numpy线性代数练习题

在线性代数中，numpy库提供了许多用于处理矩阵和向量的函数。下面是一些numpy线性代数练习题的例子:

1. 计算矩阵的特征值和特征向量:

import numpy as np
a = np.array([[1, 2], [3, 4]])
eigenvalues, eigenvectors = np.linalg.eig(a)
print("特征值:", eigenvalues)
print("特征向量:", eigenvectors)

输出:
特征值: [5.37228132 -0.37228132]
特征向量: [[-0.82456484 -0.41597356]
[ 0.56576746 -0.90937671]]

2. 计算两个数组之间的欧式距离:

import numpy as np
a = np.array([1, 2, 3, 4, 5])
b = np.array([4, 5, 6, 7, 8])
distance = np.linalg.norm(a - b)
print("欧式距离:", distance)

输出: 欧式距离: 6.708203932499369

3. 计算矩阵的行列式:

import numpy as np
x = np.array([[1, 2], [3, 4]])
determinant = np.linalg.det(x)
print("行列式:", determinant)

输出: 行列式: -2.0000000000000004

4. 解线性矩阵方程:

import numpy as np
A = np.array([[1, 2, 1], [2, -1, 3], [3, 1, 2]])
b = np.array([7, 7, 18])
x = np.linalg.solve(A, b)
print("解:", x)

输出: 解: [ 7. 1. -2.]

希望这些例子能帮助你练习numpy线性代数的应用。<span class="em">1</span><span class="em">2</span><span class="em">3</span><span class="em">4</span>