73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
# -*- coding: utf-8 -*-
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# Created by: Vincentzyx
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import os
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import torch
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from torch import nn
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from torch.utils.data import DataLoader
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from torch.utils.data.dataset import Dataset
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import time
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def EnvToOnehot(cards):
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Env2IdxMap = {3:0,4:1,5:2,6:3,7:4,8:5,9:6,10:7,11:8,12:9,13:10,14:11,17:12,20:13,30:14}
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cards = [Env2IdxMap[i] for i in cards]
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Onehot = torch.zeros((4,15))
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for i in range(0, 15):
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Onehot[:cards.count(i),i] = 1
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return Onehot
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def RealToOnehot(cards, llc):
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RealCard2EnvCard = {'3': 0, '4': 1, '5': 2, '6': 3, '7': 4,
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'8': 5, '9': 6, 'T': 7, 'J': 8, 'Q': 9,
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'K': 10, 'A': 11, '2': 12, 'X': 13, 'D': 14}
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cards = [RealCard2EnvCard[c] for c in cards]
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llcs = [RealCard2EnvCard[c] for c in llc]
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Onehot = torch.zeros((7,15))
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for i in range(0, 15):
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Onehot[:cards.count(i),i] = 1
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Onehot[4:llcs.count(i)+4,i] = 1
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return Onehot
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class Net(nn.Module):
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def __init__(self):
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super().__init__()
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self.fc1 = nn.Linear(105, 512)
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self.fc2 = nn.Linear(512, 512)
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self.fc3 = nn.Linear(512, 512)
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self.fc4 = nn.Linear(512, 512)
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self.fc5 = nn.Linear(512, 512)
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self.fc6 = nn.Linear(512, 1)
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self.dropout5 = nn.Dropout(0.5)
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self.dropout3 = nn.Dropout(0.3)
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self.dropout1 = nn.Dropout(0.1)
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def forward(self, input):
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x = self.fc1(input)
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x = torch.relu(self.dropout3(self.fc2(x)))
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x = torch.relu(self.dropout5(self.fc3(x)))
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x = torch.relu(self.dropout5(self.fc4(x)))
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x = torch.relu(self.dropout5(self.fc5(x)))
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x = self.fc6(x)
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x = torch.sigmoid(x)
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return x
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Nets = {"up": Net(), "down": Net()}
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if os.path.exists("./landlord_up_weights.pkl"):
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if torch.cuda.is_available():
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Nets["up"].load_state_dict(torch.load("./landlord_up_weights.pkl"))
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else:
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Nets["up"].load_state_dict(torch.load("./landlord_up_weights.pkl", map_location=torch.device("cpu")))
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Nets["up"].eval()
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if os.path.exists("./landlord_down_weights.pkl"):
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if torch.cuda.is_available():
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Nets["up"].load_state_dict(torch.load("./landlord_down_weights.pkl"))
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else:
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Nets["up"].load_state_dict(torch.load("./landlord_down_weights.pkl", map_location=torch.device("cpu")))
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Nets["down"].eval()
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def predict(cards, llc, type="up"):
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net = Nets[type]
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x = torch.flatten(RealToOnehot(cards, llc))
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y = net(x)[0].item()
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return y * 100 |