Python将2D numpy数组附加到3D

debugcn 发表于 Dev

Curunir

我遇到了一个有关numpy数组的理解问题。我有一个数据集，看起来像这样：

[
   [ F0, F1, F2, F3 ... F22],
   [ G0, G1, G2, G3 ... G22],
   [ H0, H1, H2, H3 ... H22],
   [ I0, I1, I2, I3 ... I22],
   [ J0, J1, J2, J3 ... J22]
]

我想将这些转换成“三包”形式：

[
   [
      [ F0, F1, F2, F3 ... F22],
      [ G0, G1, G2, G3 ... G22],
      [ H0, H1, H2, H3 ... H22]
   ],
   [
      [ G0, G1, G2, G3 ... G22],
      [ H0, H1, H2, H3 ... H22],
      [ I0, I1, I2, I3 ... I22]
   ]
   ...
]

到目前为止，我已经编写了以下代码：

data = loadtxt('./training_data/set_0.csv', delimiter=';')
batch_size=3
features=17
labels=6

trainX = np.empty((0,batch_size, features), float)
for i in range(0, len(data)-batch_size):
    row_X = data[i:i+batch_size,0:features]
    trainX = np.append(trainX, row_X)

print(trainX[0])

记录row_X的形状就可以得到(3,17)想要的结果。但是，trainX变量似乎包含这些阵列的平面的组合，我本来期望的形状trainX[0]是(batch_size,features)。

克里斯

import numpy as np

batch_size = 3


l = np.array([
   [1,2,3,4,5],
   [ 2,3,4,5,6],
   [ 3,4,5,6,7],
   [ 4,5,6,7,8],
   [ 5,6,7,8,9],
    [6,7,8,9,0],
])

output = [l[n:n+batch_size] for n in range(len(l)-batch_size+1)]

输出量

[array([[1, 2, 3, 4, 5],
        [2, 3, 4, 5, 6],
        [3, 4, 5, 6, 7]]),
 array([[2, 3, 4, 5, 6],
        [3, 4, 5, 6, 7],
        [4, 5, 6, 7, 8]]),
 array([[3, 4, 5, 6, 7],
        [4, 5, 6, 7, 8],
        [5, 6, 7, 8, 9]]),
 array([[4, 5, 6, 7, 8],
        [5, 6, 7, 8, 9],
        [6, 7, 8, 9, 0]])]

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