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module SModel.MixtureModels where
import Bio.Alphabet
import SModel.Simple
import SModel.Rate
import SModel.MixtureModel
import Tree
import Data.IntMap (IntMap)
import qualified Data.IntMap as IntMap
import qualified Data.Text as T
import Markov (CTMC)
-- Currently we are weirdly duplicating the mixture probabilities for each component.
-- Probably the actual data-type is something like [(Double,\Int->a)] or [(Double,[a])] where all the [a] should have the same length.
-- This would be a branch-dependent mixture
data MixtureModels m = MixtureModels (IntMap Int) [Discrete m]
branch_categories (MixtureModels categories _) = categories
mmm branch_cats m = MixtureModels branch_cats [m]
instance HasAlphabet m => HasAlphabet (MixtureModels m) where
getAlphabet (MixtureModels _ (m:ms)) = getAlphabet m
instance (HasSMap m, CTMC m, HasAlphabet m, RateModel m, HasBranchLengths t, SimpleSModel t m) => SimpleSModel t (MixtureModels m) where
type instance IsReversible (MixtureModels m) = IsReversible m
branch_transition_p (SModelOnTree tree smodel@(MixtureModels branchCats mms) f) b = branch_transition_p (SModelOnTree tree mx f) b
where mx = mms!!(branchCats IntMap.! undirectedName b)
distribution (SModelOnTree tree (MixtureModels _ (m:ms)) f) = distribution (SModelOnTree tree m f)
nBaseModels (SModelOnTree tree (MixtureModels _ (m:ms)) f) = nBaseModels (SModelOnTree tree m f)
stateLetters (SModelOnTree tree (MixtureModels _ (m:ms)) f) = stateLetters (SModelOnTree tree m f)
componentFrequencies (SModelOnTree tree (MixtureModels _ (m:ms)) f) i = componentFrequencies (SModelOnTree tree m f) i
-- No Attribute
getForeground Nothing = 0
-- Attribute with no value
getForeground (Just Nothing) = 1
-- Attribute with value
getForeground (Just (Just text)) = read (T.unpack text) :: Int
foregroundBranches tree key = edgeAttributes tree (T.pack key) getForeground
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