predict_GMM: Prediction function for a Gaussian Mixture Model object

In ClusterR: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids and Affinity Propagation Clustering

Prediction function for a Gaussian Mixture Model object

Description

Prediction function for a Gaussian Mixture Model object

Usage

predict_GMM(data, CENTROIDS, COVARIANCE, WEIGHTS)

## S3 method for class 'GMMCluster'
predict(object, newdata, type = c("cluster", "prob", "all"), ...)

Arguments

data	matrix or data frame
CENTROIDS	matrix or data frame containing the centroids (means), stored as row vectors
COVARIANCE	matrix or data frame (for diagonal covariance) or 3D array (for full covariance matrices)
WEIGHTS	vector containing the weights
object, newdata, ...	arguments for the 'predict' generic
type	the type of prediction to return. One of '"cluster"' (hard cluster labels), '"prob"' (cluster probabilities), or '"all"' (the complete list returned by 'predict_GMM()').

Details

This function takes the centroids, covariance matrix and weights from a trained model and returns the log-likelihoods, cluster probabilities and cluster labels for new data. The function handles both diagonal covariance matrices (2D matrix) and full covariance matrices (3D array/cube).

Value

'predict_GMM()' returns a list consisting of the log-likelihoods, cluster probabilities and cluster labels. The 'predict()' method returns the hard cluster labels when 'type = "cluster"', the cluster probabilities when 'type = "prob"', and the complete 'predict_GMM()' list when 'type = "all"'.

Author(s)

Lampros Mouselimis

Examples

data(dietary_survey_IBS)

dat = as.matrix(dietary_survey_IBS[, -ncol(dietary_survey_IBS)])

dat = center_scale(dat)

gmm = GMM(dat, 2, "maha_dist", "random_subset", 10, 10)

# pr = predict_GMM(dat, gmm$centroids, gmm$covariance_matrices, gmm$weights)

ClusterR documentation built on Sept. 8, 2026, 5:06 p.m.