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.
setMethod("transition", signature(x = "TRACDS",
from = "matrix", to = "missing"),
function(x,
from,
to,
type = c("probability", "counts", "log_odds"),
prior = TRUE) {
to <- from[, 2]
from <- from[, 1]
transition(x, from, to, type, prior)
})
setMethod("transition", signature(x = "TRACDS",
from = "data.frame", to = "missing"),
function(x,
from,
to,
type = c("probability", "counts", "log_odds"),
prior = TRUE) {
to <- from[, 2]
from <- from[, 1]
transition(x, from, to, type, prior)
})
setMethod("transition", signature(x = "TRACDS", from = "character", to =
"character"), function(x,
from,
to,
type = c("probability",
"counts", "log_odds"),
prior = TRUE) {
type <- match.arg(type)
if (length(from) != length(to))
stop("vectors from and to are not of the same length!")
### deal with empty from/to
if (length(from) < 1)
return(numeric(0))
tm <- transition_matrix(x, type, prior)
from <- match(from, states(x))
to <- match(to, states(x))
res <-
sapply(
1:length(from),
FUN = function(i)
tm[from[i], to[i]]
)
## handle missing states (NA)
res[is.na(res)] <- 0
res
})
setMethod("transition_matrix", signature(x = "TRACDS"),
function(x,
type = c("probability", "counts", "log_odds"),
prior = TRUE) {
type <- match.arg(type)
## get transition count matrix
m <- smc_countMatrix(x@tracds_d$mm)
if (prior)
m <- m + 1
if (type == "counts")
return(m)
rs <- rowSums(m)
prob <- m / rs
## we have to handle absorbing states here (row sum is 0)
absorbing <- which(rs == 0)
prob[absorbing, ] <- 0
for (i in absorbing)
prob[i, i] <- 1
switch(type,
probability = prob,
log_odds = log(prob * size(x)))
})
setMethod("initial_transition", signature(x = "TRACDS"),
function(x,
type = c("probability", "counts", "log_odds"),
prior = TRUE) {
type <- match.arg(type)
ic <- smc_initialCounts(x@tracds_d$mm)
if (prior)
ic <- ic + 1
switch(
type,
probability = ic / sum(ic),
counts = ic,
log_odds = log(ic / sum(ic) * size(x))
)
})
setMethod("transition_table", signature(x = "EMM", newdata = "numeric"),
function(x,
newdata,
type = c("probability", "counts", "log_odds"),
match_cluster = "exact",
prior = TRUE,
initial_transition = FALSE)
transition_table(
x,
as.matrix(rbind(newdata)),
type,
match_cluster,
prior,
initial_transition
))
setMethod("transition_table", signature(x = "EMM", newdata = "data.frame"),
function(x,
newdata,
type = c("probability", "counts", "log_odds"),
match_cluster = "exact",
prior = TRUE,
initial_transition = FALSE)
transition_table(
x,
as.matrix(newdata),
type,
match_cluster,
prior,
initial_transition
))
setMethod("transition_table", signature(x = "EMM", newdata = "matrix"),
function(x,
newdata,
type = c("probability", "counts", "log_odds"),
match_cluster = "exact",
prior = TRUE,
initial_transition = FALSE) {
type <- match.arg(type)
## make sure newdata is a matrix (maybe a single row)
if (!is.matrix(newdata))
newdata <- as.matrix(rbind(newdata))
n <- nrow(newdata)
## empty EMM or single state?
if (n < 2) {
df <- data.frame(from = NA, to = NA, val = NA)
names(df)[3] <- type
return(df)
}
## get sequence
ssequence <-
find_clusters(x, newdata, match_cluster = match_cluster,
dist = FALSE)
from <- ssequence[1:(n - 1)]
to <- ssequence[2:n]
## get values
res <- transition(x, from, to, type = type,
prior = prior)
if (initial_transition) {
from <- c(NA, from)
to <- c(ssequence[1], to)
res <- c(initial_transition(x, type = type,
prior = prior)[ssequence[1]],
res)
}
df <-
data.frame(
from = from,
to = to,
val = res,
stringsAsFactors = FALSE
)
names(df)[3] <- type
return(df)
})
```

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