ngclearn.utils.density package

Submodules

ngclearn.utils.density.gmm module

class ngclearn.utils.density.gmm.GMM(k, max_iter=5, assume_diag_cov=False, init_kmeans=True)[source]

Bases: object

Implements a Gaussian mixture model (GMM) – or mixture of Gaussians, MoG. Adaptation of parameters is conducted via the Expectation-Maximization (EM) learning algorithm and leverages full covariance matrices in the component multivariate Gaussians.

Note this is a (JAX) wrapper model that houses the sklearn implementation for learning. The sampling process has been rewritten to utilize GPU matrix computation.

Parameters:

  • k – the number of components/latent variables within this GMM

  • max_iter – the maximum number of EM iterations to fit parameters to data (Default = 5)

  • assume_diag_cov – if True, assumes a diagonal covariance for each component (Default = False)

  • init_kmeans – if True, first learn use the K-Means algorithm to initialize the component Gaussians of this GMM (Default = True)

fit(data)[source]

Run full fitting process of this GMM.

Parameters:

data – the dataset to fit this GMM to

sample(n_s, mode_i=-1, samples_modes_evenly=False)[source]

(Efficiently) Draw samples from the current underlying GMM model

Parameters:

  • n_s – the number of samples to draw from this GMM

  • mode_i – if >= 0, will only draw samples from a specific component of this GMM (Default = -1), ignoring the Categorical prior over latent variables/components

  • samples_modes_evenly – if True, will ignore the Categorical prior over latent variables/components and draw an approximately equal number of samples from each component

update(X)[source]

Performs a single iterative update of parameters (assuming model initialized)

Parameters:

X – the dataset / design matrix to fit this GMM to

Module contents