Gaussian Mixture Model clustering
GMM(
data,
gaussian_comps = 1,
dist_mode = "eucl_dist",
seed_mode = "random_subset",
km_iter = 10,
em_iter = 5,
verbose = FALSE,
var_floor = 1e-10,
seed = 1,
full_covariance_matrices = FALSE
)matrix or data frame
the number of gaussian mixture components
the distance used during the seeding of initial means and k-means clustering. One of, eucl_dist, maha_dist.
how the initial means are seeded prior to running k-means and/or EM algorithms. One of, static_subset, random_subset, static_spread, random_spread.
the number of iterations of the k-means algorithm
the number of iterations of the EM algorithm
either TRUE or FALSE; enable or disable printing of progress during the k-means and EM algorithms
the variance floor (smallest allowed value) for the diagonal covariances
integer value for random number generator (RNG)
a boolean. If FALSE "diagonal" covariance matrices (i.e. in each covariance matrix, all entries outside the main diagonal are assumed to be zero) otherwise "full" covariance matrices will be returned. Be aware in case of "full" covariance matrices a cube (3-dimensional) rather than a matrix for the output "covariance_matrices" value will be returned.
a list consisting of the centroids, covariance matrix ( where each row of the matrix represents a diagonal covariance matrix), weights and the log-likelihoods for each gaussian component. In case of Error it returns the error message and the possible causes.
This function is an R implementation of the 'gmm_diag' class of the Armadillo library. The only exception is that user defined parameter settings are not supported, such as seed_mode = 'keep_existing'. For probabilistic applications, better model parameters are typically learned with dist_mode set to maha_dist. For vector quantisation applications, model parameters should be learned with dist_mode set to eucl_dist, and the number of EM iterations set to zero. In general, a sufficient number of k-means and EM iterations is typically about 10. The number of training samples should be much larger than the number of Gaussians. Seeding the initial means with static_spread and random_spread can be much more time consuming than with static_subset and random_subset. The k-means and EM algorithms will run faster on multi-core machines when OpenMP is enabled in your compiler (eg. -fopenmp in GCC)
http://arma.sourceforge.net/docs.html
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)