{"id":174353,"date":"2024-05-08T18:29:53","date_gmt":"2024-05-08T10:29:53","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/174353.html"},"modified":"2024-05-08T18:29:57","modified_gmt":"2024-05-08T10:29:57","slug":"%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8python%e5%81%9a%e8%b4%9d%e5%8f%b6%e6%96%af%e7%bd%91%e7%bb%9c%e6%8e%a8%e7%90%86","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/174353.html","title":{"rendered":"\u5982\u4f55\u4f7f\u7528python\u505a\u8d1d\u53f6\u65af\u7f51\u7edc\u63a8\u7406"},"content":{"rendered":"

\"\u5982\u4f55\u4f7f\u7528python\u505a\u8d1d\u53f6\u65af\u7f51\u7edc\u63a8\u7406\"<\/p>\n

\u5728\u4f7f\u7528Python<\/strong>\u8fdb\u884c\u8d1d\u53f6\u65af\u7f51\u7edc\u63a8\u7406\u65f6\uff0c\u53ef\u4ee5\u5229\u7528\u4e13\u95e8\u7684\u5e93\uff0c\u5982pgmpy<\/strong>\u548cbnlearn<\/strong>\uff0c\u6765\u5b9a\u4e49\u7f51\u7edc\u7ed3\u6784\u3001\u8bbe\u7f6e\u53c2\u6570\u6982\u7387\u3001\u5e76\u4f7f\u7528\u4e0d\u540c\u7684\u7b97\u6cd5\u8fdb\u884c\u6761\u4ef6\u6982\u7387\u63a8\u7406\u3002\u9996\u5148\uff0c\u9700\u8981\u5b9a\u4e49\u8d1d\u53f6\u65af\u7f51\u7edc\u7684\u7ed3\u6784\uff0c\u5305\u62ec\u53d8\u91cf\u548c\u5b83\u4eec\u4e4b\u95f4\u7684\u4f9d\u8d56\u5173\u7cfb\uff1b\u7136\u540e\uff0c\u4e3a\u7f51\u7edc\u7684\u6bcf\u4e00\u4e2a\u53d8\u91cf\u6307\u5b9a\u6761\u4ef6\u6982\u7387\u8868\uff08CPTs\uff09\uff1b\u6700\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528\u67e5\u8be2\u7b97\u6cd5\uff0c\u4f8b\u5982\u53d8\u91cf\u6d88\u9664\u3001\u4fe1\u5ff5\u4f20\u64ad\u6216\u91c7\u6837\u65b9\u6cd5\uff0c\u6765\u6839\u636e\u6240\u7ed9\u7684\u8bc1\u636e\u8ba1\u7b97\u5176\u4ed6\u53d8\u91cf\u7684\u540e\u9a8c\u5206\u5e03\u3002Python\u7684\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u548c\u65b9\u6cd5\uff0c\u4ee5\u652f\u6301\u6982\u7387\u63a8\u7406\u548c\u5b66\u4e60\uff0c\u5305\u62ec\u7ed3\u6784\u5b66\u4e60\u548c\u53c2\u6570\u5b66\u4e60\u3002\u6b64\u5916\uff0c\u5b83\u4eec\u8fd8\u63d0\u4f9b\u4e86\u7528\u4e8e\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u7684\u6307\u6807\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5b89\u88c5\u4e0e\u5b9e\u4f8b\u5316\u8d1d\u53f6\u65af\u7f51\u7edc\u5e93<\/p>\n<\/p>\n

\u9996\u5148\uff0c\u4f60\u9700\u8981\u5b89\u88c5Python\u7684\u8d1d\u53f6\u65af\u7f51\u7edc\u5e93\u3002\u5728\u7ec8\u7aef\u6216\u547d\u4ee4\u884c\u754c\u9762\u4f7f\u7528pip\uff1a<\/p>\n<\/p>\n

pip install pgmpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6216\u8005\uff0c<\/p>\n<\/p>\n
pip install bnlearn<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e<\/strong>\uff0c\u4f60\u53ef\u4ee5\u5f00\u59cb\u5b9e\u4f8b\u5316\u4e00\u4e2a\u8d1d\u53f6\u65af\u7f51\u7edc\u6a21\u578b\u3002<\/p>\n<\/p>\n
from pgmpy.models import BayesianModel<\/p>\n
model = BayesianModel([('A', 'B'), ('C', 'B')])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a\u7b80\u5355\u7684\u8d1d\u53f6\u65af\u7f51\u7edc\uff0c\u53d8\u91cfB\u4f9d\u8d56\u4e8eA\u548cC\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u5b9a\u4e49\u6761\u4ef6\u6982\u7387\u8868<\/p>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u5b9a\u4e49\u6bcf\u4e2a\u8282\u70b9\u7684\u6761\u4ef6\u6982\u7387\u8868<\/strong>\u3002\u8fd9\u9700\u8981\u6839\u636e\u4f60\u7684\u6570\u636e\u6216\u95ee\u9898\u7684\u5177\u4f53\u9886\u57df\u77e5\u8bc6\u6765\u5b8c\u6210\u3002\u4f7f\u7528pgmpy\uff0c\u4f60\u53ef\u4ee5\u8fd9\u6837\u8bbe\u5b9aCPT\uff1a<\/p>\n<\/p>\n
from pgmpy.factors.discrete import TabularCPD<\/p>\n
cpd_A = TabularCPD(variable='A', variable_card=2, values=[[0.3], [0.7]])<\/p>\n
cpd_C = TabularCPD(variable='C', variable_card=2, values=[[0.6], [0.4]])<\/p>\n
cpd_B = TabularCPD(variable='B', variable_card=2,<\/p>\n
                   values=[[0.1, 0.2, 0.3, 0.4],<\/p>\n
                           [0.9, 0.8, 0.7, 0.6]],<\/p>\n
                   evidence=['A', 'C'],<\/p>\n
                   evidence_card=[2, 2])<\/p>\n
model.add_cpds(cpd_A, cpd_C, cpd_B)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u8fdb\u884c\u6982\u7387\u63a8\u7406<\/p>\n<\/p>\n
\u4e00\u65e6\u4f60\u6709\u4e86\u7f51\u7edc\u7ed3\u6784\u548c\u6761\u4ef6\u6982\u7387\u8868\uff0c\u4f60\u5c31\u53ef\u4ee5\u8fdb\u884c\u6982\u7387\u63a8\u7406<\/strong>\u4e86\u3002pgmpy\u5305\u63d0\u4f9b\u4e86\u51e0\u79cd\u63a8\u7406\u7b97\u6cd5\uff1a<\/p>\n<\/p>\n
from pgmpy.inference import VariableElimination<\/p>\n
inference = VariableElimination(model)<\/p>\n
result = inference.query(variables=['B'], evidence={'A': 1})<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u4e2a\u4f8b\u5b50\u663e\u793a\u4e86\u5728\u6761\u4ef6A\u53d1\u751f\u65f6\u53d8\u91cfB\u7684\u6982\u7387\u5206\u5e03\u3002<\/p>\n<\/p>\n
\u56db\u3001\u8fdb\u4e00\u6b65\u7684\u6982\u7387\u66f4\u65b0\u4e0e\u5b66\u4e60<\/p>\n<\/p>\n
\u9664\u4e86\u57fa\u672c\u7684\u63a8\u7406\uff0c\u4f60\u8fd8\u53ef\u4ee5\u6539\u5584\u4f60\u7684\u7f51\u7edc<\/strong>\uff0c\u901a\u8fc7\u7ed3\u6784\u548c\u53c2\u6570\u5b66\u4e60\u6839\u636e\u6570\u636e\u6765\u8c03\u6574\u5b83\u3002<\/p>\n<\/p>\n
from pgmpy.estimators import MaximumLikelihoodEstimator<\/p>\n
model.fit(data, estimator=MaximumLikelihoodEstimator)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0cdata<\/code>\u4ee3\u8868\u4e86\u7528\u6765\u4ece\u4e2d\u5b66\u4e60\u7f51\u7edc\u53c2\u6570\u7684\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u8fdb\u9636\u5e94\u7528<\/p>\n<\/p>\n
\u5728\u590d\u6742\u7684\u95ee\u9898\u4e2d\uff0c\u53ef\u80fd\u9700\u8981\u4f7f\u7528\u66f4\u9ad8\u7ea7\u7684\u6280\u672f\uff0c\u5982\u91c7\u6837\u65b9\u6cd5\uff08Gibbs Sampling\u3001Metropolis-Hastings\uff09\u6216\u7ed3\u6784\u5b66\u4e60\u7b97\u6cd5\uff0c\u8fd9\u4e9b\u90fd\u53ef\u4ee5\u5728pgmpy<\/strong>\u5e93\u4e2d\u627e\u5230\u3002<\/p>\n<\/p>\n
from pgmpy.sampling import GibbsSampling<\/p>\n
gibbs_chAI<\/a>n = GibbsSampling(model)<\/p>\n
sample = gibbs_chain.sample(size=1000)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u4f18\u5316\u4e0e\u8c03\u8bd5<\/p>\n<\/p>\n
\u6700\u540e\uff0c\u4f18\u5316\u548c\u8c03\u8bd5<\/strong>\u8d1d\u53f6\u65af\u7f51\u7edc\u6a21\u578b\u662f\u81f3\u5173\u91cd\u8981\u7684\u3002\u4f60\u9700\u8981\u786e\u4fdd\u7f51\u7edc\u7ed3\u6784\u548cCPTs\u6b63\u786e\u65e0\u8bef\uff0c\u63a8\u7406\u7b97\u6cd5\u80fd\u6b63\u786e\u5de5\u4f5c\uff0c\u5e76\u901a\u8fc7\u4ea4\u53c9\u9a8c\u8bc1\u3001A\/B\u6d4b\u8bd5\u548c\u5176\u4ed6\u7edf\u8ba1\u65b9\u6cd5\u5bf9\u4f60\u7684\u7f51\u7edc\u5b9e\u65bd\u8bc4\u4f30\u3002<\/p>\n<\/p>\n
\u8d1d\u53f6\u65af\u7f51\u7edc\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u6982\u7387\u56fe\u6a21\u578b\u5de5\u5177\uff0c\u9002\u7528\u4e8e\u591a\u79cd\u4efb\u52a1\uff0c\u5982\u8bca\u65ad\u3001\u9884\u6d4b\u4e0e\u51b3\u7b56\u652f\u6301\u3002Python\u63d0\u4f9b\u7684\u76f8\u5173\u5e93\u8ba9\u6784\u5efa\u548c\u4f7f\u7528\u8d1d\u53f6\u65af\u7f51\u7edc\u53d8\u5f97\u5bb9\u6613\uff0c\u4e14\u5177\u6709\u5f3a\u5927\u7684\u7075\u6d3b\u6027\u548c\u6269\u5c55\u6027\u3002\u901a\u8fc7\u7cbe\u5fc3\u8bbe\u8ba1\u548c\u4e0d\u65ad\u5730\u5b9e\u6218\u6d4b\u8bd5\uff0c\u4f60\u5c06\u80fd\u591f\u6784\u5efa\u4e00\u4e2a\u5f3a\u5065\u548c\u7cbe\u786e\u7684\u8d1d\u53f6\u65af\u7f51\u7edc\u6a21\u578b\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
1. \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u8d1d\u53f6\u65af\u7f51\u7edc\u63a8\u7406?<\/strong><\/p>\n
\u8d1d\u53f6\u65af\u7f51\u7edc\u662f\u4e00\u79cd\u7528\u4e8e\u5efa\u6a21\u4e0d\u786e\u5b9a\u6027\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u800cPython\u662f\u4e00\u79cd\u529f\u80fd\u5f3a\u5927\u3001\u6613\u5b66\u6613\u7528\u7684\u7f16\u7a0b\u8bed\u8a00\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684\u4e00\u4e9b\u5e93\u6216\u6846\u67b6\u6765\u8fdb\u884c\u8d1d\u53f6\u65af\u7f51\u7edc\u63a8\u7406\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u9009\u62e9\u5408\u9002\u7684\u5e93\u6216\u6846\u67b6\uff0c\u4f8b\u5982pgmpy\u3001pomegranate\u3001bayespy\u7b49\uff0c\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u548c\u7b97\u6cd5\u6765\u8fdb\u884c\u8d1d\u53f6\u65af\u7f51\u7edc\u63a8\u7406\u3002<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u60a8\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u8d1d\u53f6\u65af\u7f51\u7edc\u6a21\u578b\u3002\u8fd9\u9700\u8981\u5b9a\u4e49\u53d8\u91cf\u3001\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u4ee5\u53ca\u76f8\u5e94\u7684\u6761\u4ef6\u6982\u7387\u8868\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528\u5e93\u63d0\u4f9b\u7684API\u6765\u521b\u5efa\u6a21\u578b\uff0c\u4f8b\u5982pgmpy\u5e93\u4e2d\u7684BayesianModel\u7c7b\u3002<\/p>\n
\u4e00\u65e6\u60a8\u521b\u5efa\u4e86\u8d1d\u53f6\u65af\u7f51\u7edc\u6a21\u578b\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u4e0d\u540c\u7684\u65b9\u6cd5\u6765\u8fdb\u884c\u63a8\u7406\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u63a8\u65ad\u7b97\u6cd5\uff0c\u5982\u53d8\u91cf\u6d88\u9664\u3001\u91c7\u6837\u7b49\u6765\u8ba1\u7b97\u540e\u9a8c\u6982\u7387\u3002\u4e00\u4e9b\u5e93\u8fd8\u63d0\u4f9b\u4e86\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u5e2e\u52a9\u60a8\u66f4\u76f4\u89c2\u5730\u7406\u89e3\u7f51\u7edc\u6a21\u578b\u548c\u63a8\u7406\u7ed3\u679c\u3002<\/p>\n
2. \u54ea\u4e9bPython\u5e93\u53ef\u4ee5\u7528\u4e8e\u8d1d\u53f6\u65af\u7f51\u7edc\u63a8\u7406?<\/strong><\/p>\n
\u6709\u51e0\u4e2aPython\u5e93\u53ef\u4ee5\u7528\u4e8e\u8d1d\u53f6\u65af\u7f51\u7edc\u63a8\u7406\uff0c\u5177\u4f53\u53d6\u51b3\u4e8e\u60a8\u7684\u9700\u6c42\u548c\u504f\u597d\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u5e93\uff1a<\/p>\n
    \n
  • \n
    pgmpy\uff1a\u8fd9\u4e2a\u5e93\u662f\u4e00\u4e2a\u7eafPython\u5e93\uff0c\u63d0\u4f9b\u4e86\u5e7f\u6cdb\u7684\u8d1d\u53f6\u65af\u7f51\u7edc\u5efa\u6a21\u548c\u63a8\u7406\u529f\u80fd\u3002\u5b83\u652f\u6301\u53d8\u91cf\u6d88\u9664\u3001\u91c7\u6837\u7b49\u63a8\u7406\u7b97\u6cd5\uff0c\u5e76\u63d0\u4f9b\u65b9\u4fbf\u7684API\u6765\u521b\u5efa\u548c\u64cd\u4f5c\u7f51\u7edc\u6a21\u578b\u3002<\/p>\n<\/li>\n
  • \n
    pomegranate\uff1a\u8fd9\u4e2a\u5e93\u662f\u4e00\u4e2a\u9ad8\u6027\u80fd\u7684\u8d1d\u53f6\u65af\u7f51\u7edc\u5e93\uff0c\u63d0\u4f9b\u4e86\u7528\u4e8e\u63a8\u7406\u548c\u6a21\u578b\u8bad\u7ec3\u7684API\u3002\u5b83\u652f\u6301\u591a\u79cd\u63a8\u65ad\u7b97\u6cd5\uff0c\u6bd4\u5982\u53d8\u91cf\u6d88\u9664\u3001\u8fd1\u4f3c\u63a8\u7406\u7b49\u3002<\/p>\n<\/li>\n
  • \n
    bayespy\uff1a\u8fd9\u4e2a\u5e93\u63d0\u4f9b\u4e86\u7528\u4e8e\u8d1d\u53f6\u65af\u7f51\u7edc\u63a8\u7406\u7684\u9ad8\u7ea7API\u548c\u5de5\u5177\u3002\u5b83\u652f\u6301\u53d8\u91cf\u6d88\u9664\u3001\u91c7\u6837\u3001\u8fd1\u4f3c\u63a8\u7406\u7b49\u591a\u79cd\u63a8\u65ad\u7b97\u6cd5\u3002<\/p>\n<\/li>\n<\/ul>\n
    \u60a8\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u8fdb\u884c\u8d1d\u53f6\u65af\u7f51\u7edc\u63a8\u7406\u3002<\/p>\n
    3. \u8d1d\u53f6\u65af\u7f51\u7edc\u63a8\u7406\u5728\u54ea\u4e9b\u9886\u57df\u6709\u5e94\u7528?<\/strong><\/p>\n
    \u8d1d\u53f6\u65af\u7f51\u7edc\u63a8\u7406\u5728\u8bb8\u591a\u9886\u57df\u90fd\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u5e94\u7528\u9886\u57df\uff1a<\/p>\n
      \n
    • \n
      \u533b\u5b66\u8bca\u65ad\uff1a\u8d1d\u53f6\u65af\u7f51\u7edc\u53ef\u4ee5\u7528\u4e8e\u75be\u75c5\u8bca\u65ad\u548c\u60a3\u8005\u7ba1\u7406\u3002\u5b83\u53ef\u4ee5\u6839\u636e\u75c7\u72b6\u548c\u68c0\u6d4b\u7ed3\u679c\uff0c\u8ba1\u7b97\u51fa\u4e0d\u540c\u75be\u75c5\u7684\u540e\u9a8c\u6982\u7387\uff0c\u5e2e\u52a9\u533b\u751f\u8fdb\u884c\u51c6\u786e\u7684\u8bca\u65ad\u3002<\/p>\n<\/li>\n
    • \n
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