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, ...)

Arguments

data	matrix or data frame
CENTROIDS	matrix or data frame containing the centroids (means), stored as row vectors
COVARIANCE	matrix or data frame containing the diagonal covariance matrices, stored as row vectors
WEIGHTS	vector containing the weights
object, newdata, ...	arguments for the 'predict' generic

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.

Value

a list consisting of the log-likelihoods, cluster probabilities and cluster labels.

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 June 22, 2024, 10:28 a.m.