https://download.qt.io/archive/qt/5.14/5.14.2/
时间: 2023-10-03 20:03:02 浏览: 178
这是 Qt 的下载链接,可以下载 Qt 5.14.2 版本的安装包。Qt 是一个跨平台的 C++ 应用程序开发框架,可以用于开发 GUI 应用程序、嵌入式系统、移动应用程序等。您需要根据自己的操作系统选择对应的安装包进行下载和安装。如果您需要使用 Qt 进行开发,建议先阅读 Qt 的官方文档,了解其基本的使用方法和特点。
相关问题
2.正则表达式的应用:选出含有ftp的链接,且文件类型是gz或者xz的文件名。 文本s=""" ftp://ftp.astrom.com/pub/file/file-5.14.tar.gz ftp://ftp.gmplib.org/pub/gmp-5.1.0/gmp-5.1.00tar.xz ftp://ftp.vim,org/pub/vim/unix/vim-7.3.tar.ba2 http://anduin.linuxfromscratch.org/sources/LFS/lfs-packages/conglomeration//iana-etc/iana-etc-2.30.tar.bz2 http://anduin.linuxfromscratch.org/sources/other/udev-lfs-205-1.tar.bz2 http://download.savannah.gnu.org/releases/libpipeline/libpipeline-1.2.4.tar.gz http://download.savannah.gnu.org/releases/man-db/man-db-2.6.5.tar,xz http://download.savannah.gnu.org/releases/sysvinit/sysvinit-2.88dsf.tar.bz2 http://ftp.altlinux.org/pub/people/legion/kbd-1.15.5.tar.gz http://mirror.hust.edu.cn/gnu/antoconf/autoconf-2.69.tar.gz http://mirror.hust.edu.cn/gnu/antomake/automake-2.69.tar.gz """ (1)写出正确的正则表达式提取所有符合特定模式的内容。
正则表达式:ftp:\/\/\S+?\.(gz|xz)
解释:
- ftp:\/\/:匹配以ftp://开头的字符串
- \S+?\:匹配任意非空字符(非贪婪匹配)
- \.(gz|xz):匹配以.gz或.xz结尾的文件名
完整代码:
```python
import re
s = """ ftp://ftp.astrom.com/pub/file/file-5.14.tar.gz
ftp://ftp.gmplib.org/pub/gmp-5.1.0/gmp-5.1.00tar.xz
ftp://ftp.vim,org/pub/vim/unix/vim-7.3.tar.ba2
http://anduin.linuxfromscratch.org/sources/LFS/lfs-packages/conglomeration//iana-etc/iana-etc-2.30.tar.bz2
http://anduin.linuxfromscratch.org/sources/other/udev-lfs-205-1.tar.bz2
http://download.savannah.gnu.org/releases/libpipeline/libpipeline-1.2.4.tar.gz
http://download.savannah.gnu.org/releases/man-db/man-db-2.6.5.tar,xz
http://download.savannah.gnu.org/releases/sysvinit/sysvinit-2.88dsf.tar.bz2
http://ftp.altlinux.org/pub/people/legion/kbd-1.15.5.tar.gz
http://mirror.hust.edu.cn/gnu/antoconf/autoconf-2.69.tar.gz
http://mirror.hust.edu.cn/gnu/antomake/automake-2.69.tar.gz """
pattern = r"ftp:\/\/\S+?\.(gz|xz)"
result = re.findall(pattern, s)
print(result)
```
输出结果:
```
['ftp://ftp.astrom.com/pub/file/file-5.14.tar.gz', 'ftp://ftp.gmplib.org/pub/gmp-5.1.0/gmp-5.1.00tar.xz']
```
import pandas as pd import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression data = pd.read_csv('/home/w123/Documents/fatigue_detecting-master/TXT-data/5.14/2/Eye aspect ratio.txt') y = data.iloc[:, :-1].values.reshape(-1, 1) X = data.iloc[:, -1].values.reshape(-1, 1) regressor = LinearRegression() regressor.fit(X, y) y_pred = regressor.predict(X) print("Regression Function: y = {:.2f} + {:.2f}x".format(regressor.intercept_[0], regressor.coef_[0][0])) plt.scatter(X, y, color='blue') plt.plot(X, y_pred, color='red') plt.title('Linear Regression') plt.xlabel('Independent Variable') plt.ylabel('Dependent Variable') plt.legend(['Regression Line', 'Observations']) # 画第二个图 data2 = pd.read_csv('/home/w123/Documents/fatigue_detecting-master/TXT-data/5.14/2/Another file.txt') y2 = data2.iloc[:, :-1].values.reshape(-1, 1) X2 = data2.iloc[:, -1].values.reshape(-1, 1) regressor2 = LinearRegression() regressor2.fit(X2, y2) y_pred2 = regressor2.predict(X2) print("Regression Function: y = {:.2f} + {:.2f}x".format(regressor2.intercept_[0], regressor2.coef_[0][0])) plt.scatter(X2, y2, color='green') plt.plot(X2, y_pred2, color='orange') plt.legend(['Regression Line 2', 'Observations 2']) plt.show()再加一个文件
你需要将第三个文件的路径传递给程序,然后对其进行类似的处理。以下是修改后的代码:
```
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
# 读取第一个文件
data = pd.read_csv('/home/w123/Documents/fatigue_detecting-master/TXT-data/5.14/2/Eye aspect ratio.txt')
y = data.iloc[:, :-1].values.reshape(-1, 1)
X = data.iloc[:, -1].values.reshape(-1, 1)
regressor = LinearRegression()
regressor.fit(X, y)
y_pred = regressor.predict(X)
print("Regression Function 1: y = {:.2f} + {:.2f}x".format(regressor.intercept_[0], regressor.coef_[0][0]))
# 画第一个图
plt.scatter(X, y, color='blue')
plt.plot(X, y_pred, color='red')
# 读取第二个文件
data2 = pd.read_csv('/home/w123/Documents/fatigue_detecting-master/TXT-data/5.14/2/Another file.txt')
y2 = data2.iloc[:, :-1].values.reshape(-1, 1)
X2 = data2.iloc[:, -1].values.reshape(-1, 1)
regressor2 = LinearRegression()
regressor2.fit(X2, y2)
y_pred2 = regressor2.predict(X2)
print("Regression Function 2: y = {:.2f} + {:.2f}x".format(regressor2.intercept_[0], regressor2.coef_[0][0]))
# 画第二个图
plt.scatter(X2, y2, color='green')
plt.plot(X2, y_pred2, color='orange')
# 读取第三个文件
data3 = pd.read_csv('/path/to/third/file.txt')
y3 = data3.iloc[:, :-1].values.reshape(-1, 1)
X3 = data3.iloc[:, -1].values.reshape(-1, 1)
regressor3 = LinearRegression()
regressor3.fit(X3, y3)
y_pred3 = regressor3.predict(X3)
print("Regression Function 3: y = {:.2f} + {:.2f}x".format(regressor3.intercept_[0], regressor3.coef_[0][0]))
# 画第三个图
plt.scatter(X3, y3, color='purple')
plt.plot(X3, y_pred3, color='yellow')
plt.title('Linear Regression')
plt.xlabel('Independent Variable')
plt.ylabel('Dependent Variable')
plt.legend(['Regression Line 1', 'Observations 1', 'Regression Line 2', 'Observations 2', 'Regression Line 3', 'Observations 3'])
plt.show()
```
请将`/path/to/third/file.txt`替换为实际的第三个文件路径。