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This implements hierarchical latent Dirichlet allocation, a topic model that finds a hierarchy of topics. The structure of the hierarchy is determined by the data.

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This code implements hierarchical LDA with a fixed depth tree and a stick breaking prior on the depth weights. An infinite-depth tree can be approximated by setting the depth to be very high. This code requires that you have installed the GSL package.

The input format of the data is the same as in the LDA-C package. Each line contains

[# of unique terms] [term #] : [count] ...

The settings file controls various parameters of the model. There are several settings files contained in this directory.

IMPORTANT:

I hope that this code is useful to you, but please note that this code is UNSUPPORTED. Do not email me (David Blei) with questions. I like posting as much code as possible, but I unfortunately do not have the time to support all of it. (This paragraph is my solution to the problem.)

HLDA-C is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.

LDA-C is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

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This implements hierarchical latent Dirichlet allocation, a topic model that finds a hierarchy of topics. The structure of the hierarchy is determined by the data.

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