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States division #24
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I actually know what is happening: NaN... If I load the dataset, then do the decomposition and on the bins fill NaN, then I get an equal count for all scenarios. I need to dig more to understand why we have NaNs. I don't remember the details there. I have the feeling Maybe I need to calculate the bins for each axis before instead. This way I am sure that the binning is done on the number of sample and not the values. Need to check that hypothesis 😮💨 |
Yep we can provide bounds for the bins. I just thought that was the normal behavior. I have to check that in SciPy's code and do some poking around. So worst case I can do as you do and construct my own bounds it's not hard 👍 |
For the NaNs I don't remember why we have them, need to check as well. |
Should be fixed in a81bf18 |
Easier to discuss over a call. |
The default procedure does not return states with an equal amount of observations. The screenshot (tested in the dashboard) and the data are attached.
case1_data.csv
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