我有一个带有形状(x,y)的二维数组,我想将其转换为具有形状(x,y,1)的3d数组。有没有一个很好的Pythonic方法来做到这一点?
答案 0 :(得分:50)
除了其他答案,您还可以使用numpy.newaxis
切片:
>>> from numpy import zeros, newaxis
>>> a = zeros((6, 8))
>>> a.shape
(6, 8)
>>> b = a[:, :, newaxis]
>>> b.shape
(6, 8, 1)
甚至这个(它可以使用任意数量的维度):
>>> b = a[..., newaxis]
>>> b.shape
(6, 8, 1)
答案 1 :(得分:9)
numpy.reshape(array, array.shape + (1,))
答案 2 :(得分:4)
import numpy as np
# create a 2D array
a = np.array([[1,2,3], [4,5,6], [1,2,3], [4,5,6],[1,2,3], [4,5,6],[1,2,3], [4,5,6]])
print(a.shape)
# shape of a = (8,3)
b = np.reshape(a, (8, 3, -1))
# changing the shape, -1 means any number which is suitable
print(b.shape)
# size of b = (8,3,1)
答案 3 :(得分:2)
import numpy as np
a= np.eye(3)
print a.shape
b = a.reshape(3,3,1)
print b.shape
答案 4 :(得分:2)
希望这个功能可以帮助你将2D数组转换为3D数组。
Args:
x: 2darray, (n_time, n_in)
agg_num: int, number of frames to concatenate.
hop: int, number of hop frames.
Returns:
3darray, (n_blocks, agg_num, n_in)
def d_2d_to_3d(x, agg_num, hop):
# Pad to at least one block.
len_x, n_in = x.shape
if (len_x < agg_num): #not in get_matrix_data
x = np.concatenate((x, np.zeros((agg_num - len_x, n_in))))
# main 2d to 3d.
len_x = len(x)
i1 = 0
x3d = []
while (i1 + agg_num <= len_x):
x3d.append(x[i1 : i1 + agg_num])
i1 += hop
return np.array(x3d)
答案 5 :(得分:0)
如果您只想在(x,y,1)上添加第三个轴(x,y),则Numpy允许您使用dstack
命令轻松地做到这一点。
import numpy as np
a = np.eye(3) # your matrix here
b = np.dstack(a).T
您需要对它进行转置(.T
才能将其转换为所需的(x,y,1)格式。
答案 6 :(得分:0)
答案 7 :(得分:0)
简单的方法,带有一些数学运算
首先,您知道数组元素的数量,可以说100 然后将100分配给3个步骤,例如:
25 * 2 * 2 = 100
或:4 * 5 * 5 = 100
import numpy as np
D = np.arange(100)
# change to 3d by division of 100 for 3 steps 100 = 25 * 2 * 2
D3 = D.reshape(2,2,25) # 25*2*2 = 100
另一种方式:
another_3D = D.reshape(4,5,5)
print(another_3D.ndim)
到4D:
D4 = D.reshape(2,2,5,5)
print(D4.ndim)