foldl-incremental 0.1.0.2 → 0.1.1.0

raw patch · 5 files changed

+54/−21 lines, 5 filesPVP: major bump suggested

API removals or changes: PVP suggests a major version bump

API changes (from Hackage documentation)

Files

• .gitignore +6/−0
• .travis.yml +6/−0
• CHANGELOG.markdown +3/−0
• foldl-incremental.cabal +12/−6
• src/Control/Foldl/Incremental.hs +27/−15

@@ -0,0 +1,6 @@+.cabal-sandbox+dist+/cabal.sandbox.config+*.hi+*.o+/TAGS

@@ -0,0 +1,6 @@+language: haskell+install:  cabal install --only-dependencies --enable-tests --enable-benchmarks+script:   cabal configure --enable-benchmarks --enable-tests && cabal build && cabal test && cabal bench+notifications:+  email:+    - tonyday567@gmail.com

@@ -0,0 +1,3 @@+0.1.1+---+* Removed Increment from API

@@ -1,5 +1,5 @@ Name:                     foldl-incremental-Version:                  0.1.0.2+Version:                  0.1.1.0 Author:                   Tony Day Maintainer:               tonyday567@gmail.com License:                  MIT@@ -9,13 +9,19 @@ Build-Type:               Simple Stability:                Experimental Cabal-Version:            >= 1.10-Extra-Source-Files:       README.markdown+Extra-Source-Files:+  .travis.yml+  .gitignore+  README.markdown+  CHANGELOG.markdown Synopsis:                 incremental folds-Description:-    `foldl-incremental` allows you to create incremental folds and scans such as moving averages or moving deviations.-    .-    It supplies `Incremental` which represents a state of an exponential moving average calculation, and `incrementalize`, which turns functions into suitable step functions.+Description: This library provides incremental statistical folds based upon the +  foldl libray.  An incremental statistical fold can be thought of as +  exponentially-weighting statistics designed to be efficient computations over +  a Foldable. +  It supplies "incrementalize" which turns any unary function into a +  "Fold".  As a reference, \"incrementalize id\" is an exponentially-weighted moving average. Homepage:                 https://github.com/tonyday567/foldl-incremental Bug-Reports:              https://github.com/tonyday567/foldl-incremental/issues Tested-With:              GHC==7.6.3

@@ -1,27 +1,42 @@-{-| This module provides incremental statistics folds based upon the foldl library+{-| This module provides incremental statistical folds based upon the foldl library -    To avoid clashes, Control.Foldl should be qualified.+An incremental statsitical fold can be thought of as exponentially-weighting statistics designed to be efficient computations over a Foldable. +Some throat clearing is required, however.++The common usage term \"exponential moving ...\" refers to the cumulative effect+of the fold referencing the original data. From the point of view of a+single step, the algorithm could be better described as \"constant proportion\" or+\"geometric\" decay. Many other methods are also possible and future versions of the library may introduce some more.++A main point of the library is that the traditional simple moving average+uses a sliding window of past data and thus requires keeping track of+the last n elements in State (in a LIFO queue most likey). It may be simple for the human brain but its a more complex and costly computational than this single-pass version.++For clarity, moving average (and moving whatever) below refers to geometric decay+rather than the common usage. So with the throat clearing out of the way:++To avoid clashes, Control.Foldl should be qualified.+ >>> import Control.Foldl.Incremental >>> import qualified Control.Foldl as L -    The folds represent incremental statistics such as `moving averages`.+The folds represent incremental statistics such as moving averages`. -    Statistics are based on exponential-weighting schemes which enable statistics to be calculated in a streaming one-pass manner.  The stream of moving averages with a `rate` of 0.9 is:+The stream of moving averages with a `rate` of 0.1 is: ->>> L.scan (incMa 0.9) [1..10]+>>> L.scan (incMa 0.1) [1..    10]  or if you just want the moving average at the end. ->>> L.fold (incMa 0.9) [1..10]+>>> L.fold (incMa 0.1) [1..10]  -}  module Control.Foldl.Incremental (-  -- * Increment-    Increment(..)-  , incrementalize-  -- * common incremental folds+    -- * incrementalize+    incrementalize+    -- * common incremental folds   , incMa   , incAbs   , incSq@@ -38,7 +53,7 @@    , _rate    :: {-# UNPACK #-} !Double    } deriving (Show) -{-| Incrementalize takes a function and turns it into a `Control.Foldl.Fold` where the step incremental is an Increment with a step function iso to a step in an exponential moving average calculation.+{-| Incrementalize takes a function and turns it into a `Control.Foldl.Fold` where the step is an Increment iso to the typical step in an exponential moving average calculation.  >>> incrementalize id @@ -52,8 +67,6 @@  >>> std r = (\s ss -> sqrt (ss - s**2)) <$> incrementalize id r <*> incrementalize (*2) r -An exponential moving average approach (where `average` id abstracted to `function`) represents an efficient single-pass computation that attempts to keep track of a running average of some Foldable.- The rate is the parameter regulating the discount of current state and the introduction of the current value.  >>> incrementalize id 1@@ -62,12 +75,11 @@  >>> incrementalize id 0 -produces the latest value (ie current state is discounted to zero)+produces the latest value (ie current state is discounted (or decays) to zero)  A exponential moving average with a duration of 10 (the average lag of the values effecting the calculation) is  >>> incrementalize id (1/10)-  -} incrementalize :: (a -> Double) -> Double -> Fold a Double