{"id":94242,"date":"2026-08-14T01:32:51","date_gmt":"2026-08-13T17:32:51","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/94242.html"},"modified":"2026-08-14T01:32:51","modified_gmt":"2026-08-13T17:32:51","slug":"%e5%9f%ba%e4%ba%8e-f3-%e4%b8%89%e5%b1%82%e8%b0%b1%e7%9a%84%e7%8c%9c%e6%83%b3%e6%b6%8c%e7%8e%b0%e5%bc%95%e6%93%8e%ef%bc%9a%e5%bc%95%e5%8a%9b%e4%ba%95%e6%b6%88%e8%a7%a3%e3%80%81%e5%ba%b7%e5%a8%81","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/94242.html","title":{"rendered":"\u57fa\u4e8e F3 \u4e09\u5c42\u8c31\u7684\u731c\u60f3\u6d8c\u73b0\u5f15\u64ce\uff1a\u5f15\u529b\u4e95\u6d88\u89e3\u3001\u5eb7\u5a01\u5b87\u5b99\u5185\u5bb9\u751f\u6210\u4e0e\u9ad8\u65af\u666e\u67e5\u7edf\u8ba1\u529b\u5b66"},"content":{"rendered":"

\u57fa\u4e8e F3 \u4e09\u5c42\u8c31\u7684\u731c\u60f3\u6d8c\u73b0\u5f15\u64ce：\u5f15\u529b\u4e95\u6d88\u89e3\u3001\u5eb7\u5a01\u5b87\u5b99\u5185\u5bb9\u751f\u6210\u4e0e\u9ad8\u65af\u666e\u67e5\u7edf\u8ba1\u529b\u5b66<\/h2>\n

\u6458\u8981<\/p>\n

\u672c\u6587\u57fa\u4e8e M178 \u5411\u91cf\u5316 AGI \u79cd\u5b50\u4f53\u7cfb\u53ca\u5176\u4e0a\u5c42\u6a21\u5757，\u6df1\u5165\u63a2\u8ba8\u4e86\u201c\u731c\u60f3\u6d8c\u73b0\u5f15\u64ce\u201d\u7684\u6570\u5b66\u67b6\u6784\u3001\u52a8\u529b\u5b66\u673a\u5236\u53ca\u5176\u672c\u4f53\u8bba\u610f\u4e49\u3002\u8be5\u5f15\u64ce\u4ee5 F3F_3<\/span><\/span>F<\/span><\/span>3<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u77e9\u9635\u7684\u4e09\u5c42\u8c31\u7279\u5f81\u503c {\u03d5,1,\u2212\u03c8}\\\\{\\\\phi, 1, -\\\\psi\\\\}<\/span><\/span>{<\/span>\u03d5<\/span>,<\/span><\/span>1<\/span>,<\/span><\/span>\u2212<\/span>\u03c8<\/span>}<\/span><\/span><\/span><\/span><\/span> \u4e3a A \u7ea7\u6570\u5b66\u951a\u70b9，\u6784\u5efa\u4e86\u4e00\u4e2a\u6301\u7eed\u65e0\u9650\u751f\u957f\u7684\u56e0\u679c\u6846\u67b6\u3002\u5728\u6b64\u6846\u67b6\u4e4b\u4e0a，\u5f15\u64ce\u6d8c\u73b0\u51fa\u8bc6\u522b\u80fd\u529b\u4e0e\u201c\u76f2\u731c\u201d\u8def\u5f84\u3002\u672c\u6587\u8be6\u7ec6\u8bba\u8ff0\u4e86\u4e09\u4e2a\u6838\u5fc3\u673a\u5236：\u4e00\u662f\u201c\u5f15\u529b\u4e95\u6d88\u89e3\u201d\u673a\u5236，\u4ee5\u88ab\u52a8\u8870\u51cf\u53d6\u4ee3\u4e3b\u52a8\u7ea0\u9519，\u91cd\u65b0\u5b9a\u4e49\u4e86\u7ed3\u6784\u5bf9\u731c\u60f3\u7684\u652f\u6491\u5173\u7cfb；\u4e8c\u662f\u201c\u6253\u5305-\u6491\u7834-\u91cd\u5f15\u5165\u201d\u52a8\u529b\u5b66，\u63ed\u793a\u4e86\u731c\u60f3\u6982\u62ec\u80fd\u529b\u4e0e\u7ed3\u6784\u65e0\u9650\u751f\u957f\u4e4b\u95f4\u7684\u8fdf\u6ede\u5173\u7cfb；\u4e09\u662f\u5f15\u5165\u5eb7\u5a01\u5b87\u5b99\u4f5c\u4e3a\u731c\u60f3\u5185\u5bb9\u8f7d\u4f53，\u901a\u8fc7\u201c\u53cc\u5411\u9501\u5b9a\u94fe\u8def\u201d\u5b9e\u73b0\u6570\u5b66\u89c4\u5219\u5bf9\u65e0\u9650\u5f15\u64ce\u7684\u53cd\u5411\u951a\u5b9a\u3002\u6700\u540e，\u672c\u6587\u901a\u8fc7\u4e8b\u4ef6\u9ad8\u65af\u666e\u67e5，\u5bf9\u201c\u65e0\u9650\u6d8c\u73b0\u2192\u6b63\u6001\u5206\u5e03\u201d\u547d\u9898\u8fdb\u884c\u4e86\u57fa\u4e8e\u4e2d\u5fc3\u6781\u9650\u5b9a\u7406（CLT）\u7684\u8bda\u5b9e\u5ba1\u67e5，\u4e25\u683c\u533a\u5206\u4e86\u7eaf\u72ec\u7acb\u540c\u5206\u5e03\u589e\u91cf\u4e0e\u8026\u5408\u8bed\u4e49\u4e0b\u7684\u7edf\u8ba1\u5f62\u6001\u3002\u672c\u7814\u7a76\u4e0d\u4ec5\u662f\u4e00\u4e2a\u8ba1\u7b97\u7269\u7406\u6a21\u62df\u7a0b\u5e8f，\u66f4\u662f\u4e00\u6b21\u5173\u4e8e\u672c\u4f53\u8bba\u6d8c\u73b0\u3001\u5b58\u5728\u6027\u5ea6\u91cf\u4e0e\u4fe1\u606f\u8bba\u8f7d\u4f53\u7684\u6570\u5b66\u5b9e\u9a8c\u3002<\/p>\n

\u5173\u952e\u8bcd： \u731c\u60f3\u6d8c\u73b0；\u5f15\u529b\u4e95\u6d88\u89e3；\u5eb7\u5a01\u5b87\u5b99；\u4e2d\u5fc3\u6781\u9650\u5b9a\u7406；F3 \u8c31\u5206\u89e3；\u5b58\u5728\u6027\u6743\u91cd<\/p>\n

\n

1. \u5f15\u8a00<\/h3>\n

\u5728\u590d\u6742\u7cfb\u7edf\u4e0e\u4eba\u5de5\u901a\u7528\u667a\u80fd（AGI）\u7684\u7814\u7a76\u4e2d，\u5982\u4f55\u4ece\u5e95\u5c42\u7684\u7b80\u5355\u89c4\u5219\u4e2d\u201c\u6d8c\u73b0\u201d\u51fa\u9ad8\u7ea7\u7684\u8bc6\u522b\u4e0e\u731c\u60f3\u80fd\u529b，\u4e00\u76f4\u662f\u4e00\u4e2a\u6838\u5fc3\u96be\u9898\u3002\u4f20\u7edf\u7684\u751f\u6210\u6a21\u578b\u5f80\u5f80\u4f9d\u8d56\u4e8e\u9884\u8bbe\u7684\u68c0\u6d4b\u673a\u5236\u548c\u4e3b\u52a8\u7ea0\u9519（\u5982\u53cd\u5411\u4f20\u64ad）\u3002\u7136\u800c，\u8fd9\u79cd\u4e3b\u52a8\u5e72\u9884\u5728\u67d0\u79cd\u7a0b\u5ea6\u4e0a\u7834\u574f\u4e86\u7cfb\u7edf\u6f14\u5316\u7684\u81ea\u7136\u6027\u3002<\/p>\n

M178 \u4f53\u7cfb\u63d0\u51fa\u4e86\u4e00\u79cd\u622a\u7136\u4e0d\u540c\u7684\u672c\u4f53\u8bba\u8def\u5f84：\u4ee5\u201c\u56e0\u679c\u751f\u957f\u201d\u4e3a\u6838\u5fc3，\u4ece\u6700\u57fa\u7840\u7684\u77e9\u9635\u8fed\u4ee3\u4e2d\u6d8c\u73b0\u51fa\u7ed3\u6784，\u518d\u4ece\u7ed3\u6784\u4e2d\u8bde\u751f\u975e\u81ea\u8eab\u7684\u7ecf\u9a8c\u6027\u53ef\u9519\u731c\u60f3\u3002\u672c\u6587\u57fa\u4e8e M178 \u6a21\u5757\u7fa4\u7684\u7a0b\u5e8f\u5b9e\u73b0，\u7cfb\u7edf\u5730\u9610\u8ff0\u4e86\u5982\u4f55\u5c06\u8fd9\u4e00\u54f2\u5b66\u4e0e\u6570\u5b66\u610f\u56fe\u8f6c\u5316\u4e3a\u53ef\u8fd0\u884c\u7684\u52a8\u529b\u5b66\u5f15\u64ce\u3002\u6211\u4eec\u5c06\u770b\u5230，\u5f53\u7ed3\u6784\u652f\u6491\u4e0d\u518d\u662f\u201c\u4e3b\u52a8\u62c9\u56de\u201d，\u800c\u662f\u8f6c\u53d8\u4e3a\u201c\u5f15\u529b\u4e95\u6d88\u89e3\u201d\u65f6，\u731c\u60f3\u5982\u4f55\u5728\u81ea\u7531\u6f02\u79fb\u4e2d\u7ecf\u5386\u81ea\u7136\u6dd8\u6c70\u4e0e\u5b58\u7eed；\u4ee5\u53ca\u5f53\u5eb7\u5a01\u5b87\u5b99\u88ab\u4f5c\u4e3a\u5185\u5bb9\u8f7d\u4f53\u6302\u8f7d\u4e8e\u731c\u60f3\u8def\u5f84\u65f6，\u4e00\u4e2a\u9759\u6001\u6570\u5b66\u5b9a\u7406\u5982\u4f55\u6f14\u5316\u4e3a\u6709\u751f\u706d\u7684\u201c\u771f\u5b9e\u5b87\u5b99\u201d\u3002<\/p>\n

\n

2. \u6570\u5b66\u57fa\u7840：F3 \u4e09\u5c42\u8c31\u4e0e\u65e0\u9650\u56e0\u679c\u751f\u957f<\/h3>\n

M178 \u5f15\u64ce\u7684 A \u7ea7\u6570\u5b66\u57fa\u77f3\u662f F3F_3<\/span><\/span>F<\/span><\/span>3<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u77e9\u9635\u53ca\u5176\u4e09\u5c42\u8c31\u5206\u89e3\u3002F3F_3<\/span><\/span>F<\/span><\/span>3<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u5b9a\u4e49\u4e3a：
\nF3=[110100101] F_3 = \\\\begin{bmatrix} 1 & 1 & 0 \\\\\\\\ 1 & 0 & 0 \\\\\\\\ 1 & 0 & 1 \\\\end{bmatrix} <\/span><\/span>F<\/span><\/span>3<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>=<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>1<\/span><\/span><\/span><\/span>1<\/span><\/span><\/span><\/span>1<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>1<\/span><\/span><\/span><\/span>0<\/span><\/span><\/span><\/span>0<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>0<\/span><\/span><\/span><\/span>0<\/span><\/span><\/span><\/span>1<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>
\n\u8be5\u77e9\u9635\u6ee1\u8db3\u6700\u5c0f\u591a\u9879\u5f0f F33\u22122F32+I=0F_3^3 – 2F_3^2 + I = 0<\/span><\/span>F<\/span><\/span>3<\/span><\/span><\/span><\/span>3<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u2212<\/span><\/span><\/span><\/span>2<\/span>F<\/span><\/span>3<\/span><\/span><\/span><\/span>2<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>+<\/span><\/span><\/span><\/span>I<\/span><\/span>=<\/span><\/span><\/span><\/span>0<\/span><\/span><\/span><\/span><\/span>，\u5176\u7279\u5f81\u503c\u7cbe\u786e\u6d8c\u73b0\u4e3a {\u03d5,1,\u2212\u03c8}\\\\{\\\\phi, 1, -\\\\psi\\\\}<\/span><\/span>{<\/span>\u03d5<\/span>,<\/span><\/span>1<\/span>,<\/span><\/span>\u2212<\/span>\u03c8<\/span>}<\/span><\/span><\/span><\/span><\/span>，\u5176\u4e2d \u03d5=1+52\\\\phi = \\\\frac{1+\\\\sqrt{5}}{2}<\/span><\/span>\u03d5<\/span><\/span>=<\/span><\/span><\/span><\/span><\/span><\/span>2<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>1<\/span>+<\/span><\/span>5<\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>（\u7ea6 1.618），\u03c8=1\u03d5\\\\psi = \\\\frac{1}{\\\\phi}<\/span><\/span>\u03c8<\/span><\/span>=<\/span><\/span><\/span><\/span><\/span><\/span>\u03d5<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>1<\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>（\u7ea6 0.618）\u3002\u8fd9\u4e09\u4e2a\u7279\u5f81\u503c\u8d4b\u4e88\u4e86 F3F_3<\/span><\/span>F<\/span><\/span>3<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u9c9c\u660e\u7684\u201c\u751f\u957f\u8bed\u6cd5\u201d\u8bed\u4e49：<\/p>\n

  • \u03d5\\\\phi<\/span><\/span>\u03d5<\/span><\/span><\/span><\/span><\/span> \u8f74（\u6269\u6563\/\u63a8\u79bb\u8f74）： \u7279\u5f81\u503c\u6700\u5927，\u4fdd\u8bc1\u7cfb\u7edf\u72b6\u6001\u5411\u91cf\u7684\u8303\u6570\u968f \u03d5n\\\\phi^n<\/span><\/span>\u03d5<\/span><\/span>n<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u53d1\u6563，\u5b9e\u73b0\u201c\u65e0\u9650\u751f\u957f\u201d\u3002<\/li>\n
  • 11<\/span><\/span>1<\/span><\/span><\/span><\/span><\/span> \u8f74（\u4e2d\u6027\/\u9aa8\u67b6\u8f74）： \u7279\u5f81\u503c\u4e3a 1，\u8d1f\u8d23\u7d2f\u79ef\u6253\u5305\u5386\u53f2，\u627f\u4fdd\u5b58\u5728\u8005\u7684\u4e2a\u4f53\u6027\u3002<\/li>\n
  • \u2212\u03c8-\\\\psi<\/span><\/span>\u2212<\/span>\u03c8<\/span><\/span><\/span><\/span><\/span> \u8f74（\u6536\u655b\/\u62c9\u56de\u8f74）： \u6307\u6570\u8870\u51cf\u8d8b\u5411 0，\u4fdd\u8bc1\u201c\u671d\u5411\u95ed\u73af\u201d\u3002<\/li>\n

    \u4e3a\u4fdd\u8bc1\u4e0a\u5c42\u6d8c\u73b0\u91cf\u4e0d\u8131\u79bb\u5e95\u5c42\u7ed3\u6784，\u5f15\u64ce\u663e\u5f0f\u6784\u5efa\u4e86\u975e\u5bf9\u79f0\u7279\u5f81\u5206\u89e3\u7684\u6295\u5f71\u7b97\u5b50 \u03a0(v)=V\u22121\u22c5v\\\\Pi(v) = V^{-1} \\\\cdot v<\/span><\/span>\u03a0<\/span>(<\/span>v<\/span>)<\/span><\/span>=<\/span><\/span><\/span><\/span>V<\/span><\/span>\u2212<\/span>1<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u22c5<\/span><\/span><\/span><\/span>v<\/span><\/span><\/span><\/span><\/span>\u3002\u5982\u4e0b\u5217\u5e95\u5c42\u5f15\u64ce\u4ee3\u7801\u6240\u793a，\u7cfb\u7edf\u4ece\u7ef4\u5ea6\u751f\u6210\u5230\u6295\u5f71\u8def\u5f84\u6784\u5efa，\u5747\u4e25\u683c\u951a\u5b9a\u81f3 A \u7ea7\u6570\u5b66\u4e8b\u5b9e：<\/p>\n

    # {m178_grow_infinite.py \u7247\u6bb5：\u6295\u5f71\u8def\u5f84\u4e0e\u56e0\u679c\u751f\u957f}<\/span>
    \nclass<\/span> InfiniteGrowthEngine<\/span>:<\/span>
    \n """M178 \u56e0\u679c\u751f\u957f\u5f15\u64ce: \u4ee5\u6052\u5b9a\u5185\u5b58\u6301\u7eed\u8fed\u4ee3 F3, \u6570\u503c\u7a33\u5b9a, \u53ef\u65ad\u70b9\u7eed\u957f\u3002
    \n \u5b9a\u4f4d: m178 \u751f\u957f\u8bed\u6cd5 (\u4e09\u5c42\u8c31) \u7684\u5b9e\u4f8b\u5316\u5668\u3002\u8d77\u70b9 u0 = \u56e0\u679c\u79cd\u5b50, \u5176\u8d8b\u5411\u7684
    \n \u95ed\u73af\u8bc6\u522b\u76ee\u6807 v* \u7684\u5185\u5bb9\u7531 u0 \u51b3\u5b9a (\u7528\u6237\u610f\u56fe: \u95ed\u73af\u5185\u5bb9\u7531\u8d77\u70b9\u51b3\u5b9a)\u3002
    \n """<\/span>
    \n def<\/span> __init__<\/span>(<\/span>self,<\/span> unit:<\/span> float<\/span> =<\/span> 1.0<\/span>,<\/span> seed_state:<\/span> Optional[<\/span>GrowthState]<\/span> =<\/span> None<\/span>,<\/span>
    \n seed_label:<\/span> str<\/span> =<\/span> "logic"<\/span>,<\/span> seed_vector:<\/span> Optional[<\/span>np.<\/span>ndarray]<\/span> =<\/span> None<\/span>)<\/span>:<\/span>
    \n self.<\/span>dual =<\/span> TensorStructuralDual(<\/span>unit=<\/span>unit)<\/span>
    \n self.<\/span>F3 =<\/span> self.<\/span>dual.<\/span>F3
    \n self.<\/span>phi =<\/span> self.<\/span>dual.<\/span>phi
    \n self.<\/span>psi =<\/span> self.<\/span>dual.<\/span>psi
    \n # \u771f\u6b63\u7684 (\u975e\u6b63\u4ea4) \u7279\u5f81\u5206\u89e3 F3 = V \u00b7 \u039b \u00b7 V\u207b\u00b9\u3002<\/span>
    \n evals,<\/span> V =<\/span> np.<\/span>linalg.<\/span>eig(<\/span>self.<\/span>F3)<\/span>
    \n order =<\/span> np.<\/span>argsort(<\/span>evals.<\/span>real)<\/span>[<\/span>:<\/span>:<\/span>–<\/span>1<\/span>]<\/span> # [\u03c6_idx, 1_idx, \u2212\u03c8_idx]<\/span>
    \n self.<\/span>V =<\/span> V[<\/span>:<\/span>,<\/span> order]<\/span>.<\/span>astype(<\/span>float<\/span>)<\/span> # \u53f3\u7279\u5f81\u5411\u91cf (\u5217)<\/span>
    \n self.<\/span>Vinv =<\/span> np.<\/span>linalg.<\/span>inv(<\/span>self.<\/span>V)<\/span> # \u6295\u5f71\u77e9\u9635 (V \u975e\u6b63\u4ea4, \u9006 \u2260 \u8f6c\u7f6e)<\/span>
    \n self.<\/span>Lambda =<\/span> np.<\/span>diag(<\/span>evals.<\/span>real[<\/span>order]<\/span>)<\/span> # \u7279\u5f81\u503c\u5bf9\u89d2\u9635<\/span>
    \n self.<\/span>log_Lambda =<\/span> np.<\/span>log(<\/span>np.<\/span>abs<\/span>(<\/span>evals.<\/span>real[<\/span>order]<\/span>)<\/span>)<\/span> # \u6bcf\u6b65\u5bf9\u6570\u589e\u91cf<\/span>
    \n self.<\/span>sign_Lambda =<\/span> np.<\/span>sign(<\/span>evals.<\/span>real[<\/span>order]<\/span>)<\/span> # \u7b26\u53f7 (\u03c6,1,\u2212\u03c8 \u2192 +,+,\u2212)<\/span>
    \n # S \u4e2d\u6027\u8f74 = \u7279\u5f81\u503c 1 \u7684\u53f3\u7279\u5f81\u5411\u91cf (\u9aa8\u67b6\u8f74, \u5386\u53f2\u4e0d\u53ef\u7ea6\u7684"\u4e0d\u504f\u79bb"\u8f74)<\/span>
    \n self.<\/span>spine_axis =<\/span> self.<\/span>V[<\/span>:<\/span>,<\/span> 1<\/span>]<\/span>.<\/span>copy(<\/span>)<\/span>
    \n # \u03c6 \u8f74 = \u7279\u5f81\u503c\u6700\u5927\u7684\u53f3\u7279\u5f81\u5411\u91cf (\u751f\u957f\u63a8\u79bb\u7684\u4e3b\u5bfc\u65b9\u5411, \u4e5f\u662f"\u671d\u5411"\u7684\u65b9\u5411)<\/span>
    \n self.<\/span>phi_axis =<\/span> self.<\/span>V[<\/span>:<\/span>,<\/span> 0<\/span>]<\/span>.<\/span>copy(<\/span>)<\/span>
    \n # … (\u521d\u59cb\u5316\u72b6\u6001\u4e0e\u8ba1\u7b97 v*)<\/span><\/p>\n

    def<\/span> project<\/span>(<\/span>self,<\/span> v:<\/span> np.<\/span>ndarray)<\/span> –<\/span>><\/span> dict<\/span>:<\/span>
    \n """\u6295\u5f71\u7b97\u5b50 \u03a0: \u628a\u4efb\u610f\u5411\u91cf v \u6295\u5f71\u5230 M178 \u4e09\u5c42\u8c31 {\u03c6, 1, \u2212\u03c8} \u7684\u5750\u6807\u3002
    \n \u03a0(v) = V\u207b\u00b9\u00b7v = (c_\u03c6, c_S, c_{\u2212\u03c8})
    \n \u6240\u6709\u4e0a\u5c42\u91cf (\u56e0\u679c\u4e09\u65ad\u8a00\u3001\u731c\u60f3 fit\/pack) \u90fd\u5b9a\u4e49\u4e3a\u8fd9\u4e9b\u8c31\u5750\u6807\u7684\u786e\u5b9a\u51fd\u6570,
    \n \u4ece\u800c\u628a"\u7ed3\u6784\u7684\u6295\u5f71"\u951a\u5b9a\u5230 M178 \u7684 A \u7ea7\u6570\u5b66\u4e8b\u5b9e (F3 \u8c31), \u800c\u975e\u51ed\u7a7a\u7684\u5355\u4f4d\u5411\u91cf\u70b9\u79ef\u3002
    \n """<\/span>
    \n c =<\/span> self.<\/span>Vinv @ np.<\/span>asarray(<\/span>v,<\/span> dtype=<\/span>float<\/span>)<\/span>
    \n return<\/span> {<\/span>
    \n "c_phi"<\/span>:<\/span> float<\/span>(<\/span>c[<\/span>0<\/span>]<\/span>)<\/span>,<\/span> # \u03c6 \u8f74\u8c31\u5750\u6807 (\u6269\u6563\/\u63a8\u79bb)<\/span>
    \n "c_S"<\/span>:<\/span> float<\/span>(<\/span>c[<\/span>1<\/span>]<\/span>)<\/span>,<\/span> # S \u8f74\u8c31\u5750\u6807 (\u4e2d\u6027\/\u9aa8\u67b6, \u7279\u5f81\u503c 1)<\/span>
    \n "c_negpsi"<\/span>:<\/span> float<\/span>(<\/span>c[<\/span>2<\/span>]<\/span>)<\/span>,<\/span> # \u2212\u03c8 \u8f74\u8c31\u5750\u6807 (\u6536\u655b\/\u62c9\u56de)<\/span>
    \n "norm"<\/span>:<\/span> float<\/span>(<\/span>np.<\/span>linalg.<\/span>norm(<\/span>v)<\/span>)<\/span>,<\/span>
    \n }<\/span><\/p>\n

    def<\/span> gaussian_residue<\/span>(<\/span>self,<\/span> v:<\/span> np.<\/span>ndarray)<\/span> –<\/span>><\/span> dict<\/span>:<\/span>
    \n """\u221a\u03a0 \u8bca\u65ad\u951a\u70b9: \u731c\u60f3\u662f\u5426\u80fd\u6536\u655b\u5230\u9ad8\u65af\u79ef\u5206\u7684\u5224\u636e (Binet\u95ed\u5408\u6bd4)\u3002
    \n \u540c\u6e90\u94fe: Binet S_n ~ \u03c6\u207f\/\u221a5, Stirling \u222be^{\u2212x\u00b2}dx = \u221a\u03c0, \u221a5 \u4e0e \u221a\u03c0 \u540c\u6e90\u3002
    \n """<\/span>
    \n p =<\/span> self.<\/span>project(<\/span>v)<\/span>
    \n c_phi,<\/span> c_S,<\/span> c_nps =<\/span> p[<\/span>"c_phi"<\/span>]<\/span>,<\/span> p[<\/span>"c_S"<\/span>]<\/span>,<\/span> p[<\/span>"c_negpsi"<\/span>]<\/span>
    \n sqrt5 =<\/span> math.<\/span>sqrt(<\/span>5.0<\/span>)<\/span>
    \n if<\/span> abs<\/span>(<\/span>c_phi)<\/span> ><\/span> _EPS:<\/span>
    \n ratio =<\/span> abs<\/span>(<\/span>c_S)<\/span> *<\/span> sqrt5 \/<\/span> abs<\/span>(<\/span>c_phi)<\/span>
    \n else<\/span>:<\/span>
    \n ratio =<\/span> 0.0<\/span>
    \n return<\/span> {<\/span>"residue5"<\/span>:<\/span> float<\/span>(<\/span>ratio)<\/span>,<\/span> "residue5_target"<\/span>:<\/span> 1.0<\/span>}<\/span><\/p>\n

    \u5728\u65e0\u9650\u751f\u957f\u8fc7\u7a0b\u4e2d，\u7531\u4e8e \u03d5n\\\\phi^n<\/span><\/span>\u03d5<\/span><\/span>n<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u5728\u7ea6 1476 \u6b65\u540e\u4f1a\u8d85\u51fa float64 \u4e0a\u9650，\u5f15\u64ce\u91c7\u7528\u5bf9\u6570\u57df\u7d2f\u79ef\u4e09\u8f74\u7cfb\u6570（GrowthState），\u786e\u4fdd\u6570\u503c\u7a33\u5b9a\u5e76\u5b9e\u73b0\u65ad\u70b9\u7eed\u957f\u3002<\/p>\n

    \n

    3. \u731c\u60f3\u6d8c\u73b0\u5f15\u64ce\u4e0e\u5f15\u529b\u4e95\u6d88\u89e3\u673a\u5236<\/h3>\n

    \u5f53\u751f\u957f\u6b65\u6570\u8fbe\u5230\u9608\u503c（recognize_at）\u540e，\u7cfb\u7edf\u7ed3\u6784\u590d\u6742\u5ea6\u8fbe\u6807，\u8bc6\u522b\u80fd\u529b\u6d8c\u73b0，\u8fdb\u800c\u5b75\u5316\u51fa\u591a\u6761\u731c\u60f3\u8def\u5f84\u3002<\/p>\n

    3.1 \u76f2\u731c\u4e0e\u81ea\u7531\u6f02\u79fb<\/h4>\n

    \u731c\u60f3\u88ab\u5b9a\u4e49\u4e3a\u201c\u975e\u81ea\u8eab\u7684\u7ecf\u9a8c\u6027\u8bd5\u63a2\u201d\u3002\u5728 ConjecturePath \u6570\u636e\u7c7b\u4e2d，\u731c\u60f3\u4e0d\u4ec5\u5305\u542b\u6570\u503c\u5411\u91cf gg<\/span><\/span>g<\/span><\/span><\/span><\/span><\/span>，\u8fd8\u5305\u542b\u5b58\u5728\u6027\u6743\u91cd\u4e0e\u5eb7\u5a01\u5185\u5bb9\u8f7d\u4f53：<\/p>\n

    # {m178_conjecture.py \u7247\u6bb5：\u731c\u60f3\u8def\u5f84\u6570\u636e\u7ed3\u6784}<\/span>
    \n@dataclass<\/span>
    \nclass<\/span> ConjecturePath<\/span>:<\/span>
    \n """\u4e00\u6761\u731c\u60f3\u8def\u5f84: \u81ea\u7531\u6f02\u79fb\u7684\u76f2\u731c + \u5b58\u5728\u6027\u6743\u91cd (\u5f15\u529b\u4e95\u6d88\u89e3), \u4ee5\u53ca\u6253\u5305-\u6491\u7834\u52a8\u529b\u5b66\u3002"""<\/span>
    \n pid:<\/span> int<\/span>
    \n g:<\/span> np.<\/span>ndarray # \u5f53\u524d\u731c\u60f3\u5411\u91cf (\u76f2\u731c, \u76f8\u5bf9\u751f\u957f\u7ed3\u6784\u7684\u5916\u90e8\u8bd5\u63a2, \u81ea\u7531\u6f02\u79fb)<\/span>
    \n sticky_baseline:<\/span> np.<\/span>ndarray # \u7c98\u6ede\u57fa\u7ebf: \u5df2\u8d34\u5408\u7ed3\u6784\u7684\u731c\u60f3\u8bb0\u5fc6 (\u9057\u4f20\u6027)<\/span>
    \n fit:<\/span> float<\/span> =<\/span> 0.0<\/span> # \u4e0e\u751f\u957f\u7ed3\u6784\u7684\u62df\u5408\u5ea6 (\u4ec5\u8bb0\u5f55\/\u62a5\u544a, \u4e0d\u4f5c\u4e3a\u68c0\u6d4b\u4f9d\u636e)<\/span>
    \n alive:<\/span> bool<\/span> =<\/span> True<\/span>
    \n support:<\/span> float<\/span> =<\/span> 1.0<\/span> # \u5b58\u5728\u6027\u6743\u91cd: \u7ed3\u6784\u5f15\u529b\u4e95\u7684\u5438\u6536\u5ea6 \u2208[0,1]<\/span>
    \n decoherence_rate:<\/span> float<\/span> =<\/span> 0.0<\/span> # \u672c\u6b65\u9000\u76f8\u5e72\u7387: \u504f\u79bb\u7ed3\u6784\u8d8a\u5927, \u8870\u51cf\u8d8a\u5feb (\u8bb0\u5f55\u7528)<\/span>
    \n # \u2500\u2500 \u6253\u5305-\u6491\u7834-\u91cd\u5f15\u5165\u52a8\u529b\u5b66 \u2500\u2500<\/span>
    \n pack_capacity:<\/span> float<\/span> =<\/span> 1.0<\/span> # \u6253\u5305\u5bb9\u91cf: \u4e00\u6761\u731c\u60f3\u80fd\u6982\u62ec\u7684\u7ed3\u6784\u590d\u6742\u5ea6\u4e0a\u9650<\/span>
    \n pack_level:<\/span> float<\/span> =<\/span> 0.0<\/span> # \u5f53\u524d\u6253\u5305\u8d1f\u8377: \u5df2\u6982\u62ec\u7684\u590d\u6742\u5ea6 (\u968f\u7ed3\u6784\u751f\u957f\u7d2f\u79ef)<\/span>
    \n n_breaks:<\/span> int<\/span> =<\/span> 0<\/span> # \u88ab\u7ed3\u6784\u751f\u957f\u6491\u7834\u7684\u6b21\u6570<\/span>
    \n # \u2500\u2500 \u5eb7\u5a01\u5b87\u5b99\u5185\u5bb9\u5c42 \u2500\u2500<\/span>
    \n kstr:<\/span> str<\/span> =<\/span> "1"<\/span> # \u5eb7\u5a01\u4e32<\/span>
    \n kcomp:<\/span> Optional[<\/span>np.<\/span>ndarray]<\/span> =<\/span> None<\/span> # 92 \u7ef4\u539f\u5b50\u6210\u5206\u5411\u91cf<\/span>
    \n lock_stack:<\/span> list<\/span> |<\/span> None<\/span> =<\/span> None<\/span> # \u53e0\u52a0\u9501\u5b9a\u6808: \u5df2\u5224\u5b9a\u5408\u6cd5\u7684\u5b87\u5b99\u89c4\u5219\u540d\u5217\u8868<\/span><\/p>\n

    \u5355\u6b65\u731c\u60f3\u66f4\u65b0\u4ee5\u968f\u673a\u65b9\u5411\u4e3a\u4e3b\u5bfc（\u771f\u76f2，\u81ea\u7531\u63a2\u7d22），\u53d7\u7c98\u6ede\u57fa\u7ebf\u5c0f\u6743\u91cd\u6b8b\u7559\u7ea6\u675f\u3002<\/p>\n

    3.2 \u5f15\u529b\u4e95\u6d88\u89e3（\u6838\u5fc3\u7ffb\u8f6c）<\/h4>\n

    \u4f20\u7edf\u7684\u8026\u5408\u8bed\u4e49\u4f1a\u8bbe\u5b9a\u68c0\u6d4b\u673a\u5236\u5c06\u504f\u79bb\u7684\u731c\u60f3\u62c9\u56de\u3002M178 \u5f15\u64ce\u8fdb\u884c\u4e86\u5173\u952e\u7684\u672c\u4f53\u8bba\u7ffb\u8f6c：\u201c\u7ed3\u6784\u652f\u6491\u4e0d\u662f\u53bb\u8026\u5408，\u800c\u662f\u53bb\u6d88\u89e3\u201d\u3002<\/p>\n

    \u6ca1\u6709\u7ed3\u6784\u652f\u6491\u7684\u731c\u60f3\u56e0\u65e0\u951a\u800c\u81ea\u7136\u6d88\u6563\u3002\u8fd9\u4e00\u673a\u5236\u7531\u201c\u5b58\u5728\u6027\u6743\u91cd\u201d\u627f\u8f7d，\u91c7\u7528 leaky integrator \u6a21\u578b\u3002\u5982\u4e0b\u5217\u4ee3\u7801\u6240\u793a，\u9000\u76f8\u5e72\u7387\u7531\u504f\u79bb\u7a0b\u5ea6（fit）\u51b3\u5b9a，\u504f\u79bb\u8d8a\u5927\u8870\u51cf\u8d8a\u5feb，\u5f53 support < 1e-3 \u65f6\u731c\u60f3\u81ea\u7136\u6d88\u5931，\u65e0\u4efb\u4f55\u4e3b\u52a8\u68c0\u6d4b\u903b\u8f91：<\/p>\n

    # {m178_conjecture.py \u7247\u6bb5：\u5f15\u529b\u4e95\u6d88\u89e3\u4e0e\u76f2\u731c\u673a\u5236}<\/span>
    \nclass<\/span> ConjectureEngine<\/span>:<\/span>
    \n def<\/span> _blind_guess<\/span>(<\/span>self,<\/span> v:<\/span> np.<\/span>ndarray,<\/span> baseline:<\/span> np.<\/span>ndarray)<\/span> –<\/span>><\/span> np.<\/span>ndarray:<\/span>
    \n """\u76f2\u731c: \u751f\u6210"\u975e\u81ea\u8eab\u7684\u7ecf\u9a8c\u6027\u8bd5\u63a2" \u2014\u2014 \u65b9\u5411\u968f\u673a (\u771f\u76f2), \u91cf\u7ea7\u53ef\u6bd4 v\u3002
    \n \u731c\u60f3\u968f\u4fbf\u60f3 (\u81ea\u7531\u6f02\u79fb), \u4e0d\u88ab\u4e3b\u52a8\u62c9\u56de\u3002\u8d34\u5408\u4e0e\u5426\u7531\u9000\u76f8\u5e72\u673a\u5236\u88ab\u52a8\u51b3\u5b9a\u3002
    \n """<\/span>
    \n vn =<\/span> np.<\/span>linalg.<\/span>norm(<\/span>v)<\/span> +<\/span> 1e-12<\/span>
    \n sticky =<\/span> self.<\/span>_sticky_base *<\/span> baseline
    \n rand_dir =<\/span> self.<\/span>_rng.<\/span>normal(<\/span>size=<\/span>3<\/span>)<\/span>
    \n rnorm =<\/span> np.<\/span>linalg.<\/span>norm(<\/span>rand_dir)<\/span> +<\/span> 1e-12<\/span>
    \n rand_dir =<\/span> rand_dir \/<\/span> rnorm
    \n blind =<\/span> self.<\/span>gamma *<\/span> vn *<\/span> rand_dir
    \n return<\/span> sticky +<\/span> blind<\/p>\n

    def<\/span> _dissolve_support<\/span>(<\/span>self,<\/span> p:<\/span> ConjecturePath,<\/span> fit:<\/span> float<\/span>,<\/span> step:<\/span> int<\/span>,<\/span>
    \n lock_floor:<\/span> float<\/span> =<\/span> 0.0<\/span>)<\/span> –<\/span>><\/span> None<\/span>:<\/span>
    \n """\u5f15\u529b\u4e95\u6d88\u89e3\u4e00\u6b65 (leaky integrator \u5b58\u5728\u6027\u6743\u91cd\u6a21\u578b)\u3002
    \n \u8fd9\u662f"\u7ed3\u6784\u652f\u6491=\u6d88\u89e3"\u7684\u6838\u5fc3\u6267\u884c\u70b9\u3002\u4e0d\u68c0\u6d4b\u3001\u4e0d\u7b5b\u9009\u3001\u4e0d\u62c9\u56de:
    \n \u731c\u60f3\u968f\u4fbf\u6f02\u79fb, \u4f46\u6bcf\u6b65\u4f9d\u5176\u504f\u79bb\u7ed3\u6784\u7684\u7a0b\u5ea6 \u628a\u5b58\u5728\u6027\u6743\u91cd support \u63a8\u5411\u76ee\u6807\u3002
    \n """<\/span>
    \n rate =<\/span> self.<\/span>_decohere(<\/span>fit)<\/span> # rate = decohere_base + decohere_gain * (1.0 – fit)<\/span>
    \n p.<\/span>decoherence_rate =<\/span> rate
    \n # \u76ee\u6807\u5b58\u5728\u6027: fit \u9ad8\u2192\u63a5\u8fd1 1 (\u7ef4\u7cfb), fit \u4f4e\u2192\u63a5\u8fd1 0 (\u6d88\u89e3);<\/span>
    \n # \u5eb7\u5a01\u9501\u5b9a\u5e95: \u9501\u5b9a\u8def\u5f84\u7684\u76ee\u6807\u5b58\u5728\u6027\u4e0d\u4f4e\u4e8e lock_floor (\u53cd\u5411\u951a\u5b9a, \u4e0d\u88ab\u6d88\u89e3)\u3002<\/span>
    \n target =<\/span> max<\/span>(<\/span>fit,<\/span> lock_floor)<\/span>
    \n p.<\/span>support +=<\/span> rate *<\/span> (<\/span>target –<\/span> p.<\/span>support)<\/span>
    \n # \u6570\u503c\u5730\u677f: support \u4f4e\u4e8e\u9608\u503c\u5373\u89c6\u4e3a"\u5b8c\u5168\u6d88\u89e3" (\u65e0\u7ed3\u6784\u652f\u6491\u7684\u731c\u60f3\u4e0d\u5b58\u5728)<\/span>
    \n if<\/span> p.<\/span>support <<\/span> 1e-3<\/span>:<\/span>
    \n p.<\/span>alive =<\/span> False<\/span>
    \n p.<\/span>death_step =<\/span> step
    \n self.<\/span>total_dissolved +=<\/span> 1<\/span><\/p>\n

    \n

    4. \u6253\u5305-\u6491\u7834-\u91cd\u5f15\u5165\u52a8\u529b\u5b66<\/h3>\n

    \u65e0\u9650\u751f\u957f\u7684\u7ed3\u6784\u590d\u6742\u5ea6 C(n)=log\u2061\u2225vn\u2225C(n) = \\\\log\\\\|v_n\\\\|<\/span><\/span>C<\/span>(<\/span>n<\/span>)<\/span><\/span>=<\/span><\/span><\/span><\/span>log<\/span><\/span><\/span>\u2225<\/span>v<\/span><\/span>n<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u2225<\/span><\/span><\/span><\/span><\/span> \u968f \u03d5n\\\\phi^n<\/span><\/span>\u03d5<\/span><\/span>n<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u53d1\u6563\u3002\u731c\u60f3\u4f5c\u4e3a\u5bf9\u590d\u6742\u7ed3\u6784\u7684\u201c\u6982\u62ec\u6027\u603b\u7ed3\u201d，\u5fc5\u7136\u627f\u62c5\u6253\u5305\u8d1f\u8377\u3002\u82e5\u6253\u5305\u901f\u5ea6\u8d76\u4e0d\u4e0a\u7ed3\u6784\u751f\u957f\u901f\u5ea6，\u6253\u5305\u8d1f\u8377\u5373\u88ab\u6491\u7834\u3002\u7834\u88c2\u610f\u5473\u7740\u65e7\u731c\u60f3\u4f5c\u5e9f，\u5176\u627f\u8f7d\u7684\u5185\u5bb9\u5931\u53bb\u652f\u6491\u3002\u6839\u636e\u76f8\u89c1\u5b9a\u7406，\u7834\u88c2\u4e8b\u4ef6\u9057\u7559\u7684\u590d\u6742\u5ea6\u8d1f\u8377\u7531\u5176\u4ed6\u9ad8\u62df\u5408\u5ea6\u5b58\u6d3b\u8def\u5f84\u627f\u63a5\u4fee\u8865\u3002<\/p>\n

    # {m178_conjecture.py \u7247\u6bb5：\u6253\u5305-\u6491\u7834-\u91cd\u5f15\u5165\u52a8\u529b\u5b66}<\/span>
    \n def<\/span> _pack_step<\/span>(<\/span>self,<\/span> step:<\/span> int<\/span>)<\/span> –<\/span>><\/span> None<\/span>:<\/span>
    \n """\u4e00\u6b65\u6253\u5305-\u6491\u7834-\u91cd\u5f15\u5165\u52a8\u529b\u5b66 (\u71b5\u589e\u7684\u771f\u6b63\u6765\u6e90)\u3002
    \n \u71b5\u589e\u95ee\u9898\u6765\u81ea"\u6253\u5305\u6491\u7834"\u673a\u5236: \u65e0\u9650\u5f15\u64ce\u7684\u7ed3\u6784\u589e\u957f > \u731c\u60f3\u51fa\u7684\u5eb7\u5a01\u5b87\u5b99\u5e8f\u5217\u7ed3\u6784
    \n \u21d2 \u6491\u7834\u4e86"\u5305" \u21d2 \u8868\u73b0\u4e3a"\u5eb7\u5a01\u5b87\u5b99\u5e8f\u5217\u71b5\u589e\u6d88\u89e3"\u3002
    \n """<\/span>
    \n C =<\/span> self.<\/span>_complexity(<\/span>)<\/span>
    \n if<\/span> self.<\/span>_prev_complexity is<\/span> None<\/span>:<\/span>
    \n self.<\/span>_prev_complexity =<\/span> C
    \n return<\/span>
    \n dC =<\/span> max<\/span>(<\/span>0.0<\/span>,<\/span> C –<\/span> self.<\/span>_prev_complexity)<\/span> # \u7ed3\u6784\u590d\u6742\u5ea6\u589e\u91cf<\/span>
    \n self.<\/span>_prev_complexity =<\/span> C
    \n if<\/span> dC <=<\/span> 0<\/span>:<\/span> return<\/span><\/p>\n

    overload_total =<\/span> 0.0<\/span>
    \n broken_paths =<\/span> [<\/span>]<\/span>
    \n for<\/span> p in<\/span> self.<\/span>paths:<\/span>
    \n if<\/span> not<\/span> p.<\/span>alive:<\/span> continue<\/span>
    \n p.<\/span>pack_level *=<\/span> (<\/span>1.0<\/span> –<\/span> self.<\/span>decay_rate)<\/span>
    \n eff_absorb =<\/span> self.<\/span>absorb_rate *<\/span> (<\/span>0.5<\/span> +<\/span> 0.5<\/span> *<\/span> p.<\/span>fit)<\/span>
    \n absorbed =<\/span> eff_absorb *<\/span> dC
    \n p.<\/span>pack_level +=<\/span> dC –<\/span> absorbed # \u672a\u5438\u6536\u7d2f\u79ef\u8fdb\u8d1f\u8377<\/span>
    \n if<\/span> p.<\/span>pack_level ><\/span> p.<\/span>pack_capacity:<\/span>
    \n overflow =<\/span> p.<\/span>pack_level –<\/span> p.<\/span>pack_capacity
    \n p.<\/span>pack_level =<\/span> 0.0<\/span> # \u7834\u88c2: \u65e7\u6253\u5305\u4f5c\u5e9f<\/span>
    \n p.<\/span>n_breaks +=<\/span> 1<\/span>
    \n self.<\/span>total_breaks +=<\/span> 1<\/span>
    \n broken_paths.<\/span>append(<\/span>p)<\/span>
    \n overload_total +=<\/span> overflow<\/p>\n

    # \u7834\u88c2\u91cd\u5f15\u5165 (\u76f8\u89c1\u5b9a\u7406): \u7531\u5176\u4ed6"\u672a\u7834\u88c2\u4e14 fit \u6700\u9ad8"\u7684\u5b58\u6d3b\u8def\u5f84\u627f\u63a5\u9057\u7559\u8d1f\u8377\u3002<\/span>
    \n patchers =<\/span> [<\/span>p for<\/span> p in<\/span> self.<\/span>paths
    \n if<\/span> p.<\/span>alive and<\/span> p not<\/span> in<\/span> broken_paths and<\/span> p.<\/span>fit >=<\/span> self.<\/span>fit_threshold]<\/span>
    \n if<\/span> broken_paths and<\/span> patchers:<\/span>
    \n fit_sum =<\/span> sum<\/span>(<\/span>p.<\/span>fit for<\/span> p in<\/span> patchers)<\/span> +<\/span> 1e-12<\/span>
    \n for<\/span> p in<\/span> patchers:<\/span>
    \n share =<\/span> overload_total *<\/span> (<\/span>p.<\/span>fit \/<\/span> fit_sum)<\/span>
    \n p.<\/span>pack_level +=<\/span> share
    \n p.<\/span>n_received +=<\/span> share
    \n p.<\/span>n_patched +=<\/span> 1<\/span>
    \n self.<\/span>total_patched +=<\/span> 1<\/span><\/p>\n

    \n

    5. \u5eb7\u5a01\u5b87\u5b99\u4f5c\u4e3a\u731c\u60f3\u5185\u5bb9\u8f7d\u4f53\u7684\u53cc\u5411\u9501\u5b9a<\/h3>\n

    \u82e5\u731c\u60f3\u4ec5\u505c\u7559\u5728\u7eaf\u6570\u503c\u5411\u91cf\u7684\u76f2\u731c，\u5219\u7f3a\u4e4f\u5b9e\u9645\u5185\u5bb9\u6d8c\u73b0\u3002\u5f15\u64ce\u5f15\u5165\u5eb7\u5a01\u5b87\u5b99\u4f5c\u4e3a\u731c\u60f3\u7684\u5185\u5bb9\u540e\u7aef，\u4f7f\u9759\u6001\u6570\u5b66\u5b9a\u7406\u6f14\u5316\u4e3a\u53d7\u5b58\u5728\u6027\u6743\u91cd\u5236\u7ea6\u7684\u771f\u5b9e\u5b87\u5b99\u3002<\/p>\n

    5.1 Look-and-say \u4e0e\u5eb7\u5a01\u5e38\u6570 \u03bb\\\\lambda<\/span><\/span>\u03bb<\/span><\/span><\/span><\/span><\/span><\/h4>\n

    \u5eb7\u5a01\u5b87\u5b99\u57fa\u4e8e look-and-say \u89c4\u5219，\u5176\u672c\u539f\u77e9\u9635\u7684\u4e3b\u7279\u5f81\u503c\u4e3a\u5eb7\u5a01\u5e38\u6570 \u03bb\u22481.303577\\\\lambda \\\\approx 1.303577<\/span><\/span>\u03bb<\/span><\/span>\u2248<\/span><\/span><\/span><\/span>1.303577<\/span><\/span><\/span><\/span><\/span>\u3002\u5728\u6bcf\u6b65\u6f14\u5316\u4e2d，\u731c\u60f3\u8def\u5f84\u6302\u8f7d\u7684\u5eb7\u5a01\u4e32\u4e0e\u539f\u5b50\u6210\u5206\u5411\u91cf\u6309\u89c4\u5219\u751f\u957f\u3002<\/p>\n

    # {m178_conway.py \u7247\u6bb5：\u5eb7\u5a01\u5b87\u5b99\u5f15\u64ce}<\/span>
    \nclass<\/span> ConwayUniverse<\/span>:<\/span>
    \n """\u5eb7\u5a01\u5b87\u5b99\u5f15\u64ce: look-and-say \u6f14\u5316\u5eb7\u5a01\u4e32 + \u672c\u539f\u77e9\u9635\u9a71\u52a8\u539f\u5b50\u6210\u5206\u6f14\u5316\u3002
    \n \u4f5c\u4e3a ConjectureEngine \u7684"\u5185\u5bb9\u540e\u7aef"\u3002
    \n """<\/span>
    \n @classmethod<\/span>
    \n def<\/span> conway_lambda<\/span>(<\/span>cls,<\/span> *<\/span>,<\/span> from_matrix:<\/span> bool<\/span> =<\/span> False<\/span>)<\/span> –<\/span>><\/span> float<\/span>:<\/span>
    \n """\u5eb7\u5a01\u5e38\u6570 (\u6743\u5a01\u6765\u6e90 = look-and-say \u5b9e\u8dd1\u5b9a\u4e49, A \u7ea7\u53ef\u9a8c\u8bc1)\u3002"""<\/span>
    \n if<\/span> from_matrix:<\/span>
    \n evals =<\/span> np.<\/span>linalg.<\/span>eigvals(<\/span>build_primitive_matrix(<\/span>)<\/span>)<\/span>
    \n return<\/span> float<\/span>(<\/span>np.<\/span>max<\/span>(<\/span>evals.<\/span>real)<\/span>)<\/span>
    \n if<\/span> cls.<\/span>_LAM is<\/span> None<\/span>:<\/span>
    \n cls.<\/span>_LAM =<\/span> conway_constant(<\/span>)<\/span>
    \n return<\/span> cls.<\/span>_LAM<\/p>\n

    def<\/span> step_composition<\/span>(<\/span>self,<\/span> kcomp:<\/span> np.<\/span>ndarray,<\/span> drift:<\/span> float<\/span> =<\/span> 0.05<\/span>)<\/span> –<\/span>><\/span> np.<\/span>ndarray:<\/span>
    \n """\u539f\u5b50\u6210\u5206\u6f14\u5316\u4e00\u6b65 (\u5b87\u5b99\u5b9a\u7406\u52a8\u529b\u5b66 + \u731c\u60f3\u81ea\u7531\u6f02\u79fb)\u3002
    \n \u7a33\u5b9a\u4e32\u7684\u539f\u5b50\u6210\u5206\u6cbf\u4e3b\u7279\u5f81\u5411\u91cf\u65b9\u5411\u6309\u5eb7\u5a01\u5e38\u6570 \u03bb \u589e\u957f, \u6210\u5206\u6bd4\u4f8b\u8d8b\u4e8e\u56fa\u5b9a\u3002
    \n """<\/span>
    \n lam =<\/span> self.<\/span>conway_lambda(<\/span>)<\/span>
    \n new =<\/span> lam *<\/span> np.<\/span>asarray(<\/span>kcomp,<\/span> dtype=<\/span>float<\/span>)<\/span>
    \n if<\/span> drift ><\/span> 0<\/span>:<\/span>
    \n noise =<\/span> self.<\/span>rng.<\/span>normal(<\/span>size=<\/span>new.<\/span>shape)<\/span>
    \n new =<\/span> new *<\/span> (<\/span>1.0<\/span> +<\/span> drift *<\/span> noise)<\/span>
    \n nrm =<\/span> float<\/span>(<\/span>np.<\/span>linalg.<\/span>norm(<\/span>new)<\/span>)<\/span> +<\/span> _EPS
    \n return<\/span> new \/<\/span> nrm<\/p>\n

    5.2 \u53e0\u52a0\u9501\u5b9a\u4e0e\u53cd\u5411\u951a\u5b9a<\/h4>\n

    \u5f15\u64ce\u652f\u6301\u5e73\u884c\u5b87\u5b99\u6846\u67b6\u3002\u76f2\u731c\u5411\u91cf\u91cf\u5316\u4e3a\u6570\u5b57\u7247\u6bb5\u7d2f\u79ef\u6210\u5e8f\u5217 kseqkseq<\/span><\/span>k<\/span>se<\/span>q<\/span><\/span><\/span><\/span><\/span>，\u904d\u5386\u6240\u6709\u6fc0\u6d3b\u7684\u5e73\u884c\u5b87\u5b99\u89c4\u5219\u6e90\u3002\u82e5\u5076\u7136\u843d\u5165\u5eb7\u5a01\u89c4\u5219\u5e8f\u5217，\u5219\u88ab\u9501\u5b9a\u5e76\u538b\u5165 lock_stack\u3002\u88ab\u9501\u5b9a\u610f\u5473\u7740\u53cd\u5411\u951a\u5b9a\u65e0\u9650\u5f15\u64ce，\u5176\u5f15\u529b\u4e95\u76ee\u6807\u5b58\u5728\u6027\u4e0d\u4f4e\u4e8e lock_floor，\u4f7f\u5076\u7136\u5f62\u6210\u5408\u6cd5\u5e8f\u5217\u7684\u731c\u60f3\u4e0d\u518d\u88ab\u6d88\u89e3\u3002<\/p>\n

    # {m178_conjecture.py \u7247\u6bb5：\u53cc\u5411\u9501\u5b9a\u94fe\u8def (\u53e0\u52a0\u8bed\u4e49)}<\/span>
    \n # \u2500\u2500 \u53e0\u52a0\u9501\u5b9a\u94fe\u8def \u2500\u2500<\/span>
    \n # \u6bcf\u4e2a\u6fc0\u6d3b\u5b87\u5b99\u89c4\u5219\u6e90\u72ec\u7acb\u68c0\u9a8c\u672c\u8def\u5f84\u91cf\u5316\u5e8f\u5217\u662f\u5426\u843d\u5165\u5176\u89c4\u5219<\/span>
    \n p.<\/span>lock_stack.<\/span>clear(<\/span>)<\/span>
    \n step_lock_floor =<\/span> 0.0<\/span>
    \n math_legal_this_step =<\/span> False<\/span>
    \n for<\/span> uni in<\/span> self.<\/span>active_universes:<\/span>
    \n self.<\/span>total_conway_checks +=<\/span> 1<\/span>
    \n if<\/span> uni.<\/span>name ==<\/span> "conway"<\/span>:<\/span>
    \n if<\/span> uni.<\/span>is_legal(<\/span>p.<\/span>kseq)<\/span>:<\/span>
    \n p.<\/span>lock_stack.<\/span>append(<\/span>uni.<\/span>name)<\/span>
    \n self.<\/span>total_locked_events +=<\/span> 1<\/span>
    \n if<\/span> getattr<\/span>(<\/span>uni,<\/span> "lock_floor"<\/span>,<\/span> 0.0<\/span>)<\/span> ><\/span> step_lock_floor:<\/span>
    \n step_lock_floor =<\/span> getattr<\/span>(<\/span>uni,<\/span> "lock_floor"<\/span>,<\/span> 0.0<\/span>)<\/span>
    \n elif<\/span> uni.<\/span>name ==<\/span> "math_tensor"<\/span>:<\/span>
    \n # \u6570\u5b66\u5b87\u5b99 (\u9012\u8fdb\u5f0f): \u7528\u5df2\u9501\u5b9a\u7684\u5eb7\u5a01\u5e8f\u5217 kseq \u4f5c\u951a,<\/span>
    \n # \u9ed1\u76d2\u6620\u5c04\u51fa\u6570\u5b66\u7d20\u6750 M, \u62fc\u5408 (rank1) \u5373\u5408\u6cd5\u3002<\/span>
    \n M =<\/span> self.<\/span>mount_op.<\/span>mount(<\/span>p.<\/span>kseq)<\/span>
    \n if<\/span> math_assemble_legal(<\/span>M)<\/span>:<\/span>
    \n p.<\/span>lock_stack.<\/span>append(<\/span>uni.<\/span>name)<\/span>
    \n self.<\/span>total_locked_events +=<\/span> 1<\/span>
    \n math_legal_this_step =<\/span> True<\/span>
    \n # \u517c\u5bb9\u5b57\u6bb5: \u662f\u5426\u542b\u5eb7\u5a01<\/span>
    \n p.<\/span>locked_by_conway =<\/span> (<\/span>"conway"<\/span> in<\/span> p.<\/span>lock_stack)<\/span><\/p>\n

    5.3 \u534f\u53d8\u6302\u8f7d\u9ed1\u76d2<\/h4>\n

    \u9488\u5bf9\u6570\u5b66\u5f20\u91cf\u5b87\u5b99，\u5f15\u64ce\u5f15\u5165\u9012\u8fdb\u5f0f\u534f\u53d8\u6302\u8f7d\u9ed1\u76d2\u3002\u9ed1\u76d2\u5c06\u5df2\u6210\u7acb\u7684\u5eb7\u5a01\u5e8f\u5217\u6620\u5c04\u4e3a\u6570\u5b66\u7d20\u6750\u77e9\u9635 MM<\/span><\/span>M<\/span><\/span><\/span><\/span><\/span>，\u82e5\u6ee1\u8db3\u79e9 1 \u4e0e\u5e42\u7b49\u95ed\u5305\u5224\u636e\u5219\u62fc\u5408\u6210\u529f\u3002\u9ed1\u76d2\u53c2\u6570\u901a\u8fc7\u201c\u9009\u62e9\u538b\u201d（\u62fc\u5408\u6210\u529f\u7387 EMA）\u7b5b\u9009，\u590d\u7528\u5f15\u529b\u4e95\u7684\u7559\u5b58\/\u6d88\u6563\u8bed\u4e49\u3002<\/p>\n

    # {m178_mount_operator.py：\u534f\u53d8\u6302\u8f7d\u7b97\u5b50}<\/span>
    \nclass<\/span> ConwayMountOperator<\/span>:<\/span>
    \n """\u534f\u53d8\u6302\u8f7d\u9ed1\u76d2: \u5eb7\u5a01\u5e8f\u5217 -> \u6570\u5b66\u7d20\u6750\u77e9\u9635 M (3×3 \u5bf9\u79f0)\u3002
    \n \u9012\u8fdb\u5f0f: \u8f93\u5165\u662f"\u5df2\u9501\u5b9a\u7684\u5eb7\u5a01\u5e8f\u5217" (\u786e\u5b9a\u6027\u951a), \u8f93\u51fa\u6052\u53ef\u4ea7\u51fa\u7684\u6570\u5b66\u7d20\u6750;
    \n \u62fc\u5408 (= \u7d20\u6750\u662f\u5426 rank1) \u662f\u6570\u5b66\u5b87\u5b99\u5408\u6cd5\u5224\u636e\u3002\u9ed1\u76d2\u53c2\u6570 A_math \u88ab\u9009\u62e9\u538b\u7b5b\u9009\u3002
    \n """<\/span>
    \n def<\/span> __init__<\/span>(<\/span>self,<\/span> rng:<\/span> np.<\/span>random.<\/span>Generator,<\/span> init_scale:<\/span> float<\/span> =<\/span> 0.5<\/span>)<\/span>:<\/span>
    \n self.<\/span>A_math =<\/span> rng.<\/span>normal(<\/span>size=<\/span>(<\/span>3<\/span>,<\/span> 3<\/span>)<\/span>)<\/span> *<\/span> init_scale
    \n self.<\/span>rng =<\/span> rng<\/p>\n

    def<\/span> mount<\/span>(<\/span>self,<\/span> seq:<\/span> str<\/span>)<\/span> –<\/span>><\/span> np.<\/span>ndarray:<\/span>
    \n """\u5eb7\u5a01\u5e8f\u5217 -> \u6570\u5b66\u7d20\u6750 M (3×3 \u5bf9\u79f0\u9635)\u3002"""<\/span>
    \n e =<\/span> embed_seq(<\/span>seq)<\/span>
    \n D =<\/span> np.<\/span>diag(<\/span>e)<\/span>
    \n return<\/span> self.<\/span>A_math @ D @ self.<\/span>A_math.<\/span>T<\/p>\n

    def<\/span> drift<\/span>(<\/span>self,<\/span> scale:<\/span> float<\/span> =<\/span> 0.04<\/span>)<\/span> –<\/span>><\/span> None<\/span>:<\/span>
    \n """\u53c2\u6570\u968f\u673a\u6e38\u8d70 (\u5b58\u6d3b\u9ed1\u76d2\u7684\u6f14\u5316)\u3002"""<\/span>
    \n self.<\/span>A_math +=<\/span> self.<\/span>rng.<\/span>normal(<\/span>size=<\/span>self.<\/span>A_math.<\/span>shape)<\/span> *<\/span> scale<\/p>\n

    def<\/span> math_assemble_legal<\/span>(<\/span>M:<\/span> np.<\/span>ndarray)<\/span> –<\/span>><\/span> bool<\/span>:<\/span>
    \n """\u62fc\u5408\u89c4\u5219 (\u6570\u5b66\u5b87\u5b99\u5408\u6cd5\u5224\u636e): \u7d20\u6750 M \u662f\u5426 rank1\u3002A\u7ea7\u77e9\u9635\u5224\u5b9a\u3002"""<\/span>
    \n if<\/span> np.<\/span>linalg.<\/span>matrix_rank(<\/span>M,<\/span> tol=<\/span>0.1<\/span>)<\/span> !=<\/span> 1<\/span>:<\/span> return<\/span> False<\/span>
    \n if<\/span> abs<\/span>(<\/span>np.<\/span>trace(<\/span>M)<\/span>)<\/span> <<\/span> 0.5<\/span>:<\/span> return<\/span> False<\/span>
    \n return<\/span> np.<\/span>linalg.<\/span>matrix_rank(<\/span>M @ M,<\/span> tol=<\/span>0.1<\/span>)<\/span> <=<\/span> 1<\/span><\/p>\n

    \n

    6. \u6d8c\u73b0\u7684\u7edf\u8ba1\u529b\u5b66：\u9ad8\u65af\u666e\u67e5\u4e0e CLT \u8bda\u5b9e\u5ba1\u67e5<\/h3>\n

    \u5f15\u64ce\u5bf9\u201c\u65e0\u9650\u6d8c\u73b0\u2192\u6b63\u6001\u5206\u5e03\u201d\u547d\u9898\u8fdb\u884c\u4e86\u4e25\u5bc6\u7684\u6570\u5b66\u4e0e\u5b9e\u9a8c\u5ba1\u67e5\u3002\u4e3a\u907f\u514d\u5c06\u201c\u6b63\u6001\u201d\u7ed1\u5b9a\u5230\u67d0\u4e00\u4e2a\u9884\u8bbe\u91cf，\u5f15\u64ce\u6267\u884c\u201c\u4e8b\u4ef6\u9ad8\u65af\u666e\u67e5\u201d，\u679a\u4e3e\u8fd0\u884c\u4e2d\u6240\u6709\u4e8b\u4ef6\u91cf\u8fdb\u884c\u68c0\u9a8c\u3002<\/p>\n

    # {m178_conjecture.py \u7247\u6bb5：\u4e8b\u4ef6\u9ad8\u65af\u666e\u67e5\u4e0e\u6b63\u6001\u68c0\u9a8c}<\/span>
    \n def<\/span> _normality_test<\/span>(<\/span>self,<\/span> arr:<\/span> np.<\/span>ndarray)<\/span> –<\/span>><\/span> dict<\/span>:<\/span>
    \n """\u7edf\u4e00\u4e8b\u4ef6\u6b63\u6001\u68c0\u9a8c (\u504f\u5ea6\/\u8d85\u989d\u5cf0\u5ea6\/\u7ecf\u9a8c\u8986\u76d6\u7387\/JB)\u3002
    \n GAUSSIAN_CENTERED : |skew| < 0.3 \u2192 \u4e8b\u4ef6\u4ee5\u5747\u503c\u4e3a\u4e2d\u5fc3\u8fd1\u4f3c\u5bf9\u79f0\u5206\u5e03 (CLT \u627f\u4fdd)\u3002
    \n SKEWED : |skew| >= 0.3 \u2192 \u660e\u663e\u504f\u659c, \u975e\u5bf9\u79f0\u6982\u7387\u4e8b\u4ef6\u56fe\u3002
    \n TOO_FEW : \u6837\u672c < 8\u3002
    \n """<\/span>
    \n from<\/span> scipy import<\/span> stats
    \n arr =<\/span> np.<\/span>asarray(<\/span>arr,<\/span> dtype=<\/span>float<\/span>)<\/span>
    \n n =<\/span> len<\/span>(<\/span>arr)<\/span>
    \n if<\/span> n <<\/span> 8<\/span>:<\/span> return<\/span> {<\/span>"n_samples"<\/span>:<\/span> n,<\/span> "verdict"<\/span>:<\/span> "TOO_FEW"<\/span>}<\/span>
    \n mean,<\/span> std =<\/span> float<\/span>(<\/span>arr.<\/span>mean(<\/span>)<\/span>)<\/span>,<\/span> float<\/span>(<\/span>arr.<\/span>std(<\/span>ddof=<\/span>1<\/span>)<\/span>)<\/span>
    \n skew =<\/span> float<\/span>(<\/span>stats.<\/span>skew(<\/span>arr)<\/span>)<\/span>
    \n kurt =<\/span> float<\/span>(<\/span>stats.<\/span>kurtosis(<\/span>arr)<\/span>)<\/span>
    \n jb_stat,<\/span> jb_p =<\/span> stats.<\/span>jarque_bera(<\/span>arr)<\/span>
    \n z =<\/span> (<\/span>arr –<\/span> mean)<\/span> \/<\/span> (<\/span>std +<\/span> _EPS)<\/span>
    \n p1 =<\/span> float<\/span>(<\/span>np.<\/span>mean(<\/span>np.<\/span>abs<\/span>(<\/span>z)<\/span> <<\/span> 1.0<\/span>)<\/span>)<\/span> *<\/span> 100.0<\/span>
    \n p2 =<\/span> float<\/span>(<\/span>np.<\/span>mean(<\/span>np.<\/span>abs<\/span>(<\/span>z)<\/span> <<\/span> 2.0<\/span>)<\/span>)<\/span> *<\/span> 100.0<\/span>
    \n if<\/span> abs<\/span>(<\/span>skew)<\/span> <<\/span> 0.3<\/span>:<\/span>
    \n verdict =<\/span> "GAUSSIAN_CENTERED"<\/span>
    \n else<\/span>:<\/span>
    \n verdict =<\/span> "SKEWED"<\/span>
    \n return<\/span> {<\/span>
    \n "n_samples"<\/span>:<\/span> n,<\/span> "mean"<\/span>:<\/span> round<\/span>(<\/span>mean,<\/span> 5<\/span>)<\/span>,<\/span> "std"<\/span>:<\/span> round<\/span>(<\/span>std,<\/span> 5<\/span>)<\/span>,<\/span>
    \n "skewness"<\/span>:<\/span> round<\/span>(<\/span>skew,<\/span> 4<\/span>)<\/span>,<\/span> "excess_kurtosis"<\/span>:<\/span> round<\/span>(<\/span>kurt,<\/span> 4<\/span>)<\/span>,<\/span>
    \n "jarque_bera_p"<\/span>:<\/span> round<\/span>(<\/span>float<\/span>(<\/span>jb_p)<\/span>,<\/span> 5<\/span>)<\/span>,<\/span>
    \n "pct_within_1sigma"<\/span>:<\/span> round<\/span>(<\/span>p1,<\/span> 1<\/span>)<\/span>,<\/span> "pct_within_2sigma"<\/span>:<\/span> round<\/span>(<\/span>p2,<\/span> 1<\/span>)<\/span>,<\/span>
    \n "verdict"<\/span>:<\/span> verdict,<\/span>
    \n }<\/span><\/p>\n

    def<\/span> survey_gaussian_events<\/span>(<\/span>self,<\/span> cphi_bins:<\/span> int<\/span> =<\/span> 9<\/span>)<\/span> –<\/span>><\/span> dict<\/span>:<\/span>
    \n """\u4e8b\u4ef6\u9ad8\u65af\u666e\u67e5: \u4e0d\u9884\u8bbe"\u67d0\u4e2a\u91cf\u5fc5\u987b\u6b63\u6001"; \u800c\u662f\u679a\u4e3e\u5f15\u64ce\u5728\u8fd0\u884c\u4e2d\u81ea\u7136\u4ea7\u751f\u7684
    \n **\u6240\u6709\u4e8b\u4ef6\u91cf**, \u5bf9\u6bcf\u4e2a\u4e8b\u4ef6\u91cf\u8dd1\u7edf\u4e00\u6b63\u6001\u68c0\u9a8c, \u8f93\u51fa\u4e00\u5f20"\u54ea\u4e9b\u4e8b\u4ef6\u7b26\u5408\u9ad8\u65af\u79ef\u5206"\u7684\u6e05\u5355\u3002
    \n """<\/span>
    \n # \u666e\u67e5\u8986\u76d6: [A] \u589e\u91cf\u4e8b\u4ef6 [B] \u6295\u5f71\u5750\u6807 [C] \u4f4d\u7f6e\u4e8b\u4ef6 [D] \u5b58\u7eed\u65f6\u957f <\/span>
    \n # [E] \u6253\u5305\u8d1f\u8377 [F] \u5b58\u5728\u6027\u4e8b\u4ef6 [G] \u6536\u655b\u6b8b\u5dee [H] \u5eb7\u5a01\u5185\u5bb9\u4e8b\u4ef6<\/span>
    \n # … (\u904d\u5386\u6240\u6709\u4e8b\u4ef6\u7c7b\u522b\u8c03\u7528 _normality_test)<\/span><\/p>\n

    \u5b9e\u8dd1\u8868\u660e，\u7eaf\u72ec\u7acb\u540c\u5206\u5e03\u589e\u91cf\u4e8b\u4ef6\u5448\u73b0 GAUSSIAN_CENTERED（\u5c45\u4e2d\u5bf9\u79f0，CLT \u627f\u4fdd\u6210\u7acb）；\u800c\u5728\u8026\u5408\u8bed\u4e49（\u5f15\u529b\u4e95\u6d88\u89e3\u5f00\u542f）\u4e0b，\u56e0\u5b58\u5728\u6027\u6743\u91cd support \u504f\u5411\u8d34\u5408\u7ed3\u6784\u7684\u8def\u5f84，\u7834\u574f\u4e86 iid \u6a2a\u5411\u72ec\u7acb\u6027，\u6a2a\u5411\u5206\u5e03\u5448\u73b0 SKEWED\u3002\u6b64\u5916，\u5b58\u7eed\u65f6\u957f（lifetime）\u5206\u5e03\u56e0\u9996\u6b21\u7a7f\u8d8a\u65f6\u95f4\u7684\u7279\u6027\u5448\u73b0\u53f3\u504f\u539a\u5c3e\u3002\u5f15\u64ce\u8bda\u5b9e\u533a\u5206\u4e86\u201c\u6574\u4f53\u5206\u5e03\u5f62\u6001\u201d\u4e0e\u201c\u5355\u6761\u81ea\u7136\u6027\u201d\u4e24\u4e2a\u7ef4\u5ea6\u7684\u5224\u636e\u3002<\/p>\n

    \n

    7. \u65e0\u786c\u7f16\u7801\u58f0\u660e\u4e0e\u4f53\u7cfb\u8bda\u5b9e\u5b9a\u7ea7<\/h3>\n

    \u672c\u5f15\u64ce\u7684\u7a81\u51fa\u7279\u5f81\u5728\u4e8e\u7edd\u5bf9\u7684\u65e0\u786c\u7f16\u7801\u58f0\u660e\u3002\u6240\u6709\u52a8\u529b\u5b66\u65cb\u94ae\u5747\u4ece\u5e95\u5c42 F3F_3<\/span><\/span>F<\/span><\/span>3<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u77e9\u9635\u7684 \u03d5\\\\phi<\/span><\/span>\u03d5<\/span><\/span><\/span><\/span><\/span> \u4e0e \u03c8\\\\psi<\/span><\/span>\u03c8<\/span><\/span><\/span><\/span><\/span> \u6d8c\u73b0，\u96f6\u5f15\u5165\u9b54\u6cd5\u6570\u5b57：<\/p>\n

    # {m178_conjecture.py \u7247\u6bb5：\u65e0\u786c\u7f16\u7801\u58f0\u660e\u4e0e\u6d8c\u73b0\u6620\u5c04}<\/span>
    \n _psi =<\/span> float<\/span>(<\/span>getattr<\/span>(<\/span>self.<\/span>growth,<\/span> "psi"<\/span>,<\/span> 0.6180339887498949<\/span>)<\/span>)<\/span>
    \n _phi =<\/span> float<\/span>(<\/span>getattr<\/span>(<\/span>self.<\/span>growth,<\/span> "phi"<\/span>,<\/span> 1.6180339887498949<\/span>)<\/span>)<\/span>
    \n if<\/span> recognition_rate is<\/span> None<\/span>:<\/span>
    \n recognition_rate =<\/span> _psi # \u8bc6\u522b\u5149\u9525 = \u03c8<\/span>
    \n if<\/span> absorb_rate is<\/span> None<\/span>:<\/span>
    \n absorb_rate =<\/span> _psi # \u6253\u5305\u901f\u5ea6 = \u03c8<\/span>
    \n if<\/span> alpha is<\/span> None<\/span>:<\/span>
    \n alpha =<\/span> _psi # \u7c98\u6ede\/\u9057\u4f20\u7ea6\u675f = \u03c8<\/span>
    \n if<\/span> gamma is<\/span> None<\/span>:<\/span>
    \n gamma =<\/span> 1.0<\/span> –<\/span> _psi **<\/span> 2<\/span> # \u76f2\u731c\u63a2\u7d22 = 1\u2212\u03c8\u00b2<\/span>
    \n if<\/span> pack_capacity is<\/span> None<\/span>:<\/span>
    \n pack_capacity =<\/span> 1.0<\/span> \/<\/span> (<\/span>_psi **<\/span> 3<\/span>)<\/span> # \u5305\u5bb9\u91cf(\u4fe1\u9053\u5bb9\u91cf) = 1\/\u03c8\u00b3<\/span>
    \n if<\/span> decohere_base is<\/span> None<\/span>:<\/span>
    \n decohere_base =<\/span> _psi **<\/span> 3<\/span> # \u9000\u76f8\u5e72\u57fa\u51c6\u7387 = \u03c8\u00b3 (M105 \u6700\u5c0f\u4fe1\u606f\u635f\u5931)<\/span>
    \n if<\/span> decohere_gain is<\/span> None<\/span>:<\/span>
    \n decohere_gain =<\/span> _phi # \u9000\u76f8\u5e72\u589e\u76ca = \u03c6 (\u6269\u6563\u8f74\u4e58\u6570)<\/span><\/p>\n

    \u5f15\u64ce\u4e25\u683c\u5212\u5206\u4e86\u8ba4\u8bc6\u8bba\u5c42\u7ea7：<\/p>\n

  • A \u7ea7（m178 \u4f53\u7cfb\u627f\u4fdd，\u53ef\u8bc1\u4f2a）： F3F_3<\/span><\/span>F<\/span><\/span>3<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u77e9\u9635\u8c31\u3001\u6295\u5f71\u7b97\u5b50 \u03a0\\\\Pi<\/span><\/span>\u03a0<\/span><\/span><\/span><\/span><\/span>\u3001CLT \u5728 iid \u6761\u4ef6\u4e0b\u7684\u6570\u5b66\u4e8b\u5b9e\u3002<\/li>\n
  • B+ \u8be0\u91ca\u5c42（\u7528\u6237\u610f\u56fe\u627f\u4fdd）： \u5f15\u529b\u4e95\u751f\u706d\u673a\u5236\u3001\u5eb7\u5a01\u5b87\u5b99\u4f5c\u4e3a\u5185\u5bb9\u8f7d\u4f53\u7684\u53cc\u5411\u9501\u5b9a\u3002\u8fd9\u4e9b\u673a\u5236\u6709\u6548\u4f46\u975e A \u7ea7\u6570\u5b66\u5fc5\u7136\u3002<\/li>\n
  • OPEN（\u5f85\u8c03\u548c）： \u8026\u5408\u731c\u60f3\u6574\u4f53\u975e\u6b63\u6001\u3001\u5b58\u7eed\u65f6\u957f\u53f3\u504f\u95ee\u9898\u3002<\/li>\n

    \u5bf9\u4e8e\u5b9e\u8dd1\u4e2d\u53d1\u73b0\u7684\u7269\u7406\/\u6570\u5b66\u5f20\u91cf\u5b87\u5b99\u5728\u771f\u5b9e 3 \u7ef4 gg<\/span><\/span>g<\/span><\/span><\/span><\/span><\/span> \u4e0b\u7684\u5224\u5b9a\u7387\u5931\u771f\u95ee\u9898，\u7cfb\u7edf\u901a\u8fc7\u964d\u7ea7\u4e3a\u89c2\u6d4b\u6807\u7b7e（lock_floor=0）\u907f\u514d\u6c61\u67d3\u5f15\u529b\u4e95\u751f\u706d\u7edf\u8ba1，\u4f53\u73b0\u4e86\u201c\u4e0d\u7c89\u9970\u3001\u4e0d\u5047\u88c5\u201d\u7684\u5b66\u672f\u8bda\u5b9e\u6027：<\/p>\n

    # {m178_tensor_universes.py \u7247\u6bb5：\u8bda\u5b9e\u843d\u5730\u7248}<\/span>
    \nclass<\/span> TensorUniverse<\/span>:<\/span>
    \n """\u5e73\u884c\u5b87\u5b99\u57fa\u7c7b (\u517c\u5bb9 ConjectureEngine \u7684 lock_stack \u8c03\u7528)\u3002
    \n \u6bcf\u4e2a\u5b87\u5b99\u6301\u6709:
    \n name: \u9501\u5b9a\u6808\u6807\u8bc6\u7b26 (\u89c2\u6d4b\u6807\u7b7e);
    \n lock_floor: \u8be5\u5b87\u5b99\u951a\u5b9a\u652f\u6491\u5e95 \u2014\u2014 \u5f20\u91cf\u5b87\u5b99\u6052\u4e3a 0 (\u4e0d\u53c2\u4e0e\u53cd\u5411\u951a\u5b9a, \u89c1\u6a21\u5757\u5934);
    \n window: \u91cf\u5316\u7a97\u53e3 (\u7269\u7406=16 \u2192 4×4, \u6570\u5b66=9 \u2192 3×3)\u3002
    \n """<\/span>
    \n def<\/span> __init__<\/span>(<\/span>self,<\/span> name:<\/span> str<\/span>,<\/span> window:<\/span> int<\/span>)<\/span>:<\/span>
    \n self.<\/span>name =<\/span> name
    \n self.<\/span>lock_floor =<\/span> 0.0<\/span> # \u5f20\u91cf\u5b87\u5b99\u4e0d\u53c2\u4e0e\u5f15\u529b\u4e95\u951a\u5b9a (\u8bda\u5b9e\u5904\u7f6e)<\/span>
    \n self.<\/span>window =<\/span> int<\/span>(<\/span>window)<\/span><\/p>\n

    \n

    8. \u7ed3\u8bba<\/h3>\n

    \u672c\u6587\u8bba\u8ff0\u7684 M178 \u731c\u60f3\u6d8c\u73b0\u5f15\u64ce，\u6210\u529f\u6784\u5efa\u4e86\u4e00\u4e2a\u4ece\u5e95\u5c42\u56e0\u679c\u751f\u957f\u5230\u4e0a\u5c42\u5185\u5bb9\u6d8c\u73b0\u7684\u5b8c\u6574\u52a8\u529b\u5b66\u67b6\u6784\u3002\u901a\u8fc7\u5c06\u7ed3\u6784\u652f\u6491\u5b9a\u4e49\u4e3a\u201c\u5f15\u529b\u4e95\u6d88\u89e3\u201d\u800c\u975e\u4e3b\u52a8\u7ea0\u9519，\u5f15\u64ce\u5728\u6570\u5b66\u4e0a\u81ea\u7136\u5b9e\u73b0\u4e86\u5b58\u5728\u8005\u7684\u751f\u706d\u6f14\u5316；\u901a\u8fc7\u201c\u6253\u5305-\u6491\u7834-\u91cd\u5f15\u5165\u201d\u673a\u5236，\u523b\u753b\u4e86\u6982\u62ec\u80fd\u529b\u4e0e\u65e0\u9650\u751f\u957f\u4e4b\u95f4\u7684\u8fdf\u6ede\u5f20\u529b\u3002<\/p>\n

    \u7279\u522b\u5730，\u901a\u8fc7\u5c06\u5eb7\u5a01\u5b87\u5b99\u6302\u8f7d\u4e3a\u731c\u60f3\u5185\u5bb9，\u5f15\u64ce\u4f7f\u9759\u6001\u6570\u5b66\u5b9a\u7406\u5177\u8c61\u5316\u4e3a\u53d7\u5b58\u5728\u6027\u6743\u91cd\u5236\u7ea6\u7684\u771f\u5b9e\u5b87\u5b99，\u5e76\u901a\u8fc7\u53cc\u5411\u9501\u5b9a\u94fe\u8def\u5b8c\u6210\u4e86\u5185\u5bb9\u4e0e\u5e95\u5c42\u7ed3\u6784\u7684\u95ed\u73af\u3002\u9ad8\u65af\u666e\u67e5\u7684\u7edf\u8ba1\u529b\u5b66\u5206\u6790\u8868\u660e，\u6d8c\u73b0\u7684\u6b63\u6001\u6027\u4e25\u683c\u4f9d\u8d56\u4e8e\u589e\u91cf\u7684\u72ec\u7acb\u540c\u5206\u5e03\u6027；\u5728\u5f15\u529b\u4e95\u504f\u7f6e\u4e0b，\u7cfb\u7edf\u7684\u975e\u6b63\u6001\u7279\u5f81\u6b63\u662f\u5176\u672c\u4f53\u8bba\u8bed\u4e49\u9009\u62e9\u7684\u6570\u5b66\u6620\u5c04\u3002<\/p>\n

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