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zdebruine / RcppML

Rcpp Machine Learning: Fast robust NMF, divisive clustering, and more
GNU General Public License v2.0
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Same MSE for different crossValidate methods #49

Open avaruuser opened 7 months ago

avaruuser commented 7 months ago

I switched from python's sklearn NMF to your implementation because of the improved speed and convenient cross-validation. This also means I'm fairly new to R and I'm sorry if my question is obvious.

I have a dataset with 500000 features and 734 observations. I ran crossValidate() with methods 'predict', 'robust', and 'impute' for 2:10 components with ten repetitions each and the same random seed (everything else was default settings). The resulting MSE's were exactly the same across methods, which I believe shouldn't be the case despite using the same random seed. Any ideas why this might happen?

Thank you for developing this amazing package!

GilbertHan1011 commented 6 months ago

I encountered this problem too..... Have you solved this?

zdebruine commented 1 month ago

Any reproducible example would help. I can't reproduce. Thanks! Sorry for the delay.