LDA is a document theme generation model, also known as a three-tier Bayesian probability model that contains the words, topics and document three-tier structure. Dirichlet distribution of the document to the theme of obedience, the theme to the word obey polynomial distribution. LDA is an unsupervised machine learning techniques can be used to identify large-scale document set (document collection) or corpus (corpus) of the underlying themes of information. It uses the word bag (bag of words) of the method, which each one document as a word frequency vector, thus the text information into digital information for ease of modeling. However, the method does not consider the order of the words Bag between words, which simplifies the complexity of the problem, but also for the improvement of the model provided an opportunity. Each document represents a probability distribution of some of the topics posed, and each topic and they represent many words constituted a probability distribut

lda-c
.....\topics.py
.....\todo.txt
.....\settings.txt
.....\readme.txt
.....\license.txt
.....\inf-settings.txt
.....\Makefile
.....\utils.c
.....\lda-model.c
.....\lda-inference.c
.....\lda-estimate.c
.....\lda-data.c
.....\lda-alpha.c
.....\cokus.c
.....\utils.h
.....\lda.h
.....\lda-model.h
.....\lda-inference.h
.....\lda-estimate.h
.....\lda-data.h
.....\lda-alpha.h
.....\cokus.h

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