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After some debugging I found out that a feature in my dataset contained only zeros, which lead to errors in the standardisation process of the nipalsPCA when trying to divide by a std of 0. Perhaps there should be some handling of zero-values or at least a check if a column contains only zero values or other constants in-which the standard deviation is 0 and then throw an error. In my case, the code just ran until manually stopped instead of throwing a zero-division error.
When Xstand = True, the code runs untill manually stopped and gives the warning
RuntimeWarning: invalid value encountered in true_divide
self.arrX = (self.arrX_input - self.Xmeans) / self.Xstd
When Xstand = False, the code runs untill manually stopped and gives the warning
RuntimeWarning: invalid value encountered in true_divide
p = num / denom
The text was updated successfully, but these errors were encountered:
Thank you for reporting this issue, @martinbo94. I agree, there should be a more specific feedback that standardisation will not work if the data has one or more columns with zero variance. We'll try to update this as soon as we can.
After some debugging I found out that a feature in my dataset contained only zeros, which lead to errors in the standardisation process of the nipalsPCA when trying to divide by a std of 0. Perhaps there should be some handling of zero-values or at least a check if a column contains only zero values or other constants in-which the standard deviation is 0 and then throw an error. In my case, the code just ran until manually stopped instead of throwing a zero-division error.
When Xstand = True, the code runs untill manually stopped and gives the warning
RuntimeWarning: invalid value encountered in true_divide
self.arrX = (self.arrX_input - self.Xmeans) / self.Xstd
When Xstand = False, the code runs untill manually stopped and gives the warning
RuntimeWarning: invalid value encountered in true_divide
p = num / denom
The text was updated successfully, but these errors were encountered: