Comments (5)
Well, going to a bigger k limit does generate rational looking selection plots. Though I still get those runtime warnings about invalid error in true_divide which is irritating, but not catastrophic? Will close for now.
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Hi, can you try a larger k range. Maybe like this: mpirun -n 4 python /Users/edward/pyDNMFk/main.py --p_r=4 --p_c=1 --process='pyDNMFk' --fpath='data/' --ftype='csv' --fname='HMX' --init='nnsvd' --itr=5000 --norm='kl' --method='mu' --results_path='results/34' --perturbations=100 --noise_var=0.015 --start_k=3 --end_k=10 --step_k=1 --sill_thr=.9 --sampling='uniform' --prune=true > log34.out &
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small_HMX.csv
Here's a smallish (280x1000) data file as an example of what I'm trying to run if that helps.
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Hi @emkober, I am glad it is working now. I tried the sample data you shared with the following settings:
mpirun -n 4 python main.py --p_r=4 --p_c=1 --process='pyDNMFk' --fpath='data/' --ftype='csv' --fname='small_HMX' --init='nnsvd' --itr=2000 --norm='kl' --method='mu' --results_path='results/' --perturbations=50 --noise_var=0.015 --start_k=2 --end_k=22 --step_k=2 --sill_thr=.9 --sampling='uniform' --prune='true'
I did get same warning in the end, but the results seems okay with the high k-range. In this plot k-optimal seems to be either 6 or 14:
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