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Functions838 in github.com/Ardavans/sHDP

⨍ Functions 838◇ Types & classes 102
↓ 24 callersMethodrvs
(self,customer_counts)
core/core_distributions.py:3442
↓ 23 callersMethodmean
(d)
core/testing/test_distributions.py:74
↓ 17 callersFunctiongi
(data)
core/util/stats.py:29
↓ 16 callersFunctiongetdatasize
(data)
core/util/stats.py:33
↓ 14 callersMethodresample
(self,data=[],niter=25)
core/core_distributions.py:3479
↓ 13 callersMethod_get_statistics
(self,data)
core/core_distributions.py:3497
↓ 12 callersMethodbeta
(self)
HDP/internals/transitions.py:126
↓ 11 callersMethodatleast_2d
(data)
core/core_distributions.py:74
↓ 11 callersFunctionflattendata
(data)
core/util/stats.py:74
↓ 11 callersFunctiongetdatadimension
(data)
core/util/stats.py:47
↓ 11 callersMethodlog_likelihood
(self,x)
core/core_models.py:84
↓ 9 callersFunctionsample_discrete
samples from a one-dimensional finite pmf
core/util/stats.py:177
↓ 8 callersMethod_compute_log_partion
(self, D, kappa)
core/core_distributions.py:178
↓ 8 callersMethod_get_weighted_statistics
(self,data,weights)
core/core_distributions.py:2666
↓ 8 callersMethoddistribution_class
(self)
core/testing/mixins.py:13
↓ 8 callersFunctionmean
(datalist)
HDP/util/stats.py:93
↓ 8 callersMethodmean
(self)
HDP/basic/abstractions.py:105
↓ 8 callersMethodvar
(d)
core/testing/test_distributions.py:76
↓ 7 callersFunctioncumsum
(v,strict=False)
HDP/util/general.py:67
↓ 7 callersMethodexpected_log_likelihood
(self,x)
core/core_distributions.py:2700
↓ 7 callersMethodplot
(self,color=None,legend=True,alpha=None,plot_all_components=True)
core/core_models.py:241
↓ 7 callersMethodrvs
(self,size=None)
HDP/basic/distributions.py:150
↓ 7 callersFunctionsample_vMF
Sampling from vMF This is based on the implementation I found online here: http://stats.stackexchange.com/questions/156729/samp
core/util/stats.py:140
↓ 6 callersMethod_get_statistics
(self,data,D=None)
core/core_distributions.py:724
↓ 6 callersMethod_natural_to_standard
(self,natparam)
core/core_distributions.py:711
↓ 6 callersMethodadd_data
(self,data,**kwargs)
core/core_models.py:52
↓ 6 callersMethodadd_data
(self,data,doc_num,stateseq=None,**kwargs)
HDP/models.py:38
↓ 6 callersFunctionsample_discrete
samples from a one-dimensional finite pmf
HDP/util/stats.py:123
↓ 6 callersFunctionsgd_steps
(tau,kappa)
core/util/general.py:223
↓ 6 callersFunctionsgd_steps
(tau,kappa)
HDP/util/general.py:242
↓ 5 callersMethod_log_base_measure
(x,r)
core/core_distributions.py:2984
↓ 5 callersMethodmeanfieldupdate
(self,data,weights,**kwargs)
core/core_models.py:378
↓ 5 callersMethodmeanfieldupdate
(self)
HDP/internals/hmm_states.py:210
↓ 5 callersMethodresample
(self,data)
core/core_models.py:335
↓ 4 callersMethod_compute_expected_direction
direct : direction (mu) of the vMF concentr : concentration (kappa) of the vMF Expectation of mu is a bit tricky
core/core_distributions.py:282
↓ 4 callersMethod_get_weighted_statistics
(self,data,weights,D=None)
core/core_distributions.py:735
↓ 4 callersMethod_get_weighted_statistics
(data,weights)
core/core_distributions.py:2333
↓ 4 callersMethod_log_likelihoods
(self,x)
core/core_models.py:75
↓ 4 callersMethod_log_partition_fn
(self,alpha,beta)
core/core_distributions.py:2736
↓ 4 callersMethod_posterior_hypparams
(self,n,tot)
core/core_distributions.py:3037
↓ 4 callersMethodclear_caches
(self)
HDP/internals/hmm_states.py:80
↓ 4 callersMethodlog_likelihood
(self,x)
core/core_distributions.py:1217
↓ 4 callersMethodlog_likelihood
(self,restaurants)
core/core_distributions.py:3464
↓ 4 callersMethodmeanfieldupdate
(self,data,weights)
core/core_distributions.py:2685
↓ 4 callersMethodpmf
(self,x)
HDP/basic/abstractions.py:33
↓ 4 callersMethodresample
(self,data=[],*args,**kwargs)
HDP/basic/distributions.py:153
↓ 3 callersMethod_formatparams
(dct)
core/core_abstractions.py:43
↓ 3 callersMethod_get_statistics
(data,K)
core/core_distributions.py:2323
↓ 3 callersMethod_get_statistics
(self,data)
core/core_distributions.py:2551
↓ 3 callersMethod_get_statistics
(self,data)
core/core_distributions.py:2652
↓ 3 callersMethod_get_statistics
(self,data)
core/core_distributions.py:3008
↓ 3 callersMethod_get_weighted_statistics
(self,data,weights)
core/core_distributions.py:3026
↓ 3 callersMethod_posterior_hypparams
(self,n,ybar,sumsqc)
core/core_distributions.py:1816
↓ 3 callersMethod_sample_kappa
(self, D, kappaCurrent, weightSum, weighted_x_Dot_Expected_mu , m, sigma, method='IS',burnIn=300,extraSample=2
core/core_distributions.py:308
↓ 3 callersMethod_update_rho_mf
(self,data,weights)
core/core_distributions.py:3120
↓ 3 callersMethodexpected_log_likelihood
(self,x,*args,**kwargs)
HDP/basic/distributions.py:27
↓ 3 callersFunctionflattendata
(data)
HDP/util/stats.py:74
↓ 3 callersMethodget_vlb
(self)
core/core_models.py:364
↓ 3 callersMethodgeweke_statistics
(self,distn,data)
core/testing/mixins.py:116
↓ 3 callersMethodlog_likelihood
(self,x)
HDP/basic/distributions.py:147
↓ 3 callersMethodlog_sf
log survival function, defined by log_sf(x) = log(P[X \gt x]) = log(1-cdf(x)) where cdf(x) = P[X \leq x]
HDP/basic/abstractions.py:18
↓ 3 callersMethodmeanfield_sgdstep
(self,data,weights,minibatchfrac,stepsize)
core/core_distributions.py:2689
↓ 3 callersMethodmeanfield_sgdstep
(self,minibatch,minibatchfrac,stepsize,num_procs=0,**kwargs)
HDP/models.py:133
↓ 3 callersMethodplot
(self,data=None,indices=None,color='b',plot_params=True,label='',alpha=1.)
core/core_distributions.py:623
↓ 3 callersMethodplot
(self,data=None,color='b',**kwargs)
HDP/basic/abstractions.py:112
↓ 3 callersFunctionproject_data
(data,vecs)
core/util/plot.py:41
↓ 3 callersFunctionrcumsum
(v,strict=False)
HDP/util/general.py:75
↓ 3 callersMethodresample
(self)
core/internals/labels.py:38
↓ 3 callersMethodresample_model
(self)
core/core_models.py:89
↓ 3 callersFunctionsample_invwishart
(S,nu)
core/util/stats.py:220
↓ 3 callersFunctionsample_pareto
(x_m,alpha)
core/util/stats.py:277
↓ 2 callersMethodE_step
(self)
core/internals/labels.py:89
↓ 2 callersMethod_EM_fit
(self,method,tol=1e-1,maxiter=100,progprint=False)
core/core_abstractions.py:236
↓ 2 callersMethod__init__
(self,a_0,b_0,concentration=None)
core/core_distributions.py:3425
↓ 2 callersMethod_check_stats
(self,s1,s2)
core/testing/mixins.py:52
↓ 2 callersMethod_expected_statistics
(self,alphas,betas,mu,nus)
core/core_distributions.py:1369
↓ 2 callersMethod_generate
(self,N)
core/internals/labels.py:33
↓ 2 callersMethod_get_statistics
(self,data)
core/core_distributions.py:946
↓ 2 callersMethod_get_statistics
(self,data)
core/core_distributions.py:1044
↓ 2 callersMethod_get_statistics
(self,data,D=None)
core/core_distributions.py:1256
↓ 2 callersMethod_get_statistics
(self,data)
core/core_distributions.py:2078
↓ 2 callersMethod_get_weighted_statistics
(self,data,weights,D=None)
core/core_distributions.py:392
↓ 2 callersMethod_get_weighted_statistics
(self,data,weights)
core/core_distributions.py:1380
↓ 2 callersMethod_get_weighted_statistics
(self,data,weights)
core/core_distributions.py:2033
↓ 2 callersMethod_grad_E_log_p_pi_given_beta
(self,beta,gamma,alphatildes)
HDP/internals/transitions.py:198
↓ 2 callersMethod_grad_log_p_beta
(beta,alpha)
HDP/internals/transitions.py:193
↓ 2 callersMethod_log_Z
(self,alphas,betas,mu,nus)
core/core_distributions.py:1377
↓ 2 callersMethod_log_partition_function
(self,mu,sigma,kappa,nu)
core/core_distributions.py:865
↓ 2 callersMethod_log_partition_function
(self,alpha,beta)
core/core_distributions.py:2595
↓ 2 callersMethod_loglmbdatilde
(self)
core/core_distributions.py:848
↓ 2 callersMethod_meanfield_sgdstep_parameters
(self,mb_labels_list,minibatchfrac,stepsize)
core/core_models.py:173
↓ 2 callersMethod_meanfield_update_states_list
(self,states_list,num_procs=0)
HDP/models.py:123
↓ 2 callersMethod_messages_forwards_log
(trans_matrix_docnum, log_likelihoods)
HDP/internals/hmm_states.py:128
↓ 2 callersMethod_mf_expected_statistics
(self)
core/core_distributions.py:2705
↓ 2 callersMethod_mf_expected_statistics
(self)
core/core_distributions.py:2969
↓ 2 callersMethod_natural_to_standard
(self,natparam)
core/core_distributions.py:1321
↓ 2 callersMethod_pad_zeros
(self,counts)
HDP/internals/transitions.py:163
↓ 2 callersMethod_posterior_hypparams
(self,n,tot)
core/core_distributions.py:2526
↓ 2 callersMethod_posterior_hypparams
(self,n,tot)
core/core_distributions.py:2631
↓ 2 callersMethod_standard_to_natural
(self,mu_mf,sigma_mf,kappa_mf,nu_mf)
core/core_distributions.py:702
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