Heterogeneous refinement initial random assignment iterations

@apunjani Could you clarify if the initial random assignment iterations are always used, even if the initial references are not identical?

In other words, if I am providing known references for 3D classification, should this parameter be set to 0 rather than the default of 5?


Hi @DanielAsarnow,

Initial random assignment is actually somewhat smart about the references you provided.
In each iteration, the posterior assignment probabilities for each particle against all references are computed. In the first 5 (by default) iterations, assignment is randomized only within sets of particles that have their max probability amongst a set of repeated identical references. So if you provide 3 identical references, all particles will be randomized for the first 5 iters. If you provide 3 distinct references, particles will not be randomized (though for the first 5 iterations, classification will be binary rather than soft). If you provide 2 identical and 1 distinct reference, particles that fall into the first 2 identical classes will be randomized within those 2 classes for the first 5 iters.

Hope that helps!

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