def run_dfs(p):
results = init_results()
i = 0
progress = len(p) * 8
for puzzle in p:
# for puzzle in ["4x4_01_00001.txt", "4x4_02_00001.txt", "4x4_03_00001.txt", "4x4_04_00001.txt", "4x4_05_00001.txt", "4x4_06_00001.txt", "4x4_07_00001.txt"]:
for order in orders:
dfs = DFS(search_order=order)
dim, lay = load_config(os.path.join("puzzles", puzzle))
dfs.model.load_layout(dim, lay)
t = time()
path = dfs.run()
t = round((time() - t) * 1000, 3)
depth = int(puzzle.split('_')[1])
# print(r, puzzle, order)
if path == -1:
continue
result = {
"path_length": len(path),
"frontier": len(dfs.frontier) + len(dfs.explored),
"explored": len(dfs.explored),
"depth": dfs.deepest,
"time": t
}
results["DFS"][order][depth].append(result)
print("DFS progress: {}%".format(round((i / progress) * 100, 2)),
end='\r',
flush=True)
i += 1
return results
def main():
g = MyGraph(100)
graph = g.generate()
# graph = g.static()
d = DFS()
v = d.dfs(g.graph, 1)
print v
g.render()
def setUp(self):
self.dfs = DFS()
self.graph = Graph()
self.graph.insert_edge(create_edge(0, 1, 10))
self.graph.insert_edge(create_edge(1, 3, 10))
self.graph.insert_edge(create_edge(0, 2, 20))
self.graph.insert_edge(create_edge(0, 3, 30))
self.graph.insert_edge(create_edge(2, 3, 60))
self.graph.insert_edge(create_edge(3, 4, 120))
class TestDepthFirstSearch(unittest.TestCase):
def setUp(self):
self.dfs = DFS(ROMENIA, start="Arad")
def test_dfs_search_success(self):
self.assertEqual("Bucharest", self.dfs.search("Bucharest"))
def test_dfs_search_failure(self):
target = "Catalao"
expected = "{0} Not Found".format(target)
self.assertEqual(expected, self.dfs.search(target))
def test_get_path_success(self):
path_expected = [
"Arad",
"Sibiu",
"Rimnicu Vilcea",
"Pitesti",
"Craiova",
"Dobreta",
"Mehadia",
"Lugoj",
"Timisoara",
]
self.dfs.search("Bucharest")
self.assertEqual(path_expected, self.dfs.get_path())
def test_get_cost_success(self):
self.dfs.search("Bucharest")
self.assertEqual(9, self.dfs.cost())
def scc(self):
# calcola i tempi di completamento per ciascun vertice
dfs = DFS(self.matrice_adiacenza)
# memorizzo i vertici in una variabile locale
vertici = dfs.vertici
# calcolo il grafo trasposto formato dagli archi del grafo originale con direzioni invertite
matrice_trasposta = self.trasposta(self.matrice_adiacenza)
# chiamo DFS su grafo trasposto passando i vertici calcolati in precedenza
dfs_t = DFS(matrice_trasposta, vertici)
return dfs_t.numero_di_scc, dfs_t.dizionario_scc
def solve():
"""
Solve the puzzle by getting the required parameters from the request args.
"""
algorithm = request.args['algorithm']
arr = list(map(int, request.args['input[]'][1:-1].split(',')))
print(algorithm)
agent = None
if algorithm == "BFS":
agent = BFS()
elif algorithm == "DFS":
agent = DFS()
elif algorithm == "A start (Euclidean)":
agent = AStar(Euclidean)
elif algorithm == "A start (Manhatten)":
agent = AStar(Manhatten)
else:
return arr
start = timeit.default_timer()
res = agent.search(arr)
end = timeit.default_timer()
res['time'] = end - start
ret = jsonify(res)
return ret
def main():
# python driver.py bfs 0,8,7,6,5,4,3,2,1
arguments = sys.argv
search_type = arguments[1]
# create a list of initial states from arguments[2]
initial_state_list = [int(x) for x in arguments[2].split(',')]
# now use this list to create initial_state object from State class
initial_state = State(initial_state_list)
# let's use time var to time execution
start_time = time.time()
# create output Object from Output class
output = Output()
if search_type == 'bfs':
output = BFS(initial_state)
elif search_type == 'dfs':
output = DFS(initial_state)
# elif search_type == 'ast':
# output = solve_ast(initial_state)
else:
print('Invalid search_type: {}'.format(search_type))
# Let's add max ram uage and running time to output object
output.max_ram_usage = resource.getrusage(
resource.RUSAGE_SELF).ru_maxrss / 1024
output.running_time = time.time() - start_time
f = open('output.txt', 'w')
f.write(str(output))
f.close()
def measure_dfs_recurrent():
dfs = DFS(Graph(0))
avg_memory_consumption = []
for i in range(START_COUNTING_FROM_NODE, NUM_OF_NODES):
print(f"Measuring DFS Recurrent for {i} nodes.")
graph = Graph(i)
dfs.graph = graph
starting_node = next(iter(graph.graph))
mem_usage = memory_usage((dfs.depth_first_search, (), {
'start_node': starting_node
}))
avg_memory_consumption.append(mem_usage[-1])
return avg_memory_consumption
def topological_sort(graph):
finishing_list = []
def pos_function(vertex):
finishing_list.append(vertex)
DFS(graph, pos_function=pos_function)
finishing_list.reverse()
return finishing_list
def test_connectedness(self, v):
dfs = DFS(self, v)
for i in range(self.V):
if dfs.marked[i]:
print(str(i) + " ", end="")
if dfs.count != self.V:
print("NOT ", end="")
print("connected")
def main():
coppie = [(1, 3), (1, 2), (1, 4), (3, 7), (3, 10), (4, 5), (5, 9), (5, 8),
(6, 10), (6, 8), (7, 6), (7, 2), (9, 10)]
V, E = init_grapho(10, coppie)
G = Grafo(V, E)
DFS(G)
print(G)
def test_random():
"""
Test all approaches on random graph.
"""
num_of_nodes = 7
graph = Graph(num_of_nodes)
starting_node = next(iter(graph.graph))
print("{:-^100}".format(" TESTING RANDOM PATH "))
dfs = DFS(graph)
paths_dfs = dfs.depth_first_search(starting_node)
print("DEPTH FIRST SEARCH")
print("Number of Hamilton Cycles found:", len(paths_dfs))
print("Shortest path:", graph.get_shortest_path(paths_dfs), end="\n\n")
bfs = BFS(graph)
paths_bfs = bfs.breadth_first_search(starting_node)
print("BREADTH FIRST SEARCH")
print("Number of Hamilton Cycles found:", len(paths_bfs))
print("Shortest path:", graph.get_shortest_path(paths_bfs), end="\n\n")
greedy = Greedy(graph)
path_greedy = greedy.greedy_approach(starting_node)
print("GREEDY APPROACH")
print("Path length:",
(path_greedy, graph.calculate_path_length(path_greedy)),
end="\n\n")
a_star_custom = AStarCustom(graph)
path_a_star = a_star_custom.a_star(starting_node)
print("A* APPROACH")
print("Path length:",
(path_a_star, graph.calculate_path_length(path_a_star)),
end="\n")
print("{:-^100}".format(" END OF TEST "), end="\n\n")
def find_path(source, target, graph):
def target_check(u, v):
return v == target
parent = DFS(graph=graph, root=source, check_terminate=target_check)
print("Parent: ", parent)
path = [target]
cur_country = target
while parent[cur_country] is not None:
parent_country = parent[cur_country]
path.append(parent_country)
cur_country = parent_country
path.reverse()
return path
class TestDFS(unittest.TestCase):
def setUp(self):
self.dfs = DFS()
self.graph = Graph()
self.graph.insert_edge(create_edge(0, 1, 10))
self.graph.insert_edge(create_edge(1, 3, 10))
self.graph.insert_edge(create_edge(0, 2, 20))
self.graph.insert_edge(create_edge(0, 3, 30))
self.graph.insert_edge(create_edge(2, 3, 60))
self.graph.insert_edge(create_edge(3, 4, 120))
def test_dfs(self):
result = self.dfs.dfs(0, self.graph)
expected = [0, 1, 3, 2, 4]
self.assertEqual(expected, result)
def correr_busqueda(tipo_busqueda=0):
tablero = CMP(get_mapa_caracteres())
if tipo_busqueda == 1:
resultados, tablero = BFS(tablero)
elif tipo_busqueda == 2:
resultados, tablero = DFS(tablero)
elif tipo_busqueda == 3:
resultados, tablero = IDS(tablero)
elif tipo_busqueda == 0:
print("No se envio el parametro tipo de busqueda")
exit()
else:
print("No se envio un tipo de busqueda correcto")
exit()
return resultados, tablero
def next_direction(data, board, destination, current_pos, health_flag):
# print 'data:' , data
print('turn: ', data['turn'])
print('current pos: ', current_pos)
# direction = data['turn']
health = data['you']['health']
direction = 'up'
next_pos = DFS(current_pos, destination, board, health_flag)
if (next_pos is None):
return None
print('next pos: ', next_pos)
if next_pos[0] == current_pos[0]:
direction = ('up' if next_pos[1] < current_pos[1] else 'down')
if next_pos[1] == current_pos[1]:
direction = ('left' if next_pos[0] < current_pos[0] else 'right')
return direction
def run(self):
if self.algorithm == "DFS":
self.solution = DFS.run(self.source, self.destinations)
if self.solution is not None:
self.end_time = self.start_time + len(self.solution) - 1
self.end_time = self.end_time % 24
elif self.algorithm == "BFS":
self.solution = BFS.run(self.source, self.destinations)
if self.solution is not None:
self.end_time = self.start_time + len(self.solution) - 1
self.end_time = self.end_time % 24
elif self.algorithm == "UCS":
self.solution = UCS.run(self.start_time, self.source, self.destinations)
if self.solution is not None:
self.end_time = self.solution.pop(0)
else:
raise Exception("Unexpected Algorithm = " + self.algorithm)
class TestDepthFirstSearch(unittest.TestCase):
def setUp(self):
self.dfs = DFS(ROMENIA, start='Arad')
def test_dfs_search_success(self):
self.assertEqual('Bucharest', self.dfs.search('Bucharest'))
def test_dfs_search_failure(self):
target = 'Catalao'
expected = '{0} Not Found'.format(target)
self.assertEqual(expected, self.dfs.search(target))
def test_get_path_success(self):
path_expected = ['Arad', 'Sibiu', 'Rimnicu Vilcea', 'Pitesti',
'Craiova', 'Dobreta', 'Mehadia', 'Lugoj', 'Timisoara']
self.dfs.search('Bucharest')
self.assertEqual(path_expected, self.dfs.get_path())
def test_get_cost_success(self):
self.dfs.search('Bucharest')
self.assertEqual(9, self.dfs.cost())
def run():
label_find_the_result.config(text="Result:")
label_time.config(text="Time:")
label_steps.config(text="Steps:")
btn_run.config(state=tk.DISABLED)
btn_reset.config(state=tk.DISABLED)
radio_dfs.config(state=tk.DISABLED)
radio_bfs.config(state=tk.DISABLED)
radio_bestfs.config(state=tk.DISABLED)
radio_hc.config(state=tk.DISABLED)
algorithm = alg.get()
res = ""
steps = 0
time_start = time.time()
if algorithm == 1:
res = DFS.solve(puzzle_in, window)
print("DFS: ", res)
elif algorithm == 2:
res = BFS.solve(puzzle_in, window)
print("BFS: ", res)
elif algorithm == 3:
res = BestFirstSearch.solve(puzzle_in, window)
print("Best First Search: ", res)
elif algorithm == 4:
res = HillClimbing.solve(puzzle_in, window)
print("Hill Climbing: ", res)
time_end = time.time()
if res is None:
solution = "No!"
else:
solution = "Yes!"
steps = len(res)
output(solution, time_end - time_start, steps)
btn_run.config(state=tk.NORMAL)
btn_reset.config(state=tk.NORMAL)
radio_dfs.config(state=tk.NORMAL)
radio_bfs.config(state=tk.NORMAL)
radio_bestfs.config(state=tk.NORMAL)
radio_hc.config(state=tk.NORMAL)
# row = int(input('Enter number of Rows (Must be > 1): '))
row = 2
# col int(input('Enter number of Columns (Must be > 1): '))
col = 3
puzzel = GameController.create_puzzle(row, col)
puzzel = GameController.scramble(puzzel)
# puzzel = [[' 0 ', ' 0 ', ' 0 '], [' 1 ', ' 0 ', ' 1 ']]
puzzelorg = deepcopy(puzzel)
puzzeldfs = deepcopy(puzzel)
# -------------------------------------------------------------------------
# uncomment this block to use the dfs_ai search algorithm
moves = DFS.perform(puzzel, row, col)
# uncomment to use the A* search
# moves = AStar.perform(puzzel, row,col)
# uncomment to use the BFS
# moves = BFS.perform(puzzel, row, col)
# -------------------------------------------------------------------------
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
for r in puzzelorg:
print("".join(r))
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
for move in moves:
__author__ = 'muneeb' from node import Node from ids import IDS from dfs import DFS from bfs import BFS from graph_generator import Graph_Generator import copy start = "1" goal = "15" g = Graph_Generator(8000) graph = g.generate_graph() ids = IDS(copy.deepcopy(graph), start, goal) bfs = BFS(copy.deepcopy(graph), start, goal) dfs = DFS(copy.deepcopy(graph), start, goal) ids.walk() bfs.walk() dfs.walk()
def try_all_approaches(num_of_samples: int, num_of_nodes: int):
"""
Execute all approaches on the same graphs for given number of nodes and samples (the more samples the more
statistically accurate results).
Returns: Achieved path lengths and execution times for all approaches.
"""
graph = Graph(0)
dfs = DFS(graph)
dfs_path_lengths = []
dfs_execution_times = []
bfs = BFS(graph)
bfs_path_lengths = []
bfs_execution_times = []
greedy = Greedy(graph)
greedy_path_lengths = []
greedy_execution_times = []
a_star_custom = AStarCustom(graph)
a_star_path_lengths = []
a_star_execution_times = []
for i in range(a_star_custom.num_of_heuristics):
a_star_path_lengths.append([])
a_star_execution_times.append([])
for j in range(num_of_samples):
graph = Graph(num_of_nodes)
starting_node = next(iter(graph.graph))
start = time.time()
dfs.graph = graph
paths_dfs = dfs.depth_first_search(starting_node)
dfs_path_lengths.append(Graph.get_shortest_path(paths_dfs)[1])
execution_time = time.time() - start
dfs_execution_times.append(execution_time)
start = time.time()
bfs.graph = graph
paths_bfs = bfs.breadth_first_search(starting_node)
bfs_path_lengths.append(Graph.get_shortest_path(paths_bfs)[1])
execution_time = time.time() - start
bfs_execution_times.append(execution_time)
start = time.time()
greedy.graph = graph
path_greedy = greedy.greedy_approach(starting_node)
greedy_path_lengths.append(Graph.calculate_path_length(path_greedy))
execution_time = time.time() - start
greedy_execution_times.append(execution_time)
for i in range(a_star_custom.num_of_heuristics):
start = time.time()
a_star_custom.graph = graph
a_star_custom.heuristic_choice = i
path_a_star = a_star_custom.a_star(starting_node)
execution_time = time.time() - start
a_star_path_lengths[i].append(
Graph.calculate_path_length(path_a_star))
a_star_execution_times[i].append(execution_time)
dfs_avg_results = sum(dfs_path_lengths) / num_of_samples, sum(
dfs_execution_times) / num_of_nodes, "DFS Recurrent"
bfs_avg_results = sum(bfs_path_lengths) / num_of_samples, sum(
bfs_execution_times) / num_of_nodes, "BFS"
greedy_avg_results = sum(greedy_path_lengths) / num_of_samples, sum(
greedy_execution_times) / num_of_nodes, "Greedy"
a_star_avg_results = []
for i in range(a_star_custom.num_of_heuristics):
label = "A* heuristic " + str(i)
a_star_avg_results.append(
(sum(a_star_path_lengths[i]) / num_of_samples,
sum(a_star_execution_times[i]) / num_of_samples, label))
data = [dfs_avg_results, bfs_avg_results, greedy_avg_results]
data.extend(a_star_avg_results)
return data
from state import State
from bfs import BFS
from dfs import DFS
SEARCH_TYPE = sys.argv[1]
INITIAL_STATE = map(int, sys.argv[2].split(','))
game = State(INITIAL_STATE);
search = None
if SEARCH_TYPE == 'bfs':
search = BFS(game)
if SEARCH_TYPE == 'dfs':
search = DFS(game)
result = search.perform_search()
goal_pos = result.position
res = resource.getrusage(resource.RUSAGE_SELF)
def trace_path(goal_pos):
pos = goal_pos
path = []
while pos != None:
if (pos.node.action != None):
path.append(pos.node.action)
pos = pos.prev
from bestfirst import BestFirstSearch
from astar import AStarSearch
from board import heuristicOption
import time
"""
Project 1.1: Indonesian Dot Puzzle Board Class
COMP 472 NN
DUE: Feb 9th, 2020
Samantha Yuen (40033121), Andrew Marcos (40011252), Michael Gagnon (40030481)
This file is responsible for starting all three searches on the board test file.
"""
filename = input("Enter filename with board setup:")
listOfBoards = returnBoards(filename)
dfs = DFS()
bfs = BestFirstSearch()
afs = AStarSearch()
for board in listOfBoards:
#input('enter to continue')
start_time = time.time()
dfs.dfsSearch(board)
print("--- Board #%s finished in %s seconds for dfs ---" %
(board.num, time.time() - start_time))
start_time = time.time()
bfs.bestFirstSearch(board)
print(
"--- Board #%s finished in %s seconds for bfs with heuristic %s ---" %
(board.num, time.time() - start_time, heuristicOption))
start_time = time.time()
# row = int(input('Enter number of Rows (Must be > 1): '))
row = 2
# col int(input('Enter number of Columns (Must be > 1): '))
col = 3
puzzel = GameController.create_puzzle(row, col)
puzzel = GameController.scramble(puzzel)
# puzzel = [[' 0 ', ' 0 ', ' 0 '], [' 1 ', ' 0 ', ' 1 ']]
puzzelorg = deepcopy(puzzel)
puzzeldfs = deepcopy(puzzel)
# -------------------------------------------------------------------------
# uncomment this block to use the dfs_ai search algorithm
moves = DFS.perform(puzzel, row, col)
# uncomment to use the A* search
# moves = AStar.perform(puzzel, row,col)
# uncomment to use the BFS
# moves = BFS.perform(puzzel, row, col)
# -------------------------------------------------------------------------
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
for r in puzzelorg:
print("".join(r))
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
def solveDFS(rubik):
aux = time.time()
solution_DFS = DFS.search(rubik, lambda x: x == RubikPuzzle())
time["DFS: "](time.time() - aux) * 1000
aux = 0
return solution_DFS
def test_dfs(self):
agent = DFS()
steps = agent.search([1, 2, 5, 3, 4, 0, 6, 7, 8])
print("Solved in {} steps".format(len(steps)))
print(steps)
for line in txtFile.splitlines():
try:
if line[1] == ',':
if len(playerPositions) == 0:
playerPositions.append(int(line[0]))
playerPositions.append(int(line[2]))
else:
boxPositions.append([int(line[0]), int(line[2])])
else:
j = 0
temp = []
for a in line.replace('\n', ''):
temp.append(a)
if a == 'X':
goals.append([i, j])
j += 1
gameTable.append(temp)
i += 1
except IndexError:
pass
readLevel(sys.argv[1])
print(BFS(gameTable, playerPositions, goals, boxPositions))
print(DFS(gameTable, playerPositions, goals, boxPositions))
print(IDFS(gameTable, playerPositions, goals, boxPositions))
def setUp(self):
self.dfs = DFS(ROMENIA, start="Arad")
from dfs import DFS
from bfs import BFS
def print_steps(steps_sol):
for s in steps_sol:
print("================================")
for i in range(0, 9, 3):
print(s[i:i + 3])
agent = DFS()
steps = agent.search([1, 2, 5, 3, 4, 0, 6, 7, 8])
print_steps(steps)
agent = BFS()
steps = agent.search([1, 2, 0, 3, 4, 5, 6, 7, 8])
print_steps(steps)
import balloon_manager
import read_input_file
from cell import Cell
from dfs import DFS
from graph2 import Graph2, Vertex2
config = read_input_file.load_configuration('test_input')
selected_targets = balloon_manager.select_targets(config)
balloons = balloon_manager.assign_balloons(config, selected_targets)
g = Graph2(config)
dfs = DFS(g, config)
for balloon in balloons:
target_vertex = Vertex2(balloon.target_cell, 1)
dfs.search(dfs.start_vertex, target_vertex)
print "start_vertex: %s, target_vertex: %s" % (dfs.start_vertex, target_vertex)
print "path found: %s" % dfs.path_found
print "path: %s" % dfs.get_path(target_vertex)
if False:
for r in range(config['R']): # For all rows
for c in range(config['C']): # For all columns
v = Vertex2(Cell(r, c), 2)
adj = g.adj(v)
print "Adjacent cells for vertex %s are: %s" % (v, adj)
def iter_dfs(self, start_node):
return DFS(self, start_node)
def run_tests(filepath):
"""
runs all the tests for the program
"""
generator = GraphGenerator(filepath)
graph = generator.graph_from_text_file()
# test for object references in original graph
print "TEST: Testing object reference on original graph"
print object_references(graph)
dfs_1 = DFS(graph)
dfs_1.dfs()
# graph after first run of dfs with vertices ordered by increasing
# finishing time.
dfs_1.dfs_graph_from_call_stack()
# test for object reference after first dfs run
print "TEST: Testing object reference on dfs 1 graph"
print object_references(dfs_1.dfs_graph)
print "Printing DFS 1 Graph"
print dfs_1.dfs_graph
print "*****************************************"
# graph after transpose of the original graph
dfs_1_trans_graph = dfs_1.dfs_graph.transpose()
print "Printing transpose of DFS 1 Graph"
print dfs_1_trans_graph
print "*****************************************"
# # Second run of dfs on transposed graph
dfs_2 = DFS(dfs_1_trans_graph)
dfs_2.dfs()
dfs_2.dfs_graph_from_call_stack()
# test for object reference after first dfs run
print "TEST: Testing object reference on dfs 2 graph"
print object_references(dfs_2.dfs_graph)
print "Printing DFS 2 Graph"
print dfs_2.dfs_graph
print "*****************************************"
print "Printing DFS components"
print "*****************************************"
print_dfs_components(dfs_2.dfs_forest)
print("Iterator po krawedziach")
for i in dGraph.iteredges():
print(i)
print('BFS \n')
bfs = BFS(dGraph, 2)
iter_bfs = iter(bfs)
for i in iter_bfs:
print(i)
for i in dGraph.iter_bfs(2):
print(i)
print("DFS \n")
dfs = DFS(dGraph, 2)
iter_dfs = iter(dfs)
for i in iter_dfs:
print(i)
for i in dGraph.iter_dfs(2):
print(i)
for node in dGraph: # iterator po wierzchołkach
print("wierzchołek", node)
for node in uGraph: # iterator po wierzchołkach
print("wierzchołek ", node)
print("stopien ", len(uGraph[node]))
uGraph.__repr__()
from json import loads
from bfs import BFS
from dfs import DFS
from ids import IDS
from hc import HC
# Load tree data from file
filePath = open('./data.json')
data = filePath.read()
dataJson = loads(data)
tree = dataJson['tree']
# list of goals of DFS, DFS, IDS algorithms
goals = dataJson['goals']
hill_climb = dataJson['hill-climb']
# list of goals of HC algorithm
goals_HC = dataJson['hill-climb']['goals']
# root node
root = dataJson['root']
if __name__ == '__main__':
DFS(tree, root, goals)
BFS(tree, root, goals)
IDS(tree, root, goals, 2)
HC(hill_climb, root, goals_HC)
print "\nBFS - Map2" world = World(Util.read_file(map2), b_map2) bfs_search = BFS(world) bfs_search.search() print timeit.Timer(bfs_search.search).timeit(1) print "\nBFS - Map3" world = World(Util.read_file(map3), b_map3) bfs_search = BFS(world) bfs_search.search() print timeit.Timer(bfs_search.search).timeit(1) print "\nDFS - Map1" world = World(Util.read_file(map1), d_map1) dfs_search = DFS(world) dfs_search.search() print timeit.Timer(dfs_search.search).timeit(1) print "\nDFS - Map2" world = World(Util.read_file(map2), d_map2) dfs_search = DFS(world) dfs_search.search() print timeit.Timer(dfs_search.search).timeit(1) print "\nDFS - Map3" world = World(Util.read_file(map3), d_map3) dfs_search = DFS(world) dfs_search.search() print timeit.Timer(dfs_search.search).timeit(1)
