我正在尝试使用该功能,但未获得结果。
df_close = df['Close']
df_train = df_close[:'2019-12-31']
df_train.shape
training_set = df_close
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range = (0, 1))
training_set_scaled = sc.fit_transform(training_set)
training_set_scaled[1]
import numpy as np
X_train = []
y_train = []
for i in range(100, training_set.shape[1]):
X_train.append(training_set_scaled[i-100:i, 0])
y_train.append(training_set_scaled[i, 0])
X_train, y_train = np.array(X_train), np.array(y_train)
X_train
结果是:
array([], dtype=float64)
答案 0 :(得分:0)
如果 training_set.shape[1]
的值小于 100
,则跳过 for 循环内部,将 X_train
留空。
您可以通过在 for 循环中添加打印语句来测试这种情况。让我知道它是否有效,祝你好运!