-
Notifications
You must be signed in to change notification settings - Fork 1
Classsify_CLTomo_v31
Divide a selfile of volumes into the desired number of classes. Vector quantization with correntropy and a probabilistic criterion is used for creating the subdivisions. Correlation and the standard maximum correlation criterion can also be used and normally produce good results. Correntropy and the probabilistic clustering criterion are recommended for images with very low SNR or cases in which the correlation have clear difficulties to converge.
The algorithm is fully described in this article.
An interesting convergence criterion is the number of images changing classes between iterations. If a low percentage of the image change class, then the clustering is rather stable and clear. If many images change class, it is likely that there is not enough SNR to determine so many classes. It is recommended to reduce the number of classes
Parameters
$: Selfile with the input images $`--oroot <root`class> $`--iter <N`20> $`--nref0 <N`2> $ or`--ref0 <selfile`> $`--nref <N`16> $`--neigh <N`4> $`--minsize <N`20> $`--sparsity <f`0.975> $`--DWTsparsity <f`0.99> $`--maxShiftX <d`10> $`--maxShiftY <d`10> $`--maxShiftZ <d`10> $`--maxRot <d`360> $`--maxTilt <d`360> $`--maxPsi <d`360> $: By default, some images may not be classified. Use this option to classify them all. $--sym <sc1> $--maxFreq <w0.2> $: Use this option to avoid aligning all missing wedges $: Do not align volumes, only classify --mask <mask_typecircular> where <mask_type> can be:
-
- $
--center <x00> <y0=0> <z0=0>
The MPI program as to be called through a python wrapper that encapsulates some path setting Call the program as xmipp_classify_CLTomo [numberOfMPIProcessors] [arguments as above]
xmipp_classify_CLTomo 8 -i images.stk --nref 256 --oroot class --iter 10