{"id":1180921,"date":"2025-01-15T18:44:36","date_gmt":"2025-01-15T10:44:36","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1180921.html"},"modified":"2025-01-15T18:44:52","modified_gmt":"2025-01-15T10:44:52","slug":"python%e5%a6%82%e4%bd%95%e5%8f%96%e5%af%b9%e6%95%b0%e5%87%bd%e6%95%b0","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1180921.html","title":{"rendered":"python\u5982\u4f55\u53d6\u5bf9\u6570\u51fd\u6570"},"content":{"rendered":"

\"python\u5982\u4f55\u53d6\u5bf9\u6570\u51fd\u6570\"<\/p>\n

Python\u53d6\u5bf9\u6570\u51fd\u6570\u7684\u65b9\u5f0f\u4e3b\u8981\u6709\u4ee5\u4e0b\u51e0\u79cd\uff1a\u4f7f\u7528math\u6a21\u5757\u4e2d\u7684log\u51fd\u6570\u3001\u4f7f\u7528numpy\u5e93\u4e2d\u7684log\u51fd\u6570\u3001\u4f7f\u7528sympy\u5e93\u4e2d\u7684log\u51fd\u6570<\/strong>\u3002\u5176\u4e2d\uff0c\u6700\u5e38\u7528\u7684\u65b9\u5f0f\u662f\u4f7f\u7528math\u6a21\u5757\u4e2d\u7684log\u51fd\u6570\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u4f7f\u7528math\u6a21\u5757\u4e2d\u7684log\u51fd\u6570\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0cmath\u6a21\u5757\u63d0\u4f9b\u4e86log\u51fd\u6570\uff0c\u53ef\u4ee5\u8ba1\u7b97\u4efb\u610f\u7ed9\u5b9a\u6570\u503c\u7684\u5bf9\u6570\u3002\u8fd9\u4e2a\u51fd\u6570\u7684\u8bed\u6cd5\u4e3amath.log(x, base)\uff0c\u5176\u4e2dx\u662f\u8981\u53d6\u5bf9\u6570\u7684\u6570\u503c\uff0cbase\u662f\u5bf9\u6570\u7684\u5e95\u6570\u3002\u5982\u679c\u4e0d\u6307\u5b9abase\uff0c\u5219\u9ed8\u8ba4\u5e95\u6570\u4e3a\u81ea\u7136\u5bf9\u6570e\u3002\u5177\u4f53\u7528\u6cd5\u5982\u4e0b\uff1a<\/p>\n<\/p>\n

import math<\/p>\n
\u8ba1\u7b97\u81ea\u7136\u5bf9\u6570<\/strong><\/h2>\n
x = 10<\/p>\n
log_e = math.log(x)<\/p>\n
print(f"\u81ea\u7136\u5bf9\u6570log({x}) = {log_e}")<\/p>\n
\u8ba1\u7b97\u4ee52\u4e3a\u5e95\u7684\u5bf9\u6570<\/strong><\/h2>\n
base = 2<\/p>\n
log_base = math.log(x, base)<\/p>\n
print(f"\u4ee52\u4e3a\u5e95\u7684\u5bf9\u6570log({x}, {base}) = {log_base}")<\/p>\n
\u8ba1\u7b97\u4ee510\u4e3a\u5e95\u7684\u5bf9\u6570<\/strong><\/h2>\n
base = 10<\/p>\n
log_base = math.log(x, base)<\/p>\n
print(f"\u4ee510\u4e3a\u5e95\u7684\u5bf9\u6570log({x}, {base}) = {log_base}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u63a5\u4e0b\u6765\u5c06\u8be6\u7ec6\u4ecb\u7ecdPython\u4e2d\u53d6\u5bf9\u6570\u51fd\u6570\u7684\u5176\u4ed6\u65b9\u5f0f\u53ca\u5176\u5e94\u7528\u3002<\/p>\n<\/p>\n
\u4e00\u3001MATH\u6a21\u5757\u4e2d\u7684log\u51fd\u6570<\/h3>\n<\/p>\n
1\u3001\u8ba1\u7b97\u81ea\u7136\u5bf9\u6570<\/h4>\n<\/p>\n
\u81ea\u7136\u5bf9\u6570\u662f\u4ee5\u6570\u5b66\u5e38\u6570e\u4e3a\u5e95\u7684\u5bf9\u6570\u3002Python\u7684math\u6a21\u5757\u63d0\u4f9b\u4e86\u8ba1\u7b97\u81ea\u7136\u5bf9\u6570\u7684\u65b9\u6cd5\uff0c\u5373math.log(x)\u51fd\u6570\u3002<\/p>\n<\/p>\n
import math<\/p>\n
\u8ba1\u7b97\u81ea\u7136\u5bf9\u6570<\/strong><\/h2>\n
x = 10<\/p>\n
log_e = math.log(x)<\/p>\n
print(f"\u81ea\u7136\u5bf9\u6570log({x}) = {log_e}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u8ba1\u7b97\u4e8610\u7684\u81ea\u7136\u5bf9\u6570\u3002math.log(x)\u51fd\u6570\u9ed8\u8ba4\u4ee5e\u4e3a\u5e95\uff0c\u8fd4\u56de\u503c\u4e3a\u7ea62.302585\u3002<\/p>\n<\/p>\n
2\u3001\u8ba1\u7b97\u5176\u4ed6\u5e95\u6570\u7684\u5bf9\u6570<\/h4>\n<\/p>\n
\u9664\u4e86\u81ea\u7136\u5bf9\u6570\u5916\uff0cmath\u6a21\u5757\u8fd8\u5141\u8bb8\u6211\u4eec\u8ba1\u7b97\u5176\u4ed6\u5e95\u6570\u7684\u5bf9\u6570\u3002\u8bed\u6cd5\u4e3amath.log(x, base)\uff0c\u5176\u4e2dx\u662f\u8981\u53d6\u5bf9\u6570\u7684\u6570\u503c\uff0cbase\u662f\u5bf9\u6570\u7684\u5e95\u6570\u3002<\/p>\n<\/p>\n
import math<\/p>\n
\u8ba1\u7b97\u4ee52\u4e3a\u5e95\u7684\u5bf9\u6570<\/strong><\/h2>\n
x = 10<\/p>\n
base = 2<\/p>\n
log_base = math.log(x, base)<\/p>\n
print(f"\u4ee52\u4e3a\u5e95\u7684\u5bf9\u6570log({x}, {base}) = {log_base}")<\/p>\n
\u8ba1\u7b97\u4ee510\u4e3a\u5e95\u7684\u5bf9\u6570<\/strong><\/h2>\n
base = 10<\/p>\n
log_base = math.log(x, base)<\/p>\n
print(f"\u4ee510\u4e3a\u5e95\u7684\u5bf9\u6570log({x}, {base}) = {log_base}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5206\u522b\u8ba1\u7b97\u4e8610\u7684\u4ee52\u4e3a\u5e95\u548c\u4ee510\u4e3a\u5e95\u7684\u5bf9\u6570\u3002math.log(x, base)\u51fd\u6570\u8fd4\u56de\u5bf9\u5e94\u7684\u5bf9\u6570\u503c\u3002<\/p>\n<\/p>\n
\u4e8c\u3001NUMPY\u5e93\u4e2d\u7684log\u51fd\u6570<\/h3>\n<\/p>\n
1\u3001\u8ba1\u7b97\u81ea\u7136\u5bf9\u6570<\/h4>\n<\/p>\n
NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u5b66\u51fd\u6570\u3002NumPy\u7684log\u51fd\u6570\u53ef\u4ee5\u8ba1\u7b97\u6570\u7ec4\u4e2d\u6bcf\u4e2a\u5143\u7d20\u7684\u81ea\u7136\u5bf9\u6570\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u8ba1\u7b97\u81ea\u7136\u5bf9\u6570<\/strong><\/h2>\n
x = np.array([1, 10, 100])<\/p>\n
log_e = np.log(x)<\/p>\n
print(f"\u81ea\u7136\u5bf9\u6570log({x}) = {log_e}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u8ba1\u7b97\u4e86\u6570\u7ec4[1, 10, 100]\u4e2d\u6bcf\u4e2a\u5143\u7d20\u7684\u81ea\u7136\u5bf9\u6570\u3002np.log(x)\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u5305\u542b\u6bcf\u4e2a\u5143\u7d20\u81ea\u7136\u5bf9\u6570\u7684\u6570\u7ec4\u3002<\/p>\n<\/p>\n
2\u3001\u8ba1\u7b97\u5176\u4ed6\u5e95\u6570\u7684\u5bf9\u6570<\/h4>\n<\/p>\n
NumPy\u4e5f\u63d0\u4f9b\u4e86\u8ba1\u7b97\u5176\u4ed6\u5e95\u6570\u5bf9\u6570\u7684\u65b9\u6cd5\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528np.log10()\u8ba1\u7b97\u4ee510\u4e3a\u5e95\u7684\u5bf9\u6570\uff0c\u4f7f\u7528np.log2()\u8ba1\u7b97\u4ee52\u4e3a\u5e95\u7684\u5bf9\u6570\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u8ba1\u7b97\u4ee510\u4e3a\u5e95\u7684\u5bf9\u6570<\/strong><\/h2>\n
x = np.array([1, 10, 100])<\/p>\n
log_10 = np.log10(x)<\/p>\n
print(f"\u4ee510\u4e3a\u5e95\u7684\u5bf9\u6570log10({x}) = {log_10}")<\/p>\n
\u8ba1\u7b97\u4ee52\u4e3a\u5e95\u7684\u5bf9\u6570<\/strong><\/h2>\n
log_2 = np.log2(x)<\/p>\n
print(f"\u4ee52\u4e3a\u5e95\u7684\u5bf9\u6570log2({x}) = {log_2}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u8ba1\u7b97\u4e86\u6570\u7ec4[1, 10, 100]\u4e2d\u6bcf\u4e2a\u5143\u7d20\u7684\u4ee510\u4e3a\u5e95\u548c\u4ee52\u4e3a\u5e95\u7684\u5bf9\u6570\u3002np.log10(x)\u548cnp.log2(x)\u51fd\u6570\u5206\u522b\u8fd4\u56de\u5bf9\u5e94\u7684\u5bf9\u6570\u503c\u3002<\/p>\n<\/p>\n
\u4e09\u3001SYMPY\u5e93\u4e2d\u7684log\u51fd\u6570<\/h3>\n<\/p>\n
1\u3001\u8ba1\u7b97\u81ea\u7136\u5bf9\u6570<\/h4>\n<\/p>\n
SymPy\u662f\u4e00\u4e2a\u7b26\u53f7\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u7cbe\u786e\u7684\u6570\u5b66\u8ba1\u7b97\u529f\u80fd\u3002SymPy\u7684log\u51fd\u6570\u53ef\u4ee5\u8ba1\u7b97\u7b26\u53f7\u8868\u8fbe\u5f0f\u7684\u5bf9\u6570\u3002<\/p>\n<\/p>\n
import sympy as sp<\/p>\n
\u5b9a\u4e49\u7b26\u53f7<\/strong><\/h2>\n
x = sp.Symbol('x')<\/p>\n
\u8ba1\u7b97\u81ea\u7136\u5bf9\u6570<\/strong><\/h2>\n
log_e = sp.log(x)<\/p>\n
print(f"\u81ea\u7136\u5bf9\u6570log(x) = {log_e}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u4e00\u4e2a\u7b26\u53f7x\uff0c\u5e76\u8ba1\u7b97\u4e86\u5b83\u7684\u81ea\u7136\u5bf9\u6570\u3002sp.log(x)\u51fd\u6570\u8fd4\u56de\u7b26\u53f7\u8868\u8fbe\u5f0f\u7684\u5bf9\u6570\u3002<\/p>\n<\/p>\n
2\u3001\u8ba1\u7b97\u5176\u4ed6\u5e95\u6570\u7684\u5bf9\u6570<\/h4>\n<\/p>\n
SymPy\u4e5f\u5141\u8bb8\u6211\u4eec\u8ba1\u7b97\u5176\u4ed6\u5e95\u6570\u7684\u5bf9\u6570\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528sp.log(x, base)\u8ba1\u7b97\u4ee5base\u4e3a\u5e95\u7684\u5bf9\u6570\u3002<\/p>\n<\/p>\n
import sympy as sp<\/p>\n
\u5b9a\u4e49\u7b26\u53f7<\/strong><\/h2>\n
x = sp.Symbol('x')<\/p>\n
\u8ba1\u7b97\u4ee52\u4e3a\u5e95\u7684\u5bf9\u6570<\/strong><\/h2>\n
base = 2<\/p>\n
log_base = sp.log(x, base)<\/p>\n
print(f"\u4ee52\u4e3a\u5e95\u7684\u5bf9\u6570log(x, {base}) = {log_base}")<\/p>\n
\u8ba1\u7b97\u4ee510\u4e3a\u5e95\u7684\u5bf9\u6570<\/strong><\/h2>\n
base = 10<\/p>\n
log_base = sp.log(x, base)<\/p>\n
print(f"\u4ee510\u4e3a\u5e95\u7684\u5bf9\u6570log(x, {base}) = {log_base}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u8ba1\u7b97\u4e86\u7b26\u53f7x\u7684\u4ee52\u4e3a\u5e95\u548c\u4ee510\u4e3a\u5e95\u7684\u5bf9\u6570\u3002sp.log(x, base)\u51fd\u6570\u8fd4\u56de\u5bf9\u5e94\u7684\u7b26\u53f7\u8868\u8fbe\u5f0f\u7684\u5bf9\u6570\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5bf9\u6570\u51fd\u6570\u7684\u5e94\u7528<\/h3>\n<\/p>\n
1\u3001\u5bf9\u6570\u53d8\u6362<\/h4>\n<\/p>\n
\u5bf9\u6570\u53d8\u6362\u662f\u6570\u636e\u9884\u5904\u7406\u7684\u4e00\u79cd\u65b9\u6cd5\uff0c\u5e38\u7528\u4e8e\u5904\u7406\u5177\u6709\u5e42\u5f8b\u5173\u7cfb\u7684\u6570\u636e\u3002\u901a\u8fc7\u5bf9\u6570\u53d8\u6362\uff0c\u53ef\u4ee5\u5c06\u6570\u636e\u7684\u5206\u5e03\u53d8\u5f97\u66f4\u52a0\u5bf9\u79f0\uff0c\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u539f\u59cb\u6570\u636e<\/strong><\/h2>\n
data = np.array([1, 10, 100, 1000, 10000])<\/p>\n
\u5bf9\u6570\u53d8\u6362<\/strong><\/h2>\n
log_data = np.log(data)<\/p>\n
print(f"\u5bf9\u6570\u53d8\u6362\u540e\u7684\u6570\u636e: {log_data}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5bf9\u539f\u59cb\u6570\u636e\u8fdb\u884c\u4e86\u5bf9\u6570\u53d8\u6362\uff0c\u5f97\u5230\u7684\u6570\u636e\u5206\u5e03\u66f4\u52a0\u5bf9\u79f0\u3002<\/p>\n<\/p>\n
2\u3001\u5bf9\u6570\u5c3a\u5ea6<\/h4>\n<\/p>\n
\u5728\u7ed8\u5236\u6570\u636e\u65f6\uff0c\u5bf9\u6570\u5c3a\u5ea6\u53ef\u4ee5\u66f4\u597d\u5730\u5c55\u793a\u6570\u636e\u7684\u53d8\u5316\u3002Matplotlib\u5e93\u63d0\u4f9b\u4e86\u5bf9\u6570\u5c3a\u5ea6\u7684\u652f\u6301\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u7ed8\u5236\u5bf9\u6570\u5c3a\u5ea6\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u539f\u59cb\u6570\u636e<\/strong><\/h2>\n
x = np.linspace(1, 10000, 100)<\/p>\n
y = np.log(x)<\/p>\n
\u7ed8\u5236\u5bf9\u6570\u5c3a\u5ea6\u7684\u56fe\u8868<\/strong><\/h2>\n
plt.figure()<\/p>\n
plt.plot(x, y)<\/p>\n
plt.xscale('log')<\/p>\n
plt.yscale('log')<\/p>\n
plt.xlabel('X (log scale)')<\/p>\n
plt.ylabel('Y (log scale)')<\/p>\n
plt.title('Log-Log Scale Plot')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u7ed8\u5236\u4e86\u4e00\u4e2a\u5bf9\u6570\u5c3a\u5ea6\u7684\u56fe\u8868\u3002\u901a\u8fc7\u8bbe\u7f6ex\u8f74\u548cy\u8f74\u7684\u5c3a\u5ea6\u4e3a\u5bf9\u6570\u5c3a\u5ea6\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u5c55\u793a\u6570\u636e\u7684\u53d8\u5316\u3002<\/p>\n<\/p>\n
3\u3001\u5bf9\u6570\u56de\u5f52<\/h4>\n<\/p>\n
\u5bf9\u6570\u56de\u5f52\u662f\u4e00\u79cd\u56de\u5f52\u5206\u6790\u65b9\u6cd5\uff0c\u9002\u7528\u4e8e\u5177\u6709\u5bf9\u6570\u5173\u7cfb\u7684\u6570\u636e\u3002\u901a\u8fc7\u5bf9\u6570\u56de\u5f52\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u62df\u5408\u6570\u636e\uff0c\u63d0\u9ad8\u6a21\u578b\u7684\u9884\u6d4b\u6027\u80fd\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
\u751f\u6210\u6570\u636e<\/strong><\/h2>\n
x = np.linspace(1, 100, 100).reshape(-1, 1)<\/p>\n
y = np.log(x)<\/p>\n
\u5bf9\u6570\u56de\u5f52<\/strong><\/h2>\n
model = LinearRegression()<\/p>\n
model.fit(x, y)<\/p>\n
y_pred = model.predict(x)<\/p>\n
\u7ed8\u5236\u56de\u5f52\u7ed3\u679c<\/strong><\/h2>\n
import matplotlib.pyplot as plt<\/p>\n
plt.figure()<\/p>\n
plt.scatter(x, y, label='Original Data')<\/p>\n
plt.plot(x, y_pred, color='red', label='Log Regression')<\/p>\n
plt.xlabel('X')<\/p>\n
plt.ylabel('Y')<\/p>\n
plt.title('Log Regression')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u751f\u6210\u4e86\u4e00\u7ec4\u5177\u6709\u5bf9\u6570\u5173\u7cfb\u7684\u6570\u636e\uff0c\u5e76\u8fdb\u884c\u4e86\u5bf9\u6570\u56de\u5f52\u3002\u901a\u8fc7\u5bf9\u6570\u56de\u5f52\uff0c\u53ef\u4ee5\u5f88\u597d\u5730\u62df\u5408\u6570\u636e\uff0c\u63d0\u9ad8\u6a21\u578b\u7684\u9884\u6d4b\u6027\u80fd\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u5bf9\u6570\u51fd\u6570\u7684\u6027\u80fd\u4f18\u5316<\/h3>\n<\/p>\n
1\u3001\u4f7f\u7528NumPy\u8fdb\u884c\u6279\u91cf\u8ba1\u7b97<\/h4>\n<\/p>\n
\u5728\u5904\u7406\u5927\u91cf\u6570\u636e\u65f6\uff0c\u4f7f\u7528NumPy\u8fdb\u884c\u6279\u91cf\u8ba1\u7b97\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u6027\u80fd\u3002NumPy\u7684log\u51fd\u6570\u53ef\u4ee5\u5bf9\u6570\u7ec4\u4e2d\u7684\u6bcf\u4e2a\u5143\u7d20\u8fdb\u884c\u5bf9\u6570\u8ba1\u7b97\uff0c\u6bd4\u9010\u4e2a\u8ba1\u7b97\u6548\u7387\u66f4\u9ad8\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u751f\u6210\u5927\u91cf\u6570\u636e<\/strong><\/h2>\n
data = np.random.rand(1000000)<\/p>\n
\u4f7f\u7528NumPy\u8fdb\u884c\u6279\u91cf\u8ba1\u7b97<\/strong><\/h2>\n
log_data = np.log(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u751f\u6210\u4e86\u4e00\u767e\u4e07\u4e2a\u968f\u673a\u6570\u636e\uff0c\u5e76\u4f7f\u7528NumPy\u7684log\u51fd\u6570\u8fdb\u884c\u6279\u91cf\u8ba1\u7b97\u3002\u76f8\u6bd4\u9010\u4e2a\u8ba1\u7b97\uff0c\u6027\u80fd\u5f97\u5230\u4e86\u663e\u8457\u63d0\u5347\u3002<\/p>\n<\/p>\n
2\u3001\u4f7f\u7528Cython\u8fdb\u884c\u52a0\u901f<\/h4>\n<\/p>\n
Cython\u662f\u4e00\u79cd\u5c06Python\u4ee3\u7801\u7f16\u8bd1\u4e3aC\u4ee3\u7801\u7684\u5de5\u5177\uff0c\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u8ba1\u7b97\u6027\u80fd\u3002\u901a\u8fc7\u5c06\u5bf9\u6570\u8ba1\u7b97\u7684\u4ee3\u7801\u7528Cython\u7f16\u5199\uff0c\u53ef\u4ee5\u83b7\u5f97\u66f4\u9ad8\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n
# log_cython.pyx<\/p>\n
def log_cython(data):<\/p>\n
    cdef int n = len(data)<\/p>\n
    cdef double[:] result = np.zeros(n)<\/p>\n
    for i in range(n):<\/p>\n
        result[i] = math.log(data[i])<\/p>\n
    return result<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u7528Cython\u7f16\u5199\u4e86\u4e00\u4e2a\u5bf9\u6570\u8ba1\u7b97\u7684\u51fd\u6570\u3002\u901a\u8fc7\u7f16\u8bd1Cython\u4ee3\u7801\uff0c\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u8ba1\u7b97\u6027\u80fd\u3002<\/p>\n<\/p>\n
# \u7f16\u8bd1Cython\u4ee3\u7801<\/p>\n
$ cythonize -i log_cython.pyx<\/p>\n
<\/code><\/pre>\n<\/p>\n
# \u4f7f\u7528Cython\u8fdb\u884c\u52a0\u901f<\/p>\n
import numpy as np<\/p>\n
from log_cython import log_cython<\/p>\n
\u751f\u6210\u5927\u91cf\u6570\u636e<\/strong><\/h2>\n
data = np.random.rand(1000000)<\/p>\n
\u4f7f\u7528Cython\u8fdb\u884c\u52a0\u901f<\/strong><\/h2>\n
log_data = log_cython(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u751f\u6210\u4e86\u4e00\u767e\u4e07\u4e2a\u968f\u673a\u6570\u636e\uff0c\u5e76\u4f7f\u7528Cython\u8fdb\u884c\u5bf9\u6570\u8ba1\u7b97\u3002\u76f8\u6bd4\u7eafPython\u4ee3\u7801\uff0c\u6027\u80fd\u5f97\u5230\u4e86\u663e\u8457\u63d0\u5347\u3002<\/p>\n<\/p>\n
\u516d\u3001\u5bf9\u6570\u51fd\u6570\u7684\u6ce8\u610f\u4e8b\u9879<\/h3>\n<\/p>\n
1\u3001\u975e\u6b63\u6570\u7684\u5bf9\u6570<\/h4>\n<\/p>\n
\u5bf9\u6570\u51fd\u6570\u53ea\u5bf9\u6b63\u6570\u5b9a\u4e49\uff0c\u5bf9\u4e8e\u975e\u6b63\u6570\u7684\u8f93\u5165\u4f1a\u4ea7\u751f\u6570\u5b66\u9519\u8bef\u3002\u5728\u4f7f\u7528\u5bf9\u6570\u51fd\u6570\u65f6\uff0c\u9700\u8981\u786e\u4fdd\u8f93\u5165\u6570\u636e\u4e3a\u6b63\u6570\u3002<\/p>\n<\/p>\n
import math<\/p>\n
try:<\/p>\n
    log_value = math.log(-1)<\/p>\n
except ValueError as e:<\/p>\n
    print(f"\u6570\u5b66\u9519\u8bef: {e}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5c1d\u8bd5\u8ba1\u7b97-1\u7684\u5bf9\u6570\uff0c\u4ea7\u751f\u4e86\u6570\u5b66\u9519\u8bef\u3002\u9700\u8981\u786e\u4fdd\u8f93\u5165\u6570\u636e\u4e3a\u6b63\u6570\uff0c\u907f\u514d\u7c7b\u4f3c\u9519\u8bef\u3002<\/p>\n<\/p>\n
2\u3001\u6d6e\u70b9\u6570\u7cbe\u5ea6<\/h4>\n<\/p>\n
\u5bf9\u6570\u51fd\u6570\u7684\u8ba1\u7b97\u6d89\u53ca\u6d6e\u70b9\u6570\u8fd0\u7b97\uff0c\u53ef\u80fd\u4f1a\u53d7\u5230\u6d6e\u70b9\u6570\u7cbe\u5ea6\u7684\u5f71\u54cd\u3002\u5728\u9ad8\u7cbe\u5ea6\u8ba1\u7b97\u4e2d\uff0c\u9700\u8981\u6ce8\u610f\u6d6e\u70b9\u6570\u7cbe\u5ea6\u95ee\u9898\u3002<\/p>\n<\/p>\n
import math<\/p>\n
\u8ba1\u7b97\u5c0f\u6570\u7684\u5bf9\u6570<\/strong><\/h2>\n
x = 0.0001<\/p>\n
log_value = math.log(x)<\/p>\n
print(f"log({x}) = {log_value}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u8ba1\u7b97\u4e860.0001\u7684\u5bf9\u6570\u3002\u7531\u4e8e\u6d6e\u70b9\u6570\u7cbe\u5ea6\u7684\u5f71\u54cd\uff0c\u7ed3\u679c\u53ef\u80fd\u4f1a\u6709\u4e00\u5b9a\u8bef\u5dee\u3002\u5728\u9ad8\u7cbe\u5ea6\u8ba1\u7b97\u4e2d\uff0c\u9700\u8981\u6ce8\u610f\u6d6e\u70b9\u6570\u7cbe\u5ea6\u95ee\u9898\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u5bf9\u6570\u51fd\u6570\u7684\u6269\u5c55\u5e94\u7528<\/h3>\n<\/p>\n
1\u3001\u5bf9\u6570\u51fd\u6570\u5728\u673a\u5668\u5b66\u4e60<\/a>\u4e2d\u7684\u5e94\u7528<\/h4>\n<\/p>\n
\u5bf9\u6570\u51fd\u6570\u5728\u673a\u5668\u5b66\u4e60\u4e2d\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u4f8b\u5982\uff0c\u7279\u5f81\u53d8\u6362\u3001\u635f\u5931\u51fd\u6570\u7b49\u90fd\u53ef\u4ee5\u4f7f\u7528\u5bf9\u6570\u51fd\u6570\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
from sklearn.datasets import load_boston<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
from sklearn.metrics import mean_squared_error<\/p>\n
\u52a0\u8f7d\u6570\u636e<\/strong><\/h2>\n
data = load_boston()<\/p>\n
X = data.data<\/p>\n
y = data.target<\/p>\n
\u7279\u5f81\u53d8\u6362<\/strong><\/h2>\n
X_log = np.log(X + 1)<\/p>\n
\u5206\u5272\u6570\u636e\u96c6<\/strong><\/h2>\n
X_train, X_test, y_train, y_test = train_test_split(X_log, y, test_size=0.2, random_state=42)<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model = LinearRegression()<\/p>\n
model.fit(X_train, y_train)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
\u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\n
mse = mean_squared_error(y_test, y_pred)<\/p>\n
print(f"Mean Squared Error: {mse}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5bf9\u6ce2\u58eb\u987f\u623f\u4ef7\u6570\u636e\u96c6\u7684\u7279\u5f81\u8fdb\u884c\u4e86\u5bf9\u6570\u53d8\u6362\uff0c\u5e76\u8bad\u7ec3\u4e86\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u3002\u901a\u8fc7\u5bf9\u6570\u53d8\u6362\uff0c\u53ef\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n
2\u3001\u5bf9\u6570\u51fd\u6570\u5728\u91d1\u878d\u4e2d\u7684\u5e94\u7528<\/h4>\n<\/p>\n
\u5bf9\u6570\u51fd\u6570\u5728\u91d1\u878d\u4e2d\u4e5f\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u4f8b\u5982\uff0c\u5bf9\u6570\u6536\u76ca\u7387\u3001\u5bf9\u6570\u6b63\u6001\u5206\u5e03\u7b49\u90fd\u4f7f\u7528\u5bf9\u6570\u51fd\u6570\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u751f\u6210\u80a1\u7968\u4ef7\u683c\u6570\u636e<\/strong><\/h2>\n
prices = np.array([100, 105, 110, 115, 120])<\/p>\n
\u8ba1\u7b97\u5bf9\u6570\u6536\u76ca\u7387<\/strong><\/h2>\n
log_returns = np.diff(np.log(prices))<\/p>\n
print(f"\u5bf9\u6570\u6536\u76ca\u7387: {log_returns}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u751f\u6210\u4e86\u4e00\u7ec4\u80a1\u7968\u4ef7\u683c\u6570\u636e\uff0c\u5e76\u8ba1\u7b97\u4e86\u5bf9\u6570\u6536\u76ca\u7387\u3002\u5bf9\u6570\u6536\u76ca\u7387\u53ef\u4ee5\u66f4\u597d\u5730\u53cd\u6620\u80a1\u7968\u4ef7\u683c\u7684\u53d8\u5316\u3002<\/p>\n<\/p>\n
\u516b\u3001\u5bf9\u6570\u51fd\u6570\u7684\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u4e86\u89e3\u4e86Python\u4e2d\u53d6\u5bf9\u6570\u51fd\u6570\u7684\u591a\u79cd\u65b9\u5f0f\uff0c\u5305\u62ecmath\u6a21\u5757\u3001numpy\u5e93\u548csympy\u5e93\u3002\u6211\u4eec\u8fd8\u4ecb\u7ecd\u4e86\u5bf9\u6570\u51fd\u6570\u7684\u5e94\u7528\uff0c\u5305\u62ec\u5bf9\u6570\u53d8\u6362\u3001\u5bf9\u6570\u5c3a\u5ea6\u3001\u5bf9\u6570\u56de\u5f52\u7b49\u3002\u6b64\u5916\uff0c\u8fd8\u4ecb\u7ecd\u4e86\u5bf9\u6570\u51fd\u6570\u7684\u6027\u80fd\u4f18\u5316\u65b9\u6cd5\uff0c\u4ee5\u53ca\u5bf9\u6570\u51fd\u6570\u7684\u6ce8\u610f\u4e8b\u9879\u548c\u6269\u5c55\u5e94\u7528\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u9009\u62e9\u5408\u9002\u7684\u5bf9\u6570\u51fd\u6570\u65b9\u5f0f\uff0c\u53ef\u4ee5\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u548c\u6a21\u578b\u6027\u80fd\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5728Python\u4e2d\uff0c\u5982\u4f55\u4f7f\u7528\u5bf9\u6570\u51fd\u6570\u5904\u7406\u4e0d\u540c\u7684\u5e95\u6570\uff1f<\/strong>
Python\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u8ba1\u7b97\u5bf9\u6570\uff0c\u6700\u5e38\u7528\u7684\u662f\u4f7f\u7528math<\/code>\u6a21\u5757\u548cnumpy<\/code>\u5e93\u3002\u4f7f\u7528math.log(x, base)<\/code>\u53ef\u4ee5\u8ba1\u7b97\u4efb\u610f\u5e95\u6570\u7684\u5bf9\u6570\uff0c\u5176\u4e2dx<\/code>\u662f\u8981\u53d6\u5bf9\u6570\u7684\u6570\uff0cbase<\/code>\u662f\u5e95\u6570\u3002\u4f8b\u5982\uff0cmath.log(100, 10)<\/code>\u5c06\u8fd4\u56de2\u3002\u4f7f\u7528numpy.log<\/code>\u65b9\u6cd5\u65f6\uff0c\u9ed8\u8ba4\u5e95\u6570\u4e3ae\uff0c\u4f46\u53ef\u4ee5\u4f7f\u7528numpy.log10()<\/code>\u548cnumpy.log2()<\/code>\u5206\u522b\u8ba1\u7b97\u4ee510\u548c2\u4e3a\u5e95\u7684\u5bf9\u6570\u3002<\/p>\n
\u5982\u4f55\u5904\u7406\u8d1f\u6570\u6216\u96f6\u503c\u7684\u5bf9\u6570\u8ba1\u7b97\uff1f<\/strong>
\u5728\u6570\u5b66\u4e2d\uff0c\u8d1f\u6570\u548c\u96f6\u7684\u5bf9\u6570\u662f\u672a\u5b9a\u4e49\u7684\uff0c\u56e0\u6b64\u5728Python\u4e2d\u5c1d\u8bd5\u8ba1\u7b97\u8fd9\u4e9b\u503c\u5c06\u5f15\u53d1ValueError<\/code>\u3002\u5728\u8fdb\u884c\u5bf9\u6570\u8ba1\u7b97\u524d\uff0c\u5efa\u8bae\u4f7f\u7528\u6761\u4ef6\u8bed\u53e5\u68c0\u67e5\u6570\u503c\u662f\u5426\u5927\u4e8e\u96f6\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528if x > 0:<\/code>\u6765\u786e\u4fdd\u53ea\u5bf9\u6b63\u6570\u8fdb\u884c\u5bf9\u6570\u8ba1\u7b97\uff0c\u4ece\u800c\u907f\u514d\u7a0b\u5e8f\u5d29\u6e83\u3002<\/p>\n
\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u4f55\u65f6\u4f7f\u7528\u5bf9\u6570\u53d8\u6362\uff0c\u6548\u679c\u5982\u4f55\uff1f<\/strong>
\u5bf9\u6570\u53d8\u6362\u5728\u6570\u636e\u5206\u6790\u4e2d\u5e38\u7528\u4e8e\u5904\u7406\u504f\u6001\u5206\u5e03\uff0c\u7279\u522b\u662f\u5728\u6570\u636e\u5448\u73b0\u53f3\u504f\u6216\u5b58\u5728\u6781\u7aef\u503c\u65f6\u3002\u901a\u8fc7\u5bf9\u6570\u53d8\u6362\uff0c\u53ef\u4ee5\u4f7f\u6570\u636e\u66f4\u52a0\u7b26\u5408\u6b63\u6001\u5206\u5e03\uff0c\u4ece\u800c\u63d0\u9ad8\u7edf\u8ba1\u5206\u6790\u548c\u5efa\u6a21\u7684\u6709\u6548\u6027\u3002\u5728Python\u4e2d\uff0c\u4f7f\u7528numpy.log()<\/code>\u6216pandas.DataFrame.apply()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u8f7b\u677e\u5bf9\u6570\u636e\u96c6\u4e2d\u7684\u6570\u503c\u5217\u8fdb\u884c\u5bf9\u6570\u53d8\u6362\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u53d6\u5bf9\u6570\u51fd\u6570\u7684\u65b9\u5f0f\u4e3b\u8981\u6709\u4ee5\u4e0b\u51e0\u79cd\uff1a\u4f7f\u7528math\u6a21\u5757\u4e2d\u7684log\u51fd\u6570\u3001\u4f7f\u7528numpy\u5e93\u4e2d\u7684log\u51fd\u6570\u3001\u4f7f […]","protected":false},"author":3,"featured_media":1180942,"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\/1180921"}],"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=1180921"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1180921\/revisions"}],"predecessor-version":[{"id":1180948,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1180921\/revisions\/1180948"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1180942"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1180921"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1180921"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1180921"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}