R/build.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.


## build for EMM


## make  newdata a matrix (with a single row)
setMethod("build", signature(x = "EMM", newdata = "numeric"),
  function(x, newdata, verbose = FALSE)
    build(x,
      as.matrix(rbind(newdata), verbose)))

setMethod("build", signature(x = "EMM", newdata = "data.frame"),
  function(x, newdata, verbose = FALSE)
    build(x, as.matrix(newdata),
      verbose))

setMethod("build", signature(x = "EMM", newdata = "matrix"),
  function(x, newdata, verbose = FALSE) {
    if (verbose)
      cat("Adding", nrow(newdata) , "observations.", "\n")

    ## cluster all the data (the variable data is in an
    ## environment, so there is no need for x <- cluster(x, newdata))
    cluster(x, newdata, verbose = verbose)

    ## now update TRACDS (iterate over cluster assignments in last)
    update(x, last_clustering(x), verbose = verbose)

    invisible(x)
  })