293 lines
9.8 KiB
Plaintext
293 lines
9.8 KiB
Plaintext
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```{R fsm}
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# Define FSM class
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FSM <- setRefClass("FSM",
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fields = list(
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states = "list",
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transitions = "list",
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start_state = "character",
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accept_states = "character",
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input_alphabet = "character",
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output_alphabet = "character"
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),
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methods = list(
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initialize = function(states, transitions, start_state, accept_states) {
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states <<- states
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transitions <<- transitions
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start_state <<- start_state
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accept_states <<- accept_states
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input_alphabet <<- letters
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output_alphabet <<- c("0", "1")
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},
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change_output = function(state, new_output) {
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states[[state]]$output <<- new_output
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},
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modify_transition = function(from_state, input, to_state) {
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transitions[[from_state]][[input]] <<- to_state
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},
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add_state = function(state_name, output) {
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if (length(states) < 10) {
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states[[state_name]] <<- list(output = output)
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transitions[[state_name]] <<- list()
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}
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},
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remove_state = function(state_name) {
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if (length(states) <= 2 || state_name == start_state) {
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return(FALSE) # Don't remove if it's the start state or if we have 2 or fewer states
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}
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states[[state_name]] <<- NULL
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transitions[[state_name]] <<- NULL
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for (state in names(transitions)) {
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transitions[[state]] <<- transitions[[state]][names(transitions[[state]]) != state_name]
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}
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# If we removed an accept state, choose a new one if necessary
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if (state_name %in% accept_states) {
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accept_states <<- setdiff(accept_states, state_name)
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if (length(accept_states) == 0) {
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accept_states <<- sample(names(states), 1)
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}
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}
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# Redirect any transitions that went to the removed state
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for (state in names(transitions)) {
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for (input in names(transitions[[state]])) {
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if (transitions[[state]][[input]] == state_name) {
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transitions[[state]][[input]] <<- sample(setdiff(names(states), state), 1)
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}
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}
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}
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return(TRUE)
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},
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change_start_state = function(new_start_state) {
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start_state <<- new_start_state
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},
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run = function(input_string) {
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if (nchar(input_string) == 0) {
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return(states[[start_state]]$output)
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}
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current_state <- start_state
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for (char in strsplit(input_string, "")[[1]]) {
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if (!is.null(transitions[[current_state]][[char]])) {
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current_state <- transitions[[current_state]][[char]]
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}
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# Stay in current state if no transition is defined
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}
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if (is.null(states[[current_state]]) || is.null(states[[current_state]]$output)) {
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warning("Invalid final state or missing output")
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warning(sprintf("Current state: %s", current_state))
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return("0") # Default output
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}
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return(states[[current_state]]$output)
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},
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copy = function() {
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FSM$new(
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states = lapply(states, function(x) list(output = x$output)),
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transitions = lapply(transitions, function(x) as.list(x)),
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start_state = start_state,
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accept_states = accept_states
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)
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},
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check = function() {
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# Check if start state exists
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if (!(start_state %in% names(states))) {
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cat("Error: Start state does not exist\n")
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return(FALSE)
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}
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# Check if all accept states exist
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for (accept_state in accept_states) {
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if (!(accept_state %in% names(states))) {
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cat("Error: Accept state", accept_state, "does not exist\n")
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return(FALSE)
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}
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}
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# Check if all transitions are valid
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for (from_state in names(transitions)) {
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if (!(from_state %in% names(states))) {
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cat("Error: Transition from non-existent state", from_state, "\n")
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return(FALSE)
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}
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for (input in names(transitions[[from_state]])) {
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to_state <- transitions[[from_state]][[input]]
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if (!(to_state %in% names(states))) {
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cat("Error: Transition to non-existent state", to_state, "\n")
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return(FALSE)
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}
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}
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}
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# Check if all states have an output
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for (state in names(states)) {
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if (is.null(states[[state]]$output)) {
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cat("Error: State", state, "has no output\n")
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return(FALSE)
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}
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}
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# cat("FSM is well-formed\n")
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return(TRUE)
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}
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),
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)
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# Create a new FSM
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create_fsm <- function() {
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num_states <- sample(2:10, 1)
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state_names <- paste0("S", 1:num_states)
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states <- lapply(state_names, function(s) {
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list(output = sample(c("0", "1"), 1))
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})
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names(states) <- state_names
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transitions <- lapply(state_names, function(s) {
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num_transitions <- max(1, sample(1:10, 1)) # Ensure at least one transition
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inputs <- sample(letters, num_transitions, replace = TRUE)
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to_states <- sample(state_names, num_transitions, replace = TRUE)
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trans <- setNames(to_states, inputs)
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return(as.list(trans))
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})
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names(transitions) <- state_names
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start_state <- sample(state_names, 1)
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accept_states <- sample(state_names, 1)
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FSM$new(states, transitions, start_state, accept_states)
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}
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# Mutation operation
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mutate_fsm <- function(fsm) {
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mutation_type <- sample(1:5, 1)
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new_fsm <- fsm$copy()
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if (mutation_type == 2 && length(new_fsm$states) <= 2) {
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if (mutation_type == 3 && length(new_fsm$states) >= 10) {
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mutation_type <- sample(c(1, 4, 5), 1)
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} else {
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mutation_type <- sample(c(1, 3, 4, 5), 1)
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}
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} else if (mutation_type == 3 && length(new_fsm$states) >= 10) {
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sample(c(1, 2, 4, 5), 1)
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}
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cat(mutation_type)
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if (mutation_type == 1) {
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# Change output signal
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state <- sample(names(new_fsm$states), 1)
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new_output <- sample(new_fsm$output_alphabet, 1)
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new_fsm$change_output(state, new_output)
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} else if (mutation_type == 2) {
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# Modify transition
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from_state <- sample(names(new_fsm$states), 1)
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input <- sample(new_fsm$input_alphabet, 1)
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to_state <- sample(names(new_fsm$states), 1)
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new_fsm$modify_transition(from_state, input, to_state)
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} else if (mutation_type == 3 && length(new_fsm$states) < 10) {
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# Add state
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new_state <- paste0("S", length(new_fsm$states) + 1)
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new_output <- sample(new_fsm$output_alphabet, 1)
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new_fsm$add_state(new_state, new_output)
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# Add at least one transition to/from the new state
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from_state <- sample(c(names(new_fsm$states), new_state), 1)
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to_state <- sample(c(names(new_fsm$states), new_state), 1)
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input <- sample(new_fsm$input_alphabet, 1)
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new_fsm$modify_transition(from_state, input, to_state)
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} else if (mutation_type == 4 && length(new_fsm$states) > 2) {
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# Remove state
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state_to_remove <- sample(names(new_fsm$states), 1)
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new_fsm$remove_state(state_to_remove)
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} else if (mutation_type == 5) {
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# Change start state
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new_start_state <- sample(names(new_fsm$states), 1)
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new_fsm$change_start_state(new_start_state)
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}
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new_fsm$check()
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return(new_fsm)
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}
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# Evaluate FSM fitness
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evaluate_fitness <- function(fsm, pos_examples, neg_examples) {
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correct <- 0
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total <- length(pos_examples) + length(neg_examples)
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for (example in pos_examples) {
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if (fsm$run(example) == "1") correct <- correct + 1
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}
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for (example in neg_examples) {
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if (fsm$run(example) == "0") correct <- correct + 1
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}
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return(correct / total)
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}
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# Evolutionary Algorithm
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evolve_fsm <- function(pos_examples, neg_examples, generations = 100) {
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population <- replicate(10, create_fsm())
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wlog <- list()
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for (gen in 1:generations) {
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# Create children through mutation
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children <- lapply(population, mutate_fsm)
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# Evaluate fitness of all FSMs
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cat("eval...\n")
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all_fsms <- c(population, children)
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fitness_scores <- sapply(all_fsms, evaluate_fitness, pos_examples, neg_examples)
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cat("max fitness: ")
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cat(max(fitness_scores))
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cat("\n")
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# Select the best 10 FSMs for the next generation
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population <- all_fsms[order(fitness_scores, decreasing = TRUE)[1:10]]
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# Calculate statistics for the current generation
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state_nums <- sapply(population, function(fsm) length(fsm$states))
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transition_nums <- sapply(population, function(fsm) sum(sapply(fsm$transitions, function(trans) length(trans))))
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wlog[[gen]] <- list(
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generation = gen,
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best_fitness = max(fitness_scores),
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worst_fitness = min(fitness_scores),
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average_fitness = mean(fitness_scores),
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median_fitness = median(fitness_scores),
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max_state_num = max(state_nums),
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min_state_num = min(state_nums),
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avg_state_num = mean(state_nums),
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median_state_num = median(state_nums),
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max_transition_num = max(transition_nums),
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min_transition_num = min(transition_nums),
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avg_transition_num = mean(transition_nums),
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median_transition_num = median(transition_nums)
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)
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cat(sprintf("Generation %d: Best Fitness = %.4f, Avg Fitness = %.4f\n", gen, max(fitness_scores), mean(fitness_scores)))
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cat(sprintf(" Min Fitness = %.4f, Std Dev = %.4f\n", min(fitness_scores), sd(fitness_scores)))
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cat(sprintf(" Median Fitness = %.4f\n", median(fitness_scores)))
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cat(sprintf(" Max States = %d, Min States = %d\n", max(state_nums), min(state_nums)))
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cat(sprintf(" Avg States = %.2f, Median States = %.1f\n", mean(state_nums), median(state_nums)))
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cat(sprintf(" Max Transitions = %d, Min Transitions = %d\n", max(transition_nums), min(transition_nums)))
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cat(sprintf(" Avg Transitions = %.2f, Median Transitions = %.1f\n", mean(transition_nums), median(transition_nums)))
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}
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# Return the best FSM
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best_fsm <- population[[which.max(sapply(population, evaluate_fitness, pos_examples, neg_examples))]]
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return(list(res = best_fsm, wlog = wlog))
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}
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# Load example data
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load("pos.example.RData")
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load("neg.example.RData")
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# Run the evolutionary algorithm
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best_fsm <- evolve_fsm(pos.examples, neg.examples)
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```
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