Hi I am working on a continuous classification problem. My time series data is of the following shape
x: (n_trials, n_features, n_time)
y: (n_trials, ).
The trials are of different length, so the n_windows is different for each trial.
Then, I applied sliding window. Now, the data is of this 4D shape:
x: (n_trials, n_windows, n_features, window_length).
y: (n_trials, n_windows)
Not all trials will be used, and I will be passing a list eligible of trial number.
There are three things I would like to ask regarding the Pytorch Dataset class:
The getitem method only takes in one index, and my objective is to return one window of 2D data shaped (n_features, window_length), and one integer for y. I hope that the index would refer to a specific window number. However, to access the window, I must get into the corresponding trial first.
Also, the input trial number may not always start from 1. With only one index, how can I get to the specific window I want?
Is the logic in (1) correct? Is it a must to return one window at a time? Can I put in an entire trial instead of a specific window? My eventual goal is to make a prediction every 40ms, with a window size of 500ms.