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object --+ | Distribution --+ | LDA --+ | BatchLDA
An implementation of latent Dirichlet allocation (LDA).
Example:
>>> documents = load_data('data_train.dat') >>> model = BatchLDA(num_words=7000, num_topics=100, alpha=.1, eta=.3) >>> model.update_parameters(documents, max_epochs=100)
alpha
can be a scalar or an array with one entry for each
topic.
Instance Methods | |||
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float
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list
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tuple
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Inherited from |
Properties | |
alpha Controls Dirichlet prior over topic weights, $\theta_k$. (Inherited from trlda.models.LDA) |
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eta Controls Dirichlet prior over topics, $\beta_{ki}$. (Inherited from trlda.models.LDA) |
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lambdas Parameters governing beliefs over topics, $\beta_{ki}$. (Inherited from trlda.models.LDA) |
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num_topics Number of topics. (Inherited from trlda.models.LDA) |
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num_words Number of words. (Inherited from trlda.models.LDA) |
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Inherited from |
Method Details |
Updates beliefs over parameters.
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