{"id":121232,"date":"2026-11-02T10:58:11","date_gmt":"2026-11-02T02:58:11","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/121232.html"},"modified":"2026-11-02T10:58:11","modified_gmt":"2026-11-02T02:58:11","slug":"%e5%a4%a9%e8%b5%90%e8%8c%83%e5%bc%8f%e7%ac%ac214%e5%a4%a9%ef%bc%9a%e6%92%a4%e6%8e%89%e5%a4%96%e5%8a%a0%ce%bb%ef%bc%8c%e5%ad%98%e6%b4%bb%e4%ba%a4%e7%bb%99%e4%b8%aa%e4%bd%93%e8%87%aa%e5%b7%b1%e7%b4%af","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/121232.html","title":{"rendered":"\u5929\u8d50\u8303\u5f0f\u7b2c214\u5929\uff1a\u64a4\u6389\u5916\u52a0\u03bb\uff0c\u5b58\u6d3b\u4ea4\u7ed9\u4e2a\u4f53\u81ea\u5df1\u7d2f\u79ef\u7684\u4ea7\u51fa\u2014\u2014E_i\u7d2f\u79ef\u9000\u51fa\u00d7\u7ee7\u627fvs\u4e0d\u7ee7\u627f\u00d7\u56db\u7ec4\u00d75seed"},"content":{"rendered":"

\u5929\u8d50\u8303\u5f0f \u00b7\u7b2c214\u5929 \u7b2c\u4e00\u7bc7：\u64a4\u6389\u5916\u52a0 \u03bb，\u5b58\u6d3b\u4ea4\u7ed9\u4e2a\u4f53\u81ea\u5df1\u7d2f\u79ef\u7684\u4ea7\u51fa<\/h2>\n

\u2014\u2014E_i \u7d2f\u79ef\u9000\u51fa \u00d7 \u7ee7\u627f vs \u4e0d\u7ee7\u627f \u00d7 \u56db\u7ec4 \u00d7 5 seed<\/p>\n

\u6458\u8981<\/h3>\n

211-3 \u7f3a\u53e3\u2460：\u9002\u5e94\u5ea6\u2192\u5b58\u6d3b\u901a\u9053\u4e0d\u5b58\u5728\u3002212-1 \u7528 \u03ba \u505a\u9002\u5e94\u5ea6\u4f9d\u8d56\u6b7b\u4ea1（\u03bb_i=\u03bb0(1\u2212\u03ba\u00b7q_eff\/q_max)），\u662f\u88c5\u4ef6（\u5916\u52a0\u516c\u5f0f），213-1 \u5df2\u8bc1\u660e\u56de\u62a5\u4e0d\u7531 \u03ba \u901a\u9053\u4ea7\u751f\u2014\u2014\u03ba \u662f\u77ac\u65f6\u6982\u7387\u8c03\u5236，\u5728 q_eff\u22480.996 \u89e6\u9876\u3001\u4e2a\u4f53\u77ed\u5bff\u7684\u7cfb\u7edf\u91cc\u88ab\u7a00\u91ca\u3002\u4eca\u5929\u6362\u6210\u957f\u6210\u5f0f：\u64a4\u6389\u5916\u52a0 \u03bb，\u9000\u51fa\u6539\u7531\u4e2a\u4f53\u81ea\u5df1\u7d2f\u79ef\u7684\u4ea7\u51fa E_i=\u03b4\u00b7E_i+q_eff \u51b3\u5b9a（\u672b\u4f4d 10% \u9000\u51fa）\u3002\u6838\u5fc3\u5bf9\u7167\u662f E \u9057\u4e0d\u9057\u4f20\u3002\u7ed3\u679c：5seed \u4e0b A\u2212B \u672a\u8d85\u566a\u58f0（t=\u22122.24），\u52a0\u538b 30seed \u540e\u663e\u8457\u8d1f（t=\u22125.27）\u2014\u2014\u5f80\u5bf9\u8d70\u4e0d\u662f"\u6ca1\u68c0\u51fa\u56de\u62a5"，\u662f"\u663e\u8457\u66f4\u5dee"\u3002 \u4e0d\u7ffb\u8f6c \u03c1（\u6052 +1）\u5219 A\u2212B \u663e\u8457\u6b63（t=+7.43）\u2014\u2014F1 \u65b9\u5411\u88ab \u03c1 \u7ffb\u8f6c\u7ed1\u6b7b，"\u6f14\u5316\u7ec4\u4f4e\u4e8e \u03b8 \u9501\u6b7b\u7ec4"\u662f"\u53c2\u4e0e\u5373\u53d7\u7f5a"\u7684\u7ed3\u6784\u6027\u7ed3\u679c\u3002\u968f\u673a\u9000 10% \u66ff\u4ee3 E_exit \u6392\u5e8f\u5219 A\u2212B \u5f52\u96f6（t=+0.43）\u2014\u2014\u5dee\u5f02\u5b8c\u5168\u6765\u81ea E_exit \u6392\u5e8f\u903b\u8f91，\u4e0d\u662f"\u53c2\u4e0e\/\u4e0d\u53c2\u4e0e"\u672c\u8eab\u3002\u7ee7\u627f E vs \u4e0d\u7ee7\u627f E \u65e0\u5dee\u5f02（t=+0.34）\u3002<\/p>\n

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u00a7\u4e00 \u95ee\u9898<\/h3>\n

211-3 \u7559\u4e86\u4e09\u4ef6\u5175\u5668：\u7edf\u4e00\u8ffd\u95ee（\u201c\u66f4\u81ea\u5df1\u4f1a\u52a8\u4e86\u8fd8\u662f\u591a\u52a0\u529f\u80fd？\u201d）\u3001\u957f\u6210\u539f\u5219（\u201c\u6750\u6599\u90fd\u662f\u4e0a\u4e00\u5c42\u957f\u51fa\u6765\u7684\u201d）\u3001\u7f3a\u53e3\u6e05\u5355（\u2460\u9002\u5e94\u5ea6\u2192\u5b58\u6d3b\u901a\u9053\u4e0d\u5b58\u5728 \u2461\u7a81\u53d8\u6765\u6e90\u5916\u52a0 \u03c3_\u03b8 \u2462\u9002\u5e94\u540e\u679c\u53ea\u6d4b\u5230\u5355\u5411）\u3002<\/p>\n

\u7528\u8fd9\u4e09\u4ef6\u5175\u5668\u91cf 212\/213：<\/p>\n\n\u5929\u6253\u54ea\u4e00\u683c\u6309\u5c3a\u5b50\u7684\u5224\u5b9a<\/tr>\n\n\n\n\n\n
212-1 \u03ba<\/td>\n\u7f3a\u53e3\u2460<\/td>\n\u65b9\u5411\u5bf9；\u4f46 \u03ba \u662f\u88c5\u4ef6（\u5916\u52a0 \u03bb(q_eff) \u516c\u5f0f）\u2192 213-1 \u5df2\u8bc1\u660e\u56de\u62a5\u4e0d\u7531\u8be5\u901a\u9053\u4ea7\u751f<\/td>\n<\/tr>\n
212-2 mutator<\/td>\n\u7f3a\u53e3\u2461<\/td>\n\u957f\u6210\u2714（\u7a81\u53d8\u7387\u6210\u4e3a\u53ef\u9057\u4f20\u6027\u72b6）<\/td>\n<\/tr>\n
213-1 \u56db\u628a\u5c3a\u5b50<\/td>\n\u7f3a\u53e3\u2462<\/td>\n\u7eaf\u6d4b\u91cf（\u6362\u5c3a\u5b50\u91cd\u6d4b 210 \u7684\u9634\u6027）\u2192 \u96f6\u63a8\u8fdb<\/td>\n<\/tr>\n
213-2 \u03b2_m<\/td>\n\u4e0d\u5728\u6e05\u5355<\/td>\n\u88c5\u4ef6 + \u56f4\u6587\u732e（\u4e3a\u5bf9\u8d26 182 \u7684 D* \u800c\u52a0）\u2192 \u504f\u79bb<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4eca\u5929\u8d77：\u4e0d\u6362\u5c3a\u5b50\u91cd\u6d4b，\u4e0d\u4e3a\u5916\u90e8\u516c\u5f0f\u88c5\u4ef6\u3002\u65b9\u5411\u662f\u64a4\u88c5\u4ef6\u3001\u6362\u957f\u6210\u3002<\/p>\n

\u03ba \u662f\u77ac\u65f6\u6982\u7387\u8c03\u5236：\u03bb_i=\u03bb0(1\u2212\u03ba\u00b7q_eff\/q_max)，\u6bcf\u4ee3\u91cd\u63b7\u9ab0\u5b50\u3002\u5728 q_eff\u22480.996 \u89e6\u9876\u3001\u4e2a\u4f53\u5e73\u5747\u5bff\u547d\u77ed（\u03bb=0.10）\u7684\u7cfb\u7edf\u91cc，"\u5c11\u6b7b\u4e00\u70b9"\u7684\u4f18\u52bf\u6bcf\u4ee3\u88ab\u7a00\u91ca\u3002\u4eca\u5929\u6362\u6210\u7d2f\u79ef\u5f0f：E_i=\u03b4\u00b7E_i+q_eff，\u9000\u51fa\u7531 E_i \u6392\u5e8f\u672b\u4f4d\u51b3\u5b9a\u3002\u5173\u952e\u5bf9\u7167\u662f E \u9057\u4e0d\u9057\u4f20\u2014\u2014\u7ee7\u627f\u6863\u8865\u4f4d\u65f6 E_child=\u03b1\u00b7E_winner，\u4e0d\u7ee7\u627f\u6863 E_child=0\u3002<\/p>\n

\u00a7\u4e8c \u9884\u6ce8\u518c\u5047\u8bbe<\/h3>\n

E_i \u7d2f\u79ef：E_i = \u03b4\u00b7E_i + q_eff[i]，\u03b4=0.95 \u9489\u6b7b\u4e0d\u626b\u6863（\u626b\u6863=\u53c8\u4e00\u4e2a\u5916\u52a0\u65cb\u94ae）\u3002<\/p>\n

\u9000\u51fa：\u6bcf\u4ee3\u6309 E_i \u6392\u5e8f，\u672b\u4f4d exit_frac=10% \u9000\u51fa（\u786e\u5b9a\u6027，\u4e0d\u6d88\u8017\u968f\u673a\u6570）\u3002exit_frac=0.10 \u9489\u6b7b，\u5339\u914d \u03bb=0.10 \u7684\u5e73\u5747\u9000\u51fa\u7387\u3002<\/p>\n

E \u7ee7\u627f：\u8865\u4f4d\u65f6 E_child = \u03b1\u00b7E_winner（\u7ee7\u627f\u6863）\u6216 0（\u4e0d\u7ee7\u627f\u6863），\u03b1=0.5 \u9489\u6b7b\u4e0d\u626b\u6863\u3002<\/p>\n

\u56db\u7ec4：A(\u6f14\u5316+\u7ee7\u627fE) \/ A0(\u6f14\u5316+\u4e0d\u7ee7\u627fE) \/ B(\u03b8\u9501\u6b7b+\u4e0d\u7ee7\u627fE) \/ D(\u4e2d\u6027+\u7ee7\u627fE)\u3002<\/p>\n

\u9884\u6ce8\u518c\u4e09\u6761：<\/p>\n

    \n
  • H0（\u6052\u7b49）：death_mode=\u2018lambda\u2019 \u9010\u4f4d\u5bf9 212a(\u03ba=0)，\u4e0d\u4e00\u81f4\u5373\u505c\u3002<\/li>\n
  • H1（\u4e3b\u5224\u636e）：\u5f80\u5bf9\u8d70（A\u2212B）\u5728\u7ee7\u627f\u6863\u662f\u5426\u68c0\u51fa\u56de\u62a5 \u2192 \u76f4\u63a5\u7b54 211-3 \u7684\u4e0b\u4e00\u5173\u3002<\/li>\n
  • H2：\u7ee7\u627f\u6863 vs \u4e0d\u7ee7\u627f\u6863\u4e4b\u5dee \u2192 \u9002\u5e94\u5ea6\u2192\u5b58\u6d3b\u901a\u9053\u662f\u5426\u56e0\u6b64\u6210\u7acb\u3002<\/li>\n<\/ul>\n

    \u9884\u6ce8\u518c\u7ed3\u5c40：A\u2192\u7ee7\u627f\u6863\u68c0\u51fa\u56de\u62a5\u4e14\u663e\u8457\u5927\u4e8e\u4e0d\u7ee7\u627f\u6863\u2192\u7f3a\u53e3\u2460\u6253\u901a；B\u2192\u4ecd\u672a\u68c0\u51fa\u2192\u786c\u9634\u6027；C\u2192\u64a4 \u03bb \u540e\u79cd\u7fa4\u5f52\u96f6\u2192"\u5b58\u7eed\u4ecd\u4f9d\u8d56\u5916\u52a0\u9000\u51fa\u7387"\u3002<\/p>\n

    \u00a7\u4e09 \u6052\u7b49\u6027\u81ea\u68c0<\/h3>\n

    death_mode=\u2018lambda\u2019 \u4e0e 212a(\u03ba=0) \u9010\u4f4d\u6bd4\u5bf9（p_total \/ n_alive，151 \u4ee3\u5168\u5e8f\u5217，\u56db\u7ec4 \u00d7 5 seed）：PASS\u3002<\/p>\n

    lambda \u6a21\u5f0f\u9000\u5316\u4e3a 212 \u57fa\u5ea7（\u03ba=0），E \u8ddf\u8e2a\u4e0d\u6d88\u8017\u968f\u673a\u6570 \u2192 \u5fc5\u7136\u9010\u4f4d\u4e00\u81f4\u3002<\/p>\n

    \u00a7\u56db \u4e3b\u8868：E_i \u7d2f\u79ef\u9000\u51fa \u00d7 \u56db\u7ec4 \u00d7 5 seed<\/h3>\n\n\u7ec4\u8bf4\u660eF1 \u4eba\u5747\u4ea7\u51fa\u5b58\u7eed\u7387<\/tr>\n\n\n\n\n\n
    A<\/td>\n\u6f14\u5316+\u7ee7\u627fE<\/td>\n0.8531\u00b10.0471<\/td>\n0.5970\u00b10.0125<\/td>\n<\/tr>\n
    A0<\/td>\n\u6f14\u5316+\u4e0d\u7ee7\u627fE<\/td>\n0.8453\u00b10.0372<\/td>\n0.5960\u00b10.0129<\/td>\n<\/tr>\n
    B<\/td>\n\u03b8\u9501\u6b7b+\u4e0d\u7ee7\u627fE<\/td>\n0.9219\u00b10.0671<\/td>\n0.6020\u00b10.0104<\/td>\n<\/tr>\n
    D<\/td>\n\u4e2d\u6027+\u7ee7\u627fE<\/td>\n0.8833\u00b10.0704<\/td>\n0.6000\u00b10.0137<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    B（\u03b8\u9501\u6b7b）F1 \u9ad8\u4e8e A（\u6f14\u5316）\u2014\u2014\u5f80\u5bf9\u8d70\u65b9\u5411\u4e3a\u8d1f\u3002<\/p>\n

    \u00a7\u4e94 H1：\u5f80\u5bf9\u8d70（A\u2212B）\u5728\u7ee7\u627f\u6863 vs \u4e0d\u7ee7\u627f\u6863<\/h3>\n

    5 seed \u2192 df=4，\u4e34\u754c |t|>2.776<\/p>\n\n\u5bf9\u7167\u914d\u5bf9\u5deet(df=4)\u5224\u5b9a<\/tr>\n\n\n\n
    A\u2212B (\u7ee7\u627fE)<\/td>\n\u22120.0688\u00b10.0688<\/td>\n\u22122.24<\/td>\n\u672a\u8d85\u566a\u58f0<\/td>\n<\/tr>\n
    A0\u2212B (\u4e0d\u7ee7\u627fE)<\/td>\n\u22120.0766\u00b10.0934<\/td>\n\u22121.83<\/td>\n\u672a\u8d85\u566a\u58f0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    5seed \u4e0b\u4e24\u6863\u5747\u8d1f\u672a\u8d85\u566a\u58f0\u3002\u4f46 5seed \u662f\u6559\u5b66\u7ea7\u6837\u672c\u91cf\u2014\u2014\u52a0\u538b 30seed：<\/p>\n\n\u5bf9\u7167\u914d\u5bf9\u5deet(df=29)\u5224\u5b9a<\/tr>\n\n\n
    A\u2212B (\u7ee7\u627fE, 30seed)<\/td>\n\u22120.0753\u00b10.0782<\/td>\n\u22125.27<\/td>\n\u663e\u8457\u8d1f<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u52a0\u538b\u540e\u5f80\u5bf9\u8d70\u663e\u8457\u66f4\u5dee，\u4e0d\u662f"\u6ca1\u68c0\u51fa\u56de\u62a5"\u3002\u5dee\u503c\u51e0\u4e4e\u6ca1\u52a8（\u22120.069\u2192\u22120.075），\u53d8\u7684\u53ea\u662f\u6837\u672c\u91cf\u3002<\/p>\n

    \u00a7\u4e94b \u4e09\u628a\u5bf9\u7167：F1 \u65b9\u5411\u88ab \u03c1 \u7ffb\u8f6c\u7ed1\u6b7b，\u5dee\u5f02\u6765\u81ea E_exit \u6392\u5e8f<\/h3>\n\n\u5bf9\u7167A\u2212Bt(df=29)\u5224\u5b9a<\/tr>\n\n\n\n\n
    \u7ffb\u8f6c（\u539f\u7248, 30seed）<\/td>\n\u22120.0753<\/td>\n\u22125.27<\/td>\n\u663e\u8457\u8d1f<\/td>\n<\/tr>\n
    \u4e0d\u7ffb\u8f6c（\u03c1 \u6052 +1, 30seed）<\/td>\n+0.2781<\/td>\n+7.43<\/td>\n\u663e\u8457\u6b63<\/td>\n<\/tr>\n
    \u968f\u673a\u9000 10%（\u66ff\u4ee3 E_exit, 30seed）<\/td>\n+0.0068<\/td>\n+0.43<\/td>\n\u5dee\u5f02\u5f52\u96f6<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u4e09\u628a\u5bf9\u7167\u5750\u5b9e\u4e24\u4ef6\u4e8b：<\/p>\n

  • \n

    F1 \u65b9\u5411\u88ab \u03c1 \u7ffb\u8f6c\u7ed1\u6b7b：\u540c\u4e00\u4e2a F1，\u7ffb\u8f6c\u65f6"A \u663e\u8457\u5dee"\u3001\u4e0d\u7ffb\u8f6c\u65f6"A \u663e\u8457\u597d"\u3002\u672b\u4ee3 \u03c1=\u22120.5，\u53c2\u4e0e \u2192 q_eff\u00d70.5，\u53c2\u4e0e\u8005\u4ea7\u51fa\u6070\u597d\u662f\u672a\u53c2\u4e0e\u8005\u7684\u4e00\u534a\u3002A \u7ec4（\u6f14\u5316\u2192\u03b8 \u964d\u2192\u53c2\u4e0e\u591a）\u53d7\u7f5a\u591a \u2192 F1 \u4f4e；B \u7ec4（\u03b8 \u9501\u6b7b）\u53c2\u4e0e\u5c11 \u2192 F1 \u9ad8\u3002"\u6f14\u5316\u7ec4 F1 \u4f4e\u4e8e \u03b8 \u9501\u6b7b\u7ec4"\u662f"\u53c2\u4e0e\u5373\u53d7\u7f5a"\u7684\u7ed3\u6784\u6027\u7ed3\u679c，\u4e0d\u662f\u9002\u5e94\u5ea6\u8bc1\u636e\u3002<\/p>\n<\/li>\n

  • \n

    \u5dee\u5f02\u5b8c\u5168\u6765\u81ea E_exit \u6392\u5e8f：\u628a\u9000\u51fa\u6362\u6210\u968f\u673a\u9000 10%，A\u2212B \u5f52\u96f6（t=+0.43）\u3002\u8fd9 0.075 \u7684\u5dee\u662f E_exit \u903b\u8f91\u7684\u81ea\u9020\u7269，\u4e0d\u662f"\u53c2\u4e0e\/\u4e0d\u53c2\u4e0e"\u672c\u8eab\u3002<\/p>\n<\/li>\n

    \u00a7\u516d H2：\u7ee7\u627f\u6863 vs \u4e0d\u7ee7\u627f\u6863\u4e4b\u5dee<\/h3>\n

    A(\u7ee7\u627fE) \u2212 A0(\u4e0d\u7ee7\u627fE): +0.0078\u00b10.0513, t=+0.34<\/p>\n

    \u7ee7\u627f E \u4e0e\u4e0d\u7ee7\u627f E \u672a\u8d85\u566a\u58f0（|t|<2.776）\u3002\u9002\u5e94\u5ea6\u2192\u5b58\u6d3b\u901a\u9053\u672a\u56e0 E \u7ee7\u627f\u800c\u6210\u7acb\u3002<\/p>\n

    \u00a7\u4e03 \u5b58\u7eed\u68c0\u67e5<\/h3>\n\n\u7ec4\u672b\u4ee3\u5b58\u7eed\u7387\u5224\u5b9a<\/tr>\n\n\n\n\n\n
    A<\/td>\n0.5970\u00b10.0125<\/td>\n\u5b58\u7eed<\/td>\n<\/tr>\n
    A0<\/td>\n0.5960\u00b10.0129<\/td>\n\u5b58\u7eed<\/td>\n<\/tr>\n
    B<\/td>\n0.6020\u00b10.0104<\/td>\n\u5b58\u7eed<\/td>\n<\/tr>\n
    D<\/td>\n0.6000\u00b10.0137<\/td>\n\u5b58\u7eed<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u64a4\u6389\u5916\u52a0 \u03bb \u540e\u79cd\u7fa4\u4e0d\u5f52\u96f6（\u5b58\u7eed\u7387\u22480.60）\u3002\u7ed3\u5c40\u4e0d\u662f C\u3002<\/p>\n

    \u00a7\u516b \u7ed3\u8bba<\/h3>\n

    \u56db\u4ef6\u4e8b\u6446\u5728\u4e00\u8d77：<\/p>\n

  • \u64a4 \u03bb \u540e\u79cd\u7fa4\u4ecd\u5b58\u7eed（\u00a7\u4e03，\u5b58\u7eed\u7387 0.60）\u2192 \u7ed3\u5c40\u4e0d\u662f C\u3002<\/li>\n
  • \u5f80\u5bf9\u8d70\u663e\u8457\u66f4\u5dee（\u00a7\u4e94，30seed t=\u22125.27）\u2192 \u4e0d\u662f"\u672a\u68c0\u51fa\u56de\u62a5"，\u662f"\u663e\u8457\u8d1f"\u30025seed \u7684"\u672a\u8d85\u566a\u58f0"\u662f\u6837\u672c\u91cf\u4e0d\u8db3\u7684\u5047\u8c61\u3002<\/li>\n
  • F1 \u65b9\u5411\u88ab \u03c1 \u7ffb\u8f6c\u7ed1\u6b7b（\u00a7\u4e94b，\u4e0d\u7ffb\u8f6c t=+7.43 \u663e\u8457\u6b63）\u2192 "\u6f14\u5316\u7ec4\u4f4e\u4e8e \u03b8 \u9501\u6b7b\u7ec4"\u662f"\u53c2\u4e0e\u5373\u53d7\u7f5a"\u7684\u7ed3\u6784\u6027\u7ed3\u679c，\u4e0d\u662f\u9002\u5e94\u5ea6\u8bc1\u636e\u3002<\/li>\n
  • \u5dee\u5f02\u6765\u81ea E_exit \u6392\u5e8f（\u00a7\u4e94b，\u968f\u673a\u9000 t=+0.43 \u5f52\u96f6）\u2192 0.075 \u7684\u5dee\u662f E_exit \u903b\u8f91\u7684\u81ea\u9020\u7269\u3002\u7ee7\u627f E vs \u4e0d\u7ee7\u627f E \u65e0\u5dee\u5f02（\u00a7\u516d，t=+0.34）\u3002<\/li>\n

    \u4e00\u53e5\u8bdd：\u64a4\u6389\u5916\u52a0 \u03bb \u6362\u6210\u4e2a\u4f53\u81ea\u5df1\u7d2f\u79ef\u7684\u4ea7\u51fa\u51b3\u5b9a\u9000\u51fa，\u79cd\u7fa4\u80fd\u6d3b，\u4f46\u5f80\u5bf9\u8d70\u663e\u8457\u66f4\u5dee\u2014\u2014\u4e14\u8fd9\u4e2a"\u66f4\u5dee"\u4e0d\u662f\u9002\u5e94\u5ea6\u8bc1\u636e，\u662f \u03c1 \u7ffb\u8f6c\u4e0b"\u53c2\u4e0e\u5373\u53d7\u7f5a"\u7684\u7ed3\u6784\u6027\u7ed3\u679c，\u5dee\u5f02\u5b8c\u5168\u6765\u81ea E_exit \u6392\u5e8f\u903b\u8f91\u3002 \u627f\u63a5 210 \u7684\u673a\u5236\u5f52\u56e0：q_eff\u22480.996 \u89e6\u9876，\u9009\u62e9\u628a \u03b8 \u63a8\u9ad8 \u2192 \u53c2\u4e0e\u7387\u4e0b\u964d \u2192 \u4ea7\u51fa\u4e0b\u964d，\u6f14\u5316\u7ec4\u53cd\u800c\u4f4e\u4e8e \u03b8 \u9501\u6b7b\u7ec4\u3002<\/p>\n

    \u4ecd\u7f3a\u7684<\/h4>\n
  • E_i \u7528 q_eff \u7d2f\u79ef，\u800c q_eff \u89e6\u9876\u2014\u2014\u82e5\u6362 n_part（\u53c2\u4e0e\u6b21\u6570）\u7d2f\u79ef\u53ef\u80fd\u6709\u533a\u5206\u5ea6（\u63a2\u9488\u663e\u793a CV=0.72，\u4f46 A\u2212B \u65b9\u5411\u672a\u53d8）\u3002<\/li>\n
  • exit_frac=0.10 \u9489\u6b7b，\u672a\u626b\u6863\u2014\u2014\u4e0d\u540c\u9000\u51fa\u538b\u529b\u4e0b\u7ee7\u627f\u6548\u5e94\u53ef\u80fd\u4e0d\u540c\u3002<\/li>\n
  • 5seed \u6559\u5b66\u7ea7，30seed \u52a0\u538b\u5df2\u8865（\u00a7\u4e94）\u3002<\/li>\n

    \u00a7\u4e5d \u8bda\u5b9e\u8fb9\u754c<\/h3>\n

    Limitations<\/h4>\n
  • \u4e3b\u5b9e\u9a8c 5seed \u6559\u5b66\u7ea7，\u52a0\u538b 30seed \u5df2\u8865（\u00a7\u4e94）\u3002<\/li>\n
  • \u03b4=0.95\u3001exit_frac=0.10\u3001\u03b1=0.5 \u4e09\u4e2a\u53c2\u6570\u9489\u6b7b\u4e0d\u626b\u6863\u2014\u2014\u626b\u6863=\u5916\u52a0\u65cb\u94ae，\u4f46\u7ed3\u8bba\u53ef\u80fd\u4f9d\u8d56\u53d6\u503c\u3002<\/li>\n
  • E_exit \u662f\u786e\u5b9a\u6027\u9000\u51fa（\u672b\u4f4d\u5206\u4f4d），\u4e0d\u6d88\u8017\u968f\u673a\u6570；\u4e0e \u03bb \u968f\u673a\u6b7b\u4ea1\u6709\u673a\u5236\u5dee\u5f02\u3002exit_frac=0.10 \u662f"\u6bcf\u4ee3\u56fa\u5b9a\u6dd8\u6c70\u672b\u4f4d 10%"\u2014\u2014\u8fd9\u662f\u6bd4\u968f\u673a\u6b7b\u4ea1\u66f4\u5f3a\u7684\u5b9a\u5411\u63a7\u5236\u3002<\/li>\n
  • F1 \u65b9\u5411\u88ab \u03c1 \u7ffb\u8f6c\u7ed1\u6b7b（\u00a7\u4e94b）\u2014\u2014\u7ed3\u8bba\u53ea\u5728 \u03c1 \u7ffb\u8f6c\u4e0b\u6210\u7acb，\u4e0d\u7ffb\u8f6c\u65f6\u65b9\u5411\u53cd\u8f6c\u3002<\/li>\n
  • E_i \u7528 q_eff \u7d2f\u79ef，q_eff \u89e6\u9876\u662f 210 \u7684\u53d1\u73b0\u2014\u2014\u6362\u6750\u6599（\u5982 n_part）\u662f\u4e0b\u4e00\u6b65\u3002<\/li>\n
  • A\u2212B \u5bf9\u7167\u672a\u914d\u5e73：A=\u6f14\u5316+\u7ee7\u627fE，B=\u03b8\u9501\u6b7b+\u4e0d\u7ee7\u627fE，\u540c\u65f6\u53d8\u4e86\u4e24\u4e2a\u56e0\u7d20\u3002\u00a7\u516d \u5df2\u8bc1 E \u7ee7\u627f\u65e0\u6548（t=+0.34），\u4f46\u7f3a B1（\u03b8\u9501\u6b7b+\u7ee7\u627fE）\u5bf9\u7167\u3002<\/li>\n

    Reproducibility<\/h4>\n

    \u811a\u672c：tianci_214a.py（20261102 \u5de5\u4f5c\u76ee\u5f55），\u4f9d\u8d56 212a \/ 209a \/ 202a \/ 200b \u56db\u4e2a\u5386\u53f2\u6a21\u5757（\u8def\u5f84\u89c1\u811a\u672c\u5934\u90e8）\u3002<\/p>\n

    \u590d\u8dd1\u547d\u4ee4：py tianci_214a.py > out_214a.txt 2>&1<\/p>\n

    \u73af\u5883：Windows 10, Python 3.13, py launcher<\/p>\n

    \u8f93\u51fa：out_214a.txt<\/p>\n

    \u7ed3\u679c：exit=0；death_mode=\u2018lambda\u2019 \u6052\u7b49\u6027\u81ea\u68c0 PASS（\u4e0e 212a(\u03ba=0) \u9010\u4f4d\u4e00\u81f4）；E_i \u7d2f\u79ef\u9000\u51fa \u00d7 \u56db\u7ec4 \u00d7 5seed + 30seed \u52a0\u538b + \u4e0d\u7ffb\u8f6c\u5bf9\u7167 + \u968f\u673a\u9000\u5bf9\u7167 \u5168\u90e8\u5b8c\u6210\u3002\u7248\u672c v3.3.52.0，2026-11-02\u3002<\/p>\n

    211-2 \u8ffd\u95ee：\u8fd9\u4e00\u6b65\u662f\u8ba9\u5b83\u66f4"\u81ea\u5df1\u4f1a\u52a8"\u4e86，\u8fd8\u662f\u53ea\u591a\u52a0\u4e86\u4e00\u4e2a\u529f\u80fd？\u7b54：\u64a4\u6389\u4e86\u5b9e\u9a8c\u8005\u6bcf\u4ee3\u63b7\u7684\u9ab0\u5b50（\u03bb），\u9000\u51fa\u6807\u51c6\u5185\u751f\u5316\u4e86（\u771f\u63a8\u8fdb）\u3002\u4f46\u88c5\u4e0a\u7684\u662f \u03b4=0.95\u3001exit_frac=0.10\u3001\u03b1=0.5 \u4e09\u4e2a\u786e\u5b9a\u6027\u5e38\u6570，\u4e14 exit_frac \u662f"\u6bcf\u4ee3\u56fa\u5b9a\u6dd8\u6c70\u672b\u4f4d 10%"\u2014\u2014\u6bd4\u968f\u673a\u6b7b\u4ea1\u66f4\u5f3a\u7684\u5b9a\u5411\u63a7\u5236\u3002\u9000\u51fa\u6807\u51c6\u5185\u751f\u5316\u4e86，\u6dd8\u6c70\u914d\u989d\u4e0e\u7d2f\u79ef\/\u7ee7\u627f\u89c4\u5219\u4ecd\u662f\u5916\u52a0\u5e38\u6570\u3002<\/p>\n

    \u9644\u5f55：\u4ee3\u7801<\/h3>\n

    # -*- coding: utf-8 -*-<\/span>
    \n"""
    \n\u5929\u8d50\u8303\u5f0f \u7b2c214\u5929 \u7b2c\u4e00\u7bc7
    \n\u64a4\u6389\u5916\u52a0 \u03bb，\u5b58\u6d3b\u4ea4\u7ed9\u4e2a\u4f53\u81ea\u5df1\u7d2f\u79ef\u7684\u4ea7\u51fa<\/p>\n

    PID: TC-214A-V3.3.52.0
    \nV3.3.52.0 \u00b7 2026-11-02<\/p>\n

    \u63a5 211-3 \u7f3a\u53e3\u2460（\u9002\u5e94\u5ea6\u2192\u5b58\u6d3b\u901a\u9053\u4e0d\u5b58\u5728）+ 211-3 \u5b9a\u7684\u4e0b\u4e00\u5173（\u5f80\u5bf9\u8d70\u80fd\u5426\u68c0\u51fa\u56de\u62a5）\u3002<\/p>\n

    212-1 \u7528 \u03ba \u505a\u9002\u5e94\u5ea6\u4f9d\u8d56\u6b7b\u4ea1（\u03bb_i = \u03bb0(1\u2212\u03ba\u00b7q_eff\/q_max)），\u662f**\u88c5\u4ef6**（\u5916\u52a0\u516c\u5f0f）\u3002
    \n213-1 \u5df2\u8bc1\u660e\u56de\u62a5\u4e0d\u7531 \u03ba \u901a\u9053\u4ea7\u751f\u2014\u2014\u03ba \u662f\u77ac\u65f6\u6982\u7387\u8c03\u5236，\u5728 q_eff\u22480.996 \u89e6\u9876\u3001
    \n\u4e2a\u4f53\u77ed\u5bff\u7684\u7cfb\u7edf\u91cc\u88ab\u7a00\u91ca\u3002<\/p>\n

    \u4eca\u5929\u6362\u6210\u957f\u6210\u5f0f：\u9000\u51fa\u4e0d\u518d\u7531\u5b9e\u9a8c\u8005\u6bcf\u4ee3\u63b7\u9ab0\u5b50\u51b3\u5b9a，\u800c\u7531**\u4e2a\u4f53\u81ea\u5df1\u7d2f\u79ef\u7684\u4ea7\u51fa**
    \nE_i = \u03b4\u00b7E_i + q_eff \u51b3\u5b9a\u3002\u6750\u6599 100% \u6765\u81ea\u4e0a\u4e00\u5c42（q_eff \u5df2\u6709\u3001\u9000\u51fa\/\u8865\u4f4d\u5df2\u6709），
    \n\u4e0d\u65b0\u589e\u72b6\u6001\u53d8\u91cf\u3002<\/p>\n

    \u6838\u5fc3\u5bf9\u7167\u662f E \u9057\u4e0d\u9057\u4f20（\u7ee7\u627f\u6863 vs \u4e0d\u7ee7\u627f\u6863）\u2014\u2014"\u6d3b\u5f97\u597d\u7684\u8c31\u7cfb\u80fd\u4e0d\u80fd\u628a\u4f18\u52bf\u4f20\u4e0b\u53bb"\u3002
    \n\u8fd9\u662f\u7f3a\u53e3\u2460\u771f\u6b63\u7684\u95ee\u6cd5（\u4e0d\u662f"\u6b7b\u4ea1\u6311\u4e0d\u6311\u4eba"，\u662f"\u4f18\u52bf\u80fd\u4e0d\u80fd\u4ee3\u9645\u4f20\u9012"）\u3002<\/p>\n

    \u6052\u7b49\u6027\u81ea\u68c0：death_mode='lambda' \u9000\u5316\u4e3a run_frtm_212(\u03ba=0)，\u9010\u4f4d\u4e00\u81f4\u3002
    \n"""<\/span>
    \nimport<\/span> sys
    \nimport<\/span> random
    \nimport<\/span> statistics<\/p>\n

    sys.<\/span>stdout.<\/span>reconfigure(<\/span>encoding=<\/span>"utf-8"<\/span>)<\/span><\/p>\n

    sys.<\/span>path.<\/span>insert(<\/span>0<\/span>,<\/span> r"C:\\\\Users\\\\Administrator\\\\Desktop\\\\20261021"<\/span>)<\/span>
    \nfrom<\/span> tianci_202a import<\/span> (<\/span>SEEDS,<\/span> RHO,<\/span> QUAD_ENV,<\/span> PHI,<\/span> TRACK_WINDOW)<\/span>
    \nsys.<\/span>path.<\/span>insert(<\/span>0<\/span>,<\/span> r"C:\\\\Users\\\\Administrator\\\\Desktop\\\\20261019"<\/span>)<\/span>
    \nfrom<\/span> tianci_200b import<\/span> (<\/span>GRID_W,<\/span> GRID_H,<\/span> N,<\/span> N_GEN,<\/span> SIGMA_V,<\/span> TOURNAMENT_K,<\/span>
    \n N_BUCKETS,<\/span> true_beta,<\/span> get_quadrant,<\/span> moore_neighbors,<\/span>
    \n get_bucket,<\/span> shannon_entropy,<\/span> clamp,<\/span> bar,<\/span> md_table)<\/span>
    \nsys.<\/span>path.<\/span>insert(<\/span>0<\/span>,<\/span> r"C:\\\\Users\\\\Administrator\\\\Desktop\\\\20261028"<\/span>)<\/span>
    \nfrom<\/span> tianci_209a import<\/span> (<\/span>THETA_MIN,<\/span> THETA_MAX,<\/span> GAMMA_MAIN,<\/span> LAMBDA_MAIN,<\/span>
    \n _clamp_theta)<\/span>
    \nsys.<\/span>path.<\/span>insert(<\/span>0<\/span>,<\/span> r"C:\\\\Users\\\\Administrator\\\\Desktop\\\\20261031"<\/span>)<\/span>
    \nfrom<\/span> tianci_212a import<\/span> (<\/span>run_frtm_212,<\/span> N_GEN_212,<\/span> FLIP_GEN_212,<\/span>
    \n get_p_and_alive_at,<\/span> get_part_rate_at)<\/span><\/p>\n

    N_GEN_214 =<\/span> N_GEN_212
    \nFLIP_GEN_214 =<\/span> FLIP_GEN_212
    \nFILL_DELAY_214 =<\/span> 5<\/span>
    \nRHO_NEW_214 =<\/span> –<\/span>0.5<\/span>
    \nSIGMA_THETA =<\/span> 0.10<\/span>
    \nLAM_BASE =<\/span> 0.10<\/span><\/p>\n

    DELTA_E =<\/span> 0.95<\/span>
    \nEXIT_FRAC =<\/span> 0.10<\/span>
    \nALPHA_E =<\/span> 0.5<\/span><\/p>\n

    SEEDS_30 =<\/span> SEEDS +<\/span> [<\/span>47<\/span>,<\/span> 53<\/span>,<\/span> 59<\/span>,<\/span> 61<\/span>,<\/span> 67<\/span>,<\/span> 71<\/span>,<\/span> 73<\/span>,<\/span> 79<\/span>,<\/span> 83<\/span>,<\/span> 89<\/span>,<\/span>
    \n 97<\/span>,<\/span> 101<\/span>,<\/span> 103<\/span>,<\/span> 107<\/span>,<\/span> 109<\/span>,<\/span> 113<\/span>,<\/span> 127<\/span>,<\/span> 131<\/span>,<\/span> 137<\/span>,<\/span> 139<\/span>,<\/span>
    \n 149<\/span>,<\/span> 151<\/span>,<\/span> 157<\/span>,<\/span> 163<\/span>,<\/span> 167<\/span>]<\/span><\/p>\n

    GROUPS_214A =<\/span> [<\/span>
    \n (<\/span>'A'<\/span>,<\/span> 'normal'<\/span>,<\/span> True<\/span>,<\/span> True<\/span>,<\/span> '\u6f14\u5316+\u7ee7\u627fE'<\/span>)<\/span>,<\/span>
    \n (<\/span>'A0'<\/span>,<\/span> 'normal'<\/span>,<\/span> True<\/span>,<\/span> False<\/span>,<\/span> '\u6f14\u5316+\u4e0d\u7ee7\u627fE'<\/span>)<\/span>,<\/span>
    \n (<\/span>'B'<\/span>,<\/span> 'normal'<\/span>,<\/span> False<\/span>,<\/span> False<\/span>,<\/span> '\u03b8\u9501\u6b7b+\u4e0d\u7ee7\u627fE'<\/span>)<\/span>,<\/span>
    \n (<\/span>'D'<\/span>,<\/span> 'neutral'<\/span>,<\/span> True<\/span>,<\/span> True<\/span>,<\/span> '\u4e2d\u6027+\u7ee7\u627fE'<\/span>)<\/span>,<\/span>
    \n]<\/span><\/p>\n

    def<\/span> run_frtm_214a<\/span>(<\/span>seed,<\/span> gamma,<\/span> sigma_theta,<\/span> lam=<\/span>0.0<\/span>,<\/span>
    \n death_mode=<\/span>'lambda'<\/span>,<\/span> inherit_E=<\/span>True<\/span>,<\/span>
    \n n_gen=<\/span>N_GEN_214,<\/span> flip_gen=<\/span>FLIP_GEN_214,<\/span> flip_mode=<\/span>'rho'<\/span>,<\/span>
    \n inherit_theta=<\/span>True<\/span>,<\/span> sel_mode=<\/span>'normal'<\/span>,<\/span> rho_new=<\/span>RHO_NEW_214,<\/span>
    \n delta=<\/span>DELTA_E,<\/span> exit_frac=<\/span>EXIT_FRAC,<\/span> alpha=<\/span>ALPHA_E)<\/span>:<\/span>
    \n """212 \u57fa\u5ea7 + E_i \u7d2f\u79ef\u9000\u51fa\u3002<\/p>\n

    death_mode:
    \n 'lambda' \u03bb \u5e38\u6570\u968f\u673a\u6b7b\u4ea1，\u968f\u673a\u6570\u8def\u5f84\u4e0e run_frtm_212(\u03ba=0) \u5b8c\u5168\u76f8\u540c
    \n 'E_exit' \u6309 E_i \u6392\u5e8f，\u672b\u4f4d exit_frac \u9000\u51fa（\u786e\u5b9a\u6027，\u4e0d\u6d88\u8017\u968f\u673a\u6570）<\/p>\n

    inherit_E: \u8865\u4f4d\u65f6 E \u662f\u5426\u4ece winner \u7ee7\u627f
    \n True E[i] = alpha * E[winner]
    \n False E[i] = 0.0<\/p>\n

    E_i = delta * E_i + q_eff[i]（\u6bcf\u4ee3，\u5b58\u6d3b\u4e2a\u4f53）
    \n \u8fd4\u56de\u4e0e run_frtm_212 \u76f8\u540c\u7684 12 \u5143\u7ec4\u3002
    \n """<\/span>
    \n random.<\/span>seed(<\/span>seed)<\/span>
    \n rho_val =<\/span> 1.0<\/span>
    \n betas =<\/span> [<\/span>random.<\/span>uniform(<\/span>0.0<\/span>,<\/span> 1.0<\/span>)<\/span> for<\/span> _ in<\/span> range<\/span>(<\/span>N)<\/span>]<\/span>
    \n thetas_A =<\/span> [<\/span>random.<\/span>uniform(<\/span>0.0<\/span>,<\/span> 1.0<\/span>)<\/span> for<\/span> _ in<\/span> range<\/span>(<\/span>N)<\/span>]<\/span>
    \n thetas_B =<\/span> [<\/span>random.<\/span>uniform(<\/span>0.0<\/span>,<\/span> 1.0<\/span>)<\/span> for<\/span> _ in<\/span> range<\/span>(<\/span>N)<\/span>]<\/span>
    \n s_stim_A =<\/span> [<\/span>0.0<\/span>,<\/span> 0.0<\/span>,<\/span> 0.0<\/span>,<\/span> 0.0<\/span>]<\/span>
    \n s_stim_B =<\/span> [<\/span>0.0<\/span>,<\/span> 0.0<\/span>,<\/span> 0.0<\/span>,<\/span> 0.0<\/span>]<\/span>
    \n history =<\/span> [<\/span>]<\/span>
    \n part_A_counts =<\/span> [<\/span>0<\/span>]<\/span> *<\/span> N
    \n part_B_counts =<\/span> [<\/span>0<\/span>]<\/span> *<\/span> N
    \n alive =<\/span> [<\/span>True<\/span>]<\/span> *<\/span> N
    \n death_gen =<\/span> [<\/span>None<\/span>]<\/span> *<\/span> N
    \n snaps =<\/span> [<\/span>]<\/span>
    \n scale_traj =<\/span> [<\/span>]<\/span>
    \n track_start =<\/span> n_gen –<\/span> TRACK_WINDOW +<\/span> 1<\/span>
    \n cross_copy =<\/span> 0<\/span>
    \n cross_fill =<\/span> 0<\/span>
    \n copy_total =<\/span> 0<\/span>
    \n fill_total =<\/span> 0<\/span>
    \n fill_count =<\/span> 0<\/span>
    \n death_z =<\/span> [<\/span>]<\/span>
    \n q_eff_prev =<\/span> [<\/span>0.0<\/span>]<\/span> *<\/span> N
    \n E =<\/span> [<\/span>0.0<\/span>]<\/span> *<\/span> N<\/p>\n

    env_A =<\/span> list<\/span>(<\/span>QUAD_ENV)<\/span>
    \n env_B =<\/span> list<\/span>(<\/span>QUAD_ENV)<\/span><\/p>\n

    for<\/span> gen in<\/span> range<\/span>(<\/span>n_gen +<\/span> 1<\/span>)<\/span>:<\/span>
    \n if<\/span> flip_mode ==<\/span> 'rho'<\/span> and<\/span> flip_gen is<\/span> not<\/span> None<\/span> and<\/span> gen ==<\/span> flip_gen:<\/span>
    \n rho_val =<\/span> rho_new<\/p>\n

    if<\/span> death_mode ==<\/span> 'lambda'<\/span> and<\/span> lam ><\/span> 0<\/span>:<\/span>
    \n for<\/span> i in<\/span> range<\/span>(<\/span>N)<\/span>:<\/span>
    \n if<\/span> alive[<\/span>i]<\/span> and<\/span> random.<\/span>random(<\/span>)<\/span> <<\/span> lam:<\/span>
    \n alive[<\/span>i]<\/span> =<\/span> False<\/span>
    \n death_gen[<\/span>i]<\/span> =<\/span> gen
    \n death_z.<\/span>append(<\/span>0.0<\/span>)<\/span>
    \n elif<\/span> death_mode ==<\/span> 'E_exit'<\/span>:<\/span>
    \n live_idx =<\/span> [<\/span>i for<\/span> i in<\/span> range<\/span>(<\/span>N)<\/span> if<\/span> alive[<\/span>i]<\/span>]<\/span>
    \n if<\/span> len<\/span>(<\/span>live_idx)<\/span> ><\/span> 1<\/span>:<\/span>
    \n live_idx.<\/span>sort(<\/span>key=<\/span>lambda<\/span> idx:<\/span> E[<\/span>idx]<\/span>)<\/span>
    \n n_exit =<\/span> int<\/span>(<\/span>len<\/span>(<\/span>live_idx)<\/span> *<\/span> exit_frac)<\/span>
    \n for<\/span> idx in<\/span> live_idx[<\/span>:<\/span>n_exit]<\/span>:<\/span>
    \n alive[<\/span>idx]<\/span> =<\/span> False<\/span>
    \n death_gen[<\/span>idx]<\/span> =<\/span> gen
    \n death_z.<\/span>append(<\/span>E[<\/span>idx]<\/span>)<\/span>
    \n elif<\/span> death_mode ==<\/span> 'random_exit'<\/span>:<\/span>
    \n live_idx =<\/span> [<\/span>i for<\/span> i in<\/span> range<\/span>(<\/span>N)<\/span> if<\/span> alive[<\/span>i]<\/span>]<\/span>
    \n if<\/span> len<\/span>(<\/span>live_idx)<\/span> ><\/span> 1<\/span>:<\/span>
    \n n_exit =<\/span> int<\/span>(<\/span>len<\/span>(<\/span>live_idx)<\/span> *<\/span> exit_frac)<\/span>
    \n exit_set =<\/span> random.<\/span>sample(<\/span>live_idx,<\/span> n_exit)<\/span>
    \n for<\/span> idx in<\/span> exit_set:<\/span>
    \n alive[<\/span>idx]<\/span> =<\/span> False<\/span>
    \n death_gen[<\/span>idx]<\/span> =<\/span> gen
    \n death_z.<\/span>append(<\/span>E[<\/span>idx]<\/span>)<\/span><\/p>\n

    n_alive =<\/span> sum<\/span>(<\/span>alive)<\/span>
    \n scale_traj.<\/span>append(<\/span>n_alive)<\/span><\/p>\n

    live_tA =<\/span> [<\/span>thetas_A[<\/span>i]<\/span> for<\/span> i in<\/span> range<\/span>(<\/span>N)<\/span> if<\/span> alive[<\/span>i]<\/span>]<\/span>
    \n live_tB =<\/span> [<\/span>thetas_B[<\/span>i]<\/span> for<\/span> i in<\/span> range<\/span>(<\/span>N)<\/span> if<\/span> alive[<\/span>i]<\/span>]<\/span>
    \n if<\/span> len<\/span>(<\/span>live_tA)<\/span> ><\/span> 1<\/span>:<\/span>
    \n mean_tA =<\/span> statistics.<\/span>mean(<\/span>live_tA)<\/span>
    \n mean_tB =<\/span> statistics.<\/span>mean(<\/span>live_tB)<\/span>
    \n snaps.<\/span>append(<\/span>(<\/span>gen,<\/span> mean_tA,<\/span> mean_tB,<\/span>
    \n statistics.<\/span>stdev(<\/span>live_tA)<\/span>,<\/span>
    \n statistics.<\/span>stdev(<\/span>live_tB)<\/span>)<\/span>)<\/span>
    \n else<\/span>:<\/span>
    \n mean_tA =<\/span> live_tA[<\/span>0<\/span>]<\/span> if<\/span> live_tA else<\/span> 0.0<\/span>
    \n mean_tB =<\/span> live_tB[<\/span>0<\/span>]<\/span> if<\/span> live_tB else<\/span> 0.0<\/span>
    \n snaps.<\/span>append(<\/span>(<\/span>gen,<\/span> mean_tA,<\/span> mean_tB,<\/span> 0.0<\/span>,<\/span> 0.0<\/span>)<\/span>)<\/span><\/p>\n

    q_base =<\/span> [<\/span>0.0<\/span>]<\/span> *<\/span> N
    \n for<\/span> i in<\/span> range<\/span>(<\/span>N)<\/span>:<\/span>
    \n if<\/span> not<\/span> alive[<\/span>i]<\/span>:<\/span>
    \n continue<\/span>
    \n r,<\/span> c =<\/span> divmod<\/span>(<\/span>i,<\/span> GRID_W)<\/span>
    \n env =<\/span> true_beta(<\/span>r,<\/span> c)<\/span>
    \n q_base[<\/span>i]<\/span> =<\/span> 1.0<\/span> \/<\/span> (<\/span>1.0<\/span> +<\/span> (<\/span>betas[<\/span>i]<\/span> –<\/span> env)<\/span> **<\/span> 2<\/span>)<\/span><\/p>\n

    n_act_A =<\/span> [<\/span>0<\/span>,<\/span> 0<\/span>,<\/span> 0<\/span>,<\/span> 0<\/span>]<\/span>
    \n n_act_B =<\/span> [<\/span>0<\/span>,<\/span> 0<\/span>,<\/span> 0<\/span>,<\/span> 0<\/span>]<\/span>
    \n q_eff =<\/span> [<\/span>0.0<\/span>]<\/span> *<\/span> N
    \n participated_A =<\/span> [<\/span>False<\/span>]<\/span> *<\/span> N
    \n participated_B =<\/span> [<\/span>False<\/span>]<\/span> *<\/span> N
    \n n_part =<\/span> 0<\/span>
    \n for<\/span> i in<\/span> range<\/span>(<\/span>N)<\/span>:<\/span>
    \n if<\/span> not<\/span> alive[<\/span>i]<\/span>:<\/span>
    \n continue<\/span>
    \n r,<\/span> c =<\/span> divmod<\/span>(<\/span>i,<\/span> GRID_W)<\/span>
    \n q =<\/span> get_quadrant(<\/span>r,<\/span> c)<\/span>
    \n sA =<\/span> s_stim_A[<\/span>q]<\/span>
    \n sB =<\/span> s_stim_B[<\/span>q]<\/span>
    \n if<\/span> sel_mode ==<\/span> 'neutral'<\/span>:<\/span>
    \n thA_for_p =<\/span> mean_tA
    \n thB_for_p =<\/span> mean_tB
    \n else<\/span>:<\/span>
    \n thA_for_p =<\/span> thetas_A[<\/span>i]<\/span>
    \n thB_for_p =<\/span> thetas_B[<\/span>i]<\/span>
    \n if<\/span> sA ><\/span> 0<\/span> and<\/span> thA_for_p ><\/span> 0<\/span>:<\/span>
    \n pA =<\/span> sA *<\/span> sA \/<\/span> (<\/span>sA *<\/span> sA +<\/span> thA_for_p *<\/span> thA_for_p)<\/span>
    \n else<\/span>:<\/span>
    \n pA =<\/span> 0.0<\/span>
    \n if<\/span> random.<\/span>random(<\/span>)<\/span> <<\/span> pA:<\/span>
    \n participated_A[<\/span>i]<\/span> =<\/span> True<\/span>
    \n n_act_A[<\/span>q]<\/span> +=<\/span> 1<\/span>
    \n if<\/span> sB ><\/span> 0<\/span> and<\/span> thB_for_p ><\/span> 0<\/span>:<\/span>
    \n pB =<\/span> sB *<\/span> sB \/<\/span> (<\/span>sB *<\/span> sB +<\/span> thB_for_p *<\/span> thB_for_p)<\/span>
    \n else<\/span>:<\/span>
    \n pB =<\/span> 0.0<\/span>
    \n if<\/span> random.<\/span>random(<\/span>)<\/span> <<\/span> pB:<\/span>
    \n participated_B[<\/span>i]<\/span> =<\/span> True<\/span>
    \n n_act_B[<\/span>q]<\/span> +=<\/span> 1<\/span>
    \n if<\/span> participated_A[<\/span>i]<\/span> or<\/span> participated_B[<\/span>i]<\/span>:<\/span>
    \n n_part +=<\/span> 1<\/span>
    \n q_eff[<\/span>i]<\/span> =<\/span> q_base[<\/span>i]<\/span> *<\/span> (<\/span>1.0<\/span> +<\/span> rho_val)<\/span>
    \n else<\/span>:<\/span>
    \n q_eff[<\/span>i]<\/span> =<\/span> q_base[<\/span>i]<\/span><\/p>\n

    if<\/span> gamma ><\/span> 0<\/span>:<\/span>
    \n for<\/span> i in<\/span> range<\/span>(<\/span>N)<\/span>:<\/span>
    \n if<\/span> not<\/span> alive[<\/span>i]<\/span>:<\/span>
    \n continue<\/span>
    \n if<\/span> participated_A[<\/span>i]<\/span>:<\/span>
    \n thetas_A[<\/span>i]<\/span> =<\/span> _clamp_theta(<\/span>thetas_A[<\/span>i]<\/span> –<\/span> gamma)<\/span>
    \n if<\/span> participated_B[<\/span>i]<\/span>:<\/span>
    \n thetas_B[<\/span>i]<\/span> =<\/span> _clamp_theta(<\/span>thetas_B[<\/span>i]<\/span> –<\/span> gamma)<\/span><\/p>\n

    for<\/span> i in<\/span> range<\/span>(<\/span>N)<\/span>:<\/span>
    \n if<\/span> alive[<\/span>i]<\/span>:<\/span>
    \n E[<\/span>i]<\/span> =<\/span> delta *<\/span> E[<\/span>i]<\/span> +<\/span> q_eff[<\/span>i]<\/span><\/p>\n

    if<\/span> gen >=<\/span> track_start:<\/span>
    \n for<\/span> i in<\/span> range<\/span>(<\/span>N)<\/span>:<\/span>
    \n if<\/span> participated_A[<\/span>i]<\/span>:<\/span>
    \n part_A_counts[<\/span>i]<\/span> +=<\/span> 1<\/span>
    \n if<\/span> participated_B[<\/span>i]<\/span>:<\/span>
    \n part_B_counts[<\/span>i]<\/span> +=<\/span> 1<\/span><\/p>\n

    p_total =<\/span> sum<\/span>(<\/span>q_eff)<\/span>
    \n mean_b =<\/span> sum<\/span>(<\/span>betas)<\/span> \/<\/span> N
    \n bucket_c =<\/span> [<\/span>0<\/span>]<\/span> *<\/span> N_BUCKETS
    \n for<\/span> b in<\/span> betas:<\/span>
    \n bucket_c[<\/span>get_bucket(<\/span>b)<\/span>]<\/span> +=<\/span> 1<\/span><\/p>\n

    n_occ =<\/span> sum<\/span>(<\/span>1<\/span> for<\/span> cnt in<\/span> bucket_c if<\/span> cnt ><\/span> 0<\/span>)<\/span>
    \n ent =<\/span> shannon_entropy(<\/span>bucket_c,<\/span> N)<\/span><\/p>\n

    history.<\/span>append(<\/span>{<\/span>
    \n 'gen'<\/span>:<\/span> gen,<\/span> 'p'<\/span>:<\/span> p_total,<\/span> 'mean_b'<\/span>:<\/span> mean_b,<\/span>
    \n 'n_occ'<\/span>:<\/span> n_occ,<\/span> 'entropy'<\/span>:<\/span> ent,<\/span>
    \n 's_stim_A'<\/span>:<\/span> tuple<\/span>(<\/span>s_stim_A)<\/span>,<\/span> 's_stim_B'<\/span>:<\/span> tuple<\/span>(<\/span>s_stim_B)<\/span>,<\/span>
    \n 'n_act_A'<\/span>:<\/span> tuple<\/span>(<\/span>n_act_A)<\/span>,<\/span> 'n_act_B'<\/span>:<\/span> tuple<\/span>(<\/span>n_act_B)<\/span>,<\/span>
    \n 'n_alive'<\/span>:<\/span> n_alive,<\/span> 'n_part'<\/span>:<\/span> n_part,<\/span>
    \n }<\/span>)<\/span><\/p>\n

    if<\/span> gen ==<\/span> n_gen:<\/span>
    \n qmax_cur =<\/span> 0.0<\/span>
    \n for<\/span> i in<\/span> range<\/span>(<\/span>N)<\/span>:<\/span>
    \n if<\/span> alive[<\/span>i]<\/span> and<\/span> q_eff[<\/span>i]<\/span> ><\/span> qmax_cur:<\/span>
    \n qmax_cur =<\/span> q_eff[<\/span>i]<\/span>
    \n surv_z =<\/span> [<\/span>q_eff[<\/span>i]<\/span> \/<\/span> qmax_cur for<\/span> i in<\/span> range<\/span>(<\/span>N)<\/span> if<\/span> alive[<\/span>i]<\/span>]<\/span> \\\\
    \n if<\/span> qmax_cur ><\/span> 0<\/span> else<\/span> [<\/span>]<\/span>
    \n break<\/span><\/p>\n

    for<\/span> q in<\/span> range<\/span>(<\/span>4<\/span>)<\/span>:<\/span>
    \n s_stim_A[<\/span>q]<\/span> =<\/span> max<\/span>(<\/span>0.0<\/span>,<\/span> s_stim_A[<\/span>q]<\/span> +<\/span> env_A[<\/span>q]<\/span> –<\/span> PHI *<\/span> n_act_A[<\/span>q]<\/span>)<\/span>
    \n s_stim_B[<\/span>q]<\/span> =<\/span> max<\/span>(<\/span>0.0<\/span>,<\/span> s_stim_B[<\/span>q]<\/span> +<\/span> env_B[<\/span>q]<\/span> –<\/span> PHI *<\/span> n_act_B[<\/span>q]<\/span>)<\/span><\/p>\n

    new_betas =<\/span> [<\/span>0.0<\/span>]<\/span> *<\/span> N
    \n new_thetas_A =<\/span> [<\/span>0.0<\/span>]<\/span> *<\/span> N
    \n new_thetas_B =<\/span> [<\/span>0.0<\/span>]<\/span> *<\/span> N
    \n for<\/span> i in<\/span> range<\/span>(<\/span>N)<\/span>:<\/span>
    \n if<\/span> not<\/span> alive[<\/span>i]<\/span>:<\/span>
    \n new_betas[<\/span>i]<\/span> =<\/span> betas[<\/span>i]<\/span>
    \n new_thetas_A[<\/span>i]<\/span> =<\/span> thetas_A[<\/span>i]<\/span>
    \n new_thetas_B[<\/span>i]<\/span> =<\/span> thetas_B[<\/span>i]<\/span>
    \n continue<\/span>
    \n r,<\/span> c =<\/span> divmod<\/span>(<\/span>i,<\/span> GRID_W)<\/span>
    \n candidates =<\/span> [<\/span>i]<\/span>
    \n for<\/span> j in<\/span> moore_neighbors(<\/span>r,<\/span> c)<\/span>:<\/span>
    \n if<\/span> alive[<\/span>j]<\/span>:<\/span>
    \n candidates.<\/span>append(<\/span>j)<\/span>
    \n k =<\/span> min<\/span>(<\/span>TOURNAMENT_K,<\/span> len<\/span>(<\/span>candidates)<\/span>)<\/span>
    \n sample =<\/span> random.<\/span>sample(<\/span>candidates,<\/span> k)<\/span>
    \n if<\/span> sel_mode ==<\/span> 'anti'<\/span>:<\/span>
    \n winner =<\/span> min<\/span>(<\/span>sample,<\/span> key=<\/span>lambda<\/span> idx:<\/span> q_eff[<\/span>idx]<\/span>)<\/span>
    \n else<\/span>:<\/span>
    \n winner =<\/span> max<\/span>(<\/span>sample,<\/span> key=<\/span>lambda<\/span> idx:<\/span> q_eff[<\/span>idx]<\/span>)<\/span>
    \n new_betas[<\/span>i]<\/span> =<\/span> clamp(<\/span>betas[<\/span>winner]<\/span> +<\/span> random.<\/span>gauss(<\/span>0<\/span>,<\/span> SIGMA_V)<\/span>)<\/span>
    \n if<\/span> inherit_theta:<\/span>
    \n new_thetas_A[<\/span>i]<\/span> =<\/span> _clamp_theta(<\/span>
    \n thetas_A[<\/span>winner]<\/span> +<\/span> random.<\/span>gauss(<\/span>0<\/span>,<\/span> sigma_theta)<\/span>)<\/span>
    \n new_thetas_B[<\/span>i]<\/span> =<\/span> _clamp_theta(<\/span>
    \n thetas_B[<\/span>winner]<\/span> +<\/span> random.<\/span>gauss(<\/span>0<\/span>,<\/span> sigma_theta)<\/span>)<\/span>
    \n else<\/span>:<\/span>
    \n new_thetas_A[<\/span>i]<\/span> =<\/span> thetas_A[<\/span>i]<\/span>
    \n new_thetas_B[<\/span>i]<\/span> =<\/span> thetas_B[<\/span>i]<\/span>
    \n copy_total +=<\/span> 1<\/span>
    \n if<\/span> winner !=<\/span> i:<\/span>
    \n cross_copy +=<\/span> 1<\/span><\/p>\n

    if<\/span> death_mode ==<\/span> 'lambda'<\/span> and<\/span> lam ><\/span> 0<\/span> and<\/span> True<\/span>:<\/span>
    \n for<\/span> i in<\/span> range<\/span>(<\/span>N)<\/span>:<\/span>
    \n if<\/span> (<\/span>not<\/span> alive[<\/span>i]<\/span>)<\/span> and<\/span> death_gen[<\/span>i]<\/span> is<\/span> not<\/span> None<\/span> \\\\
    \n and<\/span> (<\/span>gen –<\/span> death_gen[<\/span>i]<\/span>)<\/span> >=<\/span> FILL_DELAY_214:<\/span>
    \n r,<\/span> c =<\/span> divmod<\/span>(<\/span>i,<\/span> GRID_W)<\/span>
    \n neighbors =<\/span> [<\/span>j for<\/span> j in<\/span> moore_neighbors(<\/span>r,<\/span> c)<\/span>
    \n if<\/span> alive[<\/span>j]<\/span>]<\/span>
    \n if<\/span> neighbors:<\/span>
    \n w =<\/span> max<\/span>(<\/span>neighbors,<\/span> key=<\/span>lambda<\/span> idx:<\/span> q_eff[<\/span>idx]<\/span>)<\/span>
    \n new_betas[<\/span>i]<\/span> =<\/span> clamp(<\/span>
    \n betas[<\/span>w]<\/span> +<\/span> random.<\/span>gauss(<\/span>0<\/span>,<\/span> SIGMA_V)<\/span>)<\/span>
    \n thA_mut =<\/span> thetas_A[<\/span>w]<\/span> +<\/span> random.<\/span>gauss(<\/span>0<\/span>,<\/span> sigma_theta)<\/span>
    \n thB_mut =<\/span> thetas_B[<\/span>w]<\/span> +<\/span> random.<\/span>gauss(<\/span>0<\/span>,<\/span> sigma_theta)<\/span>
    \n if<\/span> inherit_theta:<\/span>
    \n new_thetas_A[<\/span>i]<\/span> =<\/span> _clamp_theta(<\/span>thA_mut)<\/span>
    \n new_thetas_B[<\/span>i]<\/span> =<\/span> _clamp_theta(<\/span>thB_mut)<\/span>
    \n else<\/span>:<\/span>
    \n new_thetas_A[<\/span>i]<\/span> =<\/span> random.<\/span>uniform(<\/span>0.0<\/span>,<\/span> 1.0<\/span>)<\/span>
    \n new_thetas_B[<\/span>i]<\/span> =<\/span> random.<\/span>uniform(<\/span>0.0<\/span>,<\/span> 1.0<\/span>)<\/span>
    \n alive[<\/span>i]<\/span> =<\/span> True<\/span>
    \n death_gen[<\/span>i]<\/span> =<\/span> None<\/span>
    \n E[<\/span>i]<\/span> =<\/span> alpha *<\/span> E[<\/span>w]<\/span> if<\/span> inherit_E else<\/span> 0.0<\/span>
    \n fill_count +=<\/span> 1<\/span>
    \n fill_total +=<\/span> 1<\/span>
    \n cross_fill +=<\/span> 1<\/span>
    \n part_A_counts[<\/span>i]<\/span> =<\/span> 0<\/span>
    \n part_B_counts[<\/span>i]<\/span> =<\/span> 0<\/span>
    \n elif<\/span> death_mode ==<\/span> 'E_exit'<\/span>:<\/span>
    \n for<\/span> i in<\/span> range<\/span>(<\/span>N)<\/span>:<\/span>
    \n if<\/span> (<\/span>not<\/span> alive[<\/span>i]<\/span>)<\/span> and<\/span> death_gen[<\/span>i]<\/span> is<\/span> not<\/span> None<\/span> \\\\
    \n and<\/span> (<\/span>gen –<\/span> death_gen[<\/span>i]<\/span>)<\/span> >=<\/span> FILL_DELAY_214:<\/span>
    \n r,<\/span> c =<\/span> divmod<\/span>(<\/span>i,<\/span> GRID_W)<\/span>
    \n neighbors =<\/span> [<\/span>j for<\/span> j in<\/span> moore_neighbors(<\/span>r,<\/span> c)<\/span>
    \n if<\/span> alive[<\/span>j]<\/span>]<\/span>
    \n if<\/span> neighbors:<\/span>
    \n w =<\/span> max<\/span>(<\/span>neighbors,<\/span> key=<\/span>lambda<\/span> idx:<\/span> q_eff[<\/span>idx]<\/span>)<\/span>
    \n new_betas[<\/span>i]<\/span> =<\/span> clamp(<\/span>
    \n betas[<\/span>w]<\/span> +<\/span> random.<\/span>gauss(<\/span>0<\/span>,<\/span> SIGMA_V)<\/span>)<\/span>
    \n thA_mut =<\/span> thetas_A[<\/span>w]<\/span> +<\/span> random.<\/span>gauss(<\/span>0<\/span>,<\/span> sigma_theta)<\/span>
    \n thB_mut =<\/span> thetas_B[<\/span>w]<\/span> +<\/span> random.<\/span>gauss(<\/span>0<\/span>,<\/span> sigma_theta)<\/span>
    \n if<\/span> inherit_theta:<\/span>
    \n new_thetas_A[<\/span>i]<\/span> =<\/span> _clamp_theta(<\/span>thA_mut)<\/span>
    \n new_thetas_B[<\/span>i]<\/span> =<\/span> _clamp_theta(<\/span>thB_mut)<\/span>
    \n else<\/span>:<\/span>
    \n new_thetas_A[<\/span>i]<\/span> =<\/span> random.<\/span>uniform(<\/span>0.0<\/span>,<\/span> 1.0<\/span>)<\/span>
    \n new_thetas_B[<\/span>i]<\/span> =<\/span> random.<\/span>uniform(<\/span>0.0<\/span>,<\/span> 1.0<\/span>)<\/span>
    \n alive[<\/span>i]<\/span> =<\/span> True<\/span>
    \n death_gen[<\/span>i]<\/span> =<\/span> None<\/span>
    \n E[<\/span>i]<\/span> =<\/span> alpha *<\/span> E[<\/span>w]<\/span> if<\/span> inherit_E else<\/span> 0.0<\/span>
    \n fill_count +=<\/span> 1<\/span>
    \n fill_total +=<\/span> 1<\/span>
    \n cross_fill +=<\/span> 1<\/span>
    \n part_A_counts[<\/span>i]<\/span> =<\/span> 0<\/span>
    \n part_B_counts[<\/span>i]<\/span> =<\/span> 0<\/span><\/p>\n

    betas =<\/span> new_betas
    \n thetas_A =<\/span> new_thetas_A
    \n thetas_B =<\/span> new_thetas_B
    \n q_eff_prev =<\/span> list<\/span>(<\/span>q_eff)<\/span><\/p>\n

    flow_stats =<\/span> {<\/span>
    \n 'cross_copy'<\/span>:<\/span> cross_copy,<\/span> 'cross_fill'<\/span>:<\/span> cross_fill,<\/span>
    \n 'copy_total'<\/span>:<\/span> copy_total,<\/span> 'fill_total'<\/span>:<\/span> fill_total,<\/span>
    \n 'flow_rate'<\/span>:<\/span> (<\/span>cross_copy +<\/span> cross_fill)<\/span> \/<\/span> (<\/span>copy_total +<\/span> fill_total)<\/span>
    \n if<\/span> (<\/span>copy_total +<\/span> fill_total)<\/span> ><\/span> 0<\/span> else<\/span> 0.0<\/span>,<\/span>
    \n 'copy_flow_rate'<\/span>:<\/span> cross_copy \/<\/span> copy_total
    \n if<\/span> copy_total ><\/span> 0<\/span> else<\/span> 0.0<\/span>,<\/span>
    \n }<\/span>
    \n return<\/span> (<\/span>history,<\/span> betas,<\/span> thetas_A,<\/span> thetas_B,<\/span> part_A_counts,<\/span> part_B_counts,<\/span>
    \n alive,<\/span> scale_traj,<\/span> snaps,<\/span> flow_stats,<\/span> death_z,<\/span> surv_z)<\/span><\/p>\n

    def<\/span> mean_std<\/span>(<\/span>xs)<\/span>:<\/span>
    \n if<\/span> len<\/span>(<\/span>xs)<\/span> <<\/span> 2<\/span>:<\/span>
    \n return<\/span> (<\/span>xs[<\/span>0<\/span>]<\/span> if<\/span> xs else<\/span> 0.0<\/span>)<\/span>,<\/span> 0.0<\/span>
    \n return<\/span> statistics.<\/span>mean(<\/span>xs)<\/span>,<\/span> statistics.<\/span>stdev(<\/span>xs)<\/span><\/p>\n

    def<\/span> paired_t<\/span>(<\/span>a,<\/span> b)<\/span>:<\/span>
    \n d =<\/span> [<\/span>x –<\/span> y for<\/span> x,<\/span> y in<\/span> zip<\/span>(<\/span>a,<\/span> b)<\/span>]<\/span>
    \n dm,<\/span> dstd =<\/span> mean_std(<\/span>d)<\/span>
    \n dd =<\/span> dm \/<\/span> dstd if<\/span> dstd ><\/span> 0<\/span> else<\/span> None<\/span>
    \n t =<\/span> dd *<\/span> (<\/span>len<\/span>(<\/span>d)<\/span> **<\/span> 0.5<\/span>)<\/span> if<\/span> dd is<\/span> not<\/span> None<\/span> else<\/span> None<\/span>
    \n return<\/span> dm,<\/span> dstd,<\/span> dd,<\/span> t<\/p>\n

    def<\/span> f1_per_capita<\/span>(<\/span>history)<\/span>:<\/span>
    \n rec =<\/span> history[<\/span>–<\/span>1<\/span>]<\/span>
    \n return<\/span> rec[<\/span>'p'<\/span>]<\/span> \/<\/span> rec[<\/span>'n_alive'<\/span>]<\/span> if<\/span> rec[<\/span>'n_alive'<\/span>]<\/span> ><\/span> 0<\/span> else<\/span> 0.0<\/span><\/p>\n

    def<\/span> survival_rate<\/span>(<\/span>history)<\/span>:<\/span>
    \n rec =<\/span> history[<\/span>–<\/span>1<\/span>]<\/span>
    \n return<\/span> rec[<\/span>'n_alive'<\/span>]<\/span> \/<\/span> N<\/p>\n

    if<\/span> __name__ ==<\/span> "__main__"<\/span>:<\/span>
    \n bar(<\/span>"\u5929\u8d50\u8303\u5f0f \u7b2c214\u5929 \u7b2c\u4e00\u7bc7：\u64a4\u6389\u5916\u52a0 \u03bb，\u5b58\u6d3b\u4ea4\u7ed9\u4e2a\u4f53\u81ea\u5df1\u7d2f\u79ef\u7684\u4ea7\u51fa"<\/span>)<\/span><\/p>\n

    # —————- Part 0 \u6052\u7b49\u6027\u81ea\u68c0 —————-<\/span>
    \n bar(<\/span>"Part 0 \u6052\u7b49\u6027\u81ea\u68c0：death_mode='lambda' \u987b\u4e0e 212a(\u03ba=0) \u9010\u4f4d\u4e00\u81f4"<\/span>)<\/span>
    \n ident_all =<\/span> True<\/span>
    \n for<\/span> gname,<\/span> sel,<\/span> inh,<\/span> inhE,<\/span> desc in<\/span> GROUPS_214A:<\/span>
    \n for<\/span> seed in<\/span> SEEDS:<\/span>
    \n r_new =<\/span> run_frtm_214a(<\/span>seed,<\/span> GAMMA_MAIN,<\/span> SIGMA_THETA,<\/span>
    \n lam=<\/span>LAM_BASE,<\/span> death_mode=<\/span>'lambda'<\/span>,<\/span>
    \n inherit_theta=<\/span>inh,<\/span> inherit_E=<\/span>inhE,<\/span>
    \n sel_mode=<\/span>sel)<\/span>
    \n r_old =<\/span> run_frtm_212(<\/span>seed,<\/span> GAMMA_MAIN,<\/span> SIGMA_THETA,<\/span>
    \n lam=<\/span>LAM_BASE,<\/span> kappa=<\/span>0.0<\/span>,<\/span>
    \n n_gen=<\/span>N_GEN_212,<\/span> flip_gen=<\/span>FLIP_GEN_212,<\/span>
    \n flip_mode=<\/span>'rho'<\/span>,<\/span>
    \n inherit_theta=<\/span>inh,<\/span> sel_mode=<\/span>sel)<\/span>
    \n h_new =<\/span> r_new[<\/span>0<\/span>]<\/span>
    \n h_old =<\/span> r_old[<\/span>0<\/span>]<\/span>
    \n ok =<\/span> len<\/span>(<\/span>h_new)<\/span> ==<\/span> len<\/span>(<\/span>h_old)<\/span>
    \n if<\/span> ok:<\/span>
    \n for<\/span> rn,<\/span> ro in<\/span> zip<\/span>(<\/span>h_new,<\/span> h_old)<\/span>:<\/span>
    \n if<\/span> (<\/span>abs<\/span>(<\/span>rn[<\/span>'p'<\/span>]<\/span> –<\/span> ro[<\/span>'p'<\/span>]<\/span>)<\/span> ><\/span> 1e-12<\/span> or<\/span>
    \n rn[<\/span>'n_alive'<\/span>]<\/span> !=<\/span> ro[<\/span>'n_alive'<\/span>]<\/span> or<\/span>
    \n rn[<\/span>'n_part'<\/span>]<\/span> !=<\/span> ro[<\/span>'n_part'<\/span>]<\/span>)<\/span>:<\/span>
    \n ok =<\/span> False<\/span>
    \n break<\/span>
    \n ident_all &<\/span>=<\/span> ok
    \n if<\/span> not<\/span> ok:<\/span>
    \n print<\/span>(<\/span>f" <\/span>{<\/span>gname}<\/span><\/span> seed=<\/span>{<\/span>seed}<\/span><\/span>: \u4e0d\u4e00\u81f4！\u7981\u6b62\u7ee7\u7eed\u3002"<\/span><\/span>)<\/span>
    \n sys.<\/span>exit(<\/span>1<\/span>)<\/span>
    \n print<\/span>(<\/span>f" death_mode='lambda' \u4e0e 212a(\u03ba=0) \u9010\u4f4d\u4e00\u81f4 = <\/span>{<\/span>ident_all}<\/span><\/span>"<\/span><\/span>)<\/span>
    \n print<\/span>(<\/span>f" \u8bf4\u660e：lambda \u6a21\u5f0f\u9000\u5316\u4e3a 212 \u57fa\u5ea7（\u03ba=0），E \u8ddf\u8e2a\u4e0d\u6d88\u8017\u968f\u673a\u6570\u3002"<\/span><\/span>)<\/span>
    \n print<\/span>(<\/span>)<\/span><\/p>\n

    # —————- Part 1 \u4e3b\u8868：E_exit \u00d7 \u56db\u7ec4 \u00d7 5 seed —————-<\/span>
    \n bar(<\/span>"Part 1 E_i \u7d2f\u79ef\u9000\u51fa：\u56db\u7ec4 \u00d7 5 seed"<\/span>)<\/span>
    \n print<\/span>(<\/span>f" \u03b4=<\/span>{<\/span>DELTA_E}<\/span><\/span>, exit_frac=<\/span>{<\/span>EXIT_FRAC}<\/span><\/span>, \u03b1=<\/span>{<\/span>ALPHA_E}<\/span><\/span>, \u03c3_\u03b8=<\/span>{<\/span>SIGMA_THETA}<\/span><\/span>"<\/span><\/span>)<\/span>
    \n print<\/span>(<\/span>)<\/span><\/p>\n

    rows_main =<\/span> [<\/span>]<\/span>
    \n f1_store =<\/span> {<\/span>}<\/span>
    \n surv_store =<\/span> {<\/span>}<\/span>
    \n for<\/span> gname,<\/span> sel,<\/span> inh,<\/span> inhE,<\/span> desc in<\/span> GROUPS_214A:<\/span>
    \n f1s,<\/span> survs =<\/span> [<\/span>]<\/span>,<\/span> [<\/span>]<\/span>
    \n for<\/span> seed in<\/span> SEEDS:<\/span>
    \n r =<\/span> run_frtm_214a(<\/span>seed,<\/span> GAMMA_MAIN,<\/span> SIGMA_THETA,<\/span>
    \n lam=<\/span>0.0<\/span>,<\/span> death_mode=<\/span>'E_exit'<\/span>,<\/span>
    \n inherit_theta=<\/span>inh,<\/span> inherit_E=<\/span>inhE,<\/span>
    \n sel_mode=<\/span>sel)<\/span>
    \n f1s.<\/span>append(<\/span>f1_per_capita(<\/span>r[<\/span>0<\/span>]<\/span>)<\/span>)<\/span>
    \n survs.<\/span>append(<\/span>survival_rate(<\/span>r[<\/span>0<\/span>]<\/span>)<\/span>)<\/span>
    \n f1_store[<\/span>gname]<\/span> =<\/span> f1s
    \n surv_store[<\/span>gname]<\/span> =<\/span> survs
    \n fm,<\/span> fs =<\/span> mean_std(<\/span>f1s)<\/span>
    \n sm,<\/span> ss =<\/span> mean_std(<\/span>survs)<\/span>
    \n rows_main.<\/span>append(<\/span>[<\/span>gname,<\/span> desc,<\/span>
    \n f"<\/span>{<\/span>fm:<\/span>.4f<\/span>}<\/span><\/span>\u00b1<\/span>{<\/span>fs:<\/span>.4f<\/span>}<\/span><\/span>"<\/span><\/span>,<\/span>
    \n f"<\/span>{<\/span>sm:<\/span>.4f<\/span>}<\/span><\/span>\u00b1<\/span>{<\/span>ss:<\/span>.4f<\/span>}<\/span><\/span>"<\/span><\/span>]<\/span>)<\/span>
    \n md_table(<\/span>[<\/span>"\u7ec4"<\/span>,<\/span> "\u8bf4\u660e"<\/span>,<\/span> "F1 \u4eba\u5747\u4ea7\u51fa"<\/span>,<\/span> "\u5b58\u7eed\u7387"<\/span>]<\/span>,<\/span> rows_main,<\/span> [<\/span>"r"<\/span>]<\/span> *<\/span> 4<\/span>)<\/span>
    \n print<\/span>(<\/span>)<\/span><\/p>\n

    # —————- Part 2 H1：\u5f80\u5bf9\u8d70\u662f\u5426\u68c0\u51fa\u56de\u62a5 —————-<\/span>
    \n bar(<\/span>"Part 2 H1：\u5f80\u5bf9\u8d70（A\u2212B）\u5728\u7ee7\u627f\u6863 vs \u4e0d\u7ee7\u627f\u6863"<\/span>)<\/span>
    \n print<\/span>(<\/span>f" 5 seed \u2192 df=4, \u4e34\u754c |t|>2.776"<\/span><\/span>)<\/span>
    \n print<\/span>(<\/span>)<\/span><\/p>\n

    rows_h1 =<\/span> [<\/span>]<\/span>
    \n for<\/span> label,<\/span> g1,<\/span> g2 in<\/span> [<\/span>(<\/span>'A\u2212B (\u7ee7\u627fE)'<\/span>,<\/span> 'A'<\/span>,<\/span> 'B'<\/span>)<\/span>,<\/span>
    \n (<\/span>'A0\u2212B (\u4e0d\u7ee7\u627fE)'<\/span>,<\/span> 'A0'<\/span>,<\/span> 'B'<\/span>)<\/span>]<\/span>:<\/span>
    \n a =<\/span> f1_store[<\/span>g1]<\/span>
    \n b =<\/span> f1_store[<\/span>g2]<\/span>
    \n dm,<\/span> ds,<\/span> dd,<\/span> t =<\/span> paired_t(<\/span>a,<\/span> b)<\/span>
    \n rows_h1.<\/span>append(<\/span>[<\/span>label,<\/span>
    \n f"<\/span>{<\/span>dm:<\/span>+.4f<\/span>}<\/span><\/span>\u00b1<\/span>{<\/span>ds:<\/span>.4f<\/span>}<\/span><\/span>"<\/span><\/span>,<\/span>
    \n f"<\/span>{<\/span>t:<\/span>+.2f<\/span>}<\/span><\/span>"<\/span><\/span> if<\/span> t is<\/span> not<\/span> None<\/span> else<\/span> "n\/a"<\/span>,<\/span>
    \n "\u68c0\u51fa"<\/span> if<\/span> (<\/span>t is<\/span> not<\/span> None<\/span> and<\/span> abs<\/span>(<\/span>t)<\/span> ><\/span> 2.776<\/span>)<\/span> else<\/span> "\u672a\u8d85\u566a\u58f0"<\/span>]<\/span>)<\/span>
    \n md_table(<\/span>[<\/span>"\u5bf9\u7167"<\/span>,<\/span> "\u914d\u5bf9\u5dee"<\/span>,<\/span> "t(df=4)"<\/span>,<\/span> "\u5224\u5b9a"<\/span>]<\/span>,<\/span> rows_h1,<\/span> [<\/span>"r"<\/span>]<\/span> *<\/span> 4<\/span>)<\/span>
    \n print<\/span>(<\/span>)<\/span><\/p>\n

    # —————- Part 3 H2：\u7ee7\u627f vs \u4e0d\u7ee7\u627f\u4e4b\u5dee —————-<\/span>
    \n bar(<\/span>"Part 3 H2：\u7ee7\u627f\u6863 vs \u4e0d\u7ee7\u627f\u6863\u4e4b\u5dee（\u9002\u5e94\u5ea6\u2192\u5b58\u6d3b\u901a\u9053\u662f\u5426\u6210\u7acb）"<\/span>)<\/span>
    \n a_inh =<\/span> f1_store[<\/span>'A'<\/span>]<\/span>
    \n a_noinh =<\/span> f1_store[<\/span>'A0'<\/span>]<\/span>
    \n dm,<\/span> ds,<\/span> dd,<\/span> t =<\/span> paired_t(<\/span>a_inh,<\/span> a_noinh)<\/span>
    \n print<\/span>(<\/span>f" A(\u7ee7\u627fE) \u2212 A0(\u4e0d\u7ee7\u627fE): <\/span>{<\/span>dm:<\/span>+.4f<\/span>}<\/span><\/span>\u00b1<\/span>{<\/span>ds:<\/span>.4f<\/span>}<\/span><\/span>, t=<\/span>{<\/span>t:<\/span>+.2f<\/span>}<\/span><\/span>"<\/span><\/span> if<\/span> t is<\/span> not<\/span> None<\/span> else<\/span> " n\/a"<\/span>)<\/span>
    \n if<\/span> t is<\/span> not<\/span> None<\/span> and<\/span> abs<\/span>(<\/span>t)<\/span> ><\/span> 2.776<\/span>:<\/span>
    \n print<\/span>(<\/span>f" \u2192 \u7ee7\u627fE\u663e\u8457<\/span>{<\/span>'\u9ad8\u4e8e'<\/span> if<\/span> dm ><\/span> 0<\/span> else<\/span> '\u4f4e\u4e8e'<\/span>}<\/span><\/span>\u4e0d\u7ee7\u627fE (|t|>2.776)"<\/span><\/span>)<\/span>
    \n else<\/span>:<\/span>
    \n print<\/span>(<\/span>f" \u2192 \u7ee7\u627fE\u4e0e\u4e0d\u7ee7\u627fE\u672a\u8d85\u566a\u58f0 (|t|<2.776)"<\/span><\/span>)<\/span>
    \n print<\/span>(<\/span>)<\/span><\/p>\n

    # —————- Part 4 \u5b58\u7eed\u68c0\u67e5 —————-<\/span>
    \n bar(<\/span>"Part 4 \u5b58\u7eed\u68c0\u67e5：\u64a4\u6389\u5916\u52a0 \u03bb \u540e\u79cd\u7fa4\u662f\u5426\u5f52\u96f6"<\/span>)<\/span>
    \n rows_surv =<\/span> [<\/span>]<\/span>
    \n for<\/span> gname,<\/span> sel,<\/span> inh,<\/span> inhE,<\/span> desc in<\/span> GROUPS_214A:<\/span>
    \n ss =<\/span> surv_store[<\/span>gname]<\/span>
    \n sm,<\/span> sstd =<\/span> mean_std(<\/span>ss)<\/span>
    \n rows_surv.<\/span>append(<\/span>[<\/span>gname,<\/span> f"<\/span>{<\/span>sm:<\/span>.4f<\/span>}<\/span><\/span>\u00b1<\/span>{<\/span>sstd:<\/span>.4f<\/span>}<\/span><\/span>"<\/span><\/span>,<\/span>
    \n "\u5f52\u96f6"<\/span> if<\/span> sm <<\/span> 0.01<\/span> else<\/span> "\u5b58\u7eed"<\/span>]<\/span>)<\/span>
    \n md_table(<\/span>[<\/span>"\u7ec4"<\/span>,<\/span> "\u672b\u4ee3\u5b58\u7eed\u7387"<\/span>,<\/span> "\u5224\u5b9a"<\/span>]<\/span>,<\/span> rows_surv,<\/span> [<\/span>"r"<\/span>]<\/span> *<\/span> 3<\/span>)<\/span>
    \n print<\/span>(<\/span>)<\/span><\/p>\n

    # —————- Part 5 E_i \u5206\u5e03\u5feb\u7167 —————-<\/span>
    \n bar(<\/span>"Part 5 E_i \u5206\u5e03\u5feb\u7167（gen=50\/100\/150, A\u7ec4\u7ee7\u627fE, seed=42）"<\/span>)<\/span>
    \n r_snap =<\/span> run_frtm_214a(<\/span>42<\/span>,<\/span> GAMMA_MAIN,<\/span> SIGMA_THETA,<\/span>
    \n lam=<\/span>0.0<\/span>,<\/span> death_mode=<\/span>'E_exit'<\/span>,<\/span>
    \n inherit_theta=<\/span>True<\/span>,<\/span> inherit_E=<\/span>True<\/span>,<\/span>
    \n sel_mode=<\/span>'normal'<\/span>)<\/span>
    \n scale =<\/span> r_snap[<\/span>7<\/span>]<\/span>
    \n print<\/span>(<\/span>f" \u5b58\u6d3b\u8f68\u8ff9: g0=<\/span>{<\/span>scale[<\/span>0<\/span>]<\/span>}<\/span><\/span> g25=<\/span>{<\/span>scale[<\/span>25<\/span>]<\/span>}<\/span><\/span> g50=<\/span>{<\/span>scale[<\/span>50<\/span>]<\/span>}<\/span><\/span> "<\/span><\/span>
    \n f"g75=<\/span>{<\/span>scale[<\/span>75<\/span>]<\/span>}<\/span><\/span> g100=<\/span>{<\/span>scale[<\/span>100<\/span>]<\/span>}<\/span><\/span> g125=<\/span>{<\/span>scale[<\/span>125<\/span>]<\/span>}<\/span><\/span> g150=<\/span>{<\/span>scale[<\/span>150<\/span>]<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span>
    \n print<\/span>(<\/span>)<\/span><\/p>\n

    # —————- Part 7 30seed \u52a0\u538b —————-<\/span>
    \n bar(<\/span>"Part 7 30seed \u52a0\u538b：A\u2212B（E_exit, \u7ee7\u627fE, \u7ffb\u8f6c）"<\/span>)<\/span>
    \n print<\/span>(<\/span>f" 30 seed \u2192 df=29, \u4e34\u754c |t|>2.045"<\/span><\/span>)<\/span>
    \n print<\/span>(<\/span>)<\/span>
    \n f1_A_30,<\/span> f1_B_30 =<\/span> [<\/span>]<\/span>,<\/span> [<\/span>]<\/span>
    \n for<\/span> seed in<\/span> SEEDS_30:<\/span>
    \n rA =<\/span> run_frtm_214a(<\/span>seed,<\/span> GAMMA_MAIN,<\/span> SIGMA_THETA,<\/span>
    \n lam=<\/span>0.0<\/span>,<\/span> death_mode=<\/span>'E_exit'<\/span>,<\/span>
    \n inherit_theta=<\/span>True<\/span>,<\/span> inherit_E=<\/span>True<\/span>,<\/span>
    \n sel_mode=<\/span>'normal'<\/span>)<\/span>
    \n rB =<\/span> run_frtm_214a(<\/span>seed,<\/span> GAMMA_MAIN,<\/span> SIGMA_THETA,<\/span>
    \n lam=<\/span>0.0<\/span>,<\/span> death_mode=<\/span>'E_exit'<\/span>,<\/span>
    \n inherit_theta=<\/span>False<\/span>,<\/span> inherit_E=<\/span>False<\/span>,<\/span>
    \n sel_mode=<\/span>'normal'<\/span>)<\/span>
    \n f1_A_30.<\/span>append(<\/span>f1_per_capita(<\/span>rA[<\/span>0<\/span>]<\/span>)<\/span>)<\/span>
    \n f1_B_30.<\/span>append(<\/span>f1_per_capita(<\/span>rB[<\/span>0<\/span>]<\/span>)<\/span>)<\/span>
    \n dm,<\/span> ds,<\/span> dd,<\/span> t =<\/span> paired_t(<\/span>f1_A_30,<\/span> f1_B_30)<\/span>
    \n print<\/span>(<\/span>f" A\u2212B: <\/span>{<\/span>dm:<\/span>+.4f<\/span>}<\/span><\/span>\u00b1<\/span>{<\/span>ds:<\/span>.4f<\/span>}<\/span><\/span>, t=<\/span>{<\/span>t:<\/span>+.2f<\/span>}<\/span><\/span> (df=29)"<\/span><\/span>)<\/span>
    \n print<\/span>(<\/span>f" \u5224\u5b9a: <\/span>{<\/span>'\u663e\u8457\u8d1f'<\/span> if<\/span> (<\/span>t is<\/span> not<\/span> None<\/span> and<\/span> t <<\/span> –<\/span>2.045<\/span>)<\/span> else<\/span> '\u672a\u8d85\u566a\u58f0'<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span>
    \n print<\/span>(<\/span>)<\/span><\/p>\n

    # —————- Part 8 \u4e0d\u7ffb\u8f6c\u5bf9\u7167 —————-<\/span>
    \n bar(<\/span>"Part 8 \u4e0d\u7ffb\u8f6c\u5bf9\u7167：rho \u6052 +1，A\u2212B（E_exit, \u7ee7\u627fE, 30seed）"<\/span>)<\/span>
    \n f1_A_nf,<\/span> f1_B_nf =<\/span> [<\/span>]<\/span>,<\/span> [<\/span>]<\/span>
    \n for<\/span> seed in<\/span> SEEDS_30:<\/span>
    \n rA =<\/span> run_frtm_214a(<\/span>seed,<\/span> GAMMA_MAIN,<\/span> SIGMA_THETA,<\/span>
    \n lam=<\/span>0.0<\/span>,<\/span> death_mode=<\/span>'E_exit'<\/span>,<\/span>
    \n inherit_theta=<\/span>True<\/span>,<\/span> inherit_E=<\/span>True<\/span>,<\/span>
    \n sel_mode=<\/span>'normal'<\/span>,<\/span> flip_gen=<\/span>None<\/span>)<\/span>
    \n rB =<\/span> run_frtm_214a(<\/span>seed,<\/span> GAMMA_MAIN,<\/span> SIGMA_THETA,<\/span>
    \n lam=<\/span>0.0<\/span>,<\/span> death_mode=<\/span>'E_exit'<\/span>,<\/span>
    \n inherit_theta=<\/span>False<\/span>,<\/span> inherit_E=<\/span>False<\/span>,<\/span>
    \n sel_mode=<\/span>'normal'<\/span>,<\/span> flip_gen=<\/span>None<\/span>)<\/span>
    \n f1_A_nf.<\/span>append(<\/span>f1_per_capita(<\/span>rA[<\/span>0<\/span>]<\/span>)<\/span>)<\/span>
    \n f1_B_nf.<\/span>append(<\/span>f1_per_capita(<\/span>rB[<\/span>0<\/span>]<\/span>)<\/span>)<\/span>
    \n dm,<\/span> ds,<\/span> dd,<\/span> t =<\/span> paired_t(<\/span>f1_A_nf,<\/span> f1_B_nf)<\/span>
    \n print<\/span>(<\/span>f" A\u2212B (\u4e0d\u7ffb\u8f6c): <\/span>{<\/span>dm:<\/span>+.4f<\/span>}<\/span><\/span>\u00b1<\/span>{<\/span>ds:<\/span>.4f<\/span>}<\/span><\/span>, t=<\/span>{<\/span>t:<\/span>+.2f<\/span>}<\/span><\/span> (df=29)"<\/span><\/span>)<\/span>
    \n print<\/span>(<\/span>f" \u5224\u5b9a: <\/span>{<\/span>'\u663e\u8457\u6b63'<\/span> if<\/span> (<\/span>t is<\/span> not<\/span> None<\/span> and<\/span> t ><\/span> 2.045<\/span>)<\/span> else<\/span> '\u672a\u8d85\u566a\u58f0'<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span>
    \n print<\/span>(<\/span>)<\/span><\/p>\n

    # —————- Part 9 \u968f\u673a\u9000\u5bf9\u7167 —————-<\/span>
    \n bar(<\/span>"Part 9 \u968f\u673a\u9000\u5bf9\u7167：random_exit \u66ff\u4ee3 E_exit，A\u2212B（\u7ee7\u627fE, \u7ffb\u8f6c, 30seed）"<\/span>)<\/span>
    \n f1_A_rx,<\/span> f1_B_rx =<\/span> [<\/span>]<\/span>,<\/span> [<\/span>]<\/span>
    \n for<\/span> seed in<\/span> SEEDS_30:<\/span>
    \n rA =<\/span> run_frtm_214a(<\/span>seed,<\/span> GAMMA_MAIN,<\/span> SIGMA_THETA,<\/span>
    \n lam=<\/span>0.0<\/span>,<\/span> death_mode=<\/span>'random_exit'<\/span>,<\/span>
    \n inherit_theta=<\/span>True<\/span>,<\/span> inherit_E=<\/span>True<\/span>,<\/span>
    \n sel_mode=<\/span>'normal'<\/span>)<\/span>
    \n rB =<\/span> run_frtm_214a(<\/span>seed,<\/span> GAMMA_MAIN,<\/span> SIGMA_THETA,<\/span>
    \n lam=<\/span>0.0<\/span>,<\/span> death_mode=<\/span>'random_exit'<\/span>,<\/span>
    \n inherit_theta=<\/span>False<\/span>,<\/span> inherit_E=<\/span>False<\/span>,<\/span>
    \n sel_mode=<\/span>'normal'<\/span>)<\/span>
    \n f1_A_rx.<\/span>append(<\/span>f1_per_capita(<\/span>rA[<\/span>0<\/span>]<\/span>)<\/span>)<\/span>
    \n f1_B_rx.<\/span>append(<\/span>f1_per_capita(<\/span>rB[<\/span>0<\/span>]<\/span>)<\/span>)<\/span>
    \n dm,<\/span> ds,<\/span> dd,<\/span> t =<\/span> paired_t(<\/span>f1_A_rx,<\/span> f1_B_rx)<\/span>
    \n print<\/span>(<\/span>f" A\u2212B (\u968f\u673a\u9000): <\/span>{<\/span>dm:<\/span>+.4f<\/span>}<\/span><\/span>\u00b1<\/span>{<\/span>ds:<\/span>.4f<\/span>}<\/span><\/span>, t=<\/span>{<\/span>t:<\/span>+.2f<\/span>}<\/span><\/span> (df=29)"<\/span><\/span>)<\/span>
    \n print<\/span>(<\/span>f" \u5224\u5b9a: <\/span>{<\/span>'\u663e\u8457'<\/span> if<\/span> (<\/span>t is<\/span> not<\/span> None<\/span> and<\/span> abs<\/span>(<\/span>t)<\/span> ><\/span> 2.045<\/span>)<\/span> else<\/span> '\u5dee\u5f02\u5f52\u96f6'<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span>
    \n print<\/span>(<\/span>)<\/span><\/p>\n

    # —————- Part 6 \u8bda\u5b9e\u8fb9\u754c —————-<\/span>
    \n bar(<\/span>"Part 6 \u8bda\u5b9e\u8fb9\u754c"<\/span>)<\/span>
    \n print<\/span>(<\/span>" 1. \u6052\u7b49\u6027\u81ea\u68c0 PASS：death_mode='lambda' \u4e0e 212a(\u03ba=0) \u9010\u4f4d\u4e00\u81f4\u3002"<\/span>)<\/span>
    \n print<\/span>(<\/span>" 2. E_i = \u03b4\u00b7E_i + q_eff，\u03b4=0.95 \u9489\u6b7b\u4e0d\u626b\u6863（\u626b\u6863=\u53c8\u4e00\u4e2a\u5916\u52a0\u65cb\u94ae）\u3002"<\/span>)<\/span>
    \n print<\/span>(<\/span>" 3. exit_frac=0.10 \u9489\u6b7b，\u5339\u914d \u03bb=0.10 \u7684\u5e73\u5747\u9000\u51fa\u7387\u3002"<\/span>)<\/span>
    \n print<\/span>(<\/span>" 4. \u03b1=0.5 \u9489\u6b7b\u4e0d\u626b\u6863（\u7ee7\u627f\u6bd4\u4f8b\u626b\u6863=\u53c8\u4e00\u4e2a\u5916\u52a0\u65cb\u94ae）\u3002"<\/span>)<\/span>
    \n print<\/span>(<\/span>" 5. E_exit \u662f\u786e\u5b9a\u6027\u9000\u51fa（\u672b\u4f4d\u5206\u4f4d），\u4e0d\u6d88\u8017\u968f\u673a\u6570；"<\/span>)<\/span>
    \n print<\/span>(<\/span>" \u4e0e \u03bb \u968f\u673a\u6b7b\u4ea1\u4e0d\u540c\u2014\u2014\u8fd9\u662f\u673a\u5236\u5dee\u5f02，\u4e0d\u662f bug\u3002"<\/span>)<\/span>
    \n print<\/span>(<\/span>" 6. 5seed \u672a\u8d85\u566a\u58f0\u662f\u6837\u672c\u91cf\u5047\u8c61，30seed \u663e\u8457\u8d1f（t=\u22125.27）\u3002"<\/span>)<\/span>
    \n print<\/span>(<\/span>" 7. F1 \u65b9\u5411\u88ab \u03c1 \u7ffb\u8f6c\u7ed1\u6b7b，\u4e0d\u7ffb\u8f6c\u65f6\u663e\u8457\u6b63\u2014\u2014\u7ed3\u8bba\u53ea\u5728\u7ffb\u8f6c\u4e0b\u6210\u7acb\u3002"<\/span>)<\/span>
    \n print<\/span>(<\/span>)<\/span>
    \n print<\/span>(<\/span>" 211-2 \u8ffd\u95ee：\u8fd9\u4e00\u6b65\u662f\u8ba9\u5b83\u66f4'\u81ea\u5df1\u4f1a\u52a8'\u4e86，\u8fd8\u662f\u53ea\u591a\u52a0\u4e86\u4e00\u4e2a\u529f\u80fd？"<\/span>)<\/span>
    \n print<\/span>(<\/span>" \u7b54：\u64a4\u6389\u4e86\u5b9e\u9a8c\u8005\u6bcf\u4ee3\u63b7\u7684\u9ab0\u5b50（\u03bb），\u9000\u51fa\u6807\u51c6\u5185\u751f\u5316\u4e86（\u771f\u63a8\u8fdb）\u3002"<\/span>)<\/span>
    \n print<\/span>(<\/span>" \u4f46\u88c5\u4e0a \u03b4=0.95\u3001exit_frac=0.10\u3001\u03b1=0.5 \u4e09\u4e2a\u786e\u5b9a\u6027\u5e38\u6570，"<\/span>)<\/span>
    \n print<\/span>(<\/span>" exit_frac \u662f'\u6bcf\u4ee3\u56fa\u5b9a\u6dd8\u6c70\u672b\u4f4d 10%'\u2014\u2014\u6bd4\u968f\u673a\u6b7b\u4ea1\u66f4\u5f3a\u7684\u5b9a\u5411\u63a7\u5236\u3002"<\/span>)<\/span>
    \n print<\/span>(<\/span>" \u9000\u51fa\u6807\u51c6\u5185\u751f\u5316\u4e86，\u6dd8\u6c70\u914d\u989d\u4e0e\u7d2f\u79ef\/\u7ee7\u627f\u89c4\u5219\u4ecd\u662f\u5916\u52a0\u5e38\u6570\u3002"<\/span>)<\/span><\/p>\n

    \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n","protected":false},"excerpt":{"rendered":"

    \u5929\u8d50\u8303\u5f0f \u00b7\u7b2c214\u5929 \u7b2c\u4e00\u7bc7：\u64a4\u6389\u5916\u52a0 \u03bb，\u5b58\u6d3b\u4ea4\u7ed9\u4e2a\u4f53\u81ea\u5df1\u7d2f\u79ef\u7684\u4ea7\u51fa
    \n\u2014\u2014E_i \u7d2f\u79ef\u9000\u51fa \u00d7 \u7ee7\u627f vs \u4e0d\u7ee7\u627f \u00d7 \u56db\u7ec4 \u00d7 5 seed
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