{"id":1090411,"date":"2025-01-08T14:02:28","date_gmt":"2025-01-08T06:02:28","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1090411.html"},"modified":"2025-01-08T14:02:30","modified_gmt":"2025-01-08T06:02:30","slug":"%e7%94%a8python%e5%a6%82%e4%bd%95%e8%b7%91%e5%b1%80%e9%83%a8%e6%9c%80%e9%ab%98%e7%82%b9-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1090411.html","title":{"rendered":"\u7528Python\u5982\u4f55\u8dd1\u5c40\u90e8\u6700\u9ad8\u70b9"},"content":{"rendered":"

\"\u7528Python\u5982\u4f55\u8dd1\u5c40\u90e8\u6700\u9ad8\u70b9\"<\/p>\n

\u7528Python\u5982\u4f55\u8dd1\u5c40\u90e8\u6700\u9ad8\u70b9<\/strong>\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u6cd5\u5b9e\u73b0\uff1a\u5b9a\u4e49\u76ee\u6807\u51fd\u6570\u3001\u9009\u62e9\u5408\u9002\u7684\u4f18\u5316\u7b97\u6cd5\u3001\u8bbe\u7f6e\u521d\u59cb\u70b9\u3001\u8fd0\u884c\u7b97\u6cd5\u3002\u8fd9\u4e9b\u6b65\u9aa4\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u627e\u5230\u51fd\u6570\u5728\u7ed9\u5b9a\u533a\u95f4\u5185\u7684\u5c40\u90e8\u6700\u9ad8\u70b9\u3002\u5176\u4e2d\u9009\u62e9\u5408\u9002\u7684\u4f18\u5316\u7b97\u6cd5<\/strong>\u5c24\u5176\u91cd\u8981\uff0c\u56e0\u4e3a\u4e0d\u540c\u7684\u4f18\u5316\u7b97\u6cd5\u9002\u7528\u4e8e\u4e0d\u540c\u7684\u95ee\u9898\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u9009\u62e9\u548c\u5e94\u7528\u8fd9\u4e9b\u4f18\u5316\u7b97\u6cd5\u3002<\/p>\n<\/p>\n

\u5c40\u90e8\u6700\u4f18\u5316\u662f\u6307\u5728\u67d0\u4e2a\u7279\u5b9a\u533a\u95f4\u5185\u5bfb\u627e\u51fd\u6570\u7684\u6781\u503c\u70b9\uff0c\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u68af\u5ea6\u4e0a\u5347\u6cd5\u3001\u725b\u987f\u6cd5\u548c\u6a21\u62df\u9000\u706b\u6cd5\u7b49\u3002\u9009\u62e9\u5408\u9002\u7684\u4f18\u5316\u7b97\u6cd5\u53d6\u51b3\u4e8e\u51fd\u6570\u7684\u6027\u8d28\u548c\u95ee\u9898\u7684\u5177\u4f53\u8981\u6c42\uff0c\u4f8b\u5982\u51fd\u6570\u662f\u5426\u5149\u6ed1\u3001\u662f\u5426\u6709\u591a\u4e2a\u5c40\u90e8\u6781\u503c\u70b9\u7b49\u3002\u672c\u6587\u5c06\u4ecb\u7ecd\u51e0\u79cd\u5e38\u89c1\u7684\u4f18\u5316\u7b97\u6cd5\uff0c\u5e76\u7ed9\u51fa\u5728Python\u4e2d\u5b9e\u73b0\u8fd9\u4e9b\u7b97\u6cd5\u7684\u793a\u4f8b\u4ee3\u7801\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u68af\u5ea6\u4e0a\u5347\u6cd5<\/h3>\n<\/p>\n

\u68af\u5ea6\u4e0a\u5347\u6cd5\u662f\u4e00\u79cd\u8fed\u4ee3\u4f18\u5316\u7b97\u6cd5\uff0c\u901a\u8fc7\u6cbf\u7740\u76ee\u6807\u51fd\u6570\u68af\u5ea6\u7684\u65b9\u5411\u8fdb\u884c\u641c\u7d22\uff0c\u9010\u6b65\u903c\u8fd1\u5c40\u90e8\u6700\u9ad8\u70b9\u3002\u68af\u5ea6\u4e0a\u5347\u6cd5\u9002\u7528\u4e8e\u5149\u6ed1\u8fde\u7eed\u7684\u51fd\u6570\u3002<\/p>\n<\/p>\n

1.1 \u5b9a\u4e49\u76ee\u6807\u51fd\u6570\u548c\u68af\u5ea6<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b9a\u4e49\u76ee\u6807\u51fd\u6570\u548c\u5176\u68af\u5ea6\u3002\u5047\u8bbe\u76ee\u6807\u51fd\u6570\u4e3af(x)\uff0c\u5176\u68af\u5ea6\u4e3af'(x)\u3002<\/p>\n<\/p>\n

import numpy as np<\/p>\n
def f(x):<\/p>\n
    return -x2 + 4*x<\/p>\n
def gradient(x):<\/p>\n
    return -2*x + 4<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2 \u5b9e\u73b0\u68af\u5ea6\u4e0a\u5347\u6cd5<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5b9e\u73b0\u68af\u5ea6\u4e0a\u5347\u6cd5\u3002\u8bbe\u5b9a\u521d\u59cb\u70b9\u3001\u5b66\u4e60\u7387\u548c\u8fed\u4ee3\u6b21\u6570\u3002<\/p>\n<\/p>\n
def gradient_ascent(starting_point, learning_rate, iterations):<\/p>\n
    x = starting_point<\/p>\n
    for _ in range(iterations):<\/p>\n
        grad = gradient(x)<\/p>\n
        x += learning_rate * grad<\/p>\n
    return x<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.3 \u8fd0\u884c\u68af\u5ea6\u4e0a\u5347\u6cd5<\/h4>\n<\/p>\n
\u8bbe\u7f6e\u521d\u59cb\u70b9\u4e3a0\uff0c\u5b66\u4e60\u7387\u4e3a0.1\uff0c\u8fed\u4ee3\u6b21\u6570\u4e3a100\uff0c\u8fd0\u884c\u68af\u5ea6\u4e0a\u5347\u6cd5\u627e\u5230\u5c40\u90e8\u6700\u9ad8\u70b9\u3002<\/p>\n<\/p>\n
starting_point = 0<\/p>\n
learning_rate = 0.1<\/p>\n
iterations = 100<\/p>\n
optimal_x = gradient_ascent(starting_point, learning_rate, iterations)<\/p>\n
optimal_y = f(optimal_x)<\/p>\n
print(f"\u5c40\u90e8\u6700\u9ad8\u70b9: x = {optimal_x}, y = {optimal_y}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u725b\u987f\u6cd5<\/h3>\n<\/p>\n
\u725b\u987f\u6cd5\u662f\u4e00\u79cd\u57fa\u4e8e\u4e8c\u9636\u5bfc\u6570\u7684\u4f18\u5316\u7b97\u6cd5\uff0c\u901a\u8fc7\u6cf0\u52d2\u5c55\u5f00\u8fd1\u4f3c\u76ee\u6807\u51fd\u6570\uff0c\u8fed\u4ee3\u66f4\u65b0\u4ee5\u903c\u8fd1\u5c40\u90e8\u6700\u9ad8\u70b9\u3002\u725b\u987f\u6cd5\u9002\u7528\u4e8e\u4e8c\u9636\u5bfc\u6570\u5b58\u5728\u4e14\u8fde\u7eed\u7684\u51fd\u6570\u3002<\/p>\n<\/p>\n
2.1 \u5b9a\u4e49\u76ee\u6807\u51fd\u6570\u53ca\u5176\u4e00\u9636\u548c\u4e8c\u9636\u5bfc\u6570<\/h4>\n<\/p>\n
def f(x):<\/p>\n
    return -x2 + 4*x<\/p>\n
def gradient(x):<\/p>\n
    return -2*x + 4<\/p>\n
def hessian(x):<\/p>\n
    return -2<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2 \u5b9e\u73b0\u725b\u987f\u6cd5<\/h4>\n<\/p>\n
def newton_method(starting_point, iterations):<\/p>\n
    x = starting_point<\/p>\n
    for _ in range(iterations):<\/p>\n
        grad = gradient(x)<\/p>\n
        hess = hessian(x)<\/p>\n
        x -= grad \/ hess<\/p>\n
    return x<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.3 \u8fd0\u884c\u725b\u987f\u6cd5<\/h4>\n<\/p>\n
\u8bbe\u5b9a\u521d\u59cb\u70b9\u4e3a0\uff0c\u8fed\u4ee3\u6b21\u6570\u4e3a10\uff0c\u8fd0\u884c\u725b\u987f\u6cd5\u627e\u5230\u5c40\u90e8\u6700\u9ad8\u70b9\u3002<\/p>\n<\/p>\n
starting_point = 0<\/p>\n
iterations = 10<\/p>\n
optimal_x = newton_method(starting_point, iterations)<\/p>\n
optimal_y = f(optimal_x)<\/p>\n
print(f"\u5c40\u90e8\u6700\u9ad8\u70b9: x = {optimal_x}, y = {optimal_y}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6a21\u62df\u9000\u706b\u6cd5<\/h3>\n<\/p>\n
\u6a21\u62df\u9000\u706b\u6cd5\u662f\u4e00\u79cd\u5168\u5c40\u4f18\u5316\u7b97\u6cd5\uff0c\u901a\u8fc7\u6a21\u62df\u7269\u7406\u9000\u706b\u8fc7\u7a0b\uff0c\u5728\u641c\u7d22\u8fc7\u7a0b\u4e2d\u5141\u8bb8\u5076\u5c14\u63a5\u53d7\u52a3\u89e3\uff0c\u4ee5\u8df3\u51fa\u5c40\u90e8\u6781\u503c\u70b9\uff0c\u903c\u8fd1\u5168\u5c40\u6700\u4f18\u89e3\u3002<\/p>\n<\/p>\n
3.1 \u5b9a\u4e49\u76ee\u6807\u51fd\u6570<\/h4>\n<\/p>\n
import numpy as np<\/p>\n
def f(x):<\/p>\n
    return -x2 + 4*x<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2 \u5b9e\u73b0\u6a21\u62df\u9000\u706b\u6cd5<\/h4>\n<\/p>\n
def simulated_annealing(starting_point, temperature, cooling_rate, iterations):<\/p>\n
    x = starting_point<\/p>\n
    current_value = f(x)<\/p>\n
    best_x = x<\/p>\n
    best_value = current_value<\/p>\n
    for _ in range(iterations):<\/p>\n
        new_x = x + np.random.uniform(-1, 1)<\/p>\n
        new_value = f(new_x)<\/p>\n
        acceptance_probability = np.exp((new_value - current_value) \/ temperature)<\/p>\n
        if new_value > current_value or np.random.rand() < acceptance_probability:<\/p>\n
            x = new_x<\/p>\n
            current_value = new_value<\/p>\n
        if current_value > best_value:<\/p>\n
            best_x = x<\/p>\n
            best_value = current_value<\/p>\n
        temperature *= cooling_rate<\/p>\n
    return best_x<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.3 \u8fd0\u884c\u6a21\u62df\u9000\u706b\u6cd5<\/h4>\n<\/p>\n
\u8bbe\u5b9a\u521d\u59cb\u70b9\u4e3a0\uff0c\u521d\u59cb\u6e29\u5ea6\u4e3a100\uff0c\u51b7\u5374\u7387\u4e3a0.99\uff0c\u8fed\u4ee3\u6b21\u6570\u4e3a1000\uff0c\u8fd0\u884c\u6a21\u62df\u9000\u706b\u6cd5\u627e\u5230\u5c40\u90e8\u6700\u9ad8\u70b9\u3002<\/p>\n<\/p>\n
starting_point = 0<\/p>\n
temperature = 100<\/p>\n
cooling_rate = 0.99<\/p>\n
iterations = 1000<\/p>\n
optimal_x = simulated_annealing(starting_point, temperature, cooling_rate, iterations)<\/p>\n
optimal_y = f(optimal_x)<\/p>\n
print(f"\u5c40\u90e8\u6700\u9ad8\u70b9: x = {optimal_x}, y = {optimal_y}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u7c92\u5b50\u7fa4\u4f18\u5316<\/h3>\n<\/p>\n
\u7c92\u5b50\u7fa4\u4f18\u5316\uff08PSO\uff09\u662f\u4e00\u79cd\u57fa\u4e8e\u7fa4\u4f53\u667a\u80fd\u7684\u4f18\u5316\u7b97\u6cd5\uff0c\u901a\u8fc7\u6a21\u62df\u9e1f\u7fa4\u89c5\u98df\u884c\u4e3a\uff0c\u591a\u4e2a\u7c92\u5b50\u5728\u89e3\u7a7a\u95f4\u4e2d\u540c\u65f6\u641c\u7d22\uff0c\u4f9d\u9760\u5168\u5c40\u6700\u4f18\u89e3\u548c\u4e2a\u4f53\u6700\u4f18\u89e3\u7684\u4fe1\u606f\u66f4\u65b0\u4f4d\u7f6e\u3002<\/p>\n<\/p>\n
4.1 \u5b9a\u4e49\u76ee\u6807\u51fd\u6570<\/h4>\n<\/p>\n
def f(x):<\/p>\n
    return -x2 + 4*x<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2 \u5b9e\u73b0\u7c92\u5b50\u7fa4\u4f18\u5316<\/h4>\n<\/p>\n
class Particle:<\/p>\n
    def __init__(self, x):<\/p>\n
        self.position = x<\/p>\n
        self.velocity = np.random.uniform(-1, 1)<\/p>\n
        self.best_position = x<\/p>\n
        self.best_value = f(x)<\/p>\n
    def update_velocity(self, global_best_position, w, c1, c2):<\/p>\n
        inertia = w * self.velocity<\/p>\n
        cognitive = c1 * np.random.rand() * (self.best_position - self.position)<\/p>\n
        social = c2 * np.random.rand() * (global_best_position - self.position)<\/p>\n
        self.velocity = inertia + cognitive + social<\/p>\n
    def update_position(self):<\/p>\n
        self.position += self.velocity<\/p>\n
        value = f(self.position)<\/p>\n
        if value > self.best_value:<\/p>\n
            self.best_value = value<\/p>\n
            self.best_position = self.position<\/p>\n
def pso(num_particles, iterations, w, c1, c2):<\/p>\n
    particles = [Particle(np.random.uniform(-10, 10)) for _ in range(num_particles)]<\/p>\n
    global_best_position = max(particles, key=lambda p: p.best_value).best_position<\/p>\n
    for _ in range(iterations):<\/p>\n
        for particle in particles:<\/p>\n
            particle.update_velocity(global_best_position, w, c1, c2)<\/p>\n
            particle.update_position()<\/p>\n
        global_best_position = max(particles, key=lambda p: p.best_value).best_position<\/p>\n
    return global_best_position<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.3 \u8fd0\u884c\u7c92\u5b50\u7fa4\u4f18\u5316<\/h4>\n<\/p>\n
\u8bbe\u5b9a\u7c92\u5b50\u6570\u91cf\u4e3a30\uff0c\u8fed\u4ee3\u6b21\u6570\u4e3a100\uff0c\u60ef\u6027\u6743\u91cd\u4e3a0.5\uff0c\u4e2a\u4f53\u548c\u793e\u4f1a\u52a0\u901f\u5ea6\u7cfb\u6570\u5206\u522b\u4e3a1.5\u548c1.5\uff0c\u8fd0\u884c\u7c92\u5b50\u7fa4\u4f18\u5316\u627e\u5230\u5c40\u90e8\u6700\u9ad8\u70b9\u3002<\/p>\n<\/p>\n
num_particles = 30<\/p>\n
iterations = 100<\/p>\n
w = 0.5<\/p>\n
c1 = 1.5<\/p>\n
c2 = 1.5<\/p>\n
optimal_x = pso(num_particles, iterations, w, c1, c2)<\/p>\n
optimal_y = f(optimal_x)<\/p>\n
print(f"\u5c40\u90e8\u6700\u9ad8\u70b9: x = {optimal_x}, y = {optimal_y}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u51e0\u79cd\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Python\u4e2d\u5b9e\u73b0\u4e0d\u540c\u7684\u4f18\u5316\u7b97\u6cd5\uff0c\u4ee5\u627e\u5230\u76ee\u6807\u51fd\u6570\u7684\u5c40\u90e8\u6700\u9ad8\u70b9\u3002\u68af\u5ea6\u4e0a\u5347\u6cd5<\/strong>\u9002\u7528\u4e8e\u5149\u6ed1\u8fde\u7eed\u7684\u51fd\u6570\uff0c\u725b\u987f\u6cd5<\/strong>\u9002\u7528\u4e8e\u4e8c\u9636\u5bfc\u6570\u5b58\u5728\u4e14\u8fde\u7eed\u7684\u51fd\u6570\uff0c\u6a21\u62df\u9000\u706b\u6cd5<\/strong>\u548c\u7c92\u5b50\u7fa4\u4f18\u5316<\/strong>\u9002\u7528\u4e8e\u5168\u5c40\u4f18\u5316\u95ee\u9898\u3002\u9009\u62e9\u5408\u9002\u7684\u4f18\u5316\u7b97\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u95ee\u9898\u7684\u6027\u8d28\u548c\u8981\u6c42\u3002\u5e0c\u671b\u672c\u6587\u7684\u4ecb\u7ecd\u80fd\u5e2e\u52a9\u60a8\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u6709\u6548\u5730\u627e\u5230\u5c40\u90e8\u6700\u9ad8\u70b9\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u627e\u5230\u5c40\u90e8\u6700\u9ad8\u70b9\u7684\u7b97\u6cd5\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u627e\u5230\u5c40\u90e8\u6700\u9ad8\u70b9\u7684\u5e38\u7528\u65b9\u6cd5\u662f\u901a\u8fc7\u904d\u5386\u6570\u7ec4\uff0c\u68c0\u67e5\u6bcf\u4e2a\u5143\u7d20\u662f\u5426\u6bd4\u5b83\u7684\u76f8\u90bb\u5143\u7d20\u5927\u3002\u53ef\u4ee5\u4f7f\u7528\u7b80\u5355\u7684\u5faa\u73af\uff0c\u7ed3\u5408\u6761\u4ef6\u5224\u65ad\u6765\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\u3002\u6b64\u5916\uff0cNumPy\u5e93\u4e2d\u7684\u4e00\u4e9b\u51fd\u6570\u4e5f\u80fd\u6709\u6548\u5e2e\u52a9\u8bc6\u522b\u5c40\u90e8\u6700\u9ad8\u70b9\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n
import numpy as np\n\ndef find_local_peaks(data):\n    peaks = []\n    for i in range(1, len(data) - 1):\n        if data[i] > data[i - 1] and data[i] > data[i + 1]:\n            peaks.append(data[i])\n    return peaks\n\ndata = [1, 3, 2, 5, 4, 6, 5]\nlocal_peaks = find_local_peaks(data)\nprint(local_peaks)  # \u8f93\u51fa\u5c40\u90e8\u6700\u9ad8\u70b9\n<\/code><\/pre>\n
\u5728\u5904\u7406\u5927\u6570\u636e\u65f6\uff0c\u5982\u4f55\u4f18\u5316\u5c40\u90e8\u6700\u9ad8\u70b9\u7684\u67e5\u627e\uff1f<\/strong>
\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\uff0c\u4f18\u5316\u67e5\u627e\u5c40\u90e8\u6700\u9ad8\u70b9\u7684\u6548\u7387\u81f3\u5173\u91cd\u8981\u3002\u4e00\u79cd\u5e38\u7528\u7684\u65b9\u6cd5\u662f\u91c7\u7528\u4e8c\u5206\u67e5\u627e\u7b97\u6cd5\uff0c\u901a\u8fc7\u4e0d\u65ad\u7f29\u5c0f\u67e5\u627e\u8303\u56f4\u6765\u5feb\u901f\u5b9a\u4f4d\u5cf0\u503c\u3002\u53e6\u4e00\u79cd\u65b9\u6cd5\u662f\u4f7f\u7528\u4fe1\u53f7\u5904\u7406\u5e93\uff0c\u5982SciPy\u4e2d\u7684find_peaks<\/code>\u51fd\u6570\uff0c\u8fd9\u4e2a\u51fd\u6570\u63d0\u4f9b\u4e86\u591a\u79cd\u53c2\u6570\u8bbe\u7f6e\uff0c\u80fd\u591f\u7075\u6d3b\u9002\u5e94\u4e0d\u540c\u6570\u636e\u7279\u70b9\uff0c\u4ece\u800c\u63d0\u5347\u6548\u7387\u3002<\/p>\n
\u5c40\u90e8\u6700\u9ad8\u70b9\u5728\u6570\u636e\u5206\u6790\u4e2d\u6709\u4ec0\u4e48\u5e94\u7528\uff1f<\/strong>
\u5c40\u90e8\u6700\u9ad8\u70b9\u7684\u8bc6\u522b\u5728\u591a\u4e2a\u9886\u57df\u4e2d\u5177\u6709\u91cd\u8981\u610f\u4e49\uff0c\u4f8b\u5982\u5728\u4fe1\u53f7\u5904\u7406\u9886\u57df\uff0c\u53ef\u4ee5\u7528\u6765\u68c0\u6d4b\u97f3\u9891\u4fe1\u53f7\u4e2d\u7684\u97f3\u7b26\uff1b\u5728\u5e02\u573a\u5206\u6790\u4e2d\uff0c\u5c40\u90e8\u6700\u9ad8\u70b9\u80fd\u5e2e\u52a9\u8bc6\u522b\u4ef7\u683c\u6ce2\u52a8\u7684\u8d8b\u52bf\uff1b\u5728\u751f\u7269\u4fe1\u606f\u5b66\u4e2d\uff0c\u5c40\u90e8\u6700\u9ad8\u70b9\u7684\u67e5\u627e\u53ef\u4ee5\u7528\u4e8e\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u5206\u6790\u3002\u56e0\u6b64\uff0c\u638c\u63e1\u5c40\u90e8\u6700\u9ad8\u70b9\u7684\u67e5\u627e\u65b9\u6cd5\u5bf9\u4ece\u4e8b\u76f8\u5173\u9886\u57df\u7684\u7814\u7a76\u4eba\u5458\u975e\u5e38\u91cd\u8981\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u7528Python\u5982\u4f55\u8dd1\u5c40\u90e8\u6700\u9ad8\u70b9\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u6cd5\u5b9e\u73b0\uff1a\u5b9a\u4e49\u76ee\u6807\u51fd\u6570\u3001\u9009\u62e9\u5408\u9002\u7684\u4f18\u5316\u7b97\u6cd5\u3001\u8bbe\u7f6e\u521d\u59cb\u70b9\u3001\u8fd0\u884c\u7b97\u6cd5\u3002\u8fd9 […]","protected":false},"author":3,"featured_media":1090414,"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\/1090411"}],"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=1090411"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1090411\/revisions"}],"predecessor-version":[{"id":1090418,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1090411\/revisions\/1090418"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1090414"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1090411"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1090411"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1090411"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}