Linear_Regression_Andrew.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Linear Regression_Andrew.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyN4l25DPCzf0qqTWbaxMsL3",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hussain0048/Machine-Learning/blob/master/Linear_Regression_Andrew.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Import Library**"
      ],
      "metadata": {
        "id": "aFhgt8BlEUoe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "mjZ4jdnjEOxZ"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Data Loading**"
      ],
      "metadata": {
        "id": "hvEfJ3q0CA5x"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "atqAnm0-BmD1",
        "outputId": "2998fe6c-1486-4708-b263-019d4cf7631b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd \n",
        "data = pd.read_csv('/content/drive/MyDrive/Datasets/Andrew ML/ex1data1.txt', header=None, names=['Population', 'Profit'])\n",
        "data.head()"
      ],
      "metadata": {
        "id": "QLJ6QGfDEl3Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "id": "Oz8isQlvFPou"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Visulization** "
      ],
      "metadata": {
        "id": "D0r5ByIxFXPh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.plot(kind='scatter', x='Population', y='Profit', figsize=(12,8))"
      ],
      "metadata": {
        "id": "rEScbwTrFVq8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Implementing Simple Linear Regression**"
      ],
      "metadata": {
        "id": "I1VWaJsoFjTX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def computeCost(X, y, theta):\n",
        "    inner = np.power(((X * theta.T) - y), 2)\n",
        "    return np.sum(inner) / (2 * len(X))"
      ],
      "metadata": {
        "id": "a332MtBLFrEs"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# append a ones column to the front of the data set\n",
        "data.insert(0, 'Ones', 1)\n",
        "\n",
        "# set X (training data) and y (target variable)\n",
        "cols = data.shape[1]\n",
        "X = data.iloc[:,0:cols-1]\n",
        "y = data.iloc[:,cols-1:cols]"
      ],
      "metadata": {
        "id": "H3b7t7oiFynE"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# convert from data frames to numpy matrices\n",
        "X = np.matrix(X.values)\n",
        "y = np.matrix(y.values)\n",
        "theta = np.matrix(np.array([0,0]))"
      ],
      "metadata": {
        "id": "MKu-CyaZF-nV"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape, theta.shape, y.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Trlb3TYBGI07",
        "outputId": "55b6e536-5796-47e8-fcca-c3a6210197ed"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((97, 2), (1, 2), (97, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "computeCost(X, y, theta)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8T1Tl-rhGPIM",
        "outputId": "83e6d883-c476-403f-faab-b0f5877bc22e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32.072733877455676"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def gradientDescent(X, y, theta, alpha, iters):\n",
        "    temp = np.matrix(np.zeros(theta.shape))\n",
        "    parameters = int(theta.ravel().shape[1])\n",
        "    cost = np.zeros(iters)\n",
        "    \n",
        "    for i in range(iters):\n",
        "        error = (X * theta.T) - y\n",
        "        \n",
        "        for j in range(parameters):\n",
        "            term = np.multiply(error, X[:,j])\n",
        "            temp[0,j] = theta[0,j] - ((alpha / len(X)) * np.sum(term))\n",
        "            \n",
        "        theta = temp\n",
        "        cost[i] = computeCost(X, y, theta)\n",
        "        \n",
        "    return theta, cost"
      ],
      "metadata": {
        "id": "tL3KXbuHGVtE"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# initialize variables for learning rate and iterations\n",
        "alpha = 0.01\n",
        "iters = 1000\n",
        "\n",
        "# perform gradient descent to \"fit\" the model parameters\n",
        "g, cost = gradientDescent(X, y, theta, alpha, iters)\n",
        "g"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yd_XQPBNGcJN",
        "outputId": "75b41708-336b-4858-cf80-f3cec3536aa5"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "matrix([[-3.24140214,  1.1272942 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "computeCost(X, y, g)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f6tD8yQsGlRr",
        "outputId": "fd350dd8-9b36-4410-d41b-35d2c7b364d8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.515955503078914"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Viewing The Results**"
      ],
      "metadata": {
        "id": "nJ9qKgLDGqs-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.linspace(data.Population.min(), data.Population.max(), 100)\n",
        "f = g[0, 0] + (g[0, 1] * x)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12,8))\n",
        "ax.plot(x, f, 'r', label='Prediction')\n",
        "ax.scatter(data.Population, data.Profit, label='Traning Data')\n",
        "ax.legend(loc=2)\n",
        "ax.set_xlabel('Population')\n",
        "ax.set_ylabel('Profit')\n",
        "ax.set_title('Predicted Profit vs. Population Size')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "pQ3HmNFxGzBk",
        "outputId": "6277480c-7536-47eb-c2ba-5ca3ce801b23"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Predicted Profit vs. Population Size')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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AcOLECQBatGjBF1988ZXbX3nllVx11VWV/cr/+7//W7nqLCIiIiKNFx4rzUHs8ccfp6CggO7du2OtpXXr1mRlZfHggw/yzjvvkJycTGJiIr169frKfVu3bs3ChQsZNGgQ5eXltGnThj/84Q888MADPPTQQ6xbt445c+ZccJ9ly5aRlpbGmTNnuO6661iyZEmgnqqIiIhI2DLWWq9rqFOPHj1sxTSJCrt27eLGG2/0qCK5mH4eIiIicqmycgvJzM7nYFEJ7eJjSU9NYmBKQkBrMMZ8ZK3tcfFxrTSLiIiIiOeycguZuHY7JaXnASgsKmHi2u0AAQ/O1VFPs4iIiIh4LjM7vzIwVygpPU9mdr5HFV1IoVlEREREPHewqKRBxwNNoVlEREREPNcuPrZBxwNNoVlEREREPJeemkRsdNQFx2Kjo0hPTfKoogvpQkARERER8VzFxX5eT8+oiUJzIx0/fpx7770XgMOHDxMVFUXr1q0B2Lx5MzExMY163Pvvv58VK1YQHx9/SfUVFBRw44030rlzZ86ePUuLFi148sknGTZsWK33y8vL4+DBg9x///2XdH4RERGRhhqYkhA0IfliCs2NdPXVV1duof3CCy8QFxfH2LFjK79fVlbGZZc1/OVtyu21r7/+enJzcwHYu3cvgwYNwlrL8OHDa7xPXl4eW7ZsUWgWERERqSJiepqzcgu5I+MdOk54kzsy3iErt7DJzzFs2DDS0tL49re/zbhx49i8eTO9evUiJSWF22+/nfx8NzJl6dKlDBo0iH79+nHDDTcwbty4ysfo0KEDx44dq1wpHjFiBDfddBN9+/alpMRdPfrXv/6VLl260K1bN9LT07n55pvrrO26665jxowZ/OIXvwCotrZz587x/PPPs2rVKrp168aqVatqfA4iIiIikSQiQnPFsOzCohIs/xqW7Y/gfODAAd5//31mzJhB586dee+998jNzeVnP/sZzz33XOXt8vLyWLVqFdu3b2fVqlXs37//K4/16aefMnLkSHbs2EF8fDyvvvoqAMOHD2fBggXk5eURFRX1lfvVpHv37nzyyScA1dYWExPDz372Mx555BHy8vJ45JFHan0OIiIiIpEiItozahuW3dR9M4MHD64MsqdOneKxxx7j008/xRhDaWlp5e3uvfderrzySgCSk5P57LPPuOaaay54rI4dO9KtWzcAbr31VgoKCigqKuKLL76gV69eAAwZMoTf/e539aqt6pbptdVWVX1vJyIiIhLOImKlOZDDsq+44orKz3/6059y99138/HHH/PGG29w9uzZyu997Wtfq/w8KiqKsrKyrzxWfW7TELm5udx444111lZVfW8nIiIiEs4iIjR7NSz71KlTJCS4leylS5c2yWPGx8fTokULPvzwQwBefvnlet2voKCAsWPH8tRTT9VaW4sWLfjiiy8qv/bHcxAREREJNRERmr0alj1u3DgmTpxISkrKJa8SV/XSSy8xYsQIunXrxpdfflnZ5nGxPXv2kJKSwo033sjDDz/MT37yk8rJGTXVdvfdd7Nz587KCwH99RxEREREQomp2ucarHr06GG3bNlywbFdu3ZVthrUR1ZuYdAOy26o4uJi4uLiAMjIyODQoUPMnj3b05oa+vMQERERCUbGmI+stT0uPh4RFwJCcA/Lbqg333yTyZMnU1ZWxrXXXqu2CRERERE/i5jQHE4eeeQRHnnkEa/LEBEREYkYEdHTLCIiIiJyKUI6NIdCP3Yk0M9BREREwl3IhubLL7+c48ePK7B5zFrL8ePHufzyy70uRURERMRvQranuX379hw4cICjR496XUrEu/zyy2nfvr3XZYiIiIj4TciG5ujoaDp27Oh1GSIiIuKxcBorK8ErZEOziIiISFZuIRPXbqek9DwAhUUlTFy7HUDBWZpUyPY0i4iIiGRm51cG5golpefJzM73qCIJVwrNIiIiErIOFpU06LhIYyk0i4iISMhqFx/boOMijaXQLCIiIiErPTWJ2OioC47FRkeRnprkUUUSrnQhoIiIiISsiov9ND1D/E2hWURERELawJQEhWTxO7VniIiIiIjUQaFZRERERKQOCs0iIiIiInVQaBYRERERqYNCs4iIiIhIHTQ9Q0RERMQjWbmFGpcXIrTSLCIiIuKBrNxCJq7dTmFRCRYoLCph4trtZOUWel2at/btg+xsr6v4CoVmEREREQ9kZudTUnr+gmMlpefJzM73qCKP7dwJw4bB9de7P8vKvK7oAgrNIiIiIh44WFTSoONhKycHHnwQbroJVq+GkSPhww/hsuDqIg6uakREREQiRLv4WAqrCcjt4mM9qCbArIW334aMDNiwAVq2hEmTYNQoaNXK6+qqpZVmEREREQ+kpyYRGx11wbHY6CjSU5M8qigAyspg1Sro3h369YPdu2HGDPjsM3jhhaANzKCVZhERERFPVEzJiIjpGWfPwrJlkJkJe/ZA586wZAkMGQIxMV5XVy8KzSIiIiIeGZiSEJ4hucKpUzB/PsycCUeOQM+eLjgPGADNQqvhQaFZRERERJrW4cMwezbMnQunT0PfvjB+PNx9NxjjdXWNotAsIiIiIk1jzx6YNs21XpSWwkMPubDcvbvXlV0yhWYRERERuTR5eTBlCrzyihsVN2wYjB0LN9zgdWVNRqFZRERExGMhuZ22tbBpkxsbt349tGjhgvLo0dC2rdfVNTmFZhEREREPVWynXbE7YMV22kBwBufycnj9dbeynJMDbdrAz38OP/4xxMd7XZ3fhNZliyIiIiJhJmS20z53DpYudTv3Pfigm4Yxdy4UFMDEiWEdmEErzSIiIiKeCvrttIuLYfFimD4dDhyALl1gxQoYPDjotrr2p8h5piIiIiJBKGi30z5+HObMcR8nTsBdd8HChW4nvxAdG3cp1J4hIiIi4qGg2057/353MV9iIrz4ItxxB/zlL7BxI9x3X0QGZtBKs4iIiIingmY77V27YOpU+O1v3ddDhsC4ca6HWRSaRURERLzm6XbaOTluEkZWFsTGuikYzz4L117rTT1BSqFZREREJNJYC2+/7WYsb9gAV10Fzz8PTz0FrVp5XV1QUmgWERERiRRlZfDqqy4s5+VBQgLMmAEjRkBcnNfVBTWFZhEREZFwd/YsLFsGmZmwZw8kJcFLL8H3vgcxMV5XFxIUmkVERETC1alTMH8+zJzpNiPp2dMF5wEDoJmGqDWEQrOIiIhIuDl8GGbPdjv2nT4NffvChAnQp0/Ejoy7VArNIiIiIuFizx6YNg2WLIHSUnjoIRg/Hrp397qykOe3dXljzDXGmHeNMTuNMTuMMU/7jrc0xvzBGPOp78+r/FWDiIiISETIy4NHH4VvfhN+/Wt47DH45BNYtUqBuYn4s5mlDHjWWpsM3AaMNMYkAxOAP1lrbwD+5PtaRERERBrC2n/t0peSAm++CenpUFAACxbADTd4XWFY8Vt7hrX2EHDI9/kXxphdQAIwAOjju9kyYAMw3l91iIiIiISV8nJ44w03Ni4nB9q0gcmTIS0N4uO9ri5sBaSn2RjTAUgBPgS+4QvUAIeBbwSiBhEREZGQdu4crFzpdu/btQs6doRf/QqGD3c7+Ylf+T00G2PigFeB0dba06bKFZvWWmuMsTXc7wngCYDExER/lykiIiISnIqLYfFitwnJ/v3QpQusWAGDB8NlmukQKH4d0GeMicYF5uXW2rW+w0eMMW19328LfF7dfa21C621Pay1PVq3bu3PMkVERESCz/Hj8MILcO218MwzbmX5rbf+ddGfAnNA+XN6hgFeAnZZa2dU+dbrwGO+zx8D1vmrBhEREZGQs28fjB4NiYnw4ovw7/8Of/nLvy7605xlT/jzLcodwH8D240xeb5jzwEZwCvGmB8CnwEP+7EGERERkdCwcydMnQrLl7uvhwyBcePgppu8rUsA/07P+DNQ01uhe/11XhEREZGQkpPjJmGsW+cu6HvySRgzxrVlSNBQM4yIiIhIoFkL2dkuLG/cCFddBc8/D089Ba1aeV2dVEOhWURERCRQyspgzRoXlrdtg4QENxVjxAiIi/O6OqmFQrOIiIiIv509C0uXQmYm7N0LSUluu+uhQyEmxuvqpB4UmkVERET85dQpmDcPZs2CI0egZ0+YNg0GDIBmfp38K01MoVlERESkqR0+DLNnw9y5cPo09O0LEydC794aGReiFJpFREREmsqePW4leckSKC11u/aNHw8pKV5XJpdIoVlERETkUuXlwZQp8Morbqe+4cNh7Fjo1MnryqSJKDSLiIiINIa1sGmTm4Sxfj20aOGC8ujR0Lat19VJE1NoFhEREWmI8nJ44w0XlnNyoE0bmDwZ0tIgPt7r6sRPFJpFRERE6uPcOVi50rVh7NoFHTu6C/2GDXM7+UlYU2gWERERqU1xMSxe7DYh2b8funSB5cvh4Ydd/7JEBP2kRURERKpz/DjMmeM+TpyAu+6CBQugXz+NjYtACs0iIiIiVe3b51aVFy2CM2egf383Nu72272uTDyk0CwiIiICsHMnTJ3qWi8AhgyBcePgppu8rUuCgkKziIiIRLacHDcJY906aN4cnnwSxoyBa6/1ujIJIgrNIiIiEnmshbffdmF5wwZo2RImTYJRo6BVK6+rkyCk0CwiIiKRo6wM1qxxY+Py8qB9e9e/PGIExMV5XZ0EMYVmERERCX9nz8LSpZCZCXv3QufOsGSJ61uOifG6OgkBCs0iIiISvk6dgnnzYNYsOHIEevaE6dPdRIxmzbyuTkKIQrOIiIiEn8OHXVCeNw9On4bUVJgwAXr31oxlaRSFZhEREQkfu3fDtGmuFaO0FAYPdjOWU1K8rkxCnEKziIiIhL7cXHdx3+rVbmvr4cNh7Fjo1MnryiRMKDSLiIhIaLIWNm50Y+Oys6FFC0hPh6efhrZtva4u5GTlFpKZnc/BohLaxceSnprEwJQEr8sKGgrNIiIiElrKy+GNN2DyZPjwQ2jTxn2elgbx8V5XF5KycguZuHY7JaXnASgsKmHi2u0ACs4+umxUREREQsO5c7BsGdx8MwwcCJ9/DnPnQkGBu8hPgbnRMrPzKwNzhZLS82Rm53tUUfDRSrOIiIgEty+/hMWL3ai4/fuhSxdYscJd5HeZokxTOFhU0qDjkUh/00RERCQ4HT8Oc+a4jxMn3Li4BQugXz+NjWti7eJjKawmILeLj/WgmuCk0Bxi1KQvIiJhb/9+t6q8aBGcOQMDBrixcb16eV1Z2EpPTbqgpxkgNjqK9NQkD6sKLgrNIURN+iIiEtZ27oSpU2H5cvf10KEwbhwkJ3tbVwSoyBFamKuZQnMIqa1JX3+pRUQkZOXkuLFx69ZB8+bw5JPw7LOQmOh1ZRFlYEqC8kQtFJpDiJr0RUQkbFjrZitnZLhZyy1bwvPPw1NPQatWXlcn8hUKzSFETfoiIhLyyspgzRq3e19eHrRvDzNnwuOPQ1yc19WJ1EhzmkNIemoSsdFRFxxTk76IiISEs2dh/nxISoJHH3VfL1kCe/bA6NEKzBL0tNIcQtSkLyIiIefUKZg3D2bNgiNHoGdPNxmjf39oprU7CR0KzSFGTfoiIhISDh92QXnePDh9GlJT3di4Pn00Y1lCkkKziIiINJ09e2DaNNd6UVrqdu0bPx5SUvx2Su1hIIGg0CwiIiKXLi/PXdz3yitua+vhw2HsWOjUya+n1R4GEihqJhIREZHGsdaNi7vvPreS/OabLigXFLiL/vwcmKH2PQxEmpJWmkVERKRhysvhjTfcjOWcHGjTBv7nf9ymJPHxAS1FexhIoGilWUREROrn3DlYtgxuvhkGDnTTMObOdSvLzz0X8MAMNe9VoD0MpKkpNIuIiEjtiovdJIxOnWDYMIiJgRUr4O9/hx//GGK9C6jaw0ACRe0ZIiIiUr3jx2HOHPdx4gT07g0LFkC/fkEzNk57GEigKDSLiIjIhfbvhxkzYOFCOHMGBgxwY+N69fK6smppDwMJBIVmERERcXbuhKlTYfly9/XQoTBuHCQne1uXSBBQaBYREYl0OTluEsa6ddC8OYwcCWPGQGKi15WJBA2FZhERkUhkLWRnu7C8cSO0bAmTJsGoUdCqldfViQQdhaAil6QAACAASURBVGYREZFIUlYGa9a4sLxtG7RvDzNnwuOPQ1yc19WJBC2FZhERkUhw9iwsXQqZmbB3L3TuDEuWwJAhboSciNRKoVlERCScnToF8+a5OctHjkDPnjB9OvTvD820XYNIfSk0i4iIhKPDh11QnjcPTp+Gvn1h4kQ3azlIZiyLhBKFZhERkXCyezdMm+ZaMUpLYfBgN2M5JcXrykRCmkKziIhIOMjNhSlTYPVquOwyGD4cxo51W1+LyCVTaBYREQlV1rpxcRkZbnxcixYuKI8eDW3bel2dSFhRaBYREQk15eXw+usuLH/4IbRpA5MnQ1oaxMd7XZ1IWFJoFhERCRXnzsGKFW6r6127oGNHmDsXhg2D2FivqxMJawrNIiIiwa64GBYvdqPiDhyALl1ceB482PUvi4jf6b80ERGRYHX8OMyZ4z5OnHDj4hYuhH79NDZOJMAUmkVERILN/v0wY4YLyGfOwIABbmxcr15eVyYSsRSaRUSkRlm5hWRm53OwqIR28bGkpyYxMCXB67LC165drl/5t791Xw8Z4sJycrK3dYmIQrOIiFQvK7eQiWu3U1J6HoDCohImrt0OoODc1HJy3IzlrCxo3hxGjoQxYyAx0evKRMRHm86LiEi1MrPzKwNzhZLS82Rm53tUUZixFtavhz59XNvFpk0waRJ89pnb/lqBWSSoaKVZRESqdbCopEHHpZ7KymDNGjdjeds2aN/e9S+PGAFxcV5XJyI10EqziIhUq1189XN/azoudTh7FubPh6QkePRR+Oc/YckS2LMHnnlGgVkkyCk0i4hItdJTk4iNjrrgWGx0FOmpSR5VFKJOnXKryh06wI9/DK1awWuvwY4dblOSmBivKxSRelB7hoiIVKviYj9Nz2ikw4ddb/K8eXD6NKSmukkYffpoxrJICPJbaDbG/Br4L+Bza+3NvmMvACOAo76bPWetfctfNYiIyKUZmJKgkNxQe/ZAZiYsXQqlpW7XvvHjISXF68pE5BL4c6V5KfBL4DcXHZ9prZ3mx/OKiIgEXm6uGxu3erXb2nr4cBg7Fjp18royEWkCfgvN1tpNxpgO/np8ERERz1kLGze6nuXsbGjRAtLT4emnoW1br6sTkSbkxYWAo4wxfzPG/NoYc1VNNzLGPGGM2WKM2XL06NGabiYiIhJ45eVuI5JeveDuu90q8+TJsG+fC9AKzCJhJ9CheR5wPdANOARMr+mG1tqF1toe1toerVu3DlR9IiIiNTt3zvUq33QTPPggfP45zJ0LBQUwYQLEx3tdoYj4SUCnZ1hrj1R8boxZBPwukOcXEQkFWbmFmlgRbIqLYfFimD4dDhyALl1gxQp3kd9lGkQlEgkC+l+6MaattfaQ78sHgY8DeX4RkWCXlVvIxLXbK7evLiwqYeLa7QAKzl44fhzmzHEfJ07AXXfBwoXQr5/GxolEGH+OnFsJ9AFaGWMOAJOAPsaYboAFCoAf+ev8IiKhKDM7vzIwVygpPU9mdr5CcyDt3+9WlRctgjNn4IEHXPvF7bd7XZmIeMSf0zMerebwS/46n4hIODhYVNKg49LEdu2CqVPht791Xw8ZAuPGuR5mEYloasQSEQki7eJjKawmILeLj/WgmgiSk+NmLGdlQfPmMHIkjBkDiYleVyYiQcKLkXMiIlKD9NQkYqOjLjgWGx1FemqSRxWFMWth/Xq3rXWvXm7e8qRJ8NlnbvtrBWYRqUIrzSIiQaSib1nTM/yorAzWrHHzlLdtg/btYeZMePxxiIvzujoRCVIKzSIiQWZgSoJCsj+cPetmLGdmwt690LkzLFni+pZjYryuTkSCnEKziIiEt1OnYN4813Jx5Aj07OkmY/TvD83UpSgi9aPQLCIi4enQIZg92wXm06chNdWNjevdWzOWRaTBFJpFRCS87N7tWjCWLYPSUrdr3/jxkJLidWUiEsIUmkVEJDxs3erGxq1Z47a2HjYM0tOhUyevKxORMKDQLCIiocta2LDBTcJ4+21o0cIF5aefhrZtva5ORMKIQrOIiISe8nJYt86tLH/4IbRpA5MnQ1oaxMd7XZ2IhCGFZhERCR3nzsGKFS4sf/IJXHcdzJ3rWjFitWuiiPiPQrNEvKzcQm0kIRLsioth8WI3Ku7AAejaFVauhIcecv3LIiJ+pv/TSETLyi1k4trtlJSeB6CwqISJa7cDKDiLBINjx+CXv4Q5c+DECTcubtEiNz5OY+NEJIA01V0iWmZ2fmVgrlBSep7M7HyPKhIRAPbtg9Gj4dpr4cUX4c474f333UV//fopMItIwGmlWSLawaKSBh0XaQy1ADXAzp0wdSosX+6+HjoUxo2D5GRv6xKRiKfQLBGtXXwshdUE5HbxuqBImoZagOopJ8eNjVu3Dpo3h5EjYcwYSEz0ujIREUDtGRLh0lOTiI2OuuBYbHQU6alJHlUk4UYtQLWwFtavhz59oFcveO89mDQJPvsMZs1SYBaRoKKVZoloFSt9+tW5+ItagKpRVuZ27cvIgG3boH17mDkTHn8c4uK8rk5EpFoKzRLxBqYkKCSL36gFqIqzZ2HpUsjMhL17oXNnWLIEhgyBmBivqxMRqZXaM0RE/EgtQMCpU25VuUMH+PGPoVUreO012LHDbUqiwCwiIUArzSIifhTRLUCHD7ve5Hnz4PRpN1t5wgQ3a1kj40QkxCg0i4j4WcS1AO3eDdOmuVaM0lIYPBjGj4eUFK8rExFpNIVmERFpGrm5MGUKrF7ttrYePhzGjoVOnbyuTETkkik0i4hI41nrdumbMgWys6FFC0hPh6efhrZtva5ORKTJKDSLiEjDlZe7jUgyMmDzZmjTBiZPhrQ0iI/3ujoRkSan0CwiIvV37pzb4nrqVPjkE7juOneh32OPQWwEjtETkYih0CwiInUrLobFi2H6dDhwALp2hZUr4aGHXP+yiEiY0//pRESkZseOwZw57uPkSbjrLli0yI2P09g4EYkgCs0iIvJV+/bBjBkuIJ85AwMGuLFxvXp5XZmIiCcUmkVE5F927nT9ysuXu6+HDoVx4yA52du6REQ8ptAsIiKQk+MmYaxbB82bw5NPwrPPQmKi15WJiAQFhWYRkUhlrZutnJEBGzfCVVfBpEkwahS0auV1dSIiQUWhWUQk0pSVwZo1Lixv2wYJCa5/ecQIiIvzujoRkaCk0CwiEinOnoWlSyEzE/buhaQkeOkl+N73ICbG6+pERIKaQrNIgGTlFpKZnc/BohLaxceSnprEwJQEr8uSSHDqlNuAZNYsOHIEevaEadPcRIxmzbyuTkQkJCg0iwRAVm4hE9dup6T0PACFRSVMXLsdQMFZ/OfwYReU582D06ehb1+YMAH69AnKGct6YykiwUyhWcTHn/9gZ2bnVwbmCiWl58nMzlcokKa3e7dbSV66FEpL3a5948dD9+5eV1YjvbEUkWCn0CwRpaZg7O9/sA8WlTTouEij5ObClCmwerXb2nr4cBg7Fjp18rqyOumNpYgEO4VmqbdQ/9VpbcHY3/9gt4uPpbCagNwuPvaSH1sinLVuXFxGhhsf9/WvQ3o6PP00tG3rdXX1pjeWIhLsdAWI1EtF4CwsKsHyr8CZlVvodWn1Vlsw9vc/2OmpScRGR11wLDY6ivTUpCZ5fIlA5eXw2mtuW+u774a8PPj5z9321xkZIRWYoeY3kHpjKSLBQqFZ6qW2wBkqagvG/v4He2BKApMH3UJCfCwGSIiPZfKgW0JqpV6CxLlzsGQJ3HQTDBoER4+6C/3+8Q+YOBGuvNLrChtFbyxFJNipPUPqJRx+dVpbi0R6atIFrRvQ9P9gD0xJUEiWxisuhkWLYPp0KCyErl1h5Up3kd9lof+/8or/NkK5BUxEwlvo/59WAiIcenJrC8b6B1uC1rFjMGeO+zh5Enr3duG5X7+gHBt3KfTGUkSCmUKz1EsgVmL9ra5grH+wJajs2+dWlRctgpIStxHJ+PGuh1lERAJOoVnqJVxWYhWMJejt2AFTp8KKFe7roUNh3DhITva2LhGRCKfQLPWmwCniRx984KZevP46NG8OI0fCmDGQmOh1ZSIigkKziIh3rIX1611Y3rQJWraESZNg1Cho1crr6kREpIp6jZwzxvypPsdERKQeysrc5IuUFLj/fti7F2bOhM8+gxdeUGAWEQlCta40G2MuB5oDrYwxVwEVl2p/HdDv6UVEGqKkBJYuhcxMN1e5c2c3c3nIEIiJ8bo6ERGpRV3tGT8CRgPtgK1Vjp8GfumvokREwsqpU24Dklmz4MgR6NkTZsyA/v2hmfaYEhEJBbWGZmvtbGC2MeYpa+2cANUkIhIeDh2C2bNdYD59Gvr2dbv29e4ddjOWRUTCXV3tGfdYa98BCo0xgy7+vrV2rd8qExEJVbt3w7RprhWjtBQGD3YzllNSvK5MREQaqa72jLuAd4AHqvmeBRSaRUQq5ObClCmwerXb2nr4cBg7Fjp18royERG5RHWF5pO+P1+y1v7Z38WIiIQca2HjRjc2LjsbWrRwQXn0aGjb1uvqRESkidR1Bcpw35+/8HchIiIhpbwcsrLcttZ33+1WmSdPdttfT5miwCwiEmbqWmneZYz5FGhnjPlbleMGsNbaLv4rTUQkCJ0757a4njoVdu2C665zF/o99hjExnpdnYiI+Eld0zMeNcb8G5AN9A9MSSIiQai4GBYvhunT4cAB6NrVhefBg13/soiIhLU6/09vrT0MdDXGxADf9B3Ot9aW+rUyEZFgcOwYzJnjPk6edOPiFi2C1FSNjRMRiSD1Wh4xxvQGfgMU4FozrjHGPGat3eTH2kREvLNvn1tVXrwYzpyBAQPc2LhevbyuTEREPFDf3ynOAPpaa/MBjDHfBFYCt/qrMBERT+zY4fqVV6xwXw8Z4sJycrK3dYmIiKfqG5qjKwIzgLX278aYaD/VJCISeB984MbGvf46NG8OI0fCmDGQmOh1ZSIiEgTqG5o/MsYsBn7r+3oosMU/JYmIBIi1sH69C8ubNkHLljBpEowaBa1aeV2diIgEkfqG5jRgJPAT39fvAXP9UpGIiL+Vlbld+6ZMgW3boH17mDEDRoyAuDivqxMRkSBUZ2g2xkQB26y1nXG9zSIioamkBJYtg8xM2LsXOneGJUtc33JMjN9Om5VbSGZ2PgeLSmgXH0t6ahIDUxL8dj4REWl6de0IiLX2PJBvjGlQY58x5tfGmM+NMR9XOdbSGPMHY8ynvj+vakTNIiINU1Tkduvr0AF+/GPXevHaa+6iv2HD/B6YJ67dTmFRCRYoLCph4trtZOUW+u2cIiLS9OoMzT5XATuMMX8yxrxe8VHHfZYC/S46NgH4k7X2BuBPvq9FRPzj0CGYMAGuvRaeew5SUuCddyAnBwYOhGb1/V9g42Vm51NSev6CYyWl58nMzq/hHiIiEozq29P804Y+sLV2kzGmw0WHBwB9fJ8vAzYA4xv62CIitdq9G6ZNg6VLobTU7do3frwLzQF2sKikQcdFRCQ41RqajTGX4y4C7ARsB16y1pZdwvm+Ya095Pv8MPCNS3gsEZEL5ea6i/tWr3ZbWw8fDmPHQqdOnpXULj6WwmoCcrv4WA+qERGRxqrrd5PLgB64wHwfML2pTmyttYCt6fvGmCeMMVuMMVuOHj3aVKcVkXBjLWzYAP36Qffu8NZbkJ4OBQUwf76ngRkgPTWJ2OioC47FRkeRnprkUUUiItIYdbVnJFtrbwEwxrwEbL7E8x0xxrS11h4yxrQFPq/phtbahcBCgB49etQYrkUkQpWXu41IMjLgww+hTRt3sV9aGsTHe11dpYopGZqeISIS2uoKzaUVn1hry4wxl3q+14HHgAzfn+su9QFFJMKcO+e2uJ4yBT75BDp2hLlz3RSM2OBseRiYkqCQLCIS4uoKzV2NMad9nxsg1ve1wXVYfL2mOxpjVuIu+mtljDkATMKF5VeMMT8EPgMevsT6RSRSFBfD4sUwfTocOABdu8LKlfDQQ65/WURExI9q/ZfGWhtV2/fruO+jNXzr3sY+pohEoGPH4Je/hDlz4MQJ6NMHFi2C1FS49N9+iYiI1IuWZ0QkOO3b57a2XrQIzpyBAQPczOXbbvO6MhERiUAKzSISXHbuhKlTYfly9/X3vuemYSQne1uXiIhENIVmEQkOOTluEsa6ddC8OYwaBc88A4mJXlcmIiKi0CwiHrIW1q93kzA2boSWLWHSJHjqKbj6aq+rExERqaTQLCKBV1bmdu2bMgW2bYP27WHmTHj8cYiL87o6ERGRr1Bo9qOs3EJtaCBSVUkJLF0K06bB3r3QuTMsWQJDhkBMjNfViYiI1Eih2U+ycguZuHY7JaXnASgsKmHi2u0ACs4SeYqKYN48mDULPv8cevZ085b794dmzbyuLuToDbmISODpXys/yczOrwzMFUpKz5OZne9RRSIeOHQIxo93F/M99xykpMC777qL/gYOVGBuhIo35IVFJVj+9YY8K7fQ69JERMKa/sXyk4NFJQ06LhJWdu+GH/0IOnRwrRj33w9bt7qL/vr00aYkl0BvyEVEvKH2DD9pFx9LYTUBuV18rAfViATI1q3u4r41a9zW1sOGuRnLnTp5XVnY0BtyERFvaKXZT9JTk4iNvnAX8tjoKNJTkzyqSMRPrHUtF6mpcOut8Pvfu6BcUAALFigwN7Ga3njrDbmIiH8pNPvJwJQEJg+6hYT4WAyQEB/L5EG36GIdCR/l5fDaa25b63vugbw8mDzZbX+dkQFt23pdYVjSG3IREW+oPcOPBqYkKCRL+Dl3zm1xPXUqfPIJXHcdzJ3rWjFitdrpbxX/T9H0DBGRwFJoFpH6KS6GxYvdqLgDB6BrV1i5Eh56yPUvS8DoDbmISODpXzoRqd2xYzBnjvs4eRJ694ZFi1wPs6ZgiIhIhFBoFpHq7dsHM2a4gHzmDAwY4GYu9+rldWUiIiIBp9AsIhfaudP1Ky9f7r4eOhTGjYPkZG/rEhER8ZBCs4g4OTlu6sW6ddC8OYwaBc8843bzExERiXAKzSKRzFrIznZheeNGaNkSXngBRo6EVq28rk5ERCRoKDSLRKKyMrdrX0YGbNsG7dvDrFnw+ONwxRVeVyciIhJ0FJpFIklJCSxbBpmZsHcv3HgjLF0Kjz4KMTFeVyciIhK0FJpFIkFREcyb51aTP/8cevZ085b794dm2hhURESkLgrNIuHs0CEXlOfNgy++cLOVJ0xws5bDaMZyVm6hdsgTERG/UmgWCUe7d7sWjKVLXf/yQw+5sJyS4nVlTS4rt5CJa7dTUnoegMKiEiau3Q6g4CwiIk1Gv5cVCSdbt8Ijj0BSkgvMw4dDfj6sWhWWgRkgMzu/MjBXKCk9T2Z2vkcViYhIONJKs0iosxY2bHCTMN5+G1q0gPR0ePppaNu22ruEUzvDwaKSBh0XERFpDIVmkVBVXu42IsnIgM2boU0bmDwZ0tIgPr7Gu4VbO0O7+FgKqwnI7eJjPahGRETCldozRELNuXOwZAncdBMMGgTHjrkL/QoKXN9yLYEZwq+dIT01idjoqAuOxUZHkZ6a5FFFIiISjrTSLBIqioth8WLOZEyl+ZFD7GzTkZe/+3+4dcwIBnzr2no/TLi1M1SsjodLu4mIiAQnhWaRYHfsGMyZ4z5OnmRH4s38cnAaGzt2B2NY/fou7GWX1TskhmM7w8CUBIVkERHxK4VmkWC1bx/MmAGLFsGZMzBgAE+0vZe3r7zugptVtFbUNzSmpyZd0NMMamcQERGpi3qaRYLNzp0wbBhcfz386lcweDDs2AFZWfzhosBcoSGtFQNTEpg86BYS4mMxQEJ8LJMH3aKVWhERkVpopVkkWOTkuEkY69ZB8+bw5JPw7LOQmFh5k6ZqrVA7g4iISMMoNAeRcJqdG8yC6nW2FtavhylTYONGaNkSJk2CUaOgVauv3FytFSIiIt5QaA4S4TY7N1gFzetcVgarV7uwvG0btG8Ps2bBD38IcXE13k2TIkRERLyh0Bwkapudq0DUdDx/nUtK3PbWmZnwj3/AjTe6mctDhkBMTL0eQq0VIiIigafQHCTCbXZusPLsdT51ym1AMmsWHDkC3/42zJwJDzwAzcL3etygaoURERG5BArNQSIcZ+cGo4C/zocOwezZLjCfPg39+rld++66C4zxzzmDRNC0woiIiDSB8F3iCjHaCjgwAvY6794NP/oRdOjgWjHuvx+2boXf/x569w77wAzht123iIhENoXmIDEwJYHv3JpAlC9MRRnDd25V72pT8/uM4q1b4ZFHICkJli2DH/yAt1/bxB1dR9Bx1UHuyHiHrNzCpjlXkFPLkYiIhBO1ZwSJrNxCXv2okPPWAnDeWl79qJAe17YMy+DckF7Xpu6LbfIL6ayFDRvcjOW334avfx3GjYOnnybr0PmgbVHwd7+xWo5ERCScKDQHCc+nOgRQQ3pdg7ovtrzcbUSSkQGbN8M3vuE+T0uDK68EIHPpO0Hxc704IN/duTWvflTo19dVM6VFRCScqD0jSPjzV9lZuYXckfEOHSe8GRTtAQ3pdQ3Kvthz59yYuORkGDQIjh2D+fOhoADGj68MzBAcLQoVbzwKi0qwuIC8PGef319XbdctIiLhRCvNQcJfv8oOxpXahgTJxoZOv7QeFBfDokUwfToUFkK3bvDyy/Cd78Bl1f+nFAwtCtW98bA13Lapw7xmSouISLjQSnOQ8NdUh2Bcqa0pMFZ3vCG3rVDdyurEtdsbv8J+7Jjb2joxEcaMgRtucFMwKi76qyEwQ3BMRWlIEFa/sYiISPUUmoOEv36VHQztARdrSJBsTOhssjcK+/bB00+7sPyzn7nZyh98AO++6+Yt12NsXDC0KNQUhC+uXv3GIiIiNVN7RhDxx6+yg6E94GIVz7E+7RMNuW2FS36jsGMHTJ0KK1a4r4cOddMwkpPrd/+LeN2iUNMFed+5NYF3Pzmq3fpERETqQaE5zAXrBIOGBMmGhs5Gv1H44AM3/eL116F5cxg50rVjJCbW+9zBqDFvPERERORCCs1hLhIDU4PeKFgL69e7sLxpE7Rs6fqXR42CVq0CWLV/eb3aLSIiEuoUmiNApAWmer1RKCuD1athyhTYtg3at4eZM+HxxyEuzqPKRUREJFgpNEtYqvGNQkkJLF0KmZnwj39A585u5vKQIRATE/A6RUREJDQoNEcgf2+fHJROnYJ582DWLDhyBL79bZgxA/r3h2YaIiMiIiK1U2iOMMG42YlfHToEs2e7wHz6NKSmwsSJbnxcPUbGiYiIiIDmNEecYNzsxC9274a0NOjY0bVi3H8/5Oa6i/5691ZgFhERkQbRSnOECcbNTppUbq67uG/1aoiOhuHDYexYuP56rysTERGREKaV5gjTmG2pg561bpe+1FTo3t1tcZ2eDgUFMG8eWacv546Md+g44U3uyHin8dtpi4iISMTSSnMNwvViuWDd7KRRysth3To3Y3nzZvjGN9znaWlw5ZVABPZwi4iIiF9opbkaFUGrsKgEy7+CVjisUA5MSWDyoFtIiI/FAAnxsUwedEtoBchz59yYuJtugkGD4NgxmD/frSyPH18ZmCGCerhFRETEr7TSXI3aglZIhcsahOxmJ8XFsGgRTJ8OhYXQtSusXAkPPQSXVf9XOex7uEVERCQgFJqrEcpBKyzbSo4dgzlz3MfJk9CnD7z0EvTtW+cUjHbxsRRW83ML6R5uERERCTiF5mqEatAKZP9uQML5vn1uVXnRIreT38CBrv3ittvq/RCh0sMdlm92Akivn4iI+JtCczVCJWhdrKa2khff2FH5/aYIFX4P5zt2wNSpsGKF+3roUBg3DpKTG/xQFfUEc6DSxYqXRq+fiIgEgrHWel1DnXr06GG3bNkS0HOG4spVxwlvUtNPMzrKUHr+X9+NjY5q9AWAd2S8U+1KfEJ8LH+ZcE+DH6/SBx+46Revvw7Nm8MTT8Azz0BiYuMfMwT47fWMEHr9RESkKRljPrLW9rj4uFaaaxCKF8vFN4/m5JnSar9XNTDDpV3Y2KQ939a6XfoyMmDTJmjZEiZNgqeegquvbvjj+Zk/3kyFcg99MNDrJyIigaDQXItQWm3Oyi2k+GxZg+7T2FDRJD3fZWVu174pU2DbNmjfHmbMgBEjIC6uUXX5m7/aAEK1hz5Y6PUTEZFA8GROszGmwBiz3RiTZ4wJbN9FPYXarObM7HxKyxvWatPYUJGemkRsdNQFx+rd811S4mYqJyXBkCHwz3+6mct79rhWjCANzOC/mc+X9HqKXj8REQkIL1ea77bWHvPw/LUKtVnNta0aRzczYPhKT3NjQ0WjLq4rKoJ582DWLPj8c+jZ003G6N+frG2HyJzx56Bf0fdXG0AoXKwYzPT6iYhIIKg9owah1idZ06+oo4whc3BXoGlDRb17vg8dgtmzXWA+fRpSU93YuD59wJiQmnzgzzaAUOyhDyZ6/URExN+8Cs0WeNsYY4EF1tqFHtVRo1Drk6xpTF7VCRn+DhVVe8C/VXaCKQVv0/F3q6G0FB5+2I2NS0m54D6htKIfqqMIRURE5NJ5FZr/3VpbaIxpA/zBGPOJtXZT1RsYY54AngBI9GDkWKgFJK9/RV2xYnzd/nwmfvgq9+X/hbJmUfzjwe/SMWMSXH99tfcLpRV9r19jERER8Y4nodlaW+j783NjzGtAT2DTRbdZCCwEN6c50DWGYkDy7FfU1vKnuS+z4I/Luasgl9MxzVnw7UEsuXUAMe3b8ZcaAjOE3oq+2gBEREQiU8BDszHmCqCZtfYL3+d9gZ8Fuo76UECqQ3k5rFsHGRnM2byZo1fEk9F7GMtT7uOLr10BgKljxTjUVvRFREQkMnmx0vwN4DVjTMX5V1hr13tQR8CF0tznWp07B8uXu62uP/kErruOqQNH89L1d/HPy2IuuGldK8YNXdEPm9dQREREQkrAQ7O1/KlXsgAAF4JJREFUdi/QNdDn9VooTYmoUXExLFrkRsUVFkK3bvDyy/Cd7/DN7UdotnY7NGLF+P+2d+9BcpVlHsd/D5NBhiwwJGCEgWwiUJFLWCKzEEWWi7sJ4CVjcEEKasEbxWJ2QTQh0S2NS5UJRlbUFVAiRilclNsku4ABTahVCoRJBnLBZIkQIJOIZGHAmFkymTz7xzkdepo+ffpMX05fvp+qrvScPt395u2uk1/eed73LXZEvyH6EAAA1KVUNjdpRpXaGKMqtm8PtrYeP1665hrpmGOCra9Xr5YuvFAaNUpdUzq0YOZkdbS3ySR1tLcNW7mjHOq6DwEAQF1jneYqqadVIvZ64YVga+tbbw128psxQ5o7V5o6Ne/pxYwYl1JeUZd9CAAAGgKhuUrqapWI9euDeuWf/jT4+ZJLgjWWjz12xC/Z3dunr/3ner22c3DvsezyCim+rrmu+hAAADQUQnOF5I6onvWeQ3XPqr7aXiXiscekhQulZcuk/feXZs0KyjGOPLKkl82tRc42MDik+cvW683de2JrlSu10gaTCwEAQBxqmisgExL7+gfkCkLgPav6dP7JHRWt+R0Rd+nBB6UzzpDe/37pN78J6pdffFH61rdKDsxS/lrkbP0Dg0XVKleibjrfZzXv3rXq7u0b8WsCAIDGw0hzBURNWFu54RU9OvfslFqVY/du6a67gpHlNWukI46QbrxR+sxnpNGjy/pWI605zve8qLrpkY4W19M23gAAID2E5gqo6QlrAwPSkiXSokXS888HdcpLlkgXXSTtu2/csxPJBNlC2zm2tbZov9Z9htU6ZxRbq1zKUnQ1/VkBAICaQXlGBUSFvVQnrPX3SwsWSBMmSFdeKb3zndJ990nr1kmXXlqRwJwpe4jS3taqBTMn66sfOV5trS3DHktSq1zKUnRpfFbdvX06beEKTZx7v05buIJSEAAA6gChuQJmT59UUggsq23bpGuvDdZY/tKXpPe+V3rkkWDSX1eXtE/0V6CUcFeojrmjvU2XTB2v0e8Ypc//7CktWr6xpHrvUkaLq/1ZUUMNAEB9ojwjQikrKiTdGroiNm0KSjCWLAnqly+4IFg2bsqUop5e6u57UYHV9PZVMDITJUc6qa+Upeiq/VlRQw0AQH0iNOdRju2ai90auuxWr5auv166+26ptVW67DJp9mzp6KMTvUyp4a5QkC13cCx1KbpqflbUUAMAUJ8oz8ij7rZrdpdWrpSmT5dOPjnY4nrOHGnzZun7308cmKXSw12hsodyB8dqbOFdLjVZ7w4AAGIx0pxH3YwG7tkjLV0aLBv3xBPSuHHB/SuukA46KCgxWbJiRGUHpe6+V6jsYdHyjWXf2S+1kf2EKrVBCwAAqCxCcx41v13zrl3SHXcEW11v2CC9+93SzTcHpRj77Sep9BKTcoS7qCDbzMGxJurdAQBAYoTmPEYS6qqyFfOOHdLixdINN0hbtkgnnSTdead0/vnSqOEfZal1w5UMd80eHOtlVBwAALyF0JxH0lBXjomDBW3fLn33u8HttdekM88MwvO0aZJZ3qeUo8SkkuGO4AgAAOoJoTlCklBXzKjuiEaiX3wxGFW+9dZgJ7+urmDN5alTY9uURolJVUbbAQAAUsDqGWUQN6qbeEOL9euDXfqOOkq66SbpwgulZ54JdvArIjBLbNoBAABQToTmMohbRixqJPoLP396+G57jz0mzZghnXBCsM7yrFnSc89JP/qRdOyxidpU7WXY6m6ZPgAAgAQozyiDuImDUSPRQ+6Su45Z9WsdftNV0kvrpDFjpPnzg8A8dmzB940rh2DTDgAAgPIgNJdBoYmD3b192scsCMhZWvYM6UMbfqN/fPwuHfvKZm094BB945wrNOfub0qjR8e+Z8UnHyZU88v0AQAAlIDQXCb5RnUzwTY7ML9j8E39/bpf6fLf3qPxr7+sTWOO0BfO+7yWHfc3Gmxp1ZwiArNU+pJy5dbMay8DAIDGR2iuoOxge+D/7dAlvQ/okz3LdOjOfvUeNknXffCz+uXRp8gteWl5rZVDNPvaywAAoLERmitoa/+ADt3xqj7ds1QX9z6gA3YN6JGJJ+vmqR/Xb4884W1rLB+8f2vRr12L5RCsvQwAABoVobmAktYd3rRJ31p5s85dtVyj9uzRg5NO081TP671447Swfu3qvXN3Roceqtso7XF9NWPHF902yiHAAAAqB5Cc4QRT7RbvVq6/nrp7rv1kVGtuuuvpummzo/pxYMPkxQE20w4LqWUgXIIAACA6iE0R0g00c5deuQRaeFC6aGHpAMPlObMUctVV2m/bUMaWr5RlifYlhpwKYcAAACoDkJzhKIm2u3ZIy1dGoTlJ56Qxo2Tvv516corpYMOkiR1vSudJeAAAABQPuwIGKHgLn+7dgW79B1/vDRzprR9u3TLLdLmzdK8eXsDMwAAABoDI80R8k20G+u7dMv2HumoS6UtW6STTpLuvFM6/3xpVLpdWdKkRQAAABREaI6QPdFuYOsfNGv9L3RJzzLt+8br0hlnSIsXS9OmvW3ZuKTKEXZrbXdAAACARkNoLqBr7JC6ti2VfrhY2rlT6uqSrr1Wmjq1LK9frrBba7sDAgAANBpqmqPcfrt01FHSTTdJF1wgPfOMdN99ZQvMUuGwm0St7Q4IAADQaBhpjnL66dLnPiddc400fnxF3qJcYbcWdwcEAABoJIw0R5kwQbrxxooFZilmhY4EZk+fpLbWlmHH2B0QAACgfAjNMbp7+3TawhWaOPd+nbZwhbp7+8r22uUKu11TOrRg5mR1tLfJJHW0t2nBzMnUMwMAAJQJ5RkFxE3UK3Xli3Juhc3ugAAAAJVDaC4gbqJeOVa+IOwCAADUPkJzAYUm6pW6zFv2KHX7/q1yl14fGBx2n01KAAAAagOhuYBCq1LkOy4p8ni23LKP13YO7n0s+z6blAAAANQGJgIWUGiiXkvEToBRx7PlG6WOMpJ1mwEAAFBehOYCCq1KMeSe9zlRx7MlXYeZTUoAAADSRXlGjKiJeh0RJRodRayxXKi8I+p8AAAApIeR5hEqZY3lfM+NwiYlAAAA6WOkeYRKWWM597msngEAAFDbzIuowU1bZ2en9/T0pN2MulDqhisAAADNzMxWuXtn7nFGmhtI3A6GAAAAGBlCc41LMnJc6oYrAAAAyI/QXMOSjhwX2sEQAAAAI8fqGTWs0MhxPlFL07FkHQAAQGkIzTUs6chxKcvgjUR3b59OW7hCE+fer9MWrlB3b19F3gcAACBtlGdUSKYWua9/QC1mGnJXR8LVLKI2QYkaOS5lGbykmHQIAACaCaG5AnIDZWZr7aTBcvb0SZp919Ma3PPWsoCt+1jBkeOoHQzLjUmHAACgmVCeUQH5AmVGoZrkvCzm55Qw6RAAADQTQnMFxAXHYoPlouUbNTg0fPOZwSFPFrorhEmHAACgmRCaKyAuOLpU1MS5Wh7NrfakQwAAgDRR01xm3b192rlrd+x5xdQ3J50ImHn/akwErOakQwAAgLQRmotQbBD9l+61uuPxF+U5x0162zEpfuLc7OmThk0olAqP5lZ7RYtqTToEAABIG+UZMTJBtK9/QK63gmhuaUV3b1/ewCwFI8NR8/f6+gciSzW6pnRowczJ6gif39HepgUzJ49oG20AAACMHCPNMYpdWm3R8o15A7OkvSPU+UotpMIjwklGc2u5BhoAAKCeMdIco9ggWiiYZko6cifOZSvHiDArWgAAAFQGoTlGsUE06jyT9tZAZ0otopQ6Ihy3ogXbXgMAAIwMoTlGsUur5TvPJF08dfze8oquKR16dO7ZkcG51BHhQjXQxdZmAwAA4O2oaY6RvbRaX/+AWsyGlVJkB+LMeZka5rPec6hWbnhFE+feP2zVjaSrYiRtb74a6Gbe9rpay/ABAIDGRWguQiZgxS3nlh1Yi1n+rZpBrlknCVZ7GT4AANCYUgnNZnaOpG9LapG02N0XptGOJJKO1MadX+01jkeyUUojaOYRdgAAUD5Vr2k2sxZJ35N0rqTjJF1kZsdVux1JJR2prbWR3Wbd9rrWPgcAAFCf0pgIeIqkTe7+nLvvknSnpBkptCORpMu51dryb0k3SmkUtfY5AACA+pRGaO6Q9FLWz1vCYzUt6UhtLY7sZlbveH7hh/To3LMbPjBLtfk5AACA+lOzEwHN7HJJl0vS+PHjU25N8sl7aUz2w9vxOQAAgHIw96jNnyv0hmbvkzTf3aeHP8+TJHdfEPWczs5O7+npqVILAQAA0KzMbJW7d+YeT6M840lJx5jZRDPbV9InJC1LoR0AAABAUapenuHuu81slqTlCpacu83d11e7HQAAAECxUqlpdvcHJD2QxnsDAAAASaVRngEAAADUFUIzAAAAEKNml5yrRd29fSxdBgAA0IQIzUXq7u3TvHvXamBwSJLU1z+gefeulSSCMwAAQIOjPKNIi5Zv3BuYMwYGh7Ro+caUWgQAAIBqITQXaWv/QKLjAAAAaByE5iId3t6W6DgAAAAaB6G5SLOnT1Jba8uwY22tLZo9fVJKLQIAAEC1MBGwSJnJfqyeAQAA0HwIzQl0TekgJAMAADQhQvMIsF4zAABAcyE0J8R6zQAAAM2H0JxQofWaayU0MxIOAABQXoTmhGp9vWZGwgEAAMqPJecSqvX1mtm5EAAAoPwIzQnV+nrNtT4SDgAAUI8IzQl1TenQgpmT1dHeJpPU0d6mBTMn10zpQ62PhAMAANQjappHoJbXa549fdKwmmaptkbCAQAA6hGhucGwcyEAAED5EZobUC2PhAMAANQjapoBAACAGIRmAAAAIAahGQAAAIhBaAYAAABiEJoBAACAGIRmAAAAIAahGQAAAIhBaAYAAABiEJoBAACAGIRmAAAAIAahGQAAAIgxKu0G1KLu3j4tWr5RW/sHdHh7m2ZPn6SuKR1pNwsAAAApITTn6O7t07x712pgcEiS1Nc/oHn3rpUkgjMAAECTojwjx6LlG/cG5oyBwSEtWr4xpRYBAAAgbYTmHFv7BxIdBwAAQOMjNOc4vL0t0XEAAAA0PkJzjtnTJ6mttWXYsbbWFs2ePimlFgEAACBtTATMkZnsx+oZAAAAyCA059E1pYOQDAAAgL0ozwAAAABiEJoBAACAGIRmAAAAIAahGQAAAIhBaAYAAABiEJoBAACAGIRmAAAAIAahGQAAAIhBaAYAAABiEJoBAACAGIRmAAAAIAahGQAAAIhBaAYAAABiEJoBAACAGIRmAAAAIIa5e9ptiGVmr0h6ocpve4ik7VV+z2ZDH1cefVxZ9G/l0ceVRf9WHn1ceeXu479090NzD9ZFaE6DmfW4e2fa7Whk9HHl0ceVRf9WHn1cWfRv5dHHlVetPqY8AwAAAIhBaAYAAABiEJqj/SDtBjQB+rjy6OPKon8rjz6uLPq38ujjyqtKH1PTDAAAAMRgpBkAAACI0fSh2cw2m9laM3vKzHryPG5m9h0z22Rma8zsvWm0s16Z2aSwbzO3N8zs6pxzzjSz17PO+Upa7a0XZnabmf3RzNZlHRtjZg+b2bPhnwdHPPfS8JxnzezS6rW6fkT07yIz2xBeB+4zs/aI5xa8piAQ0cfzzawv61pwXsRzzzGzjeF1eW71Wl0/Ivr3Z1l9u9nMnop4Lt/hIpjZkWa20syeMbP1ZnZVeJxrcRkU6N/UrsVNX55hZpsldbp73vX9wov2P0k6T9Kpkr7t7qdWr4WNw8xaJPVJOtXdX8g6fqakL7r7h9NqW70xs7+RtEPST9z9hPDYNyS96u4LwyBxsLtfm/O8MZJ6JHVKckmrJJ3s7q9V9S9Q4yL6d5qkFe6+28yul6Tc/g3P26wC1xQEIvp4vqQd7v7NAs9rkfQ/kv5O0hZJT0q6yN2fqXij60i+/s15/AZJr7v7v+Z5bLP4Dscys8MkHebuq83sAAXX0y5Jl4lrcckK9O8RSula3PQjzUWYoeCi4+7+uKT28INEch+U9PvswIyRcff/lvRqzuEZkn4c3v+xgotLrumSHnb3V8OL88OSzqlYQ+tUvv5194fcfXf44+MKLtwYoYjvcDFOkbTJ3Z9z912S7lTw3UeWQv1rZibpAkn/UdVGNRh33+buq8P7f5L0O0kd4lpcFlH9m+a1mNAc/A/vITNbZWaX53m8Q9JLWT9vCY8huU8o+iL9PjN72sweNLPjq9moBjLO3beF9/8gaVyec/g+l8enJD0Y8VjcNQWFzQp/7XpbxK+1+Q6X7nRJL7v7sxGP8x1OyMwmSJoi6bfiWlx2Of2brarX4lHleJE69wF37zOzd0p62Mw2hP9DRxmZ2b6SPippXp6HVyvYsnJHWA7TLemYarav0bi7m1lz115ViJl9WdJuSXdEnMI1ZeRulnSdgn/srpN0g4J/FFFeF6nwKDPf4QTM7C8k3SPpand/IxjID3AtLl1u/2Ydr/q1uOlHmt29L/zzj5LuU/Crv2x9ko7M+vmI8BiSOVfSand/OfcBd3/D3XeE9x+Q1Gpmh1S7gQ3g5UzpUPjnH/Ocw/e5BGZ2maQPS7rYIyaEFHFNQQR3f9ndh9x9j6Rblb/v+A6XwMxGSZop6WdR5/AdLp6ZtSoIdHe4+73hYa7FZRLRv6ldi5s6NJvZ6LC4XGY2WtI0SetyTlsm6R8sMFXBxIltQlKRIxtm9q6wxk5mdoqC7+X/VrFtjWKZpMwM7EslLc1zznJJ08zs4PBX39PCY4hhZudImiPpo+6+M+KcYq4piJAzX+Rjyt93T0o6xswmhr/B+oSC7z6K87eSNrj7lnwP8h0uXvjv1g8l/c7d/y3rIa7FZRDVv6lei929aW+S3i3p6fC2XtKXw+NXSLoivG+Svifp95LWKpiJmXrb6+kmabSCEHxQ1rHsPp4V9v/TCor63592m2v9puA/INskDSqohfu0pLGSfiXpWUm/lDQmPLdT0uKs535K0qbw9sm0/y61eIvo300KahCfCm+3hOceLumB8H7eawq3ovv49vA6u0ZB8Dgst4/Dn89TsILG7+nj4vs3PL4kc+3NOpfv8Mj6+AMKSonWZF0XzuNaXPH+Te1a3PRLzgEAAABxmro8AwAAACgGoRkAAACIQWgGAAAAYhCaAQAAgBiEZgAAACAGoRkAUmBmQ2b2lJmtM7O7zGz/Mr/+I2bWGXPO1dnva2YPmFl7OdsBAI2C0AwA6Rhw95Pc/QRJuxSsXV5tV0vaG5rd/Tx370+hHQBQ8wjNAJC+X0s62szGmFm3ma0xs8fN7ERJMrP5Zna7mT1mZs+a2WfD42ea2X9lXsTM/j3cXnYYM7vZzHrMbL2ZfS089s8KNgNYaWYrw2ObM1vYm9k14Sj4OjO7Ojw2wcx+Z2a3hq/1kJm1VbZrAKA2EJoBIEVmNkrSuQp2wvuapF53P1HSlyT9JOvUEyWdLel9kr5iZocneJsvu3tn+BpnmNmJ7v4dSVslneXuZ+W06WRJn5R0qqSpkj5rZlPCh4+R9D13P15Sv6TzE/2FAaBOEZoBIB1tZvaUpB5JL0r6oYJtY2+XJHdfIWmsmR0Ynr/U3QfcfbuklZJOSfBeF5jZakm9ko6XdFzM+R+QdJ+7/9ndd0i6V9Lp4WPPu/tT4f1VkiYkaAcA1K1RaTcAAJrUgLuflH3AzAqd73l+3q3hgx/75T7JzCZK+qKkv3b318xsSb7zEngz6/6QJMozADQFRpoBoHb8WtLFUlCvLGm7u78RPjbDzPYzs7GSzpT0pKQXJB1nZu8IV734YJ7XPFDSnyW9bmbjFJSCZPxJ0gER7egys/3NbLSkj4XHAKBpMdIMALVjvqTbzGyNpJ2SLs16bI2CsoxDJF3n7lslycx+LmmdpOcVlF8M4+5Pm1mvpA2SXpL0aNbDP5D0CzPbml3X7O6rwxHpJ8JDi92918wmlOHvCAB1ydxzf+MHAKglZjZf0g53/2babQGAZkV5BgAAABCDkWYAAAAgBiPNAAAAQAxCMwAAABCD0AwAAADEIDQDAAAAMQjNAAAAQAxCMwAAABDj/wF/Yd0W/R9X/AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(12,8))\n",
        "ax.plot(np.arange(iters), cost, 'r')\n",
        "ax.set_xlabel('Iterations')\n",
        "ax.set_ylabel('Cost')\n",
        "ax.set_title('Error vs. Training Epoch')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "-x_TmgQsG771",
        "outputId": "cb9c49e2-5e8b-46b4-9f52-118c5d700f23"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Error vs. Training Epoch')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}