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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.
The text was updated successfully, but these errors were encountered:
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
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.
The text was updated successfully, but these errors were encountered: