monoid-statistics 0.3 → 0.3.1

raw patch · 3 files changed

+97/−21 lines, 3 filesPVP ok

version bump matches the API change (PVP)

API changes (from Hackage documentation)

Files

• Data/Monoid/Statistics.hs +54/−10
• Data/Monoid/Statistics/Numeric.hs +36/−9
• monoid-statistics.cabal +7/−2

@@ -9,13 +9,17 @@ -- Maintainer : Alexey Khudyakov <alexey.skladnoy@gmail.com> -- Stability  : experimental -- -module Data.Monoid.Statistics ( StatMonoid(..)-                              , evalStatistic-                                -- * Statistic monoids-                              , TwoStats(..)-                                -- * Additional information-                                -- $info-                              ) where+module Data.Monoid.Statistics ( +    -- * Type class+    StatMonoid(..)+  , evalStatistic+    -- ** Examples+    -- $examples+    -- * Generic monoid+  , TwoStats(..)+    -- * Additional information+    -- $info+  ) where   import Data.Monoid@@ -32,6 +36,7 @@ -- --   Statistic could be calculated with fold over sample. Since --   accumulator is 'Monoid' such fold could be easily parralelized.+--   Check examples section for more information. -- --   Instance must satisfy following law: --@@ -92,9 +97,48 @@ -- This indeed proves that monoid could be constructed. Monoid above -- is completely impractical. It runs in O(n) space. However for some -- statistics monoids which runs in O(1) space could be--- implemented. For example mean. +-- implemented. Simple examples of such statistics are number of+-- elements in sample or mean of a sample. -- -- On the other hand some statistics could not be implemented in such -- way. For example calculation of median require O(n) space. Variance--- could be implemented in O(1) but such implementation won't be--- numerically stable. +-- could be implemented in O(1) but such implementation will have+-- problems with numberical stability.++++-- $examples+--+-- These examples show how to find maximum and minimum of a sample in+-- one pass over data.+-- +-- This is test data. It's not limited to list but could be anything+-- what could be folded.+--+-- > > let xs = [1..100] :: [Double]+-- +-- Now let calculate maximum of test sample using two methods. First+-- one is to use generic function 'evalStatistic' and another one is+-- fold.+--+-- > > evalStatistic xs :: Max+-- > Max {calcMax = 100.0}+-- > > foldl (flip pappend) mempty xs :: Max+-- > Max {calcMax = 100.0}+--+-- More complicated example allows to combine several monoids+-- together. It allows to calculate two statistics in one pass:+--+-- > > evalStatistic xs :: TwoStats Min Max+-- > TwoStats {calcStat1 = Min {calcMin = 1.0}, calcStat2 = Max {calcMax = 100.0}}+--+-- Last example shows how to calculate nuber of elements, mean and+-- variance at once:+--+-- > > let v = evalStatistic xs :: Variance+-- > > calcCount v+-- > 100+-- > > calcMean v+-- > 50.5+-- > > calcStddev v+-- > 28.86607004772212

@@ -12,6 +12,7 @@   , Variance(..)   , asVariance     -- ** Ad-hoc accessors+    -- $accessors   , CalcCount(..)   , CalcMean(..)   , CalcVariance(..)@@ -22,10 +23,6 @@   , Min(..)   ) where -import Data.Int     (Int8, Int16, Int32, Int64)-import Data.Word    (Word8,Word16,Word32,Word64,Word)-import GHC.Float    (float2Double)- import Data.Monoid import Data.Monoid.Statistics import Data.Typeable (Typeable)@@ -158,7 +155,10 @@ -- N.B. forall (x :: Double) (x <= NaN) == False instance Monoid Min where   mempty = Min (0/0)-  mappend !(Min x) !(Min y) = Min $ if x <= y then x else y+  mappend !(Min x) !(Min y) +    | isNaN x   = Min y+    | isNaN y   = Min x+    | otherwise = Min (min x y)   {-# INLINE mempty  #-}   {-# INLINE mappend #-}   @@ -166,9 +166,6 @@   pappend !x m = mappend (Min x) m   {-# INLINE pappend #-} --- -- | Calculate maximum of sample. For empty sample returns NaN. Any -- NaN encountedred will be ignored.  newtype Max = Max { calcMax :: Double }@@ -176,7 +173,10 @@  instance Monoid Max where   mempty = Max (0/0)-  mappend !(Max x) !(Max y) = Max $ if x >= y then x else y+  mappend !(Max x) !(Max y) +    | isNaN x   = Max y+    | isNaN y   = Max x+    | otherwise = Max (max x y)   {-# INLINE mempty  #-}   {-# INLINE mappend #-}   @@ -191,14 +191,41 @@ -- Ad-hoc type class ----------------------------------------------------------------   +-- $accessors+--+-- Monoids 'Count', 'Mean' and 'Variance' form some kind of tower.+-- Every successive monoid can calculate every statistics previous+-- monoids can. So to avoid replicating accessors for each statistics+-- a set of ad-hoc type classes was added. +--+-- This approach have deficiency. It becomes to infer type of monoidal+-- accumulator from accessor function so following expression will be+-- rejected:+-- +-- > calcCount $ evalStatistics xs+--+-- Indeed type of accumulator is:+--+-- > forall a . (StatMonoid a, CalcMean a) => a+--+-- Therefore it must be fixed by adding explicit type annotation. For+-- example:+--+-- > calcMean (evalStatistics xs :: Mean)++  ++-- | Statistics which could count number of elements in the sample class CalcCount m where   -- | Number of elements in sample   calcCount :: m -> Int +-- | Statistics which could estimate mean of sample class CalcMean m where   -- | Calculate esimate of mean of a sample   calcMean :: m -> Double   +-- | Statistics which could estimate variance of sample class CalcVariance m where   -- | Calculate biased estimate of variance   calcVariance         :: m -> Double

@@ -1,5 +1,7 @@++ Name:           monoid-statistics-Version:        0.3+Version:        0.3.1 Cabal-Version:  >= 1.6 License:        BSD3 License-File:   LICENSE@@ -19,7 +21,10 @@   This packages is quite similar to monoids package but limited to   calculation on statistics. In particular it makes use of   commutatitvity of statistical monoids.-+  .+  Changes:+  .+  * 0.3.1 Better documentation; Fix in Min/Max monoids  source-repository head   type:     hg