Represent a Markov chain as a plain R list

Converts a markovchain object to a plain, self-describing R list: the same information toFile writes to disk, kept in memory. fromDictionary reverses the conversion.

Usage

toDictionary(object)

# S4 method for class 'markovchain'
toDictionary(object)

fromDictionary(d)

Arguments

object

A markovchain object.

d

A list as returned by toDictionary. d$transitionMatrix may also be a plain n x n matrix (with or without dimnames matching d$states) for convenience when building a dictionary by hand rather than from an existing markovchain object. When it is a plain matrix, d$byrow is honored exactly as new("markovchain", ...) honors its own byrow argument: the matrix is stored as given, with d$byrow only documenting whether it is row- or column-stochastic (so a column-stochastic matrix round-trips by setting d$byrow = FALSE, with no transposition performed here). When d$transitionMatrix is a nested list (as toDictionary produces), the nesting itself is always keyed [[from]][[to]] – i.e. row-stochastic – so it is always reconstructed with byrow = TRUE, regardless of d$byrow.

Value

A named list with four elements:

name

The chain's name, as a single string (possibly empty).

states

A character vector of state names, in order.

byrow

Always TRUE: the list always stores the chain row-stochastically, regardless of object's own storage convention, so that the representation is unambiguous without also having to interpret this flag.

transitionMatrix

A named list of named lists: transitionMatrix[[i]][[j]] is the probability of moving from state i to state j. This is deliberately not a plain matrix, so that the structure serializes to JSON or YAML (via toFile) as a self-describing object keyed by state name, rather than a bare array whose meaning depends on remembering a row/column order.

fromDictionary returns a markovchain object.

Details

Unlike PyDTMC's own to_dictionary()/from_dictionary(), which represent a chain as a flat mapping from every (from_state, to_state) pair to its probability – \(n^2\) entries with no state grouping – this nests the representation by source state, which is both more compact to read and directly round-trips through R's own list-of-lists idiom without any special tuple-key handling.

See also

toFile, fromFile

Examples

statesNames <- c("a", "b")
mc <- new("markovchain", states = statesNames,
          transitionMatrix = matrix(c(0.7, 0.3, 0.4, 0.6), byrow = TRUE,
                                     nrow = 2, dimnames = list(statesNames, statesNames)))
d <- toDictionary(mc)
d$transitionMatrix$a$b # 0.3: probability of moving from "a" to "b"
#> [1] 0.3

identical(fromDictionary(d)@transitionMatrix, mc@transitionMatrix)
#> [1] TRUE