70 lines
2.5 KiB
Python
70 lines
2.5 KiB
Python
import torch
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import numpy as np
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from card_model.model import CardModel
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from card_model.config import VOCAB_SIZE, EMBEDDING_DIM, MLP_HIDDEN, NUM_BINS
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import os
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def parse_cards(card_str):
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"""将 'As Ks' 等字符串转换为 0-51 的 ID 列表"""
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if not card_str.strip():
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return []
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cards = []
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for c in card_str.strip().split():
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rank = "23456789TJQKA".index(c[0].upper())
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suit = "cdhs".index(c[1].lower())
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cards.append(rank * 4 + suit)
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return cards
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def print_histogram(hist):
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"""用 ASCII 打印简单的直方图"""
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print("\n胜率直方图分布 (0% -> 100%):")
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bars = [" "] * 50
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for i, p in enumerate(hist):
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length = int(p * 50) # 缩放以便显示
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print(f"[{i*2:02d}%-{(i+1)*2:02d}%]: {'█' * length} ({p*100:.1f}%)")
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def main():
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# 1. 加载你刚刚训练好的最优模型
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model_path = os.path.join(os.path.dirname(__file__), "data", "best_card_model.pt")
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model = CardModel(VOCAB_SIZE, EMBEDDING_DIM, MLP_HIDDEN, NUM_BINS)
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model.load_state_dict(torch.load(model_path, map_location="cpu"))
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model.eval()
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print("模型加载成功！你可以输入牌型进行测试 (例如: As Ks)。输入 'q' 退出。")
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while True:
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hole_str = input("\n请输入你的底牌 (2张, 例如 'As Ks'): ")
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if hole_str.lower() == 'q': break
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board_str = input("请输入公共牌 (0到5张, 例如 'Qs Js 2d' 或直接回车留空): ")
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try:
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hole_cards = parse_cards(hole_str)
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board_cards = parse_cards(board_str)
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if len(hole_cards) != 2:
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print("底牌必须是 2 张！")
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continue
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# 补齐公共牌到 5 张
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padded_board = board_cards + [52] * (5 - len(board_cards))
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x_hole = torch.tensor([hole_cards], dtype=torch.int64)
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x_board = torch.tensor([padded_board], dtype=torch.int64)
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with torch.no_grad():
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pred_equity, pred_hist = model(x_hole, x_board)
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eq = pred_equity.item()
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hist = pred_hist[0].numpy()
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print("-" * 40)
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print(f"你的底牌: {hole_str} | 公共牌: {board_str if board_str else '(翻牌前)'}")
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print(f"=> 预测绝对胜率 (Equity): {eq * 100:.2f}%")
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print_histogram(hist)
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print("-" * 40)
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except Exception as e:
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print(f"输入格式错误: {e}")
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if __name__ == "__main__":
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main() |