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|
{- |
Module : Numeric.GSL.Fitting
Copyright : (c) Alberto Ruiz 2010
License : GPL
Maintainer : Alberto Ruiz (aruiz at um dot es)
Stability : provisional
Portability : uses ffi
Nonlinear Least-Squares Fitting
<http://www.gnu.org/software/gsl/manual/html_node/Nonlinear-Least_002dSquares-Fitting.html>
The example program in the GSL manual (see examples/fitting.hs):
@dat = [
([0.0],([6.0133918608118675],0.1)),
([1.0],([5.5153769909966535],0.1)),
([2.0],([5.261094606015287],0.1)),
...
([39.0],([1.0619821710802808],0.1))]
expModel [a,lambda,b] [t] = [a * exp (-lambda * t) + b]
expModelDer [a,lambda,b] [t] = [[exp (-lambda * t), -t * a * exp(-lambda*t) , 1]]
(sol,path) = fitModelScaled 1E-4 1E-4 20 (expModel, expModelDer) dat [1,0,0]
\> path
(6><5)
[ 1.0, 76.45780563978782, 1.6465931240727802, 1.8147715267618197e-2, 0.6465931240727797
, 2.0, 37.683816318260355, 2.858760367632973, 8.092094813253975e-2, 1.4479636296208662
, 3.0, 9.5807893736187, 4.948995119561291, 0.11942927999921617, 1.0945766509238248
, 4.0, 5.630494933603935, 5.021755718065913, 0.10287787128056883, 1.0338835440862608
, 5.0, 5.443976278682909, 5.045204331329302, 0.10405523433131504, 1.019416067207375
, 6.0, 5.4439736648994685, 5.045357818922331, 0.10404905846029407, 1.0192487112786812 ]
\> sol
[(5.045357818922331,6.027976702418132e-2),
(0.10404905846029407,3.157045047172834e-3),
(1.0192487112786812,3.782067731353722e-2)]@
-}
-----------------------------------------------------------------------------
module Numeric.GSL.Fitting (
-- * Levenberg-Marquardt
nlFitting, FittingMethod(..),
-- * Utilities
fitModelScaled, fitModel
) where
import Data.Packed.Internal
import Numeric.LinearAlgebra
import Numeric.GSL.Internal
import Foreign.Ptr(FunPtr, freeHaskellFunPtr)
import Foreign.C.Types
import System.IO.Unsafe(unsafePerformIO)
-------------------------------------------------------------------------
data FittingMethod = LevenbergMarquardtScaled -- ^ Interface to gsl_multifit_fdfsolver_lmsder. This is a robust and efficient version of the Levenberg-Marquardt algorithm as implemented in the scaled lmder routine in minpack. Minpack was written by Jorge J. More, Burton S. Garbow and Kenneth E. Hillstrom.
| LevenbergMarquardt -- ^ This is an unscaled version of the lmder algorithm. The elements of the diagonal scaling matrix D are set to 1. This algorithm may be useful in circumstances where the scaled version of lmder converges too slowly, or the function is already scaled appropriately.
deriving (Enum,Eq,Show,Bounded)
-- | Nonlinear multidimensional least-squares fitting.
nlFitting :: FittingMethod
-> Double -- ^ absolute tolerance
-> Double -- ^ relative tolerance
-> Int -- ^ maximum number of iterations allowed
-> (Vector Double -> Vector Double) -- ^ function to be minimized
-> (Vector Double -> Matrix Double) -- ^ Jacobian
-> Vector Double -- ^ starting point
-> (Vector Double, Matrix Double) -- ^ solution vector and optimization path
nlFitting method epsabs epsrel maxit fun jac xinit = nlFitGen (fi (fromEnum method)) fun jac xinit epsabs epsrel maxit
nlFitGen m f jac xiv epsabs epsrel maxit = unsafePerformIO $ do
let p = dim xiv
n = dim (f xiv)
fp <- mkVecVecfun (aux_vTov (checkdim1 n p . f))
jp <- mkVecMatfun (aux_vTom (checkdim2 n p . jac))
rawpath <- createMatrix RowMajor maxit (2+p)
app2 (c_nlfit m fp jp epsabs epsrel (fi maxit) (fi n)) vec xiv mat rawpath "c_nlfit"
let it = round (rawpath @@> (maxit-1,0))
path = takeRows it rawpath
[sol] = toRows $ dropRows (it-1) path
freeHaskellFunPtr fp
freeHaskellFunPtr jp
return (subVector 2 p sol, path)
foreign import ccall safe "nlfit"
c_nlfit:: CInt -> FunPtr TVV -> FunPtr TVM -> Double -> Double -> CInt -> CInt -> TVM
-------------------------------------------------------
checkdim1 n _p v
| dim v == n = v
| otherwise = error $ "Error: "++ show n
++ " components expected in the result of the function supplied to nlFitting"
checkdim2 n p m
| rows m == n && cols m == p = m
| otherwise = error $ "Error: "++ show n ++ "x" ++ show p
++ " Jacobian expected in nlFitting"
------------------------------------------------------------
err (model,deriv) dat vsol = zip sol errs where
sol = toList vsol
c = max 1 (chi/sqrt (fromIntegral dof))
dof = length dat - (rows cov)
chi = norm2 (fromList $ cost (resMs model) dat sol)
js = fromLists $ jacobian (resDs deriv) dat sol
cov = inv $ trans js <> js
errs = toList $ scalar c * sqrt (takeDiag cov)
-- | Higher level interface to 'nlFitting' 'LevenbergMarquardtScaled'. The optimization function and
-- Jacobian are automatically built from a model f vs x = y and its derivatives, and a list of
-- instances (x, (y,sigma)) to be fitted.
fitModelScaled
:: Double -- ^ absolute tolerance
-> Double -- ^ relative tolerance
-> Int -- ^ maximum number of iterations allowed
-> ([Double] -> x -> [Double], [Double] -> x -> [[Double]]) -- ^ (model, derivatives)
-> [(x, ([Double], Double))] -- ^ instances
-> [Double] -- ^ starting point
-> ([(Double, Double)], Matrix Double) -- ^ (solution, error) and optimization path
fitModelScaled epsabs epsrel maxit (model,deriv) dt xin = (err (model,deriv) dt sol, path) where
(sol,path) = nlFitting LevenbergMarquardtScaled epsabs epsrel maxit
(fromList . cost (resMs model) dt . toList)
(fromLists . jacobian (resDs deriv) dt . toList)
(fromList xin)
-- | Higher level interface to 'nlFitting' 'LevenbergMarquardt'. The optimization function and
-- Jacobian are automatically built from a model f vs x = y and its derivatives, and a list of
-- instances (x,y) to be fitted.
fitModel :: Double -- ^ absolute tolerance
-> Double -- ^ relative tolerance
-> Int -- ^ maximum number of iterations allowed
-> ([Double] -> x -> [Double], [Double] -> x -> [[Double]]) -- ^ (model, derivatives)
-> [(x, [Double])] -- ^ instances
-> [Double] -- ^ starting point
-> ([Double], Matrix Double) -- ^ solution and optimization path
fitModel epsabs epsrel maxit (model,deriv) dt xin = (toList sol, path) where
(sol,path) = nlFitting LevenbergMarquardt epsabs epsrel maxit
(fromList . cost (resM model) dt . toList)
(fromLists . jacobian (resD deriv) dt . toList)
(fromList xin)
cost model ds vs = concatMap (model vs) ds
jacobian modelDer ds vs = concatMap (modelDer vs) ds
-- | Model-to-residual for association pairs with sigma, to be used with 'fitModel'.
resMs :: ([Double] -> x -> [Double]) -> [Double] -> (x, ([Double], Double)) -> [Double]
resMs m v = \(x,(ys,s)) -> zipWith (g s) (m v x) ys where g s a b = (a-b)/s
-- | Associated derivative for 'resMs'.
resDs :: ([Double] -> x -> [[Double]]) -> [Double] -> (x, ([Double], Double)) -> [[Double]]
resDs m v = \(x,(_,s)) -> map (map (/s)) (m v x)
-- | Model-to-residual for association pairs, to be used with 'fitModel'. It is equivalent
-- to 'resMs' with all sigmas = 1.
resM :: ([Double] -> x -> [Double]) -> [Double] -> (x, [Double]) -> [Double]
resM m v = \(x,ys) -> zipWith g (m v x) ys where g a b = a-b
-- | Associated derivative for 'resM'.
resD :: ([Double] -> x -> [[Double]]) -> [Double] -> (x, [Double]) -> [[Double]]
resD m v = \(x,_) -> m v x
|
