https://blog.csdn.net/qq_24395387/article/details/103370434
python 解 多元一次方程
间歇性学习狂躁症 2019-12-03 16:43:20 3806 收藏 7
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1. 待解方程式
已知ai,bi,ci,求解x,y,z。
a1*x + b1*y+c1*z = 1
a2*x + b2*y+c2*z = 2
a3*x + b3*y+c3*z = 3
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2. 需要的模块与方法
numpy
np.linalg.solve(m, n)
3. 输入格式
其中 np.linalg.solve(m, n)的m,n输入格式为:
m = np.array([[a1, b1, c1], [a2, b2. c2], [a3, b3, c3]])
n = np.array([1, 2, 3]) #可替换为式子右边的常数
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4. 结果
solution = np.linalg.solve(m, n) #solution format: np.array([x, y, z])
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完整伪代码如下:
import numpy as np
"""
a1*x + b1*y+c1*z = 1
a2*x + b2*y+c2*z = 2
a3*x + b3*y+c3*z = 3
"""
m = np.array([[a1, b1, c1], [a2, b2. c2], [a3, b3, c3]])
n = np.array([0, 0, 0]) #可替换为式子右边的常数
solution = np.linalg.solve(m, n) #solution format: np.array([x, y, z])
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注意: np.linalg.solve前提是m矩阵可逆。
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原文链接:https://blog.csdn.net/qq_24395387/article/details/103370434
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rootroot@rootroot-System-Product-Name:~$ python
Python 2.7.18 (default, Mar 8 2021, 13:02:45)
[GCC 9.3.0] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> exit()
rootroot@rootroot-System-Product-Name:~$
rootroot@rootroot-System-Product-Name:~$
rootroot@rootroot-System-Product-Name:~$ python3
Python 3.8.10 (default, Sep 28 2021, 16:10:42)
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> exit9)
File "
exit9)
^
SyntaxError: unmatched ')'
>>>
>>>
>>> exit()
rootroot@rootroot-System-Product-Name:~$
rootroot@rootroot-System-Product-Name:~$
rootroot@rootroot-System-Product-Name:~$
rootroot@rootroot-System-Product-Name:~$ python3
Python 3.8.10 (default, Sep 28 2021, 16:10:42)
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
Traceback (most recent call last):
File "
ModuleNotFoundError: No module named 'numpy'
>>> exit()
rootroot@rootroot-System-Product-Name:~$
rootroot@rootroot-System-Product-Name:~$
rootroot@rootroot-System-Product-Name:~$ python3 -m pip install numpy
/usr/bin/python3: No module named pip
rootroot@rootroot-System-Product-Name:~$ sudo apt-get install pip
[sudo] password for rootroot:
Reading package lists... Done
Building dependency tree
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Note, selecting 'python3-pip' instead of 'pip'
The following additional packages will be installed:
libexpat1-dev libpython3-dev libpython3.8-dev python-pip-whl python3-dev python3-wheel python3.8-dev zlib1g-dev
The following NEW packages will be installed:
libexpat1-dev libpython3-dev libpython3.8-dev python-pip-whl python3-dev python3-pip python3-wheel python3.8-dev zlib1g-dev
0 upgraded, 9 newly installed, 0 to remove and 88 not upgraded.
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Get:1 http://cn.archive.ubuntu.com/ubuntu focal/main amd64 libexpat1-dev amd64 2.2.9-1build1 [116 kB]
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Get:8 http://cn.archive.ubuntu.com/ubuntu focal/universe amd64 python3-wheel all 0.34.2-1 [23.8 kB]
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rootroot@rootroot-System-Product-Name:~$
rootroot@rootroot-System-Product-Name:~$
rootroot@rootroot-System-Product-Name:~$
rootroot@rootroot-System-Product-Name:~$ python3 -m pip install numpy
Collecting numpy
Downloading numpy-1.21.4-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.7 MB)
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Installing collected packages: numpy
WARNING: The scripts f2py, f2py3 and f2py3.8 are installed in '/home/rootroot/.local/bin' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
Successfully installed numpy-1.21.4
rootroot@rootroot-System-Product-Name:~$
rootroot@rootroot-System-Product-Name:~$
rootroot@rootroot-System-Product-Name:~$ python3
Python 3.8.10 (default, Sep 28 2021, 16:10:42)
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> m = np.array([[a1, b1, c1], [a2, b2. c2], [a3, b3, c3]])
Traceback (most recent call last):
File "
NameError: name 'a1' is not defined
>>> m = np.array([[0.8, 0.1, 0.2], [0.15, 0.9. 0.15], [0.2, 0.1, 0.95]])
File "
m = np.array([[0.8, 0.1, 0.2], [0.15, 0.9. 0.15], [0.2, 0.1, 0.95]])
^
SyntaxError: invalid syntax
>>> m = np.array([[0.8, 0.1, 0.2], [0.15, 0.9, 0.15], [0.2, 0.1, 0.95]])
>>> n = np.array([0, 0, 0])
>>> n
array([0, 0, 0])
>>> n = np.array([1, 2, 3])
>>> solution = np.linalg.solve(m, n)
>>> solution
array([0.31063321, 1.68458781, 2.91517324])
>>>