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#!/usr/bin/perl
#=================================================
# RESCUE
# "RES.idus predi.C.tion by ne.U.ral n.E.twork"
#=================================================
#
# Author : Jean_Luc PONS
# Marc Andre Delsuc
# Centre de Biochimie Structurale
# - CNRS -
# Faculte de Pharmacie
# 15 Av. C. Flafault
# 34060 MONTPELLIER FRANCE
#
#
# This software cannot be used unless the users have been fully
# licensed to do so from the above laboratory.
# This file cannot be copied, duplicated or used in any other program,
# without written permission from the authors.
#=================================================
#=================================================
# DEFINITION FUZZY LOGIC:
$MAX_DEP = 16; # plage de deplacement chimique
$INIT_DEP = -2; # debut de plage
$SIZE = 0.5; # taille des cases de la grille d'echantillonnage
#=================================================
# NOMBRE DE VALEURS MAXIMUM PAR SYSTEME DE SPIN
%nb_shift_max = ('A',3,'G',3,'P',7,'T',4,'V',5,'I',7,'L',7,'K',11,'R',10,'AROM',10,'DN',6,'S',4,'EQ',8,'M',7);
#=================================================
# MATRICES DE POID ET DE BIAIS DES RESULTANTS DE L'APPRENTISSAGE DES RESEAUX
# DE NEURONES
# DEFINITION:
# Groupe general 1er aiguillage
#Mat_All_W1 = matrice de poid pour la couche interne
#Mat_All_W2 = matrice de poid pour la couche de sortie
#Mat_All_B1 = matrice de biais pour la couche interne
#Mat_All_B2 = matrice de biais pour la couche de sortie
# GROUPE 1_IL
#Mat_IL_W1 = matrice de poid pour la couche interne
#Mat_IL_W2 = matrice de poid pour la couche de sortie
#Mat_IL_B1 = matrice de biais pour la couche interne
#Mat_IL_B2 = matrice de biais pour la couche de sortie
# GROUPE 7_KR
#Mat_KR_W1 = matrice de poid pour la couche interne
#Mat_KR_W2 = matrice de poid pour la couche de sortie
#Mat_KR_B1 = matrice de biais pour la couche interne
#Mat_KR_B2 = matrice de biais pour la couche de sortie
# GROUPE 8_AMX
#Mat_AMX_W1 = matrice de poid pour la couche interne
#Mat_AMX_W2 = matrice de poid pour la couche de sortie
#Mat_AMX_B1 = matrice de biais pour la couche interne
#Mat_AMX_B2 = matrice de biais pour la couche de sortie
# GROUPE 9_AMPTX
#Mat_AMPTX_W1 = matrice de poid pour la couche interne
#Mat_AMPTX_W2 = matrice de poid pour la couche de sortie
#Mat_AMPTX_B1 = matrice de biais pour la couche interne
#Mat_AMPTX_B2 = matrice de biais pour la couche de sortie
# MATRIX
# GROUPE ALL
@Mat_All_W1 = (
[ 0.90025857, -0.53772297, 0.21368517, -0.02803506, 0.78259793, 0.52419367 ],
[ -0.08706467, -0.96299271, 0.64281433, -0.11059327, 0.23086470, 0.58387407 ],
[ 0.84362594, 0.47641449, -0.64746771, -0.18858757, 0.87093940, 0.83380888 ],
[ -0.17945959, 0.78729906, -0.88421739, -0.29426374, 0.62633299, -0.98027740 ],
[ -0.74645062, -0.80686229, -0.44671761, 1.59400136, 0.34819322, -0.21017302 ],
[ 0.28076754, -1.77613873, 0.44740022, 1.40441289, 3.58765693, 0.16224069 ],
[ 5.41126954, -4.20285583, -2.73923472, 0.90372691, 2.90432851, 0.20334571 ],
[ 0.96141894, -3.76254532, 2.03313960, 1.19431062, -0.15433104, 0.34768894 ],
[ -0.91133792, -3.22244928, 0.87850717, 2.95562718, 2.24937771, 2.25985624 ],
[ -4.19930809, 1.35437875, -1.53565269, 2.52961797, 6.38332421, 0.37272239 ],
[ 1.38969072, 1.24631586, 2.56229159, 2.91357666, 4.90585606, -0.40737544 ],
[ 3.62643656, -0.63854119, 3.03950179, 2.84753382, 3.27136929, -4.33008842 ],
[ 0.75940739, -1.27918595, 1.36396284, 2.80002155, 1.75617641, -5.45028412 ],
[ 0.91271603, -0.75230632, -0.78525218, 3.21304325, 2.10891164, -4.03415625 ],
[ 3.81770559, -0.80756899, -0.73923067, 2.68045414, 1.91203340, -3.79803734 ],
[ 0.92453335, 0.14037699, 1.62834671, 1.76624893, 1.63125722, -4.00578898 ],
[ 1.92454505, -1.08881580, 3.10726606, 2.63994599, 2.14650565, -2.94036043 ],
[ 0.35455188, -0.31544619, -0.91144499, -0.50723542, 0.42156007, 0.88043675 ],
[ -0.16618899, -0.64457651, -1.43539716, 1.70186617, -2.07702940, 1.09450475 ],
[ 1.92150111, -0.82667053, 0.72464953, 0.69316948, 0.63486527, 3.29724672 ],
[ 0.23333827, 0.09173669, -0.13773218, 0.99538273, 2.02309379, 2.54109746 ],
[ -0.69645676, 0.05005428, -0.40691436, 1.26409112, 0.54274220, 2.18531349 ],
[ -0.93214950, -0.27054065, -1.40072413, 0.87968166, 0.75081335, 1.96322902 ],
[ 0.14118757, -0.34698293, -0.27464392, 0.64840708, 1.65418094, 2.46435425 ],
[ -2.52917800, -0.91941413, -0.24265265, -0.71642554, -0.75822510, 1.06637913 ],
[ -0.09559978, -0.02749794, -1.02124861, -0.42543123, -0.12914441, -0.22747246 ],
[ 0.35443357, -0.64574363, -0.93279412, 0.19918324, 0.27965972, -0.96985772 ],
[ -0.96729013, -0.61985082, 0.17383694, -0.88483782, -0.26486392, 0.26290233 ],
[ 0.43526884, 0.38533879, -0.83184188, -0.09128970, -0.11634341, -0.29349909 ],
[ -0.69278727, 0.35128930, 0.39842666, 0.45501826, -0.04323124, 0.10968397 ],
[ -0.75790577, -0.09849212, 0.43176590, 0.78568322, -0.45379506, -0.49046141 ],
[ 0.73120696, -0.53529926, 0.60974349, 0.81679509, -0.53621136, -0.52137487 ]
);
@Mat_All_W2 = (
[ 3.50494671, -1.36544128, -2.52944757, -4.01782378, 0.02271937, 1.28271087, 0.21205789, 3.44927681, -3.98586196, -0.42508682 ],
[ -1.79667541, -0.10817714, 3.31326172, -3.08381992, -5.57189951, -3.91346815, -3.20654471, 5.65425882, 1.25313910, 3.92589073 ],
[ -2.88864406, 1.35334893, -3.28796945, 1.53727151, -0.02484934, 0.20878170, 5.03937195, -0.99397303, -2.35624283, 3.82904666 ],
[ 7.18049571, -6.21494500, -6.94881555, 3.09380561, -0.99739920, -7.06112806, 1.72065184, -3.03956494, 5.15696178, 6.96334992 ],
[ -1.18742430, -5.35819441, -0.49347756, 0.20880886, -3.97670943, 6.45261895, -1.05950191, 6.53632731, -4.65169005, -8.56190316 ],
[ 2.35652007, 3.06908973, -3.71240794, -6.91264731, -4.75544152, 3.30473908, 5.74744673, -0.38645315, 4.31088523, -1.44470748 ]
);
@Mat_All_B1 = (
[ 0.50082366, 4.14881426, -4.60362349, -8.82931345, -8.00106598, 3.84779946 ]
);
@Mat_All_B2 = (
[-12.75731195, -8.32935408, -10.85019454, -5.90893864, -7.34100922, -10.30855973, -7.22206541, -9.76428453, -6.22188327, -4.95703373 ]
);
# GROUPE 1_IL
@Mat_IL_W1 = (
[ 0.90025857, -0.53772297, 0.21368517 ],
[ -0.02803506, 0.78259793, 0.52419367 ],
[ -0.08706467, -0.96299271, 0.64281433 ],
[ -0.11059327, 0.23086470, 0.58387407 ],
[ 4.24060295, 0.64352665, -0.64723239 ],
[ -0.84444118, 2.47250304, 0.84228790 ],
[ 0.43107159, 1.61050428, -0.85339588 ],
[ -0.29775943, 2.46176474, -0.96824308 ],
[ -0.14620856, -5.39023564, -0.57523058 ],
[ 0.41737322, 1.61268531, -0.59693594 ],
[ -0.96945215, 0.49357135, -0.10980714 ],
[ 4.98235314, 0.39332328, -0.16030272 ],
[ 0.00518214, 1.51040851, -0.59437680 ],
[ -0.57201762, -1.37984383, -0.96285416 ],
[ -0.16666417, 1.30469954, 0.67900001 ],
[ 0.11882778, 0.59951453, -0.14117139 ],
[ -1.45653762, -1.84882086, -0.61312755 ],
[ 0.47211590, -0.39240325, 0.08334849 ],
[ 0.73514448, 1.03433435, -0.24012857 ],
[ 0.34015554, -0.55958307, 0.19017612 ],
[ 0.00249192, 5.09649875, 0.64320732 ],
[ 0.28010540, 0.09094896, 0.32250896 ],
[ -0.31381546, -0.11197222, -0.30753869 ],
[ -1.15064117, 0.87807867, -0.38318856 ],
[ 2.44676406, 1.19854059, -0.25813866 ],
[ 0.40547983, 0.09314230, -0.11023959 ],
[ 0.38913448, 0.24262026, 0.58964216 ],
[ 0.91368690, 0.04518070, 0.76028441 ],
[ -0.65408772, 0.95949379, -0.45710548 ],
[ -0.49534131, 0.75148380, 0.47461198 ],
[ -0.72696252, -0.97648663, 0.78779593 ],
[ -0.60172387, -0.40255398, 0.32288515 ]
);
@Mat_IL_W2 = (
[ 9.25268284, -8.77201413 ],
[ 5.67899725, -5.41127874 ],
[ -0.07213835, -0.42869367 ]
);
@Mat_IL_B1 = (
[ 0.34674401, 0.26440052, -0.85338468 ]
);
@Mat_IL_B2 = (
[ -0.79400368, 0.43773366 ]
);
# GROUPE 7_KR
@Mat_KR_W1 = (
[ 0.90025857, -0.53772297 ],
[ 0.21368517, -0.02803506 ],
[ 0.78259793, 0.52419367 ],
[ -0.08706467, -0.96299271 ],
[ 0.68566865, -0.11692086 ],
[ -0.02799971, 0.55303106 ],
[ -1.57990630, 1.19958761 ],
[ -0.51475854, -0.01064007 ],
[ -1.74213102, 1.56914930 ],
[ -1.60228290, 0.43904377 ],
[ -0.31383967, -0.75653035 ],
[ 3.14454381, -0.81372297 ],
[ 1.35965748, -0.57356764 ],
[ 1.29964099, -0.07626059 ],
[ -1.31282572, -1.32182574 ],
[ 0.34532194, 0.29180532 ],
[ 0.21094853, 0.81035978 ],
[ 0.34185559, -0.19611073 ],
[ 1.69793030, -0.39013709 ],
[ 1.05326438, 0.05748171 ],
[ 0.91699520, -1.08836900 ],
[ -1.31017690, 0.24968241 ],
[ 0.17402293, -0.23031189 ],
[ 1.45524665, -0.22398760 ],
[ -0.07869392, -0.64420823 ],
[ -0.62244462, 0.36602632 ],
[ -0.39447120, 0.08334771 ],
[ -0.69825405, 0.39579696 ],
[ -0.24325400, 0.72002321 ],
[ 0.70731026, 0.18712583 ],
[ -0.00689510, 0.79953835 ],
[ 0.64325832, 0.28982077 ]
);
@Mat_KR_W2 = (
[ -4.56609208, 4.64804830 ],
[ 1.53676092, -1.41410052 ]
);
@Mat_KR_B1 = (
[ 2.66038390, -0.03430645 ]
);
@Mat_KR_B2 = (
[ -1.03933542, 1.25177260 ]
);
# GROUPE 8_AMX
@Mat_AMX_W1 = (
[ 0.90025857, -0.53772297, 0.21368517 ],
[ -0.02803506, 0.78259793, 0.52419367 ],
[ -0.08706467, -0.96299271, 0.64281433 ],
[ -0.11059327, 0.23086470, 0.58387407 ],
[ 0.84362594, 0.47641449, -0.64746771 ],
[ -0.18858757, 0.87093940, 0.83380888 ],
[ -0.59305319, 3.52227948, -0.80067901 ],
[ -2.43548902, 5.82851619, -0.47491660 ],
[ -1.88981338, -1.70658029, -4.14434856 ],
[ 3.44282730, -1.17354098, 4.77735207 ],
[ -2.02762133, 7.95933339, -4.46856404 ],
[ 17.31974439, -6.49523547, 3.60473125 ],
[ 2.91919738, 1.14486626, -9.67280497 ],
[ -4.24179775, -1.53863638, -1.51587022 ],
[ -2.96555354, -0.99510925, 2.14450015 ],
[ 3.70791636, -2.34066170, 4.74331929 ],
[ 2.26174365, -5.30561871, -5.91737306 ],
[ 0.40613527, -0.41138767, 0.07889812 ],
[ 0.21573902, -0.28232860, -1.98653294 ],
[ 6.06098305, 0.75104321, 0.74762463 ],
[ -8.04628874, -0.49616497, 6.45209489 ],
[ -8.38351156, 11.83328526, -4.39962109 ],
[ -8.00666672, -5.14512267, 8.99469877 ],
[ 0.04735677, 5.06536775, -3.29166700 ],
[ -3.30233043, 1.29735523, 5.31290202 ],
[ 0.05138161, 2.99599418, 1.54831473 ],
[ 0.38913448, 0.24262026, 0.58964216 ],
[ -0.56548097, 0.11305484, 1.38904489 ],
[ -0.71571971, 0.96232188, -0.43090713 ],
[ -0.49534131, 0.75148380, 0.47461198 ],
[ -0.72696252, -0.97648663, 0.78779593 ],
[ -0.60172387, -0.40255398, 0.32288515 ]
);
@Mat_AMX_W2 = (
[ 3.68087282, -4.49085566 ],
[-10.68778703, 9.98787428 ],
[ -9.64764769, 9.20653467 ]
);
@Mat_AMX_B1 = (
[ 4.77542941, -0.79160360, -3.11709947 ]
);
@Mat_AMX_B2 = (
[ -1.70787699, 2.85820565 ]
);
# GROUPE 9_AMPTX
@Mat_AMPTX_W1 = (
[ 0.90025857, -0.53772297, 0.21368517 ],
[ -0.02803506, 0.78259793, 0.52419367 ],
[ -0.08706467, -0.96299271, 0.64281433 ],
[ -0.11059327, 0.23086470, 0.58387407 ],
[ 0.84362594, 0.47641449, -0.64746771 ],
[ -0.26057234, 0.77557740, 0.83479081 ],
[ -0.33146072, 1.71123348, -0.88725892 ],
[ 1.99430638, 0.04249910, -1.38354799 ],
[ -0.92883918, -1.37470645, 1.15480534 ],
[ 1.40507122, -1.67630923, 0.89900411 ],
[ -1.51320247, 0.74263549, 0.41739015 ],
[ 1.88692615, -0.89390206, -0.39562952 ],
[ 1.26873391, -4.48579095, -4.53637055 ],
[ -0.08047845, 2.13975954, 0.04616907 ],
[ -1.14519914, 2.90758846, 0.04184370 ],
[ -3.12032950, 2.57333742, -0.24470430 ],
[ -0.28759087, -0.58521783, -0.62294959 ],
[ 0.36444645, -0.39447120, 0.08334771 ],
[ -0.80627495, 0.39727830, -0.24288344 ],
[ -1.04018605, 1.31401468, 0.18228010 ],
[ 1.38084268, -4.37036406, -6.13741269 ],
[ 1.58135854, 6.14575813, 3.75166410 ],
[ 4.15286013, -0.63544081, -0.68874649 ],
[ 2.90789962, 0.26906345, -0.27785938 ],
[ 1.81353752, 0.56247287, -0.22516703 ],
[ 4.26369286, -0.70380305, 0.02103616 ],
[ 0.38913448, 0.24262026, 0.58964216 ],
[ 0.91368690, 0.04518070, 0.76028441 ],
[ -0.65408772, 0.95949379, -0.45710548 ],
[ -0.49534131, 0.75148380, 0.47461198 ],
[ -0.72696252, -0.97648663, 0.78779593 ],
[ -0.60172387, -0.40255398, 0.32288515 ]
);
@Mat_AMPTX_W2 = (
[ 6.88902465, -7.20582308 ],
[ -5.92395878, 6.36600994 ],
[ 5.20318888, -5.94589907 ]
);
@Mat_AMPTX_B1 = (
[ -1.98667826, 0.67735114, -4.46829737 ]
);
@Mat_AMPTX_B2 = (
[ 0.01546663, 0.28156143 ]
);
#=================================================
# Vecteurs des distances entre tests (bonnes reponses) de la
# BMRB et vecteur ideal
@Dist_tst_ideal_GRPAll_1 = (0.0524, 0.01, 0.01, 0.01, 0.01, 0.057, 0.0592, 0.01, 0.0783, 0.028, );
@Dist_tst_ideal_GRPAll_2 = (0.01, 0.0573, 0.0748, 0.01, 0.079, 0.01, 0.0123, 0.01, 0.1312, 0.01, );
@Dist_tst_ideal_GRPAll_3 = (0.01, 0.0274, 0.0474, 0.01, 0.01, 0.01, 0.01, 0.029, 0.01, 0.0603, );
@Dist_tst_ideal_GRPAll_4 = (0.0342, 0.01, 0.01, 0.0438, 0.01, 0.01, 0.0268, 0.01, 0.0457, 0.0424, );
@Dist_tst_ideal_GRPAll_5 = (0.0203, 0.0373, 0.056, 0.0386, 0.0759, 0.0215, 0.01, 0.01, 0.01, 0.0354, );
@Dist_tst_ideal_GRPAll_6 = (0.0643, 0.0161, 0.01, 0.01, 0.01, 0.0761, 0.023, 0.0272, 0.01, 0.01, );
@Dist_tst_ideal_GRPAll_7 = (0.0572, 0.01, 0.01, 0.0615, 0.01, 0.01, 0.0523, 0.01, 0.0598, 0.01, );
@Dist_tst_ideal_GRPAll_8 = (0.01, 0.01, 0.1129, 0.0173, 0.01, 0.1224, 0.0288, 0.0791, 0.0456, 0.1071, );
@Dist_tst_ideal_GRPAll_9 = (0.042, 0.01, 0.01, 0.01, 0.01, 0.0152, 0.0113, 0.0676, 0.0803, 0.01, );
@Dist_tst_ideal_GRPAll_10 = (0.01, 0.01, 0.0684, 0.01, 0.0585, 0.01, 0.01, 0.038, 0.01, 0.084, );
@Dist_tst_ideal_GRPIL_1 = (0.0978, 0.1025, );
@Dist_tst_ideal_GRPIL_2 = (0.0916, 0.0945, );
@Dist_tst_ideal_GRPKR_1 = (0.0482, 0.0504, );
@Dist_tst_ideal_GRPKR_2 = (0.05, 0.0482, );
@Dist_tst_ideal_GRPAMX_1 = (0.1765, 0.1845, );
@Dist_tst_ideal_GRPAMX_2 = (0.1151, 0.1009, );
@Dist_tst_ideal_GRPAMPTX_1 = (0.0785, 0.094, );
@Dist_tst_ideal_GRPAMPTX_2 = (0.0801, 0.0651, );
$scoremax_All_1 =93.38;
$scoremax_All_2 =94.53;
$scoremax_All_3 =94.42;
$scoremax_All_4 =96.48;
$scoremax_All_5 =90.79;
$scoremax_All_6 =93.94;
$scoremax_All_7 =91.68;
$scoremax_All_8 =89.09;
$scoremax_All_9 =93.47;
$scoremax_All_10 =88.13;
$scoremax_IL_1 =74.44;
$scoremax_IL_2 =70.84;
$scoremax_KR_1 =91.06;
$scoremax_KR_2 =80.52;
$scoremax_AMX_1 =67.87;
$scoremax_AMX_2 =59.17;
$scoremax_AMPTX_1=71.64;
$scoremax_AMPTX_2=67,81;
#=================================================
# RECUPERATION DES VALEURS D'ENTREE
if ($ARGV[0])
{
$input = $ARGV[0];
}
else
{
print "Entrez le nom du fichier contenant le(s) vecteur(s) de deplacements chimiques\n";
print "[Syntaxe dans le fichier d'entree : xx.xx yy.yy ... zz.zz]\n";
print "ou directement le vecteur d'entree ::xx.xx:yy.yy:...:zz.zz:\n";
$input = <STDIN>;
chop( $input );
}
$input_tmp = $input;
$last_char = chop($input);
if ($last_char eq ":") {
$_ = $input;
s/\://;
#print $_,"\n";
@shifts = split(":",$_);
print "\nChemical Shift Input: ",$_,"\n";
print "\nR.E.S.C.U.E. Software Result:\n";
&global_function(@shifts);
}
else {
$input = $input_tmp;
#print "file_name : ",$input,"\n";
open(FILE_IN,$input);
while(<FILE_IN>)
{
@shifts = split(" ",$_);
# Affichage
print "\nChemical Shift Input: ",$_,"\n";
print "\nR.E.S.C.U.E. Software Result:\n";
&global_function(@shifts);
}
close FILE_IN;
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# SWITCH GENERAL
sub global_function
{
local(@shift)=@_;
$rep = 0;
$nb_shift = @shift;
@fuzzy_logic_vector = &make_input_vector(@shifts);
$flvect = join(" ",@fuzzy_logic_vector);
#print $flvect," - \n";
# GENERAL SWITCH
$learn_mat = 'All';
$grp = 0;
($surete_grp,$grp,@tabres) = &check_net($learn_mat,$flvect);
# Stockage des scores de la premiere couche
$first_score = join(" ",@tabres);
# Aiguillage en fonction des resultats:
# ************ GROUPE 1 IL
if($grp == 1){
$learn_mat = 'IL';
$grp2 = 0;
($surete,$grp2,@tabres) = &check_net($learn_mat,$flvect);
# On ramene la surete 2 sur la base de la surete 1 : calcul eff1/100*eff2
$surete = ($surete_grp/100) * $surete;
&affiche_score($first_score,'IL',@tabres);
if($grp2 == 1){
printf("Result GROUP : IL RESIDU : I \n");
printf("Reliability (%%) : %d : %d\n",$surete_grp,$surete);
$rep = &check_max_shift_val("I",$nb_shift);
}
elsif($grp2 == 2){
printf("Result GROUP : IL RESIDU : L \n");
printf("Reliability (%%) : %d : %d\n",$surete_grp,$surete);
$rep = &check_max_shift_val("L",$nb_shift);
}
else { print "ERROR\n"; }
}
# ************ GROUPE 2
elsif($grp == 2){
$rep = &check_max_shift_val("A",$nb_shift);
&affiche_score($first_score,'A',@tabres);
printf("Result RESIDU : A \n");
printf("Reliability (%%) : %d\n",$surete_grp);
}
# ************ GROUPE 3
elsif($grp == 3){
$rep = &check_max_shift_val("G",$nb_shift);
&affiche_score($first_score,'G',@tabres);
printf("Result RESIDU : G \n");
printf("Reliability (%%) : %d\n",$surete_grp);
}
# ************ GROUPE 4
elsif($grp == 4){
$rep = &check_max_shift_val("P",$nb_shift);
&affiche_score($first_score,'P',@tabres);
printf("Result RESIDU : P \n");
printf("Reliability (%%) : %d\n",$surete_grp);
}
# ************ GROUPE 5
elsif($grp == 5){
$rep = &check_max_shift_val("T",$nb_shift);
&affiche_score($first_score,'T',@tabres);
printf("Result RESIDU : T \n");
printf("Reliability (%%) : %d\n",$surete_grp);
}
# ************ GROUPE 6
elsif($grp == 6){
$rep = &check_max_shift_val("V",$nb_shift);
&affiche_score($first_score,'V',@tabres);
printf("Result RESIDU : V \n");
printf("Reliability (%%) : %d\n",$surete_grp);
}
# ************ GROUPE 7
elsif($grp == 7){
$learn_mat = 'KR';
$grp2 = 0;
($surete,$grp2,@tabres) = &check_net($learn_mat,$flvect);
# On ramene la surete 2 sur la base de la surete 1 : calcul eff1/100)*eff2
$surete = ($surete_grp/100) * $surete;
&affiche_score($first_score,'KR',@tabres);
if($grp2 == 1){
printf("Result GROUP : KR RESIDU : K \n");
printf("Reliability (%%) : %d : %d\n",$surete_grp,$surete);
$rep = &check_max_shift_val("K",$nb_shift);
}
elsif($grp2 == 2){
printf("Result GROUP : KR RESIDU : R \n");
printf("Reliability (%%) : %d : %d\n",$surete_grp,$surete);
$rep = &check_max_shift_val("K",$nb_shift);
}
else { print "ERROR\n"; }
}
# ************ GROUPE 8
elsif($grp == 8){
$learn_mat = 'AMX';
$grp2 = 0;
($surete,$grp2,@tabres) = &check_net($learn_mat,$flvect);
# On ramene la surete 2 sur la base de la surete 1 : calcul eff1/100)*eff2
$surete = ($surete_grp/100) * $surete;
&affiche_score($first_score,'AMX',@tabres);
if($grp2 == 1){
printf("Result GROUP : AMX RESIDU : F,Y,W,H,C \n");
printf("Reliability (%%) : %d : %d\n",$surete_grp,$surete);
$rep = &check_max_shift_val("AROM",$nb_shift);
}
elsif($grp2 == 2){
printf("Result GROUP : AMX RESIDU : D,N \n");
printf("Reliability (%%) : %d : %d\n",$surete_grp,$surete);
$rep = &check_max_shift_val("DN",$nb_shift);
}
else { print "ERROR\n"; }
}
# ************ GROUPE 9
elsif($grp == 9){
printf("====> GROUP : AMPTX \n");
$learn_mat = 'AMPTX';
$grp2 = 0;
($surete,$grp2,@tabres) = &check_net($learn_mat,$flvect);
# On ramene la surete 2 sur la base de la surete 1 : calcul eff1/100)*eff2
$surete = ($surete_grp/100) * $surete;
&affiche_score($first_score,'AMPTX',@tabres);
if($grp2 == 1){
printf("Result GROUP : AMPTX RESIDU : E,Q \n");
printf("Reliability (%%) : %d : %d\n",$surete_grp,$surete);
$rep = &check_max_shift_val("EQ",$nb_shift);
}
elsif($grp2 == 2){
printf("Result GROUP : AMPTX RESIDU : M \n");
printf("Reliability (%%) : %d : %d\n",$surete_grp,$surete);
$rep = &check_max_shift_val("M",$nb_shift);
}
else { print "ERROR\n"; }
}
# ************ GROUPE 10
elsif($grp == 10){
$rep = &check_max_shift_val("S",$nb_shift);
&affiche_score($first_score,'S',@tabres);
printf("Result RESIDU : S \n");
printf("Reliability (%%) : %d\n",$surete_grp);
}
# ************ ERROR
else { print "ERROR\n"; }
if ($rep != 0 )
{ print " ERROR :To many data input for the selected residu !! \n"; }
printf("\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n");
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# CREATION DU VECTEUR D'ENTREE AVEC LOGIQUE FLOUE
sub make_input_vector
{
local(@shift)=@_;
local(@matrix,$i,$k,$l,$val1,$val2);
# Creation d'une matrice = au nombre total de
# cases de la grille d'echantillonnage
for($i = 0; $i<($MAX_DEP/$SIZE); $i++) { $matrix[$i] = 0; }
foreach (@shift) {
#print $_,"\n";
# Fuzzy logic
($k,$l,$val1,$val2) = &fuzz($_,$SIZE,$MAX_DEP,$INIT_DEP);
$matrix[$k] += $val1;
$matrix[$l] += $val2;
}
return(@matrix);
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# APPEL GENERAL AUX FONCTIONS DE TRANSFERT
sub check_net
{
local($learn_mat,$flvect)=@_;
# multiplication des synapses par les poids
$couche = 1;
@input = split(" ",$flvect);
@vect1_pond = &pondere($learn_mat,$couche,@input);
# premiere fonction de transfert:
@result1 = &tansig($learn_mat,$couche,@vect1_pond);
# multiplication des synapses par les poids
$couche = 2;
@vect2_pond = &pondere($learn_mat,$couche,@result1);
# seconde fonction de transfert:
@result2 = &fct5($learn_mat,$couche,@vect2_pond);
#Calcul meilleur score
$ok_indice = &calc_max(@result2);
#Calcul surete
$reliability = &calc_reliability($learn_mat,$ok_indice,@result2);
return($reliability,$ok_indice,@result2);
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# CALCUL SURETE PAR REPONSE
#
# la surete est R = exp(- ((Vout - Videal)*(Vout - Videal)) / Dist***))
#
# Avec Dist*** = sqrt(somme( ((VoutBMRB - Videal)*(VoutBMRB - Videal)) /n-1 ) )
sub calc_reliability
{
local($grp,$max,@matscore)=@_;
$size_mat = @matscore;
#$maxsc = pourcentage maximum pour le res
$varname = "scoremax_".$grp."_".$max;
$maxsc = ${$varname};
#recuperation matrice: @Dist_tst_ideal_GRPX_X
$matname = "Dist_tst_ideal_GRP".$grp."_".$max;
@matdist = @{$matname};
$max = $max - 1;
#calcul distance : dist(vect d'entree et vect ideal) / dist(test_BRMB et vect ideal)
for ( $i = 0;$i<$size_mat;$i++){ $ideal[$i] = 0;}
$ideal[$max] = 1;
$vdist = 0;
for ( $i = 0;$i<$size_mat;$i++){
$dv = (($matscore[$i] - $ideal[$i]) * ($matscore[$i] - $ideal[$i])) / $matdist[$i];
$vdist = $vdist + $dv;
}
$vdist = exp(- $vdist);
$vdistok = int($vdist*$maxsc);
return($vdistok);
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# AFFFICHAGE FORMATES DES SCORES
sub affiche_score
{
local($gen_score,$mat,@score)=@_;
# = zero si negatif:
$size_score = @score;
for ( $i = 0;$i<$size_score;$i++){
if($score[$i] < 0) {$score[$i] = 0;}
}
# Recup score premiere couche
@Score_couche1 = split(" ",$gen_score);
$size_gen_score = @Score_couche1;
for ( $i = 0;$i<$size_gen_score;$i++){
if($Score_couche1[$i] < 0) {$Score_couche1[$i] = 0;}
}
if(($mat eq 'A') || ($mat eq 'G') || ($mat eq 'P') || ($mat eq 'T') || ($mat eq 'V') || ($mat eq 'S')){
printf("\n");
printf("GROUP : SCORE \n");
printf("-------------------\n");
printf("IL : %8.4f \n",$score[0]);
printf("A : %8.4f \n",$score[1]);
printf("G : %8.4f \n",$score[2]);
printf("P : %8.4f \n",$score[3]);
printf("T : %8.4f \n",$score[4]);
printf("V : %8.4f \n",$score[5]);
printf("KR : %8.4f \n",$score[6]);
printf("AMX : %8.4f \n",$score[7]);
printf("AMPTX : %8.4f \n",$score[8]);
printf("S : %8.4f \n\n",$score[9]);
}
elsif($mat eq 'IL'){
printf("\n");
printf(" --First Switch-- --Second Switch--\n");
printf("GROUP : SCORE | RESIDU : SCORE \n");
printf("----------------------------------------------\n");
printf("IL : %8.4f | I : %8.4f \n",$Score_couche1[0],$score[0]);
printf("A : %8.4f | L : %8.4f \n",$Score_couche1[1],$score[1]);
printf("G : %8.4f | \n",$Score_couche1[2]);
printf("P : %8.4f |\n",$Score_couche1[3]);
printf("T : %8.4f |\n",$Score_couche1[4]);
printf("V : %8.4f |\n",$Score_couche1[5]);
printf("KR : %8.4f |\n",$Score_couche1[6]);
printf("AMX : %8.4f |\n",$Score_couche1[7]);
printf("AMPTX : %8.4f |\n",$Score_couche1[8]);
printf("S : %8.4f |\n\n",$Score_couche1[9]);
}
elsif($mat eq 'KR'){
printf("\n");
printf(" --First Switch-- --Second Switch--\n");
printf("GROUP : SCORE | RESIDU : SCORE \n");
printf("----------------------------------------------\n");
printf("IL : %8.4f | K : %8.4f \n",$Score_couche1[0],$score[0]);
printf("A : %8.4f | R : %8.4f \n",$Score_couche1[1],$score[1]);
printf("G : %8.4f |\n",$Score_couche1[2]);
printf("P : %8.4f |\n",$Score_couche1[3]);
printf("T : %8.4f |\n",$Score_couche1[4]);
printf("V : %8.4f |\n",$Score_couche1[5]);
printf("KR : %8.4f |\n",$Score_couche1[6]);
printf("AMX : %8.4f |\n",$Score_couche1[7]);
printf("AMPTX : %8.4f |\n",$Score_couche1[8]);
printf("S : %8.4f |\n\n",$Score_couche1[9]);
}
elsif($mat eq 'AMX'){
printf("\n");
printf(" --First Switch-- --Second Switch--\n");
printf("GROUP : SCORE | RESIDU : SCORE \n");
printf("----------------------------------------------\n");
printf("IL : %8.4f | FYWHC : %8.4f \n",$Score_couche1[0],$score[0]);
printf("A : %8.4f | DN : %8.4f \n",$Score_couche1[1],$score[1]);
printf("G : %8.4f |\n",$Score_couche1[2]);
printf("P : %8.4f |\n",$Score_couche1[3]);
printf("T : %8.4f |\n",$Score_couche1[4]);
printf("V : %8.4f |\n",$Score_couche1[5]);
printf("KR : %8.4f |\n",$Score_couche1[6]);
printf("AMX : %8.4f |\n",$Score_couche1[7]);
printf("AMPTX : %8.4f |\n",$Score_couche1[8]);
printf("S : %8.4f |\n\n",$Score_couche1[9]);
}
elsif($mat eq 'AMPTX'){
printf("\n");
printf(" --First Switch-- --Second Switch--\n");
printf("GROUP : SCORE | RESIDU : SCORE \n");
printf("----------------------------------------------\n");
printf("IL : %8.4f | EQ : %8.4f \n",$Score_couche1[0],$score[0]);
printf("A : %8.4f | M : %8.4f \n",$Score_couche1[1],$score[1]);
printf("G : %8.4f |\n",$Score_couche1[2]);
printf("P : %8.4f |\n",$Score_couche1[3]);
printf("T : %8.4f |\n",$Score_couche1[4]);
printf("V : %8.4f |\n",$Score_couche1[5]);
printf("KR : %8.4f |\n",$Score_couche1[6]);
printf("AMX : %8.4f |\n",$Score_couche1[7]);
printf("AMPTX : %8.4f |\n",$Score_couche1[8]);
printf("S : %8.4f |\n\n",$Score_couche1[9]);
}
else{print " ERROR OUTPUT SCORE \n";}
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# DETECTE LES INCOHERENCES ENTRE NBRE DE VALEUR ET SYST DE SPIN
sub check_max_shift_val
{
local($res,$nbshift)=@_;
$ok = 0;
if ( $nb_shift_max{$res} < $nbshift ){ $ok = 1; }
#print $res," -- ",$nb_shift_max{$res}," -- ",$nbshift," -- ",$ok,"\n";
return($ok);
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# RECHERCHE DU RESIDU AYANT LE MEILLEUR SCORE
sub calc_max
{
local(@res)=@_;
$res_size = @res;
$max = -10;
$indice_max = -1;
for ($i = 0;$i<$res_size;$i++){
#print $res[$i],"\n";
if ($max < $res[$i]){
$indice_max = $i+1;
$max = $res[$i];
}
}
#print $indice_max," max \n";
return($indice_max);
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# MULTIPLIE LE VECTEUR D'ENTREE AVEC LA MATRICE DE POID
sub pondere
{
local($learn_mat,$couche,@input)=@_;
local(@output);
local(@weight);
#recuperation matrice: weight[col][row] eg:Mat_All_W1[32][17]
$matname = "Mat_".$learn_mat."_W".$couche;
@weight = @{$matname};
# Calcul des tailles de matrices:
$input_size = @input;
#print $input_size," en entree \n";
$layer_size = @{$weight[0]};
#print $layer_size," ponderes\n";
for($i = 0; $i<$layer_size; $i++) { $output[$i] = 0; }
for($i = 0; $i<$layer_size; $i++) {
for($j = 0; $j<$input_size; $j++) {
$output[$i] = $output[$i] + ($input[$j]*$weight[$j][$i]);
}
}
#for($i = 0; $i<$layer_size; $i++) { print $output[$i],"\n"; }
return(@output);
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# FONCTION DE TRANSFERT TANSIG
sub tansig
{
local($learn_mat,$couche,@vect_pond)=@_;
local(@output);
local(@bias);
# Detrmination de la matrice de biais.
$matname = "Mat_".$learn_mat."_B".$couche;
@bias = @{$matname};
# Calcul des tailles
$layer_size = @vect_pond;
#print $layer_size," vect ponderes ne tansig\n";
$size_biais = @{$bias[0]};
#print $size_biais," - bias en tansig \n";
if ( $layer_size != $size_biais) {print "ERROR BIAS MATRIX (function tansig)\n";}
# Addition Input+Biais
for($i = 0; $i<$layer_size; $i++) { $output[$i] = 0; }
for ($i = 0; $i<$layer_size; $i++) {
$vect_pond[$i] = $vect_pond[$i] + $bias[0][$i];
}
#for($i = 0; $i<$layer_size; $i++) { print $vect_pond[$i],"\n"; }
# Calcul tansig
#print "TANSIG : \n";
for ($i = 0; $i<$layer_size; $i++) {
$output[$i] = 2 / (1 + exp(-2*$vect_pond[$i])) -1;
#print $output[$i],"\n";
# pour eviter les infinis:
if($output[$i]>1000){$output[$i]=1000;}
if($output[$i]<-1000){$output[$i]=-1000;}
}
return(@output);
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# FONCTION DE TRANSFERT FCT5
sub fct5
{
local($learn_mat,$couche,@vect_pond)=@_;
local(@output);
local(@bias);
# Detrmination de la matrice de biais.
$matname = "Mat_".$learn_mat."_B".$couche;
@bias = @{$matname};
# Calcul des tailles
$layer_size = @vect_pond;
$size_biais = @{$bias[0]};
if ( $layer_size != $size_biais) {print "ERROR BIAS MATRIX (function logsig)\n";}
# Addition Input+Biais
for($i = 0; $i<$layer_size; $i++) { $output[$i] = 0; }
for ($i = 0; $i<$layer_size; $i++) {
$vect_pond[$i] = $vect_pond[$i] + $bias[0][$i];
}
# Calcul fct5
for ($i = 0; $i<$layer_size; $i++) {
$tt = 0;
if($vect_pond[$i] < 0){$tt = 1;}
$max = -2; if($vect_pond[$i]>-2){$max = $vect_pond[$i];}
$output[$i] = $tt * (0.25*$max + 0.5) + (1-$tt) * (1 / (1 + exp(-$vect_pond[$i])));
}
return(@output);
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# FONCTION DE TRANSFERT PURELIN
sub purelin
{
local($learn_mat,$couche,@vect_pond)=@_;
local(@output);
local(@bias);
# Detrmination de la matrice de biais.
$matname = "Mat_".$learn_mat."_B".$couche;