def test_search_failure(self):
ts = TreeSearch()
dfs = DepthFirstSearch(ts)
# 5 - is a goal state but it's unreachable
problem = Problem(1, TestActionsFunction(3), TestResultFunction(), TestGoalTest(5))
result = dfs.search(problem)
self.assertTrue(dfs.is_failure(result))
metrics = dfs.get_metrics();
self.assertEqual(13, metrics[ts.METRIC_NODES_EXPANDED])
def test_successful_search(self):
ts = TreeSearch()
dfs = DepthFirstSearch(ts)
problem = Problem(1, TestActionsFunction(), TestResultFunction(), TestGoalTest(4))
result = dfs.search(problem)
self.assertEqual(3, len(result))
metrics = dfs.get_metrics();
self.assertEqual(3, metrics[ts.METRIC_NODES_EXPANDED])
self.assertEqual(3, metrics[ts.METRIC_PATH_COST])
def test_search_failure(self):
ts = TreeSearch()
dfs = DepthFirstSearch(ts)
# 5 - is a goal state but it's unreachable
problem = Problem(1, TestActionsFunction(3), TestResultFunction(),
TestGoalTest(5))
result = dfs.search(problem)
self.assertTrue(dfs.is_failure(result))
metrics = dfs.get_metrics()
self.assertEqual(13, metrics[ts.METRIC_NODES_EXPANDED])
def test_successful_search(self):
ts = TreeSearch()
dfs = DepthFirstSearch(ts)
problem = Problem(1, TestActionsFunction(), TestResultFunction(),
TestGoalTest(4))
result = dfs.search(problem)
self.assertEqual(3, len(result))
metrics = dfs.get_metrics()
self.assertEqual(3, metrics[ts.METRIC_NODES_EXPANDED])
self.assertEqual(3, metrics[ts.METRIC_PATH_COST])
from aima.core.environment.nqueens import NQueensGoalTest, NQResultFunction, NQIActionsFunctions, NQueensBoard
from aima.core.search.framework import TreeSearch, Problem
from aima.core.search.uninformed import DepthFirstSearch
__author__ = 'Ivan Mushketik'
##
# Example of solving N-queens problem with help of DFS
# Size of the chess board
SIZE = 5
# Using tree DFS
ts = TreeSearch()
dfs = DepthFirstSearch(ts)
# Create empty board
empty_board = NQueensBoard(SIZE)
# Define N-queen board
p = Problem(empty_board, NQIActionsFunctions(), NQResultFunction(), NQueensGoalTest())
# Get list of actions to solve a problem
actions = dfs.search(p)
board_to_show = NQueensBoard(SIZE)
rf = NQResultFunction()
# Print each action and perform each action (putting new queen on a board)
for action in actions:
print(action)
board_to_show = rf.result(board_to_show, action)
# Print result board
print(board_to_show)