我遇到了对 numpy 数组的理解问题。 我有一个读取时看起来像这样的数据集:
[
[ 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]
]
我想将它们转换成“三个一包”:
[
[
[ 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,我已经编写了这段代码:
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 的形状就像我想要的那样给我 (3,17)
。
但是 trainX
变量似乎包含这些数组的平面组合,我原以为 trainX[0]
的形状为 (batch_size,features)
。
答案 0 :(得分:1)
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
[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]])]