RNAwolf (empty) → 0.3.0.0

raw patch · 11 files changed

+1929/−0 lines, 11 filesdep +BiobaseTrainingDatadep +BiobaseXNAdep +PrimitiveArraysetup-changed

Dependencies added: BiobaseTrainingData, BiobaseXNA, PrimitiveArray, StatisticalMethods, base, bytestring, cmdargs, containers, deepseq, directory, parallel, random, vector

Files

• BioInf/Keys.hs +246/−0
• BioInf/Params.hs +105/−0
• BioInf/Params/Export.hs +33/−0
• BioInf/Params/Import.hs +73/−0
• BioInf/PassiveAggressive.hs +110/−0
• BioInf/RNAwolf.hs +265/−0
• LICENSE +675/−0
• RNAwolf.cabal +103/−0
• RNAwolf.hs +86/−0
• RNAwolfTrain.hs +231/−0
• Setup.hs +2/−0

@@ -0,0 +1,246 @@+{-# LANGUAGE PatternGuards #-}+{-# LANGUAGE TupleSections #-}++-- | Transformation of predictions and known structures into keys. Keys are+-- used for linearization.+--+-- NOTE READ THE BIG FAT KEYS WARNING+--+-- TODO Generalize and move into its own library++module BioInf.Keys where++import Data.Vector.Unboxed as VU hiding ((++),concatMap,length,concat,null)+import qualified Data.Vector.Unboxed as VU+import Data.List as L+import qualified Data.Map as M++import Biobase.Primary+import Biobase.Secondary+import Biobase.Secondary.Diagrams+import Data.PrimitiveArray+import Data.PrimitiveArray.Ix++import BioInf.Params as P+import BioInf.Params.Import as P+import BioInf.Params.Export as P++++-- | A list of "named" parameters.++paramsKeys = concat+  [ L.map (HairpinLength  . fst) . assocs . hairpinLength   $ zeroParams+  , L.map (HairpinClose   . fst) . assocs . hairpinClose    $ zeroParams+  , L.map (Stem           . fst) . assocs . stem            $ zeroParams+  , L.map (StemTriplet    . fst) . assocs . stemTriplet     $ zeroParams+  , L.map (InteriorLength . fst) . assocs . interiorLength  $ zeroParams+  , L.map (InteriorAsym   . fst) . assocs . interiorAsym    $ zeroParams+  , L.map (InteriorClose  . fst) . assocs . interiorClose   $ zeroParams+  , L.map (BulgeLength    . fst) . assocs . bulgeLength     $ zeroParams+  , L.map (BulgeTriplet   . fst) . assocs . bulgeTriplet    $ zeroParams+  , L.map (BulgeClose     . fst) . assocs . bulgeClose      $ zeroParams+  , L.map (MbClose        . fst) . assocs . mbClose         $ zeroParams+  -- scalar values for multiloops+  , [ MultiBranched, MultiHelix, MultiUnpaired ]+  -- distance between pairs+  , L.map (PairDistance   . fst) . assocs . pairDistance    $ zeroParams+  , [ InterMolInit ]+  ]++-- | Uniquely tag each key+--+-- NOTE BIG FAT WARNING: BE ABSOLUTELY SURE THAT ALL IMPORTS AND EXPORTS FOLLOW+-- THIS ORDERING EXACTLY, OTHERWISE KEYS WILL BE MAPPED TO WRONG POSITIONS+-- DURING LOOKUP AND VALUES END UP SOMEWHERE ELSE.++data Keys+  = HairpinLength   Int+  | HairpinClose    (ExtPair,Nuc,Nuc)+  | Stem            (ExtPair,ExtPair)+  | StemTriplet     (ExtPair,ExtPair)+  | InteriorLength  Int+  | InteriorAsym    Int+  | InteriorClose   (ExtPair,Nuc,Nuc)+  | BulgeLength     Int+  | BulgeTriplet    (ExtPair,ExtPair)+  | BulgeClose      ExtPair+  | MbClose         (ExtPair,Nuc,Nuc)+  | MultiBranched+  | MultiHelix+  | MultiUnpaired+  | PairDistance    Int+  | InterMolInit+  deriving (Read,Show,Eq,Ord)++-- | Training data to feature vector++featureVector :: String -> [ExtPairIdx] -> [Int]+featureVector inp xs = ys where+  ys = L.map lookupFeatureIndex tr+  tr = treeToFeatures inp $ ssTree (length inp) xs++-- | transform feature to 0-based index++lookupFeatureIndex :: Keys -> Int+lookupFeatureIndex k+  | Just v <- k `M.lookup` kvs = v+  | otherwise = error $ show ("key unknown: ", k)+  where++-- | Map param keys to thei Int-indices.++kvs = M.fromList $ L.zip paramsKeys [0..]+{-# NOINLINE kvs #-}++-- | And back from Int-indices to the keys.++vks = M.fromList $ L.zip [0 ::Int ..] paramsKeys+{-# NOINLINE vks #-}++-- | Takes a primary structure and secondary structure tree and produces a list+-- of keys.+--+-- TODO Data.Traversable ?!+--+-- TODO better handling of unknown features: we can have genuine errors+-- (pseudoknots) and uncoded features (e.g. hairpins of size > 30)++treeToFeatures :: (MkPrimary a, Show a) => a -> SSTree ExtPairIdx  t -> [Keys]+treeToFeatures inp = f where+  pri = mkPrimary inp+  swap23 (a,b,c) = (a,c,b)+  vuIndex xs k = if k<0 || k>= VU.length xs then error (show (inp,k)) else xs VU.! k+  n = VU.length pri -1+  -- Features for external loop+  f (SSExt n _ xs) = concatMap f xs+  -- Features for anything else+  f (SSTree ((i,j),ijExt) _ xs)++    -- intermolecular init+    | null xs+    , let is = VU.length . VU.filter (==nIMI) . VU.take (j-i) . VU.drop i $ pri+    , is > 0+    = L.replicate is InterMolInit++    -- hairpin+    -- TODO relax the minima?+    | null xs+    , j-i-1<=P.maxLength+    , j-i>=3+    = [ HairpinLength (j-i-1)+      , HairpinClose (((nI,nJ),ijExt),nIp1,nJm1)+--      , PairDistance  (j-i-1)+      ]++    -- normal stem+    | [SSTree ((k,l),klExt) _ _] <- xs+    , let nK = pri `vuIndex` k; nL = pri `vuIndex` l+    , i+1==k && j-1==l+    = [ Stem (((nI,nJ),ijExt),((nL,nK),swap23 klExt))+--      , PairDistance (j-i-1)+      ] ++ concatMap f xs+    {-+    -- triplet stem (right nucleotide shared)+    | [SSTree ((k,l),klExt) _ _] <- xs+    , i+1==k && j==l+    , let nK = pri `vuIndex` k+    = [ StemTriplet ( ((nI,nJ),ijExt)+                    , ((nJ,nK),swap23 klExt)+                    )+--      , PairDistance (j-i-1)+      ] ++ concatMap f xs+    -}+    {-+    -- triplet stem (left nucleotide shared)+    | [SSTree ((k,l),klExt) _ _] <- xs+    , i==k && j-1==l+    , let nL = pri `vuIndex` l+    = [ StemTriplet (((nI,nL),klExt),((nI,nJ),ijExt)) -- shared nuc first (nI), then 5' (nL) first+--      , PairDistance (j-i-1)+      ] ++ concatMap f xs+    -}+    -- interior loops+    | [SSTree ((k,l),klExt) _ _] <- xs+    , let lenI = k-i-1; lenJ = j-l-1; len = lenI+lenJ+    , lenI>=1 && lenJ>=1 && len<=P.maxLength+    , let nL = pri `vuIndex` l; nK = pri `vuIndex` k; lkExt = swap23 klExt+    , let nLm1 = pri `vuIndex` (l-1); nKp1 = pri `vuIndex` (k+1)+    , let nLp1 = pri `vuIndex` (l+1); nKm1 = pri `vuIndex` (k-1)+    = [ InteriorLength len+      , InteriorAsym $ abs (lenI-lenJ)+      , InteriorClose (((nI,nJ),ijExt),nIp1,nJm1)+      , InteriorClose (((nL,nK),lkExt),nLp1,nKm1)+--      , PairDistance (j-i-1)+      ] ++ concatMap f xs+    -- normal bulge+    | [SSTree ((k,l),klExt) _ _] <- xs+    , let lenI = k-i-1; lenJ = j-l-1; len = max lenI lenJ+    , lenI==0 && lenJ>0 || lenJ==0 && lenI>0+    , len<=P.maxLength+    , let nK = pri `vuIndex` k; nL = pri `vuIndex` l; lkExt = swap23 klExt+    = [ BulgeLength len+      , BulgeClose ((nI,nJ),ijExt)+      , BulgeClose ((nL,nK),lkExt)+--      , PairDistance (j-i-1)+      ] ++ concatMap f xs+    {-+    -- bulge triplet (left)+    | [SSTree ((k,l),klExt) _ _] <- xs+    , let lenJ = j-l-1+    , i==k && lenJ>0+    , lenJ<=P.maxLength+    , let nL = pri `vuIndex` l+    = [ BulgeLength lenJ+      , BulgeTriplet (((nI,nL),klExt),((nI,nJ),ijExt))+--      , PairDistance (j-i-1)+      ] ++ concatMap f xs+    -}+    {-+    -- bulge triplet (right)+    | [SSTree ((k,l),klExt) _ _] <- xs+    , let lenI = k-i-1+    , j==l && lenI>0+    , lenI<=P.maxLength+    , let nK = pri `vuIndex` k+    = [ BulgeLength lenI+      , BulgeTriplet (((nJ,nI),swap23 ijExt),((nJ,nK),swap23 klExt))+--      , PairDistance (j-i-1)+      ] ++ concatMap f xs+    -}+    -- close a multibranched loop+    --+    -- TODO what about shared multibranched loops? (see sequence GCGGCACCGUCCGCUCAAACAAACGG in fr3d DB)+    | length xs > 1+--    = concatMap f xs+    = [ MbClose (((nI,nJ),ijExt),nIp1,nJm1)+--      , PairDistance (j-i-1)+      , MultiBranched+      , MultiHelix+      ] ++ concat+      -- each inner part+      [ [ MbClose (((nL,nK),lkExt),nLp1,nKm1)+        , MultiHelix ]+      | SSTree ((k,l),klExt) _ _ <- xs+      , k>0 && l<n+      , let nK = pri `vuIndex` k; nL = pri `vuIndex` l+      , let nKm1 = pri `vuIndex` (k-1); nLp1 = pri `vuIndex` (l+1)+      , let lkExt = swap23 klExt+      ] ++ concatMap f xs++    | otherwise = concatMap f xs+    -- | otherwise = error $ show ("unknown features:", inp , SSTree ((i,j),ijExt) () xs)+    where+      nI    = pri `vuIndex` i+      nJ    = pri `vuIndex` j+      nIp1  = pri `vuIndex` (i+1)+      nJm1  = pri `vuIndex` (j-1)+      jiExt = swap23 ijExt++-- | Create the secondary structure tree+--+-- FIXME okPairs is ad-hoc, we should allow for other kinds of pairs!++ssTree :: Int -> [ExtPairIdx] -> SSTree ExtPairIdx ()+ssTree n xs = d2sTree . mkD2S . (n,) . L.filter okPairs $ xs where+  okPairs ((i,j),_) = j-i>2 -- we only keep pairs which have at least to free nucleotides between them

@@ -0,0 +1,105 @@+{-# LANGUAGE TupleSections #-}++-- | RNA-folding parameter space.+--+-- TODO find better names for types, functions, and minima/maxima.++module BioInf.Params where++import Data.PrimitiveArray+import Data.PrimitiveArray.Ix++import Biobase.Primary+import Biobase.Secondary++++-- | A (very) rich set of paramters.+--+-- TODO 1xn interior loops should be tested (how often do they occur?)+--+-- TODO external loop++data Params = Params+  { hairpinLength   :: PaLength+  , hairpinClose    :: PaExtPairNN+  , stem            :: Pa2ExtPairs+  , stemTriplet     :: Pa2ExtPairs+  , interiorLength  :: PaLength+  , interiorAsym    :: PaLength+  , interiorClose   :: PaExtPairNN+  , bulgeLength     :: PaLength+  , bulgeTriplet    :: Pa2ExtPairs+  , bulgeClose      :: PaExtPair+  , mbClose         :: PaExtPairNN+  , multiBranched   :: Double+  , multiHelix      :: Double+  , multiUnpaired   :: Double+  , pairDistance    :: PaDistance+  , interMolInit    :: Double+  } deriving (Read,Show)++maxLength = 1000 :: Int+maxDistance = 1000 :: Int+minExtPair = ((nN,nN),(cis,wc,wc))+maxExtPair = ((nT,nT),(trans,hoogsteen,hoogsteen))+min2ExtPairs = (minExtPair,minExtPair)+max2ExtPairs = (maxExtPair,maxExtPair)+minExtPairNN = (minExtPair,nN,nN)+maxExtPairNN = (maxExtPair,nT,nT)+minTriplet = min2ExtPairs+maxTriplet = max2ExtPairs++-- | A parameter set with all values set to zero.++zeroParams = Params+  { hairpinLength   = zeroLength+  , hairpinClose    = zeroExtPairNN+  , stem            = zero2ExtPairs+  , stemTriplet     = zeroTriplet+  , interiorLength  = zeroLength+  , interiorAsym    = zeroLength+  , interiorClose   = zeroExtPairNN+  , bulgeLength     = zeroLength+  , bulgeTriplet    = zeroTriplet+  , bulgeClose      = zeroExtPair+  , mbClose         = zeroExtPairNN+  , multiBranched   = 0+  , multiHelix      = 0+  , multiUnpaired   = 0+  , pairDistance    = zeroDistance+  , interMolInit    = 0+  }++zeroLength = fromAssocs 0 maxLength 0 []+zeroDistance = fromAssocs 0 maxDistance 0 []+zeroExtPair = fromAssocs minExtPair maxExtPair 0 []+zero2ExtPairs = fromAssocs min2ExtPairs max2ExtPairs 0 []+zeroExtPairNN = fromAssocs minExtPairNN maxExtPairNN 0 []+zeroTriplet = fromAssocs minTriplet maxTriplet 0 []++++-- ** types++-- | An array which encodes "length" information++type PaLength = PrimArray Int Double++-- | This is an experimental annotation for long-distance interactions++type PaDistance = PrimArray Int Double++-- | An array holding information for one extended pair.++type PaExtPair   = PrimArray ExtPair Double++-- | An array holding information for two extended pairs, e.g. stems.++type Pa2ExtPairs = PrimArray (ExtPair,ExtPair) Double++-- | An array holding information for one extended pair and two unpaired+-- nucleotides, closes a loop.++type PaExtPairNN = PrimArray (ExtPair,Nuc,Nuc) Double -- pair and two unpaired nucleotides, for hairpins, etc+

@@ -0,0 +1,33 @@+{-# LANGUAGE RecordWildCards #-}++-- | Exporting parameters is a bit more involved as we need the ability to+-- export into a database format as well as linearize to list form.++module BioInf.Params.Export where++import Data.PrimitiveArray as PA+import Data.PrimitiveArray.Ix as PA++import BioInf.Params++++-- | Just a long list of doubles.++toList :: Params -> [Double]+toList Params{..} = concat+  [ PA.toList hairpinLength+  , PA.toList hairpinClose+  , PA.toList stem+  , PA.toList stemTriplet+  , PA.toList interiorLength+  , PA.toList interiorAsym+  , PA.toList interiorClose+  , PA.toList bulgeLength+  , PA.toList bulgeTriplet+  , PA.toList bulgeClose+  , PA.toList mbClose+  , [multiBranched, multiHelix, multiUnpaired]+  , PA.toList pairDistance+  , [interMolInit]+  ]

@@ -0,0 +1,73 @@++-- | Given a list of doubles with the /exact required length/ import into a+-- 'Params' structure.++module BioInf.Params.Import where++import Data.PrimitiveArray as PA+import Data.PrimitiveArray.Ix as PA+import Data.Ix (rangeSize)++import BioInf.Params++++-- | Cast a list of values to parameters.+--+-- NOTE This operation is rather fragile if there are layout changes. Consider+-- Repr for this.+--+-- NOTE BIG FAT WARNING: BE ABSOLUTELY SURE THAT ALL IMPORTS AND EXPORTS FOLLOW+-- THIS ORDERING EXACTLY, OTHERWISE KEYS WILL BE MAPPED TO WRONG POSITIONS+-- DURING LOOKUP AND VALUES END UP SOMEWHERE ELSE.++fromList :: [Double] -> Params+fromList xs = Params+  { hairpinLength   = PA.fromList 0            maxLength    hpl+  , hairpinClose    = PA.fromList minExtPairNN maxExtPairNN hpc+  , stem            = PA.fromList min2ExtPairs max2ExtPairs sp+  , stemTriplet     = PA.fromList minTriplet   maxTriplet   tp+  , interiorLength  = PA.fromList 0            maxLength    il+  , interiorAsym    = PA.fromList 0            maxLength    ia+  , interiorClose   = PA.fromList minExtPairNN maxExtPairNN ip+  , bulgeLength     = PA.fromList 0            maxLength    bl+  , bulgeTriplet    = PA.fromList minTriplet   maxTriplet   bt+  , bulgeClose      = PA.fromList minExtPair   maxExtPair   bu+  , mbClose         = PA.fromList minExtPairNN maxExtPairNN mbc+  , multiBranched   = head mbranched+  , multiHelix      = head mhelix+  , multiUnpaired   = head munpaired+  , pairDistance    = PA.fromList 0            maxDistance  dst+  , interMolInit    = head intermol+  } where+    rsExtPair   = rangeSize (minExtPair,maxExtPair)+    rs2ExtPairs = rangeSize (min2ExtPairs,max2ExtPairs)+    rsTriplet   = rangeSize (minTriplet,maxTriplet)+    rsExtPairNN = rangeSize (minExtPairNN,maxExtPairNN)+    [hpl,hpc,sp,tp,il,ia,ip,bl,bt,bu,mbc,mbranched,mhelix,munpaired,dst,intermol] = splitXs+      [ maxLength+1   -- hairpin length+      , rsExtPairNN   -- hairpin close+      , rs2ExtPairs   -- stem pair+      , rsTriplet     -- triplet pair+      , maxLength+1   -- interior loop length+      , maxLength+1   -- interior loop asymmetry+      , rsExtPairNN   -- interior pair+      , maxLength+1   -- bulge length+      , rsTriplet     -- bulge triplet+      , rsExtPair     -- normal bulge with non-overlapping nucs+      , rsExtPairNN   -- multibranch pair+      , 1             -- multibranched score+      , 1             -- helix in multibranch score+      , 1             -- unpaired nucleotide score in multibranch+      , maxDistance+1 -- pair long distance+      , 1+      ]+      xs++-- | split up a list accordings to given lengths++splitXs :: [Int] -> [Double] -> [[Double]]+splitXs [k] xs+  | length xs == k = [xs]+  | otherwise      = error "splitXs encountered wrong key length on last element"+splitXs (k:ks) xs  = let (here,rest) = splitAt k xs in here : splitXs ks rest

@@ -0,0 +1,110 @@+{-# LANGUAGE RecordWildCards #-}++-- | Passive-aggressive optimization. Mainly based on:+--+-- Zakov, Shay and Goldberg, Yoav and Elhaded, Michael and Ziv-Ukelson, Michal+-- "Rich Parameterization Improves RNA Structure Prediction"+-- RECOMB 2011+--+-- and+--+-- Crammer, Koby and (et al)+-- "Online Passive-Aggressive Algorithms"+-- Journal of Machine Learning Research (2006)+--+-- TODO as always: move out of here and put in its own library++module BioInf.PassiveAggressive where++import qualified Data.Vector.Unboxed as VU+import Data.List as L+import Data.Set as S+import Control.Arrow+import Data.Map as M++import Biobase.TrainingData+import BioInf.Keys++import qualified BioInf.Params as P+import qualified BioInf.Params.Import as P+import qualified BioInf.Params.Export as P++import Statistics.ConfusionMatrix+import Statistics.PerformanceMetrics++import Data.PrimitiveArray as PA+import Data.PrimitiveArray.Ix++++-- | Default implementation of P/A.++defaultPA :: Double -> P.Params -> TrainingData -> (P.Params,Double,Double,[(Int,Double)])+defaultPA aggressiveness params td@TrainingData{..}+--  | kScore+0.02 < pScore = error $ show (pScore,kScore,pOnly,kOnly,tau,changes)+--  | pScore > kScore = error "foo"+  | L.null $ pOnly++kOnly = (params,0,1,[])+  | sty >= 0.999 = (params,0,1,[])+--  | otherwise = error $ show (pOnly,kOnly,kScore,pScore,tau,changes)+  | otherwise = ( heck+                , tau+                , sty+                , changes+                )+  where+    new1 = P.fromList . VU.toList $ VU.accum (\v pm -> v+pm) cur changes+    new2 = P.fromList . VU.toList $ VU.accum (\v pm -> v+pm) (VU.fromList $ P.toList new1) []+    heck+      | P.toList new1 == P.toList new2 = new1+      | otherwise = error "fuck" -- ignore this line ;-) (impressive, that you actually read this code!)+    pFeatures = featureVector primary predicted+    kFeatures = featureVector primary secondary+    pOnly = pFeatures L.\\ kFeatures+    kOnly = kFeatures L.\\ pFeatures+    numChanges = genericLength $ pOnly ++ kOnly+    cur = VU.fromList . P.toList $ params+    pScore = sum . L.map (cur VU.!) $ pFeatures+    kScore = sum . L.map (cur VU.!) $ kFeatures+    pScore2 = sum . L.map (cur VU.!) $ pFeatures+    kScore2 = sum . L.map (cur VU.!) $ kFeatures+    tau+      | abs ((kScore2 - pScore2) - (kScore-pScore)) > 0.1+      = error $ "abs: \n" ++ z+      | val < 0      = error $ "val<0 \n" ++ z+      | sty >= 0.999 = 0+      | otherwise    = val -- 100 * val+      where+        val = min aggressiveness $ (kScore - pScore + sqrt (1-sty)) / (numChanges ^ 2)+        z = show ( kScore,pScore,kScore - pScore+                 , kScore2,pScore2, kScore2 - pScore2+                 ) ++ "\n" ++ primary ++ "\n" ++ (concat $ intersperse "\n" comments) ++ "\n" +++                 ( L.concatMap (\x -> show x ++ "\n")+                 $ L.map (fun &&& (cur VU.!)) kOnly ) ++ " <<<\n" +++                 ( L.concatMap (\x -> show x ++ "\n") +                 $ L.map (fun &&& (cur VU.!)) pOnly ) ++ " ALL\n" +++                 ( L.concatMap (\x -> show x ++ "\n")+                 $ L.map (fun &&& (cur VU.!)) pFeatures)+        fun i = let lol = vks M.! i in (lol, fun2 lol)+        fun2 hc@(HairpinClose k) = P.hairpinClose params PA.! k+        fun2 hl@(HairpinLength l) = P.hairpinLength params PA.! l+        fun2 _ = (-1)+    sty = case fmeasure (mkConfusionMatrix td) of -- currently optimizing using F_1+            Left  _ -> 1+            Right v -> v+    changes = zip kOnly (repeat $ negate tau) ++ zip pOnly (repeat tau)++-- | Pull in the statistical interface. From the confusion matrix, we+-- automagically get everything we need.+--+-- NOTE Unfortunately, StatisticalMethods has heavy dependencies.++instance MkConfusionMatrix TrainingData where+  mkConfusionMatrix TrainingData{..} = ConfusionMatrix+    { fn = Right . fromIntegral . S.size $ k `S.difference` p+    , fp = Right . fromIntegral . S.size $ p `S.difference` k+    , tn = Right . fromIntegral $ allPs - S.size (k `S.union` p)+    , tp = Right . fromIntegral . S.size $ k `S.intersection` p+    } where+        k = S.fromList secondary+        p = S.fromList predicted+        allPs = ((length primary) * (length primary -1)) `div` 2

@@ -0,0 +1,265 @@+{-# LANGUAGE BangPatterns #-}++-- | The RNAwolf folding algorithm, version 1.9. We now have full stacking and+-- rich parameters everywhere. In general, most parameters closely follow what+-- we have for ViennaRNA 1.8 but with extended RNA secondary structures,+-- instead of canonicals only. Further refinements of the parameter system will+-- follow.+--+-- TODO right now, 1-diagrams only, 2-diagrams come back in a few days. I want+-- to be sure that the full stacking approach does not introduce subtle bugs.+--+-- TODO recast all fZZZ functions for folding to actually fuse on minimum/fZZZ.+--+-- TODO VU.! -> VU.unsafeIndex+--+-- TODO possibly very big TODO: is this being optimized? : fold $ g z where g z+-- = if z==True then [1..10] else []. If this is not optimized, we should+-- change all functions below in a way that allows optimization. (I dont think+-- fusion can fire on these objects...)+--+--   TODO rewrite minimumVU to accept "Either" ctors and specialize on them.+--   "Left" to be used for strange errors, "Right" for correct streams++++module BioInf.RNAwolf+  ( rnaWolf+  , rnaWolfBacktrack+  ) where++import Control.Monad+import Control.Monad.ST+import qualified Data.Vector.Unboxed as VU+import Control.Arrow++import Data.PrimitiveArray+import Data.PrimitiveArray.Ix+import Biobase.Primary+import Biobase.Secondary++import BioInf.Params+import BioInf.RNAwolf.Types+import qualified BioInf.RNAwolf.Bulge as Bul+import qualified BioInf.RNAwolf.Extern as Ext+import qualified BioInf.RNAwolf.Hairpin as Hp+import qualified BioInf.RNAwolf.Interior as Int+import qualified BioInf.RNAwolf.Multibranched as Mul+import qualified BioInf.RNAwolf.Stem as Stem+import qualified BioInf.RNAwolf.TripletBulge as TrB+import qualified BioInf.RNAwolf.TripletStem as TrS++import Debug.Trace++++-- * Folding++-- | Wrapper around the state monad.++rnaWolf :: Params -> Primary -> Tables+rnaWolf ps inp = {-# SCC "rnaWolf" #-} runST $ foldST ps inp++-- | Folding magic. In principle, this is not more complicated than+-- Nussinov-style folding.++foldST :: Params -> Primary -> ST s Tables+foldST ps inp = do+  let n = VU.length inp -1+  let imi = map fst . filter ((==nIMI).snd) $ zip [0..] (VU.toList inp)+  (eStemM,eStem) <- second EStem `fmap` mkExtTable n+  (nStemM,nStem) <- second NStem `fmap` mkTable n+  (nInteM,nInte) <- second NInte `fmap` mkTable n -- interior loop helper table+  (nMultM,nMult) <- second NMult `fmap` mkTable n -- multibranched loop helper table+  (nBulgM,nBulg) <- second NBulg `fmap` mkTable n -- bulge loop helper table+  (nMbrM ,nMbr ) <- second NMbr  `fmap` mkTable n+  (nMbr1M,nMbr1) <- second NMbr1 `fmap` mkTable n+  (nExtnM,nExtn) <- second NExtn `fmap` mkTableWith 0 n+  (nBulgLoopM,nBulgLoop) <- second NBulgLoop `fmap` mkTable n+  (nInteLoopM,nInteLoop) <- second NInteLoop `fmap` mkTable n -- interior loop helper table+  (nMultLoopM,nMultLoop) <- second NMultLoop `fmap` mkTable n -- multibranched loop helper table+  -- This version of the (i,j) pair generation walks along the diagonals. It is+  -- required to calculate this way, as otherwise the shared nucleotide+  -- variants will fail.+  forM_ (mkIJ n) $ \(i,j) -> do+    forM_ citr $ \ct -> forM_ wsh $ \eI -> forM_ wsh $ \eJ -> do+      -- weak table (everything is weak)+      let vHairpin  = minimumVU $ Hp.fHairpin imi    ps inp           i j ct eI eJ+      let vStem     = minimumVU $ Stem.fStem         ps inp eStem     i j ct eI eJ+      let vInterior = minimumVU $ Int.fInteriorOuter ps inp nInteLoop i j ct eI eJ+      let vMlClose  = minimumVU $ Mul.fMlClose       ps inp nMultLoop i j ct eI eJ+      let vBulge    = minimumVU $ Bul.fBulgeOuter    ps inp nBulgLoop i j ct eI eJ+      writeM eStemM ((i,j),(ct,eI,eJ)) $ minimum [vHairpin,vStem,vInterior,vMlClose,vBulge] -- FIXME vTStem+    -- fill stem table that ignores extended annotations+    writeM nStemM (i,j) . minimumVU $ Stem.fNstem ps inp eStem i j+    -- fill the inner interior table+    writeM nInteM (i,j) . minimumVU $ Int.fInteriorInner ps inp eStem i j+    -- fill the interior LOOP table (includes everything except the closing pair)+    writeM nInteLoopM (i,j) . minimumVU $ Int.fInteriorLoop ps inp nInte i j+    -- fill multibranch helper table+    writeM nMultM (i,j) . minimumVU $ Mul.fMlHelix ps inp eStem i j+    -- multibranched close helper table (should improve speed for MLs by 2x3x3)+    writeM nMultLoopM (i,j) . minimumVU $ Mul.fMlLoop ps inp nMbr nMbr1 i j+    -- fill bulge close helper table+    writeM nBulgM (i,j) . minimumVU $ Bul.fBulgeInner ps inp eStem i j+    -- fill bulge LOOP table+    writeM nBulgLoopM (i,j) . minimumVU $ Bul.fBulgeLoop ps inp nBulg i j+    -- one or more multibranched stems+    let vUnpaired = minimumVU $ Mul.fUnpairedRight ps inp nMbr i j+    let vStem = minimumVU $ Mul.fMlStem ps inp nMult i j+    let vStems = minimumVU $ Mul.fMlStems ps inp nMbr nMult i j+    writeM nMbrM (i,j) $ minimum [vUnpaired, vStem, vStems]+    -- exactly one multibranched stem+    let vUnpaired = minimumVU $ Mul.fUnpairedRight1 ps inp nMbr1 i j+    let vStem = minimumVU $ Mul.fMl1Stem ps inp nMult i j+    writeM nMbr1M (i,j) $ minimum [vUnpaired,vStem]+  let j=n+  forM_ [n-2,n-3..0] $ \i -> do+    let unp = minimumVU $ Ext.fLeftUnpaired ps inp nExtn i j+    let es  = minimumVU $ Ext.fStem ps inp nStem i j+    let esl = minimumVU $ Ext.fStems ps inp nStem nExtn i j+    let one = minimumVU $ Ext.fOne ps inp i j+    writeM nExtnM (i,j) $ minimum [unp,esl,es,one]+  return  ( eStem+          , nStem+          , nInte+          , nInteLoop+          , nBulg+          , nBulgLoop+          , nMult+          , nMbr+          , nMbr1+          , nMultLoop+          , nExtn)++++-- * Backtracking++-- | Given parameters, input, score band, and filled tables we can backtrack.+--+-- NOTE the order in which backtracking for individual functions is performed,+-- is important. In case of ties in energy, the first result is taken. This+-- should be considered!+--+-- [1] We consider unpaired stretches always first. This is kind of arbitrary.+--+-- [2] extended stems always come last. This is because they can potentially+-- introduce many co-optimal structures before they are all discarded.+--+-- TODO all the crap in comments are bug-fix backtracking options.++rnaWolfBacktrack :: Params -> Primary -> Double -> Tables -> [([ExtPairIdx],Double)]+rnaWolfBacktrack ps inp delta ( estem@(EStem eStem)+                              , nstem@(NStem nStem)+                              , ninte@(NInte nInte)+                              , ninteloop@(NInteLoop nInteLoop)+                              , nbulg@(NBulg nBulg)+                              , nbulgloop@(NBulgLoop nBulgLoop)+                              , nmult@(NMult nMult)+                              , nmbr@(NMbr nMbr)+                              , nmbr1@(NMbr1 nMbr1)+                              , nmultloop@(NMultLoop nMultLoop)+                              , nextn@(NExtn nExtn)+                              )+  | otherwise = let finalScore = nExtn ! (0,n)+                in filter ((<=0).snd) . map (second (\z -> finalScore + delta -z)) $ btE 0 n delta+  where+    btE i j d = -- trace (show ("btE",i,j,d)) $+      Ext.btOne ps inp nextn i j d ++ -- [1]+      Ext.btLeftUnpaired ps inp nextn btE i j d +++      Ext.btStem ps inp nextn nstem btNS i j d +++      Ext.btStems ps inp nstem nextn btNS btE i j d+    btNS i j d =+      Stem.btNstem ps inp nstem estem btES i j d+    btES :: Int -> Int -> CTisomerism -> Edge -> Edge -> Double -> [([ExtPairIdx],Double)]+    btES i j ct eI eJ d = -- trace (show ("btES",i,j,ct,eI,eJ,d)) $+      Hp.btHairpin ps inp estem i j ct eI eJ d +++      Int.btInteriorOuter ps inp estem ninteloop btILoop i j ct eI eJ d +++      Bul.btBulgeOuter ps inp estem nbulgloop btBULoop i j ct eI eJ d +++      Mul.btMlClose ps inp estem nmultloop btMultLoop i j ct eI eJ d +++      Stem.btStem ps inp estem btES i j ct eI eJ d -- [2]+    btILoop i j d = -- trace (show ("btILoop",i,j,d)) $+      Int.btInteriorLoop ps inp ninteloop ninte btIL i j d+    btIL i j d = -- trace (show ("btIL",i,j,d)) $+      Int.btInteriorInner ps inp ninte estem btES i j d+    btBULoop i j d = -- trace (show ("btBULoop",i,j,d)) $+      Bul.btBulgeLoop ps inp nbulgloop nbulg btBU i j d+    btBU i j d = -- trace (show ("btBU",i,j,d)) $+      Bul.btBulgeInner ps inp nbulg estem btES i j d+    btMH i j d = -- trace (show ("btMH",i,j,d)) $+      Mul.btMlHelix ps inp nmult estem btES i j d+    btMultLoop i j d =+      Mul.btMlLoop ps inp nmultloop nmbr nmbr1 btM btM1 i j d+    btM i j d = {- trace (show ("btM",i,j,d)) $ -}+      Mul.btUnpairedRight ps inp nmbr btM i j d +++      Mul.btMlStem ps inp nmbr nmult btMH i j d +++      Mul.btMlStems ps inp nmbr nmult btM btMH i j d+    btM1 i j d = let ehere = nMbr1!(i,j) in+      Mul.btUnpairedRight1 ps inp nmbr1 btM1 i j d +++      Mul.btMl1Stem ps inp nmbr1 nmult btMH i j d++    newD d here next = d - (next - here)+    testD d = d>=0+    n = VU.length inp -1+    epsilon = 0.001+    imi = map fst . filter ((==nIMI).snd) $ zip [0..] (VU.toList inp)++++-- * Helper functions++-- | Given an unboxed vector with (index,value) elements, return the minimum+-- over the values.+--+-- TODO with vector-0.7.2 / vector-0.8, rewrite using "snd . unzip" (or not,+-- see next todo)+--+-- TODO http://trac.haskell.org/vector/ticket/51++minimumVU xs = VU.foldl' (\(!acc) (!k,!v) -> min acc v) 999999 xs+{-# INLINE minimumVU #-}++-- | Create 2d-tables, initialized with "infinity"+--+-- TODO use (infinity :: Energy)++mkTable n = mkTableWith 9999999 n++-- | Create 2d-tables, initialized with 'z'++mkTableWith z n = do+  tM <- fromAssocsM (0,0) (n,n) z []+  t  <- unsafeFreezeM tM+  return (tM,t)++-- | 2d-tables with extended information.++mkExtTable n = mkExtTableWith 9999999 n++-- | 2d-tables with extended information.++mkExtTableWith z n = do+  tM <- fromAssocsM ((0,0),(cis,wc,wc)) ((n,n),(trans,hoogsteen,hoogsteen)) z []+  t  <- unsafeFreezeM tM+  return (tM,t)++-- | Produces indices in correct diagonal order.+--+-- TODO this is a stupid way to create the indices...++mkIJ n = [ (i,j) | d <- [0..n], j<-[n,n-1..0], let i=j-d, j>=0, i>=0 ]++++-- * Types+++++-- * debugging++trc k x = trace (show (k,x)) x+trc' k x = trace (show k) x+trci' c k x = if c then trace (show k) x else x+

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@@ -0,0 +1,103 @@+name:           RNAwolf+version:        0.3.0.0+author:         Christian Hoener zu Siederdissen, Stephan H Bernhart, Peter F Stadler, Ivo L Hofacker+copyright:      Christian Hoener zu Siederdissen, 2010-2011+homepage:       http://www.tbi.univie.ac.at/software/rnawolf/+maintainer:     choener@tbi.univie.ac.at+category:       Bioinformatics+license:        GPL-3+license-file:   LICENSE+build-type:     Simple+stability:      experimental+cabal-version:  >= 1.6.0+synopsis:+                RNA folding with non-canonical basepairs and base-triplets.+description:+                The algorithm implemented here-in provides extended RNA+                secondary structure prediction. Each predicted nucleotide+                pairing is extended with an annotation describing which of+                three nucleotide edges is engaged in the pairing. In addition,+                each nucleotide may be engaged in more than one pairing.+                .+                .+                The algorithm is described in+                .+                Hoener zu Siederdissen C, Bernhart SH, Stadler PF, Hofacker IL,+                .+                "A Folding Algorithm for Extended RNA Secondary Structures",+                .+                Bioinformatics (2011) 27 (13), i129-136+                .+                <http://www.tbi.univie.ac.at/software/rnawolf/>+                .+                .+                Please note that "experimental" does mean experimental. We are+                mostly concerned with determining a good set of (heuristic)+                rules for run-time reduction currently. This version does+                include stacking and is able to fold sequences of a few hundred+                nucleotides in seconds.+                .+                Triplet calculations will come back with the next version (in a+                few days). The recursions require a number of changes to keep+                the runtimes down (as has been done for the extended loops+                without triplets).+                .+                /We have recently split the Biohaskell libraries into smaller+                individual libraries. In addition, stacking, intermediate+                arrays, fusion and newtype-wrapping did require a number of+                changes. Please send a mail, if you encounter strange behaviour+                or bugs./++Flag llvm+  description: build using llvm backend+  default: False++library+  build-depends:+    base >= 4 && < 5,+    bytestring,+    containers,+    deepseq,+    directory,+    parallel,+    random,+    vector,+    PrimitiveArray,+    BiobaseXNA,+    BiobaseTrainingData == 0.1.*,+    StatisticalMethods+  exposed-modules:+    BioInf.Keys+    BioInf.Params+    BioInf.Params.Export+    BioInf.Params.Import+    BioInf.PassiveAggressive+    BioInf.RNAwolf+  ghc-options:+    -O2+  if flag(llvm)+    ghc-options:+      -fllvm++executable RNAwolfTrain+  build-depends:+    cmdargs == 0.7.*+  main-is:+    RNAwolfTrain.hs+  ghc-options:+    -O2 -rtsopts+  if flag(llvm)+    ghc-options:+      -fllvm++executable RNAwolf+  build-depends:+    cmdargs == 0.7.*+  main-is:+    RNAwolf.hs+  ghc-options:+    -O2 -rtsopts+  if flag(llvm)+    ghc-options:+      -fllvm+

@@ -0,0 +1,86 @@+{-# LANGUAGE RecordWildCards #-}+{-# LANGUAGE DeriveDataTypeable #-}++-- | RNAwolf extended secondary structure folding program. This is an extended+-- version of the algorithm first described in:++--  Hoener zu Siederdissen, Christian and Bernhart, Stephan H. and Stadler,+--  Peter F. and Hofacker, Ivo L.+--  "A Folding Algorithm for Extended RNA Secondary Structures"+--  Bioinformatics, 2011++--  http://www.tbi.univie.ac.at/software/rnawolf/++module Main where++import System.Console.CmdArgs+import Control.Monad+import Text.Printf++import Biobase.Primary++import BioInf.RNAwolf+import BioInf.RNAwolf.Types+import BioInf.Params as P++import Data.PrimitiveArray++++main :: IO ()+main = do+  o@Options{..} <- cmdArgs options+  when (null inDB) $ error "you need to give a database"+  db <- fmap read $ readFile inDB+  xs <- fmap lines $ getContents+  mapM_ (foldLine o db) xs+  return ()++foldLine :: Options -> Params -> String -> IO ()+foldLine Options{..} p inp = do+  let pri = mkPrimary inp+  let ts = rnaWolf p pri+  let bt = take coopt $ rnaWolfBacktrack p pri subopt ts+  printX inp ts+  putStrLn inp+  forM_ bt $ \(pairs,score) -> do+    printf "%s %7.4f\n" (simpleViewer inp pairs) score+    forM_ pairs $ \((i,j),ext) -> do+      printf "  %4d %4d %s\n" i j (showX ext)+  return ()++printX inp (_,_,_,_,_,_,_,_,_,_,NExtn n) = print $ n!(0,length inp -1)++showX (ct,ei,ej) = show ct ++ show ei ++ show ej++-- * options++data Options = Options+  { inDB :: FilePath+  , subopt :: Double+  , coopt :: Int+  } deriving (Show,Data,Typeable)++options = Options+  { inDB = "" &= help "specify parameter database"+  , subopt = 0.00001 &= help "calculate suboptimal in this range (returns all suboptimal results)"+  , coopt = 1 &= help "how many co-optimal structures to return"+  }++-- | simple viewer...++simpleViewer s xs = foldl f (replicate (length s) '.') xs where+  f str ((i,j),_) = upd ')' j $ upd '(' i str+  upd c k str+    |  l=='('+    && c=='('+    = pre ++ "<" ++ post+    |  l==')'+    && c==')'+    = pre ++ ">" ++ post+    | l/='.' = pre ++ "X" ++ post+    | otherwise = pre ++ [c] ++ post+    where+      pre = take k str+      l = head $ drop k str+      post = drop (k+1) str

@@ -0,0 +1,231 @@+{-# LANGUAGE BangPatterns #-}+{-# LANGUAGE RecordWildCards #-}+{-# LANGUAGE DeriveDataTypeable #-}++-- | This program trains a parameter database for RNAwolf. The user has to take+-- care to only give appropriate training data to the optimizer. The most+-- important rule is to not give any pseudoknotted data. The small helper+-- program "MkTrainingData" should be able to take care of this.+-- "MkTrainingData" is part of BiobaseTrainingData.+--+-- We currently train using an optimization scheme described in:+--+-- Zakov, Shay and Goldberg, Yoav and Elhaded, Michael and Ziv-Ukelson, Michal+-- "Rich Parameterization Improves RNA Structure Prediction"+-- RECOMB 2011+--+-- NOTE It is likely that this we extended with other methods in the (near)+-- future, again. Especially the convex-optimization-based (even though the+-- Zakov et al. scheme is derived from cvx-methods) system seems promising.+-- Right now, this version simply is faster...+--+-- TODO update the DB within IO to save creation / destruction of Params in+-- each iteration+--+-- TODO re-allow co-folding++module Main where++import Control.Monad+import System.Console.CmdArgs+import Text.Printf+import Data.List+import Data.Function (on)+import System.Random+import Control.Applicative+import Data.Ord+import Control.Arrow+import qualified Data.Vector.Unboxed as VU+import qualified Data.Map as M++import Biobase.Primary+import Biobase.TrainingData+import Biobase.TrainingData.Import+import Statistics.ConfusionMatrix+import Statistics.PerformanceMetrics+import Biobase.Secondary.Diagrams++import BioInf.Params as P+import BioInf.Params.Export as P+import BioInf.Params.Import as P+import BioInf.RNAwolf+import BioInf.PassiveAggressive+import BioInf.Keys++++-- | Entry function++main :: IO ()+main = do+  o@Options{..} <- cmdArgs options+  when (null outDB) $+    error "please set --outdb"+  when (null trainingData) $+    error "please give at least one training data file with --trainingdata"+  -- read training data+  xs <- id+      . fmap (filter (\TrainingData{..} ->+                       True+--                       length primary > 20 &&+                       && all (/='&') primary -- no co-folding right now+--                       length secondary > 5 -- at least 5 basepairs+                     )+             )+      . fmap (filter (lengthFlt maxLength))+      . fmap concat+      $ mapM fromFile trainingData+  -- read database or use zero-based parameters+  dbIn <- maybe (return . P.fromList . map (+0.01) . P.toList $ P.zeroParams) (fmap read . readFile) inDB+  -- dbOut <- foldM (foldTD $ length xs) dbIn $ zip xs [1..]+  (dbOut,_) <- foldM (doIteration o xs) (dbIn,[]) [1..numIterations]+  writeFile outDB $ show dbOut++-- | length filter for training data++lengthFlt l TrainingData{..} = maybe True (length primary <) l++-- | iterations to go++doIteration :: Options -> [TrainingData] -> (P.Params,[Double]) -> Int -> IO (P.Params,[Double])+doIteration o@Options{..} xs' (!p,rhos) !k = do+  xs <- return xs' -- shuffle xs'+  when (Iteration `elem` verbose) $ do+    putStrLn "\n======================================"+    printf "# INFO iteration: %4d / %4d starting\n"+            k+            numIterations+    putStrLn "======================================\n"+  (newp,totalchange,rhosum,cooptimality) <- foldM (foldTD o $ length xs) (p,0,0,0) $ zip xs [1..]+  let drctch = sum $ zipWith (\x y -> abs $ x-y) (P.toList p) (P.toList newp)+  let rho = rhosum / genericLength xs+  when (Iteration `elem` verbose) $ do+    putStrLn "\n======================================"+    printf "# INFO iteration: %4d / %4d ended\n"+            k+            numIterations+    printf "# INFO sum tau: %7.2f, total change: %7.2f, avg.rho: %4.2f, avg.coopt: %5d\n"+            totalchange+            drctch+            rho+            (cooptimality `div` length xs)+    putStr "# INFO history:"+    zipWithM_ (printf " %4d %4.2f") [1::Int ..] $ rhos++[rho]+    putStrLn ""+    print $ sum $ map abs $ P.toList newp+    print $ minimum $ P.toList newp+    print $ maximum $ P.toList newp+    putStrLn "======================================\n"+  writeFile (printf "%04d.db" k) . show $ newp+  return (newp,rhos++[rho])++-- | Fold one 'TrainingData', print some info and stuff++foldTD :: Options -> Int -> (P.Params,Double,Double,Int) -> (TrainingData,Int) -> IO (P.Params,Double,Double,Int)+foldTD o@Options{..} total (!p,accChange,rhosum,cooptimality) (td@TrainingData{},k) = do+  print $ length $ primary td+  let pri = mkPrimary $ primary td+  let tables = rnaWolf p pri+  let bs' = let f x = td{predicted = x} in map (first f) . take (maybe 1 id maxLoss) $ rnaWolfBacktrack p pri 0.00001 tables+  let bs = pure $ minimumBy (comparing (sensitivity . mkConfusionMatrix . fst)) bs'+  case bs of+    [(x,ddd)] -> do+      let fV = featureVector (primary x) (predicted x)+      let pVU = VU.fromList . P.toList $ p+      let sss = map (pVU VU.!) fV+      when (abs (ddd - sum sss) > 0.0001) $ do+        printf "SCORE DIFFERENCE, backtracking score: %f, sum features: %f\n"  ddd   (sum sss) -- , " ", map (vks M.!) fV, " ", sss)+        mapM_ print $ zip (map (vks M.!) fV) sss+        print "You have found a bug, now write choener to have him fix it!"+      let (newp,tau,rho,votes) = defaultPA aggressiveness p+                                $ x { comments = [ show ddd+                                                 , simpleViewer (primary x) $ secondary x+                                                 , simpleViewer (primary x) $ predicted x+                                                 , show $ predicted x+                                                 ]+                                    }+      when (Single `elem` verbose) $ do+        printf "# INFO currently at: %4d / %4d (s-tau: %7.4f, rho: %5.2f, changes: %4d)\n"+                k+                total+                (tau * genericLength votes)+                rho+                (length votes)+      when (Detailed `elem` verbose) $ do+        putStrLn $ take (length $ primary x) . concatMap show . concat . repeat $ [0..9]+        putStrLn $ primary x+        putStrLn $ simpleViewer (primary x) $ secondary x+        putStrLn $ simpleViewer (primary x) $ predicted x+        when (AllPairs `elem` verbose) $ do+          mapM_ print $ predicted x+      when (errorOnError && abs (ddd - sum sss) > 0.0001) $ error "error-ing out"+      return (newp,accChange + tau * genericLength votes, rhosum+rho, cooptimality + length bs')+    _       -> error $ "no prediction for: " ++ show td++-- | simple viewer...++simpleViewer s xs = foldl f (replicate (length s) '.') xs where+  f str ((i,j),_) = upd ')' j $ upd '(' i str+  upd c k str+    |  l=='('+    && c=='('+    = pre ++ "<" ++ post+    |  l==')'+    && c==')'+    = pre ++ ">" ++ post+    | l/='.' = pre ++ "X" ++ post+    | otherwise = pre ++ [c] ++ post+    where+      pre = take k str+      l = head $ drop k str+      post = drop (k+1) str++++-- ** program options++data Options = Options+  { inDB :: Maybe FilePath+  , outDB :: FilePath+  , trainingData :: [FilePath]+  , maxLength :: Maybe Int+  , numIterations :: Int+  , verbose :: [Verbose]+  , maxLoss :: Maybe Int+  , aggressiveness :: Double+  , errorOnError :: Bool+  } deriving (Show,Data,Typeable)++data Verbose+  = Iteration+  | Single+  | Detailed+  | AllPairs+  deriving (Show,Data,Typeable,Eq)++options = Options+  { inDB  = Nothing &= help "database from which to continue optimizing; if none is given, start from scratch"+  , outDB = ""      &= help "new database to write out"+  , trainingData = [] &= help "training data elements to read"+  , maxLength = Nothing &= help "[dev] only train using elements of length or less"+  , numIterations = 50 &= help "how many optimizer iterations"+  , verbose = [Iteration] &= help "select verbosity options: single, iteration, detailed (all switch on different verbosity options)"+  , maxLoss = Nothing &= help "use maxLoss optimization instead of prediction-based, requires maximal number of instances to search for maxLoss (default: not used)"+  , aggressiveness = 1 &= help "maximal tau for each round"+  , errorOnError = False &= help "error out if an error is detected (default: false)"+  }++++-- ** helper functions++-- | simple shuffling of a list++shuffle :: [a] -> IO [a]+shuffle [] = return []+shuffle xs = do+  r <- getStdRandom (randomR (0,length xs -1))+  let (hs,ts) = splitAt r xs+  let y = head ts+  ys <- shuffle $ hs ++ tail ts+  return $ y : ys

@@ -0,0 +1,2 @@+import Distribution.Simple+main = defaultMain