{"id":1184314,"date":"2025-01-15T19:25:00","date_gmt":"2025-01-15T11:25:00","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1184314.html"},"modified":"2025-01-15T19:25:04","modified_gmt":"2025-01-15T11:25:04","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e6%b1%82%e6%a6%82%e7%8e%87%e9%97%ae%e9%a2%98","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1184314.html","title":{"rendered":"\u5982\u4f55\u7528python\u6c42\u6982\u7387\u95ee\u9898"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25133757\/5d563637-1c61-4107-abad-e413d9de66e0.webp\" alt=\"\u5982\u4f55\u7528python\u6c42\u6982\u7387\u95ee\u9898\" \/><\/p>\n<p><p> <strong>\u7528Python\u6c42\u89e3\u6982\u7387\u95ee\u9898\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528\u7edf\u8ba1\u6a21\u62df\u65b9\u6cd5\u3001\u4f7f\u7528\u6982\u7387\u5206\u5e03\u51fd\u6570\u3001\u4f7f\u7528\u6982\u7387\u5e93\u5982SciPy\u7b49\u3002<\/strong>\u5728\u8fd9\u4e9b\u65b9\u6cd5\u4e2d\uff0c\u7edf\u8ba1\u6a21\u62df\u65b9\u6cd5\u7279\u522b\u9002\u5408\u89e3\u51b3\u590d\u6742\u7684\u6982\u7387\u95ee\u9898\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u6a21\u62df\u5927\u91cf\u968f\u673a\u8bd5\u9a8c\uff0c\u6211\u4eec\u53ef\u4ee5\u4f30\u8ba1\u4e8b\u4ef6\u53d1\u751f\u7684\u6982\u7387\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u7edf\u8ba1\u6a21\u62df\u65b9\u6cd5\u6765\u6c42\u89e3\u6982\u7387\u95ee\u9898\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u7edf\u8ba1\u6a21\u62df\u65b9\u6cd5<\/h3>\n<\/p>\n<p><p>\u7edf\u8ba1\u6a21\u62df\u65b9\u6cd5\uff0c\u4e5f\u79f0\u4e3a\u8499\u7279\u5361\u7f57\u65b9\u6cd5\uff0c\u901a\u8fc7\u968f\u673a\u751f\u6210\u5927\u91cf\u6837\u672c\u6765\u4f30\u8ba1\u4e8b\u4ef6\u7684\u6982\u7387\u3002\u8fd9\u79cd\u65b9\u6cd5\u7279\u522b\u9002\u5408\u89e3\u51b3\u590d\u6742\u4e14\u96be\u4ee5\u7528\u89e3\u6790\u65b9\u6cd5\u6c42\u89e3\u7684\u6982\u7387\u95ee\u9898\u3002<\/p>\n<\/p>\n<p><h4>1.1 \u8499\u7279\u5361\u7f57\u65b9\u6cd5\u7684\u57fa\u672c\u6b65\u9aa4<\/h4>\n<\/p>\n<ol>\n<li><strong>\u5b9a\u4e49\u95ee\u9898<\/strong>\uff1a\u660e\u786e\u8981\u8ba1\u7b97\u7684\u4e8b\u4ef6\u53ca\u5176\u6982\u7387\u3002<\/li>\n<li><strong>\u8bbe\u8ba1\u6a21\u62df\u5b9e\u9a8c<\/strong>\uff1a\u786e\u5b9a\u6a21\u62df\u5b9e\u9a8c\u7684\u6b65\u9aa4\u548c\u8981\u751f\u6210\u7684\u968f\u673a\u6837\u672c\u3002<\/li>\n<li><strong>\u8fd0\u884c\u6a21\u62df<\/strong>\uff1a\u591a\u6b21\u8fd0\u884c\u6a21\u62df\u5b9e\u9a8c\uff0c\u8bb0\u5f55\u7ed3\u679c\u3002<\/li>\n<li><strong>\u4f30\u8ba1\u6982\u7387<\/strong>\uff1a\u6839\u636e\u6a21\u62df\u7ed3\u679c\u4f30\u8ba1\u4e8b\u4ef6\u7684\u6982\u7387\u3002<\/li>\n<\/ol>\n<p><h4>1.2 \u793a\u4f8b\uff1a\u6c42\u63b7\u9ab0\u5b50\u5f97\u52306\u7684\u6982\u7387<\/h4>\n<\/p>\n<p><p>\u5047\u8bbe\u6211\u4eec\u60f3\u77e5\u9053\u63b7\u4e00\u679a\u516c\u5e73\u7684\u516d\u9762\u9ab0\u5b50\u5f97\u52306\u7684\u6982\u7387\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8499\u7279\u5361\u7f57\u65b9\u6cd5\u6765\u4f30\u8ba1\u8fd9\u4e2a\u6982\u7387\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import random<\/p>\n<p>def roll_dice(n_trials):<\/p>\n<p>    success = 0<\/p>\n<p>    for _ in range(n_trials):<\/p>\n<p>        if random.randint(1, 6) == 6:<\/p>\n<p>            success += 1<\/p>\n<p>    return success \/ n_trials<\/p>\n<h2><strong>\u6a21\u62df10000\u6b21\u63b7\u9ab0\u5b50\u5b9e\u9a8c<\/strong><\/h2>\n<p>n_trials = 10000<\/p>\n<p>estimated_probability = roll_dice(n_trials)<\/p>\n<p>print(f&quot;\u63b7\u9ab0\u5b50\u5f97\u52306\u7684\u4f30\u8ba1\u6982\u7387\u4e3a\uff1a{estimated_probability}&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u901a\u8fc7\u6a21\u62df10000\u6b21\u63b7\u9ab0\u5b50\u5b9e\u9a8c\uff0c\u6765\u4f30\u8ba1\u63b7\u9ab0\u5b50\u5f97\u52306\u7684\u6982\u7387\u3002\u7ed3\u679c\u4f1a\u63a5\u8fd1\u7406\u8bba\u6982\u73871\/6\u3002<\/p>\n<\/p>\n<p><h3>\u4e8c\u3001\u6982\u7387\u5206\u5e03\u51fd\u6570<\/h3>\n<\/p>\n<p><p>Python\u4e2d\u6709\u591a\u4e2a\u5e93\u63d0\u4f9b\u6982\u7387\u5206\u5e03\u51fd\u6570\uff0c\u8fd9\u4e9b\u5e93\u53ef\u4ee5\u76f4\u63a5\u7528\u4e8e\u8ba1\u7b97\u5404\u79cd\u6982\u7387\u5206\u5e03\u7684\u6982\u7387\u503c\u3001\u671f\u671b\u503c\u7b49\u3002<\/p>\n<\/p>\n<p><h4>2.1 \u4f7f\u7528SciPy\u5e93<\/h4>\n<\/p>\n<p><p>SciPy\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6982\u7387\u5206\u5e03\u51fd\u6570\uff0c\u53ef\u4ee5\u7528\u4e8e\u8ba1\u7b97\u5404\u79cd\u6807\u51c6\u6982\u7387\u5206\u5e03\u7684\u6982\u7387\u503c\u3002<\/p>\n<\/p>\n<p><h4>2.2 \u793a\u4f8b\uff1a\u8ba1\u7b97\u6b63\u6001\u5206\u5e03\u7684\u6982\u7387<\/h4>\n<\/p>\n<p><p>\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5747\u503c\u4e3a0\uff0c\u6807\u51c6\u5dee\u4e3a1\u7684\u6b63\u6001\u5206\u5e03\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528SciPy\u5e93\u6765\u8ba1\u7b97\u5728\u533a\u95f4[-1, 1]\u5185\u7684\u6982\u7387\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from scipy.stats import norm<\/p>\n<h2><strong>\u5b9a\u4e49\u6b63\u6001\u5206\u5e03<\/strong><\/h2>\n<p>mu = 0<\/p>\n<p>sigma = 1<\/p>\n<p>normal_dist = norm(mu, sigma)<\/p>\n<h2><strong>\u8ba1\u7b97\u533a\u95f4[-1, 1]\u5185\u7684\u6982\u7387<\/strong><\/h2>\n<p>prob = normal_dist.cdf(1) - normal_dist.cdf(-1)<\/p>\n<p>print(f&quot;\u6b63\u6001\u5206\u5e03\u5728\u533a\u95f4[-1, 1]\u5185\u7684\u6982\u7387\u4e3a\uff1a{prob}&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528SciPy\u7684<code>norm<\/code>\u51fd\u6570\u5b9a\u4e49\u4e86\u4e00\u4e2a\u6807\u51c6\u6b63\u6001\u5206\u5e03\uff0c\u7136\u540e\u4f7f\u7528\u7d2f\u79ef\u5206\u5e03\u51fd\u6570<code>cdf<\/code>\u8ba1\u7b97\u4e86\u5728\u533a\u95f4[-1, 1]\u5185\u7684\u6982\u7387\u3002<\/p>\n<\/p>\n<p><h3>\u4e09\u3001\u6c42\u89e3\u5e38\u89c1\u6982\u7387\u95ee\u9898<\/h3>\n<\/p>\n<p><h4>3.1 \u62bd\u6837\u95ee\u9898<\/h4>\n<\/p>\n<p><p>\u62bd\u6837\u95ee\u9898\u662f\u6982\u7387\u8bba\u4e2d\u7684\u5e38\u89c1\u95ee\u9898\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u901a\u8fc7\u7ec4\u5408\u6570\u5b66\u6216\u7edf\u8ba1\u6a21\u62df\u65b9\u6cd5\u6765\u6c42\u89e3\u3002<\/p>\n<\/p>\n<p><h5>\u793a\u4f8b\uff1a\u4ece\u4e00\u4e2a\u5305\u542b10\u4e2a\u7ea2\u7403\u548c5\u4e2a\u84dd\u7403\u7684\u888b\u5b50\u4e2d\uff0c\u968f\u673a\u62bd\u53d63\u4e2a\u7403\uff0c\u6c42\u81f3\u5c11\u6709\u4e00\u4e2a\u7ea2\u7403\u7684\u6982\u7387\u3002<\/h5>\n<\/p>\n<p><pre><code class=\"language-python\">import itertools<\/p>\n<p>def at_least_one_red(n_trials):<\/p>\n<p>    red_balls = 10<\/p>\n<p>    blue_balls = 5<\/p>\n<p>    total_balls = red_balls + blue_balls<\/p>\n<p>    success = 0<\/p>\n<p>    for _ in range(n_trials):<\/p>\n<p>        sample = random.sample(range(total_balls), 3)<\/p>\n<p>        red_count = sum(1 for ball in sample if ball &lt; red_balls)<\/p>\n<p>        if red_count &gt; 0:<\/p>\n<p>            success += 1<\/p>\n<p>    return success \/ n_trials<\/p>\n<h2><strong>\u6a21\u62df10000\u6b21\u62bd\u6837\u5b9e\u9a8c<\/strong><\/h2>\n<p>n_trials = 10000<\/p>\n<p>estimated_probability = at_least_one_red(n_trials)<\/p>\n<p>print(f&quot;\u81f3\u5c11\u6709\u4e00\u4e2a\u7ea2\u7403\u7684\u4f30\u8ba1\u6982\u7387\u4e3a\uff1a{estimated_probability}&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u901a\u8fc7\u6a21\u62df10000\u6b21\u62bd\u6837\u5b9e\u9a8c\uff0c\u6765\u4f30\u8ba1\u81f3\u5c11\u6709\u4e00\u4e2a\u7ea2\u7403\u7684\u6982\u7387\u3002<\/p>\n<\/p>\n<p><h4>3.2 \u8d1d\u53f6\u65af\u5b9a\u7406<\/h4>\n<\/p>\n<p><p>\u8d1d\u53f6\u65af\u5b9a\u7406\u662f\u6982\u7387\u8bba\u4e2d\u7684\u91cd\u8981\u5b9a\u7406\uff0c\u53ef\u4ee5\u7528\u4e8e\u66f4\u65b0\u4e8b\u4ef6\u7684\u6982\u7387\u3002Python\u53ef\u4ee5\u4f7f\u7528SciPy\u6216\u81ea\u5b9a\u4e49\u51fd\u6570\u6765\u5b9e\u73b0\u8d1d\u53f6\u65af\u5b9a\u7406\u3002<\/p>\n<\/p>\n<p><h5>\u793a\u4f8b\uff1a\u5047\u8bbe\u6709\u4e00\u79cd\u75be\u75c5\uff0c\u60a3\u75c5\u6982\u7387\u4e3a0.01\uff0c\u6d4b\u8bd5\u51c6\u786e\u7387\u4e3a99%\u3002\u6c42\u6d4b\u8bd5\u7ed3\u679c\u4e3a\u9633\u6027\u65f6\uff0c\u5b9e\u9645\u60a3\u75c5\u7684\u6982\u7387\u3002<\/h5>\n<\/p>\n<p><pre><code class=\"language-python\">def bayes_theorem(prior, sensitivity, specificity):<\/p>\n<p>    # P(D|+)<\/p>\n<p>    posterior = (sensitivity * prior) \/ (sensitivity * prior + (1 - specificity) * (1 - prior))<\/p>\n<p>    return posterior<\/p>\n<h2><strong>\u5148\u9a8c\u6982\u7387 P(D)<\/strong><\/h2>\n<p>prior = 0.01<\/p>\n<h2><strong>\u654f\u611f\u6027 P(+|D)<\/strong><\/h2>\n<p>sensitivity = 0.99<\/p>\n<h2><strong>\u7279\u5f02\u6027 P(-|~D)<\/strong><\/h2>\n<p>specificity = 0.99<\/p>\n<p>posterior = bayes_theorem(prior, sensitivity, specificity)<\/p>\n<p>print(f&quot;\u6d4b\u8bd5\u7ed3\u679c\u4e3a\u9633\u6027\u65f6\uff0c\u5b9e\u9645\u60a3\u75c5\u7684\u6982\u7387\u4e3a\uff1a{posterior}&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u8d1d\u53f6\u65af\u5b9a\u7406\u8ba1\u7b97\u4e86\u6d4b\u8bd5\u7ed3\u679c\u4e3a\u9633\u6027\u65f6\uff0c\u5b9e\u9645\u60a3\u75c5\u7684\u6982\u7387\u3002<\/p>\n<\/p>\n<p><h3>\u56db\u3001\u4f7f\u7528\u6982\u7387\u5e93<\/h3>\n<\/p>\n<p><p>\u9664\u4e86SciPy\u4e4b\u5916\uff0cPython\u8fd8\u6709\u5176\u4ed6\u4e00\u4e9b\u5e93\u53ef\u4ee5\u7528\u4e8e\u6982\u7387\u8ba1\u7b97\uff0c\u5982NumPy\u548cPyMC3\u7b49\u3002<\/p>\n<\/p>\n<p><h4>4.1 NumPy\u5e93<\/h4>\n<\/p>\n<p><p>NumPy\u5e93\u63d0\u4f9b\u4e86\u8bb8\u591a\u968f\u673a\u6570\u751f\u6210\u51fd\u6570\u548c\u7edf\u8ba1\u51fd\u6570\uff0c\u53ef\u4ee5\u7528\u4e8e\u6982\u7387\u8ba1\u7b97\u3002<\/p>\n<\/p>\n<p><h5>\u793a\u4f8b\uff1a\u4f7f\u7528NumPy\u751f\u621010000\u4e2a\u5747\u503c\u4e3a0\uff0c\u6807\u51c6\u5dee\u4e3a1\u7684\u6b63\u6001\u5206\u5e03\u968f\u673a\u6570\uff0c\u5e76\u8ba1\u7b97\u5176\u5747\u503c\u548c\u6807\u51c6\u5dee\u3002<\/h5>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u751f\u621010000\u4e2a\u6b63\u6001\u5206\u5e03\u968f\u673a\u6570<\/strong><\/h2>\n<p>random_numbers = np.random.normal(0, 1, 10000)<\/p>\n<h2><strong>\u8ba1\u7b97\u5747\u503c\u548c\u6807\u51c6\u5dee<\/strong><\/h2>\n<p>mean = np.mean(random_numbers)<\/p>\n<p>std_dev = np.std(random_numbers)<\/p>\n<p>print(f&quot;\u968f\u673a\u6570\u7684\u5747\u503c\u4e3a\uff1a{mean}&quot;)<\/p>\n<p>print(f&quot;\u968f\u673a\u6570\u7684\u6807\u51c6\u5dee\u4e3a\uff1a{std_dev}&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528NumPy\u751f\u6210\u4e8610000\u4e2a\u6b63\u6001\u5206\u5e03\u968f\u673a\u6570\uff0c\u5e76\u8ba1\u7b97\u4e86\u5b83\u4eec\u7684\u5747\u503c\u548c\u6807\u51c6\u5dee\u3002<\/p>\n<\/p>\n<p><h4>4.2 PyMC3\u5e93<\/h4>\n<\/p>\n<p><p>PyMC3\u662f\u4e00\u4e2a\u7528\u4e8e\u8d1d\u53f6\u65af\u7edf\u8ba1\u5efa\u6a21\u7684\u5e93\uff0c\u53ef\u4ee5\u7528\u4e8e\u6784\u5efa\u548c\u6c42\u89e3\u590d\u6742\u7684\u6982\u7387\u6a21\u578b\u3002<\/p>\n<\/p>\n<p><h5>\u793a\u4f8b\uff1a\u4f7f\u7528PyMC3\u6784\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u8d1d\u53f6\u65af\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u3002<\/h5>\n<\/p>\n<p><pre><code class=\"language-python\">import pymc3 as pm<\/p>\n<p>import numpy as np<\/p>\n<p>import matplotlib.pyplot as plt<\/p>\n<h2><strong>\u751f\u6210\u6570\u636e<\/strong><\/h2>\n<p>np.random.seed(123)<\/p>\n<p>X = np.linspace(0, 1, 100)<\/p>\n<p>true_intercept = 1<\/p>\n<p>true_slope = 2<\/p>\n<p>Y = true_intercept + true_slope * X + np.random.normal(scale=0.5, size=100)<\/p>\n<h2><strong>\u6784\u5efa\u8d1d\u53f6\u65af\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n<p>with pm.Model() as model:<\/p>\n<p>    intercept = pm.Normal(&#39;intercept&#39;, mu=0, sigma=10)<\/p>\n<p>    slope = pm.Normal(&#39;slope&#39;, mu=0, sigma=10)<\/p>\n<p>    sigma = pm.HalfNormal(&#39;sigma&#39;, sigma=1)<\/p>\n<p>    mu = intercept + slope * X<\/p>\n<p>    Y_obs = pm.Normal(&#39;Y_obs&#39;, mu=mu, sigma=sigma, observed=Y)<\/p>\n<p>    trace = pm.sample(1000, tune=1000, cores=2)<\/p>\n<h2><strong>\u7ed8\u5236\u56de\u5f52\u7ebf<\/strong><\/h2>\n<p>pm.plot_posterior_predictive_glm(trace, samples=100, label=&#39;posterior predictive regression lines&#39;)<\/p>\n<p>plt.scatter(X, Y, label=&#39;data&#39;)<\/p>\n<p>plt.legend()<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528PyMC3\u6784\u5efa\u4e86\u4e00\u4e2a\u7b80\u5355\u7684\u8d1d\u53f6\u65af\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff0c\u5e76\u7ed8\u5236\u4e86\u56de\u5f52\u7ebf\u3002<\/p>\n<\/p>\n<p><h3>\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n<p><p>\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86\u5982\u4f55\u7528Python\u6c42\u6982\u7387\u95ee\u9898\u7684\u51e0\u79cd\u65b9\u6cd5\uff0c\u5305\u62ec\u7edf\u8ba1\u6a21\u62df\u65b9\u6cd5\u3001\u6982\u7387\u5206\u5e03\u51fd\u6570\u3001\u5e38\u89c1\u6982\u7387\u95ee\u9898\u7684\u6c42\u89e3\u4ee5\u53ca\u4f7f\u7528\u6982\u7387\u5e93\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u89e3\u51b3\u5404\u79cd\u590d\u6742\u7684\u6982\u7387\u95ee\u9898\u3002<\/p>\n<\/p>\n<p><p><strong>\u7edf\u8ba1\u6a21\u62df\u65b9\u6cd5<\/strong>\uff1a\u901a\u8fc7\u6a21\u62df\u5927\u91cf\u968f\u673a\u8bd5\u9a8c\u6765\u4f30\u8ba1\u4e8b\u4ef6\u7684\u6982\u7387\uff0c\u9002\u5408\u590d\u6742\u7684\u6982\u7387\u95ee\u9898\u3002<\/p>\n<\/p>\n<p><p><strong>\u6982\u7387\u5206\u5e03\u51fd\u6570<\/strong>\uff1a\u4f7f\u7528SciPy\u7b49\u5e93\u63d0\u4f9b\u7684\u6982\u7387\u5206\u5e03\u51fd\u6570\uff0c\u53ef\u4ee5\u76f4\u63a5\u8ba1\u7b97\u5404\u79cd\u6982\u7387\u5206\u5e03\u7684\u6982\u7387\u503c\u3002<\/p>\n<\/p>\n<p><p><strong>\u6c42\u89e3\u5e38\u89c1\u6982\u7387\u95ee\u9898<\/strong>\uff1a\u4f7f\u7528\u7ec4\u5408\u6570\u5b66\u6216\u7edf\u8ba1\u6a21\u62df\u65b9\u6cd5\u6c42\u89e3\uff0c\u5982\u62bd\u6837\u95ee\u9898\u548c\u8d1d\u53f6\u65af\u5b9a\u7406\u3002<\/p>\n<\/p>\n<p><p><strong>\u4f7f\u7528\u6982\u7387\u5e93<\/strong>\uff1a\u5982NumPy\u548cPyMC3\uff0c\u53ef\u4ee5\u7528\u4e8e\u6982\u7387\u8ba1\u7b97\u548c\u8d1d\u53f6\u65af\u7edf\u8ba1\u5efa\u6a21\u3002<\/p>\n<\/p>\n<p><p>\u901a\u8fc7\u638c\u63e1\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u7075\u6d3b\u5730\u89e3\u51b3\u5404\u79cd\u6982\u7387\u95ee\u9898\uff0c\u5e76\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u505a\u51fa\u66f4\u51c6\u786e\u7684\u51b3\u7b56\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u7528Python\u89e3\u51b3\u6982\u7387\u76f8\u5173\u7684\u5b9e\u9645\u95ee\u9898\uff1f<\/strong><br \/>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0cPython\u53ef\u4ee5\u901a\u8fc7\u5185\u7f6e\u7684\u6570\u5b66\u5e93\u548c\u6982\u7387\u5e93\u6765\u89e3\u51b3\u5404\u79cd\u6982\u7387\u95ee\u9898\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u751f\u6210\u968f\u673a\u6570\uff0c\u8fdb\u884c\u6982\u7387\u5206\u5e03\u6a21\u62df\uff0c\u6216\u8005\u4f7f\u7528SciPy\u5e93\u8fdb\u884c\u66f4\u590d\u6742\u7684\u7edf\u8ba1\u5206\u6790\u3002\u901a\u8fc7\u8fd9\u4e9b\u5de5\u5177\uff0c\u7528\u6237\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u8499\u7279\u5361\u6d1b\u6a21\u62df\u3001\u8ba1\u7b97\u671f\u671b\u503c\u548c\u65b9\u5dee\u7b49\u6982\u7387\u7edf\u8ba1\u4efb\u52a1\u3002<\/p>\n<p><strong>\u6211\u9700\u8981\u54ea\u4e9bPython\u5e93\u6765\u5904\u7406\u6982\u7387\u95ee\u9898\uff1f<\/strong><br \/>\u5904\u7406\u6982\u7387\u95ee\u9898\u65f6\uff0c\u63a8\u8350\u4f7f\u7528NumPy\u548cSciPy\u8fd9\u4e24\u4e2a\u5e93\u3002NumPy\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u7ec4\u64cd\u4f5c\u548c\u968f\u673a\u6570\u751f\u6210\u51fd\u6570\uff0c\u9002\u7528\u4e8e\u57fa\u7840\u7684\u6982\u7387\u8ba1\u7b97\u3002\u800cSciPy\u5219\u63d0\u4f9b\u4e86\u66f4\u591a\u7684\u7edf\u8ba1\u529f\u80fd\uff0c\u5982\u6982\u7387\u5206\u5e03\u3001\u7edf\u8ba1\u6d4b\u8bd5\u7b49\u3002\u6b64\u5916\uff0cPandas\u5e93\u4e5f\u53ef\u4ee5\u7528\u6765\u5904\u7406\u548c\u5206\u6790\u6570\u636e\uff0c\u4fbf\u4e8e\u8fdb\u884c\u6982\u7387\u8ba1\u7b97\u3002<\/p>\n<p><strong>\u5982\u4f55\u7528Python\u5b9e\u73b0\u6982\u7387\u5206\u5e03\u7684\u53ef\u89c6\u5316\uff1f<\/strong><br \/>\u4e3a\u4e86\u53ef\u89c6\u5316\u6982\u7387\u5206\u5e03\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u548cSeaborn\u8fd9\u4e24\u4e2a\u5e93\u3002Matplotlib\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u7ed8\u5236\u76f4\u65b9\u56fe\u3001\u5bc6\u5ea6\u56fe\u7b49\uff0c\u800cSeaborn\u5219\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u7684\u56fe\u5f62\u548c\u66f4\u7b80\u4fbf\u7684\u63a5\u53e3\u3002\u901a\u8fc7\u8fd9\u4e9b\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u7528\u6237\u53ef\u4ee5\u76f4\u89c2\u5730\u7406\u89e3\u4e0d\u540c\u6982\u7387\u5206\u5e03\u7684\u7279\u5f81\uff0c\u5e76\u5206\u6790\u5176\u5bf9\u5b9e\u9645\u95ee\u9898\u7684\u5f71\u54cd\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u7528Python\u6c42\u89e3\u6982\u7387\u95ee\u9898\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528\u7edf\u8ba1\u6a21\u62df\u65b9\u6cd5\u3001\u4f7f\u7528\u6982\u7387\u5206\u5e03\u51fd\u6570\u3001\u4f7f\u7528\u6982\u7387\u5e93\u5982SciPy\u7b49\u3002\u5728\u8fd9\u4e9b\u65b9\u6cd5 [&hellip;]","protected":false},"author":3,"featured_media":1184319,"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\/1184314"}],"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=1184314"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1184314\/revisions"}],"predecessor-version":[{"id":1184320,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1184314\/revisions\/1184320"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1184319"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1184314"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1184314"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1184314"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}