R/smooth_transitions.R

In rEMM: Extensible Markov Model for Modelling Temporal Relationships Between Clusters

#######################################################################
# 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)
  })