A Gaussian Mixture model (GMM) is a probabilistic model for representing the presence of clusters within an overall population without requiring a sample to know which sub-population it belongs to beforehand. GMMs are similar to centroid-based clusterers like K Means but allow both the centers (means) and the radii (variances) to be learned as well.
Data Type Compatibility: Continuous
|1||k||int||The number of target clusters.|
|2||epochs||100||int||The maximum number of training rounds to execute.|
|3||min change||1e-3||float||The minimum change in the components necessary for the algorithm to continue training.|
|6||seeder||PlusPlus||object||The seeder used to initialize the Guassian components.|
Return the cluster prior probabilities based on their representation over all training samples:
public priors() : array
Return the running means of each feature column for each cluster:
public means() : array
Return the variance of each feature column for each cluster:
public variances() : array
use Rubix\ML\Clusterers\GaussianMixture; use Rubix\ML\Clusterers\Seeders\KMC2; $estimator = new GaussianMixture(5, 1e-4, 100, new KMC2(50));
- A. P. Dempster et al. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm.
- J. Blomer et al. (2016). Simple Methods for Initializing the EM Algorithm for Gaussian Mixture Models.