{"id":57714,"date":"2025-08-15T18:09:58","date_gmt":"2025-08-15T10:09:58","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/57714.html"},"modified":"2025-08-15T18:09:58","modified_gmt":"2025-08-15T10:09:58","slug":"%e3%80%90%e5%a4%a7%e6%a8%a1%e5%9e%8b%e5%ad%a6%e4%b9%a0%e7%ac%94%e8%ae%b0%e3%80%91%e5%a4%a7%e6%a8%a1%e5%9e%8b%e6%95%b0%e5%ad%a6%e5%85%ac%e5%bc%8f%e4%b8%8epython%e5%ba%93%e6%98%a0%e5%b0%84%e6%8c%87","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/57714.html","title":{"rendered":"\u3010\u5927\u6a21\u578b\u5b66\u4e60\u7b14\u8bb0\u3011\u5927\u6a21\u578b\u6570\u5b66\u516c\u5f0f\u4e0ePython\u5e93\u6620\u5c04\u6307\u5357"},"content":{"rendered":"

\u5efa\u8bae\u7ed3\u5408\u4e13\u680f\u6587\u7ae0\u300a\u5927\u6a21\u578b\u57fa\u7840\u6570\u5b66\u539f\u7406\u4e0e\u67b6\u6784\u7684\u5173\u7cfb\u300b\u9605\u8bfb<\/p>\n

\ud83c\udfaf \u67b6\u6784\u603b\u89c8\u56fe\uff1a\u6570\u5b66\u516c\u5f0f \u2192 Python\u5e93\u670d\u52a1\u5173\u7cfb<\/h3>\n

\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n\u2502 \u6570\u5b66\u516c\u5f0f\u4e0ePython\u5e93\u6620\u5c04\u751f\u6001 \u2502
\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518
\n \u2502 \u2502
\n \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25bc\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25bc\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n \u2502 \u6df1\u5ea6\u5b66\u4e60\u5e93 \u2502 \u2502 \u673a\u5668\u5b66\u4e60\u5e93 \u2502
\n \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502 \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502
\n \u2502 \u2502PyTorch \u2502 \u2502 \u2502 \u2502Scikit-learn\u2502 \u2502
\n \u2502 \u2502TensorFlow \u2502 \u2502 \u2502 \u2502XGBoost \u2502 \u2502
\n \u2502 \u2502JAX \u2502 \u2502 \u2502 \u2502LightGBM \u2502 \u2502
\n \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502 \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2518 \u2502
\n \u2514\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2518
\n \u2502 \u2502 \u2502 \u2502
\n \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25bc\u2500\u2500\u2500\u2500\u2510 \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25bc\u2500\u2500\u2500\u2500\u2510 \u2502
\n \u2502 \u7ebf\u6027\u4ee3\u6570\u5e93 \u2502 \u2502 \u2502 \u6982\u7387\u7edf\u8ba1\u5e93 \u2502 \u2502
\n \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502 \u2502 \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502 \u2502
\n \u2502 \u2502NumPy \u2502 \u2502 \u2502 \u2502 \u2502SciPy \u2502 \u2502 \u2502
\n \u2502 \u2502SciPy \u2502 \u2502 \u2502 \u2502 \u2502Statsmodels\u2502 \u2502 \u2502
\n \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2518 \u2502 \u2502 \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2518 \u2502 \u2502
\n \u2514\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2518 \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2518 \u2502
\n \u2502 \u2502 \u2502 \u2502 \u2502 \u2502
\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25bc\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25bc\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n\u2502 \u6570\u5b66\u8ba1\u7b97\u5e93 \u2502
\n\u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502
\n\u2502 \u2502SymPy \u2502 \u2502Matplotlib \u2502 \u2502Pandas \u2502 \u2502
\n\u2502 \u2502\u7b26\u53f7\u8ba1\u7b97 \u2502 \u2502\u53ef\u89c6\u5316 \u2502 \u2502\u6570\u636e\u5904\u7406 \u2502 \u2502
\n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502
\n\u2502 \u2502 \u2502 \u2502 \u2502
\n\u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u25bc\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25bc\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25bc\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502
\n\u2502 \u2502 \u4f18\u5316\u5e93 \u2502 \u4fe1\u53f7\u5904\u7406 \u2502 \u6570\u503c\u8ba1\u7b97 \u2502 \u2502
\n\u2502 \u2502\u00b7SciPy\u4f18\u5316 \u2502 \u00b7SciPy\u4fe1\u53f7 \u2502 \u00b7NumPy \u2502 \u2502
\n\u2502 \u2502\u00b7CVXPY \u2502 \u00b7PyWavelets \u2502 \u00b7Numba \u2502 \u2502
\n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502
\n\u2502 \u2502 \u2502 \u2502 \u2502
\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518
\n \u2502
\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25bc\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n\u2502 \u57fa\u7840\u6570\u5b66\u516c\u5f0f\u5c42 \u2502
\n\u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502
\n\u2502 \u2502 \u5fae\u79ef\u5206\u516c\u5f0f \u2502 \u2502 \u7ebf\u6027\u4ee3\u6570 \u2502 \u2502 \u6982\u7387\u7edf\u8ba1 \u2502 \u2502
\n\u2502 \u2502\u00b7\u68af\u5ea6\u4e0b\u964d \u2502 \u2502\u00b7\u77e9\u9635\u5206\u89e3 \u2502 \u2502\u00b7\u8d1d\u53f6\u65af\u5b9a\u7406 \u2502 \u2502
\n\u2502 \u2502\u00b7\u94fe\u5f0f\u6cd5\u5219 \u2502 \u2502\u00b7\u7279\u5f81\u503c\u5206\u89e3 \u2502 \u2502\u00b7\u6700\u5927\u4f3c\u7136 \u2502 \u2502
\n\u2502 \u2502\u00b7\u6cf0\u52d2\u5c55\u5f00 \u2502 \u2502\u00b7SVD\u5206\u89e3 \u2502 \u2502\u00b7KL\u6563\u5ea6 \u2502 \u2502
\n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502
\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518<\/p>\n

\ud83d\udcca \u6570\u5b66\u516c\u5f0f\u4e0ePython\u5e93\u51fd\u6570\u6620\u5c04\u8868<\/h3>\n

\ud83d\udd22 \u5fae\u79ef\u5206\u516c\u5f0f\u6620\u5c04<\/h4>\n\n\n \u6570\u5b66\u516c\u5f0f
\n Python\u5b9e\u73b0
\n \u6240\u5c5e\u5e93
\n \u51fd\u6570\/\u7c7b
\n \u5e94\u7528\u573a\u666f
\n <\/tr>\n\n\n\n\n\n\n
\u68af\u5ea6\u4e0b\u964d<\/td>\ntorch.optim.SGD<\/td>\nPyTorch<\/td>\noptim.SGD(params, lr=0.01)<\/td>\n\u795e\u7ecf\u7f51\u7edc\u8bad\u7ec3<\/td>\n<\/tr>\n
\u94fe\u5f0f\u6cd5\u5219<\/td>\ntorch.autograd<\/td>\nPyTorch<\/td>\nloss.backward()<\/td>\n\u53cd\u5411\u4f20\u64ad<\/td>\n<\/tr>\n
\u6cf0\u52d2\u5c55\u5f00<\/td>\nscipy.interpolate.taylor<\/td>\nSciPy<\/td>\ntaylor(func, x0, n)<\/td>\n\u51fd\u6570\u8fd1\u4f3c<\/td>\n<\/tr>\n
\u504f\u5bfc\u6570<\/td>\ntorch.autograd.grad<\/td>\nPyTorch<\/td>\ntorch.autograd.grad(outputs, inputs)<\/td>\n\u9ad8\u9636\u68af\u5ea6<\/td>\n<\/tr>\n
\u62c9\u683c\u6717\u65e5\u4e58\u5b50<\/td>\nscipy.optimize.minimize<\/td>\nSciPy<\/td>\nminimize(fun, x0, method=\\’SLSQP\\’, constraints=…)<\/td>\n\u7ea6\u675f\u4f18\u5316<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\ud83c\udfaf \u7ebf\u6027\u4ee3\u6570\u516c\u5f0f\u6620\u5c04<\/h4>\n\n\n \u6570\u5b66\u516c\u5f0f
\n Python\u5b9e\u73b0
\n \u6240\u5c5e\u5e93
\n \u51fd\u6570\/\u7c7b
\n \u5e94\u7528\u573a\u666f
\n <\/tr>\n\n\n\n\n\n\n
\u77e9\u9635\u4e58\u6cd5<\/td>\ntorch.matmul<\/td>\nPyTorch<\/td>\ntorch.matmul(A, B)<\/td>\n\u5168\u8fde\u63a5\u5c42\u8ba1\u7b97<\/td>\n<\/tr>\n
\u7279\u5f81\u503c\u5206\u89e3<\/td>\ntorch.linalg.eig<\/td>\nPyTorch<\/td>\ntorch.linalg.eig(A)<\/td>\nPCA\u964d\u7ef4<\/td>\n<\/tr>\n
SVD\u5206\u89e3<\/td>\ntorch.linalg.svd<\/td>\nPyTorch<\/td>\ntorch.linalg.svd(A)<\/td>\n\u63a8\u8350\u7cfb\u7edf<\/td>\n<\/tr>\n
\u77e9\u9635\u6c42\u9006<\/td>\ntorch.linalg.inv<\/td>\nPyTorch<\/td>\ntorch.linalg.inv(A)<\/td>\n\u7ebf\u6027\u56de\u5f52\u89e3\u6790\u89e3<\/td>\n<\/tr>\n
\u6b63\u4ea4\u6295\u5f71<\/td>\nnumpy.linalg.qr<\/td>\nNumPy<\/td>\nnp.linalg.qr(A)<\/td>\n\u6b63\u4ea4\u5316<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\ud83d\udcc8 \u6982\u7387\u7edf\u8ba1\u516c\u5f0f\u6620\u5c04<\/h4>\n\n\n \u6570\u5b66\u516c\u5f0f
\n Python\u5b9e\u73b0
\n \u6240\u5c5e\u5e93
\n \u51fd\u6570\/\u7c7b
\n \u5e94\u7528\u573a\u666f
\n <\/tr>\n\n\n\n\n\n\n
\u8d1d\u53f6\u65af\u5b9a\u7406<\/td>\nscipy.stats.bayes_mvs<\/td>\nSciPy<\/td>\nbayes_mvs(data, alpha=0.05)<\/td>\n\u53c2\u6570\u4f30\u8ba1<\/td>\n<\/tr>\n
\u6700\u5927\u4f3c\u7136\u4f30\u8ba1<\/td>\nscipy.stats.fit<\/td>\nSciPy<\/td>\nnorm.fit(data)<\/td>\n\u5206\u5e03\u62df\u5408<\/td>\n<\/tr>\n
KL\u6563\u5ea6<\/td>\ntorch.nn.KLDivLoss<\/td>\nPyTorch<\/td>\nnn.KLDivLoss()<\/td>\n\u77e5\u8bc6\u84b8\u998f<\/td>\n<\/tr>\n
\u9ad8\u65af\u5206\u5e03<\/td>\ntorch.distributions.Normal<\/td>\nPyTorch<\/td>\nNormal(loc, scale)<\/td>\n\u53d8\u5206\u81ea\u7f16\u7801\u5668<\/td>\n<\/tr>\n
\u4ea4\u53c9\u71b5<\/td>\ntorch.nn.CrossEntropyLoss<\/td>\nPyTorch<\/td>\nnn.CrossEntropyLoss()<\/td>\n\u5206\u7c7b\u635f\u5931<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\ud83d\udd0d \u4f18\u5316\u7b97\u6cd5\u6620\u5c04<\/h4>\n\n\n \u7b97\u6cd5\u540d\u79f0
\n \u6570\u5b66\u516c\u5f0f
\n Python\u5b9e\u73b0
\n \u6240\u5c5e\u5e93
\n \u5e94\u7528\u573a\u666f
\n <\/tr>\n\n\n\n\n\n
Adam\u4f18\u5316\u5668<\/td>\nm_t = \u03b21*m_{t-1} + (1-\u03b21)*g_t<\/td>\ntorch.optim.Adam<\/td>\nPyTorch<\/td>\n\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3<\/td>\n<\/tr>\n
RMSprop<\/td>\nv_t = \u03b2*v_{t-1} + (1-\u03b2)*g_t\u00b2<\/td>\ntorch.optim.RMSprop<\/td>\nPyTorch<\/td>\nRNN\u8bad\u7ec3<\/td>\n<\/tr>\n
L-BFGS<\/td>\n\u62df\u725b\u987f\u6cd5<\/td>\nscipy.optimize.minimize<\/td>\nSciPy<\/td>\n\u5c0f\u89c4\u6a21\u4f18\u5316<\/td>\n<\/tr>\n
SGD<\/td>\n\u03b8 = \u03b8 – \u03b1*\u2207J(\u03b8)<\/td>\ntorch.optim.SGD<\/td>\nPyTorch<\/td>\n\u57fa\u7840\u68af\u5ea6\u4e0b\u964d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\ud83c\udfd7\ufe0f \u5e93\u95f4\u670d\u52a1\u5173\u7cfb\u67b6\u6784\u56fe<\/h3>\n

1\ufe0f\u20e3 \u6838\u5fc3\u6df1\u5ea6\u5b66\u4e60\u5e93\u670d\u52a1\u94fe<\/h4>\n

PyTorch\/TensorFlow \u2190 NumPy \u2190 \u57fa\u7840\u6570\u5b66
\n \u2193
\n\u81ea\u52a8\u5fae\u5206 \u2190 \u94fe\u5f0f\u6cd5\u5219
\n \u2193
\n\u4f18\u5316\u5668 \u2190 \u68af\u5ea6\u4e0b\u964d\u7b97\u6cd5
\n \u2193
\n\u795e\u7ecf\u7f51\u7edc\u5c42 \u2190 \u7ebf\u6027\u4ee3\u6570\u8fd0\u7b97<\/p>\n

2\ufe0f\u20e3 \u673a\u5668\u5b66\u4e60\u5e93\u670d\u52a1\u5173\u7cfb<\/h4>\n

Scikit-learn
\n \u251c\u2500\u2500 \u4f7f\u7528NumPy\u8fdb\u884c\u77e9\u9635\u8fd0\u7b97
\n \u251c\u2500\u2500 \u4f7f\u7528SciPy\u8fdb\u884c\u4f18\u5316
\n \u251c\u2500\u2500 \u4f7f\u7528Matplotlib\u53ef\u89c6\u5316
\n \u2514\u2500\u2500 \u63d0\u4f9b\u9ad8\u5c42API\u5c01\u88c5<\/p>\n

XGBoost\/LightGBM
\n \u251c\u2500\u2500 \u4f7f\u7528C++\u5e95\u5c42\u4f18\u5316
\n \u251c\u2500\u2500 \u63d0\u4f9bPython\u63a5\u53e3
\n \u2514\u2500\u2500 \u96c6\u6210NumPy\u6570\u7ec4\u5904\u7406<\/p>\n

3\ufe0f\u20e3 \u6570\u5b66\u8ba1\u7b97\u5e93\u5c42\u7ea7<\/h4>\n

\u57fa\u7840\u5c42\uff1aNumPy
\n \u251c\u2500\u2500 \u6570\u7ec4\u64cd\u4f5c
\n \u251c\u2500\u2500 \u7ebf\u6027\u4ee3\u6570
\n \u2514\u2500\u2500 \u968f\u673a\u6570\u751f\u6210<\/p>\n

\u4e2d\u95f4\u5c42\uff1aSciPy
\n \u251c\u2500\u2500 \u4f18\u5316\u7b97\u6cd5
\n \u251c\u2500\u2500 \u7edf\u8ba1\u5206\u5e03
\n \u2514\u2500\u2500 \u4fe1\u53f7\u5904\u7406<\/p>\n

\u9ad8\u7ea7\u5c42\uff1aPyTorch\/TensorFlow
\n \u251c\u2500\u2500 \u81ea\u52a8\u5fae\u5206
\n \u251c\u2500\u2500 GPU\u52a0\u901f
\n \u2514\u2500\u2500 \u6df1\u5ea6\u5b66\u4e60\u6a21\u578b<\/p>\n

\ud83d\udccb \u5177\u4f53\u5b9e\u73b0\u4ee3\u7801\u6620\u5c04<\/h3>\n

\ud83c\udfaf 1. \u7ebf\u6027\u56de\u5f52\uff08\u6700\u5c0f\u4e8c\u4e58\u6cd5\uff09<\/h4>\n

\u6570\u5b66\u516c\u5f0f\uff1a<\/p>\n

# \u6b63\u89c4\u65b9\u7a0b\uff1a\u03b8 = (X^T X)^(-1) X^T y<\/span><\/p>\n

Python\u5b9e\u73b0\u6620\u5c04\uff1a<\/p>\n

import<\/span> numpy as<\/span> np
\nfrom<\/span> sklearn.<\/span>linear_model import<\/span> LinearRegression<\/p>\n

# NumPy\u539f\u751f\u5b9e\u73b0<\/span>
\nX =<\/span> np.<\/span>array(<\/span>[<\/span>[<\/span>1<\/span>,<\/span> 1<\/span>]<\/span>,<\/span> [<\/span>1<\/span>,<\/span> 2<\/span>]<\/span>,<\/span> [<\/span>2<\/span>,<\/span> 2<\/span>]<\/span>,<\/span> [<\/span>2<\/span>,<\/span> 3<\/span>]<\/span>]<\/span>)<\/span>
\ny =<\/span> np.<\/span>array(<\/span>[<\/span>6<\/span>,<\/span> 8<\/span>,<\/span> 9<\/span>,<\/span> 11<\/span>]<\/span>)<\/span>
\ntheta =<\/span> np.<\/span>linalg.<\/span>inv(<\/span>X.<\/span>T @ X)<\/span> @ X.<\/span>T @ y<\/p>\n

# Scikit-learn\u5c01\u88c5<\/span>
\nmodel =<\/span> LinearRegression(<\/span>)<\/span>
\nmodel.<\/span>fit(<\/span>X,<\/span> y)<\/span><\/p>\n

\ud83c\udfaf 2. \u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09<\/h4>\n

\u6570\u5b66\u516c\u5f0f\uff1a<\/p>\n

# \u7279\u5f81\u503c\u5206\u89e3\uff1aC = Q\u039bQ^T<\/span><\/p>\n

Python\u5b9e\u73b0\u6620\u5c04\uff1a<\/p>\n

from<\/span> sklearn.<\/span>decomposition import<\/span> PCA
\nfrom<\/span> sklearn.<\/span>preprocessing import<\/span> StandardScaler<\/p>\n

# \u624b\u52a8\u5b9e\u73b0<\/span>
\nX_std =<\/span> StandardScaler(<\/span>)<\/span>.<\/span>fit_transform(<\/span>X)<\/span>
\ncov_matrix =<\/span> np.<\/span>cov(<\/span>X_std.<\/span>T)<\/span>
\neigenvalues,<\/span> eigenvectors =<\/span> np.<\/span>linalg.<\/span>eig(<\/span>cov_matrix)<\/span><\/p>\n

# Scikit-learn\u5c01\u88c5<\/span>
\npca =<\/span> PCA(<\/span>n_components=<\/span>2<\/span>)<\/span>
\nX_pca =<\/span> pca.<\/span>fit_transform(<\/span>X)<\/span><\/p>\n

\ud83c\udfaf 3. \u795e\u7ecf\u7f51\u7edc\u53cd\u5411\u4f20\u64ad<\/h4>\n

\u6570\u5b66\u516c\u5f0f\uff1a<\/p>\n","protected":false},"excerpt":{"rendered":"

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