Code and example of generative clustering using mixed normal/multinomial distributions
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README.md
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generative_model.r

README.md

Overview

The code in generative_model.r contains a few primary functions that are very useful for general purposes:

generative_model - This function runs a generative model on a data set, using a combination of numeric and/or categorical variables, with unsupervised, semi-supervised, and supervised modes, depending on the parameters provided and the data set supplied. Its history can be recorded, which allows you to visualize each iteration of its learning.

generative.deviance - This function evaluates the cohesiveness of the model in terms of deviance. Out-of-cluster deviance needs to be implemented to get a good idea of goodness-of-fit for the data.

generative.classify - This takes a model generated by generative_model and a data set, and then performs classifies the clusters of the data set based on the model's parameters.

base.animated.multiplot - This function creates an animated plot of a model's clustering progress over time. If you are on Windows, you may need to install animation from my forked repo if your ImageMagick location is in a directory containing a space.

melt.history - A function that expands the clustering/classification history of all the points from a model.

blog link

Here is a blog post using this code (and made by the .Rhtml file): http://maxcandocia.com/article/2018/Jan/09/generative-clustering/