Operation on Dataset

[Zero-coder]

`border1s = [0, 12 * 30 * 24 - self.seq_len, 12 * 30 * 24 + 4 * 30 * 24 - self.seq_len]
border2s = [12 * 30 * 24, 12 * 30 * 24 + 4 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24]
border1 = border1s[self.set_type]
border2 = border2s[self.set_type]#没看懂

    if self.features == 'M' or self.features == 'MS':#输入
        cols_data = df_raw.columns[1:]#DataFrame.columns属性以返回给定 DataFrame 的列标签,这里不要date
        df_data = df_raw[cols_data]#再通过列名索引取出数据
    elif self.features == 'S':
        df_data = df_raw[[self.target]]#只取OT这一列作为输入

    if self.scale:#scale在_init_初始化True
        train_data = df_data[border1s[0]:border2s[0]]#不知道为什么可以问问author_wu
        self.scaler.fit(train_data.values)#简单来说，求得训练集的均值、方差、最大值、最小值等属性，对数据进行拟合
        data = self.scaler.transform(df_data.values)#在fit的基础上，进行归一化等
    else:
        data = df_data.values`  in data_provider  data_loader.py  Class Dataset_ETT_hour(Dataset)

Thanks for watching this,I wonder why and how you set the border and why train_data = df_data[border1s[0]:border2s[0]]

[Zero-coder]

in data_provider data_loader.py Class Dataset_ETT_hour(Dataset)

[wuhaixu2016]

This is quite plain, you can print the intermediate results.
For more discussion, you can refer to #20

[Zero-coder]

Thank you very much for advice

[Zero-coder]

Simple explaination for train、val、test:https://github.com/zhouhaoyi/Informer2020/issues/164

[Zero-coder]

border1s = [0, 12 * 30 * 24 - self.seq_len, 12 * 30 * 24 + 4 * 30 * 24 - self.seq_len]
border2s = [12 * 30 * 24, 12 * 30 * 24 + 4 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24]
border1 = border1s[self.set_type]#开始值，从数组border1s选一个值代表train、val、test
border2 = border2s[self.set_type]#结束值，从数组border2s选一个值代表train、val、test