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.
### smooth transitions: Each cluster gets the average of the outgoing
### transition counts of all its neightbors = within range x threshold
setMethod("smooth_transitions", signature(x = "EMM"),
function(x, range = 2, copy = TRUE) {
if (copy)
x <- copy(x)
centers <- cluster_centers(x)
tm <- transition_matrix(x, type = "counts")
tms <- matrix(NA_real_, nrow = nrow(tm), ncol = ncol(tm))
for (i in 1:nclusters(x)) {
d <- dist(centers[i, , drop = FALSE], centers, method = x@measure)
tms[i, ] <-
colMeans(tm[d <= range * x@tnn_d$var_thresholds, , drop = FALSE])
}
x@tracds_d$mm <- smc_setTransitions(
x@tracds_d$mm,
from = rep(clusters(x), nclusters(x)),
to = rep(clusters(x), each = nclusters(x)),
value = as.vector(tms)
)
if (copy)
x
else
invisible(x)
})
```

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