1], alpha_0=5., beta_0=5.) for state in range(library_size) ] hsmm = LibraryHSMMIntNegBinVariant(init_state_concentration=10., alpha=6., gamma=2., obs_distns=obs_distns, dur_distns=dur_distns) for data in training_datas: hsmm.add_data(data, left_censoring=True) for itr in progprint_xrange(resample_iter): hsmm.resample_model() ### degrade into HMM, use the same learned syllables! hmm = LibraryHMMFixedObs(init_state_concentration=10., alpha=6., gamma=2., obs_distns=hsmm.obs_distns) for data in training_datas: hmm.add_data(data) for itr in progprint_xrange(resample_iter): hmm.resample_model() ### degrade into GMM, use the same learned syllables!
model = LibraryHSMMIntNegBinVariant(init_state_concentration=10., alpha=6., gamma=6., obs_distns=obs_distns, dur_distns=dur_distns) for data in training_datas: model.add_data(data, left_censoring=True) # model.add_data_parallel(data,left_censoring=True) ################## # infer things # ################## for i in progprint_xrange(25): model.resample_model() ################# # check likes # ################# computed_directly = model.log_likelihood(test_data, left_censoring=True) # NOTE: this is like model.predictive_likelihoods(test_data,[1]) but it includes # the first frame p(y_1) term instead of just starting at p(y_2|y_1) s = model._states_class(model=model, data=test_data, stateseq=np.zeros(len(test_data)), left_censoring=True) alphal = s.messages_forwards() cmaxes = alphal.max(axis=1)
for state in range(library_size)] model = LibraryHSMMIntNegBinVariant( init_state_concentration=10., alpha=6.,gamma=6., obs_distns=obs_distns, dur_distns=dur_distns) for data in training_datas: model.add_data(data,left_censoring=True) # model.add_data_parallel(data,left_censoring=True) ################## # infer things # ################## train_likes = [] test_likes = [] for i in progprint_xrange(5): model.resample_model() # model.resample_model_parallel() train_likes.append(model.log_likelihood()) # test_likes.append(model.log_likelihood(test_data,left_censoring=True)) newmodel = model.unfreeze() for i in progprint_xrange(5): newmodel.resample_model()
### HSMM dur_distns = [NegativeBinomialIntegerRVariantDuration(np.r_[0.,0,0,0,0,0,1,1,1,1],alpha_0=5.,beta_0=5.) for state in range(library_size)] hsmm = LibraryHSMMIntNegBinVariant( init_state_concentration=10., alpha=6.,gamma=2., obs_distns=obs_distns, dur_distns=dur_distns) for data in training_datas: hsmm.add_data(data,left_censoring=True) for itr in progprint_xrange(resample_iter): hsmm.resample_model() ### degrade into HMM, use the same learned syllables! hmm = LibraryHMMFixedObs( init_state_concentration=10., alpha=6.,gamma=2., obs_distns=hsmm.obs_distns) for data in training_datas: hmm.add_data(data) for itr in progprint_xrange(resample_iter): hmm.resample_model() ### degrade into GMM, use the same learned syllables!