task5

cross_validation/debug_emd.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python3
+"""
+调试
+turn/flop阶段EMD在导出的数据与生成的数据间的差异
+"""
+
+import numpy as np
+from cross_validation import DataValidator
+
+validator = DataValidator()
+
+print("解析Turn样本...")
+turn_records = validator.parser.parse_turn_hist_with_cards(max_records=1)
+
+if turn_records:
+    record = turn_records[0]
+    player_cards = record.player_cards
+    board_cards = record.board_cards
+    src_hist = record.bins
+
+    cur_hist = validator.generator.generate_turn_histogram(
+        player_cards, board_cards, num_bins=len(src_hist)
+    )
+    
+
+    src_hist_norm = src_hist / src_hist.sum() if src_hist.sum() > 0 else src_hist
+    
+    print(f"\n牌面: {[str(c) for c in player_cards]} + {[str(c) for c in board_cards]}")
+    print(f"bin数量: {len(src_hist)} vs {len(cur_hist)}")
+    print(f"原始直方图 - 和: {src_hist.sum():.3f}, 归一化后: {src_hist_norm.sum():.3f}")
+    print(f"生成直方图 - 和: {sum(cur_hist):.3f}")
+    
+    print("\n前10个bin对比:")
+    print("Bin  原始值    归一化    生成值")
+    for i in range(min(10, len(src_hist))):
+        print(f"{i:3d}  {src_hist[i]:8.3f}  {src_hist_norm[i]:8.3f}  {cur_hist[i]:8.3f}")
+    
+    # 查看非零bin的分布
+    src_nonzero = np.nonzero(src_hist_norm)[0]
+    cur_nonzero = np.nonzero(cur_hist)[0]
+    print(f"\n非零bins位置:")
+    print(f"原始: {src_nonzero[:10]}...")
+    print(f"生成: {cur_nonzero[:10]}...")
+    
+    # 计算分布的统计特征
+    src_mean = np.average(range(len(src_hist_norm)), weights=src_hist_norm)
+    cur_mean = np.average(range(len(cur_hist)), weights=cur_hist)
+    
+    print(f"\n分布特征:")
+    print(f"原始分布重心: {src_mean:.2f}")
+    print(f"生成分布重心: {cur_mean:.2f}")
+    print(f"重心差异: {abs(src_mean - cur_mean):.2f}")