LR_Implementaiton.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "# Steps to implement\n",
        "\n",
        "\n",
        "## Training\n",
        "\n",
        "\n",
        "\n",
        "*   initialize weights as zero\n",
        "*   Initialize bias as zero\n",
        "\n",
        "## Given a data point\n",
        "\n",
        "\n",
        "*   Predict result using y = wx + b\n",
        "*   calculate error\n",
        "*   Use gradient descent to figure out new weight and bias values\n",
        "*   Reapeat n times   \n",
        "\n",
        "\n",
        "## testing\n",
        "\n",
        "Given a data point\n",
        "* Put in the values from data point in the equation y = wx + b\n",
        "\n"
      ],
      "metadata": {
        "id": "3iNteFvEK4at"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "EmIfJPO5KtMC"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "Bw72vFO6JN4R"
      },
      "outputs": [],
      "source": [
        "class LinearRegression:\n",
        "\n",
        "    def __init__(self, lr = 0.001, n_iters=10000):\n",
        "        self.lr = lr\n",
        "        self.n_iters = n_iters\n",
        "        self.weights = None\n",
        "        self.bias = None\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        n_samples, n_features = X.shape\n",
        "        self.weights = np.zeros(n_features)\n",
        "        self.bias = 0\n",
        "\n",
        "        for _ in range(self.n_iters):\n",
        "            y_pred = np.dot(X, self.weights) + self.bias\n",
        "\n",
        "            dw = (1/n_samples) * np.dot(X.T, (y_pred-y))\n",
        "            db = (1/n_samples) * np.sum(y_pred-y)\n",
        "\n",
        "            self.weights = self.weights - self.lr * dw\n",
        "            self.bias = self.bias - self.lr * db\n",
        "\n",
        "    def predict(self, X):\n",
        "        y_pred = np.dot(X, self.weights) + self.bias\n",
        "        return y_pred\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Testing it out:"
      ],
      "metadata": {
        "id": "OMNAteyJOJ5J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import datasets\n",
        "import matplotlib.pyplot as plt\n",
        "\n"
      ],
      "metadata": {
        "id": "-nMLF3L1OAUs"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X, y = datasets.make_regression(n_samples=100, n_features=1, noise=20, random_state=4)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)"
      ],
      "metadata": {
        "id": "5GkmrLD9OPtl"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(8,6))\n",
        "plt.scatter(X[:, 0], y, color = \"b\", marker = \"o\", s = 30)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "uVqCCsOtOST-",
        "outputId": "c466f8d5-e92f-4798-b931-1f1c7c5c7cfe"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg = LinearRegression(lr=0.0001)\n",
        "reg.fit(X_train,y_train)\n",
        "predictions = reg.predict(X_test)\n",
        "\n",
        "def mse(y_test, predictions):\n",
        "    return np.mean((y_test-predictions)**2)\n",
        "\n",
        "mse = mse(y_test, predictions)\n",
        "print(mse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tYoTiWksOT8I",
        "outputId": "b3642a32-a03e-43cf-8f08-e9fe4759f13a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "784.272738608998\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_line = reg.predict(X)\n",
        "cmap = plt.get_cmap('viridis')\n",
        "fig = plt.figure(figsize=(8,6))\n",
        "m1 = plt.scatter(X_train, y_train, color=cmap(0.9), s=10)\n",
        "m2 = plt.scatter(X_test, y_test, color=cmap(0.5), s=10)\n",
        "plt.plot(X, y_pred_line, color='black', linewidth=2, label='Prediction')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "FO8Vv1zfOVng",
        "outputId": "8ef0369b-cbee-46cb-99a1-e972a95f5362"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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mmzrrrLMCr2fNmiXpy/9iWbp0qebMmaM9e/bo6quv1u7duzVmzBitXLlS/fv3D7znySef1MyZMzV+/HhlZGTo4osv1m9+85tElg0AABAQzewpoTV5DDMN/mr7fD55PB55vV653W6rywEAAA5AaE2uaPMaq4cBAAAOQWi1N8s2bAEAANhJ1xGu4YwYMSKwEQvWIbwCAJAiapubtHzze6ptbrK6FEfpLrRedtllMk1TmzdvTmJVCIdlAwAApIB7XlmnxZs2BF5PG1WhuWPGWliRvZmmqYyMyHN4d955p37yk58kqSJEi5lXAAAcrra5KSi4StLiTRuYgQ1h//79MgwjYnBdtmyZTNMkuNoU4RUAAIdraGuL6Xo6amtrk2EYEY+PX79+vUzT1OTJk5NYGWLFsgEAAByuOCcnpuvp5IMPPlBJSUnEMQ0NDTr22GOTUxB6jZlXAAAcrqygUNNGVQRdm15eobKCQosqst7f//53GYYRMbju3r1bpmkSXB2GmVcAAFLA3DFjVV1yvBra2lSckxMyuHr9ddrb0aCszGJ5XKUWVJl4Tz31lC6//PKIY/bv36/MzMwkVYR4I7wCAJAiygoKw8621rcuUKNvSeB1kbtGJbmzk1Vawt1+++264447Io7p7OyM6gAC2BvhFQCAFOf11wUFV0lq9C1R3sAJjp+BnTx5sv74xz9GHMOhAqmF8AoAQIrb29EQ9rpTw2txcbG2bdsWcQyhNTURXgEASHFZmcUxXbezaD72J7SmNroNAAAQR15/nZraV8jrr7O6lACPq1RF7pqga0XuGkfNunZ3hKv0ZWgluKY+Zl4BAIgTO2+KKsmdrbyBExzXbYCZVhyOmVcAAOIg3KYou83AFh41yRHBtbuZ1vLycmZa0xThFQCAOIi0KQrR6y60VlVVyTRNvfnmm0msCnZCeAUAIA5SaVNUsnX1X40UWm+66SaZpqlVq1YlsTLYEeEVAIA4SIVNUcm2Z88eGYahPn36hB3zxBNPyDRN3XvvvUmsDHbGhi0AAOLEqZuiku3jjz/W8OHDI4554YUXdPbZZyepIjgJ4RUAgDjyuEoJrWG8/vrr+vrXvx5xzJYtW3TiiScmqSI4EcsGAABAQj388MMyDCNicG1tbZVpmgRXdIuZVwAAkBBXXXWVHn300YhjOjo61LcvcQTR4+8WAAAQVyeccILef//9iGPoz4qeIrwCAIC44DQsJAPhFQCA/+P119EpoAcIrUgmwisAAJLqWxcEHe9a5K5RSe5sCyuyP0IrrEC3AQBA2vP664KCqyQ1+pbI66+zqCJ76+40LOnL0EpwRSIQXgEAaW9vR0NM19MVoRV2wLIBAEBKi2Yda1ZmcUzX0w3LA2AnhFcAQMqKdh2rx1WqInfNEWPTfdNWd6E1Ly9PO3fuTFI1wJcIrwAAy9U2N6mhrU3FOTkqKyiMyzPDrWPNGzghZCgtyZ2tvIETjpilTbcOBAcPHuz20IDJkydr2bJlSaoICEZ4BQBY6p5X1mnxpg2B19NGVWjumLG9fm6kdazhQqjHVRp0L506ELS1tSk3NzfimAULFujGG29MUkVAaIRXAIBlapubgoKrJC3etEHVJcf3ega2p+tYu2ZaTfNATDO34Z5j9xnbd955R1/96lcjjvnrX/+qiRMnJqkiIDLCKwDAMg1tbWGv9za89mQd6+EzraFEmrkN9xw7ztiuWLFCF154YcQx77zzjk455ZQkVQREh/AKALBMcU5OTNdjFW4dayih1siGEs3MbW9mbKPV05ndW2+9Vb/4xS8ijmltbVVOnH4HQLwRXgEAlikrKNS0URVBSweml1fEbdOWdOQ61nCi6ekaTQeCnqy1jVVPZnZPOeUUvffeexHHHDhwQH369IlLjUCiEF4BAJaaO2asqkuOj3u3gVjFq6dronvGxjqzS49WpBpO2AIAWK6soFAXnnRywoOr11+npvYVIY997VojG0k0R8aGek48e8ZGexoYp2EhVTHzCgBIC9F81H7oGtl9HY3a5l14xHOi+fg/lrW2sepuZpeZVqQ6Zl4BACkv3Eft4WZgC4+apKOzvhnyWdF+/N/1nHi3yQo3s5vdv4yZVqQFZl4BACmvpwcW2PXI2ENndocOulDSTRHHE1iRSgivAICU19NNVIn8+L+3svuXdTuG0IpUxLIBAEDK680mqkR9/N9TbMRCumPmFQCQFqKdRbXrsa5sxAK+RHgFAKSN7g4ssNuxrn6/X/3794845swzz9Qrr7ySpIoA67FsAAAAxdaRINR7w/WP7YlPPvlEhmFEDK4//vGPZZomwRVpx/LwevvttwfW73R9jRgxInD/iy++0IwZM3T00UfrqKOO0sUXX6yWlhYLKwYAHKq2uUnLN7+n2uYmq0vplWib/x+uvnWBNjZN0ebP5mpj0xTVty7ocQ1r166VYRgaNmxY2DHPPPOMTNPU/fff3+PvAziZLZYNnHLKKfrb3/4WeN2377/KuuGGG/T888/rv/7rv+TxeDRz5kxddNFF+vvf/25FqQCAQ9zzyjot3rQh8HraqArNHTPWwop6ricdCWI9qjWcBx54QNdff33EMW+//bZOPfXUqJ8JpCpbhNe+ffuqoKDgiOter1ePPvqonnrqKX3rW9+SJD3++OM66aST9Nprr+nrX/96sksFAPyf2uamoOAqSYs3bVB1yfEJP+Y1EXrS17Un/WMPddFFF2n58uURx7S1tSk7O7vbZwHpwhbh9f3339fQoUPVv39/jR49WvPnz1dRUZE2btyojo4OVVVVBcaOGDFCRUVFWr9+fdjw6vf75ff7A699Pl/CfwYASDcNbW1hrzsxvEqx93Xtaf/YPn36qLOzM+KYgwcPKiPD8tV9gO1Y/k9FZWWlli5dqpUrV+qRRx5RQ0ODvvGNb+jzzz9Xc3Oz+vXrd8R/cebn56u5uTnsM+fPny+PxxP4Gj58eIJ/CgBIP8U5OTFdd4pY+rrG2j+2a29HpODa1aOV4AqEZvnM6znnnBP482mnnabKykodc8wxWrZsmQYMGNCjZ86bN0+zZs0KvPb5fARYAIizsoJCTRtVEbR0YHp5RVJmXe3UizWa2Vp6tALxY3l4PVx2drZOOOEE1dfXa8KECdq/f792794dNPva0tISco1sF5fLJZfLlYRqASC9zR0zVtUlx6uhrU3FOTlJCa5268Uqhe8fS2gF4s92n0m0t7frgw8+UGFhocrLy5WZmanVq1cH7m/dulWNjY0aPXq0hVUCALqUFRTqwpNOTtqMa097sSYTR7gCiWP5zOvs2bN1/vnn65hjjtGOHTt02223qU+fPrrsssvk8Xh05ZVXatasWcrNzZXb7dZ1112n0aNH02kAANJQb3f3JxozrUDiWR5eP/74Y1122WXatWuX8vLyNGbMGL322mvKy8uTJP36179WRkaGLr74Yvn9flVXV+vhhx+2uGoAgBV6urs/0QitQPIYZhr80+Tz+eTxeOT1euV2u60uBwDQC3Za80poBeIn2rxm+cwrACA57LRDvzdi7cUab9G2sSK0AolBeAWANNCb2cra5qakdhOIRrjd/YnUNSsUyZAhQ9TS0pKkioD0ZLtuAwCA+OrNDv17Xlmni5Y9pRtX/VUXLXtK97yyLlFl2tZbb70lwzAiBtfvfe97Mk2T4AokAeEVAFJcpB36kdQ2NwUdQCBJizdtUG1zU9xqs7MHHnhAhmGotDT8DO/SpUtlmqaefPLJJFYGpDeWDQBAiuvpDv2Gtraw1+2yfCARxo0bp7Vr10Ycs2nTJo0cOTJJFQE4FOEVAFKcx1WqInfNEWteu1szWpyTE9P1cGLdKHb4+GStuY2mc0BbW1vQiY8Ako/wCgBpoCc79MsKCjVtVEXQ0oHp5RUxBchYN4odPv7F+hote+9foXLaqArNHTM26u8fjWhCa2dnZ1TjACQefV4BABH1dObT66/TxqYpR1wvL1wWMjwfPv7Dtjzd9b8XHjHuf6Z8Ly4zsPRoBeyFPq8AgLgoKygMGRa7Ww4Q61Guh49vac8O+f7errkltALORngFAMQsmuUAsW4UO/x6/lG7Q447dM1tLOtpCa1AaqBVFgAgJtH2je3aKHaoSBvFDh9/XM6nmnJycJg8dM1tfesCbWyaos2fzdXGpimqb10Q8rmGYXQbXE3TjDq41jY3afnm99KmZRhgN8y8AoAFnHxUayzLAWLdKHb4+G8dW6pLTz1yzW24AJ03cELgeyRipvWeV9YFbWBLxAYyAJERXgEgyXpzVKsdxLocINqjXP+1MWyIygr+NT7UmttIATq7f1m336snywPCHdpQXXJ8Sve9BeyG8AoASRTNjKHd9bRvbCSxzmiGCsrji/8p6cjuBIfqzZrWdD20AbAbwisAJFGsO/Dtqid9Y8PpyYxmV4Detvu3mvCV97v9HvHYiBWvQxsA9A4btgAgiXp6VKsdeVylKjxqUq9Dd6QZzXB27Nih44++qdvgGstGrO50HdpwqFgPbQDQe8y8AkASJeIjd6eLZUbz2Wef1QUXXBDxeZWVlXrttdfiUtvh5o4Zq+qS45NyXC2A0AivAJBk8fzIPRWUFRSqdEiB6nY2/+tafkFQMJw2bZp++9vfRnzOr371K91www0Jq7NLuEMbACQH4RUALBDtDvx0UNvcFBRcJam2pVm1zU0aWTi02/dv2LBBp59+eqLKA2AzrHkFAFgq1NrWhh/P7ja4er1emaZJcAXSDDOvAJBmIh2QYMXhCYeubW34cff9bjnCFUhvhpkG/y/g8/nk8Xjk9XrldrutLgcALBPpgAQrD09IxGlYAJwl2rzGzCsApIlIByR0/TnUvUTOwEYTWlc3nCDpy/pZJwyA8AoAaSLSAQmR3pOIwBhLaE10LQCchfAKAGmiJwckxPvwhGhC6+4varWxaUrCawHgTHQbAIA00XVAwqG6DkiIdC8eDMPoNrh2nYaV6FoAOBsbtgAgzSSz20BvNmJZ0fkAgHWizWuEVwBA3NE9AECsos1rLBsAAMTF/v37o1oesLrhBO3+ojY5RQFIOYRXAECvbN26VYZhyOVyRRy3uuGEQAeBSB0OACASwisAOIjXX6em9hXy+uusLkVLliyRYRgaMWJE2DGZhQUqfmCBlmw6M+g6nQMA9BStsgDAIaw8AetQ48aN09q1ayOOyf3Ot+X51rjA65b2bB2X86kkOgcA6B3CKwA4QKTTsZIVBKPZhPX031Zp3rtHzgp/85g5+kpuK50DAPQa4RUAHCDS6ViJDoPRhNY9e/YoKytLkrTN1U+LN20I3JteXqExRWMTVh+A9EJ4BQAH6MnpWL3V03ZXc8eMVXXJ8Wpoa1NxTo7KCgoTUR6ANEV4BQAH6Dp16vA1r4mYdY1Hj9aygkJCK4CEILwCgEOU5M5W3sAJ2rV3rSRDR2fF96N4DhYA4ASEVwCIo0QfafrpnlWB2ddt3ofi0nGgp6GV41sBWIHwCgBxkuhWVvHuONCbmVa7tO0CkH44pAAA4iBcsIznYQKROg7EIpojXE3TDBtck/GzAkA4hFcAiIN4BctIwnUWaN33alTvjya0/qNpR7frWpPxswJAOIRXAIiDZLSy8rhKlZ/1nSOut+z5U8RZz2hCa/EDC1T8wAI1tLV1W4cVbbsAoAvhFQDioKuV1aES0cpqQOawkNcPn/Vsb2+PKbQGXufkdFtDsn5WAAiFDVsAECddrawStQP/8E1Sh+qa9XzjjTdUWVnZ7bP+0bRDD76+Xi9/9K/QO728IurerIn+WQEgHMIrAMSRx1WakCAXapNUlyJ3jX5553/rF78oi/iMiooKvfHGG7rnlXW6aNlTgetnHVOs6ypHx3yoQKJ+VgCIhPAKAA4QbjPUd8s/UVvrTRHfu3DhQl177bWSpNrmJi3etCHo/ssfNei6ytHxKRQAEozwCgAOcPhmqPHF/+z2PVu2bNGJJ54YdC3chqyGtjaOcwXgCIRXAHCArk1Sxx8deZZVkvbv36/MzMyQ98JtyIpmoxYA2IFjug0sXLhQxx57rPr376/Kykq98cYbVpcEAEljGEa3wbXrYIFwwVWSygoKNW1URdC1aDdqef11ampfwWEEACzliJnXZ555RrNmzdKiRYtUWVmp+++/X9XV1dq6dauGDBlidXkAkDC9OcI1nLljxqq65Hg1tLWpOCcnquDKcbAA7MIwY/1/PQtUVlaqoqJCDz30kCSps7NTw4cP13XXXaebb7652/f7fD55PB55vV653e5ElwsAvZaI0NpTXn+dNjZNOeJ6eeEyug0AiJto85rtlw3s379fGzduVFVVVeBaRkaGqqqqtH79+pDv8fv98vl8QV8AkCi1zU1avvk91TY39fqj9WgOFnh/131JC64Sx8ECsBfbLxv47LPPdPDgQeXn5wddz8/P15YtW0K+Z/78+brjjjuSUR6ANHfPK+uCWk9NLKnVd0/+ck1+LB+tRzPTurrhBElSo2+JsjKPlWH0TcoBARwHC8BObD/z2hPz5s2T1+sNfG3fvt3qkgCkoFA9U1fWl+nDtjxJX4bM7mZgo5lpXd1wQiC4dtmy66fa/NlcbWyaovrWBWHeCQCpx/bhdfDgwerTp49aWlqCrre0tKigoCDke1wul9xud9AXAMRbuJ6pLe3ZgT+H+2g9mtBqmqZ2f1HbbR2NviXa8fkfux3XUywbAGAntg+v/fr1U3l5uVavXh241tnZqdWrV2v0aE6EAWCdcL1R84/aHfjzoR+tm6YZ9UxrV2jt6u/anS27fpqwGdjulg0cuuYXABLN9mteJWnWrFm64oordPrpp+trX/ua7r//fu3Zs0c/+MEPrC4NQBrr6pl66NKBc0r+oeNyPpX05ZpXj6tUu3bt0uDBg7t93qFLA/Z2NATWspbkzlbewAna29Ggvfsb9JFvUcj3N/qWKG/ghLivge0K0Ie3yvK4So9Y8zttVIXmjhkb1+8PAIdyRHi95JJL9Omnn+rWW29Vc3OzysrKtHLlyiM2cQFAsh3eM7U4p0p7OxqUlVmsjX/fpePHR78R61CHz3Z6XKXyuEr1YduDEZ91aOiNp0MDdNcmsVBrfhdv2qDqkuM5ahZAwth+2UCXmTNn6qOPPpLf79frr7+uyspKq0sCAElfzsBeeNLJKiso/HI28mdrlN2/TOPHjw/7ngsuuCBwItbhywK6ZjVDi9wiK5EdADyuUhUeNSlQW7g1v+GuA0A8OGLmFQCcICMjo9v+q3/4wx906aWXBl0LNasZztFZ39Q278KQ9yKH3vgLt+Y33HUAiAfCKwD0UjQ9Wrdv365hw4aFvd+1LKA7odafHt1/nI7NuTbpp12FWvM7vbyCJQMAEsoRx8P2FsfDAkiEaELrwYMHlZER/xVaXn9dVDO1yVDb3BRY80twBdBT0eY1Zl4BIEbRhNZEzwtEO1ObDGUFhYRWAElDeAWACA6d4czuX9bt+DT4MAsALEV4BYAw6lsXqNG3ROOL/9ntWEIrACQH4RUAQvD663T80Td1O47QCgDJRXgFgMPYYU0rACA0xxxSAACJZhhGt8F19xe1BFcAsBAzrwBSWjQtpaKZaV3dcELSDwEAAByJ8AogZXVtuOpS5K5RSe5sSVJnZ6f69OnT7TN2f1FrWT9VO/VyBQC7ILwCSElef11QcJWkRt8Sdew+TSd/pbrb9x+6NMCK4BgpeANAOmPNK4CUtLejIej12r98rvHF/+w2uJqmafma1nDB2+uv69Uzm9pX9OoZAGAHzLwCSElZmcWSpNuv2aH/XdkecexVV12lJUuWRByTTIcH70Ov92QWmFlcAKmE8AogJUVzGtbKlStVXd39EoJk6wre0V6PJNwsbt7ACayjBeBILBsAYFs9+ag7mnZXn376qUzTtGVwlb5cY1vkrgm61tNOB5FmcQHAiZh5BWBLsX7UHU27q87OzqjG2UFJ7mzlDZzQ624D8ZzFBQA7YOYVgO3EsmEpmpnWrk1YTgmuXTyuUhUeNalXH+/HcxYXAOyAmVcAthPuI+1de9cGZiKjWdMaTdeAZPRStbpfa7xmcQHADgivAGwn3Efa27wLNb74n92+P9pWV8nYhW+Xnf4eVymhFUBKYNkAANsJ9VH3+OJ/dhtcY+nRmoheqlZ8DwBIN8y8ArClro+647U84HDx7qVq1fcAgHRDeAVgS9Fsrtr9Ra2td+Gz0x8A4o9lAwBsJZruAasbTtD7u+6z/S58dvoDQPwZptWHeCeBz+eTx+OR1+uV2+22uhwAhzlw4IAyMzO7Hbf7i9q475gP1Qkg3t0BrO42AABOEG1eY9kAAMts27ZNxcXdf4R+6H9jxzv8Hb4LPxHdAdjpDwDxw7IBAEm3bNkyGYYRMbiecsopMXUPiAe6AwCA/RFeASTND3/4QxmGoUsuuSTsmJ/fdb1M09Q777yTxMq+FKk7AADAHlg2AKQJK9ddRtM5YPHzRSo5ub+kv6i+9d8saeRPdwAAsD/CK5AGrDrlKZrQ+smu9driuyLoWqNvifIGTkh6yO7qDnD4XyvWqwKAfRBegRQXbh1nIsNhNKG1ay1rU/uKkPetauTfdTgC3QEAwJ4Ir0CKS+YpT7GE1i52/Kie7gAAYF9s2AJSXDLCYTQHC4TrHEAjfwBALJh5BVJcItdx9mSmNRQ+qgcARIvwCqSBeIfDeIXWQyXzo3pOvAIA5yK8AmkiHuEwEaE12azqvAAAiA/WvALoVm/WtNoJJ2gBgPMRXgGElSqhtQsnaAGA8xFeAQTx+/0pF1q72LEtFwAgNoRXAJKkDz74QIZhqH///mHH9O/f35GhtQttuQDA+QivQJpbtmyZDMNQSUlJ2DHTpk2TaZrat29fEitLjJLc2SovXKaTBt+j8sJlbNYCAIeh2wCQpqZOnaonnngi4pj/+Z//0YUXXhjynh3aTdU2N6mhrU3FOTkqKyiM+n2coAUAzkV4BdJMNO2utm3bpmOOOSbsfTu0m7rnlXVavGlD4PW0URWaO2ZsUmsAACQfywYAG/H669TUviIhrZui2YS1f/9+maYZMbjaod1UbXNTUHCVpMWbNqi2uSlpNQAArMHMK2ATiZrNjPfBApHaTSXro/iGtraw12NZPgAAcB5mXgEbSMRsZqLaXdmh3VRxTk5M1wEAqYPwCthAPJvnJ7pHqx3aTZUVFGraqIqga9PLK5h1BYA0wLIBwAbiMZsZ7+UBkZTkzlbewAmWdhuYO2asqkuO71G3AQCAc1k683rssccGZom6vu6+++6gMW+99Za+8Y1vqH///ho+fLjuvfdei6oFEqc3s5lWnYblcZWq8KhJlracKiso1IUnnUxwBYA0YvnM689//nPV1PzrX9qDBg0K/Nnn8+nss89WVVWVFi1apLfffls//OEPlZ2drauvvtqKcoGEiXU2M5kzrQAA2IXl4XXQoEEqKCgIee/JJ5/U/v379dhjj6lfv3465ZRTVFtbq1/96lcRw6vf75ff7w+89vl8ca8bSIRomucTWgEA6czyDVt33323jj76aI0cOVL33XefDhw4ELi3fv16jR07Vv369Qtcq66u1tatW9UWplWOJM2fP18ejyfwNXz48IT+DEAyWLU8AAAAO7E0vP7oRz/S008/rZdfflnTpk3TXXfdpTlz5gTuNzc3Kz8/P+g9Xa+bm5vDPnfevHnyer2Br+3btyfmBwAS7IsvviC0AgBwiLiH15tvvvmITViHf23ZskWSNGvWLI0bN06nnXaapk+frl/+8pd68MEHgz7y7wmXyyW32x30BTjJ9u3bZRiGBgwYEHbMhAkTCK0AgLQT9zWvN954o6ZOnRpxzHHHHRfyemVlpQ4cOKBt27bpxBNPVEFBgVpaWoLGdL0Ot04WcLLVq1erqqoq4phFixZp2rRpSaoIAAB7iXt4zcvLU15eXo/eW1tbq4yMDA0ZMkSSNHr0aP30pz9VR0eHMjMzJUmrVq3SiSeeqBxO0kEKeeaZZ3TppZdGHLNp0yaNHDkySRUBAGBPlq15Xb9+ve6//37V1dXpww8/1JNPPqkbbrhB//7v/x4Ipt/73vfUr18/XXnllXr33Xf1zDPP6IEHHtCsWbOsKhuIq65lNpGCa1tbm0zTTKng6vXXqal9Ra+OvwUApCfLWmW5XC49/fTTuv322+X3+1VcXKwbbrghKJh6PB69+OKLmjFjhsrLyzV48GDdeuut9HiF45155pl69dVXI445ePCgMjIsbwgSd/WtC9ToWxJ4XeSuUUnubAsrAgA4iWGmwW4Pn88nj8cjr9fL5i1YKicnR7t37444JpX/kfT667SxacoR18sLl6mhbQhHvQJAGos2r1l+SAGQDjhY4Et7OxpCXl/w6gY9+bY38HraqArNHTM2WWUBABwk9T6TBGyEHq3BsjKLj7j2YVteUHCVpMWbNqi2uSlZZQEAHITwCiQAoTV6Le3ZIa83RDhFDwCQvlg2AMQRywMiC7VsIP+o3SHHFtMODwAQAjOvQBww0xqdUMsGjsv5VFNLjwm6Nr28gk1bAICQmHkFeqG7wGoYhjo7O5NUjf15XKUqctcc0Srr1m9+Vxec2ES3AQBAt2iVBcTINM1u+6+OHz9ef/vb35JUkfN4/XXa29GgrMxieVylVpcDALCBaPMaywaAKH3xxRcyDCNicP3pT38q0zQJrt3wuEpVeNQkgisAIGYsGwC6sWvXLg0ePDjimKefflqXXHJJkioCACB9EV6BMLZs2aKTTjop4piNGzdq1KhRSaoIAAAQXoHDvPjii6quro44pqmpSQUFBUmqKHHivfaUtawAgEQjvAL/5+GHH9aMGTMijvH7/erXr1+SKkqs+tYFR+z6L8mdbZvnAQAQChu2kPauvfZaGYYRMbh2dnbKNM2UCa5ef11Q0JSkRt8Sef11tngeAADhEF6Rti655BIZhqFHHnkk7JiugwWiOTnLSUKddBXperKfBwBAOCwbQNopKSnRBx98EHFMqrc/DnXSVaTryX4eAADhMPOKtNF1hGuk4JouR7h2nXR1qCJ3TY83WcX7eQAAhMMJW0h50Xzknwb/GIREtwEAgF1Em9dYNoCUlQqhNdFh0OMqjetz4/08AAAOR3hFyukutJaVlekf//hHkqrpua7WUx+25amlPVun5Y/T+SfQegoAkN4Ir0gZ3YXWH/zgB3rssceSVE3vdLWe+uN7X9PK+rLA9X80/VG3fvO71hUGAIDF2LAFR+tqYxUpuN57770yTdMxwVX6ssXUh215QcFVkpbWfaTa5iZrigIAwAaYeYUjHThwQJmZmRHHvPzyyxo3blxyCoqzrMxitbRnh7zX0NamsoLCiO9n4xQAIFURXuEo7e3tGjRoUMQx7777rk4++eS4f+/a5iY1tLWpOCen2/DYWx5XqU7LHxfyXnFOTsT3ckwrACCVEV7hCC0tLSooKIg4prm5Wfn5+Qn5/ve8sk6LN20IvJ42qkJzx4xNyPfqcv4Js/WPpj9qad1HgWvTyysiBudwx7TmDZzADCwAICUQXmFrW7du1YgRIyKO2bdvn/r375+wGmqbm4KCqyQt3rRB1SXHJ3wG9tZvflcXnBj9jG+kY1oJrwCAVEB4hS29+uqrOvPMMyOOOXjwoDIyEr/nsKGtLez1RIdXSSorKIz6+3BMKwAg1dFtALayfPlyGYYRMbh2HeGajOAqhV9j2t3aUytwTCsAINUx8wpbeOihh3TddddFHGPVaVhlBYWaNqoiaOlAd2tPrVSSO1t5AyfQbQAAkJIM0+7nY8ZBtGflIvluuukmLViwIOIYu/wtmsxuA3b63gAAJEO0eY2ZV1jioosu0vLly8PeLyoq0kcffRT2vhViWXsaT1Z0OgAAwK5Y84qkOvXUU2UYRtjgOmHCBJmmabvgapVwnQ44ZQsAkK4Ir0iKriNc33333ZD3r732WpmmqRdffDHJldlbpE4HAACkI8IrEqortIZz7733yjRNLVy4MIlVOYeTOh0AAJAMhFckRHeh9amnnpJpmrrpppuSWJXzdHU6OJSdOx0AAJBobNhC3ETTe/Wll17SWWedlaSKUsPcMWNVXXI83QYAABDhFXFw4MABZWZmRhzz9ttv69RTT01SRanHqk4HAADYDeEVPbZ3714NHDgw4pjt27dr2LBhSaoIAACkOsIrYtba2qqjjz464pjdu3fL4/EkqSI4jddfxwlgAIAeIbwiatu3b1dRUVHEMR0dHerbl7+tEF596wI1+pYEXhe5a1SSO9vCigAATkK3AXTrvffek2EYEYNrZ2enTNMkuCIir78uKLhKUqNvibz+OosqAgA4DeEVYb366qsyDEOnnHJKyPs5OTkyTVOmaUZsiwV02dvRENN1AAAOR3jFEZ599lkZhqEzzzwz5P3y8nKZpqnW1tYkVwany8osjuk6AACHI7wi4LHHHpNhGLrgggtC3r/oootkmqbefPPNJFeWPrz+OjW1r0jZj9E9rlIVuWuCrhW5a9i0BQCIGgsUobvuuks//elPw97/8Y9/rPvvvz95BaWpdNnIVJI7W3kDJ9BtAADQI4TXNDZz5kwtXLgw7P177rlHc+bMSWJF6SvcRqa8gRNSMtx5XKUp+XMBABKP8JqGLrjgAj377LNh7z/xxBP6/ve/n8SKrGOXfqORNjIR8gAA+BfCaxo57bTT9Pbbb4e9//zzz+vcc89NYkXWstPH9GxkAgAgOgnbsHXnnXfqjDPOUFZWlrKzs0OOaWxs1HnnnaesrCwNGTJEN910kw4cOBA0Zs2aNRo1apRcLpdKSkq0dOnSRJWcsgYNGiTDMMIG19dee02maaZVcLVbv1E2MgEAEJ2Ezbzu379fkydP1ujRo/Xoo48ecf/gwYM677zzVFBQoFdffVVNTU36/ve/r8zMTN11112SpIaGBp133nmaPn26nnzySa1evVpXXXWVCgsLVV1dnajSU4JpmsrIiPzfJps3b9aIESOSVJG92PFjejYyAQDQPcM0TTOR32Dp0qW6/vrrtXv37qDrf/3rX/Xtb39bO3bsUH5+viRp0aJFmjt3rj799FP169dPc+fO1fPPP6933nkn8L5LL71Uu3fv1sqVK6OuwefzyePxyOv1yu12x+XnsquOjg7169cv4piPP/5Y//Zv/5akiuzJ66/TxqYpR1wvL1yWMqGxtrlJDW1tKs7JUVlBodXlAAAQUbR5zbI+r+vXr9dXv/rVQHCVpOrqavl8Pr377ruBMVVVVUHvq66u1vr16yM+2+/3y+fzBX2luj179sgwjIjBta2tTaZppn1wlaz9mD5cL9d49ni955V1umjZU7px1V910bKndM8r63r9TAAA7MCyDVvNzc1BwVVS4HVzc3PEMT6fT/v27dOAAQNCPnv+/Pm64447ElC1/Xz22WfKy8uLOGbv3r1h/1qlMys+pg+3SSzazWPRdEeobW7S4k0bgq4t3rRB1SXHMwMLAHC8mGZeb775ZhmGEfFry5Ytiao1avPmzZPX6w18bd++3eqS4u6jjz6SYRgRg+uBAwdkmibBNQKPq1SFR01K2oxrqE1iOz7/Y1Sbx+pbF2hj0xRt/myuNjZNUX3rgpDfp6GtLabrAAA4SUwzrzfeeKOmTp0accxxxx0X1bMKCgr0xhtvBF1raWkJ3Ov6365rh45xu90RA5nL5ZLL5YqqDqd56623VFoaOWh1dnbKMIwkVWQ9u/Rq7U64TWI+/1thx3f9PLEcYlCckxPyeeGuAwDgJDGF17y8vG4/oo7W6NGjdeedd2rnzp0aMmSIJGnVqlVyu906+eSTA2P+8pe/BL1v1apVGj16dFxqcJJ169bpm9/8Ztj7hYWF2rFjRxIrsgc79WrtTrierW7XadrR/kzE8bF0RygrKNS0URVBSwf+/avZKs7ZKYllAwAAZ0vYhq3GxkbV1taqsbFRBw8eVG1trWpra9Xe3i5JOvvss3XyySfr//2//6e6ujq98MIL+tnPfqYZM2YEZk2nT5+uDz/8UHPmzNGWLVv08MMPa9myZbrhhhsSVbbtLF++XIZhhA2uZ5xxhkzTTMvgarderd0Jt0ls6KDvdrt5LNZDDOaOGav/mfI9zTnD1E++sVzjiu+NuNQAAACnSFirrKlTp+qJJ5444vrLL7+scePGSfpy3eY111yjNWvWaODAgbriiit09913q2/ff00Ir1mzRjfccIPee+89DRs2TLfccku3SxcO58RWWYsXL9b06dPD3r/sssv01FNPJbEi+2lqX6HNn8094vpJg+9R4VGTkl9QlMItc+hu+UOss8zp0A4MAJA6os1rCe/zagdOCq+33357xE4JN910k+69994kVmRf6RjOYlnf69RwDwBIT9HmNctaZSFYTU2N/vM//zPs/V/96ldptVwiGl0fwx8+G5mqwVX68meO9ueLdakBAABOQHi12MSJE/XCCy+Evf/kk0/qe9/7XhIrchaOVA0vHcM9ACD1EV4tMmLECG3dujXs/RdffFETJkxIYkXOFctsZLoh3AMAUg3hNYlM01S/fv104MCBsGM2bNig008/PYlVIdUR7gEAqYTwmgSdnZ3q06dPxDHvv/++SkpKklQRAACAMxFeE2j//v3dnvTV1NQUOFEMAAAAkSXskIJ05vf79e1vfzticPV6vTJNk+AKAAAQA2Ze42znzp3Kz88Pe/+LL77odjYWAAAAoRFe4+y5554Lef3AgQPdrnsFAABAZCwbiLNvf/vbOv/88yVJt912mzo7O2WaJsEVAAAgDph5jbMhQ4boz3/+s9VlAAAApCRmXgEAAOAYhFcAAAA4BuEVAAAAjsGaV6Qkr79OezsalJVZzNGoAACkEMIrUk596wI1+pYEXhe5a1SSO9vCigAAQLywbAApxeuvCwquktToWyKvv86iigAAQDwRXpFS9nY0xHQdAAA4C+EVKSUrszim6wAAwFkIr0gpHlepitw1QdeK3DVs2gIAIEWwYQsppyR3tvIGTqDbAAAAKYjwipTkcZUmLLTShgsAAOsQXoEY0IYLAABrseYViBJtuAAAsB7hFYgSbbgAALAe4RWIEm24AACwHuEViBJtuAAAsB4btoAY0IYLAABrEV6BGCWyDRcAAIiMZQMAAABwDMIrAAAAHIPwCgAAAMcgvAIAAMAxCK8AAABwDMIrAAAAHIPwCgAAAMcgvAIAAMAxCK8AAABwDMIrAAAAHIPwCgAAAMcgvAIAAMAxCK8AAABwDMIrAAAAHIPwCgAAAMcgvAIAAMAxCK8AAABwDMIrAAAAHIPwCgAAAMdIWHi98847dcYZZygrK0vZ2dkhxxiGccTX008/HTRmzZo1GjVqlFwul0pKSrR06dJElQwAAACbS1h43b9/vyZPnqxrrrkm4rjHH39cTU1Nga9JkyYF7jU0NOi8887TWWedpdraWl1//fW66qqr9MILLySqbAAAANhY30Q9+I477pCkbmdKs7OzVVBQEPLeokWLVFxcrF/+8peSpJNOOkmvvPKKfv3rX6u6ujqu9QIAAMD+LF/zOmPGDA0ePFhf+9rX9Nhjj8k0zcC99evXq6qqKmh8dXW11q9fH/GZfr9fPp8v6AsAAADOl7CZ12j8/Oc/17e+9S1lZWXpxRdf1LXXXqv29nb96Ec/kiQ1NzcrPz8/6D35+fny+Xzat2+fBgwYEPK58+fPD8z8AgAAIHXENPN68803h9xkdejXli1bon7eLbfcojPPPFMjR47U3LlzNWfOHN13330x/xCHmzdvnrxeb+Br+/btvX4mAAAArBfTzOuNN96oqVOnRhxz3HHH9biYyspK/eIXv5Df75fL5VJBQYFaWlqCxrS0tMjtdoeddZUkl8sll8vV4zoAAABgTzGF17y8POXl5SWqFtXW1ionJycQPEePHq2//OUvQWNWrVql0aNHJ6wGAAAA2FfC1rw2NjaqtbVVjY2NOnjwoGprayVJJSUlOuqoo/Tss8+qpaVFX//619W/f3+tWrVKd911l2bPnh14xvTp0/XQQw9pzpw5+uEPf6iXXnpJy5Yt0/PPP5+osgEAAGBjhnno9v44mjp1qp544okjrr/88ssaN26cVq5cqXnz5qm+vl6maaqkpETXXHONampqlJHxr6W4a9as0Q033KD33ntPw4YN0y233NLt0oXD+Xw+eTweeb1eud3u3v5oAAAAiLNo81rCwqudEF6Tw+uv096OBmVlFsvjKrW6HAAA4CDR5jVLW2UhddS3LlCjb0ngdZG7RiW5syO8AwAAIHaWH1IA5/P664KCqyQ1+pbI66+zqCIAAJCqCK8J5PXXqal9RcqHuL0dDTFdBwAA6CmWDSRIOn2MnpVZHNN1AACAnmLmNQHS7WN0j6tURe6aoGtF7ho2bQEAgLhj5jUBIn2MnqqBriR3tvIGTqDbAAAASCjCawKk68foHlcpoRUAACQUywYSgI/RAQAAEoOZ1wThY3QAAID4I7wmEB+jAwAAxBfLBgAAAOAYhFcAAAA4BuEVAAAAjkF4BQAAgGMQXgEAAOAYhFcAAAA4BuEVAAAAjkF4BQAAgGMQXgEAAOAYhFcAAAA4BuEVAAAAjkF4BQAAgGMQXgEAAOAYhFcAAAA4BuEVAAAAjkF4BQAAgGP0tbqAVOT112lvR4OyMovlcZVaXQ4AAEDKILzGWX3rAjX6lgReF7lrVJI728KKAAAAUgfLBuLI668LCq6S1OhbIq+/zqKKAAAAUgvhNY72djTEdB0AAACxIbzGUVZmcUzXAQAAEBvCaxx5XKUqctcEXSty17BpCwAAIE7YsBVnJbmzlTdwAt0GAAAAEoDwmgAeVymhFQAAIAFYNgAAAADHILwCAADAMQivAAAAcAzCKwAAAByD8AoAAADHILwCAADAMQivAAAAcAzCKwAAAByD8AoAAADHILwCAADAMQivAAAAcAzCKwAAAByD8AoAAADHILwCAADAMQivAAAAcIy+VheQDKZpSpJ8Pp/FlQAAACCUrpzWldvCSYvw+vnnn0uShg8fbnElAAAAiOTzzz+Xx+MJe98wu4u3KaCzs1M7duzQoEGDZBiG1eXYjs/n0/Dhw7V9+3a53W6ry0Ev8ftMLfw+Uwu/z9TC7zO+TNPU559/rqFDhyojI/zK1rSYec3IyNCwYcOsLsP23G43//ClEH6fqYXfZ2rh95la+H3GT6QZ1y5s2AIAAIBjEF4BAADgGIRXyOVy6bbbbpPL5bK6FMQBv8/Uwu8ztfD7TC38Pq2RFhu2AAAAkBqYeQUAAIBjEF4BAADgGIRXAAAAOAbhFQAAAI5BeAUAAIBjEF4RsG3bNl155ZUqLi7WgAED9JWvfEW33Xab9u/fb3Vp6KE777xTZ5xxhrKyspSdnW11OeiBhQsX6thjj1X//v1VWVmpN954w+qS0APr1q3T+eefr6FDh8owDK1YscLqktAL8+fPV0VFhQYNGqQhQ4Zo0qRJ2rp1q9VlpQ3CKwK2bNmizs5OLV68WO+++65+/etfa9GiRfrJT35idWnoof3792vy5Mm65pprrC4FPfDMM89o1qxZuu2227Rp0yaVlpaqurpaO3futLo0xGjPnj0qLS3VwoULrS4FcbB27VrNmDFDr732mlatWqWOjg6dffbZ2rNnj9WlpQX6vCKi++67T4888og+/PBDq0tBLyxdulTXX3+9du/ebXUpiEFlZaUqKir00EMPSZI6Ozs1fPhwXXfddbr55pstrg49ZRiGli9frkmTJlldCuLk008/1ZAhQ7R27VqNHTvW6nJSHjOviMjr9So3N9fqMoC0s3//fm3cuFFVVVWBaxkZGaqqqtL69estrAzA4bxeryTx78skIbwirPr6ej344IOaNm2a1aUAaeezzz7TwYMHlZ+fH3Q9Pz9fzc3NFlUF4HCdnZ26/vrrdeaZZ+rUU0+1upy0QHhNAzfffLMMw4j4tWXLlqD3fPLJJ5o4caImT56smpoaiypHKD35fQIAEmPGjBl655139PTTT1tdStroa3UBSLwbb7xRU6dOjTjmuOOOC/x5x44dOuuss3TGGWfot7/9bYKrQ6xi/X3CmQYPHqw+ffqopaUl6HpLS4sKCgosqgrAoWbOnKnnnntO69at07Bhw6wuJ20QXtNAXl6e8vLyohr7ySef6KyzzlJ5ebkef/xxZWQwOW83sfw+4Vz9+vVTeXm5Vq9eHdjY09nZqdWrV2vmzJnWFgekOdM0dd1112n58uVas2aNiouLrS4prRBeEfDJJ59o3LhxOuaYY7RgwQJ9+umngXvM9DhTY2OjWltb1djYqIMHD6q2tlaSVFJSoqOOOsra4tCtWbNm6YorrtDpp5+ur33ta7r//vu1Z88e/eAHP7C6NMSovb1d9fX1gdcNDQ2qra1Vbm6uioqKLKwMPTFjxgw99dRT+tOf/qRBgwYF1qF7PB4NGDDA4upSH62yELB06dKw/1LkbxNnmjp1qp544okjrr/88ssaN25c8gtCzB566CHdd999am5uVllZmX7zm9+osrLS6rIQozVr1uiss8464voVV1yhpUuXJr8g9IphGCGvP/74490u60LvEV4BAADgGCxoBAAAgGMQXgEAAOAYhFcAAAA4BuEVAAAAjkF4BQAAgGMQXgEAAOAYhFcAAAA4BuEVAAAAjkF4BQAAgGMQXgEAAOAYhFcAAAA4xv8HK1omYCA1HZ8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}