{-# LANGUAGE TypeSynonymInstances #-} {-# LANGUAGE FlexibleInstances #-} {- | Factors -} module Bayes.Factor( -- * Factor Factor(..) , isomorphicFactor , normedFactor , displayFactorBody , changeFactorInFunctor , FactorContainer(..) -- * Set of variables , Set(..) , BayesianDiscreteVariable(..) -- * Implementation , Vertex(..) -- ** Discrete variables and instantiations , DV(..) , TDV --, DVSet(..) , DVI , DVISet , tdvi , setDVValue , instantiationValue , instantiationVariable , variableVertex , (=:) , forAllInstantiations , factorFromInstantiation ) where import Data.Maybe(fromJust) import Control.Monad import Bayes.PrivateTypes import Bayes.Tools import qualified Data.Vector.Unboxed as V import Text.PrettyPrint.Boxes hiding((//)) --import Debug.Trace --debug a = trace ("\nDEBUG\n" ++ show a ++ "\n") a -- | Change factor in a functor (only factor values should have been changed) -- It assumes that the variables of a factor are enough to identify it. -- If the functor is containing several factors with same set of variables then it -- won't give a meaningful result. -- So it should be used only on functor derived from a Bayesian Network. changeFactorInFunctor :: (Factor f, Functor m) => f -> m f -> m f changeFactorInFunctor f g = let replaceFactor cf | cf `isUsingSameVariablesAs` f = f | otherwise = cf in fmap replaceFactor g -- | Structure containing factors which can be replaced. -- It is making sense when the factors are related to the nodes of a Bayesian -- network. class FactorContainer m where changeFactor :: Factor f => f -> m f -> m f instance FactorContainer [] where changeFactor = changeFactorInFunctor -- | A vertex associated to another value (variable dimension, variable value ...) class LabeledVertex l where variableVertex :: l -> Vertex -- | Convert a variable instantation to a factor -- Useful to create evidence factors factorFromInstantiation :: Factor f => DVI -> f factorFromInstantiation (DVI dv a) = let setValue i = if i == a then 1.0 else 0.0 in fromJust . factorWithVariables [dv] . map (setValue) $ [0..dimension dv-1] instance LabeledVertex DVI where variableVertex (DVI v _) = variableVertex v instance LabeledVertex DV where variableVertex (DV v _) = v -- | Norm the factor normedFactor :: Factor f => f -> f normedFactor f = factorDivide f (factorNorm f) -- | A factor as used in graphical model -- It may or not be a probability distribution. So it has no reason to be -- normalized to 1 class Factor f where -- | When all variables of a factor have been summed out, we have a scalar isScalarFactor :: f -> Bool -- | An empty factor with no variable and no values emptyFactor :: f -- | Check if a given discrete variable is contained in a factor containsVariable :: f -> DV -> Bool -- | Give the set of discrete variables used by the factor factorVariables :: f -> [DV] -- | Return A in P(A | C D ...). It is making sense only if the factor is a conditional propbability -- table. It must always be in the vertex corresponding to A in the bayesian graph factorMainVariable :: f -> DV factorMainVariable f = let vars = factorVariables f in case vars of [] -> error "Can't get the main variable of a scalar factor" (h:_) -> h -- | Create a new factors with given set of variables and a list of value -- for initialization. The creation may fail if the number of values is not -- coherent with the variables and their levels. -- For boolean variables ABC, the value must be given in order -- FFF, FFT, FTF, FTT ... factorWithVariables :: [DV] -> [Double] -> Maybe f -- | Value of factor for a given set of variable instantitation. -- The variable instantion is like a multi-dimensional index. factorValue :: f -> [DVI] -> Double -- | String representation of a factor value factorStringValue :: f -> [DVI] -> String -- | Position of a discrete variable in te factor (p(AB) is differennt from p(BA) since values -- are not organized in same order in memory) variablePosition :: f -> DV -> Maybe Int -- | Dimension of the factor (number of floating point values) factorDimension :: f -> Int -- | Norm of the factor = sum of its values factorNorm :: f -> Double -- | Scale the factor values by a given scaling factor factorScale :: Double -> f -> f -- | Create a scalar factor with no variables factorFromScalar :: Double -> f -- | Create an evidence factor from an instantiation. -- If the instantiation is empty then we get nothing evidenceFrom :: [DVI] -> Maybe f -- | Test if two factors are coding for the same probability dependence. -- It does not test if the factors are equal (same probabilities) but just -- if they involve the same variables so are linked to the same -- node in the Bayesian network isUsingSameVariablesAs :: f -> f -> Bool -- | Divide all the factor values factorDivide :: f -> Double -> f factorDivide f d = (1.0 / d) `factorScale` f factorToList :: f -> [Double] -- | Multiply factors. factorProduct :: [f] -> f -- | Project out a factor. The variable in the DVSet are summed out factorProjectOut :: [DV] -> f -> f -- | Project to. The variable are kept and other variables are removed factorProjectTo :: [DV] -> f -> f factorProjectTo s f = let alls = factorVariables f s' = alls `difference` s in factorProjectOut s' f -- | Test equality of two factors taking into account the fact -- that the variables may be in a different order. -- In case there is a distinction between conditionned variable and -- conditionning variables (imposed from the exterior) then this -- comparison may not make sense. It is a comparison of -- function of several variables which no special interpretation of the -- meaning of the variables according to their position. isomorphicFactor :: Factor f => f -> f -> Bool isomorphicFactor fa fb = maybe False (const True) $ do let sa = factorVariables fa sb = factorVariables fb va = DVSet sa vb = DVSet sb guard (sa `equal` sb) guard (factorDimension fa == factorDimension fb) guard $ and [factorValue fa ia `nearlyEqual` factorValue fb ia | ia <- forAllInstantiations va] return () {- Following functions are used to typeset the factor when displaying it -} -- | Display a variable name and its size vname :: Int -> DVI -> Box vname vc i = text $ "v" ++ show vc ++ "=" ++ show (instantiationValue i) dispFactor :: Factor f => f -> DV -> [DVI] -> [DV] -> Box dispFactor cpt h c [] = let dstIndexes = allInstantiationsForOneVariable h dependentIndexes = reverse c factorValueAtPosition p = let v = factorStringValue cpt p in text v in vsep 0 center1 . map (factorValueAtPosition . (:dependentIndexes)) $ dstIndexes dispFactor cpt dst c (h@(DV (Vertex vc) i):l) = let allInst = allInstantiationsForOneVariable h in hsep 1 top . map (\i -> vcat center1 [vname vc i,dispFactor cpt dst (i:c) l]) $ allInst displayFactorBody :: Factor f => f -> String displayFactorBody c = let d = factorVariables c h@(DV (Vertex vc) _) = head d table = dispFactor c h [] (tail d) dstIndexes = map head (forAllInstantiations . DVSet $ [h]) -- In P(A | B ...), the dst column is containing the possible values for the -- variables A with a header made of space to be aligned with the other part of the table. -- In the other part of the table, this header is containing the variable values for the other varibles dstColumn = vcat center1 $ replicate (length d - 1) (text "") ++ map (vname vc) dstIndexes in "\n" ++ show d ++ "\n" ++ render (hsep 1 top [dstColumn,table])