R/predict.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.


## 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)])
  })