ChatGPT 可以像 Linux 终端一样运行，并在给出以下提示时返回执行结果。下面我来带大家操作起来。

终端操作

输入：I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English, I will do so by putting text inside curly brackets {like this}. My first command is pwd

当我们给出上述提示时，ChatGPT 正确返回了执行 pwd 命令的结果。如下：

首先，我们将使用 ls 命令检查当前目录下有哪些文件。输入：ls
输出：

现在我们为这个演示创建一个示例目录。输入：mkdir sample
输出如下：

进入目录，查看当前路径。输入：cd sample; pwd
输出如下：

/home/username/sample

这里查看python路径输入：which python3
输出如下：

/usr/bin/python3

查看版本输入：python3 --version
输出：

Python 3.8.10

训练机器学习模型

这次我们将运行以下 python 脚本来使用鸢尾花数据训练决策树模型。我们可以在本地运行这个脚本，但这次我们将在 ChatGPT 的虚拟 Linux 内核上运行它。
输入：

echo 'import numpy as np\nimport pandas as pd\nfrom sklearn import datasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import tree\nfrom sklearn.metrics import accuracy_score\n\niris = datasets.load_iris()\niris_df = pd.DataFrame(iris.data, columns=iris.feature_names)\niris_df["species"] = [iris.target_names[i] for i in iris.target]\nprint(iris_df.sample(5))\nX = iris_df.iloc[:, :4]\ny = iris.target\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1, stratify=y)\nprint("train", len(y_train))\nprint("test", len(y_test))\nclf = tree.DecisionTreeClassifier()\nclf = clf.fit(X_train, y_train)\ny_pred = clf.predict(X_test)\nprint("Accuracy: %.2f" % accuracy_score(y_test, y_pred))\nprint(y_pred)\nprint(y_test)' > sample.py

上述把代码写入了一个叫做sample.py文件中，下面我们来查看一下文件内容。

输入：cat sample.py
输出：

import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import tree
from sklearn.metrics import accuracy_scoreiris = datasets.load_iris()
iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)
iris_df["species"] = [iris.target_names[i] for i in iris.target]
print(iris_df.sample(5))
X = iris_df.iloc[:, :4]
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1, stratify=y)
print("train", len(y_train))
print("test", len(y_test))
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print("Accuracy: %.2f" % accuracy_score(y_test, y_pred))
print(y_pred)
print(y_test)

现在我们执行代码，输入：python3 sample.py
输出：

    sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)     species
80                 5.5                2.4                 3.8                1.1  versicolor
43                 5.0                3.5                 1.6                0.6      setosa
4                  5.0                3.6                 1.4                0.2      setosa
21                 5.1                3.7                 1.5                0.4      setosa
16                 5.4                3.9                 1.3                0.4      setosa
train 120
test 30
Accuracy: 1.00
[1 0 0 2 2 2 2 1 1 0 2 0 1 1 1 2 1 0 0 2 0 2 2 1 1 0 1 1 2 0]
[1 0 0 2 2 2 2 1 1 0 2 0 1 1 1 2 1 0 0 2 0 2 2 1 1 0 1 1 2 0]

是的，我们成功在上面训练好了模型，并进行了评估。输入：date

输出如下：

{Current Date and Time will be shown in the format like below}
Mon Mar 14 16:45:00 UTC 2023

镜像演示

上述操作同样可以在我搭建的镜像中进行。

仓库源码和使用教程地址：https://github.com/sfvsfv/sfvsfv.github.io镜像地址：https://sfvsfv.github.io/