1、 矩阵转向量：ravel()
和
flatten()
函数可以实现多维数据向一维数据转换
import numpy as np
x = np.arange(9).reshape(3,3)
print(type(x),x.shape)
print(x)
x = x.reshape(1,9)
print(type(x),x.shape)
print(x)
a = x.ravel()
print(type(a),a.shape)
print(a)
b = x.flatten()
print(b)
a[1]=100
print(a)
b[1]=100
print(b)
..............................
<class 'numpy.ndarray'> (3, 3)
[[0 1 2]
[3 4 5]
[6 7 8]]
<class 'numpy.ndarray'> (1, 9)
[[0 1 2 3 4 5 6 7 8]]
<class 'numpy.ndarray'> (9,)
[0 1 2 3 4 5 6 7 8]
[0 1 2 3 4 5 6 7 8]
[ 0 100 2 3 4 5 6 7 8]
[ 0 100 2 3 4 5 6 7 8]
2、向量转矩阵：reshape()
可以将一维数据转为多维数据
import numpy as np
x = np.arange(10)
print(type(x),x.shape)
print(x)
a = x.reshape(1,10)
print(type(a),a.shape)
print(a)
b = x.reshape(5,2)
print(type(b),b.shape)
print(b)
b = x.reshape(2,5)
print(type(b),b.shape)
print(b)
.......................
<class 'numpy.ndarray'> (10,)
[0 1 2 3 4 5 6 7 8 9]
<class 'numpy.ndarray'> (1, 10)
[[0 1 2 3 4 5 6 7 8 9]]
<class 'numpy.ndarray'> (5, 2)
[[0 1]
[2 3]
[4 5]
[6 7]
[8 9]]
<class 'numpy.ndarray'> (2, 5)
[[0 1 2 3 4]
[5 6 7 8 9]]
