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X = read($1, data_type = "frame", format = "csv", header=TRUE)
X = X[ , 2:ncol(X) - 1]
X = as.matrix(X)

# divide in train and test set
train = X[1:45,]
train = rbind(train, X[52:95,])
train = rbind(train, X[102:145,])

test = X[46:51,]
test = rbind(test, X[96:101,])
test = rbind(test, X[146:150,])

# train GMM
[labels, prob, df, bic, mu, prec_chol, w] = gmm(X=train, n_components = $2,
  model = $3, init_params = $4, iter = $5, reg_covar = $6, tol = $7, seed=$8, verbose=TRUE)
 
# predict labels
[pred, pp] = gmmPredict(test, w, mu, prec_chol, $3)  

# expected clusters/predictions
expected = matrix("6 6 5", 3, 1)

resp = matrix(1, 17, 3) * t(seq(1,3))
resp = resp == pred
cluster = t(colSums(resp))

cluster = order(target = cluster, by = 1, decreasing = FALSE, index.return=FALSE)
correct_Predictions = order(target = expected, by = 1, decreasing = FALSE, index.return=FALSE)

error = mean(abs(correct_Predictions - cluster))
write(error, $9, format = "text")