Hi Christian.
The document-topic-matrix is lda.transform(X), the word-topic-matrix is
lda.components_.
See
http://scikit-learn.org/dev/modules/decomposition.html#latent-dirichlet-allocation-lda
"When LatentDirichletAllocation
<http://scikit-learn.org/dev/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html#sklearn.decomposition.LatentDirichletAllocation>
is applied on a “document-term” matrix, the matrix will be decomposed
into a “topic-term” matrix and a “document-topic” matrix. While
“topic-term” matrix is stored as components_ in the model,
“document-topic” matrix can be calculated from transform method."
On 12/08/2015 11:04 AM, Rockenkamm, Christian wrote:
Hello,
I have a short question concerning the Latent Dirichlet Allocation in
scikit. Is it possible to acquire the topic-word-matrix and the
document-topic-matrix? If so, could someone please explain to me how
to do that?
Best regards,
Christian Rockenkamm
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