{"id":960786,"date":"2024-12-27T03:52:47","date_gmt":"2024-12-26T19:52:47","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/960786.html"},"modified":"2024-12-27T03:52:49","modified_gmt":"2024-12-26T19:52:49","slug":"python%e5%a6%82%e4%bd%95%e8%b0%83%e7%94%a8copula%e5%87%bd%e6%95%b0","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/960786.html","title":{"rendered":"Python\u5982\u4f55\u8c03\u7528copula\u51fd\u6570"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25103201\/e2075a7b-8e1e-43da-9ce4-1f481bfe6d70.webp\" alt=\"Python\u5982\u4f55\u8c03\u7528copula\u51fd\u6570\" \/><\/p>\n<p><p> <strong>\u5728Python\u4e2d\u8c03\u7528copula\u51fd\u6570\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u4e13\u95e8\u7684\u5e93\uff0c\u4f8b\u5982copulas\u5e93\u3001SciPy\u5e93\u6216statsmodels\u5e93\u7b49\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u591a\u79cdcopula\u51fd\u6570\u7684\u5b9e\u73b0\uff0c\u4f8b\u5982\u9ad8\u65afcopula\u3001t-copula\u7b49\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u5b89\u88c5\u8fd9\u4e9b\u5e93\uff0c\u5e76\u5229\u7528\u5b83\u4eec\u63d0\u4f9b\u7684\u63a5\u53e3\u6765\u521b\u5efa\u548c\u8bc4\u4f30copula\u3002\u9ad8\u65afcopula\u662f\u4e00\u4e2a\u5e38\u89c1\u7684\u9009\u62e9\uff0c\u56e0\u4e3a\u5b83\u7b80\u5355\u6613\u7528\u4e14\u8ba1\u7b97\u6548\u7387\u9ad8\u3002<\/strong><\/p>\n<\/p>\n<p><h2>\u4e00\u3001COPULA\u51fd\u6570\u6982\u8ff0<\/h2>\n<\/p>\n<p><p>Copula\u51fd\u6570\u662f\u4e00\u79cd\u7528\u4e8e\u6355\u6349\u968f\u673a\u53d8\u91cf\u4e4b\u95f4\u4f9d\u8d56\u7ed3\u6784\u7684\u5de5\u5177\u3002\u4e0e\u4f20\u7edf\u7684\u76f8\u5173\u7cfb\u6570\u4e0d\u540c\uff0ccopula\u80fd\u591f\u5728\u4fdd\u7559\u8fb9\u7f18\u5206\u5e03\u7684\u60c5\u51b5\u4e0b\u5efa\u6a21\u8054\u5408\u5206\u5e03\u3002Copula\u51fd\u6570\u5728\u91d1\u878d\u3001\u4fdd\u9669\u3001\u7ecf\u6d4e\u5b66\u3001\u5de5\u7a0b\u7b49\u9886\u57df\u4e2d\u6709\u5e7f\u6cdb\u5e94\u7528\u3002<\/p>\n<\/p>\n<p><p><strong>1. Copula\u7684\u57fa\u672c\u6982\u5ff5<\/strong><\/p>\n<\/p>\n<p><p>Copula\u51fd\u6570\u662f\u4e00\u79cd\u591a\u53d8\u91cf\u8054\u5408\u5206\u5e03\u51fd\u6570\uff0c\u5176\u8fb9\u7f18\u5206\u5e03\u4e3a\u5747\u5300\u5206\u5e03\u3002\u6362\u53e5\u8bdd\u8bf4\uff0ccopula\u5c06\u591a\u7ef4\u968f\u673a\u53d8\u91cf\u7684\u8fb9\u7f18\u5206\u5e03\u201c\u7c98\u5408\u201d\u5728\u4e00\u8d77\uff0c\u5f62\u6210\u4e00\u4e2a\u5b8c\u6574\u7684\u8054\u5408\u5206\u5e03\u3002\u901a\u8fc7copula\uff0c\u6211\u4eec\u53ef\u4ee5\u5728\u4e0d\u4f9d\u8d56\u4e8e\u7279\u5b9a\u7684\u8fb9\u7f18\u5206\u5e03\u5f62\u5f0f\u7684\u60c5\u51b5\u4e0b\uff0c\u7814\u7a76\u53d8\u91cf\u4e4b\u95f4\u7684\u4f9d\u8d56\u6027\u3002<\/p>\n<\/p>\n<p><p><strong>2. Copula\u7684\u79cd\u7c7b<\/strong><\/p>\n<\/p>\n<p><p>\u5e38\u89c1\u7684copula\u5305\u62ec\u9ad8\u65afcopula\u3001t-copula\u3001Clayton copula\u3001Gumbel copula\u548cFrank copula\u7b49\u3002\u6bcf\u79cdcopula\u5177\u6709\u4e0d\u540c\u7684\u7279\u6027\uff0c\u9002\u7528\u4e8e\u4e0d\u540c\u7684\u573a\u666f\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u9ad8\u65afCopula<\/strong>\uff1a\u9002\u7528\u4e8e\u5bf9\u79f0\u4f9d\u8d56\u7ed3\u6784\uff0c\u5bb9\u6613\u8ba1\u7b97\u3002<\/li>\n<li><strong>t-Copula<\/strong>\uff1a\u7c7b\u4f3c\u4e8e\u9ad8\u65afcopula\uff0c\u4f46\u80fd\u591f\u6355\u6349\u91cd\u5c3e\u7279\u6027\u3002<\/li>\n<li><strong>Archimedean Copulas<\/strong>\uff1a\u5982Clayton\u3001Gumbel\u548cFrank\uff0c\u7528\u4e8e\u5efa\u6a21\u975e\u5bf9\u79f0\u4f9d\u8d56\u7ed3\u6784\u3002<\/li>\n<\/ul>\n<p><h2>\u4e8c\u3001\u4f7f\u7528copulas\u5e93<\/h2>\n<\/p>\n<p><p>copulas\u5e93\u662fPython\u4e2d\u4e00\u4e2a\u6d41\u884c\u7684\u5e93\uff0c\u7528\u4e8e\u5904\u7406copula\u51fd\u6570\u3002\u5b83\u63d0\u4f9b\u4e86\u591a\u79cd\u7c7b\u578b\u7684copula\u51fd\u6570\uff0c\u5e76\u4e14\u6613\u4e8e\u4f7f\u7528\u3002<\/p>\n<\/p>\n<p><p><strong>1. \u5b89\u88c5copulas\u5e93<\/strong><\/p>\n<\/p>\n<p><p>\u5728\u5f00\u59cb\u4f7f\u7528copulas\u5e93\u4e4b\u524d\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u5b89\u88c5\u8be5\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install copulas<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>2. \u521b\u5efa\u9ad8\u65afCopula<\/strong><\/p>\n<\/p>\n<p><p>\u9ad8\u65afcopula\u662f\u6700\u5e38\u7528\u7684copula\u4e4b\u4e00\u3002\u4ee5\u4e0b\u662f\u521b\u5efa\u548c\u4f7f\u7528\u9ad8\u65afcopula\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from copulas.multivariate import GaussianMultivariate<\/p>\n<h2><strong>\u521b\u5efa\u9ad8\u65afcopula\u5b9e\u4f8b<\/strong><\/h2>\n<p>gaussian_copula = GaussianMultivariate()<\/p>\n<h2><strong>\u8bad\u7ec3\u6570\u636e\uff08\u5047\u8bbe\u6570\u636e\u5df2\u7ecf\u6807\u51c6\u5316\uff09<\/strong><\/h2>\n<p>data = [[0.1, 0.5], [0.2, 0.6], [0.3, 0.7]]<\/p>\n<h2><strong>\u62df\u5408\u6570\u636e<\/strong><\/h2>\n<p>gaussian_copula.fit(data)<\/p>\n<h2><strong>\u751f\u6210\u65b0\u7684\u6837\u672c<\/strong><\/h2>\n<p>samples = gaussian_copula.sample(10)<\/p>\n<p>print(samples)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>3. \u4f7f\u7528\u5176\u4ed6\u7c7b\u578b\u7684Copula<\/strong><\/p>\n<\/p>\n<p><p>copulas\u5e93\u8fd8\u652f\u6301\u5176\u4ed6\u7c7b\u578b\u7684copula\uff0c\u5982Clayton\u3001Gumbel\u7b49\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Clayton copula\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from copulas.multivariate import VineCopula<\/p>\n<h2><strong>\u521b\u5efaClayton copula\u5b9e\u4f8b<\/strong><\/h2>\n<p>clayton_copula = VineCopula(&#39;clayton&#39;)<\/p>\n<h2><strong>\u62df\u5408\u6570\u636e<\/strong><\/h2>\n<p>clayton_copula.fit(data)<\/p>\n<h2><strong>\u751f\u6210\u65b0\u7684\u6837\u672c<\/strong><\/h2>\n<p>samples = clayton_copula.sample(10)<\/p>\n<p>print(samples)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u4e09\u3001\u4f7f\u7528SciPy\u5e93<\/h2>\n<\/p>\n<p><p>SciPy\u5e93\u662fPython\u4e2d\u7684\u4e00\u4e2a\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u5b83\u4e5f\u63d0\u4f9b\u4e86\u4e00\u4e9b\u57fa\u672c\u7684copula\u5b9e\u73b0\u3002<\/p>\n<\/p>\n<p><p><strong>1. \u5b89\u88c5SciPy\u5e93<\/strong><\/p>\n<\/p>\n<p><p>\u5982\u679c\u5c1a\u672a\u5b89\u88c5SciPy\u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install scipy<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>2. \u4f7f\u7528SciPy\u5b9e\u73b0Copula<\/strong><\/p>\n<\/p>\n<p><p>SciPy\u5e93\u4e2d\u7684copula\u529f\u80fd\u76f8\u5bf9\u7b80\u5355\uff0c\u9002\u7528\u4e8e\u57fa\u672c\u9700\u6c42\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528SciPy\u5b9e\u73b0\u9ad8\u65afcopula\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>from scipy.stats import norm, multivariate_normal<\/p>\n<h2><strong>\u751f\u6210\u6807\u51c6\u6b63\u6001\u5206\u5e03\u7684\u6570\u636e<\/strong><\/h2>\n<p>data = np.random.normal(size=(100, 2))<\/p>\n<h2><strong>\u8ba1\u7b97\u76f8\u5173\u77e9\u9635<\/strong><\/h2>\n<p>corr_matrix = np.corrcoef(data, rowvar=False)<\/p>\n<h2><strong>\u521b\u5efa\u591a\u5143\u6b63\u6001\u5206\u5e03\u5bf9\u8c61<\/strong><\/h2>\n<p>mvn = multivariate_normal(mean=[0, 0], cov=corr_matrix)<\/p>\n<h2><strong>\u751f\u6210\u65b0\u7684\u6837\u672c<\/strong><\/h2>\n<p>samples = mvn.rvs(size=10)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u56db\u3001\u4f7f\u7528statsmodels\u5e93<\/h2>\n<\/p>\n<p><p>statsmodels\u5e93\u662f\u4e00\u4e2a\u7528\u4e8e\u7edf\u8ba1\u5efa\u6a21\u7684Python\u5e93\uff0c\u5b83\u4e5f\u63d0\u4f9b\u4e86\u4e00\u4e9bcopula\u5b9e\u73b0\u3002<\/p>\n<\/p>\n<p><p><strong>1. \u5b89\u88c5statsmodels\u5e93<\/strong><\/p>\n<\/p>\n<p><p>\u5982\u679c\u5c1a\u672a\u5b89\u88c5statsmodels\u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install statsmodels<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>2. \u4f7f\u7528statsmodels\u5b9e\u73b0Copula<\/strong><\/p>\n<\/p>\n<p><p>\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528statsmodels\u5b9e\u73b0\u9ad8\u65afcopula\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import statsmodels.api as sm<\/p>\n<p>import numpy as np<\/p>\n<h2><strong>\u751f\u6210\u6807\u51c6\u6b63\u6001\u5206\u5e03\u7684\u6570\u636e<\/strong><\/h2>\n<p>data = np.random.normal(size=(100, 2))<\/p>\n<h2><strong>\u521b\u5efa\u9ad8\u65afcopula<\/strong><\/h2>\n<p>copula = sm.copulas.GaussianCopula(data)<\/p>\n<h2><strong>\u751f\u6210\u65b0\u7684\u6837\u672c<\/strong><\/h2>\n<p>samples = copula.rvs(10)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u4e94\u3001\u5e94\u7528\u573a\u666f\u548c\u6ce8\u610f\u4e8b\u9879<\/h2>\n<\/p>\n<p><p><strong>1. \u5e94\u7528\u573a\u666f<\/strong><\/p>\n<\/p>\n<ul>\n<li><strong>\u91d1\u878d\u98ce\u9669\u7ba1\u7406<\/strong>\uff1acopula\u7528\u4e8e\u5efa\u6a21\u8d44\u4ea7\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u8bc4\u4f30\u6295\u8d44\u7ec4\u5408\u98ce\u9669\u3002<\/li>\n<li><strong>\u4fdd\u9669\u5b9a\u4ef7<\/strong>\uff1acopula\u5e2e\u52a9\u4fdd\u9669\u516c\u53f8\u8bc4\u4f30\u4e0d\u540c\u4fdd\u9669\u4ea7\u54c1\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u4f18\u5316\u5b9a\u4ef7\u7b56\u7565\u3002<\/li>\n<li><strong>\u7ecf\u6d4e\u5b66\u7814\u7a76<\/strong>\uff1acopula\u7528\u4e8e\u7814\u7a76\u7ecf\u6d4e\u53d8\u91cf\u4e4b\u95f4\u7684\u4f9d\u8d56\u5173\u7cfb\uff0c\u63ed\u793a\u7ecf\u6d4e\u6d3b\u52a8\u7684\u6f5c\u5728\u89c4\u5f8b\u3002<\/li>\n<\/ul>\n<p><p><strong>2. \u6ce8\u610f\u4e8b\u9879<\/strong><\/p>\n<\/p>\n<ul>\n<li><strong>\u6570\u636e\u9884\u5904\u7406<\/strong>\uff1a\u5728\u4f7f\u7528copula\u4e4b\u524d\uff0c\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u6807\u51c6\u5316\u548c\u53bb\u91cf\u7eb2\u5316\u5904\u7406\u3002<\/li>\n<li><strong>\u6a21\u578b\u9009\u62e9<\/strong>\uff1a\u6839\u636e\u5177\u4f53\u5e94\u7528\u573a\u666f\u9009\u62e9\u5408\u9002\u7684copula\u7c7b\u578b\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u6355\u6349\u6570\u636e\u7684\u4f9d\u8d56\u7ed3\u6784\u3002<\/li>\n<li><strong>\u53c2\u6570\u4f30\u8ba1<\/strong>\uff1a\u5bf9\u4e8e\u67d0\u4e9b\u590d\u6742\u7684copula\u7c7b\u578b\uff0c\u53c2\u6570\u4f30\u8ba1\u53ef\u80fd\u8f83\u4e3a\u590d\u6742\uff0c\u9700\u8981\u4f7f\u7528\u4f18\u5316\u7b97\u6cd5\u8fdb\u884c\u6c42\u89e3\u3002<\/li>\n<\/ul>\n<p><p>\u901a\u8fc7\u4ee5\u4e0a\u7684\u8be6\u7ec6\u4ecb\u7ecd\uff0c\u76f8\u4fe1\u4f60\u5bf9Python\u4e2d\u5982\u4f55\u8c03\u7528copula\u51fd\u6570\u6709\u4e86\u66f4\u6df1\u5165\u7684\u7406\u89e3\u3002copula\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5728\u591a\u53d8\u91cf\u5206\u6790\u4e2d\u6355\u6349\u968f\u673a\u53d8\u91cf\u4e4b\u95f4\u7684\u590d\u6742\u4f9d\u8d56\u5173\u7cfb\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u5b89\u88c5\u6240\u9700\u7684\u5e93\u4ee5\u4f7f\u7528copula\u51fd\u6570\uff1f<\/strong><br \/>\u5728Python\u4e2d\u4f7f\u7528copula\u51fd\u6570\u901a\u5e38\u9700\u8981\u5b89\u88c5\u4e00\u4e9b\u7279\u5b9a\u7684\u5e93\uff0c\u6bd4\u5982<code>copulas<\/code>\u6216<code>statsmodels<\/code>\u3002\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528pip\u547d\u4ee4\u6765\u5b89\u88c5\u8fd9\u4e9b\u5e93\u3002\u4f8b\u5982\uff0c\u6253\u5f00\u7ec8\u7aef\u5e76\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\uff1a  <\/p>\n<pre><code class=\"language-bash\">pip install copulas\n<\/code><\/pre>\n<p>\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u60a8\u5c31\u53ef\u4ee5\u5728\u4ee3\u7801\u4e2d\u5bfc\u5165\u8fd9\u4e9b\u5e93\u6765\u8c03\u7528copula\u76f8\u5173\u7684\u529f\u80fd\u4e86\u3002<\/p>\n<p><strong>\u4f7f\u7528copula\u51fd\u6570\u7684\u57fa\u672c\u6b65\u9aa4\u6709\u54ea\u4e9b\uff1f<\/strong><br \/>\u8c03\u7528copula\u51fd\u6570\u7684\u57fa\u672c\u6b65\u9aa4\u5305\u62ec\uff1a\u9996\u5148\uff0c\u5bfc\u5165\u6240\u9700\u7684\u5e93\uff1b\u63a5\u7740\uff0c\u51c6\u5907\u6570\u636e\u96c6\u5e76\u5bf9\u5176\u8fdb\u884c\u5fc5\u8981\u7684\u9884\u5904\u7406\uff1b\u7136\u540e\uff0c\u9009\u62e9\u9002\u5408\u60a8\u6570\u636e\u7684copula\u7c7b\u578b\uff0c\u4f8b\u5982\u9ad8\u65afcopula\u6216t-copula\uff1b\u6700\u540e\uff0c\u4f7f\u7528\u9009\u5b9a\u7684copula\u6a21\u578b\u8fdb\u884c\u62df\u5408\u548c\u751f\u6210\u65b0\u7684\u6837\u672c\u3002\u901a\u8fc7\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u60a8\u53ef\u4ee5\u6709\u6548\u5730\u5229\u7528copula\u51fd\u6570\u8fdb\u884c\u6570\u636e\u5efa\u6a21\u3002<\/p>\n<p><strong>copula\u51fd\u6570\u9002\u5408\u7528\u6765\u89e3\u51b3\u54ea\u4e9b\u7c7b\u578b\u7684\u95ee\u9898\uff1f<\/strong><br \/>copula\u51fd\u6570\u7279\u522b\u9002\u5408\u7528\u6765\u5904\u7406\u591a\u7ef4\u6570\u636e\u7684\u76f8\u5173\u6027\u95ee\u9898\u3002\u5b83\u4eec\u53ef\u4ee5\u7528\u4e8e\u91d1\u878d\u9886\u57df\u7684\u98ce\u9669\u7ba1\u7406\uff0c\u4f8b\u5982\u8d44\u4ea7\u7ec4\u5408\u7684\u76f8\u5173\u6027\u5206\u6790\uff1b\u8fd8\u53ef\u4ee5\u5e94\u7528\u4e8e\u6c14\u8c61\u6570\u636e\u5206\u6790\u3001\u751f\u547d\u79d1\u5b66\u6570\u636e\u5efa\u6a21\u7b49\u573a\u666f\u3002\u901a\u8fc7copula\u51fd\u6570\uff0c\u60a8\u80fd\u591f\u66f4\u597d\u5730\u7406\u89e3\u4e0d\u540c\u53d8\u91cf\u4e4b\u95f4\u7684\u4f9d\u8d56\u5173\u7cfb\uff0c\u4ece\u800c\u505a\u51fa\u66f4\u51c6\u786e\u7684\u9884\u6d4b\u548c\u51b3\u7b56\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u8c03\u7528copula\u51fd\u6570\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u4e13\u95e8\u7684\u5e93\uff0c\u4f8b\u5982copulas\u5e93\u3001SciPy\u5e93\u6216statsmod [&hellip;]","protected":false},"author":3,"featured_media":960791,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/960786"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=960786"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/960786\/revisions"}],"predecessor-version":[{"id":960793,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/960786\/revisions\/960793"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/960791"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=960786"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=960786"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=960786"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}