import numpy as np
from keras.utils import np_utils
nsample = 100
sample_space = ["HOME","DRAW","AWAY"]
array = np.random.choice(sample_space, nsample, )
uniques, coded_id = np.unique(array, return_inverse=True)
coded_array = np_utils.to_categorical(coded_id)
实施例
输入
['AWAY', 'HOME', 'DRAW', 'AWAY', ...]
输出coded_array
[[ 0. 1. 0.]
[ 0. 0. 1.]
[ 0. 0. 1.]
...,
[ 0. 0. 1.]
[ 0. 0. 1.]
[ 1. 0. 0.]]
如何反转进程并从coded_array获取原始数据?
答案 0 :(得分:10)
您可以使用np.argmax
检索这些ids
,然后只需将uniques
编入索引即可获得原始数组。因此,我们将有一个实现,如此 -
uniques[y_code.argmax(1)]
示例运行 -
In [44]: arr
Out[44]: array([5, 7, 3, 2, 4, 3, 7])
In [45]: uniques, ids = np.unique(arr, return_inverse=True)
In [46]: y_code = np_utils.to_categorical(ids, len(uniques))
In [47]: uniques[y_code.argmax(1)]
Out[47]: array([5, 7, 3, 2, 4, 3, 7])