module HLearn.Models.Distributions.Univariate.Binomial
where
import HLearn.Algebra
import HLearn.Models.Distributions.Common
import HLearn.Models.Distributions.Univariate.Internal.Moments
import HLearn.Models.Distributions.Visualization.Gnuplot
import qualified Statistics.Distribution as S
import Statistics.Distribution.Binomial
newtype Binomial prob dp = Binomial { bmoments :: (Moments3 dp) }
deriving (Read,Show,Eq,Ord,Monoid,Group)
instance (Num dp) => HomTrainer (Binomial prob dp) where
type Datapoint (Binomial prob dp) = dp
train1dp dp = Binomial $ train1dp dp
instance (Num dp) => Probabilistic (Binomial prob dp) where
type Probability (Binomial prob dp) = prob
instance (Floating prob) => PDF (Binomial Double Int) where
pdf (Binomial dist) dp = S.probability (binomial n p) dp
where
n = bin_n $ Binomial dist
p = bin_p $ Binomial dist
bin_n :: Binomial Double Int -> Int
bin_n (Binomial dist) = round $ ((fromIntegral $ m1 dist :: Double) / (fromIntegral $ m0 dist)) / (bin_p $ Binomial dist)
bin_p :: Binomial Double Int -> Double
bin_p (Binomial dist) = ((fromIntegral $ m1 dist) / (fromIntegral $ m0 dist)) + 1 (fromIntegral $ m2 dist)/(fromIntegral $ m1 dist)
instance
( PDF (Binomial prob dp)
, Show prob
, Show dp
, Ord dp
, Ord prob
, Num prob
, Integral dp
) => PlottableDistribution (Binomial prob dp)
where
plotType _ = Points
samplePoints dist = [min..max]
where
min = 0
max = maximum [20,floor $ 3*mu]
mu = 5