np.linalg.in
时间: 2023-08-06 10:10:58 浏览: 50
np.linalg.inv 是 NumPy 库中的一个函数,用于计算矩阵的逆。其语法为:
```python
numpy.linalg.inv(a)
```
其中,a 表示要求逆的矩阵。如果矩阵 a 不可逆,会抛出 LinAlgError 异常。
举个例子,假设我们有一个 2x2 的矩阵:
```python
import numpy as np
a = np.array([[1, 2], [3, 4]])
```
那么,我们可以通过 np.linalg.inv(a) 计算出矩阵 a 的逆:
```python
a_inv = np.linalg.inv(a)
```
注意,矩阵的逆并不是所有矩阵都有的,只有满足一定条件的矩阵才有逆。如果矩阵没有逆,那么 np.linalg.inv(a) 会抛出异常。
相关问题
np.linalg.svd
np.linalg.svd is a function provided by the NumPy library in Python. It stands for Singular Value Decomposition and is used for matrix factorization. SVD decomposes a matrix into three separate matrices, U, S, and V.
Here's an example of how to use np.linalg.svd:
```python
import numpy as np
# Create a matrix
A = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
# Perform SVD
U, S, V = np.linalg.svd(A)
# U: Left singular vectors
# S: Singular values
# V: Right singular vectors
print("U:")
print(U)
print("S:")
print(S)
print("V:")
print(V)
```
The output will be the U, S, and V matrices. U contains the left singular vectors, S contains the singular values, and V contains the right singular vectors.
np.linalg.norm
np.linalg.norm is a function from the NumPy library in Python that computes the Euclidean norm (magnitude or length) of a vector or matrix. It can be used to calculate the distance between two points in n-dimensional space.
The syntax for np.linalg.norm is:
np.linalg.norm(x, ord=None, axis=None, keepdims=False)
where:
- x: the input vector or matrix
- ord: the order of the norm to be computed (default is 2, i.e., Euclidean norm)
- axis: the axis along which to compute the norm (default is None, i.e., flattened array)
- keepdims: whether to keep the dimensions of the input array (default is False)
Example usage:
import numpy as np
# compute the Euclidean norm of a vector
v = np.array([1, 2, 3])
norm_v = np.linalg.norm(v)
print(norm_v) # Output: 3.7416573867739413
# compute the Euclidean norm of a matrix along rows
A = np.array([[1, 2, 3], [4, 5, 6]])
norm_A = np.linalg.norm(A, axis=1)
print(norm_A) # Output: [3.74165739 8.77496439]