model.dictionary)
policy = ActionToArray.moveEvaluations(
ActionToArray.legalMovesForState(
board.arrayBoard, board.board),
board.arrayBoard, outputs)
model.childrenPolicyEval.append(policy)
model.childrenMoveNames.append(
ActionToArray.legalMovesForState(
board.arrayBoard, board.board))
directory = model.dictionary[board.boardToString()]
if playouts > 0:
index = np.argmax(
MCTSCrazyhouse.PUCT_Algorithm(
model.childrenStateWin[directory],
model.childrenStateSeen[directory], 1,
np.sum(model.childrenStateSeen[directory]),
model.childrenValueEval[directory],
MCTSCrazyhouse.noiseEvals(
model.childrenPolicyEval[directory], noiseVal)))
else:
index = np.argmax(
MCTSCrazyhouse.noiseEvals(model.childrenPolicyEval[directory],
noiseVal))
move = model.childrenMoveNames[directory][index]
if chess.Move.from_uci(move) not in board.board.legal_moves:
move = ActionToArray.legalMovesForState(board.arrayBoard,
board.board)[0]
print("bestmove " + move)
print(
"info depth 1 score cp",
str(
def NetworkCompetitionWhite(bestNet, testingNet, playouts, round="1"):
score = 0
PGN = chess.pgn.Game()
PGN.headers["Event"] = "Neural Network Comparison Test"
PGN.headers["Site"] = "Cozy Computer Lounge"
PGN.headers["Date"] = datetime.datetime.today().strftime('%Y-%m-%d %H:%M')
PGN.headers["Round"] = round
PGN.headers["White"] = "Network: " + bestNet.nameOfNetwork
PGN.headers["Black"] = "Network: " + testingNet.nameOfNetwork
PGN.headers["Variant"] = "crazyhouse"
sim = ChessEnvironment()
while sim.result == 2:
#print("Win Probability:", ValueEvaluation.positionEval(sim, bestNet.neuralNet))
noiseVal = 1.0 / (2 * (sim.plies // 2 + 1))
if sim.plies % 2 == 0:
if playouts > 0:
bestNet.competitivePlayoutsFromPosition(playouts, sim)
else:
position = sim.boardToString()
if position not in bestNet.dictionary:
state = torch.from_numpy(sim.boardToState())
nullAction = torch.from_numpy(np.zeros(1)) # this will not be used, is only a filler
testSet = DoubleHeadDataset(state, nullAction, nullAction)
generatePredic = torch.utils.data.DataLoader(dataset=testSet, batch_size=len(state), shuffle=False)
with torch.no_grad():
for images, labels1, labels2 in generatePredic:
bestNet.neuralNet.eval()
#start = time.time()
outputs = bestNet.neuralNet(images)[0]
#end = time.time()
#print(end-start)
if playouts > 0:
bestNet.addPositionToMCTS(sim.boardToString(),
ActionToArray.legalMovesForState(sim.arrayBoard,
sim.board),
sim.arrayBoard, outputs, sim)
else:
bestNet.dictionary[sim.boardToString()] = len(bestNet.dictionary)
policy = ActionToArray.moveEvaluations(ActionToArray.legalMovesForState(sim.arrayBoard,
sim.board),
sim.arrayBoard, outputs)
bestNet.childrenMoveNames.append(ActionToArray.legalMovesForState(sim.arrayBoard,
sim.board))
bestNet.childrenPolicyEval.append(policy)
directory = bestNet.dictionary[sim.boardToString()]
if playouts > 0:
index = np.argmax(
MCTSCrazyhouse.PUCT_Algorithm(bestNet.childrenStateWin[directory], bestNet.childrenStateSeen[directory], 1,
np.sum(bestNet.childrenStateSeen[directory]),
bestNet.childrenValueEval[directory],
MCTSCrazyhouse.noiseEvals(bestNet.childrenPolicyEval[directory], noiseVal))
)
else:
index = np.argmax(MCTSCrazyhouse.noiseEvals(bestNet.childrenPolicyEval[directory], noiseVal))
move = bestNet.childrenMoveNames[directory][index]
if chess.Move.from_uci(move) not in sim.board.legal_moves:
move = ActionToArray.legalMovesForState(sim.arrayBoard, sim.board)[0]
#print(move)
sim.makeMove(move)
sim.gameResult()
elif sim.plies % 2 == 1:
if playouts > 0:
testingNet.competitivePlayoutsFromPosition(playouts, sim)
else:
position = sim.boardToString()
if position not in testingNet.dictionary:
state = torch.from_numpy(sim.boardToState())
nullAction = torch.from_numpy(np.zeros(1)) # this will not be used, is only a filler
testSet = DoubleHeadDataset(state, nullAction, nullAction)
generatePredic = torch.utils.data.DataLoader(dataset=testSet, batch_size=len(state), shuffle=False)
with torch.no_grad():
for images, labels1, labels2 in generatePredic:
testingNet.neuralNet.eval()
outputs = testingNet.neuralNet(images)[0]
if playouts > 0:
testingNet.addPositionToMCTS(sim.boardToString(),
ActionToArray.legalMovesForState(sim.arrayBoard,
sim.board),
sim.arrayBoard, outputs, sim)
else:
testingNet.dictionary[sim.boardToString()] = len(testingNet.dictionary)
policy = ActionToArray.moveEvaluations(ActionToArray.legalMovesForState(sim.arrayBoard,
sim.board),
sim.arrayBoard, outputs)
testingNet.childrenMoveNames.append(ActionToArray.legalMovesForState(sim.arrayBoard,
sim.board))
testingNet.childrenPolicyEval.append(policy)
directory = testingNet.dictionary[sim.boardToString()]
if playouts > 0:
index = np.argmax(
MCTSCrazyhouse.PUCT_Algorithm(testingNet.childrenStateWin[directory], testingNet.childrenStateSeen[directory], 1,
np.sum(testingNet.childrenStateSeen[directory]),
testingNet.childrenValueEval[directory],
MCTSCrazyhouse.noiseEvals(testingNet.childrenPolicyEval[directory],
noiseVal))
)
else:
index = np.argmax(MCTSCrazyhouse.noiseEvals(testingNet.childrenPolicyEval[directory], noiseVal))
move = testingNet.childrenMoveNames[directory][index]
if chess.Move.from_uci(move) not in sim.board.legal_moves:
move = ActionToArray.legalMovesForState(sim.arrayBoard, sim.board)[0]
#print(move)
sim.makeMove(move)
sim.gameResult()
if sim.plies == 1:
node = PGN.add_variation(chess.Move.from_uci(move))
else:
node = node.add_variation(chess.Move.from_uci(move))
#print(sim.board)
if sim.result == 1:
PGN.headers["Result"] = "1-0"
if sim.result == 0:
PGN.headers["Result"] = "1/2-1/2"
score = 0.5
if sim.result == -1:
PGN.headers["Result"] = "0-1"
score = 1
print(PGN)
return score
print("PLAYOUTS:", ENGINE_PLAYOUTS)
# START SEARCHING FOR MCTS TREE
if ENGINE_PLAYOUTS > 0:
start = time.time()
print("CHOSEN DEPTH:",model.DEPTH_VALUE)
model.competitivePlayoutsFromPosition(ENGINE_PLAYOUTS, board)
end = time.time()
TIME_SPENT = end-start
directory = model.dictionary[board.boardToString()]
if board.plies > 10 or board.plies < 2:
index = np.argmax(model.childrenStateSeen[directory])
else:
index = np.argmax(MCTSCrazyhouse.noiseEvals(model.childrenPolicyEval[directory], noiseVal))
move = model.childrenMoveNames[directory][index]
else:
state = torch.from_numpy(board.boardToState())
# moves in a position
moveNames = ActionToArray.legalMovesForState(board.arrayBoard, board.board)
mate = isThereMate(board, moveNames, model.matefinder)
if mate != None:
index = mate
print("I see mate!")
else:
model.neuralNet.eval()
outputs = model.neuralNet(state)[0]
def NetworkCompetitionWhite(bestNet, playouts, round="1"):
PGN = chess.pgn.Game()
PGN.headers["Event"] = "Neural Network Comparison Test"
PGN.headers["Site"] = "Cozy Computer Lounge"
PGN.headers["Date"] = datetime.datetime.today().strftime('%Y-%m-%d %H:%M')
PGN.headers["Round"] = round
PGN.headers["White"] = "Network: " + bestNet.nameOfNetwork
PGN.headers["Black"] = "You"
PGN.headers["Variant"] = "crazyhouse"
sim = ChessEnvironment()
while sim.result == 2:
noiseVal = 0.0 / (10 * (sim.plies // 2 + 1))
if sim.plies % 2 == 0:
if playouts > 0:
start = time.time()
bestNet.competitivePlayoutsFromPosition(playouts, sim)
end = time.time()
print(end - start)
else:
position = sim.boardToString()
if position not in bestNet.dictionary:
image = torch.from_numpy(sim.boardToState())
outputs = bestNet.neuralNet(image)[0]
if playouts > 0:
bestNet.addPositionToMCTS(
sim.boardToString(),
ActionToArray.legalMovesForState(
sim.arrayBoard, sim.board), sim.arrayBoard,
outputs, sim)
else:
bestNet.dictionary[sim.boardToString()] = len(
bestNet.dictionary)
policy = ActionToArray.moveEvaluations(
ActionToArray.legalMovesForState(
sim.arrayBoard, sim.board), sim.arrayBoard,
outputs)
bestNet.childrenMoveNames.append(
ActionToArray.legalMovesForState(
sim.arrayBoard, sim.board))
bestNet.childrenPolicyEval.append(policy)
directory = bestNet.dictionary[sim.boardToString()]
if playouts > 0:
index = np.argmax(
MCTSCrazyhouse.PUCT_Algorithm(
bestNet.childrenStateWin[directory],
bestNet.childrenStateSeen[directory], 1,
np.sum(bestNet.childrenStateSeen[directory]),
bestNet.childrenValueEval[directory],
MCTSCrazyhouse.noiseEvals(
bestNet.childrenPolicyEval[directory], noiseVal)))
else:
index = np.argmax(
MCTSCrazyhouse.noiseEvals(
bestNet.childrenPolicyEval[directory], noiseVal))
move = bestNet.childrenMoveNames[directory][index]
if chess.Move.from_uci(move) not in sim.board.legal_moves:
move = ActionToArray.legalMovesForState(
sim.arrayBoard, sim.board)[0]
# PRINT WIN PROBABILITY W/ MCTS?
print("-----")
print(move)
print("Win Probability: {:.4f} %".format(
100 * ValueEvaluation.positionEval(sim, bestNet.neuralNet)))
if playouts > 0 and bestNet.childrenStateSeen[directory][index] > 0:
mctsWinRate = 100 * bestNet.childrenStateWin[directory][
index] / bestNet.childrenStateSeen[directory][index]
print("MCTS Win Probability: {:.4f} %".format(mctsWinRate))
totalWinRate = (100 * ValueEvaluation.positionEval(
sim, bestNet.neuralNet) + mctsWinRate) / 2
print("Total Win Probability: {:.4f} %".format(totalWinRate))
print("-----")
sim.makeMove(move)
sim.gameResult()
elif sim.plies % 2 == 1:
legal = False
while not legal:
move = input("Enter move: ")
if len(move) == 4 or len(move) == 5:
if chess.Move.from_uci(move) in sim.board.legal_moves:
legal = True
else:
print("Illegal move! Try again:")
else:
print("Illegal move! Try again:")
print(move)
sim.makeMove(move)
sim.gameResult()
if sim.plies == 1:
node = PGN.add_variation(chess.Move.from_uci(move))
else:
node = node.add_variation(chess.Move.from_uci(move))
print(sim.board)
print("WHITE POCKET")
print(sim.whiteCaptivePieces)
print("BLACK POCKET")
print(sim.blackCaptivePieces)
if sim.result == 1:
PGN.headers["Result"] = "1-0"
if sim.result == 0:
PGN.headers["Result"] = "1/2-1/2"
if sim.result == -1:
PGN.headers["Result"] = "0-1"
print(PGN)