gensim.models.HdpModel¶
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class
gensim.models.HdpModel(corpus, id2word, max_chunks=None, max_time=None, chunksize=256, kappa=1.0, tau=64.0, K=15, T=150, alpha=1, gamma=1, eta=0.01, scale=1.0, var_converge=0.0001, outputdir=None)[source]¶ The constructor estimates Hierachical Dirichlet Process model parameters based on a training corpus:
>>> hdp = HdpModel(corpus, id2word) >>> hdp.print_topics(show_topics=20, num_words=10)
Inference on new documents is based on the approximately LDA-equivalent topics.
Model persistency is achieved through its load/save methods.
Methods¶
__init__(corpus, id2word[, max_chunks, ...]) |
gamma: first level concentration |
doc_e_step(doc, ss, Elogsticks_1st, ...) |
e step for a single doc |
evaluate_test_corpus(corpus) |
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hdp_to_lda() |
Compute the LDA almost equivalent HDP. |
inference(chunk) |
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load(fname[, mmap]) |
Load a previously saved object from file (also see save). |
optimal_ordering() |
ordering the topics |
print_topics([num_topics, num_words]) |
Alias for show_topics() that prints the num_words most probable words for topics number of topics to log. |
save(fname_or_handle[, separately, ...]) |
Save the object to file (also see load). |
save_options() |
legacy method; use self.save() instead |
save_topics([doc_count]) |
legacy method; use self.save() instead |
show_topics([num_topics, num_words, log, ...]) |
Print the num_words most probable words for topics number of topics. |
update(corpus) |
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update_chunk(chunk[, update, opt_o]) |
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update_expectations() |
Since we’re doing lazy updates on lambda, at any given moment the current state of lambda may not be accurate. |
update_finished(start_time, ...) |
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update_lambda(sstats, word_list, opt_o) |