def main():
""" Initial state node """
initialState = nodo.node(None, ist0)
#initialState.randMatrix()
""" Final state node """
finalState = nodo.node(None, fst0)
print('\n\nInitial state:')
initialState.printM()
print('Final state')
finalState.printM()
""" Try to solve the puzzle using best first search """
bfs_solution = bfs.astar(initialState, finalState)
""" Try to solve the puzzle using A* """
#path1 = bfs.astar(initialState,finalState)
""" Print the path to file """
if len(bfs_solution[0]) > 0:
with open('bfs_solution.txt', 'w') as file:
file.write('Solution statistics:')
file.write('\n\nNode expansions: ' + str(bfs_solution[1]))
file.write('\nBlock movements: ' + str(bfs_solution[2]))
file.write('\nSolution time: ' + str(bfs_solution[3]))
file.write('\n\n\n')
for matrix in bfs_solution[0]:
for row in matrix:
file.write(str(row))
file.write('\n')
file.write('\n')
def __init__(self, matrix):
n = 0
for x in range(len(matrix)):
nodos = []
for y in range(len(matrix)):
if int(matrix[x][y][0]) == 255 and int(
matrix[x][y][1]) == 0 and int(
matrix[x][y][2]) == 0 and n == 0:
nodos.append(
node(True, x, y,
(int(matrix[x][y][0]), int(
matrix[x][y][1]), int(matrix[x][y][2]))))
n = 1
else:
nodos.append(
node(False, x, y,
(int(matrix[x][y][0]), int(
matrix[x][y][1]), int(matrix[x][y][2]))))
self.nodes.append(nodos)
def bfsearch(start, goal):
openl = [] # Open nodes list
closedl = [] # Closed nodes list
path = [] # Path of the solution
moves = 0 # Number of movements to solve the puzzle
expansions = 0 # Number of node expansions
start_time = time.time() # Start time of the search
start.h = 0
openl.append([start.h, start])
while openl:
current = openl.pop(0) # Pop item from queue
cm = current[1].matrix
gm = goal.matrix
if cm == gm:
end_time = time.time() # End time of the search
# Form the path by going from
# the goal node to the root node
node = current[1]
while node.parent:
path.append(node.matrix)
node = node.parent
moves += 1
path.append(node.matrix)
break
else:
# Expanssion of the valid nodes (returns a matrix)
for node_matrix in validMoves(current[1]):
if node_matrix not in closedl:
n = nodo.node(current[1], node_matrix)
n.h = n.heuristic(goal.matrix)
current[1].childs.append([n.h, n])
openl.append([n.h, n])
openl = sorted(openl,
key=lambda x: x[0]) # Sort list based on heuristic
closedl.append(current[1].matrix)
expansions += 1
if len(path) > 0:
print('The puzzle was solved!')
print('\nSolution statistics: ')
print('\nNode expansions: ', expansions)
print('Block movements : ', moves)
print('Solution time: ', float(end_time - start_time), '[s]\n')
path.reverse()
else:
print('\nNo solution was found...')
return [path, expansions, moves, float(end_time - start_time)]
def astar(start, goal):
openl = []
closedl = []
path = []
start_time = time.time()
expansions = 0
moves = 0
start.h = 0
openl.append([start.h, start])
while openl:
current = openl.pop(0)
cm = current[1].matrix
gm = goal.matrix
if cm == gm:
end_time = time.time()
node = current[1]
while node.parent:
path.append(node.matrix)
node = node.parent
moves += 1
path.append(node.matrix)
break
else:
# Expanssion of the valid nodes (returns a matrix)
for node_matrix in validMoves(current[1]):
if node_matrix not in closedl:
n = nodo.node(current[1], node_matrix)
n.h = n.heuristic(goal.matrix) + n.cost()
current[1].childs.append([n.h, n])
openl.append([n.h, n])
openl = sorted(openl,
key=lambda x: x[0]) # Sort list based on heuristic
closedl.append(current[1].matrix)
expansions += 1
if len(path) > 0:
print('The puzzle was solved!')
path.reverse()
else:
print('\nNo solution was found...')
return [path, expansions, moves, float(end_time - start_time)]
def bfsearch(N,goal):
opnNds = [] # Open nodes list
cldNds = [] # Closed nodes list
path = [] # Path of the solution
moves = 0 # Number of movements to solve the puzzle
expansions = 0 # Number of node expansions
start_time = time.time() # Start time of the search
N.h = 0
opnNds.append([N.h,N])
while opnNds:
current = opnNds.pop(0) # Pop item from queue
cm = current[1].matrix
gm = goal.matrix
if cm == gm:
end_time = time.time() # End time of the search
# Form the path by going from
# the goal node to the root node
node = current
while node[1].parent:
path.append(node[1].matrix)
node = node[1].parent
moves += 1
path.append(node[1].matrix)
break
else:
# Expanssion of the valid nodes
nsum = current[1].shiftUp()
nsdm = current[1].shiftDown()
nslm = current[1].shiftLeft()
nsrm = current[1].shiftRight()
if len(nsum) > 0 and nsum not in cldNds:
nsu = nodo.node(current,nsum) # New node creation
nsu.h = nsu.heuristic(goal.matrix) # Compute the heuristic
current[1].childs.append([nsu.h,nsu]) # Append to current node's child list
opnNds.append([nsu.h,nsu])
if len(nsdm) > 0 and nsdm not in cldNds:
nsd = nodo.node(current,nsdm)
nsd.h = nsd.heuristic(goal.matrix)
current[1].childs.append([nsd.h,nsd])
opnNds.append([nsd.h,nsd])
if len(nslm) > 0 and nslm not in cldNds:
nsl = nodo.node(current,nslm)
nsl.h = nsl.heuristic(goal.matrix)
current[1].childs.append([nsl.h,nsl])
opnNds.append([nsl.h,nsl])
if len(nsrm) > 0 and nsrm not in cldNds:
nsr = nodo.node(current,nsrm)
nsr.h = nsr.heuristic(goal.matrix)
current[1].childs.append([nsr.h,nsr])
opnNds.append([nsr.h,nsr])
opnNds = sorted(opnNds,key=lambda x: x[0]) # Sort list based on heuristic
cldNds.append(current[1].matrix)
expansions += 1
if len(path) > 0:
print('The puzzle was solved!')
print('\nSolution statistics: ')
print('\nNode expansions: ', expansions)
print('Block movements : ', moves)
print('Solution time: ',float(end_time-start_time),'[s]\n')
path.reverse()
else:
print('\nNo solution was found...')
return path
for r in m:
print(r)
print('\n')
#initialState = nodo.node(None,[[1, 2, 6, 3],[4, 9, 5, 7], [8, 13, 11, 15],[12, 14, 10, 0]])
#finalState = nodo.node(None,[[0, 1, 2, 3],[4, 5, 6, 7], [8, 9, 10, 11],[12, 13, 14, 15]])
ist = randMatrix()
fst = [[1,2,3,4],
[5,6,7,8],
[9,10,11,12],
[13,14,15,0]]
print('Initial state:')
printM(ist)
print('Goal state:')
printM(fst)
initialState = nodo.node(None,ist)
finalState = nodo.node(None,fst)
# Try to solve puzzle using Best First Search
path = bfs.bfsearch(initialState,finalState)
# Print path to the file
if len(path) > 0:
with open('solution.txt','w') as file:
for matrix in path:
for row in matrix:
file.write(str(row))
file.write('\n')
file.write('\n')