gmm-gpu 0.1.1

A small library for quick fitting of multiple Gaussian Mixture Models in parallel on the GPU.

gmm_gpu

Documentation

A small library that provides a GMM class for fitting multiple instances of Gaussian Mixture Models.

This may be useful if you have a large number of independent small problems and you want to fit a GMM on each one. You can create a single large 3D tensor (three dimensional matrix) with the data for all your instances (i.e. a batch) and then send the tensor to the GPU and process the whole batch in parallel. This would work best if all the instances have roughly the same number of points.

If you have a single big problem (one GMM instance with many points) that you want to fit using the GPU, maybe Pomegranate would be a better option.

Example usage:

Import pytorch and the GMM class

>>> from gmm_gpu.gmm import GMM
>>> import pytorch

Generate some test data: We create a batch of 1000 instances, each with 200 random points. Half of the points are sampled from distribution centered at the origin (0, 0) and the other half from a distribution centered at (1.5, 1.5).

>>> X1 = torch.randn(1000, 100, 2)
>>> X2 = torch.randn(1000, 100, 2) + torch.tensor([1.5, 1.5])
>>> X = torch.cat([X1, X2], dim=1)

Fit the model:

>>> gmm = GMM(n_components=2, device='cuda')
>>> gmm.fit(X)

Predict the components: This will return a matrix with shape (1000, 200) where each value is the predicted component for the point.

>>> gmm.predict(X)