20_8_2022_Assignment2.ipynb

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        "<a href=\"https://colab.research.google.com/github/SWi7CHbladE/Pattern-Recognition-Assigment/blob/main/20_8_2022_Assignment2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive/\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import copy\n",
        "import os\n",
        "from scipy.stats import norm\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive/\",force_remount = True)"
      ]
    },
    {
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              "       R  S      L      D      H      WW      SW      VW      LW\n",
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              "4      7  I  0.330  0.255  0.080  0.2050  0.0895  0.0395  0.0550\n",
              "...   .. ..    ...    ...    ...     ...     ...     ...     ...\n",
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      ],
      "source": [
        "data = pd.read_csv(\"/content/gdrive/MyDrive/Colab_Files/abalonedata.csv\")\n",
        "col = [\"L\",\"D\",\"H\",\"WW\",\"SW\",\"VW\",\"LW\"]\n",
        "data"
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    {
      "cell_type": "markdown",
      "source": [
        "**Small-data dataset**"
      ],
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        "id": "aa2pXcefrfnt"
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      "id": "aa2pXcefrfnt"
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              "      R  S      L      D      H      WW      SW      VW      LW\n",
              "42    5  I  0.240  0.175  0.045  0.0700  0.0315  0.0235  0.0200\n",
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              "      <td>0.245</td>\n",
              "      <td>0.175</td>\n",
              "      <td>0.055</td>\n",
              "      <td>0.0785</td>\n",
              "      <td>0.0400</td>\n",
              "      <td>0.0180</td>\n",
              "      <td>0.0200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4119</th>\n",
              "      <td>4</td>\n",
              "      <td>I</td>\n",
              "      <td>0.300</td>\n",
              "      <td>0.215</td>\n",
              "      <td>0.050</td>\n",
              "      <td>0.1185</td>\n",
              "      <td>0.0480</td>\n",
              "      <td>0.0225</td>\n",
              "      <td>0.0420</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>189 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae11ccd8-fd0e-4294-8503-115766efd9ca')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
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              "  <style>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ae11ccd8-fd0e-4294-8503-115766efd9ca');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "data_S = copy.deepcopy(data.loc[(data.R <= 5) & (data.R >= 1)])\n",
        "data_S"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "93661b80",
      "metadata": {
        "id": "93661b80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "18fc61fc-9756-43c3-adc4-43d5d6525369"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        mean       std\n",
              "L   0.255820  0.068613\n",
              "D   0.187963  0.052799\n",
              "H   0.062302  0.019796\n",
              "WW  0.096683  0.072998\n",
              "SW  0.045958  0.055320\n",
              "VW  0.021058  0.016036\n",
              "LW  0.028545  0.020270"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-82135b24-5ddd-4642-9ca7-8de9b190cc99\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>L</th>\n",
              "      <td>0.255820</td>\n",
              "      <td>0.068613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.187963</td>\n",
              "      <td>0.052799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>H</th>\n",
              "      <td>0.062302</td>\n",
              "      <td>0.019796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WW</th>\n",
              "      <td>0.096683</td>\n",
              "      <td>0.072998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SW</th>\n",
              "      <td>0.045958</td>\n",
              "      <td>0.055320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VW</th>\n",
              "      <td>0.021058</td>\n",
              "      <td>0.016036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LW</th>\n",
              "      <td>0.028545</td>\n",
              "      <td>0.020270</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82135b24-5ddd-4642-9ca7-8de9b190cc99')\"\n",
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              "              style=\"display:none;\">\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    }\n",
              "\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-82135b24-5ddd-4642-9ca7-8de9b190cc99 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-82135b24-5ddd-4642-9ca7-8de9b190cc99');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "tmp = data_S[col].describe().loc[[\"mean\",\"std\"]]\n",
        "tmp.transpose()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.arange(-0.1, 0.5, 0.001)\n",
        "\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'L'), tmp._get_value(\"std\", 'L')), label='L')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'D'), tmp._get_value(\"std\", 'D')), label='D')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'H'), tmp._get_value(\"std\", 'H')), label='H')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'WW'), tmp._get_value(\"std\", 'WW')), label='WW')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'SW'), tmp._get_value(\"std\", 'SW')), label='SW')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'VW'), tmp._get_value(\"std\", 'VW')), label='VW')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'LW'), tmp._get_value(\"std\", 'LW')), label='LW')\n",
        "\n",
        "\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "LAIunJUfuoX0",
        "outputId": "0aee688a-f23c-4c8b-9063-d95166f49d7e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f6adeac8fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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oJDJepOkINnTflbDR04gn6GGE9fQeeETgjzuO98dsiUSSAEgBjzOebjzwsC9EqM2HLqtr+AROLHiGrYJgkwclGO4ypsJRAdCrB55lykKn0VHlrIrGfIlEEkekgMcZr9PZRcCDjWpYRNfD4mRkfMgUhLBacn8qFXZVwHvzwLUaLfmWfCngEskZiBTwOONzOTEmd04VjIRF9Nmn98C9ST51fH3XMEq5vRyd0JFnyevVhnxLPpWOyn7ZLZFI4o8U8DiiKEq3i5iBejcI0GV0L+CRzR88Qs1ACXQTB69wVFBgLUCn6T1TVHrgEkn/+eUvf8nEiRM599xzmTp1Kvfffz9LlizpOP/QQw8xevSJIrr169dz1VVXDagNMg88jgS8HsKhULchFG26EaHr/vkaWcT0ep1orDaCjV1DKOX28l5TCCMUWAto9bXi9Du77VookUg6s3XrVl5//XV27tyJwWCgsbERl8vF008/3WlMSkoK9fX1ZGdnU1JSMmBNrCJIDzyOnGhk1Vk0g81edOnGHq/TG01otFq8Tge6DGO3MfAaVw3Dkof1yY58i1qpKb1wiaRv1NTUkJmZicFgACAzM5ORI0eSkpLCkSNqC4uqqiquvfbaTm1lB6qJVQTpgccRTw9l9KFmL0mTM3u8TgiB0WLF63CgSzfiO9rW6bw74Mbhd5CTnNMnOyKtZCudlYxL777xlUSSiGx+6RCNx/u2O1VfyRxu4UvXd9/BM8JXvvIVfvGLXzB27FguueQSbrjhBi688MKOJlehUIgxY8YwZ84c3nrrLa688kr27NnDeeedN6C2Sg88jkT6oJiSTwh42Bsk7A6e1gMHtfTe61QFPGT3dUolrHPXAZBj7qOAW9oFXC5kSiR9wmKxsGPHDlauXElWVhY33HADq1at6tJmdtasWXz00Ufs2rWL8ePHYzSe/u+6v0gPPI50F0KJhEO0vQm4NQWvy6GOUyDY4u3oiRIR8Nzkvu34kZKUgkVvkSEUyRlHb57yYKLVarnooou46KKLmDx5Mi+88AK/+c1vWLFiBaFQiNtvvx2r1YrX62Xjxo0DHv8G6YHHle46EYbaBVyX3n0GSgSjxYLH6ezw1E+Og0f6mvTVAxdCUGAtkAIukfSRgwcPcvjw4Y7Xu3fvZuTIkZxzzjlUV1ezZcsWpk2bBsDUqVP54x//OODxb5AeeFyJCLihGw+8txCKyWKlofxYh9CHThLwOpfqgWebT7/d2snkW/JlW1mJpI84nU7uuusuWltb0el0jB49mpUrVyKEYPbs2bS1taHXq11A586dy8qVKwfFA5cCHke8Lie6JAP6JEPHsWCzF2HSoTGd/p/GaLHgdTrRWPUIvYZg00kC7q4j1ZCKUdf3eFtuci7barb1/01IJF9AZsyY0ZFdciobNmzo9PqWW27hlltuGRQ7ZAgljnid3VRh9pJCGMGYbG3PIw+iTe+cSljnquuX9w2Qa87FFXDh8Dv6dZ1EIokfUsDjSHdVmKG+CniknL49Dt4phOKu63P8O0JkwbPWVduv6yQSSfyQAh5HvK7OAq6EFYItfRRwa0TA1VTCYLO3Y/ePOnddn3PAI0TGRzJYJBJJ4iMFPI6onQhPLGCG7H4IKb2mEMIJD9zjdKBNM6L4Q4RdAfwhP83e5v574GbpgUskZxpSwOPIqSGUUGt7BkqqoadLOjCdHEJJU8eH2vz9TiGMkGnORCM0UsAlkjMIKeBx5NRe4KE2PwDaPgh4R0MrpwOtrV3AW70nqjD7GULRa/RkGjNlCEUiOYOQAh4nAn4fQb+v0270oTa1v3dEkE/HiUVMR4fgB1t9HTngkZBIf8hNzpUeuETSCwsWLOCtt97qdOzxxx9nwYIFvPLKKx3Hxo0bx4MPPtjx+tprr2Xt2rUDakuvAi6E+JMQol4Ise+kY/cJIaqEELvbv746oFZ9AfB1lNGfHELxIQxaNMbe0/OTTGaERqPmgifrQach1Obr8KD7m0YIqtcuBVwiOT3Lli1j9erVnY6tXr2aBQsWdOSGNzU1kZyczNatWzvGbN26NS7tZFcBl3Vz/DFFUaa2f70xoFZ9Aei2jL7N1yfvG9o7EiZb8DrtCCHQpRoItaoCnqxPjqqvd445hzp3XUc2i0Qi6crSpUvZsGEDfr8a8iwrK6O6upqLL764U+vYRYsW0dDQgKIoHDt2DJPJRG5u/z8Zn45eXT1FUTYJIQoH9K6S7htZtfnQ2pL6PIfRmoKnfR5tRMBd/c8Bj5CbnIsn6MHut2Mz2KKaQyIZSj5YtZL68tIBnTN7ZDELblne4/n09HRmzZrFm2++yeLFi1m9ejXXX389M2fOZN++ffj9fkpKSrjwwgspLS3lwIED7Nq1K+GaWd0phPi0PcSS1tMgIcRyIcR2IcT2hoaGGG53duF1tQv4KTHwvnrgECmnVz15re2EBx6LgINMJZRIeuPkMMrq1atZtmwZBoOBiRMnsnPnTrZt28bs2bOZO3duR3vZRGpm9TTwAKC0f38EuLW7gYqirARWAsycOVN+Nm/n1BCKEgwTdgb6JeAmixVnSzPQ7oE7/DS6GhiVPyoqmyLCX+eukxs7SM4ITucpDyaLFy/m7rvvZufOnbjdbmbMmAHA/Pnz2bRpEw6Hg7S0NObMmcOTTz7Jrl27uOOOOwbcjqg8cEVR6hRFCSmKEgaeBWYNrFlnP6cKeMjuB6VvOeAR1Bi46snrUg2ggGIPSg9cIhlkLBYLCxYs4NZbb2XZsmUdx+fNm8czzzzDlClTADj33HPZtm0bFRUVTJo0acDtiErAhRB5J728GtjX01hJ93hdToRGQ5KpvR2sve8phBGMFuuJEEq78GcEbP3OAY+QZcpCK7RSwCWSPrBs2TL27NnTRcBLS0uZO3cuADqdjuzsbGbOnIlGM/BZ272GUIQQ/wAuAjKFEJXAz4GLhBBTUUMoZcDAfzY4y4lUYQohADWFEOjfIqbFit/jJhQMdgh4diA9ag9cq9GSaZLFPBJJX1iyZEmXjK3s7OwuxzZu3DhoNvQlC2VZN4efHwRbvlB4ulRhtgt4f0Io7RksPpcTo02dKysGAQc1jBIpBpJIJImNrMSME16no1Mv8FCbH2HUojH0fV3ZaE0B1IZWmiQtfkOIrEBaTAKeY86h1i1DKBLJmYAU8DjhdTowtQswqGXw/Yl/A5iSI/1Q1IVMp8lLTigjphzunOQc6t31sphHIjkDkAIeJ9TdeKLPAYfO/VAAWpLs5AWzOuLq0ZBjzuko5pFIJImNFPA40aWVbJuvXymE0FXA63XNZARiq6CMhF8ibWklEkniIgU8DoSCQfwed9cinpS+Z6BA523VACo1tZiCBsLeYNS2yZ15JJIzByngccDndgEnskiiyUABMJjNIARep52wEuYYxzvNFw2RLobSA5dIesZyUg8jRVHIzMykpaUFgJqaGoQQbNmypWNMVlYWTU1NA26HFPA40KUKM7KRQz9j4EKjwZhsweN00uxtpl7b1Gm+aMg2qQIuUwklkr4hhGDOnDkdrWNLSkqYNm1aR2fCgwcPkpGRQUZGxoDfWwp4HOgq4P2vwoxgsqrVmHXuOhr0LZ3miwa9Vk+6MV2GUCSSfjBv3rxOrWTvvvvuToI+GI2sIPpmVpIY8DgiAq5+DAvGIODG5HYBd9XRpGtFQU1JjIVIX3CJJNFpXX8Uf7VrQOdMGpZM6qL+NYSbP38+999/PwAff/wx999/P0888QSgCvhgtJIF6YHHhYgHbrKoeeChNh/CqENj0PZ7LrWlrJM6dx0hEUZYdDF54KAKuIyBSyR957zzzmPXrl24XC4CgQAWi4Xi4mKOHDkiPfCzDe8p26mFWn3oUvuXgRLBaLHSXFOFy12PTujQpxpjF/DkHHY17IppDolkKOivpzxYmM1mxowZw5/+9CemT58OwJw5c3jjjTeor69n3LjBac8sPfA44HU5QAg1iwS1lWw04RM40ZGwzlVHtjkbnc0Qs4Bnm7Np87XhDXpjmkci+SIxb948Hn/88Y5OhHPnzuWJJ55gzpw5MRXXnQ4p4HFA7YNiQbS3lwxFUUYfwWix4HO5qHPWkm3Obt9azR9TKbws5pFITo/b7aagoKDj69FHH2X+/PmdWslOnz6dysrKQYt/gwyhxAWv09mxgKkEwoRd/duJ52QiYZjmtnpG5Y1HKwwo/hCKL4Tow+723RHJBa9z1zEiZURUc0gkZzPhcLjb4yc7TgaDAZ8vtk/DvSE98Dhwchl9x0YO/SziiWBqn6ettZEcc07HgyAUQyaKrMaUSM4MpIDHgZMFPBjFRg4n09FPxRtQBbz9QRBLHLxjb0xZzCORJDRSwOPAyZ0IQ/boqjAjRAQ8KaAhJ/mEBx6MQcCT9clY9BYZA5ckLGdru+P+vi8p4HGgUwilNfoiHjhRDGQIaFUP3JoEIrYQCqhxcBlCkSQiRqORpqams07EFUWhqakJo9HY52vkIuYQEw6H8LpdncroNWYdmqT+F/HACQ/cENCQm5yL0Aq01qSY+qGALOaRJC4FBQVUVlbS0NAQb1MGHKPRSEFBQZ/HSwEfYnwuFygKppM6EUbrfQMY2rdlM/i1ZJjUZjnaAcoFP1p9NKY5JJLBQK/XU1RUFG8zEgIZQhliOhpZtW+nFksOOIBGo0UxaElVzOg1ekDNaBmIasxGbyPBcPS9xSUSyeAiBXyIOVFGf7IHHl0GSoSAAawhU8friAceazFPWAnT6GmMyTaJRDJ4SAEfYjo88GQrSiBE2B2MOgc8gk8Xwhw68RDQ2pJQ/GEUTww788hqTIkk4ZECPsSc3As8GNnIISU2AXfp/BgCJ/4pT6QSxrCxw0nVmBKJJDGRAj7EeE4KoUS7ldrJuAIu3NoAOv+JcMmAFPMkSw9cIkl0pIAPMSdCKJaOXG1dDIuYde46fEkhOClc0lFOH4OApxnS0Gv0shpTIklgpIAPMV6XA4M5GY1We9JWatEvYta56vDpw4S9PsLhkDqfNQk0sQm4EEIW80gkCY4U8CHm5E6EoTYfmmQdQh9dEQ+0e+D6MCjgc7sBEJr2Yh65tZpEclYjBXyI6VRG3+aPeQGzzlWHLyncMXeEgSjmkdWYEkliIwV8iPE6OvdBiTWFsM5dR1L7zj6dBDzVEHM5fbY5mzpX3VnXc0IiOVvoVcCFEH8SQtQLIfaddCxdCPGOEOJw+/e0wTXz7MHjsGOKVGHaY6vCBFXALSmpwIkiIVBTE2Mu5knOwR/20+Zri8lGiUQyOPTFA18FXHbKsf8C3lMUZQzwXvtrSR/wOOyYUlII+9uLeGIVcFcdqbYsoKsHrgTChN3RF/PIXHCJJLHpVcAVRdkENJ9yeDHwQvvPLwBLBtius5JQMIjP7cJkTRmQHHBQ87TTU9WcbY+jcwwcBmhjByngEklCEm0MPEdRlJr2n2uBnJ4GCiGWCyG2CyG2n43tH/uDx2EHwGS1dQirLoYUQl/IR4uvheyMYcCpi5jqvFLAJZKzl5gXMRU1yNpjoFVRlJWKosxUFGVmVlZWrLc7ozkh4CknNnJI7Xvz9lOpd6kZIjmWXAzmZLyuEwKuG4BqzExzJgIhM1EkkgQlWgGvE0LkAbR/l3/hfcBjP0XABWhTovfAa921gOopGy2WTouYGksSaERMmSh6jZ4MU4asxpRIEpRoBfw14JvtP38TeHVgzDm7iXjg5pQUgq0+NJYkhC76D0GR0EZOcg5Gi7VTCEVoBNqUgSnmkR64RJKY9CWN8B/AVmCcEKJSCPFt4NfApUKIw8Al7a8lvdARQklRY+Ax54C3e8aqB95ZwGHgduaRMXCJJDHpdUs1RVGW9XDq4gG25azHY1fzqY0WK45WH/rc5Jjmq3XVYk2ykqxPxphswd7Q2VPW2pIIVDl7uLpv5Jhz2F63PaY5JBLJ4CArMYcQj8N+opFVjFupgSrgucm5gPpQ8JzqgacaCLb5Yy7mcfgduAPumGyVSCQDjxTwISRShRl2B1EC4ZhDKLXuWvKS8wBVwH1OJ0o43HFeazNAMLZiHrkzj0SSuEgBH0IiAt7RBzxWAXfVkmtWPXCT1YqihDs6EsKJPuOxLGTKakyJJHGRAj6EeOxqGf2JHPDoBdwT9NDqa40K2WgAACAASURBVO0IoUT6q3gcJ/qWDGQ1pvTAJZLEQwr4EOJ2tHWqwowlBl7rUnPAIwJuTrGp92gbWAGXHrhEkrhIAR8iFEXB2+6BB1t9oBNokvVRz3eqgJtsakdCt721Y4zGogetiEnAzXoz1iSrLOaRSBIQKeBDRNDnIxjwt8fAvehsBoRGRD1fFw/c1tUD7yjmibEvuNyZRyJJTKSADxGn9kEZiBRCgeiIUXeEUE7ywEENowRlNaZEclYiBXyIGOgqzFp3LRmmDJK0ai8VrU6PMdmCu62rgMtqTInk7EQK+BDhjlRhmi2E7P6YBbzGWdORQhjBZEvtFEKBEwIeazFPk6eJQDgQ9RwSiWTgkQI+REQ8Y5POAkrsGznUumvJs+R1OmZOsXUJoehsSRBSCLuiF98ccw4KCo3uxqjnkEgkA48U8CHC1doCgEExASeKbKJBURRqXbUd8e8IZputqwfe3m9cFvNIJGcfUsCHCHdbCzqDAeFSQxnatOg3crD77XiCno4MlAjmlNSOUE0EbZr6oAg2e6O+n9yZRyJJTHrtRigZGFytrSSnphFqF1JdDAIeSSGM9EGJYLbZ8DrshEMhNFqtep/0dg+8ZQAE/EzLBQ+H4fg2OPoB1H4KrRUQ9IHeBLYCGDYdii+Eglmgkb6M5MxDCvgQ4W5rIdmWRrDFh8aahNBHLxin5oBHMKeoxTweh53k1DQANEYdGrMuJg/cZrBh0pmocdX0PjgR8Lvg42fhk+ehrQKEBrLGQ1qRKt5+F7SWw6G3YOOvIHUEzPkezLhFPS+RnCFIAR8iXK2tpOXlE2rxdnjF0dKdgIdCYTQ6tb94c009ZlsqQqiFQtp0Y0wCLoQg35JPlbMqBquHAEWBnX+BD34JzjoougAu+TmMvgRMqV3He1pVEd/5Avz7v2DLY3DpL+DcG0BEX2QlkQwVUsCHCFdbKwXnTCLY6MVQmBLTXNWuapIwYD+osG/fAWpL7bTUugj7KwFY93AJJmstaXnJDD8nnQKDFk0MAg6Qb8mn2lkd0xyDSlsVvPp9KP0Ahs+B6/8CI+ac/hpTKky5Qf0q2wLv3g/r7oC9L8NVT0JK3umvl0jijAz8DQGhYBCvw05ySppaxBODB+5x+nFvsfD1T+7nrWf2U7qrgZQMIzMvL2TWogkAjD0vhXGzcxFCsOPNMg7tbybQ5OXzkmrCoXAvd+ieYZZhVDmrYsonHzRK/w/+OB+OfwRXPAK3/rt38T6VwvPh1rfg8t9CeQk8c4H6XSJJYKQHPgREcrMtxjRQolvADAXC7HqnnB1vVWDzFdOWf5xrrl7E8HPS0GjV57DXmc2Wf0DWcC0zrhjXfixAxatH0Oxt5MO/fs7OtyuYf90YRk7M6Nf98y35OANO7H47NoOt3/YPGp88B2/8J2SOgRv/Dhmjop9Lo4HZd6ihl9VfgxcWwZWPwfRvDJy9EskAIj3wIcDdqgp4slYNnfTXA6891sY/f/kxH712jJET0nnzvD8QvKSckZMyOsQbwJCsbtd2cjm90aJnxCw1Vv7lq0cRDim8vmIP7/xpPz5334t78i35AIkVRtn0O9hwL4y5FL79TmzifTLZ58DyD6DoQnjtLjU2noifPCRfeKSADwFdinj66IErYYVdb1ew7nc7CfhDXPH9c/nSrcVU6A5TYCnoMl4IgTnFhqv1lGrM9vtlpxtZ9vPZnHdlEYe317P6gY+pLW3rMk93DLMMA0iMhUxFgfd+Ae8/COfeCDe8CMbY1hW6YLTBstUwaSm8e5+6MCqRJBgyhDIEuNpUAdcHkvBr+raRQ9Af4t0/f8bRXQ0UT8viyzePx2DWc7D5IAD51vxur0tOy8DV2tzpmDbVAAKCLV60Og2zriyicHIGbz23n3WP7mTB18Yzfu7pF+wiHnhCCPh7v4Atj6ppf1c8Nng53LokuOZZNbVw0+/U71+6d3DuJZFEgRTwISASQtF4BFqbAaE9fYqa2+7njac/pa7MzrxrRzP1kuEdKYERAe3OAwewpGfQVtc5X1voNGpTq5MyUbJHpnDdf83krWf38d4LB2iudjH3mlEd9zmVlKQULHpL/EMoJU+2i/e31Pj0YKf7aTSw6Am1AOi9X4DeDHO+O7j3lEj6iBTwIcDV1kKSyUy41d9r+MTe6OHVx3fhavNz2fJJjJqW3el8pUNNFYx4xKdiSUun6sC+Lsd13eSCG5P1LLprClteOsyudyrwugJc9PXxaLrZaEII0ZGJEjd2/wPe/ilMWKxmmwxVrrZGC0uehqBHzRe35MCka4bm3hLJaZAx8CEgUkYfbPKgy+y50s/e6OGVR3fhcwdZcve0LuINqgdu1plJNXRTmILqgXtdTgL+zs2rtGndF/NotBq+dONYzruikAMlNbz93H5Cwe5TDeNazHNsE7x2p5ohcs2zqqgOJVodXPOcmmO+7jtw/JOhvb9E0g1SwIcAd2sLKbYswq5gjwJub/Sw7tGd+L1BFv9wGrnF3afqVTmryLfm9xjqsKSr6YGu5s5xcF26kbDDjxIIdblGCMGsRcXMu3Y0R3fW887z+7vNF48I+JDngjeXwkvfgPRRcMPfQBdbK96o0RvhxhfVAp/Vy6ClPD52SCTtSAEfAlytLaS19+7WZXQVcEezl3WP7iTgDbH4h9PIGmHtca4qZ1WP4RNQQygAzuamTscj5fvBlp7byk67dATzl47m6K4GPvjr5yjhzkJdYC3AE/TQ5G3qYYZBwGuHv9+o/nzTajU7JJ4kZ8JNL0PID3+/HnyOqKbxB8OEwzI1URIbMgY+yCiKgqO5EduoTAB0mZ1j4F5XgPW/343fHWTJPdNPK96KolDlrGJOXs9VhhEP3NHSWWS1Ge0C3uhBn23u8fqpl4zA7w3xyevH0Bu0fOnGsR3e/siUkQCU28vJNGX2OMeAEQ7Bmm9D81G4eR2kFw/+PftC1li4/q/w16vhle+pZfvdfCLyBkLsPt7KjvIWDtc5ONLgpLbNh90bwN8epkrSarCZ9RSkmRiRbmbisBSmj0hjUr4No36Iw0SSMw4p4IOMz+Ui6PNh0aSCAF36CQ884A+x4Q+f0tbo4aofTD2teAM0ehrxBD0UWLvPQIGTQyidBVzfHroJNnp6tfm8KwoJeIPsfvc4RoueWYtU4YwIeIW9ghk5M3qdJ2Y2PwqH31YXLIsuGPz79YfiC+GS++Cd/4WSFTD/BwC0uQO89Vktr39aw0elTfjahXqYzciobAuT822kmPRYknQEwwq+YJgWl5/jLW62l7Xw6m41y8eo13DBmCy+MjGXr0zMIcWoj9MblSQyUsAHGUezug2ZUTGjTTV0tJENh8K8/dx+ao+1sfC2SeSPTet1rjJ7GQBFKUU9jjGYk9EZDDhP8cA1Zj2aZD3Bht4FXAjBvGtH43UF+GRDGSmZJsZMtpLRFGRMrQb7R1tx20cgtFo0KSnoMjPRWK09xuWj4thmtdXr5Oth5rcHbt6BZN5dULUd5d2fc0g7iqfKhvHG3hoCIYXh6Sa+Nnskc0dlMKswHZu5bwJc7/Cyq6KVD4808vb+Ot7+rA7TK1oWTcnja7NHMmV494vXki8mUsAHmUgsWufVdSxgKorCptWHKPu0kQtuHMvoGV2zTbojIuAjbSN7HCOEwJKWjuOURUwAXZaJQIO71/v4K6vw7NzBOWWf0hAcyfurQjTt+QPprQdR6xHXU876zvc1mzEUF2MYMwbj5Ekkn3ceSaNHRyfqzno1dJI+amhyvaNEAbZMuI/CgzvJePM77Oc3fG32FK6els+5Bbao3nu21cjCibksnJjL/VdNZNfxVl765Div7anmpe2VzB+dwZ0LxjCnOH1gH5iSM5KYBFwIUQY4gBAQVBRl5kAYdTbhaGrfCNgRRjdaFfC9G6vYv7ma6QtHMPminsMhp1LeVk6SJqnLTjynYknPwNXSdaFRl2nC+3lXYVcUBe+ePTjeex/H++/jP3oUAI3ZzIwJU9hmW8L+Gd/nK3M8/KvxRerCrfx8zs8gFCLUZifY2Eigphr/kSM4N2+mbd06ALQZGVi//GWsCxeSPHsWQt8HLzQcgrW3g7dNjXsbLH34zQw9JUcaefjtg+ysaGWe7T95gZ/wVu6f0V7xBmgHJtwhhGD6iDSmj0jjp1ecw+qPj/PMplKWPbuNWYXp/PdXxzNtRO+f3CRnLwPhgS9QFEVuV94DzuZGDFoz+MLoMk0c/7yZLS8fpvDcTOYs7l/zpXJ7OSNSRqARp08esqRlUHP48y7H9Vkm3NsDhD1BNCYdwYYGWl95hbY1a/GXlYFOh/m8maTdcD3mWbMwjBmD0GrJafKw5jc72HQgmfBls9lY8Q9+M29ut3YoikKgshL3xx/j+vBD7Bs20Pryy2htNmxLlpB6ww0YinsOAbH5ESjdCFetgJyJ/fr9DAXHm9088PpnvP1ZHcNsRn519WSWzihAf8Csfmr44JdqbHyAsRr13H5BMTfPHck/PznOkx8c4eqnSrh6Wj7/edk48mxyJ6EvIjKEMsg4mprISh0BgE+r4a2V+0jLNXPprRMQ3VQ8no4yexmjU0f3Os6SnoGzpRlFUTp9zNZlqtkn7l0Hsb/6V9o2bIBgENPMGeQtX471kovRpnRtCpWSYeKK75/L2od3Yvu/CfgLAtS767ts6Qaq15g0fDhJw4eTeu21hL1eXB9+SNv612l+8UWaX3gB85w5ZHz7VpLPP79zGODYJtj4kLojzrSb+/W7GWx8wRBPbzzK0xuPohGCHy8cx21fKsKga88UmbxUtX/LY2pv8dGXDIodRr2Wb84r5NoZBTy98QjPbj7Gv/fVcu9XxvKt+UVo+/l/SnJmE2seuAK8LYTYIYRY3t0AIcRyIcR2IcT2hoaGGG935uFsaSIjRQ2TbHqzDCEEV3zvXJKM/Xt2BsIBKh2VFNoKex1rScsgFAjgdXbOUQ41q4UnNT9/GPvbb5N20zKK33yDwr/9jdRrru5WvCNkj0zhyzePJ1iVxPyya6mwV/TJbo3RiPXiiyl4/DHGfPA+WT/8If6yMo7fvpyypddhf+cdlHC4Pe59mxr3vuLRhIp7761s46oVH/L4u4e5dEIO7//oQr6/YPQJ8Y5w2a8hewKsvQPsg7t/qMWg48cLx/PePRcyd1QGD244wLVPl3CwNrq8dMmZSawCfr6iKNOBy4HvCyG65HopirJSUZSZiqLMzMrKivF2Zx6OxgZSjdmEBdQ1erls+SRSTlNO3xPVzmqCSrAjle90WDPVHG17o/rA9JUeo/KuH3B8+TdQlBCWBVcw+v33yP2f/8FQdJpwximMnZXL2AUZTKybz4Ettf1+D7qsLDK/cwej336LvAcfIOR0UHXXDyi74QZcj31NjXtf/0LCxL19wRAPv3WQJU99SKvHz/PfnMmTN03vOVyRZIalf4aAW43jh7tWvQ40w9PNPP/NmTxx41TKm1xcuWIzK947TEgWCX0hiEnAFUWpav9eD6wDZg2EUWcLiqJgb6gnyWfFHlD40o1jyR8X3aJTuV31ngtTCnsda8vKAaD1WCk1999P6aJFuD78kMw7v4suw0zSyIno0qKzY8HSSRxPO4DjfTNVh1qimkMkJZG6dCmjNmwg76GHCFaWUvFiJcc/m4XPGduGzwNFZYubpU9v5ckPjrBkaj5v//BCLj4np/cLs8fDVx+Gss1qC9ohQAjB4qn5vHvPhSycmMsj7xxi2cptVLX2njIqObOJWsCFEMlCCGvkZ+ArQNc2eF9gPA47AZ8XY9CMNsvEpAt6LoHvjbK2MqBvAm7NUD3w0od+SevL/yLthhsY9fZbZH3/++hzkgnU9Z5K2BM6rZby87biszj598p92PtQGNQTQqcjdWo6oy4pJWthIe5DtZQuXkL9I48QdkdvY6z836EGrlyxhbJGF3/8+gweuX5Kn/O4AZh6k7rRxMZfq3HxISLDYmDFsmk8ct0U9le3cfnjm3hj7+CGciTxJRYPPAfYIoTYA3wMbFAU5d8DY9bZwbHdR9FrDJi1SQyb1Qfv7TSUtpWSakgl1Xj6Qg7vZ59Ru3w52lAYf2YGxa+sI/dn/4uuPayiz0sm2OhGCUS3uTFAcfZINk18ESWs8MbTe/F7g9FN1B731mSPIvO3LzPq7bewXXUVTc8+R+mVi3C8/37UNkZDOKzw+/cOc8ufPyY3xchrd53PZZO6LtT2ihBq9WjGKFhzOziHbu1HCMG1Mwp44z++RFGWhe+9uJP/Xvsp3m6amEnOfKIWcEVRShVFmdL+NVFRFLnn1Ek4W7xs/ucObHpVOJPyYovrHm45zNi0sT2eD/v91D/yCMeWXkewspqUjEyUaVMxjO6ctaLPTYYwBOqj93BHp46mVHzO/G8W0Vzt5P0XDnRpfNUr4ZC6aOm1d8S9denpDPvVLxn5t7+iSTZT+b3vc/zOOwkOweJ3mzvAbX/ZzqPvHGLJ1HzWfm8eRZnJ0U9osMB1q8DTAuvugHD0D8xoGJmRzL++M5fvXjSKf3x8nKV/LOF4c/w+1UgGB9mNcBAI+EO88fReAr4WbEntnm9Ozw2keiOshDnc2rOAew8eouy662l69jlSr72GUW++Qdqo0dgb6rqM1eepohSodUVtTySV0Z1br7ag3dXAJxuO9W+STQ/Dsf+Dr/6uS763eeZMitauJftH9+LavIWjVy6ibf36QWtju7+6jUVPbmHz4QZ+sXgij14/BXPSAGTY5k6Gyx6Co+/Bh4/HPl8/0Ws1/OSy8Tz3jZmUN7m5csUWPjhYP+R2SAYPKeADjKIofPCXAzQcd5A/Rkt68jBEklbdlzJKKh2VeIIexqSN6XyvUIim5/9E2dKlBJuaKHj6KfIeeABtSgq2rBzaGuq6iJ4uwwQ6DYGa6AU8YsfR1qNMuXg44+fl8cmGMg5v7/rA6JajH7Tne98I077e7RCh15Nx220UrVuHoaiI6h//J5V33UWwcWBrxl7efpxrnirBHwzzzzvm8o25hQNboj7zVph4tboBc9mHAzdvP7hkQg6v33U+w1JN3LrqEx5755BsZXuWIAV8gNnxZjmHt9czd8kolFAbaaZc9DnmmEThcMthgE4euL+yiopv3kL9736H5aILKX7tVawLFnSct2Xn4Pd48LqcneYSGoE+10ygLnoBzzHnYNFbONxyGCEEFy0bR26xjfdfOEBDRS95yPZqNXSSNQ6u7D3f21BcxMgX/0b2j3+Ma9NmSq+4Ui1AihFfMMR/r93Lj//1KTNGpvH6D85n+mCUpQsBi34P6UXwr2+Bo48PuQFmZEYya787j2umFfDEe4f51qpPaHH542KLZOCQAj6AlO5q4KPXShk7O4dpXxlBW30dVpGmxp1j4FDLIQSCUamjUBSF1jVrObZ4Md4DB8h76CHyf/97dOnpna6xZauLb221XbMQ9DnJMXngQghGp47mSOsRALR6DZd/ZzJGq543nv4UV1sPm0aEAvCvWyHgUXtoJ/Xt9yK0WjK+fStFr6xDXziS6nt/RNU99xBsiS6NsarVw/V/3Mo/Pq7guxeN4i+3ziLTMoi7/BhT1Pfrtavl9qEoF31jxJSk5eHrzuVXV09m69EmFj25hX1VbXGxRTIwSAEfIBorHbyz6jNyilJY8PXxhEMhAk0edIoefUFsC5iHWg4xImUE+jY3lXfeRc1Pf4pxwgSKXn2V1KuXdOvdp+WpKYstNV33sNTnJRN2Bgg5ovfARqepAh4J0ZhTkvjqd8/F6wrw5h/3Euwu6+G9X0DFVnWX96xx/b6nobiYwr//nax77sH+zrscu2oxzk39S9PbdKiBK3+/mdIGF8/cPIOfXDYenXYI/gxyJqqfOMra2+TGCSEEN80ewUvfmUsorHDt0yWs2VEZN3sksSEFfABw2/1seOpTDCYdl39nMjq9lrb6OtL0apvYpILTb9TQG4dbD3PJcRulVy3GtWkT2f/5n4x4YRVJBT3nldtychFCQ3N3At7+iSAWL3xs2lhafa3UuU+EBLKGW7nkWxOoO2bng7993jn+/vkbUPJ7NSZ87nVR31dotWQuv52il19Cm5bG8eV3UPOznxN2nf69hMMKK947zDf//DHZVjVFcOHEKFIEY2HqTTD9G2rDrkNvDe29TzVleCrr7zqfaSNSufflPfzs1X0duwRJzhykgMdIMBDi38/sxesI8NXvTibZpn4Ub6mpJC0pFzSxZaA4Wxu4fHUpC5/aiS4ri8J//YuMW7+F0Jz+n06n12PLzqG5uquAJxVYQID/ePR9MyZlTAJgX2Pn2q1R07KZfVURhz6qY9fb7f1Smo/BK9+BvCmw8KGo73kyxvHjKfzXy2Tc9m1aX36Z0iVX4965s9uxbZ4At/9lO4+8c4jFU4ax7vsxpgjGwuW/VbNT1i6H1r71kxksMi0G/vbt2dz+pSL+srWcZc9uo87ujatNkv4hBTwGlLDCe6sOUHO0jS9/8xyyR55oBtVcXUW6IRddrhmhi+7X7N6xg/Krr+WiPQquGxdS+NI/MY7rORf8VNLyhnUbQtEYdeiyzDEJ+Lj0ceg0OvY27u1ybsblhYyemc3WV45y9KNyWH0TIOC6F9Sd3QcITVIS2T/6ESP/9ldQFMq/9nXqH36YsP9EaOizajuLVmxhU3uK4GM3TB2YFMFo0ZvUeLiiwD9vBn98c7N1Wg0/vWICK5ZN40CNnStXbOGTsq494yWJiRTwGChZe4QjO+qZd81oxszsXGnZUlVJuiEXw4j+76IeKcop//rN+EN+fv51LYU/+X9okpL6NU/asAJaaqrUbn+nkDTCir/CHnVudZI2iXFp49jfuL/LOSEEF3/jHHILU3hn1UGqqzRqUUt63xtn9QfzjBkUvfIKqdddR9Nzz1O29Dq8n3/Omh2VXP3Uh/iDYVYvH4QUwWhJL4ZrnoGaPfDanaqYx5lFU4ax7nvzsRh0LFu5jVUfHhu0vHvJwCEFPEo+/eA4u989zuSLCph66fAu5z1Vreg1BjVc0Q86FeUsvZaXfjoHx/j8qHaBT8vLJ+jz4WjuujtP0ggrYXeQUFP0H5knZU5iX9M+wkrXB4QuScsV576HVVPLG477aDYPbp8zrSWZvF/cz/Bn/kiwuZkj1y5l2y8fY0aBjdd/cD4zRibYzjXjLodLfg771sDmh+NtDQDjcq28eud8LhqXzX3rP+Oel/bg8csS/ERGCngUlO5qYPNLhymaksn514/p4tUpioJoVkWtrwuYalHO82pRTmMjBU+pRTm7nJ8zKXNSVHZmFKgPlqbKrrHWpOFquCemOHjmJFwBV0ejrU7sfwXjR79i0fy9aI0m1q/Yjau1h/TCAaRl8nn8bPFP+TBnIrd+9ga/3LiClKb+t74dEub/UN284v0H4cD63scPASlGPStvnsG9l47lld1VXPN0CRVNsgQ/UZEC3k8qD7bw9p/2k1OYwqXfnoimmx1QXC3N2JQMwrowuuzeFzD95eWUf/1m6n/3MMkXXkDx+tewfnkBjZ5GqpxVUQt41gg1ZNFQ3rXMXZ9jRiRp8FXYo5obYHLmZAD2NZ3ShPL4J7DuO1Awi5TrH+TKO6fgcwVZv2IPPncg6vv1xruf1XHF7zez3yUo/P3jDHv4YfylpZQuuZqW1asTLyQQKfLJn6kuatbsibdFAGg0grsuHsOfbjmPqhY3i56UJfiJihTwflBztI0NT32KLUvdYkyfpO12XH15KVnG4WhyDafdNk0Jh2l+8UVKl1yN78gRhv3m1xSsWNFRlLOzTs2qmJ4zPSp7jRYL1sysbgVcaARJBVb85dELeGFKIcn6ZPbUnyQ8jUfg79dDSh7c+HfQGcgaYeWyOybRUuti/Yo90Xcv7IFgKMxDbx7gtr9sZ3i6mQ13fYmFk/KwXXkFxetfwzx9OrX33c/x25cTqItPJWSP6I1w44tgSoe/LVUzdhKEBeOyWX/X+eTZjNy66hNWvHdYluAnGFLA+0h9uZ3XV+wm2ZbEVf8xFZOl5wXFpkPlpCRlkDw+u8cxgepqKr79beoeeBDzjBkUr38N2+LFncIx2+u2Y9KZmJAxIWq7s0YUdivgAIZRqQRqXIRc0XnFWo2WadnT+KTuE/WAsx7+dg0IDXx9DVhO7MA0YkIGC2+bRH25g9ef3EPANzCx1do2Lzc9+xHP/F8pN80ewZrvzmNExolPPfqcHIY/9yy5P/8Z7h07KF10FW2vx16KP6BYc+HmtRAOqL+/IWw/2xsjM5JZ9735LJ4yjEfeOcS3Vn1Cg2PwQ2GSviEFvA80VTl57fe7MZj1LP7htI5c757wlrYCYB7bdeFRCYdpefllShddhXfPp+T+4n6GP7sSfW7XopLtdduZkjUFvaYfmwmcQtbIYpqrKwn6u1ZdGsakggK+I61Rzz8rdxbH2o7R2HwUXlwKrgb42ktqpsUpFE/L4tJbJ1Db/kkmGOMC2Rt7a1j4+Cb2VrXx+A1T+dXVkzHqu34qEkKQtmwZxevWYigupvpHsZXiDwpZ4+Cml9S9NP9+HficvV8zRJiStDx2w1QeWDyRbaVNXP7EJhlSSRCkgPdCfbmddY/uRKfXsvjuaVjTe89jTmrSEhQBkvI7L2D6jh6l/BvfoPZ/f6aWwr/2KmnXX99talurt5XDLYeZmTMzJvuzRhahhMM0Hi/vame+FWHU4T0cvZDNylWzSz5Z9w2o+0zNcc6f0eP4MTNzuPiWCVQdamHDU59G5Yk7fUF+9PIevvfiTkZmmNnwg/NZMq333Y6SCgsZ+be/knX33VGX4g8qw2fBdX+Gmk/VMJQ/+krZgUYIwc1zC3ntzvPJSDbwrT9/wv3r98uNIuKMFPDTUH24lVce20WSUcfV907HltX7ZsRuh510cvCnBRFaVZjDPh8Nv1+hxroPHyHvwQfaS+ELepynpLoEgLnD5sb0HvLGqD1Hqg993uWc0AqMo2z4jrRGvcA3k9fo6AAAGwFJREFUPrkAqyLY5qlRN2YYc2mv14ybncvF3ziHqoMtvPbELrz9COHsrGjhq09sZu3OSu5cMJo1351HcVbfUzWFTkfmHcspeumfaFNTOb78Dqr/3/9LHG983OVwzUq1Z8yL1yWUiMOJVMNb5hXy5w/LWPKHD2VDrDgiBbwHyvc1sf73u7GkGrjmRzP6JN4AtR9/hklnxTReXYh0bfuIY4uX0PjUU6QsXMioDa+TunRpr6Xwm6s2k2ZIY2LGxNOO642UzCwsGZlUHzrQ7XnDmDRCrT6C0ext6W1Du3oZ89wuNqfnEh53eZ8vHT83j8uWT6a+wsG6R3b23MGwHY8/xC83fMbSp0sIhRVWL5/LjxaOQx9lIyrjOedQuOZfZNx+G23rXqH0sstpWf1PlFACeJSTl8I1z7aLeGJ54gBGvZb7rprIn26ZSaPTz5I/fMgjbx/EF0yA390XDCng3bB3YyUb/rCH1FwzS+6ZjiWt761GXXvVLIfUohQq7/oBFbfcghIMMvzZZ8l/+Hcde1OejlA4xIdVHzIvfx5aTfeZLv1h2NhzehRw41i1wMV7oJ/l0856WHUFHP+ICyd+jYaAgwNN3d+jJ4qnZXHlnVOwN3lZ89sdNFV1H/ctOdLIwsc38ezmY9xw3gje/OGXmFWU3u3Y/qBJSiL73nspWrcWw9ix1N53H2U33Ijn009jnjtmOkS8BF64Clxdi7HizZfH5/DuPRdw1ZRhrHj/CItWbGHP8ejXUyT9Rwr4SYTDCptfOsSm1YcYOSmDq++djjmlf+XrSbU6WvzVVN20FOeWLWT9xw8oXv8ali+d3+c5djfspsXXwgX5F/T3LXRL/tjxOBobcDR13c1Gl25En2/BvbcfO900H4PnvwJNR2HZPzl/9t1ohIYPjn/Qb9uGj0/n6numEQqGWfPbHZR9esKOJqePn/zrU2567iM0AlYvn8ND10wmxRj9om53GMeOZcRfXmDY735HsK6OshtupPonPyFQ1bWPzJAyeSlc/1eo2wfPXwotZfG1pxtSzUk8esNU/nTLTOyeIFc/9SE/e3UfbYOY7y85gRTwdjxOPxv+sIdP369kypeHc/l3zyXJ2PemR4rfT91zq0kmBXv1DlK++lVG/ftNMr/7XTSmvoVfIrx57E2MWiMXDb+on++iewomqAU35Xt3d3veNDmTwHEHwZY+lNVXbFPFxNsK31wPYy4hzZjGjJwZvFX2VlSx9OyRKVz3X+eRmmNmw9Of8sm/y3h+cykLHt7Iv3ZWcseFxfz7hxcwpzij33P3FSEEtkVXUvzmG2R8+1bsb/6bo5ddTt1vfkuoNY5e5TlXwjdeBXcTPHcpVO2Iny2n4cvjc3j7ngv4+pyR/G1bOQse2cjqjytk3vggIwUcqDnSyj8f/ISqg61c9LVxnH/9mG4rLLtDCQZpXbNG/WN/dz9hJUz2tfMZ9ptfo8/J6X2CUwiGg7xT/g4XFFyAWR99G9qTyRpZRHJaOsd2d//Hb56shnXcO3tJDdv+Z1h1JRiscOvbUHAiQ+aKoisos5fxWdNnUdloSTNw9Y+mYxtj4+P/396Zh0dVngv8982ZObNnDyFAFiDsSoSgBQVxgYobUm2rVqm2tVq0e63V6u31alut3sflKS5Y61JvvWK9FVERFRUVlD0hyJoQliRkJclMZs3MnO/+MQMECGQyCVnw/PKcnO0757zvfOe85zvf8r5LKti6eDdFg5N5/5czuOfScR12DzwVKA4Hg+68k5HvLyfpiitoeuklymd/k8bn/kbE00d10blT4UcfgNECL8yBjS/3jRydkGQx8cBVZ/DOz2YwMtPO3f/ewreeXs2aiv5X/XO68LU24JGIxoZle3nzsWKMJgPX3FXEhBmdd0eDqMfAlv/7NxWXX0HNvfehpGXgHH4+dcF9DJ93WcIyrapeRVOgictGJH6OYxFCkF84mX2lm9A6aKQzplsxj0zGu6EW2VGJKeiBJXfAO7+EETPhxx9D5tFubWfnz8ZkMPF2RWI+PdbvbWL+S+u4r66WTRlQoBmZuSeMtblvwo+ZsrMZ8tCfGb5kCbbJk2l47DHKL76YhoVP9U2JPHMM3LoS8qfD2z+Ht+6Ihqbrh4wfksTrt03jiWvPotYd4Lrn1vD9F9bpvVVOAV9bA95Q2cobD29g7dIKRk7O5Lu/P5vM3M4dT0Xcbhr/9jd2XzyLmnvvRVitDHv6KQb94a+YpAVvphejKfE62td2vMYg6yBmDpuZ8Dk6YsSkKQS9Xqp3HO/+FcB+TjaR5uDxfcKrN8KiGVDyTzj/t9HBJtbjPfslqUnMzpvNW+Vv4QvF7/yopLKF+X9fy3ee/ZLyei9/uHI8T98/k+/cMwWTxciSx4tZ+epOgv6+MeSWMaPJWfQs+a8vxlZUROPChZRfdDF1jz5K6MCB3hXGng43vBHNh+L/gecugAPFvStDnAghmDdpKJ/+9kJ+f9lYSqtauOKvq7j9nxvZdiBx9w06RyN608HPlClT5IYNG3rteh3RFgizcfk+Sj7Yj9luZOb1Yxg5+cRD3g8RLCujefHruN58E83rxX7uNNJ++CPs550LQNV/f4m39iByXhLjZiRmfCtcFVy15CpuL7ydBWctSOgcJyIUDPDMrfMZM206l/zkF8ftl2GN2kfWo6RbGHRbYbTr2sqH4cunokO9r34uWvo7CaUNpdyw7AbuOecevjfueydMp2mSj3fU8/yqCtZUNJFqM/GTmSP5/rR8rO38y4SCEdYuraD040psSSozrhvNiLMy+9Snd2DnLg4uWoR7+XIAnBdfROoNN2L7xjm9K1f5CnjrZ+Cpgxm/hhl39miwjJ7GHQjx/Od7+PvnFXjbIswYlcGt549gekFG//DR3s8RQmyUUh43qu9rY8A1TbLjixrWLK3A725jzNTBTP/2KCyOE5eWNb8f9/vv07L4dfzFxQiTCeecOaT/4GYs44/4Jwnsaqbxha8odn3MnCfvxqQmFuH8rs/uYmXlSt67+j3SrT3fYLf8mScoW7uanyx6BZP5+Ie9dVU1rncqyLzEg3nzXdGQX5Pmwzcf7LDU3RE3LruRWm8t7179Lmbl6N+hNRBiSXE1L6zey55GL0OSLdx0bj43TM3DYT5xg3HdXjefvLKDg9UehoxKYdrVIxk8vOuBMnqSUHU1za+9Rsvr/yLicqEWjCTlW1eTdOUVmAZ1XiDoEfwtsPxu2Py/kJIL3/wTjLsy6uWwn+Lyhfjnun28uHovDa1Bxmcn8f1peVxROOSk98DXna+tAdciGmUb6tm4fB/NNV4Gj0jmvO8UnNAAyLY2PKtX4172Hp6PPkLz+VDz80m59lqS512FMfVoQyYjGrWPb8BdW8/+UXuZfdtPE5JzR9MOvvv2d/nBGT/gV0W/SugcnVG17SsW/9fdzLrlDgpnHz/oRiv7nLqX3Bi0egYNXYS48jHIO7dL11hbs5ZbPriFO6fcyU0TbkLTJF9WHORfGypZvrWWQEijMCeFW6YPZ84Zg+MeiBOJaGxfdYB17+zB3xpi5KRMii7Nj6va61SiBQK4311G8+uLCWwuBYMB+3nnkTx3Lo4LL0BxdC2gR0Ls+Qze+x3Ub4P8GXDRfdGGz35MMBxhSXE1f1+1h111HmyqwpUTh3DtOTlMyknRS+XH8LUz4EFfiJ1raylZUUnrwQBpQ+ycfflwRk4+/hM84nbjXb0az6ef0frJJ2guF0pyMs5LLiHpisuxnX32CW8o98pK3Mv3sqr+38x+8NekDz0+Ok9nRLQI89+bT7WnmqXzlpJsPjWlSyklr973G/xuFz984jkMigKaBuUfwpcLYc9n+ExX0dT6Y5Ivz8c5o+u6ACz4cAEb6jYyO+lRPt0WobrFj9NiZG7hEL4zJYfCYckJP6BtgTDFH+6n9KNK2gIRho1NZdLsXHLGpZ3UdW9vENyzB9dbb+FaupTwgRqEyYRt2lScs2bhvOiiuAZxJUwkDBtfhJUPRbsc5s+AGb+BERf06xK5lJJN+1tYvH4/b2+uwR+KkJtm49IzB3PZGdlM7Ma9cjrxtTDgUpPU7HaxffUByjfWEw5pZA1PoujSfPLPSD/8gMu2NgLbtuFdtx7PZ5/iLy6BSAQlORnHBTNJuuwy7NOmITqJQRnc76bh2c1Ue8toyG/gil/9LiG5FxYvZFHpIh6e8TCXj7g8oXPEy+6Na1nyyINceN31TM52w/rn4WA5OIfA1AXIs2/h4Kt7CJQ3M2jBWahD4ytBtvjaWFPRxKe7Gvhg53aCg/+CDAylyHw310zO45IJg3u0K2DQH2brZ9Vs/rgSn6sNZ5qFsedmM3bqYJIyutbvvqeRmoa/uJjWD1fQumIFoaoqAMzjxmGfNg37tKnYioow2Hqmm+hRtHmj3QxXPwmeWsgYDUU3Q+H1YOv+6NVTSWsgxLItNby7pZYvyhsJa5KhKVYuGjuI6aMymDYyvccHcQ0UTlsDHm6LUFPuoqKkgYqSBnzuNkxmhdHnZDF++hAG5SURbmggsH07/pISfBs24i8tRQaig1bM48bhmHk+jvNnYi2ciFDiMzKheh8Nz27G53fzYc0r3PDoYyRldr3uc+nupdy76l7mFczjgXMfOLWljdZaZNkK3nzxdaoaQswfsYnU/PEw9XaYMA+U6MMR8bRR/9cSpKaReVshpg4MYp07wObKFjbua+aL3Qf56oALKcGuKlwwZhCDhmzljf2PcNXIq3jgvAcwiFPT4SkS0thdUs+OL2qo3NEMEjJznQwvzGB4YQbpQx19WoKTUhLctQvPxx/j/XIN/uJiZCgEJhPWCROwFk7EcuZErIUTMQ0b1nOyhgLw1Ruw8SWoWg+KCiMujNaRj7ks2qOlH9Pia+PDbXUs/6qWL3YfxB+KoBgEZ+WkcM7wNCblpDApN5VMZ2LtTQON08aAB7whGipbqatwUbWjmZoKF1pYYlQN5IxOIm9wmCxDLdqeMgLbdxDYsYNIY2x4tsGAZexYrEVF2IqKsBVNxpiZefILdoB/ZxNNr+0k1Obn/b0vMu0HNzBx1pwunUOTGi9vfZknNj3B2YPPZuFFC7EYe7gXgbcRqjZA5Zpor4XaLQC4Lfm8sm041qQUrv3j49hTjm+gDNV5aVhUigQCc/LYZRWU13vYUu1ic1ULde6o8ymTIpiUm8q5I9M5ryCDwmEpqMaosX6m5Bme3vw0s3Jncf+595+yqqFDuA/6Kd9Qz57NDdTucYMEW7LKkIIUsgtSyC5IJm2IHSVBB1g9geb349u0Cd+aNfg2FRPYuvVwYUJJScE8ejTmggLUgpGYCwowjxqFktLNOuG6rVDyKmxbCq790YAb2YXRXkX550fd2FpTekjDnicYjrBpXwuryhtYVdbI1gNuwrHxCsNSrRTmpDA2y8moLCejsxzkpdtR+rg6rac5JQZcCDEHeBJQgOellA+fLH2iBnzX+lrK19fRsM+Fx3WoP7AkxewnI1JLStMOnHvWIprb+fMwmTAXFGAZOxbLuLGYx47FMn58txqV2g54aF1Zib+0Eb/i5aO9rzDu0ouZeeMP4z6HlJJ1tet4ctOTbGncwuy82fxp+p+wGhP87JcSgm5oqoDGMmjcBQ07o/EVW2I+wA1GyPkGFMyKToPPpHrndt74839gsTspuvE25NAxHGjxc6AlQI3LT3WzH1+dl582Qx4Ky2njn7RBhpXCYclMHJZCYU4y47OTj+r6dyz/2PoPHt/4OGnWNBYULmDuyLmoStf8yySCz93G3i2N0Zd8eQue5ugLx6AIUrPtZAx1kDbUTnKmlaR0K850CxZ773+ey3CYYFkZ/tItBL7aQnBXGcHdu9E8Rxx7GRwOTEOHHpmGDMGUnY0xIx0lPR1jejoGp7NzIy9l9L7YuQz2roqWzCOxQB8peZA9EbLOhIwCSB0OacPj7n3UmwRCEb6qdlG8v4WSyhY2V7VQ1XxkUJNqNJCfbmNYqo2cVCvDUm0MS7WSnWIl3a6S6TT32sjenqLHDbgQQgF2AbOBKmA9cL2U8oRjqRM14Cvu/AeV9SYcrZU4PZU4WytxevZjEmFMWVmoubmY8nJRc/NQ8/JQc3NQc3M7rcPuCCklMqQh/WEi7jZCDT58e5po292CbAoTIcJO1zp2tK5j+vfmM+nSuR0+OFJKApEA7qCbGm8N+1v3U9pQyurq1VR5qsi0ZvLLSb/gyrzZCC0E4bbow3RoCvkg4EYGXEQCbjSfCy3oRvpdCE8dwlOLwVOL4qnFED4ycEYKA15bDs3O0dQ6J1Bpm8B+8yhawiotvjaafKHo3NuGOFjNBdXLSQm7qFczKLMXUGvOQnOkkZqRTn6mk1FpdmY0hBhc5kKEJaZsO+ZRqajZdoyZVgwOFcVuQphOXKrd2riVB9c8yNaDW3GanEwfOp2JmRMZkzaGLFsWGdaMHnMb0BFSSloPBqitcHGw2kNjlZeD1R68LUe7sFWtRhypZqwOExaHCatDjc6dJlSLEaOqYLIomFQFkzk6GVUFgyIOT4piwKCIbjWoSikJ19URLCsnuLucUFU1oeojk+Y9fki/MJlQ0tNR0lJRHE4MTieKw47B4cTgcKA4HRgcDgw2G0I1I8xqVE7XbkRzGcJVgaF5F6J1P0KRCIMEAcKSBEnZiKRMcGQiHFnRUHmWZFCdYHaA6ojNnWCyRqtrFFNsUsFggk7cJ3cXbzBMeb2HXXWtlNV72NvoparZT2Wzj9YOYrDaVYUMp5l0u0qaXcVhNuKwGHFaTDjMRpwWIw6zEbvZiNloQDUaMBsVzEbDUetqbNloECgGgSJE3G44usKpMODTgPullJfE1u8BkFI+dKJjEjXg7//ij+QoE5ACBAIExP4R23LUHHH81vZL0eXDqQ+30gsEijCiiKP7o4a1EI3BKmp8FewMbqYiq5HyPDc+25EbQ7abSyBoEISOMew2TaMwEGKOJ8Bsnwen7Lr/ZJ800yiTqCOVOplGnUylTqawT2axWw5hv8yijaNLkopBYFMVUm0qqXaVVJuJNJtKik0l1QzWPRsJbf8SX83+IwcJgUk1Y1RVFFXFYrQz1FjAIGUYKYZMDOLoEkxEhtHQkGiH5zL2d/g3khJNamhSa/e7HdkvYr+dOLzvWNrv7TkEot3F2t0n8pBU3aWDZ+wUnVan/1LhKOPKB+5M6NgTGfDu9JwfClS2W68CvtHBhW8FbgXIzc1N6ELeNGhoPRRN/NAjf2R+3FZ5ZItExh6Wk6c/tB6RYYIEaCOADy8topEmtZmwM0zQESFsA0UYGUMaxOyvQMRMy5GXghpWsKFglUZSpcogzUKGNGNEQbMrFDtMRAwmIiI2GVQiwhibm9CMFsJGB2E1Cc3kIGJOQqoOFKOKUTFgUgRGg4EMRTDYYGCqasBqMmJTFexmBatqxGZSsJkVVMXQyef1eGA+PlcLdRXluBsb8DQ3EQr4CYdChNuCaOEwQRmmkr1Uyn2YwxbMYTNKxIhRM6FoxuhrUcam2HJ72r9CIzJCWAujSY2I1GL50M7kH/l3+Og+s1iH9IgZdNHesLcz/O3X2xcWOhQ7IVUStfryeLlOdH0p47tKh8f3cP6cZi8oNannR8qe8qFPUsrngOcgWgJP5BxX/+d9PSqTTsfYklMYPql7MTh1dHR6j+5UTFUD7Ud6DItt09HR0dHpBbpjwNcDo4QQw4UQKnAdsLRnxNLR0dHR6YyEq1CklGEhxE+B94l2I3xBStmxr1IdHR0dnR6nW3XgUsplwLIekkVHR0dHpwt8bQM66Ojo6Ax0dAOuo6OjM0DRDbiOjo7OAEU34Do6OjoDlF71RiiEaAD2JXh4BtDYaaqBga5L/+R00eV00QN0XQ6RJ6U8znVqrxrw7iCE2NCRL4CBiK5L/+R00eV00QN0XTpDr0LR0dHRGaDoBlxHR0dngDKQDPhzfS1AD6Lr0j85XXQ5XfQ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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "id": "LAIunJUfuoX0"
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the plot, we can see that the probability distribution of the bell curves of VW, LW and H have the minimum spread. Hence for the S data dataset, VW, LW and H will be the optimum features for classification."
      ],
      "metadata": {
        "id": "zWVdIdI7VL5v"
      },
      "id": "zWVdIdI7VL5v"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "0e3e7f9e",
      "metadata": {
        "id": "0e3e7f9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "286b9d35-3908-42b1-e632-ead97d71483f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           L         D         H        WW        SW        VW        LW\n",
              "L   1.000000  0.976550  0.852423  0.928089  0.600235  0.881295  0.935311\n",
              "D   0.976550  1.000000  0.833777  0.913866  0.590875  0.865123  0.920756\n",
              "H   0.852423  0.833777  1.000000  0.823764  0.525951  0.796471  0.833174\n",
              "WW  0.928089  0.913866  0.823764  1.000000  0.619960  0.955098  0.981555\n",
              "SW  0.600235  0.590875  0.525951  0.619960  1.000000  0.555413  0.618641\n",
              "VW  0.881295  0.865123  0.796471  0.955098  0.555413  1.000000  0.935846\n",
              "LW  0.935311  0.920756  0.833174  0.981555  0.618641  0.935846  1.000000"
            ],
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              "      <th></th>\n",
              "      <th>L</th>\n",
              "      <th>D</th>\n",
              "      <th>H</th>\n",
              "      <th>WW</th>\n",
              "      <th>SW</th>\n",
              "      <th>VW</th>\n",
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              "      <th>L</th>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.852423</td>\n",
              "      <td>0.928089</td>\n",
              "      <td>0.600235</td>\n",
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              "      <th>D</th>\n",
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              "      <td>0.913866</td>\n",
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              "      <th>H</th>\n",
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              "      <td>0.525951</td>\n",
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              "      <td>0.833174</td>\n",
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              "    <tr>\n",
              "      <th>WW</th>\n",
              "      <td>0.928089</td>\n",
              "      <td>0.913866</td>\n",
              "      <td>0.823764</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.619960</td>\n",
              "      <td>0.955098</td>\n",
              "      <td>0.981555</td>\n",
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              "    <tr>\n",
              "      <th>SW</th>\n",
              "      <td>0.600235</td>\n",
              "      <td>0.590875</td>\n",
              "      <td>0.525951</td>\n",
              "      <td>0.619960</td>\n",
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              "      <td>0.555413</td>\n",
              "      <td>0.618641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VW</th>\n",
              "      <td>0.881295</td>\n",
              "      <td>0.865123</td>\n",
              "      <td>0.796471</td>\n",
              "      <td>0.955098</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.935846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LW</th>\n",
              "      <td>0.935311</td>\n",
              "      <td>0.920756</td>\n",
              "      <td>0.833174</td>\n",
              "      <td>0.981555</td>\n",
              "      <td>0.618641</td>\n",
              "      <td>0.935846</td>\n",
              "      <td>1.000000</td>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "            L         D         H       WW        SW        VW        LW\n",
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      ],
      "source": [
        "data_S[col].corr().describe().loc[[\"min\"]]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Mid-data dataset**"
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        "id": "31zjh-XIr4en"
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    },
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "data_M = copy.deepcopy(data.loc[(data.R <= 10) & (data.R >= 6)])\n",
        "data_M"
      ]
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      },
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        mean       std\n",
              "L   0.505742  0.105709\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>L</th>\n",
              "      <td>0.505742</td>\n",
              "      <td>0.105709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.391316</td>\n",
              "      <td>0.086828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>H</th>\n",
              "      <td>0.131537</td>\n",
              "      <td>0.039003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WW</th>\n",
              "      <td>0.714939</td>\n",
              "      <td>0.415353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SW</th>\n",
              "      <td>0.323103</td>\n",
              "      <td>0.197330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VW</th>\n",
              "      <td>0.156723</td>\n",
              "      <td>0.094497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LW</th>\n",
              "      <td>0.199830</td>\n",
              "      <td>0.109703</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f02eccf4-fe1a-437e-9ae0-5feb3cb8e541')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f02eccf4-fe1a-437e-9ae0-5feb3cb8e541 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f02eccf4-fe1a-437e-9ae0-5feb3cb8e541');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "tmp = data_M[col].describe().loc[[\"mean\",\"std\"]]\n",
        "tmp.transpose()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.arange(-0.2, 1.8, 0.001)\n",
        "\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'L'), tmp._get_value(\"std\", 'L')), label='L')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'D'), tmp._get_value(\"std\", 'D')), label='D')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'H'), tmp._get_value(\"std\", 'H')), label='H')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'WW'), tmp._get_value(\"std\", 'WW')), label='WW')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'SW'), tmp._get_value(\"std\", 'SW')), label='SW')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'VW'), tmp._get_value(\"std\", 'VW')), label='VW')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'LW'), tmp._get_value(\"std\", 'LW')), label='LW')\n",
        "\n",
        "\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "89ac81bc-2d88-4a76-d658-3ac55338283b",
        "id": "oJZHf5Xu_fg3"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f6addad4f90>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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ZRnn66afZsmULRqOR+fPnU1JSwq5du1i1ahWrV68ebi/7UWxm9UvgDVVVNyuKYgD8W7lygQu4hGIY2lbNIQFciEBMlCmH00033cT9999PSUkJNpuN5cuXA7BmzRp27NhBX18fcXFxFBYW8uijj1JaWsrnP//5kI8j6AxcUZQYYD3wewBVVR2qqnaHamDTWaABPFLn/bknGbgQ04PFYmHjxo3cfffdbNmyZfh4UVERjz32GIsXLwZg0aJF7Nq1i9raWhYsWBDycUylhJILtAF/VBSlVFGU3ymKMipiKYpyj6Io+xRF2dfW1jaFt5s+Ag3gJp0JjaKRGrgQ08iWLVs4ePDgqABeWVnJ6tXearJOpyM5OZmCggI0mtDPGZlKCUUHLAPuU1V1t6IovwT+HfjWmSepqvo48DhAQUHBRbFWPNAArigKZp0Zm0tmoQgxXdx8882j2l8kJyePOrZ9+/awjWEqPxLqgXpVVXcPfb0Vb0C/6AUawAHMBrNk4EKIgAQdwFVVbQbqFEWZM3TocuBYSEY1zVmdVjSKhgit/xuYmnXSUlYIEZipzkK5D3hyaAZKJXDX1Ic0/fmW0SuK4vc1ZoMEcCFEYKYUwFVVPQAUhGgsF4xA+qD4SAYuhAiULKUPA6vT6ncvcB+LQXamF0IERgJ4GFidVsyGADNw2ZVHCBEgCeBhYHUFnoGb9WZpZiXENGGxnN7vVlVVEhMT6erqAqCpqQlFUfjggw+Gz0lKSqKjoyPk45AAHgZWRxA1cL0Zq8sq26oJMc0oikJhYeFw69ji4mKWLl063JnwxIkTJCQkkJCQEPL3lgAeBlaX/xsa+5j1ZjyqZ7iXuBBi+igqKhrRSvb+++8fEdDD0cgKpJ1sWFid1uEe3/7ynR/IbvZCXOy6Xz6FozG0D/8NaWZib5gV0DVr1qzhwQcfBGDPnj08+OCD/PKXvwS8ATwcrWRBMvCQU1U1qGmEvqAtDzKFmH5WrFhBaWkpVqsVp9OJxWJh5syZVFRUSAY+nQy6B/GonoCzaNmVR4jABZoph0tkZCT5+fn84Q9/YNkyb0eRwsJCXnvtNVpbW5kzZ84kdwiOZOAh5pvLHWgJRfbFFGJ6Kyoq4uGHHx7uRLh69Wp++ctfUlhYGNCq7EBIAA+xYBpZnXm+LOYR4qPPZrORkZEx/PHzn/+cNWvWjGglu2zZMurr68NW/wYpoYRcsAH8zIeYQoiPNo/HM+bxM6cBG41G7HZ7WMchGXiIBVtC8dXMJYALIfwlATzEfD29g83ApQYuhPCXBPAQ8wXgQAO4UWtEq2hlFooQfrhQVywH+n1JAA8xXwD27TTvL0VRpKGVEH6IiIigo6PjggviqqrS0dFBRIT/G8HIQ8wQCzYD910jNXAhJpaRkUF9fT0X4ibpERERZGRk+H2+BPAQszqtaBVtQNup+UgAF2Jyer2e3Nzc8z2MjwQpoYRYv7OfSH1kUBP3LXqLlFCEEH6TAB5iwTSy8pGe4EKIQEgAD7FgGln5yENMIUQgpAYeYv3O/ill4OetBt52Eg78BZyDsODjkFV4fsYhhPCbBPAQszqsxETEBHXteQvgR56F5z7v/Vyrhz2PweXfhnVfOfdjEUL4TUooIdbv7A94P0wfXw3co47dZyEs6vfBc/dAxgr4Shn8v1OwYDNsexAOPXPuxiGECJgE8BCzOq0BL+LxsegtqKjnbls1lx2e+xxEpcGWp8CSBIZIuOU3kFkIr30V+lrOzViEEAGTAB5iU3mIec4bWu37A3RWwvW/AFPs6eNaPdz0KDis8N6Pzs1YhBABkwAeQm6PG5vLFvRDzHPa0Mphgx3/DbkbIO/y0a8n5sOyT0PJ/0F3XfjHI4QImATwELK5vHO4g83AfaUXq+McZOCHnwFbB2z4Goy36Gjt0EPMnf8b/vEIIQImATyEAtnMwe1yUVm6l0Pb3qClsgJVVYnUDZVQXGEO4KoKux+DlIWQPcFmq7GZcMkNcPCv4DxHdXkhhN9kGmEI+XqBT1ZCaaut5uWf/5CupobhY7MKCpl1+/XAOcjA63ZD61G48ZHxs2+fgrvg6HNw9AVYsiW84xJCBGTKAVxRFC2wD2hQVfX6qQ9p+vJlzhNl4B31tTz9X1/DEBHBjQ/8B8k5szhevIPiv/+Fro5mtLOV8Gfgh/4OOhPMv2Xyc3PWQUIelP6fBHAhPmJCUUL5MlAWgvtMe77MebxphI4BGy/890PoDAa2PPRT8lcWEZOcwqqbb+X6f/s6ndU1FB6NH87kw8LthKPPw9zrwBg1+fmKAgtvg5pi6G0K37iEEAGbUgBXFCUD+Bjwu9AMZ3qbrBd48dan6G5p5oZ/+3eik5JHvJa/sojlN95Cfr2FnhPV4RvkqXdgoBMW3ur/NQs+Dqhw7IWwDUsIEbipZuAPA18DzuHSwY+uiR5ittdWU/Laiyy67Goy5i0Y8/o1mz5Fr9mF7Y0DuF2u8Azy6PMQEQuzxpg6OJ7EfO8DzyPPhWdMQoigBB3AFUW5HmhVVXX/JOfdoyjKPkVR9l2IO2icyZeBj/UQc+fWp9AbI1i75Z/GvV5vNHJsoR26bBx7/53QD9DjhpNvwuyrQWcI7Nr5N0P9HuhpmPxcIcQ5MZUMfA1wo6Io1cDTwGWKovzl7JNUVX1cVdUCVVULkpKSpvB2H32+DNy3otKno76Wk3uKWXrNDZiioie8R1+mEVeSid3P/x2P2x3aAdbt8ZZPZl8T+LVzP+b9s/yt0I5JCBG0oAO4qqrfUFU1Q1XVHOCTwDuqqt4RspFNQ1anlQhtBHqNfsTxPS9uRWcwsOy6Gye9h9lgpnNZND0tzZzc/WFoB3jyddDoxl55OZmkuRCTJQFciI8QmQceQv3O/lH1b1tvDyeKd7Dw8muIjJ68zaxZb6YjDfKTUzj41mvMLVofugGeeMO7cCeYdreKArOvggN/9fYM14/c81NVVd472cZLBxtp7B4gLdbETUvSWZ+fGNT2ckKIyYVkJaaqqtsv9jng4J1GeHYAP/beNtwuF4uvvNave5j1ZvpdVhZfeR31ZUdoq60OzeA6K6H9BMzxbxxjyr8anDao+WDE4d5BJ1/4y37u/ONetp9ow+VWefd4K5/5wx6+8Jf99A06pzh4IcRYZCl9CJ2dgauqyqFtb5A2Zx6Jmdl+3cOsN2N1WJm3aiMRBgsH3349NIMrf9v75+yrg79H7jrvAqCTbw4f6re7+PTvdrOtrJVvXDuXXd+4nK3/UsTu/7iCf792Lv8oa+WO3+2m3x6mWTVCXMQkgIdQn6OPaOPph5T1xw7T1dTI4iv8e2ioOj0UVc/noQP30PWLo9yU/kXSymbQt7sB1aNObXBVOyA2G+JnBn8PvQly10PFPwDweFT+9ckSjjT28us7lvP5DbMw6Lz/pAw6DV/YMIvH7ljOkcZe7v1rCZ6pfg9CiBEkgIdQr6OXaMPpAF724XvoI0zkryqa9FpX9yCtvzrAyrI86g0txHxsJq5FOlxuBz3PV9Lxp6N4BoPMYj0eqP7Am0FP1cxLveWY7jp+90ElO0628eCN87lyXsqYp18xL4Xv3Dif7SfaePz9yqm/vxBimATwEOpz9BFl8C5Pd7uclO/6kLyCVeiNERNe5+610/abQ7g6B9m9rppvZP4S89oZZN62gg/6X6TOUsFgeRftvz+CxxHE1MKWIzDY7e1rMlUzN3hvefBN/vvNE1wzP5VPrcqa8JI7VmVx7YJUfvbWCU61nYNe50JcJCSAh1Cfo48ovTeAVx8sYdDaz9y1Gya8xuNw0/6Ho3hsLpI+t5D+HO+iVpvThlanY27RenYff4moTbk46vvofOo4qhpgKaL6fe+foQjgyfNQzUlU7H4Vk17L929ZMOksE0VR+O5NC4jQa/nOS0cDH78QYkwSwEPE4XYw6B4croEf/3AHEVHRZC9cOuF1Pa9U4myxknDHJRgyooYfgvpWdV6y9lLcTid1fceJuX4mg2Wd9Bc3Bja4qve9te+Y9MC/sbMpCs3xq8i3lnD/FfkkWIx+XZYUZeSrV83h/fJ23j4m+2wKEQoSwEOkz9EHQJQhCufgIBX7djF7VRFa3fhT7QfKOrDuacayPoOI2XHA6WX4Nqd3d5/UvNlYEhKp2LsTS1EaEZfE0/NaFc4WP1vOetzeToKhyL4Bt0flybYckpVu7sgbDOjaT63KYmaimZ+/fVIeaAoRAhLAQ+TMAF59qASX3c7swrXjnq863XS/dApdciQxV56eYuhbhu/LwBVFIX/laqoPluC0DxK3KR/FoKX7xVP+lSKaDoK9xzt7JAReP9LECz35AOhr3g/oWp1Ww5evyOd4cx+vHpbWtEJMlQTwEPEF8GhDNKf27cZoNpNxydhdBwF6t9fj7rITe9MsFN3p/wxjbWycv7IIt9NJVel+tBYDMVdnY6/sYeBQ++QDG65/j//DxF8ej8oj2yqISMpFjcuByu0B3+P6RWnMTrHwP9vKpRYuxBRJAA+RXkcvAGZtJKdK9jJz6YpxyyfuXgd979VjWpxExKzYEa/5auC+EgpA+tx5mKJjKN9T7D1n5Qz0aWZ6Xq9CdU3SybfqfUicDVGpwX5rw/5R1sKJlj7uuywPZeal3qmJnsBmxWg1Cl/YMIvy1n7eO3lhd6cUItwkgIeILwN31rYz2NdL3orC8c/dXgceDzFXjV6defZDTACNRktewSqqSvficjpRNAox1+Ti7rZj3dc8/qDcLqjdGZLsG+CPH1aTHmviYwtneGvq9l5viSZA1y9KIyXayO8/qArJuIS4WEkADxFfBt555CRanY6cxcvGPM/Vbad/dxPm5anoEkyjXveVUHytaX3yVxbhGBig9vABAIz5sRhyoul9pw7VOU4W3HQAHP0heYB5ormPnZUd3FGYjU6rOX3Pqh0B38ug0/CZohzeL2+nrKl3ymMT4mIlATxE+hx9oELDgYNkLViMwRQ59nnb6wCIuixzzNeHM/Cz9sXMXLAYfYSJU/t3A96Hm9FXZuPpddC/Z5ws3BdcQxDA/7SzGqNOwydXDI07KsXbYrY6sAeZPrevzMKk1/IHycKFCJoE8BDpc/SRaI2gt7WFvBWrxzzH3e/Auq8F8/IUdHFjr87Ua/WYdKbhjN5Hp9eTvXAJlaX7hh/+RcyKxZAdTf8HDajuMR4IVr8PSZeAZWobafTYnDxf0sDNS9KJM5+xk0/OOqjZ6d0oOUCxkQZuXprGy4ca6ZVuhUIERQJ4iPQ5+pjV7n0gOXP5yjHPse5qApcHy9qJF9TEGmPptnePOj5z+Qr6O9ppqzmdtUatT8fdZWfgyFkzUlwOqN0Vkv4nz+yvY8Dp5jNFOSNfyF0HTis0lAR13y0rsxh0enixVLZpEyIYEsBDpM/RR3qz0bvwJi5+1Ouq003/ziYi5sajTx67vOITY4yh1z66Njxz6QoAqkr3DR+LuCQBXaKJvh31I6flNZZ4e3dPsXyiqipb99ezJDOWeWlnbQfnu3d14HVwgIXpMcxPi+bJ3bUypVCIIEgADxFrTxfRnTBz2YqxXy9txWN1Ylk3+XL2GEMMPY6eUcfNsXGkzMznVMme4WOKRsGyLh1nQz/2yjOuCdH876ONvRxv7mPz8ozRL0bGe3errwquDq4oCltWZnG8uY+D9aO/XyHExCSAh4imylvymLlsdPlEVVX6329An2bGOHPy7cyijdH02McOaDOXraCp/AS23tOvm5clo4nUYd15Ro+UqvchZYE3yE7B1v31GHQabliUNvYJueugbje47EHd/6YlaZj0Wp7aXTuFUQpxcZIAHiKRdQO4zTqSc0ZvmGCv7MHVNoBlTbpf+0OOVwOHoQxfVak+sH/4mKLXElmQysCxDtw9dm8wrds95fKJw+XhxQMNXDkvhZhI/dgn5a4H1yDU7w3qPaIi9Ny4OI2XDjZilV17hAiIBPAQcLtcxDWpeHJjxwzQ1t1NKCYdkYsS/bqfrwY+Vl04JXcW5tg4KktGBkzLqlRQ8U4prN/nDapTfID57olWumxONi8bo3zik10EiiboMgrA5oIMBpxu3jgywaIkIcQoEsBDoP74UfQuBWP+6Pq2u9/BwNEOzMuSUfRav+4XY4jBpbqwuWyjXlM0Gi0FZCEAACAASURBVHKXFlB9sAS363TGqkswETE7DuueZtTKDwDFG1ynYOv+epKijKzLn+AHT0QMzFgc1IIen4LsOLLiI3m2pD7oewhxMZIAHgIn9n2IW6MSN2d0+cS2vxXcKuaV/vciiTF66+QT1cHtNiuNJ46NOG4unIGnz8HAkRaYsQhMcQF8FyN19Nt593grtyxN9668nEjuem8JxTH6B44/FEXh48vS2VnZQUP3QFD3EOJiJAE8BKpL99McP0h89MgFM6pHxbqnCUNONPoU8zhXj+bbFGK8Onj2wiVotDoqz5hOCBAxJx5trAFrc96U698vHmjE5VHZNFH5xCdnPXic3rp7kD6+NANVhRdkTrgQfpMAPkXdzU30NbdQnzxArHFkZ0F7ZQ+ujkHMq2YEdE/ffcbLwA2mSDLnL+TU/j0jjisaBfNsO3bPQpxxE2/lNpmt++tZmB7DnNSoyU/OKgSNbkpllKyESFbmxPPs/nqZEy6EnySAT1FlqfdhYn3S6ABu3TP08HJBQkD3jDEMlVDGmAvuM3PZCroa6+lqHrm9mtm0C3BibRy714o/jjX2cqypd+y532MxWiB9edB9UXw2LU+nst3Kgbqxf/MQQowkAXyKKkv2ok+Moc/sGhHAg3l46TNcAx+cKIB755tX7h85G0XbuA2T+TjWg93jdymcxLMl9ei1CjcuHmfu91hy1nmX1Nv7gnpPgGsXzsCo08jDTCH8JAF8ChyDA9QfO4wuLwWA2IjTAdy2v8X78DLA8gmcEcAnyMBjU1JJyMiisuSMurO9Hxr2YZ7jRB1wYfNnx56zON3eud+Xz00Z2bhqMrnrQHV7m1sFKTpCz9XzU3n5YBN2V3A/fIS4mEgAn4Lawwdxu1w4cqLRKlqi9N56sepR6d/TjCE3etK+J2MxaA2YdKZxa+A+M5evpL7sKHbbUO/w2l3gcWFcthBdognreG1mJ/DeiTba+x1s8rd84pO5CrSGoPui+GxankHPgJN3ylqndB8hLgYSwKegsmQPBlMkPUkqMcaY4UU89spu3B2DWFYGnn37xBhjJg/gy1bgcbupPjjUDbDqPdDoUbIKMa+agaOmF0eTn7vXD9m6v54Es4FL5wTYglZvgoyVU3qQCbA2L5HkKKOUUYTwgwTwIKmqSlXpPnIWLaXb2Uuc8fSca+vuZjSROkwL/Ft5OZYYQ8y40wh90mbPJSIq+vRslKodkLkSDJGYlyeDTsG62//d37usDrYdb+HmpenoJ5v7PZbcddB0CAa6Ar92iFajcMuydN490UZbX3D9VYS4WAQdwBVFyVQU5V1FUY4pinJUUZQvh3JgH3Wt1ZX0d3WSu2wF3fbu4bq1u8/BwLEOIpeloOiD//kYHxFP52DnhOdoNFpmLllOVek+PP3t3v0pc73TBzWReiIXJmErbcVj96+e/NLBRpxuP+d+jyVnHaBCTXFw1w/ZvCwDt0flxQMyJ1yIiUwlA3cBD6iqOg8oBL6oKMq80Azro6+qZC8oCrlLltNt7yYuwpuBW30PLwNYeTmWBFPCpAEcvHXwwf4+Gj94DlC9qyKHmAtnoNrdDBz0b/f3Z0vqmTcjenTfb39lFIDONOUySn5KFIszYti6X8ooQkxEF+yFqqo2AU1Dn/cpilIGpAPHJrzwAlGxbzczZs3GHBtHt72bxcbFQysvmzHkxgT18PJMCREJdAx0oKrqhB0McxYvQ6PVUrn3Q+I1qTQ0Z9FReorOJivWrkEWAr3PlrP/pUp0Bi2WOCNRCSYS082kzIwhId2CRqNwormPQ/U9fOv6KfwM1hkha9WUGlv5bF6ewbdePMrRxh7mp03egleIi1HQAfxMiqLkAEuBUWupFUW5B7gHICsrKxRvd971trfRUlnOutvvxKN66B70ZuD2U924OweJuSp7yu+RYEpg0D2IzWUb3uh4LAN9ClGJsyg93szhqF/DH0+gaBRik01Y4iPoM2qZ0WYjN91CL9DfZaelqpejO7yNsCLMenIWJbDPNYhBUbh5SQBzv8eSsw7eeQis7WAO/hnADYvTeOiVMrbur5cALsQ4phzAFUWxAM8C/6aq6qh9wFRVfRx4HKCgoOCCWCPt2xk+b0UhPfYeXKqLRFMi1j1DDy/nBx+4fOIjvBsxdAx0jArgHreHU6VtHHqnjubKXtz2NFyukxTMLSf/li0kpFvQ6rzVMc+gi6bv72Z+som4TfmA9wFsb/sgzZU91B7roPJAG5YBN/+qN3Hq3QYi1qVjjjUGN/DcDcBD3lWZ828J+vuPjTRw5bwUXjzQyDeuvQSDTp63C3G2Kf1foSiKHm/wflJV1edCM6SPvoq9u4hPyyA+LYP2Ae9imRQ1kYGjU3946ZNg8i6/7xjsGD7mdns4sqOBJ7+9i7d+d5SBfidFH89j863eBUSmVDfJ2dHDwRtAE6HDtDgJ24FWPIPerFtRFGKSTMxZlcqVd80n5+7ZPGe2E5NmZu9r1fz5P4vZ8fRJrN1BzAJJWwIGS0jKKJuWp9NpdbD9hMwJF2IsQWfgircw+3ugTFXVn4duSB9tg/391B87TMH13uzSF8DTT0WDx4V51dQeXvokRAwF8KE6eNXBdnY+f4ruFhspudGs2ZRPzuJENBoFtu4kPsLOqZP1LBvjXpbCGdj2tWArbcWyenSJZOuBBnridNzxtQJsnYOUvFHD0R0NHPugkUWXZVBwXQ6GCD//qWj1kLV6yn1RANbnJ5FoMbJ1fz1XzQ/N36sQF5KppIprgE8DlymKcmDo47oQjesjq6p0Lx63m7wVqwFvAFdUBctR1fvwMmlqDy99fBl4a2MPLz5cyuu/OYyiwHX/spBNX1vOzKVJ3uDtccOpbeTnJlB37MiIvTJ9DBlR6NMtWHc3jer019FvZ1tZKzcv8c79jkmKZOOnL+H2BwvJK0im9K1anvz2Lk7sbva/S2Duemg/Cb3+z0Efi06r4ePL0nnneCsd/TInXIizBR3AVVX9QFVVRVXVRaqqLhn6eC2Ug/soqti7C3NcPKmzvPXkjoEOllrnonS7vduahUi0LoZl9VfS9ZcY2uv62bBlNp/81kpyFyeNnJXSWAoDXcxesxFV9VCxd+xeJOZVqTibbThqRzab8vX9vrVgZPfCmCQTV9w5j01fX44l1sg//niMFx8upbfdjw0XfFu5VX8Q0Pc8lk3LMnB5VF480Dj5yUJcZOTJUACcDjtVB/aTV7AKReP9q2sfaOeGng0he3gJ0NHQzws/PsDKuutxZHay5durWLAhA81YqyPL3wZFQ1LRZmJTZ3By14dj3jNycTKKUYt11+msWFVVntlfz6KM8ft+p+bGsPnrBVz6qTm01vTx9EN7OLKjYeJsPHVoN6BT2wL6vscyJzWKhekyJ1yIsUgAD0BVyV6c9kFmF64dPmbvtLKydwHmlakheXh5fGcTW3+0j4E+J6XLXqJ21U7MMRPMCKl4G9KXo5gTmF24ltojB8cso2iMWiKXJmM73IbH5gTgQF03ZU29o7Lvsykahfnr0vnkt1aSkhvNe389wcv/c2D8h5waLeRfBSffBPfUd5q/rSCDY029HJQ+4UKMIAE8AMc/3IE5No6MeQuGj+WdSkYFzIVTmz/tcrh55//K2PanMlJmRnPbN1egZvcNPyQdk7XD24M77woAZq9ag+rxULF315inm1fNAJeKdb93VsdfdtViNmi5ZenozZjHEp1g4sYvL2HD7XNoOtXD376/h5qjHWOfPOdaGOiE+j1jvx6Am5emE2nQ8pddNVO+lxAXEgngfrLbbFSW7mX26rVoNN4NGjwON0sb8qhIaUQX7LxpoLvFxtYf76fswyaWX5vNjV9eijnGSEpkCi22lvEvLH8TUL3ZLpCcO4uYlFTKd49dRjHMMGPIisK6p4kuq51XDjVy89J0LEb/JyMpisKC9enc+o0VREYbeOWRgxQ/V4Hb7Rl54qzLQaOHE6/7fe/xREXouXlpOi8dbKRn6LcHIYQEcL+d2rcLt9PJ3KLTvUZspa1EuiOonDNOFuqHiv2t/P2He+nvHuT6exdTeNMs7+wSINWcSqutFbdnnGZUZS9DdAakLQW8wdVXRhnoG7WmCvBm4a62Ad59uxK7y8MdhcGtGo2fYWbz1wuYvz6d0rdqef6nJfR2nPGAMyIactaGJIAD3LEqG7vLwzP760JyPyEuBBLA/XS8eAfRScnMyJ8LDK1m/KCekxE16DMtAd/P7fLw/t9O8uZvjxA/w8wnvrmS7LP2zkw1p+JW3WOXUez9ULENLrkezpiVMrdoPR63mxPFY8/DjlyUiGLSQUkby7PjuGRGkI2rAJ1By6W3z+Hqzy2gq8nKMz/YR92xMxpwzbkWOsqhvSLo9/CZlxbN8uw4ntxdi8dzQSzoFWLKJID7wdbbQ82hUuasXnd604bybjxtg7wUt53UqMCmD/Z1DvL8z0o49G49iy7L4JYHlhEVHzHqvFSz975jllEq/gFuO8y9fsTh5JyZJGXlcPS9f4z53opeS1d+DMsd8M/zg99w4kx5y5O9JZUYAy89coB9r1ejelSYfY33hBOvhuR97ijMoqrdSvGp4H/jEeJCIgHcD2Xvb8fjdnPJuo3Dx3rfrcNlUXkvZh8zzP4HwpojHfzt+3vobLJy9ecWsO622SOWvp8pJdK712azdYyt0cpehsgE76rHs8y/9AqaT5XTUT92ueE31j7cwIrW0NWTY1Mi2fz1AvILUtj9YiWvP3YYe0Q6zFgCR0LTZeHaBTOINxt4orgqJPcTYrqTAD4JVVU58u5bpM7KJykrBwB7dQ+Oqh5q5/fiUtx+BXCP28OuF07xyqMHscRFcNs3VpC3PHnCa3wZ+KgA7rJD+Vsw5zrQjn4AOXfNBhSNhqM7Rs/DPtHcxyun2qnPiMRe2oq7N3QrHPVGLVfePY+1t+VTc7iDZ36wl46MT0HTAeg4NeX7R+i1/NPqbP5R1kpFa9/kFwhxgZMAPonmUydpr6thwcarho/1ba9HY9ZxKL0SnUZHomniBTzWHjsv/fIA+9+oYd6aGWz+2nJiUyZfch9tiMakM9FsOyuAn3wD7L0w/+YxrzPHxpG7ZDllO97Bc9YD0N++X4lJr2X+zbPBo9L3QWhXOCqKwuLLMrnpK0tx2t1sfSOfkwNr4cizIbn/P63OIUKv4fEdlSG5nxDTmQTwSRx55210RiNz13i3KnM09jN4vBPL2nTqBhtIiUxBo4z/19hwoou/f38vLVW9XP6ZS9j46UvQGbR+vbeiKKSaU0dn4AefBksq5F467rULLr2S/q5OKkv2DR9r6R3kxQMN3FaQQUJGNKZFSVh3NQ0v7AmltLxYbvvmCpKyo3m75wHef2sQt8u/rd0mEm82cFtBJi+UNtLSOxiCkQoxfUkAn4DdZuN48XvMKVyLMdKbMff+oxbFqMVSmEaztXm4zHE21aOy/41qXny4FINJx+Z/L2Du6sAfGmZGZVLbW3v6gLXDWz5ZuHnM8onPrIJVWBISOfDmK8PHfv9BFW6Pyt1rcwGI3piJ6nTT+154lqmbY4zcdP9SFs/v4VDHWl74cTH9XVMPup9dOxOXx8MfPpRauLi4SQCfwNHtb+MYGGDJ1d6ZHvaaXgaPdRC1IQONSUdNbw2ZUaOXoQ/0O3j1V4fY9UIls5Ync+s3CkhID3yqIUBWVBa1fbWne48cfQ48Llj8yQmv02i1LL7iWmoOldLRUEdr3yB/3lnNzUvSyU7wbhChTzUTuSSZ/g8bQ1oLP5NWq2Ht3eu5Ku5hOhoH+Nv39lI73upNP2UlRPKxRWn8384a2qVLobiIhWRLtQuRx+Om5I2XSZszj9RZ+aiqSs8bVWgseixr0+lz9NEx2EFOdM6I6xoruoc2W3Cw/pOzWbAhfcI9LSeTHZ3NgGuAtoE2kk1JUPoXSJ4PqQsnvXbR5Vez69mnOPDmq7yfuA6nW+VLl+ePOCf6iixsB9vo3VZL3C3549xpiswJ5C9PJKnsm7yhPsLLjx6k4NocVlyfO7xoaTyqx4PHNoDHasVj7R/608aXovtorT3Mi480ceviFFSXC9XpRHW7vY3GNFoU7Zl/alD0ejQmE4rJhCYy0vt5RIT388jI4QZlQkwXEsDHUbl/Lz0tzay//U4ABk904ajqJfamWWgMWmravX05cmJyAG/JpOStGna/VEVUQgSbv1ZAUtbYHf4CkRXl3Ue0preG5M4674yO637q17WRMbHMWb2OI9v/wbPpKWxaPpOcxJHbs+kSTJhXpmLd04RlbXrI+pmPsvxOYo/cwMevq2RH6Rz2vVZN3d4qivI6MNg6cHV04u7sxNXp/dPd04Onvx+PzQbjdD78DsAuaAjF+BQFbXQ02thYNLExaGNj0cXGoo2NRRsXhy4pCV1yCrrkZPQpyWhiYqb0g1mIUJAAPgZVVdnz4jNEJSaRt2I1qstDzyuV6BIiMK/w1ryrerz119zoXGy9DrY9cYzaY53kFSSz8VNzMZhC81ebFe0N4LW9taw4+rZ3u7JFn/D7+oIbN3Hs/XeZ332I+y67esxzoi/PwlbaSvfLlSTeNX9KgcljteKoq8NRV4erqQlnUzPOpibv5xVpuP72EOkqaFJWccL9SV6t1zPv+JskOhvQJsSji4tHn5lJxIIFaCxmtBYLGrMZjdmCxmJBY45EY4pEMRhotbn43FMHuXxBGl+5dh6KXg9aLXg8qB4PuN2obg+oHlS325uhDwzgGRjwZvUDNtTBQe/n/X24u3twd3d7P9racZRX4O7u9v4QOYtiMKBLTkaXkoI+NRV9Rgb6jHQMmZnez1NTUXTyv5cIL/kXNoaagyU0lZ/gis9+EY1WS+87tbjaB0i8ewHK0KKbmt4aNIoGd3UkT/91Dw6biw23z2H+urSQZmYzzDPQa/TUdJzwLohZ9mlvnxE/NWliORWZS0H/EZJHL/YEQBtlIPrKbHpeqWTwWMekfc3dvb04ampx1NbgrK3FUVuHo9b7tbtt5LJ/xWhEn5qKbsYMzEvmou/ei+7qfyNz7krmKjG8+1Y/B4xfYtHGDFbfMsvvGToAOcD6bjOPfVjFx3RxLEwPz+71HrsdV1sbrtZWXC0tuFpbcba24mrxfj1w6BC9b74JrjNa52q16GfMQJ+RgSEzA0NOLobcXAy5ORgyMyW4i5CQf0VnUVWV4mf+SlRiEgs2XoGrc5C+d+swLUggYnbc8Hk1HfVcU3MXbxUfIyHdzI1fWkJiRnAPKiei1WjJicmhvHa7d+n8ynsC+l6+89JR+mcUMuvUUxx48xVW3XLbmOdaVqdh29dM98uVGPPj0Bi0uPutOCrKGSwvxz78UYG7fWSQ1iUnY8jKwrJ+PYasbAzZWegzMtGnp6GNjT39A22wF36xAGIOwvqvYgE+scrNzudPcejdeurKOrny7vkBlZ6+fEU+Lx5o5L9eOsKzXyiatKYeDI3RiCEjA0NGxrjnqC4XzuYWnPX1OOvrcNTX46xvwFlXR9+2d3B3ntEjRq/HkJmJITcX48zc4eBunDUTbUx4fgiJC5ME8LNUluylqeIEV37uXjQaHe3PHQcg5vqZw+c0nOwi+dVVmAajWXZ1Niuvz0Ubgs0cxnNJ9CyK21+DS26EpDl+X/f3fXWU1Hbzk83r0W+rYM+LW1l42VVExsSOOM9jt+OorESX2MLAkSgavvZH7Ieewtl4epGPYjJhnDULy7p1GGfNRJ+d7Q3WmRloIv2sm0dEw8rPwvs/9za4SsxDZ9Cy7hOzyVmYyLY/HWPrj/ZR8LEcll2dPW6LgTNFR+j5xrVzeeCZg2wtqee2STanCBdFp8OQkY4hIx1YNep1d08Pjqoq7FXVOCorcVRXYa+son/HDnCenoevS07GmJ9/+mN2PsZZs/z/OxYXFcXvjWpDoKCgQN23b9/kJ54nbpeTP331i6Bo+Mx/P8LAbm9dOPaWPCyrZjDY76T4+QrKPmyix9hG3LUD/Ms1d4Z9XP/30l38pGsf7274XxJz1k9+AdDQPcA1v9jB/PRo/vrZQrqa6vnz/7uXeQWFrJ637IyMuhxHTQ14vP28jYs+iWHmZeAqJiIv1htA8vPRp6eHZpZGfys8vND7w2jTb0e8NGh1suOpE5TvayU+zcylt89hRl7sODc6zeNR+cTjOznR3Meb969nRoxp6uM8R1SXC2dDA/bKShynTmEvr/D+dzl1CtU+NEVSUdBnZIwM7Pn5GHNzUAyG8zp+cW4oirJfVdWCUcclgJ+296Vn2fHkH/n4Nx4kY8ZcWh45QMSsGOI/M4+Te1r4cGsFdpuL1NV6HnJ+mV9e9QvWZ/gXUIPW38rex1Zyd2IUv77i16xNXzvpJR63my89/Dq9Zcf5r/kRmBprsZeXU9rfSXW8hbUn64l2uDBkZWHMzxsRFPRpmbT+5jCeQTcpX16G1qwP/ff0j+/AB7+Ae96DtCWjXq4+1M57T5+gv9POvHVprL55FhGTjKO63cp1//M+y7Li+PPdK8NSSjmXVLcbZ13dWeWrchxV1eAeWtGq02HIycaYn0/E7Nmn/xtmZsqUyAuMBPBJdDU38uev3UfWgsXcdN9/0Pq/B/AMutDcnE/xG9U0VfSQkhvNpZ+ay+u9z/OTvT/hnVvfISkyKbwDe+k+eg8+xZqsGdy39D7uWXS6Bq6qKq62thH/g9vLK7CeOInWfnrFoy5tBsb8fJTcHF4qK8ESG8ft3/85esvYNXtHQz+tvz6AMTuaxLsXomhDHAwHe+B/lkLKfPinl0b0Mx8ew6CLva9UcXBbHUaznpXX5zJvXRrasTZ2HvLk7hq++fwRvnndJXxu/cxxz5vOPA4Hjqrq0/+9T57EXlGBs+5050klIgJj3hk/mIeCuy45SaY+TlMSwCfg8bj523e+QUd9Df/0k0dxvNzCYHk3VTMsHDraiSlKz6obZzJvTRqKRuHf3v03jnce541Nb4R3YA374beXQ+G/8qnOw8zuMnFf3M3eTGzoV213z+kNjLUJCQymZ/OaNZKoS+Zw1+2XY8zPQxt1+qFg+e5iXvr5Dyi67VOs3rRl3Le27muha+tJLGvSiL1hVui/tz2/hde+Ch//LSwa+8EqQFtdHx9uLafhRDexKZEUbcojZ2HCmIFIVVW+8Jf9/KOslT/dtZK1+RPPprmQeKxW7KdODQV1b3AfLD85YlaQJiYGY37eiGzdmJ8vD06nAQngE/jw70+y69mnuPaLD5BQm4LzSAeHBt3UuWHJlVksvSoLQ4T3ea9H9bD+b+vZmLmRh9Y8FPKxuLu7sVdUYD9Rhv2F/8be7sRuj8Pd2TV8jiYqanQ9ND+PSqeeW3+zk6QoIy9+cQ3mcfa6fOWXP6F894ds/s/vkTlv/BWd3S+dor+4kZhrc4naMP4MjKB43PCHa7w79vzrbohKGfdUVVWpPtxB8bMVdLfYSMmNpuC6HLIXjA7k/XYXm35VTHPvIM//axEzk0I/M2g6cXV1jQjqvg9P3+l2vLqUlFHZunHWTDSm6fMs4UInAXwc5XuKeelnPyB/xQZy3BuI77Jzwu5BU5DCiutyscSN3Ky4rKOM2165jR+s/QE3zLohqPdUVRV3R8fQrIQqb8AuL8deUTEyY9J5MMyaiXHBMuoSFf6n+3keuPVhls6/YlTgqu+ysfnXO3GrKs/9SxGZ8ePPWrDbrDz5zQcY7O/jjh/+gujEsfuSqx6VzqePM3CondibZmFZnRbU9zuutpPwm7WQswY+tRU0E88Bd7s9lH3YRMkbNfR1DpKUFcXya7PJXZw0ouZd22Hjll99iF6r4e+fX01WgszgOJOqqriam0eUYQbLy3FUnEJ1OLwnKQr6rMzhbN2QO9M7jz0nB63FPPEbiJCTAD6G2iOHeO6H30Efkcqy6FvJNOroSYok6655RMWPnX08UvoIvzv8O9659R0STAljnuPjsdlw1NTgqK7GXlWFo6oaR7X348wMSDGZvDVL3wfVGI8/iu7yL6Bc8wMA+hx9rP/beu645A4eKHhgxPtUtPbx6d/vod/u4m/3rGZe2uQLfToa6vjrNx8gKiGR2/7rB6OmFvqobg8dfyljsKyTqMuziL4iK7R11P1/gpe/BGu+DFd+169L3G4PJ3c3s//1GnraBoiKj2D++jTmrUnDFOWdlVHW1MuW3+7CbNDx539eyayLPBP3h+p246itHZmtnzw5YpYSgC4paWhRkjegG3JzMObmemcqyQKlsJAAfobORiv7Xv2AI9sew6RLZm36J4lTtJg2ZBB/Tc64AUpVVW584UaSI5P5/dW/92bS3d1DizfqcdT5/qzFUVWNq3lkH2/djBkYc3O8Czdycob/J9CnzTg9a6DsZXjmTshdD7c/M6Jl7L3b7uV453He2vzWcA/y98vb+NJTpWg1Gv5890q/grdP3dFDPPejB4lNSWXzf34Pc2zcmOepbg9dz1Vg29+CaXEScR/PQzNOeSYor3wF9v0ervo+FN3r92Uet4eqg+0cfq+ehhPdaHQKs5YkMXtlKpnz4ilr6eMzf9iD0+3hN3cspyjv4qmJh5LHbsdZW+tNQqprcFRVDX+c+QxmeIFSTg6GzAz06RnoM70LoPQZGVKSmYKLOoCrqkpno5Xqw+2c3NNCa9UuXLZt5MQuZ0Xy5WhRiPt4PpFLk0dd5+7qwtXSgrOpmVPHi3l755Os18wlqduDs74ej9U64hptXJx3+XSuNysZDtTZ2ZP/Az70d3jhXyBtKdzxLESMfLj0etXrfG3H1/jV5b9iVeoaHn23gkfeKSc/2cJv/6lguE1sIGqPHOT5H3+XiKgobv7qf5IyM2/cv8O+d+vofbsGbXwE8bfOxpgToodfbic8+89w7EW47D9h3VfHnJkykc5GK0d2NHBybzN2qwujWUfesmSiZkXzHx+cpLzDyr9emseXLs/H4McCIeEfV1fX6d8sq6pwVFd5P69vQB0YXxM44wAAD1FJREFUGHGuNjERQ3r6UN8Yb4sBfVoautRU9CkpaMxSmhnPRRfAbb0Omk51U3usk9ojHfR32VE9VrTKDgy2DlZl3ECsmog2TkPEbBtqfwuulmaczS24mptxtrTgamk5XRMcYteDJXsWxozM0/8Ih/5B6tMzgqsPOgdg23dh168gew1seXrMfidOt5Nrn7sWszaJ3qp7qGqzsWlZBg/dPJ9IQ/AZcUvVKV786fewdXdRuGkLK27chHacX4XtVT10/u0E7m47piVJxFyVgy5+nCYrgXA7vT+8Dj8D826C6x+GyPjAb+PyUFfWyck9LVQdbMPl8KDVa+iL1lJss6JJieCLN13CxrnJMqUujFRVxd3ZibOuDkd9g/c30/o6b3uB+nqcTU2n57MP0URFoU9NQZeSii4lGX1KKrpUb7MwXUoKuoQEtHFxKFr/++VcKC7oAD5oddBR1Ul7ZSfNVT201A/S1+f9vnQaNwnaJtx9ewEtedHLSInMRXVYsZe9gLP6fVC99T1Frx/OBnSpqd5/TMkp6FJTKFVr+eaph/nc+q/ymQV3hmbgHg8cfwXe/hZ0VcPKz8PV3wft6EUrg04328pa+enOP9Ie8RSRPXfwo6vvYuOciTdG9pett4dtf/gNJ3e+T2zqDAo//knmrtkwZiD3ONz0ba+jb0c9uFVMCxOxrE7DkB2NMpUFNKoKxf/j/WEWEQOXfQuW3A464+TXjsHlcNNQ3k3NkQ5qDrfT2+6dGz+IijVKS+7ceAqXpZKSFUVUfIQE9HPI2zumGWdDI67WFpzNzbiaW3C2NHubhDU342pvH91KWFG87X0T4tHGJ3iDekLC0Nfx6BIT0cbGoY2JRhsdjSYmBs0FsFo1LAFcUZRr+P/tnXuMXNV9xz+/e+femZ31zOyLXRs7NrZry7FRAoRSbEXkASnENBDUSAXlAYE8SCBSFakCZCmKIkV5VFXS0khV5ebRFBEaUEvaAqnBRE0D5mHAwSaxd712cPzaZWzPzrLe2Z2ZX/64Z3avxzO7s7sz4x3lfFZX99zzuPe7v3vu7557zp174O8BF9iuqt+cKf98HfjIU0+R/c0A2VMTjGYLjI4JYxMeWW1n1O1i3J8egPMmRujIDNKe7ceVM0S6+oh1refi+Dp8N0axeJZI2wm83hxeXyduV3fgqJcuDe7uFS7i3Sd3c88z97AysZKHbnwIz1ngrxPTB4PugtcegvQA9KyHG/8u6PcOMTQyzvODaX7Z/xY/33eC7HieFV1REpdsZ3higAc/+CCbL968MC1lDL7yEv//yI8ZPjxIPNXBhi3XsPbKq7l4/QYiZRdCPpNj9FfHePuF42iugJv0iW3qJrqmg+jqJO6SeV44J/cF/eJHdkFiGbz7Nth0SzCJxTydrKoy8tZZjhw4w3MvHOWtQ1lSoalAxXfoXNZO7/IlJLtjJHvaptZtSb/lf9nZiujkJPnh4amn4Xw6TSF9ivypNIW0+YZ8Ok0+naY4Olp1P9LWFnzrPZnETaVwUqmpbSeVxG1vR+LxqbUTjwefMJ5at+PE2y7or1vr7sBFxAUOAB8Cfg+8BNymqm9UKzNfB/7Tux9iCDOfpCouBXwdp90dIxEZo90rEPXBc1yk4OIV4rRJkpgbvD5WcAp46xJ0bl5N7E86pj4JWw1VJZPLsP/0fp489CSPDzzO8sRytv/59qpzYIYKw8TbkMvCxCiMnoQzb8KZI3ByL3p0NzISTEGQW3ol6Uvv5HDftQy/XeDkyDjHM+MMDI2y/0SWoWzwLYxUm8e1G3q55YrlbFnbQ2biNHc+dSeDmUFuWnsTW1dvZWP3RlLR+kwyoKocevVl9v5iB4O7X6SQz+N6Hr2r19KzYiVdy99BoruHeKqD9o5Ool4bhcNnmXgjQ27gDDoZPNG4SZ9IXxzvojhuysdJRnETHk7cw4m6SNRFfBfxnPN1q8Lgs/D89+Dgs6AFiHfDij+Fvkuh8xLoXAXtFwWt9VgHeG01O/hiUdm57wQ7dx1lcOA0S84qPQWhG4d44fx9ePEIbQmPeNInnvRpT/j4bRH8WAQ/5uKZtR+LEPFdXE9wIw5uxMFxnbJtsa39OlPM5YIJQdKnKJw+RSEzQmEkQ3FkJAhnMsF2ZoTCiFkyGbTCt96rMeXcYzEkGkWiPo4fRWKx6XA0isSi0+GoH+T3o6Ru+SiRzsovCsx67AY48M3AV1X1erP9AICqfqNamfk68F9++V9YEVmFK96MM8ADjBfHOKXDHHdPMLjkCK90/pYj0WOoBP+noqiICQml/15DS14gZ1pcnip/kZ3gs6fPkigGOUqXnpTtwUGJkcOlsk2PaC+vFteyu7ieHYX3cIzz34po81zW9S1hfV+CDUsTXL2mm3cuS+KWtQDHJsd48NUHeaz/Mc7mg8Eiz/FIRVNEnAiuuEScCMLCnIU7oaSGofMkLDmtxDPgzzQNpeOQii/lIn8FHV4PKa+HlNuF51TvBilqkSJFlCJFNWuKqOpUfIAGJyhk35lqr1ZNraG8goTONFMhCRWqlxOe4zVofX/9aUJPcnpThmvu+tS8ylZz4At5F2w5cCS0/XsqfEdTRD4HfA5g5cqV8zrQifY3GRlPM2n+8s4kk0ww6UwwHsmR9bNkoyNkYlny3mTpuAAkFDYWOqYvRZm+KKV0kUrpmgguWReHzqJHXzHKukKCGC5HOwN3Hbh+CbX0pgqjOEw6MXKRJeScdibcOGNeB2f8ZYz4S1HXJ+67LPUjfN53afNd4r5LV9ynNxmjNxklEY3U5HDjXpz7rrqPey+/lz1De+g/0096PE12Iku+mKdQLJAv5ilSnHVfs6G9CptgjGCR8QLu23mcs3ncsQIyUUAmFckXkbwymh/jWHE/sH/qrugVI8S1jXixnSgxIhrBm/rzcXAQHByR4KaDY+KC0DQl2xTNvs+9qU4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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "id": "oJZHf5Xu_fg3"
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the plot, we can see that the probability distribution of the bell curves of H, D and VW have the minimum spread. Hence for the M data dataset, H, D and VW will be the optimum features for classification."
      ],
      "metadata": {
        "id": "rEYI7aVnVt60"
      },
      "id": "rEYI7aVnVt60"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "54fc2470",
      "metadata": {
        "id": "54fc2470",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "fd86c50d-d133-4b6a-bb2b-b2670e1ad005"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           L         D         H        WW        SW        VW        LW\n",
              "L   1.000000  0.983610  0.746355  0.934598  0.911976  0.909623  0.924398\n",
              "D   0.983610  1.000000  0.750696  0.932879  0.907485  0.904899  0.928663\n",
              "H   0.746355  0.750696  1.000000  0.757821  0.733973  0.741299  0.755670\n",
              "WW  0.934598  0.932879  0.757821  1.000000  0.981556  0.969876  0.965296\n",
              "SW  0.911976  0.907485  0.733973  0.981556  1.000000  0.938065  0.920389\n",
              "VW  0.909623  0.904899  0.741299  0.969876  0.938065  1.000000  0.933944\n",
              "LW  0.924398  0.928663  0.755670  0.965296  0.920389  0.933944  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dd7eb72f-61a5-45ef-ac53-898c2439de29\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>L</th>\n",
              "      <th>D</th>\n",
              "      <th>H</th>\n",
              "      <th>WW</th>\n",
              "      <th>SW</th>\n",
              "      <th>VW</th>\n",
              "      <th>LW</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>L</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.983610</td>\n",
              "      <td>0.746355</td>\n",
              "      <td>0.934598</td>\n",
              "      <td>0.911976</td>\n",
              "      <td>0.909623</td>\n",
              "      <td>0.924398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.983610</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.750696</td>\n",
              "      <td>0.932879</td>\n",
              "      <td>0.907485</td>\n",
              "      <td>0.904899</td>\n",
              "      <td>0.928663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>H</th>\n",
              "      <td>0.746355</td>\n",
              "      <td>0.750696</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.757821</td>\n",
              "      <td>0.733973</td>\n",
              "      <td>0.741299</td>\n",
              "      <td>0.755670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WW</th>\n",
              "      <td>0.934598</td>\n",
              "      <td>0.932879</td>\n",
              "      <td>0.757821</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.981556</td>\n",
              "      <td>0.969876</td>\n",
              "      <td>0.965296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SW</th>\n",
              "      <td>0.911976</td>\n",
              "      <td>0.907485</td>\n",
              "      <td>0.733973</td>\n",
              "      <td>0.981556</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.938065</td>\n",
              "      <td>0.920389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VW</th>\n",
              "      <td>0.909623</td>\n",
              "      <td>0.904899</td>\n",
              "      <td>0.741299</td>\n",
              "      <td>0.969876</td>\n",
              "      <td>0.938065</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.933944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LW</th>\n",
              "      <td>0.924398</td>\n",
              "      <td>0.928663</td>\n",
              "      <td>0.755670</td>\n",
              "      <td>0.965296</td>\n",
              "      <td>0.920389</td>\n",
              "      <td>0.933944</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dd7eb72f-61a5-45ef-ac53-898c2439de29')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "            L         D         H        WW        SW        VW       LW\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6330dbff-a179-4ff2-845d-c5fc215f8cbf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6330dbff-a179-4ff2-845d-c5fc215f8cbf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "data_L = copy.deepcopy(data.loc[(data.R <= 15) & (data.R >= 11)])                                                   # & is a bitwise AND operator, whereas 'and' is a logical operator \n",
        "data_L"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "b46ff45f",
      "metadata": {
        "id": "b46ff45f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "95a444ca-52ad-42c5-f818-8d4d080a27eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        mean       std\n",
              "L   0.589545  0.085464\n",
              "D   0.463786  0.069827\n",
              "H   0.161454  0.029282\n",
              "WW  1.106674  0.463021\n",
              "SW  0.468113  0.226401\n",
              "VW  0.242624  0.105617\n",
              "LW  0.321548  0.121588"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2e8913d8-efd6-435a-a9e7-c0ee1680b28a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>L</th>\n",
              "      <td>0.589545</td>\n",
              "      <td>0.085464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.463786</td>\n",
              "      <td>0.069827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>H</th>\n",
              "      <td>0.161454</td>\n",
              "      <td>0.029282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WW</th>\n",
              "      <td>1.106674</td>\n",
              "      <td>0.463021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SW</th>\n",
              "      <td>0.468113</td>\n",
              "      <td>0.226401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VW</th>\n",
              "      <td>0.242624</td>\n",
              "      <td>0.105617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LW</th>\n",
              "      <td>0.321548</td>\n",
              "      <td>0.121588</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2e8913d8-efd6-435a-a9e7-c0ee1680b28a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2e8913d8-efd6-435a-a9e7-c0ee1680b28a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2e8913d8-efd6-435a-a9e7-c0ee1680b28a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "tmp = data_L[col].describe().loc[[\"mean\",\"std\"]]\n",
        "tmp.transpose()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.arange(-0.01, 2.2, 0.001)\n",
        "\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'L'), tmp._get_value(\"std\", 'L')), label='L')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'D'), tmp._get_value(\"std\", 'D')), label='D')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'H'), tmp._get_value(\"std\", 'H')), label='H')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'WW'), tmp._get_value(\"std\", 'WW')), label='WW')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'SW'), tmp._get_value(\"std\", 'SW')), label='SW')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'VW'), tmp._get_value(\"std\", 'VW')), label='VW')\n",
        "plt.plot(x, norm.pdf(x, tmp._get_value(\"mean\", 'LW'), tmp._get_value(\"std\", 'LW')), label='LW')\n",
        "\n",
        "\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "e83361f9-b6a2-43fb-8748-a6b71bdac6d3",
        "id": "kgJPvOMY_nGK"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f6adda14f90>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "id": "kgJPvOMY_nGK"
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the plot, we can see that the probability distribution of the bell curves of H, D and L have the minimum spread. Hence for the L data dataset, H, D and L will be the optimum features for classification."
      ],
      "metadata": {
        "id": "xj3LLzG4WCD2"
      },
      "id": "xj3LLzG4WCD2"
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "1cb7193a",
      "metadata": {
        "id": "1cb7193a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "bd8afaa2-ac20-4671-a21b-43daa970c273"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           L         D         H        WW        SW        VW        LW\n",
              "L   1.000000  0.971832  0.784630  0.935477  0.901784  0.896240  0.888882\n",
              "D   0.971832  1.000000  0.797463  0.928154  0.887991  0.881108  0.898311\n",
              "H   0.784630  0.797463  1.000000  0.806313  0.745546  0.773979  0.811868\n",
              "WW  0.935477  0.928154  0.806313  1.000000  0.961206  0.941564  0.927172\n",
              "SW  0.901784  0.887991  0.745546  0.961206  1.000000  0.895374  0.842154\n",
              "VW  0.896240  0.881108  0.773979  0.941564  0.895374  1.000000  0.845434\n",
              "LW  0.888882  0.898311  0.811868  0.927172  0.842154  0.845434  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-26365173-b460-4e2c-a9da-8e767f5c6827\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>L</th>\n",
              "      <th>D</th>\n",
              "      <th>H</th>\n",
              "      <th>WW</th>\n",
              "      <th>SW</th>\n",
              "      <th>VW</th>\n",
              "      <th>LW</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>L</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.971832</td>\n",
              "      <td>0.784630</td>\n",
              "      <td>0.935477</td>\n",
              "      <td>0.901784</td>\n",
              "      <td>0.896240</td>\n",
              "      <td>0.888882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.971832</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.797463</td>\n",
              "      <td>0.928154</td>\n",
              "      <td>0.887991</td>\n",
              "      <td>0.881108</td>\n",
              "      <td>0.898311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>H</th>\n",
              "      <td>0.784630</td>\n",
              "      <td>0.797463</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.806313</td>\n",
              "      <td>0.745546</td>\n",
              "      <td>0.773979</td>\n",
              "      <td>0.811868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WW</th>\n",
              "      <td>0.935477</td>\n",
              "      <td>0.928154</td>\n",
              "      <td>0.806313</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.961206</td>\n",
              "      <td>0.941564</td>\n",
              "      <td>0.927172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SW</th>\n",
              "      <td>0.901784</td>\n",
              "      <td>0.887991</td>\n",
              "      <td>0.745546</td>\n",
              "      <td>0.961206</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.895374</td>\n",
              "      <td>0.842154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VW</th>\n",
              "      <td>0.896240</td>\n",
              "      <td>0.881108</td>\n",
              "      <td>0.773979</td>\n",
              "      <td>0.941564</td>\n",
              "      <td>0.895374</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.845434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LW</th>\n",
              "      <td>0.888882</td>\n",
              "      <td>0.898311</td>\n",
              "      <td>0.811868</td>\n",
              "      <td>0.927172</td>\n",
              "      <td>0.842154</td>\n",
              "      <td>0.845434</td>\n",
              "      <td>1.000000</td>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "data_L[col].corr()"
      ]
    },
    {
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      "id": "a0cabc1a",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           L         D         H        WW        SW        VW        LW\n",
              "min  0.78463  0.797463  0.745546  0.806313  0.745546  0.773979  0.811868"
            ],
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              "      <th>L</th>\n",
              "      <th>D</th>\n",
              "      <th>H</th>\n",
              "      <th>WW</th>\n",
              "      <th>SW</th>\n",
              "      <th>VW</th>\n",
              "      <th>LW</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.78463</td>\n",
              "      <td>0.797463</td>\n",
              "      <td>0.745546</td>\n",
              "      <td>0.806313</td>\n",
              "      <td>0.745546</td>\n",
              "      <td>0.773979</td>\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3944413e-74d2-478b-a090-8735a1f8926c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3944413e-74d2-478b-a090-8735a1f8926c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "data_L[col].corr().describe().loc[[\"min\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "7acea479",
      "metadata": {
        "id": "7acea479"
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      "outputs": [],
      "source": [
        ""
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