矩阵运算

import numpy as np
import matplotlib.pyplot as plt

# 定义矩阵
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])

# 矩阵的四则运算
addition = A + B
subtraction = A - B
multiplication = A * B
division = A / B

# 带变元的矩阵计算
x = 9
scalar_multiplication = A * x

# 特征值和特征向量的计算
eigenvalues, eigenvectors = np.linalg.eig(A)

# 可视化矩阵A
plt.imshow(A, cmap='magma')
plt.colorbar(label='Value')
plt.title('Matrix A')
plt.xticks(np.arange(A.shape[1]))
plt.yticks(np.arange(A.shape[0]))
plt.grid(True, color='gray', linestyle='dotted')
plt.show()

# 打印结果
print("矩阵A：")
print(A)
print("\n矩阵B：")
print(B)
print("\n加法结果：")
print(addition)
print("\n减法结果：")
print(subtraction)
print("\n逐元素乘法结果：")
print(multiplication)
print("\n逐元素除法结果：")
print(division)
print("\n带变元的乘法结果：")
print(scalar_multiplication)
print("\n矩阵A的特征值：")
print(eigenvalues)
print("\n矩阵A的特征向量：")
print(eigenvectors)

 可以可视化