mirror of
https://git.cs.ou.nl/joshua.moerman/mealy-decompose.git
synced 2025-04-30 02:07:44 +02:00
175 lines
6.2 KiB
Haskell
175 lines
6.2 KiB
Haskell
{-# LANGUAGE OverloadedStrings #-}
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{-# OPTIONS_GHC -Wno-unrecognised-pragmas #-}
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module Main where
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import Data.Partition
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import Data.Preorder
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import DotParser (readDotFile)
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import DotWriter
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import Mealy
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import MealyRefine
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import Merger
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import Data.Bifunctor
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import Data.List (sortOn)
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import Data.List.Ordered (nubSort)
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import Data.Map.Strict qualified as Map
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import Data.Maybe (isNothing)
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import Data.Set qualified as Set
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import Data.Text qualified as T
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import Data.Text.IO qualified as T
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import Data.Tuple (swap)
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import System.Environment
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-- | This functions inverts a map. In the new map the values are lists.
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converseRelation :: Ord b => Map.Map a b -> Map.Map b [a]
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converseRelation = Map.fromListWith (++) . fmap (second pure . swap) . Map.assocs
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-- TODO: use Data.Text here
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myWriteFile :: FilePath -> String -> IO ()
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myWriteFile filename = writeFile ("results/" ++ filename)
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main :: IO ()
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main = do
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-- Read dot file
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[dotFile] <- getArgs
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print dotFile
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machine <- readDotFile dotFile
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-- print some basic info
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putStrLn $ (show . length $ states machine) <> " states, " <> (show . length $ inputs machine) <> " inputs and " <> (show . length $ outputs machine) <> " outputs"
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putStrLn "Small sample:"
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let
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printPartition p = putStrLn $ "number of states = " <> show (numBlocks p)
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let
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outputFuns = [(i, fun) | i <- inputs machine, let fun s = fst (behaviour machine s i)]
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reverseTransitionMaps i = Map.fromListWith (++) [(t, [s]) | s <- states machine, let t = snd (behaviour machine s i)]
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reverseFuns = [(i, fun) | i <- inputs machine, let mm = reverseTransitionMaps i, let fun s = Map.findWithDefault [] s mm]
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-- Minimise input, so we know the actual number of states
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printPartition (refineMealy3 outputFuns reverseFuns (states machine))
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putStrLn ""
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-- Then compute each projection
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let
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outs = outputs machine
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mappedOutputFuns o = [(i, (o ==) . f) | (i, f) <- outputFuns]
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projections = [(o, refineMealy3 (mappedOutputFuns o) reverseFuns (states machine)) | o <- outs]
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-- Print number of states of each projection
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mapM_
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( \(o, partition) -> do
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T.putStr o
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putStr " -> "
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printPartition partition
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)
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projections
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-- First we check for equivalent partitions, so that we skip redundant work.
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let
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(equiv, uniqPartitions) = equivalenceClasses comparePartitions projections
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putStrLn ""
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putStrLn "Representatives"
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print . fmap fst $ uniqPartitions
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putStrLn ""
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putStrLn "Equivalences"
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mapM_
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( \(o2, o1) -> do
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putStrLn $ " " <> show o2 <> " == " <> show o1
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)
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(Map.assocs equiv)
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-- Then we compare each pair of partitions. We only keep the finest
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-- partitions, since the coarse ones don't provide value to us.
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let
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(topMods, downSets) = maximalElements comparePartitions uniqPartitions
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foo (a, b) = (numBlocks b, a)
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putStrLn ""
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putStrLn "Top modules"
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mapM_
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( \(b, o) -> do
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putStrLn $ " " <> show o <> " has size " <> show b
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)
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(sortOn (negate . fst) . fmap foo $ topMods)
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-- Then we try to combine paritions, so that we don't end up with
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-- too many components. (Which would be too big to be useful.)
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let strategy MergerStats{..}
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| numberOfComponents <= 4 = Stop
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| otherwise = Continue
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projmap <- heuristicMerger topMods strategy
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print projmap
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-- Now we are going to output the components we found.
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let
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equivInv = converseRelation equiv
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projmapN = zip projmap [1 :: Int ..]
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action ((os, p), componentIdx) = do
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let
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name = T.intercalate "x" os
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osWithRel = concat $ os : [Map.findWithDefault [] o downSets | o <- os]
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osWithRelAndEquiv = concat $ osWithRel : [Map.findWithDefault [] o equivInv | o <- osWithRel]
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componentOutputs = Set.fromList osWithRelAndEquiv
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proj = projectToComponent (`Set.member` componentOutputs) machine
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-- Sanity check: compute partition again
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partition = refineMealy2 proj
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putStrLn ""
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putStrLn $ "Component " <> show os
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putStrLn $ "Correct? " <> show (comparePartitions p partition)
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putStrLn $ "Size = " <> show (numBlocks p)
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do
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let
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filename = "partition_" <> show componentIdx <> ".dot"
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content = T.unpack . T.unlines . fmap T.unwords . toBlocks $ p
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putStrLn $ "Output (partition) in file " <> filename
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myWriteFile filename content
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do
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let
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MealyMachine{..} = proj
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-- We enumerate all transitions in the full automaton
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transitions = [(s, i, o, t) | s <- states, i <- inputs, let (o, t) = behaviour s i]
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-- This is the quotient map, from state to block
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state2block = (Map.!) (getPartition p)
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-- We apply this to each transition, and then nubSort the duplicates away
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transitionsBlocks = nubSort [(state2block s, i, o, state2block t) | (s, i, o, t) <- transitions]
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-- The initial state should be first
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initialBlock = state2block initialState
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-- Sorting on "/= initialBlock" puts the initialBlock in front
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initialFirst = sortOn (\(s, _, _, _) -> s /= initialBlock) transitionsBlocks
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-- Convert to a file
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filename1 = "component_" <> show componentIdx <> ".dot"
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content1 = toString . mealyToDot (T.unpack name) $ initialFirst
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-- So far so good, `initialFirst` could serve as our output
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-- But we do one more optimisation on the machine
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-- We remove inputs, on which the machine does nothing
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deadInputs0 = Map.fromListWith (++) . fmap (\(s, i, o, t) -> (i, [(s, o, t)])) $ initialFirst
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deadInputs = Map.keysSet . Map.filter (all (\(s, o, t) -> s == t && isNothing o)) $ deadInputs0
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result = filter (\(_, i, _, _) -> i `Set.notMember` deadInputs) initialFirst
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-- Convert to a file
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filename2 = "component_reduced_" <> show componentIdx <> ".dot"
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content2 = toString . mealyToDot (T.unpack name) $ result
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putStrLn $ "Output (reduced machine) in file " <> filename1
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myWriteFile filename1 content1
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putStrLn $ "Dead inputs = " <> show (Set.size deadInputs)
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putStrLn $ "Output (reduced machine) in file " <> filename2
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myWriteFile filename2 content2
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mapM_ action projmapN
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