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()`).

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"`.

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).

Author

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)