def TransformData(data,
bits=12,
conv=False,
startingplayer=False,
startingplayerbits=False):
transformed = []
#print("length:",len(data))
#for i in range(len(data[0])):
for entry in data:
#print(i)
bitboard = entry[0]
if conv:
if not startingplayer:
bitboard = TL.FENtoBits(bitboard,
numberOfBits=bits,
startingplayerbits=startingplayer)
else:
bitboard = TL.FENtoBitsWStartingPlayer(bitboard,
numberOfBits=bits)
bitboard = np.array(bitboard).reshape((8, 8, bits))
else:
if not startingplayer:
bitboard = TL.FENtoBits(bitboard,
numberOfBits=bits,
startingplayerbits=startingplayer)
else:
bitboard = TL.FENtoBitsWStartingPlayer(bitboard,
numberOfBits=bits)
bitboard = np.array(bitboard)
transformed.append([bitboard, entry[1]])
return transformed
def DataForm(self, data):
winOther = []
lossDraw = []
#draw = []
convolutional = True
X = 8
if self.double_bits:
X = X * 2
for entry in data:
reshaped = TL.FENtoBits(entry[0],
self.bits,
startingplayerbits=self.double_bits)
if convolutional:
reshaped = np.reshape(reshaped, (X, 8, self.bits))
if entry[1] == 0:
lossDraw.append([reshaped, 0])
winOther.append([reshaped, 0])
elif entry[1] == 1:
winOther.append([reshaped, 1])
else:
winOther.append([reshaped, 0])
lossDraw.append([reshaped, 1])
return (winOther, lossDraw)
def MassPredict(self, inp):
score = 0
tData = []
#TestData = [[],[]]
#train = data[0]
#test = data[1]
X = 8
if self.double_bits:
X = X * 2
for entry in inp:
#print(entry)
reshaped = np.array(
TL.FENtoBits(entry,
self.bits,
True,
startingplayerbits=self.double_bits))
#print(len(reshaped))
reshaped = np.reshape(reshaped, (X, 8, self.bits))
tData.append(reshaped)
#print(TrainData)
#score = self.Machine[0].Score(inp)
#return [self.Machine[0].Pred(inp)[0], score]
return self.Machine[0].MassScore(tData)
def LoadData():
Data = DL.GetData(transform=False, includeDraw=False)
TData = []
bits = 7
for entry in Data:
reshaped = TL.FENtoBits(entry[0], bits, True)
reshaped = np.reshape(reshaped, (8, 8, bits))
TData.append([reshaped, entry[1]])
TData = DL.StartifiedData(TData)
return TData
def Predict(self, inp):
score = 0
X = 8
if self.double_bits:
X = X * 2
bit_board = np.array(
TL.FENtoBits(inp,
self.Machine[0].bits,
True,
startingplayerbits=self.double_bits))
inp = np.reshape(bit_board, (1, X, 8, self.Machine[0].bits))
#print(inp)
#print(self.Machine[0].Pred(inp))
score = self.Machine[0].Score(inp)
return [self.Machine[0].Pred(inp)[0], score]
def MassPredict(self, inp):
data = []
win = []
winIndex = []
other = []
otherIndex = []
convolutional = True
X = 8
if self.double_bits:
X = X * 2
for entry in inp:
reshaped = TL.FENtoBits(entry[0],
self.bits,
startingplayerbits=self.double_bits)
if convolutional:
reshaped = np.reshape(reshaped, (X, 8, self.bits))
data.append(reshaped)
results = self.Machine[0].MassScore(data)
for i in range(len(results)):
if results[i][0] == 1:
win.append(results[i])
winIndex.append(i)
else:
other.append(results[i])
otherIndex.append(i)
otherres = aelf.Machine[1].MassScore(other)
indecies = winIndex + otherIndex
res = win + otherres
def sorter(inp1):
return inp1[1]
sorting = []
for i in range(len(res)):
sorting.append((res[i], indecies[i]))
sorting.sort(key=sorter)
return [i[0] for i in sorting]
def Predict(self, inp):
if not len(self.Machine) == 1:
print("Not correct predict. Amount of machines found:",
str(len(self.Machine)), "expected 1")
return
machine = self.Machine[0]
bit_board = np.array(
TL.FENtoBits(inp,
machine.bits,
startingplayerbits=self.double_bits))
if machine.convolutional:
bit_board = np.reshape(bit_board, (1, 8, 8, machine.bits))
else:
bit_board = np.reshape(bit_board, (1, 1))
score = machine.Score(bit_board)
return [machine.Pred(bit_board)[0], score]
def Predict(self, inp):
X = 8
if self.double_bits:
X = X * 2
bit_board = np.array(
TL.FENtoBits(inp,
self.Machine[0].bits,
True,
startingplayerbits=self.double_bits))
inp = np.reshape(bit_board, (1, X, 8, self.Machine[0].bits))
pred = self.Machine[0].Pred(inp)[0]
score = self.Machine[0].Score(inp)
if pred == 0:
pred2 = self.Machine[1].Pred(inp)[0]
score = self.Machine[1].Score(inp)
if pred2 == 0:
return [0, score]
return [2, score]
else:
return [1, score]
def DataForm(self, data):
tData = []
#TestData = [[],[]]
#train = data[0]
#test = data[1]
X = 8
if self.double_bits:
X = X * 2
for entry in data:
reshaped = np.array(
TL.FENtoBits(entry[0],
self.bits,
True,
startingplayerbits=self.double_bits))
#print(len(reshaped))
reshaped = np.reshape(reshaped, (X, 8, self.bits))
tData.append([reshaped, entry[1]])
#print(TrainData)
return tData
