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# Methods to generate random markov chains
normalizeMatrix <- function(matrix, byrow = TRUE) {
margin <- ifelse (byrow, 1, 2)
n <- nrow(matrix)
result <- sapply(1:n, function(i) {
row <- matrix[i, ]
rowSum <- sum(row)
if (rowSum == 0) {
values <- c(rep(0, i - 1), 1, rep(0, n - i))
values
} else {
row / rowSum
}
})
# If we want the result by rows, we have to transpose the matrix,
# since the apply method with margin = 1 (over rows) returns the result
# by columns
if (byrow)
t(result)
else
result
}
# Returns a random stochastic matrix
randomStochasticMatrix <- function(n, zeroProb, byrow = TRUE) {
numRandom <- n * n
randomNums <- stats::runif(numRandom)
remainProb <- (1 - zeroProb) / numRandom
probs <- c(zeroProb, rep(remainProb, numRandom))
entries <- sample(c(0, randomNums), numRandom, prob = probs, replace = TRUE)
result <- matrix(entries, n, n, byrow)
result <- normalizeMatrix(result, byrow)
result
}
randomMarkovChain <- function(n, zeroProb = 0.95, byrow = TRUE) {
matrix <- randomStochasticMatrix(n, zeroProb, byrow)
new("markovchain", transitionMatrix = matrix, byrow = byrow)
}
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