search

ratschlab / RGAN

Recurrent (conditional) generative adversarial networks for generating real-valued time series data.
https://arxiv.org/abs/1706.02633
MIT License
639 stars 183 forks source link

Improper scaling #21

Open anoojpatel opened 6 years ago

anoojpatel commented 6 years ago

I believe your scale_data() is scaling horizontally across the signals rather than down the time series for each of signal itself. I used:

def scale_linear_bycolumn(rawpoints, high=1.0, low=-1.0): mins = np.min(rawpoints, axis=0) maxs = np.max(rawpoints, axis=0) rng = maxs - mins return high - (((high - low) * (maxs - rawpoints)) / rng)

I think it would make sense to scale the whole signals's timeseries (down the column) rather than across at each time step. I noticed this when I was inputing signals with different amplitudes and the values starting to fill as NaN because it was being scaled across signals at a timestep rather than down the signal to all the values for that signal's time series.

data-boss commented 5 years ago 
Hi,

I'm also trying to train the RGAN to reproduce the results on the eICU data. I have not got the eICU dataset, could you give me a brief introduction abot the structure of the dataset?
what are the 7 lables in the manuscript?
thank you

contact me : zhangxuewen2018@gmail.com