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.
synthetic_stream <-
function(k = 10,
d = 2,
n_subseq = 100,
p_transition = .5,
p_swap = 0,
n_train = 5000,
n_test = 1000,
p_outlier = .01,
rangeVar = c(0, 0.005)) {
test <- NA
train <- NA
outlier_position <- NA
sequence_train <- NA
sequence_test <- NA
swap_train <- NA
swap_test <- NA
get_mu <- function(k)
runif(k, min = .1, max = .9)
get_outlier <- function(k)
runif(k, min = 0, max = 1)
mu <- replicate(d, get_mu(k))
Sigma <- replicate(k,
genPositiveDefMat("unifcorrmat",
rangeVar = rangeVar, dim = d)$Sigma,
simplify = FALSE)
#subseq <- sample(1:k, replace=TRUE)
subseq <- integer(n_subseq)
subseq[1] <- 1L
for (i in 2:n_subseq) {
if (runif(1) < p_transition)
subseq[i] <- sample(1:k, 1)
else
subseq[i] <- subseq[i - 1]
}
model <- list(
k = k,
d = d,
mu = mu,
Sigma = Sigma,
subseq = subseq
)
if (n_train > 0) {
sequence_train <- rep(subseq, n_train / n_subseq)
## randomly mess up sequence missing
if (p_swap > 0) {
swap_train <- which(runif(n_train - 1) < p_swap) + 1L
for (i in swap_train) {
sequence_train[(i - 1):i] <-
rev(sequence_train[(i - 1):i])
}
#tmp <- sequence_train[swap_train]
#sequence_train[swap_train] <- sequence_train[swap_train-1]
#sequence_train[swap_train-1] <- tmp
}
train <- t(sapply(
sequence_train,
FUN = function(i)
mvrnorm(1, mu = mu[i, ], Sigma = Sigma[[i]])
))
}
if (n_test > 0) {
sequence_test <- rep(subseq, n_test / n_subseq)
## randomly mess up sequence missing
if (p_swap > 0) {
swap_test <- which(runif(n_test - 1) < p_swap) + 1L
for (i in swap_test) {
sequence_test[(i - 1):i] <-
rev(sequence_test[(i - 1):i])
}
#swap_test <- which(runif(n_test-1)<p_swap)+1L
#tmp <- sequence_test[swap_test]
#sequence_test[swap_test] <- sequence_test[swap_test-1]
#sequence_test[swap_test-1] <- tmp
}
test <- t(sapply(
sequence_test,
FUN = function(i)
mvrnorm(1, mu = mu[i, ], Sigma = Sigma[[i]])
))
## outliers are random points
outlier_position <- runif(n_test) < p_outlier
n_outliers <- sum(outlier_position)
if (d > 1)
test[outlier_position,] <- replicate(d,
get_outlier(n_outliers))
else
test[outlier_position] <- get_outlier(n_outliers)
}
list(
test = test,
train = train,
sequence_test = sequence_test,
sequence_train = sequence_train,
swap_test = swap_test,
swap_train = swap_train,
outlier_position = outlier_position,
model = model
)
}
```

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