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ldeecke
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Gaussian mixture models in PyTorch.

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This repository contains an implementation of a simple Gaussian mixture model (GMM) fitted with Expectation-Maximization in pytorch. The interface closely follows that of sklearn.

Example of a fit via a Gaussian Mixture model.


A new model is instantiated by calling

gmm.GaussianMixture(..)
and providing as arguments the number of components, as well as the tensor dimension. Note that once instantiated, the model expects tensors in a flattened shape
(n, d)
.

The first step would usually be to fit the model via

model.fit(data)
, then predict with
model.predict(data)
. To reproduce the above figure, just run the provided
example.py
.

Some sanity checks can be executed by calling

python test.py
. To fit data on GPUs, ensure that you first call
model.cuda()
.

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