--
-- Static evaluation functions for board positions
--
module AI.Eval (
  simpleVal,
  fullVal
  ) where

import           Board
import           AI.Gametree
import           AI.Minimax
import qualified Data.Vector.Unboxed as Vec

-- simple static board valuation (material score only)
simpleVal :: Board -> Value
simpleVal b = if endGame b then (-maxBound) else material b

-- better static board valuation (material and positional scores)
fullVal :: Board -> Value
fullVal b = if endGame b then (-maxBound) else material b + positional b

-- | Material score
-- * multiply sum of heights by counts
-- * height weights for kinds with lowest counts
material :: Board -> Value
{-# INLINE material #-}
material b = fromIntegral
              (Vec.sum (Vec.zipWith (\n h -> (30-n)^2*h) counts heights) -
               Vec.sum (Vec.zipWith (\n h -> (30-n)^2*h) counts' heights'))
  where
      -- stacks counts by piece kinds for each player
      counts = activeCounts b
      counts'= inactiveCounts b
      -- sum of heights by piece kinds
      heights = activeHeights b
      heights'= inactiveHeights b


-- | Positional score
-- for each kind, count opponent's pieces in each player's zone of control
positional :: Board -> Value
{-# INLINE positional #-}
positional b = fromIntegral
               (Vec.sum (Vec.zipWith (\n m -> pw*m`quot`n) counts' zoc_counts) -
                Vec.sum (Vec.zipWith (\n m -> pw*m`quot`n) counts zoc_counts'))
  where
    counts = activeCounts b
    counts'= inactiveCounts b
    -- zone of control of each player
    zoc = zoneOfControl (active b) (pieces b)
    zoc'= zoneOfControl (inactive b) (pieces b)
    -- count pieces in each zone of control
    zoc_counts = Vec.fromList $ countStacks (inactive b) zoc
    zoc_counts'= Vec.fromList $ countStacks (active b) zoc'
    pw = 1000