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Documentation | ||
============= | ||
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.. _gallery_top: | ||
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We welcome contributions to our documentation via GitHub pull requests, whether it's fixing a typo or authoring an entirely new tutorial or guide. | ||
If you're thinking about contributing documentation, please see :ref:`sphx_glr_auto_examples_howtos_run_doc.py`. |
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# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html | ||
# Based on Copyright (C) 2016 Radim Rehurek <[email protected]> | ||
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"""Lda Sequence model, inspired by `David M. Blei, John D. Lafferty: "Dynamic Topic Models" | ||
"""Lda Sequence model, inspired by | ||
`David M. Blei, John D. Lafferty: "Dynamic Topic Models" | ||
<https://mimno.infosci.cornell.edu/info6150/readings/dynamic_topic_models.pdf>`_. | ||
The original C/C++ implementation can be found on `blei-lab/dtm <https://github.com/blei-lab/dtm>`_. | ||
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return self.zeta | ||
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def compute_post_variance(self, word, chain_variance): | ||
r"""Get the variance, based on the `Variational Kalman Filtering approach for Approximate Inference (section 3.1) | ||
r"""Get the variance, based on the | ||
`Variational Kalman Filtering approach for Approximate Inference (section 3.1) | ||
<https://mimno.infosci.cornell.edu/info6150/readings/dynamic_topic_models.pdf>`_. | ||
This function accepts the word to compute variance for, along with the associated sslm class object, | ||
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