如何用 NumPY 计算给定方阵的特征值和右特征向量？
原文:https://www . geeksforgeeks . org/如何使用-numpy/ 计算给定方阵的特征值和右特征向量
在本文中，我们将讨论如何使用 NumPy 库计算给定方阵的特征值和右特征向量。
示例:
Suppose we have a matrix as:
[[1,2],
[2,3]]
Eigenvalue we get from this matrix or square array is:
[-0.23606798 4.23606798]
Eigenvectors of this matrix are:
[[-0.85065081 -0.52573111],
[ 0.52573111 -0.85065081]]
要知道它们是如何被数学计算的，请看这个特征值和特征向量的计算。在下面的例子中，我们使用了numpy . linalg . EIG()来寻找给定方阵的特征值和特征向量。
语法: numpy.linalg.eig()
参数:一个方阵。
Return: 它会返回两个值第一个是特征值，第二个是特征向量。
例 1:
蟒蛇 3
# importing numpy library
import numpy as np
# create numpy 2d-array
m = np.array([[1, 2],
[2, 3]])
print("Printing the Original square array:\n",
m)
# finding eigenvalues and eigenvectors
w, v = np.linalg.eig(m)
# printing eigen values
print("Printing the Eigen values of the given square array:\n",
w)
# printing eigen vectors
print("Printing Right eigenvectors of the given square array:\n"
v)
输出:
Printing the Original square array:
[[1 2]
[2 3]]
Printing the Eigen values of the given square array:
[-0.23606798 4.23606798]
Printing Right eigenvectors of the given square array:
[[-0.85065081 -0.52573111]
[ 0.52573111 -0.85065081]]
例 2:
蟒蛇 3
# importing numpy library
import numpy as np
# create numpy 2d-array
m = np.array([[1, 2, 3],
[2, 3, 4],
[4, 5, 6]])
print("Printing the Original square array:\n",
m)
# finding eigenvalues and eigenvectors
w, v = np.linalg.eig(m)
# printing eigen values
print("Printing the Eigen values of the given square array:\n",
w)
# printing eigen vectors
print("Printing Right eigenvectors of the given square array:\n",
v)
输出:
Printing the Original square array:
[[1 2 3]
[2 3 4]
[4 5 6]]
Printing the Eigen values of the given square array:
[ 1.08309519e+01 -8.30951895e-01 1.01486082e-16]
Printing Right eigenvectors of the given square array:
[[ 0.34416959 0.72770285 0.40824829]
[ 0.49532111 0.27580256 -0.81649658]
[ 0.79762415 -0.62799801 0.40824829]]
