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Hello community,
here is the log from the commit of package ghc-statistics for openSUSE:Factory
checked in at 2022-02-11 23:09:41
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Comparing /work/SRC/openSUSE:Factory/ghc-statistics (Old)
and /work/SRC/openSUSE:Factory/.ghc-statistics.new.1956 (New)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Package is "ghc-statistics"
Fri Feb 11 23:09:41 2022 rev:4 rq:953535 version:0.16.0.1
Changes:
--------
--- /work/SRC/openSUSE:Factory/ghc-statistics/ghc-statistics.changes
2021-09-09 23:08:00.848863684 +0200
+++ /work/SRC/openSUSE:Factory/.ghc-statistics.new.1956/ghc-statistics.changes
2022-02-11 23:11:39.851337240 +0100
@@ -1,0 +2,21 @@
+Sat Jan 8 15:39:32 UTC 2022 - Peter Simons <psim...@suse.com>
+
+- Update statistics to version 0.16.0.1.
+ ## Changes in 0.16.0.0
+
+ * Random number generation switched to API introduced in random-1.2
+
+ * Support of GHC<7.10 is dropped
+
+ * Fix for chi-squared test (#167) which was completely wrong
+
+ * Computation of CDF and quantiles of Cauchy distribution is now numerically
+ stable.
+
+ * Fix loss of precision in computing of CDF of gamma distribution
+
+ * Log-normal and Weibull distributions added.
+
+ * `DiscreteGen` instance added for `DiscreteUniform`
+
+-------------------------------------------------------------------
Old:
----
statistics-0.15.2.0.tar.gz
New:
----
statistics-0.16.0.1.tar.gz
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Other differences:
------------------
++++++ ghc-statistics.spec ++++++
--- /var/tmp/diff_new_pack.hr0qpO/_old 2022-02-11 23:11:40.347338675 +0100
+++ /var/tmp/diff_new_pack.hr0qpO/_new 2022-02-11 23:11:40.351338687 +0100
@@ -1,7 +1,7 @@
#
# spec file for package ghc-statistics
#
-# Copyright (c) 2021 SUSE LLC
+# Copyright (c) 2022 SUSE LLC
#
# All modifications and additions to the file contributed by third parties
# remain the property of their copyright owners, unless otherwise agreed
@@ -19,7 +19,7 @@
%global pkg_name statistics
%bcond_with tests
Name: ghc-%{pkg_name}
-Version: 0.15.2.0
+Version: 0.16.0.1
Release: 0
Summary: A library of statistical types, data, and functions
License: BSD-2-Clause
@@ -28,7 +28,6 @@
BuildRequires: ghc-Cabal-devel
BuildRequires: ghc-aeson-devel
BuildRequires: ghc-async-devel
-BuildRequires: ghc-base-orphans-devel
BuildRequires: ghc-binary-devel
BuildRequires: ghc-data-default-class-devel
BuildRequires: ghc-deepseq-devel
@@ -37,6 +36,7 @@
BuildRequires: ghc-monad-par-devel
BuildRequires: ghc-mwc-random-devel
BuildRequires: ghc-primitive-devel
+BuildRequires: ghc-random-devel
BuildRequires: ghc-rpm-macros
BuildRequires: ghc-vector-algorithms-devel
BuildRequires: ghc-vector-binary-instances-devel
@@ -84,11 +84,10 @@
%prep
%autosetup -n %{pkg_name}-%{version}
+find . -type f -exec chmod -x {} +
%build
%ghc_lib_build
-chmod a-x README.markdown changelog.md
-chmod a-x examples/kde/*
%install
%ghc_lib_install
++++++ statistics-0.15.2.0.tar.gz -> statistics-0.16.0.1.tar.gz ++++++
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/statistics-0.15.2.0/Statistics/Correlation/Kendall.hs
new/statistics-0.16.0.1/Statistics/Correlation/Kendall.hs
--- old/statistics-0.15.2.0/Statistics/Correlation/Kendall.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Correlation/Kendall.hs 2001-09-09
03:46:40.000000000 +0200
@@ -1,4 +1,4 @@
-{-# LANGUAGE BangPatterns, CPP, FlexibleContexts #-}
+{-# LANGUAGE BangPatterns, FlexibleContexts #-}
-- |
-- Module : Statistics.Correlation.Kendall
--
@@ -131,11 +131,6 @@
wroteLow low (iLow+1) high iHigh eHigh (iIns+1)
{-# INLINE merge #-}
-#if !MIN_VERSION_base(4,6,0)
-modifySTRef' :: STRef s a -> (a -> a) -> ST s ()
-modifySTRef' = modifySTRef
-#endif
-
-- $references
--
-- * William R. Knight. (1966) A computer method for calculating Kendall's Tau
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/statistics-0.15.2.0/Statistics/Distribution/Binomial.hs
new/statistics-0.16.0.1/Statistics/Distribution/Binomial.hs
--- old/statistics-0.15.2.0/Statistics/Distribution/Binomial.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Distribution/Binomial.hs 2001-09-09
03:46:40.000000000 +0200
@@ -1,4 +1,5 @@
{-# LANGUAGE OverloadedStrings #-}
+{-# LANGUAGE PatternGuards #-}
{-# LANGUAGE DeriveDataTypeable, DeriveGeneric #-}
-- |
-- Module : Statistics.Distribution.Binomial
@@ -31,7 +32,7 @@
import Data.Data (Data, Typeable)
import GHC.Generics (Generic)
import Numeric.SpecFunctions (choose,logChoose,incompleteBeta,log1p)
-import Numeric.MathFunctions.Constants (m_epsilon)
+import Numeric.MathFunctions.Constants (m_epsilon,m_tiny)
import qualified Statistics.Distribution as D
import qualified Statistics.Distribution.Poisson.Internal as I
@@ -104,9 +105,16 @@
| n == 0 = 1
-- choose could overflow Double for n >= 1030 so we switch to
-- log-domain to calculate probability
- | n < 1000 = choose n k * p^k * (1-p)^(n-k)
- | otherwise = exp $ logChoose n k + log p * k' + log1p (-p) * nk'
+ --
+ -- We also want to avoid underflow when computing p^k &
+ -- (1-p)^(n-k).
+ | n < 1000
+ , pK >= m_tiny
+ , pNK >= m_tiny = choose n k * pK * pNK
+ | otherwise = exp $ logChoose n k + log p * k' + log1p (-p) * nk'
where
+ pK = p^k
+ pNK = (1-p)^(n-k)
k' = fromIntegral k
nk' = fromIntegral $ n - k
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/statistics-0.15.2.0/Statistics/Distribution/CauchyLorentz.hs
new/statistics-0.16.0.1/Statistics/Distribution/CauchyLorentz.hs
--- old/statistics-0.15.2.0/Statistics/Distribution/CauchyLorentz.hs
2001-09-09 03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Distribution/CauchyLorentz.hs
2001-09-09 03:46:40.000000000 +0200
@@ -94,23 +94,43 @@
instance D.Distribution CauchyDistribution where
- cumulative (CD m s) x = 0.5 + atan( (x - m) / s ) / pi
+ cumulative (CD m s) x
+ | y < -1 = atan (-1/y) / pi
+ | otherwise = 0.5 + atan y / pi
+ where
+ y = (x - m) / s
+ complCumulative (CD m s) x
+ | y > 1 = atan (1/y) / pi
+ | otherwise = 0.5 - atan y / pi
+ where
+ y = (x - m) / s
instance D.ContDistr CauchyDistribution where
density (CD m s) x = (1 / pi) / (s * (1 + y*y))
where y = (x - m) / s
quantile (CD m s) p
- | p > 0 && p < 1 = m + s * tan( pi * (p - 0.5) )
- | p == 0 = -1 / 0
- | p == 1 = 1 / 0
- | otherwise =
- error $ "Statistics.Distribution.CauchyLorentz.quantile: p must be in
[0,1] range. Got: "++show p
+ | p == 0 = -1 / 0
+ | p == 1 = 1 / 0
+ | p == 0.5 = m
+ | p < 0 = err
+ | p < 0.5 = m - s / tan( pi * p )
+ | p < 1 = m + s / tan( pi * (1 - p) )
+ | otherwise = err
+ where
+ err = error
+ $ "Statistics.Distribution.CauchyLorentz.quantile: p must be in
[0,1] range. Got: "++show p
complQuantile (CD m s) p
- | p > 0 && p < 1 = m + s * tan( pi * (0.5 - p) )
- | p == 0 = 1 / 0
- | p == 1 = -1 / 0
- | otherwise =
- error $ "Statistics.Distribution.CauchyLorentz.complQuantile: p must be
in [0,1] range. Got: "++show p
+ | p == 0 = 1 / 0
+ | p == 1 = -1 / 0
+ | p == 0.5 = m
+ | p < 0 = err
+ | p < 0.5 = m + s / tan( pi * p )
+ | p < 1 = m - s / tan( pi * (1 - p) )
+ | otherwise = err
+ where
+ err = error
+ $ "Statistics.Distribution.CauchyLorentz.quantile: p must be in
[0,1] range. Got: "++show p
+
instance D.ContGen CauchyDistribution where
genContVar = D.genContinuous
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/statistics-0.15.2.0/Statistics/Distribution/DiscreteUniform.hs
new/statistics-0.16.0.1/Statistics/Distribution/DiscreteUniform.hs
--- old/statistics-0.15.2.0/Statistics/Distribution/DiscreteUniform.hs
2001-09-09 03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Distribution/DiscreteUniform.hs
2001-09-09 03:46:40.000000000 +0200
@@ -28,10 +28,11 @@
, rangeTo
) where
-import Control.Applicative ((<$>), (<*>), empty)
+import Control.Applicative (empty)
import Data.Aeson (FromJSON(..), ToJSON, Value(..), (.:))
import Data.Binary (Binary(..))
import Data.Data (Data, Typeable)
+import System.Random.Stateful (uniformRM)
import GHC.Generics (Generic)
import qualified Statistics.Distribution as D
@@ -94,6 +95,12 @@
instance D.MaybeEntropy DiscreteUniform where
maybeEntropy = Just . D.entropy
+instance D.ContGen DiscreteUniform where
+ genContVar d = fmap fromIntegral . D.genDiscreteVar d
+
+instance D.DiscreteGen DiscreteUniform where
+ genDiscreteVar (U a b) = uniformRM (a,b)
+
-- | Construct discrete uniform distribution on support {1, ..., n}.
-- Range /n/ must be >0.
discreteUniform :: Int -- ^ Range
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/statistics-0.15.2.0/Statistics/Distribution/FDistribution.hs
new/statistics-0.16.0.1/Statistics/Distribution/FDistribution.hs
--- old/statistics-0.15.2.0/Statistics/Distribution/FDistribution.hs
2001-09-09 03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Distribution/FDistribution.hs
2001-09-09 03:46:40.000000000 +0200
@@ -109,15 +109,36 @@
cumulative :: FDistribution -> Double -> Double
cumulative (F n m _) x
| x <= 0 = 0
- | isInfinite x = 1 -- Only matches +???
- | otherwise = let y = n*x in incompleteBeta (0.5 * n) (0.5 * m) (y / (m +
y))
+ -- Only matches +???
+ | isInfinite x = 1
+ -- NOTE: Here we rely on implementation detail of incompleteBeta. It
+ -- computes using series expansion for sufficiently small x
+ -- and uses following identity otherwise:
+ --
+ -- I(x; a, b) = 1 - I(1-x; b, a)
+ --
+ -- Point is we can compute 1-x as m/(m+y) without loss of
+ -- precision for large x. Sadly this switchover point is
+ -- implementation detail.
+ | n >= (n+m)*bx = incompleteBeta (0.5 * n) (0.5 * m) bx
+ | otherwise = 1 - incompleteBeta (0.5 * m) (0.5 * n) bx1
+ where
+ y = n * x
+ bx = y / (m + y)
+ bx1 = m / (m + y)
complCumulative :: FDistribution -> Double -> Double
complCumulative (F n m _) x
- | x <= 0 = 1
- | isInfinite x = 0 -- Only matches +???
- | otherwise = let y = n*x
- in incompleteBeta (0.5 * m) (0.5 * n) (m / (m + y))
+ | x <= 0 = 1
+ -- Only matches +???
+ | isInfinite x = 0
+ -- See NOTE at cumulative
+ | m >= (n+m)*bx = incompleteBeta (0.5 * m) (0.5 * n) bx
+ | otherwise = 1 - incompleteBeta (0.5 * n) (0.5 * m) bx1
+ where
+ y = n*x
+ bx = m / (m + y)
+ bx1 = y / (m + y)
logDensity :: FDistribution -> Double -> Double
logDensity (F n m fac) x
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/Statistics/Distribution/Gamma.hs
new/statistics-0.16.0.1/Statistics/Distribution/Gamma.hs
--- old/statistics-0.15.2.0/Statistics/Distribution/Gamma.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Distribution/Gamma.hs 2001-09-09
03:46:40.000000000 +0200
@@ -36,6 +36,7 @@
import Numeric.MathFunctions.Constants (m_pos_inf, m_NaN, m_neg_inf)
import Numeric.SpecFunctions (incompleteGamma, invIncompleteGamma, logGamma,
digamma)
import qualified System.Random.MWC.Distributions as MWC
+import qualified Numeric.Sum as Sum
import Statistics.Distribution.Poisson.Internal as Poisson
import qualified Statistics.Distribution as D
@@ -126,7 +127,11 @@
density = density
logDensity (GD k theta) x
| x <= 0 = m_neg_inf
- | otherwise = log x * (k - 1) - (x / theta) - logGamma k - log theta * k
+ | otherwise = Sum.sum Sum.kbn [ log x * (k - 1)
+ , - (x / theta)
+ , - logGamma k
+ , - log theta * k
+ ]
quantile = quantile
instance D.Variance GammaDistribution where
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/statistics-0.15.2.0/Statistics/Distribution/Lognormal.hs
new/statistics-0.16.0.1/Statistics/Distribution/Lognormal.hs
--- old/statistics-0.15.2.0/Statistics/Distribution/Lognormal.hs
1970-01-01 01:00:00.000000000 +0100
+++ new/statistics-0.16.0.1/Statistics/Distribution/Lognormal.hs
2001-09-09 03:46:40.000000000 +0200
@@ -0,0 +1,172 @@
+{-# LANGUAGE MultiParamTypeClasses #-}
+{-# LANGUAGE DeriveDataTypeable, DeriveGeneric #-}
+-- |
+-- Module : Statistics.Distribution.Lognormal
+-- Copyright : (c) 2020 Ximin Luo
+-- License : BSD3
+--
+-- Maintainer : infini...@pwned.gg
+-- Stability : experimental
+-- Portability : portable
+--
+-- The log normal distribution. This is a continuous probability
+-- distribution that describes data whose log is clustered around a
+-- mean. For example, the multiplicative product of many independent
+-- positive random variables.
+
+module Statistics.Distribution.Lognormal
+ (
+ LognormalDistribution
+ -- * Constructors
+ , lognormalDistr
+ , lognormalDistrErr
+ , lognormalDistrMeanStddevErr
+ , lognormalStandard
+ ) where
+
+import Data.Aeson (FromJSON, ToJSON)
+import Data.Binary (Binary (..))
+import Data.Data (Data, Typeable)
+import GHC.Generics (Generic)
+import Numeric.MathFunctions.Constants (m_huge, m_sqrt_2_pi)
+import Numeric.SpecFunctions (expm1, log1p)
+import qualified Data.Vector.Generic as G
+
+import qualified Statistics.Distribution as D
+import qualified Statistics.Distribution.Normal as N
+import Statistics.Internal
+
+
+-- | The lognormal distribution.
+newtype LognormalDistribution = LND N.NormalDistribution
+ deriving (Eq, Typeable, Data, Generic)
+
+instance Show LognormalDistribution where
+ showsPrec i (LND d) = defaultShow2 "lognormalDistr" m s i
+ where
+ m = D.mean d
+ s = D.stdDev d
+instance Read LognormalDistribution where
+ readPrec = defaultReadPrecM2 "lognormalDistr" $
+ (either (const Nothing) Just .) . lognormalDistrErr
+
+instance ToJSON LognormalDistribution
+instance FromJSON LognormalDistribution
+
+instance Binary LognormalDistribution where
+ put (LND d) = put m >> put s
+ where
+ m = D.mean d
+ s = D.stdDev d
+ get = do
+ m <- get
+ sd <- get
+ either fail return $ lognormalDistrErr m sd
+
+instance D.Distribution LognormalDistribution where
+ cumulative = cumulative
+ complCumulative = complCumulative
+
+instance D.ContDistr LognormalDistribution where
+ logDensity = logDensity
+ quantile = quantile
+ complQuantile = complQuantile
+
+instance D.MaybeMean LognormalDistribution where
+ maybeMean = Just . D.mean
+
+instance D.Mean LognormalDistribution where
+ mean (LND d) = exp (m + v / 2)
+ where
+ m = D.mean d
+ v = D.variance d
+
+instance D.MaybeVariance LognormalDistribution where
+ maybeStdDev = Just . D.stdDev
+ maybeVariance = Just . D.variance
+
+instance D.Variance LognormalDistribution where
+ variance (LND d) = expm1 v * exp (2 * m + v)
+ where
+ m = D.mean d
+ v = D.variance d
+
+instance D.Entropy LognormalDistribution where
+ entropy (LND d) = logBase 2 (s * exp (m + 0.5) * m_sqrt_2_pi)
+ where
+ m = D.mean d
+ s = D.stdDev d
+
+instance D.MaybeEntropy LognormalDistribution where
+ maybeEntropy = Just . D.entropy
+
+instance D.ContGen LognormalDistribution where
+ genContVar d = D.genContinuous d
+
+-- | Standard log normal distribution with mu 0 and sigma 1.
+--
+-- Mean is @sqrt e@ and variance is @(e - 1) * e@.
+lognormalStandard :: LognormalDistribution
+lognormalStandard = LND N.standard
+
+-- | Create log normal distribution from parameters.
+lognormalDistr
+ :: Double -- ^ Mu
+ -> Double -- ^ Sigma
+ -> LognormalDistribution
+lognormalDistr mu sig = either error id $ lognormalDistrErr mu sig
+
+-- | Create log normal distribution from parameters.
+lognormalDistrErr
+ :: Double -- ^ Mu
+ -> Double -- ^ Sigma
+ -> Either String LognormalDistribution
+lognormalDistrErr mu sig
+ | sig >= sqrt (log m_huge - 2 * mu) = Left $ errMsg mu sig
+ | otherwise = LND <$> N.normalDistrErr mu sig
+
+errMsg :: Double -> Double -> String
+errMsg mu sig =
+ "Statistics.Distribution.Lognormal.lognormalDistr: sigma must be > 0 && < "
+ ++ show lim ++ ". Got " ++ show sig
+ where lim = sqrt (log m_huge - 2 * mu)
+
+-- | Create log normal distribution from mean and standard deviation.
+lognormalDistrMeanStddevErr
+ :: Double -- ^ Mu
+ -> Double -- ^ Sigma
+ -> Either String LognormalDistribution
+lognormalDistrMeanStddevErr m sd = LND <$> N.normalDistrErr mu sig
+ where r = sd / m
+ sig2 = log1p (r * r)
+ sig = sqrt sig2
+ mu = log m - sig2 / 2
+
+-- | Variance is estimated using maximum likelihood method
+-- (biased estimation) over the log of the data.
+--
+-- Returns @Nothing@ if sample contains less than one element or
+-- variance is zero (all elements are equal)
+instance D.FromSample LognormalDistribution Double where
+ fromSample = fmap LND . D.fromSample . G.map log
+
+logDensity :: LognormalDistribution -> Double -> Double
+logDensity (LND d) x
+ | x > 0 = let lx = log x in D.logDensity d lx - lx
+ | otherwise = 0
+
+cumulative :: LognormalDistribution -> Double -> Double
+cumulative (LND d) x
+ | x > 0 = D.cumulative d $ log x
+ | otherwise = 0
+
+complCumulative :: LognormalDistribution -> Double -> Double
+complCumulative (LND d) x
+ | x > 0 = D.complCumulative d $ log x
+ | otherwise = 1
+
+quantile :: LognormalDistribution -> Double -> Double
+quantile (LND d) = exp . D.quantile d
+
+complQuantile :: LognormalDistribution -> Double -> Double
+complQuantile (LND d) = exp . D.complQuantile d
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/statistics-0.15.2.0/Statistics/Distribution/Normal.hs
new/statistics-0.16.0.1/Statistics/Distribution/Normal.hs
--- old/statistics-0.15.2.0/Statistics/Distribution/Normal.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Distribution/Normal.hs 2001-09-09
03:46:40.000000000 +0200
@@ -19,6 +19,7 @@
-- * Constructors
, normalDistr
, normalDistrE
+ , normalDistrErr
, standard
) where
@@ -55,7 +56,7 @@
parseJSON (Object v) = do
m <- v .: "mean"
sd <- v .: "stdDev"
- maybe (fail $ errMsg m sd) return $ normalDistrE m sd
+ either fail return $ normalDistrErr m sd
parseJSON _ = empty
instance Binary NormalDistribution where
@@ -63,7 +64,7 @@
get = do
m <- get
sd <- get
- maybe (fail $ errMsg m sd) return $ normalDistrE m sd
+ either fail return $ normalDistrErr m sd
instance D.Distribution NormalDistribution where
cumulative = cumulative
@@ -111,7 +112,7 @@
normalDistr :: Double -- ^ Mean of distribution
-> Double -- ^ Standard deviation of distribution
-> NormalDistribution
-normalDistr m sd = maybe (error $ errMsg m sd) id $ normalDistrE m sd
+normalDistr m sd = either error id $ normalDistrErr m sd
-- | Create normal distribution from parameters.
--
@@ -120,13 +121,20 @@
normalDistrE :: Double -- ^ Mean of distribution
-> Double -- ^ Standard deviation of distribution
-> Maybe NormalDistribution
-normalDistrE m sd
- | sd > 0 = Just ND { mean = m
- , stdDev = sd
- , ndPdfDenom = log $ m_sqrt_2_pi * sd
- , ndCdfDenom = m_sqrt_2 * sd
- }
- | otherwise = Nothing
+normalDistrE m sd = either (const Nothing) Just $ normalDistrErr m sd
+
+-- | Create normal distribution from parameters.
+--
+normalDistrErr :: Double -- ^ Mean of distribution
+ -> Double -- ^ Standard deviation of distribution
+ -> Either String NormalDistribution
+normalDistrErr m sd
+ | sd > 0 = Right $ ND { mean = m
+ , stdDev = sd
+ , ndPdfDenom = log $ m_sqrt_2_pi * sd
+ , ndCdfDenom = m_sqrt_2 * sd
+ }
+ | otherwise = Left $ errMsg m sd
errMsg :: Double -> Double -> String
errMsg _ sd = "Statistics.Distribution.Normal.normalDistr: standard deviation
must be positive. Got " ++ show sd
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/statistics-0.15.2.0/Statistics/Distribution/Transform.hs
new/statistics-0.16.0.1/Statistics/Distribution/Transform.hs
--- old/statistics-0.15.2.0/Statistics/Distribution/Transform.hs
2001-09-09 03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Distribution/Transform.hs
2001-09-09 03:46:40.000000000 +0200
@@ -18,11 +18,9 @@
) where
import Data.Aeson (FromJSON, ToJSON)
-import Control.Applicative ((<*>))
import Data.Binary (Binary)
import Data.Binary (put, get)
import Data.Data (Data, Typeable)
-import Data.Functor ((<$>))
import GHC.Generics (Generic)
import qualified Statistics.Distribution as D
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/statistics-0.15.2.0/Statistics/Distribution/Uniform.hs
new/statistics-0.16.0.1/Statistics/Distribution/Uniform.hs
--- old/statistics-0.15.2.0/Statistics/Distribution/Uniform.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Distribution/Uniform.hs 2001-09-09
03:46:40.000000000 +0200
@@ -22,11 +22,11 @@
) where
import Control.Applicative
-import Data.Aeson (FromJSON(..), ToJSON, Value(..), (.:))
-import Data.Binary (Binary(..))
-import Data.Data (Data, Typeable)
-import GHC.Generics (Generic)
-import qualified System.Random.MWC as MWC
+import Data.Aeson (FromJSON(..), ToJSON, Value(..), (.:))
+import Data.Binary (Binary(..))
+import Data.Data (Data, Typeable)
+import System.Random.Stateful (uniformRM)
+import GHC.Generics (Generic)
import qualified Statistics.Distribution as D
import Statistics.Internal
@@ -117,4 +117,4 @@
maybeEntropy = Just . D.entropy
instance D.ContGen UniformDistribution where
- genContVar (UniformDistribution a b) gen = MWC.uniformR (a,b) gen
+ genContVar (UniformDistribution a b) = uniformRM (a,b)
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/statistics-0.15.2.0/Statistics/Distribution/Weibull.hs
new/statistics-0.16.0.1/Statistics/Distribution/Weibull.hs
--- old/statistics-0.15.2.0/Statistics/Distribution/Weibull.hs 1970-01-01
01:00:00.000000000 +0100
+++ new/statistics-0.16.0.1/Statistics/Distribution/Weibull.hs 2001-09-09
03:46:40.000000000 +0200
@@ -0,0 +1,224 @@
+{-# LANGUAGE MultiParamTypeClasses #-}
+{-# LANGUAGE OverloadedStrings #-}
+{-# LANGUAGE DeriveDataTypeable, DeriveGeneric #-}
+-- |
+-- Module : Statistics.Distribution.Lognormal
+-- Copyright : (c) 2020 Ximin Luo
+-- License : BSD3
+--
+-- Maintainer : infini...@pwned.gg
+-- Stability : experimental
+-- Portability : portable
+--
+-- The weibull distribution. This is a continuous probability
+-- distribution that describes the occurrence of a single event whose
+-- probability changes over time, controlled by the shape parameter.
+
+module Statistics.Distribution.Weibull
+ (
+ WeibullDistribution
+ -- * Constructors
+ , weibullDistr
+ , weibullDistrErr
+ , weibullStandard
+ , weibullDistrApproxMeanStddevErr
+ ) where
+
+import Control.Applicative
+import Data.Aeson (FromJSON(..), ToJSON, Value(..), (.:))
+import Data.Binary (Binary(..))
+import Data.Data (Data, Typeable)
+import GHC.Generics (Generic)
+import Numeric.MathFunctions.Constants (m_eulerMascheroni)
+import Numeric.SpecFunctions (expm1, log1p, logGamma)
+import qualified Data.Vector.Generic as G
+
+import qualified Statistics.Distribution as D
+import qualified Statistics.Sample as S
+import Statistics.Internal
+
+
+-- | The weibull distribution.
+data WeibullDistribution = WD {
+ wdShape :: {-# UNPACK #-} !Double
+ , wdLambda :: {-# UNPACK #-} !Double
+ } deriving (Eq, Typeable, Data, Generic)
+
+instance Show WeibullDistribution where
+ showsPrec i (WD k l) = defaultShow2 "weibullDistr" k l i
+instance Read WeibullDistribution where
+ readPrec = defaultReadPrecM2 "weibullDistr" $
+ (either (const Nothing) Just .) . weibullDistrErr
+
+instance ToJSON WeibullDistribution
+instance FromJSON WeibullDistribution where
+ parseJSON (Object v) = do
+ k <- v .: "wdShape"
+ l <- v .: "wdLambda"
+ either fail return $ weibullDistrErr k l
+ parseJSON _ = empty
+
+instance Binary WeibullDistribution where
+ put (WD k l) = put k >> put l
+ get = do
+ k <- get
+ l <- get
+ either fail return $ weibullDistrErr k l
+
+instance D.Distribution WeibullDistribution where
+ cumulative = cumulative
+ complCumulative = complCumulative
+
+instance D.ContDistr WeibullDistribution where
+ logDensity = logDensity
+ quantile = quantile
+ complQuantile = complQuantile
+
+instance D.MaybeMean WeibullDistribution where
+ maybeMean = Just . D.mean
+
+instance D.Mean WeibullDistribution where
+ mean (WD k l) = l * exp (logGamma (1 + 1 / k))
+
+instance D.MaybeVariance WeibullDistribution where
+ maybeStdDev = Just . D.stdDev
+ maybeVariance = Just . D.variance
+
+instance D.Variance WeibullDistribution where
+ variance (WD k l) = l * l * (exp (logGamma (1 + 2 * invk)) - q * q)
+ where
+ invk = 1 / k
+ q = exp (logGamma (1 + invk))
+
+instance D.Entropy WeibullDistribution where
+ entropy (WD k l) = m_eulerMascheroni * (1 - 1 / k) + log (l / k) + 1
+
+instance D.MaybeEntropy WeibullDistribution where
+ maybeEntropy = Just . D.entropy
+
+instance D.ContGen WeibullDistribution where
+ genContVar d = D.genContinuous d
+
+-- | Standard weibull distribution with scale factor (lambda) 1.
+weibullStandard :: Double -> WeibullDistribution
+weibullStandard k = weibullDistr k 1.0
+
+-- | Create weibull distribution from parameters.
+--
+-- If the shape (first) parameter is @1.0@, the distribution is equivalent to a
+-- 'Statistics.Distribution.Exponential.ExponentialDistribution' with parameter
+-- @1 / lambda@ the scale (second) parameter.
+weibullDistr
+ :: Double -- ^ Shape
+ -> Double -- ^ Lambda (scale)
+ -> WeibullDistribution
+weibullDistr k l = either error id $ weibullDistrErr k l
+
+-- | Create weibull distribution from parameters.
+--
+-- If the shape (first) parameter is @1.0@, the distribution is equivalent to a
+-- 'Statistics.Distribution.Exponential.ExponentialDistribution' with parameter
+-- @1 / lambda@ the scale (second) parameter.
+weibullDistrErr
+ :: Double -- ^ Shape
+ -> Double -- ^ Lambda (scale)
+ -> Either String WeibullDistribution
+weibullDistrErr k l | k <= 0 = Left $ errMsg k l
+ | l <= 0 = Left $ errMsg k l
+ | otherwise = Right $ WD k l
+
+errMsg :: Double -> Double -> String
+errMsg k l =
+ "Statistics.Distribution.Weibull.weibullDistr: both shape and lambda must be
positive. Got shape "
+ ++ show k
+ ++ " and lambda "
+ ++ show l
+
+-- | Create weibull distribution from mean and standard deviation.
+--
+-- The algorithm is from "Methods for Estimating Wind Speed Frequency
+-- Distributions", C. G. Justus, W. R. Hargreaves, A. Mikhail, D. Graber, 1977.
+-- Given the identity:
+--
+-- \[
+-- (\frac{\sigma}{\mu})^2 = \frac{\Gamma(1+2/k)}{\Gamma(1+1/k)^2} - 1
+-- \]
+--
+-- \(k\) can be approximated by
+--
+-- \[
+-- k \approx (\frac{\sigma}{\mu})^{-1.086}
+-- \]
+--
+-- \(\lambda\) is then calculated straightforwardly via the identity
+--
+-- \[
+-- \lambda = \frac{\mu}{\Gamma(1+1/k)}
+-- \]
+--
+-- Numerically speaking, the approximation for \(k\) is accurate only within a
+-- certain range. We arbitrarily pick the range \(0.033 \le \frac{\sigma}{\mu}
\le 1.45\)
+-- where it is good to ~6%, and will refuse to create a distribution outside of
+-- this range. The paper does not cover these details but it is straightforward
+-- to check them numerically.
+weibullDistrApproxMeanStddevErr
+ :: Double -- ^ Mean
+ -> Double -- ^ Stddev
+ -> Either String WeibullDistribution
+weibullDistrApproxMeanStddevErr m s = if r > 1.45 || r < 0.033
+ then Left msg
+ else weibullDistrErr k l
+ where r = s / m
+ k = (s / m) ** (-1.086)
+ l = m / exp (logGamma (1 + 1/k))
+ msg = "Statistics.Distribution.Weibull.weibullDistr: stddev-mean ratio
"
+ ++ "outside approximation accuracy range [0.033, 1.45]. Got "
+ ++ "stddev " ++ show s ++ " and mean " ++ show m
+
+-- | Uses an approximation based on the mean and standard deviation in
+-- 'weibullDistrEstMeanStddevErr', with standard deviation estimated
+-- using maximum likelihood method (unbiased estimation).
+--
+-- Returns @Nothing@ if sample contains less than one element or
+-- variance is zero (all elements are equal), or if the estimated mean
+-- and standard-deviation lies outside the range for which the
+-- approximation is accurate.
+instance D.FromSample WeibullDistribution Double where
+ fromSample xs
+ | G.length xs <= 1 = Nothing
+ | v == 0 = Nothing
+ | otherwise = either (const Nothing) Just $
+ weibullDistrApproxMeanStddevErr m (sqrt v)
+ where
+ (m,v) = S.meanVarianceUnb xs
+
+logDensity :: WeibullDistribution -> Double -> Double
+logDensity (WD k l) x
+ | x < 0 = 0
+ | otherwise = log k + (k - 1) * log x - k * log l - (x / l) ** k
+
+cumulative :: WeibullDistribution -> Double -> Double
+cumulative (WD k l) x | x < 0 = 0
+ | otherwise = -expm1 (-(x / l) ** k)
+
+complCumulative :: WeibullDistribution -> Double -> Double
+complCumulative (WD k l) x | x < 0 = 1
+ | otherwise = exp (-(x / l) ** k)
+
+quantile :: WeibullDistribution -> Double -> Double
+quantile (WD k l) p
+ | p == 0 = 0
+ | p == 1 = inf
+ | p > 0 && p < 1 = l * (-log1p (-p)) ** (1 / k)
+ | otherwise =
+ error $ "Statistics.Distribution.Weibull.quantile: p must be in [0,1]
range. Got: " ++ show p
+ where inf = 1 / 0
+
+complQuantile :: WeibullDistribution -> Double -> Double
+complQuantile (WD k l) q
+ | q == 0 = inf
+ | q == 1 = 0
+ | q > 0 && q < 1 = l * (-log q) ** (1 / k)
+ | otherwise =
+ error $ "Statistics.Distribution.Weibull.complQuantile: q must be in [0,1]
range. Got: " ++ show q
+ where inf = 1 / 0
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/Statistics/Distribution.hs
new/statistics-0.16.0.1/Statistics/Distribution.hs
--- old/statistics-0.15.2.0/Statistics/Distribution.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Distribution.hs 2001-09-09
03:46:40.000000000 +0200
@@ -29,18 +29,15 @@
, ContGen(..)
, DiscreteGen(..)
, genContinuous
- , genContinous
-- * Helper functions
, findRoot
, sumProbabilities
) where
-import Control.Applicative ((<$>), Applicative(..))
-import Control.Monad.Primitive (PrimMonad,PrimState)
import Prelude hiding (sum)
-import Statistics.Function (square)
+import Statistics.Function (square)
import Statistics.Sample.Internal (sum)
-import System.Random.MWC (Gen, uniform)
+import System.Random.Stateful (StatefulGen, uniformDouble01M)
import qualified Data.Vector.Unboxed as U
import qualified Data.Vector.Generic as G
@@ -56,7 +53,7 @@
-- > cumulative d +??? = 1
-- > cumulative d -??? = 0
cumulative :: d -> Double -> Double
-
+ cumulative d x = 1 - complCumulative d x
-- | One's complement of cumulative distribution:
--
-- > complCumulative d x = 1 - cumulative d x
@@ -67,17 +64,18 @@
-- encouraged to provide more precise implementation.
complCumulative :: d -> Double -> Double
complCumulative d x = 1 - cumulative d x
+ {-# MINIMAL (cumulative | complCumulative) #-}
+
-- | Discrete probability distribution.
class Distribution d => DiscreteDistr d where
-- | Probability of n-th outcome.
probability :: d -> Int -> Double
probability d = exp . logProbability d
-
-- | Logarithm of probability of n-th outcome
logProbability :: d -> Int -> Double
logProbability d = log . probability d
-
+ {-# MINIMAL (probability | logProbability) #-}
-- | Continuous probability distribution.
--
@@ -89,22 +87,20 @@
-- [/x/,/x+/δ/x/) equal to /density(x)/⋅δ/x/
density :: d -> Double -> Double
density d = exp . logDensity d
-
+ -- | Natural logarithm of density.
+ logDensity :: d -> Double -> Double
+ logDensity d = log . density d
-- | Inverse of the cumulative distribution function. The value
-- /x/ for which P(/X/≤/x/) = /p/. If probability is outside
-- of [0,1] range function should call 'error'
quantile :: d -> Double -> Double
-
+ quantile d x = complQuantile d (1 - x)
-- | 1-complement of @quantile@:
--
-- > complQuantile x ??? quantile (1 - x)
complQuantile :: d -> Double -> Double
complQuantile d x = quantile d (1 - x)
-
- -- | Natural logarithm of density.
- logDensity :: d -> Double -> Double
- logDensity d = log . density d
-
+ {-# MINIMAL (density | logDensity), (quantile | complQuantile) #-}
-- | Type class for distributions with mean. 'maybeMean' should return
-- 'Nothing' if it's undefined for current value of data
@@ -126,9 +122,10 @@
-- Minimal complete definition is 'maybeVariance' or 'maybeStdDev'
class MaybeMean d => MaybeVariance d where
maybeVariance :: d -> Maybe Double
- maybeVariance d = (*) <$> x <*> x where x = maybeStdDev d
+ maybeVariance = fmap square . maybeStdDev
maybeStdDev :: d -> Maybe Double
- maybeStdDev = fmap sqrt . maybeVariance
+ maybeStdDev = fmap sqrt . maybeVariance
+ {-# MINIMAL (maybeVariance | maybeStdDev) #-}
-- | Type class for distributions with variance. If distribution have
-- finite variance for all valid parameter values it should be
@@ -140,6 +137,8 @@
variance d = square (stdDev d)
stdDev :: d -> Double
stdDev = sqrt . variance
+ {-# MINIMAL (variance | stdDev) #-}
+
-- | Type class for distributions with entropy, meaning Shannon entropy
-- in the case of a discrete distribution, or differential entropy in the
@@ -161,13 +160,13 @@
-- | Generate discrete random variates which have given
-- distribution.
class Distribution d => ContGen d where
- genContVar :: PrimMonad m => d -> Gen (PrimState m) -> m Double
+ genContVar :: (StatefulGen g m) => d -> g -> m Double
-- | Generate discrete random variates which have given
-- distribution. 'ContGen' is superclass because it's always possible
-- to generate real-valued variates from integer values
class (DiscreteDistr d, ContGen d) => DiscreteGen d where
- genDiscreteVar :: PrimMonad m => d -> Gen (PrimState m) -> m Int
+ genDiscreteVar :: (StatefulGen g m) => d -> g -> m Int
-- | Estimate distribution from sample. First parameter in sample is
-- distribution type and second is element type.
@@ -181,16 +180,11 @@
-- | Generate variates from continuous distribution using inverse
-- transform rule.
-genContinuous :: (ContDistr d, PrimMonad m) => d -> Gen (PrimState m) -> m
Double
+genContinuous :: (ContDistr d, StatefulGen g m) => d -> g -> m Double
genContinuous d gen = do
- x <- uniform gen
+ x <- uniformDouble01M gen
return $! quantile d x
--- | Backwards compatibility with genContinuous.
-genContinous :: (ContDistr d, PrimMonad m) => d -> Gen (PrimState m) -> m
Double
-genContinous = genContinuous
-{-# DEPRECATED genContinous "Use genContinuous" #-}
-
data P = P {-# UNPACK #-} !Double {-# UNPACK #-} !Double
-- | Approximate the value of /X/ for which P(/x/>/X/)=/p/.
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/Statistics/Internal.hs
new/statistics-0.16.0.1/Statistics/Internal.hs
--- old/statistics-0.15.2.0/Statistics/Internal.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Internal.hs 2001-09-09
03:46:40.000000000 +0200
@@ -25,8 +25,6 @@
import Control.Applicative
import Control.Monad
import Text.Read
-import Data.Orphans ()
-
----------------------------------------------------------------
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/Statistics/Quantile.hs
new/statistics-0.16.0.1/Statistics/Quantile.hs
--- old/statistics-0.15.2.0/Statistics/Quantile.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Quantile.hs 2001-09-09
03:46:40.000000000 +0200
@@ -1,4 +1,3 @@
-{-# LANGUAGE CPP #-}
{-# LANGUAGE DeriveDataTypeable #-}
{-# LANGUAGE DeriveFoldable #-}
{-# LANGUAGE DeriveFunctor #-}
@@ -55,7 +54,6 @@
import Data.Aeson (ToJSON,FromJSON)
import Data.Data (Data,Typeable)
import Data.Default.Class
-import Data.Functor
import qualified Data.Foldable as F
import Data.Vector.Generic ((!))
import qualified Data.Vector as V
@@ -196,7 +194,7 @@
| F.any (badQ nQ) qs = modErr "quantiles" "Wrong quantile number"
| G.any isNaN xs = modErr "quantiles" "Sample contains NaNs"
-- Doesn't matter what we put into empty container
- | fnull qs = 0 <$ qs
+ | null qs = 0 <$ qs
| otherwise = fmap (estimateQuantile sortedXs) ks'
where
ks' = fmap (\q -> toPk param n q nQ) qs
@@ -210,14 +208,6 @@
{-# SPECIALIZE quantiles
:: (Functor f, F.Foldable f) => ContParam -> f Int -> Int -> S.Vector
Double -> f Double #-}
--- COMPAT
-fnull :: F.Foldable f => f a -> Bool
-#if !MIN_VERSION_base(4,8,0)
-fnull = F.foldr (\_ _ -> False) True
-#else
-fnull = null
-#endif
-
-- | O(/k??n/??log /n/). Same as quantiles but uses 'G.Vector' container
-- instead of 'Foldable' one.
quantilesVec :: (G.Vector v Double, G.Vector v Int)
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/Statistics/Regression.hs
new/statistics-0.16.0.1/Statistics/Regression.hs
--- old/statistics-0.15.2.0/Statistics/Regression.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Regression.hs 2001-09-09
03:46:40.000000000 +0200
@@ -13,7 +13,6 @@
, bootstrapRegress
) where
-import Control.Applicative ((<$>))
import Control.Concurrent.Async (forConcurrently)
import Control.DeepSeq (rnf)
import Control.Monad (when)
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/Statistics/Resampling.hs
new/statistics-0.16.0.1/Statistics/Resampling.hs
--- old/statistics-0.15.2.0/Statistics/Resampling.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Resampling.hs 2001-09-09
03:46:40.000000000 +0200
@@ -1,5 +1,4 @@
{-# LANGUAGE BangPatterns #-}
-{-# LANGUAGE CPP #-}
{-# LANGUAGE DeriveDataTypeable #-}
{-# LANGUAGE DeriveFoldable #-}
{-# LANGUAGE DeriveFunctor #-}
@@ -40,7 +39,6 @@
) where
import Data.Aeson (FromJSON, ToJSON)
-import Control.Applicative
import Control.Concurrent.Async (forConcurrently_)
import Control.Monad (forM_, forM, replicateM, liftM2)
import Control.Monad.Primitive (PrimMonad(..))
@@ -88,9 +86,7 @@
, resamples :: v a
}
deriving (Eq, Read, Show , Generic, Functor, T.Foldable, T.Traversable
-#if __GLASGOW_HASKELL__ >= 708
, Typeable, Data
-#endif
)
instance (Binary a, Binary (v a)) => Binary (Bootstrap v a) where
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/Statistics/Sample/Histogram.hs
new/statistics-0.16.0.1/Statistics/Sample/Histogram.hs
--- old/statistics-0.15.2.0/Statistics/Sample/Histogram.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Sample/Histogram.hs 2001-09-09
03:46:40.000000000 +0200
@@ -75,7 +75,7 @@
go (i+1)
write' bins' b !e = GM.write bins' b e
len = G.length xs
- d = ((hi - lo) * (1 + realToFrac m_epsilon)) / fromIntegral numBins
+ d = ((hi - lo) / fromIntegral numBins) * (1 + realToFrac m_epsilon)
{-# INLINE histogram_ #-}
-- | /O(n)/ Compute decent defaults for the lower and upper bounds of
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/Statistics/Test/ChiSquared.hs
new/statistics-0.16.0.1/Statistics/Test/ChiSquared.hs
--- old/statistics-0.15.2.0/Statistics/Test/ChiSquared.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Test/ChiSquared.hs 2001-09-09
03:46:40.000000000 +0200
@@ -39,7 +39,7 @@
| n > 0 = Just Test
{ testSignificance = mkPValue $ complCumulative d chi2
, testStatistics = chi2
- , testDistribution = chiSquared ndf
+ , testDistribution = chiSquared n
}
| otherwise = Nothing
where
@@ -66,7 +66,7 @@
| n > 0 = Just Test
{ testSignificance = mkPValue $ complCumulative d chi2
, testStatistics = chi2
- , testDistribution = chiSquared ndf
+ , testDistribution = chiSquared n
}
| otherwise = Nothing
where
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/Statistics/Test/KruskalWallis.hs
new/statistics-0.16.0.1/Statistics/Test/KruskalWallis.hs
--- old/statistics-0.15.2.0/Statistics/Test/KruskalWallis.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Test/KruskalWallis.hs 2001-09-09
03:46:40.000000000 +0200
@@ -17,7 +17,6 @@
) where
import Data.Ord (comparing)
-import Data.Foldable (foldMap)
import qualified Data.Vector.Unboxed as U
import Statistics.Function (sort, sortBy, square)
import Statistics.Distribution (complCumulative)
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/Statistics/Test/MannWhitneyU.hs
new/statistics-0.16.0.1/Statistics/Test/MannWhitneyU.hs
--- old/statistics-0.15.2.0/Statistics/Test/MannWhitneyU.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Test/MannWhitneyU.hs 2001-09-09
03:46:40.000000000 +0200
@@ -24,7 +24,6 @@
-- $references
) where
-import Control.Applicative ((<$>))
import Data.List (findIndex)
import Data.Ord (comparing)
import Numeric.SpecFunctions (choose)
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/Statistics/Test/WilcoxonT.hs
new/statistics-0.16.0.1/Statistics/Test/WilcoxonT.hs
--- old/statistics-0.15.2.0/Statistics/Test/WilcoxonT.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/Statistics/Test/WilcoxonT.hs 2001-09-09
03:46:40.000000000 +0200
@@ -39,7 +39,6 @@
-- the sum of negative ranks (the ranks of the differences where the second
parameter is higher).
-- to the length of the shorter sample.
-import Control.Applicative ((<$>))
import Data.Function (on)
import Data.List (findIndex)
import Data.Ord (comparing)
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/Statistics/Types.hs
new/statistics-0.16.0.1/Statistics/Types.hs
--- old/statistics-0.15.2.0/Statistics/Types.hs 2001-09-09 03:46:40.000000000
+0200
+++ new/statistics-0.16.0.1/Statistics/Types.hs 2001-09-09 03:46:40.000000000
+0200
@@ -1,4 +1,3 @@
-{-# LANGUAGE CPP #-}
{-# LANGUAGE ScopedTypeVariables #-}
{-# LANGUAGE MultiParamTypeClasses #-}
{-# LANGUAGE TypeFamilies #-}
@@ -72,12 +71,6 @@
import Data.Vector.Unboxed (Unbox)
import Data.Vector.Unboxed.Deriving (derivingUnbox)
import GHC.Generics (Generic)
-
-#if __GLASGOW_HASKELL__ == 704
-import qualified Data.Vector.Generic
-import qualified Data.Vector.Generic.Mutable
-#endif
-
import Statistics.Internal
import Statistics.Types.Internal
import Statistics.Distribution
@@ -324,9 +317,7 @@
, estError :: !(e a)
-- ^ Confidence interval for estimate.
} deriving (Eq, Read, Show, Generic
-#if __GLASGOW_HASKELL__ >= 708
, Typeable, Data
-#endif
)
instance (Binary (e a), Binary a) => Binary (Estimate e a) where
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/changelog.md
new/statistics-0.16.0.1/changelog.md
--- old/statistics-0.15.2.0/changelog.md 2001-09-09 03:46:40.000000000
+0200
+++ new/statistics-0.16.0.1/changelog.md 2001-09-09 03:46:40.000000000
+0200
@@ -1,3 +1,21 @@
+## Changes in 0.16.0.0
+
+ * Random number generation switched to API introduced in random-1.2
+
+ * Support of GHC<7.10 is dropped
+
+ * Fix for chi-squared test (#167) which was completely wrong
+
+ * Computation of CDF and quantiles of Cauchy distribution is now numerically
+ stable.
+
+ * Fix loss of precision in computing of CDF of gamma distribution
+
+ * Log-normal and Weibull distributions added.
+
+ * `DiscreteGen` instance added for `DiscreteUniform`
+
+
## Changes in 0.15.2.0
* Test suite is finally fixed (#42, #123). It took very-very-very long
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/statistics.cabal
new/statistics-0.16.0.1/statistics.cabal
--- old/statistics-0.15.2.0/statistics.cabal 2001-09-09 03:46:40.000000000
+0200
+++ new/statistics-0.16.0.1/statistics.cabal 2001-09-09 03:46:40.000000000
+0200
@@ -1,5 +1,5 @@
name: statistics
-version: 0.15.2.0
+version: 0.16.0.1
synopsis: A library of statistical types, data, and functions
description:
This library provides a number of common functions and types useful
@@ -24,14 +24,14 @@
license: BSD2
license-file: LICENSE
-homepage: https://github.com/bos/statistics
-bug-reports: https://github.com/bos/statistics/issues
+homepage: https://github.com/haskell/statistics
+bug-reports: https://github.com/haskell/statistics/issues
author: Bryan O'Sullivan <b...@serpentine.com>, Alexey Khudaykov
<alexey.sklad...@gmail.com>
-maintainer: Bryan O'Sullivan <b...@serpentine.com>, Alexey Khudaykov
<alexey.sklad...@gmail.com>
+maintainer: Alexey Khudaykov <alexey.sklad...@gmail.com>
copyright: 2009-2014 Bryan O'Sullivan
category: Math, Statistics
build-type: Simple
-cabal-version: >= 1.8
+cabal-version: >= 1.10
extra-source-files:
README.markdown
benchmark/bench.hs
@@ -46,19 +46,17 @@
tests/utils/fftw.c
tested-with:
- GHC ==7.4.2
- || ==7.6.3
- || ==7.8.4
- || ==7.10.3
- || ==8.0.2
+ GHC ==8.0.2
|| ==8.2.2
|| ==8.4.4
|| ==8.6.5
- || ==8.8.1
- , GHCJS ==8.4
-
+ || ==8.8.4
+ || ==8.10.7
+ || ==9.0.1
+ || ==9.2.1
library
+ default-language: Haskell2010
exposed-modules:
Statistics.Autocorrelation
Statistics.ConfidenceInt
@@ -76,11 +74,13 @@
Statistics.Distribution.Geometric
Statistics.Distribution.Hypergeometric
Statistics.Distribution.Laplace
+ Statistics.Distribution.Lognormal
Statistics.Distribution.Normal
Statistics.Distribution.Poisson
Statistics.Distribution.StudentT
Statistics.Distribution.Transform
Statistics.Distribution.Uniform
+ Statistics.Distribution.Weibull
Statistics.Function
Statistics.Quantile
Statistics.Regression
@@ -108,11 +108,11 @@
Statistics.Internal
Statistics.Test.Internal
Statistics.Types.Internal
- build-depends: base >= 4.5 && < 5
- , base-orphans >= 0.6 && <0.9
+ build-depends: base >= 4.9 && < 5
--
- , math-functions >= 0.3
- , mwc-random >= 0.13.0.0
+ , math-functions >= 0.3.4.1
+ , mwc-random >= 0.15.0.0
+ , random >= 1.2
--
, aeson >= 0.6.0.0
, async >= 2.2.2 && <2.3
@@ -134,7 +134,8 @@
ghc-prim
ghc-options: -O2 -Wall -fwarn-tabs -funbox-strict-fields
-test-suite tests
+test-suite statistics-tests
+ default-language: Haskell2010
type: exitcode-stdio-1.0
hs-source-dirs: tests
main-is: tests.hs
@@ -165,7 +166,6 @@
, aeson
, ieee754 >= 0.7.3
, math-functions
- , mwc-random
, primitive
, tasty
, tasty-hunit
@@ -176,4 +176,4 @@
source-repository head
type: git
- location: https://github.com/bos/statistics
+ location: https://github.com/haskell/statistics
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/tests/Tests/Distribution.hs
new/statistics-0.16.0.1/tests/Tests/Distribution.hs
--- old/statistics-0.15.2.0/tests/Tests/Distribution.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/tests/Tests/Distribution.hs 2001-09-09
03:46:40.000000000 +0200
@@ -2,10 +2,10 @@
ViewPatterns #-}
module Tests.Distribution (tests) where
-import Control.Applicative ((<$), (<$>), (<*>))
import qualified Control.Exception as E
import Data.List (find)
import Data.Typeable (Typeable)
+import Data.Word
import Numeric.MathFunctions.Constants (m_tiny,m_huge,m_epsilon)
import Numeric.MathFunctions.Comparison
import Statistics.Distribution
@@ -19,11 +19,13 @@
import Statistics.Distribution.Geometric
import Statistics.Distribution.Hypergeometric
import Statistics.Distribution.Laplace (LaplaceDistribution)
+import Statistics.Distribution.Lognormal (LognormalDistribution)
import Statistics.Distribution.Normal (NormalDistribution)
import Statistics.Distribution.Poisson (PoissonDistribution)
import Statistics.Distribution.StudentT
import Statistics.Distribution.Transform (LinearTransform)
import Statistics.Distribution.Uniform (UniformDistribution)
+import Statistics.Distribution.Weibull (WeibullDistribution)
import Statistics.Distribution.DiscreteUniform (DiscreteUniform)
import Test.Tasty (TestTree, testGroup)
import Test.Tasty.QuickCheck (testProperty)
@@ -46,8 +48,10 @@
, contDistrTests (T :: T ExponentialDistribution )
, contDistrTests (T :: T GammaDistribution )
, contDistrTests (T :: T LaplaceDistribution )
+ , contDistrTests (T :: T LognormalDistribution )
, contDistrTests (T :: T NormalDistribution )
, contDistrTests (T :: T UniformDistribution )
+ , contDistrTests (T :: T WeibullDistribution )
, contDistrTests (T :: T StudentT )
, contDistrTests (T :: T (LinearTransform NormalDistribution))
, contDistrTests (T :: T FDistribution )
@@ -71,8 +75,7 @@
contDistrTests t = testGroup ("Tests for: " ++ typeName t) $
cdfTests t ++
[ testProperty "PDF sanity" $ pdfSanityCheck t
- ] ++
- [ (if quantileIsInvCDF_enabled t then id else ignoreTest)
+ , (if quantileIsInvCDF_enabled t then id else ignoreTest)
$ testProperty "Quantile is CDF inverse" $ quantileIsInvCDF t
, testProperty "quantile fails p<0||p>1" $ quantileShouldFail t
, testProperty "log density check" $ logDensityCheck t
@@ -94,7 +97,8 @@
cdfTests t =
[ testProperty "C.D.F. sanity" $ cdfSanityCheck t
, testProperty "CDF limit at +inf" $ cdfLimitAtPosInfinity t
- , testProperty "CDF limit at -inf" $ cdfLimitAtNegInfinity t
+ , (if cdfLimitAtNegInfinity_enabled t then id else ignoreTest)
+ $ testProperty "CDF limit at -inf" $ cdfLimitAtNegInfinity t
, testProperty "CDF at +inf = 1" $ cdfAtPosInfinity t
, testProperty "CDF at -inf = 1" $ cdfAtNegInfinity t
, testProperty "CDF is nondecreasing" $ cdfIsNondecreasing t
@@ -143,11 +147,14 @@
-- CDF's complement is implemented correctly
-cdfComplementIsCorrect :: (Distribution d, Param d) => T d -> d -> Double ->
Bool
+cdfComplementIsCorrect :: (Distribution d, Param d) => T d -> d -> Double ->
Property
cdfComplementIsCorrect _ d x
- = 1 - (cumulative d x + complCumulative d x) <= tol
+ = counterexample ("err. tolerance = " ++ show tol)
+ $ counterexample ("difference = " ++ show delta)
+ $ delta <= tol
where
- tol = prec_complementCDF d
+ tol = prec_complementCDF d
+ delta = 1 - (cumulative d x + complCumulative d x)
-- CDF for discrete distribution uses <= for comparison
cdfDiscreteIsCorrect :: (Param d, DiscreteDistr d) => T d -> d -> Property
@@ -168,29 +175,34 @@
p1 = cumulative d $ fromIntegral i
dp = probability d i
relerr = ((p1 - p) - dp) / max p1 dp
- , not (p == 0 && p1 == 0 && dp == 0)
- && relerr > tol
+ , p > m_tiny || p == 0
+ , p1 > m_tiny
+ , dp > m_tiny
+ , relerr > tol
]
tol = prec_discreteCDF d
-logDensityCheck :: (ContDistr d) => T d -> d -> Double -> Property
+logDensityCheck :: (Param d, ContDistr d) => T d -> d -> Double -> Property
logDensityCheck _ d x
= not (isDenorm x)
==> ( counterexample (printf "density = %g" p)
$ counterexample (printf "logDensity = %g" logP)
$ counterexample (printf "log p = %g" (log p))
- $ counterexample (printf "eps = %g" (abs (logP - log p) / max
(abs (log p)) (abs logP)))
+ $ counterexample (printf "ulps[log] = %i" ulpsLog)
+ $ counterexample (printf "ulps[lin] = %i" ulpsLin)
$ or [ p == 0 && logP == (-1/0)
, p <= m_tiny && logP < log m_tiny
-- To avoid problems with roundtripping error in case
-- when density is computed as exponent of logDensity we
-- accept either inequality
- , (ulpDistance (log p) logP <= 32)
- || (ulpDistance p (exp logP) <= 32)
+ , (ulpsLog <= n) || (ulpsLin <= n)
])
where
- p = density d x
- logP = logDensity d x
+ p = density d x
+ logP = logDensity d x
+ n = prec_logDensity d
+ ulpsLog = ulpDistance (log p) logP
+ ulpsLin = ulpDistance p (exp logP)
-- PDF is positive
pdfSanityCheck :: (ContDistr d) => T d -> d -> Double -> Bool
@@ -201,6 +213,8 @@
complQuantileCheck _ d (Double01 p)
= counterexample (printf "x0 = %g" x0)
$ counterexample (printf "x1 = %g" x1)
+ $ counterexample (printf "abs err = %g" $ abs (x1 - x0))
+ $ counterexample (printf "rel err = %g" $ relativeError x1 x0)
-- We avoid extreme tails of distributions
--
-- FIXME: all parameters are arbitrary at the moment
@@ -208,7 +222,7 @@
, p < 0.99
, not $ isInfinite x0
, not $ isInfinite x1
- ] ==> (abs (x1 - x0) < 1e-6)
+ ] ==> (if x0 < 1e6 then abs (x1 - x0) < 1e-6 else relativeError x1 x0
< 1e-12)
where
x0 = quantile d (1 - p)
x1 = complQuantile d p
@@ -273,23 +287,26 @@
p1 = cumulative d (fromIntegral m + 0.5) - cumulative d (fromIntegral n -
0.5)
p2 = sum $ map (probability d) [n .. m]
-logProbabilityCheck :: (DiscreteDistr d) => T d -> d -> Int -> Property
+logProbabilityCheck :: (Param d, DiscreteDistr d) => T d -> d -> Int ->
Property
logProbabilityCheck _ d x
= counterexample (printf "probability = %g" p)
$ counterexample (printf "logProbability = %g" logP)
$ counterexample (printf "log p = %g" (log p))
- $ counterexample (printf "eps = %g" (abs (logP - log p) / max
(abs (log p)) (abs logP)))
+ $ counterexample (printf "ulps[log] = %i" ulpsLog)
+ $ counterexample (printf "ulps[lin] = %i" ulpsLin)
$ or [ p == 0 && logP == (-1/0)
, p < 1e-308 && logP < 609
-- To avoid problems with roundtripping error in case
-- when density is computed as exponent of logDensity we
-- accept either inequality
- , (ulpDistance (log p) logP <= 32)
- || (ulpDistance p (exp logP) <= 32)
+ , (ulpsLog <= n) || (ulpsLin <= n)
]
where
p = probability d x
logP = logProbability d x
+ n = prec_logDensity d
+ ulpsLog = ulpDistance (log p) logP
+ ulpsLin = ulpDistance p (exp logP)
-- | Parameters for distribution testing. Some distribution require
@@ -298,6 +315,9 @@
-- | Whether quantileIsInvCDF is enabled
quantileIsInvCDF_enabled :: T a -> Bool
quantileIsInvCDF_enabled _ = True
+ -- | Whether cdfLimitAtNegInfinity is enabled
+ cdfLimitAtNegInfinity_enabled :: T a -> Bool
+ cdfLimitAtNegInfinity_enabled _ = True
-- | Precision for 'quantileIsInvCDF' test
prec_quantile_CDF :: a -> (Double,Double)
prec_quantile_CDF _ = (16,16)
@@ -307,33 +327,53 @@
-- | Precision of CDF's complement
prec_complementCDF :: a -> Double
prec_complementCDF _ = 1e-14
+ -- | Precision for logDensity check
+ prec_logDensity :: a -> Word64
+ prec_logDensity _ = 32
instance Param StudentT where
-- FIXME: disabled unless incompleteBeta troubles are sorted out
quantileIsInvCDF_enabled _ = False
+
instance Param BetaDistribution where
-- FIXME: See https://github.com/bos/statistics/issues/161 for details
quantileIsInvCDF_enabled _ = False
+
instance Param FDistribution where
-- FIXME: disabled unless incompleteBeta troubles are sorted out
quantileIsInvCDF_enabled _ = False
+ -- We compute CDF and complement using same method so precision
+ -- should be very good here.
+ prec_complementCDF _ = 2 * m_epsilon
instance Param ChiSquared where
prec_quantile_CDF _ = (32,32)
instance Param BinomialDistribution where
- prec_discreteCDF _ = 1e-13
-instance Param CauchyDistribution
+ prec_discreteCDF _ = 1e-12
+ prec_logDensity _ = 48
+instance Param CauchyDistribution where
+ -- Distribution is long-tailed enough that we may never get to zero
+ cdfLimitAtNegInfinity_enabled _ = False
+
instance Param DiscreteUniform
instance Param ExponentialDistribution
-instance Param GammaDistribution
+instance Param GammaDistribution where
+ -- We lose precision near `incompleteGamma 10` because of error
+ -- introuced by exp . logGamma. This could only be fixed in
+ -- math-function by implementing gamma
+ prec_quantile_CDF _ = (24,24)
+ prec_logDensity _ = 64
instance Param GeometricDistribution
instance Param GeometricDistribution0
instance Param HypergeometricDistribution
instance Param LaplaceDistribution
+instance Param LognormalDistribution where
+ prec_quantile_CDF _ = (64,64)
instance Param NormalDistribution
instance Param PoissonDistribution
instance Param UniformDistribution
+instance Param WeibullDistribution
instance Param a => Param (LinearTransform a)
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/tests/Tests/Orphanage.hs
new/statistics-0.16.0.1/tests/Tests/Orphanage.hs
--- old/statistics-0.15.2.0/tests/Tests/Orphanage.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/tests/Tests/Orphanage.hs 2001-09-09
03:46:40.000000000 +0200
@@ -16,11 +16,13 @@
import Statistics.Distribution.Geometric
import Statistics.Distribution.Hypergeometric
import Statistics.Distribution.Laplace (LaplaceDistribution, laplace)
+import Statistics.Distribution.Lognormal (LognormalDistribution,
lognormalDistr)
import Statistics.Distribution.Normal (NormalDistribution,
normalDistr)
import Statistics.Distribution.Poisson (PoissonDistribution, poisson)
import Statistics.Distribution.StudentT
import Statistics.Distribution.Transform (LinearTransform, scaleAround)
import Statistics.Distribution.Uniform (UniformDistribution,
uniformDistr)
+import Statistics.Distribution.Weibull (WeibullDistribution,
weibullDistr)
import Statistics.Distribution.DiscreteUniform (DiscreteUniform,
discreteUniformAB)
import Statistics.Types
@@ -38,7 +40,7 @@
instance QC.Arbitrary LaplaceDistribution where
arbitrary = laplace <$> QC.choose (-10,10) <*> QC.choose (0, 2)
instance QC.Arbitrary GammaDistribution where
- arbitrary = gammaDistr <$> QC.choose (0.1,10) <*> QC.choose (0.1,10)
+ arbitrary = gammaDistr <$> QC.choose (0.1,100) <*> QC.choose (0.1,100)
instance QC.Arbitrary BetaDistribution where
arbitrary = betaDistr <$> QC.choose (1e-3,10) <*> QC.choose (1e-3,10)
instance QC.Arbitrary GeometricDistribution where
@@ -50,6 +52,9 @@
m <- QC.choose (0,l)
k <- QC.choose (1,l)
return $ hypergeometric m l k
+instance QC.Arbitrary LognormalDistribution where
+ -- can't choose sigma too big, otherwise goes outside of double-float limit
+ arbitrary = lognormalDistr <$> QC.choose (-100,100) <*> QC.choose (1e-10, 20)
instance QC.Arbitrary NormalDistribution where
arbitrary = normalDistr <$> QC.choose (-100,100) <*> QC.choose (1e-3, 1e3)
instance QC.Arbitrary PoissonDistribution where
@@ -60,6 +65,8 @@
arbitrary = do a <- QC.arbitrary
b <- QC.arbitrary `suchThat` (/= a)
return $ uniformDistr a b
+instance QC.Arbitrary WeibullDistribution where
+ arbitrary = weibullDistr <$> QC.choose (1e-3,1e3) <*> QC.choose (1e-3, 1e3)
instance QC.Arbitrary CauchyDistribution where
arbitrary = cauchyDistribution
<$> arbitrary
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/tests/Tests/Serialization.hs
new/statistics-0.16.0.1/tests/Tests/Serialization.hs
--- old/statistics-0.15.2.0/tests/Tests/Serialization.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/tests/Tests/Serialization.hs 2001-09-09
03:46:40.000000000 +0200
@@ -16,11 +16,13 @@
import Statistics.Distribution.Geometric
import Statistics.Distribution.Hypergeometric
import Statistics.Distribution.Laplace (LaplaceDistribution)
+import Statistics.Distribution.Lognormal (LognormalDistribution)
import Statistics.Distribution.Normal (NormalDistribution)
import Statistics.Distribution.Poisson (PoissonDistribution)
import Statistics.Distribution.StudentT
import Statistics.Distribution.Transform (LinearTransform)
import Statistics.Distribution.Uniform (UniformDistribution)
+import Statistics.Distribution.Weibull (WeibullDistribution)
import Statistics.Types
import Test.Tasty (TestTree, testGroup)
@@ -50,8 +52,10 @@
, serializationTests (T :: T ExponentialDistribution )
, serializationTests (T :: T GammaDistribution )
, serializationTests (T :: T LaplaceDistribution )
+ , serializationTests (T :: T LognormalDistribution )
, serializationTests (T :: T NormalDistribution )
, serializationTests (T :: T UniformDistribution )
+ , serializationTests (T :: T WeibullDistribution )
, serializationTests (T :: T StudentT )
, serializationTests (T :: T (LinearTransform NormalDistribution))
, serializationTests (T :: T FDistribution )
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/statistics-0.15.2.0/tests/Tests/Transform.hs
new/statistics-0.16.0.1/tests/Tests/Transform.hs
--- old/statistics-0.15.2.0/tests/Tests/Transform.hs 2001-09-09
03:46:40.000000000 +0200
+++ new/statistics-0.16.0.1/tests/Tests/Transform.hs 2001-09-09
03:46:40.000000000 +0200
@@ -8,7 +8,6 @@
import Data.Bits ((.&.), shiftL)
import Data.Complex (Complex((:+)))
-import Data.Functor ((<$>))
import Numeric.Sum (kbn, sumVector)
import Statistics.Function (within)
import Statistics.Transform (CD, dct, fft, idct, ifft)
