线性方程组 python

Prerequisites:

先决条件：

• Defining a Vectors

  定义向量

• Defining a Matrix

  定义矩阵

In this article, we are going to learn how to represent a linear equation in Python using Linear Algebra. For example we are considering an equation with 3 variables (x,y,z and t).

在本文中，我们将学习如何使用线性代数在Python中表示线性方程。 例如，我们正在考虑具有3个变量( x，y，z 和t )的方程。

    3x + 4y - 7z + 12t = 46
2x + 7y - 13z + 3t = 65
34x + 4y - 4z + 34t = 78

The above equation has a form as below in linear Algebra:

上式的线性代数形式如下：

    Ax = b, x = (x y z t)

Application:

应用：

1. Machine Learning

   机器学习

2. Calculus

   结石

3. Linear Programming

   线性规划

4. Physics and Kinetic Studies

   物理与动力学研究

用于表示线性方程组的Python代码 (Python code for Representation of a system of linear equation)

# Linear Algebra Learning Sequence
# Representation of a System of Linear Equation
import numpy as np
# Use of np.array() to define an Vector
A = np.array([[3, 4, -7, 12], [2, 7, -13, 3], [34, 4, -4, 34]])
b = np.array([46, 65, 78])
print("The Matrix A : \n",A)
x = np.array(['x', 'y', 'z', 't'])
print("\nThe Vector x : ",x)
print("\nThe Vector b : ",b)
print("\n---Now the equations is represented in form of vector: Ax = b---")
print("This is just a python intrepetation of understanding a linear equation")

Output:

输出：

The Matrix A : 
[[  3   4  -7  12]
[  2   7 -13   3]
[ 34   4  -4  34]]
The Vector x :  ['x' 'y' 'z' 't']
The Vector b :  [46 65 78]
---Now the equations is represented in form of vector: Ax = b---
This is just a python intrepetation of understanding a linear equation

翻译自: https://www.includehelp.com/python/representation-of-a-system-of-linear-equation.aspx

线性方程组 python