{"id":1021571,"date":"2024-12-27T13:24:38","date_gmt":"2024-12-27T05:24:38","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1021571.html"},"modified":"2024-12-27T13:24:41","modified_gmt":"2024-12-27T05:24:41","slug":"python-%e5%a6%82%e4%bd%95%e7%94%9f%e6%88%90%e9%9a%8f%e6%9c%ba%e7%9f%a9%e9%98%b5","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1021571.html","title":{"rendered":"python \u5982\u4f55\u751f\u6210\u968f\u673a\u77e9\u9635"},"content":{"rendered":"

\"python<\/p>\n

\u5728Python\u4e2d\u751f\u6210\u968f\u673a\u77e9\u9635\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u3001SciPy\u5e93\u3001\u6216\u8005Python\u7684\u6807\u51c6\u5e93random\u6a21\u5757<\/strong>\u3002\u5176\u4e2d\uff0cNumPy\u5e93\u63d0\u4f9b\u4e86\u6700\u4e3a\u7b80\u4fbf\u548c\u9ad8\u6548\u7684\u65b9\u6cd5\uff0c\u7279\u522b\u662f\u5728\u9700\u8981\u751f\u6210\u5927\u89c4\u6a21\u968f\u673a\u77e9\u9635\u65f6\u5c24\u4e3a\u660e\u663e\u3002\u6b64\u5916\uff0cSciPy\u5e93\u4e2d\u7684\u4e00\u4e9b\u51fd\u6570\u4e5f\u53ef\u4ee5\u7528\u4e8e\u751f\u6210\u7279\u5b9a\u5206\u5e03\u7684\u968f\u673a\u77e9\u9635\u3002NumPy\u5e93\u7684rand()\u51fd\u6570\u975e\u5e38\u9002\u5408\u751f\u6210\u5747\u5300\u5206\u5e03\u7684\u968f\u673a\u77e9\u9635<\/strong>\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u751f\u6210\u968f\u673a\u77e9\u9635\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528NumPy\u751f\u6210\u968f\u673a\u77e9\u9635<\/h3>\n<\/p>\n

NumPy\u662fPython\u4e2d\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u7684\u6838\u5fc3\u5e93\u4e4b\u4e00\uff0c\u5176\u63d0\u4f9b\u4e86\u591a\u79cd\u751f\u6210\u968f\u673a\u6570\u548c\u77e9\u9635\u7684\u529f\u80fd\u3002<\/p>\n<\/p>\n

1. \u4f7f\u7528numpy.random.rand<\/code>\u751f\u6210\u5747\u5300\u5206\u5e03\u7684\u968f\u673a\u77e9\u9635<\/h4>\n<\/p>\n

numpy.random.rand<\/code>\u51fd\u6570\u53ef\u4ee5\u751f\u6210\u4e00\u4e2a\u5747\u5300\u5206\u5e03\u7684\u968f\u673a\u77e9\u9635\uff0c\u6570\u503c\u8303\u56f4\u57280\u52301\u4e4b\u95f4\u3002<\/p>\n<\/p>\n

import numpy as np<\/p>\n
\u751f\u62103x3\u7684\u968f\u673a\u77e9\u9635<\/strong><\/h2>\n
matrix_uniform = np.random.rand(3, 3)<\/p>\n
print("\u5747\u5300\u5206\u5e03\u968f\u673a\u77e9\u9635\uff1a")<\/p>\n
print(matrix_uniform)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u4e2a\u51fd\u6570\u662f\u751f\u6210\u968f\u673a\u77e9\u9635\u6700\u7b80\u5355\u7684\u65b9\u6cd5\u4e4b\u4e00\u3002\u901a\u8fc7\u4f20\u5165\u77e9\u9635\u7684\u5f62\u72b6\u53c2\u6570\uff0c\u53ef\u4ee5\u5f97\u5230\u4efb\u610f\u5927\u5c0f\u7684\u968f\u673a\u77e9\u9635\u3002<\/p>\n<\/p>\n
2. \u4f7f\u7528numpy.random.randn<\/code>\u751f\u6210\u6b63\u6001\u5206\u5e03\u7684\u968f\u673a\u77e9\u9635<\/h4>\n<\/p>\n
\u5982\u679c\u9700\u8981\u751f\u6210\u670d\u4ece\u6b63\u6001\u5206\u5e03\uff08\u5747\u503c\u4e3a0\uff0c\u65b9\u5dee\u4e3a1\uff09\u7684\u968f\u673a\u77e9\u9635\uff0c\u53ef\u4ee5\u4f7f\u7528numpy.random.randn<\/code>\u3002<\/p>\n<\/p>\n
# \u751f\u62103x3\u7684\u6b63\u6001\u5206\u5e03\u968f\u673a\u77e9\u9635<\/p>\n
matrix_normal = np.random.randn(3, 3)<\/p>\n
print("\u6b63\u6001\u5206\u5e03\u968f\u673a\u77e9\u9635\uff1a")<\/p>\n
print(matrix_normal)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u9700\u8981\u6a21\u62df\u6216\u5e94\u7528\u6b63\u6001\u5206\u5e03\u7684\u573a\u5408\uff0c\u6bd4\u5982\u5728\u67d0\u4e9b\u7edf\u8ba1\u5206\u6790\u6216\u673a\u5668\u5b66\u4e60<\/a>\u7684\u521d\u59cb\u6743\u91cd\u4e2d\u3002<\/p>\n<\/p>\n
3. \u4f7f\u7528numpy.random.randint<\/code>\u751f\u6210\u6574\u6570\u968f\u673a\u77e9\u9635<\/h4>\n<\/p>\n
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u751f\u6210\u968f\u673a\u6574\u6570\u77e9\u9635\u53ef\u80fd\u66f4\u4e3a\u5408\u9002\u3002numpy.random.randint<\/code>\u53ef\u4ee5\u7528\u4e8e\u751f\u6210\u7ed9\u5b9a\u8303\u56f4\u5185\u7684\u968f\u673a\u6574\u6570\u77e9\u9635\u3002<\/p>\n<\/p>\n
# \u751f\u62103x3\u7684\u968f\u673a\u6574\u6570\u77e9\u9635\uff0c\u8303\u56f4\u57280\u523010\u4e4b\u95f4<\/p>\n
matrix_int = np.random.randint(0, 10, (3, 3))<\/p>\n
print("\u968f\u673a\u6574\u6570\u77e9\u9635\uff1a")<\/p>\n
print(matrix_int)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u4e2a\u51fd\u6570\u5141\u8bb8\u6307\u5b9a\u968f\u673a\u6574\u6570\u7684\u8303\u56f4\u548c\u77e9\u9635\u7684\u5f62\u72b6\uff0c\u975e\u5e38\u7075\u6d3b\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528SciPy\u751f\u6210\u968f\u673a\u77e9\u9635<\/h3>\n<\/p>\n
SciPy\u5e93\u662f\u57fa\u4e8eNumPy\u7684\u9ad8\u7ea7\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u591a\u7684\u6982\u7387\u5206\u5e03\u51fd\u6570\u548c\u77e9\u9635\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
1. \u4f7f\u7528scipy.stats<\/code>\u751f\u6210\u7279\u5b9a\u5206\u5e03\u7684\u968f\u673a\u77e9\u9635<\/h4>\n<\/p>\n
SciPy\u7684stats<\/code>\u6a21\u5757\u63d0\u4f9b\u4e86\u5927\u91cf\u7684\u6982\u7387\u5206\u5e03\uff0c\u53ef\u4ee5\u7528\u4e8e\u751f\u6210\u7b26\u5408\u7279\u5b9a\u5206\u5e03\u7684\u968f\u673a\u77e9\u9635\u3002<\/p>\n<\/p>\n
from scipy.stats import uniform<\/p>\n
\u751f\u62103x3\u7684\u5747\u5300\u5206\u5e03\u968f\u673a\u77e9\u9635\uff0c\u6570\u503c\u8303\u56f4\u5728[0, 1]<\/strong><\/h2>\n
matrix_scipy_uniform = uniform.rvs(size=(3, 3))<\/p>\n
print("SciPy\u5747\u5300\u5206\u5e03\u968f\u673a\u77e9\u9635\uff1a")<\/p>\n
print(matrix_scipy_uniform)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7scipy.stats<\/code>\u6a21\u5757\uff0c\u53ef\u4ee5\u751f\u6210\u4efb\u610f\u6982\u7387\u5206\u5e03\u7684\u968f\u673a\u77e9\u9635\uff0c\u6bd4\u5982\u6307\u6570\u5206\u5e03\u3001\u6cca\u677e\u5206\u5e03\u7b49\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Python\u6807\u51c6\u5e93\u751f\u6210\u968f\u673a\u77e9\u9635<\/h3>\n<\/p>\n
\u867d\u7136Python\u7684\u6807\u51c6\u5e93random<\/code>\u6a21\u5757\u6ca1\u6709\u76f4\u63a5\u751f\u6210\u77e9\u9635\u7684\u529f\u80fd\uff0c\u4f46\u53ef\u4ee5\u901a\u8fc7\u5217\u8868\u63a8\u5bfc\u5f0f\u6216\u5faa\u73af\u6765\u751f\u6210\u3002<\/p>\n<\/p>\n
1. \u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f\u751f\u6210\u968f\u673a\u77e9\u9635<\/h4>\n<\/p>\n
import random<\/p>\n
\u751f\u62103x3\u7684\u968f\u673a\u77e9\u9635\uff0c\u6570\u503c\u8303\u56f4\u57280\u52301\u4e4b\u95f4<\/strong><\/h2>\n
matrix_random = [[random.random() for _ in range(3)] for _ in range(3)]<\/p>\n
print("\u5217\u8868\u63a8\u5bfc\u5f0f\u968f\u673a\u77e9\u9635\uff1a")<\/p>\n
print(matrix_random)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u79cd\u65b9\u6cd5\u9002\u5408\u4e8e\u5c0f\u89c4\u6a21\u77e9\u9635\u7684\u751f\u6210\uff0c\u4f46\u6548\u7387\u76f8\u5bf9\u8f83\u4f4e\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5e94\u7528\u573a\u666f\u548c\u4f18\u5316\u5efa\u8bae<\/h3>\n<\/p>\n
\u751f\u6210\u968f\u673a\u77e9\u9635\u5728\u6570\u636e\u5206\u6790\u3001\u673a\u5668\u5b66\u4e60\u3001\u7edf\u8ba1\u6a21\u62df\u7b49\u9886\u57df\u6709\u5e7f\u6cdb\u5e94\u7528\u3002\u4e3a\u4e86\u63d0\u9ad8\u6027\u80fd\uff0c\u7279\u522b\u662f\u5728\u5927\u89c4\u6a21\u77e9\u9635\u751f\u6210\u65f6\uff0c\u5efa\u8bae\u4f7f\u7528NumPy\u5e93\u3002\u5176\u5e95\u5c42\u5b9e\u73b0\u662fC\u8bed\u8a00\uff0c\u5177\u6709\u8f83\u9ad8\u7684\u6548\u7387\u548c\u6027\u80fd\u3002<\/p>\n<\/p>\n
1. \u4f7f\u7528NumPy\u7684\u968f\u673a\u6570\u79cd\u5b50<\/h4>\n<\/p>\n
\u4e3a\u4e86\u4fdd\u8bc1\u5b9e\u9a8c\u7684\u53ef\u91cd\u590d\u6027\uff0c\u5c24\u5176\u662f\u5728\u673a\u5668\u5b66\u4e60\u6a21\u578b\u8bad\u7ec3\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u7684\u968f\u673a\u6570\u79cd\u5b50\u529f\u80fd\u3002<\/p>\n<\/p>\n
np.random.seed(42)<\/p>\n
matrix_with_seed = np.random.rand(3, 3)<\/p>\n
print("\u8bbe\u7f6e\u79cd\u5b50\u7684\u968f\u673a\u77e9\u9635\uff1a")<\/p>\n
print(matrix_with_seed)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u5927\u89c4\u6a21\u77e9\u9635\u7684\u5b58\u50a8\u548c\u5904\u7406<\/h4>\n<\/p>\n
\u5728\u5904\u7406\u5927\u89c4\u6a21\u968f\u673a\u77e9\u9635\u65f6\uff0c\u9700\u8981\u8003\u8651\u5b58\u50a8\u548c\u5185\u5b58\u4f7f\u7528\u7684\u95ee\u9898\u3002\u53ef\u4ee5\u4f7f\u7528NumPy\u7684memmap<\/code>\u529f\u80fd\u5c06\u77e9\u9635\u5b58\u50a8\u5728\u78c1\u76d8\u4e0a\uff0c\u5e76\u8fdb\u884c\u9010\u5757\u5904\u7406\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u5927\u89c4\u6a21\u968f\u673a\u77e9\u9635\u5e76\u5b58\u50a8<\/p>\n
large_matrix = np.memmap('large_matrix.dat', dtype='float32', mode='w+', shape=(10000, 10000))<\/p>\n
large_matrix[:] = np.random.rand(10000, 10000)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u79cd\u65b9\u6cd5\u53ef\u4ee5\u6709\u6548\u51cf\u5c11\u5185\u5b58\u4f7f\u7528\uff0c\u63d0\u9ad8\u5904\u7406\u6548\u7387\u3002<\/p>\n<\/p>\n
3. \u5e76\u884c\u8ba1\u7b97\u548cGPU\u52a0\u901f<\/h4>\n<\/p>\n
\u5728\u9700\u8981\u9ad8\u6027\u80fd\u8ba1\u7b97\u7684\u573a\u5408\uff0c\u53ef\u4ee5\u5229\u7528Python\u7684\u5e76\u884c\u8ba1\u7b97\u548cGPU\u52a0\u901f\u5e93\uff0c\u5982Dask\u548cCuPy\u3002<\/p>\n<\/p>\n
import cupy as cp<\/p>\n
\u4f7f\u7528CuPy\u5728GPU\u4e0a\u751f\u6210\u5927\u89c4\u6a21\u968f\u673a\u77e9\u9635<\/strong><\/h2>\n
matrix_gpu = cp.random.rand(10000, 10000)<\/p>\n
<\/code><\/pre>\n<\/p>\n
CuPy\u4e0eNumPy\u63a5\u53e3\u7c7b\u4f3c\uff0c\u4f46\u5728GPU\u4e0a\u8fd0\u884c\uff0c\u9002\u5408\u4e8e\u6df1\u5ea6\u5b66\u4e60\u548c\u9ad8\u6027\u80fd\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u968f\u673a\u77e9\u9635\u5728\u4e0d\u540c\u9886\u57df\u7684\u5e94\u7528<\/h3>\n<\/p>\n
1. \u5728\u673a\u5668\u5b66\u4e60\u4e2d\u7684\u5e94\u7528<\/h4>\n<\/p>\n
\u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u968f\u673a\u77e9\u9635\u5e38\u7528\u4e8e\u521d\u59cb\u5316\u6a21\u578b\u7684\u6743\u91cd\u3002\u901a\u8fc7\u5408\u7406\u7684\u6743\u91cd\u521d\u59cb\u5316\uff0c\u53ef\u4ee5\u52a0\u5feb\u6a21\u578b\u7684\u6536\u655b\u901f\u5ea6\uff0c\u63d0\u9ad8\u8bad\u7ec3\u6548\u679c\u3002<\/p>\n<\/p>\n
2. \u5728\u7edf\u8ba1\u6a21\u62df\u4e2d\u7684\u5e94\u7528<\/h4>\n<\/p>\n
\u968f\u673a\u77e9\u9635\u53ef\u4ee5\u7528\u4e8e\u6a21\u62df\u5404\u79cd\u968f\u673a\u73b0\u8c61\uff0c\u6bd4\u5982\u8499\u7279\u5361\u7f57\u6a21\u62df\u3001\u968f\u673a\u8fc7\u7a0b\u6a21\u62df\u7b49\u3002\u5728\u91d1\u878d\u3001\u7269\u7406\u7b49\u9886\u57df\uff0c\u968f\u673a\u6a21\u62df\u662f\u91cd\u8981\u7684\u5206\u6790\u5de5\u5177\u3002<\/p>\n<\/p>\n
3. \u5728\u56fe\u50cf\u5904\u7406\u4e2d\u7684\u5e94\u7528<\/h4>\n<\/p>\n
\u5728\u56fe\u50cf\u5904\u7406\u9886\u57df\uff0c\u968f\u673a\u77e9\u9635\u7528\u4e8e\u751f\u6210\u566a\u58f0\u56fe\u50cf\u3001\u6a21\u62df\u968f\u673a\u7eb9\u7406\u7b49\u3002\u901a\u8fc7\u5bf9\u6bd4\u5206\u6790\uff0c\u53ef\u4ee5\u63d0\u9ad8\u56fe\u50cf\u5904\u7406\u7b97\u6cd5\u7684\u9c81\u68d2\u6027\u3002<\/p>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u751f\u6210\u968f\u673a\u77e9\u9635\u662fPython\u7f16\u7a0b\u4e2d\u5e38\u89c1\u7684\u4efb\u52a1\uff0c\u901a\u8fc7\u4f7f\u7528NumPy\u548cSciPy\u5e93\uff0c\u53ef\u4ee5\u8f7b\u677e\u751f\u6210\u5404\u79cd\u7c7b\u578b\u7684\u968f\u673a\u77e9\u9635\u3002\u5bf9\u4e8e\u4e0d\u540c\u7684\u5e94\u7528\u573a\u666f\uff0c\u53ef\u4ee5\u9009\u62e9\u5408\u9002\u7684\u751f\u6210\u65b9\u6cd5\uff0c\u5e76\u7ed3\u5408\u5e76\u884c\u8ba1\u7b97\u548cGPU\u52a0\u901f\u6765\u63d0\u9ad8\u6027\u80fd\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u8fd8\u9700\u8981\u6ce8\u610f\u968f\u673a\u6570\u79cd\u5b50\u7684\u8bbe\u7f6e\u548c\u5927\u89c4\u6a21\u6570\u636e\u7684\u5b58\u50a8\u4e0e\u5904\u7406\u3002\u901a\u8fc7\u5408\u7406\u5229\u7528\u8fd9\u4e9b\u6280\u672f\uff0c\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u7684\u6548\u7387\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u751f\u6210\u7279\u5b9a\u5927\u5c0f\u7684\u968f\u673a\u77e9\u9635\uff1f<\/strong>
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