kNN_with_CICAndMal2017.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PjDulR923sXL"
      },
      "source": [
        "# **1. Dowload data**</a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Upb3qvGXvUUI"
      },
      "outputs": [],
      "source": [
        "!wget -c http://205.174.165.80/CICDataset/CICMalAnal2017/Dataset/APKs/Adware-APKs.zip -O /content/drive/MyDrive/Adware.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rzroyJYJ5xa5"
      },
      "outputs": [],
      "source": [
        "!wget -c http://205.174.165.80/CICDataset/CICMalAnal2017/Dataset/APKs/Benign-APKs-2015.zip -O /content/drive/MyDrive/Benign.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KZ7B2UKo8sy7"
      },
      "outputs": [],
      "source": [
        "!wget -c http://205.174.165.80/CICDataset/CICMalAnal2017/Dataset/APKs/SMSmalware-APKs.zip -O /content/drive/MyDrive/SMSmalware.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O56bq1Ii9D07"
      },
      "outputs": [],
      "source": [
        "!wget -c http://205.174.165.80/CICDataset/CICMalAnal2017/Dataset/APKs/Scareware-APKs.zip -O /content/drive/MyDrive/Scareware.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "618s5kFH2XxD"
      },
      "outputs": [],
      "source": [
        "# Can't dowload because of \"Android/Banker.LH!tr\".\n",
        "!wget -c http://205.174.165.80/CICDataset/CICMalAnal2017/Dataset/APKs/Ransomware-APKs.zip -O /content/drive/MyDrive/Ransomware.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3IPalyYr4JUq"
      },
      "source": [
        "# **2. Unzip to APKS**</a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUjwqFOpJfep",
        "outputId": "58b781d5-0dfc-4249-e316-c1341642e198"
      },
      "outputs": [],
      "source": [
        "!unzip /content/drive/MyDrive/Adware.zip -d '<your_path>'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OcnEI95qDnKI",
        "outputId": "050da8cf-2376-4899-cdf9-d892b4269879"
      },
      "outputs": [],
      "source": [
        "!unzip /content/drive/MyDrive/Benign.zip -d '<your_path>'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "awZMP3wpMHQ0",
        "outputId": "5425a3dc-0bb9-4abd-85cd-f42ee40afe54"
      },
      "outputs": [],
      "source": [
        "!unzip /content/drive/MyDrive/SMSmalware.zip -d '<your_path>'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-sCZabfAMIpZ",
        "outputId": "a7eed10d-d8d2-4dcc-da0d-29d0736f46a3"
      },
      "outputs": [],
      "source": [
        "!unzip /content/drive/MyDrive/Scareware.zip -d '<your_path>'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B1V5MwvSC1gh"
      },
      "source": [
        "# **3. Convert APKS to image**</a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TWC56OdlUFAG"
      },
      "outputs": [],
      "source": [
        "#Thanks to this topic. Ref: https://stackoverflow.com/questions/60193896/convert-file-into-grayscale-image\n",
        "\n",
        "import numpy as np\n",
        "from math import sqrt, ceil\n",
        "import zipfile, cv2, pathlib\n",
        "from concurrent.futures import ThreadPoolExecutor\n",
        "\n",
        "def apk_to_img(input, save_location):\n",
        "    save_filename = ''\n",
        "    name = pathlib.Path(input).stem\n",
        "    data = bytearray()\n",
        "    with zipfile.ZipFile(input, 'r') as apkFile:\n",
        "        for file in sorted(apkFile.namelist()):\n",
        "            if file.endswith('.dex') or file == 'AndroidManifest.xml' or file == 'resources.arsc':\n",
        "                data.extend(apkFile.read(file))\n",
        "\n",
        "    if save_location[len(save_location) - 1] != '/':\n",
        "        save_location += '/'\n",
        "\n",
        "    data_len = len(data)\n",
        "    d = np.frombuffer(data, dtype=np.uint8)\n",
        "    img_size = int(ceil(sqrt(data_len)))\n",
        "    pad_len = img_size * img_size - data_len\n",
        "    shape = (img_size, img_size)\n",
        "\n",
        "    name += '_GrayScale'\n",
        "    save_filename = save_location + name + '.png'\n",
        "    padded_d = np.hstack((d, np.zeros(pad_len, np.uint8)))\n",
        "    im = np.reshape(padded_d, shape)\n",
        "    cv2.imwrite(save_filename, im)\n",
        "\n",
        "def apks_to_imgs(input, save_location):\n",
        "    apkFiles = []\n",
        "    directory = pathlib.Path(input)\n",
        "    for entry in directory.iterdir():\n",
        "        if entry.is_file() and entry.name.endswith('.apk'):\n",
        "            apkFiles.append(entry)\n",
        "    with ThreadPoolExecutor() as pool:\n",
        "        for file in apkFiles:\n",
        "            pool.submit(apk_to_img, file, save_location)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "sO7DxtfiKfmi"
      },
      "outputs": [],
      "source": [
        "adware_family = pathlib.Path('<your_path>')\n",
        "for entry in adware_family.iterdir():\n",
        "  apks_to_imgs(entry, '<your_path>')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pjDV34GlK_kk"
      },
      "outputs": [],
      "source": [
        "SMSmalware_family = pathlib.Path('<your_path>')\n",
        "for entry in SMSmalware_family.iterdir():\n",
        "  apks_to_imgs(entry, '<your_path>')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dlgWU4JXLBSN"
      },
      "outputs": [],
      "source": [
        "scareware_family = pathlib.Path('<your_path>')\n",
        "for entry in scareware_family.iterdir():\n",
        "  apks_to_imgs(entry, '<your_path>')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "apks_to_imgs('<your_malware_path>', '<your_grayscale_path>')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZxG7LuiCc9R7"
      },
      "source": [
        "# **4. Prepare data**</a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-svnALcC5Mdl"
      },
      "outputs": [],
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt # for data visualization purposes\n",
        "import seaborn as sns # for data visualization\n",
        "import os\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cXtPF9PBlPzU"
      },
      "outputs": [],
      "source": [
        "# Path to dataset\n",
        "data = '<your_path>'\n",
        "\n",
        "# set size of the images in the model\n",
        "image_height = 64\n",
        "image_width = 64"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0NY8qd1QXKHt"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "direction = Path(data)\n",
        "\n",
        "#get file directory\n",
        "folders = [directory for directory in direction.iterdir() if directory.is_dir()]\n",
        "folders.sort()\n",
        "#get classes\n",
        "classes = [folder.name for folder in folders]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M1UEAwU7YWQb"
      },
      "outputs": [],
      "source": [
        "from skimage.io import imread\n",
        "from tensorflow.keras.preprocessing import image\n",
        "import pandas as pd\n",
        "import cv2 as cv\n",
        "\n",
        "from PIL import Image\n",
        "Image.MAX_IMAGE_PIXELS = None\n",
        "\n",
        "imgs = []  # images arr\n",
        "y = []  # labels arr\n",
        "\n",
        "for i, direc in enumerate(folders):\n",
        "    for file in direc.iterdir():\n",
        "        img = imread(file)\n",
        "        img_pred = cv.resize(img, (image_height, image_width), interpolation=cv.INTER_AREA)\n",
        "        img_pred = image.img_to_array(img_pred)\n",
        "        img_pred = img_pred / 255\n",
        "        imgs.append(img_pred)\n",
        "        y.append(i)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "5tv8gerp5GXz",
        "outputId": "a4e34909-198a-4e79-d362-3401546e2244"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df = pd.DataFrame(y)\n",
        "\n",
        "#pie chart\n",
        "(df.iloc[:, -1].value_counts()).plot(kind='pie', autopct='%.1f%%')\n",
        "\n",
        "plt.title('Value Counts of Labels')\n",
        "plt.xticks(rotation= 45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "nfLkN1vZZzy_",
        "outputId": "a5fba9d4-9853-43cf-c36a-ccf23e396c51"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "(df.iloc[:, -1].value_counts()/len(df)).plot(kind='bar', color='skyblue')\n",
        "\n",
        "plt.title('Value of each Labels')\n",
        "plt.xticks(rotation= 45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z8zcRD5wzJLa"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "# convert to  numpy arrays\n",
        "import numpy as np\n",
        "X = np.array(imgs)\n",
        "X_reshape = X.reshape(803, 4096)\n",
        "\n",
        "# undelsampling\n",
        "undersample = RandomUnderSampler(sampling_strategy= {1:100})\n",
        "\n",
        "# fit and apply the transform\n",
        "X_over, y_over = undersample.fit_resample(X_reshape, y)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_over, y_over, test_size=0.2, random_state=53)\n",
        "\n",
        "# Reshape the image data into rows\n",
        "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], -1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "luFuK-D6a5cw",
        "outputId": "7fe1c123-79e5-4373-f675-8277c7c6b632"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df = pd.DataFrame(y_over)\n",
        "\n",
        "#pie chart\n",
        "(df.iloc[:, -1].value_counts()).plot(kind='pie', autopct='%.1f%%')\n",
        "\n",
        "plt.title('Value Counts of Labels')\n",
        "plt.xticks(rotation= 45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "pVF2mZB6IVR7",
        "outputId": "af16dd70-0481-4aec-b384-2324a922cf37"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "(df.iloc[:, -1].value_counts()).plot(kind='bar', color='skyblue')\n",
        "\n",
        "plt.title('Value of each Labels')\n",
        "plt.xticks(rotation= 45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qt7KHhksdQfI"
      },
      "source": [
        "# **5. Train model**</a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 836
        },
        "id": "Qc0u09V0zQCZ",
        "outputId": "ac19e6e4-163a-474b-fecb-09f1c3685aee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n",
            "10\n",
            "11\n",
            "12\n",
            "13\n",
            "14\n",
            "15\n",
            "16\n",
            "17\n",
            "18\n",
            "19\n",
            "20\n"
          ]
        },
        {
          "data": {
            "image/png": 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o2rVrlWrPzs6GlZUVFAoFXzVCRESkJ6r6/S3ZFaSioiJERkYiKCjon2JkMgQFBSEiIqLCfXbt2oXAwEBMmzYNTk5OaNOmDT799FMolcoq9xkZGYni4mKNNi1btkSTJk0qPS4RERE1LJIFpIyMDCiVSjg5OWmsd3JyQmpqaoX73Lx5E9u2bYNSqcQff/yBjz/+GMuWLcMnn3xS5T5TU1NhbGwMa2vrKh8XAAoLC5Gdna2x6IJKJeJcQiaKSlQ66Z+IiIieTPJB2tpQqVRwdHTEd999B39/f4wYMQIffvghVq9erfNjh4aGwsrKSr24u7vr5DiDvj6BV1dH4FR8hk76JyIioieTLCDZ29vDwMCg3NNjaWlpcHZ2rnAfFxcXtGjRAgYGBup1rVq1QmpqKoqKiqrUp7OzM4qKipCVlVXl4wLAnDlzoFAo1EtycrI2p1tlHZpYAwD2xlR+NYuIiIh0S7KAZGxsDH9/f4SHh6vXqVQqhIeHIzAwsMJ9unfvjri4OKhU/9x+un79OlxcXGBsbFylPv39/WFkZKTRJjY2FklJSZUeFwDkcjksLS01Fl0Y2MYFALD/SiqKlbzNRkREJAVJb7GFhIRgzZo12LBhA65evYqpU6ciLy8P48ePBwAEBwdjzpw56vZTp05FZmYmpk+fjuvXr2PPnj349NNPMW3atCr3aWVlhQkTJiAkJASHDx9GZGQkxo8fj8DAwCo/waZLXZrawtbcGFn5xThzM1PqcoiIiBokQykPPmLECNy7dw9z585Famoq/Pz8sG/fPvUg66SkJMhk/2Q4d3d37N+/HzNnzkS7du3g5uaG6dOnY/bs2VXuEwC++OILyGQyDBs2DIWFhejXrx+++eab2jvxxzA0kKFfayf8fDYZf1xKwTPN7aUuiYiIqMGRdB4kfabLeZCO37iH19eehZ25Mc78X18YGujVWHoiIqI6q87Pg0SV6+plB2szI9zPK8LZBN5mIyIiqm0MSHWQkYEMz/uW3hLk02xERES1jwGpjhrQtvRptn2XU6FU8S4oERFRbWJAqqO6N7OHhYkh7uUUIjLxgdTlEBERNSgMSHWUsaEMz/19m+2PmBSJqyEiImpYGJDqsBce3Wa7lAoVb7MRERHVGgakOuyZ5vZoJDdEanYBzifzNhsREVFtYUCqw+SGBghq5QgA+INPsxEREdUaBqQ67tHTbHtjUsA5PYmIiGoHA1Id16uFA8yMDXBXUYALtxVSl0NERNQgMCDVcSZGBujTsvQ2214+zUZERFQrGJD0wMC/b7P9cYm32YiIiGoDA5Ie6O3jCFMjAyRnPsTlu9lSl0NERFTvMSDpAVNjA/Ru6QAA2MPbbERERDrHgKQnBrTh02xERES1hQFJT/Ru6Qi5oQwJ9/NxNSVH6nKIiIjqNQYkPdFIboheLUpvs+29xNtsREREusSApEcePc22h7fZiIiIdIoBSY/0beUIYwMZbt7Lw430XKnLISIiqrcYkPSIhYkRerawBwD8wafZiIiIdIYBSc88epqNAYmIiEh3GJD0TFArJxgZCLielou4dD7NRkREpAsMSHrGyswI3b1Lb7PtjUmVuBoiIqL6iQFJDw18dJvtEgMSERGRLjAg6aHnWzvBUCbgako2bmXkSV0OERFRvcOApIeszYwR2MwOACeNJCIi0gUGJD31aNJIjkMiIiKqeQxIeup5XyfIBCDmjgJJ9/OlLoeIiKheYUDSU3aN5OjqxdtsREREusCApMcGtOXTbERERLrAgKTH+rV2giAAF5KzcPsBb7MRERHVFAYkPeZoYYIunrYAgH28ikRERFRjGJD0nPppNgYkIiKiGsOApOf6t3EGAEQmPkCqokDiaoiIiOqHOhGQVq5cCU9PT5iYmCAgIABnz56ttO369eshCILGYmJiotGm7PZHy9KlS9VtPD09y21fvHixzs5RV5wsTdDJwwYAn2YjIiKqKZIHpC1btiAkJATz5s1DVFQU2rdvj379+iE9Pb3SfSwtLZGSkqJeEhMTNbb/e1tKSgrWrVsHQRAwbNgwjXYLFy7UaPfOO+/o5Bx1bQAnjSQiIqpRkgek5cuXY9KkSRg/fjx8fX2xevVqmJmZYd26dZXuIwgCnJ2d1YuTk5PG9n9vc3Z2xs6dO9G7d294eXlptLOwsNBoZ25urpNz1LUBf99mO5eYifRs3mYjIiJ6WpIGpKKiIkRGRiIoKEi9TiaTISgoCBEREZXul5ubCw8PD7i7u2PIkCG4fPlypW3T0tKwZ88eTJgwody2xYsXw87ODh06dMDSpUtRUlLydCckEVdrU3RoYg1RBPZf5lUkIiKipyVpQMrIyIBSqSx3BcjJyQmpqRV/0fv4+GDdunXYuXMnNm3aBJVKhW7duuH27dsVtt+wYQMsLCzw8ssva6x/9913ERYWhsOHD2PKlCn49NNP8f7771daa2FhIbKzszWWumRgm78njeRtNiIioqdmKHUB2goMDERgYKD6527duqFVq1b49ttvsWjRonLt161bh9GjR5cbyB0SEqL+73bt2sHY2BhTpkxBaGgo5HJ5uX5CQ0OxYMGCGjyTmtW/jTP++8dVnLl1Hxm5hbBvVP4ciIiIqGokvYJkb28PAwMDpKWlaaxPS0uDs7NzlfowMjJChw4dEBcXV27b8ePHERsbi4kTJz6xn4CAAJSUlCAhIaHC7XPmzIFCoVAvycnJVaqvtrjbmqFdYyuoeJuNiIjoqUkakIyNjeHv74/w8HD1OpVKhfDwcI2rRI+jVCoRExMDFxeXctvWrl0Lf39/tG/f/on9REdHQyaTwdHRscLtcrkclpaWGktdM6ANn2YjIiKqCZLfYgsJCcHYsWPRqVMndOnSBStWrEBeXh7Gjx8PAAgODoabmxtCQ0MBlD6a37VrV3h7eyMrKwtLly5FYmJiuatE2dnZ2Lp1K5YtW1bumBEREThz5gx69+4NCwsLREREYObMmRgzZgxsbGx0f9I6MrCtM5bsu4aIm/eRmVcEW3NjqUsiIiLSS5IHpBEjRuDevXuYO3cuUlNT4efnh3379qkHbiclJUEm++dC14MHDzBp0iSkpqbCxsYG/v7+OHXqFHx9fTX6DQsLgyiKGDVqVLljyuVyhIWFYf78+SgsLETTpk0xc+ZMjXFJ+sjDzhytXS1x+W42DlxJxYjOTaQuiYiISC8JoiiKUhehj7Kzs2FlZQWFQlGnbretPByHpftj0auFAza80UXqcoiIiOqUqn5/Sz5RJNWsR5NGnozLgCK/WOJqiIiI9BMDUj3j5dAILZ0tUKISceBq2pN3ICIionIYkOqhAepJI/nyWiIioupgQKqHBrYtvc12/MY9ZBfwNhsREZG2GJDqoeZOFmju2AjFShHhvM1GRESkNQakempAW76bjYiIqLoYkOqpR7fZjl6/h9zCEomrISIi0i8MSPWUj5MFvOzNUVSiwqFr6VKXQ0REpFcYkOopQRAw4O+rSHv5NBsREZFWGJDqsUeP+x+OTUd+EW+zERERVRUDUj3W2tUSTWzNUFCswuFr96Quh3SksESJTacT8dI3J/Hj6USpyyEiqhckf1kt6Y4gCBjY1gWrj8bjj0speKGdi9QlUQ3KLyrBz2eT8d2xeKRlFwIALt1RoFszOzRzaCRxdURE+o1XkOq5R0+zHb6WjodFSomroZqQXVCMlYfj8MySw1i0+wrSsgvhYmWC1q6WKFaKmL/rMvgOaiKip8MrSPVcWzcruFmb4k7WQxy9fg/9/36ZLemfB3lF+OHkLfxwKgE5BaVjyprYmmHqs83wckc3pCoK8NwXx3D8Rgb2XUpVz4VFRETa4xWkeq70NtvfT7Nd4tNs+ig9pwCf/nEV3ZccwpeH4pBTUAJvx0b4YkR7HHqvF0Z1aQK5oQE87MzxZk8vAMCi3Vc4MJ+I6CnwClIDMKCtC9Ycv4Xwq+koKFbCxMhA6pKoCu5kPcS3R+MRdi4ZRSUqAICviyXe6eONfq2dIZMJ5faZ+qw3fo26gztZD7HycBz+069lbZdNRFQvMCA1AH6NreFiZYIURQGO38jAc75OUpdEj5GQkYdVR+Lxa9RtlKhKxxJ1aGKNd/p4o7ePIwShfDB6xNTYAHMH+WLKj5FYc+wWXvF3R1N789oqnYio3uAttgZAJhPUcyJx0si663paDqaHnUefZUew5a9klKhEBHrZ4aeJAdg+tRv6tHR6bDh65HlfJ/Rq4YAipYoDtomIqolXkBqIgW2dse7kLRy4mobCEiXkhrzNVlfE3Fbg68M3sP9ymnpdbx8HvN3HG/4etlr3JwgC5g9ujX5fHMPR6/fw55U09GvNwflERNpgQGogOjaxgaOFHOk5hTgVdx+9WzpKXVKD91dCJr4+HIcjsaWTeAoC0L+1M6b19kYbN6un6rupvTkm9WyKlYfjsfD3K+jZ3AGmxgzFRERVxVtsDUTpbbbSqwh/8DabZERRxMm4DIz8LgKvrI7Akdh7kAnASx3c8OeMnlg1xv+pw9Ej03p7w9XKBHeyHmLVkbga6ZOIqKHgFaQGZEBbF2yISMTuiymQG8nQvZk9unrZwcbcWOrSGoRT8Rn4bF8sopOzAABGBgJe8W+MN3s1g4ddzQ+kNjM2xMcv+mLq5iisPnYTw/wb6+Q4RET1kSByBGe1ZGdnw8rKCgqFApaWllKXUyVKlYig5UdxKyNPvU4QSh8d79bMDt287dHF0xbmcubmmnYtNRsvfnkCJSoRckMZRnVpgsk9veBqbarT44qiiOB1Z3H8RgZ6+zhg3bjOVRroTURUX1X1+5sBqZr0MSABQF5hCU7GZeBU/H2cis/A9bRcje2GMgHt3a3RvZkdApvZo6OHNQd0PyVRFDHi29M4m5CJHs3tsXy4Hxws5LV2/Ph7uei/4hiKlSLWBHfiNA9E1KAxIOmYvgaksu7lFOJUfAYi4u/jVPx9JGXma2yXG8rQ2dMW3bzt0K2ZPdq4WsLQgEPXtLE96jZCfrkAUyMDHHyvF9x0fNWoIkv2XcOqI/FobGOKgyG9OFkoETVYDEg6Vl8CUlnJmfmIiL+Pk/GlV5nu5RRqbLcwMURAUzt0a2aH7t72aOHUiLdsHkPxsBh9lx1BRm4R3u/vg7ee9ZakjvyiEvRddhQpigJM79scM59rIUkdRERSY0DSsfoakP5NFEXEpeeqb8dFxN9HdoHm+73sGxkjsJl96RimZnZoYmvGwPQv83ddxvpTCfByMMe+6T1hbCjd1bc9F1Mw7acoGBvKcHBmLzSxM5OsFiIiqTAg6VhDCEhlKVUirtzNVl9dOncrEw+LlRpt3KxN8W5fb4zo3ESiKuuOy3cVGPTVCahEYPPEAHT3tpe0HlEUMWbtGZyMu4+gVo74fmxnSeshIpICA5KONcSAVFZRiQrRyVk4FZ+BU3H3cT75AYqVImQCsPXNwGrNAl1fqFQiXll9ClFJWXixnQu+fq2j1CUBAOLSc9B/xXGUqESsG9cJfVpywDYRNSxV/f7maFuqNmNDGbo0tcWMoBb45c1AXJj3PIb6uUIlAjO2RCO3sOTJndRT26JuIyopC2bGBvjoBV+py1HzdrTAhGeaAgDm77qCgjJXAImIqBQDEtUYM2NDLBraBo1tTJGc+RALdl2WuiRJZOUXYfHeawCAGUHN4WxlInFFmt7p2xxOlnIkZebju2M3pS6HiKhOYkCiGmVhYoTlw/0gCMDWyNvYd6nhvdbk8z9jkZlXhOaOjTC+e1OpyymnkdwQH/59VWvl4Tgkl5nagYiIGJBIB7o0tcWbvZoBAOZsj0F6doHEFdWemNsKbD6TBABYOKQNjOronFGD2rkg0MsOhSUqLNp9RepyiIjqnLr5tzfpvZlBLdDa1RIP8ovxn20X0RCeBVCpRHy08xJEERji54rAZnZSl1QpQRCwYEhrGMoE/HklDUdi06UuiYioTmFAIp0wNpThfyP9IDeU4ej1e/jxdKLUJenclr+ScSE5q/QW1sBWUpfzRC2cLDCumyeA0vmaCks4YJuI6JE6EZBWrlwJT09PmJiYICAgAGfPnq207fr16yEIgsZiYqI5CHbcuHHl2vTv31+jTWZmJkaPHg1LS0tYW1tjwoQJyM3VfC8ZPR1vRwvMGdASAPDfPVcRl54jcUW6k5lXhCX7Sgdmz3yuBRwt69bA7MpMD2oOBws5Eu7n4/vjt6Quh4iozpA8IG3ZsgUhISGYN28eoqKi0L59e/Tr1w/p6ZVf8re0tERKSop6SUwsf3Wif//+Gm1+/vlnje2jR4/G5cuXceDAAezevRvHjh3D5MmTa/z8GrrgQE/0bOGAwhIVZmyJRlGJSuqSdGLp/mvIyi9GS2cLjA30kLqcKrMwMVJf7frq0A3cyXoocUVERHWD5AFp+fLlmDRpEsaPHw9fX1+sXr0aZmZmWLduXaX7CIIAZ2dn9eLkVH6yO7lcrtHGxsZGve3q1avYt28fvv/+ewQEBOCZZ57BV199hbCwMNy9e1cn59lQyWQClr7SDtZmRrh0Jxv/C78udUk17nzSA4SdSwZQOjBb317mO8TPFV2a2qKgWIVFv3PANhERIHFAKioqQmRkJIKCgtTrZDIZgoKCEBERUel+ubm58PDwgLu7O4YMGYLLl8vPt3PkyBE4OjrCx8cHU6dOxf3799XbIiIiYG1tjU6dOqnXBQUFQSaT4cyZMxUes7CwENnZ2RoLVY2TpQlCX2oLAFh1JB7nEjIlrqjmKFUiPv57YPbLHd3Qpan+zR4uCAIWDmkNA5mAfZdTcez6PalLIiKSnKQBKSMjA0qlstwVICcnJ6Smpla4j4+PD9atW4edO3di06ZNUKlU6NatG27fvq1u079/f2zcuBHh4eFYsmQJjh49igEDBkCpLB2EmpqaCkdHR41+DQ0NYWtrW+lxQ0NDYWVlpV7c3d2f5tQbnAFtXfCKf2OoRGDmlmjkFBRLXVKN+OlsEi7dyYaFiSHmDKj7A7Mr09LZEsF/3xrkgG0iojpwi01bgYGBCA4Ohp+fH3r16oXt27fDwcEB3377rbrNyJEjMXjwYLRt2xZDhw7F7t27ce7cORw5cqTax50zZw4UCoV6SU5OroGzaVjmDfJFYxtT3H7wEAvqwa2cjNxCLP17YPas533gYCGXuKKnM/O5FrBvJMfNjDysPcEB20TUsEkakOzt7WFgYIC0tDSN9WlpaXB2dq5SH0ZGRujQoQPi4uIqbePl5QV7e3t1G2dn53KDwEtKSpCZmVnpceVyOSwtLTUW0o6FiRG+GOEHmQBsi7yNvTH6Pcv2kr3XkF1QgtaulhjTVX8GZlfG0sRI/dThV+FxuMsB20TUgEkakIyNjeHv74/w8HD1OpVKhfDwcAQGBlapD6VSiZiYGLi4uFTa5vbt27h//766TWBgILKyshAZGaluc+jQIahUKgQEBFTzbKgqOnvaYuqzf8+y/VsM0vR0lu3IxExsjSy9rbtwSBsYyASJK6oZL3d0QycPGzwsVuK/e65KXQ4RkWQkv8UWEhKCNWvWYMOGDbh69SqmTp2KvLw8jB8/HgAQHByMOXPmqNsvXLgQf/75J27evImoqCiMGTMGiYmJmDhxIoDSAdz/+c9/cPr0aSQkJCA8PBxDhgyBt7c3+vXrBwBo1aoV+vfvj0mTJuHs2bM4efIk3n77bYwcORKurq61/4fQwEzv2wJt3CyRlV+MWVsvQKXSr1m2S5QqfLSj9MGA4Z0aw9/D5gl76I/SAdttIBOAPTEpOHEjQ+qSiIgkIXlAGjFiBD7//HPMnTsXfn5+iI6Oxr59+9QDt5OSkpCS8s+tmAcPHmDSpElo1aoVBg4ciOzsbJw6dQq+vqUv3zQwMMDFixcxePBgtGjRAhMmTIC/vz+OHz8OufyfMSKbN29Gy5Yt0bdvXwwcOBDPPPMMvvvuu9o9+QbK2FCGFSM6wMRIhuM3MrAxIkHqkrSy6XQirqZkw8rUCLP7t5S6nBrn62qJ1/++ZThv16V6O3cVEdHjCGJDeEmWDmRnZ8PKygoKhYLjkappY0QC5u68DLmhDLvfeQbNnSykLumJ0nMK0Pfzo8gpLMEnQ9vUi7FHFVE8LEafz4/gfl4R5gxoiSl/v3yYiEjfVfX7W/IrSNRwvd7VA730bJbtxX9cQ05hCdo1tsKoLk2kLkdnrEyNMPvvAdv/C7+BVIV+jhUjIqouBiSSjCCUzrJtY2aEy3ez8cXBuj3L9pmb97H9/B0IArCoHg3MrswrHRujQxNr5Bcp8d8/OGCbiBoWBiSSlKOlCUJfbgcAWH00Hmdv1c1ZtouVKszdWTowe2TnJmjvbi1tQbVAJhOwaEgbCALw+4W7OBXPAdtE1HAwIJHk+rdxxqv+jSH+Pct2dh2cZXvDqQTEpuXAxswI7/fzkbqcWtPGzQqjA0pvJc7beRnFyrp/G5SIqCYwIFGdMG9wazSxNcOdrIeYv6v8u/WklJZdgBUHbwAAZvdvCRtzY4krql2znveBrbkxbqTnYv3JBKnLISKqFQxIVCc0khviixHtIROA7VF38EcdmmX7v3uuIrewBH7u1hjeqeG9g8/azBiz+5deNVtx8DpSFJxhm4jqPwYkqjP8PWzx1rPeAID/+y2mTjw5dSo+A7su3IUgAJ8MbQNZPR+YXZlX/d3RsYk18oqU+GQ3B2wTUf3HgER1yvSg5mjX2ApZ+cX4zzZpZ9kuKvlnYPaYAA+0cbOSrBapyWQCPhnaVj3D9tHr96QuiYhIpxiQqE4xMpDhixF+6lm2N0g4y/YPJ28hLj0XdubGmPV8wxmYXRlfV0uM69YUADBv5yUUFCslroiISHcYkKjOaebQCB++UPrqmMV7r+F6Wk6t15CieIj/hZcOzP5gQEtYmRnVeg110cznmsPRQo6E+/n47thNqcshItIZBiSqk8YENEFvn79n2Q6r/Vm2P9l9FflFSnTysMGwjo1r9dh1mYWJET56sTS8fn04Don38ySuiIhINxiQqE4SBAFLXmkHW3NjXEnJxvIDtTfL9vEb97AnJgUyAaVvtm+gA7MrM6idC7p726GoRIX5uy6Dr3MkovqIAYnqLEcLE4S+3BYA8O2xeJy+eV/nxywsUWLe3wOzgwM94evKFxGXJQgCFg5pAyMDAYdj72H/5TSpSyIiqnEMSFSn9WvtjBGd3CGKwHu/XND5LNvfH7+Fmxl5sG8kR8jzLXR6LH3WzKERJvf0AgAs/P0y8otKJK6IiKhmMSBRnffxIN9/ZtneWf1ZtkVRhEolokSpQlGJCgXFSjwsUiKvsAS5hSWIS8/FV4dKB2Z/+EJLWJpwYPbjvN27OdysTXFXUYAvw+OkLoeIqEYJIgcQVEt2djasrKygUChgacnbMLoWmfgAr64+BZUIOFjIIYp/Bx5RhEoEVKL4r3X//Kz6V5uq6tLUFlsmd4UgcOzRkxy8koaJG/+CoUzA3uk90NzJQuqSiIgeq6rf37yCRHrB38MG0/uW3vK6l1OIjNxC3M8rwoP8YigeFiOnoPQqUF6REg+LlSgsUaFIqUKJSrtwZGNmhP8ObcNwVEVBvk4IauWEEpWIj3Zc4oBtIqo3DKUugKiq3u3rjRfauaCoRAWZDJAJAmRC6aBhmSBAQOk6QSid+VkmQL1eEP75WSYIEGQov48gwFAm8Kk1Lc0b5IsTcfdw5lYmdkTfwUsdOC0CEek/BiTSG4IgwNuxkdRlUBnutmZ4p09zLN0fi//uuYY+LZ1gZcrxW0Sk33iLjYie2sQeTeHlYI6M3EIs/zNW6nKIiJ4aAxIRPTW5oQEWDWkDAPjxdCJibiskroiI6OloHZDmzZuHxMREXdRCRHqsu7c9Brd3hUoEPtp5CSptRscTEdUxWgeknTt3olmzZujbty9++uknFBYW6qIuItJDH73QCo3khriQnIWwc8lSl0NEVG1aB6To6GicO3cOrVu3xvTp0+Hs7IypU6fi3LlzuqiPiPSIo6UJQp4rnY5hyb5ruJ/Lf0ARkX6q1hikDh064Msvv8Tdu3exdu1a3L59G927d0e7du3wv//9DwoFxx8QNVTBgR5o5WIJxcNiLN57TepyiIiq5akGaYuiiOLiYhQVFUEURdjY2ODrr7+Gu7s7tmzZUlM1EpEeMTSQ4ZOhpQO2t0bexl8JmRJXRESkvWoFpMjISLz99ttwcXHBzJkz0aFDB1y9ehVHjx7FjRs38N///hfvvvtuTddKRHrC38MGIzq5AwA+2nEJJUqVxBUREWlH64DUtm1bdO3aFbdu3cLatWuRnJyMxYsXw9vbW91m1KhRuHfvXo0WSkT6ZfaAlrA2M8K11BysP5UgdTlERFrROiANHz4cCQkJ2LNnD4YOHQoDA4Nybezt7aFS8V+MRA2ZrbkxPujfEgDwxYHrSFUUSFwREVHVaR2QPv74Y7i5uemiFiKqZ4Z3ckeHJtbIK1Ji0Z4rUpdDRFRlWgekYcOGYcmSJeXWf/bZZ3j11VdrpCgiqh9kMgGfDG0DmQDsuZiC4zd4652I9IPWAenYsWMYOHBgufUDBgzAsWPHaqQoIqo/WrtaITjQEwAwd+dlFJYopS2IiKgKtA5Iubm5MDY2LrfeyMgI2dnZNVIUEdUvIc+3gIOFHLcy8vDd0ZtSl0NE9ETVeoqtojmOwsLC4OvrWyNFEVH9YmlihI9eaAUA+PpwHJIz8yWuiIjo8ao1SHvRokUYO3YsNmzYgA0bNiA4OBj//e9/8fHHH1eriJUrV8LT0xMmJiYICAjA2bNnK227fv16CIKgsZiYmKi3FxcXY/bs2Wjbti3Mzc3h6uqK4OBg3L17V6MfT0/Pcv0sXry4WvUT0ZMNbu+Kbs3sUFiiwrxdlyGKfJktEdVdWgekQYMGYceOHYiLi8Nbb72F9957D7dv38bBgwcxdOhQrQvYsmULQkJCMG/ePERFRaF9+/bo168f0tPTK93H0tISKSkp6iUxMVG9LT8/H1FRUfj4448RFRWF7du3IzY2FoMHDy7Xz8KFCzX6eeedd7Sun4iqRhAELBzSBkYGAg5dS8eBK2lSl0REVClBlPifcQEBAejcuTO+/vprAIBKpYK7uzveeecdfPDBB+Xar1+/HjNmzEBWVlaVj3Hu3Dl06dIFiYmJaNKkCYDSK0gzZszAjBkzqlV3dnY2rKysoFAoYGlpWa0+iBqiz/ZdwzdH4uFmbYoDIT1hZmwodUlE1IBU9fv7qd7F9rSKiooQGRmJoKAg9TqZTIagoCBERERUul9ubi48PDzg7u6OIUOG4PLly489jkKhgCAIsLa21li/ePFi2NnZoUOHDli6dClKSkoq7aOwsBDZ2dkaCxFp750+zeFmbYo7WQ/x1aE4qcshIqqQ1gFJqVTi888/R5cuXeDs7AxbW1uNRRsZGRlQKpVwcnLSWO/k5ITU1NQK9/Hx8cG6deuwc+dObNq0CSqVCt26dcPt27crbF9QUIDZs2dj1KhRGknx3XffRVhYGA4fPowpU6bg008/xfvvv19praGhobCyslIv7u7uWp0rEZUyNTbA/MGtAQBrjt1EXHqOxBUREZWndUBasGABli9fjhEjRkChUCAkJAQvv/wyZDIZ5s+fr4MSNQUGBiI4OBh+fn7o1asXtm/fDgcHB3z77bfl2hYXF2P48OEQRRGrVq3S2BYSEoJnn30W7dq1w5tvvolly5bhq6++QmFhYYXHnTNnDhQKhXpJTk7WyfkRNQTP+Tqhb0tHlKhEfLyDA7aJqO7ROiBt3rwZa9aswXvvvQdDQ0OMGjUK33//PebOnYvTp09r1Ze9vT0MDAyQlqY5WDMtLQ3Ozs5V6sPIyAgdOnRAXJzmpfpH4SgxMREHDhx44jihgIAAlJSUICEhocLtcrkclpaWGgsRVd/8wa0hN5Qh4uZ97Lpw98k7EBHVIq0DUmpqKtq2bQsAaNSoERQKBQDgxRdfxJ49e7Tqy9jYGP7+/ggPD1evU6lUCA8PR2BgYJX6UCqViImJgYuLi3rdo3B048YNHDx4EHZ2dk/sJzo6GjKZDI6OjlqdAxFVj7utGd7p4w0AWLT7KrILiiWuiIjoH1oHpMaNGyMlJQUA0KxZM/z5558ASp8Uk8vlWhcQEhKCNWvWYMOGDbh69SqmTp2KvLw8jB8/HgAQHByMOXPmqNsvXLgQf/75J27evImoqCiMGTMGiYmJmDhxIoDScPTKK6/gr7/+wubNm6FUKpGamorU1FQUFRUBACIiIrBixQpcuHABN2/exObNmzFz5kyMGTMGNjY2Wp8DEVXPpJ5e8LI3R0ZuIZb/eV3qcoiI1LR+vvall15CeHg4AgIC8M4772DMmDFYu3YtkpKSMHPmTK0LGDFiBO7du4e5c+ciNTUVfn5+2Ldvn3rgdlJSEmSyf3LcgwcPMGnSJKSmpsLGxgb+/v44deqUehbvO3fuYNeuXQAAPz8/jWMdPnwYzz77LORyOcLCwjB//nwUFhaiadOmmDlzJkJCQrSun4iqT25ogIVD2mDM2jPYGJGAkV3c0dKZt6+JSHpPPQ/S6dOncerUKTRv3hyDBg2qqbrqPM6DRFRzpm6KxN5LqQhq5Yjvx3aWuhwiqsd0Mg9ScXEx3njjDdy6dUu9rmvXrggJCWlQ4YiIatasfj4wkAk4eDUdfyVkSl0OEZF2AcnIyAi//vqrrmohogaqmUMjDO/UGACwZN81PvZPRJLTepD20KFDsWPHDh2UQkQN2bt9m0NuKMO5hAc4EntP6nKIqIHTepB28+bNsXDhQpw8eRL+/v4wNzfX2P7uu+/WWHFE1HC4WJliXDdPfHvsJpbsu4ZeLRwgkwlSl0VEDZTWg7SbNm1aeWeCgJs3bz51UfqAg7SJal5WfhF6fHYYOQUlWDHCD0M7uEldEhHVM1X9/tb6CtK/B2gTEdUkazNjvNmrGZbuj8WyA7EY2NYFxoaSvlObiBoo/s1DRHXK+O6esG8kR3LmQ4SdS5K6HCJqoLS+gvTGG288dvu6deuqXQwRkZmxIab39cbHOy/jy/A4DOvYGOZyrf+qIiJ6KlpfQXrw4IHGkp6ejkOHDmH79u3IysrSQYlE1NCM7NIEHnZmyMgtxA8neVufiGqf1v8s++2338qtU6lUmDp1Kpo1a1YjRRFRw2ZkIEPIcy0wPSwa3x69idEBHrAxN5a6LCJqQGpkDJJMJkNISAi++OKLmuiOiAiD2rmilYslcgpLsOpovNTlEFEDU2ODtOPj41FSUlJT3RFRAyeTCXi/vw8AYP2pBKQoHkpcERE1JFrfYiv7xntRFJGSkoI9e/Zg7NixNVYYEdGzLRzQpaktzt7KxP8O3sDiYe2kLomIGgitA9L58+c1fpbJZHBwcMCyZcue+IQbEZE2BEHA7P4+GLYqAr/8lYyJPbzg7dhI6rKIqAHQOiAdPnxYF3UQEVXI38MWQa2ccPBqGpYfiMU3o/2lLomIGgCtxyDdunULN27cKLf+xo0bSEhIqImaiIg0/KefDwQB+CMmFReSs6Quh4gaAK0D0rhx43Dq1Kly68+cOYNx48bVRE1ERBp8nC3w0t/vZVu6P1biaoioIdA6IJ0/fx7du3cvt75r166Ijo6uiZqIiMqZGdQCRgYCTsRl4MSNDKnLIaJ6TuuAJAgCcnJyyq1XKBRQKpU1UhQRUVnutmYYHeABAPhs/zWIoihxRURUn2kdkHr27InQ0FCNMKRUKhEaGopnnnmmRosjIvq3t/t4w8zYABdvK7DvUqrU5RBRPab1U2xLlixBz5494ePjgx49egAAjh8/juzsbBw6dKjGCyQiesS+kRwTe3jhy/AbWPpnLJ7zdYKhQY3Nd0tEpKb13yy+vr64ePEihg8fjvT0dOTk5CA4OBjXrl1DmzZtdFEjEZHapB5NYWNmhJv38vBr1G2pyyGiekoQeSO/WrKzs2FlZQWFQgFLS0upyyFqUL4/fhOf7LkKFysTHJ71LEyMDKQuiYj0RFW/v7W+gvTDDz9g69at5dZv3boVGzZs0LY7IiKtjenqATdrU6QoCvBjRKLU5RBRPaR1QAoNDYW9vX259Y6Ojvj0009rpCgioscxMTLAjKDmAICVR+KQXVAscUVEVN9oHZCSkpLQtGnTcus9PDyQlJRUI0URET3Jyx0bo7ljI2TlF2PNsZtSl0NE9YzWAcnR0REXL14st/7ChQuws7OrkaKIiJ7EQCZgVj8fAMD3x2/hXk6hxBURUX2idUAaNWoU3n33XRw+fBhKpRJKpRKHDh3C9OnTMXLkSF3USERUoed9neDnbo2HxUp8daj8OyKJiKpL64C0aNEiBAQEoG/fvjA1NYWpqSmef/559OnTh2OQiKhWCYKA2f1bAgB+OpOEpPv5EldERPVFtR/zv379Oi5cuABTU1O0bdsWHh4eNV1bncbH/InqjuB1Z3Hs+j0M9XPFipEdpC6HiOqwqn5/az2T9iMtWrRAixYtqrs7EVGNeb+fD45dv4edF+5ics9m8HXlP1qI6OlUKyDdvn0bu3btQlJSEoqKijS2LV++vEYKIyKqqjZuVnixnQt2X0zB53/GYt24zlKXRER6TuuAFB4ejsGDB8PLy0v9epGEhASIooiOHTvqokYioid673kf7L2UikPX0nH2Via6NLWVuiQi0mNaD9KeM2cOZs2ahZiYGJiYmODXX39FcnIyevXqhVdffVUXNRIRPVFTe3OM6OwOAPhs3zXwLUpE9DS0DkhXr15FcHAwAMDQ0BAPHz5Eo0aNsHDhQixZsqRaRaxcuRKenp4wMTFBQEAAzp49W2nb9evXQxAEjcXExESjjSiKmDt3LlxcXGBqaoqgoCDcuKH5CHBmZiZGjx4NS0tLWFtbY8KECcjNza1W/URUN0zv2xxyQxn+SnyAQ9fSpS6HiPSY1gHJ3NxcPe7IxcUF8fHx6m0ZGRlaF7BlyxaEhIRg3rx5iIqKQvv27dGvXz+kp1f+l5ulpSVSUlLUS2Ki5ruYPvvsM3z55ZdYvXo1zpw5A3Nzc/Tr1w8FBQXqNqNHj8bly5dx4MAB7N69G8eOHcPkyZO1rp+I6g4nSxOM71460/9n+2KhVPEqEhFVk6ilIUOGiN99950oiqL43nvvid7e3uInn3widuzYUezbt6+23YldunQRp02bpv5ZqVSKrq6uYmhoaIXtf/jhB9HKyqrS/lQqlejs7CwuXbpUvS4rK0uUy+Xizz//LIqiKF65ckUEIJ47d07dZu/evaIgCOKdO3eqVLdCoRABiAqFokrtiah2ZOUViW3n7RM9Zu8Wt0clS10OEdUxVf3+1voK0vLlyxEQEAAAWLBgAfr27YstW7bA09MTa9eu1aqvoqIiREZGIigoSL1OJpMhKCgIERERle6Xm5sLDw8PuLu7Y8iQIbh8+bJ6261bt5CamqrRp5WVFQICAtR9RkREwNraGp06dVK3CQoKgkwmw5kzZyo8ZmFhIbKzszUWIqp7rMyM8OazzQAAy/68jqISlcQVEZE+0jogeXl5oV27dgBKb7etXr0aFy9exK+//qr1ZJEZGRlQKpVwcnLSWO/k5ITU1NQK9/Hx8cG6deuwc+dObNq0CSqVCt26dcPt27cBQL3f4/pMTU2Fo6OjxnZDQ0PY2tpWetzQ0FBYWVmpF3d3d63OlYhqz/huTeFoIcftBw/x81m+RJuItKd1QJJaYGAggoOD4efnh169emH79u1wcHDAt99+q9PjzpkzBwqFQr0kJyfr9HhEVH2mxgaYHtQcAPDVoRvIKyyRuCIi0jeSBiR7e3sYGBggLS1NY31aWhqcnZ2r1IeRkRE6dOiAuLg4AFDv97g+nZ2dyw0CLykpQWZmZqXHlcvlsLS01FiIqO4a3skdnnZmyMgtwroTt6Quh4j0jKQBydjYGP7+/ggPD1evU6lUCA8PR2BgYJX6UCqViImJgYuLCwCgadOmcHZ21ugzOzsbZ86cUfcZGBiIrKwsREZGqtscOnQIKpVKPb6KiPSbkYEM7z3vAwD47thNZOYVPWEP0oXMvCKOAyO9JPkttpCQEKxZswYbNmzA1atXMXXqVOTl5WH8+PEAgODgYMyZM0fdfuHChfjzzz9x8+ZNREVFYcyYMUhMTMTEiRMBlL7de8aMGfjkk0+wa9cuxMTEIDg4GK6urhg6dCgAoFWrVujfvz8mTZqEs2fP4uTJk3j77bcxcuRIuLq61vqfARHpxgttXdDa1RI5hSWYv+syYm4rUKzkl3VtOXEjA11DwzHwy+PIymdAJf1S7ZfV1pQRI0bg3r17mDt3LlJTU+Hn54d9+/apB1knJSVBJvsnxz148ACTJk1CamoqbGxs4O/vj1OnTsHX11fd5v3330deXh4mT56MrKwsPPPMM9i3b5/GhJKbN2/G22+/jb59+0Imk2HYsGH48ssva+/EiUjnZDIB7/dvibHrzmLXhbvYdeEuTI0M0K6xFTp62KBjExt0bGINu0ZyqUutd26k5WDq5kgUlagQl56LqZuisHFCFxgZSP7vcqIqEURRu/n4lUol1q9fj/DwcKSnp0Ol0vzX2KFDh2q0wLoqOzsbVlZWUCgUHI9EVIeJoojtUXew88JdnE96gJyC8gO2PezM4N/EBh08SgOTj5MFDPlFXm0ZuYV46ZuTSM58iLZuVrh5Lxd5RUqM6uKOT19qC0EQpC6RGrCqfn9rfQVp+vTpWL9+PV544QW0adOG/6MTUZ0mCAKG+TfGMP/GUKlExN/LRVTSA0QlZiEy6QHi0nOReD8fiffzsf38HQCAmbEB2je2hr+HDTp6WKODuw1szI0lPhP9UFCsxOSNfyE58yGa2JphwxtdEJ38ABM3/IWfzyajmUMjTOzhJXWZRE+k9RUke3t7bNy4EQMHDtRVTXqBV5CI6gdFfjHOJz9AVFIWzic9QHRSFnIqmBbAy94cHZqUBiZ/Dxs0d7SAgYz/QPw3URTxblg0fr9wF5Ymhtj+Vnd4OzYCAHx//CY+2XMVggCsHdsJfVo6PaE3It2o6ve31gHJ1dUVR44cQYsWLZ66SH3GgERUPylVIm6k5yAqMav0SlPSA9y8l1euXSO5IfzcrdGxiTU6NLGBpamRzmqyMTOCl0MjnfVfU5YfuI4vw2/AUCZg4xtd0M3bXr1NFEX8328x+PlsMsyNDfDrW93Q0pl/d1Lt01lAWrZsGW7evImvv/66Qd9eY0Aiajge5BWVXmX6OzRdSM5CXpGyVmt4o3tTfPRCK8jq6FWr387fxswtFwAAS4a1xYjOTcq1KVaqMHbdWZyKvw83a1PsmNYdDhYcIE+1S2cB6aWXXsLhw4dha2uL1q1bw8hI819N27dvr17FeoYBiajhUqpExKbmqK8wxdxWoFBHc/2IEJGc+RAA8HIHNyx5pV2dexLsXEImRq85gyKlCm/2aoYPBrSstG1WfhFe+uYUbmXkoWMTa/w0qStMjAxqsVpq6HQWkB7NT1SZH374QZvu9BYDEhHVlu1Rt/GfbRehVIno29IRK0d3rDOhIvF+HoauPIkH+cXo39oZ34zu+MSrXDfv5eKlb05B8bAYQ/xcsWKEX4O+I0G1S2cBiUoxIBFRbQq/moa3NkehsESFLp62WDO2E6x0OO6pKhT5xXhp1UncvJeHdo2tsGVyIEyNqxbcTsVlIHjdWZSoRIQ81wLv9m2u42qJSlX1+7tuXaclIqIK9W3lhB8nBMBCboizCZkY+d1p3MsplKyeohIV3twUiZv38uBiZYLvgztVORwBQDdveywc0gZA6eDu3Rfv6qpUomqp1hWkbdu24ZdffkFSUhKKijSnj4+Kiqqx4uoyXkEiIilcvqvA2HXnkJFbCE87M/w4IQDutma1WoMoivjg1xhs+av0ibStb3aDr2v1/h5ctPsK1p64BbmhDL9MCUR7d+uaLZaoDJ1dQfryyy8xfvx4ODk54fz58+jSpQvs7Oxw8+ZNDBgw4KmKJiKix2vtaoVtbwaisY0pEu7nY9iqU4hNzanVGr49dhNb/kqGTAC+eq1DtcMRAPzfwFbo09IRhSUqTNz4F+5mPazBSomqT+uA9M033+C7777DV199BWNjY7z//vs4cOAA3n33XSgUCl3USERE/+Jpb45fp3aDj5MF0nMKMfzbCEQmPqiVY++7lILFe68BAOa+6PvUEz4ayAT8b6QffJwscC+nEBM3/IW8CibqJKptWgekpKQkdOvWDQBgamqKnJzSf7m8/vrr+Pnnn2u2OiIiqpCTpQm2TOmKjk2soXhYjDHfn8HR6/d0esyLt7MwY0s0AGBsoAfGdW9aI/1amBjh+7GdYGdujCsp2Zi5JRoqFZ8fImlpHZCcnZ2RmZkJAGjSpAlOnz4NALh16xb4QBwRUe2xNjPGpokB6NXCAQ+LlZi44Rx+v6Cbwc53sx5iwoa/UFCswrM+Dvj4Rd8a7d/d1gzfBfvD2ECGP6+k4bP9sTXaP5G2tA5Iffr0wa5duwCUzok0c+ZMPPfccxgxYgReeumlGi+QiIgqZ2ZsiDXBnTCovSuKlSLeDTuPH08n1ugxcgtL8Mb6c7iXUwgfJwt8NaoDDHUwWaW/hy0+e6UdAGD10Xhs/Su5xo9BVFVaP8WmUqmgUqlgaGgIAAgLC8OpU6fQvHlzTJkyBcbGDeON13yKjYjqEpVKxLxdl9XhKOS5Fninj/dTT8BYolRh0sa/cDj2HuwbybFjWjc0ttHtU3PL/ozFV4fiYGQgYNOEAAR42en0eNSwcKJIHWNAIqK6RhRFfHHwBr4MvwEAGN/dEx+/4PtU72+bv+sy1p9KgNxQhi1TAuFXC4/hq1Qi3v45Cn/EpMLGzAg7pnWHh525zo9LDYNOJ4o8fvw4xowZg8DAQNy5cwcA8OOPP+LEiRPVq5aIiJ6aIAgIea4F5g0qHR/0w8kEvLf1AoqV1XtP3IZTCVh/KgEA8MUIv1oJRwAgkwlY9qof2jW2woP8YkzY8BeyC4pr5dhEj2gdkH799Vf069cPpqamOH/+PAoLS2dyVSgU+PTTT2u8QCIi0s747k2xYoQfDGQCfjt/B2/+GImCYqVWfRy+lo4Fv18GALzf3wcD27rootRKmRobYE1wJzhbmiAuPRfTNkehpJpBj6g6tA5In3zyCVavXo01a9bAyOif9wB17969wcyiTURU1w3t4IY1wf6QG8oQfi0dwWvPQvGwaldhrqZk4+2foqASgVf9G2Nqr2Y6rrZiTpYm+H5sJ5gaGeD4jQws3H1FkjqoYdI6IMXGxqJnz57l1ltZWSErK6smaiIiohrQp6UTNk0MgIXJP+9vS88peOw+6TkFmLD+HPKKlOjqZYv/vtT2qQd6P402blb4YoQfAGBjRCI2RiRIVgs1LNWaBykuLq7c+hMnTsDLy6tGiiIioprR2dMWWyYHwr6RHFdTsvHq6ggkZ+ZX2PZhkRKTNkbirqIATe3NsXqMP4wNpX+nef82zni/vw8AYMHvV3BMxxNiEgHVCEiTJk3C9OnTcebMGQiCgLt372Lz5s2YNWsWpk6dqosaiYjoKfi6WuLXqYFwtzVF4t/vb7uWmq3RRqUS8d7WaFxIzoK1mRHWjesMa7O6M23L1F7N8HJHNyhVIqZtjsKNtNp9/xw1PFo/5i+KIj799FOEhoYiP7/0XyFyuRyzZs3CokWLdFJkXcTH/IlI36RnFyB43VlcS82BpYkhfhjfBf4eNgCAz/ZdwzdH4uv03EOFJUqM+f4MziU8QBNbM+yY1h225nUnxJF+0Pk8SEVFRYiLi0Nubi58fX3RqFGjaherjxiQiEgfKfKL8caGc4hMfABTIwOsGtMR6TmFeH/bRQDAslfbY5h/Y4mrrNz93EIM/eYkkjMfoounLX6c2AVyQwOpyyI9wokidYwBiYj0VX5RCd7aHIUjsfdg+PckkiUqEe/08cZ7z/tIXN2T3UjLwcvfnEJOYQle8W+Mpa+0k3QgOemXGg9Ib7zxRpUOvG7duqpVqOcYkIhInxUrVZi19QJ2Rpe+3PbFdi74cmSHp5p1uzYdiU3HG+vPQSUCnwxtgzFdPaQuifREjQckmUwGDw8PdOjQAY/b5bffftO+Wj3EgERE+k6lErHqaDxSFQX48IVWMDHSr1tVq4/GY/Hea2hqb45D7/XiVSSqkqp+fxtWtcOpU6fi559/xq1btzB+/HiMGTMGtra2NVIsERHVPplMwLTe3lKXUW2vd/XAl+E3cCsjD38lPkBnT34nUc2p8mP+K1euREpKCt5//338/vvvcHd3x/Dhw7F///7HXlEiIiLSBXO5IV5sV/oKlF/OJUtcDdU3Ws2DJJfLMWrUKBw4cABXrlxB69at8dZbb8HT0xO5ubm6qpGIiKhCwzu5AwD2xKQgt7BE4mqoPqn2FKkymQyCIEAURSiV2r0EkYiIqCb4e9jAy94c+UVK7Ll4V+pyqB7RKiAVFhbi559/xnPPPYcWLVogJiYGX3/9NZKSkhrcPEhERCQ9QRDw6t9XkX7567bE1VB9UuWA9NZbb8HFxQWLFy/Giy++iOTkZGzduhUDBw6ETCb9u3qIiKhhGtbRDQYyAZGJDxCXzuEeVDO0esy/SZMm6NChw2Mfpdy+fXuNFVeX8TF/IqK6Y+KGczh4NR1TenlhzoBWUpdDdVhVv7+rfOknODgYvXv3hrW1NaysrCpdqmPlypXw9PSEiYkJAgICcPbs2SrtFxYWBkEQMHToUI31giBUuCxdulTdxtPTs9z2xYsXV6t+IiKS1qPbbL9G3kGxUiVxNVQfVHkepPXr1+ukgC1btiAkJASrV69GQEAAVqxYgX79+iE2NhaOjo6V7peQkIBZs2ahR48e5balpKRo/Lx3715MmDABw4YN01i/cOFCTJo0Sf2zhYXFU54NERFJoU9LR9g3MkZGbiGOxt5DkK+T1CWRnpN88NDy5csxadIkjB8/Hr6+vli9ejXMzMwe+8oSpVKJ0aNHY8GCBfDy8iq33dnZWWPZuXMnevfuXa6thYWFRjtzc/MaPz8iItI9IwMZXurgBgD45S/OiURPT9KAVFRUhMjISAQFBanXyWQyBAUFISIiotL9Fi5cCEdHR0yYMOGJx0hLS8OePXsqbLt48WLY2dmhQ4cOWLp0KUpKKp9Do7CwENnZ2RoLERHVHY9usx26lo57OYUSV0P6TtKAlJGRAaVSCScnzUuhTk5OSE1NrXCfEydOYO3atVizZk2VjrFhwwZYWFjg5Zdf1lj/7rvvIiwsDIcPH8aUKVPw6aef4v3336+0n9DQUI2xVu7u7lU6PhER1Y4WThbwc7dGiUrEb+f5yD89HclvsWkjJycHr7/+OtasWQN7e/sq7bNu3TqMHj0aJiYmGutDQkLw7LPPol27dnjzzTexbNkyfPXVVygsrPhfHXPmzIFCoVAvycm8hEtEVNcM/9ecSHwNFj2NKg/S1gV7e3sYGBggLS1NY31aWhqcnZ3LtY+Pj0dCQgIGDRqkXqdSlT6tYGhoiNjYWDRr1ky97fjx44iNjcWWLVueWEtAQABKSkqQkJAAHx+fctvlcjnkcnmVz42IiGrfi+1dsHD3ZcSl5+J8chY6NrGRuiTSU5JeQTI2Noa/vz/Cw8PV61QqFcLDwxEYGFiufcuWLRETE4Po6Gj1MnjwYPTu3RvR0dHlbnutXbsW/v7+aN++/RNriY6Ohkwme+yTc0REVLdZmhhhYNvSF9hu5WBtegqSXkECSm91jR07Fp06dUKXLl2wYsUK5OXlYfz48QBK519yc3NDaGgoTExM0KZNG439ra2tAaDc+uzsbGzduhXLli0rd8yIiAicOXMGvXv3hoWFBSIiIjBz5kyMGTMGNjb81wYRkT4b3skd26Pu4PcLKfj4RV+YGUv+VUd6SPL/a0aMGIF79+5h7ty5SE1NhZ+fH/bt26ceuJ2UlFStV5mEhYVBFEWMGjWq3Da5XI6wsDDMnz8fhYWFaNq0KWbOnImQkJCnPh8iIpJWQFNbeNiZIfF+PvbGpGKYf2OpSyI9VOVXjZAmvmqEiKju+vrQDXz+53UENLXFlinlh2xQw1XjrxohIiLSF8P8G0MQgDO3MpGQkSd1OaSHGJCIiKjecbEyRc/mDgCAbZGcE4m0x4BERET10qM5kbZF3oZSxdEkpB0GJCIiqpeCfB1hbWaE1OwCHLtxT+pySM8wIBERUb0kNzTAUL/SF9hyTiTSFgMSERHVW49usx24kobMvCKJqyF9woBERET1lq+rJdq6WaFYKWLH+TtSl0N6hAGJiIjqteGdSieK/OWvZL7AlqqMAYmIiOq1we3dYGwow7XUHFy6ky11OaQnGJCIiKheszIzQv/WzgBKryIRVQUDEhER1XuPBmvvjL6DgmKlxNWQPmBAIiKieq9bMzu4WZsiu6AE+y+nSl0O6QEGJCIiqvdkMgGv+P8zWJvoSRiQiIioQXjl7xfYnoy7j+TMfKnLoTqOAYmIiBoEd1szdG9mD4AvsKUnY0AiIqIG49W/50TaFnkbKr7Alh6DAYmIiBqMfq2dYWliiDtZD3Eq/r7U5VAdxoBEREQNhomRAYb8/QJbDtamx2FAIiKiBuXRnEj7LqdCkV8scTVUVzEgERFRg9LGzRItnS1QVKLCrgt8gS1VjAGJiIgaFEEQ1FeRfvmLT7NRxRiQiIiowRnawQ1GBgJi7ihw5S5fYEvlMSAREVGDY2tujOd8nQBwsDZVjAGJiIgapEe32XZE30FhCV9gS5oYkIiIqEHq0dwBzpYmyMovxsEr6VKXQ3UMAxIRETVIBnyBLT0GAxIRETVYjwLSsRv3cDfrocTVUF3CgERERA2Wp705ApraQhSB7VF85J/+wYBEREQN2r/nROILbOkRBiQiImrQBrR1RiO5IZIy83E2IVPqcqiOYEAiIqIGzczYEIPauwDgYG36BwMSERE1eI9us/0Rk4LsAr7AlhiQiIiI4OdujeaOjVBQrMLuCylSl0N1AAMSERE1eJovsOVtNqojAWnlypXw9PSEiYkJAgICcPbs2SrtFxYWBkEQMHToUI3148aNgyAIGkv//v012mRmZmL06NGwtLSEtbU1JkyYgNzc3Jo6JSIi0jNDO7jBUCYgOjkL19NypC6HJCZ5QNqyZQtCQkIwb948REVFoX379ujXrx/S0x8/7XtCQgJmzZqFHj16VLi9f//+SElJUS8///yzxvbRo0fj8uXLOHDgAHbv3o1jx45h8uTJNXZeRESkXxws5OjT0hEAsJVXkRo8yQPS8uXLMWnSJIwfPx6+vr5YvXo1zMzMsG7dukr3USqVGD16NBYsWAAvL68K28jlcjg7O6sXGxsb9barV69i3759+P777xEQEIBnnnkGX331FcLCwnD37t0aP0ciItIPj26zbY+6g2KlSuJqSEqSBqSioiJERkYiKChIvU4mkyEoKAgRERGV7rdw4UI4OjpiwoQJlbY5cuQIHB0d4ePjg6lTp+L+/fvqbREREbC2tkanTp3U64KCgiCTyXDmzJmnPCsiItJXz/o4wMFCjvt5RTh0jS+wbcgkDUgZGRlQKpVwcnLSWO/k5ITU1NQK9zlx4gTWrl2LNWvWVNpv//79sXHjRoSHh2PJkiU4evQoBgwYAKVSCQBITU2Fo6Ojxj6GhoawtbWt9LiFhYXIzs7WWIiIqH4xNJDh5Y5uAHibraEzlLoAbeTk5OD111/HmjVrYG9vX2m7kSNHqv+7bdu2aNeuHZo1a4YjR46gb9++1Tp2aGgoFixYUK19iYhIf7zq745vj97E4dh7SM8ugKOlidQlkQQkvYJkb28PAwMDpKWlaaxPS0uDs7Nzufbx8fFISEjAoEGDYGhoCENDQ2zcuBG7du2CoaEh4uPjKzyOl5cX7O3tERcXBwBwdnYuNwi8pKQEmZmZFR4XAObMmQOFQqFekpP5LwsiovrI27EROnnYQKkS8WvUHanLIYlIGpCMjY3h7++P8PBw9TqVSoXw8HAEBgaWa9+yZUvExMQgOjpavQwePBi9e/dGdHQ03N3dKzzO7du3cf/+fbi4lE4lHxgYiKysLERGRqrbHDp0CCqVCgEBARX2IZfLYWlpqbEQEVH99Giw9ta/kiGKfIFtQyT5LbaQkBCMHTsWnTp1QpcuXbBixQrk5eVh/PjxAIDg4GC4ubkhNDQUJiYmaNOmjcb+1tbWAKBen5ubiwULFmDYsGFwdnZGfHw83n//fXh7e6Nfv34AgFatWqF///6YNGkSVq9ejeLiYrz99tsYOXIkXF1da+/kiYioThrYzgXzf7+Mmxl5CL+ajiBfpyfvRPWK5I/5jxgxAp9//jnmzp0LPz8/REdHY9++feqB20lJSUhJqfq07wYGBrh48SIGDx6MFi1aYMKECfD398fx48chl8vV7TZv3oyWLVuib9++GDhwIJ555hl89913NX5+RESkfxrJDTGic+lVpJm/RCMunRMJNzSCyGuH1ZKdnQ0rKysoFArebiMiqocKS5QY8/0ZnEt4AA87M+x4qztszI2lLoueUlW/vyW/gkRERFQXyQ0NsHqMP9xtTZF4Px9TNkWiqISTRzYUDEhERESVsGskx9qxnWEhN8TZW5n4aEcMB203EAxIREREj9HCyQJfvdYBMgH45a/bWHP8ptQlUS1gQCIiInqCZ30cMfdFXwBA6N5rOHAl7Ql7kL5jQCIiIqqCsd08MaZrE4giMD3sPC7fVUhdEukQAxIREVEVCIKAeYNa4xlve+QXKTFpw19IzymQuizSEQYkIiKiKjIykGHl6I7wcjDHXUUBJm2MREGxUuqySAcYkIiIiLRgZWqEdWM7w9rMCBeSs/CfbRf5ZFs9xIBERESkJU97c6we4w8jAwG/X7iL/4XfkLokqmEMSERERNXQ1csO/x3aFgCw4uAN7LpwV+KKqCYxIBEREVXT8M7umNzTCwAwa+sFnE96IHFFVFMYkIiIiJ7C7P4tEdTKCUUlKkzaGIk7WQ+lLolqAAMSERHRUzCQCfjfSD+0dLZARm4hJqw/h9zCEqnLoqfEgERERPSUzOWGWDuuM+wbyXEtNQczws5DqeKTbfqMAYmIiKgGuFmbYk2wP4wNZTh4NR1L9l2TuiR6CgxIRERENaRDExt8/mp7AMB3x25iy7kkiSui6mJAIiIiqkGD27tiet/mAIAPf7uE0zfvS1wRVQcDEhERUQ2bEdQcL7ZzQYlKxJubIpGQkSd1SaQlBiQiIqIaJggCPn+1Pdq7WyMrvxhvbDgHRX6x1GWRFhiQiIiIdMDEyABrgv3hamWCm/fyMO2nKBQrVVKXRVXEgERERKQjjhYm+H5sZ5gZG+BEXAYW/H6ZL7bVEwxIREREOuTraon/jewAQQA2nU7ChlMJUpdEVcCAREREpGPP+Trhg/4tAQALd1/Bkdh0iSuiJ2FAIiIiqgWTe3rhVf/GUInA2z+dx/W0HKlLosdgQCIiIqoFgiDgvy+1RZemtsgtLMGEDedwP7dQ6rKoEgxIREREtcTYUIZvx/jDw84MyZkPMfnHSL7Yto5iQCIiIqpFNubGWDu2MyxMDBGZ+ABjvj+DrPwiqcuiMhiQiIiIapm3YyNsnhgAazMjRCdnYeR3p3Evh7fb6hIGJCIiIgm0a2yNLZMD4WAhx7XUHAz/NgJ3sh5KXRb9jQGJiIhIIj7OFtg6JRBu1qa4lZGHV1edwi2+t61OYEAiIiKSkKe9ObZNDYSXgznuKgrw6uoIXE3JlrosSeUXlSD+Xq6kNTAgERERSczFyhS/TAmEr4slMnILMfK70zif9EDqsiShUomYuSUaQ78+iRM3MiSrgwGJiIioDrBvJMfPk7vC38MGiofFGP39GZyKly4gSGXJ/mvYfzkNhSUqyI2kiykMSERERHWElakRfpzQBc942yO/SIlxP5xD+NU0qcuqNWFnk/Dt0ZsAgM9eaYfOnraS1VInAtLKlSvh6ekJExMTBAQE4OzZs1XaLywsDIIgYOjQoep1xcXFmD17Ntq2bQtzc3O4uroiODgYd+/e1djX09MTgiBoLIsXL67J0yIiItKambEhvh/bCc/5OqGoRIUpP0bi9wt3n7yjnjsVl4GPdlwCAEzv2xxDO7hJWo/kAWnLli0ICQnBvHnzEBUVhfbt26Nfv35IT3/8i/wSEhIwa9Ys9OjRQ2N9fn4+oqKi8PHHHyMqKgrbt29HbGwsBg8eXK6PhQsXIiUlRb288847NXpuRERE1WFiZIBvRnfEUD9XlKhEvBt2HmFnk6QuS2fi0nPx5qZIlKhEDG7vihlBzaUuCYIoiqKUBQQEBKBz5874+uuvAQAqlQru7u5455138MEHH1S4j1KpRM+ePfHGG2/g+PHjyMrKwo4dOyo9xrlz59ClSxckJiaiSZMmAEqvIM2YMQMzZsyoVt3Z2dmwsrKCQqGApaVltfogIiJ6HJVKxMc7L2HzmdJw9NELrTCxh5fEVdWszLwiDF15EkmZ+fD3sMHmiQEwMTLQ2fGq+v0t6RWkoqIiREZGIigoSL1OJpMhKCgIERERle63cOFCODo6YsKECVU6jkKhgCAIsLa21li/ePFi2NnZoUOHDli6dClKSvg+HCIiqjtkMgGfDG2DKb1KQ9Ene65ixcHrkPjaRo0pLFFi8sa/kJSZD3dbU3z3ur9Ow5E2DKU8eEZGBpRKJZycnDTWOzk54dq1axXuc+LECaxduxbR0dFVOkZBQQFmz56NUaNGaSTFd999Fx07doStrS1OnTqFOXPmICUlBcuXL6+wn8LCQhQW/jMNfHZ2w56jgoiIaocgCPigf0tYmhhh6f5YrDh4A7kFJfjwhVYQBEHq8qpNFEXM3nYRfyU+gIWJIdaN7Qy7RnKpy1KTNCBpKycnB6+//jrWrFkDe3v7J7YvLi7G8OHDIYoiVq1apbEtJCRE/d/t2rWDsbExpkyZgtDQUMjl5T+g0NBQLFiw4OlPgoiISEuCIGBab2+YGxtg/u9X8P2JW8gtLMF/X2oLA5l+hqQvw+OwI/ouDGQCVo32R3MnC6lL0iBpQLK3t4eBgQHS0jQfYUxLS4Ozs3O59vHx8UhISMCgQYPU61QqFQDA0NAQsbGxaNasGYB/wlFiYiIOHTr0xHFCAQEBKCkpQUJCAnx8fMptnzNnjkaoys7Ohru7e9VPloiI6CmN694UjUyM8P62Cwg7l4zcwhJ8McIPRgaSP3OllZ3Rd/DFwesAgE+GtsEzzZ980aO2SfonamxsDH9/f4SHh6vXqVQqhIeHIzAwsFz7li1bIiYmBtHR0epl8ODB6N27N6Kjo9WB5VE4unHjBg4ePAg7O7sn1hIdHQ2ZTAZHR8cKt8vlclhaWmosREREte0V/8ZY+VpHGBkI2H0xBVN+jERBsVLqsqosMjET/9l2EQAwuacXRnVpInFFFZP8FltISAjGjh2LTp06oUuXLlixYgXy8vIwfvx4AEBwcDDc3NwQGhoKExMTtGnTRmP/RwOvH60vLi7GK6+8gqioKOzevRtKpRKpqakAAFtbWxgbGyMiIgJnzpxB7969YWFhgYiICMycORNjxoyBjY1N7Z08ERFRNQxo64I1xgZ4c1MkDl1Lx7gfzuL7sZ3RSC751/pjJd3Px+SNkSgqUeF5XyfM7t9S6pIqJfmf5IgRI3Dv3j3MnTsXqamp8PPzw759+9QDt5OSkiCTVf1C1507d7Br1y4AgJ+fn8a2w4cP49lnn4VcLkdYWBjmz5+PwsJCNG3aFDNnztS4hUZERFSXPevjiI1vBOCN9edw+mYmRn9/BhvGd4a1mbHUpVVI8bAYb2w4h/t5RWjjZokVI/3q9PgpyedB0lecB4mIiOqCi7ezMHbdWTzIL0ZLZwtsnNAFjhYmUpeloVipwvgfzuFEXAacLU2w8+3ucLKUpka9mAeJiIiInk67xtbYMiUQjhZyXEvNwYhvT+NO1kOpy1ITRRFzd17CibgMmBkb4PuxnSQLR9pgQCIiItJzLZwssPXNQDS2McWtjDy8uuoUbt7LlbosAMD3x2/h57PJEATgy5Ed0MbNSuqSqoQBiYiIqB7wsDPH1jcD0czBHHcVBRj+bQSik7MkrenPy6n4dO9VAMBHL/giyNfpCXvUHQxIRERE9YSLlSl+mRKI1q6WyMgtfcfZhPXnEJn4oNZruXRHgelh0RBFYEzXJniju2et1/A0GJCIiIjqEbtGcvw0qSuG+LlCJgDh19IxbNUpjPwuAseu36uV97ilKB5iwoZzeFisRI/m9pg/qLXevRaFT7FVE59iIyKiuu5WRh6+PRqPX6Nuo1hZ+nXf1s0K03o3w/O+zpDp4DH7vMISvLo6AldSstHCqRG2Te0GSxOjGj9OdVX1+5sBqZoYkIiISF+kKB5izbFb+PlsEh7+Peu2t2MjvNmrGYb4udbYq0qUKhFTfvwLB6+mw76RMX57qzvcbc1qpO+awoCkYwxIRESkb+7nFmL9qQSsP5WAnIISAICbtSmm9PLC8E7uMDEyeKr+F+2+grUnbsHYUIawyV3RsUndezsFA5KOMSAREZG+yikoxqbTSVh74hYycgsBAPaNjPHGM00xpqtHtW6JbTqdiI92XAIAfDWqAwa1d63RmmsKA5KOMSAREZG+KyhWYutfyVh99KZ6ckkLE0OMDfTE+O6esGskr1I/x67fw/j156BUiZj1fAu83ae5Lst+KgxIOsaARERE9UWxUoVd0XfxzZE4xN/LAwCYGMkwqksTTOrhBVdr00r3vZ6Wg2HfnEJOYQle7uiGZa+2r9NPrDEg6RgDEhER1TcqlYg/r6Rh5eE4xNxRAACMDAS81MENb/ZqBi+HRhrt7+UUYujKk7iT9RBdmtrixwldIDd8unFMusaApGMMSEREVF+JoogTcRlYeTgOp29mAgAEARjYxgVTn22GNm5WKChWYtSa0ziflAVPOzP89lZ32JgbS1z5kzEg6RgDEhERNQSRiZn45nA8wq+lq9c96+MAA0FA+LV0WJka4be3upW7ulRXMSDpGAMSERE1JFdTsrHqSDx2X7wL1d/JwVAm4McJAQhsZidtcVpgQNIxBiQiImqIEjLy8O2xeByJvYfZ/VtiaAc3qUvSSlW/vw1rsSYiIiLSc5725gh9uZ3UZegcX1ZLREREVAYDEhEREVEZDEhEREREZTAgEREREZXBgERERERUBgMSERERURkMSERERERlMCARERERlcGARERERFQGAxIRERFRGQxIRERERGUwIBERERGVwYBEREREVAYDEhEREVEZhlIXoK9EUQQAZGdnS1wJERERVdWj7+1H3+OVYUCqppycHACAu7u7xJUQERGRtnJycmBlZVXpdkF8UoSiCqlUKty9excWFhYQBEHqcnQmOzsb7u7uSE5OhqWlpdTl6FRDOlegYZ0vz7X+akjny3OtGaIoIicnB66urpDJKh9pxCtI1SSTydC4cWOpy6g1lpaW9f4X8pGGdK5Awzpfnmv91ZDOl+f69B535egRDtImIiIiKoMBiYiIiKgMBiR6LLlcjnnz5kEul0tdis41pHMFGtb58lzrr4Z0vjzX2sVB2kRERERl8AoSERERURkMSERERERlMCARERERlcGARERERFQGA1IDFhoais6dO8PCwgKOjo4YOnQoYmNjH7vP+vXrIQiCxmJiYlJLFVff/Pnzy9XdsmXLx+6zdetWtGzZEiYmJmjbti3++OOPWqr26Xl6epY7X0EQMG3atArb69PneuzYMQwaNAiurq4QBAE7duzQ2C6KIubOnQsXFxeYmpoiKCgIN27ceGK/K1euhKenJ0xMTBAQEICzZ8/q6Ayq7nHnWlxcjNmzZ6Nt27YwNzeHq6srgoODcffu3cf2WZ3fhdrypM923Lhx5Wrv37//E/vVt88WQIW/v4IgYOnSpZX2WVc/26p81xQUFGDatGmws7NDo0aNMGzYMKSlpT223+r+rlcVA1IDdvToUUybNg2nT5/GgQMHUFxcjOeffx55eXmP3c/S0hIpKSnqJTExsZYqfjqtW7fWqPvEiROVtj116hRGjRqFCRMm4Pz58xg6dCiGDh2KS5cu1WLF1Xfu3DmNcz1w4AAA4NVXX610H335XPPy8tC+fXusXLmywu2fffYZvvzyS6xevRpnzpyBubk5+vXrh4KCgkr73LJlC0JCQjBv3jxERUWhffv26NevH9LT03V1GlXyuHPNz89HVFQUPv74Y0RFRWH79u2IjY3F4MGDn9ivNr8LtelJny0A9O/fX6P2n3/++bF96uNnC0DjHFNSUrBu3ToIgoBhw4Y9tt+6+NlW5btm5syZ+P3337F161YcPXoUd+/excsvv/zYfqvzu64Vkehv6enpIgDx6NGjlbb54YcfRCsrq9orqobMmzdPbN++fZXbDx8+XHzhhRc01gUEBIhTpkyp4cpqx/Tp08VmzZqJKpWqwu36+rkCEH/77Tf1zyqVSnR2dhaXLl2qXpeVlSXK5XLx559/rrSfLl26iNOmTVP/rFQqRVdXVzE0NFQndVdH2XOtyNmzZ0UAYmJiYqVttP1dkEpF5zt27FhxyJAhWvVTXz7bIUOGiH369HlsG335bMt+12RlZYlGRkbi1q1b1W2uXr0qAhAjIiIq7KO6v+va4BUkUlMoFAAAW1vbx7bLzc2Fh4cH3N3dMWTIEFy+fLk2yntqN27cgKurK7y8vDB69GgkJSVV2jYiIgJBQUEa6/r164eIiAhdl1njioqKsGnTJrzxxhuPfbGyvn6u/3br1i2kpqZqfHZWVlYICAio9LMrKipCZGSkxj4ymQxBQUF693krFAoIggBra+vHttPmd6GuOXLkCBwdHeHj44OpU6fi/v37lbatL59tWloa9uzZgwkTJjyxrT58tmW/ayIjI1FcXKzxObVs2RJNmjSp9HOqzu+6thiQCACgUqkwY8YMdO/eHW3atKm0nY+PD9atW4edO3di06ZNUKlU6NatG27fvl2L1WovICAA69evx759+7Bq1SrcunULPXr0QE5OToXtU1NT4eTkpLHOyckJqamptVFujdqxYweysrIwbty4Stvo6+da1qPPR5vPLiMjA0qlUu8/74KCAsyePRujRo167Ms9tf1dqEv69++PjRs3Ijw8HEuWLMHRo0cxYMAAKJXKCtvXl892w4YNsLCweOItJ334bCv6rklNTYWxsXG5YP+4z6k6v+vaMqyRXkjvTZs2DZcuXXri/erAwEAEBgaqf+7WrRtatWqFb7/9FosWLdJ1mdU2YMAA9X+3a9cOAQEB8PDwwC+//FKlf5Xps7Vr12LAgAFwdXWttI2+fq5Uqri4GMOHD4coili1atVj2+rz78LIkSPV/922bVu0a9cOzZo1w5EjR9C3b18JK9OtdevWYfTo0U98cEIfPtuqftfUBbyCRHj77bexe/duHD58GI0bN9ZqXyMjI3To0AFxcXE6qk43rK2t0aJFi0rrdnZ2LvcERVpaGpydnWujvBqTmJiIgwcPYuLEiVrtp6+f66PPR5vPzt7eHgYGBnr7eT8KR4mJiThw4MBjrx5V5Em/C3WZl5cX7O3tK61d3z9bADh+/DhiY2O1/h0G6t5nW9l3jbOzM4qKipCVlaXR/nGfU3V+17XFgNSAiaKIt99+G7/99hsOHTqEpk2bat2HUqlETEwMXFxcdFCh7uTm5iI+Pr7SugMDAxEeHq6x7sCBAxpXWfTBDz/8AEdHR7zwwgta7aevn2vTpk3h7Oys8dllZ2fjzJkzlX52xsbG8Pf319hHpVIhPDy8zn/ej8LRjRs3cPDgQdjZ2Wndx5N+F+qy27dv4/79+5XWrs+f7SNr166Fv78/2rdvr/W+deWzfdJ3jb+/P4yMjDQ+p9jYWCQlJVX6OVXnd706hVMDNXXqVNHKyko8cuSImJKSol7y8/PVbV5//XXxgw8+UP+8YMECcf/+/WJ8fLwYGRkpjhw5UjQxMREvX74sxSlU2XvvvSceOXJEvHXrlnjy5EkxKChItLe3F9PT00VRLH+eJ0+eFA0NDcXPP/9cvHr1qjhv3jzRyMhIjImJkeoUtKZUKsUmTZqIs2fPLrdNnz/XnJwc8fz58+L58+dFAOLy5cvF8+fPq5/cWrx4sWhtbS3u3LlTvHjxojhkyBCxadOm4sOHD9V99OnTR/zqq6/UP4eFhYlyuVxcv369eOXKFXHy5MmitbW1mJqaWuvn92+PO9eioiJx8ODBYuPGjcXo6GiN3+HCwkJ1H2XP9Um/C1J63Pnm5OSIs2bNEiMiIsRbt26JBw8eFDt27Cg2b95cLCgoUPdRHz7bRxQKhWhmZiauWrWqwj705bOtynfNm2++KTZp0kQ8dOiQ+Ndff4mBgYFiYGCgRj8+Pj7i9u3b1T9X5Xf9aTAgNWAAKlx++OEHdZtevXqJY8eOVf88Y8YMsUmTJqKxsbHo5OQkDhw4UIyKiqr94rU0YsQI0cXFRTQ2Nhbd3NzEESNGiHFxcertZc9TFEXxl19+EVu0aCEaGxuLrVu3Fvfs2VPLVT+d/fv3iwDE2NjYctv0+XM9fPhwhf/fPjoflUolfvzxx6KTk5Mol8vFvn37lvsz8PDwEOfNm6ex7quvvlL/GXTp0kU8ffp0LZ1R5R53rrdu3ar0d/jw4cPqPsqe65N+F6T0uPPNz88Xn3/+edHBwUE0MjISPTw8xEmTJpULOvXhs33k22+/FU1NTcWsrKwK+9CXz7Yq3zUPHz4U33rrLdHGxkY0MzMTX3rpJTElJaVcP//epyq/609D+PugRERERPQ3jkEiIiIiKoMBiYiIiKgMBiQiIiKiMhiQiIiIiMpgQCIiIiIqgwGJiIiIqAwGJCIiIqIyGJCIiIiIymBAIiL627hx4zB06FCNddu2bYOJiQmWLVsmTVFEJAlDqQsgIqqrvv/+e0ybNg2rV6/G+PHjpS6HiGoRryAREVXgs88+wzvvvIOwsDCGI6IGiFeQiIjKmD17Nr755hvs3r0bffv2lbocIpIAAxIR0b/s3bsXO3fuRHh4OPr06SN1OUQkEd5iIyL6l3bt2sHT0xPz5s1Dbm6u1OUQkUQYkIiI/sXNzQ1HjhzBnTt30L9/f+Tk5EhdEhFJgAGJiKgMDw8PHD16FKmpqQxJRA0UAxIRUQXc3d1x5MgRpKeno1+/fsjOzpa6JCKqRQxIRESVaNy4MY4cOYKMjAyGJKIGRhBFUZS6CCIiIqK6hFeQiIiIiMpgQCIiIiIqgwGJiIiIqAwGJCIiIqIyGJCIiIiIymBAIiIiIiqDAYmIiIioDAYkIiIiojIYkIiIiIjKYEAiIiIiKoMBiYiIiKgMBiQiIiKiMv4fhp3AZXH2cWsAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1\n"
          ]
        }
      ],
      "source": [
        "#a range of k values to try\n",
        "k_values = [i for i in range (1, 21)]\n",
        "best_k = None\n",
        "best_accuracy = 0\n",
        "score = []\n",
        "\n",
        "\n",
        "for k in k_values:\n",
        "      print(k)\n",
        "      knn_model = KNeighborsClassifier(n_neighbors=k, p = 2, weights = 'uniform')\n",
        "      # 10-fold cross-validation\n",
        "      scores = cross_val_score(knn_model, X_train, y_train, cv=10, scoring='accuracy')\n",
        "      mean_accuracy = scores.mean()\n",
        "      score.append(mean_accuracy)\n",
        "\n",
        "      if mean_accuracy > best_accuracy:\n",
        "          best_accuracy = mean_accuracy\n",
        "          best_k = k\n",
        "\n",
        "plt.plot(k_values, score)\n",
        "plt.title('kNN mean accuracy')\n",
        "plt.xlabel('K')\n",
        "plt.ylabel('Mean accuracy')\n",
        "plt.show()\n",
        "print(best_k)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YCfFHo708YZ4"
      },
      "outputs": [],
      "source": [
        "knn = KNeighborsClassifier(n_neighbors= 1, p = 2, weights = 'uniform')\n",
        "knn.fit(X_train,y_train)\n",
        "y_pred = knn.predict(X_test)\n",
        "\n",
        "knn_accuracy = accuracy_score(y_test, y_pred)\n",
        "knn_precision = precision_score(y_test, y_pred, average='weighted')\n",
        "knn_recall = recall_score(y_test, y_pred, average='weighted')\n",
        "knn_f1 = f1_score(y_test, y_pred, average='weighted')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Po0cGs-I858X",
        "outputId": "e0db70ca-1b8a-4d1e-a0ff-ae82f7ff7427"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[11,  2,  3,  1],\n",
              "       [ 1, 19,  3,  3],\n",
              "       [ 2,  1,  8,  5],\n",
              "       [ 2,  0,  5, 15]])"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x1200 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "conf = confusion_matrix(y_pred, y_test)\n",
        "display(conf)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=conf, display_labels=[classes[i] for i in range(0, 4)])\n",
        "fig, ax = plt.subplots(figsize=(12, 12))\n",
        "disp.plot(ax=ax, xticks_rotation='vertical')\n",
        "plt.title(\"Confusion matrix\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jxl0YuI0_fBO",
        "outputId": "b02e89c2-618a-4d4b-d1a9-04d716a2d6ac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best k: 1\n",
            "K-Nearest Neighbors Classifier Accuracy: 0.6543\n",
            "K-Nearest Neighbors Classifier Precision: 0.6456\n",
            "K-Nearest Neighbors Classifier Recall: 0.6543\n",
            "K-Nearest Neighbors Classifier F1-Score: 0.6472\n"
          ]
        }
      ],
      "source": [
        "print(f\"Best k: {1}\")\n",
        "print(f\"K-Nearest Neighbors Classifier Accuracy: {knn_accuracy:.4f}\")\n",
        "print(f\"K-Nearest Neighbors Classifier Precision: {knn_precision:.4f}\")\n",
        "print(f\"K-Nearest Neighbors Classifier Recall: {knn_recall:.4f}\")\n",
        "print(f\"K-Nearest Neighbors Classifier F1-Score: {knn_f1:.4f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V4WG4BHPdY_J"
      },
      "source": [
        "# **6. Improve**</a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7BrAVrv_9lFb",
        "outputId": "c58335f6-dd9a-4f56-ca09-9d3d0078d35a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1\n",
            "1\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "2\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "3\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "2\n",
            "1\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "2\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "3\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "3\n",
            "1\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "2\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "3\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "4\n",
            "1\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "2\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "3\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "5\n",
            "1\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "2\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "3\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "6\n",
            "1\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "2\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "3\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "7\n",
            "1\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "2\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "3\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "8\n",
            "1\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "2\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "3\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "9\n",
            "1\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "2\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "3\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "10\n",
            "1\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "2\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "3\n",
            "uniform\n",
            "distance\n",
            "<function myweight at 0x7cae43a00310>\n",
            "4\n",
            "distance\n",
            "1\n"
          ]
        }
      ],
      "source": [
        "def myweight(distances):\n",
        "    sigma2 = .5\n",
        "    return np.exp(-distances**2/sigma2)\n",
        "\n",
        "#a range of k values to try\n",
        "k_values = [i for i in range (1, 11)]\n",
        "weight = ['uniform', 'distance', myweight]\n",
        "score_k = []\n",
        "best_accuracy = 0\n",
        "best_k = None\n",
        "best_w = None\n",
        "best_p = None\n",
        "\n",
        "for k in k_values:\n",
        "  score_p = []\n",
        "  print(k)\n",
        "  for p_val in range(1, 4):\n",
        "    score_w = []\n",
        "    print(p_val)\n",
        "    for w in weight:\n",
        "      print(w)\n",
        "      knn_model = KNeighborsClassifier(n_neighbors=k, p = p_val, weights = w)\n",
        "      # 10-fold cross-validation\n",
        "      scores = cross_val_score(knn_model, X_train, y_train, cv=10, scoring='accuracy')\n",
        "      mean_accuracy = scores.mean()\n",
        "      score_w.append(mean_accuracy)\n",
        "\n",
        "      if mean_accuracy > best_accuracy:\n",
        "          best_accuracy = mean_accuracy\n",
        "          best_k = k\n",
        "          best_w = w\n",
        "          best_p = p_val\n",
        "    score_p.append(score_w)\n",
        "  score_k.append(score_p)\n",
        "\n",
        "print(best_k)\n",
        "print(best_w)\n",
        "print(best_p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tu86BUCQcz4R"
      },
      "outputs": [],
      "source": [
        "knn = KNeighborsClassifier(n_neighbors= 4, p = 1, weights = 'distance')\n",
        "knn.fit(X_train,y_train)\n",
        "y_pred = knn.predict(X_test)\n",
        "\n",
        "knn_accuracy = accuracy_score(y_test, y_pred)\n",
        "knn_precision = precision_score(y_test, y_pred, average='weighted')\n",
        "knn_recall = recall_score(y_test, y_pred, average='weighted')\n",
        "knn_f1 = f1_score(y_test, y_pred, average='weighted')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wzC_0Nz4dAjw",
        "outputId": "4cb07f3b-9376-4607-fb82-129c971c46c7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[11,  2,  5,  2],\n",
              "       [ 0, 18,  2,  1],\n",
              "       [ 2,  0,  7,  6],\n",
              "       [ 3,  2,  5, 15]])"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x1200 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "conf = confusion_matrix(y_pred, y_test)\n",
        "display(conf)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=conf, display_labels=[classes[i] for i in range(0, 4)])\n",
        "fig, ax = plt.subplots(figsize=(12, 12))\n",
        "disp.plot(ax=ax, xticks_rotation='vertical')\n",
        "plt.title(\"Confusion matrix\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o7ose_C0dHGc",
        "outputId": "333f199e-559e-432c-863c-9c7bbb4345c4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best parameters: k = 4, p = 1, weight = 'distance'\n",
            "K-Nearest Neighbors Classifier Accuracy: 0.6296\n",
            "K-Nearest Neighbors Classifier Precision: 0.6287\n",
            "K-Nearest Neighbors Classifier Recall: 0.6296\n",
            "K-Nearest Neighbors Classifier F1-Score: 0.6261\n"
          ]
        }
      ],
      "source": [
        "print(f\"Best parameters: k = 4, p = 1, weight = 'distance'\")\n",
        "print(f\"K-Nearest Neighbors Classifier Accuracy: {knn_accuracy:.4f}\")\n",
        "print(f\"K-Nearest Neighbors Classifier Precision: {knn_precision:.4f}\")\n",
        "print(f\"K-Nearest Neighbors Classifier Recall: {knn_recall:.4f}\")\n",
        "print(f\"K-Nearest Neighbors Classifier F1-Score: {knn_f1:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ebrW0pVVWbaL",
        "outputId": "9438b140-5387-47e5-d646-7efc83d3d6a2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ],
      "source": [
        "model_score = [[0.6543, 0.6456, 0.6543, 0.6472]]\n",
        "\n",
        "#logistic regression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "logreg = LogisticRegression(random_state=0)\n",
        "logreg.fit(X_train, y_train)\n",
        "y_pred = logreg.predict(X_test)\n",
        "\n",
        "lr_accuracy = accuracy_score(y_test, y_pred)\n",
        "lr_precision = precision_score(y_test, y_pred, average='weighted')\n",
        "lr_recall = recall_score(y_test, y_pred, average='weighted')\n",
        "lr_f1 = f1_score(y_test, y_pred, average='weighted')\n",
        "model_score.append([lr_accuracy, lr_precision, lr_recall, lr_f1])\n",
        "\n",
        "#random forest\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rfc = RandomForestClassifier(random_state=0)\n",
        "rfc.fit(X_train, y_train)\n",
        "y_pred = rfc.predict(X_test)\n",
        "\n",
        "rfc_accuracy = accuracy_score(y_test, y_pred)\n",
        "rfc_precision = precision_score(y_test, y_pred, average='weighted')\n",
        "rfc_recall = recall_score(y_test, y_pred, average='weighted')\n",
        "rfc_f1 = f1_score(y_test, y_pred, average='weighted')\n",
        "model_score.append([rfc_accuracy, rfc_precision, rfc_recall, rfc_f1])\n",
        "\n",
        "#support vector machine\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "svc=SVC()\n",
        "svc.fit(X_train,y_train)\n",
        "y_pred=svc.predict(X_test)\n",
        "\n",
        "rfc_accuracy = accuracy_score(y_test, y_pred)\n",
        "rfc_precision = precision_score(y_test, y_pred, average='weighted')\n",
        "rfc_recall = recall_score(y_test, y_pred, average='weighted')\n",
        "rfc_f1 = f1_score(y_test, y_pred, average='weighted')\n",
        "model_score.append([rfc_accuracy, rfc_precision, rfc_recall, rfc_f1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZHaJMeSndfQt"
      },
      "source": [
        "# **7. Compare with others model**</a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "s_-ID1N5WuXr",
        "outputId": "1cd323d7-f970-4092-b302-6f47bbbf7bbe"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "alg = ['knn', 'lg', 'rf', 'svm']\n",
        "\n",
        "plt.title(\"Accuracy each algorithm\")\n",
        "plt.plot(alg, [(s[0] + s[1] + s[2] + s[3])/4 for s in model_score])\n",
        "plt.xlabel('Algorithm')\n",
        "plt.ylabel('Mean score')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h140QKCaly22"
      },
      "source": [
        "# **8. Other.**</a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kUPM_j-Dlkgt"
      },
      "outputs": [],
      "source": [
        "y = np.array(y)\n",
        "imgs = np.array(imgs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N0pPe3Oolkg0"
      },
      "outputs": [],
      "source": [
        "avg_be = np.mean(imgs[y == 0], axis=0)\n",
        "avg_ad = np.mean(imgs[y == 1], axis=0)\n",
        "avg_sms = np.mean(imgs[y == 2], axis=0)\n",
        "avg_scare = np.mean(imgs[y == 3], axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Kt8gF5p5lkg0",
        "outputId": "61fd194f-52c6-49ff-86f8-7883cc3ce3ff"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7c47ee08f100>"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(avg_be)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "JxPb995Elkg1",
        "outputId": "7ec43c0e-15c1-4b55-98d5-087884c1a4d2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7c47eddcf1f0>"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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Ek5YfUDs/Qn9pK9tHlmgvpWxvtGg/H2Vr9VbaL0aNWXOeWcaQdn4s1wng5/wo14kTlrY+WZcKLjFu6ENtjDvhunJOS45YindAREQUBCcgIiIKghMQEREFwQmIiIiCqNogBMQxEJX3wE98OKY8uPPysFhLa1HmIkzFYpQHmpaH+VbiQ0et7Wo6EqV+2gJcTkotZHw4r9GOKZ1/68Nc7fwIY0h7mGs+l8r+UWrkpao+tFbLLv8ht68gEe2aEMs3pNQCoI9DA/XzwEP7rWWrwTAWynXlhuSgAGc5pNbf0djrzTsgIiIKghMQEREFwQmIiIiC4ARERERBcAIiIqIgqjcKLpEYGV3hIw2GEjXlYIjs0qKptIgshRqB4uOYQuQZADgpYsWaAsR6TGH/KLKVYSacTjUiLa2kfDKcTwdPi9dpUUyWiDftvGntl/rKGtmknTelHOeEg2qRkcbxZqmHOmat5Uh9rn3WSG0frWyNVHdjJC5iwzG18yONqzLPDe+AiIgoCE5AREQUBCcgIiIKghMQEREFwQmIiIiCqNooODeUhzs1sESLqLHkhFKiM7RccFIElxYwZ40EimDIh6ZF1GhtV6NQpIgVLZ+aMV+bpf2GiLlRyzbUXT3H2uKFhvaouQStUX2moazkGkt4uKyN9dbrYsh3qI5lY4ShmGvNV35FD3XUrqsyF3Eb7ZhSzkBglOtKaU8kRPW5vFJGPLIM58qLxuMdEBERBcEJiIiIguAEREREQXACIiKiIDgBERFREFUbBYdIyAWn7WpYXTNK1ojb1ZUbpbKtubY85LjSo1iMpAiulJ9oN2fJK2VkWrEWkMeOmtvOcO61shXW82YayzV+Vi31EaUoRU2Ntr84Do1lqFGNwnVojlL0lQvPIqnkJPSwEqkWjajXRYgi1fI3SivqOgeUEQjHOyAiIgqCExAREQXBCYiIiILgBEREREGYJqD29nZceOGFmD59Os444wxcccUVOHjwYMk+g4ODaG1txaxZszBt2jSsWbMGPT09XitNREQTnykKrqOjA62trbjwwgsxPDyMv/u7v8OHP/xhHDhwAFOnTgUAbNiwAT/60Y+wY8cOZLNZrF+/HqtXr8ZPf/pTU8USU2uRiEqjQtyQkitJinDRVk9NKHnZauToOJFWtpKHKVJW3HRDQ3I5Uo4nY3vUOkp9Zc21pUQUWWKyIjFfF+C0uljbb6mLtiKqRopKUuoX1RgDTZV+Ec+Rtq9GGZ9iH2rt0fpbidRSayiVr12blpU4ofS51p50rbhdzbFovd4MZWjXhEaso/X8ZDJl769em1K5LgmceOv9TFfHrl27Sv7/4IMP4owzzkBnZyf+/M//HH19fXjggQfw8MMP49JLLwUAbNu2DYsXL8bevXtx8cUXWw5HREST2Lh+fezr6wMAzJw5EwDQ2dmJfD6PlpaW4j6LFi3CvHnzsGfPHrGMXC6H/v7+khcREU1+Y56A4jjGDTfcgBUrVuCcc84BAHR3dyOdTqO+vr5k34aGBnR3d4vltLe3I5vNFl9NTU1jrRIREU0gY56AWltb8fLLL2P79u3jqkBbWxv6+vqKr66urnGVR0REE8OYUvGsX78eP/zhD/H000/jzDPPLG5vbGzE0NAQent7S+6Cenp60NjYKJaVyWSQER6ERaedhihxyvZMeYscjcqa1kJKsWFNrSMs2AScbGPZ1DQyxgXcrClGfBDqogV9RMaACFfQghOE9CDaQ17rw9+8YeEwy7ga9aCGIATrMSs5JpSxLy7KZq2fJSWWsY3mJEdaXXxQAzyEa8h67rXrTUhZprZQSqsUJ8oKQjCdFecc1q9fj507d+LJJ5/EggULSt5ftmwZampqsHv37uK2gwcP4siRI2hubrYcioiIJjnTHVBraysefvhhfP/738f06dOLz3Wy2SymTJmCbDaLq6++Ghs3bsTMmTNRV1eH6667Ds3NzYyAIyKiEqYJaOvWrQCAv/iLvyjZvm3bNvz1X/81AODOO+9EIpHAmjVrkMvlsHLlStxzzz1eKktERJOHaQIq5w+RamtrsWXLFmzZsmXMlSIiosmPueCIiCiI6l2QLpEYkVZCTZcjRQOpETJapI0hikVLu6Gm4zAupiYW4aEMQI560dJ3aHe8lnQxlnqMVhdFZMigpB7T2E4xNYy1T5JayhQPC5tZU/T4SPNjHSs+ytZoKYcstHGoLFxpSWeklq31Va2WLkcoX00hpFwolmvfkoqnzNREvAMiIqIgOAEREVEQnICIiCgITkBERBQEJyAiIgqiaqPgXCYNlyxdKCxSIoScFlEk7myLqImkXGvWqBwtIsRTbjITS8STNWrMkkHLekwffaUtgqdETIrnfpT9LbSxbIoCDBF55is6LkRdLGVo25UFBsXPIKXe2rgyk8aKtgCi9TxI4zOlLQwojeXyxjHvgIiIKAhOQEREFAQnICIiCoITEBERBcEJiIiIgqjaKDgpF5yrZNSLEiXiksL+vqLUtHKkKCstcsa6UqoUkecr55sxj5upbGufS/tbV1u1NMfYJ+pYtuTmskR/AvqYKPd4Y9nfFDmlsLZTYo0885FLUinD+aqLjwhdyzWulS2kmXOF8urBOyAiIgqCExAREQXBCYiIiILgBEREREFwAiIioiCqNgouPi2DOFm6EmC5q+yNqszojCLLFO0rv5lEq7cUpTeWcgwiLWIwZYjg0qK9rHnztPZIxWvDRzvHljxh1nprY8JSR0/tiYZHFiRGf47hmJFyfpy0qqyP8wAAQnuQ9nRtWqIdfUWkWceWRDkP6rUsla2NCaEMRsEREVFV4wRERERBcAIiIqIgOAEREVEQVRuE4NIJuFPSeLhYmS8NqWsibY017TmnJXuJMSBAS8eiLlYmsS6OJvWLNZ2PlmJEa4/hYax5sTftGbLQh+rChdohDWUDSsoZbbE77eG8pfm+yp4y8g0f53LUuliDZ8YpSHs8pc+KnDy2TGOlgqTPSFcolPWzvAMiIqIgOAEREVEQnICIiCgITkBERBQEJyAiIgqiaqPg8tNq4FKlKx1FWkYKS5SVp2gYsWxrVJK2v1CXWIkaUqP0DM2JjAtkqZFqxmgysQgtNYjWV0rdXTRyaPs4x1rZ5kg6rS6WqEtrnxjGm1ZGrKbFkTer15tUvlaGNt4sp9MaMeZnqLz9rOs2qtePtBBn+WUMDzMKjoiIqhgnICIiCoITEBERBcEJiIiIguAEREREQVRtFFyi4JAoMxGbGCXjKfpIKlsrQ4t201iisrQIQC+skTOWRayU/aNhW44wrW/VPpR+tdK620f7lfNjjuAyRNMllJyBlmg3rWztXJpz9WmkS9YSMWdljBa1RhjKO5e/K+ApQreS7bREepZZZd4BERFREJyAiIgoCE5AREQUBCcgIiIKghMQEREFUbVRcHEyGpH/zLI6qRaxEafkOVeNykoJBWlpjtRIE3m7Ftkm7u9p9Utx9UItv5eV0n4xj51xRUx1JVtLqJH1/FQwH1gsjatRiOdNabt1pUx5TJS/739VRvkBZXfDmIvVqL6yi/Cy4jGgR2lK5fsaP+p5FsatVr+E8vmmkcanqb/LHIS8AyIioiA4ARERURCcgIiIKAhOQEREFIRpAtq6dSuWLFmCuro61NXVobm5GY899ljx/cHBQbS2tmLWrFmYNm0a1qxZg56eHm+VdZH8sogKTnzZKqK8IvkVOfll2l+pt0tG4iuKIb58sJ4HsT3G+mntNNH620NfaX2ivdQxYShfHYdGPuqniWInvuSKyK9EwYkvtf3Cy9weY99ayjb1CU4GG0gvcdxqn28e2qNe9+MYh6YJ6Mwzz8TmzZvR2dmJ/fv349JLL8Xll1+OV155BQCwYcMGPProo9ixYwc6Ojpw9OhRrF692nIIIiJ6h4icG986xTNnzsQdd9yBj33sY5g9ezYefvhhfOxjHwMA/OIXv8DixYuxZ88eXHzxxWWV19/fj2w2ixUf3IRUqrasn/ES7qj9UiaFI2ohjZ6imS3U5J0VrKMa0qrdNUj7G0OirWHoJsYQYrEID6HP1nJMYfxG5mtKG26WJdatZVj+fMDXktwerh/rkumW6836Jx8+xrhU9vDwIJ7puBV9fX2oq6tTyxzzUC0UCti+fTsGBgbQ3NyMzs5O5PN5tLS0FPdZtGgR5s2bhz179qjl5HI59Pf3l7yIiGjyM09AL730EqZNm4ZMJoNrr70WO3fuxNlnn43u7m6k02nU19eX7N/Q0IDu7m61vPb2dmSz2eKrqanJ3AgiIpp4zBPQWWedhRdffBH79u3DunXrsHbtWhw4cGDMFWhra0NfX1/x1dXVNeayiIho4jCn4kmn03j3u98NAFi2bBmee+45fP3rX8eVV16JoaEh9Pb2ltwF9fT0oLGxUS0vk8kgk8mM2O6SCbhT0uao329angNY084I3z/HGdu8bf2+35IaRaU9G5Lqoj0G1BYlC/CsS/2u2hAJp54H6/fmHsRKtRNaaiXh+YD5MY3lkYk1MNSY6sY0hozPHF1S2FdJn2UeywHGvtZObQyJZfh45jhK8OKp4qi8z8hxP66M4xi5XA7Lli1DTU0Ndu/eXXzv4MGDOHLkCJqbm8d7GCIimmRMd0BtbW1YtWoV5s2bhxMnTuDhhx/GU089hccffxzZbBZXX301Nm7ciJkzZ6Kurg7XXXcdmpuby46AIyKidw7TBHT8+HF8+tOfxrFjx5DNZrFkyRI8/vjj+NCHPgQAuPPOO5FIJLBmzRrkcjmsXLkS99xzT0UqTkREE9u4/w7Itz/8HdCftdyKVE3p3wFVyzMgLV5fPWSIZ0CWukzkZ0CWU19Fz4C0PrQ8A/J1TAmfAdnqUklqO6vkGZBkOD+In/2fTZX7OyAiIqLxqNoF6RLDMRIonfp9/Oat/VW1StrdEg4CVPS3pspmQrD11UT9DTvh4Q5Vo97pGI8p3RmpbVTultS7K2kMjZKbTCzb2ofSNl9jYljYV7lOknkty4JyUA8ZOXyN5Uioi1oPH59NhkwVblhbtbMU74CIiCgITkBERBQEJyAiIgqCExAREQXBCYiIiIKo2ii41IkhpFJjnx/VFQaNf9ti+bsZ7W81RlvtsGzWv19SSO1Uo4+sEYOe6mhi+RsmY3vU8yn9nZYWjahFnnmIsNP4WJuokvWrOMPf7vn4Gz3tmBW/HmLhD4QSymemtO9opHK060ca44Wh8g5jqBIREZE3nICIiCgITkBERBQEJyAiIgqCExAREQVRtVFwiXyMRFyaT0iLNDJRIlOifPlRImo+OS3qxZjF20nlaMm2NFo7pYg8penWvHnq+ZHKt/7qY85MLdRdO6bWfkN+LzVztlZ22SX/F6nuFSw70qKphuWDamNFHMuAvJKt1t/GsSKeN2NEWpkLeo7OkqUf0Nup1V06FUq0mzkHZqG8XG4AxOi4qMyoO94BERFREJyAiIgoCE5AREQUBCcgIiIKomqDEODciIdbiUH5wZj0oNP6UNT8kE4sxE/qDbEU4wN0L/XQ+lBdBG/8ZftiGRPmlEMSX2lXLHXRjqmmipIHkelcGPsqsvSL9uBaC4iw1EN7o4Lpprx91vgYn1oZWt9K6YyU1GhS0EdUYBACERFVMU5AREQUBCcgIiIKghMQEREFwQmIiIiCmFBRcD6iSszpZSxRPJWMVDOmyzGlxdFofRXbzoPUh17SKkFPlyPVRe+TAAvpaXycN2PfOqn9WhnWdDlavaX9k0l5X+0atEQBGsey+VdzqS6WVFsY5foxRAGay1CLllau1MbV2KP0eAdERERBcAIiIqIgOAEREVEQnICIiCgITkBERBRE9UbBSbS0RT5Wj1KjZMqvhznaTYs0kuqiRKA4LcuVmvtJ2KaVbYxKcpaF+jzlgnMpDxFsWhkVzJGm5uBS86EJ24y54NRjRoaoMSvLpalGzBlz+FmuQx/RblpdtLKV86BePxop0rOSUX1aNaQo1zJ/nndAREQUBCcgIiIKghMQEREFwQmIiIiC4ARERERBVG0UnMuk4JI1pdsMAU9R7GelQ+mYUtDQyZ0NUWCVpkVTSXUxtB0YpW8tfWgtQ82RpkV8Cfsr+2p1cVIZCnVMWDktUs0whozj0DTGrce0sF4nIaIulTEh9peal00pQxuHykqk0ni2njcvn6lpIQquIK9efSreARERURCcgIiIKAhOQEREFAQnICIiCqJqgxAKmRSi1CnV85UeRGB5KK5lu9AeAKoPsyvZHien0bE8dNTLtu0vHtP6wNlH+iNrf/t48F9B5vGmMaR+ChJoYz2mj/ZUUoigJE2FAjkKw0oar1PwDoiIiILgBEREREFwAiIioiA4ARERURCcgIiIKIhxRcFt3rwZbW1tuP7663HXXXcBAAYHB3HjjTdi+/btyOVyWLlyJe655x40NDSYyn5zdhqpmnTJNlP0lRbcYYx6KXdhpVHLtkYrSZu1avsIqPHUV9VEOm+VPPdW1vQ/Yt19RcFJx1OjQo3HtIxb4zg0nR9rYKAx0lVtZyVV8Nofb9nD+fI6ZMx3QM899xy+9a1vYcmSJSXbN2zYgEcffRQ7duxAR0cHjh49itWrV4/1MERENEmNaQJ6/fXXcdVVV+H+++/HjBkzitv7+vrwwAMP4Gtf+xouvfRSLFu2DNu2bcPPfvYz7N2711uliYho4hvTBNTa2oqPfOQjaGlpKdne2dmJfD5fsn3RokWYN28e9uzZI5aVy+XQ399f8iIiosnP/Axo+/bteP755/Hcc8+NeK+7uxvpdBr19fUl2xsaGtDd3S2W197ejltvvdVaDSIimuBMd0BdXV24/vrr8Z3vfAe1tbVeKtDW1oa+vr7iq6ury0u5RERU3Ux3QJ2dnTh+/DjOP//84rZCoYCnn34a3/zmN/H4449jaGgIvb29JXdBPT09aGxsFMvMZDLIZDIjtr95egLJdOn8qEWgRFI+sApGpahrhlVyMSgt4kkrwxLFYoyEEft7tOKrJf+csWztPFvGlrfz5mO9t0peEx7aY+4rC2O/VrKvrLy0X2Fpp6UehaHy7m1ME9Bll12Gl156qWTbZz7zGSxatAhf/OIX0dTUhJqaGuzevRtr1qwBABw8eBBHjhxBc3Oz5VBERDTJmSag6dOn45xzzinZNnXqVMyaNau4/eqrr8bGjRsxc+ZM1NXV4brrrkNzczMuvvhif7UmIqIJz/tyDHfeeScSiQTWrFlT8oeoREREfyxyrrr+3L2/vx/ZbBbnXP1VJNOlgQ58BmQog8+Axl02nwGVj8+AKmdiPgMaxEvb/hf6+vpQV1en7sdccEREFETVrog6ODNCMlM65frIBaf+VqsVIyzsp94BePqtqVK/lViP5+MO4OQBDLsqfWttp9Qm87nX7nQLtnLkQipYho+7Je08aH1orUsF7zBM516rt3aNV3Ix0wre5Xr5pkQjHLOQK69g3gEREVEQnICIiCgITkBERBQEJyAiIgqCExAREQVRvVFwc4aRmDJculH7u5RCJVdGHBniEfkKzDf8/Y1Uj9FEceX6xAetPRXtWw/nHlDqqEVNefoVz3r+JVrfWsr2UYaVOiYsh6zkuR9lf1MZSduKypZr3Pz5IdTR0ifxm+WFivIOiIiIguAEREREQXACIiKiIDgBERFREFUbhFA7+w0kTyt9shspD8HiuPx5VCtD2y5xykNErQxt/0TCckxbXTTSMbWyI+3Zr6f95X1tfWspR+vvWHmYq+0vtcd6HjTWdkp89aFchrUu4y/Hx9j3MX4A2xjSP2u0Y8rbLftbz48PUtsLbwyW9bO8AyIioiA4ARERURCcgIiIKAhOQEREFAQnICIiCqJqo+BmTXsDqaml6RxiQ9RLwhj1opWdSozMsTJsiLobjVQ2IEfPWCObCoY6WvtKYzk/vsrW6i5tt543rexKttPCuJK6SmpniDZaziVgizq1jh9tf2s5ljI0BcMxtZJ9fB5axttwlCvrOLwDIiKiIDgBERFREJyAiIgoCE5AREQUBCcgIiIKomqj4Jqm96Jmarpkmx6pVt7iRwAwHCfF7T4iwXxF2kj7JyI5Yk5rT6zEwyQMq3iZI4Sq/JjavmpdlHpbxop23jRDsXxJWvpQux5iV7nfN32M/RDRmL6iZd/usq3H1MahNiak/S375lNDeE6r5B//bBn7EBEReccJiIiIguAEREREQXACIiKiIDgBERFREFUbBZdNDSJdU150myViw1KGRitbi7JKReVH6Y1WvuWYWv6opBRhp0RY+SjbyhLtBdii4KyRQNaouXLrMZq8y4vbLX1rP+bI9mvHq+RYtp4fazlvNy0iTYtctZc//nb6OD+SoZQ8jkeUWXaJREREHnECIiKiIDgBERFREJyAiIgoiKoNQsi7CFGZD8iSwsPiXMHWNB9pQLSHdLlCpmJ1sadAGX8ZmuF4/OX4ehDtI62JJaWNVu+88/PAOWl4+C8FFZwso/zzoAWaAHJ7rNebxDzetNQwHlI/WY/pQzoxbPsBoS6+rhNLOjDpmENlDlfeARERURCcgIiIKAhOQEREFAQnICIiCoITEBERBVG1UXD9w1NQky9dkG441hZEEhYfUxev0yI5lAXPDNFUPiKvAFt7fJTti74YVvnt8RWRV8nFvaSyrceztscWSVi5vvLVTh8L0vkYKz4Wi9RYPq8AYABpcbtFiM8J6Zj5fHnRn7wDIiKiIDgBERFREJyAiIgoCE5AREQUBCcgIiIKwhQF9+Uvfxm33nprybazzjoLv/jFLwAAg4ODuPHGG7F9+3bkcjmsXLkS99xzDxoaGswVGxyuQWG4pmTbkKeFnCYTfdGr8n+3cNoCc8aIQa2cqIIRXJXMm2ctp5Lk3Fy2c2/Z31e0pI8oOB8Rqlr0q5WPKDhfx/QRSWg9Zrn1yBfK+3lzD733ve/FsWPHiq9nnnmm+N6GDRvw6KOPYseOHejo6MDRo0exevVq6yGIiOgdwPx3QKlUCo2NjSO29/X14YEHHsDDDz+MSy+9FACwbds2LF68GHv37sXFF18slpfL5ZDL5Yr/7+/vt1aJiIgmIPMd0KFDhzB37ly8613vwlVXXYUjR44AADo7O5HP59HS0lLcd9GiRZg3bx727Nmjltfe3o5sNlt8NTU1jaEZREQ00ZgmoOXLl+PBBx/Erl27sHXrVhw+fBjvf//7ceLECXR3dyOdTqO+vr7kZxoaGtDd3a2W2dbWhr6+vuKrq6trTA0hIqKJxfQV3KpVq4r/XrJkCZYvX4758+fje9/7HqZMmTKmCmQyGWQytgXbiIho4htXLrj6+nq85z3vwauvvooPfehDGBoaQm9vb8ldUE9Pj/jM6K3k4wTcKVFvBQ9RJbGweiqgr6JoieCy8hE1VsmILK2/rX1YMKzSqAXPWCOhpKgnaxScjygma2SXtZzKleEnaswSMZk3jiv1mFI5SnO0sWk9Ztn1gH0canWRyvfSV5DrmFJWRJVWiR0eLm/8jOsKe/311/GrX/0Kc+bMwbJly1BTU4Pdu3cX3z948CCOHDmC5ubm8RyGiIgmIdMd0Oc//3l89KMfxfz583H06FFs2rQJyWQSn/zkJ5HNZnH11Vdj48aNmDlzJurq6nDdddehublZjYAjIqJ3LtME9Nvf/haf/OQn8Z//+Z+YPXs2LrnkEuzduxezZ88GANx5551IJBJYs2ZNyR+iEhERnSpyzlXuIccY9Pf3I5vN4rIf/U+kppYGJ/AZ0NtbD421D7X9TcfkM6CK1UWSVL7v90Uac9ZxpZHK0drDZ0AVegY0kMMz/2ML+vr6UFdXpx6fueCIiCiIql0Rtat3BpJDpXdAcVx+RI12X5dKybO4tn8kBdQo9bDeuUhlA0DCQ94q7a7G0leJhLU9b//douXuTevXWLnT0dpTKIzcX9vX112u5Vz4OKa1PVqutYJ25yr0eZjxYytHu2Z9lK2dY2m8jba/hdbn2mecROrbwhuDZf0s74CIiCgITkBERBQEJyAiIgqCExAREQVRtUEI+VwShWR51RMfrCv7xsoDPTUs1hIuqz1EtabL8fAwVjuiqeQK1tsamG19sC5tVfukkudHK9t6jiu5CJ5UF0/BE5a9fQTfAHKggDMEMI1Ga4+lFGuAh9qHhrprx0wklaAsQxCCJM6Xt3go74CIiCgITkBERBQEJyAiIgqCExAREQXBCYiIiIKo3ii4N2qQcDXey9VSg5jCW6yhMD6i43xF2FnK1orWImQs/aIdUys76SFNi1a2tW99pIwJMQ59ZLrR+tCaFkYopqDtqxVdyZy9Ia4rtRxluyVo0HqrYQojHbkpfvNtWJCOiIhorDgBERFREJyAiIgoCE5AREQUBCcgIiIKomqj4FL/rwaJ2tIoODVYSdimBppYI2csESuVDKYyRs6oRYsRabZCLOdB5SsSqIJ96CXxl0YLErL8SugpCkxMBWcuxLi/dEwlfZh6nVQwOs58iqXzafz13nTNaj/gKQJyvB978WB5UwvvgIiIKAhOQEREFAQnICIiCoITEBERBcEJiIiIgqjaKLjIlR8lZorY8JQOzFSRCqZxq2REVohANWvknRpNZon2s1VFLCfSEpkZ83h5GRPWxVY9HFI9b5YytHFYwZxv1pR8GnEVVmsZynYXIj+gIcJuPJca74CIiCgITkBERBQEJyAiIgqCExAREQXBCYiIiIKo2ii4Qq2Dqz0lFMNHNIg1v5k0RRsjTbQIKaf1vhQNpK62qmz30FdOWYU0GtZWxVTKkaLGrMcsyMd0ykqc4v6+oqmkqlivJA+/+qkRcz5COi39CtijryxjvJIrv2rXrDFKUcynZ424lTfrDJFq+lhRNkvtN1QwTpW3M++AiIgoCE5AREQUBCcgIiIKghMQEREFUbVBCKmBCIlTH3hWMCWHJWeINV2K+tBxSNlfeACoLdblZZE19bmyj7wwStyD+gBZfkNNxyLlQAH8jBVPi3uZWB64ezpvclyKdZAbd/exCJ6lbE/NUU+P6boylAElEAqjpH8yMMWrGAIWolx5Hc47ICIiCoITEBERBcEJiIiIguAEREREQXACIiKiIKo2Ci4/dwiJKaXzo7owk0SLpkppOTaUckyr3Rn2NYqUFDXmxcfikQ2KlLQr5sgmNQ3I+DsmFuo9GrGdWj206B6tPYY0MlI9AL3P1f099KHzstqdLKGMz9iQukftkwrWW6Wm8iq/LqbPK+jjTS1f6lvtWtauH2s6pzLFbyohvqfgHRAREQXBCYiIiILgBEREREFwAiIioiDME9Brr72GT33qU5g1axamTJmC973vfdi/f3/xfeccbrnlFsyZMwdTpkxBS0sLDh065LXSREQ08Zmi4H7/+99jxYoV+OAHP4jHHnsMs2fPxqFDhzBjxoziPrfffjvuvvtuPPTQQ1iwYAFuvvlmrFy5EgcOHEBtbW3Zx1r+7sOomZou2TYUywnREkLERsIYHhYr4SBSOZZ9x3JMi+FY/h1C6hMAGCqMPOUJbfUto1hJWiWVb9n35HYlykqJSpLKTyeHTWVYaPVL+epbYaxo9dbqoo1Pyzj0NcalcavW23jupf2186CVMayOT1vfWmh95WN8arR+SSXKTzQ3FI/8TMkPDOG35Ry/7KMA+Id/+Ac0NTVh27ZtxW0LFiwo/ts5h7vuugtf+tKXcPnllwMAvv3tb6OhoQGPPPIIPvGJT1gOR0REk5jpK7gf/OAHuOCCC/Dxj38cZ5xxBs477zzcf//9xfcPHz6M7u5utLS0FLdls1ksX74ce/bsEcvM5XLo7+8veRER0eRnmoB+/etfY+vWrVi4cCEef/xxrFu3Dp/73Ofw0EMPAQC6u7sBAA0NDSU/19DQUHzvVO3t7chms8VXU1PTWNpBREQTjGkCiuMY559/Pm677Tacd955uOaaa/DZz34W995775gr0NbWhr6+vuKrq6trzGUREdHEYZqA5syZg7PPPrtk2+LFi3HkyBEAQGNjIwCgp6enZJ+enp7ie6fKZDKoq6sreRER0eRnCkJYsWIFDh48WLLtl7/8JebPnw/gZEBCY2Mjdu/ejXPPPRcA0N/fj3379mHdunWmin2+8ceYNr10fswpy4LWCJEcA05umhZ9VVAiUJJCdEuNshShVoaVdExp22gs7RlU+korozaSo8ny6rKtI/mKvPPBV99KtPGm9aE0lrVjWqOjtAguidYnlnE1mrzQL1rbrdF7PiLS8srv5lo7LdGyGh+fH1K/AnrfWs6zZby9fiLGI2XsZ5qANmzYgD/7sz/Dbbfdhr/6q7/Cs88+i/vuuw/33XcfACCKItxwww34yle+goULFxbDsOfOnYsrrrjCcigiIprkTBPQhRdeiJ07d6KtrQ1///d/jwULFuCuu+7CVVddVdznC1/4AgYGBnDNNdegt7cXl1xyCXbt2mX6GyAiIpr8IuesScMrq7+/H9lsFntfbuRXcG+xbTT8Cq58/ApuJH4Fx6/gTmX9Cu4D73sNfX19oz7XZy44IiIKomoXpGtIAtNH/EJdfnqIhPIbZqzc8BWU3wSSwm8CmUjutqTyC0LCOM+/4UYu5iTVAwBqIvmuIxPViNsLwmpqsdL2mki5W1TumIYN5+eNOC9uz2t1Mf52KNVdOw95pd55ceU5vV/EfSGfnzzksrVjWuSr6EuNpLLKWkJov96v2p24cucutD/n5M+DhFK/vCt/LAPa9Wn7rLGe+YFYuOtSLpMaZXtBW79O2JZRTo80wk9Lltca3gEREVEQnICIiCgITkBERBQEJyAiIgqCExAREQVRtVFwq178NJKnZUq2xcria7EW+iGIlL+FyOeVxe4S5UcUuQouHOWUNvo4ZjJljPhRIlyUgCLT+dH21cpOJJSFxoSxUigoEVnKOdb6Vgoy0+qnnTcY/iZHKz8uyNeDVu9YaX8ktF/tE6V+MPSVVpDaV8Zjioz9ba2LdH7UtivnAUn5B7SxJVGPqV1XyjHFsi3X8ZuDAL78lvvxDoiIiILgBEREREFwAiIioiA4ARERURBVF4Twh9yohTdyI97TgxDKL199UJ5XHugaghBMD0WNKhmEAGMQgvVhqeX8WIMQtPMjlaM9hFfP8WQLQlD7VqhLiCAE61hmEEL5xwwShPDfn+eaqsuG/dvf/hZNTU2hq0FEROPU1dWFM888U32/6iagOI5x9OhRTJ8+HSdOnEBTUxO6urom9VLd/f39bOck8U5oI8B2Tja+2+mcw4kTJzB37lwkEvqTnqr7Ci6RSBRnzOi/7j3r6uom9cn/A7Zz8ngntBFgOycbn+3MZrNvuQ+DEIiIKAhOQEREFERVT0CZTAabNm1CJpN5650nMLZz8ngntBFgOyebUO2suiAEIiJ6Z6jqOyAiIpq8OAEREVEQnICIiCgITkBERBQEJyAiIgqiqiegLVu24E//9E9RW1uL5cuX49lnnw1dpXF5+umn8dGPfhRz585FFEV45JFHSt53zuGWW27BnDlzMGXKFLS0tODQoUNhKjtG7e3tuPDCCzF9+nScccYZuOKKK3Dw4MGSfQYHB9Ha2opZs2Zh2rRpWLNmDXp6egLVeGy2bt2KJUuWFP9yvLm5GY899ljx/cnQxlNt3rwZURThhhtuKG6bDO388pe/jCiKSl6LFi0qvj8Z2vgHr732Gj71qU9h1qxZmDJlCt73vvdh//79xfff7s+gqp2Avvvd72Ljxo3YtGkTnn/+eSxduhQrV67E8ePHQ1dtzAYGBrB06VJs2bJFfP/222/H3XffjXvvvRf79u3D1KlTsXLlSgwODr7NNR27jo4OtLa2Yu/evXjiiSeQz+fx4Q9/GAMDA8V9NmzYgEcffRQ7duxAR0cHjh49itWrVwestd2ZZ56JzZs3o7OzE/v378ell16Kyy+/HK+88gqAydHGP/bcc8/hW9/6FpYsWVKyfbK0873vfS+OHTtWfD3zzDPF9yZLG3//+99jxYoVqKmpwWOPPYYDBw7gH//xHzFjxoziPm/7Z5CrUhdddJFrbW0t/r9QKLi5c+e69vb2gLXyB4DbuXNn8f9xHLvGxkZ3xx13FLf19va6TCbj/vVf/zVADf04fvy4A+A6OjqccyfbVFNT43bs2FHc59///d8dALdnz55Q1fRixowZ7p/+6Z8mXRtPnDjhFi5c6J544gn3gQ98wF1//fXOuclzLjdt2uSWLl0qvjdZ2uicc1/84hfdJZdcor4f4jOoKu+AhoaG0NnZiZaWluK2RCKBlpYW7NmzJ2DNKufw4cPo7u4uaXM2m8Xy5csndJv7+voAADNnzgQAdHZ2Ip/Pl7Rz0aJFmDdv3oRtZ6FQwPbt2zEwMIDm5uZJ18bW1lZ85CMfKWkPMLnO5aFDhzB37ly8613vwlVXXYUjR44AmFxt/MEPfoALLrgAH//4x3HGGWfgvPPOw/333198P8RnUFVOQL/73e9QKBTQ0NBQsr2hoQHd3d2BalVZf2jXZGpzHMe44YYbsGLFCpxzzjkATrYznU6jvr6+ZN+J2M6XXnoJ06ZNQyaTwbXXXoudO3fi7LPPnlRt3L59O55//nm0t7ePeG+ytHP58uV48MEHsWvXLmzduhWHDx/G+9//fpw4cWLStBEAfv3rX2Pr1q1YuHAhHn/8caxbtw6f+9zn8NBDDwEI8xlUdcsx0OTR2tqKl19+ueT79MnkrLPOwosvvoi+vj7827/9G9auXYuOjo7Q1fKmq6sL119/PZ544gnU1taGrk7FrFq1qvjvJUuWYPny5Zg/fz6+973vYcqUKQFr5lccx7jgggtw2223AQDOO+88vPzyy7j33nuxdu3aIHWqyjug008/HclkckSkSU9PDxobGwPVqrL+0K7J0ub169fjhz/8IX7yk5+UrIjY2NiIoaEh9Pb2luw/EduZTqfx7ne/G8uWLUN7ezuWLl2Kr3/965OmjZ2dnTh+/DjOP/98pFIppFIpdHR04O6770YqlUJDQ8OkaOep6uvr8Z73vAevvvrqpDmXADBnzhycffbZJdsWL15c/LoxxGdQVU5A6XQay5Ytw+7du4vb4jjG7t270dzcHLBmlbNgwQI0NjaWtLm/vx/79u2bUG12zmH9+vXYuXMnnnzySSxYsKDk/WXLlqGmpqaknQcPHsSRI0cmVDslcRwjl8tNmjZedtlleOmll/Diiy8WXxdccAGuuuqq4r8nQztP9frrr+NXv/oV5syZM2nOJQCsWLFixJ9E/PKXv8T8+fMBBPoMqkhogwfbt293mUzGPfjgg+7AgQPummuucfX19a67uzt01cbsxIkT7oUXXnAvvPCCA+C+9rWvuRdeeMH95je/cc45t3nzZldfX+++//3vu5///Ofu8ssvdwsWLHBvvvlm4JqXb926dS6bzbqnnnrKHTt2rPh64403ivtce+21bt68ee7JJ590+/fvd83Nza65uTlgre1uuukm19HR4Q4fPux+/vOfu5tuuslFUeR+/OMfO+cmRxslfxwF59zkaOeNN97onnrqKXf48GH305/+1LW0tLjTTz/dHT9+3Dk3OdronHPPPvusS6VS7qtf/ao7dOiQ+853vuNOO+009y//8i/Ffd7uz6CqnYCcc+4b3/iGmzdvnkun0+6iiy5ye/fuDV2lcfnJT37iAIx4rV271jl3Mgzy5ptvdg0NDS6TybjLLrvMHTx4MGyljaT2AXDbtm0r7vPmm2+6v/3bv3UzZsxwp512mvvLv/xLd+zYsXCVHoO/+Zu/cfPnz3fpdNrNnj3bXXbZZcXJx7nJ0UbJqRPQZGjnlVde6ebMmePS6bT7kz/5E3fllVe6V199tfj+ZGjjHzz66KPunHPOcZlMxi1atMjdd999Je+/3Z9BXA+IiIiCqMpnQERENPlxAiIioiA4ARERURCcgIiIKAhOQEREFAQnICIiCoITEBERBcEJiIiIguAEREREQXACIiKiIDgBERFREP8f1LF5ITWigd4AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(avg_ad)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "gvVBo8yulkg1",
        "outputId": "8524e956-2250-43b0-b736-601e23ae4980"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7c47ee0582e0>"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(avg_scare)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "5vmTEyK8lkg1",
        "outputId": "daa64568-746b-4488-f41d-3e154e6f9912"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7c47edfb5060>"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(avg_sms)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kBWe1bjElkg1",
        "outputId": "e0e477cf-d8b2-4436-a972-32a70f02a934"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.9519436\n"
          ]
        }
      ],
      "source": [
        "dist = np.linalg.norm(avg_sms - avg_scare)\n",
        "print(dist)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvrITjPplkg1",
        "outputId": "46eee70a-4ca1-41f0-f67e-f1fcd1cfdfb6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3.3065255\n"
          ]
        }
      ],
      "source": [
        "dist = np.linalg.norm(avg_sms - avg_ad)\n",
        "print(dist)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pKxulszzlkg2",
        "outputId": "e5832dcf-6f56-49f8-bf38-bbf630052f9f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.7529628\n"
          ]
        }
      ],
      "source": [
        "dist = np.linalg.norm(avg_sms - avg_be)\n",
        "print(dist)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V4GXacC4lkg2",
        "outputId": "03fa1563-50e6-49e3-dfe3-282c2434e005"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.6980965\n"
          ]
        }
      ],
      "source": [
        "dist = np.linalg.norm(avg_scare - avg_ad)\n",
        "print(dist)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "98PY7zQmlkg3",
        "outputId": "7aab2d1b-78c6-4914-a533-7fa1799c3ec9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.3619074\n"
          ]
        }
      ],
      "source": [
        "dist = np.linalg.norm(avg_scare - avg_be)\n",
        "print(dist)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mgp4ghZJlkg4",
        "outputId": "302a9ec2-d429-4404-d295-8fa8cf0e736a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3.1105115\n"
          ]
        }
      ],
      "source": [
        "dist = np.linalg.norm(avg_ad - avg_be)\n",
        "print(dist)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VY4ApycDFLzG"
      },
      "source": [
        "# **8. Using XGBoost feature selection.**</a>\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bPkCdIg33DQJ",
        "outputId": "9e0dc910-d5b2-4284-c90f-4a454467dc4d"
      },
      "outputs": [],
      "source": [
        "# use feature importance for feature selection\n",
        "from numpy import loadtxt\n",
        "from numpy import sort\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "\n",
        "model = XGBClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "thresholds = sort(model.feature_importances_)\n",
        "for thresh in thresholds[::-1]:\n",
        "    # select features using threshold\n",
        "    selection = SelectFromModel(model, threshold=thresh, prefit=True)\n",
        "    select_X_train = selection.transform(X_train)\n",
        "    # train model\n",
        "    knn = KNeighborsClassifier(n_neighbors= 1, p = 2, weights = 'uniform')\n",
        "    knn.fit(select_X_train, y_train)\n",
        "    # eval model\n",
        "    select_X_test = selection.transform(X_test)\n",
        "    y_pred = knn.predict(select_X_test)\n",
        "    predictions = [round(value) for value in y_pred]\n",
        "    accuracy = accuracy_score(y_test, predictions)\n",
        "    print(\"Thresh=%.3f, n=%d, Accuracy: %.2f%%\" % (thresh, select_X_train.shape[1], accuracy*100.0))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}