如何使 2d numpy 数组成为 3d 数组

Question

我有一个形状为(x，y(的2d数组，我想将其转换为形状为(x，y，1(的3d数组。有没有一种很好的 Python 方法来做到这一点？

Answer 1

除了其他答案之外，您还可以将切片与numpy.newaxis一起使用：

>>> 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(适用于任意数量的维度(：

>>> b = a[..., newaxis]
>>> b.shape
(6, 8, 1)

Answer 2

numpy.reshape(array, array.shape + (1,))

Answer 3

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)

Answer 4

import numpy as np
a= np.eye(3)
print a.shape
b = a.reshape(3,3,1)
print b.shape

Answer 5

希望此功能可以帮助您将 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)

Answer 6

如果您只想将第 3 轴 (x，y( 添加到 (x，y，1(，Numpy 允许您使用 dstack 命令轻松执行此操作。

import numpy as np
a = np.eye(3) # your matrix here
b = np.dstack(a).T

您需要转置(.T(它以使其转换为所需的(x，y，1(格式。

Answer 7

你可以通过重塑来做到这一点

例如，您有一个形状为 35 x 750(二维(的数组 A，您可以使用 A.reshape(35， 25， 30( 将形状更改为 35 x 25 x 30(三维(

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Answer 8

简单的方法，有一些数学

起初你知道数组元素的数量，假设100然后通过 100 个步骤划分 3 个，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)

Answer 9

import numpy as np
# create a 2-D ndarray
a = np.array([[2,3,4], [5,6,7]])
print(a.ndim)
>> 2
print(a.shape)
>> (2, 3)
# add 3rd dimension

第一个选项：重塑

b = np.reshape(a, a.shape + (1,))
print(b.ndim)
>> 3
print(b.shape)
>> (2, 3, 1)

第二种选择：expand_dims

c = np.expand_dims(a, axis=2)
print(c.ndim)
>> 3
print(c.shape)
>> (2, 3, 1)

Answer 10

如果你有一个数组

a2 = np.array([[1, 2, 3.3],
           [4, 5, 6.5]])

然后，您可以使用以下命令将此数组更改为 3-D shape (2, 3, 3)数组：

a2_new = np.reshape(a2, a2.shape + (1,)) a2_new

您的输出将是：

array([[[1. ],
    [2. ],
    [3.3]],
   [[4. ],
    [5. ],
    [6.5]]])

或您可以尝试：

a2.reshape(2, 3, 1)

这会将您的 2-D 数组更改为 3-D 的 shape(2, 3, 1) 。