packages feed

maxent (empty) → 0.1.0.0

raw patch · 5 files changed

+224/−0 lines, 5 filesdep +addep +basedep +nonlinear-optimizationsetup-changed

Dependencies added: ad, base, nonlinear-optimization, vector

Files

+ LICENSE view
@@ -0,0 +1,30 @@+Copyright (c) 2013, Jonathan Fischoff++All rights reserved.++Redistribution and use in source and binary forms, with or without+modification, are permitted provided that the following conditions are met:++    * Redistributions of source code must retain the above copyright+      notice, this list of conditions and the following disclaimer.++    * Redistributions in binary form must reproduce the above+      copyright notice, this list of conditions and the following+      disclaimer in the documentation and/or other materials provided+      with the distribution.++    * Neither the name of Jonathan Fischoff nor the names of other+      contributors may be used to endorse or promote products derived+      from this software without specific prior written permission.++THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ Setup.hs view
@@ -0,0 +1,2 @@+import Distribution.Simple+main = defaultMain
+ maxent.cabal view
@@ -0,0 +1,78 @@+-- Initial maxent.cabal generated by cabal init.  For further +-- documentation, see http://haskell.org/cabal/users-guide/++-- The name of the package.+name:                maxent++-- The package version.  See the Haskell package versioning policy (PVP) +-- for standards guiding when and how versions should be incremented.+-- http://www.haskell.org/haskellwiki/Package_versioning_policy+-- PVP summary:      +-+------- breaking API changes+--                   | | +----- non-breaking API additions+--                   | | | +--- code changes with no API change+version:             0.1.0.0++-- A short (one-line) description of the package.+synopsis:            Compute Maximum Entropy Distrubtions++-- A longer description of the package.+description: Use this package to compute maximum entropy distributions given a list of values and+  list of constraints.+  .+  Here is a the example from Probability the Logic of Science +  .+   > maxent ([1,2,3], [average 1.5])+  .+  Right [0.61, 0.26, 0.11]+  .+  The classic dice example+  .+   > maxent ([1,2,3,4,5,6], [average 4.5])+  .+  Right [.05, .07, 0.11, 0.16, 0.23, 0.34]+  +  I will document this more ... soonish+  +-- URL for the project homepage or repository.+homepage:            https://github.com/jfischoff/maxent++-- The license under which the package is released.+license:             BSD3++-- The file containing the license text.+license-file:        LICENSE++-- The package author(s).+author:              Jonathan Fischoff++-- An email address to which users can send suggestions, bug reports, and +-- patches.+maintainer:          jonathangfischoff@gmail.com++-- A copyright notice.+-- copyright:           ++category:            Math++build-type:          Simple++-- Constraint on the version of Cabal needed to build this package.+cabal-version:       >=1.8+++library+  -- Modules exported by the library.+  exposed-modules:     MaxEnt+  +  -- Modules included in this library but not exported.+  other-modules:     MaxEnt.Internal+  +  -- Other library packages from which modules are imported.+  build-depends: base ==4.6.*,+                 nonlinear-optimization ==0.3.*,+                 vector ==0.9.*, +                 ad ==3.2.*+  +  -- Directories containing source files.+  hs-source-dirs:      src+
+ src/MaxEnt.hs view
@@ -0,0 +1,27 @@+-- |+-- Use this package to compute maximum entropy distributions given a list of values and+-- list of constraints.+-- +-- Here is a the example from Probability the Logic of Science+-- +-- > maxent ([1,2,3], [average 1.5])+-- +-- Right [0.61, 0.26, 0.11]+-- +-- The classic dice example+-- +-- > maxent ([1,2,3,4,5,6], [average 4.5])+-- +-- Right [.05, .07, 0.11, 0.16, 0.23, 0.34]+module MaxEnt (+    Constraint,+    constraint,+    average,+    variance,+    maxent+) where+import MaxEnt.Internal (Constraint,+                        constraint,+                        average,+                        variance,+                        maxent)
+ src/MaxEnt/Internal.hs view
@@ -0,0 +1,87 @@+{-# LANGUAGE TupleSections, Rank2Types #-}+module MaxEnt.Internal where+import Numeric.Optimization.Algorithms.HagerZhang05+import qualified Data.Vector.Unboxed as U+import qualified Data.Vector.Storable as S+import Numeric.AD+import GHC.IO                   (unsafePerformIO)++sumWith :: Num c => (a -> b -> c) -> [a] -> [b] -> c +sumWith f xs = sum . zipWith f xs++pOfK :: Floating a => [a] -> [a -> a] -> [a] -> Int -> a+pOfK values fs ls k = exp (negate . sumWith (\l f -> l * f (values !! k)) ls $ fs) / +    partitionFunc values fs ls ++probs :: Floating b => [b] -> [b -> b] -> [b] -> [b]    +probs values fs ls = map (pOfK values fs ls) [0..length values - 1] ++partitionFunc :: Floating a => [a] -> [a -> a] -> [a] -> a+partitionFunc values fs ls = sum $ [ exp ((-l) * f x) | x <- values, (f, l) <- zip fs ls]++objectiveFunc :: Floating a => [a] -> [a -> a] -> [a] -> [a] -> a+objectiveFunc values fs moments ls = log (partitionFunc values fs ls) + sumWith (*) ls moments++toFunction :: (forall a. Floating a => [a] -> a) -> Function Simple+toFunction f = VFunction (f . U.toList)++toGradient :: (forall a. Floating a => [a] -> a) -> Gradient Simple+toGradient f = VGradient (U.fromList . grad f . U.toList)++toDoubleF :: (forall a. Floating a => [a] -> a) -> [Double] -> Double+toDoubleF f x = f x ++-- | Constraint type. Think of this as f and c in sum pi (f x) = c+type Constraint a = (a -> a, a)++-- make a constraint from function and constant+constraint :: Floating a => (a -> a) -> a -> Constraint a+constraint = (,)++-- The average constraint+average :: Floating a => a -> Constraint a+average m = constraint id m++-- The variance constraint+variance :: Floating a => a -> Constraint a+variance sigma = constraint (^(2 :: Int)) sigma++-- | The main entry point for computing discrete maximum entropy distributions.+--   +maxent :: (forall a. Floating a => ([a], [Constraint a])) -- ^ A pair of values that the distributions is over and the constraints+       -> Either (Result, Statistics) [Double] -- ^ Either the a discription of what wrong or the probability distribution +maxent params = result where+    obj :: Floating a => [a] -> a+    obj = uncurry (objectiveFunc values) fsmoments+    +    values :: Floating a => [a]+    values = fst params+    +    constraints :: Floating a => [(a -> a, a)]+    constraints = snd params+    +    fsmoments :: Floating a => ([a -> a], [a])+    fsmoments = unzip constraints +    +    fs :: [Double -> Double]+    fs = fst fsmoments+    +    -- hmm maybe there is a better way to get rid of the defaulting+    guess = U.fromList $ replicate +        (length (constraints :: [(Double -> Double, Double)])) (1.0 :: Double) +    +    result = case unsafePerformIO (optimize defaultParameters 0.00001 guess +                        (toFunction obj)+                        (toGradient obj)+                        Nothing) of+        (vs, ToleranceStatisfied, _) -> Right $ probs values fs (S.toList vs)+        (_, x, y) -> Left (x, y)++--test = maxent ([1.0,2.0,3.0], [average 1.5])+++    +    +++