commit9e2088b80c86ce3358227d53c897e45c1316c6acparenteb9408953e098ffb4f3592f851c89e65f40d770fAuthor:Jared Tobin <jared@jtobin.ca>Date:Tue, 19 Nov 2013 20:35:21 +1300 Remove old tests.Diffstat:

D | tests/Test.hs | | | 130 | ------------------------------------------------------------------------------- |

1 file changed, 0 insertions(+), 130 deletions(-)diff --git a/tests/Test.hs b/tests/Test.hs@@ -1,130 +0,0 @@ --- Simple examples that demonstrate some measure-fu. - -import Control.Applicative -import Control.Monad -import Data.Vector (singleton) -import Measurable.Core -import Numeric.SpecFunctions -import Statistics.Distribution hiding (mean, variance) -import Statistics.Distribution.Normal -import Statistics.Distribution.Beta -import Statistics.Distribution.ChiSquared -import System.Random.MWC -import System.Random.MWC.Distributions - --- | Some workhorse densities (with respect to Lebesgue measure). -genNormal m v = density $ normalDistr m v -genBeta a b = density $ betaDistr a b -genChiSq d = density $ chiSquared d - --- | A binomial density (with respect to counting measure). -binom p n k - | n <= 0 = 0 - | k < 0 = 0 - | n < k = 0 - | otherwise = n `choose` k * p ^ k * (1 - p) ^ (n - k) - --- | Measures created from densities. Notice that the binomial measure has to --- be treated differently than the measures absolutely continuous WRT Lebesgue --- measure. -normalMeasure m v = fromDensityLebesgue $ genNormal m v -betaMeasure a b = fromDensityLebesgue $ genBeta a b -chiSqMeasure d = fromDensityLebesgue $ genChiSq d -binomMeasure n p = fromDensityCounting (binom p n) [0..n] - --- | Sampling functions. -generateExpSamples n l g = replicateM n (exponential l g) -generateNormalSamples n m v g = replicateM n (normal m v g) - --- | A standard beta-binomial conjugate model. Notice how naturally it's --- expressed using do-notation! -betaBinomialConjugate :: Double -> Double -> Int -> Measure Double Int -betaBinomialConjugate a b n = do - p <- betaMeasure a b - binomMeasure n p - -main :: IO () -main = do - -- Initialize our PRNG. - g <- initialize (singleton 42) - - -- Generate some samples (in practice we'd usually create measures directly - -- from samples). - expSamples <- generateExpSamples 1000 1 g - normSamples <- generateNormalSamples 1000 0 1 g - - -- Create a couple of measures from those. - let observedExpMeasure = fromObservations expSamples - observedNormalMeasure = fromObservations normSamples - - putStrLn "X ~ N(0, 1)" - putStrLn "Y ~ empirical (observed from exponential(1))" - putStrLn "Z ~ empirical (observed from N(0, 1))" - putStrLn "W ~ ChiSquared(5)" - putStrLn "" - - -- We can mingle our empirical measures with those created directly from - -- densities. We can literally just add measures together (there's a - -- convolution happening under the hood). - - let mu = normalMeasure 0 1 + observedExpMeasure - putStrLn $ "E(X + Y): " ++ show (expectation mu) - - -- We can create pushforward/image measures by.. pushing functions onto - -- measures. - -- - -- The pushforward operator happens to be trusty old 'fmap', (as infix, <$>). - - let nu = (cos <$> normalMeasure 0 1) * (sin <$> observedNormalMeasure) - putStrLn $ "E(cos X * sin Z): " ++ show (expectation nu) - - let eta = exp <$> nu - putStrLn $ "E[e^(cos X * sin Z)]: " ++ show (expectation eta) - - -- At present the complexity of each Measure operation seems to *explode*, so - -- you can't do more than a few of them without your machine locking up. I - -- have to look into what could be done to make this reasonably efficient. - -- But hey, experiments and such.. - - let zeta = (exp . tanh) <$> (chiSqMeasure 5 * normalMeasure 0 1) - putStrLn $ "E[e^(tanh (X * W))]: " ++ show (expectation zeta) - - putStrLn "" - - -- We can do probability by just taking the expectation of an indicator - -- function, and there's a built-in cumulative distribution function. - -- - -- P(X < 0) for example. It should be 0.5, but there is some error due to - -- quadrature. - - putStrLn $ "P(X < 0): " ++ show (cdf (normalMeasure 0 1) 0) - - -- Everyone knows that for X ~ N(0, 1), P(0 < X < 1) is about 0.341.. - - putStrLn $ "P(0 < X < 1): " - ++ show (expectation $ 0 `to` 1 <$> normalMeasure 0 1) - - putStrLn "" - - -- The coolest trick of all is that monadic bind is Bayesian inference. - -- Getting posterior predictive expectations & probabilities is thus really - -- declarative. - - putStrLn "X | p ~ binomial(10, p)" - putStrLn "p ~ beta(1, 4)" - - let phi = fromIntegral <$> betaBinomialConjugate 1 4 10 - - putStrLn $ "E(X) (unconditional): " - ++ show (expectation phi) - - putStrLn $ "P(X == 5): " - ++ show (expectation $ 5 `to` 5 <$> phi) - - putStrLn $ "P(1 <= X <= 5): " - ++ show (expectation $ 1 `to` 5 <$> phi) - - putStrLn $ "var(X): " ++ show (variance phi) - - -- Lots of kinks to be worked out, but this is a cool concept. -