notebooks/Aula 16 - Redes neurais artificiais.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "443a57e3",
   "metadata": {},
   "source": [
    "SIN-392 - Introdução ao Processamento Digital de Imagens (2023-1)\n",
    "\n",
    "# Aula 16 - Redes neurais artificiais\n",
    "\n",
    "Prof. João Fernando Mari ([*joaofmari.github.io*](https://joaofmari.github.io/))\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82143f90",
   "metadata": {},
   "source": [
    "## Importando as bibliotecas\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c3f119c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import datasets, metrics, preprocessing, model_selection\n",
    "\n",
    "### %matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a7f8636",
   "metadata": {},
   "source": [
    "## Plots the decision surface\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2b897acc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decision_surface(X, y, w, b, str_title):\n",
    "    \"\"\"\n",
    "    Plots the decision surface for the current epoch\n",
    "    \"\"\"\n",
    "\n",
    "    # Defines the extreme points of the decision surface line segment.\n",
    "    # p0 = (x0_min, f_x0_min); p1 = (x0_max, f_x0_max)\n",
    "    # ----------\n",
    "    x0_min = X.min() - 1.\n",
    "    x0_max = X.max() + 1.\n",
    "    # ----------\n",
    "    f_x0_min = -(w[0] / w[1]) * x0_min - (b / w[1])\n",
    "    f_x0_max = -(w[0] / w[1]) * x0_max - (b / w[1])\n",
    "\n",
    "    colors = ['r', 'g', 'b', 'y', 'c', 'm']\n",
    "\n",
    "    plt.figure()\n",
    "    # Plots the trainning set\n",
    "    for y_ in np.unique(y):\n",
    "        plt.scatter(X[y==y_][:,0], X[y==y_][:,1], color=colors[y_], label=str(y_))\n",
    "\n",
    "    # Labels, legends and title \n",
    "    plt.xlabel('$x_0$')\n",
    "    plt.ylabel('$x_1$')\n",
    "    plt.legend()\n",
    "    plt.title(str_title)\n",
    "\n",
    "    # Set the limits of the visualization space.\n",
    "    plt.xlim(X.min()-.5, X.max()+.5)\n",
    "    plt.ylim(X.min()-.5, X.max()+.5)\n",
    "\n",
    "    # Plots the surface decision.\n",
    "    plt.plot([x0_min, x0_max], [f_x0_min, f_x0_max], color='b')\n",
    "\n",
    "    # Show de figure.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eb97aec",
   "metadata": {},
   "source": [
    "## Perceptron learning algorithm\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "530c5eaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(X, y, plot=True):\n",
    "    \"\"\"\n",
    "    Train the model.\n",
    "    \"\"\"\n",
    "    print('Iniciatialization')\n",
    "    print('====================')\n",
    "    \n",
    "    # Weights (w) initialization\n",
    "    w = np.random.rand(X.shape[1]) \n",
    "    print(f'\\nWeights: {w}')\n",
    "\n",
    "    # Bias (b) initialization\n",
    "    b = np.random.rand() \n",
    "    print(f'Bias: {b}')\n",
    "\n",
    "    # List with the epoch errors\n",
    "    erros_epocas = []\n",
    "\n",
    "    # Plot the decision surface\n",
    "    if plot:\n",
    "        decision_surface(X, y, w, b, 'Inicialização')\n",
    "\n",
    "    # Iterates along the training epochs\n",
    "    for i in range(max_iter):\n",
    "        print('\\nÉpoca %i' % i)\n",
    "        print('====================')\n",
    "\n",
    "        # Epoch error\n",
    "        erro_epoca = 0\n",
    "\n",
    "        # Iterates across the dataset samples\n",
    "        for j in range(X.shape[0]):\n",
    "            print('\\nIteração %i' % j)\n",
    "            print('--------------------')\n",
    "            print(f'x: {X[j,:]}, y: {y[j]}')\n",
    "\n",
    "            # v: Inne product\n",
    "            v = np.dot(X[j,:], w) + b\n",
    "            print(f'v: {v}')\n",
    "\n",
    "            # y^: Step activation function.\n",
    "            y_out = np.where(v >= 0., 1, 0)\n",
    "            print(f'y_out: {y_out}')\n",
    "\n",
    "            # Error\n",
    "            erro = y[j] - y_out\n",
    "            print(f'Erro: {erro}')\n",
    "\n",
    "            # Update the epoch error. Squared error.\n",
    "            erro_epoca = erro_epoca + erro**2\n",
    "\n",
    "            # Weights (w) update\n",
    "            w = w +  learning_rate * np.dot(erro, X[j,:])\n",
    "            print(f'Pesos: {w}')\n",
    "\n",
    "            # Bias (b) update\n",
    "            b = b +  learning_rate * erro.sum()\n",
    "            print(f'Bias: {b}')\n",
    "\n",
    "        # Epoch error (mean squared error).\n",
    "        erro_epoca = erro_epoca / 2.\n",
    "        print('--------------------')\n",
    "        print(f'\\nEpoch error: {erro_epoca}')\n",
    "\n",
    "        # Append the epoch error to the list.\n",
    "        erros_epocas.append(erro_epoca)\n",
    "\n",
    "        # Plot the decision surface\n",
    "        if plot:\n",
    "            decision_surface(X, y, w, b, f'Epoch {i}')\n",
    "\n",
    "        # Stop training if epoch error is less than a pre-determined threshold.\n",
    "        if np.abs(erro_epoca) <= 0.01:\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3234925b",
   "metadata": {},
   "source": [
    "## Training the model with binary functions\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dc5f177b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data\n",
    "X = np.array([[0, 0],\n",
    "              [0, 1],\n",
    "              [1, 0],\n",
    "              [1, 1]])\n",
    "\n",
    "# Binary functions\n",
    "# ================\n",
    "# AND\n",
    "y_and = np.array([0, 0, 0, 1])\n",
    "\n",
    "# OR\n",
    "y_or = np.array([0, 1, 1, 1])\n",
    "\n",
    "# XOR\n",
    "y_xor = np.array([0, 1, 1, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21e22495",
   "metadata": {},
   "source": [
    "### AND function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3ceda2be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iniciatialization\n",
      "====================\n",
      "\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.4377277390071145\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 0\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: 0.4377277390071145\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.3377277390071145\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 0\n",
      "v: 0.9598365100469464\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.19151945 0.52210877]\n",
      "Bias: 0.2377277390071145\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 0\n",
      "v: 0.4292471893860068\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.09151945 0.52210877]\n",
      "Bias: 0.1377277390071145\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 1\n",
      "v: 0.7513559604258386\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.09151945 0.52210877]\n",
      "Bias: 0.1377277390071145\n",
      "--------------------\n",
      "\n",
      "Epoch error: 1.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 1\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: 0.1377277390071145\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.09151945 0.52210877]\n",
      "Bias: 0.037727739007114486\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 0\n",
      "v: 0.5598365100469463\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.09151945 0.42210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 0\n",
      "v: 0.02924718938600676\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [-0.00848055  0.42210877]\n",
      "Bias: -0.16227226099288553\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 1\n",
      "v: 0.25135596042583863\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [-0.00848055  0.42210877]\n",
      "Bias: -0.16227226099288553\n",
      "--------------------\n",
      "\n",
      "Epoch error: 1.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 2\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: -0.16227226099288553\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [-0.00848055  0.42210877]\n",
      "Bias: -0.16227226099288553\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 0\n",
      "v: 0.2598365100469464\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [-0.00848055  0.32210877]\n",
      "Bias: -0.26227226099288553\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 0\n",
      "v: -0.27075281061399326\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [-0.00848055  0.32210877]\n",
      "Bias: -0.26227226099288553\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 1\n",
      "v: 0.051355960425838676\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [-0.00848055  0.32210877]\n",
      "Bias: -0.26227226099288553\n",
      "--------------------\n",
      "\n",
      "Epoch error: 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 3\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: -0.26227226099288553\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [-0.00848055  0.32210877]\n",
      "Bias: -0.26227226099288553\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 0\n",
      "v: 0.0598365100469464\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [-0.00848055  0.22210877]\n",
      "Bias: -0.36227226099288556\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 0\n",
      "v: -0.3707528106139933\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [-0.00848055  0.22210877]\n",
      "Bias: -0.36227226099288556\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 1\n",
      "v: -0.14864403957416136\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.09151945 0.32210877]\n",
      "Bias: -0.2622722609928856\n",
      "--------------------\n",
      "\n",
      "Epoch error: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 4\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: -0.2622722609928856\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.09151945 0.32210877]\n",
      "Bias: -0.2622722609928856\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 0\n",
      "v: 0.059836510046946345\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.09151945 0.22210877]\n",
      "Bias: -0.36227226099288556\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 0\n",
      "v: -0.27075281061399326\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.09151945 0.22210877]\n",
      "Bias: -0.36227226099288556\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 1\n",
      "v: -0.04864403957416136\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.19151945 0.32210877]\n",
      "Bias: -0.2622722609928856\n",
      "--------------------\n",
      "\n",
      "Epoch error: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 5\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: -0.2622722609928856\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.32210877]\n",
      "Bias: -0.2622722609928856\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 0\n",
      "v: 0.059836510046946345\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.19151945 0.22210877]\n",
      "Bias: -0.36227226099288556\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 0\n",
      "v: -0.17075281061399328\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.22210877]\n",
      "Bias: -0.36227226099288556\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 1\n",
      "v: 0.051355960425838676\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.22210877]\n",
      "Bias: -0.36227226099288556\n",
      "--------------------\n",
      "\n",
      "Epoch error: 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 6\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: -0.36227226099288556\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.22210877]\n",
      "Bias: -0.36227226099288556\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 0\n",
      "v: -0.14016348995305364\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.22210877]\n",
      "Bias: -0.36227226099288556\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 0\n",
      "v: -0.17075281061399328\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.22210877]\n",
      "Bias: -0.36227226099288556\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 1\n",
      "v: 0.051355960425838676\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.22210877]\n",
      "Bias: -0.36227226099288556\n",
      "--------------------\n",
      "\n",
      "Epoch error: 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Maximum number of epochs.\n",
    "max_iter = 20\n",
    "\n",
    "# Learning rate.\n",
    "learning_rate = 0.1\n",
    "\n",
    "# Sets a value for the random number generation seed.\n",
    "np.random.seed(1234)\n",
    "\n",
    "# Training the model\n",
    "fit(X, y_and)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ba97910",
   "metadata": {},
   "source": [
    "### OR function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "480c45a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iniciatialization\n",
      "====================\n",
      "\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.4377277390071145\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 0\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: 0.4377277390071145\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.3377277390071145\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 1\n",
      "v: 0.9598365100469464\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.3377277390071145\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 1\n",
      "v: 0.5292471893860068\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.3377277390071145\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 1\n",
      "v: 1.1513559604258385\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.3377277390071145\n",
      "--------------------\n",
      "\n",
      "Epoch error: 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 1\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: 0.3377277390071145\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.2377277390071145\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 1\n",
      "v: 0.8598365100469464\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.2377277390071145\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 1\n",
      "v: 0.4292471893860068\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.2377277390071145\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 1\n",
      "v: 1.0513559604258387\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.2377277390071145\n",
      "--------------------\n",
      "\n",
      "Epoch error: 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 2\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: 0.2377277390071145\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.1377277390071145\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 1\n",
      "v: 0.7598365100469464\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.1377277390071145\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 1\n",
      "v: 0.3292471893860068\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.1377277390071145\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 1\n",
      "v: 0.9513559604258386\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.1377277390071145\n",
      "--------------------\n",
      "\n",
      "Epoch error: 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 3\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: 0.1377277390071145\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.037727739007114486\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 1\n",
      "v: 0.6598365100469463\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.037727739007114486\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 1\n",
      "v: 0.22924718938600677\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.037727739007114486\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 1\n",
      "v: 0.8513559604258386\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.037727739007114486\n",
      "--------------------\n",
      "\n",
      "Epoch error: 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 4\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: 0.037727739007114486\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 1\n",
      "v: 0.5598365100469463\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 1\n",
      "v: 0.12924718938600677\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 1\n",
      "v: 0.7513559604258386\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: -0.06227226099288552\n",
      "--------------------\n",
      "\n",
      "Epoch error: 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 5\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: -0.06227226099288552\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 1\n",
      "v: 0.5598365100469463\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 1\n",
      "v: 0.12924718938600677\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 1\n",
      "v: 0.7513559604258386\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: -0.06227226099288552\n",
      "--------------------\n",
      "\n",
      "Epoch error: 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Maximum number of epochs.\n",
    "max_iter = 10\n",
    "\n",
    "# Learning rate\n",
    "learning_rate = 0.1\n",
    "\n",
    "# Sets a value for the random number generation seed.\n",
    "np.random.seed(1234)\n",
    "\n",
    "# Training the model\n",
    "fit(X, y_or)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d3c3d66",
   "metadata": {},
   "source": [
    "### XOR function\n",
    "\n",
    "* The XOR function is non-linear, so the Perceptron algorithm does not converge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "109c22d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iniciatialization\n",
      "====================\n",
      "\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.4377277390071145\n"
     ]
    },
    {
     "data": {
      "image/png": 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N+zdVuaL1c0ZGB1wHtGn/pkYc1bmLsAUAAOok93hug9Y1d14Vtur6cc/U1FRdffXV6tixoxwOh2JiYvTOO+941MydO1c2m81j6927t4WzAADAu4W0DWnQuubOq8JWXT/u+eGHH+rqq6/W22+/rW3btmno0KEaNWqUPv/8c4+6Sy+9VLm5ue7to48+smL4AAA0C7FdY9XF0UU22ap93iabwhxhiu0a2+DHLioqUlZWlrKysiRJ2dnZysrK0v79+xv8WA3Fq5Z+qOvHPRcuXOjx+KGHHtLrr7+uN998U/3793e3t2jRQsHBwQ01TAAAmjW7j12LEhZp3KvjZJPN40b5ygC2MGGh7D72Bj/2Z599pqFDh7ofJyUlSZImT56slStXNvjxGoJXXdk6W+Xl5Tp+/Lg6dOjg0f7NN98oNDRUPXr00I033njGdFxSUiKXy+WxAQBwPhl7yVitH79enR2dPdq7OLpo/fj1lq2zNWTIEBljqmznatCSvOzK1tl6/PHHVVRUpPHjx7vbnE6nVq5cqYiICOXm5mrevHmKjY3Vl19+qbZt21bbz4IFCzRv3rzGGjYAAOeksZeM1eiI0dq0f5Nyj+cqpG2IYrvGWnJFy5udN2HrlVde0bx58/T666+rU6dO7vafvy3Zt29fOZ1OdevWTa+++qpuvvnmavtKTk52X7aUJJfLpbCwMOsGDwDAOcruY9eQ8CFNPYxz2nkRttasWaNbbrlF69atU1xc3Glr27Vrp4svvlh79uypscbPz09+fn4NPUwAANAMNft7tlavXq0pU6Zo9erVGjly5Bnri4qKtHfvXoWE8HFVAABw9rzqylZRUZHHFafKj3t26NBBXbt2VXJysnJycvTiiy9KqnjrcPLkyVq0aJGcTqfy8vIkSa1atVJAQIAk6Z577tGoUaPUrVs3ff/990pJSZHdbtf111/f+BMEAKARGVP1z+00N+fCHL3qytZnn32m/v37u5dtSEpKUv/+/TVnzhxJUm5urscnCf/+97/r1KlTuu222xQSEuLe7rrrLnfNwYMHdf311ysiIkLjx4/XhRdeqE8++UQdO3Zs3MkBANBIWrZsKUk6ceJEE4/EepVzrJxzU7CZcyHyeTmXy6WAgAAVFhbK4XA09XAAADij3NxcFRQUqFOnTmrdurVstuoXKPVWxhidOHFChw4dUrt27aq9Paixfn971duIAACgYVQu5n3o0KEmHom12rVr1+QLlxO2AAA4D9lsNoWEhKhTp046efJkUw/HEi1btpTd3vRrfhG2AAA4j9nt9nMikDRnXnWDPAAAgLchbAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgoRZNPQDAm5SVl2nT/k3KPZ6rkLYhiu0aK7uPvamHBaAhlJVJmzZJublSSIgUGyvZeX3j7HnVla0PP/xQo0aNUmhoqGw2mzZs2HDGfTIyMjRgwAD5+fmpV69eWrlyZZWaxYsXKzw8XP7+/nI6ndq6dWvDDx5eL3VnqsIXhWvoqqG6IfUGDV01VOGLwpW6M7WphwbgbKWmSuHh0tCh0g03VHwND69oB86SV4Wt4uJi9evXT4sXL65VfXZ2tkaOHKmhQ4cqKytLd999t2655Ra988477pq1a9cqKSlJKSkp2r59u/r166f4+HgdOnTIqmnAC6XuTNW4V8fpoOugR3uOK0fjXh1H4AK8WWqqNG6cdNDz9a2cnIp2AhfOks0YY5p6EPVhs9n02muvacyYMTXW3HfffXrrrbf05ZdfutsmTpyogoICpaWlSZKcTqcuv/xyPfvss5Kk8vJyhYWF6Y477tCsWbNqNRaXy6WAgAAVFhbK4XDUf1I4J5WVlyl8UXiVoFXJJpu6OLoo+65s3lIEvE1ZWcUVrF8GrUo2m9Sli5SdzVuKzVBj/f72qitbdZWZmam4uDiPtvj4eGVmZkqSSktLtW3bNo8aHx8fxcXFuWuqU1JSIpfL5bGh+dq0f1ONQUuSjIwOuA5o0/5NjTgqAA1i06aag5YkGSMdOFBRB9RTsw5beXl5CgoK8mgLCgqSy+XSjz/+qCNHjqisrKzamry8vBr7XbBggQICAtxbWFiYJePHuSH3eG6D1gE4h+TW8nVb2zqgGs06bFklOTlZhYWF7u3AgQNNPSRYKKRtSIPWATiHhNTydVvbOqAazXrph+DgYOXn53u05efny+FwqFWrVrLb7bLb7dXWBAcH19ivn5+f/Pz8LBkzzj2xXWPVxdFFOa4cGVW9xbHynq3YrrFNMDoAZyU2tuKerJycircMf6nynq1YXt+ov2Z9ZSsmJkbp6ekebRs3blRMTIwkydfXV9HR0R415eXlSk9Pd9cAdh+7FiUsklQRrH6u8vHChIXcHA94I7tdWlTx+pbN8/XtfrxwITfH46x4VdgqKipSVlaWsrKyJFUs7ZCVlaX9+/dLqnh7b9KkSe76W2+9Vd9++63uvfde7dq1S3/729/06quv6k9/+pO7JikpSUuXLtWqVau0c+dOTZ8+XcXFxZoyZUqjzg3ntrGXjNX68evV2dHZo72Lo4vWj1+vsZeMbaKRAThrY8dK69dLnT1f3+rSpaJ9LK9vnB2vWvohIyNDQ4cOrdI+efJkrVy5UomJidq3b58yMjI89vnTn/6kr7/+Wl26dNHs2bOVmJjosf+zzz6rxx57THl5eYqKitLTTz8tp9NZ63Gx9MP5gxXkgWaMFeTPO431+9urwta5irAFAID3YZ0tAACAZoCwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhbwubC1evFjh4eHy9/eX0+nU1q1ba6wdMmSIbDZblW3kyJHumsTExCrPJyQkNMZUAADAeaBFUw+gLtauXaukpCQtWbJETqdTCxcuVHx8vHbv3q1OnTpVqU9NTVVpaan78dGjR9WvXz/97ne/86hLSEjQihUr3I/9/PysmwQAADiveNWVrSeffFJTp07VlClTFBkZqSVLlqh169Zavnx5tfUdOnRQcHCwe9u4caNat25dJWz5+fl51LVv374xpgMAAM4DXhO2SktLtW3bNsXFxbnbfHx8FBcXp8zMzFr1sWzZMk2cOFEXXHCBR3tGRoY6deqkiIgITZ8+XUePHj1tPyUlJXK5XB4bAABAdbwmbB05ckRlZWUKCgryaA8KClJeXt4Z99+6dau+/PJL3XLLLR7tCQkJevHFF5Wenq5HHnlE//73vzVixAiVlZXV2NeCBQsUEBDg3sLCwuo3KQAA0Ox51T1bZ2PZsmXq06ePBg0a5NE+ceJE97/79Omjvn37qmfPnsrIyNCwYcOq7Ss5OVlJSUnuxy6Xi8AFAACq5TVXtgIDA2W325Wfn+/Rnp+fr+Dg4NPuW1xcrDVr1ujmm28+43F69OihwMBA7dmzp8YaPz8/ORwOjw0AAKA6XhO2fH19FR0drfT0dHdbeXm50tPTFRMTc9p9161bp5KSEt10001nPM7Bgwd19OhRhYSEnPWYAQAAvCZsSVJSUpKWLl2qVatWaefOnZo+fbqKi4s1ZcoUSdKkSZOUnJxcZb9ly5ZpzJgxuvDCCz3ai4qKNHPmTH3yySfat2+f0tPTNXr0aPXq1Uvx8fGNMicAANC8edU9WxMmTNDhw4c1Z84c5eXlKSoqSmlpae6b5vfv3y8fH8/8uHv3bn300Ud69913q/Rnt9v1xRdfaNWqVSooKFBoaKiGDx+u+fPns9YWAABoEDZjjGnqQXg7l8ulgIAAFRYWcv8WAABeorF+f3vV24gAAADehrAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGChBg1bW7ZsacjuAAAAvF6Dhq3f/e53DdkdAACA12tR1x3Gjx9fbbsxRseOHTvrAZ3J4sWL9dhjjykvL0/9+vXTM888o0GDBlVbu3LlSk2ZMsWjzc/PTz/99JP7sTFGKSkpWrp0qQoKCvTrX/9azz33nC666CJL5wEAAM4PdQ5b7733nv7xj3+oTZs2Hu3GGH344YcNNrDqrF27VklJSVqyZImcTqcWLlyo+Ph47d69W506dap2H4fDod27d7sf22w2j+cfffRRPf3001q1apW6d++u2bNnKz4+Xl9//bX8/f0tnQ8AAGj+6hy2hgwZorZt2+qqq66q8lzfvn0bZFA1efLJJzV16lT31aolS5borbfe0vLlyzVr1qxq97HZbAoODq72OWOMFi5cqAceeECjR4+WJL344osKCgrShg0bNHHiRGsmAgAAzhu1vmfr+PHjkqTU1NRqg5Ykbdy4sWFGVY3S0lJt27ZNcXFx7jYfHx/FxcUpMzOzxv2KiorUrVs3hYWFafTo0frqq6/cz2VnZysvL8+jz4CAADmdztP2WVJSIpfL5bEBAABUp9ZhKzY2Vnl5eVaO5bSOHDmisrIyBQUFebQHBQXVOK6IiAgtX75cr7/+ul566SWVl5friiuu0MGDByXJvV9d+pSkBQsWKCAgwL2FhYWdzdQAAEAzVuuw1b9/fzmdTu3atcujPSsrS9dcc02DD6whxMTEaNKkSYqKitLgwYOVmpqqjh076vnnnz+rfpOTk1VYWOjeDhw40EAjBgAAzU2tw9aKFSuUmJioK6+8Uh999JH++9//avz48YqOjpbdbrdyjJKkwMBA2e125efne7Tn5+fXeE/WL7Vs2VL9+/fXnj17JMm9X1379PPzk8Ph8NgAAACqU6d1tubNm6ekpCRdffXVuuyyy3T8+HFlZmbqzTfftGp8br6+voqOjlZ6erq7rby8XOnp6YqJialVH2VlZdqxY4dCQkIkSd27d1dwcLBHny6XS1u2bKl1nwAAAKdT608j5ufn66GHHtLSpUsVGRmpXbt2KTExscY1rqyQlJSkyZMna+DAgRo0aJAWLlyo4uJi96cTJ02apM6dO2vBggWSpAcffFC/+tWv1KtXLxUUFOixxx7Td999p1tuuUVSxScV7777bv3lL3/RRRdd5F76ITQ0VGPGjGm0eQEAgOar1mGre/fuioiI0Lp16zRy5EilpaVpwoQJ2r9/v2bOnGnlGN0mTJigw4cPa86cOcrLy1NUVJTS0tLcN7jv379fPj7/d7Huhx9+0NSpU5WXl6f27dsrOjpamzdvVmRkpLvm3nvvVXFxsaZNm6aCggJdeeWVSktLY40tAADQIGzGGFObwjVr1lRZd2r79u269tprdd1112nx4sWWDNAbuFwuBQQEqLCwkPu3AADwEo31+7vW92xVt8DngAEDtHnzZr3//vsNOigAAIDm4qz/EHV4eLg2b97cEGMBAABods46bElS+/btG6IbAACAZqdBwhYAAACqR9gCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAt5XdhavHixwsPD5e/vL6fTqa1bt9ZYu3TpUsXGxqp9+/Zq37694uLiqtQnJibKZrN5bAkJCVZPAwAAnCe8KmytXbtWSUlJSklJ0fbt29WvXz/Fx8fr0KFD1dZnZGTo+uuv1wcffKDMzEyFhYVp+PDhysnJ8ahLSEhQbm6ue1u9enVjTAcAAJwHbMYY09SDqC2n06nLL79czz77rCSpvLxcYWFhuuOOOzRr1qwz7l9WVqb27dvr2Wef1aRJkyRVXNkqKCjQhg0b6j0ul8ulgIAAFRYWyuFw1LsfAADQeBrr97fXXNkqLS3Vtm3bFBcX527z8fFRXFycMjMza9XHiRMndPLkSXXo0MGjPSMjQ506dVJERISmT5+uo0ePnrafkpISuVwujw0AAKA6XhO2jhw5orKyMgUFBXm0BwUFKS8vr1Z93HfffQoNDfUIbAkJCXrxxReVnp6uRx55RP/+9781YsQIlZWV1djPggULFBAQ4N7CwsLqNykAANDstWjqATSWhx9+WGvWrFFGRob8/f3d7RMnTnT/u0+fPurbt6969uypjIwMDRs2rNq+kpOTlZSU5H7scrkIXAAAoFpec2UrMDBQdrtd+fn5Hu35+fkKDg4+7b6PP/64Hn74Yb377rvq27fvaWt79OihwMBA7dmzp8YaPz8/ORwOjw0AAKA6XhO2fH19FR0drfT0dHdbeXm50tPTFRMTU+N+jz76qObPn6+0tDQNHDjwjMc5ePCgjh49qpCQkAYZNwAAOL95TdiSpKSkJC1dulSrVq3Szp07NX36dBUXF2vKlCmSpEmTJik5Odld/8gjj2j27Nlavny5wsPDlZeXp7y8PBUVFUmSioqKNHPmTH3yySfat2+f0tPTNXr0aPXq1Uvx8fFNMkcAANC8eNU9WxMmTNDhw4c1Z84c5eXlKSoqSmlpae6b5vfv3y8fn//Lj88995xKS0s1btw4j35SUlI0d+5c2e12ffHFF1q1apUKCgoUGhqq4cOHa/78+fLz82vUuQEAgObJq9bZOlexzhYAAN6HdbYAAACaAcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFWjT1AACvUlYmbdok5eZKISFSbKxktzf1qAA0gLLyMm3av0m5x3MV0jZEsV1jZffh9Y2z53VXthYvXqzw8HD5+/vL6XRq69atp61ft26devfuLX9/f/Xp00dvv/22x/PGGM2ZM0chISFq1aqV4uLi9M0331g5BXir1FQpPFwaOlS64YaKr+HhFe0AvFrqzlSFLwrX0FVDdUPqDRq6aqjCF4UrdSevb5w9rwpba9euVVJSklJSUrR9+3b169dP8fHxOnToULX1mzdv1vXXX6+bb75Zn3/+ucaMGaMxY8boyy+/dNc8+uijevrpp7VkyRJt2bJFF1xwgeLj4/XTTz811rTgDVJTpXHjpIMHPdtzciraCVyA10rdmapxr47TQZfn6zvHlaNxr44jcOGs2YwxpqkHUVtOp1OXX365nn32WUlSeXm5wsLCdMcdd2jWrFlV6idMmKDi4mL985//dLf96le/UlRUlJYsWSJjjEJDQzVjxgzdc889kqTCwkIFBQVp5cqVmjhxYq3G5XK5FBAQoMLCQjkcjgaYKc4pZWUVV7B+GbQq2WxSly5SdjZvKQJepqy8TOGLwqsErUo22dTF0UXZd2XzlmIz1Fi/v73mylZpaam2bdumuLg4d5uPj4/i4uKUmZlZ7T6ZmZke9ZIUHx/vrs/OzlZeXp5HTUBAgJxOZ419SlJJSYlcLpfHhmZs06aag5YkGSMdOFBRB8CrbNq/qcagJUlGRgdcB7RpP69v1J/XhK0jR46orKxMQUFBHu1BQUHKy8urdp+8vLzT1ld+rUufkrRgwQIFBAS4t7CwsDrPB14kN7dh6wCcM3KP1+51W9s6oDpeE7bOJcnJySosLHRvBw4caOohwUohIQ1bB+CcEdK2dq/b2tYB1fGasBUYGCi73a78/HyP9vz8fAUHB1e7T3Bw8GnrK7/WpU9J8vPzk8Ph8NjQjMXGVtyTZbNV/7zNJoWFVdQB8CqxXWPVxdFFNlX/+rbJpjBHmGK78vpG/XlN2PL19VV0dLTS09PdbeXl5UpPT1dMTEy1+8TExHjUS9LGjRvd9d27d1dwcLBHjcvl0pYtW2rsE+chu11atKji378MXJWPFy7k5njAC9l97FqUUPH6/mXgqny8MGEhN8fjrHhN2JKkpKQkLV26VKtWrdLOnTs1ffp0FRcXa8qUKZKkSZMmKTk52V1/1113KS0tTU888YR27dqluXPn6rPPPtPtt98uSbLZbLr77rv1l7/8RW+88YZ27NihSZMmKTQ0VGPGjGmKKeJcNXastH691LmzZ3uXLhXtY8c2zbgAnLWxl4zV+vHr1dnh+fru4uii9ePXa+wlvL5xdrxqBfkJEybo8OHDmjNnjvLy8hQVFaW0tDT3De779++Xj8//5ccrrrhCr7zyih544AHdf//9uuiii7RhwwZddtll7pp7771XxcXFmjZtmgoKCnTllVcqLS1N/v7+jT4/nOPGjpVGj2YFeaAZGnvJWI2OGM0K8rCEV62zda5inS0AALwP62wBAAA0A4QtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAs5DVh69ixY7rxxhvlcDjUrl073XzzzSoqKjpt/R133KGIiAi1atVKXbt21Z133qnCwkKPOpvNVmVbs2aN1dMBAADniRZNPYDauvHGG5Wbm6uNGzfq5MmTmjJliqZNm6ZXXnml2vrvv/9e33//vR5//HFFRkbqu+++06233qrvv/9e69ev96hdsWKFEhIS3I/btWtn5VQAAMB5xGaMMU09iDPZuXOnIiMj9emnn2rgwIGSpLS0NF1zzTU6ePCgQkNDa9XPunXrdNNNN6m4uFgtWlTkTJvNptdee01jxoyp9/hcLpcCAgJUWFgoh8NR734AAEDjaazf317xNmJmZqbatWvnDlqSFBcXJx8fH23ZsqXW/VR+MyuDVqXbbrtNgYGBGjRokJYvX64z5c+SkhK5XC6PDQAAoDpe8TZiXl6eOnXq5NHWokULdejQQXl5ebXq48iRI5o/f76mTZvm0f7ggw/qN7/5jVq3bq13331Xf/zjH1VUVKQ777yzxr4WLFigefPm1X0iAADgvNOkV7ZmzZpV7Q3qP9927dp11sdxuVwaOXKkIiMjNXfuXI/nZs+erV//+tfq37+/7rvvPt1777167LHHTttfcnKyCgsL3duBAwfOeowAAKB5atIrWzNmzFBiYuJpa3r06KHg4GAdOnTIo/3UqVM6duyYgoODT7v/8ePHlZCQoLZt2+q1115Ty5YtT1vvdDo1f/58lZSUyM/Pr9oaPz+/Gp8DAAD4uSYNWx07dlTHjh3PWBcTE6OCggJt27ZN0dHRkqT3339f5eXlcjqdNe7ncrkUHx8vPz8/vfHGG/L39z/jsbKystS+fXvCFAAAaBBecc/WJZdcooSEBE2dOlVLlizRyZMndfvtt2vixInuTyLm5ORo2LBhevHFFzVo0CC5XC4NHz5cJ06c0EsvveRxI3vHjh1lt9v15ptvKj8/X7/61a/k7++vjRs36qGHHtI999zTlNMFAADNiFeELUl6+eWXdfvtt2vYsGHy8fHRb3/7Wz399NPu50+ePKndu3frxIkTkqTt27e7P6nYq1cvj76ys7MVHh6uli1bavHixfrTn/4kY4x69eqlJ598UlOnTm28iQEAgGbNK9bZOtexzhYAAN6HdbYAAACaAcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAW8pqwdezYMd14441yOBxq166dbr75ZhUVFZ12nyFDhshms3lst956q0fN/v37NXLkSLVu3VqdOnXSzJkzderUKSunAgAAziMtmnoAtXXjjTcqNzdXGzdu1MmTJzVlyhRNmzZNr7zyymn3mzp1qh588EH349atW7v/XVZWppEjRyo4OFibN29Wbm6uJk2apJYtW+qhhx6ybC4AAOD8YTPGmKYexJns3LlTkZGR+vTTTzVw4EBJUlpamq655hodPHhQoaGh1e43ZMgQRUVFaeHChdU+/69//UvXXnutvv/+ewUFBUmSlixZovvuu0+HDx+Wr69vrcbncrkUEBCgwsJCORyOuk8QAAA0usb6/e0VV7YyMzPVrl07d9CSpLi4OPn4+GjLli267rrratz35Zdf1ksvvaTg4GCNGjVKs2fPdl/dyszMVJ8+fdxBS5Li4+M1ffp0ffXVV+rfv3+1fZaUlKikpMT9uLCwUFLFSQMAAN6h8ve21dedvCJs5eXlqVOnTh5tLVq0UIcOHZSXl1fjfjfccIO6deum0NBQffHFF7rvvvu0e/dupaamuvv9edCS5H58un4XLFigefPmVWkPCwur9ZwAAMC54ejRowoICLCs/yYNW7NmzdIjjzxy2pqdO3fWu/9p06a5/92nTx+FhIRo2LBh2rt3r3r27FnvfpOTk5WUlOR+XFBQoG7dumn//v2WnqxzjcvlUlhYmA4cOHBevX3KvJn3+YB5M+/zQWFhobp27aoOHTpYepwmDVszZsxQYmLiaWt69Oih4OBgHTp0yKP91KlTOnbsmIKDg2t9PKfTKUnas2ePevbsqeDgYG3dutWjJj8/X5JO26+fn5/8/PyqtAcEBJxX/5FWcjgczPs8wrzPL8z7/HK+ztvHx9rFGZo0bHXs2FEdO3Y8Y11MTIwKCgq0bds2RUdHS5Lef/99lZeXuwNUbWRlZUmSQkJC3P3+9a9/1aFDh9xvU27cuFEOh0ORkZF1nA0AAEBVXrHO1iWXXKKEhARNnTpVW7du1ccff6zbb79dEydOdH8SMScnR71793Zfqdq7d6/mz5+vbdu2ad++fXrjjTc0adIkXXXVVerbt68kafjw4YqMjNTvf/97/ec//9E777yjBx54QLfddlu1V64AAADqyivCllTxqcLevXtr2LBhuuaaa3TllVfq73//u/v5kydPavfu3Tpx4oQkydfXV++9956GDx+u3r17a8aMGfrtb3+rN998072P3W7XP//5T9ntdsXExOimm27SpEmTPNblqg0/Pz+lpKScdwGNeTPv8wHzZt7nA+Zt7by9Yp0tAAAAb+U1V7YAAAC8EWELAADAQoQtAAAACxG2AAAALETYAgAAsBBhqxaOHTumG2+8UQ6HQ+3atdPNN9+soqKi0+4zZMgQ2Ww2j+3WW2/1qNm/f79Gjhyp1q1bq1OnTpo5c6ZOnTpl5VTqpK7zPnbsmO644w5FRESoVatW6tq1q+688073H+qu9Mvvi81m05o1a6yeTo0WL16s8PBw+fv7y+l0VvmrAr+0bt069e7dW/7+/urTp4/efvttj+eNMZozZ45CQkLUqlUrxcXF6ZtvvrFyCvVSl3kvXbpUsbGxat++vdq3b6+4uLgq9YmJiVXOa0JCgtXTqLO6zHvlypVV5uTv7+9R0xzPd3U/v2w2m0aOHOmu8Ybz/eGHH2rUqFEKDQ2VzWbThg0bzrhPRkaGBgwYID8/P/Xq1UsrV66sUlPXnxmNra7zTk1N1dVXX62OHTvK4XAoJiZG77zzjkfN3Llzq5zv3r17WziLuqvrvDMyMqr97/yXfxu5Qc63wRklJCSYfv36mU8++cRs2rTJ9OrVy1x//fWn3Wfw4MFm6tSpJjc3170VFha6nz916pS57LLLTFxcnPn888/N22+/bQIDA01ycrLV06m1us57x44dZuzYseaNN94we/bsMenp6eaiiy4yv/3tbz3qJJkVK1Z4fG9+/PFHq6dTrTVr1hhfX1+zfPly89VXX5mpU6eadu3amfz8/GrrP/74Y2O3282jjz5qvv76a/PAAw+Yli1bmh07drhrHn74YRMQEGA2bNhg/vOf/5j/+Z//Md27d2+yOVanrvO+4YYbzOLFi83nn39udu7caRITE01AQIA5ePCgu2by5MkmISHB47weO3assaZUK3Wd94oVK4zD4fCYU15enkdNczzfR48e9Zjzl19+aex2u1mxYoW7xhvO99tvv23+/Oc/m9TUVCPJvPbaa6et//bbb03r1q1NUlKS+frrr80zzzxj7Ha7SUtLc9fU9XvZFOo677vuuss88sgjZuvWrea///2vSU5ONi1btjTbt29316SkpJhLL73U43wfPnzY4pnUTV3n/cEHHxhJZvfu3R7zKisrc9c01PkmbJ3B119/bSSZTz/91N32r3/9y9hsNpOTk1PjfoMHDzZ33XVXjc+//fbbxsfHx+MH93PPPWccDocpKSlpkLGfjfrO+5deffVV4+vra06ePOluq82LoLEMGjTI3Hbbbe7HZWVlJjQ01CxYsKDa+vHjx5uRI0d6tDmdTvOHP/zBGGNMeXm5CQ4ONo899pj7+YKCAuPn52dWr15twQzqp67z/qVTp06Ztm3bmlWrVrnbJk+ebEaPHt3QQ21QdZ33ihUrTEBAQI39nS/n+6mnnjJt27Y1RUVF7jZvON8/V5ufO/fee6+59NJLPdomTJhg4uPj3Y/P9nvZ2Or78zYyMtLMmzfP/TglJcX069ev4QZmsbqErR9++KHGmoY637yNeAaZmZlq166dBg4c6G6Li4uTj4+PtmzZctp9X375ZQUGBuqyyy5TcnKye3X7yn779OmjoKAgd1t8fLxcLpe++uqrhp9IHZ3NvH+usLBQDodDLVp4/hnO2267TYGBgRo0aJCWL18u0wRr65aWlmrbtm2Ki4tzt/n4+CguLk6ZmZnV7pOZmelRL1Wct8r67Oxs5eXledQEBATI6XTW2Gdjq8+8f+nEiRM6efKkOnTo4NGekZGhTp06KSIiQtOnT9fRo0cbdOxno77zLioqUrdu3RQWFqbRo0d7vD7Pl/O9bNkyTZw4URdccIFH+7l8vuvjTK/vhvheeoPy8nIdP368yuv7m2++UWhoqHr06KEbb7xR+/fvb6IRNqyoqCiFhITo6quv1scff+xub8jz3aR/iNob5OXluf9IdaUWLVqoQ4cOVd7X/bkbbrhB3bp1U2hoqL744gvdd9992r17t1JTU939/jxoSXI/Pl2/jaW+8/65I0eOaP78+Zo2bZpH+4MPPqjf/OY3at26td5991398Y9/VFFRke68884GG39tx1dWVlbtedi1a1e1+9R03iq/J5VfT1fT1Ooz71+67777FBoa6vFDKCEhQWPHjlX37t21d+9e3X///RoxYoQyMzNlt9sbdA71UZ95R0REaPny5erbt68KCwv1+OOP64orrtBXX32lLl26nBfne+vWrfryyy+1bNkyj/Zz/XzXR02vb5fLpR9//FE//PDDWb92vMHjjz+uoqIijR8/3t3mdDq1cuVKRUREKDc3V/PmzVNsbKy+/PJLtW3btglHW38hISFasmSJBg4cqJKSEr3wwgsaMmSItmzZogEDBjTIz8pK523YmjVrlh555JHT1uzcubPe/f88YPTp00chISEaNmyY9u7dq549e9a737Nl9bwruVwujRw5UpGRkZo7d67Hc7Nnz3b/u3///iouLtZjjz3W6GEL9fPwww9rzZo1ysjI8LhZfOLEie5/9+nTR3379lXPnj2VkZGhYcOGNcVQz1pMTIxiYmLcj6+44gpdcsklev755zV//vwmHFnjWbZsmfr06aNBgwZ5tDfH8w3plVde0bx58/T66697/A/3iBEj3P/u27evnE6nunXrpldffVU333xzUwz1rEVERCgiIsL9+IorrtDevXv11FNP6R//+EeDHuu8DVszZsxQYmLiaWt69Oih4OBgHTp0yKP91KlTOnbsmIKDg2t9PKfTKUnas2ePevbsqeDg4CqfaMjPz5ekOvVbV40x7+PHjyshIUFt27bVa6+9ppYtW5623ul0av78+SopKWnUP4IaGBgou93u/r5Xys/Pr3GOwcHBp62v/Jqfn6+QkBCPmqioqAYcff3VZ96VHn/8cT388MN677331Ldv39PW9ujRQ4GBgdqzZ8858cv3bOZdqWXLlurfv7/27Nkjqfmf7+LiYq1Zs0YPPvjgGY9zrp3v+qjp9e1wONSqVSvZ7faz/m/oXLZmzRrdcsstWrduXZW3U3+pXbt2uvjii92vheZi0KBB+uijjyQ1zM+MSuftPVsdO3ZU7969T7v5+voqJiZGBQUF2rZtm3vf999/X+Xl5e4AVRtZWVmS5P6BHBMTox07dngEmo0bN8rhcCgyMrJhJlkNq+ftcrk0fPhw+fr66o033qjyMfnqZGVlqX379o3+1+Z9fX0VHR2t9PR0d1t5ebnS09M9rmb8XExMjEe9VHHeKuu7d++u4OBgjxqXy6UtW7bU2Gdjq8+8JenRRx/V/PnzlZaW5nEvX00OHjyoo0ePeoSQplTfef9cWVmZduzY4Z5Tcz7fUsUyJyUlJbrpppvOeJxz7XzXx5le3w3x39C5avXq1ZoyZYpWr17tscRHTYqKirR3716vPt/VycrKcs+pQc93nW6nP08lJCSY/v37my1btpiPPvrIXHTRRR5LIBw8eNBERESYLVu2GGOM2bNnj3nwwQfNZ599ZrKzs83rr79uevToYa666ir3PpVLPwwfPtxkZWWZtLQ007Fjx3Nu6Ye6zLuwsNA4nU7Tp08fs2fPHo+P0p46dcoYY8wbb7xhli5danbs2GG++eYb87e//c20bt3azJkzp0nmuGbNGuPn52dWrlxpvv76azNt2jTTrl0796dEf//735tZs2a56z/++GPTokUL8/jjj5udO3ealJSUapd+aNeunXn99dfNF198YUaPHn1OLgVQl3k//PDDxtfX16xfv97jvB4/ftwYY8zx48fNPffcYzIzM012drZ57733zIABA8xFF11kfvrppyaZY3XqOu958+aZd955x+zdu9ds27bNTJw40fj7+5uvvvrKXdMcz3elK6+80kyYMKFKu7ec7+PHj5vPP//cfP7550aSefLJJ83nn39uvvvuO2OMMbNmzTK///3v3fWVSz/MnDnT7Ny50yxevLjapR9O9708F9R13i+//LJp0aKFWbx4scfru6CgwF0zY8YMk5GRYbKzs83HH39s4uLiTGBgoDl06FCjz68mdZ33U089ZTZs2GC++eYbs2PHDnPXXXcZHx8f895777lrGup8E7Zq4ejRo+b66683bdq0MQ6Hw0yZMsX9S8YYY7Kzs40k88EHHxhjjNm/f7+56qqrTIcOHYyfn5/p1auXmTlzpsc6W8YYs2/fPjNixAjTqlUrExgYaGbMmOGxREJTq+u8Kz9GW92WnZ1tjKlYPiIqKsq0adPGXHDBBaZfv35myZIlHuuaNLZnnnnGdO3a1fj6+ppBgwaZTz75xP3c4MGDzeTJkz3qX331VXPxxRcbX19fc+mll5q33nrL4/ny8nIze/ZsExQUZPz8/MywYcPM7t27G2MqdVKXeXfr1q3a85qSkmKMMebEiRNm+PDhpmPHjqZly5amW7duZurUqefUL6BKdZn33Xff7a4NCgoy11xzjcfaQ8Y0z/NtjDG7du0yksy7775bpS9vOd81/UyqnOvkyZPN4MGDq+wTFRVlfH19TY8ePTzWFqt0uu/luaCu8x48ePBp642pWAIjJCTE+Pr6ms6dO5sJEyaYPXv2NO7EzqCu837kkUdMz549jb+/v+nQoYMZMmSIef/996v02xDn22ZME3zmHgAA4Dxx3t6zBQAA0BgIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAPC/Vq9erVatWik3N9fdNmXKFPXt21eFhYVNODIA3ow/1wMA/8sYo6ioKF111VV65plnlJKSouXLl+uTTz5R586dm3p4ALwUV7YA4H/ZbDb99a9/1dKlS/XXv/5VzzzzjNLS0txB65///KciIiJ00UUX6YUXXmji0QLwFlzZAoBfGDBggL766iu9++67Gjx4sCTp1KlTioyM1AcffKCAgABFR0dr8+bNuvDCC5t4tADOdVzZAoCfSUtL065du1RWVqagoCB3+9atW3XppZeqc+fOatOmjUaMGKF33323CUcKwFsQtgDgf23fvl3jx4/XsmXLNGzYMM2ePdv93Pfff+9x31bnzp2Vk5PTFMME4GVaNPUAAOBcsG/fPo0cOVL333+/rr/+evXo0UMxMTHavn27BgwY0NTDA+DFuLIF4Lx37NgxJSQkaPTo0Zo1a5Ykyel0asSIEbr//vslSaGhoR5XsnJychQaGtok4wXgXbhBHgBq4dSpU7rkkkuUkZHBDfIA6oS3EQGgFlq0aKEnnnhCQ4cOVXl5ue69916CFoBa4coWAACAhbhnCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALPT/ATu6ciccTLl7AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 0\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: 0.4377277390071145\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.3377277390071145\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 1\n",
      "v: 0.9598365100469464\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.3377277390071145\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 1\n",
      "v: 0.5292471893860068\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.3377277390071145\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 0\n",
      "v: 1.1513559604258385\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.09151945 0.52210877]\n",
      "Bias: 0.2377277390071145\n",
      "--------------------\n",
      "\n",
      "Epoch error: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 1\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: 0.2377277390071145\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.09151945 0.52210877]\n",
      "Bias: 0.1377277390071145\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 1\n",
      "v: 0.6598365100469463\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.09151945 0.52210877]\n",
      "Bias: 0.1377277390071145\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 1\n",
      "v: 0.22924718938600677\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.09151945 0.52210877]\n",
      "Bias: 0.1377277390071145\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 0\n",
      "v: 0.7513559604258386\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [-0.00848055  0.42210877]\n",
      "Bias: 0.037727739007114486\n",
      "--------------------\n",
      "\n",
      "Epoch error: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 2\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: 0.037727739007114486\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [-0.00848055  0.42210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 1\n",
      "v: 0.3598365100469464\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [-0.00848055  0.42210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 1\n",
      "v: -0.07075281061399324\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.09151945 0.42210877]\n",
      "Bias: 0.037727739007114486\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 0\n",
      "v: 0.5513559604258387\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [-0.00848055  0.32210877]\n",
      "Bias: -0.06227226099288552\n",
      "--------------------\n",
      "\n",
      "Epoch error: 1.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 3\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: -0.06227226099288552\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [-0.00848055  0.32210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 1\n",
      "v: 0.2598365100469464\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [-0.00848055  0.32210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 1\n",
      "v: -0.07075281061399324\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.09151945 0.32210877]\n",
      "Bias: 0.037727739007114486\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 0\n",
      "v: 0.4513559604258387\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [-0.00848055  0.22210877]\n",
      "Bias: -0.06227226099288552\n",
      "--------------------\n",
      "\n",
      "Epoch error: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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Tm6dstmyerdGjR8sYU21pqUFL8rEzW431xBNPqLi4WFOmTHG3OZ1OrVu3TlFRUcrLy9PSpUsVFxenvXv3qlOnTjXuZ/ny5Vq6dGlzdRsAgBZp8sDJmhg1UdtztyvvTJ7COoUprlecJWe0fFmbCVsvvfSSli5dqtdff13du3d3t1/4seSQIUPkdDrVu3dvbdq0SbNnz65xX8nJye7TlpLkcrkUERFhXecBAGih7H52jY4c7e1utGhtImxt3LhRt99+u1599VXFx8dftLZz58668sordfDgwVprAgICFBAQ0NTdBAAArVCrv2br5Zdf1qxZs/Tyyy9rwoQJl6wvLi7WoUOHFBbG7aoAAKDxfOrMVnFxsccZp6rbPbt27apevXopOTlZx44d04YNGyRVfnQ4Y8YMPf3003I6ncrPz5ckdejQQUFBQZKk+++/XzfeeKN69+6tr776SosXL5bdbtfNN9/c/AMEAKAZGVP963Zam5YwRp86s/Xxxx9r2LBh7mkbkpKSNGzYMC1atEiSlJeX53En4R//+EedP39ed955p8LCwtzLPffc4645evSobr75ZkVFRWnKlCm6/PLL9dFHH6lbt27NOzgAAJpJ+/btJUlnz571ck+sVzXGqjF7g820hMjn41wul4KCglRUVCSHw+Ht7gAAcEl5eXkqLCxU9+7d1bFjR9lsNU9Q6quMMTp79qyOHz+uzp0713h5UHP9/fapjxEBAEDTqJrM+/jx417uibU6d+7s9YnLCVsAALRBNptNYWFh6t69u86dO+ft7liiffv2stu9P+cXYQsAgDbMbre3iEDSmvnUBfIAAAC+hrAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIXaebsDgC8pryjX9tztyjuTp7BOYYrrFSe7n93b3QLQFMrLpe3bpbw8KSxMiouT7Ly/0Xg+dWbrvffe04033qjw8HDZbDZt2bLlkttkZmZq+PDhCggIUL9+/bRu3bpqNampqYqMjFRgYKCcTqd27tzZ9J2Hz0vbl6bIpyM1Zv0Y3ZJ2i8asH6PIpyOVti/N210D0FhpaVJkpDRmjHTLLZX/RkZWtgON5FNhq6SkREOHDlVqamqd6nNycjRhwgSNGTNG2dnZuvfee3X77bfr7bffdte88sorSkpK0uLFi7V7924NHTpUCQkJOn78uFXDgA9K25emmzbdpKOuox7tx1zHdNOmmwhcgC9LS5Nuukk66vn+1rFjle0ELjSSzRhjvN2JhrDZbHrttdc0adKkWmseeugh/fWvf9XevXvdbdOmTVNhYaHS09MlSU6nU9///vf13HPPSZIqKioUERGhu+++WwsWLKhTX1wul4KCglRUVCSHw9HwQaFFKq8oV+TTkdWCVhWbbOrp6Kmce3L4SBHwNeXllWewvhu0qthsUs+eUk4OHym2Qs3199unzmzVV1ZWluLj4z3aEhISlJWVJUkqKyvTrl27PGr8/PwUHx/vrqlJaWmpXC6Xx4LWa3vu9lqDliQZGR1xHdH23O3N2CsATWL79tqDliQZIx05UlkHNFCrDlv5+fkKCQnxaAsJCZHL5dI333yjkydPqry8vMaa/Pz8Wve7fPlyBQUFuZeIiAhL+o+WIe9MXpPWAWhB8ur4vq1rHVCDVh22rJKcnKyioiL3cuTIEW93CRYK6xTWpHUAWpCwOr5v61oH1KBVT/0QGhqqgoICj7aCggI5HA516NBBdrtddru9xprQ0NBa9xsQEKCAgABL+oyWJ65XnHo6euqY65iMql/iWHXNVlyvOC/0DkCjxMVVXpN17FjlR4bfVXXNVhzvbzRcqz6zFRsbq4yMDI+2bdu2KTY2VpLk7++vESNGeNRUVFQoIyPDXQPY/ex6OvFpSZXB6kJV6ymJKVwcD/giu116uvL9LZvn+9u9npLCxfFoFJ8KW8XFxcrOzlZ2drakyqkdsrOzlZubK6ny473p06e76++44w79z//8jx588EHt379f//Vf/6VNmzbpvvvuc9ckJSVp9erVWr9+vfbt26d58+appKREs2bNataxoWWbPHCyNk/ZrB6OHh7tPR09tXnKZk0eONlLPQPQaJMnS5s3Sz0839/q2bOyfTLvbzSOT039kJmZqTFjxlRrnzFjhtatW6eZM2fq8OHDyszM9Njmvvvu0+eff66ePXtq4cKFmjlzpsf2zz33nFauXKn8/HxFR0frmWeekdPprHO/mPqh7WAGeaAVYwb5Nqe5/n77VNhqqQhbAAD4HubZAgAAaAUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWMjnwlZqaqoiIyMVGBgop9OpnTt31lo7evRo2Wy2asuECRPcNTNnzqz2eGJiYnMMBQAAtAHtvN2B+njllVeUlJSkVatWyel0KiUlRQkJCTpw4IC6d+9erT4tLU1lZWXu9VOnTmno0KH6+c9/7lGXmJiotWvXutcDAgKsGwQAAGhTfOrM1pNPPqk5c+Zo1qxZGjRokFatWqWOHTtqzZo1NdZ37dpVoaGh7mXbtm3q2LFjtbAVEBDgUdelS5fmGA4AAGgDfCZslZWVadeuXYqPj3e3+fn5KT4+XllZWXXaxwsvvKBp06bpsssu82jPzMxU9+7dFRUVpXnz5unUqVMX3U9paalcLpfHAgAAUBOfCVsnT55UeXm5QkJCPNpDQkKUn59/ye137typvXv36vbbb/doT0xM1IYNG5SRkaEVK1boH//4h8aPH6/y8vJa97V8+XIFBQW5l4iIiIYNCgAAtHo+dc1WY7zwwgsaPHiwYmJiPNqnTZvm/nnw4MEaMmSIrrjiCmVmZmrs2LE17is5OVlJSUnudZfLReACAAA18pkzW8HBwbLb7SooKPBoLygoUGho6EW3LSkp0caNGzV79uxLPk/fvn0VHBysgwcP1loTEBAgh8PhsQAAANTEZ8KWv7+/RowYoYyMDHdbRUWFMjIyFBsbe9FtX331VZWWluq222675PMcPXpUp06dUlhYWKP7DAAA4DNhS5KSkpK0evVqrV+/Xvv27dO8efNUUlKiWbNmSZKmT5+u5OTkatu98MILmjRpki6//HKP9uLiYj3wwAP66KOPdPjwYWVkZGjixInq16+fEhISmmVMAACgdfOpa7amTp2qEydOaNGiRcrPz1d0dLTS09PdF83n5ubKz88zPx44cEDvv/++tm7dWm1/drtdn376qdavX6/CwkKFh4dr3LhxWrZsGXNtAQCAJmEzxhhvd8LXuVwuBQUFqaioiOu3AADwEc3199unPkYEAADwNYQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALNWnY2rFjR1PuDgAAwOc1adj6+c9/3pS7AwAA8Hnt6rvBlClTamw3xuj06dON7tClpKamauXKlcrPz9fQoUP17LPPKiYmpsbadevWadasWR5tAQEB+vbbb93rxhgtXrxYq1evVmFhoX74wx/q97//vfr372/pOAAAQNtQ77D1zjvv6E9/+pO+973vebQbY/Tee+81Wcdq8sorrygpKUmrVq2S0+lUSkqKEhISdODAAXXv3r3GbRwOhw4cOOBet9lsHo8//vjjeuaZZ7R+/Xr16dNHCxcuVEJCgj7//HMFBgZaOh4AAND61TtsjR49Wp06ddJ1111X7bEhQ4Y0Sadq8+STT2rOnDnus1WrVq3SX//6V61Zs0YLFiyocRubzabQ0NAaHzPGKCUlRb/5zW80ceJESdKGDRsUEhKiLVu2aNq0adYMBAAAtBl1vmbrzJkzkqS0tLQag5Ykbdu2rWl6VYOysjLt2rVL8fHx7jY/Pz/Fx8crKyur1u2Ki4vVu3dvRUREaOLEifrss8/cj+Xk5Cg/P99jn0FBQXI6nRfdZ2lpqVwul8cCAABQkzqHrbi4OOXn51vZl4s6efKkysvLFRIS4tEeEhJSa7+ioqK0Zs0avf766/rzn/+siooKXXPNNTp69Kgkuberzz4lafny5QoKCnIvERERjRkaAABoxeoctoYNGyan06n9+/d7tGdnZ+uGG25o8o41hdjYWE2fPl3R0dEaNWqU0tLS1K1bN/3hD39o1H6Tk5NVVFTkXo4cOdJEPQYAAK1NncPW2rVrNXPmTF177bV6//339e9//1tTpkzRiBEjZLfbreyjJCk4OFh2u10FBQUe7QUFBbVek/Vd7du317Bhw3Tw4EFJcm9X330GBATI4XB4LAAAADWp1zxbS5cuVVJSkq6//npdffXVOnPmjLKysvTmm29a1T83f39/jRgxQhkZGe62iooKZWRkKDY2tk77KC8v1549exQWFiZJ6tOnj0JDQz326XK5tGPHjjrvEwAA4GLqfDdiQUGBHn30Ua1evVqDBg3S/v37NXPmzFrnuLJCUlKSZsyYoZEjRyomJkYpKSkqKSlx3504ffp09ejRQ8uXL5ckPfLII/rBD36gfv36qbCwUCtXrtSXX36p22+/XVLlnYr33nuvfvvb36p///7uqR/Cw8M1adKkZhsXAABoveoctvr06aOoqCi9+uqrmjBhgtLT0zV16lTl5ubqgQcesLKPblOnTtWJEye0aNEi5efnKzo6Wunp6e4L3HNzc+Xn938n677++mvNmTNH+fn56tKli0aMGKEPP/xQgwYNctc8+OCDKikp0dy5c1VYWKhrr71W6enpzLEFAACahM0YY+pSuHHjxmrzTu3evVs//vGP9dOf/lSpqamWdNAXuFwuBQUFqaioiOu3AADwEc3197vO12zVNMHn8OHD9eGHH+rdd99t0k4BAAC0Fo3+IurIyEh9+OGHTdEXAACAVqfRYUuSunTp0hS7AQAAaHWaJGwBAACgZoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAu183YHAAAA6sMY6dw56dtvpdLSyuXCn7+7XttjLlfz9JewBQAALskYqays8QGnqR7zJYQtAABaqIoKz+BRn2DS1AGnrMzbr0bt2reXAgL+bwkMrPnn767bbNLzz1vfP8IWAAAXKC9veMBp6sfOnfP2q1E7f/+GBRwrHvNr4BXoLhdhCwDQRpSXe/cjqQvXz5/39qtRuwvDhjcDjr9/wwNOW0TYAoA26vz5lhNwysu9/WrUzupAU5+AY7N5+9VAQxC2AKCZGFM94Hgz7FRUePsVqZnN5t2PpC78uX17Ag4aj7AFoFVrqlvEm+oxY7z9itTMz6/lBJx27Qg4aF0IWwCanDENv8DYioDTUrVr1zKuv6kKOACswdsLaCUac4t4Uz/WGm8Rt+Ixu93brwaA5kDYAhqBW8Tr5sJbxK2+iPhSj3EHFYDmRtiCz/nuLeJWBJq61vrKLeLevP6GW8QBtHWELdRJS7qDqi3fIl6fgMMFxgDQMhC2WihuEa+bi90i3tzz4XCLOACgJj4XtlJTU7Vy5Url5+dr6NChevbZZxUTE1Nj7erVq7Vhwwbt3btXkjRixAg9+uijHvUzZ87U+vXrPbZLSEhQenp6vfv2zDOVf2ybKuD44i3i3riDioADAGjJfCpsvfLKK0pKStKqVavkdDqVkpKihIQEHThwQN27d69Wn5mZqZtvvlnXXHONAgMDtWLFCo0bN06fffaZevTo4a5LTEzU2rVr3esBAQEN6t/ChQ3arE64RRwAAN9kM6alnj+pzul06vvf/76ee+45SVJFRYUiIiJ09913a8GCBZfcvry8XF26dNFzzz2n6dOnS6o8s1VYWKgtW7Y0uF8ul0tBQUGaMqVInTo5uEUcAAAfUPX3u6ioSA6Hw7Ln8ZlzFGVlZdq1a5eSk5PdbX5+foqPj1dWVlad9nH27FmdO3dOXbt29WjPzMxU9+7d1aVLF/3oRz/Sb3/7W11++eW17qe0tFSlpaXudZfLJUlavVqy8FgBAAAf5DM3ZJ88eVLl5eUKCQnxaA8JCVF+fn6d9vHQQw8pPDxc8fHx7rbExERt2LBBGRkZWrFihf7xj39o/PjxKr/ILW/Lly9XUFCQe4mIiGjYoAAAQKvnM2e2Guuxxx7Txo0blZmZqcDAQHf7tGnT3D8PHjxYQ4YM0RVXXKHMzEyNHTu2xn0lJycrKSnJve5yuQhcAACgRj5zZis4OFh2u10FBQUe7QUFBQoNDb3otk888YQee+wxbd26VUOGDLlobd++fRUcHKyDBw/WWhMQECCHw+GxAAAA1MRnwpa/v79GjBihjIwMd1tFRYUyMjIUGxtb63aPP/64li1bpvT0dI0cOfKSz3P06FGdOnVKYWFhTdJvAADQtvlM2JKkpKQkrV69WuvXr9e+ffs0b948lZSUaNasWZKk6dOne1xAv2LFCi1cuFBr1qxRZGSk8vPzlZ+fr+LiYklScXGxHnjgAX300Uc6fPiwMjIyNHHiRPXr108JCQleGSMAAGhdfOqaralTp+rEiRNatGiR8vPzFR0drfT0dPdF87m5ufK74EvYfv/736usrEw33XSTx34WL16sJUuWyG6369NPP9X69etVWFio8PBwjRs3TsuWLWvwXFsAAAAX8ql5tlqq5pqnAwAANJ3m+vvtUx8jAgAA+BrCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhdp5uwOATykvl7Zvl/LypLAwKS5Ostu93SsATaC8olzbc7cr70yewjqFKa5XnOx+vL/ReD53Zis1NVWRkZEKDAyU0+nUzp07L1r/6quvasCAAQoMDNTgwYP11ltveTxujNGiRYsUFhamDh06KD4+Xl988YWVQ4CvSkuTIiOlMWOkW26p/DcysrIdgE9L25emyKcjNWb9GN2SdovGrB+jyKcjlbaP9zcaz6fC1iuvvKKkpCQtXrxYu3fv1tChQ5WQkKDjx4/XWP/hhx/q5ptv1uzZs/XJJ59o0qRJmjRpkvbu3euuefzxx/XMM89o1apV2rFjhy677DIlJCTo22+/ba5hwRekpUk33SQdPerZfuxYZTuBC/BZafvSdNOmm3TU5fn+PuY6pps23UTgQqPZjDHG252oK6fTqe9///t67rnnJEkVFRWKiIjQ3XffrQULFlSrnzp1qkpKSvSXv/zF3faDH/xA0dHRWrVqlYwxCg8P1/z583X//fdLkoqKihQSEqJ169Zp2rRpdeqXy+VSUFCQioqK5HA4mmCkaFHKyyvPYH03aFWx2aSePaWcHD5SBHxMeUW5Ip+OrBa0qthkU09HT+Xck8NHiq1Qc/399pkzW2VlZdq1a5fi4+PdbX5+foqPj1dWVlaN22RlZXnUS1JCQoK7PicnR/n5+R41QUFBcjqdte5TkkpLS+VyuTwWtGLbt9cetCTJGOnIkco6AD5le+72WoOWJBkZHXEd0fZc3t9oOJ8JWydPnlR5eblCQkI82kNCQpSfn1/jNvn5+Retr/q3PvuUpOXLlysoKMi9RERE1Hs88CF5eU1bB6DFyDtTt/dtXeuAmvhM2GpJkpOTVVRU5F6OHDni7S7BSmFhTVsHoMUI61S3921d64Ca+EzYCg4Olt1uV0FBgUd7QUGBQkNDa9wmNDT0ovVV/9Znn5IUEBAgh8PhsaAVi4urvCbLZqv5cZtNioiorAPgU+J6xamno6dsqvn9bZNNEY4IxfXi/Y2G85mw5e/vrxEjRigjI8PdVlFRoYyMDMXGxta4TWxsrEe9JG3bts1d36dPH4WGhnrUuFwu7dixo9Z9og2y26Wnn678+buBq2o9JYWL4wEfZPez6+nEyvf3dwNX1XpKYgoXx6NRfCZsSVJSUpJWr16t9evXa9++fZo3b55KSko0a9YsSdL06dOVnJzsrr/nnnuUnp6u//zP/9T+/fu1ZMkSffzxx7rrrrskSTabTffee69++9vf6o033tCePXs0ffp0hYeHa9KkSd4YIlqqyZOlzZulHj0823v2rGyfPNk7/QLQaJMHTtbmKZvVw+H5/u7p6KnNUzZr8kDe32gcn5pBfurUqTpx4oQWLVqk/Px8RUdHKz093X2Be25urvz8/i8/XnPNNXrppZf0m9/8Rg8//LD69++vLVu26Oqrr3bXPPjggyopKdHcuXNVWFioa6+9Vunp6QoMDGz28aGFmzxZmjiRGeSBVmjywMmaGDWRGeRhCZ+aZ6ulYp4tAAB8D/NsAQAAtAKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALOQzYev06dO69dZb5XA41LlzZ82ePVvFxcUXrb/77rsVFRWlDh06qFevXvr1r3+toqIijzqbzVZt2bhxo9XDAQAAbUQ7b3egrm699Vbl5eVp27ZtOnfunGbNmqW5c+fqpZdeqrH+q6++0ldffaUnnnhCgwYN0pdffqk77rhDX331lTZv3uxRu3btWiUmJrrXO3fubOVQAABAG2Izxhhvd+JS9u3bp0GDBumf//ynRo4cKUlKT0/XDTfcoKNHjyo8PLxO+3n11Vd12223qaSkRO3aVeZMm82m1157TZMmTWpw/1wul4KCglRUVCSHw9Hg/QAAgObTXH+/feJjxKysLHXu3NkdtCQpPj5efn5+2rFjR533U/ViVgWtKnfeeaeCg4MVExOjNWvW6FL5s7S0VC6Xy2MBAACoiU98jJifn6/u3bt7tLVr105du3ZVfn5+nfZx8uRJLVu2THPnzvVof+SRR/SjH/1IHTt21NatW/WrX/1KxcXF+vWvf13rvpYvX66lS5fWfyAAAKDN8eqZrQULFtR4gfqFy/79+xv9PC6XSxMmTNCgQYO0ZMkSj8cWLlyoH/7whxo2bJgeeughPfjgg1q5cuVF95ecnKyioiL3cuTIkUb3EQAAtE5ePbM1f/58zZw586I1ffv2VWhoqI4fP+7Rfv78eZ0+fVqhoaEX3f7MmTNKTExUp06d9Nprr6l9+/YXrXc6nVq2bJlKS0sVEBBQY01AQECtjwEAAFzIq2GrW7du6tat2yXrYmNjVVhYqF27dmnEiBGSpHfffVcVFRVyOp21budyuZSQkKCAgAC98cYbCgwMvORzZWdnq0uXLoQpAADQJHzimq2BAwcqMTFRc+bM0apVq3Tu3DndddddmjZtmvtOxGPHjmns2LHasGGDYmJi5HK5NG7cOJ09e1Z//vOfPS5k79atm+x2u958800VFBToBz/4gQIDA7Vt2zY9+uijuv/++705XAAA0Ir4RNiSpBdffFF33XWXxo4dKz8/P/3sZz/TM88843783LlzOnDggM6ePStJ2r17t/tOxX79+nnsKycnR5GRkWrfvr1SU1N13333yRijfv366cknn9ScOXOab2AAAKBV84l5tlo65tkCAMD3MM8WAABAK0DYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQj4Ttk6fPq1bb71VDodDnTt31uzZs1VcXHzRbUaPHi2bzeax3HHHHR41ubm5mjBhgjp27Kju3bvrgQce0Pnz560cCgAAaEPaebsDdXXrrbcqLy9P27Zt07lz5zRr1izNnTtXL7300kW3mzNnjh555BH3eseOHd0/l5eXa8KECQoNDdWHH36ovLw8TZ8+Xe3bt9ejjz5q2VgAAEDbYTPGGG934lL27dunQYMG6Z///KdGjhwpSUpPT9cNN9ygo0ePKjw8vMbtRo8erejoaKWkpNT4+N/+9jf9+Mc/1ldffaWQkBBJ0qpVq/TQQw/pxIkT8vf3r1P/XC6XgoKCVFRUJIfDUf8BAgCAZtdcf7994sxWVlaWOnfu7A5akhQfHy8/Pz/t2LFDP/3pT2vd9sUXX9Sf//xnhYaG6sYbb9TChQvdZ7eysrI0ePBgd9CSpISEBM2bN0+fffaZhg0bVuM+S0tLVVpa6l4vKiqSVHnQAACAb6j6u231eSefCFv5+fnq3r27R1u7du3UtWtX5efn17rdLbfcot69eys8PFyffvqpHnroIR04cEBpaWnu/V4YtCS51y+23+XLl2vp0qXV2iMiIuo8JgAA0DKcOnVKQUFBlu3fq2FrwYIFWrFixUVr9u3b1+D9z5071/3z4MGDFRYWprFjx+rQoUO64oorGrzf5ORkJSUludcLCwvVu3dv5ebmWnqwWhqXy6WIiAgdOXKkTX18yrgZd1vAuBl3W1BUVKRevXqpa9eulj6PV8PW/PnzNXPmzIvW9O3bV6GhoTp+/LhH+/nz53X69GmFhobW+fmcTqck6eDBg7riiisUGhqqnTt3etQUFBRI0kX3GxAQoICAgGrtQUFBbeo/0ioOh4NxtyGMu21h3G1LWx23n5+1kzN4NWx169ZN3bp1u2RdbGysCgsLtWvXLo0YMUKS9O6776qiosIdoOoiOztbkhQWFube7+9+9zsdP37c/THltm3b5HA4NGjQoHqOBgAAoDqfmGdr4MCBSkxM1Jw5c7Rz50598MEHuuuuuzRt2jT3nYjHjh3TgAED3GeqDh06pGXLlmnXrl06fPiw3njjDU2fPl3XXXedhgwZIkkaN26cBg0apF/84hf617/+pbffflu/+c1vdOedd9Z45goAAKC+fCJsSZV3FQ4YMEBjx47VDTfcoGuvvVZ//OMf3Y+fO3dOBw4c0NmzZyVJ/v7+eueddzRu3DgNGDBA8+fP189+9jO9+eab7m3sdrv+8pe/yG63KzY2VrfddpumT5/uMS9XXQQEBGjx4sVtLqAxbsbdFjBuxt0WMG5rx+0T82wBAAD4Kp85swUAAOCLCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbdXD69Gndeuutcjgc6ty5s2bPnq3i4uKLbjN69GjZbDaP5Y477vCoyc3N1YQJE9SxY0d1795dDzzwgM6fP2/lUOqlvuM+ffq07r77bkVFRalDhw7q1auXfv3rX7u/qLvKd18Xm82mjRs3Wj2cWqWmpioyMlKBgYFyOp3VvlXgu1599VUNGDBAgYGBGjx4sN566y2Px40xWrRokcLCwtShQwfFx8friy++sHIIDVKfca9evVpxcXHq0qWLunTpovj4+Gr1M2fOrHZcExMTrR5GvdVn3OvWras2psDAQI+a1ni8a/r9ZbPZNGHCBHeNLxzv9957TzfeeKPCw8Nls9m0ZcuWS26TmZmp4cOHKyAgQP369dO6deuq1dT3d0Zzq++409LSdP3116tbt25yOByKjY3V22+/7VGzZMmSasd7wIABFo6i/uo77szMzBr/O//udyM3yfE2uKTExEQzdOhQ89FHH5nt27ebfv36mZtvvvmi24waNcrMmTPH5OXluZeioiL34+fPnzdXX321iY+PN5988ol56623THBwsElOTrZ6OHVW33Hv2bPHTJ482bzxxhvm4MGDJiMjw/Tv39/87Gc/86iTZNauXevx2nzzzTdWD6dGGzduNP7+/mbNmjXms88+M3PmzDGdO3c2BQUFNdZ/8MEHxm63m8cff9x8/vnn5je/+Y1p37692bNnj7vmscceM0FBQWbLli3mX//6l/nJT35i+vTp47Ux1qS+477llltMamqq+eSTT8y+ffvMzJkzTVBQkDl69Ki7ZsaMGSYxMdHjuJ4+fbq5hlQn9R332rVrjcPh8BhTfn6+R01rPN6nTp3yGPPevXuN3W43a9euddf4wvF+6623zP/7f//PpKWlGUnmtddeu2j9//zP/5iOHTuapKQk8/nnn5tnn33W2O12k56e7q6p72vpDfUd9z333GNWrFhhdu7caf7973+b5ORk0759e7N79253zeLFi81VV13lcbxPnDhh8Ujqp77j/vvf/24kmQMHDniMq7y83F3TVMebsHUJn3/+uZFk/vnPf7rb/va3vxmbzWaOHTtW63ajRo0y99xzT62Pv/XWW8bPz8/jF/fvf/9743A4TGlpaZP0vTEaOu7v2rRpk/H39zfnzp1zt9XlTdBcYmJizJ133uleLy8vN+Hh4Wb58uU11k+ZMsVMmDDBo83pdJpf/vKXxhhjKioqTGhoqFm5cqX78cLCQhMQEGBefvllC0bQMPUd93edP3/edOrUyaxfv97dNmPGDDNx4sSm7mqTqu+4165da4KCgmrdX1s53k899ZTp1KmTKS4udrf5wvG+UF1+7zz44IPmqquu8mibOnWqSUhIcK839rVsbg39fTto0CCzdOlS9/rixYvN0KFDm65jFqtP2Pr6669rrWmq483HiJeQlZWlzp07a+TIke62+Ph4+fn5aceOHRfd9sUXX1RwcLCuvvpqJScnu2e3r9rv4MGDFRIS4m5LSEiQy+XSZ5991vQDqafGjPtCRUVFcjgcatfO82s477zzTgUHBysmJkZr1qyR8cLcumVlZdq1a5fi4+PdbX5+foqPj1dWVlaN22RlZXnUS5XHrao+JydH+fn5HjVBQUFyOp217rO5NWTc33X27FmdO3dOXbt29WjPzMxU9+7dFRUVpXnz5unUqVNN2vfGaOi4i4uL1bt3b0VERGjixIke78+2crxfeOEFTZs2TZdddplHe0s+3g1xqfd3U7yWvqCiokJnzpyp9v7+4osvFB4err59++rWW29Vbm6ul3rYtKKjoxUWFqbrr79eH3zwgbu9KY+3V7+I2hfk5+e7v6S6Srt27dS1a9dqn+te6JZbblHv3r0VHh6uTz/9VA899JAOHDigtLQ0934vDFqS3OsX229zaei4L3Ty5EktW7ZMc+fO9Wh/5JFH9KMf/UgdO3bU1q1b9atf/UrFxcX69a9/3WT9r2v/ysvLazwO+/fvr3Gb2o5b1WtS9e/FarytIeP+roceekjh4eEev4QSExM1efJk9enTR4cOHdLDDz+s8ePHKysrS3a7vUnH0BANGXdUVJTWrFmjIUOGqKioSE888YSuueYaffbZZ+rZs2ebON47d+7U3r179cILL3i0t/Tj3RC1vb9dLpe++eYbff31141+7/iCJ554QsXFxZoyZYq7zel0at26dYqKilJeXp6WLl2quLg47d27V506dfJibxsuLCxMq1at0siRI1VaWqrnn39eo0eP1o4dOzR8+PAm+V1Zpc2GrQULFmjFihUXrdm3b1+D939hwBg8eLDCwsI0duxYHTp0SFdccUWD99tYVo+7isvl0oQJEzRo0CAtWbLE47GFCxe6fx42bJhKSkq0cuXKZg9baJjHHntMGzduVGZmpsfF4tOmTXP/PHjwYA0ZMkRXXHGFMjMzNXbsWG90tdFiY2MVGxvrXr/mmms0cOBA/eEPf9CyZcu82LPm88ILL2jw4MGKiYnxaG+NxxvSSy+9pKVLl+r111/3+B/u8ePHu38eMmSInE6nevfurU2bNmn27Nne6GqjRUVFKSoqyr1+zTXX6NChQ3rqqaf0pz/9qUmfq82Grfnz52vmzJkXrenbt69CQ0N1/Phxj/bz58/r9OnTCg0NrfPzOZ1OSdLBgwd1xRVXKDQ0tNodDQUFBZJUr/3WV3OM+8yZM0pMTFSnTp302muvqX379hetdzqdWrZsmUpLS5v1S1CDg4Nlt9vdr3uVgoKCWscYGhp60fqqfwsKChQWFuZREx0d3YS9b7iGjLvKE088occee0zvvPOOhgwZctHavn37Kjg4WAcPHmwRf3wbM+4q7du317Bhw3Tw4EFJrf94l5SUaOPGjXrkkUcu+Twt7Xg3RG3vb4fDoQ4dOshutzf6v6GWbOPGjbr99tv16quvVvs49bs6d+6sK6+80v1eaC1iYmL0/vvvS2qa3xlV2uw1W926ddOAAQMuuvj7+ys2NlaFhYXatWuXe9t3331XFRUV7gBVF9nZ2ZLk/oUcGxurPXv2eASabdu2yeFwaNCgQU0zyBpYPW6Xy6Vx48bJ399fb7zxRrXb5GuSnZ2tLl26NPu3zfv7+2vEiBHKyMhwt1VUVCgjI8PjbMaFYmNjPeqlyuNWVd+nTx+FhoZ61LhcLu3YsaPWfTa3hoxbkh5//HEtW7ZM6enpHtfy1ebo0aM6deqURwjxpoaO+0Ll5eXas2ePe0yt+XhLldOclJaW6rbbbrvk87S0490Ql3p/N8V/Qy3Vyy+/rFmzZunll1/2mOKjNsXFxTp06JBPH++aZGdnu8fUpMe7XpfTt1GJiYlm2LBhZseOHeb99983/fv395gC4ejRoyYqKsrs2LHDGGPMwYMHzSOPPGI+/vhjk5OTY15//XXTt29fc91117m3qZr6Ydy4cSY7O9ukp6ebbt26tbipH+oz7qKiIuN0Os3gwYPNwYMHPW6lPX/+vDHGmDfeeMOsXr3a7Nmzx3zxxRfmv/7rv0zHjh3NokWLvDLGjRs3moCAALNu3Trz+eefm7lz55rOnTu77xL9xS9+YRYsWOCu/+CDD0y7du3ME088Yfbt22cWL15c49QPnTt3Nq+//rr59NNPzcSJE1vkVAD1Gfdjjz1m/P39zebNmz2O65kzZ4wxxpw5c8bcf//9Jisry+Tk5Jh33nnHDB8+3PTv3998++23XhljTeo77qVLl5q3337bHDp0yOzatctMmzbNBAYGms8++8xd0xqPd5Vrr73WTJ06tVq7rxzvM2fOmE8++cR88sknRpJ58sknzSeffGK+/PJLY4wxCxYsML/4xS/c9VVTPzzwwANm3759JjU1tcapHy72WrYE9R33iy++aNq1a2dSU1M93t+FhYXumvnz55vMzEyTk5NjPvjgAxMfH2+Cg4PN8ePHm318tanvuJ966imzZcsW88UXX5g9e/aYe+65x/j5+Zl33nnHXdNUx5uwVQenTp0yN998s/ne975nHA6HmTVrlvuPjDHG5OTkGEnm73//uzHGmNzcXHPdddeZrl27moCAANOvXz/zwAMPeMyzZYwxhw8fNuPHjzcdOnQwwcHBZv78+R5TJHhbfcdddRttTUtOTo4xpnL6iOjoaPO9733PXHbZZWbo0KFm1apVHvOaNLdnn33W9OrVy/j7+5uYmBjz0UcfuR8bNWqUmTFjhkf9pk2bzJVXXmn8/f3NVVddZf761796PF5RUWEWLlxoQkJCTEBAgBk7dqw5cOBAcwylXuoz7t69e9d4XBcvXmyMMebs2bNm3Lhxplu3bqZ9+/amd+/eZs6cOS3qD1CV+oz73nvvddeGhISYG264wWPuIWNa5/E2xpj9+/cbSWbr1q3V9uUrx7u230lVY50xY4YZNWpUtW2io6ONv7+/6du3r8fcYlUu9lq2BPUd96hRoy5ab0zlFBhhYWHG39/f9OjRw0ydOtUcPHiweQd2CfUd94oVK8wVV1xhAgMDTdeuXc3o0aPNu+++W22/TXG8bcZ44Z57AACANqLNXrMFAADQHAhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsA8L9efvlldejQQXl5ee62WbNmaciQISoqKvJizwD4Mr6uBwD+lzFG0dHRuu666/Tss89q8eLFWrNmjT766CP16NHD290D4KM4swUA/8tms+l3v/udVq9erd/97nd69tlnlZ6e7g5af/nLXxQVFaX+/fvr+eef93JvAfgKzmwBwHcMHz5cn332mbZu3apRo0ZJks6fP69Bgwbp73//u4KCgjRixAh9+OGHuvzyy73cWwAtHWe2AOAC6enp2r9/v8rLyxUSEuJu37lzp6666ir16NFD3/ve9zR+/Hht3brViz0F4CsIWwDwv3bv3q0pU6bohRde0NixY7Vw4UL3Y1999ZXHdVs9evTQsWPHvNFNAD6mnbc7AAAtweHDhzVhwgQ9/PDDuvnmm9W3b1/FxsZq9+7dGj58uLe7B8CHcWYLQJt3+vRpJSYmauLEiVqwYIEkyel0avz48Xr44YclSeHh4R5nso4dO6bw8HCv9BeAb+ECeQCog/Pnz2vgwIHKzMzkAnkA9cLHiABQB+3atdN//ud/asyYMaqoqNCDDz5I0AJQJ5zZAgAAsBDXbAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIX+Pz+lBDBWi56OAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 4\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: -0.06227226099288552\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [-0.00848055  0.22210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 1\n",
      "v: 0.1598365100469464\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [-0.00848055  0.22210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 1\n",
      "v: -0.07075281061399324\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.09151945 0.22210877]\n",
      "Bias: 0.037727739007114486\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 0\n",
      "v: 0.3513559604258387\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [-0.00848055  0.12210877]\n",
      "Bias: -0.06227226099288552\n",
      "--------------------\n",
      "\n",
      "Epoch error: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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UqVOnOvezYsUKLV26tKmaDQBAizR14FRNjp6sXXm7VHCuQOGdwhXfK96SK1q+rM2Erddff11Lly7V22+/re7du7vLLx2WHDJkiJxOp3r37q0tW7Zozpw5de4rJSXFfdlSklwulyIjI61rPAAALZTdz64xUWOauxktWpsIW5s3b9a9996rN998UwkJCZet27lzZ9144406cuRIvXUCAgIUEBDg7WYCAIBWqNXfs7Vp0ybNnj1bmzZt0sSJE69Yv7S0VEePHlV4OI+rAgCAa+dTV7ZKS0s9rjjVPO7ZtWtX9erVSykpKcrPz9err74qqXrocObMmVqzZo2cTqcKCwslSR06dFBwcLAk6ZFHHtGkSZPUu3dvffPNN0pNTZXdbtddd93V9B0EAKAJGVP743Zam5bQR5+6svXpp59q2LBh7mkbkpOTNWzYMC1evFiSVFBQ4PEk4b//+7/r4sWLuu+++xQeHu5eFixY4K5z4sQJ3XXXXYqOjtadd96p66+/Xp988om6devWtJ0DAKCJtG/fXpJ0/vz5Zm6J9Wr6WNPn5mAzLSHy+TiXy6Xg4GCVlJTI4XA0d3MAALiigoICFRcXq3v37goKCpLNVvcEpb7KGKPz58/r5MmT6ty5c523BzXV32+fGkYEAADeUTOZ98mTJ5u5Jdbq3Llzs09cTtgCAKANstlsCg8PV/fu3XXhwoXmbo4l2rdvL7u9+ef8ImwBANCG2e32FhFIWjOfukEeAADA1xC2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALBQu+ZuAOBLKqsqtStvlwrOFSi8U7jie8XL7mdv7mYB8IbKSmnXLqmgQAoPl+LjJTvvb1w7n7qy9cEHH2jSpEmKiIiQzWbTtm3brrhNVlaWhg8froCAAPXr108bNmyoVSctLU1RUVEKDAyU0+nUnj17vN94+Lz0g+mKWhOlsRvH6u70uzV241hFrYlS+sH05m4agGuVni5FRUljx0p33139NSqquhy4Rj4VtsrKyjR06FClpaU1qH5ubq4mTpyosWPHKicnRw899JDuvfdevfvuu+46b7zxhpKTk5Wamqp9+/Zp6NChSkxM1MmTJ63qBnxQ+sF03bHlDp1wnfAoz3fl644tdxC4AF+Wni7dcYd0wvP9rfz86nICF66RzRhjmrsRV8Nms+mtt97SlClT6q3z+OOP689//rMOHDjgLps+fbqKi4uVkZEhSXI6nfrRj36kl156SZJUVVWlyMhIPfDAA1q4cGGD2uJyuRQcHKySkhI5HI6r7xRapMqqSkWtiaoVtGrYZFNPR0/lLshlSBHwNZWV1Vewfhi0athsUs+eUm4uQ4qtUFP9/fapK1uNlZ2drYSEBI+yxMREZWdnS5IqKiq0d+9ejzp+fn5KSEhw16lLeXm5XC6Xx4LWa1fernqDliQZGR13HdeuvF1N2CoAXrFrV/1BS5KMkY4fr64HXKVWHbYKCwsVGhrqURYaGiqXy6XvvvtOp0+fVmVlZZ11CgsL693vihUrFBwc7F4iIyMtaT9ahoJzBV6tB6AFKWjg+7ah9YA6tOqwZZWUlBSVlJS4l+PHjzd3k2Ch8E7hXq0HoAUJb+D7tqH1gDq06qkfwsLCVFRU5FFWVFQkh8OhDh06yG63y26311knLCys3v0GBAQoICDAkjaj5YnvFa+ejp7Kd+XLqPYtjjX3bMX3im+G1gG4JvHx1fdk5edXDxn+UM09W/G8v3H1WvWVrbi4OGVmZnqU7dy5U3FxcZIkf39/jRgxwqNOVVWVMjMz3XUAu59da5LWSKoOVpeqWV+dtJqb4wFfZLdLa6rf37J5vr/d66tXc3M8rolPha3S0lLl5OQoJydHUvXUDjk5OcrLy5NUPbw3Y8YMd/1f//rX+u///m899thjOnTokP71X/9VW7Zs0W9+8xt3neTkZK1bt04bN27UwYMHNX/+fJWVlWn27NlN2je0bFMHTtXWO7eqh6OHR3lPR09tvXOrpg6c2kwtA3DNpk6Vtm6Veni+v9WzZ3X5VN7fuDY+NfVDVlaWxo4dW6t85syZ2rBhg2bNmqVjx44pKyvLY5vf/OY3+vLLL9WzZ08tWrRIs2bN8tj+pZde0rPPPqvCwkLFxMTohRdekNPpbHC7mPqh7WAGeaAVYwb5Nqep/n77VNhqqQhbAAD4HubZAgAAaAUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWMjnwlZaWpqioqIUGBgop9OpPXv21Ft3zJgxstlstZaJEye668yaNavW60lJSU3RFQAA0Aa0a+4GNMYbb7yh5ORkrV27Vk6nU6tXr1ZiYqIOHz6s7t2716qfnp6uiooK9/qZM2c0dOhQ/eM//qNHvaSkJL3yyivu9YCAAOs6AQAA2hSfurL13HPPae7cuZo9e7YGDRqktWvXKigoSOvXr6+zfteuXRUWFuZedu7cqaCgoFphKyAgwKNely5dmqI7AACgDfCZsFVRUaG9e/cqISHBXebn56eEhARlZ2c3aB8vv/yypk+fro4dO3qUZ2VlqXv37oqOjtb8+fN15syZy+6nvLxcLpfLYwEAAKiLz4St06dPq7KyUqGhoR7loaGhKiwsvOL2e/bs0YEDB3Tvvfd6lCclJenVV19VZmamVq5cqf/8z//UhAkTVFlZWe++VqxYoeDgYPcSGRl5dZ0CAACtnk/ds3UtXn75ZQ0ePFixsbEe5dOnT3d/P3jwYA0ZMkQ33HCDsrKyNG7cuDr3lZKSouTkZPe6y+UicAEAgDr5zJWtkJAQ2e12FRUVeZQXFRUpLCzsstuWlZVp8+bNmjNnzhWP07dvX4WEhOjIkSP11gkICJDD4fBYAAAA6uIzYcvf318jRoxQZmamu6yqqkqZmZmKi4u77LZvvvmmysvL9ctf/vKKxzlx4oTOnDmj8PDwa24zAACAz4QtSUpOTta6deu0ceNGHTx4UPPnz1dZWZlmz54tSZoxY4ZSUlJqbffyyy9rypQpuv766z3KS0tL9eijj+qTTz7RsWPHlJmZqcmTJ6tfv35KTExskj4BAIDWzafu2Zo2bZpOnTqlxYsXq7CwUDExMcrIyHDfNJ+Xlyc/P8/8ePjwYX344YfasWNHrf3Z7XZ9/vnn2rhxo4qLixUREaHx48dr+fLlzLUFAAC8wmaMMc3dCF/ncrkUHByskpIS7t8CAMBHNNXfb58aRgQAAPA1hC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQu2auwEAAADeUl4uuVzSuXP/u9S3fuZM07SJsAUAAJqNMVJZ2ZWDUUPXL1xo7h7VRtgCAACNUlnpvXB07lx14PK2oCDJ4ZA6dfrf5Yfr7dtLK1Z4/9g/RNgCAKANKC/3TjhyuaTvvvN++2y2K4ejhq5fd51kt1/5mC4XYQsAgDbLGOn8ee+EI6uG19q39ww81xKWgoKqA1drRNgCAMBLLh1eu9ZwVFoqVVV5v41BQd4JR506SQEB3m9fa0TYAgC0aTXDa964B+n8ee+3z2bzXji67jqpHX/5mxw/cgCAT6kZXvPWDdoVFd5vY/v23glHDkfrHl5rKwhbAADLVVZWD4t56+k1K4bXOnTw3g3aDK/hUoQtAECdKiq89/Sa1cNr3nh6jeE1WIV/WgDQShhT/Ui+t55es2J4rV077z695seHzsEHELYAoBldOrzmjXuQrBpe8+bTa9x/hLaGsAUAjVQzvOaNcFRW5v322WzVw2LeCEedOjG8Blwr3kIAWr2a4TVvPb1WXu79NrZr592n1xheA1oOwhaAFqmqyrtPr1VWer+NgYHefXqN4TWgdSJsAfCaCxe89/SaFcNrknefXmvf3po2AmhdCFtAG2aM9P333nt6zYrhNbvde0+vdezI8BqApufVsLV79245nU5v7hLAD1w6vOaNe5CsGl7z1tNrgYEMrwHwbV4NW//4j/+ovLw8b+4SaBVqhte8EY5KS61pozefXmN4DQD+V6PD1p133llnuTFGZ8+eveYGXUlaWpqeffZZFRYWaujQoXrxxRcVGxtbZ90NGzZo9uzZHmUBAQH6/vvv3evGGKWmpmrdunUqLi7WT37yE/3ud79T//79Le0HWraa4TVvPb12yT85r7Hbvff0GsNrAGCdRoet9957T3/4wx903XXXeZQbY/TBBx94rWF1eeONN5ScnKy1a9fK6XRq9erVSkxM1OHDh9W9e/c6t3E4HDp8+LB73faD8YhnnnlGL7zwgjZu3Kg+ffpo0aJFSkxM1JdffqnAwEBL+wPvqqqqvqnaWzdoWzG8FhDgvafXGF4DAN/Q6LA1ZswYderUSbfeemut14YMGeKVRtXnueee09y5c91Xq9auXas///nPWr9+vRYuXFjnNjabTWFhYXW+ZozR6tWr9eSTT2ry5MmSpFdffVWhoaHatm2bpk+fbk1H4HbxonefXjPG+22sGV7zxj1IDK8BQNvT4LB17tw5derUSenp6fXW2blzp1caVZeKigrt3btXKSkp7jI/Pz8lJCQoOzu73u1KS0vVu3dvVVVVafjw4Xrqqad00003SZJyc3NVWFiohIQEd/3g4GA5nU5lZ2fXG7bKy8tVfsljVy6X61q75zOMqX7izFtPr1k5vOaNcHTddQyvAQCuTYPDVnx8vDIyMuq9SmS106dPq7KyUqGhoR7loaGhOnToUJ3bREdHa/369RoyZIhKSkq0atUqjRo1Sl988YV69uypwsJC9z5+uM+a1+qyYsUKLV269Bp71HQuHV7zxj1IFy96v40BAd57eq1DB4bXAAAtR4PD1rBhw+R0OvXuu+9qwIAB7vKcnBw98cQT2r59uyUNvBZxcXGKi4tzr48aNUoDBw7Uv/3bv2n58uVXvd+UlBQlJye7110ulyIjI6+prT9UM7zmrafXrBhe69jRe0+v+ft7v30AALQEDQ5br7zyilJTU3XLLbdo27Zt6t69u5588kn9v//3/3T77bdb2UZJUkhIiOx2u4qKijzKi4qKGny1rX379ho2bJiOHDkiSe7tioqKFB4e7rHPmJiYevcTEBCggICAWuXff189xOaNe5C++65BXWoUPz/vPr1mt3u/jQAAtDaNukF+6dKlCggI0G233abKykqNGzdO2dnZ9U694E3+/v4aMWKEMjMzNWXKFElSVVWVMjMzdf/99zdoH5WVldq/f787HPbp00dhYWHKzMx0hyuXy6Xdu3dr/vz5jW7jD0YjvcLf33tPrzG8BgBA02tw2CoqKtJTTz2ldevWadCgQTp06JBmzZrVJEGrRnJysmbOnKmRI0cqNjZWq1evVllZmfvpxBkzZqhHjx5asWKFJGnZsmX68Y9/rH79+qm4uFjPPvusvv76a917772Sqp9UfOihh/TP//zP6t+/v3vqh4iICHeguxo1w2veuAeJ4TUAAHxbg8NWnz59FB0drTfffFMTJ05URkaGpk2bpry8PD366KNWttFt2rRpOnXqlBYvXqzCwkLFxMQoIyPDfYN7Xl6e/C55dOzbb7/V3LlzVVhYqC5dumjEiBH6+OOPNWjQIHedxx57TGVlZZo3b56Ki4t1yy23KCMj46rm2MrLkyIiGF4DAAD/y2ZMw26d3rx5c62pEPbt26ef/exn+vnPf660tDRLGugLXC6XgoODVVJSIofD0dzNAQAADdBUf78bPINQXXNODR8+XB9//LHef/99rzYKAACgtbjm6RqjoqL08ccfe6MtAAAArY5X5sbu0qWLN3YDAADQ6vBBJAAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhXwubKWlpSkqKkqBgYFyOp3as2dPvXXXrVun+Ph4denSRV26dFFCQkKt+rNmzZLNZvNYkpKSrO4GAABoI3wqbL3xxhtKTk5Wamqq9u3bp6FDhyoxMVEnT56ss35WVpbuuusu/fWvf1V2drYiIyM1fvx45efne9RLSkpSQUGBe9m0aVNTdAcAALQBNmOMae5GNJTT6dSPfvQjvfTSS5KkqqoqRUZG6oEHHtDChQuvuH1lZaW6dOmil156STNmzJBUfWWruLhY27Ztu+p2uVwuBQcHq6SkRA6H46r3AwAAmk5T/f32mStbFRUV2rt3rxISEtxlfn5+SkhIUHZ2doP2cf78eV24cEFdu3b1KM/KylL37t0VHR2t+fPn68yZM5fdT3l5uVwul8cCAABQF58JW6dPn1ZlZaVCQ0M9ykNDQ1VYWNigfTz++OOKiIjwCGxJSUl69dVXlZmZqZUrV+o///M/NWHCBFVWVta7nxUrVig4ONi9REZGXl2nAABAq9euuRvQVJ5++mlt3rxZWVlZCgwMdJdPnz7d/f3gwYM1ZMgQ3XDDDcrKytK4cePq3FdKSoqSk5Pd6y6Xi8AFAADq5DNXtkJCQmS321VUVORRXlRUpLCwsMtuu2rVKj399NPasWOHhgwZctm6ffv2VUhIiI4cOVJvnYCAADkcDo8FAACgLj4Ttvz9/TVixAhlZma6y6qqqpSZmam4uLh6t3vmmWe0fPlyZWRkaOTIkVc8zokTJ3TmzBmFh4d7pd0AAKBt85mwJUnJyclat26dNm7cqIMHD2r+/PkqKyvT7NmzJUkzZsxQSkqKu/7KlSu1aNEirV+/XlFRUSosLFRhYaFKS0slSaWlpXr00Uf1ySef6NixY8rMzNTkyZPVr18/JSYmNksfAQBA6+JT92xNmzZNp06d0uLFi1VYWKiYmBhlZGS4b5rPy8uTn9//5sff/e53qqio0B133OGxn9TUVC1ZskR2u12ff/65Nm7cqOLiYkVERGj8+PFavny5AgICmrRvAACgdfKpebZaKubZAgDA9zDPFgAAQCtA2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsFC75m4A4FMqK6Vdu6SCAik8XIqPl+z25m4VAC+orKrUrrxdKjhXoPBO4YrvFS+7H+9vXDufu7KVlpamqKgoBQYGyul0as+ePZet/+abb2rAgAEKDAzU4MGDtX37do/XjTFavHixwsPD1aFDByUkJOirr76ysgvwVenpUlSUNHasdPfd1V+joqrLAfi09IPpiloTpbEbx+ru9Ls1duNYRa2JUvpB3t+4dj4Vtt544w0lJycrNTVV+/bt09ChQ5WYmKiTJ0/WWf/jjz/WXXfdpTlz5uizzz7TlClTNGXKFB04cMBd55lnntELL7ygtWvXavfu3erYsaMSExP1/fffN1W34AvS06U77pBOnPAsz8+vLidwAT4r/WC67thyh064PN/f+a583bHlDgIXrpnNGGOauxEN5XQ69aMf/UgvvfSSJKmqqkqRkZF64IEHtHDhwlr1p02bprKyMv3pT39yl/34xz9WTEyM1q5dK2OMIiIi9PDDD+uRRx6RJJWUlCg0NFQbNmzQ9OnTG9Qul8ul4OBglZSUyOFweKGnaFEqK6uvYP0waNWw2aSePaXcXIYUAR9TWVWpqDVRtYJWDZts6unoqdwFuQwptkJN9ffbZ65sVVRUaO/evUpISHCX+fn5KSEhQdnZ2XVuk52d7VFfkhITE931c3NzVVhY6FEnODhYTqez3n1KUnl5uVwul8eCVmzXrvqDliQZIx0/Xl0PgE/Zlber3qAlSUZGx13HtSuP9zeuns+ErdOnT6uyslKhoaEe5aGhoSosLKxzm8LCwsvWr/namH1K0ooVKxQcHOxeIiMjG90f+JCCAu/WA9BiFJxr2Pu2ofWAuvhM2GpJUlJSVFJS4l6OHz/e3E2ClcLDvVsPQIsR3qlh79uG1gPq4jNhKyQkRHa7XUVFRR7lRUVFCgsLq3ObsLCwy9av+dqYfUpSQECAHA6Hx4JWLD6++p4sm63u1202KTKyuh4AnxLfK149HT1lU93vb5tsinREKr4X729cPZ8JW/7+/hoxYoQyMzPdZVVVVcrMzFRcXFyd28TFxXnUl6SdO3e66/fp00dhYWEedVwul3bv3l3vPtEG2e3SmjXV3/8wcNWsr17NzfGAD7L72bUmqfr9/cPAVbO+Omk1N8fjmvhM2JKk5ORkrVu3Ths3btTBgwc1f/58lZWVafbs2ZKkGTNmKCUlxV1/wYIFysjI0L/8y7/o0KFDWrJkiT799FPdf//9kiSbzaaHHnpI//zP/6x33nlH+/fv14wZMxQREaEpU6Y0RxfRUk2dKm3dKvXo4Vnes2d1+dSpzdMuANds6sCp2nrnVvVweL6/ezp6auudWzV1IO9vXBufmkF+2rRpOnXqlBYvXqzCwkLFxMQoIyPDfYN7Xl6e/Pz+Nz+OGjVKr7/+up588kk98cQT6t+/v7Zt26abb77ZXeexxx5TWVmZ5s2bp+LiYt1yyy3KyMhQYGBgk/cPLdzUqdLkycwgD7RCUwdO1eToycwgD0v41DxbLRXzbAEA4HuYZwsAAKAVIGwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhnwlbZ8+e1T333COHw6HOnTtrzpw5Ki0tvWz9Bx54QNHR0erQoYN69eqlBx98UCUlJR71bDZbrWXz5s1WdwcAALQR7Zq7AQ11zz33qKCgQDt37tSFCxc0e/ZszZs3T6+//nqd9b/55ht98803WrVqlQYNGqSvv/5av/71r/XNN99o69atHnVfeeUVJSUludc7d+5sZVcAAEAbYjPGmOZuxJUcPHhQgwYN0t/+9jeNHDlSkpSRkaHbb79dJ06cUERERIP28+abb+qXv/ylysrK1K5ddc602Wx66623NGXKlKtun8vlUnBwsEpKSuRwOK56PwAAoOk01d9vnxhGzM7OVufOnd1BS5ISEhLk5+en3bt3N3g/NT/MmqBV47777lNISIhiY2O1fv16XSl/lpeXy+VyeSwAAAB18YlhxMLCQnXv3t2jrF27duratasKCwsbtI/Tp09r+fLlmjdvnkf5smXL9NOf/lRBQUHasWOH/umf/kmlpaV68MEH693XihUrtHTp0sZ3BAAAtDnNemVr4cKFdd6gfuly6NChaz6Oy+XSxIkTNWjQIC1ZssTjtUWLFuknP/mJhg0bpscff1yPPfaYnn322cvuLyUlRSUlJe7l+PHj19xGAADQOjXrla2HH35Ys2bNumydvn37KiwsTCdPnvQov3jxos6ePauwsLDLbn/u3DklJSWpU6dOeuutt9S+ffvL1nc6nVq+fLnKy8sVEBBQZ52AgIB6XwMAALhUs4atbt26qVu3blesFxcXp+LiYu3du1cjRoyQJL3//vuqqqqS0+msdzuXy6XExEQFBATonXfeUWBg4BWPlZOToy5duhCmAACAV/jEPVsDBw5UUlKS5s6dq7Vr1+rChQu6//77NX36dPeTiPn5+Ro3bpxeffVVxcbGyuVyafz48Tp//rz+4z/+w+NG9m7duslut+uPf/yjioqK9OMf/1iBgYHauXOnnnrqKT3yyCPN2V0AANCK+ETYkqTXXntN999/v8aNGyc/Pz/94he/0AsvvOB+/cKFCzp8+LDOnz8vSdq3b5/7ScV+/fp57Cs3N1dRUVFq37690tLS9Jvf/EbGGPXr10/PPfec5s6d23QdAwAArZpPzLPV0jHPFgAAvod5tgAAAFoBwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABYibAEAAFiIsAUAAGAhwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcIWAACAhQhbAAAAFiJsAQAAWIiwBQAAYCHCFgAAgIUIWwAAABbymbB19uxZ3XPPPXI4HOrcubPmzJmj0tLSy24zZswY2Ww2j+XXv/61R528vDxNnDhRQUFB6t69ux599FFdvHjRyq4AAIA2pF1zN6Ch7rnnHhUUFGjnzp26cOGCZs+erXnz5un111+/7HZz587VsmXL3OtBQUHu7ysrKzVx4kSFhYXp448/VkFBgWbMmKH27dvrqaeesqwvAACg7bAZY0xzN+JKDh48qEGDBulvf/ubRo4cKUnKyMjQ7bffrhMnTigiIqLO7caMGaOYmBitXr26ztf/8pe/6Gc/+5m++eYbhYaGSpLWrl2rxx9/XKdOnZK/v3+D2udyuRQcHKySkhI5HI7GdxAAADS5pvr77RNXtrKzs9W5c2d30JKkhIQE+fn5affu3fr5z39e77avvfaa/uM//kNhYWGaNGmSFi1a5L66lZ2drcGDB7uDliQlJiZq/vz5+uKLLzRs2LA691leXq7y8nL3eklJiaTqkwYAAHxDzd9tq687+UTYKiwsVPfu3T3K2rVrp65du6qwsLDe7e6++2717t1bERER+vzzz/X444/r8OHDSk9Pd+/30qAlyb1+uf2uWLFCS5curVUeGRnZ4D4BAICW4cyZMwoODrZs/80athYuXKiVK1dets7Bgwevev/z5s1zfz948GCFh4dr3LhxOnr0qG644Yar3m9KSoqSk5Pd68XFxerdu7fy8vIsPVktjcvlUmRkpI4fP96mhk/pN/1uC+g3/W4LSkpK1KtXL3Xt2tXS4zRr2Hr44Yc1a9asy9bp27evwsLCdPLkSY/yixcv6uzZswoLC2vw8ZxOpyTpyJEjuuGGGxQWFqY9e/Z41CkqKpKky+43ICBAAQEBtcqDg4Pb1D/SGg6Hg363IfS7baHfbUtb7befn7WTMzRr2OrWrZu6det2xXpxcXEqLi7W3r17NWLECEnS+++/r6qqKneAaoicnBxJUnh4uHu/v/3tb3Xy5En3MOXOnTvlcDg0aNCgRvYGAACgNp+YZ2vgwIFKSkrS3LlztWfPHn300Ue6//77NX36dPeTiPn5+RowYID7StXRo0e1fPly7d27V8eOHdM777yjGTNm6NZbb9WQIUMkSePHj9egQYP0q1/9Sv/1X/+ld999V08++aTuu+++Oq9cAQAANJZPhC2p+qnCAQMGaNy4cbr99tt1yy236N///d/dr1+4cEGHDx/W+fPnJUn+/v567733NH78eA0YMEAPP/ywfvGLX+iPf/yjexu73a4//elPstvtiouL0y9/+UvNmDHDY16uhggICFBqamqbC2j0m363BfSbfrcF9NvafvvEPFsAAAC+ymeubAEAAPgiwhYAAICFCFsAAAAWImwBAABYiLAFAABgIcJWA5w9e1b33HOPHA6HOnfurDlz5qi0tPSy24wZM0Y2m81j+fWvf+1RJy8vTxMnTlRQUJC6d++uRx99VBcvXrSyK43S2H6fPXtWDzzwgKKjo9WhQwf16tVLDz74oPuDumv88Odis9m0efNmq7tTr7S0NEVFRSkwMFBOp7PWpwr80JtvvqkBAwYoMDBQgwcP1vbt2z1eN8Zo8eLFCg8PV4cOHZSQkKCvvvrKyi5clcb0e926dYqPj1eXLl3UpUsXJSQk1Ko/a9asWuc1KSnJ6m40WmP6vWHDhlp9CgwM9KjTGs93Xb+/bDabJk6c6K7jC+f7gw8+0KRJkxQRESGbzaZt27ZdcZusrCwNHz5cAQEB6tevnzZs2FCrTmN/ZzS1xvY7PT1dt912m7p16yaHw6G4uDi9++67HnWWLFlS63wPGDDAwl40XmP7nZWVVee/8x9+NrJXzrfBFSUlJZmhQ4eaTz75xOzatcv069fP3HXXXZfdZvTo0Wbu3LmmoKDAvZSUlLhfv3jxorn55ptNQkKC+eyzz8z27dtNSEiISUlJsbo7DdbYfu/fv99MnTrVvPPOO+bIkSMmMzPT9O/f3/ziF7/wqCfJvPLKKx4/m++++87q7tRp8+bNxt/f36xfv9588cUXZu7cuaZz586mqKiozvofffSRsdvt5plnnjFffvmlefLJJ0379u3N/v373XWefvppExwcbLZt22b+67/+y/zDP/yD6dOnT7P1sS6N7ffdd99t0tLSzGeffWYOHjxoZs2aZYKDg82JEyfcdWbOnGmSkpI8zuvZs2ebqksN0th+v/LKK8bhcHj0qbCw0KNOazzfZ86c8ejzgQMHjN1uN6+88oq7ji+c7+3bt5v/+3//r0lPTzeSzFtvvXXZ+v/93/9tgoKCTHJysvnyyy/Niy++aOx2u8nIyHDXaezPsjk0tt8LFiwwK1euNHv27DF///vfTUpKimnfvr3Zt2+fu05qaqq56aabPM73qVOnLO5J4zS233/961+NJHP48GGPflVWVrrreOt8E7au4MsvvzSSzN/+9jd32V/+8hdjs9lMfn5+vduNHj3aLFiwoN7Xt2/fbvz8/Dx+cf/ud78zDofDlJeXe6Xt1+Jq+/1DW7ZsMf7+/ubChQvusoa8CZpKbGysue+++9zrlZWVJiIiwqxYsaLO+nfeeaeZOHGiR5nT6TT/5//8H2OMMVVVVSYsLMw8++yz7teLi4tNQECA2bRpkwU9uDqN7fcPXbx40XTq1Mls3LjRXTZz5kwzefJkbzfVqxrb71deecUEBwfXu7+2cr6ff/5506lTJ1NaWuou84XzfamG/N557LHHzE033eRRNm3aNJOYmOhev9afZVO72t+3gwYNMkuXLnWvp6ammqFDh3qvYRZrTNj69ttv663jrfPNMOIVZGdnq3Pnzho5cqS7LCEhQX5+ftq9e/dlt33ttdcUEhKim2++WSkpKe7Z7Wv2O3jwYIWGhrrLEhMT5XK59MUXX3i/I410Lf2+VElJiRwOh9q18/wYzvvuu08hISGKjY3V+vXrZZphbt2Kigrt3btXCQkJ7jI/Pz8lJCQoOzu7zm2ys7M96kvV562mfm5urgoLCz3qBAcHy+l01rvPpnY1/f6h8+fP68KFC+ratatHeVZWlrp3767o6GjNnz9fZ86c8Wrbr8XV9ru0tFS9e/dWZGSkJk+e7PH+bCvn++WXX9b06dPVsWNHj/KWfL6vxpXe3974WfqCqqoqnTt3rtb7+6uvvlJERIT69u2re+65R3l5ec3UQu+KiYlReHi4brvtNn300Ufucm+e72b9IGpfUFhY6P6Q6hrt2rVT165da43rXuruu+9W7969FRERoc8//1yPP/64Dh8+rPT0dPd+Lw1aktzrl9tvU7nafl/q9OnTWr58uebNm+dRvmzZMv30pz9VUFCQduzYoX/6p39SaWmpHnzwQa+1v6Htq6ysrPM8HDp0qM5t6jtvNT+Tmq+Xq9PcrqbfP/T4448rIiLC45dQUlKSpk6dqj59+ujo0aN64oknNGHCBGVnZ8tut3u1D1fjavodHR2t9evXa8iQISopKdGqVas0atQoffHFF+rZs2ebON979uzRgQMH9PLLL3uUt/TzfTXqe3+7XC599913+vbbb6/5veMLVq1apdLSUt15553uMqfTqQ0bNig6OloFBQVaunSp4uPjdeDAAXXq1KkZW3v1wsPDtXbtWo0cOVLl5eX6/e9/rzFjxmj37t0aPny4V35X1mizYWvhwoVauXLlZescPHjwqvd/acAYPHiwwsPDNW7cOB09elQ33HDDVe/3Wlnd7xoul0sTJ07UoEGDtGTJEo/XFi1a5P5+2LBhKisr07PPPtvkYQtX5+mnn9bmzZuVlZXlcbP49OnT3d8PHjxYQ4YM0Q033KCsrCyNGzeuOZp6zeLi4hQXF+deHzVqlAYOHKh/+7d/0/Lly5uxZU3n5Zdf1uDBgxUbG+tR3hrPN6TXX39dS5cu1dtvv+3xH+4JEya4vx8yZIicTqd69+6tLVu2aM6cOc3R1GsWHR2t6Oho9/qoUaN09OhRPf/88/rDH/7g1WO12bD18MMPa9asWZet07dvX4WFhenkyZMe5RcvXtTZs2cVFhbW4OM5nU5J0pEjR3TDDTcoLCys1hMNRUVFktSo/TZWU/T73LlzSkpKUqdOnfTWW2+pffv2l63vdDq1fPlylZeXN+mHoIaEhMhut7t/7jWKiorq7WNYWNhl69d8LSoqUnh4uEedmJgYL7b+6l1Nv2usWrVKTz/9tN577z0NGTLksnX79u2rkJAQHTlypEX88b2Wftdo3769hg0bpiNHjkhq/ee7rKxMmzdv1rJly654nJZ2vq9Gfe9vh8OhDh06yG63X/O/oZZs8+bNuvfee/Xmm2/WGk79oc6dO+vGG290vxdai9jYWH344YeSvPM7o0abvWerW7duGjBgwGUXf39/xcXFqbi4WHv37nVv+/7776uqqsodoBoiJydHkty/kOPi4rR//36PQLNz5045HA4NGjTIO52sg9X9drlcGj9+vPz9/fXOO+/Ueky+Ljk5OerSpUuTf9q8v7+/RowYoczMTHdZVVWVMjMzPa5mXCouLs6jvlR93mrq9+nTR2FhYR51XC6Xdu/eXe8+m9rV9FuSnnnmGS1fvlwZGRke9/LV58SJEzpz5oxHCGlOV9vvS1VWVmr//v3uPrXm8y1VT3NSXl6uX/7yl1c8Tks731fjSu9vb/wbaqk2bdqk2bNna9OmTR5TfNSntLRUR48e9enzXZecnBx3n7x6vht1O30blZSUZIYNG2Z2795tPvzwQ9O/f3+PKRBOnDhhoqOjze7du40xxhw5csQsW7bMfPrppyY3N9e8/fbbpm/fvubWW291b1Mz9cP48eNNTk6OycjIMN26dWtxUz80pt8lJSXG6XSawYMHmyNHjng8Snvx4kVjjDHvvPOOWbdundm/f7/56quvzL/+67+aoKAgs3jx4mbp4+bNm01AQIDZsGGD+fLLL828efNM586d3U+J/upXvzILFy501//oo49Mu3btzKpVq8zBgwdNampqnVM/dO7c2bz99tvm888/N5MnT26RUwE0pt9PP/208ff3N1u3bvU4r+fOnTPGGHPu3DnzyCOPmOzsbJObm2vee+89M3z4cNO/f3/z/fffN0sf69LYfi9dutS8++675ujRo2bv3r1m+vTpJjAw0HzxxRfuOq3xfNe45ZZbzLRp02qV+8r5PnfunPnss8/MZ599ZiSZ5557znz22Wfm66+/NsYYs3DhQvOrX/3KXb9m6odHH33UHDx40KSlpdU59cPlfpYtQWP7/dprr5l27dqZtLQ0j/d3cXGxu87DDz9ssrKyTG5urvnoo49MQkKCCQkJMSdPnmzy/tWnsf1+/vnnzbZt28xXX31l9u/fbxYsWGD8/PzMe++9567jrfNN2GqAM2fOmLvuustcd911xuFwmNmzZ7v/yBhjTG5urpFk/vrXvxpjjMnLyzO33nqr6dq1qwkICDD9+vUzjz76qMc8W8YYc+zYMTNhwgTToUMHExISYh5++GGPKRKaW2P7XfMYbV1Lbm6uMaZ6+oiYmBhz3XXXmY4dO5qhQ4eatWvXesxr0tRefPFF06tXL+Pv729iY2PNJ5984n5t9OjRZubMmR71t2zZYm688Ubj7+9vbrrpJvPnP//Z4/WqqiqzaNEiExoaagICAsy4cePM4cOHm6IrjdKYfvfu3bvO85qammqMMeb8+fNm/Pjxplu3bqZ9+/amd+/eZu7cuS3qD1CNxvT7oYcectcNDQ01t99+u8fcQ8a0zvNtjDGHDh0yksyOHTtq7ctXznd9v5Nq+jpz5kwzevToWtvExMQYf39/07dvX4+5xWpc7mfZEjS236NHj75sfWOqp8AIDw83/v7+pkePHmbatGnmyJEjTduxK2hsv1euXGluuOEGExgYaLp27WrGjBlj3n///Vr79cb5thnTDM/cAwAAtBFt9p4tAACApkDYAgAAsBBhCwAAwEKELQAAAAsRtgAAACxE2AIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCgP+xadMmdejQQQUFBe6y2bNna8iQISopKWnGlgHwZXxcDwD8D2OMYmJidOutt+rFF19Uamqq1q9fr08++UQ9evRo7uYB8FFc2QKA/2Gz2fTb3/5W69at029/+1u9+OKLysjIcAetP/3pT4qOjlb//v31+9//vplbC8BXcGULAH5g+PDh+uKLL7Rjxw6NHj1aknTx4kUNGjRIf/3rXxUcHKwRI0bo448/1vXXX9/MrQXQ0nFlCwAukZGRoUOHDqmyslKhoaHu8j179uimm25Sjx49dN1112nChAnasWNHM7YUgK8gbAHA/9i3b5/uvPNOvfzyyxo3bpwWLVrkfu2bb77xuG+rR48eys/Pb45mAvAx7Zq7AQDQEhw7dkwTJ07UE088obvuukt9+/ZVXFyc9u3bp+HDhzd38wD4MK5sAWjzzp49q6SkJE2ePFkLFy6UJDmdTk2YMEFPPPGEJCkiIsLjSlZ+fr4iIiKapb0AfAs3yANAA1y8eFEDBw5UVlYWN8gDaBSGEQGgAdq1a6d/+Zd/0dixY1VVVaXHHnuMoAWgQbiyBQAAYCHu2QIAALAQYQsAAMBChC0AAAALEbYAAAAsRNgCAACwEGELAADAQoQtAAAACxG2AAAALETYAgAAsBBhCwAAwEKELQAAAAv9f+1HK7W0C4m7AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 5\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: -0.06227226099288552\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [-0.00848055  0.12210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 1\n",
      "v: 0.0598365100469464\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [-0.00848055  0.12210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 1\n",
      "v: -0.07075281061399324\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.09151945 0.12210877]\n",
      "Bias: 0.037727739007114486\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 0\n",
      "v: 0.2513559604258387\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [-0.00848055  0.02210877]\n",
      "Bias: -0.06227226099288552\n",
      "--------------------\n",
      "\n",
      "Epoch error: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 6\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: -0.06227226099288552\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [-0.00848055  0.02210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 1\n",
      "v: -0.040163489953053605\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [-0.00848055  0.12210877]\n",
      "Bias: 0.037727739007114486\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 1\n",
      "v: 0.02924718938600676\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [-0.00848055  0.12210877]\n",
      "Bias: 0.037727739007114486\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 0\n",
      "v: 0.15135596042583868\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [-0.10848055  0.02210877]\n",
      "Bias: -0.06227226099288552\n",
      "--------------------\n",
      "\n",
      "Epoch error: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 7\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: -0.06227226099288552\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [-0.10848055  0.02210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 1\n",
      "v: -0.040163489953053605\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [-0.10848055  0.12210877]\n",
      "Bias: 0.037727739007114486\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 1\n",
      "v: -0.07075281061399324\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [-0.00848055  0.12210877]\n",
      "Bias: 0.1377277390071145\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 0\n",
      "v: 0.2513559604258387\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [-0.10848055  0.02210877]\n",
      "Bias: 0.037727739007114486\n",
      "--------------------\n",
      "\n",
      "Epoch error: 1.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 8\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: 0.037727739007114486\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [-0.10848055  0.02210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 1\n",
      "v: -0.040163489953053605\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [-0.10848055  0.12210877]\n",
      "Bias: 0.037727739007114486\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 1\n",
      "v: -0.07075281061399324\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [-0.00848055  0.12210877]\n",
      "Bias: 0.1377277390071145\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 0\n",
      "v: 0.2513559604258387\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [-0.10848055  0.02210877]\n",
      "Bias: 0.037727739007114486\n",
      "--------------------\n",
      "\n",
      "Epoch error: 2.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 9\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [0 0], y: 0\n",
      "v: 0.037727739007114486\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [-0.10848055  0.02210877]\n",
      "Bias: -0.06227226099288552\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [0 1], y: 1\n",
      "v: -0.040163489953053605\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [-0.10848055  0.12210877]\n",
      "Bias: 0.037727739007114486\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [1 0], y: 1\n",
      "v: -0.07075281061399324\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [-0.00848055  0.12210877]\n",
      "Bias: 0.1377277390071145\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [1 1], y: 0\n",
      "v: 0.2513559604258387\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [-0.10848055  0.02210877]\n",
      "Bias: 0.037727739007114486\n",
      "--------------------\n",
      "\n",
      "Epoch error: 2.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Maximum number of iterations.\n",
    "max_iter = 10\n",
    "\n",
    "# Learning rate.\n",
    "learning_rate = 0.1\n",
    "\n",
    "# Sets a value for the random number generation seed.\n",
    "np.random.seed(1234)\n",
    "\n",
    "# Perceptron Training\n",
    "# -------------------\n",
    "fit(X, y_xor)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21440a3b",
   "metadata": {},
   "source": [
    "## Training the model with the Iris dataset\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "351992a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the IRIS dataset\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "# Select only 2 classes: Setosa (0) and Virginica (1).\n",
    "# Select only 2 attributes: sepal length (0) and sepal width (1).\n",
    "X = iris.data[iris.target < 2, :2]\n",
    "y = iris.target[iris.target < 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f76f5eda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean and standard deviation:\n",
      "[1.77001271e-15 9.01659699e-16] [1. 1.]\n"
     ]
    }
   ],
   "source": [
    "# Split the datase in train (70 %) and test (30 %)\n",
    "X, X_test, y, y_test = model_selection.train_test_split(X, y, test_size=0.30, random_state=42)\n",
    "\n",
    "# Data normalization\n",
    "X = preprocessing.scale(X)\n",
    "print('Mean and standard deviation:')\n",
    "print(X.mean(axis=0), X.std(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "88608f9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iniciatialization\n",
      "====================\n",
      "\n",
      "Weights: [0.19151945 0.62210877]\n",
      "Bias: 0.4377277390071145\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 0\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [-1.0280556   0.72748234], y: 0\n",
      "v: 0.6934082412757296\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.20180001 0.61483395]\n",
      "Bias: 0.42772773900711447\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [-1.32853114  0.29036849], y: 0\n",
      "v: 0.3381585497565798\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.21508532 0.61193026]\n",
      "Bias: 0.41772773900711446\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [0.77479765 0.72748234], y: 1\n",
      "v: 1.0295437983085423\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.21508532 0.61193026]\n",
      "Bias: 0.41772773900711446\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [-0.42710452  0.72748234], y: 0\n",
      "v: 0.7710322888366263\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.21935636 0.60465544]\n",
      "Bias: 0.40772773900711445\n",
      "\n",
      "Iteration 4\n",
      "--------------------\n",
      "x: [-0.72758006 -1.67664385], y: 1\n",
      "v: -0.7656634036044404\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.21208056 0.587889  ]\n",
      "Bias: 0.41772773900711446\n",
      "\n",
      "Iteration 5\n",
      "--------------------\n",
      "x: [-0.12662898  1.82026698], y: 0\n",
      "v: 1.4609871289962166\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.21334685 0.56968633]\n",
      "Bias: 0.40772773900711445\n",
      "\n",
      "Iteration 6\n",
      "--------------------\n",
      "x: [ 0.17384656 -0.14674537], y: 1\n",
      "v: 0.3612185273157523\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.21334685 0.56968633]\n",
      "Bias: 0.40772773900711445\n",
      "\n",
      "Iteration 7\n",
      "--------------------\n",
      "x: [1.82646204 0.07181156], y: 1\n",
      "v: 0.8383077315074111\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.21334685 0.56968633]\n",
      "Bias: 0.40772773900711445\n",
      "\n",
      "Iteration 8\n",
      "--------------------\n",
      "x: [-0.72758006  0.29036849], y: 0\n",
      "v: 0.4179197821854388\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.22062265 0.56678265]\n",
      "Bias: 0.39772773900711444\n",
      "\n",
      "Iteration 9\n",
      "--------------------\n",
      "x: [-0.12662898  1.82026698], y: 0\n",
      "v: 1.401486252380594\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.22188894 0.54857998]\n",
      "Bias: 0.38772773900711444\n",
      "\n",
      "Iteration 10\n",
      "--------------------\n",
      "x: [-0.72758006  0.50892542], y: 0\n",
      "v: 0.5054720610852312\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.22916474 0.54349072]\n",
      "Bias: 0.3777277390071144\n",
      "\n",
      "Iteration 11\n",
      "--------------------\n",
      "x: [-0.87781783  0.07181156], y: 0\n",
      "v: 0.21559175844275796\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.23794292 0.54277261]\n",
      "Bias: 0.3677277390071144\n",
      "\n",
      "Iteration 12\n",
      "--------------------\n",
      "x: [-0.72758006  0.72748234], y: 0\n",
      "v: 0.5894627009991171\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.24521872 0.53549778]\n",
      "Bias: 0.3577277390071144\n",
      "\n",
      "Iteration 13\n",
      "--------------------\n",
      "x: [ 0.32408433 -0.14674537], y: 1\n",
      "v: 0.3586174674948439\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.24521872 0.53549778]\n",
      "Bias: 0.3577277390071144\n",
      "\n",
      "Iteration 14\n",
      "--------------------\n",
      "x: [-0.42710452  0.94603927], y: 0\n",
      "v: 0.7595956462159641\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.24948977 0.52603739]\n",
      "Bias: 0.3477277390071144\n",
      "\n",
      "Iteration 15\n",
      "--------------------\n",
      "x: [-0.57734229  1.60171005], y: 0\n",
      "v: 1.046246120703901\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.25526319 0.51002029]\n",
      "Bias: 0.3377277390071144\n",
      "\n",
      "Iteration 16\n",
      "--------------------\n",
      "x: [ 0.02360879 -1.45808693], y: 1\n",
      "v: -0.3998997227994029\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.25549928 0.49543942]\n",
      "Bias: 0.3477277390071144\n",
      "\n",
      "Iteration 17\n",
      "--------------------\n",
      "x: [-0.72758006 -0.14674537], y: 0\n",
      "v: 0.08912811972501122\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.26277508 0.49690687]\n",
      "Bias: 0.3377277390071144\n",
      "\n",
      "Iteration 18\n",
      "--------------------\n",
      "x: [ 0.77479765 -1.89520078], y: 1\n",
      "v: -0.40041304492792884\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.27052306 0.47795487]\n",
      "Bias: 0.3477277390071144\n",
      "\n",
      "Iteration 19\n",
      "--------------------\n",
      "x: [-1.77924446 -0.14674537], y: 0\n",
      "v: -0.2037365690388776\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.27052306 0.47795487]\n",
      "Bias: 0.3477277390071144\n",
      "\n",
      "Iteration 20\n",
      "--------------------\n",
      "x: [-1.0280556   0.72748234], y: 0\n",
      "v: 0.41731872234350886\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.28080361 0.47068004]\n",
      "Bias: 0.3377277390071144\n",
      "\n",
      "Iteration 21\n",
      "--------------------\n",
      "x: [-1.32853114  0.07181156], y: 0\n",
      "v: -0.001528335143380466\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.28080361 0.47068004]\n",
      "Bias: 0.3377277390071144\n",
      "\n",
      "Iteration 22\n",
      "--------------------\n",
      "x: [-0.57734229  0.94603927], y: 0\n",
      "v: 0.6208897431265934\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.28657703 0.46121965]\n",
      "Bias: 0.3277277390071144\n",
      "\n",
      "Iteration 23\n",
      "--------------------\n",
      "x: [-1.62900669 -0.14674537], y: 0\n",
      "v: -0.20679001188813728\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.28657703 0.46121965]\n",
      "Bias: 0.3277277390071144\n",
      "\n",
      "Iteration 24\n",
      "--------------------\n",
      "x: [-1.62900669 -0.36530229], y: 0\n",
      "v: -0.3075927613581615\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.28657703 0.46121965]\n",
      "Bias: 0.3277277390071144\n",
      "\n",
      "Iteration 25\n",
      "--------------------\n",
      "x: [ 0.77479765 -0.36530229], y: 1\n",
      "v: 0.3812823551124904\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.28657703 0.46121965]\n",
      "Bias: 0.3277277390071144\n",
      "\n",
      "Iteration 26\n",
      "--------------------\n",
      "x: [-1.32853114  0.72748234], y: 0\n",
      "v: 0.2825303755507903\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.29986235 0.45394483]\n",
      "Bias: 0.3177277390071144\n",
      "\n",
      "Iteration 27\n",
      "--------------------\n",
      "x: [ 0.17384656 -0.36530229], y: 1\n",
      "v: 0.2040306914078472\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.29986235 0.45394483]\n",
      "Bias: 0.3177277390071144\n",
      "\n",
      "Iteration 28\n",
      "--------------------\n",
      "x: [0.02360879 0.94603927], y: 0\n",
      "v: 0.7542567595512311\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.29962626 0.44448443]\n",
      "Bias: 0.30772773900711436\n",
      "\n",
      "Iteration 29\n",
      "--------------------\n",
      "x: [ 0.02360879 -1.23953   ], y: 1\n",
      "v: -0.23615023779508248\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.29986235 0.43208913]\n",
      "Bias: 0.3177277390071144\n",
      "\n",
      "Iteration 30\n",
      "--------------------\n",
      "x: [1.22551096 0.50892542], y: 1\n",
      "v: 0.9051134720609303\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.29986235 0.43208913]\n",
      "Bias: 0.3177277390071144\n",
      "\n",
      "Iteration 31\n",
      "--------------------\n",
      "x: [ 0.32408433 -0.58385922], y: 1\n",
      "v: 0.16262920302994383\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.29986235 0.43208913]\n",
      "Bias: 0.3177277390071144\n",
      "\n",
      "Iteration 32\n",
      "--------------------\n",
      "x: [ 1.5259865  -0.58385922], y: 1\n",
      "v: 0.5230344058718597\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.29986235 0.43208913]\n",
      "Bias: 0.3177277390071144\n",
      "\n",
      "Iteration 33\n",
      "--------------------\n",
      "x: [-0.72758006  0.94603927], y: 0\n",
      "v: 0.5083271642026371\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.30713815 0.42262874]\n",
      "Bias: 0.30772773900711436\n",
      "\n",
      "Iteration 34\n",
      "--------------------\n",
      "x: [2.27717535 0.29036849], y: 1\n",
      "v: 1.1298532250775402\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.30713815 0.42262874]\n",
      "Bias: 0.30772773900711436\n",
      "\n",
      "Iteration 35\n",
      "--------------------\n",
      "x: [ 0.4743221  -0.80241615], y: 1\n",
      "v: 0.11428602492191076\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.30713815 0.42262874]\n",
      "Bias: 0.30772773900711436\n",
      "\n",
      "Iteration 36\n",
      "--------------------\n",
      "x: [-0.57734229  1.60171005], y: 0\n",
      "v: 0.8073326012299552\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.31291157 0.40661164]\n",
      "Bias: 0.29772773900711436\n",
      "\n",
      "Iteration 37\n",
      "--------------------\n",
      "x: [ 1.07527319 -1.89520078], y: 1\n",
      "v: -0.1364175402449775\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.3236643  0.38765963]\n",
      "Bias: 0.30772773900711436\n",
      "\n",
      "Iteration 38\n",
      "--------------------\n",
      "x: [ 0.62455988 -0.14674537], y: 1\n",
      "v: 0.4529882201661588\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.3236643  0.38765963]\n",
      "Bias: 0.30772773900711436\n",
      "\n",
      "Iteration 39\n",
      "--------------------\n",
      "x: [ 1.07527319 -0.36530229], y: 1\n",
      "v: 0.514142331227207\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.3236643  0.38765963]\n",
      "Bias: 0.30772773900711436\n",
      "\n",
      "Iteration 40\n",
      "--------------------\n",
      "x: [ 0.32408433 -1.02097307], y: 1\n",
      "v: 0.01683222135906831\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.3236643  0.38765963]\n",
      "Bias: 0.30772773900711436\n",
      "\n",
      "Iteration 41\n",
      "--------------------\n",
      "x: [-1.47876892 -1.67664385], y: 0\n",
      "v: -0.8208641100347128\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.3236643  0.38765963]\n",
      "Bias: 0.30772773900711436\n",
      "\n",
      "Iteration 42\n",
      "--------------------\n",
      "x: [ 1.67622427 -0.36530229], y: 1\n",
      "v: 0.7086487435275736\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.3236643  0.38765963]\n",
      "Bias: 0.30772773900711436\n",
      "\n",
      "Iteration 43\n",
      "--------------------\n",
      "x: [-0.27686675  1.38315312], y: 0\n",
      "v: 0.7543084888651957\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.32643297 0.3738281 ]\n",
      "Bias: 0.29772773900711436\n",
      "\n",
      "Iteration 44\n",
      "--------------------\n",
      "x: [-0.57734229 -1.23953   ], y: 1\n",
      "v: -0.35410696615897364\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.32065955 0.3614328 ]\n",
      "Bias: 0.30772773900711436\n",
      "\n",
      "Iteration 45\n",
      "--------------------\n",
      "x: [-0.87781783 -1.45808693], y: 1\n",
      "v: -0.5007533718894978\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.31188137 0.34685193]\n",
      "Bias: 0.3177277390071144\n",
      "\n",
      "Iteration 46\n",
      "--------------------\n",
      "x: [ 1.67622427 -0.14674537], y: 1\n",
      "v: 0.7896119430952008\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.31188137 0.34685193]\n",
      "Bias: 0.3177277390071144\n",
      "\n",
      "Iteration 47\n",
      "--------------------\n",
      "x: [-0.42710452  2.25738083], y: 0\n",
      "v: 0.9674987017293837\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.31615241 0.32427812]\n",
      "Bias: 0.30772773900711436\n",
      "\n",
      "Iteration 48\n",
      "--------------------\n",
      "x: [ 0.17384656 -0.80241615], y: 1\n",
      "v: 0.10248374652888614\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.31615241 0.32427812]\n",
      "Bias: 0.30772773900711436\n",
      "\n",
      "Iteration 49\n",
      "--------------------\n",
      "x: [-0.42710452 -0.80241615], y: 1\n",
      "v: -0.08750838821711338\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.31188137 0.31625396]\n",
      "Bias: 0.3177277390071144\n",
      "\n",
      "Iteration 50\n",
      "--------------------\n",
      "x: [ 0.92503542 -0.36530229], y: 1\n",
      "v: 0.490700752178289\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.31188137 0.31625396]\n",
      "Bias: 0.3177277390071144\n",
      "\n",
      "Iteration 51\n",
      "--------------------\n",
      "x: [-0.12662898 -0.14674537], y: 1\n",
      "v: 0.2318257168257007\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.31188137 0.31625396]\n",
      "Bias: 0.3177277390071144\n",
      "\n",
      "Iteration 52\n",
      "--------------------\n",
      "x: [-0.87781783  1.1645962 ], y: 0\n",
      "v: 0.41226087565866293\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.32065955 0.304608  ]\n",
      "Bias: 0.30772773900711436\n",
      "\n",
      "Iteration 53\n",
      "--------------------\n",
      "x: [-1.17829337  0.29036849], y: 0\n",
      "v: 0.01834528603102642\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.33244248 0.30170432]\n",
      "Bias: 0.29772773900711436\n",
      "\n",
      "Iteration 54\n",
      "--------------------\n",
      "x: [-0.87781783 -0.14674537], y: 0\n",
      "v: -0.03836990763122261\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.33244248 0.30170432]\n",
      "Bias: 0.29772773900711436\n",
      "\n",
      "Iteration 55\n",
      "--------------------\n",
      "x: [2.12693758 0.07181156], y: 1\n",
      "v: 1.026478000903127\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.33244248 0.30170432]\n",
      "Bias: 0.29772773900711436\n",
      "\n",
      "Iteration 56\n",
      "--------------------\n",
      "x: [-0.57734229  1.38315312], y: 0\n",
      "v: 0.5230979036582905\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.3382159  0.28787278]\n",
      "Bias: 0.28772773900711435\n",
      "\n",
      "Iteration 57\n",
      "--------------------\n",
      "x: [-1.17829337  0.29036849], y: 0\n",
      "v: -0.027200632086931797\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.3382159  0.28787278]\n",
      "Bias: 0.28772773900711435\n",
      "\n",
      "Iteration 58\n",
      "--------------------\n",
      "x: [-0.57734229  0.50892542], y: 0\n",
      "v: 0.23896717194763378\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.34398933 0.28278353]\n",
      "Bias: 0.27772773900711434\n",
      "\n",
      "Iteration 59\n",
      "--------------------\n",
      "x: [ 1.22551096 -1.67664385], y: 1\n",
      "v: 0.22516315801334802\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.34398933 0.28278353]\n",
      "Bias: 0.27772773900711434\n",
      "\n",
      "Iteration 60\n",
      "--------------------\n",
      "x: [ 0.92503542 -0.14674537], y: 1\n",
      "v: 0.5544328754815371\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.34398933 0.28278353]\n",
      "Bias: 0.27772773900711434\n",
      "\n",
      "Iteration 61\n",
      "--------------------\n",
      "x: [ 1.37574873 -0.36530229], y: 1\n",
      "v: 0.6476691441935476\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.34398933 0.28278353]\n",
      "Bias: 0.27772773900711434\n",
      "\n",
      "Iteration 62\n",
      "--------------------\n",
      "x: [1.82646204 0.07181156], y: 1\n",
      "v: 0.9263183112995456\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.34398933 0.28278353]\n",
      "Bias: 0.27772773900711434\n",
      "\n",
      "Iteration 63\n",
      "--------------------\n",
      "x: [ 0.4743221  -0.80241615], y: 1\n",
      "v: 0.21397940891087677\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.34398933 0.28278353]\n",
      "Bias: 0.27772773900711434\n",
      "\n",
      "Iteration 64\n",
      "--------------------\n",
      "x: [-0.12662898  0.72748234], y: 0\n",
      "v: 0.43988874759461716\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.34525561 0.27550871]\n",
      "Bias: 0.26772773900711433\n",
      "\n",
      "Iteration 65\n",
      "--------------------\n",
      "x: [-0.72758006 -2.33231464], y: 1\n",
      "v: -0.6260463525195928\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.33797981 0.25218556]\n",
      "Bias: 0.27772773900711434\n",
      "\n",
      "Iteration 66\n",
      "--------------------\n",
      "x: [ 0.92503542 -0.58385922], y: 1\n",
      "v: 0.44313017294901735\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.33797981 0.25218556]\n",
      "Bias: 0.27772773900711434\n",
      "\n",
      "Iteration 67\n",
      "--------------------\n",
      "x: [0.4743221  2.03882391], y: 0\n",
      "v: 0.9522009858672573\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.33323659 0.23179732]\n",
      "Bias: 0.26772773900711433\n",
      "\n",
      "Iteration 68\n",
      "--------------------\n",
      "x: [ 0.4743221  -1.02097307], y: 1\n",
      "v: 0.18913039737559523\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.33323659 0.23179732]\n",
      "Bias: 0.26772773900711433\n",
      "\n",
      "Iteration 69\n",
      "--------------------\n",
      "x: [1.37574873 0.29036849], y: 1\n",
      "v: 0.7934841969510483\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.33323659 0.23179732]\n",
      "Bias: 0.26772773900711433\n",
      "--------------------\n",
      "\n",
      "Epoch error: 17.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 1\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [-1.0280556   0.72748234], y: 0\n",
      "v: 0.0937704508342205\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.34351715 0.2245225 ]\n",
      "Bias: 0.2577277390071143\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [-1.32853114  0.29036849], y: 0\n",
      "v: -0.13345123404526127\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.34351715 0.2245225 ]\n",
      "Bias: 0.2577277390071143\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [0.77479765 0.72748234], y: 1\n",
      "v: 0.6872201709527865\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.34351715 0.2245225 ]\n",
      "Bias: 0.2577277390071143\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [-0.42710452  0.72748234], y: 0\n",
      "v: 0.2743461649932556\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.34778819 0.21724767]\n",
      "Bias: 0.2477277390071143\n",
      "\n",
      "Iteration 4\n",
      "--------------------\n",
      "x: [-0.72758006 -1.67664385], y: 1\n",
      "v: -0.3695629959561024\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.34051239 0.20048124]\n",
      "Bias: 0.2577277390071143\n",
      "\n",
      "Iteration 5\n",
      "--------------------\n",
      "x: [-0.12662898  1.82026698], y: 0\n",
      "v: 0.5795383767231073\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.34177868 0.18227857]\n",
      "Bias: 0.2477277390071143\n",
      "\n",
      "Iteration 6\n",
      "--------------------\n",
      "x: [ 0.17384656 -0.14674537], y: 1\n",
      "v: 0.28039625375750254\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.34177868 0.18227857]\n",
      "Bias: 0.2477277390071143\n",
      "\n",
      "Iteration 7\n",
      "--------------------\n",
      "x: [1.82646204 0.07181156], y: 1\n",
      "v: 0.8850632401093038\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.34177868 0.18227857]\n",
      "Bias: 0.2477277390071143\n",
      "\n",
      "Iteration 8\n",
      "--------------------\n",
      "x: [-0.72758006  0.29036849], y: 0\n",
      "v: 0.051984335240820745\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.34905448 0.17937488]\n",
      "Bias: 0.2377277390071143\n",
      "\n",
      "Iteration 9\n",
      "--------------------\n",
      "x: [-0.12662898  1.82026698], y: 0\n",
      "v: 0.5200375001074848\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.35032077 0.16117221]\n",
      "Bias: 0.2277277390071143\n",
      "\n",
      "Iteration 10\n",
      "--------------------\n",
      "x: [-0.72758006  0.50892542], y: 0\n",
      "v: 0.05486596363028512\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.35759657 0.15608296]\n",
      "Bias: 0.21772773900711428\n",
      "\n",
      "Iteration 11\n",
      "--------------------\n",
      "x: [-0.87781783  0.07181156], y: 0\n",
      "v: -0.08496835005248338\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.35759657 0.15608296]\n",
      "Bias: 0.21772773900711428\n",
      "\n",
      "Iteration 12\n",
      "--------------------\n",
      "x: [-0.72758006  0.72748234], y: 0\n",
      "v: 0.07109519698808786\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.36487238 0.14880813]\n",
      "Bias: 0.20772773900711428\n",
      "\n",
      "Iteration 13\n",
      "--------------------\n",
      "x: [ 0.32408433 -0.14674537], y: 1\n",
      "v: 0.3041402557825958\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.36487238 0.14880813]\n",
      "Bias: 0.20772773900711428\n",
      "\n",
      "Iteration 14\n",
      "--------------------\n",
      "x: [-0.42710452  0.94603927], y: 0\n",
      "v: 0.1926674370738015\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.36914342 0.13934774]\n",
      "Bias: 0.19772773900711427\n",
      "\n",
      "Iteration 15\n",
      "--------------------\n",
      "x: [-0.57734229  1.60171005], y: 0\n",
      "v: 0.20780030934035623\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.37491684 0.12333064]\n",
      "Bias: 0.18772773900711426\n",
      "\n",
      "Iteration 16\n",
      "--------------------\n",
      "x: [ 0.02360879 -1.45808693], y: 1\n",
      "v: 0.016752277457980663\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.37491684 0.12333064]\n",
      "Bias: 0.18772773900711426\n",
      "\n",
      "Iteration 17\n",
      "--------------------\n",
      "x: [-0.72758006 -0.14674537], y: 0\n",
      "v: -0.10315248093641571\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.37491684 0.12333064]\n",
      "Bias: 0.18772773900711426\n",
      "\n",
      "Iteration 18\n",
      "--------------------\n",
      "x: [ 0.77479765 -1.89520078], y: 1\n",
      "v: 0.2444760995536294\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.37491684 0.12333064]\n",
      "Bias: 0.18772773900711426\n",
      "\n",
      "Iteration 19\n",
      "--------------------\n",
      "x: [-1.77924446 -0.14674537], y: 0\n",
      "v: -0.4974391764612006\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.37491684 0.12333064]\n",
      "Bias: 0.18772773900711426\n",
      "\n",
      "Iteration 20\n",
      "--------------------\n",
      "x: [-1.0280556   0.72748234], y: 0\n",
      "v: -0.10798675881367337\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.37491684 0.12333064]\n",
      "Bias: 0.18772773900711426\n",
      "\n",
      "Iteration 21\n",
      "--------------------\n",
      "x: [-1.32853114  0.07181156], y: 0\n",
      "v: -0.3015043981681227\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.37491684 0.12333064]\n",
      "Bias: 0.18772773900711426\n",
      "\n",
      "Iteration 22\n",
      "--------------------\n",
      "x: [-0.57734229  0.94603927], y: 0\n",
      "v: 0.0879480194794045\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.38069027 0.11387025]\n",
      "Bias: 0.17772773900711425\n",
      "\n",
      "Iteration 23\n",
      "--------------------\n",
      "x: [-1.62900669 -0.14674537], y: 0\n",
      "v: -0.459129181405071\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.38069027 0.11387025]\n",
      "Bias: 0.17772773900711425\n",
      "\n",
      "Iteration 24\n",
      "--------------------\n",
      "x: [-1.62900669 -0.36530229], y: 0\n",
      "v: -0.4840163129731445\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.38069027 0.11387025]\n",
      "Bias: 0.17772773900711425\n",
      "\n",
      "Iteration 25\n",
      "--------------------\n",
      "x: [ 0.77479765 -0.36530229], y: 1\n",
      "v: 0.4310885986506463\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.38069027 0.11387025]\n",
      "Bias: 0.17772773900711425\n",
      "\n",
      "Iteration 26\n",
      "--------------------\n",
      "x: [-1.32853114  0.72748234], y: 0\n",
      "v: -0.24519254117980377\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.38069027 0.11387025]\n",
      "Bias: 0.17772773900711425\n",
      "\n",
      "Iteration 27\n",
      "--------------------\n",
      "x: [ 0.17384656 -0.36530229], y: 1\n",
      "v: 0.20231237074469838\n",
      "y_out: 1\n",
      "Error: 0\n",
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      "Bias: 0.17772773900711425\n",
      "\n",
      "Iteration 28\n",
      "--------------------\n",
      "x: [0.02360879 0.94603927], y: 0\n",
      "v: 0.2944411031766523\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.38045418 0.10440986]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 29\n",
      "--------------------\n",
      "x: [ 0.02360879 -1.23953   ], y: 1\n",
      "v: 0.04729065431397622\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.38045418 0.10440986]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 30\n",
      "--------------------\n",
      "x: [1.22551096 0.50892542], y: 1\n",
      "v: 0.6871153332234101\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.38045418 0.10440986]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 31\n",
      "--------------------\n",
      "x: [ 0.32408433 -0.58385922], y: 1\n",
      "v: 0.2300663212521686\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.38045418 0.10440986]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 32\n",
      "--------------------\n",
      "x: [ 1.5259865  -0.58385922], y: 1\n",
      "v: 0.6873350224749323\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.38045418 0.10440986]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 33\n",
      "--------------------\n",
      "x: [-0.72758006  0.94603927], y: 0\n",
      "v: -0.010307311841913247\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.38045418 0.10440986]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 34\n",
      "--------------------\n",
      "x: [2.27717535 0.29036849], y: 1\n",
      "v: 1.0644059495824951\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.38045418 0.10440986]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 35\n",
      "--------------------\n",
      "x: [ 0.4743221  -0.80241615], y: 1\n",
      "v: 0.2644054116941802\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.38045418 0.10440986]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 36\n",
      "--------------------\n",
      "x: [-0.57734229  1.60171005], y: 0\n",
      "v: 0.11530976744343338\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.3862276  0.08839276]\n",
      "Bias: 0.15772773900711423\n",
      "\n",
      "Iteration 37\n",
      "--------------------\n",
      "x: [ 1.07527319 -1.89520078], y: 1\n",
      "v: 0.4055059045170974\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.3862276  0.08839276]\n",
      "Bias: 0.15772773900711423\n",
      "\n",
      "Iteration 38\n",
      "--------------------\n",
      "x: [ 0.62455988 -0.14674537], y: 1\n",
      "v: 0.38597877453795293\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.3862276  0.08839276]\n",
      "Bias: 0.15772773900711423\n",
      "\n",
      "Iteration 39\n",
      "--------------------\n",
      "x: [ 1.07527319 -0.36530229], y: 1\n",
      "v: 0.5407378471155492\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.3862276  0.08839276]\n",
      "Bias: 0.15772773900711423\n",
      "\n",
      "Iteration 40\n",
      "--------------------\n",
      "x: [ 0.32408433 -1.02097307], y: 1\n",
      "v: 0.1926514310872541\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.3862276  0.08839276]\n",
      "Bias: 0.15772773900711423\n",
      "\n",
      "Iteration 41\n",
      "--------------------\n",
      "x: [-1.47876892 -1.67664385], y: 0\n",
      "v: -0.5616168018215831\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.3862276  0.08839276]\n",
      "Bias: 0.15772773900711423\n",
      "\n",
      "Iteration 42\n",
      "--------------------\n",
      "x: [ 1.67622427 -0.36530229], y: 1\n",
      "v: 0.7728417424758586\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.3862276  0.08839276]\n",
      "Bias: 0.15772773900711423\n",
      "\n",
      "Iteration 43\n",
      "--------------------\n",
      "x: [-0.27686675  1.38315312], y: 0\n",
      "v: 0.1730548740959399\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.38899627 0.07456122]\n",
      "Bias: 0.14772773900711422\n",
      "\n",
      "Iteration 44\n",
      "--------------------\n",
      "x: [-0.57734229 -1.23953   ], y: 1\n",
      "v: -0.16927713179911136\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.38322285 0.06216592]\n",
      "Bias: 0.15772773900711423\n",
      "\n",
      "Iteration 45\n",
      "--------------------\n",
      "x: [-0.87781783 -1.45808693], y: 1\n",
      "v: -0.26931542983801615\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.37444467 0.04758505]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 46\n",
      "--------------------\n",
      "x: [ 1.67622427 -0.14674537], y: 1\n",
      "v: 0.7883980928218949\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.37444467 0.04758505]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 47\n",
      "--------------------\n",
      "x: [-0.42710452  2.25738083], y: 0\n",
      "v: 0.11521831930877934\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.37871571 0.02501125]\n",
      "Bias: 0.15772773900711423\n",
      "\n",
      "Iteration 48\n",
      "--------------------\n",
      "x: [ 0.17384656 -0.80241615], y: 1\n",
      "v: 0.20349673627049528\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.37871571 0.02501125]\n",
      "Bias: 0.15772773900711423\n",
      "\n",
      "Iteration 49\n",
      "--------------------\n",
      "x: [-0.42710452 -0.80241615], y: 1\n",
      "v: -0.02409288153544703\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [0.37444467 0.01698708]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 50\n",
      "--------------------\n",
      "x: [ 0.92503542 -0.36530229], y: 1\n",
      "v: 0.5078968973016453\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.37444467 0.01698708]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 51\n",
      "--------------------\n",
      "x: [-0.12662898 -0.14674537], y: 1\n",
      "v: 0.11781941737256628\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.37444467 0.01698708]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 52\n",
      "--------------------\n",
      "x: [-0.87781783  1.1645962 ], y: 0\n",
      "v: -0.14118337294894479\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.37444467 0.01698708]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 53\n",
      "--------------------\n",
      "x: [-1.17829337  0.29036849], y: 0\n",
      "v: -0.2685454174917884\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.37444467 0.01698708]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 54\n",
      "--------------------\n",
      "x: [-0.87781783 -0.14674537], y: 0\n",
      "v: -0.16345924333729395\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.37444467 0.01698708]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 55\n",
      "--------------------\n",
      "x: [2.12693758 0.07181156], y: 1\n",
      "v: 0.9653680445668719\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.37444467 0.01698708]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 56\n",
      "--------------------\n",
      "x: [-0.57734229  1.38315312], y: 0\n",
      "v: -0.0249592636002762\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.37444467 0.01698708]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 57\n",
      "--------------------\n",
      "x: [-1.17829337  0.29036849], y: 0\n",
      "v: -0.2685454174917884\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.37444467 0.01698708]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 58\n",
      "--------------------\n",
      "x: [-0.57734229  0.50892542], y: 0\n",
      "v: -0.039809843859175686\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [0.37444467 0.01698708]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 59\n",
      "--------------------\n",
      "x: [ 1.22551096 -1.67664385], y: 1\n",
      "v: 0.5981324911972403\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.37444467 0.01698708]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 60\n",
      "--------------------\n",
      "x: [ 0.92503542 -0.14674537], y: 1\n",
      "v: 0.5116095423663702\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.37444467 0.01698708]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 61\n",
      "--------------------\n",
      "x: [ 1.37574873 -0.36530229], y: 1\n",
      "v: 0.6766640937275619\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.37444467 0.01698708]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 62\n",
      "--------------------\n",
      "x: [1.82646204 0.07181156], y: 1\n",
      "v: 0.8528565802829277\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.37444467 0.01698708]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 63\n",
      "--------------------\n",
      "x: [ 0.4743221  -0.80241615], y: 1\n",
      "v: 0.33170441074627954\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [0.37444467 0.01698708]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 64\n",
      "--------------------\n",
      "x: [-0.12662898  0.72748234], y: 0\n",
      "v: 0.13266999763146572\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [0.37571096 0.00971226]\n",
      "Bias: 0.15772773900711423\n",
      "\n",
      "Iteration 65\n",
      "--------------------\n",
      "x: [-0.72758006 -2.33231464], y: 1\n",
      "v: -0.13828411188260847\n",
      "y_out: 0\n",
      "Error: 1\n",
      "Weights: [ 0.36843516 -0.01361088]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 66\n",
      "--------------------\n",
      "x: [ 0.92503542 -0.58385922], y: 1\n",
      "v: 0.5164901486337259\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [ 0.36843516 -0.01361088]\n",
      "Bias: 0.16772773900711424\n",
      "\n",
      "Iteration 67\n",
      "--------------------\n",
      "x: [0.4743221  2.03882391], y: 0\n",
      "v: 0.3147344805034711\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [ 0.36369194 -0.03399912]\n",
      "Bias: 0.15772773900711423\n",
      "\n",
      "Iteration 68\n",
      "--------------------\n",
      "x: [ 0.4743221  -1.02097307], y: 1\n",
      "v: 0.3649470536109357\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [ 0.36369194 -0.03399912]\n",
      "Bias: 0.15772773900711423\n",
      "\n",
      "Iteration 69\n",
      "--------------------\n",
      "x: [1.37574873 0.29036849], y: 1\n",
      "v: 0.6482041832852494\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [ 0.36369194 -0.03399912]\n",
      "Bias: 0.15772773900711423\n",
      "--------------------\n",
      "\n",
      "Epoch error: 10.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 2\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [-1.0280556   0.72748234], y: 0\n",
      "v: -0.2409015556274175\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [ 0.36369194 -0.03399912]\n",
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      "\n",
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      "\n",
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      "Error: 0\n",
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      "Iteration 62\n",
      "--------------------\n",
      "x: [1.82646204 0.07181156], y: 1\n",
      "v: 0.7878777389564786\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [ 0.34866816 -0.09300949]\n",
      "Bias: 0.15772773900711423\n",
      "\n",
      "Iteration 63\n",
      "--------------------\n",
      "x: [ 0.4743221  -0.80241615], y: 1\n",
      "v: 0.39774107392457164\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [ 0.34866816 -0.09300949]\n",
      "Bias: 0.15772773900711423\n",
      "\n",
      "Iteration 64\n",
      "--------------------\n",
      "x: [-0.12662898  0.72748234], y: 0\n",
      "v: 0.0459134815123175\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [ 0.34993445 -0.10028432]\n",
      "Bias: 0.14772773900711422\n",
      "\n",
      "Iteration 65\n",
      "--------------------\n",
      "x: [-0.72758006 -2.33231464], y: 1\n",
      "v: 0.12701699353136597\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [ 0.34993445 -0.10028432]\n",
      "Bias: 0.14772773900711422\n",
      "\n",
      "Iteration 66\n",
      "--------------------\n",
      "x: [ 0.92503542 -0.58385922], y: 1\n",
      "v: 0.5299814209815\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [ 0.34993445 -0.10028432]\n",
      "Bias: 0.14772773900711422\n",
      "\n",
      "Iteration 67\n",
      "--------------------\n",
      "x: [0.4743221  2.03882391], y: 0\n",
      "v: 0.10924731885839017\n",
      "y_out: 1\n",
      "Error: -1\n",
      "Weights: [ 0.34519123 -0.12067256]\n",
      "Bias: 0.1377277390071142\n",
      "\n",
      "Iteration 68\n",
      "--------------------\n",
      "x: [ 0.4743221  -1.02097307], y: 1\n",
      "v: 0.4246629997538467\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [ 0.34519123 -0.12067256]\n",
      "Bias: 0.1377277390071142\n",
      "\n",
      "Iteration 69\n",
      "--------------------\n",
      "x: [1.37574873 0.29036849], y: 1\n",
      "v: 0.5775846235824591\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [ 0.34519123 -0.12067256]\n",
      "Bias: 0.1377277390071142\n",
      "--------------------\n",
      "\n",
      "Epoch error: 3.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 3\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [-1.0280556   0.72748234], y: 0\n",
      "v: -0.3049351907071842\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [ 0.34519123 -0.12067256]\n",
      "Bias: 0.1377277390071142\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [-1.32853114  0.29036849], y: 0\n",
      "v: -0.35590906531672184\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [ 0.34519123 -0.12067256]\n",
      "Bias: 0.1377277390071142\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [0.77479765 0.72748234], y: 1\n",
      "v: 0.3173939352256029\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [ 0.34519123 -0.12067256]\n",
      "Bias: 0.1377277390071142\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [-0.42710452  0.72748234], y: 0\n",
      "v: -0.09749214872958845\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [ 0.34519123 -0.12067256]\n",
      "Bias: 0.1377277390071142\n",
      "\n",
      "Iteration 4\n",
      "--------------------\n",
      "x: [-0.72758006 -1.67664385], y: 1\n",
      "v: 0.08889838536754616\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [ 0.34519123 -0.12067256]\n",
      "Bias: 0.1377277390071142\n",
      "\n",
      "Iteration 5\n",
      "--------------------\n",
      "x: [-0.12662898  1.82026698], y: 0\n",
      "v: -0.1256397436889416\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [ 0.34519123 -0.12067256]\n",
      "Bias: 0.1377277390071142\n",
      "\n",
      "Iteration 6\n",
      "--------------------\n",
      "x: [ 0.17384656 -0.14674537], y: 1\n",
      "v: 0.21544618600652787\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [ 0.34519123 -0.12067256]\n",
      "Bias: 0.1377277390071142\n",
      "\n",
      "Iteration 7\n",
      "--------------------\n",
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      "Iteration 69\n",
      "--------------------\n",
      "x: [1.37574873 0.29036849], y: 1\n",
      "v: 0.542067243490947\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [ 0.34021192 -0.15052119]\n",
      "Bias: 0.11772773900711421\n",
      "--------------------\n",
      "\n",
      "Epoch error: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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SpTHv62JKbEREEinC6uiWBffdB42NMHUqTJliIFbxhPzcfCrvqGRwZuslGgKZASrvqIzbOjbjx4/Hsqw2j6eeeiou/TXTrSgRkUS5VHV0n8+ujn7rrQSf87Njh32VpqQk4ZGKx+Tn5nPrNbdSVVNF/cl6sjKyyMvJ88wU74u56orN6tWrGTVqFBkZGQwcOJDbbruNd99913RYIiKRibA6+pkX/5PCQntTcTEMHZqY8MTb/Cl+xg8Zz/S/ns74IeM9mdSAyxKbV155hdmzZ/P666/z4osv8pe//IWvfe1rnD592nRoIiKXFmHV89Wb+lFba8+Aai6hICKRcdWtqO3bt7d6/tRTTzFw4ED++Mc/Mm7cOENRiYhEKILiTvsZyprnvwjYt6DS0+MdlIi3uCqx+bQTJ04A0K9fv3Z/fv78ec6fP9/yvKGhISFxiYi0q7k6el1d++NsfD4K054gdC6FiRPh9tsTH6I4k9Xe58VjYnWMrroVdbFwOMz8+fMZO3YsX/rSl9rdZ/Xq1fTu3bvlkZ2dneAoRUQu0lwdHS5UQ2/m87HN+gbbzt1Cjx7w+ONtd5Hk0/P/ioKdOXPGcCTx13yMPbtZCM1nuTQN/MEPfsC///u/s3PnTgKBQLv7tHfFJjs7mxMnTpCZmZmoUEVEWgsG7dlRFw0kPhcYxpca/8SBw5/l/vthzRqD8Ymj1NfXc/z4cQYOHEh6ejo+j2W8lmVx5swZPv74Y/r06UNWO7dsGxoa6N27d0Tf365MbObMmcOvf/1rXn31Va688sqIX9eVEyMiElefWnl4ZdU4lixNISsL3n0XMjJMByhOYVkWhw8f5vjx46ZDias+ffpwxRVXtJu4deX721VjbCzLYu7cufzqV7/i5Zdf7lJSIyLiKBdVR6+pgZX/twDfunVKaqQ1n89HVlYWAwcO5C9/+YvpcOKiZ8+e+NspKRINVyU2s2fPZuvWrfz6178mIyODw4cPA9C7d2969eplODoRkegsWABnz9pji6dPNx2NOJXf74/Zl7+XuepWVEf3Fbds2cKMGTMu+XrdihIRp9mxAyZOhJQU+NOfYPhw0xGJOI+nb0WJiHhFKARz59rt2bOV1IjEgqsSGxFJQqEQbNwIBw7YtQVmzYLUVNNRxUR5OezbBwMGwEMPmY5GxBuU2IiIcxUXw2OP2TOImi1cCEVFrp8PXV8Py5fb7UcegT59TEYj4h1KbETEmYqLYe3attubmi5sd3FyU1wMp07B6NEQwRBBEYmQqwYPd5cGD4u4RChkF0m6+ErNp/n9cOaMK29LVVXBuHH2ysK7dsGoUaYjEnG2rnx/u7akgoh42MaNnSc1YP9848bExBNDjY0wZ47dnjlTSY1IrCmxERHnOXAgtvs5yKZN8Pbb0LcvrFplOhoR71FiIyLOM3RobPdziCNHYMkSu71iBfTvbzYeES9SYiMizjNrlj2GpjN+v72fiyxeDMePw4gRcO+9pqMR8SYlNiLiPKmp9pTuzhQVuWrg8O7dsHmz3a6ouHTeJiLR0XRvEXGm5qncn17Hxu933To24bA9YNiy4M47YexY0xGJeJeme4uIs3lg5eHNm+0ZUBkZ8O67kJVlOiIRd/FsrSgRSUKpqTB/vukoonbsGCxaZLeXL1dSIxJvGmMjIhJHS5fC0aOQm3uh4KWIxI8SGxGRONm798IaguXl0LOn2XhEkoFuRYmIJ8axOI1l2QOGw2GYNg0mTDAdkUhyUGIjkuw8XEHbpK1bYedOu+TVunWmoxFJHkpsRJKZxytom9LQYOeGAA88ADk5ZuMRSSaa7i2SrDxeQduk+++3r9IMGwbvvANpaaYjEnE3VfcWkUvzcAVtk6qrobTUbpeVKakRSTQlNiLJysMVtE2xLLjvPmhshKlTYcoU0xGJJB8lNiLJyqMVtE0KBmHHDvsqTUmJ6WhEkpMSG5Fk5dEK2qacOQOFhXa7uFj5oIgpSmxEkpUHK2ibtHo11NbaM6CaSyiISOJpurdIMvNQBW2T9u+/cKpKSuzJZiJihqZ7i4hWHu6mqVNh2zaYOBF+9zvw+UxHJOItqu4tIl3j8graJm3bZj969IDHH1dSI2KaxtiIiETp3LkL+WBhIVx7rdFwRAQlNiIiUVu/3r57l5UFDz5oOhoRASU2IiJRqamBlSvt9rp1kJFhNh4RsWmMjYjYM6KqqqC+3r78kJd36TVunNxPAixYAGfP2ocwfbrpaESkmRIbkWQXDMK8efDhhxe2BQJ2oaP8fPf1kwA7dkBlJaSkQEWFBgyLOIluRYkks2AQCgpaJxsAdXX29mDQXf0kQCgEc+fa7dmzYfhws/GISGtax0YkWTU1wZAhbZONZj6ffUXl4MHu3S5KVD8Jsn49LFwIAwbAe+9Bnz6mIxLxvq58f+uKjUiyqqrqONkAu1R1ba29nxv6SYD6eli+3G4/8oiSGhEnUmIjkqzq62O7n+l+EqC4GE6dgtGjYcYM09GISHuU2Igkq6ys2O5nup84q6qCp5+275xVVNgDh0XEefS/pkiyysuzx7Z0NKXH54PsbHs/N/QTR42NMGeO3Z45E0aNMhuPiHRMiY1IsvL77anW0DbpaH5eWtr9Ab2J6ieONm2Ct9+Gvn1h1SrT0YhIZ5TYiCSz/Hx7QZbBg1tvDwTs7bFaXyZR/cTBkSOwZIndXrEC+vc3G4+IdE7TvUVEKw934p574Cc/gREj4M03HR+uiCd15ftbKw+LiP1tPX68d/qJkd27YfNmu11RoaRGxA10K0pEpB3hsD1g2LLgzjth7FjTEYlIJJTYiIi0Y8sWeOMNu2r3o4+ajkZEIqVbUSJO5cLxKF5x7BgsWmS3ly93/BI7InIRJTYiTuShSthutHQpHD0KubkXCl6KiDvoVpSI03ioErYb7d0LGzfa7fJy6NnTbDwi0jVKbEScpKnJvlLT3ioMzdvmz7f3k5izLHvAcDgM06bBhAmmIxKRrlJiI+IkHqqE7UZbt8LOnZCeDuvWmY5GRKKhxEbESTxUCdttGhpg4UK7/cADkJNjNh4RiY4SGxEn8UglbDd6+GE4fBiGDYMFC0xHIyLRUmIj4iQeqITtRtXVdh1OsCeepaUZDUdEukGJjYiTeKAStttYFtx3HzQ2wtSpMGWK6YhEpDuU2Ig4jYsrYbtRMAg7dthXaUpKTEcjIt2lBfpEnCg/H269VSsPx9mZM1BYaLeLi2HoULPxiEj3KbERcSqXVcJ2o9Wr7dnzOTkXSiiIiLu56lbUq6++ytSpUxk0aBA+n4/nnnvOdEgi4lL798OaNXa7pMReu0ZE3M9Vic3p06e57rrr2LBhg+lQRMTlCgshFIKJE+H2201HIyKx4qpbUZMnT2by5MmmwxBxrmgrgkfzOqdXH+8kvm3b7EePHvD44x3PrhcR93FVYtNV58+f5/z58y3PGxoaDEYjEmfRVgSP5nVOrz7eSXznpuQzf769qbAQrr3WSIQiEieuuhXVVatXr6Z3794tj+zsbNMhicRHtBXBo3md06uPXyK+9d/9MwcO2BdxHnzQTIgiEj8+y2qvjLDz+Xw+fvWrX3Hbbbd1uE97V2yys7M5ceIEmZmZCYhSJAGammDIkI6LZ/p89tWKgwdb3yqK5nXR9pUol4ivhhyu9e3jrNWLn/0MvvWtxIYnItFpaGigd+/eEX1/e/qKTVpaGpmZma0eIp4TbUXwaF7n9Orjl4hvAes4a/Uib/hxpk9PYFwikjCeTmxEkkK0FcGjeZ3Tq4930u8OJlDJNFJoouJbf9CAYRGPctXg4VOnTrF///6W5wcPHmTPnj3069ePnJwcg5GJGBRtRfBoXuf06uMd9BuiJ3MpB2A2Gxg+ZngioxKRBHLVGJuXX36Zm2++uc32u+66i6eeeuqSr+/KPToR12geV1JXZ98K+rRLjbHpyuui7StROohvPUUsZD0D+Jj3Bv8tfT7Y66yp6SLSKc+OsRk/fjyWZbV5RJLUiHhWtBXBo3md06uPtxNfPVewnOUAPMI/0ufxh5TUiHiYqxIbEelAtBXBo3md06uPfyq+YtZwigxGp/6JGf/6DfPxiUhcuepWVHfpVpR4nlYevqCpiaoNbzNu3vX4fBa7Xgsz6kYHxSciEevK97cSGxHxpMZGGDkS3n4b7rkHfvQj0xGJSLQ8O8ZGRCRSmzbZSU3fvrBqleloRCRRlNiIiOccOQJLltjtFSugf3+z8YhI4iixERHPWbwYjh+HESPg3ntNRyMiieSqBfpEYiqRg19DIdi4EQ4cgKFDYdYsSE2NfT+JPKZE9dXFfnbvhs2b7XZFhbPGM5vQFG6iqqaK+pP1ZGVkkZeThz8lyU+KeJuVRE6cOGEB1okTJ0yHIqY9+6xlBQKWZS/jZj8CAXt7rN1/v2X5/a378vvt7bGUyGNKVF9d7KepybJGjbJ3u/PO2IbiRs/+97NW4LGAxXJaHoHHAtaz/x2Hz4RIHHXl+1uzoiT5BINQUNB25dzmBeZiuRZLcTGsXdvxz++/H9as6X4/iTymRPUVRT+bN8PMmZCRAe++a66ygxMEq4MUPFOARevz58M+f5V3VJKfqzV9xB003bsDSmykZcn9jipAx7IkQCgE6el2nx3x++HMme7dlkrkMSWqryj6OXYMrr4ajh6F9euhqCj67t2uKdzEkLIhfNjQ/vnz4SOQGeDgvIO6LSWuoOneIh2pqur4yxLsqwO1tfZ+3bVxY+dJDdg/37ixe/0k8pgS1VcU/Sxdaic1ubkwd273une7qpqqDpMaAAuL2oZaqmpi8JkQcRglNpJc6utju19nDhyI7X4dSeQxJaqvLvazd++F/LC8HHr27F73bld/MrLzF+l+Im6ixEaSS6SDLmIxOGPo0Nju15FEHlOi+upCP5YFc+ZAOAzTpsGECd3r2guyMiI7f5HuJ+ImGmMjyaV57EZdXdtBqeDuMTaJOKZE9dWFfn72Cz/f/rZ9qqurIScn+m69onmMTV1DXZvBw6AxNuI+GmMj0hG/H8rK7Hbz7Jpmzc9LS2Oz+Elq6qVHsBYVdX89m0QeU6L6irCfhtN+Fi60nz7wgJKaZv4UP2WT7PPXPAuqWfPz0kmlSmrEk5TYSPLJz7enCg8e3Hp7IBDbadFgT+W+//62X/R+f+ymekNijylRfUXQz8MPw+HDMGwYLFgQm269Ij83n8o7Khmc2fr8BTIDmuotnqZbUZK8tPKwO/rqoJ/qahg+3K7i/fzzMGVK7Lv2Aq08LF6gdWw6oMRGxBssC772NdixA6ZOhd/8xnREIhJPGmMjIp4WDNpJTVoalJSYjkZEnESJjYi4ypkzUFhot4uLuz9bXkS8RdW9Rbwk2nEviRyb002rV9uLDufkwKJFpqOR7tIYIIk1JTYiXhEMwrx5rUsRBAL2tOnOZipF+zoD9u+/MJGspMReu0bcK1gdZN72ea3KPwQyA5RNKtOsLYmabkWJeEFzJexP11eqq7O3B4OxfZ0hhYX2BLOJE+H2201HI93RXH380zWt6hrqKHimgGC1sz574h6aFSXidtFW3E5kVfAY2LbNngHVowf813/Btdeajkiiperj0lWaFSWSTKKtuJ3IquDddO4czJ9vtwsLldS4naqPSzwpsRFxu2grbieyKng3rV9vr22YlQUPPmg6GukuVR+XeFJiI+J20VbcTmRV8G6oqYGVK+32unWQkWE0HIkBVR+XeFJiI+J2eXn2WJhPF4ts5vNBdra9Xyxel2ALFsDZs3YY06cbDUViJC8nj0BmoE2BzmY+fGRnZpOXY/azJ+6kxEbE7aKtuJ3IquBR2rHDrneZkgIVFR3nYOIuqj4u8aTERsQLoq24nciq4F0UCsHcuXZ79my74KV4h6qPS7xoureIl3ho5eH162HhQhgwAN57D/r0MRqOxIlWHpZIqLp3B5TYiLhDfT1cfTWcOgWbN8N3v2s6IhExSevYiIirFRfbSc3o0TBjhuloRMRNlNiIiKNUVcHTT9sDhSsq7IHDIiKR0p8MEXGMxkaYM8duz5wJo0aZjUdE3EfVvSV5RTNgNhSCjRvtZXCHDoVZsyA1NTHxRsKBg4C7YtMmePtt6NsXVq0yHY04lQYcd4/Xz58SG0lOwSDMm9e6VlIgYK/r0tEU5+JieOwxO3lotnAhFBXBmjXxjTcS0RyTgxw5AkuW2O0VK6B/f7PxiDMFq4PM2z6vVa2pQGaAskllmiIegWQ4f5oVJcknGISCArvI48WaV39rb/2W4mJYu7bj97z/frPJTTTH5DD33AM/+QmMGAFvvumqC02SIMHqIAXPFGDR+nPevKif1r/pnJvPn6Z7d0CJjdDUBEOGdFzV2uezr3IcPHjhmzUUgvT01ldqPs3vhzNnzNyWiuaYHGb3brjxRjsv27kTxo41HZE4TVO4iSFlQzqsCu7DRyAzwMF5Bz11WyVW3H7+NN1bpCNVVR0nAGB/s9bW2vs127ix86QG7J9v3BibGLsqmmNykHDYHjBsWXDnnUpqpH1VNVUdfikDWFjUNtRSVePMz7lpyXT+lNhIcqmv7/p+Bw5E9ppI94u1aI7JQbZsgTfesKt2P/qo6WjEqepPRvb5jXS/ZJNM50+JjSSXrKyu7zd0aGSviXS/WIvmmBzi2DFYtMhuL1/uyBDFIbIyIvtwRLpfskmm86cxNpJcmsej1NW1HWgL7h5j05Vjcoi5c+1F+HJzYe9e6NnTdETiVM1jROoa6toMfgXnjxExze3nT2NsRDri99vTn+HCjKFmzc9LS1snAKmp9pTuzhQVmVvPJppjcoC9ey8MSyovV1IjnfOn+CmbZH/Om2fxNGt+Xjqp1JFfyk6QTOdPiY0kn/x8e/rz4MGttwcCHU+LXrPGntL96eTA7zc/1RuiOyaDLMseMBwOw7RpMGGC6YjEDfJz86m8o5LBma0/54HMgKOnKjtFspw/3YqS5KWVh4352c/g29+27/BVV0NOjumIxE28vnJuvLnx/Gkdmw4osRExr6EBrrkGDh+GlSth8WLTEYmI02mMjYg41sMP20nNsGGwYIHpaETEa5TYiEjCVFfb45jBHu+clmY0HBHxIBXBlOTl9PEoTo+viywL7rsPGhth6lSYMsV0RCLxEWoMsfHNjRz45ABD+w1l1g2zSO0Rn7F4bhwvE29KbCQ5Ob0SttPji0IwCDt22FdpSkpMRyMSH8UvFvPYHx6jybqw7tXCFxZSdFMRaybGdvZkMlTqjoZuRUnyaa6E/en6SnV19vZg0ExczZweXxTOnIHCQrtdXGxukWaReCp+sZi1r61tldQANFlNrH1tLcUvFsesr+ZK3Z+u/1TXUEfBMwUEq933dyJWNCtKkovTK2E7Pb4oPfggrFhhT+uurraneYt4SagxRPqq9DZJzcX8Pj9nFp/p9m0pt1fqjoZmRYl0xOmVsJ0eXxT277+wfmFJiZIa8aaNb27sNKkB+8rNxjc3druvZKrUHQ0lNpJcnF4J2+nxRaGw0F7XcOJEuP1209GIxMeBTw7EdL/OJFOl7mjENLHZtWtXLN+uQxs2bGDIkCFcdtlljBkzht27dyekX/EAp1fCdnp8XbRtm/3o0QMef7xtKSsRrxjaL7KBY5Hu15lkqtQdjZgmNtOmTYvl27Xrl7/8JUVFRSxbtoy33nqL6667jq9//et8/PHHce9bPCAvzx6j0tE3rM8H2dn2fiY4Pb4uOHcO5s+324WFcO21RsMRiatZN8zC7+t8PIvf52fWDbO63VdeTh6BzECbYpbNfPjIzswmL8f5fyfiocvTve+44452t1uWxSeffNLtgC7lscce45577uHuu+8G4IknnuD555/nySefZNGiRXHvX1yuuRJ2QYGdJFw8dt4JlbCdHl8XrF9vl9TKyrIHD4t4WWqPVIpuKmLta2s73KfopqKYrGfTXKm74JkCfPiwuPB3wmuVuqPR5VlR/fr141/+5V/47Gc/22q7ZVl885vf5KOPPoppgBcLhUKkp6dTWVnJbbfd1rL9rrvu4vjx4/z6179utf/58+c5f/58y/OGhgays7M1K0raXycmO9tOGpywTozT47uEmhr7Cs3Zs3bBy299y3REIonR3jo2fp8/YevYZGdmUzqp1HPr2HRlVlSXr9iMHz+ejIwMxo0b1+Znw4cP7+rbdcnRo0dpamri8ssvb7X98ssvZ9++fW32X716Nf/0T/8U15jEpfLz4dZbnbuyr9Pju4QFC+ykJi8Ppk83HY1I4qyZuIYVN69IyMrD+bn53HrNrVp5+FMivmJz8uRJMjIy4h1Ppw4dOsTgwYN57bXXuOmmm1q2FxcX88orr7QZvKwrNiKJt2OHPQMqJQX+9CeI8793RCQJxGUdm7y8PA4fPtzt4Lqjf//++P3+Nre7PvroI6644oo2+6elpZGZmdnqISLxEwrB3Ll2e/ZsJTUikngRJzbXX389Y8aMaXPLZ8+ePUxJUDW71NRURo4cyUsvvdSyLRwO89JLL7W6giMiZpSXw759MGAAPPSQ6WhEJBlFPMZmy5YtLFu2jL/5m7/hueeeY+DAgSxZsoRnn302YYkNQFFREXfddRc33HADo0ePprS0lNOnT7fMkhIHibY6tceqWkctmvNg8NzV18Py5Xb7kUegT5/YvG8iqxdH05fT43NyP26gc+FCVhetXLnSuuyyy6yePXtakyZNsnbt2tXVt+i28vJyKycnx0pNTbVGjx5tvf766xG97sSJExZgnThxIs4RivXss5YVCFiWPWHZfgQC9vZ4vM5rojkPhs/dt79tdzl6tGU1NcXmPZ/972etwGMBi+W0PAKPBaxn/zv2xxRNX06Pz8n9uIHOhXN05fs74sHDH330EatWreLHP/4xubm57Nu3jyeffJJvfvOb8c28YkhFMBOkuTr1pz9azeuwVFa2P2U52td5TTTnwfC5q6qCcePs7nbtglGjuv+ezdWLL16jAy6s01F5R2XMprRG05fT43NyP26gc+EsXfn+jjixSU9P55prrmHFihV84xvfYPv27Xzzm99kyZIl3H///TEJPN6U2CRAtNWpPVrVusuiOQ+Gz11jI4wcCW+/DffcAz/6UfffM5HVi6Ppy+nxObkfN9C5cJ64zIp68skn+dOf/sQ3vvENACZNmsTvf/97SkpKmD17dvciFu+Itjq1B6taRyWa82D43G3aZCc1ffvCqlWxec9EVi+Opi+nx+fkftxA58LdIk5s/v7v/77Nti9/+cu89tpr/Md//EdMgxIXi7Y6tQerWkclmvNg8NwdOQJLltjtFSugf//YvG8iqxdH05fT43NyP26gc+Fu3S6COWTIEF577bVYxCJeEG11ao9VtY5aNOfB4LlbvBiOH4cRI+Dee2P3vomsXhxNX06Pz8n9uIHOhbvFpLp33759Y/E24gXRVqf2UFXrbonmPBg6d7t3w+bNdruiIrbDdxJZvTiavpwen5P7cQOdC3eLSWIj0qK5OjW0/aLtrDp1tK/zmmjOg4FzFw7DnDn28J0774SxY2P21sCF6sVAmy+XWFcvjqYvp8fn5H7cQOfC3ZTYSOzl59vTiwcPbr09EOh82nG0r/OaaM5Dgs/dli3wxhuQkQGPPhrTt26Rn5tP5R2VDM5sfUyBzEDMp9pG05fT43NyP26gc+FeEU/39gJN904wrTzcPQ5defjYMbj6ajh6FNavh6KimL59G05f2dfp8Tm5HzfQuXCGuKxj4wVKbES6b+5ce0xNbi7s3Qs9e5qOSES8Li7r2IiI7N0LGzfa7fJyJTUi4jxKbEQkIpZlDxgOh2HaNJgwwXREIiJtKbERkYhs3Qo7d0J6OqxbZzoaEZH29TAdgEgbGjzsOA0NsHCh3X7gAcjJMRtPvHhxoGgiB0R7cXCzFz8TXqfERpwlGIR581rXPgoE7HVakmW6twM9/DAcPgzDhsGCBaajiY9gdZB52+e1qhEUyAxQNqnMtVN7ozmmaM9Dos5fIn9PXvxMJAPNihLnCAahoMAezHGx5kXmkmktGweprobhw+0q3s8/D1OmmI4o9oLVQQqeKcCi9WeveTE2N65bEs0xRXseEnX+Evl78uJnws003bsDSmwcrKkJhgzpuEq1z2dfuTl4ULelEsiy4Gtfgx07YOpU+M1vTEcUe03hJoaUDemwmrMPH4HMAAfnHXTNLYhojina85Co85fI35MXPxNup+ne4j5VVR0nNWB/w9bW2vtJwgSDdlKTlgYlJaajiY+qmqoOv8AALCxqG2qpqnHPZy+aY4r2PCTq/CXy9+TFz0QyUWIjzlBfH9v9pNvOnIHCQrtdXAxDh5qNJ17qT0b2mYp0PyeI5piiPQ+JOn+J/D158TORTJTYiDNkZcV2P+m21avti2Q5ObBokelo4icrI7LPVKT7OUE0xxTteUjU+Uvk78mLn4lkosRGnCEvzx5D8+nq1M18PsjOtveTuNu/H9assdslJfbaNV6Vl5NHIDPQpopzMx8+sjOzyctxz2cvmmOK9jwk6vwl8vfkxc9EMlFiI87g99tTuqFtctP8vLRUA4cTpLAQQiGYOBFuv910NPHlT/FTNsn+7H36i6z5eemkUlcNEo3mmKI9D4k6f4n8PXnxM5FMlNiIc+Tn21O6Bw9uvT0Q0FTvBNq2zX706AGPP97xRTQvyc/Np/KOSgZntv7sBTIDrp3WG80xRXseEnX+Evl78uJnIllourc4j1YeNubcOfjSl+DAAbj//gu3o5KFF1eZ1crD7ulLOqZ1bDqgxEakcytXwpIldj757ruQkWE6IhERrWMjIlGoqbETG7CLXCqpERE3UmIjIoBdA+rsWfvO3/TppqMREYmOimCKCDt22OOzU1KgoiI5BgzHihfHYHjxmMC7xyWtKbERSXKhEMyda7dnz7YLXkpkvFj92YvHBN49LmlLg4dFktz69bBwIQwYAO+9B336mI7IHbxY/dmLxwTePa5kosHDIhKR+npYvtxuP/KIkppINYWbmLd9XpsvSqBl2/zt82kKNyU6tKh58ZjAu8clHVNiI5LEiovh1CkYPRpmzDAdjXt4sfqzF48JvHtc0jElNiJJqqoKnn7aHihcUWEPHJbIeLH6sxePCbx7XNIx/SkTSUKNjTBnjt2eORNGjTIbj9t4sfqzF48JvHtc0jElNiJJaNMmePtt6NsXVq0yHY37eLH6sxePCbx7XNIxJTYiSebIEbtsAsCKFdC/v9l43MiL1Z+9eEzg3eOSjimxEUkyixfD8eMwYgTce6/paNzLi9WfvXhM4N3jkvZpHRuRJLJ7N9x4I1gW7NwJY8eajsj9vLiarRePCbx7XMlA1b07oMRGklk4bCc1b7wBd94JP/2p6YhERCKjBfpEpI0tW+ykJiMDHn3UdDQiIvGhxEYkCRw7BosW2e3lyyFLM1tFxKOU2IgkgaVL4ehRyM29UPBSRMSLVN1bxOP27oWNG+12eTn07Bmb9/XiQMxEHlOoMcTGNzdy4JMDDO03lFk3zCK1R2pc+hJJJkpsRDzMsuwVhsNhmDYNJkyIzfsGq4PM2z6vVQ2eQGaAskllrp06m8hjKn6xmMf+8BhN1oXCiwtfWEjRTUWsmbgmpn2JJBvdihLxsK1b7Wnd6emwbl1s3jNYHaTgmYI2hQXrGuooeKaAYHUwNh0lUCKPqfjFYta+trZVUgPQZDWx9rW1FL9YHLO+RJKRpnuLeFRDA1xzDRw+DCtX2gvzdVdTuIkhZUM6rJbsw0cgM8DBeQddc1sqkccUagyRviq9TVJzMb/Pz5nFZ3RbSuQimu4tIjz8sJ3UDBsGCxbE5j2raqo6TAAALCxqG2qpqqmKTYcJkMhj2vjmxk6TGrCv3Gx8c2O3+xJJVkpsRDyouhpKS+12WRmkpcXmfetP1sd0PydI5DEd+ORATPcTkbaU2Ih4jGXBffdBYyNMnQpTpsTuvbMyIlsAJ9L9nCCRxzS039CY7icibSmxEfGYYBB27LCv0pSUxPa983LyCGQG2lRJbubDR3ZmNnk5ebHtOI4SeUyzbpiF39f5OB2/z8+sG2Z1uy+RZKXERsRDzpyBwkK7XVwMQ2P8D39/ip+ySWUAbRKB5uelk0pdM3AYEntMqT1SKbqpqNN9im4q0sBhkW5QYiPiIatXQ20t5ORcKKEQa/m5+VTeUcngzMGttgcyA1TeUenKdWwSeUxrJq7h/q/c3+bKjd/n5/6v3K91bES6SdO9RTxi/374q7+CUAiefRby45xfaOXh7tHKwyKR68r3txIbEY+YOhW2bYOJE+F3vwNf+0NGRERcR+vYiCSZbdvsR48e8PjjSmpEJHkpsRFxuXPnYP58u11YCNdeazQcERGjXFMEc+XKlTz//PPs2bOH1NRUjh8/bjokEUdYvx4OHICsLHjwQdPRJJ9ox+V4cYySiBO4JrEJhUJMmzaNm266ic2bN5sOR8QRamrsOlBgF7nMyDAbT7KJtiK4F6ujiziF6wYPP/XUU8yfPz+qKzYaPCxeM20aVFZCXh688orG1iRSc0Vwi9Z/QpvXvulomni0rxNJZho8/H/Onz9PQ0NDq4eIV+zYYSc1KSlQUaGkJpGawk3M2z6vTXICtGybv30+TeGmmLxORCLn6cRm9erV9O7du+WRnZ1tOiSRmAiFYO5cuz17NgwfbjaeZBNtRXAvVkcXcRqjic2iRYvw+XydPvbt2xf1+//jP/4jJ06caHnU1tbGMHoRc8rLYd8+GDAAHnrIdDTJJ9qK4F6sji7iNEYHDy9YsIAZM2Z0us9VV10V9funpaWRlpYW9etFnKi+HpYvt9uPPAJ9+piMJjlFWxHci9XRRZzGaGIzYMAABgwYYDIEEdcpLoZTp2D0aLjEvwskTporgtc11LU7XsaHj0BmoE1F8GhfJyKRc80Ym5qaGvbs2UNNTQ1NTU3s2bOHPXv2cOrUKdOhiSRMVRU8/bQ9ULiiwh44LIkXbUVwL1ZHF3Ea1/xZXLp0Kddffz3Lli3j1KlTXH/99Vx//fW8+eabpkMTSYjGRpgzx27PnAmjRpmNJ9lFWxHci9XRRZzEdevYdIfWsRE327DBTmz69oX33oP+/U1HJKCVh0USoSvf365ZeVgkmR05AkuW2O0VK5TUOIk/xc/4IeMT9joR6ZxrbkWJJLPFi+H4cRgxAu6913Q0IiLOpcRGxOF274bm8mgVFeDX3QoRkQ7pVpSIg4XD9rgay4I774SxY+PTjxfHe3jxmETk0pTYiDjYli3wxht21e5HH41PH16sNO3FYxKRyOhWlIhDHTsGixbZ7eXLISsOi9E2V5r+dP2iuoY6Cp4pIFgdjH2ncebFYxKRyCmxEXGopUvh6FHIzb1Q8DKWvFhp2ovHJCJdo8RGxIH27oWNG+12eTn07Bn7PrxYadqLxyQiXaPERsRhLMseMBwOw7RpMGFCfPrxYqVpLx6TiHSNEhsRh9m6FXbuhPR0WLcufv14sdK0F49JRLpGiY2IgzQ0wMKFdvuBByAnJ359NVea/nQxxmY+fGRnZruq0rQXj0lEukaJjYiDPPwwHD4Mw4bBggXx7cuLlaa9eEwi0jVKbEQcoroaSkvtdlkZpKXFv08vVpr24jGJSORU3VvEASwLvvY12LEDpk6F3/wmsf17cZVeLx6TSLLqyve3EhsRB3j2WSgosK/S/PnPMHSo6YhERJyjK9/fuhUlYtiZM1BYaLeLi5XUiIh0hxIbEcNWr4baWnsGVHMJBRERiY4SGxGD9u+HNWvsdkmJvXaNiIhET9W9RQwqLIRQCCZOhNtvb/0zDX7tHp0/keSkxEbEkG3b7EePHvD44+C7aNmVYHWQedvntap7FMgMUDapTNOVI6DzJ5K8dCtKxIBz52D+fLtdWAjXXnvhZ8HqIAXPFLQp5ljXUEfBMwUEq4OJC9SFdP5EkpsSGxED1q+HAwcgKwsefPDC9qZwE/O2z8Oi7SoMzdvmb59PU7gpUaG6is6fiCixEUmwmhpYudJur1sHGRkXflZVU9XmSsPFLCxqG2qpqqmKc5TupPMnIkpsRBJswQI4exby8mD69NY/qz9ZH9F7RLpfstH5ExElNiIJtGMHVFZCSgpUVLQeMAyQlZEV0ftEul+y0fkTESU2IgkSCsHcuXZ79mwYPrztPnk5eQQyA20qUzfz4SM7M5u8nLw4RupeOn8iosRGJEHKy2HfPhgwAB56qP19/Cl+yiaVAbT5cm5+XjqpVOuxdEDnT0SU2IgkQH09LF9utx95BPr06Xjf/Nx8Ku+oZHDm4FbbA5kBKu+o1Dosl6DzJ5LcVN1bJAHuvBOefhpGj4Y//MEeY3MpWjm3e3T+RLyjK9/fSmxE4qyqCsaNswcK79oFo0aZjkhExF268v2tW1EicdTYCHPm2O2ZM5XUiIjEmxIbkTjatAnefhv69oVVq0xHIyLifSqCKd7Q1GTf86mvt+sU5OWB3+x4iiNHYMkSu71iBfTvH/8+Na5E3EafWYk1JTbifsEgzJsHH160lH4gAGVlkG9uBszixXD8OIwYAffeG//+VNFa3EafWYkHDR4WdwsGoaAAPv0xbl7St7LSSHKzezfceKMd1s6dMHZsfPtrrmj96eKPzWu3aJqzOI0+s9IVGjwsyaGpyb5S015u3rxt/nx7vwQKh+0Bw5ZlT/OOd1KjitbiNvrMSjwpsRH3qqpqffvp0ywLamvt/RJoyxZ44w27avejj8a/P1W0FrfRZ1biSYmNuFd9hBWaI90vBo4dg0WL7Pby5fY45nhTRWtxG31mJZ6U2Ih7RZo1JCK7+D9Ll8LRo5Cbe6HgZbyporW4jT6zEk9KbMS98vLs2U++9is54/NBdra9XwLs3QsbN9rt8nLo2TMh3aqitbiOPrMST0psxL38fntKN7RNbpqfl5YmZD0by7IHDIfDMG0aTJgQ9y5bqKK1uI0+sxJPSmzE3fLz7Sndg1tXciYQSOhU761b7Wnd6emwbl1CumxFFa3FbfSZlXjROjbiDQZXHm5ogGuugcOHYeVKe2E+U7SKq7iNPrMSCVX37oASG4mH+++3r9IMGwbvvANpaaYjEhHxFi3QJ5Ig1dX2MB6wh/soqRERMUuJjUiULAvuuw8aG2HqVJgyxXREIiKixEYkSsEg7NhhX6UpKTEdjYiIgKp7i0TlzBkoLLTbxcUwdKjZeEQ6ksjBuRoILE6gxEYkCqtX22WocnIulFAQcZpgdZB52+e1qssUyAxQNqks5tOpE9mXSGd0K0qki/bvhzVr7HZJib12jYjTBKuDFDxT0KbYZF1DHQXPFBCsDrqyL5FLUWIj0kWFhRAKwcSJcPvtpqMRaasp3MS87fOwaLuaR/O2+dvn0xRuclVfIpFQYiPSBdu22Y8ePeDxxzsuUyViUlVNVZurJxezsKhtqKWqpspVfYlEQomNSITOnYP58+12YSFce63RcEQ6VH+yPqb7OaUvkUgosRGJ0Pr1cOCAXbHhwQdNRyPSsayMrJju55S+RCKhxEYkAjU1dh0osMsnZGSYjUekM3k5eQQyA20qZzfz4SM7M5u8nDxX9SUSCdckNu+//z7f+973uPLKK+nVqxdDhw5l2bJlhEIh06FJEliwAM6etWtrTp9uOhqRzvlT/JRNKgNok3A0Py+dVBqTNWYS2ZdIJFyT2Ozbt49wOMymTZv485//TElJCU888QSLTZZSlqSwYwdUVkJKClRUaMCwuEN+bj6Vd1QyOHNwq+2BzACVd1TGdG2ZRPYlcimuru69du1afvjDH/I///M/7f78/PnznD9/vuV5Q0MD2dnZqu4tEQuF4LrrYN8+mDvXngkl4iZaeVi8oCvVvV298vCJEyfo169fhz9fvXo1//RP/5TAiMRrysvtpGbAAHjoIdPRiHSdP8XP+CHjPdeXSEdce8Vm//79jBw5knXr1nHPPfe0u4+u2Eh31NfD1VfDqVOweTN897umIxIRSU5duWJjfIzNokWL8Pl8nT727dvX6jV1dXVMmjSJadOmdZjUAKSlpZGZmdnqIRKp4mI7qRk9GmbMMB2NiIhEwvgVmyNHjvC///u/ne5z1VVXkZqaCsChQ4cYP348N954I0899RQpKZHnZl3J+CS5VVXBuHH2QOFdu2DUKNMRiYgkL1eNsRkwYAADBgyIaN+6ujpuvvlmRo4cyZYtW7qU1IhEqrER5syx2zNnKqkREXET44lNpOrq6hg/fjyf//znWbduHUeOHGn52RVXXGEwMvGaTZvg7behb19Ytcp0NCIi0hWuSWxefPFF9u/fz/79+wkEAq1+5tLxz+JAR47AkiV2e8UK6N/fbDwiItI1rrmXM2PGDCzLavchEiuLF8Px4zBiBNx7r+loRESkq1yT2IjE2+7d9rRusFcY9mtdMRER11FiIwKEw/aAYcuCO++EsWNNRyQiItFQYiMCbNkCb7xhV+1+9FHT0YiISLSU2EjSO3YMFi2y28uXQ1aW0XBERKQblNhI0lu6FI4ehdxcu9CliIi4lxIbSWp798LGjXa7vBx69jQbj4iIdI8SG0lalmUPGA6HYdo0mDDBdEQiItJdSmwkaW3dCjt3Qno6rFtnOhoREYkFJTaSlBoaYOFCu/3AA5CTYzYeERGJDdeUVBCJpYcfhsOHYdgwWLDAdDSx0xRuoqqmivqT9WRlZJGXk4c/RSsNikjyUGIjSae6GkpL7XZZGaSlGQ0nZoLVQeZtn8eHDR+2bAtkBiibVEZ+br7ByEREEke3oiSpWBbcdx80NsLUqTBliumIYiNYHaTgmYJWSQ1AXUMdBc8UEKwOGopMRCSxlNhIUgkGYccO+ypNSYnpaGKjKdzEvO3zsGhbELZ52/zt82kKNyU6NBGRhFNiI0njzBkoLLTbxcUwdKjZeGKlqqaqzZWai1lY1DbUUlVTlcCoRETMUGIjSWP1aqittWdANZdQ8IL6k/Ux3U9ExM2U2EhS2L8f1qyx2yUl9to1XpGVEVlxq0j3ExFxMyU2khQKCyEUgokT4fbbTUcTW3k5eQQyA/jwtftzHz6yM7PJy8lLcGQiIomnxEY8b9s2+9GjBzz+OPja//53LX+Kn7JJZQBtkpvm56WTSrWejYgkBSU24mnnzsH8+Xa7sBCuvdZoOHGTn5tP5R2VDM4c3Gp7IDNA5R2VWsdGRJKGFugTT1u/Hg4cgKwsePBB09HEV35uPrdec6tWHhaRpKbERjyrpgZWrrTb69ZBRobZeBLBn+Jn/JDxpsMQETFGt6LEsxYsgLNnIS8Ppk83HY2IiCSCEhvxpB07oLISUlKgosJ7A4ZFRKR9SmzEc0IhmDvXbs+eDcOHm41HREQSR4mNeE55OezbBwMGwEMPmY5GREQSSYmNeEp9PSxfbrcfeQT69DEZjYiIJJoSG/GU4mI4dQpGj4YZM0xHIyIiiabERjyjqgqeftoeKFxRYQ8cFhGR5KI//eIJjY0wZ47dnjkTRo0yG4+IiJihxEY8YdMmePtt6NsXVq0yHY2IiJiixEZc78gRWLLEbq9YAf37m41HRETMUWIjrrd4MRw/DiNGwL33mo5GRERMUmIjrrZ7N2zebLcrKsCveo8iIklNiY24VjhsDxi2LLjzThg71nREIiJimhIbca0tW+CNN+yq3Y8+ajoaERFxAiU24krHjsGiRXZ7+XLIyjIajoiIOIQSG3GlpUvh6FHIzb1Q8FJERESJjbjO3r2wcaPdLi+Hnj3NxiMiIs6hxEZcxbLsAcPhMEybBhMmmI5IREScRImNuMrWrbBzJ6Snw7p1pqMRERGnUWIjrtHQAAsX2u0HHoCcHLPxiIiI8yixEdd4+GE4fBiGDYMFC0xHIyIiTqTERlyhuhpKS+12WRmkpRkNR0REHEqJjTieZcF990FjI0ydClOmmI5IREScSomNOF4wCDt22FdpSkpMRyMiIk6mxEYc7cwZKCy028XFMHSo2XhERMTZepgOIJEsywKgoaHBcCQSqRUroLYWAgGYNcueGSUiIsml+Xu7+Xu8M0mV2Jw8eRKA7Oxsw5FIV334oepBiYgku5MnT9K7d+9O9/FZkaQ/HhEOhzl06BAZGRn4fD7T4ThWQ0MD2dnZ1NbWkpmZaTocz9H5jT+d4/jS+Y0vnd+2LMvi5MmTDBo0iJSUzkfRJNUVm5SUFAKBgOkwXCMzM1P/U8WRzm/86RzHl85vfOn8tnapKzXNNHhYREREPEOJjYiIiHiGEhtpIy0tjWXLlpGm5X3jQuc3/nSO40vnN750frsnqQYPi4iIiLfpio2IiIh4hhIbERER8QwlNiIiIuIZSmxERETEM5TYiIiIiGcosZEOvf/++3zve9/jyiuvpFevXgwdOpRly5YRCoVMh+YZK1eu5Ctf+Qrp6en06dPHdDiesGHDBoYMGcJll13GmDFj2L17t+mQPOPVV19l6tSpDBo0CJ/Px3PPPWc6JE9ZvXo1o0aNIiMjg4EDB3Lbbbfx7rvvmg7LdZTYSIf27dtHOBxm06ZN/PnPf6akpIQnnniCxYsXmw7NM0KhENOmTeMHP/iB6VA84Ze//CVFRUUsW7aMt956i+uuu46vf/3rfPzxx6ZD84TTp09z3XXXsWHDBtOheNIrr7zC7Nmzef3113nxxRf5y1/+wte+9jVOnz5tOjRX0To20iVr167lhz/8If/zP/9jOhRPeeqpp5g/fz7Hjx83HYqrjRkzhlGjRlFRUQHYhW+zs7OZO3cuixYtMhydt/h8Pn71q19x2223mQ7Fs44cOcLAgQN55ZVXGDdunOlwXENXbKRLTpw4Qb9+/UyHIdJGKBTij3/8I7fcckvLtpSUFG655Rb+8Ic/GIxMJDonTpwA0N/cLlJiIxHbv38/5eXl3HvvvaZDEWnj6NGjNDU1cfnll7fafvnll3P48GFDUYlEJxwOM3/+fMaOHcuXvvQl0+G4ihKbJLRo0SJ8Pl+nj3379rV6TV1dHZMmTWLatGncc889hiJ3h2jOr4jIxWbPns0777zDL37xC9OhuE4P0wFI4i1YsIAZM2Z0us9VV13V0j506BA333wzX/nKV/jRj34U5+jcr6vnV2Kjf//++P1+Pvroo1bbP/roI6644gpDUYl03Zw5c9i2bRuvvvoqgUDAdDiuo8QmCQ0YMIABAwZEtG9dXR0333wzI0eOZMuWLaSk6CLfpXTl/ErspKamMnLkSF566aWWAa3hcJiXXnqJOXPmmA1OJAKWZTF37lx+9atf8fLLL3PllVeaDsmVlNhIh+rq6hg/fjyf//znWbduHUeOHGn5mf4FHBs1NTV88skn1NTU0NTUxJ49ewAYNmwYn/3sZ80G50JFRUXcdddd3HDDDYwePZrS0lJOnz7N3XffbTo0Tzh16hT79+9veX7w4EH27NlDv379yMnJMRiZN8yePZutW7fy61//moyMjJaxYb1796ZXr16Go3MRS6QDW7ZssYB2HxIbd911V7vn9/e//73p0FyrvLzcysnJsVJTU63Ro0dbr7/+uumQPOP3v/99u5/Xu+66y3RontDR39stW7aYDs1VtI6NiIiIeIYGTIiIiIhnKLERERERz1BiIyIiIp6hxEZEREQ8Q4mNiIiIeIYSGxEREfEMJTYiIiLiGUpsRERExDOU2IiIiIhnKLEREdf5+c9/Tq9evaivr2/ZdvfddzN8+HBOnDhhMDIRMU0lFUTEdSzLYsSIEYwbN47y8nKWLVvGk08+yeuvv87gwYNNhyciBumKjYi4js/nY+XKlfz4xz9m5cqVlJeXs3379pakZtu2bVxzzTV84Qtf4Cc/+YnhaEUkkXTFRkRc68tf/jJ//vOfeeGFF/jqV78KQGNjI1/84hf5/e9/T+/evRk5ciSvvfYan/vc5wxHKyKJoCs2IuJK27dvZ9++fTQ1NXH55Ze3bN+9ezd/9Vd/xeDBg/nsZz/L5MmTeeGFFwxGKiKJpMRGRFznrbfe4o477mDz5s1MmDCBBx98sOVnhw4dajXOZvDgwdTV1ZkIU0QM6GE6ABGRrnj//ff5xje+weLFi5k+fTpXXXUVN910E2+99RZf/vKXTYcnIobpio2IuMYnn3zCpEmTuPXWW1m0aBEAY8aMYfLkySxevBiAQYMGtbpCU1dXx6BBg4zEKyKJp8HDIuIpjY2N5Obm8vLLL2vwsEgS0q0oEfGUHj16sH79em6++WbC4TDFxcVKakSSiK7YiIiIiGdojI2IiIh4hhIbERER8QwlNiIiIuIZSmxERETEM5TYiIiIiGcosRERERHPUGIjIiIinqHERkRERDxDiY2IiIh4hhIbERER8QwlNiIiIuIZ/x/3HryutcRW+AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 4\n",
      "====================\n",
      "\n",
      "Iteration 0\n",
      "--------------------\n",
      "x: [-1.0280556   0.72748234], y: 0\n",
      "v: -0.3415305367771743\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [ 0.34021192 -0.15052119]\n",
      "Bias: 0.11772773900711421\n",
      "\n",
      "Iteration 1\n",
      "--------------------\n",
      "x: [-1.32853114  0.29036849], y: 0\n",
      "v: -0.3779610003603502\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [ 0.34021192 -0.15052119]\n",
      "Bias: 0.11772773900711421\n",
      "\n",
      "Iteration 2\n",
      "--------------------\n",
      "x: [0.77479765 0.72748234], y: 1\n",
      "v: 0.27182162579035696\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [ 0.34021192 -0.15052119]\n",
      "Bias: 0.11772773900711421\n",
      "\n",
      "Iteration 3\n",
      "--------------------\n",
      "x: [-0.42710452  0.72748234], y: 0\n",
      "v: -0.13707981592133045\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [ 0.34021192 -0.15052119]\n",
      "Bias: 0.11772773900711421\n",
      "\n",
      "Iteration 4\n",
      "--------------------\n",
      "x: [-0.72758006 -1.67664385], y: 1\n",
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      "--------------------\n",
      "x: [ 0.92503542 -0.58385922], y: 1\n",
      "v: 0.5203189965385565\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [ 0.34021192 -0.15052119]\n",
      "Bias: 0.11772773900711421\n",
      "\n",
      "Iteration 67\n",
      "--------------------\n",
      "x: [0.4743221  2.03882391], y: 0\n",
      "v: -0.02778842517180384\n",
      "y_out: 0\n",
      "Error: 0\n",
      "Weights: [ 0.34021192 -0.15052119]\n",
      "Bias: 0.11772773900711421\n",
      "\n",
      "Iteration 68\n",
      "--------------------\n",
      "x: [ 0.4743221  -1.02097307], y: 1\n",
      "v: 0.43277585274142005\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [ 0.34021192 -0.15052119]\n",
      "Bias: 0.11772773900711421\n",
      "\n",
      "Iteration 69\n",
      "--------------------\n",
      "x: [1.37574873 0.29036849], y: 1\n",
      "v: 0.542067243490947\n",
      "y_out: 1\n",
      "Error: 0\n",
      "Weights: [ 0.34021192 -0.15052119]\n",
      "Bias: 0.11772773900711421\n",
      "--------------------\n",
      "\n",
      "Epoch error: 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Defines hyperparameters and initial settings\n",
    "# -----------------------------------------------\n",
    "# Maximum number of epochs.\n",
    "max_iter = 40\n",
    "\n",
    "# Learning rate\n",
    "learning_rate = 0.01\n",
    "\n",
    "# Sets a value for the random number generation seed.\n",
    "np.random.seed(1234)\n",
    "\n",
    "# Training the model\n",
    "fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5875b50",
   "metadata": {},
   "source": [
    "## Bibliografia\n",
    "---\n",
    "\n",
    "* GONZALEZ, R.C.; WOODS, R.E. Digital Image Processing. 3rd ed. Pearson, 2007."
   ]
  }
 ],
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   "title_cell": "Índice",
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