[project @ 1999-06-09 09:35:54 by simonpj]

[ghc-hetmet.git] / ghc / tests / programs / okeefe_neural / Main.hs

module Main(main) where

newtype RNG_State = RNG_State (Int, Int, Int)

new_random i = RNG_State (i `mod` 30269, i `mod` 30307, i `mod` 30323)

next_unit_random (RNG_State (a,b,c)) =
  (r, RNG_State (a1,b1,c1))
  where a1 = (a * 171) `mod` 30269
        b1 = (b * 172) `mod` 30307
        c1 = (c * 170) `mod` 30323
        t  = fromInt a1/30269.0 +
             fromInt b1/30307.0 +
             fromInt c1/30323.0
        r  = t - fromInt (truncate t)

next_random_bool p s =
  (r < p, s1)
  where (r,s1) = next_unit_random s

next_random_list f n s =
  loop [] n s
  where loop ps 0 s = (ps, s)
        loop ps m s = loop (p:ps) (m-1) s1
                      where (p,s1) = f s

rpart []     _ _ ps qs s = ((ps, qs), s)
rpart (x:xs) n d ps qs s =
    if   d*r < n
    then rpart xs (n-1.0) (d-1.0) (x:ps) qs s1
    else rpart xs    n    (d-1.0) ps (x:qs) s1
    where (r, s1) = next_unit_random s

random_permutation xs s =
  rperm xs (length xs) [] s
  where rperm []      0 ps s = (ps, s)
        rperm [x1]    1 ps s = ((x1:ps), s)
        rperm [x1,x2] 2 ps s =
          if b then (x1:x2:ps, s1) else (x2:x1:ps, s1)
          where (b, s1) = next_random_bool 0.5 s
        rperm xs d ps s =
          let n = d `div` 2
              ((ys, zs), s1) = rpart xs (fromInt n) (fromInt d) [] [] s
              (zps, s2) = rperm zs (d-n) ps s1
          in rperm ys n zps s2
          
minit m n g =
  outer (m-1) []
  where outer (-1) xss = xss
        outer m xss = outer (m-1) (inner (n-1) [] : xss)
                      where inner (-1) xs = xs
                            inner n    xs = inner (n-1) (g m n : xs)

vadd [] [] = []
vadd (x:xs) (y:ys) = x+y : vadd xs ys

madd [] [] = []
madd (xs:xss) (ys:yss) = vadd xs ys : madd xss yss

vaddx [] [] _ = []
vaddx (x:xs) (y:ys) a = x+y*a : vaddx xs ys a

vsub [] [] = []
vsub (x:xs) (y:ys) = x-y : vsub xs ys

vmulx [] _ = []
vmulx (x:xs) a = x*a : vmulx xs a

mmulx [] _ = []
mmulx (xs:xss) a = vmulx xs a : mmulx xss a

vdist xs ys =
  loop xs ys 0.0
  where loop []     []     s = s
        loop (x:xs) (y:ys) s = loop xs ys (s+d*d)
                               where d = x-y

vdot xs ys =
  loop xs ys 0.0
  where loop []     []     s = s
        loop (x:xs) (y:ys) s = loop xs ys (s+x*y)

vmmul []     []     = []
vmmul (x:xs) (r:rs) =
  loop xs rs (vmulx r x)
  where loop []     []     ws = ws
        loop (x:xs) (r:rs) ws = loop xs rs (vaddx ws r x)

vupdate []     _  []     _ = []
vupdate (r:rs) xs (y:ys) c = vaddx r xs (y*c) : vupdate rs xs ys c

vscale [] [] = []
vscale (x:xs) (y:ys) = x*y*(1.0-y) : vscale xs ys

sigmoid x = 1.0/(1.0 + exp (0.0-x))

mrand m n l u s =
  next_random_list (next_random_list f n) m s
  where f s = (l + (u-l)*r, s1)
        (r, s1) = next_unit_random s

train pats i_h_wt i_h_ch h_o_wt h_o_ch epochs s =
  loop pats1 i_h_ch h_o_ch 0.0
  where
    (pats1, s1) = random_permutation pats s
    loop [] i_h_ch h_o_ch e0
      | e <  criterion = ((e,0,i_h_wt,h_o_wt), s1)
      | epochs <= 0    = ((e,1,i_h_wt,h_o_wt), s1)
      | True =
        train pats (madd i_h_wt i_h_ch) (mmulx i_h_ch momentum)
                   (madd h_o_wt h_o_ch) (mmulx h_o_ch momentum) (epochs-1) s1
      where e = e0/(fromInt (length pats) * fromInt (length h_o_wt))
    loop ((in_pat, out_pat):ps) i_h_ch h_o_ch e =
        loop ps (vupdate i_h_ch in_act  (tail hid_err) rate)
                (vupdate h_o_ch hid_act out_err        rate)
                (e + vdist out_pat out_act)
        where
          in_act  = 1.0:in_pat
          hid_act = 1.0:map (\w -> sigmoid (vdot in_act  w)) i_h_wt
          out_act =     map (\w -> sigmoid (vdot hid_act w)) h_o_wt
          out_err = vscale (vsub out_pat out_act) out_act
          hid_err = vscale (vmmul out_err h_o_wt) hid_act

main = putStrLn (show ans)
    where (in1,out1)   = head patterns
          nin          = length in1
          nhid         = nin
          nout         = length out1
          s0           = new_random 353721
          (i_h_wt, s1) = mrand nhid (nin +1) (0.0-0.3) 0.3 s0
          i_h_ch       = minit nhid (nin +1) (\i j -> 0.0)
          (h_o_wt, s2) = mrand nout (nhid+1) (0.0-0.3) 0.3 s1
          h_o_ch       = minit nout (nhid+1) (\i j -> 0.0)
          (ans, _)     = train patterns i_h_wt i_h_ch h_o_wt h_o_ch epochs s2

rate = 0.05

momentum = 0.9

criterion = 0.01

--epochs = 100

--patterns = [([1.0,0.0],[1.0]),
--            ([0.0,1.0],[1.0]),
--            ([0.0,0.0],[0.0]),
--            ([1.0,1.0],[0.0])]

epochs = 90

patterns =
 [([0.955809, 0.095706, 0.269204, 0.824671, 0.448012, 0.722709,
    0.327677, 0.148595, 0.063143, 0.007019, 0.135166, 0.928800, 0.298593,
    0.931150, 0.711234, 0.432630, 0.913450, 0.545061, 0.651082, 0.692984,
    0.371532, 0.940641, 0.202521, 0.064852, 0.965941, 0.523637, 0.914884,
    0.159062, 0.031648, 0.866848, 0.516526, 0.684317, 0.152135, 0.923734,
    0.177435, 0.228339, 0.766503, 0.885159, 0.812220, 0.510208, 0.082949,
    0.459792, 0.580920, 0.134190, 0.705741, 0.859523, 0.243171, 0.377117,
    0.880551, 0.271218, 0.521989, 0.039888, 0.765862, 0.096347, 0.133702,
    0.895138, 0.635548, 0.738853, 0.640126, 0.761162, 0.419599, 0.223579,
    0.779260, 0.734947, 0.488937, 0.426038, 0.430799, 0.613788, 0.090487,
    0.194372, 0.872494, 0.038179, 0.339183, 0.145482, 0.483932, 0.386425,
    0.172430, 0.416150, 0.553850, 0.952422, 0.174200, 0.150365, 0.347056,
    0.438063, 0.674154, 0.004456, 0.087527, 0.291269, 0.028474, 0.450819,
    0.013642, 0.920896, 0.488815, 0.161840, 0.162084, 0.319803, 0.564989,
    0.255287, 0.036348, 0.127750, 0.288461, 0.681906, 0.816370, 0.664449,
    0.282235, 0.537126, 0.265328, 0.964965, 0.043672, 0.577898, 0.990173,
    0.348186, 0.346446, 0.725700, 0.211463, 0.549821, 0.167943, 0.837062,
    0.700003, 0.950896, 0.048341, 0.523087, 0.034761, 0.933439, 0.287393,
    0.299081, 0.840144, 0.894284, 0.885464, 0.845302, 0.157476, 0.948210,
    0.635060, 0.660421, 0.607990, 0.852107, 0.268868, 0.888760, 0.502518,
    0.180609, 0.070650, 0.602802, 0.555223, 0.285440, 0.855495, 0.321665,
    0.356700, 0.068758, 0.725059, 0.047487, 0.263344, 0.102695, 0.504959,
    0.600452, 0.858974, 0.125217, 0.269662, 0.533982, 0.644642, 0.444838,
    0.861721, 0.411145, 0.113712, 0.284371, 0.811182, 0.723930, 0.867702,
    0.595538, 0.561968, 0.235847, 0.929167, 0.375408, 0.410901, 0.030122,
    0.908536, 0.016297, 0.065859, 0.043977, 0.180425, 0.719810, 0.229469,
    0.370403, 0.726096, 0.506699, 0.114353, 0.108921, 0.797998, 0.853908,
    0.296274, 0.046419, 0.063143, 0.489364, 0.811396, 0.949217, 0.380902,
    0.376751, 0.903958, 0.747703, 0.872127, 0.420392, 0.330729, 0.395032,
    0.435102, 0.922697, 0.628071, 0.680502, 0.365703, 0.324259, 0.248299,
    0.779656, 0.236610, 0.258950, 0.971496, 0.821680, 0.692892, 0.454146,
    0.498856, 0.255776, 0.319895, 0.508377, 0.634022, 0.297617, 0.239479,
    0.076937, 0.958342, 0.237007, 0.591144, 0.497055, 0.406842, 0.888241,
    0.554064, 0.383587, 0.650624],
   [0.234896, 0.423061, 0.263443, 0.123426,
    0.794525, 0.243195, 0.176474, 0.282455, 0.620580, 0.126517, 0.858114,
    0.150592, 0.351949, 0.153976, 0.480803, 0.272537, 0.062543, 0.525171,
    0.896010, 0.872203, 0.431661, 0.702341, 0.385838, 0.951113, 0.058039,
    0.563982, 0.981357, 0.319594, 0.190779, 0.159156, 0.182152, 0.912154,
    0.046355, 0.322471, 0.190853, 0.146977, 0.556987, 0.432033, 0.849777,
    0.397618, 0.800925, 0.817440, 0.103982, 0.417800, 0.982832, 0.123455,
    0.075878, 0.578246, 0.575774, 0.662847, 0.121769, 0.310270, 0.451505,
    0.871721, 0.263607, 0.963375, 0.198854, 0.249971, 0.273863, 0.592648,
    0.133579, 0.119994, 0.679855, 0.626287, 0.174323, 0.164256, 0.841121,
    0.752313, 0.501021, 0.569367, 0.804985, 0.639864, 0.272153, 0.055158,
    0.281015, 0.090302, 0.648710, 0.526850, 0.121161, 0.620315, 0.383709,
    0.487889, 0.739381, 0.609108, 0.181023, 0.419480, 0.453455, 0.658062,
    0.054643, 0.832708, 0.255309, 0.744293, 0.909823, 0.895573, 0.934952,
    0.332162, 0.514298, 0.129617, 0.568588, 0.459891, 0.512871, 0.708730,
    0.244905, 0.504490, 0.408360, 0.724549, 0.263859, 0.126921, 0.917366,
    0.128843, 0.761606, 0.295994, 0.485532, 0.364426, 0.209157, 0.331918,
    0.923155, 0.832404, 0.344705, 0.992287, 0.797664, 0.513933, 0.197295,
    0.898075, 0.321190, 0.766076, 0.896433, 0.510118, 0.555644, 0.731407,
    0.336166, 0.961858, 0.108792, 0.274650, 0.447974, 0.322401, 0.519439,
    0.127972, 0.743228, 0.095587, 0.442358, 0.065578, 0.427339, 0.867874,
    0.115209, 0.391970, 0.222726, 0.427568, 0.562064, 0.073965, 0.500635,
    0.103580, 0.193423, 0.463678, 0.352990, 0.872354, 0.266832, 0.066053,
    0.567679, 0.465254, 0.715300, 0.890676, 0.962122, 0.823967, 0.085568,
    0.195321, 0.730473, 0.142718, 0.589829, 0.954951, 0.190184, 0.350517,
    0.593301, 0.582699, 0.060864, 0.072237, 0.305630, 0.417032, 0.670941,
    0.430787, 0.876681, 0.244844, 0.060370, 0.274557, 0.598552, 0.194049,
    0.661775, 0.251685, 0.143953, 0.228740, 0.268005, 0.353289, 0.234517,
    0.866899, 0.382406, 0.826395, 0.551468, 0.910383, 0.677785, 0.487901,
    0.408431, 0.465832, 0.226357, 0.897554, 0.717515, 0.630160, 0.664560,
    0.807919, 0.184460, 0.314334, 0.122805, 0.073591, 0.617129, 0.220999,
    0.050125, 0.388057, 0.262980, 0.898668, 0.103295, 0.798561, 0.110919,
    0.451100, 0.852923, 0.263962, 0.169917, 0.891759, 0.902670, 0.074144,
    0.746980, 0.667262, 0.158418, 0.192804, 0.468950]),

  ([0.964202, 0.066500, 0.226936, 0.692160, 0.655660, 0.898740, 0.524033,
    0.579821, 0.196814, 0.235450, 0.473617, 0.647664, 0.698416, 0.424787,
    0.767571, 0.187048, 0.029542, 0.048921, 0.508835, 0.956023, 0.030000,
    0.973724, 0.787866, 0.432691, 0.833460, 0.532731, 0.948271, 0.219459,
    0.583239, 0.502701, 0.073183, 0.503311, 0.322489, 0.025788, 0.723991,
    0.979400, 0.338694, 0.135716, 0.461501, 0.143620, 0.745018, 0.043428,
    0.182409, 0.360729, 0.578967, 0.839015, 0.800714, 0.613727, 0.199988,
    0.765618, 0.306711, 0.341594, 0.267281, 0.722098, 0.733726, 0.270852,
    0.078555, 0.150273, 0.064547, 0.647755, 0.551744, 0.963408, 0.608631,
    0.776727, 0.630360, 0.671590, 0.880856, 0.272225, 0.393933, 0.361126,
    0.513932, 0.929319, 0.654927, 0.683248, 0.235145, 0.619770, 0.322947,
    0.509049, 0.438612, 0.119938, 0.318155, 0.406720, 0.384625, 0.276345,
    0.314371, 0.597186, 0.557329, 0.415448, 0.093722, 0.544572, 0.211493,
    0.403851, 0.794000, 0.837703, 0.640553, 0.265084, 0.829402, 0.518754,
    0.195807, 0.759392, 0.766900, 0.515610, 0.589740, 0.832545, 0.938505,
    0.333811, 0.512986, 0.340007, 0.071291, 0.664602, 0.097476, 0.953063,
    0.716330, 0.065249, 0.256539, 0.652181, 0.757408, 0.367809, 0.432722,
    0.715354, 0.781518, 0.972747, 0.785791, 0.606159, 0.591449, 0.683523,
    0.350780, 0.668813, 0.642537, 0.238472, 0.198431, 0.035859, 0.684408,
    0.196112, 0.128605, 0.655354, 0.388958, 0.330393, 0.928220, 0.843074,
    0.246040, 0.191717, 0.724906, 0.266549, 0.507981, 0.990509, 0.467574,
    0.767113, 0.463881, 0.458998, 0.065279, 0.925687, 0.825770, 0.848079,
    0.215400, 0.779382, 0.506851, 0.714957, 0.452528, 0.594592, 0.573901,
    0.658132, 0.449507, 0.102847, 0.654500, 0.655812, 0.101474, 0.105991,
    0.580767, 0.164403, 0.424482, 0.856655, 0.550645, 0.632588, 0.799554,
    0.120334, 0.587909, 0.587939, 0.594195, 0.325510, 0.582446, 0.952055,
    0.691580, 0.707785, 0.691580, 0.740196, 0.959563, 0.854366, 0.384228,
    0.802393, 0.190100, 0.555376, 0.908933, 0.761345, 0.541063, 0.367840,
    0.466506, 0.804132, 0.010590, 0.799005, 0.869655, 0.483566, 0.976623,
    0.514817, 0.969665, 0.185705, 0.331492, 0.480422, 0.632160, 0.419385,
    0.244026, 0.438002, 0.543016, 0.584490, 0.079379, 0.629322, 0.625263,
    0.374889, 0.508713, 0.168279, 0.348827, 0.472396, 0.339213, 0.970336,
    0.714225, 0.688009, 0.096377, 0.644551, 0.190954, 0.206671, 0.889401,
    0.255043, 0.392529],
   [ 0.347267, 0.395278, 0.312221, 0.271034, 0.606466,
    0.281430, 0.206116, 0.433894, 0.811608, 0.149699, 0.930589, 0.131576,
    0.606860, 0.168244, 0.510042, 0.318780, 0.017318, 0.390086, 0.887702,
    0.856325, 0.410205, 0.731879, 0.429107, 0.951366, 0.090044, 0.460008,
    0.982247, 0.365122, 0.274204, 0.137115, 0.153039, 0.951211, 0.065363,
    0.265502, 0.089683, 0.080594, 0.583010, 0.702345, 0.741734, 0.195394,
    0.694880, 0.634725, 0.110399, 0.510145, 0.984661, 0.095715, 0.060022,
    0.603920, 0.771879, 0.818352, 0.082082, 0.618599, 0.573476, 0.893264,
    0.229809, 0.985823, 0.317123, 0.178742, 0.319886, 0.742494, 0.070001,
    0.097983, 0.799138, 0.627735, 0.234819, 0.174270, 0.832352, 0.747531,
    0.647704, 0.705891, 0.773991, 0.515965, 0.364954, 0.068576, 0.245708,
    0.052415, 0.660207, 0.516471, 0.116974, 0.402301, 0.668778, 0.478975,
    0.759934, 0.675118, 0.254879, 0.420252, 0.301185, 0.501663, 0.106245,
    0.885773, 0.233874, 0.852706, 0.920644, 0.903723, 0.918905, 0.397496,
    0.508660, 0.350752, 0.525033, 0.525978, 0.501128, 0.591062, 0.327965,
    0.501709, 0.383894, 0.685330, 0.354505, 0.031559, 0.873401, 0.127268,
    0.798431, 0.353377, 0.460709, 0.275419, 0.208795, 0.362425, 0.876697,
    0.927418, 0.263693, 0.985030, 0.725649, 0.371198, 0.304175, 0.897437,
    0.436677, 0.633838, 0.883890, 0.471352, 0.563572, 0.763623, 0.364169,
    0.958443, 0.068425, 0.260482, 0.313543, 0.394317, 0.443647, 0.121875,
    0.780465, 0.211723, 0.268817, 0.067533, 0.660574, 0.821465, 0.084727,
    0.520244, 0.288125, 0.675550, 0.580184, 0.116487, 0.701141, 0.082960,
    0.262049, 0.478233, 0.452062, 0.902427, 0.540173, 0.098391, 0.338498,
    0.422226, 0.634723, 0.927242, 0.954212, 0.803277, 0.096551, 0.200374,
    0.689030, 0.291707, 0.580938, 0.939620, 0.151359, 0.232785, 0.503464,
    0.695066, 0.052623, 0.060584, 0.175948, 0.382867, 0.590377, 0.137340,
    0.854693, 0.227445, 0.042913, 0.300106, 0.598812, 0.232287, 0.684412,
    0.365377, 0.094614, 0.097589, 0.241374, 0.555737, 0.246608, 0.794405,
    0.244149, 0.733437, 0.394148, 0.893992, 0.702754, 0.580066, 0.313107,
    0.633119, 0.305982, 0.934299, 0.642923, 0.551348, 0.624630, 0.790857,
    0.331221, 0.474498, 0.189123, 0.110964, 0.484088, 0.151825, 0.119752,
    0.369902, 0.186865, 0.815287, 0.074211, 0.710128, 0.272505, 0.428258,
    0.913809, 0.370850, 0.101142, 0.907882, 0.813830, 0.169027, 0.828513,
    0.535021, 0.157700, 0.154209, 0.657740] ),

  ([0.091800, 0.590747, 0.357860, 0.768944, 0.705832, 0.861995, 0.219123,
    0.097995, 0.202826, 0.055971, 0.621387, 0.056795, 0.750175, 0.527818,
    0.164159, 0.654408, 0.634266, 0.384625, 0.464583, 0.807123, 0.907865,
    0.206702, 0.164922, 0.837733, 0.734306, 0.770074, 0.152898, 0.179693,
    0.085452, 0.302530, 0.901242, 0.067660, 0.949400, 0.304209, 0.332316,
    0.495651, 0.703909, 0.463515, 0.115024, 0.223945, 0.812586, 0.700278,
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