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informed.py
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informed.py
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from graphviz import Digraph
import os
import copy
import functools
import time
counter = 1
initial_state = [[0, 1, 3],
[6, 2, 4],
[7, 5, 8]]
final_state = [[1, 2, 3],
[6, 5, 4],
[7, 8, 0]]
class State(object):
def __init__(self, self_state, parent_id, parent_move, level):
global counter
self.self_id = counter
self.self_state = self_state
self.parent_id = parent_id
self.parent_move = parent_move
self.level = level
self.cost = 0
self.l_child = -1
self.u_child = -1
self.r_child = -1
self.d_child = -1
counter += 1
def set_child(self, state_id, move):
if move == "left":
self.l_child = state_id
elif move == "right":
self.r_child = state_id
elif move == "up":
self.u_child = state_id
elif move == "down":
self.d_child = state_id
def __repr__(self):
return str(self.self_state[0])+"\n"+str(self.self_state[1])+"\n"+str(self.self_state[2])
def create_child(parent_state, move):
i = 0
j = 0
found = False
matrix = copy.deepcopy(parent_state)
for i in range(0, 3):
for j in range(0, 3):
if parent_state[i][j] == 0:
found = True
break
if found:
break
if move == "left":
if j == 0:
return -1
else:
matrix[i][j] = matrix[i][j - 1]
matrix[i][j - 1] = 0
return matrix
if move == "right":
if j == 2:
return -1
else:
matrix[i][j] = matrix[i][j + 1]
matrix[i][j + 1] = 0
return matrix
if move == "up":
if i == 0:
return -1
else:
matrix[i][j] = matrix[i - 1][j]
matrix[i - 1][j] = 0
return matrix
if move == "down":
if i == 2:
return -1
else:
matrix[i][j] = matrix[i + 1][j]
matrix[i + 1][j] = 0
return matrix
explored = []
unexplored = []
def custom_sort(a, b):
if a.cost == b.cost:
return 0
elif a.cost < b.cost:
return 1
else:
return -1
def manhattan_distance(current_matrix):
goal_matrix = [[1,2,3],
[6,5,4],
[7,8,0]]
distance = 0
for i in range(0,3):
for j in range(0,3):
if current_matrix[i][j] != 0:
for item in goal_matrix:
if current_matrix[i][j] in item:
x = goal_matrix.index(item)
y = item.index(current_matrix[i][j])
distance = distance + (abs(x - i) + abs(y - j))
return distance
def A_star():
global state_space_tree, explored, unexplored
global id_to_state
current_state = id_to_state[1]
counter_g = 0
dot = Digraph(comment='BFS graph')
dot.node(str(current_state.self_id), current_state.__str__())
while True:
explored.append(current_state)
# print(current_state.self_id, len(explored))
# # graph
# dot.node(str(current_state.self_id), current_state.__str__())
# dot.edge(str(current_state.parent_id), str(current_state.self_id), label=current_state.parent_move)
if current_state.self_state == final_state:
break
if current_state.u_child != -1:
child_state = id_to_state[current_state.u_child]
cost_child = counter_g + manhattan_distance(child_state.self_state)
child_state.cost = cost_child
if child_state not in explored and child_state not in unexplored:
unexplored.append(child_state)
# graph
dot.node(str(child_state.self_id), child_state.__str__())
dot.edge(str(current_state.self_id), str(child_state.self_id), label=child_state.parent_move)
if current_state.d_child != -1:
child_state = id_to_state[current_state.d_child]
cost_child = counter_g + manhattan_distance(child_state.self_state)
child_state.cost = cost_child
if child_state not in explored and child_state not in unexplored:
unexplored.append(child_state)
# graph
dot.node(str(child_state.self_id), child_state.__str__())
dot.edge(str(current_state.self_id), str(child_state.self_id), label=child_state.parent_move)
if current_state.l_child != -1:
child_state = id_to_state[current_state.l_child]
cost_child = counter_g + manhattan_distance(child_state.self_state)
child_state.cost = cost_child
if child_state not in explored and child_state not in unexplored:
unexplored.append(child_state)
# graph
dot.node(str(child_state.self_id), child_state.__str__())
dot.edge(str(current_state.self_id), str(child_state.self_id), label=child_state.parent_move)
if current_state.r_child != -1:
child_state = id_to_state[current_state.r_child]
cost_child = counter_g + manhattan_distance(child_state.self_state)
child_state.cost = cost_child
if child_state not in explored and child_state not in unexplored:
unexplored.append(child_state)
# graph
dot.node(str(child_state.self_id), child_state.__str__())
dot.edge(str(current_state.self_id), str(child_state.self_id), label=child_state.parent_move)
unexplored.sort(key=lambda x:x.cost)
current_state = unexplored.pop(0)
print([item.self_id for item in unexplored])
print([item.cost for item in unexplored])
print("\n")
# time.sleep(1)
counter_g += 1
dot.render(str(os.getcwd() + '/outputs/A_star_graph.gv'), view=True)
def Greedy_best_first():
global state_space_tree, explored, unexplored
global id_to_state
current_state = id_to_state[1]
explored = []
unexplored = []
dot = Digraph(comment='BFS graph')
dot.node(str(current_state.self_id), current_state.__str__())
while True:
explored.append(current_state)
# print(current_state.self_id, len(explored))
# # graph
# dot.node(str(current_state.self_id), current_state.__str__())
# dot.edge(str(current_state.parent_id), str(current_state.self_id), label=current_state.parent_move)
if current_state.self_state == final_state:
break
if current_state.u_child != -1:
child_state = id_to_state[current_state.u_child]
cost_child = manhattan_distance(child_state.self_state)
child_state.cost = cost_child
if child_state not in explored and child_state not in unexplored:
unexplored.append(child_state)
# graph
dot.node(str(child_state.self_id), child_state.__str__())
dot.edge(str(current_state.self_id), str(child_state.self_id), label=child_state.parent_move+str(child_state.cost))
if current_state.d_child != -1:
child_state = id_to_state[current_state.d_child]
cost_child = manhattan_distance(child_state.self_state)
child_state.cost = cost_child
if child_state not in explored and child_state not in unexplored:
unexplored.append(child_state)
# graph
dot.node(str(child_state.self_id), child_state.__str__())
dot.edge(str(current_state.self_id), str(child_state.self_id), label=child_state.parent_move+str(child_state.cost))
if current_state.l_child != -1:
child_state = id_to_state[current_state.l_child]
cost_child = manhattan_distance(child_state.self_state)
child_state.cost = cost_child
if child_state not in explored and child_state not in unexplored:
unexplored.append(child_state)
# graph
dot.node(str(child_state.self_id), child_state.__str__())
dot.edge(str(current_state.self_id), str(child_state.self_id), label=child_state.parent_move+str(child_state.cost))
if current_state.r_child != -1:
child_state = id_to_state[current_state.r_child]
cost_child = manhattan_distance(child_state.self_state)
child_state.cost = cost_child
if child_state not in explored and child_state not in unexplored:
unexplored.append(child_state)
# graph
dot.node(str(child_state.self_id), child_state.__str__())
dot.edge(str(current_state.self_id), str(child_state.self_id), label=child_state.parent_move+str(child_state.cost))
unexplored.sort(key=lambda x:x.cost)
current_state = unexplored.pop(0)
dot.render(str(os.getcwd() + '/outputs/greedy_best_first_graph.gv'), view=True)
state_space_tree = dict()
id_to_state = dict()
if __name__ == "__main__":
# for graph
dot = Digraph(comment='State space graph')
opposite_movements = {"up": "down", "down": "up", "left": "right", "right": "left"}
goal_found = False
current_state = State(initial_state, 1, "null", 1)
id_to_state[1] = current_state
explore_queue = [current_state.self_id]
dot.node(str(current_state.self_id), current_state.__str__())
goal_lvl = 9999
while (True):
current_state = id_to_state[explore_queue.pop(0)]
if current_state.level > goal_lvl + 1:
break
parent_move = current_state.parent_move
possible_movements = ["up", "left", "down", "right"]
if (parent_move != "null"):
possible_movements.remove(opposite_movements[parent_move])
childs = []
for move in possible_movements:
matrix = create_child(current_state.self_state, move)
if matrix != -1:
state_obj = State(matrix, current_state.self_id, move, current_state.level+1)
current_state.set_child(state_obj.self_id, move)
# put the child id to queue
explore_queue.append(state_obj.self_id)
# save the child object
id_to_state[state_obj.self_id] = state_obj
childs.append(state_obj.self_id)
# check if this child is the goal state
if matrix == final_state and goal_lvl == 9999:
goal_lvl = state_obj.level - 1
if matrix == final_state:
dot.node(str(state_obj.self_id), state_obj.__str__(), color='green')
else:
dot.node(str(state_obj.self_id), state_obj.__str__())
dot.edge(str(state_obj.parent_id), str(state_obj.self_id), label=move)
A_star()
Greedy_best_first()
dot.render(str(os.getcwd() + '/outputs/state_space_tree_informed.gv'), view=True)