def resolver(metodo_busqueda, posiciones_aparatos):
viewer = BaseViewer()
initial = []
robot = [3, 3, 1]
namespace = __import__(__name__)
initial.append(robot)
for a in posiciones_aparatos:
aparato = [a[0], a[1], 300]
initial.append(aparato)
initial = list2tuple(initial)
result = getattr(namespace, metodo_busqueda)(Bomberobot(initial),
viewer=viewer,
graph_search=True)
print('Result state: ')
print(result.state)
print('Result path: ')
for action, state in result.path():
print('Action: ', action)
print('State: ', state)
print('')
print(viewer.stats)
return result
def try_search_method(search_method, problem_class, initial_state):
print()
print('=' * 50)
print("corriendo:", search_method.__name__)
visor = BaseViewer()
problem = problem_class(initial_state)
result = search_method(problem, graph_search=True, viewer=visor)
print('estado final:')
if result is None:
print("RESULTADO NO ENCONTRADO")
return
else:
print(result.state)
print('-' * 50)
print('estadísticas:')
print('cantidad de acciones hasta la meta:', len(result.path()))
print(visor.stats)
draw_path = input("Do you want to draw the path? [y/N]: ")
if draw_path.lower() == "y":
for _, state in result.path():
problem.print_state_representation(state)
continue_printing = input("Print the next state [Y/n]: ")
if continue_printing.lower() == "n":
break
def resolver(metodo_busqueda, posiciones_personas, mostrar_stats=False):
problema = ProblemaRobot(posiciones_personas)
if mostrar_stats:
viewer = BaseViewer()
else:
viewer = None
funciones = {
'breadth_first': breadth_first,
'depth_first': depth_first,
'greedy': greedy,
'astar': astar
}
funcion_busqueda = funciones[metodo_busqueda]
resultado = funcion_busqueda(problema, graph_search=True, viewer=viewer)
if mostrar_stats:
print(
"Metodo: {}, Costo: {}, Tamanio maximo frontera: {}, Nodos visitados: {}"
.format(metodo_busqueda, resultado.cost,
viewer.stats['max_fringe_size'],
viewer.stats['visited_nodes']))
return resultado
def searchSolution(map, configuration, state, aiBaseName, tracep):
''' Creates a gameProblem object, and calls its initialization
Passes the description of the map both in matrix and in dictionary form
Then executes the search algorithm defined upon initialization
Transforms the solution in a plan in the format required by the game
'''
result = False
if tracep:
debugCall(map, configuration, state)
mapAsPositions = transformMap(map, configuration)
problem = GameProblem()
problem.initializeProblem(map=map,
positions=mapAsPositions,
conf=configuration,
aiBaseName=aiBaseName)
algorithm = problem.ALGORITHM
useViewer = BaseViewer()
print("----------------------- PROBLEM INFORMATION ------------------")
print("-- Initial State --")
print(problem.initial_state)
print("-- Final State --")
print(problem.GOAL)
print("-- Search Algorithm --")
print(problem.ALGORITHM)
print("------------- EXECUTING SEARCH -------------------")
result = algorithm(problem, graph_search=True, viewer=useViewer)
if result:
print("------------- SEARCH RESULTS -------------------")
print("Reached final state: {0}".format(result.state))
print(searchInfo(problem, result, useViewer))
print("Solution as path (length:{0}): {1}".format(
len(result.path()), result.path()))
detailed_path = result.path()[1:]
plan = list((a[0], {
'showText':
'Executing {0} -> State {1}'.format(a[0], problem.printState(
a[1])),
'onState':
problem.getStateData(a[1])
}) for a in detailed_path)
return plan, problem, result, useViewer
else:
print("WARNING: A solution was not found for the final state: {0}".
format(problem.GOAL))
return None, None, None, None
def resolver(metodo_busqueda, posiciones_personas):
state = (posicionRobot, hielosRotos, posiciones_personas)
if metodo_busqueda == 'astar':
visor=BaseViewer()
result = astar(RescateSobreHielo(state),graph_search=True, viewer=visor)
elif metodo_busqueda =='breadth_first':
visor=BaseViewer()
result = breadth_first(RescateSobreHielo(state),graph_search=True, viewer=visor)
elif metodo_busqueda =='depth_first':
visor=BaseViewer()
result = depth_first(RescateSobreHielo(state),graph_search=True,viewer=visor)
elif metodo_busqueda =='greedy':
visor=BaseViewer()
result = greedy(RescateSobreHielo(state),graph_search=True,viewer=visor)
return result
def resolver(metodo_busqueda, posicion_rey, controlar_estados_repetidos):
problema = HnefataflProblem(posicion_rey)
visor = BaseViewer()
if metodo_busqueda == "breadth_first":
resultado = breadth_first(problema, graph_search=controlar_estados_repetidos)
return resultado
if metodo_busqueda == "depth_first":
return depth_first(problema, graph_search=controlar_estados_repetidos)
if metodo_busqueda == "greedy":
return greedy(problema, graph_search=controlar_estados_repetidos)
if metodo_busqueda == "astar":
return astar(problema, graph_search=controlar_estados_repetidos)
def planear_camiones(metodo, camiones=TRUCKS_EXAMPLE, paquetes=PACKAGES_EXAMPLE, debug=False):
# a state is defined by a tuple of two elements:
# - trucks with info wich is a tuple of (id, city, oil_liters)
# - packages info wich is a tuple of (id, city)
initial_state = (tuple(camiones), tuple(x[:-1] for x in paquetes))
packages_goal = tuple((x[0], x[2]) for x in paquetes)
trucks_liters = {x[0]: x[-1] for x in camiones}
problem = MercadoArtificalProblem(trucks_liters, packages_goal, initial_state)
search_methods = {
"astar": astar,
"breadth_first": breadth_first,
"depth_first": depth_first,
"uniform_cost": uniform_cost,
"greedy": greedy,
}
method = search_methods[metodo]
if debug:
visor = ConsoleViewer()
# visor = WebViewer()
else:
visor = BaseViewer()
result = method(problem, graph_search=True, viewer=visor)
if debug:
print('estadísticas:')
print('cantidad de acciones hasta la meta:', len(result.path()))
print(visor.stats)
print("FINAL STATE:")
print(result.state)
for i, step in enumerate(result.path()):
cont = input("print step by step? Y/n")
if cont.upper() == "N":
break
print(f"----------- STEP {i} ----------")
action, state = step
print("ACTION")
print(action)
print("STATE")
print(state)
return [action for action, _ in result.path() if action is not None]
def main():
problema = PresidentesProblem()
visor = BaseViewer()
inicio = datetime.datetime.now()
resultado = astar(problema, graph_search=True, viewer=visor)
tiempo = (datetime.datetime.now() - inicio).total_seconds()
for i, (accion, estado) in enumerate(resultado.path()):
print('Acción N: {} {} ## Estado: {}'.format(i, accion, estado))
print("Costo: {}".format(resultado.cost))
print("Nodos explorados: {}".format(visor.stats['visited_nodes']))
print("Tamaño máximo frontera: {}".format(visor.stats['max_fringe_size']))
print("Tiempo transcurrido: {} segundos".format(tiempo))
def resolver(metodo_busqueda, posicion_rey, controlar_estados_repetidos):
INITIAL = posicion_rey
problema = HnefataflProblema(posicion_rey)
visor = BaseViewer()
#Busqueda en amplitud
if (metodo_busqueda == 'breadth_first'):
resultado = breadth_first(problema,
graph_search=controlar_estados_repetidos,
viewer=visor)
return resultado
#Busqueda en profundidad
if (metodo_busqueda == 'depth_first'):
resultado = depth_first(problema,
graph_search=controlar_estados_repetidos,
viewer=visor)
return resultado
#Busqueda avara
if (metodo_busqueda == 'greedy'):
resultado = greedy(problema,
graph_search=controlar_estados_repetidos,
viewer=visor)
return resultado
#Busqueda A Estrella
if (metodo_busqueda == 'astar'):
resultado = astar(problema,
graph_search=controlar_estados_repetidos,
viewer=visor)
return resultado
# for action, state in resultado.path():
# print action
# print resultado.path()
# print 'produndidad: ' + str(resultado.depth)
# print state
# print 'El costo es: ' + str(resultado.cost)
# print visor.stats
#if __name__ == '__main__':
# resolver('breadth_first', (5,3),True)
def resolver(metodo_busqueda, posiciones_personas):
state = ((0, 0), (posiciones_personas), ())
my_viewer = BaseViewer()
if 'astar':
result = astar(TpProblem(state), graph_search=True, viewer=my_viewer)
elif 'breadth_first':
result = breadth_first(TpProblem(state),
graph_search=True,
viewer=my_viewer)
elif 'depth_first':
result = depth_first(TpProblem(state),
graph_search=True,
viewer=my_viewer)
elif 'greedy':
result = greedy(TpProblem(state), graph_search=True, viewer=my_viewer)
return result
def resolver(metodo_busqueda, posicion_rey, controlar_estados_repetidos):
problema = HnefataflProblema(posicion_rey)
visor = BaseViewer()
#Busquedas, Grafo -> graph_search=True
if (metodo_busqueda == 'breadth_first'): # En amplitud
resultado = breadth_first(problema,
graph_search=controlar_estados_repetidos,
viewer=visor)
elif (metodo_busqueda == 'depth_first'): # Profundidad
resultado = depth_first(problema,
graph_search=controlar_estados_repetidos,
viewer=visor)
elif (metodo_busqueda == 'greedy'): # Avara
resultado = greedy(problema,
graph_search=controlar_estados_repetidos,
viewer=visor)
elif (metodo_busqueda == 'astar'): # Estrella
resultado = astar(problema,
graph_search=controlar_estados_repetidos,
viewer=visor)
print(visor.stats)
return resultado
def resolver(metodo_busqueda, franceses, piratas):
viewer = BaseViewer()
barcos_piratas = []
for pirata in piratas:
barcos_piratas.append((pirata, 0))
initial = (tuple(franceses), tuple(barcos_piratas))
problem = entrega1(initial)
if metodo_busqueda == "breadth_first":
resultado = breadth_first(problem, graph_search=True, viewer=viewer)
elif metodo_busqueda == "greedy":
resultado = greedy(problem, graph_search=True, viewer=viewer)
elif metodo_busqueda == "depth_first":
resultado = depth_first(problem, graph_search=True, viewer=viewer)
elif metodo_busqueda == "astar":
resultado = astar(problem, graph_search=True, viewer=viewer)
elif metodo_busqueda == "uniform_cost":
resultado = uniform_cost(problem, graph_search=True, viewer=viewer)
return resultado
def resolver(metodo_busqueda, iteraciones, haz, reinicios):
print '··· Se van a ejecutar las 10 iteraciones para la busqueda {} ···'.format(
metodo_busqueda)
#print 'Haz:', haz
#print 'Reinicios:', reinicios
prob = HnefataflProblem(INITIAL)
visor = BaseViewer()
if (metodo_busqueda == 'hill_climbing'): # Ascenso de colina
for x in range(10):
print 'Ejecutando Iteracion {} ...'.format(x)
inicio = datetime.now()
resultado = hill_climbing(problem=prob,
iterations_limit=iteraciones)
fin = datetime.now()
print 'Tiempo de iteracion {} : {} segundos.'.format(
x, (fin - inicio).total_seconds())
grabar('1', max(apuntos))
elif (metodo_busqueda == 'hill_climbing_stochastic'
): # Ascenso de colina, variante estocástica
for x in range(10):
print 'Ejecutando Iteracion {} ...'.format(x)
inicio = datetime.now()
resultado = hill_climbing_stochastic(problem=prob,
iterations_limit=iteraciones)
fin = datetime.now()
print 'Tiempo de iteracion {} : {} segundos.'.format(
x, (fin - inicio).total_seconds())
grabar('2', max(apuntos))
elif (metodo_busqueda == 'beam'): # Haz local
for x in range(10):
print 'Ejecutando Iteracion {} ...'.format(x)
inicio = datetime.now()
resultado = beam(problem=HnefataflProblem(None),
iterations_limit=iteraciones,
beam_size=haz)
fin = datetime.now()
print 'Tiempo de iteracion {} : {} segundos.'.format(
x, (fin - inicio).total_seconds())
grabar('3', max(apuntos))
elif (metodo_busqueda == 'hill_climbing_random_restarts'
): # Ascenso de colina con reinicios aleatorios
for x in range(10):
print 'Ejecutando Iteracion {} ...'.format(x)
inicio = datetime.now()
resultado = hill_climbing_random_restarts(
problem=HnefataflProblem(None),
iterations_limit=iteraciones,
restarts_limit=reinicios)
fin = datetime.now()
print 'Tiempo de iteracion {} : {} segundos.'.format(
x, (fin - inicio).total_seconds())
grabar('4', max(apuntos))
elif (metodo_busqueda == 'simulated_annealing'): # Temple simulado
for x in range(10):
print 'Ejecutando Iteracion {} ...'.format(x)
inicio = datetime.now()
resultado = simulated_annealing(problem=prob,
iterations_limit=iteraciones)
fin = datetime.now()
print 'Tiempo de iteracion {} : {} segundos.'.format(
x, (fin - inicio).total_seconds())
grabar('5', max(apuntos))
return resultado
if use_viewer:
stats = [{'name': stat.replace('_', ' '), 'value': value}
for stat, value in list(use_viewer.stats.items())]
for s in stats:
print ('{0}: {1}'.format(s['name'],s['value']))
return result
def getTotalCost (problem,result):
originState = problem.initial_state
totalCost = 0
for action,endingState in result.path():
if action is not None:
totalCost += problem.cost(originState,action,endingState)
originState = endingState
return totalCost
# END MapExercise
# ------------------------- PROBLEM SOLVING ----------------------
initial_state = None
final_state = None
map_problem = None
problem = MapProblem(initial_state)
problem.mapProblem = map_problem
problem.final_state = final_state
MapExercise(problem,algorithm=breadth_first,use_viewer=BaseViewer())
# pero despues restamos los precios de las cosas "rotas" por conflicto
for item_a, item_b in CONFLICTOS:
if item_a in state and item_b in state:
suma -= PRECIOS[item_a]
suma -= PRECIOS[item_b]
return suma
def generate_random_state(self):
items_disponibles = list(ITEMS) # creamos una copia para nosotros
random.shuffle(items_disponibles)
items_equipados = []
for i in range(4):
items_equipados.append(items_disponibles.pop())
return tuple(items_equipados)
print 'Hill climbing:'
v = BaseViewer()
result = hill_climbing(ItemsDota(('A', 'B', 'C', 'D')), viewer=v)
print result.state
print result.value
print v.stats
print 'Hill climbing random restarts'
result = hill_climbing_random_restarts(ItemsDota(), restarts_limit=100)
print result.state
print result.value
# # REPRESENTACION DE LOS ESTADOS # # # REPRESENTACION DE LAS ACCIONES # # ------------------------- RESOLUCION DE LOS PROBLEMAS ---------------------- # RESOLUCION DEL PROBLEMA 3.1.1 estado_inicial = None estado_final = None mapa = None problem = MapProblem(estado_inicial) problem.mapaProblema = mapa problem.estado_final = estado_final # NOTA: ejercicioMapa es una funcion de este modulo, no un metodo # Aqui es donde podemos seleccionar WebViewer,ConsoleViewer o BaseViewer # BaseViewer() simplemente ejecuta y muestra las trazas y estadisticas # ConsoleViewer() permite ejecutar paso a paso por pantalla # NOTA: Para usar esto, mejor hacemos un parche a este viewer, # Si no, hay que poner la opcion entre comillas # WebViewer() usa el interfaz web # # ejercicioMapa(problem,algorithm=breadth_first,use_viewer=WebViewer()) ejercicioMapa(problem, algorithm=breadth_first, use_viewer=BaseViewer())
estimated_cost += distance
return estimated_cost
INITIAL_HARDER = (
(3, 0, 7),
(8, 2, 1),
(4, 6, 5),
)
problem = EightPuzzle(INITIAL_HARDER)
# viewer = WebViewer()
viewer = BaseViewer()
# result = limited_depth_first(problem, graph_search=True, viewer=viewer, depth_limit=3)
#result = astar(problem, graph_search=True)
#result = astar(problem, graph_search=True, viewer=viewer)
result = breadth_first(problem, graph_search=True, viewer=viewer)
print("Goal node:", result)
print("Path from initial to goal:")
for action, state in result.path():
print("Action:", action)
print("State:", state)
print("Stats:")
print(viewer.stats)
lugar = 'A'
duracion -= VALORES[z]
return (lugar, duracion), tuple(alist), tuple(blist)
def cost(self, state1, action, state2):
mov, a = action
if mov == 'Mover':
x, y = a
return max(VALORES[x], VALORES[y])
else:
return VALORES[a]
def heuristic(self, state):
linterna, a, b = state
sincruzar = len(a)
if linterna[0] == 'A':
return sincruzar + (sincruzar - 1)
else:
if sincruzar == 0:
return 0
else:
return sincruzar * 2
problema = Problema(ESTADO)
visor = BaseViewer()
respuesta = astar(problema, graph_search=True, viewer=visor)
for a in respuesta.path():
print a
print 'respuesta final', respuesta
state = string2list(state)
cost = 0
for number, correct_position in GOAL_POSITIONS.items():
current_position = find_number(state, number)
diff_x = abs(current_position[0] - correct_position[0])
diff_y = abs(current_position[1] - correct_position[1])
cost += diff_x + diff_y
return cost
def heuristic_MALA(self, state):
cost = 0
for i in range(len(state)):
if state[i] != '0' and state[i] != GOAL[i]:
cost += 1
return cost
visor = BaseViewer() # o WebViewer() o ConsoleViewer()
result = astar(EightPuzzleProblem("7,2,4|5,0,6|8,3,1"),
viewer=visor,
graph_search=True)
print result.state
print result.path()
print len(result.path())
print visor.stats