goal = np.zeros((3, 3)).astype(int)
end_state = 'end.txt'
with open(end_state) as fil:
# read the goal state
row = 0 # the currebt row
line = fil.readline()
while line:
for col, no in enumerate(line.split()):
goal[row][col] = no
line = fil.readline()
row = row + 1
if len(sys.argv) < 2:
print("Start File not enterd hence generation it from random")
# raise Exception("Enter the start file atleast")
state = generate_puzzle(goal, steps=50)
else:
start_file = sys.argv[1]
end_state = 'end.txt'
if len(sys.argv) > 2:
end_state = sys.argv[2]
state = np.zeros((3, 3)).astype(int)
goal = np.zeros((3, 3)).astype(int)
with open(start_file) as fil:
# read the goal state
row = 0 # the currebt row
line = fil.readline()
for row in range(len(state)):
for col in range(len(state[0])):
goal_hash[goal[row][col]] = [row,col]
state_hash[state[row][col]] = [row,col]
print(goal_hash)
open_list = dict()
close_list = dict()
all_states = [] # a list which will store all the states and we will use the index as a hash value for index of the lists
iterations = 0 # the g(n) cost encountered till now
depth = dict() # analogous to real cost
parents = []
parent = dict()
# randomly procude the start state
state = generate_puzzle(goal, steps = 200).astype(int)
print("The start state : \n{}\nThe Goal state is : \n{}".format(state, goal))
all_states.append(state)
depth[0] = 0
parent[0] = -1
parents.append(-1)
open_list[0] = 0 # cost initially
success = False
goal_index = 0
while bool(open_list): # empty dictionaries evaluate to False
# print(depth)
sno = min(open_list, key=open_list.get) # state no with the lowest cost
current_state = all_states[sno] # get the current state
del open_list[sno]
end_state = sys.argv[2]
state = np.zeros((3, 3)).astype(int)
goal = np.zeros((3, 3)).astype(int)
with open(end_state) as fil:
# read the goal state
row = 0 # the currebt row
line = fil.readline()
while line:
for col, no in enumerate(line.split()):
goal[row][col] = no
line = fil.readline()
row = row + 1
state = generate_puzzle(goal, steps=100)
print(state)
searchObjecth4 = []
searchObjecth3 = []
while True:
searchObjecth3 = run_experiment(start_state=state,
goal_state=goal,
h_function=h3)
searchObjecth4 = run_experiment(start_state=state,
goal_state=goal,
h_function=h4)
if searchObjecth3.depth[searchObjecth3.goal_no] < searchObjecth4.depth[
searchObjecth4.goal_no]:
break
state = generate_puzzle(goal, steps=100)
print("Found")