这是我的代码,我的逻辑错了
Xs = random.randint(1, nb_samples) #random
window_size = 16
stride = 6
nb_samples = len(Feeding) #100%
train_ratio = 0.6 #60%
from random import randrange, uniform
import random
Xs = random.randint(1, nb_samples) #random
X_feeding_train = [Feeding[i:i+window_size] for i in range(0, int(Xs*train_ratio), stride) if i+window_size<=int(len(Feeding))]
#X_feeding_train = [Feeding[i:i+window_size] for i in range(0, int(len(Feeding)*train_ratio), stride) if i+window_size<=int(len(Feeding))]
X_feeding_test = [Feeding[i:i+window_size] for i in range(irand, len(Feeding), stride) if i+window_size<=len(Feeding)]
X_lying_train = [Lying[i:i+window_size] for i in range(0, int(len(Lying)*train_ratio), stride) if i+window_size<=int(len(Lying))]
X_lying_test = [Lying[i:i+window_size] for i in range(int(len(Lying)*train_ratio), len(Lying), stride) if i+window_size<=len(Lying)]
X_standing_train = [Standing[i:i+window_size] for i in range(0, int(len(Standing)*train_ratio), stride) if i+window_size<=int(len(Standing))]
X_standing_test = [Standing[i:i+window_size] for i in range(int(len(Standing)*train_ratio), len(Standing), stride) if i+window_size<=len(Standing)]
X_normalw_train = [Normal_walking[i:i+window_size] for i in range(0, int(len(Normal_walking)*train_ratio), stride) if i+window_size<=int(len(Normal_walking))]
X_normalw_test = [Normal_walking[i:i+window_size] for i in range(int(len(Normal_walking)*train_ratio), len(Normal_walking), stride) if i+window_size<=len(Normal_walking)]
#add
feedi = len(X_feeding_train) + len(X_feeding_test)
print('X_feeding_train_and_test: ', feedi)
# print('X_feeding_train: ', len(X_feeding_train))
# print('X_feeding_test: ', len(X_feeding_test))
print('X_lying_train: ', len(X_lying_train))
print('X_lying_test: ', len(X_lying_test))
print('X_standing_train: ', len(X_standing_train))
print('X_standing_test: ', len(X_standing_test))
print('X_normalw_train: ', len(X_normalw_train))
print('X_normalw_test: ', len(X_normalw_test))
print('Total: \n')
print('Train: ', len(X_feeding_train)+len(X_lying_train)+len(X_standing_train)+
len(X_normalw_train))
print('Test: ', len(X_feeding_test)+len(X_lying_test)+len(X_standing_test)+ len(X_normalw_test))
我应该使用
feedi = len(X_feeding_train) + len(X_feeding_test)
?
我想在100%的训练中随机抽取60%(
nb_样本=len(喂食)#总计100%
),剩下40%用于测试
然后我想让它打印结果
