Find density modes with HMM-VB
This function finds the density modes with HMM-VB. First, for each data point it finds an optimal state sequence using Viterbi algorithm. Next, it uses Modal Baum-Welch algorithm (MBW) to find the modes of distinct Viterbi state sequences. Data points associated the same modes form clusters.
hmmvbFindModes(data, model = NULL, nthread = 1, bicObj = NULL)
data |
A numeric vector, matrix, or data frame of observations. Categorical values are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
model |
An object of class 'HMMVB' that contains trained HMM-VB obtained
by the call to function |
nthread |
An integer specifying the number of threads used in clustering. |
bicObj |
An object of class 'HMMVBBIC' which stores results of model selection.
If provided, argument |
An object of class 'HMMVBclust'.
# find modes using trained HMM-VB Vb <- vb(1, dim=4, numst=2) set.seed(12345) hmmvb <- hmmvbTrain(iris[,1:4], VbStructure=Vb) modes <- hmmvbFindModes(iris[,1:4], model=hmmvb) show(modes) # find modes using HMMVBBIC object obtained in model selection Vb <- vb(1, dim=4, numst=1) set.seed(12345) modelBIC <- hmmvbBIC(iris[,1:4], VbStructure=Vb) modes <- hmmvbClust(iris[,1:4], bicObj=modelBIC) show(modes)
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