] ################ # build HSMM # ################ dur_distns = [ NegativeBinomialIntegerRVariantDuration(np.r_[0., 0, 0, 0, 0, 1, 1, 1], alpha_0=5., beta_0=5.) 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 # ################## for i in progprint_xrange(25): model.resample_model() #################
for i in range(Nmax) ] ################ # build HSMM # ################ dur_distns = [ NegativeBinomialIntegerRVariantDuration(np.r_[0., 0, 0, 1, 1, 1, 1, 1], alpha_0=5., beta_0=5.) for state in range(Nmax) ] model = LibraryHSMMIntNegBinVariant(init_state_concentration=10., alpha=6., gamma=6., obs_distns=obs_distns, dur_distns=dur_distns) for data in datas: model.add_data_parallel(data, left_censoring=True) ################## # infer things # ################## for i in progprint_xrange(25): model.resample_model_parallel() # plt.figure() # truemodel.plot()
Nmax = 4*library_size obs_distns = [FrozenMixtureDistribution( components=component_library, alpha_0=4.) for i in range(Nmax)] ################ # build HSMM # ################ dur_distns = [NegativeBinomialIntegerRVariantDuration(np.r_[0.,0,0,1,1,1,1,1],alpha_0=5.,beta_0=5.) for state in range(Nmax)] model = LibraryHSMMIntNegBinVariant( init_state_concentration=10., alpha=6.,gamma=6., obs_distns=obs_distns, dur_distns=dur_distns) for data in datas: model.add_data_parallel(data,left_censoring=True) ################## # infer things # ################## for i in progprint_xrange(25): model.resample_model_parallel() # plt.figure() # truemodel.plot()
obs_distns = [FrozenMixtureDistribution( components=component_library, alpha_0=4, weights=row) for row in init_weights] ################ # build HSMM # ################ dur_distns = [NegativeBinomialIntegerRVariantDuration(np.r_[0.,0,0,0,0,1,1,1],alpha_0=5.,beta_0=5.) 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) ################## # infer things # ################## samples1 = [model.resample_and_copy() for i in progprint_xrange(1)] samples2 = [model.resample_and_copy() for i in progprint_xrange(10)] samples3 = [model.resample_and_copy() for i in progprint_xrange(100)] # samples4 = [model.resample_and_copy() for i in progprint_xrange(1000)]
models = collections.OrderedDict() ### 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:
obs_distns = [FrozenMixtureDistribution( components=component_library, alpha_0=4., weights=row) for row in init_weights] ################ # build HSMM # ################ dur_distns = [NegativeBinomialIntegerRVariantDuration(np.r_[0.,0,0,1,1,1,1,1],alpha_0=5.,beta_0=5.) for state in range(library_size)] model = LibraryHSMMIntNegBinVariant( init_state_concentration=10., alpha=6.,gamma=6., obs_distns=obs_distns, dur_distns=dur_distns) model.add_data(data) ##################### # sample and save # ##################### models = [model.resample_and_copy() for itr in progprint_xrange(100)] with open('models.pickle','w') as outfile: cPickle.dump(models,outfile,protocol=-1) with open('model.pickle','w') as outfile:
obs_distns = [FrozenMixtureDistribution( components=component_library, alpha_0=4, weights=row) for row in init_weights] ################ # build HSMM # ################ dur_distns = [NegativeBinomialIntegerRVariantDuration(np.r_[0.,0,0,0,0,1,1,1],alpha_0=5.,beta_0=5.) 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 # ################## for i in progprint_xrange(25): model.resample_model()
] ################ # build HSMM # ################ dur_distns = [ NegativeBinomialIntegerRVariantDuration(np.r_[0., 0, 0, 1, 1, 1, 1, 1], alpha_0=5., beta_0=5.) for state in range(library_size) ] model = LibraryHSMMIntNegBinVariant(init_state_concentration=10., alpha=6., gamma=6., obs_distns=obs_distns, dur_distns=dur_distns) ##################### # add_data timing # ##################### print 'this one should be slower!' tic = time.time() model.add_data(data) toc = time.time() print '...done in %f seconds' % (toc - tic) print '' print 'this one sholud be faster!'
obs_distns = [FrozenMixtureDistribution( components=component_library, alpha_0=4., weights=row) for row in init_weights] ################ # build HSMM # ################ dur_distns = [NegativeBinomialIntegerRVariantDuration(np.r_[0.,0,0,1,1,1,1,1],alpha_0=5.,beta_0=5.) for state in range(library_size)] model = LibraryHSMMIntNegBinVariant( init_state_concentration=10., alpha=6.,gamma=6., obs_distns=obs_distns, dur_distns=dur_distns) model.add_data(data,left_censoring=True) ################## # infer things # ################## for i in progprint_xrange(50): model.resample_model() likes = model.Viterbi_EM_fit() plt.figure()
for row in init_weights] ################ # fit models # ################ models = collections.OrderedDict() ### 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:
] ################ # build HSMM # ################ dur_distns = [ NegativeBinomialIntegerRVariantDuration(np.r_[0., 0, 0, 0, 0, 1, 1, 1], alpha_0=5., beta_0=5.) 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):
obs_distns = [FrozenMixtureDistribution( components=component_library, alpha_0=4, weights=row) for row in init_weights] ################ # build HSMM # ################ dur_distns = [NegativeBinomialIntegerRVariantDuration(np.r_[0.,0,0,0,0,1,1,1],alpha_0=5.,beta_0=5.) 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) ################## # infer things # ################## train_likes = [] test_likes = [] for i in progprint_xrange(5): # for i in progprint_xrange(50):
] ################ # build HSMM # ################ dur_distns = [ NegativeBinomialIntegerRVariantDuration(np.r_[0., 0, 0, 0, 0, 1, 1, 1], alpha_0=5., beta_0=5.) 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) ################## # infer things # ################## train_likes = [] test_likes = [] for i in progprint_xrange(5): # for i in progprint_xrange(50):
obs_distns = [FrozenMixtureDistribution( components=component_library, alpha_0=4, weights=row) for row in init_weights] ################ # build HSMM # ################ dur_distns = [NegativeBinomialIntegerRVariantDuration(np.r_[0.,0,0,0,0,1,1,1],alpha_0=5.,beta_0=5.) 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(50):
] ################ # build HSMM # ################ dur_distns = [ NegativeBinomialIntegerRVariantDuration(np.r_[0., 0, 0, 1, 1, 1, 1, 1], alpha_0=5., beta_0=5.) for state in range(library_size) ] model = LibraryHSMMIntNegBinVariant(init_state_concentration=10., alpha=6., gamma=6., obs_distns=obs_distns, dur_distns=dur_distns) model.add_data(data, left_censoring=True) ################## # infer things # ################## for i in progprint_xrange(50): model.resample_model() likes = model.Viterbi_EM_fit() plt.figure()
obs_distns = [FrozenMixtureDistribution( components=component_library, alpha_0=4., weights=row) for row in init_weights] ################ # build HSMM # ################ dur_distns = [NegativeBinomialIntegerRVariantDuration(np.r_[0.,0,0,1,1,1,1,1],alpha_0=5.,beta_0=5.) for state in range(library_size)] model = LibraryHSMMIntNegBinVariant( init_state_concentration=10., alpha=6.,gamma=6., obs_distns=obs_distns, dur_distns=dur_distns) model.add_data(data) ################## # infer things # ################## for i in progprint_xrange(50): model.resample_model() plt.figure() truemodel.plot() plt.gcf().suptitle('truth')
] ################ # build HSMM # ################ dur_distns = [ NegativeBinomialIntegerRVariantDuration(np.r_[0., 0, 0, 0, 0, 1, 1, 1], alpha_0=5., beta_0=5.) 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) ################## # infer things # ################## samples1 = [model.resample_and_copy() for i in progprint_xrange(1)] samples2 = [model.resample_and_copy() for i in progprint_xrange(10)] samples3 = [model.resample_and_copy() for i in progprint_xrange(100)] # samples4 = [model.resample_and_copy() for i in progprint_xrange(1000)]
obs_distns = [FrozenMixtureDistribution( components=component_library, alpha_0=4, weights=row) for row in init_weights] ################ # build HSMM # ################ dur_distns = [NegativeBinomialIntegerRVariantDuration(np.r_[0.,0,0,0,0,1,1,1],alpha_0=5.,beta_0=5.) 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):
] ################ # build HSMM # ################ dur_distns = [ NegativeBinomialIntegerRVariantDuration(np.r_[0., 0, 0, 0, 0, 1, 1, 1], alpha_0=5., beta_0=5.) 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(50):
obs_distns = [FrozenMixtureDistribution( components=component_library, a_0=1.0,b_0=0.05) for i in xrange(Nmax)] ################ # build HSMM # ################ dur_distns = [NegativeBinomialIntegerRVariantDuration(np.r_[0.,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1],alpha_0=25.,beta_0=25.) for state in range(Nmax)] model = LibraryHSMMIntNegBinVariant( init_state_concentration=10., alpha_a_0=1.0,alpha_b_0=0.1, gamma_a_0=0.1,gamma_b_0=200., obs_distns=obs_distns, dur_distns=dur_distns) for data in training_datas: model.add_data_parallel(data,left_censoring=True) # model.add_data(data,left_censoring=True) ################## # infer things # ################## train_likes = [] for i in progprint_xrange(50): model.resample_model_parallel()
obs_distns = [FrozenMixtureDistribution( components=component_library, alpha_0=4., weights=row) for row in init_weights] ################ # build HSMM # ################ dur_distns = [NegativeBinomialIntegerRVariantDuration(np.r_[0.,0,0,1,1,1,1,1],alpha_0=5.,beta_0=5.) for state in range(library_size)] model = LibraryHSMMIntNegBinVariant( init_state_concentration=10., alpha=6.,gamma=6., obs_distns=obs_distns, dur_distns=dur_distns) ##################### # add_data timing # ##################### print 'this one should be slower!' tic = time.time() model.add_data(data) toc = time.time() print '...done in %f seconds' % (toc-tic) print '' print 'this one sholud be faster!'