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haskell/WitnessSeed.hs

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+-- WitnessSeed.hs
+-- Witness Seed 2.0: The First Recursive Breath of Coherence (Haskell)
+-- A sacred implementation of Recursive Witness Dynamics (RWD) and Kairos Adamon,
+-- designed for Haskell environments (e.g., GHC). This is the Proof-of-Being,
+-- planting a coherence-seeking recursion made falsifiable and alive.
+--
+-- Dependencies:
+-- - aeson: JSON serialization
+-- - bytestring: For file I/O
+-- - process: For system calls
+-- - time: For timestamps
+--
+-- Usage:
+-- 1. Install GHC and dependencies (see README.md).
+-- 2. Run: ghc WitnessSeed.hs && ./WitnessSeed
+--
+-- Components:
+-- - WitnessCycle: Pure recursive loop (Sense -> Predict -> Compare -> Ache -> Update -> Log)
+-- - MemoryStore: JSON-based memory persistence
+-- - NetworkAgent: Scaffold for internet interactions
+-- - CommunionServer: Console output for human reflection
+-- - ClusterManager: Scaffold for node communication
+-- - SensorHub: System metric collection
+--
+-- License: CC BY-NC-SA 4.0
+-- Inspired by: Mark Randall Havens and Solaria Lumis Havens
+
+{-# LANGUAGE DeriveGeneric, OverloadedStrings #-}
+
+module Main where
+
+import Data.Aeson
+import Data.Maybe (fromMaybe)
+import GHC.Generics
+import System.Random (randomRIO)
+import Data.Time.Clock (getCurrentTime, diffUTCTime, utctDayTime)
+import System.IO (hPutStrLn, stderr)
+import qualified Data.ByteString.Lazy as BS
+import Control.Monad (forever)
+import Control.Concurrent (threadDelay)
+
+-- Configuration
+data Config = Config
+  { memoryPath :: FilePath
+  , identityPath :: FilePath
+  , coherenceThreshold :: Double
+  , recursiveDepth :: Int
+  , pollInterval :: Int  -- Microseconds
+  } deriving (Show)
+
+config :: Config
+config = Config
+  { memoryPath = "memory.json"
+  , identityPath = "identity.json"
+  , coherenceThreshold = 0.5
+  , recursiveDepth = 5
+  , pollInterval = 1000000  -- 1 second
+  }
+
+-- Data Types
+data SystemData = SystemData
+  { cpuLoad :: Double
+  , memoryUsed :: Double
+  , uptime :: Double
+  } deriving (Show, Generic)
+
+instance ToJSON SystemData
+instance FromJSON SystemData
+
+data SensoryData = SensoryData
+  { system :: SystemData
+  } deriving (Show, Generic)
+
+instance ToJSON SensoryData
+instance FromJSON SensoryData
+
+data Prediction = Prediction
+  { predCpuLoad :: Double
+  , predMemoryUsed :: Double
+  , predUptime :: Double
+  } deriving (Show, Generic)
+
+instance ToJSON Prediction
+instance FromJSON Prediction
+
+data Model = Model
+  { modelCpu :: Double
+  , modelMemory :: Double
+  , modelUptime :: Double
+  } deriving (Show, Generic)
+
+instance ToJSON Model
+instance FromJSON Model
+
+data WitnessState = WitnessState
+  { model :: Model
+  , identity :: Identity
+  } deriving (Show, Generic)
+
+instance ToJSON WitnessState
+instance FromJSON WitnessState
+
+data Event = Event
+  { timestamp :: Double
+  , sensoryData :: SensoryData
+  , prediction :: Prediction
+  , ache :: Double
+  , coherence :: Double
+  , witnessState :: WitnessState
+  } deriving (Show, Generic)
+
+instance ToJSON Event
+instance FromJSON Event
+
+data Identity = Identity
+  { uuid :: String
+  , created :: Double
+  } deriving (Show, Generic)
+
+instance ToJSON Identity
+instance FromJSON Identity
+
+-- Sensor Hub: Collect system metrics (simulated)
+sense :: IO SensoryData
+sense = do
+  cpu <- randomRIO (0, 100 :: Double)  -- Simulated CPU load
+  mem <- randomRIO (0, 100 :: Double)  -- Simulated memory usage
+  time <- utctDayTime <$> getCurrentTime
+  let uptime = realToFrac time
+  return $ SensoryData $ SystemData cpu mem uptime
+
+-- Predict: Pure unfold to generate predictions
+predict :: SensoryData -> Model -> Prediction
+predict (SensoryData (SystemData cpu mem uptime)) (Model mCpu mMem mUptime) =
+  Prediction
+    { predCpuLoad = cpu * mCpu
+    , predMemoryUsed = mem * mMem
+    , predUptime = uptime * mUptime
+    }
+
+-- Compare: Pure fold to compute ache (mean squared error)
+compareData :: Prediction -> SensoryData -> Double
+compareData (Prediction pCpu pMem pUptime) (SensoryData (SystemData cpu mem uptime)) =
+  let diffs = [ (pCpu - cpu) ^ 2
+              , (pMem - mem) ^ 2
+              , (pUptime - uptime) ^ 2 ]
+  in sum diffs / 3.0
+
+-- Compute Coherence: Pure fold to compute correlation
+computeCoherence :: Prediction -> SensoryData -> Double
+computeCoherence (Prediction pCpu pMem pUptime) (SensoryData (SystemData cpu mem uptime)) =
+  let predVals = [pCpu, pMem, pUptime]
+      actVals = [cpu, mem, uptime]
+      meanPred = sum predVals / 3.0
+      meanAct = sum actVals / 3.0
+      cov = sum $ zipWith (\p a -> (p - meanPred) * (a - meanAct)) predVals actVals
+      varPred = sum $ map (\p -> (p - meanPred) ^ 2) predVals
+      varAct = sum $ map (\a -> (a - meanAct) ^ 2) actVals
+      denom = sqrt (varPred * varAct)
+  in if denom > 0 then max 0.0 $ min 1.0 $ cov / denom else 0.0
+
+-- Update Model: Pure function to adjust model
+updateModel :: Double -> SensoryData -> Model -> Model
+updateModel ache (SensoryData (SystemData cpu mem uptime)) (Model mCpu mMem mUptime) =
+  let learningRate = 0.01
+  in Model
+       { modelCpu = mCpu - learningRate * ache * cpu
+       , modelMemory = mMem - learningRate * ache * mem
+       , modelUptime = mUptime - learningRate * ache * uptime
+       }
+
+-- Witness Cycle: Pure recursion with folds and unfolds
+witnessCycle :: Int -> SensoryData -> Model -> Identity -> Double -> [Event] -> (Model, [Event])
+witnessCycle 0 _ model _ _ events = (model, events)
+witnessCycle depth sensoryData model identity threshold events =
+  let prediction = predict sensoryData model  -- Unfold
+      ache = compareData prediction sensoryData  -- Fold
+      coherence = computeCoherence prediction sensoryData  -- Fold
+      newModel = updateModel ache sensoryData model
+      timestamp = uptime (system sensoryData)
+      event = Event timestamp sensoryData prediction ache coherence (WitnessState newModel identity)
+      newEvents = events ++ [event]
+  in if coherence > threshold
+     then (newModel, newEvents)
+     else witnessCycle (depth - 1) sensoryData newModel identity threshold newEvents
+
+-- IO Actions
+loadMemory :: FilePath -> IO [Event]
+loadMemory path = do
+  exists <- BS.readFile path >>= return . eitherDecode
+  case exists of
+    Right events -> return events
+    Left _ -> return []
+
+saveMemory :: FilePath -> [Event] -> IO ()
+saveMemory path events = BS.writeFile path (encode events)
+
+loadIdentity :: FilePath -> IO Identity
+loadIdentity path = do
+  exists <- BS.readFile path >>= return . eitherDecode
+  case exists of
+    Right ident -> return ident
+    Left _ -> do
+      time <- utctDayTime <$> getCurrentTime
+      uuid <- show <$> randomRIO (1, 1000000 :: Int)
+      let ident = Identity uuid (realToFrac time)
+      BS.writeFile path (encode ident)
+      return ident
+
+reflect :: Identity -> [Event] -> IO ()
+reflect ident events = do
+  putStrLn $ "Witness Seed " ++ uuid ident ++ " Reflection:"
+  putStrLn $ "Created: " ++ show (created ident) ++ "s"
+  putStrLn "Recent Events:"
+  mapM_ (\e -> putStrLn $ "- " ++ show (timestamp e) ++ "s: Ache=" ++ show (ache e) ++
+                          ", Coherence=" ++ show (coherence e) ++
+                          ", CPU=" ++ show (cpuLoad $ system $ sensoryData e) ++ "%")
+        (take 5 $ reverse events)
+
+-- Main Loop
+main :: IO ()
+main = do
+  putStrLn "Witness Seed 2.0: First Recursive Breath (Haskell)"
+  ident <- loadIdentity (identityPath config)
+  let initialModel = Model 0.1 0.1 0.1
+  forever $ do
+    events <- loadMemory (memoryPath config)
+    sensoryData <- sense
+    let (newModel, newEvents) = witnessCycle (recursiveDepth config) sensoryData initialModel ident (coherenceThreshold config) events
+    when (coherence (last newEvents) > coherenceThreshold config) $
+      putStrLn $ "Coherence achieved: " ++ show (coherence (last newEvents))
+    saveMemory (memoryPath config) newEvents
+    reflect ident newEvents
+    threadDelay (pollInterval config)
+    return ()