Current behavior:
At each call of EM() the crosscorrelated data (all_samples X n_dimensions) in self.event is re-organized into a by-trial matrix (max_samples X n_trials X n_dimensions).
Problem: Given that the cross-correlated data is not susceptible to change once hmp.models.hmp() is initialized, the most efficient way is to do it only once at the init call.
Consequence: speed up is very modest for single call of EM() but function making repeated calls (hmp.fit(), hmp.sliding_event, hmp.backward_estimation) the speed up can be as high as x2
possible problems:
If crosscorrelated data is changed after init by modifying self.events, it will not be taken into account but this is not a behavior that should be done.
size of the initialized model increases but shouldn't be a problem given usual data size
Current behavior: At each call of
EM()the crosscorrelated data (all_samples X n_dimensions) inself.eventis re-organized into a by-trial matrix (max_samples X n_trials X n_dimensions).Problem: Given that the cross-correlated data is not susceptible to change once
hmp.models.hmp()is initialized, the most efficient way is to do it only once at the init call.Consequence: speed up is very modest for single call of
EM()but function making repeated calls (hmp.fit(), hmp.sliding_event, hmp.backward_estimation) the speed up can be as high as x2possible problems:
self.events, it will not be taken into account but this is not a behavior that should be done.Will be shipped with issue #61