{"id":1154088,"date":"2025-01-13T17:46:58","date_gmt":"2025-01-13T09:46:58","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1154088.html"},"modified":"2025-01-13T17:47:01","modified_gmt":"2025-01-13T09:47:01","slug":"python%e4%b8%ad%e5%a6%82%e4%bd%95%e7%94%bblogistic","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1154088.html","title":{"rendered":"python\u4e2d\u5982\u4f55\u753blogistic"},"content":{"rendered":"

\"python\u4e2d\u5982\u4f55\u753blogistic\"<\/p>\n

\u5728Python\u4e2d\uff0c\u7ed8\u5236Logistic\u51fd\u6570\u7684\u56fe\u50cf\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u5de5\u5177\u548c\u5e93\uff0c\u5982Matplotlib\u3001Numpy\u7b49\u3002\u6838\u5fc3\u6b65\u9aa4\u5305\u62ec\uff1a\u5bfc\u5165\u76f8\u5173\u5e93\u3001\u5b9a\u4e49Logistic\u51fd\u6570\u3001\u751f\u6210\u6570\u636e\u5e76\u7ed8\u56fe\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\u7ed8\u5236Logistic\u51fd\u6570\u56fe\u50cf\uff0c\u5177\u4f53\u6b65\u9aa4\u5982\u4e0b\uff1a<\/strong><\/p>\n<\/p>\n

1. \u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/strong><\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165NumPy\u548cMatplotlib\u5e93\u3002NumPy\u7528\u4e8e\u751f\u6210\u6570\u636e\uff0c\u800cMatplotlib\u7528\u4e8e\u7ed8\u5236\u56fe\u5f62\u3002<\/p>\n<\/p>\n

import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u5b9a\u4e49Logistic\u51fd\u6570<\/strong><\/p>\n
Logistic\u51fd\u6570\u7684\u516c\u5f0f\u4e3a\uff1a<\/p>\n<\/p>\n
[ f(x) = \\frac{1}{1 + e^{-x}} ]<\/p>\n<\/p>\n
\u4e0b\u9762\u6211\u4eec\u7528Python\u4ee3\u7801\u5b9e\u73b0\u8fd9\u4e2a\u51fd\u6570\uff1a<\/p>\n<\/p>\n
def logistic(x):<\/p>\n
    return 1 \/ (1 + np.exp(-x))<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u751f\u6210\u6570\u636e<\/strong><\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u9700\u8981\u751f\u6210x\u8f74\u7684\u6570\u636e\u70b9\u3002\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u53ef\u4ee5\u751f\u6210\u4e00\u7ec4\u5747\u5300\u5206\u5e03\u7684\u6570\u503c\u6765\u4f5c\u4e3ax\u8f74\u7684\u6570\u636e\u70b9\u3002<\/p>\n<\/p>\n
x = np.linspace(-10, 10, 100)<\/p>\n
y = logistic(x)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u7ed8\u5236\u56fe\u5f62<\/strong><\/p>\n
\u6700\u540e\uff0c\u6211\u4eec\u4f7f\u7528Matplotlib\u5e93\u7ed8\u5236Logistic\u51fd\u6570\u7684\u56fe\u50cf\u3002<\/p>\n<\/p>\n
plt.plot(x, y, label='Logistic Function')<\/p>\n
plt.xlabel('x')<\/p>\n
plt.ylabel('f(x)')<\/p>\n
plt.title('Logistic Function')<\/p>\n
plt.legend()<\/p>\n
plt.grid(True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u6210\u529f\u7ed8\u5236\u51faLogistic\u51fd\u6570\u7684\u56fe\u50cf\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u6bcf\u4e00\u6b65\u7684\u5177\u4f53\u5185\u5bb9\u548c\u6ce8\u610f\u4e8b\u9879\u3002<\/p>\n<\/p>\n
\n
\u4e00\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h2>\n<\/p>\n
\u5728Python\u4e2d\u7ed8\u5236\u56fe\u5f62\uff0c\u6211\u4eec\u901a\u5e38\u9700\u8981\u4f7f\u7528NumPy\u548cMatplotlib\u5e93\u3002NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0cMatplotlib\u662f\u4e00\u4e2a\u975e\u5e38\u7075\u6d3b\u548c\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\u3002\u8fd9\u4e24\u4e2a\u5e93\u5728\u6570\u636e\u79d1\u5b66\u548c\u673a\u5668\u5b66\u4e60<\/a>\u9886\u57df\u90fd\u975e\u5e38\u5e38\u7528\u3002<\/p>\n<\/p>\n
1. NumPy\u5e93<\/h3>\n<\/p>\n
NumPy\uff08Numerical Python\uff09\u662f\u4e00\u4e2a\u7528\u6765\u8fdb\u884c\u79d1\u5b66\u8ba1\u7b97\u7684\u57fa\u7840\u5e93\u3002\u5b83\u63d0\u4f9b\u4e86\u652f\u6301\u9ad8\u6027\u80fd\u591a\u7ef4\u6570\u7ec4\u548c\u77e9\u9635\u7684\u8ba1\u7b97\uff0c\u5e76\u5305\u542b\u4e86\u5927\u91cf\u7684\u6570\u5b66\u51fd\u6570\u5e93\u3002\u4f7f\u7528NumPy\u751f\u6210\u6570\u636e\u7684\u8fc7\u7a0b\u975e\u5e38\u9ad8\u6548\u4e14\u7b80\u6d01\u3002<\/p>\n<\/p>\n
2. Matplotlib\u5e93<\/h3>\n<\/p>\n
Matplotlib\u662f\u4e00\u4e2a\u5e7f\u6cdb\u4f7f\u7528\u76842D\u7ed8\u56fe\u5e93\uff0c\u5b83\u53ef\u4ee5\u751f\u6210\u5404\u79cd\u9ad8\u8d28\u91cf\u7684\u56fe\u5f62\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u6563\u70b9\u56fe\u3001\u67f1\u72b6\u56fe\u7b49\u3002Matplotlib\u7684\u7075\u6d3b\u6027\u548c\u53ef\u5b9a\u5236\u6027\u4f7f\u5f97\u5b83\u5728\u6570\u636e\u53ef\u89c6\u5316\u9886\u57df\u975e\u5e38\u53d7\u6b22\u8fce\u3002<\/p>\n<\/p>\n
\u5728\u4f7f\u7528\u8fd9\u4e24\u4e2a\u5e93\u4e4b\u524d\uff0c\u9700\u8981\u901a\u8fc7pip\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install numpy matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\u5728\u4ee3\u7801\u4e2d\u5bfc\u5165\u5b83\u4eec\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u5bfc\u5165\u8fd9\u4e9b\u5e93\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528\u5b83\u4eec\u63d0\u4f9b\u7684\u5404\u79cd\u51fd\u6570\u548c\u65b9\u6cd5\u6765\u751f\u6210\u6570\u636e\u548c\u7ed8\u5236\u56fe\u5f62\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u5b9a\u4e49Logistic\u51fd\u6570<\/h2>\n<\/p>\n
Logistic\u51fd\u6570\u662f\u4e00\u79cd\u5e38\u89c1\u7684S\u5f62\u66f2\u7ebf\uff08Sigmoid\u66f2\u7ebf\uff09\uff0c\u5728\u673a\u5668\u5b66\u4e60\u548c\u7edf\u8ba1\u5b66\u4e2d\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u3002Logistic\u51fd\u6570\u7684\u516c\u5f0f\u4e3a\uff1a<\/p>\n<\/p>\n
[ f(x) = \\frac{1}{1 + e^{-x}} ]<\/p>\n<\/p>\n
1. Logistic\u51fd\u6570\u7684\u6570\u5b66\u80cc\u666f<\/h3>\n<\/p>\n
Logistic\u51fd\u6570\u6700\u521d\u5728\u751f\u7269\u5b66\u4e2d\u88ab\u7528\u6765\u63cf\u8ff0\u79cd\u7fa4\u589e\u957f\u3002\u5b83\u7684\u5f62\u5f0f\u662f\u4e00\u4e2aS\u5f62\u66f2\u7ebf\uff0c\u5f53x\u8d8b\u5411\u4e8e\u6b63\u65e0\u7a77\u65f6\uff0c\u51fd\u6570\u503c\u8d8b\u5411\u4e8e1\uff1b\u5f53x\u8d8b\u5411\u4e8e\u8d1f\u65e0\u7a77\u65f6\uff0c\u51fd\u6570\u503c\u8d8b\u5411\u4e8e0\u3002Logistic\u51fd\u6570\u7684\u8fd9\u4e00\u7279\u6027\u4f7f\u5f97\u5b83\u5728\u5206\u7c7b\u95ee\u9898\u4e2d\u88ab\u5e7f\u6cdb\u4f7f\u7528\uff0c\u4f8b\u5982\u5728\u903b\u8f91\u56de\u5f52\u6a21\u578b\u4e2d\u3002<\/p>\n<\/p>\n
2. \u5728Python\u4e2d\u5b9e\u73b0Logistic\u51fd\u6570<\/h3>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u63d0\u4f9b\u7684\u6570\u5b66\u51fd\u6570\u6765\u5b9e\u73b0Logistic\u51fd\u6570\u3002\u5177\u4f53\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
def logistic(x):<\/p>\n
    return 1 \/ (1 + np.exp(-x))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u5b9e\u73b0\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86NumPy\u63d0\u4f9b\u7684exp<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u6307\u6570\u8fd0\u7b97\uff0c\u8fd9\u6bd4\u76f4\u63a5\u4f7f\u7528Python\u7684\u5185\u5efa\u51fd\u6570\u66f4\u9ad8\u6548\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u751f\u6210\u6570\u636e<\/h2>\n<\/p>\n
\u4e3a\u4e86\u7ed8\u5236Logistic\u51fd\u6570\u7684\u56fe\u50cf\uff0c\u6211\u4eec\u9700\u8981\u751f\u6210\u4e00\u7ec4x\u8f74\u7684\u6570\u636e\u70b9\uff0c\u5e76\u8ba1\u7b97\u5bf9\u5e94\u7684y\u8f74\u6570\u636e\u70b9\u3002<\/p>\n<\/p>\n
1. \u751f\u6210x\u8f74\u6570\u636e<\/h3>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528NumPy\u7684linspace<\/code>\u51fd\u6570\u6765\u751f\u6210\u4e00\u7ec4\u5747\u5300\u5206\u5e03\u7684\u6570\u503c\u4f5c\u4e3ax\u8f74\u7684\u6570\u636e\u70b9\u3002linspace<\/code>\u51fd\u6570\u7684\u7b2c\u4e00\u4e2a\u53c2\u6570\u662f\u8d77\u59cb\u503c\uff0c\u7b2c\u4e8c\u4e2a\u53c2\u6570\u662f\u7ec8\u6b62\u503c\uff0c\u7b2c\u4e09\u4e2a\u53c2\u6570\u662f\u751f\u6210\u7684\u6570\u503c\u4e2a\u6570\u3002<\/p>\n<\/p>\n
x = np.linspace(-10, 10, 100)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u884c\u4ee3\u7801\u751f\u6210\u4e86\u4ece-10\u523010\u7684100\u4e2a\u5747\u5300\u5206\u5e03\u7684\u6570\u503c\u3002<\/p>\n<\/p>\n
2. \u8ba1\u7b97y\u8f74\u6570\u636e<\/h3>\n<\/p>\n
\u4f7f\u7528\u6211\u4eec\u4e4b\u524d\u5b9a\u4e49\u7684Logistic\u51fd\u6570\u6765\u8ba1\u7b97\u6bcf\u4e2ax\u503c\u5bf9\u5e94\u7684y\u503c\u3002<\/p>\n<\/p>\n
y = logistic(x)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6837\uff0c\u6211\u4eec\u5c31\u5f97\u5230\u4e86\u7ed8\u5236Logistic\u51fd\u6570\u56fe\u50cf\u6240\u9700\u7684\u6240\u6709\u6570\u636e\u3002<\/p>\n<\/p>\n
\u56db\u3001\u7ed8\u5236\u56fe\u5f62<\/h2>\n<\/p>\n
\u6709\u4e86\u6570\u636e\u4e4b\u540e\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528Matplotlib\u6765\u7ed8\u5236Logistic\u51fd\u6570\u7684\u56fe\u50cf\u3002<\/p>\n<\/p>\n
1. \u57fa\u672c\u7ed8\u56fe<\/h3>\n<\/p>\n
\u9996\u5148\uff0c\u4f7f\u7528plot<\/code>\u51fd\u6570\u7ed8\u5236x\u548cy\u7684\u6570\u636e\u70b9\u3002<\/p>\n<\/p>\n
plt.plot(x, y, label='Logistic Function')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u6dfb\u52a0\u6807\u7b7e\u548c\u6807\u9898<\/h3>\n<\/p>\n
\u4e3a\u4e86\u4f7f\u56fe\u5f62\u66f4\u5177\u53ef\u8bfb\u6027\uff0c\u6211\u4eec\u53ef\u4ee5\u6dfb\u52a0x\u8f74\u548cy\u8f74\u7684\u6807\u7b7e\uff0c\u4ee5\u53ca\u56fe\u5f62\u7684\u6807\u9898\u3002<\/p>\n<\/p>\n
plt.xlabel('x')<\/p>\n
plt.ylabel('f(x)')<\/p>\n
plt.title('Logistic Function')<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u6dfb\u52a0\u56fe\u4f8b\u548c\u7f51\u683c<\/h3>\n<\/p>\n
\u4f7f\u7528legend<\/code>\u51fd\u6570\u6dfb\u52a0\u56fe\u4f8b\uff0c\u4f7f\u7528grid<\/code>\u51fd\u6570\u6dfb\u52a0\u7f51\u683c\u3002<\/p>\n<\/p>\n
plt.legend()<\/p>\n
plt.grid(True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u663e\u793a\u56fe\u5f62<\/h3>\n<\/p>\n
\u6700\u540e\uff0c\u4f7f\u7528show<\/code>\u51fd\u6570\u663e\u793a\u56fe\u5f62\u3002<\/p>\n<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u5b8c\u6574\u4ee3\u7801\u793a\u4f8b<\/h2>\n<\/p>\n
\u5c06\u4ee5\u4e0a\u6240\u6709\u6b65\u9aa4\u6574\u5408\u5728\u4e00\u8d77\uff0c\u5f97\u5230\u4ee5\u4e0b\u5b8c\u6574\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
def logistic(x):<\/p>\n
    return 1 \/ (1 + np.exp(-x))<\/p>\n
x = np.linspace(-10, 10, 100)<\/p>\n
y = logistic(x)<\/p>\n
plt.plot(x, y, label='Logistic Function')<\/p>\n
plt.xlabel('x')<\/p>\n
plt.ylabel('f(x)')<\/p>\n
plt.title('Logistic Function')<\/p>\n
plt.legend()<\/p>\n
plt.grid(True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd0\u884c\u8fd9\u6bb5\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u4e00\u4e2a\u6807\u51c6\u7684Logistic\u51fd\u6570\u56fe\u50cf\uff0c\u5177\u6709\u5e73\u6ed1\u7684S\u5f62\u66f2\u7ebf\u3002<\/p>\n<\/p>\n
\u516d\u3001\u6269\u5c55\u4e0e\u5e94\u7528<\/h2>\n<\/p>\n
\u9664\u4e86\u7ed8\u5236\u6807\u51c6\u7684Logistic\u51fd\u6570\u56fe\u50cf\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u5bf9Logistic\u51fd\u6570\u8fdb\u884c\u53d8\u6362\u548c\u6269\u5c55\uff0c\u4ee5\u9002\u5e94\u4e0d\u540c\u7684\u5e94\u7528\u573a\u666f\u3002<\/p>\n<\/p>\n
1. \u8c03\u6574Logistic\u51fd\u6570\u7684\u53c2\u6570<\/h3>\n<\/p>\n
\u6807\u51c6\u7684Logistic\u51fd\u6570\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u53c2\u6570\u6765\u6539\u53d8\u5176\u5f62\u72b6\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u5f15\u5165\u53c2\u6570k\u548cx0\u6765\u8868\u793a\u51fd\u6570\u7684\u659c\u7387\u548c\u4e2d\u5fc3\u4f4d\u7f6e\u3002<\/p>\n<\/p>\n
[ f(x) = \\frac{1}{1 + e^{-k(x – x0)}} ]<\/p>\n<\/p>\n
\u5728Python\u4e2d\u5b9e\u73b0\u8fd9\u4e00\u53d8\u79cd\u7684Logistic\u51fd\u6570\uff1a<\/p>\n<\/p>\n
def logistic(x, k=1, x0=0):<\/p>\n
    return 1 \/ (1 + np.exp(-k * (x - x0)))<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u7ed8\u5236\u4e0d\u540c\u53c2\u6570\u4e0b\u7684Logistic\u51fd\u6570<\/h3>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u7ed8\u5236\u4e0d\u540c\u53c2\u6570\u4e0b\u7684Logistic\u51fd\u6570\uff0c\u4ee5\u89c2\u5bdf\u5176\u53d8\u5316\u3002<\/p>\n<\/p>\n
x = np.linspace(-10, 10, 100)<\/p>\n
y1 = logistic(x, k=1, x0=0)<\/p>\n
y2 = logistic(x, k=2, x0=0)<\/p>\n
y3 = logistic(x, k=1, x0=2)<\/p>\n
plt.plot(x, y1, label='k=1, x0=0')<\/p>\n
plt.plot(x, y2, label='k=2, x0=0')<\/p>\n
plt.plot(x, y3, label='k=1, x0=2')<\/p>\n
plt.xlabel('x')<\/p>\n
plt.ylabel('f(x)')<\/p>\n
plt.title('Logistic Function with Different Parameters')<\/p>\n
plt.legend()<\/p>\n
plt.grid(True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u8c03\u6574\u53c2\u6570k\u548cx0\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230Logistic\u51fd\u6570\u5728\u659c\u7387\u548c\u4f4d\u7f6e\u4e0a\u7684\u53d8\u5316\u3002<\/p>\n<\/p>\n
3. Logistic\u51fd\u6570\u5728\u673a\u5668\u5b66\u4e60\u4e2d\u7684\u5e94\u7528<\/h3>\n<\/p>\n
Logistic\u51fd\u6570\u5728\u673a\u5668\u5b66\u4e60\u4e2d\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u7279\u522b\u662f\u5728\u5206\u7c7b\u95ee\u9898\u4e2d\u3002\u6700\u5e38\u89c1\u7684\u5e94\u7528\u662fLogistic\u56de\u5f52\u6a21\u578b\uff0c\u5b83\u7528\u4e8e\u4e8c\u5206\u7c7b\u4efb\u52a1\u3002Logistic\u56de\u5f52\u6a21\u578b\u7684\u8f93\u51fa\u662f\u4e00\u4e2a\u6982\u7387\u503c\uff0c\u8868\u793a\u6837\u672c\u5c5e\u4e8e\u67d0\u4e00\u7c7b\u522b\u7684\u6982\u7387\u3002<\/p>\n<\/p>\n
\u5728Logistic\u56de\u5f52\u4e2d\uff0c\u4f7f\u7528Logistic\u51fd\u6570\u5c06\u7ebf\u6027\u6a21\u578b\u7684\u8f93\u51fa\u6620\u5c04\u5230\u6982\u7387\u503c\u3002\u5047\u8bbe\u7ebf\u6027\u6a21\u578b\u4e3a\uff1a<\/p>\n<\/p>\n
[ z = w \\cdot x + b ]<\/p>\n<\/p>\n
\u5219Logistic\u56de\u5f52\u6a21\u578b\u7684\u8f93\u51fa\u4e3a\uff1a<\/p>\n<\/p>\n
[ \\hat{y} = \\frac{1}{1 + e^{-z}} ]<\/p>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Scikit-Learn\u5e93\u6765\u5b9e\u73b0Logistic\u56de\u5f52\u6a21\u578b\u3002\u9996\u5148\uff0c\u901a\u8fc7pip\u5b89\u88c5Scikit-Learn\uff1a<\/p>\n<\/p>\n
pip install scikit-learn<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\uff0c\u4f7f\u7528Scikit-Learn\u5b9e\u73b0Logistic\u56de\u5f52\uff1a<\/p>\n<\/p>\n
from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
from sklearn.linear_model import LogisticRegression<\/p>\n
from sklearn.datasets import make_classification<\/p>\n
from sklearn.metrics import accuracy_score<\/p>\n
\u751f\u6210\u6570\u636e\u96c6<\/strong><\/h2>\n
X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, n_redundant=0, random_state=42)<\/p>\n
\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)<\/p>\n
\u521b\u5efaLogistic\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n
model = LogisticRegression()<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model.fit(X_train, y_train)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
\u8ba1\u7b97\u51c6\u786e\u7387<\/strong><\/h2>\n
accuracy = accuracy_score(y_test, y_pred)<\/p>\n
print(f'Accuracy: {accuracy:.2f}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u8bad\u7ec3\u4e00\u4e2aLogistic\u56de\u5f52\u6a21\u578b\uff0c\u5e76\u8bc4\u4f30\u5176\u5728\u6d4b\u8bd5\u96c6\u4e0a\u7684\u8868\u73b0\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u603b\u7ed3<\/h2>\n<\/p>\n
\u7ed8\u5236Logistic\u51fd\u6570\u56fe\u50cf\u7684\u8fc7\u7a0b\u5305\u62ec\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\u3001\u5b9a\u4e49Logistic\u51fd\u6570\u3001\u751f\u6210\u6570\u636e\u548c\u7ed8\u5236\u56fe\u5f62\u3002\u901a\u8fc7\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u76f4\u89c2\u5730\u89c2\u5bdfLogistic\u51fd\u6570\u7684\u7279\u6027\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u5bf9Logistic\u51fd\u6570\u8fdb\u884c\u6269\u5c55\u548c\u53d8\u6362\uff0c\u4ee5\u9002\u5e94\u4e0d\u540c\u7684\u5e94\u7528\u573a\u666f\u3002\u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0cLogistic\u51fd\u6570\u88ab\u5e7f\u6cdb\u7528\u4e8e\u5206\u7c7b\u95ee\u9898\uff0c\u7279\u522b\u662f\u5728Logistic\u56de\u5f52\u6a21\u578b\u4e2d\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u5e0c\u671b\u8bfb\u8005\u80fd\u591f\u638c\u63e1\u5982\u4f55\u5728Python\u4e2d\u7ed8\u5236Logistic\u51fd\u6570\u56fe\u50cf\uff0c\u4ee5\u53caLogistic\u51fd\u6570\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u7684\u4e00\u4e9b\u57fa\u672c\u77e5\u8bc6\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5728Python\u4e2d\uff0c\u5982\u4f55\u7ed8\u5236Logistic\u51fd\u6570\u7684\u56fe\u5f62\uff1f<\/strong>
\u8981\u7ed8\u5236Logistic\u51fd\u6570\u7684\u56fe\u5f62\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u548cNumpy\u5e93\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u5b9a\u4e49Logistic\u51fd\u6570\u7684\u516c\u5f0f\uff0c\u7136\u540e\u751f\u6210\u4e00\u7cfb\u5217x\u503c\uff0c\u6700\u540e\u4f7f\u7528Matplotlib\u7ed8\u5236\u8fd9\u4e9b\u70b9\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u6765\u5b8c\u6210\u8fd9\u4e2a\u4efb\u52a1\uff1a<\/p>\n
import numpy as np\nimport matplotlib.pyplot as plt\n\ndef logistic(x):\n    return 1 \/ (1 + np.exp(-x))\n\nx = np.linspace(-10, 10, 100)\ny = logistic(x)\n\nplt.plot(x, y)\nplt.title('Logistic Function')\nplt.xlabel('x')\nplt.ylabel('Logistic(x)')\nplt.grid()\nplt.show()\n<\/code><\/pre>\n
\u5982\u4f55\u81ea\u5b9a\u4e49Logistic\u56fe\u7684\u6837\u5f0f\u548c\u989c\u8272\uff1f<\/strong>
\u60a8\u53ef\u4ee5\u901a\u8fc7Matplotlib\u7684\u53c2\u6570\u6765\u81ea\u5b9a\u4e49\u56fe\u5f62\u7684\u6837\u5f0f\u548c\u989c\u8272\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u7ebf\u6761\u7684\u989c\u8272\u3001\u7ebf\u578b\u548c\u6807\u8bb0\u6765\u589e\u5f3a\u56fe\u5f62\u7684\u53ef\u89c6\u6027\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n
plt.plot(x, y, color='blue', linestyle='--', marker='o')\n<\/code><\/pre>\n
\u901a\u8fc7\u8c03\u6574\u8fd9\u4e9b\u53c2\u6570\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u56fe\u5f62\u6837\u5f0f\u66f4\u6539\u4e3a\u6240\u9700\u7684\u89c6\u89c9\u6548\u679c\u3002<\/p>\n
Logistic\u51fd\u6570\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u6709\u4ec0\u4e48\u7528\u9014\uff1f<\/strong>
Logistic\u51fd\u6570\u5728\u8bb8\u591a\u9886\u57df\u90fd\u6709\u5e7f\u6cdb\u5e94\u7528\uff0c\u5305\u62ec\u673a\u5668\u5b66\u4e60\u3001\u751f\u6001\u5b66\u548c\u7ecf\u6d4e\u5b66\u3002\u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u5b83\u5e38\u7528\u4e8e\u4e8c\u5206\u7c7b\u95ee\u9898\u7684\u903b\u8f91\u56de\u5f52\u6a21\u578b\u3002\u5728\u751f\u6001\u5b66\u4e2d\uff0cLogistic\u6a21\u578b\u53ef\u4ee5\u63cf\u8ff0\u79cd\u7fa4\u589e\u957f\u7684\u52a8\u6001\u3002\u5728\u7ecf\u6d4e\u5b66\u4e2d\uff0c\u5b83\u6709\u52a9\u4e8e\u5efa\u6a21\u5e02\u573a\u9971\u548c\u5ea6\u7b49\u73b0\u8c61\u3002\u4e86\u89e3Logistic\u51fd\u6570\u7684\u8fd9\u4e9b\u5e94\u7528\u53ef\u4ee5\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u7406\u89e3\u5176\u91cd\u8981\u6027\u53ca\u5176\u5728\u4e0d\u540c\u9886\u57df\u7684\u8868\u73b0\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u7ed8\u5236Logistic\u51fd\u6570\u7684\u56fe\u50cf\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u5de5\u5177\u548c\u5e93\uff0c\u5982Matplotlib\u3001Numpy\u7b49\u3002\u6838 […]","protected":false},"author":3,"featured_media":1154093,"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\/1154088"}],"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=1154088"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1154088\/revisions"}],"predecessor-version":[{"id":1154097,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1154088\/revisions\/1154097"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1154093"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1154088"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1154088"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1154088"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}