{"id":1118918,"date":"2025-01-08T18:39:53","date_gmt":"2025-01-08T10:39:53","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1118918.html"},"modified":"2025-01-08T18:39:55","modified_gmt":"2025-01-08T10:39:55","slug":"python%e5%a6%82%e4%bd%95%e8%bf%9b%e8%a1%8c%e8%92%99%e7%89%b9%e5%8d%a1%e6%b4%9b%e6%a8%a1%e6%8b%9f","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1118918.html","title":{"rendered":"python\u5982\u4f55\u8fdb\u884c\u8499\u7279\u5361\u6d1b\u6a21\u62df"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25082210\/3ae595f1-3266-4d89-b498-aa05a417fe69.webp\" alt=\"python\u5982\u4f55\u8fdb\u884c\u8499\u7279\u5361\u6d1b\u6a21\u62df\" \/><\/p>\n<p><p> <strong>Python\u8fdb\u884c\u8499\u7279\u5361\u6d1b\u6a21\u62df\u7684\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528\u968f\u673a\u6570\u751f\u6210\u5668\u3001\u6a21\u62df\u5927\u91cf\u6837\u672c\u3001\u8ba1\u7b97\u6982\u7387\u5206\u5e03\u3002<\/strong> \u8499\u7279\u5361\u6d1b\u6a21\u62df\u662f\u4e00\u79cd\u901a\u8fc7\u91cd\u590d\u968f\u673a\u91c7\u6837\u6765<a href=\"https:\/\/docs.pingcode.com\/agile\/project-management\/estimation\" target=\"_blank\">\u4f30\u7b97<\/a>\u6570\u503c\u7684\u65b9\u6cd5\u3002\u5b83\u53ef\u4ee5\u7528\u6765\u89e3\u51b3\u5404\u79cd\u590d\u6742\u95ee\u9898\uff0c\u4ece\u91d1\u878d\u98ce\u9669\u8bc4\u4f30\u5230\u7269\u7406\u7cfb\u7edf\u6a21\u62df\u3002\u5177\u4f53\u64cd\u4f5c\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n<ol>\n<li><strong>\u4f7f\u7528\u968f\u673a\u6570\u751f\u6210\u5668<\/strong>\uff1a\u5728Python\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u4f7f\u7528<code>numpy<\/code>\u5e93\u6765\u751f\u6210\u968f\u673a\u6570\u3002\u901a\u8fc7\u6a21\u62df\u5927\u91cf\u7684\u968f\u673a\u6837\u672c\uff0c\u6211\u4eec\u53ef\u4ee5\u903c\u8fd1\u771f\u5b9e\u7684\u6982\u7387\u5206\u5e03\u3002<\/li>\n<li><strong>\u6a21\u62df\u5927\u91cf\u6837\u672c<\/strong>\uff1a\u901a\u8fc7\u53cd\u590d\u8fdb\u884c\u968f\u673a\u91c7\u6837\uff0c\u6211\u4eec\u53ef\u4ee5\u751f\u6210\u5927\u91cf\u7684\u6837\u672c\u6570\u636e\u3002\u8fd9\u4e9b\u6837\u672c\u6570\u636e\u53ef\u4ee5\u7528\u6765\u4f30\u7b97\u6211\u4eec\u611f\u5174\u8da3\u7684\u7edf\u8ba1\u91cf\u3002<\/li>\n<li><strong>\u8ba1\u7b97\u6982\u7387\u5206\u5e03<\/strong>\uff1a\u901a\u8fc7\u5206\u6790\u6837\u672c\u6570\u636e\uff0c\u6211\u4eec\u53ef\u4ee5\u8ba1\u7b97\u51fa\u6240\u9700\u7684\u6982\u7387\u5206\u5e03\u3002\u8fd9\u4e2a\u8fc7\u7a0b\u901a\u5e38\u6d89\u53ca\u8ba1\u7b97\u6837\u672c\u7684\u5747\u503c\u3001\u65b9\u5dee\u7b49\u7edf\u8ba1\u91cf\u3002<\/li>\n<\/ol>\n<p><p>\u4e0b\u9762\u662f\u4e00\u4e2a\u8be6\u7ec6\u7684\u4ecb\u7ecd\u548c\u793a\u4f8b\u4ee3\u7801\u3002<\/p>\n<\/p>\n<p><h2>\u4e00\u3001\u4f7f\u7528\u968f\u673a\u6570\u751f\u6210\u5668<\/h2>\n<\/p>\n<p><p>\u8499\u7279\u5361\u6d1b\u6a21\u62df\u7684\u7b2c\u4e00\u6b65\u662f\u751f\u6210\u968f\u673a\u6570\u3002\u5728Python\u4e2d\uff0c<code>numpy<\/code>\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u968f\u673a\u6570\u751f\u6210\u529f\u80fd\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<code>numpy.random<\/code>\u6a21\u5757\u6765\u751f\u6210\u5404\u79cd\u5206\u5e03\u7684\u968f\u673a\u6570\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u751f\u62101000\u4e2a\u5747\u5300\u5206\u5e03\u7684\u968f\u673a\u6570<\/strong><\/h2>\n<p>random_numbers = np.random.rand(1000)<\/p>\n<p>print(random_numbers)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u751f\u6210\u4e861000\u4e2a\u5747\u5300\u5206\u5e03\u57280\u52301\u4e4b\u95f4\u7684\u968f\u673a\u6570\u3002\u8fd9\u4e9b\u968f\u673a\u6570\u53ef\u4ee5\u7528\u6765\u6a21\u62df\u5404\u79cd\u968f\u673a\u8fc7\u7a0b\u3002<\/p>\n<\/p>\n<p><h2>\u4e8c\u3001\u6a21\u62df\u5927\u91cf\u6837\u672c<\/h2>\n<\/p>\n<p><p>\u751f\u6210\u968f\u673a\u6570\u540e\uff0c\u6211\u4eec\u9700\u8981\u6a21\u62df\u5927\u91cf\u7684\u6837\u672c\u6570\u636e\u3002\u8fd9\u4e2a\u8fc7\u7a0b\u901a\u5e38\u6d89\u53ca\u53cd\u590d\u8fdb\u884c\u968f\u673a\u91c7\u6837\uff0c\u5e76\u8bb0\u5f55\u6bcf\u6b21\u91c7\u6837\u7684\u7ed3\u679c\u3002<\/p>\n<\/p>\n<p><h3>\u793a\u4f8b\uff1a\u4f30\u7b97\u5706\u5468\u7387<\/h3>\n<\/p>\n<p><p>\u4f30\u7b97\u5706\u5468\u7387\u662f\u8499\u7279\u5361\u6d1b\u6a21\u62df\u7684\u7ecf\u5178\u793a\u4f8b\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5728\u4e00\u4e2a\u5355\u4f4d\u6b63\u65b9\u5f62\u4e2d\u968f\u673a\u751f\u6210\u70b9\uff0c\u7136\u540e\u8ba1\u7b97\u8fd9\u4e9b\u70b9\u843d\u5728\u5355\u4f4d\u5706\u5185\u7684\u6bd4\u4f8b\u6765\u4f30\u7b97\u5706\u5468\u7387\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u751f\u621010000\u4e2a\u968f\u673a\u70b9<\/strong><\/h2>\n<p>num_samples = 10000<\/p>\n<p>x = np.random.rand(num_samples)<\/p>\n<p>y = np.random.rand(num_samples)<\/p>\n<h2><strong>\u8ba1\u7b97\u843d\u5728\u5355\u4f4d\u5706\u5185\u7684\u70b9\u7684\u6570\u91cf<\/strong><\/h2>\n<p>inside_circle = (x&lt;strong&gt;2 + y&lt;\/strong&gt;2) &lt;= 1<\/p>\n<p>pi_estimate = 4 * np.sum(inside_circle) \/ num_samples<\/p>\n<p>print(f&quot;\u4f30\u7b97\u7684\u5706\u5468\u7387\uff1a{pi_estimate}&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u751f\u6210\u4e8610000\u4e2a\u968f\u673a\u70b9\uff0c\u5e76\u8ba1\u7b97\u4e86\u843d\u5728\u5355\u4f4d\u5706\u5185\u7684\u70b9\u7684\u6570\u91cf\u3002\u901a\u8fc7\u5c06\u8fd9\u4e2a\u6570\u91cf\u4e58\u4ee54\u5e76\u9664\u4ee5\u603b\u6837\u672c\u6570\uff0c\u6211\u4eec\u5f97\u5230\u4e86\u5706\u5468\u7387\u7684\u4f30\u7b97\u503c\u3002<\/p>\n<\/p>\n<p><h2>\u4e09\u3001\u8ba1\u7b97\u6982\u7387\u5206\u5e03<\/h2>\n<\/p>\n<p><p>\u6700\u540e\u4e00\u6b65\u662f\u8ba1\u7b97\u6837\u672c\u6570\u636e\u7684\u6982\u7387\u5206\u5e03\u3002\u8fd9\u4e2a\u8fc7\u7a0b\u901a\u5e38\u6d89\u53ca\u8ba1\u7b97\u6837\u672c\u7684\u5747\u503c\u3001\u65b9\u5dee\u7b49\u7edf\u8ba1\u91cf\u3002<\/p>\n<\/p>\n<p><h3>\u793a\u4f8b\uff1a\u80a1\u7968\u4ef7\u683c\u6a21\u62df<\/h3>\n<\/p>\n<p><p>\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8499\u7279\u5361\u6d1b\u6a21\u62df\u6765\u6a21\u62df\u80a1\u7968\u4ef7\u683c\u7684\u53d8\u5316\u3002\u5047\u8bbe\u80a1\u7968\u4ef7\u683c\u9075\u5faa\u51e0\u4f55\u5e03\u6717\u8fd0\u52a8\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u516c\u5f0f\u8fdb\u884c\u6a21\u62df\uff1a<\/p>\n<\/p>\n<p><p>[ S_t = S_0 \\exp((\\mu &#8211; 0.5\\sigma^2)t + \\sigma W_t) ]<\/p>\n<\/p>\n<p><p>\u5176\u4e2d\uff0c( S_t ) \u662f\u65f6\u95f4 ( t ) \u7684\u80a1\u7968\u4ef7\u683c\uff0c( S_0 ) \u662f\u521d\u59cb\u80a1\u7968\u4ef7\u683c\uff0c( \\mu ) \u662f\u80a1\u7968\u7684\u9884\u671f\u6536\u76ca\u7387\uff0c( \\sigma ) \u662f\u80a1\u7968\u7684\u6ce2\u52a8\u7387\uff0c( W_t ) \u662f\u6807\u51c6\u5e03\u6717\u8fd0\u52a8\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u53c2\u6570\u8bbe\u7f6e<\/strong><\/h2>\n<p>S0 = 100  # \u521d\u59cb\u80a1\u7968\u4ef7\u683c<\/p>\n<p>mu = 0.1  # \u9884\u671f\u6536\u76ca\u7387<\/p>\n<p>sigma = 0.2  # \u6ce2\u52a8\u7387<\/p>\n<p>T = 1.0  # \u6a21\u62df\u65f6\u95f4<\/p>\n<p>num_steps = 1000  # \u65f6\u95f4\u6b65\u6570<\/p>\n<p>num_simulations = 10000  # \u6a21\u62df\u6b21\u6570<\/p>\n<h2><strong>\u65f6\u95f4\u6b65\u957f<\/strong><\/h2>\n<p>dt = T \/ num_steps<\/p>\n<h2><strong>\u751f\u6210\u6807\u51c6\u6b63\u6001\u5206\u5e03\u7684\u968f\u673a\u6570<\/strong><\/h2>\n<p>random_numbers = np.random.randn(num_simulations, num_steps)<\/p>\n<h2><strong>\u7d2f\u79ef\u968f\u673a\u6570<\/strong><\/h2>\n<p>W = np.cumsum(random_numbers * np.sqrt(dt), axis=1)<\/p>\n<h2><strong>\u8ba1\u7b97\u80a1\u7968\u4ef7\u683c<\/strong><\/h2>\n<p>time_grid = np.linspace(0, T, num_steps)<\/p>\n<p>S = S0 * np.exp((mu - 0.5 * sigma2) * time_grid + sigma * W)<\/p>\n<h2><strong>\u8ba1\u7b97\u80a1\u7968\u4ef7\u683c\u7684\u5747\u503c\u548c\u65b9\u5dee<\/strong><\/h2>\n<p>mean_price = np.mean(S[:, -1])<\/p>\n<p>std_price = np.std(S[:, -1])<\/p>\n<p>print(f&quot;\u80a1\u7968\u4ef7\u683c\u7684\u5747\u503c\uff1a{mean_price}&quot;)<\/p>\n<p>print(f&quot;\u80a1\u7968\u4ef7\u683c\u7684\u65b9\u5dee\uff1a{std_price}&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u6a21\u62df\u4e8610000\u6b21\u80a1\u7968\u4ef7\u683c\u53d8\u5316\uff0c\u5e76\u8ba1\u7b97\u4e86\u6bcf\u6b21\u6a21\u62df\u7ed3\u675f\u65f6\u7684\u80a1\u7968\u4ef7\u683c\u3002\u901a\u8fc7\u8ba1\u7b97\u8fd9\u4e9b\u80a1\u7968\u4ef7\u683c\u7684\u5747\u503c\u548c\u65b9\u5dee\uff0c\u6211\u4eec\u53ef\u4ee5\u4f30\u7b97\u80a1\u7968\u4ef7\u683c\u7684\u6982\u7387\u5206\u5e03\u3002<\/p>\n<\/p>\n<p><h2>\u56db\u3001\u8499\u7279\u5361\u6d1b\u6a21\u62df\u7684\u5e94\u7528<\/h2>\n<\/p>\n<p><p>\u8499\u7279\u5361\u6d1b\u6a21\u62df\u53ef\u4ee5\u5e94\u7528\u4e8e\u5404\u79cd\u9886\u57df\uff0c\u5305\u62ec\u91d1\u878d\u3001\u7269\u7406\u3001\u5de5\u7a0b\u7b49\u3002\u4e0b\u9762\u4ecb\u7ecd\u4e00\u4e9b\u5e38\u89c1\u7684\u5e94\u7528\u573a\u666f\u3002<\/p>\n<\/p>\n<p><h3>1\u3001\u91d1\u878d\u98ce\u9669\u7ba1\u7406<\/h3>\n<\/p>\n<p><p>\u5728\u91d1\u878d\u9886\u57df\uff0c\u8499\u7279\u5361\u6d1b\u6a21\u62df\u5e38\u7528\u4e8e\u98ce\u9669\u7ba1\u7406\u3002\u4f8b\u5982\uff0c\u6295\u8d44\u7ec4\u5408\u7684\u98ce\u9669\u8bc4\u4f30\u3001\u671f\u6743\u5b9a\u4ef7\u7b49\u3002\u901a\u8fc7\u6a21\u62df\u5404\u79cd\u5e02\u573a\u60c5\u666f\uff0c\u6211\u4eec\u53ef\u4ee5\u4f30\u7b97\u6295\u8d44\u7ec4\u5408\u7684\u6f5c\u5728\u635f\u5931\u548c\u6536\u76ca\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u53c2\u6570\u8bbe\u7f6e<\/strong><\/h2>\n<p>initial_investment = 100000  # \u521d\u59cb\u6295\u8d44\u91d1\u989d<\/p>\n<p>mu = 0.1  # \u9884\u671f\u6536\u76ca\u7387<\/p>\n<p>sigma = 0.2  # \u6ce2\u52a8\u7387<\/p>\n<p>T = 1.0  # \u6295\u8d44\u671f\u9650<\/p>\n<p>num_simulations = 10000  # \u6a21\u62df\u6b21\u6570<\/p>\n<h2><strong>\u6a21\u62df\u6295\u8d44\u56de\u62a5<\/strong><\/h2>\n<p>random_returns = np.random.normal(mu, sigma, num_simulations)<\/p>\n<p>final_investment = initial_investment * np.exp(random_returns * T)<\/p>\n<h2><strong>\u8ba1\u7b97\u6295\u8d44\u56de\u62a5\u7684\u5747\u503c\u548c\u65b9\u5dee<\/strong><\/h2>\n<p>mean_return = np.mean(final_investment)<\/p>\n<p>std_return = np.std(final_investment)<\/p>\n<p>print(f&quot;\u6295\u8d44\u56de\u62a5\u7684\u5747\u503c\uff1a{mean_return}&quot;)<\/p>\n<p>print(f&quot;\u6295\u8d44\u56de\u62a5\u7684\u65b9\u5dee\uff1a{std_return}&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u6a21\u62df\u4e8610000\u6b21\u6295\u8d44\u56de\u62a5\uff0c\u5e76\u8ba1\u7b97\u4e86\u6295\u8d44\u56de\u62a5\u7684\u5747\u503c\u548c\u65b9\u5dee\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u8bc4\u4f30\u6295\u8d44\u7684\u98ce\u9669\u548c\u6536\u76ca\u3002<\/p>\n<\/p>\n<p><h3>2\u3001\u7269\u7406\u7cfb\u7edf\u6a21\u62df<\/h3>\n<\/p>\n<p><p>\u5728\u7269\u7406\u5b66\u4e2d\uff0c\u8499\u7279\u5361\u6d1b\u6a21\u62df\u5e38\u7528\u4e8e\u6a21\u62df\u590d\u6742\u7684\u7269\u7406\u7cfb\u7edf\u3002\u4f8b\u5982\uff0c\u7c92\u5b50\u8fd0\u52a8\u3001\u8f90\u5c04\u4f20\u8f93\u7b49\u3002\u901a\u8fc7\u6a21\u62df\u5927\u91cf\u7684\u7c92\u5b50\u8fd0\u52a8\uff0c\u6211\u4eec\u53ef\u4ee5\u4f30\u7b97\u7cfb\u7edf\u7684\u884c\u4e3a\u548c\u7279\u6027\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u53c2\u6570\u8bbe\u7f6e<\/strong><\/h2>\n<p>num_particles = 1000  # \u7c92\u5b50\u6570\u91cf<\/p>\n<p>num_steps = 1000  # \u65f6\u95f4\u6b65\u6570<\/p>\n<p>dt = 0.01  # \u65f6\u95f4\u6b65\u957f<\/p>\n<h2><strong>\u751f\u6210\u7c92\u5b50\u521d\u59cb\u4f4d\u7f6e\u548c\u901f\u5ea6<\/strong><\/h2>\n<p>positions = np.zeros((num_particles, 2))<\/p>\n<p>velocities = np.random.randn(num_particles, 2)<\/p>\n<h2><strong>\u6a21\u62df\u7c92\u5b50\u8fd0\u52a8<\/strong><\/h2>\n<p>for step in range(num_steps):<\/p>\n<p>    positions += velocities * dt<\/p>\n<h2><strong>\u8ba1\u7b97\u7c92\u5b50\u4f4d\u7f6e\u7684\u5747\u503c\u548c\u65b9\u5dee<\/strong><\/h2>\n<p>mean_position = np.mean(positions, axis=0)<\/p>\n<p>std_position = np.std(positions, axis=0)<\/p>\n<p>print(f&quot;\u7c92\u5b50\u4f4d\u7f6e\u7684\u5747\u503c\uff1a{mean_position}&quot;)<\/p>\n<p>print(f&quot;\u7c92\u5b50\u4f4d\u7f6e\u7684\u65b9\u5dee\uff1a{std_position}&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u6a21\u62df\u4e861000\u4e2a\u7c92\u5b50\u7684\u8fd0\u52a8\uff0c\u5e76\u8ba1\u7b97\u4e86\u7c92\u5b50\u4f4d\u7f6e\u7684\u5747\u503c\u548c\u65b9\u5dee\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u4e86\u89e3\u7c92\u5b50\u7cfb\u7edf\u7684\u884c\u4e3a\u548c\u7279\u6027\u3002<\/p>\n<\/p>\n<p><h3>3\u3001\u5de5\u7a0b\u53ef\u9760\u6027\u5206\u6790<\/h3>\n<\/p>\n<p><p>\u5728\u5de5\u7a0b\u9886\u57df\uff0c\u8499\u7279\u5361\u6d1b\u6a21\u62df\u5e38\u7528\u4e8e\u53ef\u9760\u6027\u5206\u6790\u3002\u4f8b\u5982\uff0c\u8ba1\u7b97\u7ed3\u6784\u7684\u5931\u6548\u6982\u7387\u3001\u8bc4\u4f30\u7cfb\u7edf\u7684\u53ef\u9760\u6027\u7b49\u3002\u901a\u8fc7\u6a21\u62df\u5404\u79cd\u5de5\u51b5\uff0c\u6211\u4eec\u53ef\u4ee5\u4f30\u7b97\u7cfb\u7edf\u7684\u5931\u6548\u6982\u7387\u548c\u53ef\u9760\u6027\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u53c2\u6570\u8bbe\u7f6e<\/strong><\/h2>\n<p>num_simulations = 10000  # \u6a21\u62df\u6b21\u6570<\/p>\n<p>mean_strength = 1000  # \u7ed3\u6784\u5f3a\u5ea6\u5747\u503c<\/p>\n<p>std_strength = 100  # \u7ed3\u6784\u5f3a\u5ea6\u6807\u51c6\u5dee<\/p>\n<p>mean_load = 800  # \u8f7d\u8377\u5747\u503c<\/p>\n<p>std_load = 50  # \u8f7d\u8377\u6807\u51c6\u5dee<\/p>\n<h2><strong>\u6a21\u62df\u7ed3\u6784\u5f3a\u5ea6\u548c\u8f7d\u8377<\/strong><\/h2>\n<p>strength = np.random.normal(mean_strength, std_strength, num_simulations)<\/p>\n<p>load = np.random.normal(mean_load, std_load, num_simulations)<\/p>\n<h2><strong>\u8ba1\u7b97\u5931\u6548\u6b21\u6570<\/strong><\/h2>\n<p>f<a href=\"https:\/\/docs.pingcode.com\/blog\/59162.html\" target=\"_blank\">AI<\/a>lures = np.sum(strength &lt; load)<\/p>\n<h2><strong>\u8ba1\u7b97\u5931\u6548\u6982\u7387<\/strong><\/h2>\n<p>failure_probability = failures \/ num_simulations<\/p>\n<p>print(f&quot;\u7ed3\u6784\u5931\u6548\u6982\u7387\uff1a{failure_probability}&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u6a21\u62df\u4e8610000\u6b21\u7ed3\u6784\u5f3a\u5ea6\u548c\u8f7d\u8377\uff0c\u5e76\u8ba1\u7b97\u4e86\u7ed3\u6784\u7684\u5931\u6548\u6b21\u6570\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u4f30\u7b97\u7ed3\u6784\u7684\u5931\u6548\u6982\u7387\u548c\u53ef\u9760\u6027\u3002<\/p>\n<\/p>\n<p><h2>\u603b\u7ed3<\/h2>\n<\/p>\n<p><p>\u8499\u7279\u5361\u6d1b\u6a21\u62df\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u6570\u503c\u65b9\u6cd5\uff0c\u901a\u8fc7\u6a21\u62df\u5927\u91cf\u7684\u968f\u673a\u6837\u672c\uff0c\u6211\u4eec\u53ef\u4ee5\u4f30\u7b97\u5404\u79cd\u590d\u6742\u95ee\u9898\u7684\u6570\u503c\u89e3\u3002\u672c\u6587\u4ecb\u7ecd\u4e86Python\u8fdb\u884c\u8499\u7279\u5361\u6d1b\u6a21\u62df\u7684\u57fa\u672c\u6b65\u9aa4\uff0c\u5e76\u901a\u8fc7\u591a\u4e2a\u793a\u4f8b\u5c55\u793a\u4e86\u8499\u7279\u5361\u6d1b\u6a21\u62df\u5728\u4e0d\u540c\u9886\u57df\u7684\u5e94\u7528\u3002\u65e0\u8bba\u662f\u5728\u91d1\u878d\u3001\u7269\u7406\u8fd8\u662f\u5de5\u7a0b\u9886\u57df\uff0c\u8499\u7279\u5361\u6d1b\u6a21\u62df\u90fd\u63d0\u4f9b\u4e86\u4e00\u79cd\u6709\u6548\u7684\u6570\u503c\u5206\u6790\u65b9\u6cd5\u3002\u901a\u8fc7\u638c\u63e1\u8fd9\u4e00\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u597d\u5730\u89e3\u51b3\u5404\u79cd\u590d\u6742\u95ee\u9898\uff0c\u63d0\u9ad8\u5206\u6790\u548c\u51b3\u7b56\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u4ec0\u4e48\u662f\u8499\u7279\u5361\u6d1b\u6a21\u62df\uff0c\u5b83\u5728Python\u4e2d\u7684\u5e94\u7528\u662f\u4ec0\u4e48\uff1f<\/strong><br \/>\u8499\u7279\u5361\u6d1b\u6a21\u62df\u662f\u4e00\u79cd\u5229\u7528\u968f\u673a\u62bd\u6837\u548c\u7edf\u8ba1\u5b66\u65b9\u6cd5\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\u7684\u6280\u672f\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u98ce\u9669\u8bc4\u4f30\u3001\u91d1\u878d\u5efa\u6a21\u3001\u7269\u7406\u4eff\u771f\u7b49\u9886\u57df\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u5e93\u5982NumPy\u548cSciPy\u6765\u5b9e\u73b0\u8499\u7279\u5361\u6d1b\u6a21\u62df\uff0c\u501f\u52a9\u8fd9\u4e9b\u5e93\u7684\u5f3a\u5927\u6570\u5b66\u8ba1\u7b97\u80fd\u529b\uff0c\u7528\u6237\u53ef\u4ee5\u5feb\u901f\u751f\u6210\u968f\u673a\u6570\uff0c\u8fdb\u884c\u590d\u6742\u7684\u6982\u7387\u5206\u6790\u548c\u6a21\u62df\u3002<\/p>\n<p><strong>\u4f7f\u7528Python\u8fdb\u884c\u8499\u7279\u5361\u6d1b\u6a21\u62df\u65f6\uff0c\u5982\u4f55\u751f\u6210\u968f\u673a\u6837\u672c\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u7684<code>numpy.random<\/code>\u6a21\u5757\u751f\u6210\u968f\u673a\u6837\u672c\u3002\u4f8b\u5982\uff0c\u4f7f\u7528<code>numpy.random.rand()<\/code>\u53ef\u4ee5\u751f\u6210\u5747\u5300\u5206\u5e03\u7684\u968f\u673a\u6570\uff0c\u800c<code>numpy.random.normal()<\/code>\u5219\u53ef\u4ee5\u751f\u6210\u6b63\u6001\u5206\u5e03\u7684\u968f\u673a\u6570\u3002\u901a\u8fc7\u8bbe\u7f6e\u4e0d\u540c\u7684\u53c2\u6570\uff0c\u7528\u6237\u53ef\u4ee5\u6839\u636e\u9700\u8981\u751f\u6210\u5404\u7c7b\u968f\u673a\u6837\u672c\uff0c\u4ee5\u652f\u6301\u8499\u7279\u5361\u6d1b\u6a21\u62df\u7684\u9700\u6c42\u3002<\/p>\n<p><strong>\u6709\u54ea\u4e9b\u5e38\u89c1\u7684\u9519\u8bef\u9700\u8981\u907f\u514d\uff0c\u5728\u8fdb\u884c\u8499\u7279\u5361\u6d1b\u6a21\u62df\u65f6\uff1f<\/strong><br \/>\u5728\u8fdb\u884c\u8499\u7279\u5361\u6d1b\u6a21\u62df\u65f6\uff0c\u5e38\u89c1\u7684\u9519\u8bef\u5305\u62ec\u6837\u672c\u6570\u91cf\u4e0d\u8db3\u3001\u968f\u673a\u6570\u751f\u6210\u4e0d\u5747\u5300\u3001\u7edf\u8ba1\u5206\u6790\u65b9\u6cd5\u9009\u62e9\u4e0d\u5f53\u7b49\u3002\u6837\u672c\u6570\u91cf\u4e0d\u8db3\u53ef\u80fd\u5bfc\u81f4\u6a21\u62df\u7ed3\u679c\u7684\u4e0d\u51c6\u786e\uff0c\u56e0\u6b64\u5efa\u8bae\u8fdb\u884c\u8db3\u591f\u7684\u8fed\u4ee3\u3002\u786e\u4fdd\u968f\u673a\u6570\u751f\u6210\u7684\u5747\u5300\u6027\u4e5f\u81f3\u5173\u91cd\u8981\uff0c\u7528\u6237\u5e94\u4ed4\u7ec6\u9009\u62e9\u5408\u9002\u7684\u968f\u673a\u6570\u751f\u6210\u65b9\u6cd5\u3002\u6700\u540e\uff0c\u9009\u62e9\u9002\u5f53\u7684\u7edf\u8ba1\u5206\u6790\u65b9\u6cd5\u5bf9\u4e8e\u7ed3\u679c\u7684\u89e3\u8bfb\u548c\u51b3\u7b56\u81f3\u5173\u91cd\u8981\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u8fdb\u884c\u8499\u7279\u5361\u6d1b\u6a21\u62df\u7684\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528\u968f\u673a\u6570\u751f\u6210\u5668\u3001\u6a21\u62df\u5927\u91cf\u6837\u672c\u3001\u8ba1\u7b97\u6982\u7387\u5206\u5e03\u3002 \u8499\u7279\u5361\u6d1b\u6a21\u62df\u662f\u4e00\u79cd\u901a\u8fc7\u91cd [&hellip;]","protected":false},"author":3,"featured_media":1118929,"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\/1118918"}],"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=1118918"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1118918\/revisions"}],"predecessor-version":[{"id":1118932,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1118918\/revisions\/1118932"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1118929"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1118918"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1118918"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1118918"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}