_dict[state1][state2] |
| 54 | + |
| 55 | +Fig[3, 12] = Graph('A', ['G'], {'A':[('B', 1), ('C', 1)], |
| 56 | + 'B':[('D', 1), ('E', 1)], |
| 57 | + 'C':[('F', 1), ('G', 1)], |
| 58 | + 'D':[], |
| 59 | + 'E':[], |
| 60 | + 'F':[], |
| 61 | + 'G':[]}) |
| 62 | + |
| 63 | +def test_breadth_first_tree_search(): |
| 64 | + solution_node = breadth_first_tree_search(Fig[3, 12]) |
| 65 | + assert solution_node.solution() == ['C', 'G'] |
| 66 | + assert [node.action for node in solution_node.path()] == [None, 'C', 'G'] |
| 67 | + #Test BFS if no goal is present |
| 68 | + Fig[3, 12].goal = [] |
| 69 | + assert breadth_first_tree_search(Fig[3, 12]) is None |
| 70 | + Fig[3, 12].goal = ['G'] |
| 71 | + |
| 72 | +if __name__ == '__main__': |
| 73 | + pytest.main() |
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