srtree 2.0.0.3 → 2.0.0.4

raw patch · 3 files changed

+6/−96 lines, 3 files

Files

• ChangeLog.md +4/−0
• srtree.cabal +1/−1
• test/Spec.hs +1/−95

@@ -1,5 +1,9 @@ # Changelog for srtree +## 2.0.0.4++- Cleaned up test cases (they were deprecated), will include new ones later + ## 2.0.0.3  - Fixed compatibility with random-1.3.0 and GHC-9.12.1 

@@ -5,7 +5,7 @@ -- see: https://github.com/sol/hpack  name:           srtree-version:        2.0.0.3+version:        2.0.0.4 synopsis:       A general library to work with Symbolic Regression expression trees. description:    A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category:       Math, Data, Data Structures

@@ -1,98 +1,4 @@-import Data.SRTree-import Data.SRTree.Eval-import Data.SRTree.Derivative-import Data.SRTree.Datasets-import Algorithm.SRTree.AD--import qualified Data.Vector as V-import Numeric.AD.Double ( grad ) import Test.HUnit -import qualified Data.Massiv.Array as M-import Data.Massiv.Array (D, S, Ix1, Ix2, Comp(..), Sz(..))-import qualified Foreign as M --- test expressions-exprs = [-    param 0 * sin ( param 1)-  , sin (param 0) + cos (param 1)-  , 0.5 * sin (param 0) + 0.7 * cos (param 1)-  , log (param 0) + param 0 * param 1 - sin (param 1)-  , 1 / param 0 * param 1-  , param 0 + param 1 + param 0 * param 1 + sin (param 0) + sin (param 1) + cos (param 0) + cos (param 1) + sin (param 0 * param 1) + cos (param 0 * param 1)-  , sin (exp (param 0) + param 1)-  , param 0 / param 1-  , param 0 ** param 1-  ]---- autodiff with multiple occurrences of vars-autoDiffMult :: [[Double]]-autoDiffMult =  [ grad (\[x,y] -> x * sin y) [2,3]-          , grad (\[x,y] -> sin x + cos y) [2,3]-          , grad (\[x,y] -> 0.5 * sin x + 0.7 * cos y) [2,3]-          , grad (\[x,y] -> log x + x*y - sin y) [2,3]-          , grad (\[x,y] -> 1 / x * y) [2,3]-          , grad (\[x,y] -> x + y + x * y + sin x + sin y + cos x + cos y + sin (x * y) + cos (x * y)) [2,3]-          , grad (\[x,y] -> sin (exp x + y)) [2,3]-          , grad (\[x,y] -> x/y) [2,3]-          , grad (\[x,y] -> x ** y) [2,3]-          ]---- autodiff with single occurrences of vars-autoDiffSingle :: [[Double]]-autoDiffSingle = [ grad (\[x,y] -> x * sin y) [2,3]-          , grad (\[x,y] -> sin x + cos y) [2,3]-          , grad (\[x,y] -> 0.5 * sin x + 0.7 * cos y) [2,3]-          , grad (\[x,y,v,w] -> log x + y*v - sin w) [2,3,2,3]-          , grad (\[x,y] -> 1 / x * y) [2,3]-          , grad (\[a,b,c,d,e,f,g,h,i,j,k,l] -> a + b + c * d + sin e + sin f + cos g + cos h + sin (i * j) + cos (k * l)) [2,3,2,3,2,3,2,3,2,3,2,3]-          , grad (\[x,y] -> sin (exp x + y)) [2,3]-          , grad (\[x,y] -> x/y) [2,3]-          , grad (\[x,y] -> x ** y) [2,3]-          ]---- xs is empty since we are interested in theta-xs :: M.Array S Ix2 Double-xs = M.singleton 0--xs' :: M.Array S Ix2 Double -xs' = M.singleton 0 --err = M.singleton 1---- theta values-thetaMulti, thetaSingle :: M.Array S Ix1 Double-thetaMulti  = M.fromList Seq [2.0, 3.0]-thetaSingle = M.fromList Seq [2.0, 3.0, 2.0, 3.0, 2.0, 3.0, 2.0, 3.0, 2.0, 3.0, 2.0, 3.0]---- values from forward mode--- forwardVals :: [[Double]]-forwardVals = map (M.toList . snd . forwardMode xs' thetaMulti err) exprs---- values from grad--- we must relabel the parameters of the expression to sequence values---gradVals :: [(Double, [Double])]-gradVals = map (M.toList . snd . forwardModeUnique xs' thetaSingle err . relabelParams) exprs---gradVals' = map (M.toList . snd . reverseModeUnique xs' thetaSingle err . relabelParams) exprs---- values of the evaluated expressions---exprVals :: [Double]-exprVals = map (evalTree xs' thetaSingle . relabelParams) exprs----refGrad :: [(Double, [Double])]-refGrad = zip exprVals autoDiffSingle--testDiff :: (Eq a, Show a) => String -> String -> a -> a -> Test-testDiff lbl name a b = TestLabel lbl $ TestCase (assertEqual name a b)--tests :: Test-tests = TestList $-     zipWith (testDiff "forward mode" "autodiff x forward mode") autoDiffMult forwardVals-  <> zipWith (testDiff "forward mode" "autodiff x forward mode unique") autoDiffSingle gradVals-  -- <> zipWith (testDiff "reverse mode" "autodiff x reverse mode unique") autoDiffSingle gradVals'- main :: IO ()-main = do-    result <- runTestTT tests-    putStrLn $ showCounts result-    --ds <- loadDataset "test/wine.csv:3:10:alcohol:liver,deaths,heart" True-    --print ds+main = pure ()