# predict_MBatchKMeans: Prediction function for Mini-Batch-k-means

## Description

Prediction function for Mini-Batch-k-means

## Usage

predict_MBatchKMeans(data, CENTROIDS, fuzzy = FALSE)

## Arguments

CENTROIDS

a matrix of initial cluster centroids. The rows of the CENTROIDS matrix should be equal to the number of clusters and the columns should equal the columns of the data.

fuzzy

either TRUE or FALSE. If TRUE then prediction probabilities will be calculated using the distance between observations and centroids.

## Value

if fuzzy = TRUE the function returns a list with two attributes: a vector with the clusters and a matrix with cluster probabilities. Otherwise, it returns a vector with the clusters.

## Details

This function takes the data and the output centroids and returns the clusters.

## Examples

# NOT RUN {
data(dietary_survey_IBS)
dat = dietary_survey_IBS[, -ncol(dietary_survey_IBS)]
dat = center_scale(dat)
MbatchKm = MiniBatchKmeans(dat, clusters = 2, batch_size = 20, num_init = 5, early_stop_iter = 10)
pr = predict_MBatchKMeans(dat, MbatchKm$centroids, fuzzy = FALSE)
# }