Skip to content

Instantly share code, notes, and snippets.

@mattherbert1

mattherbert1/khf2.ipynb

Last active October 15, 2020 14:15
Show Gist options
  • Save mattherbert1/854fe25b74ecdd674ab1acaa7b117cf9 to your computer and use it in GitHub Desktop.
Save mattherbert1/854fe25b74ecdd674ab1acaa7b117cf9 to your computer and use it in GitHub Desktop.
Download ZIP
khf2.ipynb
Display the source blob
Display the rendered blob
Raw
khf2.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "khf2.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyM9f89fSYzHCwUdMK7BmoFN",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/mattherbert1/854fe25b74ecdd674ab1acaa7b117cf9/khf2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Mt4XrJmh8WXZ"
},
"source": [
"Herbert Máté - LNHG6Y\n",
"Usage: comment out the methods in the code which is not used in the particular scenario"
]
},
{
"cell_type": "code",
"metadata": {
"id": "GAWeFmC1sFjM"
},
"source": [
"import numpy as np\n",
"from sklearn import preprocessing\n",
"import copy"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-6bClFPKscUF"
},
"source": [
"def activation(x):\n",
" return 1 / (1 + np.exp(-x))"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "TvSEAd5WtaBG"
},
"source": [
"def dactivation(x):\n",
" return np.exp(-x)/((1+np.exp(-x))**2)"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yInbWKD6tce8"
},
"source": [
"# MLP osztály létrehozása.\n",
"class MLP:\n",
" \n",
" # A hálózat inicializálása az argumentumként megadott méretek alapján.\n",
" def __init__(self, *args):\n",
" # random seed megadása\n",
" np.random.seed(123)\n",
" # A hálózat formája (rétegek száma), amely megegyezik a paraméterek számával\n",
" self.shape = args\n",
" n = len(args)\n",
" # Rétegek létrehozása\n",
" self.layers = []\n",
" # Bemeneti réteg létrehozása (+1 egység a BIAS-nak)\n",
" self.layers.append(np.ones(self.shape[0]+1))\n",
" # Rejtett réteg(ek) és a kimeneti réteg létrehozása\n",
" for i in range(1,n):\n",
" self.layers.append(np.ones(self.shape[i]))\n",
" # Súlymátrix létrehozása\n",
" self.weights = []\n",
" for i in range(n-1):\n",
" self.weights.append(np.zeros((self.layers[i].size,\n",
" self.layers[i+1].size)))\n",
" # dw fogja tartalmazni a súlyok utolsó módosításait (később pl. a momentum módszer számára)\n",
" self.dw = [0,]*len(self.weights)\n",
" # Súlyok újrainicializálása\n",
" self.reset()\n",
" \n",
" # Súlyok újrainicializálási függvényének definiálása\n",
" def reset(self):\n",
" for i in range(len(self.weights)):\n",
" # véletlen számok [0,1) tartományban \n",
" Z = np.random.random((self.layers[i].size,self.layers[i+1].size))\n",
" # átskálázzuk a súlyokat -1..1 tartományba\n",
" self.weights[i][...] = (2*Z-1)*1\n",
"\n",
" # A bemenő adatok végigküldése a hálózaton, kimeneti rétegig (forward propagation)\n",
" def propagate_forward(self, data):\n",
" # Bemeneti réteg beállítása (tanító adatok)\n",
" self.layers[0][0:-1] = data\n",
" # Az adatok végigküldése a bemeneti rétegtől az utolsó előtti rétegig (az utolsó ugyanis a kimeneti réteg).\n",
" # A szigmoid aktivációs függvény használatával, mátrixszorzások alkalmazásával.\n",
" # Az előadáson a \"layers\" változót jelöltük \"a\"-val.\n",
" for i in range(1,len(self.shape)):\n",
" self.layers[i][...] = activation(np.dot(self.layers[i-1],self.weights[i-1]))\n",
" # Visszatérés a hálózat által becsült eredménnyel\n",
" return self.layers[-1]\n",
"\n",
" # Hibavisszaterjesztés (backpropagation) definiálása. \n",
" # A a learning rate (tanulási ráta) paraméter befolyásolja, hogy a hálózat súlyait milyen\n",
" # mértékben módosítsuk a gradiens függvényében. Ha ez az érték túl magas, akkor a háló \n",
" # \"oszcillálhat\" egy lokális vagy globális minimum körül. Ha túl kicsi értéket választunk,\n",
" # akkor pedig jelentősen több időbe telik mire elérjük a legjobb megoldást vagy leakad egy lokális \n",
" # minimumban és sose éri el azt.\n",
" \n",
" def propagate_backward(self, target, lrate=0.1):\n",
" deltas = []\n",
" # Hiba kiszámítása a kimeneti rétegen\n",
" error = -(target-self.layers[-1]) # y-y_kalap\n",
" # error*dactivation(s(3))\n",
" delta = np.multiply(error,dactivation(np.dot(self.layers[-2],self.weights[-1])))\n",
" deltas.append(delta)\n",
" # Gradiens kiszámítása a rejtett réteg(ek)ben\n",
" for i in range(len(self.shape)-2,0,-1):\n",
" # pl. utolsó rejtett réteg: delta(3)*(W(2).T)*dactivation(s(2)) (lásd előadás)\n",
" delta=np.dot(deltas[0],self.weights[i].T)*dactivation(np.dot(self.layers[i-1],self.weights[i-1]))\n",
" deltas.insert(0,delta) \n",
" # Súlyok módosítása\n",
" for i in range(len(self.weights)):\n",
" layer = np.atleast_2d(self.layers[i])\n",
" delta = np.atleast_2d(deltas[i])\n",
" # pl. utolsó rétegben: delta(3)*a(2) (lásd előadás)\n",
" dw = -lrate*np.dot(layer.T,delta)\n",
"\n",
" # HF2 start l1reg\n",
" # weight modification part of l1reg\n",
" # dw -= self.l1reg(self.weights[i],10**(-5),lrate)\n",
" # HF2 end l1reg\n",
"\n",
" # HF2 start l2reg\n",
" # weight modification part of l2reg\n",
" # dw -= self.l2reg(self.weights[i],10**(-5),lrate)\n",
" # HF2 end l2reg\n",
"\n",
" # HF2 start momentum\n",
" # weight modification part of momentum\n",
" # dw -= self.momentum(self.dw[i], 0.75)\n",
" # HF2 end momentum\n",
"\n",
" # súlyok módosítása\n",
" self.weights[i] += dw \n",
"\n",
" # a súlymódosítás eltárolása\n",
" self.dw[i] = dw\n",
"\n",
" cost = 0 # used to cummulate the cost caused by the particular procedure (l1 or l2reg)\n",
"\n",
" # HF2 start l1reg\n",
" # cost part of l1reg\n",
" # cost = l1reg_cost(10**(-5))\n",
" # HF2 end l1reg\n",
"\n",
" # HF2 start l2reg\n",
" # cost part of l2reg\n",
" # cost = l2reg_cost(10**(-5))\n",
" # HF2 end l2reg\n",
"\n",
" err_ret = (error**2).sum() + cost\n",
"\n",
" # Visszatérés a hibával\n",
" return err_ret\n",
" \n",
" # HF2 start l1reg\n",
" def l1reg(weights, lambda_1=10**(-5), lrate=0.1):\n",
" return lrate * lambda_1 * np.sign(weights)\n",
"\n",
" def l1reg_cost(lambda_1=10**(-5)):\n",
" return lambda_1 * np.abs(self.weights).sum()\n",
" # HF2 end l1reg\n",
" \n",
" # HF2 start l2reg\n",
" def l2reg(weights, lambda_2=10**(-5), lrate=0.1):\n",
" return lrate * lambda_2 * weights\n",
"\n",
" def l2reg_cost(lambda_2=10**(-5)):\n",
" return 0.5 * lambda_2 * (self.weights**2).sum()\n",
" # HF2 end l2reg\n",
"\n",
" # HF2 start momentum\n",
" def momentum(dw, alpha=0.75):\n",
" return alpha * dw\n",
"\n",
" # HF2 end momentum"
],
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "EwFKCIPPuAck"
},
"source": [
"def learn(network, X, Y, valid_split, test_split, epochs=20, lrate=0.1):\n",
"\n",
" # train-validation-test minták különválasztása\n",
" X_train = X[0:int(nb_samples*(1-valid_split-test_split))]\n",
" Y_train = Y[0:int(nb_samples*(1-valid_split-test_split))]\n",
" X_valid = X[int(nb_samples*(1-valid_split-test_split)):int(nb_samples*(1-test_split))]\n",
" Y_valid = Y[int(nb_samples*(1-valid_split-test_split)):int(nb_samples*(1-test_split))]\n",
" X_test = X[int(nb_samples*(1-test_split)):]\n",
" Y_test = Y[int(nb_samples*(1-test_split)):]\n",
" \n",
" # standardizálás\n",
" scaler = preprocessing.StandardScaler().fit(X_train)\n",
" X_train = scaler.transform(X_train)\n",
" X_valid = scaler.transform(X_valid)\n",
" X_test = scaler.transform(X_test)\n",
" \n",
" # ugyanolyan sorrendben keverjük be a bemeneteket és kimeneteket, a három külön adatbázisra\n",
" randperm = np.random.permutation(len(X_train))\n",
" X_train, Y_train = X_train[randperm], Y_train[randperm]\n",
" \n",
" # Tanítási fázis, epoch-szor megyünk át 1-1 véltelenszerűen kiválasztott mintán.\n",
" for i in range(epochs):\n",
" # Jelen megoldás azt a módszert használja, hogy a megadott \n",
" # tanító adatokon végigmegyünk és minden elemet először végigküldünk\n",
" # a hálózaton, majd terjeszti vissza a kapott eltérést az\n",
" # elvárt eredménytől. Ezt hívjuk SGD-ek (stochastic gradient descent).\n",
" train_err = 0\n",
" for k in range(X_train.shape[0]):\n",
" network.propagate_forward( X_train[k] )\n",
" train_err += network.propagate_backward( Y_train[k], lrate )\n",
" train_err /= X_train.shape[0]\n",
"\n",
" # validációs fázis\n",
" valid_err = 0\n",
" o_valid = np.zeros(X_valid.shape[0])\n",
" for k in range(X_valid.shape[0]):\n",
" o_valid[k] = network.propagate_forward(X_valid[k])\n",
" valid_err += (o_valid[k]-Y_valid[k])**2\n",
" valid_err /= X_valid.shape[0]\n",
"\n",
" print(\"%d epoch, train_err: %.4f, valid_err: %.4f\" % (i, train_err, valid_err))\n",
"\n",
" # Tesztelési fázis\n",
" print(\"\\n--- TESZTELÉS ---\\n\")\n",
" test_err = 0\n",
" o_test = np.zeros(X_test.shape[0])\n",
" for k in range(X_test.shape[0]):\n",
" o_test[k] = network.propagate_forward(X_test[k])\n",
" test_err += (o_test[k]-Y_test[k])**2\n",
" print(k, X_test[k], '%.2f' % o_test[k], ' (elvart eredmeny: %.2f)' % Y_test[k])\n",
" test_err /= X_test.shape[0]"
],
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "CzhTRhQJuJB1"
},
"source": [
"# Mesterséges neurális hálózat létrehozása, 2 bemenettel, 10 rejtett neuronnal és 1 kimenettel\n",
"network = MLP(2,10,1)"
],
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "WO7JohWLufT3"
},
"source": [
"# Tanító, validációs és teszt adatok megadása a rendszernek (zajjal terhelt XOR adatok)\n",
"nb_samples=1000\n",
"X = np.zeros((nb_samples,2))\n",
"Y = np.zeros(nb_samples)\n",
"for i in range(0,nb_samples,4):\n",
" noise = np.random.normal(0,1,8)\n",
" X[i], Y[i] = (-2+noise[0],-2+noise[1]), 0\n",
" X[i+1], Y[i+1] = (2+noise[2],-2+noise[3]), 1\n",
" X[i+2], Y[i+2] = (-2+noise[4],2+noise[5]), 1\n",
" X[i+3], Y[i+3] = (2+noise[6],2+noise[7]), 0"
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "oEtfgbdwusN3",
"outputId": "ee251b9e-905a-4e51-ab0c-ad22cd1407fd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"# Tanítás/Tesztelés indítása\n",
"network.reset()\n",
"learn(network, X, Y, 0.2, 0.1)"
],
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"text": [
"0 epoch, train_err: 0.2575, valid_err: 0.2487\n",
"1 epoch, train_err: 0.2515, valid_err: 0.2455\n",
"2 epoch, train_err: 0.2472, valid_err: 0.2403\n",
"3 epoch, train_err: 0.2395, valid_err: 0.2306\n",
"4 epoch, train_err: 0.2253, valid_err: 0.2131\n",
"5 epoch, train_err: 0.2017, valid_err: 0.1865\n",
"6 epoch, train_err: 0.1699, valid_err: 0.1549\n",
"7 epoch, train_err: 0.1366, valid_err: 0.1260\n",
"8 epoch, train_err: 0.1091, valid_err: 0.1041\n",
"9 epoch, train_err: 0.0894, valid_err: 0.0890\n",
"10 epoch, train_err: 0.0760, valid_err: 0.0787\n",
"11 epoch, train_err: 0.0667, valid_err: 0.0715\n",
"12 epoch, train_err: 0.0601, valid_err: 0.0662\n",
"13 epoch, train_err: 0.0553, valid_err: 0.0623\n",
"14 epoch, train_err: 0.0516, valid_err: 0.0592\n",
"15 epoch, train_err: 0.0488, valid_err: 0.0568\n",
"16 epoch, train_err: 0.0465, valid_err: 0.0548\n",
"17 epoch, train_err: 0.0446, valid_err: 0.0531\n",
"18 epoch, train_err: 0.0430, valid_err: 0.0517\n",
"19 epoch, train_err: 0.0417, valid_err: 0.0505\n",
"\n",
"--- TESZTELÉS ---\n",
"\n",
"0 [-0.49551261 -0.7444607 ] 0.11 (elvart eredmeny: 0.00)\n",
"1 [ 0.89288499 -1.26981044] 0.94 (elvart eredmeny: 1.00)\n",
"2 [-0.17687074 1.45002022] 0.72 (elvart eredmeny: 1.00)\n",
"3 [0.23518118 1.54853674] 0.36 (elvart eredmeny: 0.00)\n",
"4 [-0.91921004 -1.15158256] 0.04 (elvart eredmeny: 0.00)\n",
"5 [ 0.45007887 -0.45520451] 0.63 (elvart eredmeny: 1.00)\n",
"6 [-0.7232066 0.52937545] 0.86 (elvart eredmeny: 1.00)\n",
"7 [0.52408431 0.74905089] 0.11 (elvart eredmeny: 0.00)\n",
"8 [-1.13124799 -0.64346812] 0.09 (elvart eredmeny: 0.00)\n",
"9 [ 1.05619852 -1.16867333] 0.95 (elvart eredmeny: 1.00)\n",
"10 [-1.64331284 0.32178486] 0.84 (elvart eredmeny: 1.00)\n",
"11 [1.27592446 0.47490425] 0.10 (elvart eredmeny: 0.00)\n",
"12 [-0.85234509 -1.67766207] 0.06 (elvart eredmeny: 0.00)\n",
"13 [ 1.06439281 -0.01519469] 0.45 (elvart eredmeny: 1.00)\n",
"14 [-1.49598073 1.1721386 ] 0.98 (elvart eredmeny: 1.00)\n",
"15 [1.00867052 1.33834844] 0.04 (elvart eredmeny: 0.00)\n",
"16 [-0.96073952 -0.75856217] 0.06 (elvart eredmeny: 0.00)\n",
"17 [ 0.96820285 -0.62312564] 0.91 (elvart eredmeny: 1.00)\n",
"18 [-1.27731201 1.01086655] 0.97 (elvart eredmeny: 1.00)\n",
"19 [1.24921224 1.45125364] 0.03 (elvart eredmeny: 0.00)\n",
"20 [-0.68034828 -1.39272014] 0.07 (elvart eredmeny: 0.00)\n",
"21 [ 0.19203454 -0.69725794] 0.54 (elvart eredmeny: 1.00)\n",
"22 [-1.35447081 1.3051462 ] 0.98 (elvart eredmeny: 1.00)\n",
"23 [0.75811891 1.38066171] 0.07 (elvart eredmeny: 0.00)\n",
"24 [ 0.04865448 -0.90985725] 0.47 (elvart eredmeny: 0.00)\n",
"25 [ 0.83494361 -0.42420374] 0.81 (elvart eredmeny: 1.00)\n",
"26 [-0.42625033 1.69214366] 0.85 (elvart eredmeny: 1.00)\n",
"27 [0.9747302 0.63829576] 0.05 (elvart eredmeny: 0.00)\n",
"28 [-0.89859695 -1.16324684] 0.04 (elvart eredmeny: 0.00)\n",
"29 [ 0.99406789 -1.27367092] 0.95 (elvart eredmeny: 1.00)\n",
"30 [-0.4986541 1.23885647] 0.90 (elvart eredmeny: 1.00)\n",
"31 [0.94899899 0.93553572] 0.04 (elvart eredmeny: 0.00)\n",
"32 [-0.98989922 -0.28933404] 0.25 (elvart eredmeny: 0.00)\n",
"33 [ 1.4178541 -0.81594234] 0.93 (elvart eredmeny: 1.00)\n",
"34 [-1.28515493 0.39272723] 0.87 (elvart eredmeny: 1.00)\n",
"35 [1.01544978 0.6359506 ] 0.05 (elvart eredmeny: 0.00)\n",
"36 [-0.68920096 -1.05628831] 0.06 (elvart eredmeny: 0.00)\n",
"37 [ 1.23285696 -0.78808995] 0.94 (elvart eredmeny: 1.00)\n",
"38 [-0.45570044 -0.08445807] 0.36 (elvart eredmeny: 1.00)\n",
"39 [0.78528024 1.92471042] 0.17 (elvart eredmeny: 0.00)\n",
"40 [-2.02150252 -0.50132607] 0.34 (elvart eredmeny: 0.00)\n",
"41 [ 1.07375928 -0.30132133] 0.78 (elvart eredmeny: 1.00)\n",
"42 [-0.97967981 1.46134931] 0.97 (elvart eredmeny: 1.00)\n",
"43 [1.43104178 1.01282183] 0.03 (elvart eredmeny: 0.00)\n",
"44 [-1.42261241 -0.46956705] 0.19 (elvart eredmeny: 0.00)\n",
"45 [ 0.45148347 -0.9437387 ] 0.85 (elvart eredmeny: 1.00)\n",
"46 [-0.47857327 1.4169799 ] 0.89 (elvart eredmeny: 1.00)\n",
"47 [0.73120781 1.20702752] 0.06 (elvart eredmeny: 0.00)\n",
"48 [-1.59189506 -1.0949691 ] 0.04 (elvart eredmeny: 0.00)\n",
"49 [ 1.54374629 -0.27527979] 0.76 (elvart eredmeny: 1.00)\n",
"50 [-0.6027629 0.85996166] 0.91 (elvart eredmeny: 1.00)\n",
"51 [1.26573007 0.91350389] 0.03 (elvart eredmeny: 0.00)\n",
"52 [-0.82681195 -0.8418245 ] 0.06 (elvart eredmeny: 0.00)\n",
"53 [ 0.53999516 -0.55402011] 0.76 (elvart eredmeny: 1.00)\n",
"54 [-1.38054512 1.27422799] 0.98 (elvart eredmeny: 1.00)\n",
"55 [0.45722329 0.59802441] 0.15 (elvart eredmeny: 0.00)\n",
"56 [-2.17252156 -0.95218351] 0.16 (elvart eredmeny: 0.00)\n",
"57 [ 0.97142945 -0.50835919] 0.88 (elvart eredmeny: 1.00)\n",
"58 [-0.31580073 0.25126539] 0.54 (elvart eredmeny: 1.00)\n",
"59 [1.32725243 0.7145116 ] 0.05 (elvart eredmeny: 0.00)\n",
"60 [-0.93427312 -0.92924414] 0.05 (elvart eredmeny: 0.00)\n",
"61 [ 0.68579039 -1.28345398] 0.92 (elvart eredmeny: 1.00)\n",
"62 [-0.7771762 1.29016349] 0.96 (elvart eredmeny: 1.00)\n",
"63 [0.32342975 0.91520552] 0.19 (elvart eredmeny: 0.00)\n",
"64 [-1.18335154 -0.38699305] 0.20 (elvart eredmeny: 0.00)\n",
"65 [ 1.10755805 -0.20065101] 0.69 (elvart eredmeny: 1.00)\n",
"66 [-1.5946805 1.39085526] 0.98 (elvart eredmeny: 1.00)\n",
"67 [0.95956766 0.59662601] 0.06 (elvart eredmeny: 0.00)\n",
"68 [-0.68326656 -0.17477146] 0.31 (elvart eredmeny: 0.00)\n",
"69 [ 1.60443149 -0.51055056] 0.85 (elvart eredmeny: 1.00)\n",
"70 [-1.345509 1.06387085] 0.97 (elvart eredmeny: 1.00)\n",
"71 [1.0399581 1.48467107] 0.04 (elvart eredmeny: 0.00)\n",
"72 [-0.97578159 -0.8746935 ] 0.05 (elvart eredmeny: 0.00)\n",
"73 [ 0.59456082 -0.58012958] 0.80 (elvart eredmeny: 1.00)\n",
"74 [-0.99838184 1.00794573] 0.97 (elvart eredmeny: 1.00)\n",
"75 [0.67683025 0.94531504] 0.07 (elvart eredmeny: 0.00)\n",
"76 [-1.38522558 -0.80909313] 0.07 (elvart eredmeny: 0.00)\n",
"77 [ 0.61733912 -0.56602641] 0.80 (elvart eredmeny: 1.00)\n",
"78 [-0.07583425 1.30576314] 0.63 (elvart eredmeny: 1.00)\n",
"79 [0.38597268 0.76179308] 0.16 (elvart eredmeny: 0.00)\n",
"80 [-1.62192946 -0.87559713] 0.07 (elvart eredmeny: 0.00)\n",
"81 [ 0.0933582 -1.42405863] 0.67 (elvart eredmeny: 1.00)\n",
"82 [-0.41178207 1.31268606] 0.87 (elvart eredmeny: 1.00)\n",
"83 [0.20688708 0.72200264] 0.28 (elvart eredmeny: 0.00)\n",
"84 [-0.6947673 -0.70454052] 0.09 (elvart eredmeny: 0.00)\n",
"85 [ 0.15912428 -0.76434734] 0.53 (elvart eredmeny: 1.00)\n",
"86 [-1.17388859 1.46510224] 0.98 (elvart eredmeny: 1.00)\n",
"87 [0.79729717 1.08618224] 0.05 (elvart eredmeny: 0.00)\n",
"88 [-0.95084721 -0.58087122] 0.10 (elvart eredmeny: 0.00)\n",
"89 [ 0.54332732 -0.58005466] 0.77 (elvart eredmeny: 1.00)\n",
"90 [-0.36137803 1.75261358] 0.82 (elvart eredmeny: 1.00)\n",
"91 [1.59511841 0.68196924] 0.09 (elvart eredmeny: 0.00)\n",
"92 [-0.64393552 -0.51719602] 0.14 (elvart eredmeny: 0.00)\n",
"93 [ 1.15069897 -0.54158161] 0.90 (elvart eredmeny: 1.00)\n",
"94 [-0.71245188 0.67784608] 0.90 (elvart eredmeny: 1.00)\n",
"95 [0.97944428 1.30625314] 0.04 (elvart eredmeny: 0.00)\n",
"96 [-0.6956089 -1.37664792] 0.06 (elvart eredmeny: 0.00)\n",
"97 [ 0.49433522 -1.06646624] 0.88 (elvart eredmeny: 1.00)\n",
"98 [-0.99364269 0.99498732] 0.96 (elvart eredmeny: 1.00)\n",
"99 [0.44984778 1.28285523] 0.15 (elvart eredmeny: 0.00)\n"
],
"name": "stdout"
}
]
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment