def addChildren(self, moves, model):
gameState = af.fenToTensor(self.state)
inputState = np.expand_dims(gameState, axis=0)
policy, value = model.predict(inputState)
# Extract move with the highest value: find legit moves, max index and lookup in the dict
moves = [str(x) for x in list(game.board.legal_moves)]
kala = {move: policy[0][enumDict[move].value] for move in moves}
move = max(kala, key=kala.get)
objects = [
MctsNode(move, value)
for move, value in zip(moves, self.calc_policy(moves))
]
self.children = self.children + objects
def move(self, game, enumDict): # Create legitimate moves and pick one
if self.model is None:
moves = list(game.legal_moves)
move = random.choice(moves)
self.moves.append(move)
value = np.nan
return move, value
else:
gameState = game.fen()
gameState = af.fenToTensor(gameState)
inputState = np.expand_dims(gameState, axis=0)
policy, value = self.model.predict(inputState)
# Move with the highest value: find legit moves, max index and lookup in the dict
moves = [str(x) for x in list(game.legal_moves)]
kala = {move: policy[0][enumDict[move].value] for move in moves}
move = max(kala, key=kala.get)
self.moves.append(move)
return move, value
a7 = Node(7, 71, a4)
def visitor(node):
if node.parent:
kala = node.parent
print(kala.value)
kala.value = kala.value + node.value
return(visitor(kala))
else:
return 1
visitor(a7)
# for i in range(20):
import anxilliaryfunctions as af
import numpy as np
fen = 'rnbqkbnr/pppppppp/8/8/4P3/8/PPPP1PPP/RNBQKBNR b KQkq - 0 1'
a = af.fenToTensor(fen)
sum_pieces = np.sum(a, axis=(1, 2))
# {'P':1,'N':2,'B':3,'R':4,'Q':5,'K':6,'p':7,'n':8,'b':9,'r':10,'q':11,'k':12}
piece_values = [1, 3, 3.5, 5, 9, 10, -1, -3, -3.5, -5, -9, -10]
total_value = sum_pieces * piece_values
bala = sum(total_value)
def nextMove():
pass
# game = chess.Board()
# state = game.board_fen()
# node = MctsNode(state, 0)
i = 0
while i < 10:
i += 1
node.update()
gameState = game.board.fen()
gameState = af.fenToTensor(gameState)
inputState = np.expand_dims(gameState, axis=0)
policy, value = model.predict(inputState)
# moves = [str(x) for x in list(game.board.legal_moves)]
if not node.children:
possible_moves = [str(x) for x in list(game.board.legal_moves)]
node.addChildren(possible_moves, model)
# node = MctsNode(state, 0)
# best = node.pickBest()
# makemove with chess
ba = node.children
files = glob.glob(
'C:\\Users\\Watson\\Projects\\remus\\input\\final_input\\*.pkl')
# Convert and merge all file in the numpy arrays
stateList = []
moveList = []
resultList = []
for file in files:
data = pd.read_pickle(file)
# Extract and save states
tqdm.pandas(mininterval=10)
data['TensorState'] = data['Fen'].progress_apply(
lambda x: af.fenToTensor(x))
inputStates = data['TensorState'].values
inputStates = np.array([x for x in inputStates])
# Extract and save result and best move for a game
target1 = data['Result'].values.astype(np.int8)
target2 = af.convertMoves(data[['Move']])
target2 = np.array([x for x in target2])
# Save results
stateList = stateList.append(inputStates)
moveList = moveList.append(target1)
resultList = resultList.append(target2)
stateArray = np.array(stateList)