Test GMM; it fails

1 files changed, 0 insertions, 114 deletions

diff --git a/bench/Bench/GMM.hs b/bench/Bench/GMM.hs
deleted file mode 100644
index 9b84d23..0000000
--- a/bench/Bench/GMM.hs
+++ /dev/null
@@ -1,114 +0,0 @@
-{-# LANGUAGE DataKinds #-}
-{-# LANGUAGE OverloadedLabels #-}
-{-# LANGUAGE TypeApplications #-}
-module Bench.GMM where
-
-import Language
-
-
-type R = TScal TF64
-type I64 = TScal TI64
-type TVec = TArr (S Z)
-type TMat = TArr (S (S Z))
-
--- N, D, K: integers > 0
--- alpha, M, Q, L: the active parameters
--- X: inactive data
--- m: integer
--- k1: 1/2 N D log(2 pi)
--- k2: 1/2 gamma^2
--- k3: K * (n' D (log(gamma) - 1/2 log(2)) - log MultiGamma(1/2 n', D))
---       where n' = D + m + 1
---
--- Inputs from the file are: N, D, K, alpha, M, Q, L, gamma, m.
---
--- See:
--- - "A benchmark of selected algorithmic differentiation tools on some problems
---   in computer vision and machine learning". Optim. Methods Softw. 33(4-6):
---   889-906 (2018).
---   <https://www.tandfonline.com/doi/full/10.1080/10556788.2018.1435651>
---   <https://github.com/microsoft/ADBench>
--- - 2021 Tom Smeding: “Reverse Automatic Differentiation for Accelerate”.
---   Master thesis at Utrecht University. (Appendix B.1)
---   <https://studenttheses.uu.nl/bitstream/handle/20.500.12932/38958/report.pdf?sequence=1&isAllowed=y>
---   <https://tomsmeding.com/f/master.pdf>
-objective :: Ex [R, R, R, I64, TMat R, TMat R, TMat R, TMat R, TVec R, I64, I64, I64] R
-objective = fromNamed $
-  lambda #N $ lambda #D $ lambda #K $
-  lambda #alpha $ lambda #M $ lambda #Q $ lambda #L $
-  lambda #X $ lambda #m $
-  lambda #k1 $ lambda #k2 $ lambda #k3 $
-  body $
-    let -- We have:
-        -- sum (exp (x - max(x)))
-        -- = sum (exp x / exp (max(x)))
-        -- = sum (exp x) / exp (max(x))
-        -- Hence:
-        -- sum (exp x) = sum (exp (x - max(x))) * exp (max(x))    (*)
-        --
-        -- So:
-        -- d/dxi log (sum (exp x))
-        -- = 1/(sum (exp x)) * d/dxi sum (exp x)
-        -- = 1/(sum (exp x)) * sum (d/dxi exp x)
-        -- = 1/(sum (exp x)) * exp xi
-        -- = exp xi / sum (exp x)
-        -- (by (*))
-        -- = exp xi / (sum (exp (x - max(x))) * exp (max(x)))
-        -- = exp (xi - max(x)) / sum (exp (x - max(x)))
-        logsumexp' = lambda @(TVec R) #vec $ body $
-                       custom (#_ :-> #v :->
-                                 let_ #m (idx0 (maximum1i #v)) $
-                                   log (idx0 (sum1i (map_ (#x :-> exp (#x - #m)) #v))) + #m)
-                              (#_ :-> #v :->
-                                 let_ #m (idx0 (maximum1i #v)) $
-                                 let_ #ex (map_ (#x :-> exp (#x - #m)) #v) $
-                                 let_ #s (idx0 (sum1i #ex)) $
-                                   pair (log #s + #m)
-                                        (pair #ex #s))
-                              (#tape :-> #d :->
-                                 map_ (#exi :-> #exi / snd_ #tape * #d) (fst_ #tape))
-                              nil #vec
-        logsumexp v = inline logsumexp' (SNil .$ v)
-
-        mulmatvec = lambda @(TMat R) #mat $ lambda @(TVec R) #vec $ body $
-                      let_ #hei (snd_ (fst_ (shape #mat))) $
-                      let_ #wid (snd_ (shape #mat)) $
-                        build1 #hei $ #i :->
-                          idx0 (sum1i (build1 #wid $ #j :->
-                                         #mat ! pair (pair nil #i) #j * #vec ! pair nil #j))
-        m *@ v = inline mulmatvec (SNil .$ m .$ v)
-
-        subvec = lambda @(TVec R) #a $ lambda @(TVec R) #b $ body $
-                    build1 (snd_ (shape #a)) $ #i :-> #a ! pair nil #i - #b ! pair nil #i
-        a .- b = inline subvec (SNil .$ a .$ b)
-
-        matrow = lambda @(TMat R) #mat $ lambda @TIx #i $ body $
-                   build1 (snd_ (shape #mat)) (#j :-> #mat ! pair (pair nil #i) #j)
-        m .! i = inline matrow (SNil .$ m .$ i)
-
-        normsq' = lambda @(TVec R) #vec $ body $
-                    idx0 (sum1i (build (SS SZ) (shape #vec) (#i :-> let_ #x (#vec ! #i) $ #x * #x)))
-        normsq v = inline normsq' (SNil .$ v)
-
-        qmat' = lambda @(TVec R) #q $ lambda @(TVec R) #l $ body $
-                  let_ #n (snd_ (shape #q)) $
-                    build (SS (SS SZ)) (pair (pair nil #n) #n) $ #idx :->
-                      let_ #i (snd_ (fst_ #idx)) $
-                      let_ #j (snd_ #idx) $
-                        if_ (#i .== #j)
-                          (exp (#q ! pair nil #i))
-                          (if_ (#i .> #j)
-                            (toFloat_ $ #i * (#i - 1) `idiv` 2 + 1 + #j)
-                            0.0)
-        qmat q l = inline qmat' (SNil .$ q .$ l)
-    in - #k1
-       + idx0 (sum1i (build1 #N $ #i :->
-             logsumexp (build1 #K $ #k :->
-                 #alpha ! pair nil #k
-                 + idx0 (sum1i (#Q .! #k))
-                 - 0.5 * normsq (qmat (#Q .! #k) (#L .! #k) *@ ((#X .! #i) .- (#M .! #k))))))
-       - toFloat_ #N * logsumexp #alpha
-       + idx0 (sum1i (build1 #K $ #k :->
-                        #k2 * (normsq (map_ (#x :-> exp #x) (#Q .! #k)) + normsq (#L .! #k))
-                        - toFloat_ #m * idx0 (sum1i (#Q .! #k))))
-       - #k3