Nothing

```
#######################################################################
# rEMM - Extensible Markov Model (EMM) for Data Stream Clustering in R
# Copyright (C) 2011 Michael Hahsler
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
## predict next n states using P^n
setMethod("predict", signature(object = "TRACDS"),
function(object,
current_state = NULL,
n = 1,
probabilities = FALSE,
randomized = FALSE,
prior = FALSE) {
## probabilistic max with random tie breaking
.prob_max <- function(x) {
m <- which(x == max(x))
if (length(m) > 1)
m <- sample(m, 1)
m
}
## randomized
.randomized <- function(x)
sample((1:length(x))[x > 0], 1, prob = x[x > 0])
if (is.null(current_state))
current_state <- current_state(object)
else
current_state <- as.character(current_state)
current_state_i <- which(states(object) == current_state)
## check is state exists!
if (!is.element(current_state, states(object)))
stop("State does not exist")
P <- transition_matrix(object, prior = prior)
## calculate P^n
if (n > 1)
for (i in 1:(n - 1))
P <- P %*% P
prob <- P[current_state_i, ]
## create result
if (probabilities)
return(prob)
if (randomized)
return(states(object)[.randomized(prob)])
return(states(object)[.prob_max(prob)])
})
```

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