I have a quick question about the randomizationWindow parameter of the reader. It says in the documentation it controls how much of the data is in memory – but I’m a little unclear what effect it will have on the randomness of the data. If the training data file starts with one distribution of data, and ends in another completely different distribution, will setting a randomization window smaller than the data size cause the data fed to the trainer not to be from a homogenous distribution? I just wanted to double check.
When the randomizationWindow is set to a window smaller than the entire data size, the entire data size is chunked into randomizationWindow sized chunks and the order of chunks is randomized. Then within each chunk, the samples are randomized.