Ayant la possibilité d'évaluer qualitativement la situation dans le jeu à un moment donné et la possibilité de simuler le monde du jeu lors de la création d'un bot pour l'une des solutions, il ne reste plus qu'à s'efforcer d'effectuer de telles actions qui conduisent à une amélioration de cette évaluation dans un proche avenir.
Fonction d'estimation de position - renvoie une valeur matérielle où moins signifie pire. A l'entrée d'une telle fonction, je n'ai soumis que le vecteur position et vitesse de la balle. Initialement, cette fonction a été implémentée avec des formules assez simples et quelques ifs. Cependant, cela a fourni une bonne base pour tricher sur l'ensemble de journaux localrunner pour la formation ultérieure du réseau neuronal. J'ai donc fait défiler 300 jeux (18 000 ticks chacun) localement, ce qui a donné au total environ 12 Go de journaux et plus de 145 des meilleurs journaux de jeu ont été téléchargés depuis le serveur (5,7 Go).
Ensuite, il a fallu isoler les échantillons d'apprentissage et de test de ces journaux. Je l'ai fait comme suit: à partir d'un but, j'ai regardé le «passé» pendant 300 ticks (5 secondes) et par incréments de 5 ticks chaque position et vitesse du ballon + a pris un score de référence comme exemple.
Un point important: le score de référence (sortie) ici a été calculé par la formule
$$ affichage $$ O = S / exp (T / 60) $$ affichage $$
où S = -1 si le ballon vole dans "mon" but et 1 sinon, et T est le temps restant en ticks avant le but.
Autre point moins important, mais aussi important: le terrain de jeu est symétrique et, en conséquence, le score de référence doit également être inversement symétrique lorsqu'il est vu du point de vue de l'adversaire. C'est-à-dire si quelque chose est évalué du point de vue «mon» comme X, alors la même position doit être évaluée du point de vue de l'adversaire comme -X. Cela signifie que si vous "pliez en deux" tout l'espace d'entrée du réseau neuronal par n'importe quel paramètre, le réseau apprendra mieux, relativement parlant, "2 fois", et plus important encore, il fournira une réponse symétrique garantie (ce qui est, au moins, tout simplement magnifique). J'ai "plié" la vitesse du ballon le long de l'axe Z. Autrement dit, si le ballon vole de "mon" but, alors je regarde de mon propre point de vue, sinon - du point de vue de l'adversaire. Il s'avère que pour un réseau neuronal, la balle vole toujours dans une direction positive le long de Z. La même chose peut être faite pour la symétrie longitudinale (le long de l'axe X), bien que dans ce cas, nous continuons à regarder du point de vue de la même équipe, mais, pour ainsi dire, dans le miroir situé dans avions avec une normale (1, 0, 0).
Voici donc le code pour préparer un échantillon de test et de formation à partir des journaux en Python:
import json from pprint import pprint import glob import numpy as np import random xtrain = [] ytrain = [] xtest = [] ytest = [] f1 = r"F:\Home\Projects\MailRuAI\Codeball2018\LocalRunner\logs_archive\logs_01/*.txt" f2 = r"F:\Home\Projects\MailRuAI\Codeball2018\LocalRunner\logs_archive\logs_02/*.txt" f3 = r"F:\Home\Projects\MailRuAI\Codeball2018\LocalRunner\logs_archive\logs_03/*.txt" f7 = r"F:\Home\Projects\MailRuAI\Codeball2018\downloaded_games/*.txt" for file in (glob.glob(f1) + glob.glob(f2) + glob.glob(f3) + glob.glob(f7)): with open(file) as f: content = f.readlines() print(len(content)) print(file) sumofscores = 0 lastscore0 = 0 lastscore1 = 0 ticksbackward = 300 ticksbackwardinc = 5 for x in range(0, len(content)): data = json.loads(content[x]) if "scores" in data and sum(data["scores"]) > sumofscores: sumofscores = sum(data["scores"]) value = 0 if data["scores"][0] > lastscore0: lastscore0 = data["scores"][0] value = 1 if data["scores"][1] > lastscore1: lastscore1 = data["scores"][1] value = -1 for y in range(ticksbackwardinc, ticksbackward, ticksbackwardinc): dataY = json.loads(content[x - y]) if "scores" in dataY and sum(dataY["scores"]) == sumofscores - 1: sign = 1 if dataY['ball']['velocity']['z'] < 0: sign = -1 signX = 1 if dataY['ball']['velocity']['x'] * sign < 0: signX = -1 inputs = np.zeros(6) inputs[0] = dataY['ball']['velocity']['x'] * sign * signX inputs[1] = dataY['ball']['velocity']['y'] inputs[2] = dataY['ball']['velocity']['z'] * sign inputs[3] = dataY['ball']['position']['x'] * sign * signX inputs[4] = dataY['ball']['position']['y'] inputs[5] = dataY['ball']['position']['z'] * sign outputs = np.zeros(2) outputs[0] = value*sign outputs[1] = y if (random.random() > 0.2): xtrain.append(inputs) ytrain.append(outputs) else: xtest.append(inputs) ytest.append(outputs) else: print("exceeded") print(len(xtrain)) print(len(xtest)) np.save("F:/Home/Projects/MailRuAI/Codeball2018/nnet/xtrain_BR.npy", np.asarray(xtrain)) np.save("F:/Home/Projects/MailRuAI/Codeball2018/nnet/ytrain_BR.npy", np.asarray(ytrain)) np.save("F:/Home/Projects/MailRuAI/Codeball2018/nnet/xtest_BR.npy", np.asarray(xtest)) np.save("F:/Home/Projects/MailRuAI/Codeball2018/nnet/ytest_BR.npy", np.asarray(ytest))
Les plus attentifs ont probablement déjà remarqué que les sorties contiennent deux sorties et pas du tout ce que j'ai décrit ci-dessus, mais ne vous inquiétez pas, c'est un rudiment et la transformation s'ensuit avant la formation elle-même:
import numpy as np from keras.datasets import boston_housing from keras.models import Model, Sequential from keras.layers import Input, Dense, Concatenate, Add import random import datetime np.set_printoptions(edgeitems=50) xtrain = np.load("F:/Home/Projects/MailRuAI/Codeball2018/nnet/xtrain_BR.npy") ytrain = np.load("F:/Home/Projects/MailRuAI/Codeball2018/nnet/ytrain_BR.npy") xtest = np.load("F:/Home/Projects/MailRuAI/Codeball2018/nnet/xtest_BR.npy") ytest = np.load("F:/Home/Projects/MailRuAI/Codeball2018/nnet/ytest_BR.npy") ytrain = np.exp(-(ytrain[:,1])/60) * ytrain[:,0] ytest = np.exp(-(ytest[:,1])/60) * ytest[:,0] inp = Input(shape=(xtrain.shape[1],)) d1 = Dense(6, activation='relu')(inp) d2 = Dense(6, activation='linear')(inp) d3 = Dense(6, activation='sigmoid')(inp) added = Concatenate()([d1, d2, d3]) d21 = Dense(3, activation='relu')(added) d22 = Dense(3, activation='linear')(added) d23 = Dense(3, activation='sigmoid')(added) added2 = Concatenate()([d21, d22, d23]) d31 = Dense(3, activation='relu')(added2) d32 = Dense(3, activation='linear')(added2) d33 = Dense(3, activation='sigmoid')(added2) added3 = Concatenate()([d31, d32, d33]) out = Dense(1)(added3) model = Model(inputs=inp, outputs=out) model.compile(optimizer='adam', loss='mse', metrics=['mae'])
Pourquoi ne pas demander exactement 3 couches internes et juste une telle configuration? Je ne me connais pas. Cependant, l'intuition et les jours d'expériences y ont conduit précisément.
Et enfin, dernière question, comment utiliser un réseau de neurones déjà formé en Python en C # sans avoir de classes toutes faites? Créez une classe! Avec une configuration aussi simple du réseau neuronal et considérant que nous n'avons besoin que d'implémenter la fonction «prédire» (c'est-à-dire simplement balayer d'entrée à sortie), c'est assez simple. Le voici:
public enum Activation { relu, linear, sigmoid }; public class layer { public int Count = 0; public List<List<double>> weights = new List<List<double>>(); public List<double> Ps = new List<double>(); public List<Activation> funcs = new List<Activation>(); public List<double> Values = new List<double>(); public void Add(Activation aact) { Count++; weights.Add(new List<double>()); Ps.Add(0); funcs.Add(aact); Values.Add(0); } public void Add(Activation aact, int acnt) { for (int i = 0; i < acnt; i++) Add(aact); } public void Calculate(List<double> ainps) { for (int i = 0; i < Count; i++) { Values[i] = Ps[i]; for (int j = 0; j < ainps.Count; j++) Values[i] += weights[i][j] * ainps[j]; switch (funcs[i]) { case Activation.linear: break; case Activation.relu: Values[i] = System.Math.Max(0, Values[i]); break; case Activation.sigmoid: Values[i] = (double)(1.0 / (1.0 + System.Math.Exp(-Values[i]))); break; } } } } public class nnet { public int inputCount = 0; public List<layer> layers = new List<layer>(); public layer outputLayer = null; public nnet(int ainputcount, int aoutputcount) { inputCount = ainputcount; outputLayer = new layer(); outputLayer.Add(Activation.linear, aoutputcount); } public List<double> predict(List<double> ainput) { for (int i = 0; i < layers.Count + 1; i++) { List<double> inps = ainput; if (i > 0) inps = layers[i - 1].Values; layer lr = outputLayer; if (i < layers.Count) lr = layers[i]; lr.Calculate(inps); } return outputLayer.Values; } }
Il ne reste plus qu'à resserrer les poids du réseau formé (au fait, je cite ici les poids qui fonctionnent vraiment dans ma dernière version):
public class trained_nnet : nnet { void FillLayer(layer al, double[] atp, double[,] atw) { al.Ps.Clear(); al.Ps.AddRange(atp); al.weights.Clear(); for (int i = 0; i < atw.GetLength(0); i++) { al.weights.Add(new List<double>()); for (int j = 0; j < atw.GetLength(1); j++) { al.weights[i].Add(atw[i, j]); } } } public trained_nnet() : base(6, 1) { layer lr1 = new layer(); lr1.Add(Activation.relu, 6); lr1.Add(Activation.linear, 6); lr1.Add(Activation.sigmoid, 6); base.layers.Add(lr1); layer lr2 = new layer(); lr2.Add(Activation.relu, 3); lr2.Add(Activation.linear, 3); lr2.Add(Activation.sigmoid, 3); base.layers.Add(lr2); layer lr3 = new layer(); lr3.Add(Activation.relu, 3); lr3.Add(Activation.linear, 3); lr3.Add(Activation.sigmoid, 3); base.layers.Add(lr3); double[] t = { 3.6843767166137695, -9.454026222229004, -5.089229106903076, -2.850287437438965, -6.96286153793335, -9.751116752624512, 10.384811401367188, -4.214056968688965, 1.2072025537490845, 1.4019242525100708, -0.13174889981746674, -13.1264066696167, -4.265004634857178, 1.8926845788955688, -0.0813497006893158, -1.4616785049438477, -5.361510753631592, -1.1896661520004272 }; double[,] t2 = { { 0.1477939784526825, 0.03613739833235741, -0.09796690940856934, 1.942456841468811, -0.3508949875831604, -0.5551134347915649 }, { -0.25495094060897827, 0.049018844962120056, -0.15976546704769135, -1.881699562072754, -1.3928385972976685, 0.017490295693278313 }, { 0.314727246761322, -0.7985705733299255, -0.16902890801429749, 0.7290273308753967, -3.3613057136535645, -0.501738965511322 }, { -0.14706645905971527, 0.013889106921851635, -8.41325855255127, 0.08269797265529633, -0.8194255232810974, 0.054869525134563446 }, { -0.11769858002662659, 0.024719441309571266, -32.9736213684082, -0.06565750390291214, -0.38925793766975403, -0.30816638469696045 }, { -0.09536012262105942, -0.4411015212535858, -0.3092011511325836, 0.061532989144325256, -1.3718899488449097, -0.9904148578643799 }, { 0.03862301632761955, -0.2239271104335785, -0.3054073452949524, 0.013336590491235256, -0.0404842384159565, -0.09027290344238281 }, { -0.317527711391449, -0.14433158934116364, 0.06079907342791557, -0.4572157561779022, 0.2782846987247467, 0.17747753858566284 }, { 0.01980031281709671, 0.015361669473350048, -0.03606397658586502, 0.013219496235251427, -0.03483833745121956, -0.01729537360370159 }, { -0.003958317916840315, 0.09587077051401138, -0.08213665336370468, -0.027169639244675636, 0.032037656754255295, -0.030492693185806274 }, { -0.04885690286755562, -0.06349656730890274, 0.013905149884521961, 0.018028201535344124, 0.012719585560262203, 0.002531017642468214 }, { 0.016520477831363678, -0.00018591046682558954, -0.003657651599496603, 0.06888063997030258, -0.2127065807580948, 0.6427022218704224 }, { -0.5308891534805298, 0.13539844751358032, 0.03864796832203865, 1.5582681894302368, -1.929693341255188, -3.2511842250823975 }, { 0.032178860157728195, 1.1472656726837158, -2.020042896270752, -0.05141841620206833, -0.4635908901691437, 0.2636871039867401 }, { 0.01480827759951353, 0.33971744775772095, -0.15343432128429413, 0.03558071702718735, 3.364596366882324, -0.7852638959884644 }, { 0.0028303645085543394, 1.2297841310501099, -0.4412313997745514, 0.3644706606864929, 2.2155861854553223, -0.43303439021110535 }, { -0.3666411340236664, 0.0464097335934639, 5.143652439117432, -2.2230076789855957, 0.3511424660682678, 1.0514445304870605 }, { 0.014482858590781689, -0.4740144610404968, -1.6240901947021484, 1.7327706813812256, -1.5116417407989502, -1.6811648607254028 } }; double[] t3 = { -3.09689998626709, -1.2031112909317017, -7.121585369110107, 2.0653932094573975, -2.8601508140563965, -1.6219528913497925, 0.16301754117012024, -6.890131950378418, 3.8225107192993164 }; double[,] t4 = { { -0.6246452927589417, -0.3575346767902374, 0.6897052526473999, -2.2513232231140137, -0.23217444121837616, 0.17847181856632233, -0.3863859176635742, -0.01201619766652584, 0.050539981573820114, 0.028343766927719116, 0.0034856200218200684, 0.5547005534172058, -0.4277774691581726, -1.0249099731445312, -8.995088577270508, -3.4937169551849365, 0.7673622369766235, -1.6504380702972412 }, { -1.0006977319717407, -0.8660659790039062, -0.0415676049888134, -0.5476861000061035, -0.7828258872032166, -0.05350146442651749, 0.005586389917880297, -0.052493464201688766, 0.07955628633499146, -0.08084911853075027, 0.09794406592845917, -0.031214063987135887, -0.7785998582839966, -0.27977627515792847, -0.4096711277961731, -0.24633635580539703, -1.5932326316833496, -0.5430923104286194 }, { -0.2330777496099472, -0.07477551698684692, -1.0634428262710571, -1.772096872329712, -1.4657013416290283, 0.6256936192512512, -0.1179097518324852, 0.07645376771688461, 0.008837736211717129, 0.030952733010053635, -0.013960030861198902, 1.0339184999465942, 0.20350944995880127, -0.047291483730077744, -4.043337345123291, -0.7629795670509338, -5.41167688369751, -3.7755305767059326 }, { 0.00979659240692854, 0.11435728520154953, -0.4749748706817627, 1.5166815519332886, -5.3047380447387695, 0.9597445130348206, 0.08123911172151566, 0.039479970932006836, -0.01649349369108677, -0.04941410943865776, 0.020120851695537567, -0.16329358518123627, 0.36106961965560913, 0.5348165035247803, 0.11825983971357346, 0.2075480818748474, -1.8661850690841675, 1.4093444347381592 }, { -0.35534173250198364, 0.3471201956272125, -0.2657061517238617, -2.4178225994110107, -3.890836238861084, 0.5999298691749573, -0.10068143904209137, 0.530009388923645, 0.023632165044546127, -0.006245455238968134, 0.031124670058488846, 0.016797777265310287, 1.720144510269165, -0.3200121223926544, 0.17827671766281128, -1.0847045183181763, 0.7679504156112671, 1.1521148681640625 }, { 0.047243088483810425, -0.07313758134841919, -0.13496115803718567, -1.0498348474502563, -2.083388328552246, 0.3018227815628052, 0.019016921520233154, 0.00780009850859642, -0.02416112646460533, -0.012299800291657448, 0.019720694050192833, 0.019809948280453682, -1.637327790260315, 0.09307140856981277, 2.963168144226074, 0.515803337097168, 0.02399904653429985, -3.9851980209350586 }, { -0.6250298023223877, -0.4796958863735199, 0.4311320185661316, -1.4590528011322021, -4.861763000488281, -1.1894060373306274, 0.31154727935791016, -0.028901753947138786, 0.07241783291101456, 0.0573900043964386, -0.16387903690338135, -0.7621306777000427, 2.864539623260498, 1.126343011856079, -0.729159414768219, 15.2516450881958, -0.5845442414283752, -0.2593745291233063 }, { -0.4520488679409027, -0.37348034977912903, -0.22873088717460632, 2.816544532775879, 0.635391891002655, 1.7192658185958862, -0.042334891855716705, -0.012391769327223301, -0.00944773480296135, -0.047271229326725006, 0.045244403183460236, 1.1044175624847412, -2.682516098022461, -1.797003984451294, -5.227936744689941, 0.3994572162628174, -3.361297130584717, -0.16535422205924988 }, { 1.3437395095825195, 0.05596136301755905, -0.6534030437469482, -3.2173333168029785, -3.256056785583496, 3.164973020553589, -0.6149216294288635, 0.3425371050834656, -0.13111716508865356, -0.42127469182014465, -0.0668950155377388, 0.19484268128871918, 2.005012273788452, -3.41219425201416, -0.3146309554576874, -2.1181774139404297, 2.2965285778045654, 5.287317276000977 } }; double[] t5 = { -1.173705816268921, -1.8888208866119385, -2.566594123840332, 0.1278465986251831, 0.05948356166481972, -0.021375492215156555, -1.554726243019104, -2.2256762981414795, 1.3142614364624023 }; double[,] t6 = { { -0.023421021178364754, 0.17735084891319275, -0.1922600418329239, -0.11634820699691772, 0.05003879591822624, 0.07409390062093735, -0.131203755736351, -0.11743484437465668, -1.1311017274856567 }, { -0.6256148219108582, -0.08678799867630005, 0.08910120278596878, -0.06354714930057526, 0.05225379019975662, 0.028936704620718956, -2.069547176361084, 0.16652414202690125, 0.4840211570262909 }, { -0.9266191720962524, 0.1542767435312271, -1.511458396911621, -2.2593629360198975, 0.32768234610557556, 0.728438138961792, 1.4113644361495972, -2.9423279762268066, -1.1225157976150513 }, { -0.31864309310913086, -0.06739992648363113, 1.8643943071365356, 0.12609687447547913, 0.003282073885202408, -0.08565603941679001, 0.22951357066631317, -3.9096572399139404, -0.5148558020591736 }, { 0.0030701414216309786, 0.22653144598007202, -0.1772366166114807, 0.01472154725342989, 0.006688127294182777, 0.029435427859425545, -0.049562305212020874, -0.01126908790320158, -0.09357477724552155 }, { -0.003160204039886594, 0.004133348818868399, 0.003914407920092344, 0.013578329235315323, 0.0036796496715396643, 0.028364477679133415, 0.025828130543231964, -0.030584659427404404, -0.0449080727994442 }, { -0.15649960935115814, 0.7045242786407471, 4.971825122833252, 0.26150253415107727, 0.25615766644477844, -0.007457265630364418, 0.4002840220928192, -4.386100769042969, -0.14405106008052826 }, { -1.283564805984497, -1.0451316833496094, -9.010445594787598, -0.23629669845104218, 0.8792487978935242, 0.12951965630054474, 2.7414908409118652, -10.04093074798584, 0.08805646747350693 }, { 0.5142691731452942, 0.27933982014656067, 17.242839813232422, -0.14753387868404388, 0.35601550340652466, -0.03304799646139145, -0.3745580017566681, 3.6696081161499023, 0.18306805193424225 } }; double[] t7 = { 0.057645831257104874 }; double[,] t8 = { { 0.02502649463713169, 0.030625218525528908, -0.04921620339155197, -0.06382419914007187, -0.0018273837631568313, -0.002946096006780863, -0.3073849678039551, -0.0770358145236969, 0.44145819544792175 } }; FillLayer(lr1, t, t2); FillLayer(lr2, t3, t4); FillLayer(lr3, t5, t6); FillLayer(outputLayer, t7, t8); } } - public class trained_nnet : nnet { void FillLayer(layer al, double[] atp, double[,] atw) { al.Ps.Clear(); al.Ps.AddRange(atp); al.weights.Clear(); for (int i = 0; i < atw.GetLength(0); i++) { al.weights.Add(new List<double>()); for (int j = 0; j < atw.GetLength(1); j++) { al.weights[i].Add(atw[i, j]); } } } public trained_nnet() : base(6, 1) { layer lr1 = new layer(); lr1.Add(Activation.relu, 6); lr1.Add(Activation.linear, 6); lr1.Add(Activation.sigmoid, 6); base.layers.Add(lr1); layer lr2 = new layer(); lr2.Add(Activation.relu, 3); lr2.Add(Activation.linear, 3); lr2.Add(Activation.sigmoid, 3); base.layers.Add(lr2); layer lr3 = new layer(); lr3.Add(Activation.relu, 3); lr3.Add(Activation.linear, 3); lr3.Add(Activation.sigmoid, 3); base.layers.Add(lr3); double[] t = { 3.6843767166137695, -9.454026222229004, -5.089229106903076, -2.850287437438965, -6.96286153793335, -9.751116752624512, 10.384811401367188, -4.214056968688965, 1.2072025537490845, 1.4019242525100708, -0.13174889981746674, -13.1264066696167, -4.265004634857178, 1.8926845788955688, -0.0813497006893158, -1.4616785049438477, -5.361510753631592, -1.1896661520004272 }; double[,] t2 = { { 0.1477939784526825, 0.03613739833235741, -0.09796690940856934, 1.942456841468811, -0.3508949875831604, -0.5551134347915649 }, { -0.25495094060897827, 0.049018844962120056, -0.15976546704769135, -1.881699562072754, -1.3928385972976685, 0.017490295693278313 }, { 0.314727246761322, -0.7985705733299255, -0.16902890801429749, 0.7290273308753967, -3.3613057136535645, -0.501738965511322 }, { -0.14706645905971527, 0.013889106921851635, -8.41325855255127, 0.08269797265529633, -0.8194255232810974, 0.054869525134563446 }, { -0.11769858002662659, 0.024719441309571266, -32.9736213684082, -0.06565750390291214, -0.38925793766975403, -0.30816638469696045 }, { -0.09536012262105942, -0.4411015212535858, -0.3092011511325836, 0.061532989144325256, -1.3718899488449097, -0.9904148578643799 }, { 0.03862301632761955, -0.2239271104335785, -0.3054073452949524, 0.013336590491235256, -0.0404842384159565, -0.09027290344238281 }, { -0.317527711391449, -0.14433158934116364, 0.06079907342791557, -0.4572157561779022, 0.2782846987247467, 0.17747753858566284 }, { 0.01980031281709671, 0.015361669473350048, -0.03606397658586502, 0.013219496235251427, -0.03483833745121956, -0.01729537360370159 }, { -0.003958317916840315, 0.09587077051401138, -0.08213665336370468, -0.027169639244675636, 0.032037656754255295, -0.030492693185806274 }, { -0.04885690286755562, -0.06349656730890274, 0.013905149884521961, 0.018028201535344124, 0.012719585560262203, 0.002531017642468214 }, { 0.016520477831363678, -0.00018591046682558954, -0.003657651599496603, 0.06888063997030258, -0.2127065807580948, 0.6427022218704224 }, { -0.5308891534805298, 0.13539844751358032, 0.03864796832203865, 1.5582681894302368, -1.929693341255188, -3.2511842250823975 }, { 0.032178860157728195, 1.1472656726837158, -2.020042896270752, -0.05141841620206833, -0.4635908901691437, 0.2636871039867401 }, { 0.01480827759951353, 0.33971744775772095, -0.15343432128429413, 0.03558071702718735, 3.364596366882324, -0.7852638959884644 }, { 0.0028303645085543394, 1.2297841310501099, -0.4412313997745514, 0.3644706606864929, 2.2155861854553223, -0.43303439021110535 }, { -0.3666411340236664, 0.0464097335934639, 5.143652439117432, -2.2230076789855957, 0.3511424660682678, 1.0514445304870605 }, { 0.014482858590781689, -0.4740144610404968, -1.6240901947021484, 1.7327706813812256, -1.5116417407989502, -1.6811648607254028 } }; double[] t3 = { -3.09689998626709, -1.2031112909317017, -7.121585369110107, 2.0653932094573975, -2.8601508140563965, -1.6219528913497925, 0.16301754117012024, -6.890131950378418, 3.8225107192993164 }; double[,] t4 = { { -0.6246452927589417, -0.3575346767902374, 0.6897052526473999, -2.2513232231140137, -0.23217444121837616, 0.17847181856632233, -0.3863859176635742, -0.01201619766652584, 0.050539981573820114, 0.028343766927719116, 0.0034856200218200684, 0.5547005534172058, -0.4277774691581726, -1.0249099731445312, -8.995088577270508, -3.4937169551849365, 0.7673622369766235, -1.6504380702972412 }, { -1.0006977319717407, -0.8660659790039062, -0.0415676049888134, -0.5476861000061035, -0.7828258872032166, -0.05350146442651749, 0.005586389917880297, -0.052493464201688766, 0.07955628633499146, -0.08084911853075027, 0.09794406592845917, -0.031214063987135887, -0.7785998582839966, -0.27977627515792847, -0.4096711277961731, -0.24633635580539703, -1.5932326316833496, -0.5430923104286194 }, { -0.2330777496099472, -0.07477551698684692, -1.0634428262710571, -1.772096872329712, -1.4657013416290283, 0.6256936192512512, -0.1179097518324852, 0.07645376771688461, 0.008837736211717129, 0.030952733010053635, -0.013960030861198902, 1.0339184999465942, 0.20350944995880127, -0.047291483730077744, -4.043337345123291, -0.7629795670509338, -5.41167688369751, -3.7755305767059326 }, { 0.00979659240692854, 0.11435728520154953, -0.4749748706817627, 1.5166815519332886, -5.3047380447387695, 0.9597445130348206, 0.08123911172151566, 0.039479970932006836, -0.01649349369108677, -0.04941410943865776, 0.020120851695537567, -0.16329358518123627, 0.36106961965560913, 0.5348165035247803, 0.11825983971357346, 0.2075480818748474, -1.8661850690841675, 1.4093444347381592 }, { -0.35534173250198364, 0.3471201956272125, -0.2657061517238617, -2.4178225994110107, -3.890836238861084, 0.5999298691749573, -0.10068143904209137, 0.530009388923645, 0.023632165044546127, -0.006245455238968134, 0.031124670058488846, 0.016797777265310287, 1.720144510269165, -0.3200121223926544, 0.17827671766281128, -1.0847045183181763, 0.7679504156112671, 1.1521148681640625 }, { 0.047243088483810425, -0.07313758134841919, -0.13496115803718567, -1.0498348474502563, -2.083388328552246, 0.3018227815628052, 0.019016921520233154, 0.00780009850859642, -0.02416112646460533, -0.012299800291657448, 0.019720694050192833, 0.019809948280453682, -1.637327790260315, 0.09307140856981277, 2.963168144226074, 0.515803337097168, 0.02399904653429985, -3.9851980209350586 }, { -0.6250298023223877, -0.4796958863735199, 0.4311320185661316, -1.4590528011322021, -4.861763000488281, -1.1894060373306274, 0.31154727935791016, -0.028901753947138786, 0.07241783291101456, 0.0573900043964386, -0.16387903690338135, -0.7621306777000427, 2.864539623260498, 1.126343011856079, -0.729159414768219, 15.2516450881958, -0.5845442414283752, -0.2593745291233063 }, { -0.4520488679409027, -0.37348034977912903, -0.22873088717460632, 2.816544532775879, 0.635391891002655, 1.7192658185958862, -0.042334891855716705, -0.012391769327223301, -0.00944773480296135, -0.047271229326725006, 0.045244403183460236, 1.1044175624847412, -2.682516098022461, -1.797003984451294, -5.227936744689941, 0.3994572162628174, -3.361297130584717, -0.16535422205924988 }, { 1.3437395095825195, 0.05596136301755905, -0.6534030437469482, -3.2173333168029785, -3.256056785583496, 3.164973020553589, -0.6149216294288635, 0.3425371050834656, -0.13111716508865356, -0.42127469182014465, -0.0668950155377388, 0.19484268128871918, 2.005012273788452, -3.41219425201416, -0.3146309554576874, -2.1181774139404297, 2.2965285778045654, 5.287317276000977 } }; double[] t5 = { -1.173705816268921, -1.8888208866119385, -2.566594123840332, 0.1278465986251831, 0.05948356166481972, -0.021375492215156555, -1.554726243019104, -2.2256762981414795, 1.3142614364624023 }; double[,] t6 = { { -0.023421021178364754, 0.17735084891319275, -0.1922600418329239, -0.11634820699691772, 0.05003879591822624, 0.07409390062093735, -0.131203755736351, -0.11743484437465668, -1.1311017274856567 }, { -0.6256148219108582, -0.08678799867630005, 0.08910120278596878, -0.06354714930057526, 0.05225379019975662, 0.028936704620718956, -2.069547176361084, 0.16652414202690125, 0.4840211570262909 }, { -0.9266191720962524, 0.1542767435312271, -1.511458396911621, -2.2593629360198975, 0.32768234610557556, 0.728438138961792, 1.4113644361495972, -2.9423279762268066, -1.1225157976150513 }, { -0.31864309310913086, -0.06739992648363113, 1.8643943071365356, 0.12609687447547913, 0.003282073885202408, -0.08565603941679001, 0.22951357066631317, -3.9096572399139404, -0.5148558020591736 }, { 0.0030701414216309786, 0.22653144598007202, -0.1772366166114807, 0.01472154725342989, 0.006688127294182777, 0.029435427859425545, -0.049562305212020874, -0.01126908790320158, -0.09357477724552155 }, { -0.003160204039886594, 0.004133348818868399, 0.003914407920092344, 0.013578329235315323, 0.0036796496715396643, 0.028364477679133415, 0.025828130543231964, -0.030584659427404404, -0.0449080727994442 }, { -0.15649960935115814, 0.7045242786407471, 4.971825122833252, 0.26150253415107727, 0.25615766644477844, -0.007457265630364418, 0.4002840220928192, -4.386100769042969, -0.14405106008052826 }, { -1.283564805984497, -1.0451316833496094, -9.010445594787598, -0.23629669845104218, 0.8792487978935242, 0.12951965630054474, 2.7414908409118652, -10.04093074798584, 0.08805646747350693 }, { 0.5142691731452942, 0.27933982014656067, 17.242839813232422, -0.14753387868404388, 0.35601550340652466, -0.03304799646139145, -0.3745580017566681, 3.6696081161499023, 0.18306805193424225 } }; double[] t7 = { 0.057645831257104874 }; double[,] t8 = { { 0.02502649463713169, 0.030625218525528908, -0.04921620339155197, -0.06382419914007187, -0.0018273837631568313, -0.002946096006780863, -0.3073849678039551, -0.0770358145236969, 0.44145819544792175 } }; FillLayer(lr1, t, t2); FillLayer(lr2, t3, t4); FillLayer(lr3, t5, t6); FillLayer(outputLayer, t7, t8); } }
Appel réseau neuronal:
public double StateRatingByNNet() { double result = 0; List<double> xdata = new List<double>(); double sign = 1; if (ball.velocity.Z < 0) sign = -1; double signX = 1; if (ball.velocity.X * sign < 0) signX = -1; xdata.Add(ball.velocity.X * sign * signX); xdata.Add(ball.velocity.Y); xdata.Add(ball.velocity.Z * sign); xdata.Add(ball.position.X * sign * signX); xdata.Add(ball.position.Y); xdata.Add(ball.position.Z * sign); List<double> o = nnet.predict(xdata); return result + o[0] * sign; }
Merci de votre intérêt!