epileptic_seizures.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "id": "O8h8mqhOSMTT"
      },
      "outputs": [],
      "source": [
        "# นําเข้า Library ที่สําคัญสําหรับการใช้งานในโปรเจกต์ของเรา\n",
        "\n",
        "import imblearn\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import normalize\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.utils import shuffle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "KlCWWXzAXDkz"
      },
      "outputs": [],
      "source": [
        "# อ่านไฟล์ CSV และนําเข้าตัวแปร epileptic_seizure\n",
        "\n",
        "epileptic_seizure = pd.read_csv('/content/epileptic_seizure_recognition.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "L7ApaBHDee4N",
        "outputId": "bd5e1ecc-83e6-449a-ffba-cfa54a1870b4"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>Unnamed</th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
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              "      <th>X4</th>\n",
              "      <th>X5</th>\n",
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              "      <th>X27</th>\n",
              "      <th>X28</th>\n",
              "      <th>X29</th>\n",
              "      <th>X30</th>\n",
              "      <th>X31</th>\n",
              "      <th>X32</th>\n",
              "      <th>X33</th>\n",
              "      <th>X34</th>\n",
              "      <th>X35</th>\n",
              "      <th>X36</th>\n",
              "      <th>X37</th>\n",
              "      <th>X38</th>\n",
              "      <th>X39</th>\n",
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              "      <th>X140</th>\n",
              "      <th>X141</th>\n",
              "      <th>X142</th>\n",
              "      <th>X143</th>\n",
              "      <th>X144</th>\n",
              "      <th>X145</th>\n",
              "      <th>X146</th>\n",
              "      <th>X147</th>\n",
              "      <th>X148</th>\n",
              "      <th>X149</th>\n",
              "      <th>X150</th>\n",
              "      <th>X151</th>\n",
              "      <th>X152</th>\n",
              "      <th>X153</th>\n",
              "      <th>X154</th>\n",
              "      <th>X155</th>\n",
              "      <th>X156</th>\n",
              "      <th>X157</th>\n",
              "      <th>X158</th>\n",
              "      <th>X159</th>\n",
              "      <th>X160</th>\n",
              "      <th>X161</th>\n",
              "      <th>X162</th>\n",
              "      <th>X163</th>\n",
              "      <th>X164</th>\n",
              "      <th>X165</th>\n",
              "      <th>X166</th>\n",
              "      <th>X167</th>\n",
              "      <th>X168</th>\n",
              "      <th>X169</th>\n",
              "      <th>X170</th>\n",
              "      <th>X171</th>\n",
              "      <th>X172</th>\n",
              "      <th>X173</th>\n",
              "      <th>X174</th>\n",
              "      <th>X175</th>\n",
              "      <th>X176</th>\n",
              "      <th>X177</th>\n",
              "      <th>X178</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>X21.V1.791</td>\n",
              "      <td>135</td>\n",
              "      <td>190</td>\n",
              "      <td>229</td>\n",
              "      <td>223</td>\n",
              "      <td>192</td>\n",
              "      <td>125</td>\n",
              "      <td>55</td>\n",
              "      <td>-9</td>\n",
              "      <td>-33</td>\n",
              "      <td>-38</td>\n",
              "      <td>-10</td>\n",
              "      <td>35</td>\n",
              "      <td>64</td>\n",
              "      <td>113</td>\n",
              "      <td>152</td>\n",
              "      <td>164</td>\n",
              "      <td>127</td>\n",
              "      <td>50</td>\n",
              "      <td>-47</td>\n",
              "      <td>-121</td>\n",
              "      <td>-138</td>\n",
              "      <td>-125</td>\n",
              "      <td>-101</td>\n",
              "      <td>-50</td>\n",
              "      <td>11</td>\n",
              "      <td>39</td>\n",
              "      <td>24</td>\n",
              "      <td>48</td>\n",
              "      <td>64</td>\n",
              "      <td>46</td>\n",
              "      <td>13</td>\n",
              "      <td>-19</td>\n",
              "      <td>-61</td>\n",
              "      <td>-96</td>\n",
              "      <td>-130</td>\n",
              "      <td>-132</td>\n",
              "      <td>-116</td>\n",
              "      <td>-115</td>\n",
              "      <td>-71</td>\n",
              "      <td>...</td>\n",
              "      <td>54</td>\n",
              "      <td>90</td>\n",
              "      <td>111</td>\n",
              "      <td>107</td>\n",
              "      <td>64</td>\n",
              "      <td>32</td>\n",
              "      <td>18</td>\n",
              "      <td>-25</td>\n",
              "      <td>-69</td>\n",
              "      <td>-65</td>\n",
              "      <td>-44</td>\n",
              "      <td>-33</td>\n",
              "      <td>-57</td>\n",
              "      <td>-88</td>\n",
              "      <td>-114</td>\n",
              "      <td>-130</td>\n",
              "      <td>-114</td>\n",
              "      <td>-83</td>\n",
              "      <td>-53</td>\n",
              "      <td>-79</td>\n",
              "      <td>-72</td>\n",
              "      <td>-85</td>\n",
              "      <td>-109</td>\n",
              "      <td>-98</td>\n",
              "      <td>-72</td>\n",
              "      <td>-65</td>\n",
              "      <td>-63</td>\n",
              "      <td>-11</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>-17</td>\n",
              "      <td>-15</td>\n",
              "      <td>-31</td>\n",
              "      <td>-77</td>\n",
              "      <td>-103</td>\n",
              "      <td>-127</td>\n",
              "      <td>-116</td>\n",
              "      <td>-83</td>\n",
              "      <td>-51</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>X15.V1.924</td>\n",
              "      <td>386</td>\n",
              "      <td>382</td>\n",
              "      <td>356</td>\n",
              "      <td>331</td>\n",
              "      <td>320</td>\n",
              "      <td>315</td>\n",
              "      <td>307</td>\n",
              "      <td>272</td>\n",
              "      <td>244</td>\n",
              "      <td>232</td>\n",
              "      <td>237</td>\n",
              "      <td>258</td>\n",
              "      <td>212</td>\n",
              "      <td>2</td>\n",
              "      <td>-267</td>\n",
              "      <td>-605</td>\n",
              "      <td>-850</td>\n",
              "      <td>-1001</td>\n",
              "      <td>-1109</td>\n",
              "      <td>-1090</td>\n",
              "      <td>-967</td>\n",
              "      <td>-746</td>\n",
              "      <td>-464</td>\n",
              "      <td>-152</td>\n",
              "      <td>118</td>\n",
              "      <td>318</td>\n",
              "      <td>427</td>\n",
              "      <td>473</td>\n",
              "      <td>485</td>\n",
              "      <td>447</td>\n",
              "      <td>397</td>\n",
              "      <td>339</td>\n",
              "      <td>312</td>\n",
              "      <td>314</td>\n",
              "      <td>326</td>\n",
              "      <td>335</td>\n",
              "      <td>332</td>\n",
              "      <td>324</td>\n",
              "      <td>310</td>\n",
              "      <td>...</td>\n",
              "      <td>27</td>\n",
              "      <td>146</td>\n",
              "      <td>229</td>\n",
              "      <td>269</td>\n",
              "      <td>297</td>\n",
              "      <td>307</td>\n",
              "      <td>303</td>\n",
              "      <td>305</td>\n",
              "      <td>306</td>\n",
              "      <td>307</td>\n",
              "      <td>280</td>\n",
              "      <td>231</td>\n",
              "      <td>159</td>\n",
              "      <td>85</td>\n",
              "      <td>51</td>\n",
              "      <td>43</td>\n",
              "      <td>62</td>\n",
              "      <td>63</td>\n",
              "      <td>63</td>\n",
              "      <td>69</td>\n",
              "      <td>89</td>\n",
              "      <td>123</td>\n",
              "      <td>136</td>\n",
              "      <td>127</td>\n",
              "      <td>102</td>\n",
              "      <td>95</td>\n",
              "      <td>105</td>\n",
              "      <td>131</td>\n",
              "      <td>163</td>\n",
              "      <td>168</td>\n",
              "      <td>164</td>\n",
              "      <td>150</td>\n",
              "      <td>146</td>\n",
              "      <td>152</td>\n",
              "      <td>157</td>\n",
              "      <td>156</td>\n",
              "      <td>154</td>\n",
              "      <td>143</td>\n",
              "      <td>129</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>X8.V1.1</td>\n",
              "      <td>-32</td>\n",
              "      <td>-39</td>\n",
              "      <td>-47</td>\n",
              "      <td>-37</td>\n",
              "      <td>-32</td>\n",
              "      <td>-36</td>\n",
              "      <td>-57</td>\n",
              "      <td>-73</td>\n",
              "      <td>-85</td>\n",
              "      <td>-94</td>\n",
              "      <td>-99</td>\n",
              "      <td>-94</td>\n",
              "      <td>-96</td>\n",
              "      <td>-104</td>\n",
              "      <td>-103</td>\n",
              "      <td>-92</td>\n",
              "      <td>-75</td>\n",
              "      <td>-69</td>\n",
              "      <td>-69</td>\n",
              "      <td>-53</td>\n",
              "      <td>-37</td>\n",
              "      <td>-14</td>\n",
              "      <td>-10</td>\n",
              "      <td>-39</td>\n",
              "      <td>-78</td>\n",
              "      <td>-102</td>\n",
              "      <td>-98</td>\n",
              "      <td>-80</td>\n",
              "      <td>-54</td>\n",
              "      <td>-40</td>\n",
              "      <td>-35</td>\n",
              "      <td>-39</td>\n",
              "      <td>-32</td>\n",
              "      <td>-13</td>\n",
              "      <td>7</td>\n",
              "      <td>34</td>\n",
              "      <td>41</td>\n",
              "      <td>33</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>-82</td>\n",
              "      <td>-107</td>\n",
              "      <td>-126</td>\n",
              "      <td>-124</td>\n",
              "      <td>-108</td>\n",
              "      <td>-84</td>\n",
              "      <td>-68</td>\n",
              "      <td>-61</td>\n",
              "      <td>-56</td>\n",
              "      <td>-63</td>\n",
              "      <td>-62</td>\n",
              "      <td>-33</td>\n",
              "      <td>1</td>\n",
              "      <td>28</td>\n",
              "      <td>45</td>\n",
              "      <td>37</td>\n",
              "      <td>48</td>\n",
              "      <td>62</td>\n",
              "      <td>80</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>-11</td>\n",
              "      <td>-39</td>\n",
              "      <td>-44</td>\n",
              "      <td>-42</td>\n",
              "      <td>-45</td>\n",
              "      <td>-48</td>\n",
              "      <td>-42</td>\n",
              "      <td>-6</td>\n",
              "      <td>29</td>\n",
              "      <td>57</td>\n",
              "      <td>64</td>\n",
              "      <td>48</td>\n",
              "      <td>19</td>\n",
              "      <td>-12</td>\n",
              "      <td>-30</td>\n",
              "      <td>-35</td>\n",
              "      <td>-35</td>\n",
              "      <td>-36</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>X16.V1.60</td>\n",
              "      <td>-105</td>\n",
              "      <td>-101</td>\n",
              "      <td>-96</td>\n",
              "      <td>-92</td>\n",
              "      <td>-89</td>\n",
              "      <td>-95</td>\n",
              "      <td>-102</td>\n",
              "      <td>-100</td>\n",
              "      <td>-87</td>\n",
              "      <td>-79</td>\n",
              "      <td>-72</td>\n",
              "      <td>-68</td>\n",
              "      <td>-74</td>\n",
              "      <td>-80</td>\n",
              "      <td>-83</td>\n",
              "      <td>-73</td>\n",
              "      <td>-68</td>\n",
              "      <td>-61</td>\n",
              "      <td>-58</td>\n",
              "      <td>-59</td>\n",
              "      <td>-64</td>\n",
              "      <td>-79</td>\n",
              "      <td>-84</td>\n",
              "      <td>-97</td>\n",
              "      <td>-94</td>\n",
              "      <td>-84</td>\n",
              "      <td>-77</td>\n",
              "      <td>-75</td>\n",
              "      <td>-72</td>\n",
              "      <td>-68</td>\n",
              "      <td>-76</td>\n",
              "      <td>-76</td>\n",
              "      <td>-72</td>\n",
              "      <td>-67</td>\n",
              "      <td>-69</td>\n",
              "      <td>-69</td>\n",
              "      <td>-69</td>\n",
              "      <td>-67</td>\n",
              "      <td>-68</td>\n",
              "      <td>...</td>\n",
              "      <td>-69</td>\n",
              "      <td>-66</td>\n",
              "      <td>-74</td>\n",
              "      <td>-69</td>\n",
              "      <td>-61</td>\n",
              "      <td>-51</td>\n",
              "      <td>-45</td>\n",
              "      <td>-45</td>\n",
              "      <td>-49</td>\n",
              "      <td>-58</td>\n",
              "      <td>-64</td>\n",
              "      <td>-78</td>\n",
              "      <td>-80</td>\n",
              "      <td>-90</td>\n",
              "      <td>-87</td>\n",
              "      <td>-83</td>\n",
              "      <td>-78</td>\n",
              "      <td>-64</td>\n",
              "      <td>-38</td>\n",
              "      <td>-22</td>\n",
              "      <td>-29</td>\n",
              "      <td>-42</td>\n",
              "      <td>-51</td>\n",
              "      <td>-68</td>\n",
              "      <td>-71</td>\n",
              "      <td>-69</td>\n",
              "      <td>-69</td>\n",
              "      <td>-74</td>\n",
              "      <td>-74</td>\n",
              "      <td>-80</td>\n",
              "      <td>-82</td>\n",
              "      <td>-81</td>\n",
              "      <td>-80</td>\n",
              "      <td>-77</td>\n",
              "      <td>-85</td>\n",
              "      <td>-77</td>\n",
              "      <td>-72</td>\n",
              "      <td>-69</td>\n",
              "      <td>-65</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>X20.V1.54</td>\n",
              "      <td>-9</td>\n",
              "      <td>-65</td>\n",
              "      <td>-98</td>\n",
              "      <td>-102</td>\n",
              "      <td>-78</td>\n",
              "      <td>-48</td>\n",
              "      <td>-16</td>\n",
              "      <td>0</td>\n",
              "      <td>-21</td>\n",
              "      <td>-59</td>\n",
              "      <td>-90</td>\n",
              "      <td>-103</td>\n",
              "      <td>-84</td>\n",
              "      <td>-43</td>\n",
              "      <td>-9</td>\n",
              "      <td>3</td>\n",
              "      <td>-21</td>\n",
              "      <td>-60</td>\n",
              "      <td>-96</td>\n",
              "      <td>-103</td>\n",
              "      <td>-75</td>\n",
              "      <td>-29</td>\n",
              "      <td>14</td>\n",
              "      <td>55</td>\n",
              "      <td>78</td>\n",
              "      <td>73</td>\n",
              "      <td>28</td>\n",
              "      <td>-13</td>\n",
              "      <td>-43</td>\n",
              "      <td>-68</td>\n",
              "      <td>-78</td>\n",
              "      <td>-75</td>\n",
              "      <td>-55</td>\n",
              "      <td>-41</td>\n",
              "      <td>-19</td>\n",
              "      <td>-20</td>\n",
              "      <td>-29</td>\n",
              "      <td>-36</td>\n",
              "      <td>-20</td>\n",
              "      <td>...</td>\n",
              "      <td>38</td>\n",
              "      <td>26</td>\n",
              "      <td>10</td>\n",
              "      <td>-4</td>\n",
              "      <td>-13</td>\n",
              "      <td>-8</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>19</td>\n",
              "      <td>29</td>\n",
              "      <td>57</td>\n",
              "      <td>63</td>\n",
              "      <td>45</td>\n",
              "      <td>7</td>\n",
              "      <td>-13</td>\n",
              "      <td>-23</td>\n",
              "      <td>-9</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>-1</td>\n",
              "      <td>-2</td>\n",
              "      <td>4</td>\n",
              "      <td>18</td>\n",
              "      <td>27</td>\n",
              "      <td>27</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>-12</td>\n",
              "      <td>-32</td>\n",
              "      <td>-41</td>\n",
              "      <td>-65</td>\n",
              "      <td>-83</td>\n",
              "      <td>-89</td>\n",
              "      <td>-73</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 180 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Unnamed   X1   X2   X3   X4   X5  ...  X174  X175  X176  X177  X178  y\n",
              "0  X21.V1.791  135  190  229  223  192  ...  -103  -127  -116   -83   -51  4\n",
              "1  X15.V1.924  386  382  356  331  320  ...   157   156   154   143   129  1\n",
              "2     X8.V1.1  -32  -39  -47  -37  -32  ...   -12   -30   -35   -35   -36  5\n",
              "3   X16.V1.60 -105 -101  -96  -92  -89  ...   -85   -77   -72   -69   -65  5\n",
              "4   X20.V1.54   -9  -65  -98 -102  -78  ...   -41   -65   -83   -89   -73  5\n",
              "\n",
              "[5 rows x 180 columns]"
            ]
          },
          "execution_count": 95,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# โชว์ Data เฉพาะส่วนหัวจํานวน 5 Rows\n",
        "\n",
        "epileptic_seizure.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "3JFPG_yvefYk",
        "outputId": "eaeafd8f-21bd-4457-8f0c-4aab5fb2485e"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Unnamed</th>\n",
              "      <th>X1</th>\n",
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              "      <th>X29</th>\n",
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              "      <th>X31</th>\n",
              "      <th>X32</th>\n",
              "      <th>X33</th>\n",
              "      <th>X34</th>\n",
              "      <th>X35</th>\n",
              "      <th>X36</th>\n",
              "      <th>X37</th>\n",
              "      <th>X38</th>\n",
              "      <th>X39</th>\n",
              "      <th>...</th>\n",
              "      <th>X140</th>\n",
              "      <th>X141</th>\n",
              "      <th>X142</th>\n",
              "      <th>X143</th>\n",
              "      <th>X144</th>\n",
              "      <th>X145</th>\n",
              "      <th>X146</th>\n",
              "      <th>X147</th>\n",
              "      <th>X148</th>\n",
              "      <th>X149</th>\n",
              "      <th>X150</th>\n",
              "      <th>X151</th>\n",
              "      <th>X152</th>\n",
              "      <th>X153</th>\n",
              "      <th>X154</th>\n",
              "      <th>X155</th>\n",
              "      <th>X156</th>\n",
              "      <th>X157</th>\n",
              "      <th>X158</th>\n",
              "      <th>X159</th>\n",
              "      <th>X160</th>\n",
              "      <th>X161</th>\n",
              "      <th>X162</th>\n",
              "      <th>X163</th>\n",
              "      <th>X164</th>\n",
              "      <th>X165</th>\n",
              "      <th>X166</th>\n",
              "      <th>X167</th>\n",
              "      <th>X168</th>\n",
              "      <th>X169</th>\n",
              "      <th>X170</th>\n",
              "      <th>X171</th>\n",
              "      <th>X172</th>\n",
              "      <th>X173</th>\n",
              "      <th>X174</th>\n",
              "      <th>X175</th>\n",
              "      <th>X176</th>\n",
              "      <th>X177</th>\n",
              "      <th>X178</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11495</th>\n",
              "      <td>X22.V1.114</td>\n",
              "      <td>-22</td>\n",
              "      <td>-22</td>\n",
              "      <td>-23</td>\n",
              "      <td>-26</td>\n",
              "      <td>-36</td>\n",
              "      <td>-42</td>\n",
              "      <td>-45</td>\n",
              "      <td>-42</td>\n",
              "      <td>-45</td>\n",
              "      <td>-49</td>\n",
              "      <td>-57</td>\n",
              "      <td>-64</td>\n",
              "      <td>-73</td>\n",
              "      <td>-79</td>\n",
              "      <td>-76</td>\n",
              "      <td>-70</td>\n",
              "      <td>-63</td>\n",
              "      <td>-57</td>\n",
              "      <td>-57</td>\n",
              "      <td>-50</td>\n",
              "      <td>-45</td>\n",
              "      <td>-34</td>\n",
              "      <td>-33</td>\n",
              "      <td>-32</td>\n",
              "      <td>-30</td>\n",
              "      <td>-24</td>\n",
              "      <td>-24</td>\n",
              "      <td>-18</td>\n",
              "      <td>-9</td>\n",
              "      <td>-2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>-3</td>\n",
              "      <td>-5</td>\n",
              "      <td>-13</td>\n",
              "      <td>-23</td>\n",
              "      <td>-39</td>\n",
              "      <td>-53</td>\n",
              "      <td>-59</td>\n",
              "      <td>...</td>\n",
              "      <td>45</td>\n",
              "      <td>55</td>\n",
              "      <td>60</td>\n",
              "      <td>69</td>\n",
              "      <td>68</td>\n",
              "      <td>58</td>\n",
              "      <td>54</td>\n",
              "      <td>44</td>\n",
              "      <td>40</td>\n",
              "      <td>38</td>\n",
              "      <td>39</td>\n",
              "      <td>39</td>\n",
              "      <td>32</td>\n",
              "      <td>23</td>\n",
              "      <td>22</td>\n",
              "      <td>14</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>18</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>25</td>\n",
              "      <td>28</td>\n",
              "      <td>29</td>\n",
              "      <td>29</td>\n",
              "      <td>29</td>\n",
              "      <td>26</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>20</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>-1</td>\n",
              "      <td>-18</td>\n",
              "      <td>-37</td>\n",
              "      <td>-47</td>\n",
              "      <td>-48</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11496</th>\n",
              "      <td>X19.V1.354</td>\n",
              "      <td>-47</td>\n",
              "      <td>-11</td>\n",
              "      <td>28</td>\n",
              "      <td>77</td>\n",
              "      <td>141</td>\n",
              "      <td>211</td>\n",
              "      <td>246</td>\n",
              "      <td>240</td>\n",
              "      <td>193</td>\n",
              "      <td>136</td>\n",
              "      <td>78</td>\n",
              "      <td>8</td>\n",
              "      <td>-66</td>\n",
              "      <td>-132</td>\n",
              "      <td>-180</td>\n",
              "      <td>-210</td>\n",
              "      <td>-227</td>\n",
              "      <td>-225</td>\n",
              "      <td>-212</td>\n",
              "      <td>-192</td>\n",
              "      <td>-168</td>\n",
              "      <td>-144</td>\n",
              "      <td>-117</td>\n",
              "      <td>-88</td>\n",
              "      <td>-54</td>\n",
              "      <td>-21</td>\n",
              "      <td>6</td>\n",
              "      <td>29</td>\n",
              "      <td>46</td>\n",
              "      <td>60</td>\n",
              "      <td>76</td>\n",
              "      <td>92</td>\n",
              "      <td>110</td>\n",
              "      <td>128</td>\n",
              "      <td>152</td>\n",
              "      <td>171</td>\n",
              "      <td>150</td>\n",
              "      <td>91</td>\n",
              "      <td>21</td>\n",
              "      <td>...</td>\n",
              "      <td>69</td>\n",
              "      <td>58</td>\n",
              "      <td>38</td>\n",
              "      <td>16</td>\n",
              "      <td>25</td>\n",
              "      <td>75</td>\n",
              "      <td>165</td>\n",
              "      <td>260</td>\n",
              "      <td>343</td>\n",
              "      <td>406</td>\n",
              "      <td>456</td>\n",
              "      <td>471</td>\n",
              "      <td>461</td>\n",
              "      <td>412</td>\n",
              "      <td>319</td>\n",
              "      <td>175</td>\n",
              "      <td>-5</td>\n",
              "      <td>-171</td>\n",
              "      <td>-293</td>\n",
              "      <td>-357</td>\n",
              "      <td>-378</td>\n",
              "      <td>-370</td>\n",
              "      <td>-346</td>\n",
              "      <td>-316</td>\n",
              "      <td>-278</td>\n",
              "      <td>-241</td>\n",
              "      <td>-201</td>\n",
              "      <td>-162</td>\n",
              "      <td>-126</td>\n",
              "      <td>-94</td>\n",
              "      <td>-65</td>\n",
              "      <td>-33</td>\n",
              "      <td>-7</td>\n",
              "      <td>14</td>\n",
              "      <td>27</td>\n",
              "      <td>48</td>\n",
              "      <td>77</td>\n",
              "      <td>117</td>\n",
              "      <td>170</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11497</th>\n",
              "      <td>X8.V1.28</td>\n",
              "      <td>14</td>\n",
              "      <td>6</td>\n",
              "      <td>-13</td>\n",
              "      <td>-16</td>\n",
              "      <td>10</td>\n",
              "      <td>26</td>\n",
              "      <td>27</td>\n",
              "      <td>-9</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "      <td>-1</td>\n",
              "      <td>-10</td>\n",
              "      <td>14</td>\n",
              "      <td>44</td>\n",
              "      <td>77</td>\n",
              "      <td>61</td>\n",
              "      <td>42</td>\n",
              "      <td>32</td>\n",
              "      <td>29</td>\n",
              "      <td>22</td>\n",
              "      <td>26</td>\n",
              "      <td>35</td>\n",
              "      <td>81</td>\n",
              "      <td>66</td>\n",
              "      <td>50</td>\n",
              "      <td>57</td>\n",
              "      <td>91</td>\n",
              "      <td>121</td>\n",
              "      <td>111</td>\n",
              "      <td>73</td>\n",
              "      <td>38</td>\n",
              "      <td>23</td>\n",
              "      <td>35</td>\n",
              "      <td>21</td>\n",
              "      <td>11</td>\n",
              "      <td>-12</td>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "      <td>61</td>\n",
              "      <td>...</td>\n",
              "      <td>-90</td>\n",
              "      <td>-62</td>\n",
              "      <td>-38</td>\n",
              "      <td>-40</td>\n",
              "      <td>-21</td>\n",
              "      <td>-23</td>\n",
              "      <td>-11</td>\n",
              "      <td>-4</td>\n",
              "      <td>-9</td>\n",
              "      <td>-22</td>\n",
              "      <td>-42</td>\n",
              "      <td>-49</td>\n",
              "      <td>-48</td>\n",
              "      <td>-40</td>\n",
              "      <td>-40</td>\n",
              "      <td>-46</td>\n",
              "      <td>-43</td>\n",
              "      <td>-67</td>\n",
              "      <td>-67</td>\n",
              "      <td>-50</td>\n",
              "      <td>-18</td>\n",
              "      <td>-14</td>\n",
              "      <td>-39</td>\n",
              "      <td>-74</td>\n",
              "      <td>-86</td>\n",
              "      <td>-75</td>\n",
              "      <td>-68</td>\n",
              "      <td>-57</td>\n",
              "      <td>-78</td>\n",
              "      <td>-42</td>\n",
              "      <td>-65</td>\n",
              "      <td>-48</td>\n",
              "      <td>-61</td>\n",
              "      <td>-62</td>\n",
              "      <td>-67</td>\n",
              "      <td>-30</td>\n",
              "      <td>-2</td>\n",
              "      <td>-1</td>\n",
              "      <td>-8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11498</th>\n",
              "      <td>X10.V1.932</td>\n",
              "      <td>-40</td>\n",
              "      <td>-25</td>\n",
              "      <td>-9</td>\n",
              "      <td>-12</td>\n",
              "      <td>-2</td>\n",
              "      <td>12</td>\n",
              "      <td>7</td>\n",
              "      <td>19</td>\n",
              "      <td>22</td>\n",
              "      <td>29</td>\n",
              "      <td>22</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>-28</td>\n",
              "      <td>-37</td>\n",
              "      <td>-35</td>\n",
              "      <td>-35</td>\n",
              "      <td>-45</td>\n",
              "      <td>-64</td>\n",
              "      <td>-105</td>\n",
              "      <td>-140</td>\n",
              "      <td>-157</td>\n",
              "      <td>-157</td>\n",
              "      <td>-147</td>\n",
              "      <td>-153</td>\n",
              "      <td>-147</td>\n",
              "      <td>-126</td>\n",
              "      <td>-112</td>\n",
              "      <td>-83</td>\n",
              "      <td>-56</td>\n",
              "      <td>-41</td>\n",
              "      <td>-40</td>\n",
              "      <td>-38</td>\n",
              "      <td>-34</td>\n",
              "      <td>-47</td>\n",
              "      <td>-41</td>\n",
              "      <td>-40</td>\n",
              "      <td>-42</td>\n",
              "      <td>-49</td>\n",
              "      <td>...</td>\n",
              "      <td>-98</td>\n",
              "      <td>-77</td>\n",
              "      <td>-60</td>\n",
              "      <td>-73</td>\n",
              "      <td>-88</td>\n",
              "      <td>-97</td>\n",
              "      <td>-118</td>\n",
              "      <td>-108</td>\n",
              "      <td>-100</td>\n",
              "      <td>-97</td>\n",
              "      <td>-91</td>\n",
              "      <td>-109</td>\n",
              "      <td>-122</td>\n",
              "      <td>-134</td>\n",
              "      <td>-137</td>\n",
              "      <td>-107</td>\n",
              "      <td>-95</td>\n",
              "      <td>-67</td>\n",
              "      <td>-54</td>\n",
              "      <td>-40</td>\n",
              "      <td>-31</td>\n",
              "      <td>-19</td>\n",
              "      <td>7</td>\n",
              "      <td>22</td>\n",
              "      <td>46</td>\n",
              "      <td>68</td>\n",
              "      <td>87</td>\n",
              "      <td>97</td>\n",
              "      <td>105</td>\n",
              "      <td>114</td>\n",
              "      <td>121</td>\n",
              "      <td>135</td>\n",
              "      <td>148</td>\n",
              "      <td>143</td>\n",
              "      <td>116</td>\n",
              "      <td>86</td>\n",
              "      <td>68</td>\n",
              "      <td>59</td>\n",
              "      <td>55</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11499</th>\n",
              "      <td>X16.V1.210</td>\n",
              "      <td>29</td>\n",
              "      <td>41</td>\n",
              "      <td>57</td>\n",
              "      <td>72</td>\n",
              "      <td>74</td>\n",
              "      <td>62</td>\n",
              "      <td>54</td>\n",
              "      <td>43</td>\n",
              "      <td>31</td>\n",
              "      <td>23</td>\n",
              "      <td>13</td>\n",
              "      <td>11</td>\n",
              "      <td>-3</td>\n",
              "      <td>-5</td>\n",
              "      <td>-9</td>\n",
              "      <td>-14</td>\n",
              "      <td>1</td>\n",
              "      <td>27</td>\n",
              "      <td>60</td>\n",
              "      <td>69</td>\n",
              "      <td>69</td>\n",
              "      <td>50</td>\n",
              "      <td>33</td>\n",
              "      <td>20</td>\n",
              "      <td>15</td>\n",
              "      <td>4</td>\n",
              "      <td>-5</td>\n",
              "      <td>-4</td>\n",
              "      <td>-8</td>\n",
              "      <td>-15</td>\n",
              "      <td>-13</td>\n",
              "      <td>-2</td>\n",
              "      <td>21</td>\n",
              "      <td>39</td>\n",
              "      <td>48</td>\n",
              "      <td>37</td>\n",
              "      <td>10</td>\n",
              "      <td>-23</td>\n",
              "      <td>-47</td>\n",
              "      <td>...</td>\n",
              "      <td>-108</td>\n",
              "      <td>-83</td>\n",
              "      <td>-46</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>39</td>\n",
              "      <td>44</td>\n",
              "      <td>33</td>\n",
              "      <td>22</td>\n",
              "      <td>8</td>\n",
              "      <td>-13</td>\n",
              "      <td>-33</td>\n",
              "      <td>-61</td>\n",
              "      <td>-70</td>\n",
              "      <td>-75</td>\n",
              "      <td>-74</td>\n",
              "      <td>-58</td>\n",
              "      <td>-18</td>\n",
              "      <td>19</td>\n",
              "      <td>54</td>\n",
              "      <td>71</td>\n",
              "      <td>76</td>\n",
              "      <td>74</td>\n",
              "      <td>65</td>\n",
              "      <td>56</td>\n",
              "      <td>18</td>\n",
              "      <td>-28</td>\n",
              "      <td>-75</td>\n",
              "      <td>-98</td>\n",
              "      <td>-94</td>\n",
              "      <td>-59</td>\n",
              "      <td>-25</td>\n",
              "      <td>-4</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>-2</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 180 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Unnamed  X1  X2  X3  X4   X5  ...  X174  X175  X176  X177  X178  y\n",
              "11495  X22.V1.114 -22 -22 -23 -26  -36  ...    -1   -18   -37   -47   -48  2\n",
              "11496  X19.V1.354 -47 -11  28  77  141  ...    27    48    77   117   170  1\n",
              "11497    X8.V1.28  14   6 -13 -16   10  ...   -67   -30    -2    -1    -8  5\n",
              "11498  X10.V1.932 -40 -25  -9 -12   -2  ...   116    86    68    59    55  3\n",
              "11499  X16.V1.210  29  41  57  72   74  ...     5     4    -2     2    20  4\n",
              "\n",
              "[5 rows x 180 columns]"
            ]
          },
          "execution_count": 96,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# โชว์ Data เฉพาะส่วนท้ายจํานวน 5 Rows\n",
        "\n",
        "epileptic_seizure.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rYB5KlBvelm2",
        "outputId": "b527956d-70c4-4f34-ef98-860bffb287e6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "5    2300\n",
              "4    2300\n",
              "3    2300\n",
              "2    2300\n",
              "1    2300\n",
              "Name: y, dtype: int64"
            ]
          },
          "execution_count": 97,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# จํานวนข้อมูลของพฤติกรรมการตรวจลมบ้าหมูใน 5 ประเภท\n",
        "# 1) 5 หมายถึง เวลาบันทึก EEG ของสมอง เวลาที่ผู้ป่วยลืมตาขึ้น\n",
        "# 2) 4 หมายถึง เวลาบันทึก EEG ของสมอง เวลาที่ผู้ป่วยปิดตาลง\n",
        "# 3) 3 หมายถึง ระบุตำแหน่งของเนื้องอกในสมองและบันทึกกิจกรรม EEG จากบริเวณสมองที่แข็งแรง\n",
        "# 4) 2 หมายถึง บันทึก EEG จากบริเวณที่เกิดเนื้องอก\n",
        "# 5) 1 หมายถึง  นทึกกิจกรรมชัก ในหมายเลขที่ 2, 3, 4 และ 5 เป็นกลุ่มที่ไม่เป็นโรคลมบ้าหมู เฉพาะหมายเลข 1 เท่านั้นที่เป็นโรคลมชัก\n",
        "\n",
        "epileptic_seizure['y'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ihbWhUzeesQw",
        "outputId": "d536da2d-3181-4225-e88c-886739fd963a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0f5cd9ca10>"
            ]
          },
          "execution_count": 98,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# สร้างกราฟของแต่ละ Classes แบบคร่าวๆ จะเห็นได้ว่าทุก กราฟนั้นมีข้อมูลเท่ากันหมด\n",
        "\n",
        "epileptic_seizure.y.hist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "LXJme7-Qe-k9"
      },
      "outputs": [],
      "source": [
        "# เปลี่ยน Column ที่ y (เป็นการจัดประเภท Binary)\n",
        "\n",
        "binary_dict = {5: 0, 4: 0, 3: 0, 2: 0, 1: 1}\n",
        "epileptic_seizure['y'] = epileptic_seizure['y'].map(binary_dict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "YQ5D6s6JeurY",
        "outputId": "b3d6444a-5d3f-42df-bbb6-3b4b6407da84"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 3600x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# สร้าง Plot เพื่อจําลองคลื่นสมองของผู้ป่วยและคนปกติ\n",
        "# จะเห็นได้ว่าเส้นสีส้มและสีนํ้าเงินเป็นผู้ป่วยลมบ้าหมูจะเห็นว่าคลื่นสมองจะกว้างและไม่ถี่เหมือนคนปกตินั่นถือว่าเป็นสัญญาณอาการของคนชัก\n",
        "\n",
        "plt.figure(figsize=(50,4))\n",
        "plt.subplot(131)\n",
        "[plt.plot(epileptic_seizure.values[i][1:-1]) for i in range(23)];"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "H49q5YNWeL5c"
      },
      "outputs": [],
      "source": [
        "# ลบ Column Index ที่มีชื่อว่า Unnamed และสับเปลี่ยนข้อมูลของ CSV ที่เรานําเข้ามา\n",
        "\n",
        "epileptic_seizure = epileptic_seizure.drop('Unnamed', axis=1)\n",
        "epileptic_seizure = shuffle(epileptic_seizure)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "Yi4vo2CD4Qkc",
        "outputId": "9d462c9c-af94-4e02-c8f2-74c161e95220"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
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              "      <th>X4</th>\n",
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              "      <th>X17</th>\n",
              "      <th>X18</th>\n",
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              "      <th>X20</th>\n",
              "      <th>X21</th>\n",
              "      <th>X22</th>\n",
              "      <th>X23</th>\n",
              "      <th>X24</th>\n",
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              "      <th>X33</th>\n",
              "      <th>X34</th>\n",
              "      <th>X35</th>\n",
              "      <th>X36</th>\n",
              "      <th>X37</th>\n",
              "      <th>X38</th>\n",
              "      <th>X39</th>\n",
              "      <th>X40</th>\n",
              "      <th>...</th>\n",
              "      <th>X140</th>\n",
              "      <th>X141</th>\n",
              "      <th>X142</th>\n",
              "      <th>X143</th>\n",
              "      <th>X144</th>\n",
              "      <th>X145</th>\n",
              "      <th>X146</th>\n",
              "      <th>X147</th>\n",
              "      <th>X148</th>\n",
              "      <th>X149</th>\n",
              "      <th>X150</th>\n",
              "      <th>X151</th>\n",
              "      <th>X152</th>\n",
              "      <th>X153</th>\n",
              "      <th>X154</th>\n",
              "      <th>X155</th>\n",
              "      <th>X156</th>\n",
              "      <th>X157</th>\n",
              "      <th>X158</th>\n",
              "      <th>X159</th>\n",
              "      <th>X160</th>\n",
              "      <th>X161</th>\n",
              "      <th>X162</th>\n",
              "      <th>X163</th>\n",
              "      <th>X164</th>\n",
              "      <th>X165</th>\n",
              "      <th>X166</th>\n",
              "      <th>X167</th>\n",
              "      <th>X168</th>\n",
              "      <th>X169</th>\n",
              "      <th>X170</th>\n",
              "      <th>X171</th>\n",
              "      <th>X172</th>\n",
              "      <th>X173</th>\n",
              "      <th>X174</th>\n",
              "      <th>X175</th>\n",
              "      <th>X176</th>\n",
              "      <th>X177</th>\n",
              "      <th>X178</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5708</th>\n",
              "      <td>-27</td>\n",
              "      <td>-38</td>\n",
              "      <td>-46</td>\n",
              "      <td>-43</td>\n",
              "      <td>-43</td>\n",
              "      <td>-48</td>\n",
              "      <td>-42</td>\n",
              "      <td>-46</td>\n",
              "      <td>-45</td>\n",
              "      <td>-46</td>\n",
              "      <td>-27</td>\n",
              "      <td>-17</td>\n",
              "      <td>-9</td>\n",
              "      <td>-6</td>\n",
              "      <td>-6</td>\n",
              "      <td>-15</td>\n",
              "      <td>-20</td>\n",
              "      <td>-27</td>\n",
              "      <td>-32</td>\n",
              "      <td>-46</td>\n",
              "      <td>-51</td>\n",
              "      <td>-66</td>\n",
              "      <td>-80</td>\n",
              "      <td>-89</td>\n",
              "      <td>-95</td>\n",
              "      <td>-102</td>\n",
              "      <td>-101</td>\n",
              "      <td>-99</td>\n",
              "      <td>-92</td>\n",
              "      <td>-95</td>\n",
              "      <td>-93</td>\n",
              "      <td>-89</td>\n",
              "      <td>-87</td>\n",
              "      <td>-74</td>\n",
              "      <td>-61</td>\n",
              "      <td>-49</td>\n",
              "      <td>-40</td>\n",
              "      <td>-29</td>\n",
              "      <td>-17</td>\n",
              "      <td>-14</td>\n",
              "      <td>...</td>\n",
              "      <td>-56</td>\n",
              "      <td>-59</td>\n",
              "      <td>-62</td>\n",
              "      <td>-56</td>\n",
              "      <td>-52</td>\n",
              "      <td>-43</td>\n",
              "      <td>-35</td>\n",
              "      <td>-21</td>\n",
              "      <td>-14</td>\n",
              "      <td>-1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>-4</td>\n",
              "      <td>-23</td>\n",
              "      <td>-22</td>\n",
              "      <td>-35</td>\n",
              "      <td>-39</td>\n",
              "      <td>-50</td>\n",
              "      <td>-58</td>\n",
              "      <td>-64</td>\n",
              "      <td>-67</td>\n",
              "      <td>-61</td>\n",
              "      <td>-71</td>\n",
              "      <td>-74</td>\n",
              "      <td>-80</td>\n",
              "      <td>-81</td>\n",
              "      <td>-82</td>\n",
              "      <td>-80</td>\n",
              "      <td>-75</td>\n",
              "      <td>-67</td>\n",
              "      <td>-67</td>\n",
              "      <td>-68</td>\n",
              "      <td>-57</td>\n",
              "      <td>-50</td>\n",
              "      <td>-44</td>\n",
              "      <td>-38</td>\n",
              "      <td>-39</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6240</th>\n",
              "      <td>-54</td>\n",
              "      <td>-39</td>\n",
              "      <td>-42</td>\n",
              "      <td>-96</td>\n",
              "      <td>-132</td>\n",
              "      <td>-142</td>\n",
              "      <td>-152</td>\n",
              "      <td>-162</td>\n",
              "      <td>-155</td>\n",
              "      <td>-147</td>\n",
              "      <td>-156</td>\n",
              "      <td>-127</td>\n",
              "      <td>-80</td>\n",
              "      <td>-45</td>\n",
              "      <td>-53</td>\n",
              "      <td>-72</td>\n",
              "      <td>-75</td>\n",
              "      <td>-90</td>\n",
              "      <td>-91</td>\n",
              "      <td>-69</td>\n",
              "      <td>-49</td>\n",
              "      <td>-51</td>\n",
              "      <td>-50</td>\n",
              "      <td>-79</td>\n",
              "      <td>-110</td>\n",
              "      <td>-121</td>\n",
              "      <td>-105</td>\n",
              "      <td>-81</td>\n",
              "      <td>-75</td>\n",
              "      <td>-69</td>\n",
              "      <td>-69</td>\n",
              "      <td>-68</td>\n",
              "      <td>-69</td>\n",
              "      <td>-70</td>\n",
              "      <td>-83</td>\n",
              "      <td>-90</td>\n",
              "      <td>-93</td>\n",
              "      <td>-112</td>\n",
              "      <td>-129</td>\n",
              "      <td>-150</td>\n",
              "      <td>...</td>\n",
              "      <td>-62</td>\n",
              "      <td>-56</td>\n",
              "      <td>-45</td>\n",
              "      <td>-29</td>\n",
              "      <td>-14</td>\n",
              "      <td>-17</td>\n",
              "      <td>-31</td>\n",
              "      <td>-58</td>\n",
              "      <td>-45</td>\n",
              "      <td>-55</td>\n",
              "      <td>-84</td>\n",
              "      <td>-98</td>\n",
              "      <td>-87</td>\n",
              "      <td>-82</td>\n",
              "      <td>-93</td>\n",
              "      <td>-80</td>\n",
              "      <td>-110</td>\n",
              "      <td>-134</td>\n",
              "      <td>-111</td>\n",
              "      <td>-80</td>\n",
              "      <td>-83</td>\n",
              "      <td>-104</td>\n",
              "      <td>-92</td>\n",
              "      <td>-91</td>\n",
              "      <td>-89</td>\n",
              "      <td>-83</td>\n",
              "      <td>-88</td>\n",
              "      <td>-123</td>\n",
              "      <td>-139</td>\n",
              "      <td>-132</td>\n",
              "      <td>-110</td>\n",
              "      <td>-107</td>\n",
              "      <td>-130</td>\n",
              "      <td>-150</td>\n",
              "      <td>-147</td>\n",
              "      <td>-129</td>\n",
              "      <td>-89</td>\n",
              "      <td>-65</td>\n",
              "      <td>-62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9839</th>\n",
              "      <td>-42</td>\n",
              "      <td>-39</td>\n",
              "      <td>-34</td>\n",
              "      <td>-32</td>\n",
              "      <td>-32</td>\n",
              "      <td>-13</td>\n",
              "      <td>-2</td>\n",
              "      <td>3</td>\n",
              "      <td>-4</td>\n",
              "      <td>-5</td>\n",
              "      <td>-4</td>\n",
              "      <td>-4</td>\n",
              "      <td>10</td>\n",
              "      <td>19</td>\n",
              "      <td>22</td>\n",
              "      <td>18</td>\n",
              "      <td>27</td>\n",
              "      <td>40</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "      <td>31</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>-1</td>\n",
              "      <td>-6</td>\n",
              "      <td>-6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-9</td>\n",
              "      <td>-8</td>\n",
              "      <td>-4</td>\n",
              "      <td>16</td>\n",
              "      <td>41</td>\n",
              "      <td>57</td>\n",
              "      <td>60</td>\n",
              "      <td>45</td>\n",
              "      <td>21</td>\n",
              "      <td>-10</td>\n",
              "      <td>-21</td>\n",
              "      <td>...</td>\n",
              "      <td>106</td>\n",
              "      <td>92</td>\n",
              "      <td>68</td>\n",
              "      <td>40</td>\n",
              "      <td>14</td>\n",
              "      <td>-4</td>\n",
              "      <td>-23</td>\n",
              "      <td>-31</td>\n",
              "      <td>-29</td>\n",
              "      <td>-23</td>\n",
              "      <td>-24</td>\n",
              "      <td>-24</td>\n",
              "      <td>-17</td>\n",
              "      <td>-7</td>\n",
              "      <td>-1</td>\n",
              "      <td>11</td>\n",
              "      <td>31</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>40</td>\n",
              "      <td>39</td>\n",
              "      <td>36</td>\n",
              "      <td>26</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>-2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-14</td>\n",
              "      <td>-24</td>\n",
              "      <td>-29</td>\n",
              "      <td>-40</td>\n",
              "      <td>-43</td>\n",
              "      <td>-45</td>\n",
              "      <td>-45</td>\n",
              "      <td>-40</td>\n",
              "      <td>-37</td>\n",
              "      <td>-21</td>\n",
              "      <td>-12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6242</th>\n",
              "      <td>-39</td>\n",
              "      <td>-45</td>\n",
              "      <td>-39</td>\n",
              "      <td>-46</td>\n",
              "      <td>-52</td>\n",
              "      <td>-64</td>\n",
              "      <td>-66</td>\n",
              "      <td>-68</td>\n",
              "      <td>-62</td>\n",
              "      <td>-44</td>\n",
              "      <td>-31</td>\n",
              "      <td>-10</td>\n",
              "      <td>13</td>\n",
              "      <td>21</td>\n",
              "      <td>11</td>\n",
              "      <td>-6</td>\n",
              "      <td>-8</td>\n",
              "      <td>-15</td>\n",
              "      <td>-3</td>\n",
              "      <td>-5</td>\n",
              "      <td>-12</td>\n",
              "      <td>-32</td>\n",
              "      <td>-56</td>\n",
              "      <td>-61</td>\n",
              "      <td>-53</td>\n",
              "      <td>-34</td>\n",
              "      <td>-15</td>\n",
              "      <td>-7</td>\n",
              "      <td>-2</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>28</td>\n",
              "      <td>35</td>\n",
              "      <td>50</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>49</td>\n",
              "      <td>52</td>\n",
              "      <td>41</td>\n",
              "      <td>40</td>\n",
              "      <td>...</td>\n",
              "      <td>43</td>\n",
              "      <td>56</td>\n",
              "      <td>65</td>\n",
              "      <td>89</td>\n",
              "      <td>104</td>\n",
              "      <td>114</td>\n",
              "      <td>94</td>\n",
              "      <td>68</td>\n",
              "      <td>40</td>\n",
              "      <td>32</td>\n",
              "      <td>28</td>\n",
              "      <td>24</td>\n",
              "      <td>31</td>\n",
              "      <td>29</td>\n",
              "      <td>35</td>\n",
              "      <td>39</td>\n",
              "      <td>54</td>\n",
              "      <td>66</td>\n",
              "      <td>66</td>\n",
              "      <td>64</td>\n",
              "      <td>60</td>\n",
              "      <td>48</td>\n",
              "      <td>27</td>\n",
              "      <td>9</td>\n",
              "      <td>-15</td>\n",
              "      <td>-17</td>\n",
              "      <td>-7</td>\n",
              "      <td>-12</td>\n",
              "      <td>-11</td>\n",
              "      <td>-30</td>\n",
              "      <td>-51</td>\n",
              "      <td>-64</td>\n",
              "      <td>-73</td>\n",
              "      <td>-55</td>\n",
              "      <td>-52</td>\n",
              "      <td>-43</td>\n",
              "      <td>-36</td>\n",
              "      <td>-30</td>\n",
              "      <td>-22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>-18</td>\n",
              "      <td>-17</td>\n",
              "      <td>-31</td>\n",
              "      <td>-36</td>\n",
              "      <td>-56</td>\n",
              "      <td>-74</td>\n",
              "      <td>-88</td>\n",
              "      <td>-74</td>\n",
              "      <td>-52</td>\n",
              "      <td>-27</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>13</td>\n",
              "      <td>36</td>\n",
              "      <td>33</td>\n",
              "      <td>43</td>\n",
              "      <td>22</td>\n",
              "      <td>7</td>\n",
              "      <td>-26</td>\n",
              "      <td>-51</td>\n",
              "      <td>-65</td>\n",
              "      <td>-36</td>\n",
              "      <td>8</td>\n",
              "      <td>18</td>\n",
              "      <td>31</td>\n",
              "      <td>10</td>\n",
              "      <td>16</td>\n",
              "      <td>56</td>\n",
              "      <td>62</td>\n",
              "      <td>73</td>\n",
              "      <td>38</td>\n",
              "      <td>53</td>\n",
              "      <td>48</td>\n",
              "      <td>22</td>\n",
              "      <td>-19</td>\n",
              "      <td>-40</td>\n",
              "      <td>-29</td>\n",
              "      <td>-14</td>\n",
              "      <td>-16</td>\n",
              "      <td>-22</td>\n",
              "      <td>...</td>\n",
              "      <td>28</td>\n",
              "      <td>3</td>\n",
              "      <td>-8</td>\n",
              "      <td>-5</td>\n",
              "      <td>-14</td>\n",
              "      <td>-14</td>\n",
              "      <td>-29</td>\n",
              "      <td>-22</td>\n",
              "      <td>-9</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>-16</td>\n",
              "      <td>-51</td>\n",
              "      <td>-86</td>\n",
              "      <td>-53</td>\n",
              "      <td>-54</td>\n",
              "      <td>-44</td>\n",
              "      <td>-57</td>\n",
              "      <td>-82</td>\n",
              "      <td>-55</td>\n",
              "      <td>-31</td>\n",
              "      <td>-5</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>24</td>\n",
              "      <td>71</td>\n",
              "      <td>79</td>\n",
              "      <td>63</td>\n",
              "      <td>49</td>\n",
              "      <td>56</td>\n",
              "      <td>49</td>\n",
              "      <td>71</td>\n",
              "      <td>80</td>\n",
              "      <td>66</td>\n",
              "      <td>66</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 179 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      X1  X2  X3  X4   X5   X6   X7  ...  X173  X174  X175  X176  X177  X178  y\n",
              "5708 -27 -38 -46 -43  -43  -48  -42  ...   -68   -57   -50   -44   -38   -39  0\n",
              "6240 -54 -39 -42 -96 -132 -142 -152  ...  -150  -147  -129   -89   -65   -62  0\n",
              "9839 -42 -39 -34 -32  -32  -13   -2  ...   -45   -45   -40   -37   -21   -12  0\n",
              "6242 -39 -45 -39 -46  -52  -64  -66  ...   -55   -52   -43   -36   -30   -22  0\n",
              "2006 -18 -17 -31 -36  -56  -74  -88  ...    49    71    80    66    66    42  0\n",
              "\n",
              "[5 rows x 179 columns]"
            ]
          },
          "execution_count": 102,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# โชว์ข้อมูลส่วนหัว 5 Rows พร้อมการเปลี่ยนแปลง ที่ Column 0 Unnamed จะถูกลบหายออกไป\n",
        "\n",
        "epileptic_seizure.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "OY7Oce-afM-1",
        "outputId": "bc875ec4-3868-4b39-a2a9-6b2f97516a6d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>X3</th>\n",
              "      <th>X4</th>\n",
              "      <th>X5</th>\n",
              "      <th>X6</th>\n",
              "      <th>X7</th>\n",
              "      <th>X8</th>\n",
              "      <th>X9</th>\n",
              "      <th>X10</th>\n",
              "      <th>X11</th>\n",
              "      <th>X12</th>\n",
              "      <th>X13</th>\n",
              "      <th>X14</th>\n",
              "      <th>X15</th>\n",
              "      <th>X16</th>\n",
              "      <th>X17</th>\n",
              "      <th>X18</th>\n",
              "      <th>X19</th>\n",
              "      <th>X20</th>\n",
              "      <th>X21</th>\n",
              "      <th>X22</th>\n",
              "      <th>X23</th>\n",
              "      <th>X24</th>\n",
              "      <th>X25</th>\n",
              "      <th>X26</th>\n",
              "      <th>X27</th>\n",
              "      <th>X28</th>\n",
              "      <th>X29</th>\n",
              "      <th>X30</th>\n",
              "      <th>X31</th>\n",
              "      <th>X32</th>\n",
              "      <th>X33</th>\n",
              "      <th>X34</th>\n",
              "      <th>X35</th>\n",
              "      <th>X36</th>\n",
              "      <th>X37</th>\n",
              "      <th>X38</th>\n",
              "      <th>X39</th>\n",
              "      <th>X40</th>\n",
              "      <th>...</th>\n",
              "      <th>X140</th>\n",
              "      <th>X141</th>\n",
              "      <th>X142</th>\n",
              "      <th>X143</th>\n",
              "      <th>X144</th>\n",
              "      <th>X145</th>\n",
              "      <th>X146</th>\n",
              "      <th>X147</th>\n",
              "      <th>X148</th>\n",
              "      <th>X149</th>\n",
              "      <th>X150</th>\n",
              "      <th>X151</th>\n",
              "      <th>X152</th>\n",
              "      <th>X153</th>\n",
              "      <th>X154</th>\n",
              "      <th>X155</th>\n",
              "      <th>X156</th>\n",
              "      <th>X157</th>\n",
              "      <th>X158</th>\n",
              "      <th>X159</th>\n",
              "      <th>X160</th>\n",
              "      <th>X161</th>\n",
              "      <th>X162</th>\n",
              "      <th>X163</th>\n",
              "      <th>X164</th>\n",
              "      <th>X165</th>\n",
              "      <th>X166</th>\n",
              "      <th>X167</th>\n",
              "      <th>X168</th>\n",
              "      <th>X169</th>\n",
              "      <th>X170</th>\n",
              "      <th>X171</th>\n",
              "      <th>X172</th>\n",
              "      <th>X173</th>\n",
              "      <th>X174</th>\n",
              "      <th>X175</th>\n",
              "      <th>X176</th>\n",
              "      <th>X177</th>\n",
              "      <th>X178</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.00000</td>\n",
              "      <td>11500.00000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.00000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "      <td>11500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-11.581391</td>\n",
              "      <td>-10.911565</td>\n",
              "      <td>-10.187130</td>\n",
              "      <td>-9.143043</td>\n",
              "      <td>-8.009739</td>\n",
              "      <td>-7.003478</td>\n",
              "      <td>-6.502087</td>\n",
              "      <td>-6.68713</td>\n",
              "      <td>-6.55800</td>\n",
              "      <td>-6.168435</td>\n",
              "      <td>-5.827478</td>\n",
              "      <td>-6.042174</td>\n",
              "      <td>-6.687304</td>\n",
              "      <td>-7.088870</td>\n",
              "      <td>-7.211217</td>\n",
              "      <td>-7.088261</td>\n",
              "      <td>-6.806696</td>\n",
              "      <td>-6.744696</td>\n",
              "      <td>-6.484783</td>\n",
              "      <td>-6.448435</td>\n",
              "      <td>-6.355391</td>\n",
              "      <td>-6.543130</td>\n",
              "      <td>-6.884348</td>\n",
              "      <td>-6.883217</td>\n",
              "      <td>-6.118870</td>\n",
              "      <td>-5.15913</td>\n",
              "      <td>-4.785652</td>\n",
              "      <td>-4.874783</td>\n",
              "      <td>-5.393565</td>\n",
              "      <td>-5.935478</td>\n",
              "      <td>-6.349913</td>\n",
              "      <td>-6.212522</td>\n",
              "      <td>-5.859826</td>\n",
              "      <td>-5.299826</td>\n",
              "      <td>-4.955652</td>\n",
              "      <td>-5.278087</td>\n",
              "      <td>-6.162261</td>\n",
              "      <td>-7.316609</td>\n",
              "      <td>-7.998522</td>\n",
              "      <td>-7.818870</td>\n",
              "      <td>...</td>\n",
              "      <td>-6.997304</td>\n",
              "      <td>-7.339391</td>\n",
              "      <td>-7.911565</td>\n",
              "      <td>-8.177304</td>\n",
              "      <td>-8.608870</td>\n",
              "      <td>-8.578087</td>\n",
              "      <td>-8.240000</td>\n",
              "      <td>-7.691391</td>\n",
              "      <td>-7.356522</td>\n",
              "      <td>-7.228783</td>\n",
              "      <td>-6.980870</td>\n",
              "      <td>-6.756783</td>\n",
              "      <td>-6.355304</td>\n",
              "      <td>-6.423826</td>\n",
              "      <td>-6.465217</td>\n",
              "      <td>-6.316435</td>\n",
              "      <td>-5.808000</td>\n",
              "      <td>-5.255913</td>\n",
              "      <td>-4.682087</td>\n",
              "      <td>-4.638609</td>\n",
              "      <td>-4.770261</td>\n",
              "      <td>-5.650870</td>\n",
              "      <td>-7.170348</td>\n",
              "      <td>-8.759826</td>\n",
              "      <td>-9.784783</td>\n",
              "      <td>-10.096261</td>\n",
              "      <td>-9.842957</td>\n",
              "      <td>-9.701739</td>\n",
              "      <td>-9.351391</td>\n",
              "      <td>-9.498870</td>\n",
              "      <td>-10.145739</td>\n",
              "      <td>-11.630348</td>\n",
              "      <td>-12.943478</td>\n",
              "      <td>-13.668870</td>\n",
              "      <td>-13.363304</td>\n",
              "      <td>-13.045043</td>\n",
              "      <td>-12.705130</td>\n",
              "      <td>-12.426000</td>\n",
              "      <td>-12.195652</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>165.626284</td>\n",
              "      <td>166.059609</td>\n",
              "      <td>163.524317</td>\n",
              "      <td>161.269041</td>\n",
              "      <td>160.998007</td>\n",
              "      <td>161.328725</td>\n",
              "      <td>161.467837</td>\n",
              "      <td>162.11912</td>\n",
              "      <td>162.03336</td>\n",
              "      <td>160.436352</td>\n",
              "      <td>160.471017</td>\n",
              "      <td>161.181118</td>\n",
              "      <td>165.071121</td>\n",
              "      <td>169.198359</td>\n",
              "      <td>170.641967</td>\n",
              "      <td>168.930355</td>\n",
              "      <td>164.646296</td>\n",
              "      <td>162.062661</td>\n",
              "      <td>162.571333</td>\n",
              "      <td>162.339911</td>\n",
              "      <td>162.823325</td>\n",
              "      <td>163.909723</td>\n",
              "      <td>165.844176</td>\n",
              "      <td>166.009100</td>\n",
              "      <td>164.715153</td>\n",
              "      <td>162.77423</td>\n",
              "      <td>162.902294</td>\n",
              "      <td>164.799831</td>\n",
              "      <td>165.391697</td>\n",
              "      <td>164.291120</td>\n",
              "      <td>163.165167</td>\n",
              "      <td>163.408052</td>\n",
              "      <td>163.561408</td>\n",
              "      <td>162.548119</td>\n",
              "      <td>160.813772</td>\n",
              "      <td>160.441049</td>\n",
              "      <td>162.881958</td>\n",
              "      <td>166.235164</td>\n",
              "      <td>166.314965</td>\n",
              "      <td>164.513017</td>\n",
              "      <td>...</td>\n",
              "      <td>168.567495</td>\n",
              "      <td>168.662705</td>\n",
              "      <td>167.643458</td>\n",
              "      <td>167.414439</td>\n",
              "      <td>167.922773</td>\n",
              "      <td>168.274009</td>\n",
              "      <td>167.808218</td>\n",
              "      <td>166.739558</td>\n",
              "      <td>165.678558</td>\n",
              "      <td>167.130911</td>\n",
              "      <td>168.156296</td>\n",
              "      <td>167.459868</td>\n",
              "      <td>165.061414</td>\n",
              "      <td>164.248830</td>\n",
              "      <td>166.226332</td>\n",
              "      <td>168.143625</td>\n",
              "      <td>167.067064</td>\n",
              "      <td>166.446540</td>\n",
              "      <td>167.554020</td>\n",
              "      <td>169.418457</td>\n",
              "      <td>170.639171</td>\n",
              "      <td>170.817824</td>\n",
              "      <td>168.145372</td>\n",
              "      <td>166.817974</td>\n",
              "      <td>166.999981</td>\n",
              "      <td>167.756860</td>\n",
              "      <td>166.988820</td>\n",
              "      <td>167.062497</td>\n",
              "      <td>166.606345</td>\n",
              "      <td>165.235574</td>\n",
              "      <td>164.652883</td>\n",
              "      <td>166.149790</td>\n",
              "      <td>168.554058</td>\n",
              "      <td>168.556486</td>\n",
              "      <td>167.257290</td>\n",
              "      <td>164.241019</td>\n",
              "      <td>162.895832</td>\n",
              "      <td>162.886311</td>\n",
              "      <td>164.852015</td>\n",
              "      <td>0.400017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1839.000000</td>\n",
              "      <td>-1838.000000</td>\n",
              "      <td>-1835.000000</td>\n",
              "      <td>-1845.000000</td>\n",
              "      <td>-1791.000000</td>\n",
              "      <td>-1757.000000</td>\n",
              "      <td>-1832.000000</td>\n",
              "      <td>-1778.00000</td>\n",
              "      <td>-1840.00000</td>\n",
              "      <td>-1867.000000</td>\n",
              "      <td>-1765.000000</td>\n",
              "      <td>-1803.000000</td>\n",
              "      <td>-1833.000000</td>\n",
              "      <td>-1867.000000</td>\n",
              "      <td>-1870.000000</td>\n",
              "      <td>-1839.000000</td>\n",
              "      <td>-1867.000000</td>\n",
              "      <td>-1856.000000</td>\n",
              "      <td>-1844.000000</td>\n",
              "      <td>-1717.000000</td>\n",
              "      <td>-1764.000000</td>\n",
              "      <td>-1856.000000</td>\n",
              "      <td>-1844.000000</td>\n",
              "      <td>-1866.000000</td>\n",
              "      <td>-1863.000000</td>\n",
              "      <td>-1866.00000</td>\n",
              "      <td>-1781.000000</td>\n",
              "      <td>-1860.000000</td>\n",
              "      <td>-1818.000000</td>\n",
              "      <td>-1867.000000</td>\n",
              "      <td>-1743.000000</td>\n",
              "      <td>-1517.000000</td>\n",
              "      <td>-1552.000000</td>\n",
              "      <td>-1720.000000</td>\n",
              "      <td>-1864.000000</td>\n",
              "      <td>-1814.000000</td>\n",
              "      <td>-1829.000000</td>\n",
              "      <td>-1851.000000</td>\n",
              "      <td>-1840.000000</td>\n",
              "      <td>-1673.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>-1867.000000</td>\n",
              "      <td>-1826.000000</td>\n",
              "      <td>-1866.000000</td>\n",
              "      <td>-1854.000000</td>\n",
              "      <td>-1789.000000</td>\n",
              "      <td>-1872.000000</td>\n",
              "      <td>-1866.000000</td>\n",
              "      <td>-1675.000000</td>\n",
              "      <td>-1778.000000</td>\n",
              "      <td>-1733.000000</td>\n",
              "      <td>-1862.000000</td>\n",
              "      <td>-1861.000000</td>\n",
              "      <td>-1857.000000</td>\n",
              "      <td>-1839.000000</td>\n",
              "      <td>-1860.000000</td>\n",
              "      <td>-1843.000000</td>\n",
              "      <td>-1863.000000</td>\n",
              "      <td>-1861.000000</td>\n",
              "      <td>-1845.000000</td>\n",
              "      <td>-1820.000000</td>\n",
              "      <td>-1864.000000</td>\n",
              "      <td>-1868.000000</td>\n",
              "      <td>-1855.000000</td>\n",
              "      <td>-1852.000000</td>\n",
              "      <td>-1870.000000</td>\n",
              "      <td>-1847.000000</td>\n",
              "      <td>-1578.000000</td>\n",
              "      <td>-1838.000000</td>\n",
              "      <td>-1763.000000</td>\n",
              "      <td>-1860.000000</td>\n",
              "      <td>-1867.000000</td>\n",
              "      <td>-1865.000000</td>\n",
              "      <td>-1642.000000</td>\n",
              "      <td>-1723.000000</td>\n",
              "      <td>-1866.000000</td>\n",
              "      <td>-1863.000000</td>\n",
              "      <td>-1781.000000</td>\n",
              "      <td>-1727.000000</td>\n",
              "      <td>-1829.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-54.000000</td>\n",
              "      <td>-55.000000</td>\n",
              "      <td>-54.000000</td>\n",
              "      <td>-54.000000</td>\n",
              "      <td>-54.000000</td>\n",
              "      <td>-54.000000</td>\n",
              "      <td>-54.000000</td>\n",
              "      <td>-55.00000</td>\n",
              "      <td>-55.00000</td>\n",
              "      <td>-54.000000</td>\n",
              "      <td>-54.000000</td>\n",
              "      <td>-54.000000</td>\n",
              "      <td>-54.000000</td>\n",
              "      <td>-54.000000</td>\n",
              "      <td>-53.000000</td>\n",
              "      <td>-53.000000</td>\n",
              "      <td>-53.000000</td>\n",
              "      <td>-52.000000</td>\n",
              "      <td>-53.000000</td>\n",
              "      <td>-53.000000</td>\n",
              "      <td>-53.000000</td>\n",
              "      <td>-53.000000</td>\n",
              "      <td>-53.000000</td>\n",
              "      <td>-52.000000</td>\n",
              "      <td>-52.000000</td>\n",
              "      <td>-52.00000</td>\n",
              "      <td>-51.000000</td>\n",
              "      <td>-50.000000</td>\n",
              "      <td>-50.000000</td>\n",
              "      <td>-50.000000</td>\n",
              "      <td>-51.000000</td>\n",
              "      <td>-52.000000</td>\n",
              "      <td>-52.000000</td>\n",
              "      <td>-51.000000</td>\n",
              "      <td>-51.000000</td>\n",
              "      <td>-51.000000</td>\n",
              "      <td>-51.000000</td>\n",
              "      <td>-51.000000</td>\n",
              "      <td>-52.000000</td>\n",
              "      <td>-52.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>-54.000000</td>\n",
              "      <td>-55.000000</td>\n",
              "      <td>-55.000000</td>\n",
              "      <td>-56.000000</td>\n",
              "      <td>-56.000000</td>\n",
              "      <td>-56.000000</td>\n",
              "      <td>-55.000000</td>\n",
              "      <td>-57.000000</td>\n",
              "      <td>-55.000000</td>\n",
              "      <td>-55.000000</td>\n",
              "      <td>-54.000000</td>\n",
              "      <td>-54.000000</td>\n",
              "      <td>-53.000000</td>\n",
              "      <td>-53.000000</td>\n",
              "      <td>-53.000000</td>\n",
              "      <td>-52.000000</td>\n",
              "      <td>-52.250000</td>\n",
              "      <td>-52.000000</td>\n",
              "      <td>-52.000000</td>\n",
              "      <td>-53.000000</td>\n",
              "      <td>-52.000000</td>\n",
              "      <td>-52.000000</td>\n",
              "      <td>-52.000000</td>\n",
              "      <td>-53.000000</td>\n",
              "      <td>-54.000000</td>\n",
              "      <td>-54.000000</td>\n",
              "      <td>-55.000000</td>\n",
              "      <td>-55.000000</td>\n",
              "      <td>-55.000000</td>\n",
              "      <td>-55.000000</td>\n",
              "      <td>-55.000000</td>\n",
              "      <td>-56.000000</td>\n",
              "      <td>-56.000000</td>\n",
              "      <td>-56.000000</td>\n",
              "      <td>-55.000000</td>\n",
              "      <td>-56.000000</td>\n",
              "      <td>-55.000000</td>\n",
              "      <td>-55.000000</td>\n",
              "      <td>-55.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-8.00000</td>\n",
              "      <td>-7.00000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-6.00000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>-5.000000</td>\n",
              "      <td>-5.000000</td>\n",
              "      <td>-5.000000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-9.000000</td>\n",
              "      <td>-9.000000</td>\n",
              "      <td>-9.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-9.000000</td>\n",
              "      <td>-9.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-9.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-9.000000</td>\n",
              "      <td>-9.000000</td>\n",
              "      <td>-9.000000</td>\n",
              "      <td>-9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>34.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>36.00000</td>\n",
              "      <td>36.00000</td>\n",
              "      <td>35.250000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>38.00000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>38.250000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>35.250000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1726.000000</td>\n",
              "      <td>1713.000000</td>\n",
              "      <td>1697.000000</td>\n",
              "      <td>1612.000000</td>\n",
              "      <td>1518.000000</td>\n",
              "      <td>1816.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>2047.00000</td>\n",
              "      <td>2047.00000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>1829.000000</td>\n",
              "      <td>1631.000000</td>\n",
              "      <td>1532.000000</td>\n",
              "      <td>1441.000000</td>\n",
              "      <td>1352.000000</td>\n",
              "      <td>1504.000000</td>\n",
              "      <td>1821.000000</td>\n",
              "      <td>2022.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>2040.000000</td>\n",
              "      <td>1879.000000</td>\n",
              "      <td>1666.000000</td>\n",
              "      <td>1435.00000</td>\n",
              "      <td>1340.000000</td>\n",
              "      <td>1257.000000</td>\n",
              "      <td>1452.000000</td>\n",
              "      <td>1551.000000</td>\n",
              "      <td>1446.000000</td>\n",
              "      <td>1381.000000</td>\n",
              "      <td>1259.000000</td>\n",
              "      <td>1213.000000</td>\n",
              "      <td>1364.000000</td>\n",
              "      <td>1220.000000</td>\n",
              "      <td>1304.000000</td>\n",
              "      <td>1344.000000</td>\n",
              "      <td>1298.000000</td>\n",
              "      <td>1518.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>2045.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>1934.000000</td>\n",
              "      <td>1644.000000</td>\n",
              "      <td>1524.000000</td>\n",
              "      <td>1565.000000</td>\n",
              "      <td>1682.000000</td>\n",
              "      <td>1977.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>1902.000000</td>\n",
              "      <td>1681.000000</td>\n",
              "      <td>1502.000000</td>\n",
              "      <td>1314.000000</td>\n",
              "      <td>1431.000000</td>\n",
              "      <td>1543.000000</td>\n",
              "      <td>1556.000000</td>\n",
              "      <td>1597.000000</td>\n",
              "      <td>1629.000000</td>\n",
              "      <td>1726.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>1777.000000</td>\n",
              "      <td>1472.000000</td>\n",
              "      <td>1319.000000</td>\n",
              "      <td>1436.000000</td>\n",
              "      <td>1733.000000</td>\n",
              "      <td>1958.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>2047.000000</td>\n",
              "      <td>1915.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 179 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 X1            X2  ...          X178             y\n",
              "count  11500.000000  11500.000000  ...  11500.000000  11500.000000\n",
              "mean     -11.581391    -10.911565  ...    -12.195652      0.200000\n",
              "std      165.626284    166.059609  ...    164.852015      0.400017\n",
              "min    -1839.000000  -1838.000000  ...  -1829.000000      0.000000\n",
              "25%      -54.000000    -55.000000  ...    -55.000000      0.000000\n",
              "50%       -8.000000     -8.000000  ...     -9.000000      0.000000\n",
              "75%       34.000000     35.000000  ...     34.000000      0.000000\n",
              "max     1726.000000   1713.000000  ...   1915.000000      1.000000\n",
              "\n",
              "[8 rows x 179 columns]"
            ]
          },
          "execution_count": 103,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# สร้างสถิติเชิงบรรยายสําหรับ CSV ที่เรานําเข้า ไม่ว่าจะเปิด ค่าเฉลี่ยนหรือค่า ตํ่าสุดหรือน้อยสุด เป็นต้น ฯลฯ\n",
        "\n",
        "epileptic_seizure.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vksMpa2ffOif",
        "outputId": "f38466ad-5c2c-4708-8307-168a6f5bb7c3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 11500 entries, 5708 to 9913\n",
            "Columns: 179 entries, X1 to y\n",
            "dtypes: int64(179)\n",
            "memory usage: 15.8 MB\n"
          ]
        }
      ],
      "source": [
        "# แสดงข้อมูลเกี่ยวกับไฟล์ CSV ที่เราได้ทําการนําเข้ามาพยากรณ์\n",
        "\n",
        "epileptic_seizure.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WFTy5w07fTkn",
        "outputId": "e889ef36-8589-4df8-bd3b-4a170f62ad49"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of records of Non-Epileptic 9200 vs Epileptic 2300\n"
          ]
        }
      ],
      "source": [
        "# แสดงผลตัวเลขจํานวนคนปกติและคนที่เป็นผู้ป่วย\n",
        "\n",
        "print('Number of records of Non-Epileptic {0} vs Epileptic {1}'.format(len(epileptic_seizure[epileptic_seizure['y'] == 0]), len(epileptic_seizure[epileptic_seizure['y'] == 1])))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "J7lUhYCafbXU",
        "outputId": "a5f78c9c-fc7b-4cb8-b0a4-30e1dd3b411f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "        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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>X1</th>\n",
              "      <td>9200.0</td>\n",
              "      <td>-8.992609</td>\n",
              "      <td>70.455286</td>\n",
              "      <td>-566.0</td>\n",
              "      <td>-44.0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1726.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X2</th>\n",
              "      <td>9200.0</td>\n",
              "      <td>-8.877174</td>\n",
              "      <td>70.560110</td>\n",
              "      <td>-609.0</td>\n",
              "      <td>-44.0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1713.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X3</th>\n",
              "      <td>9200.0</td>\n",
              "      <td>-8.910435</td>\n",
              "      <td>70.372582</td>\n",
              "      <td>-594.0</td>\n",
              "      <td>-45.0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>1697.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X4</th>\n",
              "      <td>9200.0</td>\n",
              "      <td>-8.969783</td>\n",
              "      <td>70.030409</td>\n",
              "      <td>-549.0</td>\n",
              "      <td>-45.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1612.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X5</th>\n",
              "      <td>9200.0</td>\n",
              "      <td>-9.085326</td>\n",
              "      <td>69.377958</td>\n",
              "      <td>-603.0</td>\n",
              "      <td>-45.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1437.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X175</th>\n",
              "      <td>9200.0</td>\n",
              "      <td>-9.848587</td>\n",
              "      <td>69.550894</td>\n",
              "      <td>-570.0</td>\n",
              "      <td>-45.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1958.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X176</th>\n",
              "      <td>9200.0</td>\n",
              "      <td>-9.620435</td>\n",
              "      <td>70.353607</td>\n",
              "      <td>-594.0</td>\n",
              "      <td>-46.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2047.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X177</th>\n",
              "      <td>9200.0</td>\n",
              "      <td>-9.395435</td>\n",
              "      <td>70.934300</td>\n",
              "      <td>-563.0</td>\n",
              "      <td>-45.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2047.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X178</th>\n",
              "      <td>9200.0</td>\n",
              "      <td>-9.240435</td>\n",
              "      <td>71.185850</td>\n",
              "      <td>-559.0</td>\n",
              "      <td>-45.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1915.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>y</th>\n",
              "      <td>9200.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>179 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       count      mean        std    min   25%  50%   75%     max\n",
              "X1    9200.0 -8.992609  70.455286 -566.0 -44.0 -7.0  26.0  1726.0\n",
              "X2    9200.0 -8.877174  70.560110 -609.0 -44.0 -7.0  27.0  1713.0\n",
              "X3    9200.0 -8.910435  70.372582 -594.0 -45.0 -7.0  28.0  1697.0\n",
              "X4    9200.0 -8.969783  70.030409 -549.0 -45.0 -8.0  27.0  1612.0\n",
              "X5    9200.0 -9.085326  69.377958 -603.0 -45.0 -8.0  27.0  1437.0\n",
              "...      ...       ...        ...    ...   ...  ...   ...     ...\n",
              "X175  9200.0 -9.848587  69.550894 -570.0 -45.0 -9.0  27.0  1958.0\n",
              "X176  9200.0 -9.620435  70.353607 -594.0 -46.0 -8.0  27.0  2047.0\n",
              "X177  9200.0 -9.395435  70.934300 -563.0 -45.0 -9.0  27.0  2047.0\n",
              "X178  9200.0 -9.240435  71.185850 -559.0 -45.0 -8.0  27.0  1915.0\n",
              "y     9200.0  0.000000   0.000000    0.0   0.0  0.0   0.0     0.0\n",
              "\n",
              "[179 rows x 8 columns]"
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# สร้างสถิติเชิงบรรยายสําหรับคนปกติที่ไม่ได้เป็นอะไร\n",
        "\n",
        "epileptic_seizure[epileptic_seizure['y'] == 0].describe().T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "WG_HsYRmffWF",
        "outputId": "ea3f2f5e-9df8-4284-ba01-9bd0861466b9"
      },
      "outputs": [
        {
          "data": {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>X1</th>\n",
              "      <td>2300.0</td>\n",
              "      <td>-21.936522</td>\n",
              "      <td>342.361939</td>\n",
              "      <td>-1839.0</td>\n",
              "      <td>-193.25</td>\n",
              "      <td>-16.0</td>\n",
              "      <td>159.00</td>\n",
              "      <td>1314.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X2</th>\n",
              "      <td>2300.0</td>\n",
              "      <td>-19.049130</td>\n",
              "      <td>343.398782</td>\n",
              "      <td>-1838.0</td>\n",
              "      <td>-191.25</td>\n",
              "      <td>-18.0</td>\n",
              "      <td>168.25</td>\n",
              "      <td>1356.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X3</th>\n",
              "      <td>2300.0</td>\n",
              "      <td>-15.293913</td>\n",
              "      <td>337.489643</td>\n",
              "      <td>-1835.0</td>\n",
              "      <td>-187.00</td>\n",
              "      <td>-12.5</td>\n",
              "      <td>169.25</td>\n",
              "      <td>1274.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X4</th>\n",
              "      <td>2300.0</td>\n",
              "      <td>-9.836087</td>\n",
              "      <td>332.354833</td>\n",
              "      <td>-1845.0</td>\n",
              "      <td>-184.00</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>166.25</td>\n",
              "      <td>1226.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X5</th>\n",
              "      <td>2300.0</td>\n",
              "      <td>-3.707391</td>\n",
              "      <td>332.211163</td>\n",
              "      <td>-1791.0</td>\n",
              "      <td>-174.25</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>170.00</td>\n",
              "      <td>1518.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>X175</th>\n",
              "      <td>2300.0</td>\n",
              "      <td>-25.830870</td>\n",
              "      <td>339.650467</td>\n",
              "      <td>-1863.0</td>\n",
              "      <td>-195.00</td>\n",
              "      <td>-14.5</td>\n",
              "      <td>153.25</td>\n",
              "      <td>1205.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X176</th>\n",
              "      <td>2300.0</td>\n",
              "      <td>-25.043913</td>\n",
              "      <td>335.747017</td>\n",
              "      <td>-1781.0</td>\n",
              "      <td>-192.00</td>\n",
              "      <td>-18.0</td>\n",
              "      <td>150.00</td>\n",
              "      <td>1371.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X177</th>\n",
              "      <td>2300.0</td>\n",
              "      <td>-24.548261</td>\n",
              "      <td>335.244512</td>\n",
              "      <td>-1727.0</td>\n",
              "      <td>-190.25</td>\n",
              "      <td>-21.5</td>\n",
              "      <td>151.25</td>\n",
              "      <td>1445.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X178</th>\n",
              "      <td>2300.0</td>\n",
              "      <td>-24.016522</td>\n",
              "      <td>339.819309</td>\n",
              "      <td>-1829.0</td>\n",
              "      <td>-189.00</td>\n",
              "      <td>-23.0</td>\n",
              "      <td>157.25</td>\n",
              "      <td>1380.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>y</th>\n",
              "      <td>2300.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>179 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       count       mean         std     min     25%   50%     75%     max\n",
              "X1    2300.0 -21.936522  342.361939 -1839.0 -193.25 -16.0  159.00  1314.0\n",
              "X2    2300.0 -19.049130  343.398782 -1838.0 -191.25 -18.0  168.25  1356.0\n",
              "X3    2300.0 -15.293913  337.489643 -1835.0 -187.00 -12.5  169.25  1274.0\n",
              "X4    2300.0  -9.836087  332.354833 -1845.0 -184.00  -6.0  166.25  1226.0\n",
              "X5    2300.0  -3.707391  332.211163 -1791.0 -174.25 -12.0  170.00  1518.0\n",
              "...      ...        ...         ...     ...     ...   ...     ...     ...\n",
              "X175  2300.0 -25.830870  339.650467 -1863.0 -195.00 -14.5  153.25  1205.0\n",
              "X176  2300.0 -25.043913  335.747017 -1781.0 -192.00 -18.0  150.00  1371.0\n",
              "X177  2300.0 -24.548261  335.244512 -1727.0 -190.25 -21.5  151.25  1445.0\n",
              "X178  2300.0 -24.016522  339.819309 -1829.0 -189.00 -23.0  157.25  1380.0\n",
              "y     2300.0   1.000000    0.000000     1.0    1.00   1.0    1.00     1.0\n",
              "\n",
              "[179 rows x 8 columns]"
            ]
          },
          "execution_count": 107,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# สร้างสถิติเชิงบรรยายสําหรับคนที่เป็นผู้ป่วย\n",
        "\n",
        "epileptic_seizure[epileptic_seizure['y'] == 1].describe().T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LDjD0A-WfjCN",
        "outputId": "2c2ea5e5-89b1-47d6-b741-3ee33f807ed2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total Mean VALUE for Epiletic: 290.12936095888404\n",
            "Total Std VALUE for Epiletic: 53.56315864740058\n",
            "Total Mean VALUE for NON Epiletic: 1260.0989272626161\n",
            "Total Std VALUE for NON Epiletic: 15.561044289100964\n"
          ]
        }
      ],
      "source": [
        "# แสดงผลค่า Mean และ Std สําหรับผู้ที่ไม่ป่วยและผู้ที่ป่วย\n",
        "\n",
        "print('Total Mean VALUE for Epiletic: {}'.format((epileptic_seizure[epileptic_seizure['y'] == 1].describe().mean()).mean()))\n",
        "print('Total Std VALUE for Epiletic: {}'.format((epileptic_seizure[epileptic_seizure['y'] == 1].describe().std()).std()))\n",
        "\n",
        "print('Total Mean VALUE for NON Epiletic: {}'.format((epileptic_seizure[epileptic_seizure['y'] == 0].describe().mean()).mean()))\n",
        "print('Total Std VALUE for NON Epiletic: {}'.format((epileptic_seizure[epileptic_seizure['y'] == 0].describe().std()).std()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eMeZmhJGfpru",
        "outputId": "50cef756-b19b-4b8a-af86-6f0ffde5f02b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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mmC/rf+8L+tY9t9/vANRcaEVtPHYhBMDVucrjTQsxpq0Z382NpOFGJVLK88AdAEIIKzAJfB54N/BnUso/afQ9FApFdeQNdDEXt173LAT5MZMbxd5+H0KQn7B1ORTBYbWwo3e96z3o09Z9bS7Gdy7PcfOwnzt3lZ66pVAAPHZxFoCxGgz0fDTFeb1V7uUSZYCtxOxOYq8ELksprxXGkBQKRWtIZUsraKNZyaDfVfTxVuJ2WBnpdudVyqWZCHv6vViLTMUymq78+Me+S07C3bt7+OeffklL16vYXORykm9fDCEETC0nSKSzuOzWiq8zRpvet7+f71yerfp1zcLsq/TtwKcK/v2zQojTQoiPCCF6ir1ACPEeIcQJIcSJUChk8nIUis6irItbN3QbHX822Bf0rVLQhtt7LTt63Rwc9PG624Z58FCQF26ES3YhUygAzlxfYiGW5sFDA8BKd7pKHB+dw2W38OYjI+SkNjd9IzHNQAshHMD3Af+sH/obYB+a+/sG8L+LvU5K+UEp5V1SyruCwcpD3xW1c3U2Wrb3sWLrkNQNdLEyK6Mfd71TrMxm/4Avn8k9Nh9jX9Bb9Hkeh42v/uLL+at3HuXVh4eIJDNMLMRbvFrFZsKIP//QvTuBlWTEShy/Ms/RnT3cNKz1st/oOLSZCvp1wEkp5TSAlHJaSpmVUuaADwH3mPheihr4nS+e5Zc+88xGL6MpTC7GebbGMoqtTDpbOgbtdli5dSTA3buLOrNazr6gj0Q6x3cuz5KTWmZ3JQ5vCwBw9sZys5en2MQ8dmGWW0cC+fGm16pQwkuxNC9MLXNsTx/7gnqOxMwWUdDAOyhwbwshhgseezNwxsT3UlSJlJJnJ5aYj27NcX2/+8Wz/OTHT2z0MtqGci5ugC/+3Mv44RfvbuGKSmO4tB8+o9WbFsvgXsuhQT8WAS8oA60oQTiR5uTYAvcfCNLtcRBw2apS0N+9Oo+UcGxvLy67le097pL94luFKUliQggv8GrgpwoO/7EQ4g5AAlfXPKZoEdPLSWYjSYTQEicsRZJwNitSSk5cWyAUThJLZfA41PTUckli7YZhoB95oXoD7XZY2d3vVQZaUZLnry+TyUmO7e0DYHe/l2tVxKBPTy5hEXDHDq1X/L6gL1/+t1GYckeTUkaBvjXHftiMcysa4/SE5v6VEiKpDAGXfYNXZB6Ti/F8bez4fJxDQ/4Kr9j6lCuzajd6vQ56PHbmoylGut24HdVly948HFBhDUVJIgmtoU2vR6uf39nr4bnJpYqvm15K0Odz5rO29wd9PHllbkOFTftfxYqGOFPwxVyOpzdwJeZzcmzlJj1eZZbmVqeSi7vdMFR0qQzuYhweDjCxEGc5sbW+zwpziKY0A+1xaoZ2d5+XyYV4Pj+jFNPhBIOBlWY++wa0HInJxY1LSNwcV7Gibk4XGOilrWagry1g03e24wvKQMNKFvdmMdCGW7sa97bB4WEtUezcjXBT1qTY3MRSWn93rx7y2tnnIZOTXK9gaGeWkwzqlQ5Q0O1uA+PQm+MqVtSFlJIzk0v5sprl+NbqZXxqfJGjO3vwOKxV1zludVJlsrjbkXoU9M26gT57vbLbUtF5RJPrFTRULrWaCSfy3fZgZdP4+MVZTo0tbEir2c1xFSvq4sZSgtlIivv29wNsKZdgIp3l7PUljuzqZkePh/F5VRcLm8/FbZRNGX9Xw2DASY/HzgtKQSuKYChojx5L3tWntY8tlyiWzuaYjaRWubh7vQ4GA07+7tujvPmvv8MPfOCJJq66OJvjKlbUhZEYcd8B3UBvIRf3mckl0lnJ0Z097Oj1qBi0zmYz0C/e28dXf/H+fOZsNQghuHk4wAtTrcvkjqUyqnvZJiGayuC0WbDpXqQBvxOX3cK12dI1zYY6HihwcQN8+j0v5qPvvpvvfdE2xhdiLf8ObI6rWFEXz00sYbWI/Ai/5cTWcXEbndE0A+1mfCGm5gUDqYymHjaLi1sIwcHB2rPvbxoKcGE63JLfeTSZ4b4/+gafqHFsoWJjiCWzeJ0rBUpCCHb1li+1mtENdKGCBtjT7+XBQwMc2dFNOitbnsezOa5iRV2cnlzi4KCffp8TIbaWgj41tsiOXjdBv5MdPR5iqeymb8ZihrHZTHXQjRD0O0mkc/mkuGby6IUQ89EUF9tg/KCiMtFUBs+akr1dfR6ullHQ08sJAAYDrqKPG33sZyOtjUNv7au4g8lkc5yeWOS2kQAWi8DntG2pLO7TE0vcsUNr47dTH1G4mRPFrs1FufW3Hm64Z/pmc3HXS8CtKaRWbDoffn4KgLnI5t4AdgqxZDafwW2wN+jj2lyMTIlSqxndQA+sUdAG/fos9VYnim3tq7iDeexiiMVYmlfdPAhAwGXfUklis5Ekw13abteYITy+iQcofPH0DaKpLOenGkt8SmU1Fb5ZXNz14tcb7jT7O53K5Pj6uRkAQi1WT4r6iKYy+Qxug31BL6lsruQ9Yno5idUi6PMWN9CGgm71d2BrX8UdzGefnqTX6+ABfdxawG3fMmVWyUyWZCZHwKXtkrfr4xM3c6LYikpr7AaQyuRwWC1s9Xnsxu9+qcnf6SeuzBFOZOhy21vu3lTURyy1XkEbZXylWnfOhBP0+xxF55FDgYFWClrRKEuxNI+8MM33vWhb3tXZ5bZtGQUd1pPdDBXlddro9znWGejf+rcz/I/PP9fy9dXK9cU4pye0jPvZBt2oqUxuy7u3QdtwgjYYoZk8/PwUHoeV1906pFzcm4Rocn0Mel+FpiPTy8mS8WfQNoQOq0UpaEXjfPG566QyOd56dHv+WMBl3zJJYisGemWXvL3Hs6qbWCyV4dPfHedfTkw0/SbeKF/V1bPHYW040S2VzXaGgc67uJunoHM5ySNnp3ngUJCRbjdL8XQ+xq9oX6KpzKosbtC+LwN+Z0kFPb2cWFdiVYgQgqDfqRS0onE+d3KSg4M+bh1Zaf6gubjb21BVi2FwCwd/7Oj1rEoSe/R8iGQmRyqb4xvnQy1fYy08/Pw0+wd83DwcYC5qjot7q2O4uJv5nT41vkgonOShW4bo05OEGv39KJpPLJldp6BB6wxWSkHPhJPrSqzW0u93NuzhqpWtfyV3GOPzMZ6+tsBbjm5fFYfUksS2RgzaiKUXKuidvW6uLybyWZoPPz9Ft8dOv8+Rj++2IwvRFE9dneehWwbp8zoadqN2mou7mWGb71yaBeD+A0H6fdpkpNmwcnO3O8UUNGhx6MszEaSUJNJZfvjDxzl+ZY5UJsd8NFXWxQ0Q9DlarqDVAN0thtFv9ujOnlXHA24bkWSGTDaX77CzWTEUtL9QQfd4yOYkV2aj7O7z8rVzMzx0yxB2q+ALz1wnkc7mx8i1E18/N0M2J3noliHmo+OrJnTVQyqbw27d2gliAE6bBYfV0tTEx+Oj89w05KfH66DfqINVCrqtyeYkiXSuhIL2spzIEIokeWZskW9dnCXgtrNdrwIZ8JdX0EG/k2fGW9v/fXPfqRXriCQN47U+BqM9vvlVtBGDNmphAV52MIjPaePXP/ccj1+aJZzI8NAtQ7zmliGiqSzfuTy7Ucsty4Re9nF4OECf18FCLNVQO0FNQbffRsRshBAE3Lam5RekszmevraQ78LXr5ffzG7AwARF9cT0UZO+ogpa61h3eSbKw89PA/DNczP55NLKCtrJfDRJtoXtPpWB3mIYxmvtF7TLcAnGM2Rzkvf9y7Obduj9chEFPdLt5vfefCtPX1vgl//5WTwOKy870M9L9vXhc9p4+Mz0Ri23LPF0FodV6xvc53OQzTXWTjDZIS5u0H7/zQrbnJ5YIp7OcmxvHwD9ft3FrTK525r8oAzHegO9b0CbanV+apmvnZtmpNtNNJXl8ycngdJNSgz6/U5ykpZ2LDTtShZCXBVCPCeEeEYIcUI/1iuEeEQIcVH/u6fSeRSNYSjkdQq6IGY3Nh/jn05M8EdfOdfy9ZnBcolNyBvvGOEH7trBfDTFyw8GcdmtOG1WHrxpgP98YbqlO99qSaSzOO3aZWhGIlIqk8O5yUMY1RJw2ZqWJHZ8dA6Ae3QF7XHY8DisDdepK5qLMWrS61zvRRoKuPA6rHz6u+MsxtK877WH8Dlt/NuzmoGuRkFDa2uhzb6SH5RS3iGlvEv/968BX5NSHgC+pv9b0URKKeiVxg5pRme1TMbvXJ7j7PXWTQQyi3Aijd9pK9pU4P3fdwtvumMbP3bfnvyxV940wFw0xQs32u+zJtJZ3HpsvN/buEpLZTtHQQfczeuOd/zKPPsHfPkWjwB9PodqVtLmlFPQQgj2Dfg4NxXGabPw6sODPHjTAIl0DptF0OtxlD13/wb04272lfxG4GP6zx8D3tTk9+t4IskMbrt1XSJYXkHH01wJaU3jHTYLH/72aMvX2CjL8cw6D4GB22Hl/7z9CHfv7s0fO7ZX+/nJK3MtWV8tFCav5RV0Iwa6g1zcAZc9vyE1k0w2x4mr8/n4s0G/r/VlNorayCvoIkliAPuDWsOS+w8G8ThsPHSL1go56HdiKdFFzGCzK2gJfFUI8bQQ4j36sUEp5Q395ylgcO2LhBDvEUKcEEKcCIXau151MxBOZPAVMV6FLu7R2Shdbjtvv3sH//7sdWbCiVYvsyHCifSq+HMlhrvc7Oz1cHx0vomrqo94gYLu1RV0oy7uTqiDBi2M0wwX9/PXl4mmVuLPBpqBVgq6nckr6CJJYrDSUeyhW4YAeODQAA6bhYEK7m3YmIlWZl7J90kpjwKvA94rhLi/8EGpzdJbFwSUUn5QSnmXlPKuYDBo4nI6k0gyg7/Il3OlsUOGq3NR9vR7efdL95DO5fjHJzbXnNtwIrMqg7saju3p5btX51s+cL0SiXQOlx6D7vHYEaIxBZ1WLu6GeUrfyN27TkE7lIJuc6Kp8gr6gUNBju3p5dWHNa3oc9r4sZfu4XW3DlU8t9dpw223bk4FLaWc1P+eAT4P3ANMCyGGAfS/Z8x6P0VxIol0UQXtc9qwCF1Bh6Ls7feyp9/LnTt7+M7l9nP9lmO5RgUNcGxvH4uxNBdmGpsWZTbxAhe3zWqhx+NoXEF3ioF22Uikc6a337w4E2bA71ynqvo3oMxGURuxZHkFfcu2Lj7zUy/OV7UA/NrrbuKnX76vqvMH/c6W9uM25UoWQniFEH7jZ+A1wBngC8C79Ke9C/g3M95PUZpwIlO0BlCrG7UztZTg+lKC3f1aycFgl6ulZQNmEE6UjkGXwognHr/SXm7u5JoGKo12E+u0JDEwf2DGYiydDzcU0ud1kJOwENtc10snEakQg26UoL+1YQ6zruRB4NtCiGeBp4AvSSm/Avwh8GohxEXgVfq/FU0kkixtvAIuO89Nap1w9ugGut+7+TJTw4n0qj7c1bCj18NItztfPtMuFMagQcsUbsRAJzssBg3mD8xYjKdXKSwDI4tXTbVqX4xGJcWyuM2gv8XtPk35FFLKK8CLihyfA15pxnsoqkNT0MWNV8Bty5dVGQa6z+dkOZHZNK5RKSXLdSho0FT0YxdDSCnbZl5yYQwaoM/r5IWp+svBUpkczk3wezSD/EQrkxPFlmJpdvV51h03Sq5mI0kO4Tf1PRXmEE1lsVtF0+5lQb8zn6PQCjrjSt5gLociXCox5sxsKiloI3y2YqA1V95mcXPH01myOZl3b9bCsb29zEZSXC4x0WYjWNsjvM/nqPt3IaXUe3F3xmXdrIEZS/E03Z4iCtoYmLHJPE6dRCyZaZp6Bgj6XCzE0qSzrRk72hlX8gbzi595hl//3Ommv4+UkkiyeAwaVtp9Dgac+Wkvfd7NNUav2CzoajmiDxB5vo2as8TXxaCdLNZ5A8jkJFKyKTwhZmAoaLNroRfjKbqLNK1YUdCbYzPbiURT2abFn2Gl5WurwhydcSVvILFUhuevLzOpD0VoJoa6LJbFDSs3tN193vwxQ0Fvlria4c6sNYsboNvdnBt6IyTTuXUKGrQxlLViZDN3ioH2N2EmdCKdJZHOFY1Bd7nt2CxCKeg2JpbKlMzgNoNWNyvpjCt5A3luYolsTjITTja9BjdSos2ngVE7vDdYYKBNaI7RSoyEoEAdCtrwGkTbZKJXNqe5pAuTxFW5T8cAACAASURBVFbcqA0YaOXirhtjUEkxAy2E0Np9qolWbUs02VwF/dL9/Xz7Vx/k5uHW5CB0xpW8gRjzfTM52fRZsuESgzIMDAVtxJ/BnPaSraTYLOhq8TisCNE+BjqR1mo2C5PEehsIOaSynaWgvQ6rVttv4kxow0AXi0GD6ibW7sRSzY1Be502tvd41rVSbhadcSVvICfHFvI/Ty8198KOVIjPGopjT79v5ZjLht0qNk1crREFLYTA57AR0ZsZbDRx3UC7Hetd3PUkinWai9uo7TezDnoxphtod/HBCUG/kxmloNuWaDJbdJLVZqUzruQNQkrJqbFFDuj9X6eWm9vz2ijSL1Vmta3bjUXAocEV94wQgj6v1iFpM2DcjOvJ4gZtB9x2CtpW4OL21p+IlNQNdKeUWYHej9vEnIJFvQlJMRc3wI4eD2PzMbTOxYp2o9kKutV0zpXcIqSU+Yt8YiHObCSZ7/PabANtGK9SMehX3jTA13/5AXauqfFstDlGK2kkixu0ObGRdjPQBQo64LZhs4i65g4bmd+dEoMGLWyzNkksmcnW/TterODi3tXnIZzI5JW2or2IppSCVpTh25dmOfo7j/D1c9N59/arDw9htQiml5ptoMsbL4tF5Ft8FtLrdTC7Seqgl+NprBaxKrGqFnxOWxsZaM2gugoUrxCCXm99tdCd5uIG3UCvcXG//wvP89r/8xjxVO2hDMPYd5U00Nr1c3UuWvO5Fc2n2XXQraZzruQWcSUUJSfhl//pWf7juSk8Dis3D/sJ+pwtc3HXqi77fc66FNtGEE5kCLhsdXcCaycXd7EYNGjJbMZjtdBpSWKgeRwKy+aklHzjXIiJhTgfebz2WeeLMW0DWGwiHMBu3fs0Nh+rb8GKppHLSWLp5mZxt5rOuZJbxEw4gUVo8cCvPD/F7du7sFktDHa5mG62gdZvVN4a6wAbHdDQSmqdBb0Wb1spaCOLe/UNxWmz5h+rhU4rswItm7/QxT02H2NqOYHPaeOvv3Gp5nrVxXiq7AZwR69moK/OKgPdbsTTWaSs/f7XznTOldwiZpaTBP1OfvdNtwJw5y6te9VQwMlUk13ckWQGl91Sc6vHPp+TeDqbbzTfztTbh9vA77TlZ8ZuNCsu7tUG2mW35BO+aqFzXdwrv09jWtmf/JfbSWRy/N+vXajpfIuxdNEuYgYuu5XhLhfX5pWLu90wrutmNippNZ1zJbeIUCTJgN/FW45u5+9+5C5+/L69AAwFXE13cS+XGZRRjs3UTayeSVaFeJ22vKdho1lxca++DOtV0MlONNBuzSNizGh+cnSOXq+Dh24Z4p337ORTT40zE67+ulsqMcmqkJ29HsbmlIJuN4xZ0MrFrSjJzHKSAX0s3asOD+bnyg52uQgnMqao1HAizSv+5Jscv7J6dGK5QRnlWOkmthkMdGMKWotBt0cdtGGEnWsUtLNeBd2hWdywEt45fmWee3b3IoTgFTcPkM1Jxuerb7NbjYHe3eflqjLQbUe0yaMmN4LOuZJbRCiiubjXMhRwAZji5j4zucyV2Sgnri2sOh5JpEuWWJVjpZtY+yeKLccbi0H7nFZS2VzeHbyRJEokibns1rz7uxY60cW9MhM6zcRCjMnFOMf29gIrNeW1fK81F3cFBd3nYTaSbJtkQ4VGTM/aV2VWiqJkc5K5yIqCLiRvoE1wc1+cCQOsSzprWEFvChd3Jt9TvB7aqR936SQxC8lGksQ6yEAbDWuW4ul8/PnevX1AQeimBs/QUjydH6pSCmPYzDWlotsK45pWClpRlLlIkpyEoG6MCxns0o6Zkcl9fkoz0GvVeDhRetRkOeq5kW0EuZwkkso0qKC1/592yOSOp9bXQYNmsOtLEtOMeie6uD/51BhfPTtFt8ee75RnhJeqrSnP5iTLiTRdZZLEQGtWAjDWpoliT43O866PPEWmRTOL2wWloIsghNghhPiGEOKsEOJ5IcQv6MffL4SYFEI8o/95fePLbW+MHr3GSLJCVlzcjbuRL05HgPXGPpzIlBw1WQ6Pw4bHYW17F3c4mUHK+vpwG7STgU5kstitYl3jfafNUl+ZVQfWQd++vYvX3zbEp54a4+Hnp7l7dy8Wi1Yi5bJb8TttVQ+3CCfSSFm6zaeB0YmvXePQj16Y4dELIaY7rGe4oaC9W0hBm/FJMsAvSylPCiH8wNNCiEf0x/5MSvknJrzHpsCouRwIrDfQXqcNv9PWsIKWUnJ+WlfQxVzcdZYY9Pkcba+g8324G8zihvZwccdT2XXubWhEQXeegfY6bfz1D97J1dko/3RinFcdHlz1eG8NbWxXBmWU/34FXHZ6vY62dXHf0D1rc5EkI93uDV5N6zAUtGcLZXE3bKCllDeAG/rPYSHEC8BIo+fdjBjlHMVi0KC5uRtNEpsJJ7U4mcdOKJwkk81hs1qQUhJJ1qegQRtz2O5j9Brtww0rBrodFHQyU8pA16ugtVKjTnJxG+zu9/K+19607nif11H16M5KoyYL2dXnaVsX941F7R7T7tez2RhZ3KpRSQmEELuBI8Bx/dDPCiFOCyE+IoToKfGa9wghTgghToRCITOX03IMBd1fxMUN5tRCX9DV8337+8nJlalHiXSObE7WVQcN0L8JuokZCqeSC7IcvryC3vhSK01Br78EnTYrmZysOYaYyuRwWC11t0HdivT5nNUr6Hj1369dvZ627SZm3GNmw+19PZtNLJnFIrbWNDfTPokQwgd8FvhvUspl4G+AfcAdaAr7fxd7nZTyg1LKu6SUdwWDQbOWsyHMhJN0ue1FVRHAYKDxdp9Ggtj9B7X/K+NiDCe1m0u96rLPV9+AhlZirK/XVz6JpxxGAkk7uLgT6VzRoR+G0a7VzZ3K5DrKvV0N/TWEbowpdNUpaC83luIkMxu/0StESsmNJa3uO9RhCnpqOUGfz7mlNqimXM1CCDuacf6ElPJzAFLKaSllVkqZAz4E3GPGe7UzhU1KijHUpQ17N7oe1cPF6Qh9XgeHhwPASiZ3o+7fPp+TuWiyrefczus3UCM7tx78uoch3A4GuoSL22hcUqubO5XNKgO9Bm3WeYpcFdfcUl5BV/5+7ez1kJMr7uR2YTGWztfQt7tHzGxGZ6PsLTKtbzNjRha3AD4MvCCl/NOC48MFT3szcKbR92p3SjUpMRjp9pDNyYbc3Oenwxwc9DO0pmzL6KRUT5kVQI/HTjoridYxoq9VLOhKqKdCGUw52klBl04Sa0BBd2D8uRy9XgfZnMwb33Is1RBCGenRkq8mFqrvUtYKbhTkuHRaDHp0NsreoDLQa3kp8MPAK9aUVP2xEOI5IcRp4EHgF014r7ZmJpwoq6D36Lu70VB9ySVSSi5Ohzk05KfX48BuFXljbyQ91Wug2ym7uRTz0RR+l63mYSCF2KwWnDZLW3zORCZXMosb6lDQysW9jpUa/8rGajGexuOwVvV/uF030JOL7RWHnlrWNgwOm6UpBvrSTIRPHL9m+nkbZSmWZj6ayjeR2SqYkcX9baCY0//LjZ57MyGlzE+yKoWxuxudjXDfgf6a32NyMU40leXAoA+LRTDgdzG9xsVdbxa3YdjDiQyDgbpO0XTmo6mG3NsGvjYZOZlIZRkqUpJnJLnU2u4zlc1ht26d+JsZGAmbs5EU+wfKP3cxVrmLmMFQwIXVItpOQV/XXe43Dwea4uL+xPFrfPTxq7zpjpG2ypYendNEzx7l4lYUI5zMkMzkGPCv7yJmMOB34nFYuTJbn4I2MriNTkmDAec6Be2vM4vbiF23g+EqxULMHAOtDczY+M9ZMgatH6s1AUlT0FunBtQMDAVdTQLkUjxVsYuYgc1qYSjgajsDPbWUwGoR3Dzkb4qCvr6ofd52qwEfndWaNykXt6IoM8ulm5QYCCHY3edltE4D/fzkMgAHhzQDPdS1UrYVSTSWxW2UZ7XLKMZizEdT9DYQfzZoFwUdT2WLZnHXq6CTysW9jr4aBmZok6yqv35GetxMtpmBvrGUYNDvZCDgYj6WMr3dp6HQ260GfDQUxSJgR69no5diKupqNgmjSUmxNp+F7Al6uVqngT45tsCBAV++k9ZgQGt8IqXMu7jrdTu1UwvMUixEU/RsJRd3unQnMahPQTtVktgqevSSqdkq3L2ai7v679f2bjcTC+2lJG8sxRnqchH0OZASFmKVk+NqPT+0n4K+Mhtle49n3ejWzY66mk2iXJvPQvb2exlfiNc87lBKyanxRY7uXOn3MhRwEUtlCScznJsO0+d11K2g2t3FLaVkzqQYtNdpbYtGJYl0iSSxfJlV7TFopaBXY7Na6PHYq0oSm6txA7i9x83UcoJ0Gw2lmFpKMNzlzo+QNdPNnUhn8xuddutDPjob3XLxZ1AG2jQMAx0sE4MGLYkhm5OM17jzHp2NshhLc3RXd/6YUWp1cTrCI2enecPtw6VeXpF8C8yEuTtus4insyQzuS0Tg87mJKlsrngnsXyZlcriNoNquonFUhnmo6l8dnY1jPS4yUlzZrybgdakJMFwl6sgOc48A11YwtVOLm4pJVeVgVaUYyacxGGzVJy0VG+p1cmxRQCOFCjoQX1C1kceHyWVyfGWo9trOmchRn1wuyrofBexLRKDLjULuvBYskYFnc6qOuhiaP24yxtoI5Zci4He3qPFO9slUWwpniaezjLU5aLfZ/6MdyNBbDDgbKs2p6Fwkmgqqwy0ojSXZiJs73ZXbDOXN9A1xqFPji3gd9nYH/TljxkjLL/83A32D/i4fXtXjatewWnT6j/bocNWMQwDvVVi0IaBLpskphS0KfT7nBWTxCZ041PL9CfjuUYcenw+lvekbQSGwm2Wi3tS/z96yb7+tmpzalTFKAOtKEo0meHbl2Z5+aHKvcS7PQ56vY6aS61OXlvgjh3d+Vm3sOLilhLecnSk4R60fqetbbO48wraW/+gDAOv00Ysla2q/WOzSOg5CMVc3PUqaGWgi9NbhYKeyCvo6rOAh7tdCKEZrlxO8vYPPslv//vzDa21EYwEruFuFwGXDYfVYmo/7uuLcYSAY3t6ycn28RyMKgOtKMejF0KkMjkeumWoqufv7vPk6/aqIZLMcGE6vCpBDLQbeZfbjhDw5iONT/j0uTY+NluKhXwf7vJJeNWQn2iV2rjPGk+VcXHny6xq7cWtDHQx+nwOFmPpsslckwtx7FZRthPgWpw2KwN+JxMLcZ6dWGRyMc6VOrsEmsGKgnYhhNAGhZjs4g76nBwY1Lx4Y22SKHZ1NorDZmHbFpx9ra5mE/jKmSl6vQ7u3t1b1fP39PtqiuGcHl8kJ+HIzu51j+0Nenn5wSDDXY1/OdvB9VuK+aiWvGZGDNrbBiMny8WgbVYLVouo2cWdVL24i2K4exfKqOiJhRjbut2rPFTVsL3Hw+RCnIefnwZW3MAbwdRSAotYKfXs85k74/3GUoJt3W529mpK9dpc6zYj0WSmpHi4MhtlV68Ha42/u82AupobJJXJ8Y1zM7zq5oGqvyB7g16mlhNVq9WTYwsAHNmxfqT2h991N3/xjiPVL7gMXqctX0/dbsxHk1gtou5GLIW0Q0JcuRg0aCq6Hhf3VpqFaxb9XqMfd2kDPbkYrylBzGCk283EYoyvPj8FaIla4Q2qhLi+mGAw4MKmb9L6fQ7TY9Aj3W76fQ68DmtLS63e99nT3PdHX+eUfi8EeHZ8kfd+8iRfe2Gaw9vatD9xg6iruUG+c3mWcDJTtXsbVmIlV6vcgZ6ZXGZvv5euInNqe70O/K7G47Kgx6DbWEH3eOw1K5xi+NpgMIhR41xqdrjTbq1JQUsplYu7BIaCLufunViI15QgZrC9x834fJwrs1GO7dE8aBulosfmo6vcvP1VlJdVi5SS64vxvPt8Z5+XsfnWGehnxhZZiKV554eO84FHL/O2DzzBG//qcR47H+InX7aX3/reW1q2llairuY6WYimmFyM84Vnr+N1WHnp/uqHXxgTV6rtxrMYT+XrGpuJz9W+BnrBpCYl0B5d0+ImK+hMTiIlDU362qpUmmiVSGcJhZM1JYgZjBSo7ne/dDfAhrT/jCQzPDO+yF27V7xsRv23GTPeF/Q508YGYFevp2qB0SjxVJbJxTg/8uJd7A16+YP/OMfkQpzf/J6b+c6vv4Jff/3Npt0b2o32GUeyibg2F+XBP/kmRhLw99w+XFIJFaOWBv4AsVSWvhZ8AX3tnMUdSzU0B7oQbxsY6JUYdHGD6rJb85ne1WB0plMKej3GtVOq3ef1OkqsDAyjfseObo7u0ozjRmQ3P3l5jnRW8vIDK5Uk/T4HqWyO5XimqPetFoz/o7yB7vfw9XMzZHOy6bHfyyEtofbevX386mtv4tTYIvfu7c278rcyykDXwdh8jJyEn3lgH7v7tSStWjAGwlczRB40Q7KzBU3g21lBz0dTHBjwVX5iFbSDizteJkkMNENbSxZ33kB3wE2rVowQ0HKJ681wSdcTg96lX5evvXWIoM+J02bZEBf3YxdDuO1W7ixQ0Mbo21Ak2bCBnlyzidnV6yWVzTG1nKhrY1MLhoHeF/ThddrqGtW7WVEGug4Mw/rGO0Y4pE+WqgWX3YrTZil5w1hLNJnB62j+r8rvtJHM5NqyntasQRlQmMW9cQY6WcFAu+xWkrUo6KxS0KWwWgR+p43lEslbhuIdqcNA7+738tEfvZsX7+tDCKEljW3AAI3HLoR48b6+VcMi+n0rk7z2N7i5XVHQWu+FXX3axuTabLT5BnomgkXA7v6tNamqGtTVXAeGge6qcrh7Mbrc9qoVdDSZbclw9HYwXMXI5SQLsZRpbv6VGPTGlVnFK7i4nfUqaGWgixJw21mOF/9eTy7EsVpEvjNfrTx400B+o7URIyjH5mJcnYtx/xplaRjoGRO6m91YSuC0WfKxXsMoX29BH/LLoSg7e7fepKpqaPrVLIR4rRDivBDikhDi15r9fq3AMKzdDbiNutx2FqsYBSelJJrK4HM2/8vZDslTxVhOpMlJTItBu+xanXEkuXGDQSplcdeqoI3vZMCkjP6tht9lK1n+NLEQY6igPKkRtve4Wx6DfvRiCID714TadvV5sFkE56aWG34Po8TK6FZodDGcWmr+Z700E2Ff0Jzw1majqQZaCGEF/gp4HXAYeIcQ4nAz37MVLMXTOGyWmhLD1lKtgo6lskhZ/5znWjBqjNutFnou3+bTHAMthMDr2NiRk4l0FptFlMy6dtkteTd4Nax0Wtua2ayNEnDZS7q4662BLsb2Hg9z0VS+U1wreOxCiJFu97pWly67lQODfp6bbNxATyzEGe5e8TC47FZ6vY6mK+hsTjI6G23YRb9ZabaCvge4JKW8IqVMAZ8G3tjk92w6S7F0Q+5tqN5AG+7mVhhon1P7TO2moBdMHJRh4NvgpizxdLZkiRVobSRrcXEv6N6YngaTgbYqAbetrIu7nhKrYhiu38nF1sShsznJE5fnuP9gsGgv/ttHunhuYrGhUqt0Nsf5qWUODa5uBjIUcDV91Ob4fIxUNqcUdJMYAcYL/j2hH8sjhHiPEOKEEOJEKBRq8nLMYSneOgNtGEtfKwy0qz1j0EY5mpmlZl0eB0tx8/oU10oincNZxkC77JaaXNzN2MRsJUop6LSRiWyagjYmXLXGzX1jKU4kmSk5ye627V0sxNINZZa/cGOZRDq3ahY9aAljN5psoPMZ3EpBbwxSyg9KKe+SUt4VDNZWrrRRmGKgPfaqsrgNN2xrFLTu4m4zA224b800PtXMCG4miXQWt6P05VergjY2Md0Nfi+3KloMev33emopQU7CdpMykUdabKDH57X3KVWGeduIZrifm1iq+z1OXtPaa64d1jPU5cpP0GoWl2Y0A71fKeimMAnsKPj3dv3YpmYpnm74RtjlthNOZsiUmbADKxOXvK1MEmvXGLRJSWKgxWqrbRTTDBLpLK4yWam1KujFWIqAy9YRzRvqIeC2E06k140YzU+A6q4vg3stA34XdqtoWS30uN5uc0cJF/1Nw37sVsHpyfoN9KnxRQYDToa7Vv8fDXe5WYylmxpvvxyK0O9zNlzHvVlp9tX8XeCAEGKPEMIBvB34QpPfs+mY5eIGWK5gDKMb4OLeyOzmYizFtKQ8t8O8TUqv18G8iaP4aiWezpb9PC67pqCrjR3Ox9IqQawMAZednFw/YnR6WTPQg3WWWK3FahEMd7Uuk3t8IYZFlN5gOG1WDg76OdOAgT45tsDRnT3rYtyGwW6mitYyuLfenOdqaaqBllJmgJ8FHgZeAP5JSrlxE81NYimWJmCSga4UhzZi0J4WNCrx2K0I0X4K2owN0Vr6vA7CyQzJGkc6mkUlBe20WchJSGerM9CLsRTdJnoYthoBt3b9rN0Q5w203xwDDVqi2GSLmpWMzWtjMsv1YL99exenJ5aQUvLI2Wn+6Cvnqj5/KJxkfD6+zr0NhaVWzYlDSym5HOrcDG5oQQxaSvllKeVBKeU+KeXvNfv9mk02JwknM6Yp6EoG2ohBt0JBWywCn8PWdjHo5YT5BrpX74e+EN0Yb0EsVVlBA1VvIOZNHCayFTHqw9fWQofCSZw2S96Am8H2HjfjC3FThlRUYnw+VtK9bXDrSBdL8TTfvBDiFz59ir/55mXmqhxDaYy6XZsgBuRn0DcrUSyWyrIUT5uWYb8ZUQGrGlk2oYtY4esrG+jWxaBBc3O3WxZ3sxQ0lJ5w1GwiyUw+pFAMY65zosqJVouxdEONc7Y6K/241yvogYCzaIlSvdyxs5tQOMnT1xYqP7lBxhfi7Ogtn+B2+4hmXN/7iZNkdI/MU6PzVZ3/1Ngidqvglm3rs8Sb7eI2RmX2+zp346kMdI2Y0UWs8PXVurhb0Ysb9IlWbWigA2WMWT30erU2iBuVKBZJZPCV+Z0661HQysVdkryLe831Nr2cNNW9DfDmIyN0ue18+Nujpp53LfGUNiaz0iCdg0M+HFYLsVSWv3jnEVx2C8erNNAnxxY4vK2raFMml91Kj8feNAU9q2+eWzFqt11RBrpGzOjDDeRj2NUoaI/DiqXJI90MvBvcwKMYy/HGQwprMdzBG2WgoxUUtHFDrEZBJ9JZ4umsqoEug+HiXlsLPR1OmJYgZuBx2HjnsZ08/PxUPsu6GRhDOXZUMNBOm5W33b2d//7QIR66ZYi7dvXy5JW5iudPZ3Ocnljk6M717m2DoS5302LQhoLuUwpaUS2LZru4Y+UNRDSVaUkNtIG/DUdONtXFXUMmt5SSjz9xtWGjnstJoqnyA1BWXNyVFXS+Tlwp6JIYG+K1m8/QcpKBgPkK7V0v3o1FCD76+FXTz20wNl+dgQb43Tfdxnsf3A/AsT29nJ8Os1jh3nMlFCWRzvGi7aUN9LYuV9Pafc7rCrqTcyuUga4RsxS002bFZbdU4eLOtiRBzMDntLVVFncuJ1lONJ41v5Yutx2rRdRkbEdno/zPf3uez3x3vPKTy2CU+vjL/F5XksQqK2gj0U21+SyN0We+0MUdTWYIJzMMmOziBi3D+Q23D/NPJ8ZL9gBvlEo10KU4trcPKSvHoc9PhwHKjtQd6nI1bWDGrKGgvcrFragSswy0cY5qXNytShCD9otBR1IZpDTn/7sQi0XQ47HX1E3s+qKmFF640djwgUgV/dUNBV3NwIxmdFrbatitFtx26ypjaYxhHGyCggZ457FdRJIZnrhc2Z1cD+MLcdx2a81JVC/a0YXTVjkOfXE6jNUi2FumDnlbt5uFWLqmrnfVMhdJ4XVYTe1/sNlQBrpGjB24GYquWgPdihpoA1+bubiXYub9f69F6yZWfRa3MbS+UQOdbz5TRQy6KgWtJllVxdqBGWY3KVnLLdu04RIXdSVqNmPzMXb0umvOQHfarBzZ2c3x0fIbh/NTYXb3lZ/DbMzQbkai2Fw0SV8HJ4iBMtA1sxRP47I3NmrSoNvtqGygU5mWurj9uoIuVsOZzUm+cW6GdIX2pGZiKJ5mzDmutd2n0b7xcijSkGIw4qDlZny77DXEoI0+3MrFXZaAy064oEveioFujhHwOm1s73FzYTrSlPNXUwNdimN7+jh7fZmPPj7KPzx5jZnwegN7cSbCwcHS7m1obqnVfDTV0QlioAx0zSzGUqa5WwNuO4uxyo1KWpkk5nXakFJrErCWR85O8e6//y4/+fETxFKtUdlmhhTW0ud11uji1m5COQkXGlBFK81nSn8mQ7VUo6Dn8zHozr6ZVcLvWq2gQ7qLO9iEGLTBoUF/Q9+VUkgpNQNdRYJYMV5x0wAS+O1/P8v/969n+NBjV1Y9nkhnuToXrWyg9SEjNxbNV9CzkVRHx59BGeiaMTOjuMtdeaJVJJkpq7TMZqUf93oDfFFXAo9dCPGODx3PK7dmYlZjmGLUqqBvLCXy2d+NuLmNXuflcgtqUtCxFH6nrWy7R4W2IS6MQU8vJ3DZLabX2BdyYNDP5VDEdK/TQixNNJWt20C/aEc3p3/rNTz9m6/i4KCPy6HoqscvzUSQsnyCGKy4uKeWm+DijiRNHTG7GemIK/ridNi0elezDXRVSWKtjEEbIyeLZHKPzkbZ1uXib3/oTs5eX+LP/vNC09djKB4zWzEa9HodLMbSFSeKGVxfjHPPnl68DitnrzdioDWj669CQVdroFWCWGUCrtUb4unlJIMBl6ldxNZyaMhHOiu5Nhet/OQaWMngrn9Mpt9lp8/nZP+Aj9HZ1eszVP/BwfJ9sN0OKwGXLR8uMItcTioXN1vYQOdykv88O83bPvAEr/6zx3j/F8yZ0bFkYtOMLredaCpbcnedy0liFeplzcZfRkGPzkXZE/TymluGeOiWIb7w7HVSNYxErIemuriNftwVwgyguRQnF+Ns73Fz03CAF27U77aM6CqufJKYnsVdVZJYWpVYVUHAvboJz/RywvQuYmsxXMTnp8yNQxvTsszoU72n38v4PC4V8gAAIABJREFUfGzVfej8dBiH1cKuvsqTpLo9jqpm29fCciJNJidVkthGL6BZnJsK8xMfP8HkQpzdfR7ONph5a7AcT9PlNmdX11Wi/aCBUS/b2jpo7UZfrBZ6dDbKbv2Cfeud21mMpfn6uZmmrmcpnsYimvN/UEs3sfloimQmx7ZuNzcP+3lharnuYQhRPb5fzsW9oqCrqYNWCroa/C7NxW383mbCSYJNShAz2Bf0YRGN5SwUI6QndZnRZGVPv49MTq4akXlxOsLeoLeqsEnAbavoCawVIzekk/twwxY20Ie3Bfj4j93Do//9AV532zDX5qKmxIHMdHEb4wFLfbmNZKKNUNBrmyssRFMsxtLs6dcM9Mv29xP0O/ncyYmmrsdoUtIMN2RvDQMzjBpozUAHCCcydc/8DScyOKyWsuUrVovAbhVV9eJeiKVUglgVBFx20lmZ3/TMtEBBu+xWdvd5TTfQs5EUVosw5fduXNOjsysq//xUuGKCmEGX215xrn2tzKkmJcAWNtAA9x8MYrNa2BfU4kBjDfbFTWdzREwYNWlgnGexhIGOtHiSFUDQr10QM2tiSlf0GJXRtMBmtfCmO7bxjfMzTe1n3Yw2nwZ9NQzMMEqsRrrdHB7W6lvr9cpU23zGZbNWr6CVga7IykzoNJFkhmgq27QSq0IODPryXbnMIhTWEqisJvToNwz0FT1RLJLMMLkYr5ggZrA2tm8GxjhMFYPuAIyB35dnGosDrWQUm6NoKw3MMEqZWpkk1utxYLcKppZXq0ojiWRP/0rSyFuObiedlfz7s9ebtp5mGuhaXNxGnee2bjeHhvwIUX8md6VRkwZOu4VEBQWdzGSJprIqBl0FhTOhm92kpJBDg36uzcVWJfxJKYkXKWWsllAkadqUpx6PnS63PX+NG41VDgyUTxAzCLjsprcznY0aCloZ6C2PofouhRoz0PmEJZNuhobhKbX7rKYlpNlYLIIB//r+uldno9gsgu0FWaM3Dwe4acjPl5670bT1LMfTTWlSAiu9q6sZmHF9MY7TZqHHY8fjsDXktoxUmZnvtFlJVlDQRh29ikFXxgjfLMUzeQM94G++gj445Cebk3mFCvDohRBHfuer+VrsWpmNJPPerkYRQrCn38tVPdP8jF6hULWCXtOhzQwMBd3p3+uGDLQQ4n8JIc4JIU4LIT4vhOjWj+8WQsSFEM/of/7WnOXWR8BlZzDg5PJMY6UOZmcUd1VQ0CsNLVpnoEFvgL/GxT06G2VHr2dd0siLtnevuvGYTTMVtM1qodtjr0pBX19MMNK90lZxKOCq++YaSWTyxqIc1Sho1eazegyP1XIizYzuIRpogYI2YrmFG7pzU2ES6Vzdm7xQ2DwFDbC338uofh1/7YVpdvZ6Ks6ZNgi47MTTWVMrOuajKbo99o6v7W/00z8C3CqlvB24APx6wWOXpZR36H9+usH3aZh9QZ95Ctq0LG5j5GQpA936GDRoxmd6jYv7ymw0H6sqZEevm9lIsiF3XTmW4pmm9OE2qLZZyeRinG3dK96DXp+jpi5khUSS1Y0QdVWhoOdVm8+qyc+Ejqfz+SitiEHv6fdiswguzqwYY2Nzd22u9rwYKaWpCtpY4/WlBDPhBI9fmuWhWwarTsw0PIpmurnnIqmOd29DgwZaSvlVKaXh23gS2N74kprD/gEfV2YidZfGgPkK2mGz4HFYSypow8XdagU9GHAxtZTI/1/lcpKrJQ20tss2hsebjZbF3bzP3+d1VJnFHWdb94ra6quxC1kh0WR1/dVddkvFLG5j1KRS0JVZSRLL8KXTN7hjRzf+JoVPCrFbLQwGXPlKAFiZpFXYwOTnP3WKX/2X0xXPtxRPk85Kcw20Hgb86ONXSWclD90yVPVrCzc+ZjEbUYMywNwY9I8B/1Hw7z1CiFNCiEeFEC8r9SIhxHuEECeEECdCoZCJy1nN/gEf4WQmf2HUQzOaZnS57SWzuKMbEIMGGOpyEk9n86UT0+EE8XS2rIFuNEO+GAndbdYsFzdUp6BTmRyhSHK1gq6xC1kh4SoNtNNmrdhJLD9qUmVxV8QwJE9emeP8dJi33tk6PaHNTS4w0HoIyVDQUkoevRDiX5+ZLNrn/gf/7kk+/O1RYEV9m1kjbPQ3+Mcnr9Hvc3J0Z0/Vry3c+JjFXDTV8TXQUIWBFkL8pxDiTJE/byx4zm8AGeAT+qEbwE4p5RHgl4BPCiECxc4vpfyglPIuKeVdwWCw8U9Ugn3BxjO5DVe0mQajnIGIJjMIAZ4Wz0M1MluNRBoju3NvMQOtdzIab4KBbmYXMYNer7Niktj0cgIpWWWgDfdbNV3I1lKbgi6/ATDWrgx0ZZw2Cw6rha+cmcJhtfC9tw+37L21sNGKgQ7pSVBGYtZMOMlSPE0yk+OxC6uFynw0xeOX5vjWxdCq15rt4gatRv/Vhwex1FC+1QwFPRdJKq8QVRhoKeWrpJS3FvnzbwBCiB8F3gD8oNR9olLKpJRyTv/5aeAycLBpn6IKjFKrRuLQc1FtKIHDZp7jod/nZDZSXNVHklm8DltTewUXY7hLM0TGjt8w0LuLGOh+nwO33cp4nU07ypGfvd1EN2TQ52A+liqrhI0a6G1dhQq6+hrqQrI1tG+tRkHPRpJ0ue2mfie3KkIIAm4b2ZzklTcP5BsFtYLBgJZ4aYSNQnqOx9h8DCkl56dW4tMPPz+96rXPTS4BK9ehoaDNzED3Om35ePxDtwzW9NpK5aK1ksnmWIynO75JCTSexf1a4H3A90kpYwXHg0IIq/7zXuAAcKX4WVrDgN+Jz2lrSEHPRpL0m1yW0edzMFvC7V5tQwuzWTuh5kooistuyR8vRAjBjl53U1zcrVDQwYALKSmb8DWpbz6GC2LQtXQhK8Ro31pNFnc1Cno2klSuwBowNntvOdradJmhLiexVJZwMkNc/3swoB2bjaTy2dwPHgrytRemV3U9PKMb6PH5mBZuybu4zb0X7en34nfaeMm+/ppe1+U2N0lsIZZGStXmExqPQf8l4AceWVNOdT9wWgjxDPAvwE9LKecbfK+GEEKwb6CxTO7ZJow/C/qczEZTRZPXoqnWTrIyMPr7TusK+uz1ZQ4N+ku6vXb0eDatizuo3+TKlUw9M76Ix2FdVXZidDiqVUEbPc6ryuK2Wytmx89FUqbfqLcyfredXq+DBw41L5xWjHzYaCmR/67dtbsX0BLFLkyH6fM6eMc9O1lOZDh+ZeV2eXpiEdDmkI/Nx5iNpLBbhenXxS+88iB/9P231+yNWXFxmxODNja9KkkMGrr7Syn3lzj+WeCzjZy7GewLevnWxVmklHW5jeciqXws2yz6fU5SmRzhZGadKzdaZTmO2bjsVno8dqaWE+RykjPXl/i+F20r+fwdvR6Oj87X/f9aCmNH3swyK2MzMhNOAF1Fn3N8dI47d/WsqsmspQtZIdEaMvMH/Fr4I5nJluzbPRtJcvNw0fQORRHe+8A+JLS8vrbQK+W2a7/Lu3f18KXTN7g2F+P8dISDg37uPxjEbbfy8PNT3HdAU7JnJpfZ2ethbD7G1dlovgba7NDXi/f11fU6l92C3SpMU9BGXoWKQXdIJzGD+/b3Ewon+dbF2bper7m4zf3SGEqsmJs7msxuiIsbtB3/9HKCa/MxwokMt28vbrxAM9CRZKauhKlyNCMpby0D+d7jxRX0fDTFhekI9+5dffPq8TgQorouZIWEazDQ+wZ85CRcnS3tnQgpF3dNGKNSW81Ql26glxL5SpIjO3uwCC1R7NJ0mENDflx2Ky8/GOSrZ6fI5iRzkSSTi3G+90VaQtvobJSQyTXQjSKEMLUft5GTozxDHWag33D7NoJ+J3+nlyvUQjqbYyGWNv1LY5yvWAw0UmW2bzMwuokZ7rXbRrpLPtcYGm+2m3tJd5kFqojX1kt/BRf3U6NzANy7t3fVcatF0O2urgtZIXkFXcVnylcelAjLJDNZwomMupFtAgorI4zv2kiPm23dbr5zeY5oKsuBQe33/aYj25heTvLI2el8gth9+4P0eh1cmY0yG07mQzPtQpfbblqSmPH/006bkI2iowy0w2bhXS/exWMXQjW32DNuxGbHRYyba1EFndoYFzdoLrmppSRnJpdw2Cz5m0cxdvbppVYmNytZTqTxOW3YmuiOdNmtdLntJevjn7wyj8tuKbpBqbYLWSH5GHQVuQWGgb5UIrHRUO9mJy4qzMdlt9Kth41mwgmsFkGvx8GuPg8nxxYAbagGwKsPD7G9x81Hvj3KcxOagb51JMDuPg+jsxFTB2WYhd/EkZOhSBKHzdLUjflmoaMMNMA7j+3CabPwkRpVtOF2CZrsTjTck8VKrTYqBg3ajn8umuTk2CKHhwNlY3ZGLbTZmdxL8XRLLtIBv1OPQa/n+Og8d+7qKZo40+d11pzFbXSHqyaL2+2wMtLtLqmgje+Maom4OTA2vTPLWljCYhHs6vNi5Ice0A201SJ490v38NTVeT57coK9/V78Ljt7+n1cDkWZj6baTl0GXDbTXNwh3UPQ6vLSdqTjDHSv18Fb79zO505N5iemVMOsoVZM3rn2erVY5myRWOZGu7ilhFNjC9w2Ujr+DFpGcq/Xwfi8ubXQS/F0UxPEDIJ+Z1EX91IszbmpZY7tKZ48U5eCrrE73P4Bn1LQWwQjryMUSTLg11zeu/TKgKGAa1Wuxdvu2o7PaePqXIzb9PyPvUEvoXCSbE62Xd5BwG3eyMnZSEp9p3U6zkADvPnICKlMjmf1+Go1GC5os13cNquFHo9jnYJOZXIk0jn8G+jiBq2047YyCWIGO3o9pvfjXm7iJKtCNAW93kA/dXUeKeHYnt4ir9IGZtQbg642+W9f0MeVUJRcbn0ZXr6jVJu5OxXFGdKblcwsJ/PJibv08NDBNaMd/S47b797B0B+g1zYajfob/4UrlrQksRMcnGHk6Z7KjcrHWmgjZnGN5aKuzWLYbgym7Fz7fetN9CLRo/lDXJfFg6yr6SgQUsUG5uPcWnm/7V35sGRHfd9//bMYA7MDO7BLha7y93FYnma4rFkKFOUaIk6GNmmfEh2nCiRpYTFOE5KVpRESlIuRS7FrkiyymXGcihFlsuxKFoSTVkyTZkUE4miaJLL+1gu98QusFhgcM6FuTt/dP/m9Ty8GcxgMAcGv0/Vqzfz3uvj9evuX//6+HUcX3r0zZKt4UZYSmZbYsJytM+P+Xhm3Vr0p88swutx4S37nCfIDQe9WE7lHIVnJeKZPLxuV8VlU3YOj4awlivg4ur63olSFzdXZtuCXf1+LCQymF1dK3VRX6ZtYF/uMMfjY7cdxA37B/DOK0YB2AV0ZzXK+gNqFncjmxER0XhnzVJvJztSQI+G/XC7RJnx+o1YSGTh87ia0uU87GAPeqnNmyDQshCfx4XJ0Y3Xfu8b6sXUYgp3/OGP8Uc/PInPfv/1huMwH8+0ZDvAiF6Lbp/kcmIujiv10hcnhoJeFIqyrtmryUy+phncxITeZcipm3sxkUWv143eNhizYepnt7Zat5zKlTTogyNB3HRgEO+6cr15zbH+AB78rVtxSE8WpA0tgM6zstUX8CBbKG5o+W4jCkWJpWTnzVJvFztSQLtdAqNhX9n2bxux0CTjAIAaQ7Rr0LSN4GCwPfv8DvYq+85X7+mraRb12w6P4NBIEB+/YxIfvfUgvv/ybGl26mZI5wpYXcth1MG86FZDxkqitoli0XimaviWuc/au7kT6frMt5IN+dPR5Lp7Cx04m5epzO5+61tFdL7y97jxrXt+dt06eycCXjfGdMO50zTMrdowYymZRVHyvApiRwpoABjr9+NSrPZJTQtN3P5MdXGXV/Lt3kZQCIG3T0bwvmtqM+pw6+ERPP7J2/HxO47g37/nCEZCPvz3vz2+6S4vMhyyqwUCOlLBWMlGAnAz1sQSmQJCvtobXUNBLwZ6exw1aLVnbmdpUkxlzLy8WQ3x4EiwaT15jdC3Rfa4S2ugueEJYEcL6EBdY9CkQTeDkZAPiUy+bOciEtDtNHf31X9xFHe/faJud0GfB5949xEcm1rGD167tKmw57Q2u5U79lSCwogavRj5QhGLGyxnsQR07asBEpkcQnVo0EIITERCjkutFuJsh3s7YW42M7rJoZu3TY7g5oNDHbcEiZZDrjY4UWyhCVtpbmd2rIDe3e/H7Eq6Zg2vmd2JTmuhl7VWNtDbni7uRvnQ0b0YHwjgO8/PbMo97Z3bGg1ahWFq0EvJLKSsXlHQdnj1dHEnM4W6tZ/DkZDjLmyLSe7i3k4MBb3w6uGizTY8f+v2w/iLj/2jrYzWllDa0arBLu5m7dS1XdmxAnqs34+1XKGmpQHFosRiMtu07sSSNTGjm3s5lUPQ6655tm+n4XG7cPnuMKY3uU+01cXd/ILa5/fA53GVGSuJ1mCYhuYHLNVhjzuxCeMzE6NBLCazpUYbQJNpmjfswmw9QoiS5txtAqjWLu5kJl91bgpr0OXsYAGtl1rVMA69upbTxgGak2lobfWiTYNu1xKrrWLvYGDTa6Pn4ml4Pa6WrIMWQqwzVlKLPWCfx42wz1PfJLFMviYrYiaTo2qN7ElDiy5Npumyir7bIYMklVYGbFdqnST2lSfO4IN/+lRpGamdaDyDXq+7bRYUO40dK6BpGdFsDTO5S7urNKlV59TFvZRqzRrgZjI+EEA8nd+UEX0y5tCqsTa7sZJaLcfVa6wkka5/j+8rxpSAPj4bK12z1uWzgN5OXDEWLtnc7iao0bmRPe5nzy2hUJR4c87ZOl4n2hlvJzu2mbJnQAvoGiaKRRPNM1Ki/HXu4t7+GrSykjSzvFa3JjwXS7dk/JkYDftxypiIVetYWD3mPgtFibVcoa510IDSugZ6e8oE9EKcNm/Z3nlkp/G7P381iltgzKPT8Pe44fO4qmrQhaLESxfU5h8n5uK42cFC30KHbaXZbhrSoIUQnxFCzAghXtTHPzbufVoIcUoIcUII8d7Go7q1REI+uARwycFCk53FJtnhJvw9boR8nnWTxAa36QQxYlxbbJtZqX8cWgno1hVUpy7uYA1dbcNBb81d3NSTEPbX912FELhqrK9cQPOeudsSr8fVdd3bxEZbTp6cj5ds0Z+ssJtgNM77m5tsRRf3l6SU1+njYQAQQlwF4NcBXA3gfQD+RAjRUbnS43ZhNOzHxRo06FZUhva10Mtd0MVNJlU3Mw49H7c2FGgFo2EfVtdypaVuC4lMTUMaSoOubZkV7ZdN+2fXw5VjfTgxF0dBmxVdYDvcTIex0YYZz0+pvQ8iYR9OXKosoFmDtmjWGPRdAL4ppcxIKc8COAXg5iaFtWnGBvw1mftcSGTgdgkMNHHC0kjIV9qQI1coIp7Ob3sBPRz0wt/jwkydM7lT2Tzi6fym14puBsuaWKZ0rkX4DYd8eknWxt2W5xaVNbDLDJONtXLlWB/SuSLOLig/FhJZ9LgF+gI7dpSK6TDUlpOVx6CfP7+MoaAX77x8FG/OxdeVmVyhiOVUDpFQZ20E0k62QkD/thDiZSHE14QQg/raOIALxjPT+lpHMdbvd9yEwM5iIouhoNq/tVmMhKy9hVdSqhU61CYzn1uFEALjA4G6l1qVlli1VINWYV3S669rnawSCfmQK8jSN6vG+UWlQe/XWwzWw1VjfQCA13U390Iig+Eg75nLdA4badAvnF/G9fsGcPnuMJZTuXXWE63tU7e3YrKVbCighRCPCSFedTjuAvBlABMArgMwC+CL9UZACHG3EOKYEOJYNBqt+wUaYaw/gEurGxsraYXN45GwtzSLmKyIDWxzDRpQE8XqHYNupZESgmxeU9dbrZNVSmZCHbartDO1lMKuPh8C3vpHew6PhtDjFqVx6HMLyZb2MDDMRqgtJ50F9Eoqi9PRJG64bBCX660137SNQ/OwzXo2FNBSyjuklNc4HN+VUs5JKQtSyiKAr8Dqxp4BsM/wZq++5uT/fVLKo1LKo5FIpNH3qYuxfj9S2cKGSwOiieYbhDgwHMRKKoclwyBFO818bhXjm1gLPRdvnZESYu9gAGG/B8dnY8jmi1hJ5WoS0CUzobUI6MXkprq3ATW5aCISwvHZGI7PxnBsarlmO+kM0wqqTRJ74YIaf75+3wAm9daadgFdi+2BnUZDA1hCiDEp5az++0sAXtW//wbAN4QQfwhgD4BJAM80ElYzKK2FXq2+DCgaS+NwZKSpcZko7VqUaPtGGVvJ3sEAllM5JOuwoEV7SbdiJytCCIEr9UzpetYYUxzn4xvPZZhaTOEdRzbfCL1qTx+ePLWAr/3kLAI9bvzGzfs37RfDbDV9AQ9i6TyklBBCIJsv4vbP/1+MDQQw2NsDlwDesm8AvV43Bnt7KgpoXplg0egY9P8QQrwihHgZwM8B+B0AkFK+BuCvALwO4BEA/0ZKWajsTXsoWROrMlGsWJQt2Zf4sN7z9fR8Aktt3mpyKxkfsJZavXZxFTf+3qOliU6VmI9n4PO4Sgb4W8VVY31441Icc7HaW/K1dnGnsnnMxzO4bLj+8WczfnOxDB56cQa/euPerhgCYbqHPn8PCkWJVFZV9YvJDC6upvHqzCoeOz6Py3f3IejzQAiBI7vC64yVRNnM5zoaqgGllB+ucu9zAD7XiP/NhvZWrTaTeymVRb4omz4eOj4QgM/jwqn5RMlASXdo0JaxkseOz2ExmcWLF5ZxcKRyVy8ZKWn1BKirxvqQyhbw3JSyFVzLsEbI50Gv171hF/d5vcRqs13cgJrJDQD5osRv3npg0/4wTDMw7XEHfZ5Sd/cXPvgW+Dwu7Bmwlhce2RXGQy/MlLRtQGnQYZ+na9eJb4YdvUZjNKyMlcxWmcREM4qbve2hyyVwSG8rOIEQAj3ursiotBb6dDSB7710EYDq6q1Gq42UECQAf/ymmqxYa0vebibUCXrnRjRoit+7rhjFId3jwjCdgrWjVR5j/cBqaTWKF7ceLh8iPLI7jHgmj9nVdElwN3NDou3KjhbQHrcLu/v8mK4ioEv7ErdgPPTwaAgvXljGUNDXFRPEADUj0+t24S+fPo9YOg8hNhbQ8/FMSRi1ksldIbhdAk+fXQRQ+1hYJOwrjZtXYorWQA9tXoMeCnrx+V+9FrccGt60HwzTLGjDDNKc6ew0v+fIqDVRjAT0UjLTNfXeVrFjN8sgxgcDVQ1pzJeW/DRfo5uIBDG9vIZLsbVtuw+0HZdLYM+AH2cXktjd58dNB4ZKwqoStFFGq/H3uHFoJIh0roiwv/auttGwvzR+VompxRQGenvQ3+B3/eDRfdi3iXXUDNNsyGhOrAYBvVfnYXN4Udmb4PFnkx0voPcO9lY1pFHPhKFGOTwagpTAi+dXuqolSePQH7h+HBORYFUNeiWVRSKTb6mZT5Or9ijNvZ7vHQn7EI2tF9DffOY8Pv3gK5BSYmox1dD4M8N0OqUtJ9PlArrPQUAPBx128EtmS9cZxY4X0OMDAVyKpZEvFB3vz8fTGOztgc/T/PHgCT2umMwWumqGLo1D/8oN49g/FMRiMot4BYtDX3niDADgtsnmLmurBHWt17PUIxL2IZ7JYy1bvlDhgWMXcP8z5/H4G/OYWkriMtZ8mS6mNEnM0KBdAgg7LK/096i91MmamJQSy6kshngMugwW0IMBFIqyZOLRzlws0zKLVgdHgqCJy0Nd0sUNAB+6aR8++Z4jmNwVxgE9ScpJi764soavPnEWH7huD64Z7291NAFYAroeDdrJWEm+UMTrF5XVr889fBwXV9INTRBjmE6nz7Yn9OpaDn2BnoomkkfCvpIGHUvnkStI1qBt7HgBTdpdpXHo+Vi6ZQYz/D1u7NPdwd2kQd+wfxC//c5JAMB+LaRo2ZHJF/7+BCSAT7738lZGrwyyeV2PuUEnYyUn5xPI5It4/8+M4Uw0iUJRchc309V43C4Eve6ySWLVDECpHfyUgF7qIuuJW8mOF9BkSKPSOPRcLINdLZywRDahuzWjkpA6Z5so9trFVfz1CzP46K0HS2PW7SAS9uFf3XYQP3/tWO1uQuuNlbwyrTam/8R7juDmA2pjetagmW6nL9BT1sVdTUAPB32lLm7asrVb673NsuMF9B7D0pWdQlEimsi0dFOCiYgSYINdmlFDPg9GQt7Szk7E//rRGYS8Hvzr2yfaFDOL//L+q3BUC9VaoPxhLrV6eWYFYZ8HB4eD+OwHrsb7rt6Na/a0p9ueYVpFn9/a0WoltYEGHfZiUWvQtJPVMM/iLmPHC2h/jxuRsM9xQ4elZBaFFlgRMyENerCLxqDt7B/qLdOgL66s4W9fmcWv3bSvaoHuVIZ6vXC7RNlSq1dmYrh6vA8ul8AVu/vwpx++cVO7WDHMdqIvYO0JHdNj0JUYCfmwnMohVyhaXdw8SayMHS+gATUO7aRB07aHrVzy87bJCN56aBhXd7G2dWA4WKZB//lT5yClxEe2qflKl0tgJOQtWZ3LFYo4PhvDtXsH2hwzhmkt5o5Wq2s5DFTr4tZDQ0vJLBaTpEGzgDZhAQ01Du00Bk2TflppdnJ8IID7776lq8di9g/3YjaWRjpXQDKTxzeePo87rxlr69hzo4yG/aUx6Dfn4sjmi22bic4w7YK6uKWUG45BR0LWWuilZBa93u4wb7yVsICGMqRxcWUNxaIsu05GSlq57eFO4MBwEFIC08sp3P/MecTTeXzstoPtjlZDjIZ9pWVWNEHsWhbQzA6DJomlsgXki3KDWdxK8VlIZLGUzHa1UrJZWEBDrYXOFeS6DQ+oy7KeJTfMxtBSq3sfP4Xf/7s38LMTw7hh/2CbY9UYEWPDjJdnVhH2e3jWNrPj6PN7EM/kS2PKNQnoeEZtlMECeh0soGGthbZPFJuLpzEc9MLr4WTaSg7opVYPvXgRbz00jPv++dE2x6hxRsM+LCYzyBeKeGV6FT8z3t/y7TIZpt30BXoq3MtbAAATWElEQVQgpZr4CVQX0LRz1WIywxtlVIAlD4C9FZZatdJIyU5isLcHE5Egfun6cXztIzch5GAKcLsR6fNDSuDTD76CV2ZW122vxzA7AZq1fUHP6am2OUzI54HP41Jd3LxRhiPbv2bcAsYHnY2VKDOfnGm2GiEEHv2dd1Q0AbgdoWGQbz03jQ/fchnueUf713MzTKuhDTPIUmA1DVoIgZGQz+ri5iVW62hIQAshHgBAdhkHAKxIKa8TQhwAcBzACX3vH6SU9zQSVjPp9XowFPSuE9Dz8TSuHAu3KVbdTTcJZ0CtX/e4BP7duybxb995mLu3mR0JbTk5XYOABpS5z/NLKWTyRe7idqAhAS2l/DX6LYT4IoBV4/ZpKeV1jfjfSvYOBsrGoAtFiWi8dRtlMNubw6MhvPrf3svLRJgdTT0aNKAmij17bgkAm/l0YkvGoIVSFz4E4P6t8K8d7B0M4IKxgcNcLI2iBAtopmZYODM7nf7SGHQKbpfYcH7JSMhX2v2KZ3GvZ6smid0GYE5KedK4dlAI8YIQ4kdCiNsqORRC3C2EOCaEOBaNRrcoOvUzEQnhwvIaMnm1p++ZqDJFeSjCOxAxDMPUAk0Sm4tl0Of3bDjUY447swa9ng0FtBDiMSHEqw7HXcZj/wTl2vMsgP1SyusBfALAN4QQfU7+Synvk1IelVIejUQijbxLQ0xEQigUZWmf4lPzcQDA4UiobXFiGIbZToR9ntKe9rVsmTti2JjgjTLWs+EYtJTyjmr3hRAeAL8M4EbDTQZARv9+TghxGsARAMcaim0ToU0qTs8ncGRXGKejSYT9HkRauNUkwzDMdsalu7Xj6XzVjTKIEaN+HQxuv41yms1WdHHfAeANKeU0XRBCRIQQbv37EIBJAGe2IKymQV3Zp+YTpfNEJMSzcRmGYeqAJorVsjPdiO7W9rpdXWEPYavZCgH961g/OeztAF4WQrwI4NsA7pFSLm1BWE2j1+vB+EAAp6NKQJ+OJkpaNcMwDFMbJJhrEtBagx4KelkZcqDhJouU8iMO174D4DuN+t1qDkWCOB1NIpbOYT6ewQSPPzMMw9QFrYXuD2wsXmgMmieIOcOmPg0Oj4ZwOprAyblE6T/DMAxTO/V0cQ8EeuB2CbYiVgEW0AYTkRBS2QKePLWg//MSK4ZhmHqgyWEDgY2FrsslMBr28WTcCvCovAFpzD947RJ63AL7h3i7QIZhmHqoR4MGgHt/4waMsoB2hAW0AY05v3YxhsnREDxu7mBgGIapBxLMtSyzAoAbL9vee8E3E5ZABiMhbylz8fgzwzBM/ViTxHhdc6OwgDYQQpTGnVlAMwzD1A8J5oEqe0EztcEC2gYJZl5ixTAMUz/vunIXPnXnFbh8F2/V2yg8Bm2DBDRr0AzDMPXTH+jBPe+YaHc0ugIW0DZ+4S17sJTM4Yrd3PpjGIZh2gcLaBtj/QF86s4r2h0NhmEYZofDY9AMwzAM04GwgGYYhmGYDoQFNMMwDMN0ICygGYZhGKYDYQHNMAzDMB0IC2iGYRiG6UBYQDMMwzBMB8ICmmEYhmE6ECGlbHccSgghogCmttjbEQALVc7VntnsvXa77+S4dfO7cdz43bZT3Lr53VoVt63iMillZN1VKWVXHwCOVTs341673Xdy3Lr53Thu/G7bKW7d/G6tiluzD+7iZhiGYZgOhAU0wzAMw3QgO0FA37fBuRn32u2+k+PWze/GceN3205x6+Z3a1XcmkpHTRJjGIZhGEaxEzRohmEYhtl2sIBmGIZhmA7E0+4IbAVCiH0AfgzgRgBBAD8B4AYgAUwDuAXASwCuBTADYBxAUR9JAAPaq7MADgDIA+gBkAXg1fckAGEEmwAQAvCy9td+HwAKOh4JAAH9myhCNZDo7ERSvw/5Y0LhZXQchcM9pzgRaQD+DfzOQqWDkx/SONw2d+R3HEDYcFM0ns8bvykdgPK0yOnwnXCKtx36fmY60G9yb96ja+Z3t7MG9S0pbk5pXO2b5lG53JG7au/m5LeZF3wV3NG9au9I/jilO92LQ+V7+ztXy2vVviPFo9o7V/O7WloT1dK8mt+1lKNa8mE1941S7f1rCbeZcatGtW/SKLXkic1SgMrPfh0OyQoJVTecB3AlgNeg0vULUso/22xgXaFBSykvAPgygD/Qv2cBXATwxwD2AHgOwCSAJ6CE5eehEvc1qMoWUAmbBrAMJRgvALgEVYFIAI9rN2sA/g+U4HwRwFUATgOY1+5I6H/d8BcAzkF9XEA1ICSA53WYX4aqQJP6+qp+liquSwBOaren9HNpqMwxr58paLcZACl9PqffNw1VsUr9fgWoTJXXaUXGYV7R9zP6mDb8zhjpswbgjPaThF0RKuOSIFjS4SW034s6rtDuU9o/od2mtPs4gJh269LnnD4/pt2f1ukqtdssgGP6/1/r8xSsiuesvvYpHfYL+h6lRdaI55R+hzXt95RO8zyAp3S60XtIAHM6fn8O4ASA/6fv5bS/s0Y6FPQ7U5p+Qfu7rJ+PwWo4UryXjLTP63jn9ZE1nk/BaujQ+9M7LOt4Z2AJnBxUPqNvsajvkT+ULgUdd/KXwj0Nq7Ki+FKDbUX7K6EasG7bsxlYjece7b9Lp2Ve+5XSz07r/xlYZZG++wva3ZIOj/K4WR7y+pkVwx2V47T2+1XDndThrOl7WR32S8a1RX1e0u92wQh7TYd5Qj9z3hbvAoA39b2L+ln6DhQvQOUbwKqD8jqMHJSSIY1zwThS+jyj3azBqnfofVf1+ZQRL6d3A6zvTuEA6tsDVjlN60Nq9/TdKQ+ldRpe1Pde1+lG+SAOK198Xb8jff+8/g2o+gzaPeUlyr/0fR+BVSdR+aTy8oCOyyVYeYO+G30jqj/mob7Tk/paDsB/1eHN6bh+VafFIwB+E8AhqHrovJTyGgC3A/iiEKJSY39DukJAa74E4BYhxMehtDY/1AfYBeBZ/f97AA5CJXARyiIMVeLTACagMm8AqnU3DkvLosxUhPqoABDVz90HYAjqo1IFuAaVvgsAenVYlMEn9XPP6Hid0v8D2v+Qdk9CNALg77RboePohcpsu2BVqNC/vVAFZRyqELmhGhQSQJ/2k94rouMBHRcBq2GwVz9XNK4HdVrs09foMIU+Cd0QrMJMmgbFtVe/LxX+Xp2Waf2btGvS5EmgUrrvgVX5QrsB1LcXOmzSbMb0eUiH+aB2R4VSQDUqXFCF3W+kJb0fpYFH+0t+D+p4vAzV+/JD7e6Cfp7SPQCrJyWpnzli/F4E0A9VEXh0eLv1/3M6fq/Dyq/CeHeXcdA9iqPU/tF9ch/UYffoNKdKZFq7o2tJw0+p4xiFys8pWN+U0ikP9Q1ICM0ZYVP+8MLSPChNBYBvwdKqAkaYlP6ULylPTOhnkzp9plCeX0z3pn/Q/lBe/YnNHX1rwOqhmoGlKa0afkCH64fVqCzAEngL2n1O/y/Cyl+XdPotwCoL1NghxWEG5d+P/BSwvlVGXxdQgt2l71G6Ul5N2M4Bwx0JwZ/q8/eMd6S0jOlrT+jzKqxvDpSX2Z/Cyn/UcEzoe2/o82lY+TgPS+D2QOUfanxRL9guHc6zsPJtAErwU/lPaj9PGulBvUvk17S+RnWzMOJGjdgndZhXA/iuDu9lqHLzqPbHB+D7AN4K4CEp5TxUb+oZIQTVQUtG+tRPqyyitOIA8F6dqO82fn9Kn4/b/pN2YLaco/pMLWo6SDgvQwkz8xqd74PVKiNtLm3z/5jhJ2nL1IL7S1ia4qrN/acNd/R73hb+Y7Bau9RSPWfE29Q8zPMs1r+n2WqmxobZijbPPzWeJ4FH8f4Dw78LRvqY7k86uKdnkyiPh3mYGrSpTZhHwuFawRZW0bhG6b9s+79ohEPPph3c07WU8SylKbXw6ZtQD8PvGmlCcYoZ7hO2exSW0ztlbNftGqeT+6JxL2V7NmnEhfLkS/r8iBFvu3/2PGL/5gXDH/u3z6E8b5juzHxpL1sprH9v0uaoXJtaLpVFpzxG6RirEJZTWn/FSJ8knONtvps9TcwjV+H/GqrnA/u3tj+bx/qyTdrkc1X8pOsxhzDMw54fKN1yDu9kD6uA9XWT0zexh1/NjVN46QrXnzauZaEaKDMAvg0lmKlndBnA72t3MSghv6TT71/q309A1asJAO9nS2IWd0IlzDXG7zuhCuqo/n8XVOI+CVVIc1CVPXWTACqjUSEDVIvtElSL9HpYrWHSIGYB3AyrBT6CcqFOXZBrhp8+438WwC/C6uYM6+s0ZviLsDLwf9J+D+r4ktaZg6Uh7NJ+j8HSlp/VvynuVGDOwtJMv62vTcPqepNQ3fuUNtRFS4X1Zv3+plbu0edf0e8G412pa5Eqi6QRJ3MctaDPMaiMTloCFaglWNrGEoBh/S5Fw2/qus/CEogw/AFUVySVA4o/xZnSk8Z1SWPPG+khYfUgUPdgAKrgfg3W2GtIuxvX514dx7u0Hwv62RgsbSqvf6eMNKV0fNR4B6oYL+n/1F1YgBJK5nMXjDQgITan/5MWSN+jF5ZmTRrKpPbn5wx/TUiDAyzNibq4qZsbUGWU0g76mSgsbQ1Q5YjGE6kSpoqY0p80VsofElYDdVBf69PufPq6C6p3CFDfmgRZFlYZBFQ5JK2OKnPqjjXnpkQB/LL+v9e4TsMcVHZpaIrSJAOrHJLwnofVi0Dfm+JzFlbepuepTF9EuZAze+Ao35M2TP9XoMoNoLpn6R7lPcASaICl+S/DmquQ188kYJUf6PDJv5PGe9KwDGmt1BthNu6pF4P8AcrTm96DIP/MOrtoOwNWrw35c8m4fy2srnC61g8lnCf1OQ1VTm+E6nV5UqdJBkoh/D0ooX4MqofvOgD3CiH6sFnarfVuofZ8HdSY8n6oSuIEgPdBffjP6kT/qD7/R6gPT2MdEtbYzTKsioy0gRyAo7DGdv+DdvMNff6n2s0LsDLhq9r9/bAKsdkiLkJ141DBlTreJ1GueT2jnyENPQFVSdD4MVXuZsuyoP00GxpvAviB7dq8/v+f9f8klFCjMTQqMEXjKEAJcLOyrHY4PVOPO0r3BSMNz+s4Pq//f13/nzGu2VvzF2EJaTrOQuUDGqejysSslGdhaQCPG9/X3ro/hfIK8ihUxRozrlMD4kX9zAnt7wqAh2HlDfP9k7AaI3TtGf1dVgy/z+szaY2vG996yoinqTU8rt9lxUivn+jf/wCrEpuCJQzyUHkpC2uoyP7dclA9QhLAd2A1qCSUdkHvYu8p+ZjhD70XxdmugdFQk4TqKjU196QRXsaWLua4/Yo+L2F92ps9H0VYgpbKtoTVSH0IVsOQ4k1l2xTuFHcKj56hMvaSEQ7VS6eM+JDwOGGks73cUM+aU4+SvWxV08KrHXmbX3cb6Uo9RfRuNJ5ftL2LPT9SulG9Q+/2Cw5xXUP5e9D/pHHNfs/8FnTcDauOM/NUEao8FqB6vOJQPZQkC6hnixSHKajG5DmoMn6bIZceB3DzpuVauwXrFglnATWB593691moscCnANyrM81ZnVmeBvBN/f9H+qM9rc//W3+oRaiWHRXuN6EEbUZfP6V/r8DqBnwQ1mSaLFQFOW2E/bqRgaKwJkmR9kmVwQUdPlWuBahKkyZN5HSGoC6UP4E1EawIa8JJFmrcmuJ0TD/zPMornO/C6uZa1s88rMN/WIeXMOIS1ednUV6o5qAmjpkTmO6F1V0c08/8WMfvh/raig5zTT+bhRqjojh+X8dh1vC3qONNz9A4z3PaL6ocfqDDjsOaiEWFMa3d0qQ3qiSysCo3mpxD32rBuPYUrAqAniGBkwPwN7AKb1G/ax4qL5BWS3nseX2NGmeU7nntLgcr79BkqRgswUaa3yosQUXvFzfin4c11LNk84sahXmdttQrQxPmzmh3L8BqDFG+ozSgNJzSYdonsl0yvrEZHj1H35jyufm+a7CGGui7k4A2JyCRMM4ZfmV1WpkNsVVYvWfUQCFBEjPcUqOdwjEbJtRLkYPKcxR+HqqOWIA1CZDyVt7wb9WI97ROzzWb3xnjfUjIrOo404RQ+taUD/KGewlVLqjeofJA9Q0JdCrLlAepYZLX7kzB+gYsIbeo3djjnYbVO2a+x6rx/df0e2eM8BdQLlzfMO5R/qLyRo0F+q6UFkUjTFJiiij//ss6vU1lIwtVrmnSZAFKMXoESoP+nnb3AJRS8ICO73mo+SJ5qF7Iz2i5tAuqnIzsdAF9N4AHjN9/BVVJPA5LIyIhSNoIFaw3jGtRWJU4FSia0eykKZityWpaob1VV80f87BrPNVas9XGsjbrN1UKTvGnHoezsCpnqoyphR+3uSkYfs7Dmj1NYZEQoh6BRZTHx/yOleJtavop27NmT8AlrH838ttJM6GDKoBKfksAn7GlXRFWpVxJ6yxCCb8CrF4AipMpsOYd4k2/K8XbdG92NUqbW3p/e57eKG+blbfTYR+T5qO1h1P53+lHtTxd6chBNbz+JyxlZg6qDpyGKrd7APw91IqYVwH8s0ZkG5v6ZBiGYZgOpNsmiTEMwzBMV8ACmmEYhmE6EBbQDMMwDNOBsIBmGIZhmA6EBTTDMAzDdCAsoBmGYRimA2EBzTAMwzAdyP8HHXI3wSNb+7YAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# สร้างกราฟจําลองคลื่นสมองมนุษย์สําหรับผู้ที่ไม่ได้ป่วยลมบ้าหมู\n",
        "\n",
        "[(plt.figure(figsize=(8,4)), plt.title('Not Epileptic'), plt.plot(epileptic_seizure[epileptic_seizure['y'] == 0].iloc[i][0:-1])) for i in range(5)];"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "RnCrtMsAfs_N",
        "outputId": "7fcbbc03-6dc5-4acb-e520-bd2cca1f3917"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# สร้างกราฟจําลองคลื่นสมองมนุษย์สําหรับผู้ที่ป่วยเป็นลมบ้าหมู\n",
        "\n",
        "[(plt.figure(figsize=(8,4)), plt.title('Epileptic'), plt.plot(epileptic_seizure[epileptic_seizure['y'] == 1].iloc[i][0:-1])) for i in range(5)];"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "id": "Mk8gPA6qmgG6"
      },
      "outputs": [],
      "source": [
        "# ทำให้ข้อมูลเป็นมาตรฐานและป้องกันปัญหาที่ไม่สมดุล\n",
        "\n",
        "oversample = imblearn.over_sampling.RandomOverSampler(sampling_strategy='minority')\n",
        "x, y = oversample.fit_resample(epileptic_seizure.drop('y', axis=1), epileptic_seizure['y'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VbEIF0bSmgnO",
        "outputId": "47d91af6-daa2-436b-e811-73ab59b6914f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((18400, 178), (18400,))"
            ]
          },
          "execution_count": 112,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# แสดงผลค่า Shape ของ x และ y\n",
        "\n",
        "x.shape, y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XLosIaQVmjWG",
        "outputId": "ed48ac27-209c-4caf-a543-f88bf1cc4864"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of records of Non-Epileptic 18400 vs Epileptic 18400\n"
          ]
        }
      ],
      "source": [
        "# แสดงผลจํานวนผู้ที่ป่วยและผู้ที่ไม่ป่วย\n",
        "\n",
        "print('Number of records of Non-Epileptic {0} vs Epileptic {1}'.format(len(y == True), len(y == False)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "id": "192lgKrdmmDW"
      },
      "outputs": [],
      "source": [
        "# ประกาศตัวแปรเพื่อสร้าง DataFrame ของตัวแปร x และทําการ Normalization มัน\n",
        "\n",
        "normalized = pd.DataFrame(normalize(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "3tTm7E3nmoSO",
        "outputId": "e679fa7f-dece-46ab-9345-0a1375cd32bc"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>...</th>\n",
              "      <th>138</th>\n",
              "      <th>139</th>\n",
              "      <th>140</th>\n",
              "      <th>141</th>\n",
              "      <th>142</th>\n",
              "      <th>143</th>\n",
              "      <th>144</th>\n",
              "      <th>145</th>\n",
              "      <th>146</th>\n",
              "      <th>147</th>\n",
              "      <th>148</th>\n",
              "      <th>149</th>\n",
              "      <th>150</th>\n",
              "      <th>151</th>\n",
              "      <th>152</th>\n",
              "      <th>153</th>\n",
              "      <th>154</th>\n",
              "      <th>155</th>\n",
              "      <th>156</th>\n",
              "      <th>157</th>\n",
              "      <th>158</th>\n",
              "      <th>159</th>\n",
              "      <th>160</th>\n",
              "      <th>161</th>\n",
              "      <th>162</th>\n",
              "      <th>163</th>\n",
              "      <th>164</th>\n",
              "      <th>165</th>\n",
              "      <th>166</th>\n",
              "      <th>167</th>\n",
              "      <th>168</th>\n",
              "      <th>169</th>\n",
              "      <th>170</th>\n",
              "      <th>171</th>\n",
              "      <th>172</th>\n",
              "      <th>173</th>\n",
              "      <th>174</th>\n",
              "      <th>175</th>\n",
              "      <th>176</th>\n",
              "      <th>177</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.036793</td>\n",
              "      <td>-0.051782</td>\n",
              "      <td>-0.062684</td>\n",
              "      <td>-0.058596</td>\n",
              "      <td>-0.058596</td>\n",
              "      <td>-0.065409</td>\n",
              "      <td>-0.057233</td>\n",
              "      <td>-0.062684</td>\n",
              "      <td>-0.061321</td>\n",
              "      <td>-0.062684</td>\n",
              "      <td>-0.036793</td>\n",
              "      <td>-0.023166</td>\n",
              "      <td>-0.012264</td>\n",
              "      <td>-0.008176</td>\n",
              "      <td>-0.008176</td>\n",
              "      <td>-0.020440</td>\n",
              "      <td>-0.027254</td>\n",
              "      <td>-0.036793</td>\n",
              "      <td>-0.043606</td>\n",
              "      <td>-0.062684</td>\n",
              "      <td>-0.069497</td>\n",
              "      <td>-0.089938</td>\n",
              "      <td>-0.109016</td>\n",
              "      <td>-0.121280</td>\n",
              "      <td>-0.129456</td>\n",
              "      <td>-0.138995</td>\n",
              "      <td>-0.137632</td>\n",
              "      <td>-0.134907</td>\n",
              "      <td>-0.125368</td>\n",
              "      <td>-0.129456</td>\n",
              "      <td>-0.126731</td>\n",
              "      <td>-0.121280</td>\n",
              "      <td>-0.118554</td>\n",
              "      <td>-0.100839</td>\n",
              "      <td>-0.083124</td>\n",
              "      <td>-0.066772</td>\n",
              "      <td>-0.054508</td>\n",
              "      <td>-0.039518</td>\n",
              "      <td>-0.023166</td>\n",
              "      <td>-0.019078</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.068135</td>\n",
              "      <td>-0.076311</td>\n",
              "      <td>-0.080399</td>\n",
              "      <td>-0.084487</td>\n",
              "      <td>-0.076311</td>\n",
              "      <td>-0.070860</td>\n",
              "      <td>-0.058596</td>\n",
              "      <td>-0.047694</td>\n",
              "      <td>-0.028617</td>\n",
              "      <td>-0.019078</td>\n",
              "      <td>-0.001363</td>\n",
              "      <td>0.006813</td>\n",
              "      <td>0.004088</td>\n",
              "      <td>0.005451</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.005451</td>\n",
              "      <td>-0.031342</td>\n",
              "      <td>-0.029979</td>\n",
              "      <td>-0.047694</td>\n",
              "      <td>-0.053145</td>\n",
              "      <td>-0.068135</td>\n",
              "      <td>-0.079036</td>\n",
              "      <td>-0.087212</td>\n",
              "      <td>-0.091300</td>\n",
              "      <td>-0.083124</td>\n",
              "      <td>-0.096751</td>\n",
              "      <td>-0.100839</td>\n",
              "      <td>-0.109016</td>\n",
              "      <td>-0.110378</td>\n",
              "      <td>-0.111741</td>\n",
              "      <td>-0.109016</td>\n",
              "      <td>-0.102202</td>\n",
              "      <td>-0.091300</td>\n",
              "      <td>-0.091300</td>\n",
              "      <td>-0.092663</td>\n",
              "      <td>-0.077674</td>\n",
              "      <td>-0.068135</td>\n",
              "      <td>-0.059959</td>\n",
              "      <td>-0.051782</td>\n",
              "      <td>-0.053145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.049483</td>\n",
              "      <td>-0.035738</td>\n",
              "      <td>-0.038487</td>\n",
              "      <td>-0.087970</td>\n",
              "      <td>-0.120958</td>\n",
              "      <td>-0.130122</td>\n",
              "      <td>-0.139285</td>\n",
              "      <td>-0.148449</td>\n",
              "      <td>-0.142034</td>\n",
              "      <td>-0.134703</td>\n",
              "      <td>-0.142951</td>\n",
              "      <td>-0.116376</td>\n",
              "      <td>-0.073308</td>\n",
              "      <td>-0.041236</td>\n",
              "      <td>-0.048567</td>\n",
              "      <td>-0.065977</td>\n",
              "      <td>-0.068726</td>\n",
              "      <td>-0.082472</td>\n",
              "      <td>-0.083388</td>\n",
              "      <td>-0.063228</td>\n",
              "      <td>-0.044901</td>\n",
              "      <td>-0.046734</td>\n",
              "      <td>-0.045818</td>\n",
              "      <td>-0.072392</td>\n",
              "      <td>-0.100799</td>\n",
              "      <td>-0.110878</td>\n",
              "      <td>-0.096217</td>\n",
              "      <td>-0.074224</td>\n",
              "      <td>-0.068726</td>\n",
              "      <td>-0.063228</td>\n",
              "      <td>-0.063228</td>\n",
              "      <td>-0.062312</td>\n",
              "      <td>-0.063228</td>\n",
              "      <td>-0.064145</td>\n",
              "      <td>-0.076057</td>\n",
              "      <td>-0.082472</td>\n",
              "      <td>-0.085221</td>\n",
              "      <td>-0.102631</td>\n",
              "      <td>-0.118209</td>\n",
              "      <td>-0.137453</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.038487</td>\n",
              "      <td>-0.056814</td>\n",
              "      <td>-0.051316</td>\n",
              "      <td>-0.041236</td>\n",
              "      <td>-0.026574</td>\n",
              "      <td>-0.012829</td>\n",
              "      <td>-0.015578</td>\n",
              "      <td>-0.028407</td>\n",
              "      <td>-0.053148</td>\n",
              "      <td>-0.041236</td>\n",
              "      <td>-0.050399</td>\n",
              "      <td>-0.076973</td>\n",
              "      <td>-0.089802</td>\n",
              "      <td>-0.079722</td>\n",
              "      <td>-0.075141</td>\n",
              "      <td>-0.085221</td>\n",
              "      <td>-0.073308</td>\n",
              "      <td>-0.100799</td>\n",
              "      <td>-0.122791</td>\n",
              "      <td>-0.101715</td>\n",
              "      <td>-0.073308</td>\n",
              "      <td>-0.076057</td>\n",
              "      <td>-0.095300</td>\n",
              "      <td>-0.084304</td>\n",
              "      <td>-0.083388</td>\n",
              "      <td>-0.081555</td>\n",
              "      <td>-0.076057</td>\n",
              "      <td>-0.080639</td>\n",
              "      <td>-0.112711</td>\n",
              "      <td>-0.127373</td>\n",
              "      <td>-0.120958</td>\n",
              "      <td>-0.100799</td>\n",
              "      <td>-0.098049</td>\n",
              "      <td>-0.119126</td>\n",
              "      <td>-0.137453</td>\n",
              "      <td>-0.134703</td>\n",
              "      <td>-0.118209</td>\n",
              "      <td>-0.081555</td>\n",
              "      <td>-0.059563</td>\n",
              "      <td>-0.056814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.067135</td>\n",
              "      <td>-0.062340</td>\n",
              "      <td>-0.054348</td>\n",
              "      <td>-0.051151</td>\n",
              "      <td>-0.051151</td>\n",
              "      <td>-0.020780</td>\n",
              "      <td>-0.003197</td>\n",
              "      <td>0.004795</td>\n",
              "      <td>-0.006394</td>\n",
              "      <td>-0.007992</td>\n",
              "      <td>-0.006394</td>\n",
              "      <td>-0.006394</td>\n",
              "      <td>0.015985</td>\n",
              "      <td>0.030371</td>\n",
              "      <td>0.035166</td>\n",
              "      <td>0.028772</td>\n",
              "      <td>0.043159</td>\n",
              "      <td>0.063939</td>\n",
              "      <td>0.071931</td>\n",
              "      <td>0.076726</td>\n",
              "      <td>0.049552</td>\n",
              "      <td>0.023977</td>\n",
              "      <td>0.011189</td>\n",
              "      <td>0.015985</td>\n",
              "      <td>-0.001598</td>\n",
              "      <td>-0.009591</td>\n",
              "      <td>-0.009591</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.001598</td>\n",
              "      <td>-0.014386</td>\n",
              "      <td>-0.012788</td>\n",
              "      <td>-0.006394</td>\n",
              "      <td>0.025575</td>\n",
              "      <td>0.065537</td>\n",
              "      <td>0.091112</td>\n",
              "      <td>0.095908</td>\n",
              "      <td>0.071931</td>\n",
              "      <td>0.033568</td>\n",
              "      <td>-0.015985</td>\n",
              "      <td>-0.033568</td>\n",
              "      <td>...</td>\n",
              "      <td>0.169437</td>\n",
              "      <td>0.169437</td>\n",
              "      <td>0.147059</td>\n",
              "      <td>0.108696</td>\n",
              "      <td>0.063939</td>\n",
              "      <td>0.022378</td>\n",
              "      <td>-0.006394</td>\n",
              "      <td>-0.036765</td>\n",
              "      <td>-0.049552</td>\n",
              "      <td>-0.046355</td>\n",
              "      <td>-0.036765</td>\n",
              "      <td>-0.038363</td>\n",
              "      <td>-0.038363</td>\n",
              "      <td>-0.027174</td>\n",
              "      <td>-0.011189</td>\n",
              "      <td>-0.001598</td>\n",
              "      <td>0.017583</td>\n",
              "      <td>0.049552</td>\n",
              "      <td>0.063939</td>\n",
              "      <td>0.055946</td>\n",
              "      <td>0.063939</td>\n",
              "      <td>0.062340</td>\n",
              "      <td>0.057545</td>\n",
              "      <td>0.041560</td>\n",
              "      <td>0.022378</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.003197</td>\n",
              "      <td>0.001598</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.022378</td>\n",
              "      <td>-0.038363</td>\n",
              "      <td>-0.046355</td>\n",
              "      <td>-0.063939</td>\n",
              "      <td>-0.068734</td>\n",
              "      <td>-0.071931</td>\n",
              "      <td>-0.071931</td>\n",
              "      <td>-0.063939</td>\n",
              "      <td>-0.059143</td>\n",
              "      <td>-0.033568</td>\n",
              "      <td>-0.019182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.053701</td>\n",
              "      <td>-0.061962</td>\n",
              "      <td>-0.053701</td>\n",
              "      <td>-0.063339</td>\n",
              "      <td>-0.071601</td>\n",
              "      <td>-0.088124</td>\n",
              "      <td>-0.090878</td>\n",
              "      <td>-0.093632</td>\n",
              "      <td>-0.085370</td>\n",
              "      <td>-0.060585</td>\n",
              "      <td>-0.042685</td>\n",
              "      <td>-0.013769</td>\n",
              "      <td>0.017900</td>\n",
              "      <td>0.028916</td>\n",
              "      <td>0.015146</td>\n",
              "      <td>-0.008262</td>\n",
              "      <td>-0.011016</td>\n",
              "      <td>-0.020654</td>\n",
              "      <td>-0.004131</td>\n",
              "      <td>-0.006885</td>\n",
              "      <td>-0.016523</td>\n",
              "      <td>-0.044062</td>\n",
              "      <td>-0.077109</td>\n",
              "      <td>-0.083994</td>\n",
              "      <td>-0.072978</td>\n",
              "      <td>-0.046816</td>\n",
              "      <td>-0.020654</td>\n",
              "      <td>-0.009639</td>\n",
              "      <td>-0.002754</td>\n",
              "      <td>0.006885</td>\n",
              "      <td>0.009639</td>\n",
              "      <td>0.038554</td>\n",
              "      <td>0.048193</td>\n",
              "      <td>0.068847</td>\n",
              "      <td>0.061962</td>\n",
              "      <td>0.061962</td>\n",
              "      <td>0.067470</td>\n",
              "      <td>0.071601</td>\n",
              "      <td>0.056455</td>\n",
              "      <td>0.055078</td>\n",
              "      <td>...</td>\n",
              "      <td>0.059209</td>\n",
              "      <td>0.059209</td>\n",
              "      <td>0.077109</td>\n",
              "      <td>0.089501</td>\n",
              "      <td>0.122548</td>\n",
              "      <td>0.143202</td>\n",
              "      <td>0.156971</td>\n",
              "      <td>0.129433</td>\n",
              "      <td>0.093632</td>\n",
              "      <td>0.055078</td>\n",
              "      <td>0.044062</td>\n",
              "      <td>0.038554</td>\n",
              "      <td>0.033047</td>\n",
              "      <td>0.042685</td>\n",
              "      <td>0.039931</td>\n",
              "      <td>0.048193</td>\n",
              "      <td>0.053701</td>\n",
              "      <td>0.074355</td>\n",
              "      <td>0.090878</td>\n",
              "      <td>0.090878</td>\n",
              "      <td>0.088124</td>\n",
              "      <td>0.082617</td>\n",
              "      <td>0.066093</td>\n",
              "      <td>0.037177</td>\n",
              "      <td>0.012392</td>\n",
              "      <td>-0.020654</td>\n",
              "      <td>-0.023408</td>\n",
              "      <td>-0.009639</td>\n",
              "      <td>-0.016523</td>\n",
              "      <td>-0.015146</td>\n",
              "      <td>-0.041308</td>\n",
              "      <td>-0.070224</td>\n",
              "      <td>-0.088124</td>\n",
              "      <td>-0.100517</td>\n",
              "      <td>-0.075732</td>\n",
              "      <td>-0.071601</td>\n",
              "      <td>-0.059209</td>\n",
              "      <td>-0.049570</td>\n",
              "      <td>-0.041308</td>\n",
              "      <td>-0.030293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.025879</td>\n",
              "      <td>-0.024442</td>\n",
              "      <td>-0.044570</td>\n",
              "      <td>-0.051759</td>\n",
              "      <td>-0.080514</td>\n",
              "      <td>-0.106393</td>\n",
              "      <td>-0.126522</td>\n",
              "      <td>-0.106393</td>\n",
              "      <td>-0.074763</td>\n",
              "      <td>-0.038819</td>\n",
              "      <td>0.008626</td>\n",
              "      <td>0.008626</td>\n",
              "      <td>0.018691</td>\n",
              "      <td>0.051759</td>\n",
              "      <td>0.047446</td>\n",
              "      <td>0.061823</td>\n",
              "      <td>0.031630</td>\n",
              "      <td>0.010064</td>\n",
              "      <td>-0.037381</td>\n",
              "      <td>-0.073325</td>\n",
              "      <td>-0.093454</td>\n",
              "      <td>-0.051759</td>\n",
              "      <td>0.011502</td>\n",
              "      <td>0.025879</td>\n",
              "      <td>0.044570</td>\n",
              "      <td>0.014377</td>\n",
              "      <td>0.023004</td>\n",
              "      <td>0.080514</td>\n",
              "      <td>0.089140</td>\n",
              "      <td>0.104955</td>\n",
              "      <td>0.054634</td>\n",
              "      <td>0.076201</td>\n",
              "      <td>0.069012</td>\n",
              "      <td>0.031630</td>\n",
              "      <td>-0.027317</td>\n",
              "      <td>-0.057510</td>\n",
              "      <td>-0.041695</td>\n",
              "      <td>-0.020128</td>\n",
              "      <td>-0.023004</td>\n",
              "      <td>-0.031630</td>\n",
              "      <td>...</td>\n",
              "      <td>0.011502</td>\n",
              "      <td>0.040257</td>\n",
              "      <td>0.004313</td>\n",
              "      <td>-0.011502</td>\n",
              "      <td>-0.007189</td>\n",
              "      <td>-0.020128</td>\n",
              "      <td>-0.020128</td>\n",
              "      <td>-0.041695</td>\n",
              "      <td>-0.031630</td>\n",
              "      <td>-0.012940</td>\n",
              "      <td>0.001438</td>\n",
              "      <td>0.028755</td>\n",
              "      <td>0.005751</td>\n",
              "      <td>-0.023004</td>\n",
              "      <td>-0.073325</td>\n",
              "      <td>-0.123646</td>\n",
              "      <td>-0.076201</td>\n",
              "      <td>-0.077638</td>\n",
              "      <td>-0.063261</td>\n",
              "      <td>-0.081952</td>\n",
              "      <td>-0.117895</td>\n",
              "      <td>-0.079076</td>\n",
              "      <td>-0.044570</td>\n",
              "      <td>-0.007189</td>\n",
              "      <td>0.012940</td>\n",
              "      <td>0.010064</td>\n",
              "      <td>0.011502</td>\n",
              "      <td>0.012940</td>\n",
              "      <td>0.034506</td>\n",
              "      <td>0.102080</td>\n",
              "      <td>0.113582</td>\n",
              "      <td>0.090578</td>\n",
              "      <td>0.070450</td>\n",
              "      <td>0.080514</td>\n",
              "      <td>0.070450</td>\n",
              "      <td>0.102080</td>\n",
              "      <td>0.115020</td>\n",
              "      <td>0.094891</td>\n",
              "      <td>0.094891</td>\n",
              "      <td>0.060385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18395</th>\n",
              "      <td>-0.027379</td>\n",
              "      <td>-0.028161</td>\n",
              "      <td>-0.027379</td>\n",
              "      <td>-0.027379</td>\n",
              "      <td>-0.027770</td>\n",
              "      <td>-0.027379</td>\n",
              "      <td>-0.028944</td>\n",
              "      <td>-0.029335</td>\n",
              "      <td>-0.030508</td>\n",
              "      <td>-0.031682</td>\n",
              "      <td>-0.032464</td>\n",
              "      <td>-0.031291</td>\n",
              "      <td>-0.030508</td>\n",
              "      <td>-0.030117</td>\n",
              "      <td>-0.029335</td>\n",
              "      <td>-0.027770</td>\n",
              "      <td>-0.025424</td>\n",
              "      <td>-0.023468</td>\n",
              "      <td>-0.021121</td>\n",
              "      <td>-0.019557</td>\n",
              "      <td>-0.017992</td>\n",
              "      <td>-0.015645</td>\n",
              "      <td>-0.013298</td>\n",
              "      <td>-0.010952</td>\n",
              "      <td>-0.008605</td>\n",
              "      <td>-0.005085</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.005476</td>\n",
              "      <td>0.008996</td>\n",
              "      <td>0.012516</td>\n",
              "      <td>0.014863</td>\n",
              "      <td>0.016819</td>\n",
              "      <td>0.021121</td>\n",
              "      <td>0.024250</td>\n",
              "      <td>0.025815</td>\n",
              "      <td>0.028161</td>\n",
              "      <td>0.030508</td>\n",
              "      <td>0.032855</td>\n",
              "      <td>0.034028</td>\n",
              "      <td>0.035593</td>\n",
              "      <td>...</td>\n",
              "      <td>0.027379</td>\n",
              "      <td>0.036375</td>\n",
              "      <td>0.053194</td>\n",
              "      <td>0.070404</td>\n",
              "      <td>0.088005</td>\n",
              "      <td>0.088787</td>\n",
              "      <td>0.070795</td>\n",
              "      <td>0.032464</td>\n",
              "      <td>-0.016819</td>\n",
              "      <td>-0.060625</td>\n",
              "      <td>-0.094654</td>\n",
              "      <td>-0.107952</td>\n",
              "      <td>-0.104823</td>\n",
              "      <td>-0.093480</td>\n",
              "      <td>-0.081355</td>\n",
              "      <td>-0.070795</td>\n",
              "      <td>-0.057496</td>\n",
              "      <td>-0.042242</td>\n",
              "      <td>-0.025032</td>\n",
              "      <td>-0.010952</td>\n",
              "      <td>0.000782</td>\n",
              "      <td>0.007431</td>\n",
              "      <td>0.014863</td>\n",
              "      <td>0.019948</td>\n",
              "      <td>0.024250</td>\n",
              "      <td>0.029726</td>\n",
              "      <td>0.034420</td>\n",
              "      <td>0.037940</td>\n",
              "      <td>0.036375</td>\n",
              "      <td>0.034811</td>\n",
              "      <td>0.032464</td>\n",
              "      <td>0.028161</td>\n",
              "      <td>0.025424</td>\n",
              "      <td>0.023077</td>\n",
              "      <td>0.018774</td>\n",
              "      <td>0.012125</td>\n",
              "      <td>0.004694</td>\n",
              "      <td>-0.002738</td>\n",
              "      <td>-0.009778</td>\n",
              "      <td>-0.016428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18396</th>\n",
              "      <td>0.005145</td>\n",
              "      <td>0.032210</td>\n",
              "      <td>0.051000</td>\n",
              "      <td>0.058158</td>\n",
              "      <td>0.057039</td>\n",
              "      <td>0.048763</td>\n",
              "      <td>0.039816</td>\n",
              "      <td>0.028184</td>\n",
              "      <td>0.013421</td>\n",
              "      <td>0.001342</td>\n",
              "      <td>-0.010513</td>\n",
              "      <td>-0.020131</td>\n",
              "      <td>-0.031987</td>\n",
              "      <td>-0.040487</td>\n",
              "      <td>-0.049434</td>\n",
              "      <td>-0.059052</td>\n",
              "      <td>-0.068223</td>\n",
              "      <td>-0.077394</td>\n",
              "      <td>-0.090815</td>\n",
              "      <td>-0.113184</td>\n",
              "      <td>-0.126381</td>\n",
              "      <td>-0.106026</td>\n",
              "      <td>-0.051894</td>\n",
              "      <td>0.017000</td>\n",
              "      <td>0.069118</td>\n",
              "      <td>0.092828</td>\n",
              "      <td>0.108263</td>\n",
              "      <td>0.123473</td>\n",
              "      <td>0.132868</td>\n",
              "      <td>0.137341</td>\n",
              "      <td>0.129289</td>\n",
              "      <td>0.110499</td>\n",
              "      <td>0.089473</td>\n",
              "      <td>0.065539</td>\n",
              "      <td>0.042947</td>\n",
              "      <td>0.023263</td>\n",
              "      <td>0.005816</td>\n",
              "      <td>-0.004921</td>\n",
              "      <td>-0.007158</td>\n",
              "      <td>-0.010289</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.067329</td>\n",
              "      <td>-0.084776</td>\n",
              "      <td>-0.104013</td>\n",
              "      <td>-0.127276</td>\n",
              "      <td>-0.129289</td>\n",
              "      <td>-0.109605</td>\n",
              "      <td>-0.064197</td>\n",
              "      <td>-0.006934</td>\n",
              "      <td>0.036460</td>\n",
              "      <td>0.073368</td>\n",
              "      <td>0.097302</td>\n",
              "      <td>0.106249</td>\n",
              "      <td>0.106249</td>\n",
              "      <td>0.098644</td>\n",
              "      <td>0.089026</td>\n",
              "      <td>0.076723</td>\n",
              "      <td>0.065986</td>\n",
              "      <td>0.053684</td>\n",
              "      <td>0.041829</td>\n",
              "      <td>0.028184</td>\n",
              "      <td>0.028184</td>\n",
              "      <td>0.013197</td>\n",
              "      <td>0.003579</td>\n",
              "      <td>-0.011184</td>\n",
              "      <td>-0.027289</td>\n",
              "      <td>-0.046750</td>\n",
              "      <td>-0.063302</td>\n",
              "      <td>-0.077842</td>\n",
              "      <td>-0.099986</td>\n",
              "      <td>-0.098197</td>\n",
              "      <td>-0.083434</td>\n",
              "      <td>-0.052565</td>\n",
              "      <td>-0.025052</td>\n",
              "      <td>-0.014539</td>\n",
              "      <td>-0.012303</td>\n",
              "      <td>-0.010066</td>\n",
              "      <td>-0.010737</td>\n",
              "      <td>-0.011632</td>\n",
              "      <td>-0.013868</td>\n",
              "      <td>-0.016553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18397</th>\n",
              "      <td>0.140015</td>\n",
              "      <td>0.025509</td>\n",
              "      <td>-0.084462</td>\n",
              "      <td>-0.150218</td>\n",
              "      <td>-0.179695</td>\n",
              "      <td>-0.200102</td>\n",
              "      <td>-0.204070</td>\n",
              "      <td>-0.180829</td>\n",
              "      <td>-0.130945</td>\n",
              "      <td>-0.086730</td>\n",
              "      <td>-0.076526</td>\n",
              "      <td>-0.088997</td>\n",
              "      <td>-0.098067</td>\n",
              "      <td>-0.092965</td>\n",
              "      <td>-0.071991</td>\n",
              "      <td>-0.044782</td>\n",
              "      <td>-0.021541</td>\n",
              "      <td>-0.007369</td>\n",
              "      <td>0.010204</td>\n",
              "      <td>0.035712</td>\n",
              "      <td>0.056119</td>\n",
              "      <td>0.052151</td>\n",
              "      <td>0.035145</td>\n",
              "      <td>0.007369</td>\n",
              "      <td>-0.010204</td>\n",
              "      <td>-0.013605</td>\n",
              "      <td>-0.006235</td>\n",
              "      <td>0.001134</td>\n",
              "      <td>0.010770</td>\n",
              "      <td>0.023241</td>\n",
              "      <td>0.025509</td>\n",
              "      <td>0.016439</td>\n",
              "      <td>0.000567</td>\n",
              "      <td>-0.012471</td>\n",
              "      <td>-0.020407</td>\n",
              "      <td>-0.032878</td>\n",
              "      <td>-0.041381</td>\n",
              "      <td>-0.056686</td>\n",
              "      <td>-0.068023</td>\n",
              "      <td>-0.071991</td>\n",
              "      <td>...</td>\n",
              "      <td>0.064622</td>\n",
              "      <td>0.064622</td>\n",
              "      <td>0.066890</td>\n",
              "      <td>0.064055</td>\n",
              "      <td>0.058954</td>\n",
              "      <td>0.052718</td>\n",
              "      <td>0.047616</td>\n",
              "      <td>0.043648</td>\n",
              "      <td>0.036846</td>\n",
              "      <td>0.031744</td>\n",
              "      <td>0.024942</td>\n",
              "      <td>0.019840</td>\n",
              "      <td>0.016439</td>\n",
              "      <td>0.013038</td>\n",
              "      <td>0.009070</td>\n",
              "      <td>0.002267</td>\n",
              "      <td>0.001701</td>\n",
              "      <td>0.000567</td>\n",
              "      <td>0.001134</td>\n",
              "      <td>-0.004535</td>\n",
              "      <td>-0.013038</td>\n",
              "      <td>-0.018706</td>\n",
              "      <td>-0.021541</td>\n",
              "      <td>-0.022108</td>\n",
              "      <td>-0.029477</td>\n",
              "      <td>-0.042515</td>\n",
              "      <td>-0.054986</td>\n",
              "      <td>-0.050451</td>\n",
              "      <td>-0.013038</td>\n",
              "      <td>0.043648</td>\n",
              "      <td>0.115640</td>\n",
              "      <td>0.123009</td>\n",
              "      <td>0.058387</td>\n",
              "      <td>-0.047050</td>\n",
              "      <td>-0.138881</td>\n",
              "      <td>-0.179695</td>\n",
              "      <td>-0.180262</td>\n",
              "      <td>-0.180262</td>\n",
              "      <td>-0.174026</td>\n",
              "      <td>-0.150218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18398</th>\n",
              "      <td>-0.046224</td>\n",
              "      <td>-0.059654</td>\n",
              "      <td>-0.076207</td>\n",
              "      <td>-0.092448</td>\n",
              "      <td>-0.101661</td>\n",
              "      <td>-0.096195</td>\n",
              "      <td>-0.079486</td>\n",
              "      <td>-0.061684</td>\n",
              "      <td>-0.046848</td>\n",
              "      <td>-0.038416</td>\n",
              "      <td>-0.027797</td>\n",
              "      <td>-0.017802</td>\n",
              "      <td>-0.005622</td>\n",
              "      <td>0.011244</td>\n",
              "      <td>0.028578</td>\n",
              "      <td>0.046848</td>\n",
              "      <td>0.065744</td>\n",
              "      <td>0.086513</td>\n",
              "      <td>0.105721</td>\n",
              "      <td>0.119776</td>\n",
              "      <td>0.128208</td>\n",
              "      <td>0.123524</td>\n",
              "      <td>0.105565</td>\n",
              "      <td>0.085576</td>\n",
              "      <td>0.066056</td>\n",
              "      <td>0.056062</td>\n",
              "      <td>0.047317</td>\n",
              "      <td>0.037166</td>\n",
              "      <td>0.020301</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.020926</td>\n",
              "      <td>-0.039353</td>\n",
              "      <td>-0.052002</td>\n",
              "      <td>-0.057936</td>\n",
              "      <td>-0.058561</td>\n",
              "      <td>-0.054656</td>\n",
              "      <td>-0.050440</td>\n",
              "      <td>-0.051065</td>\n",
              "      <td>-0.053251</td>\n",
              "      <td>-0.058248</td>\n",
              "      <td>...</td>\n",
              "      <td>0.159285</td>\n",
              "      <td>0.123367</td>\n",
              "      <td>0.081985</td>\n",
              "      <td>0.048098</td>\n",
              "      <td>0.021394</td>\n",
              "      <td>0.003904</td>\n",
              "      <td>-0.007652</td>\n",
              "      <td>-0.015460</td>\n",
              "      <td>-0.016397</td>\n",
              "      <td>-0.016709</td>\n",
              "      <td>-0.016085</td>\n",
              "      <td>-0.024049</td>\n",
              "      <td>-0.035292</td>\n",
              "      <td>-0.048722</td>\n",
              "      <td>-0.061528</td>\n",
              "      <td>-0.069960</td>\n",
              "      <td>-0.075738</td>\n",
              "      <td>-0.080111</td>\n",
              "      <td>-0.084171</td>\n",
              "      <td>-0.086513</td>\n",
              "      <td>-0.086826</td>\n",
              "      <td>-0.084171</td>\n",
              "      <td>-0.077144</td>\n",
              "      <td>-0.066837</td>\n",
              "      <td>-0.055906</td>\n",
              "      <td>-0.046068</td>\n",
              "      <td>-0.038260</td>\n",
              "      <td>-0.033419</td>\n",
              "      <td>-0.029983</td>\n",
              "      <td>-0.033887</td>\n",
              "      <td>-0.047161</td>\n",
              "      <td>-0.071678</td>\n",
              "      <td>-0.093228</td>\n",
              "      <td>-0.106034</td>\n",
              "      <td>-0.099787</td>\n",
              "      <td>-0.069960</td>\n",
              "      <td>-0.024205</td>\n",
              "      <td>0.033575</td>\n",
              "      <td>0.094634</td>\n",
              "      <td>0.149603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18399</th>\n",
              "      <td>-0.143202</td>\n",
              "      <td>-0.158949</td>\n",
              "      <td>-0.173712</td>\n",
              "      <td>-0.183062</td>\n",
              "      <td>-0.194873</td>\n",
              "      <td>-0.199794</td>\n",
              "      <td>-0.212588</td>\n",
              "      <td>-0.215049</td>\n",
              "      <td>-0.213573</td>\n",
              "      <td>-0.185523</td>\n",
              "      <td>-0.155997</td>\n",
              "      <td>-0.124994</td>\n",
              "      <td>-0.121549</td>\n",
              "      <td>-0.134836</td>\n",
              "      <td>-0.157473</td>\n",
              "      <td>-0.174697</td>\n",
              "      <td>-0.175681</td>\n",
              "      <td>-0.170760</td>\n",
              "      <td>-0.163378</td>\n",
              "      <td>-0.156981</td>\n",
              "      <td>-0.158457</td>\n",
              "      <td>-0.155505</td>\n",
              "      <td>-0.151076</td>\n",
              "      <td>-0.115644</td>\n",
              "      <td>-0.083165</td>\n",
              "      <td>-0.043797</td>\n",
              "      <td>-0.026574</td>\n",
              "      <td>-0.034447</td>\n",
              "      <td>-0.057576</td>\n",
              "      <td>-0.087102</td>\n",
              "      <td>-0.107771</td>\n",
              "      <td>-0.105802</td>\n",
              "      <td>-0.092023</td>\n",
              "      <td>-0.070863</td>\n",
              "      <td>-0.050687</td>\n",
              "      <td>-0.030018</td>\n",
              "      <td>-0.012303</td>\n",
              "      <td>-0.003937</td>\n",
              "      <td>-0.000984</td>\n",
              "      <td>-0.003445</td>\n",
              "      <td>...</td>\n",
              "      <td>0.042813</td>\n",
              "      <td>0.043305</td>\n",
              "      <td>0.045273</td>\n",
              "      <td>0.046750</td>\n",
              "      <td>0.042813</td>\n",
              "      <td>0.042321</td>\n",
              "      <td>0.040352</td>\n",
              "      <td>0.036908</td>\n",
              "      <td>0.034939</td>\n",
              "      <td>0.030510</td>\n",
              "      <td>0.027066</td>\n",
              "      <td>0.022637</td>\n",
              "      <td>0.019684</td>\n",
              "      <td>0.017224</td>\n",
              "      <td>0.015255</td>\n",
              "      <td>0.014763</td>\n",
              "      <td>0.013779</td>\n",
              "      <td>0.012303</td>\n",
              "      <td>0.011810</td>\n",
              "      <td>0.011810</td>\n",
              "      <td>0.009842</td>\n",
              "      <td>0.014271</td>\n",
              "      <td>0.014763</td>\n",
              "      <td>0.020668</td>\n",
              "      <td>0.023621</td>\n",
              "      <td>0.018208</td>\n",
              "      <td>0.002953</td>\n",
              "      <td>-0.006889</td>\n",
              "      <td>-0.023129</td>\n",
              "      <td>0.012303</td>\n",
              "      <td>0.079229</td>\n",
              "      <td>0.085134</td>\n",
              "      <td>0.041829</td>\n",
              "      <td>-0.034939</td>\n",
              "      <td>-0.053147</td>\n",
              "      <td>-0.000492</td>\n",
              "      <td>0.031987</td>\n",
              "      <td>0.028050</td>\n",
              "      <td>-0.022637</td>\n",
              "      <td>-0.068402</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>18400 rows × 178 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            0         1         2    ...       175       176       177\n",
              "0     -0.036793 -0.051782 -0.062684  ... -0.059959 -0.051782 -0.053145\n",
              "1     -0.049483 -0.035738 -0.038487  ... -0.081555 -0.059563 -0.056814\n",
              "2     -0.067135 -0.062340 -0.054348  ... -0.059143 -0.033568 -0.019182\n",
              "3     -0.053701 -0.061962 -0.053701  ... -0.049570 -0.041308 -0.030293\n",
              "4     -0.025879 -0.024442 -0.044570  ...  0.094891  0.094891  0.060385\n",
              "...         ...       ...       ...  ...       ...       ...       ...\n",
              "18395 -0.027379 -0.028161 -0.027379  ... -0.002738 -0.009778 -0.016428\n",
              "18396  0.005145  0.032210  0.051000  ... -0.011632 -0.013868 -0.016553\n",
              "18397  0.140015  0.025509 -0.084462  ... -0.180262 -0.174026 -0.150218\n",
              "18398 -0.046224 -0.059654 -0.076207  ...  0.033575  0.094634  0.149603\n",
              "18399 -0.143202 -0.158949 -0.173712  ...  0.028050 -0.022637 -0.068402\n",
              "\n",
              "[18400 rows x 178 columns]"
            ]
          },
          "execution_count": 115,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# แสดงผลค่าของใน DataFrame\n",
        "\n",
        "normalized"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8jqLk-R4mqVO",
        "outputId": "24546d7a-80f1-462e-82f5-2fff551cc6b1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Normalized Total Mean VALUE for Epiletic: 1150.0133318356786\n",
            "Normalized Total Std VALUE for Epiletic: 0.022565249182846223\n",
            "Normalized Total Mean VALUE for NOT Epiletic: 1150.0065271687788\n",
            "Normalized Total Std VALUE for NOT Epiletic: 0.0020074295510300252\n"
          ]
        }
      ],
      "source": [
        "# ประกาศตัวแปร normalized ใน column y ให้เป็น ตัวแปร y และแสดงผลค่า Mean และ Std ของผู้ที่ป่วยและไม่ป่วย\n",
        "\n",
        "normalized['y'] = y\n",
        "\n",
        "print('Normalized Total Mean VALUE for Epiletic: {}'.format((normalized[normalized['y'] == 1].describe().mean()).mean()))\n",
        "print('Normalized Total Std VALUE for Epiletic: {}'.format((normalized[normalized['y'] == 1].describe().std()).std()))\n",
        "\n",
        "print('Normalized Total Mean VALUE for NOT Epiletic: {}'.format((normalized[normalized['y'] == 0].describe().mean()).mean()))\n",
        "print('Normalized Total Std VALUE for NOT Epiletic: {}'.format((normalized[normalized['y'] == 0].describe().std()).std()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "id": "qG39s60DmsH3"
      },
      "outputs": [],
      "source": [
        "# แบ่งชุดข้อมูลของเราบนรถไฟและทดสอบและเรียกใช้วิธีการตรวจสอบความถูกต้อง\n",
        "\n",
        "x = normalized.drop('y', axis=1)\n",
        "y = normalized['y']\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.3, random_state=42)\n",
        "\n",
        "check_shapes = x_train, x_test, y_train, y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NFbioI_Dmvou",
        "outputId": "88b52c38-1832-4de6-ed8d-833a9f2862a8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[(12880, 178), (5520, 178), (12880,), (5520,)]"
            ]
          },
          "execution_count": 118,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ลองเรียกใช้งานค่าต่างๆ เพื่อตรวจเช็คความถูกต้อง\n",
        "\n",
        "[arr.shape for arr in check_shapes]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "id": "gWfi7B8KX-hT"
      },
      "outputs": [],
      "source": [
        "# สร้างฟังก์ชั่นสําหรับรับค่า Dimensions และ Input_Shape เพื่อนําไปสร้าง ML Model เพื่อนําไป Train\n",
        "\n",
        "def keras_dense_block(dimensions, input_shape):\n",
        "    block = tf.keras.layers.BatchNormalization() (input_shape)\n",
        "    block = tf.keras.layers.Dense(dimensions, activation=tf.keras.layers.LeakyReLU(0.2)) (block)\n",
        "    block = tf.keras.layers.Dropout(0.4) (block)\n",
        "    block = tf.keras.layers.Dense(dimensions, activation=tf.keras.layers.LeakyReLU(0.2)) (block)\n",
        "    block = tf.keras.layers.Dropout(0.4) (block)\n",
        "    block = tf.keras.layers.Dense(dimensions, activation=tf.keras.layers.LeakyReLU(0.2)) (block)\n",
        "    block = tf.keras.layers.Dropout(0.4) (block)\n",
        "    block = tf.keras.layers.Dense(178, activation=tf.keras.layers.LeakyReLU(0.2)) (block)\n",
        "\n",
        "    return block"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "id": "Pu-sOHydY9Sk"
      },
      "outputs": [],
      "source": [
        "# ประกาศรับค่า input, shape ของ dataset เราและประกาศตัวแปร first_block, second_block, third_block เพื่อรับค่าของ Dims ที่ต่างกันและนําไป\n",
        "# Concatenate หรือว่ารวมกันและนําไปต่อกับ Dense ชุดสุดท้ายพร้อม activation เป็น sigmoid เพื่อปรับใช้กับ Dataset ของเรา และนําตัวแปร input และ output\n",
        "# ไปใส่ใน model เพื่อพร้อมทําการ Train Model ต่อไป\n",
        "\n",
        "input = tf.keras.layers.Input(shape=(178, ), name='input')\n",
        "first_block = keras_dense_block(256, input)\n",
        "second_block = keras_dense_block(512, input)\n",
        "third_block = keras_dense_block(1024, input)\n",
        "\n",
        "concatenated = tf.keras.layers.Concatenate()([first_block, second_block, third_block])\n",
        "concatenated = tf.keras.layers.Dense(128, activation=tf.keras.layers.LeakyReLU(0.2)) (concatenated)\n",
        "output = tf.keras.layers.Dense(1, activation='sigmoid', name='output') (concatenated)\n",
        "\n",
        "model = tf.keras.models.Model(input, output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZnqB4fnHbd5V",
        "outputId": "6ed028d9-40e4-43d8-f0eb-f35bacbf58be"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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goqICDx8+hJ+fn9SlNJj2QkK7XoMxQkNDsWjRIly+fBlr1qzRe8zT0xMAqr2Iq88x0y4OnpiYCCGE3l9GRoZJzwUAf/7zn7F9+3YcO3YMbdu2NXo7jUYDjUajuwh2dXVF586dcfHiRYO+lZWV8PDwMGgvLy8HgBrv8iQikhKzzXKz7VnPZpapTMlKZhsRWRNmofVk4bP8/f3h5uaGq1ev6rVr1y/s1auXXntdWbht2zYcPXoUdnZ2ugFI7WuKj4+HTCYzWNuPmUlExuAgHhERUSNIT0+HEAIhISG6NgcHhzqnZzFHrVu3hkwmw6NHj0zabs2aNejatSvOnDmj196zZ0+4uroaXLCcOnUK5eXleP75503aT7t27aBQKHD27FmTtnuWEAIxMTG4cOECDhw4UO0dpFovvviiQVtmZiaEEAgNDdW1TZ06FWfOnEFOTo6urbS0FFevXkVwcLDBc2iPcZs2bRryUoiImgSzzfKyDTA+s4xhSlZqMduIyJowCy0zC43h4OCAUaNG4fjx49BoNLr2w4cPQyaTYezYsQCMz8LU1FSDwUftLzjj4uIghDCYepSZSUTG4CAeERFRPWg0Gjx48ACVlZU4f/48oqOj0b59e0RGRur6BAYG4v79+zhw4AAqKipw584dg7v8AKBFixa4desWcnNzUVRUhIqKChw+fBju7u6Ij49vxlf1hLOzMwICAnDjxg2TttNOt/LswuAKhQKLFy/G/v37sX37dhQWFuLChQuIioqCSqXC7NmzTd7Pm2++iZ07dyIlJQWFhYWoqqrCjRs3kJeXBwCIiIhAmzZtcPr06Rqf5+LFi9iwYQO2bt0KR0dHvSlbZDIZPvjgA13fmzdvYteuXXj48CEqKiqQkZGBmTNnon379oiKitL1W7RoETp06IDIyEhcu3YN9+7dQ0xMDNRqdbULwmuPcXUDfEREzY3ZZsjSsg0wPrOMYUpWajHbiMiSMQsNWWIWGmv58uXIz8/HihUrUFJSgoyMDGzcuBGRkZEICgoCUL8sNBYzk4iMwUE8IiKyOX/5y1/Qv39/AEBMTAzGjRuHlJQUJCYmAngybUZOTg62bt2KxYsXAwBGjhyJy5cv656jrKwMwcHBUCqVGDRoELp06YJ//vOfetNUzZkzB0OHDsUrr7yCoKAgrFmzRjdNRmhoKK5fvw4AiIqKQuvWrdG9e3eMGjUK9+/fb5bjUJvRo0cjKytLb92Dv//97wgMDER2djb69++PP/7xjwbbhYSEYNGiRQbtK1asQEJCAlavXo2WLVsiPDwcHTt2RHp6OlxcXADApHOQlJSEhQsXYv369fD29oZKpUJ0dDQePHgA4Mm0JAUFBTh48GCNr1EIYfTxGDlyJJYtWwY/Pz84OztjypQpGDhwIE6ePAlvb29dPy8vL3z33Xfw8/NDnz594Ovri3/96184dOgQ+vTpY/C8mZmZ8PX1NZiqhYjIVMy2utlCtmmf05jMOnnyJF544QW0bdsWp06dwrlz56BSqTBw4EAcP34cgGlZqcVsIyKpMAvrZitZaEzGAUCPHj1w5MgRfP311/D29sbEiRMxY8YMfPjhh7o+9clCYzEzicgogoioDmlpaYIfF9KYNGmSmDRpktRlmBVzeD/Onj1btGjRQtIaTAVApKWlGd3/8uXLwsHBQXz66adNWFXTqaqqEoMGDRLbtm2TupQa3b17VygUCvHBBx+YvK2p55MM8fNVOnz/GjKH96OlZVt9vg8w25pefbPNHL7f2SJ+HkuD7/fqmcP70dKysD7fH5iF5oPXg0RkLP4Sj4iIqB5MWQzcEgUGBmL16tVYvXo1iouLpS7HJFVVVThw4ACKiooQEREhdTk1WrlyJfr06YP58+dLXQoREQBmmzljthERNQ9mofmylCw0FjOTiIzFQTwiIiKqVmxsLCZPnoyIiAiTFz+XUnp6Ovbt24fDhw/D2dlZ6nKqtWnTJpw9exZffvklHB0dpS6HiMhmMNuaDrONiMgyMAulx8wkIlNwEI+ImsXMmTPh5uYGmUyGs2fPSl1OoygrK0PXrl2xbNmyah/XaDRITExEWFhYs9W0b98+BAQEGCy0LJfL0bp1awwZMgQbN27UzSdPplu6dClSU1Px6NEj+Pv7Y+/evVKX1KTi4+Mxf/58vP/++1KXYrRhw4Zhx44d8PHxkbqUah08eBCPHz9Geno6vLy8pC6HGsCWsm3t2rUG2SKTydCzZ88mrYe51jyYbeaP2UbmyJZysKKiAu+99x4CAgIgl8vh6+uLd955R29dsabCLGwezELzZ+5ZaCxmJhGZioN4RNQsPv74Y2zdulXqMhpVXFwcLl26VO1jly9fxuDBg7Fo0SKUlpY2W00TJ05ETk4OOnXqBA8PDwghoNFoUFBQgN27d8Pf3x8xMTHo0aMHfvzxx2ary5okJCTg8ePHEELgt99+w6RJk6QuqcmNGDEC69atk7oMqzFu3DjExsbC3t5e6lKogWwt26TAXGsezDZqKGabbbKlHIyOjsbGjRuRkJCAe/fuYceOHdi6dStmzpzZ5DUxC5sHs5CaCzOTiEzFQTwionr44Ycf8NNPP1X72Llz57BkyRJERUWhT58+zVyZIZlMBk9PTwwZMgSpqanYvXs38vPzMXr0aIuaOoOIiJpWbdkGAJ9++imEEHp/tfVvKsw1IiJqCjXlYE5ODrZs2YLXX38dERERcHNzw5AhQzB//nx89tln+Pnnn5u9VmYhERGR7eAgHhE1G5lMJnUJjUKtVuPdd99FUlJStY/37t0b+/btw6uvvgonJ6dmrq5ukyZNQmRkJAoKCrBlyxapyyEismi2km3mjLlGRCQdW8jBzMxMaDQaDBgwQK995MiRAIAjR440S421YRYSERFZLw7iEVGTEEJg48aNCAoKgpOTEzw8PPDuu+8a9KuqqsJ7772H9u3bQ6lUolevXkhLSwMApKSkwMXFBc7Ozjh48CBeeukluLu7w8/PDzt37tR7nm+//Ra/+93v4OzsDHd3dwQHB6OwsLDOfdRHXFwc5s6di1atWtX7OaQWGRkJADh8+LCuzRLPBRFRc2K2mS/mGhFR07PVHLSze/K/zpRKpV57586dAUCSX+JVh1lIRERknTiIR0RNYvny5YiJicHs2bORn5+P27dvY8mSJQb9lixZgg0bNiAxMRF5eXkYM2YMpk2bhh9//BFz5szBwoULoVar4ebmhrS0NGRnZyMgIACzZs1CRUUFAKCkpARjx47FpEmTcP/+fVy+fBldunRBeXl5nfsw1ffff4/s7GxMmzatYQdIYtppPnNycnRtlnYuiIiam61nW2xsLLy8vCCXy+Hv74/x48cjMzPT5P01BeYaEVHTs9Uc7Nq1KwDDwTpvb28AwJ07d0zeZ1NgFhIREVkpQURUh7S0NGHKx0VpaalwdnYWw4cP12vfuXOnACDOnDkjhBBCrVYLZ2dnERERobetk5OTmDNnjhBCiLi4OAFAqNVqXZ/k5GQBQFy5ckUIIcRPP/0kAIgvvvjCoBZj9mHK6+rXr5+4ceOGEEKIO3fuCAAiLi6uxm0GDBggevfubdJ+njZp0iQxadIkk7fr1KmT8PDwqLWPTCYTnp6eQgjLOhemvh/pCQAiLS1N6jKokfB8Npypn6+2nm3Xrl0Tp0+fFkVFReLx48ciIyND9O3bVyiVSvHTTz+ZtM/6vH+tOdeEqH/e2zJ+H7AuPJ/SMOXz2NZzcOTIkaJFixbi6NGjQq1Wi7y8PLF7924hk8nEH/7wB5P2Wd/3u7VnIb/fmo7fH2wX/70Q2RaHJh4jJCIbdOXKFZSWlmLYsGG19rt06RJKS0vRs2dPXZtSqYSPjw9++eWXGreTy+UAoLszMCAgAK1bt8b06dOxYMECREZGomPHjg3aR3WWLl2Kt956C76+viZtZ45KSkoghIC7uzsAyzsXgPWsv9Gcpk6diqlTp0pdBpFFsvVsa9euHdq1a6f775CQEKSmpqJPnz5ITk5GSkqKSfttbNaQa3v37mW21QOPGVHzsPUc3LVrF2JiYvD666/j/v37UKlUGDBgAIQQul/kSc0aspDXK/XDLCQism4cxCOiRnfjxg0AqHNdnZKSEgDAsmXLsGzZMr3HVCqV0ftTKpU4duwYlixZgvj4eKxevRpTpkxBampqo+3jxIkTuHDhAjZt2mT0Nubs119/BfCfqWEs6VxocZ0F00ydOhXR0dEIDQ2VuhRqBPyfG82P2WYoODgY9vb2ukyRkjXkWkhICBYuXGjydrYqIyMDSUlJ/D5gJbTnk8yXreegh4cHtmzZoteWl5eHnTt3om3btkbvsylZQxbyesU0iYmJAMDvDzaI14NEtoWDeETU6BQKBQDg8ePHtfbTXgAmJiYiOjq6Qfvs0aMH/vGPf+DOnTvYtGkT1q1bhx49eiAiIqJR9rFt2zYcPXpUt6j50+Lj4xEfH4/MzEz069ev3vtoTl999RUA4KWXXgJgWedCa8qUKQ1+DlsydepUhIaG8rhZCV60NT9mmyGNRgONRgMnJ6d619BYrCHX/Pz8+BltoqSkJB4zK8JBPPPGHDSkXRd26NCh9a6hMVlDFvJ6xTR79uwBwGtjW8TrQSLbYvhNhYiogXr27Ak7Ozt8++23tfZr164dFAoFzp4926D93bp1CxcvXgTw5ELl/fffx3PPPYeLFy822j5SU1MhhND70y5gHhcXByGExQzg3b59G4mJifDz88OMGTMAWNa5ICKSgq1n24svvmiwfWZmJoQQkt8xz1wjImp6tp6D1dm6dSv8/f0RHh7eoDoaA7OQiIjIenEQj4gaXatWrTBx4kTs3bsX27ZtQ2FhIc6fP4+PPvpIr59CocCbb76JnTt3IiUlBYWFhaiqqsKNGzeQl5dn9P5u3bqFt99+G7/88gvKy8tx5swZXL16FSEhIY22D0skhEBxcTE0Go3ugjQtLQ0DBw6Evb09Dhw4oFsvgeeCiKh2tp5tN2/exK5du/Dw4UNUVFQgIyMDM2fORPv27REVFdUk+3wWc42ISDq2noO/+93vcPXqVVRWViI3NxfvvPMOvvnmG2zbtk23hlxzYBYSERHZIEFEVIe0tDRh6sdFUVGRmDlzpvD29haurq7ihRdeEO+9954AIPz8/MS5c+eEEEI8fvxYxMTEiPbt2wsHBwfRqlUrMXHiRJGVlSWSk5OFs7OzACA6d+4ssrOzxUcffSTc3d0FANGhQwfx66+/itzcXBEWFia8vLyEvb29aNu2rYiLixOVlZV17qMh7ty5IwCIuLg4vfaMjAwxcOBAoVKpBAABQPj4+IiwsDDx7bffmrSPSZMmiUmTJhnd//PPPxe9evUSzs7OQi6XCzs7OwFAyGQy4enpKX73u9+J1atXi3v33Q8gKAAAIABJREFU7hlsaynnoj7vRxICgEhLS5O6DGokPJ8NZ+rnqxC2nW2LFy8WnTp1Ei4uLsLBwUH4+fmJWbNmiVu3bpm8D1Pev7aQa0LU7/1o6/h9wLrwfErD1O8TtpyDw4cPF56ensLBwUF4eXmJ0aNHi8zMzHrtw9T3u61kIb/fmo7fH2wX/70Q2RaZEEI09UAhEVm23bt3Y+rUqeDHRfObPHkygP/MdU98P9aXTCZDWloa10uwEjyfDcfPV+nw/WuI70fT8fuAdeH5lAY/j6XB93v1+H40Hb8/2C7+eyGyLZxOk4iIiIiIiIiIiIiIiMjMcBCPiGzWL7/8AplMVudfRESE1KUSSeqbb75BbGws9u3bh4CAAN2/jddee82g74gRI+Dm5gZ7e3v06NEDp0+flqBi461evRrdu3eHu7s7nJycEBgYiD/96U8oLi7W67d27dpqPx969uxp8JwVFRVISEhAYGAg5HI5PD090bNnT+Tm5gIAPv/8c6xfvx5VVVXN8RLJxjDbiIxjzdlmSmYBgEajQWJiIsLCwqp93JisZLaRuWAOEhnPmrNQq66MA4ATJ05g4MCBcHZ2hkqlQkxMDB4/fqzXx9jrxmeVlZWha9euWLZsma6NmUlEpuIgHhHZrK5du0IIUeffrl27pC6VSDIrVqzA5s2bsXTpUkycOBE5OTno1KkTvL29sX37dhw6dEiv/9dff409e/ZgzJgxyMrKwnPPPSdR5cY5duwY5s2bh9zcXNy9excJCQlISkrSTU1TH1OnTsXf/vY37NixA6Wlpfj555/RqVMn3QXe2LFjoVAoMGzYMDx8+LCxXgoRAGYbkTGsPdtMcfnyZQwePBiLFi1CaWlptX2MyUpmG5kL5iCRcWwhC43JuKysLIwYMQLDhg3DnTt3sH//fvz1r39FVFSUXr/6XjfGxcXh0qVLem3MTCIyFQfxiIiITKRWq2u9k89S9lGXdevWYdeuXdi9ezfc3Nz0Htu8eTPs7Owwe/ZsPHr0SKIKG87V1RWzZ89GixYt4ObmhilTpmDChAn46quvcP36db2+n376qcH/APrpp5/0+uzatQsHDhzAnj17MGDAADg4OEClUuHgwYN6v4BYsGABevfujVGjRqGysrJZXisRUW2YbdaTbYBxmXXu3DksWbIEUVFR6NOnT43PZWxWMtuIyJLZSg4CtpGFxmbcmjVr4OPjg1WrVsHFxQWhoaGIiYnBJ598gl9++UXXz5TrRq0ffvjBIHu1mJlEZAoO4hEREZlo27ZtKCgosPh91ObKlStYvnw5Vq1aBYVCYfB4WFgYoqOjcfPmTbzzzjsSVNg4vvjiC9jb2+u1tWzZEgBqvFuzNh9++CGee+45BAcH19l35cqVOHv2LJKSkkzeDxFRY2O2WU+2Gat3797Yt28fXn31VTg5OdXYz5SsZLYRkaWyhRwEbCcLjcm4yspKHDp0COHh4ZDJZLr2l156CUIIHDx4UNdm6nWjWq3Gu+++W2seMjOJyFgcxCMiIqsnhMCmTZvQrVs3ODk5wcvLC+PHj9e7s27+/PmQy+Xw8fHRtc2dOxcuLi6QyWS4e/cuACA6OhqLFy9GdnY2ZDIZAgMDsXnzZigUCrRu3Rpvv/02VCoVFAoFwsLCcOrUqUbZBwB89dVXcHd3R3x8fJMeL+DJHZhCCIwdO7bGPmvXrkWXLl3w8ccf45tvvqn1+Yw5BykpKXBxcYGzszMOHjyIl156Ce7u7vDz88POnTv1nq+qqgrvvfce2rdvD6VSiV69eiEtLa1hL/r/3Lx5E0qlEv7+/iZtV15ejpMnT9Z6p+fTvLy8EB4ejqSkJAgh6lMqEdkwZpvpbDnbGltNWclsI6LmwhysH2bhf+Tk5KC4uBjt27fXa+/UqRMA4Pz587VuX9t1Y1xcHObOnYtWrVrVuD0zk4iMxUE8IiKyeitXrkRsbCzi4uJQUFCA48eP4/r16xg0aBDy8/MBPLmYmTJlit52ycnJWLVqlV5bUlISxowZg06dOkEIgStXrmD+/PmIjIxEaWkpFixYgNzcXJw+fRqVlZUYPny4bnqNhuwDgG7ha41G03gHpwaHDh1CUFAQnJ2da+yjVCrxySefwM7ODrNmzUJJSUmNfY05B3PmzMHChQuhVqvh5uaGtLQ0ZGdnIyAgALNmzUJFRYXu+ZYsWYINGzYgMTEReXl5GDNmDKZNm4Yff/yxQa+7tLQUx44dw6xZsyCXy/Uei42NhZeXF+RyOfz9/TF+/HhkZmbqHr916xbKy8vx73//G0OHDtVd6Hfr1g3JycnVXpj17dsXN2/exLlz5xpUNxHZHmab6Wwp2+rKrIaoLSsBZhsRNQ/mYP3YUhbW5fbt2wBgMKWoQqGAUqnU1V+d2rLw+++/R3Z2NqZNm1ZnDcxMIjIGB/GIiMiqqdVqbNq0CS+//DKmT58ODw8PBAcHY8uWLbh79y4++uijRtuXg4OD7g7E7t27IyUlBUVFRUhNTW2U5x89ejQKCwuxfPnyRnm+mpSUlOC3337T3YFYm9DQUCxcuBC5ublYsmRJtX3qcw7CwsLg7u6OVq1aISIiAiUlJbh27RoAoKysDCkpKZgwYQImTpwIT09PLFu2DI6Ojg0+1gkJCVCpVFi7dq1e+xtvvIHPP/8c169fR3FxMXbu3Ilr164hPDwcWVlZAIDi4mIAQKtWrRAfH4+srCzk5+dj/PjxmDdvHj777DOD/XXu3BkAcOHChQbVTUS2hdlmOlvKNmMyqyFqykotZhsRNTXmYP3YUhYa4/HjxwBgME0mADg6OkKtVte4bU1ZqFarER0djZSUFKNqYGYSkTE4iEdERFYtKysLxcXF6Nevn157//79IZfL9aZCaWz9+vWDs7Oz3lQilqCgoABCiFrvznza2rVrERQUhOTkZJw4ccLg8YaeA+2djdo7NC9duoTS0lL07NlT10epVMLHx6dBx3r//v3YvXs3jhw5YnA3Zrt27dC3b1+4urpCLpcjJCQEqampUKvVSE5OBgDdWgs9evRAWFgYWrRoAQ8PD6xatQoeHh7VXsRqj3Ftd3kSET2L2WY6W8o2YzKrvmrLSi1mGxE1NeZg/dhSFhpDuyZgZWWlwWPl5eVQKpXVbldbFi5duhRvvfUWfH19jaqBmUlExuAgHhERWbWHDx8CAFxdXQ0e8/T0RFFRUZPu38nJCXfu3GnSfTS2srIyAKhxAfBnKRQKpKamQiaTYcaMGQZ3LDb2OdBO57Js2TLIZDLd39WrV6tdVNwYu3btwrp165Ceno6OHTsatU1wcDDs7e3x66+/AgBUKhUA6Na90JLL5ejQoQOys7MNnkN7Yag95kRExmC2mc4Ws+1pz2ZWfRiblcw2ImpqzMH6sfUsfJZ2HcPCwkK99tLSUpSVlemu755WWxaeOHECFy5cwMyZM42ugZlJRMbgIB4REVk1T09PAKj2AuLhw4fw8/Nrsn1XVFQ0+T6agvZCQrs+gzFCQ0OxaNEiXL58GWvWrNF7rLHPgXZx8MTERAgh9P4yMjJMei4A+POf/4zt27fj2LFjaNu2rdHbaTQaaDQa3UWwq6srOnfujIsXLxr0rayshIeHh0F7eXk5ANR4lycRUXWYbaaztWx71rOZZSpTspLZRkRNjTlYP7aehc/y9/eHm5sbrl69qteuXa+wV69eeu11ZeG2bdtw9OhR2NnZ6QYgta8pPj4eMpnMYG0/ZiYRGYODeEREZNV69uwJV1dXgy/Lp06dQnl5OZ5//nldm4ODg96i2g2Vnp4OIQRCQkKabB9NoXXr1pDJZHj06JFJ261ZswZdu3bFmTNn9NpNOQfGaNeuHRQKBc6ePWvSds8SQiAmJgYXLlzAgQMHqr2DVOvFF180aMvMzIQQAqGhobq2qVOn4syZM8jJydG1lZaW4urVqwgODjZ4Du0xbtOmTUNeChHZGGab6Wwl2wDjM8sYpmSlFrONiJoac7B+bCkLjeHg4IBRo0bh+PHj0Gg0uvbDhw9DJpNh7NixAIzPwtTUVIPBR+0vNuPi4iCEMJh6lJlJRMbgIB4REVk1hUKBxYsXY//+/di+fTsKCwtx4cIFREVFQaVSYfbs2bq+gYGBuH//Pg4cOICKigrcuXPH4K48AGjRogVu3bqF3NxcFBUV6S7YNBoNHjx4gMrKSpw/fx7R0dFo3749IiMjG2Ufhw8fhru7O+Lj4xv/QD3F2dkZAQEBuHHjhknbaadbeXZhcFPOgbH7efPNN7Fz506kpKSgsLAQVVVVuHHjBvLy8gAAERERaNOmDU6fPl3j81y8eBEbNmzA1q1b4ejoqDdli0wmwwcffKDre/PmTezatQsPHz5ERUUFMjIyMHPmTLRv3x5RUVG6fosWLUKHDh0QGRmJa9eu4d69e4iJiYFara52QXjtMa5ugI+IqCbMNtPZSrYBxmeWMUzJSi1mGxE1NeZg/dhSFhpr+fLlyM/Px4oVK1BSUoKMjAxs3LgRkZGRCAoKAlC/LDQWM5OIjMFBPCIisnorVqxAQkICVq9ejZYtWyI8PBwdO3ZEeno6XFxcdP3mzJmDoUOH4pVXXkFQUBDWrFmjm9YiNDQU169fBwBERUWhdevW6N69O0aNGoX79+8DeDKPfXBwMJRKJQYNGoQuXbrgn//8p97UVQ3dR3MZPXo0srKy9NY9+Pvf/47AwEBkZ2ejf//++OMf/2iwXUhICBYtWmTQbsw5SElJQWJiIoAnU5fk5ORg69atWLx4MQBg5MiRuHz5MgAgKSkJCxcuxPr16+Ht7Q2VSoXo6Gg8ePAAwJNpSQoKCnDw4MEaX6MQwujjMXLkSCxbtgx+fn5wdnbGlClTMHDgQJw8eRLe3t66fl5eXvjuu+/g5+eHPn36wNfXF//6179w6NAh9OnTx+B5MzMz4evrazBVCxFRXZhtprOFbNM+pzGZdfLkSbzwwgto27YtTp06hXPnzkGlUmHgwIE4fvw4ANOyUovZRkTNgTlYP7aShcZkHAD06NEDR44cwddffw1vb29MnDgRM2bMwIcffqjrU58sNBYzk4iMIoiI6pCWlib4cSGNSZMmiUmTJkldhlkx1/fj7NmzRYsWLaQuo0YARFpamtH9L1++LBwcHMSnn37ahFU1naqqKjFo0CCxbds2qUup0d27d4VCoRAffPCByduaej7JED9fpcP3ryFzfT+ac7bV5/sAs63p1TfbzPX7nbXj57E0+H6vnjm+H805B4Wo3/cHZqH54PUgERmLv8QjIiJqJKYsEG7uAgMDsXr1aqxevRrFxcVSl2OSqqoqHDhwAEVFRYiIiJC6nBqtXLkSffr0wfz586UuhYioRsw288BsIyKShjXlIMAsNCfMTCIyFgfxiIiIqFqxsbGYPHkyIiIiTF78XErp6enYt28fDh8+DGdnZ6nLqdamTZtw9uxZfPnll3B0dJS6HCIim8FsazrMNiIiy8AslB4zk4hMwUE8IiKiBlq6dClSU1Px6NEj+Pv7Y+/evVKX1Gji4+Mxf/58vP/++1KXYrRhw4Zhx44d8PHxkbqUah08eBCPHz9Geno6vLy8pC6HiKhazDbzwmwjImpe1pyDALNQSsxMIjKVg9QFEBERWbqEhAQkJCRIXUaTGTFiBEaMGCF1GVZj3LhxGDdunNRlEBHVitlGpmC2EZG1sfYcBJiFUmFmEpGp+Es8IiIiIiIiIiIiIiIiIjPDQTwiIiIiIiIiIiIiIiIiM8NBPCIiIiIiIiIiIiIiIiIzw0E8IiIiIiIiIiIiIiIiIjPjIHUBRGQ5du/eLXUJNufGjRsAeOyflpGRAYDHpD60x46Inrhx4wY/SyTCzyN9zHvT8fuAdeFngnR47JsfP79qxvejafj9gYjINsiEEELqIojIvO3evRtTp06VugwiInpKWloapkyZInUZFmvy5MnYu3ev1GUQEdFT+L8nmpdMJpO6BCIiqideDxLZDg7iERGRTZo9ezauXLmCo0ePSl0KERFRvclkMv5PHCIisnn37t1Dy5Yt8c0332DYsGFSl0NERNRouCYeERHZJJVKhdu3b0tdBhERERERETVQXl4eAMDHx0fiSoiIiBoXB/GIiMgm+fj46C70iIiIiIiIyHJpb9BUqVQSV0JERNS4OIhHREQ2SaVS4cGDB1Cr1VKXQkRERERERA2Ql5cHJycneHl5SV0KERFRo+IgHhER2STtNCv5+fkSV0JEREREREQNkZeXBx8fH8hkMqlLISIialQcxCMiIpuknWaF6+IRERERERFZttu3b3M9PCIiskocxCMiIpukvUuT6+IRERERERFZtry8PK6HR0REVomDeEREZJPkcjlatGjBQTwiIiIiIiILd/v2bQ7iERGRVeIgHhER2SwfHx9Op0lERERERGThtGviERERWRsO4hERkc1SqVT8JR4REREREZGF43SaRERkrTiIR0RENouDeERERERERJattLQUhYWF/CUeERFZJQ7iERGRzeJ0mkRERERERJZNe2Mmf4lHRETWiIN4RERks/hLPCIiIiIiIsumvTGTg3hERGSNOIhHREQ2y8fHBwUFBaiqqpK6FCIiIiIiIqqHvLw8yGQytG7dWupSiIiIGh0H8YiIyGapVCpUVlbi7t27UpdCRERERERE9ZCXl4eWLVvC0dFR6lKIiIgaHQfxiIjIZmmnW+G6eERERERERJbp9u3bnEqTiIisFgfxiIjIZmkv9LguHhERERERkWXKy8vjIB4REVktDuIREZHNcnd3h7OzMwfxiIiIiIiILBR/iUdERNaMg3hERGTTfHx8OJ0mERERERGRhcrLy4OPj4/UZRARETUJDuIREZFNU6lU/CUeERERERGRheJ0mkREZM04iEdERDZNpVLxl3hEREREREQWqKqqCnfv3uUv8YiIyGpxEI+IiGyaj48Pf4lHRERERERkgQoKClBVVcVf4hERkdXiIB4REdk0TqdJRERERERkmbTXchzEIyIia8VBPCIismn8JR4REREREZFl0l7LcTpNIiKyVhzEIyIim6ZSqVBaWoqioiKpSyEiIiIiIiIT3L59G66urnB1dZW6FCIioibBQTwiIrJp2mlX+Gs8IiIiIiIiy5KXl8epNImIyKpxEI+IiGyadtoVDuIRERERERFZltu3b3MQj4iIrBoH8YiIyKa1bt0aDg4OuH37ttSlEBERERERkQny8vK4Hh4REVk1DuIREZFNs7OzQ6tWrfhLPCIiIiIiIgvD6TSJiMjacRCPiIhsnkql4i/xiIiIiIiILMzt27f5SzwiIrJqHMQjIiKb5+Pjw1/iERERERERWRiuiUdERNaOg3hERGTzVCoVB/GIiIiIiIgsyMOHD6FWqzmIR0REVo2DeEREZPM4nSYREREREZFl0d6Iyek0iYjImnEQj4iIbF5102mWlJQgNzdXmoKIiIiIiIhIp7S0FDdu3EBFRYWuTXsjJn+JR0RE1kwmhBBSF0FERNRcysrKcPnyZdy8eRP5+fnIy8vDyZMn8e233yIoKAi3b99Gfn4+ysrK8F//9V84evSo1CUTEREBAGbPno1Lly7ptZ0+fRr+/v7w8vLStdnb2+N//ud/4Ofn19wlEhERNYk7d+6gTZs2EELAy8sLLVu2hKurK3JycjB79my0bdsWbdq0Qdu2bdG2bVsEBgZKXTIREVGjcJC6ACIiouZUVVWFIUOG4P79+7C3t4eDgwOEEKioqMCpU6d0/ezt7TF48GAJKyUiItLXpk0bfPTRRwbt58+f1/vvgIAADuAREZFVadWqFQIDA3H58mU8ePAADx48APDkuu2///u/ddd0Qgi89tpr+Nvf/iZxxURERI2D02kSEZFNcXFxwbx58+Dg4ICqqio8fvwY5eXlePaH6VVVVQgPD5eoSiIiIkPTpk2rs49cLkdkZGTTF0NERNTMfv/730Mul+u1PXtNJ5PJ8O6770pUIRERUePjIB4REdmc+fPnw8Gh9h+jOzo6YsCAAc1UERERUd26du2KHj16QCaT1dinvLwcU6dObcaqiIiImsfgwYP11sR7loODA0aMGIHg4OBmrIqIiKhpcRCPiIhsjre3N/7f//t/cHR0rLHP888/D6VS2YxVERER1e3111+Hvb19tY/JZDL07t0bXbp0aeaqiIiIml54eLjBDCpPq6ysRGxsbDNWRERE1PQ4iEdERDZp8eLFqKqqqvYxuVyO3//+981cERERUd1eeeWVGvPL3t4eb7zxRjNXRERE1DxUKhU6dOhQ7WP29vbo06cPl0QgIiKrw0E8IiKySf7+/pg4cWK1v8YrLy/H4MGDJaiKiIiodu3atUNISAjs7Awv5aqqqjBlyhQJqiIiImoew4cPr/YaTqPRYPny5RJURERE1LQ4iEdERDYrLi4OlZWVBu329vYICQmRoCIiIqK6vfbaawbr4tnZ2eGFF16Ar6+vRFURERE1vcGDBxv8Il0mk6Fdu3YYN26cRFURERE1HQ7iERGRzerduzfCw8Ph4OBg0O7m5iZRVURERLWbPHmyQZtMJsPrr78uQTVERETNZ8iQIdBoNHptdnZ2iIuLq3HNWCIiIkvGQTwiIrJpS5cu1fs1nlwux/DhwyWsiIiIqHYtW7bEsGHD9P5npUwmw4QJEySsioiIqOm1a9cObdu21Wvz8PDAa6+9JlFFRERETYuDeEREZNOGDx+O4OBg3f8I5Xp4RERkCaZPnw4hBIAn00C/+OKL8Pb2lrgqIiKipvf73/9ety6eg4MD3nnnHSiVSomrIiIiahocxCMiIpsXGxurm5LFzs4OYWFhEldERERUu5dffhlyuRwAIITA9OnTJa6IiIioeYSHh+vWxXN0dERUVJTEFRERETUdDuIREZHNmzx5Mnx9fQEA3bt3h6enp8QVERER1c7FxQV/+MMfADyZCnrMmDESV0RERNQ8wsPDodFoIJPJEBUVxes3IiKyahzEIyIim+fg4ICYmBgA4Hp4RERkMV599VUAwIQJE+Di4iJxNURERM2jU6dOaNOmDezs7LBgwQKpyyEiImpSMqFdSIGIbNrkyZOxd+9eqcsgIiIzZg5fG5lXRERUF3PIK1Mx34iIqLFMmjQJe/bskboMImokDlIXQETmIyQkBAsXLpS6DKqnjIwMJCUlIS0tTepSLNbevXvx4osvws3NTa89MTERAPjvwwR8P1oX7fk0F8wry8bPh8a1fft2REREwMHBgXlVD3w/WhdzyytTMd8sGz9Pmk51+Xb06FF06dIF7dq1k6oss8b3o+3S/nshIuvBQTwi0vHz88OUKVOkLoMaICkpieewAcaOHQuFQmHQrr2DjcfWNHw/Whdz+p+izCvLx8+HxvN0djGv6ofvR+tiTnllKuab5ePnSdOoLt9qunaj/+D70TbxF3hE1odr4hEREf0fXgQSEZGlYXYREZEtYv4REZGt4CAeERERERERERERERERkZnhIB4RERERERERERERERGRmeEgHhEREREREREREREREZGZ4SAeERERERERERERERERkZnhIB4REVEz+fLLL+Hh4YF//OMfUpdi9r755hvExsZi3759CAgIgEwmg0wmw2uvvWbQd8SIEXBzc4O9vT169OiB06dPS1Cx8VavXo3u3bvD3d0dTk5OCAwMxJ/+9CcUFxfr9Vu7dq3udT/917NnT4PnrKioQEJCAgIDAyGXy+Hp6YmePXsiNzcXAPD5559j/fr1qKqqao6XSEQWjnllPGvOK1NyCAA0Gg0SExMRFhZW7ePG5B/zioiaEvPNeNacb1p15RYAnDhxAgMHDoSzszNUKhViYmLw+PFjvT7GXt89q6ysDF27dsWyZct0bcxBIqoOB/GIiIiaiRBC6hIswooVK7B582YsXboUEydORE5ODjp16gRvb29s374dhw4d0uv/9ddfY8+ePRgzZgyysrLw3HPPSVS5cY4dO4Z58+YhNzcXd+/eRUJCApKSkjB58uR6P+fUqVPxt7/9DTt27EBpaSl+/vlndOrUSXfhOHbsWCgUCgwbNgwPHz5srJdCRFaKeWUca88rU1y+fBmDBw/GokWLUFpaWm0fY/KPeUVETYn5ZhxbyDdjcisrKwsjRozAsGHDcOfOHezfvx9//etfERUVpdevvtd3cXFxuHTpkl4bc5CIqsNBPCIiomYyevRoPHr0CGPGjJG6FKjV6lrvOJTKunXrsGvXLuzevRtubm56j23evBl2dnaYPXs2Hj16JFGFDefq6orZs2ejRYsWcHNzw5QpUzBhwgR89dVXuH79ul7fTz/9FEIIvb+ffvpJr8+uXbtw4MAB7NmzBwMGDICDgwNUKhUOHjyo92uJBQsWoHfv3hg1ahQqKyub5bUSkWViXtXNFvIKMC6Hzp07hyVLliAqKgp9+vSp8bmMzT/mFRE1FeZb3Wwh34zNrTVr1sDHxwerVq2Ci4sLQkNDERMTg08++QS//PKLrp8p13daP/zwg0GeajEHiehZHMQjIiKyQdu2bUNBQYHUZei5cuUKli9fjlWrVkGhUBg8HhYWhujoaNy8eRPvvPOOBBU2ji+++AL29vZ6bS1btgSAGu8Crc2HH36I5557DsHBwXX2XblyJc6ePYukpCST90NEJAXmlfnr3bs39u3bh1dffRVOTk419jMl/5hXRGTtmG/SMSa3KisrcejQIYSHh0Mmk+naX3rpJQghcPDgQV2bqdd3arUa7777bq0ZxxwkoqdxEI+IiKgZnDhxAu3bt4dMJsNf/vIXAEBKSgpcXFzg7OyMgwcP4qWXXoK7uzv8/Pywc+dO3babN2+GQqFA69at8fbbb0OlUkGhUCAsLAynTp3S9Zs/fz7kcjl8fHx0bXPnzoWLiwtkMhnu3r0LAIiOjsbixYuRnZ0NmUyGwMBAAMBXX30Fd3d3xMfHN8chMbB582YIITB27Nga+6xduxZdunSZ3TxnAAAgAElEQVTBxx9/jG+++abW5xNCYNOmTejWrRucnJzg5eWF8ePH6901aew5AICqqiq89957aN++PZRKJXr16oW0tLSGvej/c/PmTSiVSvj7+5u0XXl5OU6ePFnrHaRP8/LyQnh4OJKSkjidEBFVi3lVN1vOq8ZWU/4xr4iosTHf6sZ8+4+cnBwUFxejffv2eu2dOnUCAJw/f77W7Wu7vouLi8PcuXPRqlWrGrdnDhLR0ziIR0RE1AxeeOEF/PDDD3ptc+bMwcKFC6FWq+Hm5oa0tDRkZ2cjICAAs2bNQkVFBYAnF4ORkZEoLS3FggULkJubi9OnT6OyshLDhw/XTdGxefNmTJkyRW8fycnJWLVqlV5bUlISxowZg06dOkEIgStXrgCAbvFsjUbTJMegLocOHUJQUBCcnZ1r7KNUKvHJJ5/Azs4Os2bNQklJSY19V65cidjYWMTFxaGgoADHjx/H9evXMWjQIOTn5wMw/hwAwJIlS7BhwwYkJiYiLy8PY8aMwbRp0/Djjz826HWXlpbi2LFjmDVrFuRyud5jsbGx8PLyglwuh7+/P8aPH4/MzEzd47du3UJ5eTn+/e9/Y+jQobr/odCtWzckJydXe8HXt29f3Lx5E+fOnWtQ3URknZhXdbOlvKorhxqitvwDmFdE1LiYb3WzpXyry+3btwHAYEpRhUIBpVKpq786teXb999/j+zsbEybNq3OGpiDRKTFQTwiIiIzEBYWBnd3d7Rq1QoREREoKSnBtWvX9Po4ODjo7mLs3r07UlJSUFRUhNTU1EapYfTo0SgsLMTy5csb5flMUVJSgt9++013Z2NtQkNDsXDhQuTm5mLJkiXV9lGr1di0aRNefvllTJ8+HR4eHggODsaWLVtw9+5dfPTRRwbb1HYOysrKkJKSggkTJmDixInw9PTEsmXL4Ojo2ODjn5CQAJVKhbVr1+q1v/HGG/j8889x/fp1FBcXY+fOnbh27RrCw8ORlZUFACguLgYAtGrVCvHx8cjKykJ+fj7Gjx+PefPm4bPPPjPYX+fOnQEAFy5caFDdRGSbmFe2k1fG5FBD1JR/WswrImpOzDfbyTdjPH78GAAMpskEAEdHR6jV6hq3rSnf1Go1oqOjkZKSYlQNzEEi0uIgHhERkZnR3q339F2H1enXrx+cnZ31piOxVAUFBRBC1HrX59PWrl2LoKAgJCcn48SJEwaPZ2Vlobi4GP369dNr79+/P+Ryud60N9V59hxcunQJpaWl6Nmzp66PUqmEj49Pg47//v37sXv3bhw5csTgLs927dqhb9++cHV1hVwuR0hICFJTU6FWq5GcnAwAujUcevTogbCwMLRo0QIeHh5YtWoVPDw8qr041h7j2u4eJSIyBvOqbpacV8bkUH3Vln9azCsikgrzrW6WnG/G0K4JWFlZafBYeXk5lEpltdvVlm9Lly7FW2+9BV9fX6NqYA4SkRYH8YiIiCyYk5MT7ty5I3UZDVZWVgYANS4s/iyFQoHU1FTIZDLMmDHD4E7Ihw8fAgBcXV0NtvX09ERRUZFJ9WmniVm2bBlkMpnu7+rVq9UuVm6MXbt2Yd26dUhPT0fHjh2N2iY4OBj29vb49ddfAQAqlQoAdOtraMnlcnTo0AHZ2dkGz6G94NQecyKi5sC8sty8etqzOVQfxuYf84qILAHzzTry7VnadQ0LCwv12ktLS1FWVqa7Dntabfl24sQJXLhwATNnzjS6BuYgEWlxEI+IiMhCVVRU4OHDh/Dz85O6lAbTXqBo14EwRmhoKBYtWoTLly9jzZo1eo95enoCQLUXh/U5ZtpFxxMTEyGE0PvLyMgw6bkA4M9//jO2b9+OY8eOoW3btkZvp9FooNFodBfXrq6u6Ny5My5evGjQt7KyEh4eHgbt5eXlAFDj3aNERI2NeWW5efWsZ3PIVKbkH/OKiMwd88168u1Z/v7+cHNzw9WrV/XatesX9urVS6+9rnzbtm0bjh49Cjs7O90ApPY1xcfHQyaTGaztxxwkIi0O4hEREVmo9PR0CCEQEhKia3NwcKhz2hdz1Lp1a8hkMjx69Mik7dasWYOuXbvizJkzeu09e/aEq6urwYXQqVOnUF5ejueff96k/bRr1w4KhQJnz541abtnCSEQExODCxcu4MCBA9Xemar14osvGrRlZmZCCIHQ0FBd29SpU3HmzBnk5OTo2kpLS3H16lUEBwcbPIf2GLdp06YhL4WIyGjMK8vLK8D4HDKGKfmnxbwiInPHfLPMfDOGg4MDRo0ahePHj0Oj0ejaDx8+DJlMhrFjxwIwPt9SU1MNBh+1v+CMi4uDEMJg6lHmIBFpcRCPiIjIQmg0Gjx48ACVlZU4f/48oqOj0b59e0RGRur6BAYG4v79+zhw4AAqKipw584dg7sHAaBFixa4desWcnNzUVRUhIqKChw+fBju7u6Ij49vxlf1hLOzMwICAnDjxg2TttNO4/LsguMKhQKLFy/G/v37sX37dhQWFuLChQuIioqCSqXC7NmzTd7Pm2++iZ07dyIlJQWFhYWoqqrCjRs3kJeXBwCIiIhAmzZtcPr06Rqf5+LFi9iwYQO2bt0KR0dHvalgZDIZPvjgA13fmzdvYteuXXj48CEqKiqQkZGBmTNnon379oiKitL1W7RoETp06IDIyEhcu3YN9+7dQ0xMDNRqdbULzWuPcXUDfEREjYF5ZcjS8gowPoeMYUr+aTGviMjcMN8MWWK+GWv58uXIz8/HihUrUFJSgoyMDGzcuBGRkZEICgoCUL98MxZzkIi0OIhHRETUDP7yl7+gf//+AICYmBiMGzcOKSkpSExMBPBkOo6cnBxs3boVixcvBgCMHDkSly9f1j1HWVkZgoODoVQqMWjQIHTp0gX//Oc/9aa0mjNnDoYOHYpXXnkFQUFBWLNmjW76jdDQUFy/fh0AEBUVhdatW6N79+4YNWoU7t+/3yzHoTajR49GVlaW3noKf//73xEYGIjs7Gz0798ff/zjHw22CwkJwaJFiwzaV6xYgYSEBKxevRotW7ZEeHg4OnbsiPT0dLi4uACASecgKSkJCxcuxPr16+Ht7Q2VSoXo6Gg8ePAAwJPpTgoKCnDw4MEaX6MQwujjMXLkSCxbtgx+fn5wdnbGlClTMHDgQJw8eRLe3t66fl5eXvjuu+/g5+eHPn36wNfXF//6179w6NAh9OnTx+B5MzMz4evrazAFDBERwLwyhi3klfY5jcmhkydP4oUXXkDbtm1x6tQpnDt3DiqVCgMHDsTx48cBmJZ/WswrImpMzLe62Uq+GZNbANCjRw8cOXIEX3/9Nby9vTFx4kTMmDEDH374oa5PffLNWMxBItKSiab8tCEiizF58mQAwJ49eySuhOpr9+7dmDp1apN+ibRV5vDv4+2338aePXtw7949yWowRX3ej1euXEG3bt2QmpqK6dOnN2F1TUOj0WDIkCGIjIzEjBkzpC6nWvfu3YOfnx/Wrl2ruzA2hjl9vpjDv0dqGHN6P1kbc/j3wbwyf8wr82QO/36pYSz5/WfuzOHfB/PN/FlCvhmrvjkImMe/FyJqXPwlHhERkYUwZZFxSxQYGIjVq1dj9erVKC4ulrock1RVVeHAgQMoKipCRESE1OXUaOXKlejTpw/mz58vdSlEZMWYV+aLeUVEVH/MN/NlKflmLOYgET2Ng3hE1GhmzpwJNzc3yGSyZlts2FytXr0a3bt3h7u7O5ycnBAYGIg//elPBl+EKyoq8N577yEgIAByuRy+vr5455139KavaCr79u1DQECAwZztcrkcrVu3xpAhQ7Bx40bd1BREzSE2NhaTJ09GRESEyYuqSyk9PR379u3D4cOH4ezsLHU51dq0aRPOnj2LL7/8Eo6OjlKXIynm1X+sX78eXbt2hVKphIuLC7p27Yrly5ejsLBQr5+xudYUmFdkjphXTYd5VX/Mt/8wNt/Wrl1rkC8ymQw9e/Zs8hqZb2SOmG/SYw4S0bM4iEdEjebjjz/G1q1bpS7DLBw7dgzz5s1Dbm4u7t69i4SEBCQlJemmNdCKjo7Gxo0bkZCQgHv37mHHjh3YunUrZs6c2eQ1Tpw4ETk5OejUqRM8PDwghIBGo0FBQQF2794Nf39/xMTEoEePHvjxxx+bvB6q2dKlS5GamopHjx7B398fe/fulbqkJhUfH4/58+fj/fffl7oUow0bNgw7duyAj4+P1KVU6+DBg3j8+DHS09Ph5eUldTmSY179x3fffYdZs2bh2rVryM/Px5o1a7B+/XpMmjRJr5+xudYUmFeWg3ll/phX1o359h/G5puUmG+Wg/lm/sw934zFHCSi6nAQj4ioBmq1GmFhYfXa1tXVFbNnz0aLFi3g5uaGKVOmYMKECfjqq690C1nn5ORgy5YteP311xEREQE3NzcMGTIE8+fPx2effYaff/65MV+OUWQyGTw9PTFkyBCkpqZi9+7dyM/Px+jRoy3qLryaNOScSikhIQGPHz+GEAK//fabWf3Ph6YyYsQIrFu3TuoyrMa4ceMQGxsLe3t7qUuhJtCQzza5XI65c+eiVatWcHV1xeTJkzF+/Hj87//+L/Ly8nT9jMm15sS8Mk/MK2oo5hU9rTnyDQA+/fRTCCH0/n766afGeAkmY76ZJ+YbNRfmIBFVh4N4RNSoZDKZ1CU0mm3btqGgoKBe237xxRcGX7patmwJACgtLQUAZGZmQqPRYMCAAXr9Ro4cCQA4cuRIvfbdmCZNmoTIyEgUFBRgy5YtUpfTYA05p0RkXZhXT+zfvx8KhUKvzdfXFwD0pso0JtekxLwiInqC+faEsflm7phvRERExEE8Iqo3IQQ2btyIoKAgODk5wcPDA++++65enw0bNsDZ2Rlubm4oKCjA4sWL4evri0uXLkEIgU2bNqFbt25wcnKCl5cXxo8fj19++UW3/ebNm6FQKNC6dWu8/fbbUKlUUCgUCAsLw6lTpwzqqev55s+fD7lcrjfFwty5c+Hi4gKZTIa7d+8CeDLN5eLFi5GdnQ2ZTIbAwMAGH6+bN29CqVTC398fAGBn9+QjWKlU6vXr3LkzAEjyS7zq/H/27jwuqnr/H/hr2IcdERUFFMEdDE1l0UJzKfXhlqhkdiMfepXymkuFiuZOpn2Fr12wr16j782uLOkFb17Nr5KZBqa5EZYLhIorosgyIOB8fn/4Y27jsMyweGZ5PR+P83jUZz7nnPfMmZmXh8+Z84mIiAAA7N+/HwCPKREZHuaVbi5fvgxnZ2d07ty5wX5P55rUmFdEZGqYb7rRNt/0DfONiIjIxAkiIiFEWFiYCAsL02md6OhoIZPJxH/913+JBw8eCIVCIeLj4wUAcebMGbV+AMS7774rPv30U/Hqq6+KX3/9VXz44YfCyspKfPnll6K4uFicP39e9O/fX7Rt21bcvn1btf6cOXOEnZ2duHDhgqisrBQ5OTli4MCBwsHBQVy7dk3VT9vtvf7666J9+/Zqz2XTpk0CgCgsLFS1TZ48Wfj4+Oj0mtSnvLxcODg4iPnz56vazp8/LwCIFStWqPWtqakRAMSkSZN02kdycrJoyte6j4+PcHJyqvfxkpISAUB4enqq2kztmDbl82Hqmvp+JP2kT8eTedU6eVVVVSUKCgrEp59+KqytrcWXX37ZYP+6ck1bzCvmlT7Rp+83aj5DPp7MN2nybe3atcLDw0M4OzsLS0tL0aVLFzFhwgTx008/6bwv5hvzTZ8Y8vchNQ8/L0TGh7/EI6ImqaioQGxsLEaMGIFFixbB2dkZcrkcbdq0qXedDRs2YN68edi9ezc6d+6MzZs349VXX8WMGTPg5OQEf39/fPbZZ7h37x62bdumtq6FhYXq6r/evXsjISEBpaWlSExMVNWjy/aetZiYGLi7u2PdunWqNn9/f7zyyiuIj49HRkYGKisrcfv2bezZswcymQzV1dUSVvwfDg4OkMlkKC0t1XjMlI8pERkG5pV2PD094eHhgVWrVmHjxo2YNm1ag/3ryjWpMa+IyJQw37TTWL69+eab2Lt3L65fv46ysjLs2rUL165dQ2hoKHJyciSp+WnMNyIiItNmIXUBRGSYrly5AoVCgeHDhzdp/ZycHJSVlWHAgAFq7QMHDoSVlZXGbTyeNmDAANja2qpu49Hc7bWmPXv2ICUlBQcPHoSDg4PaY0lJSYiKisKf/vQn3L9/H+7u7ggMDIQQAq6urhJVrK68vBxCCDg6OjbYz9iPaUFBAVJSUp7pPg1ZZmYmAPA1MxK1x9MQMa+0c/36dRQXF+PMmTNYunQptm3bhoyMDLRr106jb0O5JiXm1RPMK90wr4yLIeeVrphv2mks3zw9PeHp6anqHxQUhMTERAQEBCA+Ph4JCQmS1P1HzLcnmG+6Yb6ZroKCAnh4eEhdBhG1IA7iEVGTFBQUAADc3NyatH5xcTEAwN7eXuMxZ2fnOq8yfJq1tTUKCwtbbHutISkpCZs3b8aRI0fQsWNHjcednJw0Jii/desWdu3aVWd/KVy6dAkA0LNnzwb7GfsxzcrKavSXKaSJrxlJjXmlHUtLS7i5uWHUqFHw9vZG9+7dERMTg7i4OLV+jeWalJhXTzCvmoavGRka5pt2tM23P/L394e5ubkqV6TGfHuC+dY0fM1MU1hYmNQlEFEL4u00iahJbGxsAACPHj1q0vrOzs4AUOc//IuLixu9aqi6ulqtX3O31xo+/fRT7Ny5ExkZGTr9ofPkyZMAgGHDhrVWaTo5cOAAAGD06NEN9jP2YxoWFgYhBBctl+TkZACQvA4uLXs8DRHzSne+vr4wNzfXuI1YU3PtWWFePcG8atr3m9R1cGnZ42kKmG+6qy/fnqZUKqFUKmFtbf2MKmsY8+0J5lvTvg+lroPLs184gEdkfDiIR0RN4ufnBzMzM3z//fdNXt/e3h6nTp1Saz9x4gSqqqrw/PPPN7j+kSNHIIRAUFCQztuzsLBo1fnmhBCIiopCdnY20tLS6rxysSHbt2+Ht7c3QkNDW6lC7d2+fRuxsbHw8PDAzJkzG+xrzMeUiAwX86p+RUVFmD59ukb75cuX8fjxY9XtxZqba88C84qITA3zrX7a5hsAvPzyyxr9Tp48CSEEgoODW61GbTHfiIiIiIN4RNQkbm5umDx5Mr7++mvs2LEDJSUlOH/+vNaTW9vY2GDx4sXYs2cPdu7ciZKSEmRnZyMyMhLu7u6YM2eOWn+lUokHDx6gpqYG58+fx4IFC+Dl5YWIiAidt+fr64v79+8jLS0N1dXVKCwsxNWrVzVqbNOmDW7evIn8/HyUlpZqfVJy4cIFbNy4Edu3b4elpSVkMpna8sknn6j6Dho0CFevXkVNTQ3y8/Px3nvv4dChQ9ixYwesrKy02l9LEEKgrKwMSqUSQggUFhYiOTkZgwcPhrm5OdLS0hqdg8GYjykRGS7mVf3s7Oxw8OBBZGRkoKSkBNXV1Thz5gzefPNN2NnZYdGiRQB0y7XWxrwiInqC+VY/bfMNAG7cuIGkpCQUFxejuroamZmZmDVrFry8vBAZGanV/loC842IiIjqJYiIhBBhYWEiLCxMp3VKS0vFrFmzhKurq7C3txdDhgwRH374oQAgPDw8xLlz58THH38s5HK5ACA8PT3Fl19+qVpfqVSKTZs2iW7duglLS0vh4uIiJk2aJC5evKi2nzlz5ghLS0vRqVMnYWFhIRwdHcXEiRNFbm6uWj9tt1dUVCSGDRsmbGxshLe3t/jLX/4i3n//fQFA+Pr6imvXrgkhhDh9+rTo3LmzkMvlYsiQIeL27dtavS7Z2dkCQL3Lpk2bVH1HjhwpnJ2dhYWFhXBxcRFjx44VJ0+e1Ok41EpOTha6fK3v3btX9O3bV9ja2gorKythZmYmAAiZTCacnZ3FoEGDxJo1a0RRUZHaeqZ4TJvy+TB1ur4fSb/p0/FkXrXcd5sQQowfP154e3sLe3t7YW1tLXx8fER4eLjIzs5W9dEl17TBvGJe6RN9+n6j5jPk48l8e/b5JoQQixcvFj4+PsLOzk5YWFgIDw8PMXv2bHHz5k2t91WL+cZ80yeG/H1IzcPPC5HxkQkhROsNERKRoZgyZQoAIDU1VeJKNM2dOxepqakoKiqSuhS9lpKSgmnTpsEQvtYN7Zjq8+dDXxnS+5Eap0/HU58/j4b23SYVfXo/NcbQjqk+fz70lSG9H6lxhnw89fnza2jfhVIxpPefoR1Tff586CtDej9Sy+Lnhcj48HaaRGQQHj9+LHUJ1MJ4TInIGPG7zfjwmBIR8bvQGPGYEhERGQYO4hERaeG3337TmAOoriU8PFzqUomIyIQxr4iIyBgx34iIiMhUcRCPiPTasmXLkJiYiIcPH8Lb2xtff/21JHX07NkTQohGl6SkJEnqMyT6ckxJvx06dAhLly7F7t270bVrV9UfZt544w2NvqNGjYKDgwPMzc3Rp08fnD59WoKKtbdmzRr07t0bjo6OsLa2hq+vLz744AOUlZWp9Vu3bl2df5zy8/PT2GZ1dTViYmLg6+sLKysrODs7w8/PD/n5+QCAvXv34uOPP+YV161IX77bmFctR1+OKek3Y84rXXIIAJRKJWJjYxESElLn49rkH/NK/+jLdyHzreXoyzEl/WbM+VarsdwCgGPHjmHw4MGwtbWFu7s7oqKi8OjRI7U+2p7fPa2yshI9e/bE8uXLVW3MQSKqCwfxiEivxcTE4NGjRxBC4Pfff0dYWJjUJVEz8ZhSY1auXIktW7Zg2bJlmDx5MvLy8uDj4wNXV1fs3LkT+/btU+t/8OBBpKamYty4ccjJyUH//v0lqlw7GRkZmDdvHvLz83Hv3j3ExMQgLi5ONXdBU0ybNg1///vf8dVXX0GhUODXX3+Fj4+P6sRx/PjxsLGxwfDhw1FcXNxST4X+gN9txofHlBpj7Hmli8uXL+PFF1/EokWLoFAo6uyjTf4xr/QPvwuND48pNcYU8k2b3MrJycGoUaMwfPhwFBYWYs+ePfj8888RGRmp1q+p53fR0dG4ePGiWhtzkIjqwkE8IiIiA1BRUdHgFYKGso/GbNiwAUlJSUhJSYGDg4PaY1u2bIGZmRnmzJmDhw8fSlRh89nb22POnDlo06YNHBwcMHXqVEyaNAkHDhzA9evX1fp++eWXGleY//LLL2p9kpKSkJaWhtTUVAQGBsLCwgLu7u5IT09X+7XEu+++i+eeew5jxoxBTU3NM3muRGR6mFfGk1eAdjl07tw5LFmyBJGRkQgICKh3W9rmH/OKiPQR88148k3b3Fq7di06dOiA1atXw87ODsHBwYiKisIXX3yB3377TdVPl/O7Wj/++KNGntZiDhLR0ziIR0REZAB27NiBu3fvGvw+GnLlyhWsWLECq1evho2NjcbjISEhWLBgAW7cuIH33ntPggpbxjfffANzc3O1trZt2wJAvVeBNmTr1q3o378//P39G+27atUqnD17FnFxcTrvh4hIG8wr48krbT333HPYvXs3Xn/9dVhbW9fbT5f8Y14Rkb5hvhlPvmmTWzU1Ndi3bx9CQ0Mhk8lU7aNHj4YQAunp6ao2Xc/vKioq8P777zeYccxBIvojDuIRERG1AiEENm/ejF69esHa2houLi6YOHGi2hV78+fPh5WVFTp06KBqe+edd2BnZweZTIZ79+4BABYsWIDFixcjNzcXMpkMvr6+2LJlC2xsbNCuXTvMnTsX7u7usLGxQUhICE6cONEi+wCAAwcOwNHREevXr2/V1wt4cmWnEALjx4+vt8+6devQvXt3/O1vf8OhQ4ca3J42xyAhIQF2dnawtbVFeno6Ro8eDUdHR3h4eGDXrl1q23v8+DE+/PBDeHl5QS6Xo2/fvkhOTm7ek/7/bty4AblcDm9vb53Wq6qqQlZWVoNXkP6Ri4sLQkNDERcXByFEU0olIiPDvNKdKedVS6sv/5hXRNRczDfdMd/+Iy8vD2VlZfDy8lJr9/HxAQCcP3++wfUbOr+Ljo7GO++8Azc3t3rXZw4S0R9xEI+IiKgVrFq1CkuXLkV0dDTu3r2Lo0eP4vr163jhhRdw584dAE9OkqZOnaq2Xnx8PFavXq3WFhcXh3HjxsHHxwdCCFy5cgXz589HREQEFAoF3n33XeTn5+P06dOoqanByJEjVbftaM4+AKgm1FYqlS334tRj37596NGjB2xtbevtI5fL8cUXX8DMzAyzZ89GeXl5vX21OQZvv/02Fi5ciIqKCjg4OCA5ORm5ubno2rUrZs+ejerqatX2lixZgo0bNyI2Nha3bt3CuHHjMH36dJw6dapZz1uhUCAjIwOzZ8+GlZWV2mNLly6Fi4sLrKys4O3tjYkTJ+LkyZOqx2/evImqqir8/PPPGDZsmOqPB7169UJ8fHydJ3z9+vXDjRs3cO7cuWbVTUTGgXmlO1PKq8ZyqDkayj+AeUVEzcN8050p5Vtjbt++DQAatxS1sbGBXC5X1V+XhvLt+PHjyM3NxfTp0xutgTlIRLU4iEdERNTCKioqsHnzZrz66quYMWMGnJyc4O/vj88++wz37t3Dtm3bWmxfFhYWqisbe/fujYSEBJSWliIxMbFFtj927FiUlJRgxYoVLbK9+pSXl+P3339XXdnYkODgYCxcuBD5+flYsmRJnX2acgxCQkLg6OgINzc3hIeHo7y8HNeuXQMAVFZWIiEhAZMmTcLkyZPh7OyM5cuXw9LSstmvdUxMDNzd3bFu3Tq19jfffBN79+7F9evXUVZWhl27duHatWsIDQ1FTk4OAKCsrAwA4ObmhvXr1yMnJwd37tzBxIkTMW/ePPzjH//Q2F+3bt0AANnZ2c2qm4gMH/NKd6aUV9rkUHPUl3+1mFdE1FTMN92ZUr5p49GjRwCgcZtMALC0tERFRUW969aXbxUVFViwYAESEhK0qoE5SES1OIhHRETUwnJyclBWVoYBAwaotQ8cOBBWVhG2OXIAACAASURBVFZqt1dpaQMGDICtra3aLUoMwd27dyGEaPCqzz9at24devTogfj4eBw7dkzj8eYeg9orJmuv/Lx48SIUCgX8/PxUfeRyOTp06NCs13rPnj1ISUnBt99+q3GVp6enJ/r16wd7e3tYWVkhKCgIiYmJqKioQHx8PACo5nDo06cPQkJC0KZNGzg5OWH16tVwcnKq8+S49jVu6OpRIjINzCvdmVJeaZNDTdVQ/tViXhFRUzHfdGdK+aaN2jkBa2pqNB6rqqqCXC6vc72G8m3ZsmX485//jE6dOmlVA3OQiGpxEI+IiKiFFRcXAwDs7e01HnN2dkZpaWmr7t/a2hqFhYWtuo+WVllZCQD1Tiz+NBsbGyQmJkImk2HmzJkaV0K29DGovU3M8uXLIZPJVMvVq1frnKxcG0lJSdiwYQOOHDmCLl26aLWOv78/zM3NcenSJQCAu7s7AKjm0qhlZWWFzp07Izc3V2MbtSecta85EZku5pXuTDGv/ujpHGoKbfOPeUVETcV8052p59vTaucwLCkpUWtXKBSorKxUnYf9UUP5duzYMWRnZ2PWrFla18AcJKJaHMQjIiJqYc7OzgBQ54lJcXExPDw8Wm3f1dXVrb6P1lB7glI754M2goODsWjRIly+fBlr165Ve6ylj0HtpOOxsbEQQqgtmZmZOm0LAD799FPs3LkTGRkZ6Nixo9brKZVKKJVK1cm1vb09unXrhgsXLmj0rampgZOTk0Z7VVUVANR79SgRmQ7mle5MLa+e9nQO6UqX/GNeEVFTMd90Z+r59jRvb284ODjg6tWrau21cxX27dtXrb2xfNuxYwcOHz4MMzMz1QBk7XNav349ZDKZxtx+zEEiqsVBPCIiohbm5+cHe3t7jX+EnzhxAlVVVXj++edVbRYWFmqTdTfXkSNHIIRAUFBQq+2jNbRr1w4ymQwPHz7Uab21a9eiZ8+eOHPmjFq7LsdAG56enrCxscHZs2d1Wu9pQghERUUhOzsbaWlpdV6ZWuvll1/WaDt58iSEEAgODla1TZs2DWfOnEFeXp6qTaFQ4OrVq/D399fYRu1r3L59++Y8FSIyAswr3ZlKXgHa55A2dMm/WswrImoq5pvuTCnftGFhYYExY8bg6NGjUCqVqvb9+/dDJpNh/PjxALTPt8TERI3Bx9pfa0ZHR0MIoXHrUeYgEdXiIB4REVELs7GxweLFi7Fnzx7s3LkTJSUlyM7ORmRkJNzd3TFnzhxVX19fX9y/fx9paWmorq5GYWGhxtV+ANCmTRvcvHkT+fn5KC0tVZ0EKpVKPHjwADU1NTh//jwWLFgALy8vREREtMg+9u/fD0dHR6xfv77lX6g/sLW1RdeuXVFQUKDTerW3cXl6wnFdjoG2+3nrrbewa9cuJCQkoKSkBI8fP0ZBQQFu3boFAAgPD0f79u1x+vTperdz4cIFbNy4Edu3b4elpaXarWBkMhk++eQTVd8bN24gKSkJxcXFqK6uRmZmJmbNmgUvLy9ERkaq+i1atAidO3dGREQErl27hqKiIkRFRaGioqLOieZrX+O6BviIyLQwr3RnKnkFaJ9D2tAl/2oxr4ioqZhvujOlfNPWihUrcOfOHaxcuRLl5eXIzMzEpk2bEBERgR49egBoWr5pizlIRLU4iEdERNQKVq5ciZiYGKxZswZt27ZFaGgounTpgiNHjsDOzk7V7+2338awYcPw2muvoUePHli7dq3qdhnBwcG4fv06ACAyMhLt2rVD7969MWbMGNy/fx/Ak/vj+/v7Qy6X44UXXkD37t3x3Xffqd3mqrn7eFbGjh2LnJwctfkU/vnPf8LX1xe5ubkYOHAg/vKXv2isFxQUhEWLFmm0a3MMEhISEBsbC+DJLVHy8vKwfft2LF68GADwyiuv4PLlywCAuLg4LFy4EB9//DFcXV3h7u6OBQsW4MGDBwCe3O7k7t27SE9Pr/c5CiG0fj1eeeUVLF++HB4eHrC1tcXUqVMxePBgZGVlwdXVVdXPxcUFP/zwAzw8PBAQEIBOnTrhp59+wr59+xAQEKCx3ZMnT6JTp04at4AhItPEvNKdKeRV7Ta1yaGsrCwMGTIEHTt2xIkTJ3Du3Dm4u7tj8ODBOHr0KADd8q8W84qImoP5pjtTyTdtcgsA+vTpg2+//RYHDx6Eq6srJk+ejJkzZ2Lr1q2qPk3JN20xB4molky05rcNERmMKVOmAABSU1MlroSaKiUlBdOmTWvVf0SaKn39fMydOxepqakoKiqSuhQNTXk/XrlyBb169UJiYiJmzJjRitW1DqVSiaFDhyIiIgIzZ86Uupw6FRUVwcPDA+vWrVOdGGtDn75f9PXzSNrTp/eTsdHXzwfzSr8wr/STvn5+SXuG/P7Td/r6+WC+6RdDyDdtNTUHAf39vBBR0/GXeERERAZMl4nH9Z2vry/WrFmDNWvWoKysTOpydPL48WOkpaWhtLQU4eHhUpdTr1WrViEgIADz58+XuhQiMjHMK/3AvCIialnMN/1gKPmmLeYgEf0RB/GIiIhIbyxduhRTpkxBeHi4zpOqS+nIkSPYvXs39u/fD1tbW6nLqdPmzZtx9uxZ/Pvf/4alpaXU5RARGTTmVethXhERSYf5Jj3mIBE9jYN4REREBmjZsmVITEzEw4cP4e3tja+//lrqklrM+vXrMX/+fHz00UdSl6K14cOH46uvvkKHDh2kLqVO6enpePToEY4cOQIXFxepyyEiE8K80i/MKyKilsF80y/6nm/aYg4SUV0spC6AiIiIdBcTE4OYmBipy2g1o0aNwqhRo6Quw2hMmDABEyZMkLoMIjJBzCvSBfOKiAwF841aA3OQiOrCX+IRERERERERERERERER6RkO4hERERERERERERERERHpGQ7iEREREREREREREREREekZDuIRERERERERERERERER6RkLqQsgIv2RlZWFKVOmSF0GNVFBQQEA8Bi2gqysLAB8bXXB96NxqT2e+oJ5Zdj4/dB6mFe64/vRuOhbXumK+WbY+H3SephvuuP70XRlZWUhKChI6jKIqAXJhBBC6iKISHqbN29GZmam1GUQ6b2SkhIcP34cQ4cOhVwul7ocomcqNTVV6hKYV0RP2b9/P/r164cOHTpIXQqR3tCHvNIV841MnRAC3377LQICAphpRM0UHByMRYsWSV0GEbUQDuIRERHpoLS0FC4uLkhOTsbkyZOlLoeIiEycTCZDcnIypk6dKnUpRERETZaVlYXg4GBcuHABvXr1krocIiIivcE58YiIiHTg4OCA3r1748SJE1KXQkREREREZBQOHTqEjh07cgCPiIjoKRzEIyIi0lFgYCAH8YiIiIiIiFrI4cOHMWLECKnLICIi0jscxCMiItJRYGAgTp06hZqaGqlLISIiIiIiMmgKhQKZmZkYPny41KUQERHpHQ7iERER6SgoKAgKhQLZ2dlSl0JERERERGTQfvjhBzx69AgvvfSS1KUQERHpHQ7iERER6ah3795wdHTkLTWJiIiIiIia6fDhw+jduzc8PDykLoWIiEjvcBCPiIhIR2ZmZhgwYAAH8YiIiIiIiJrp0KFDnA+PiIioHhzEIyIiaoKgoCBkZWVJXQYREREREZHBKioqwrlz5zgfHhERUT04iEdERNQEgYGBuHjxIh48eCB1KURERERERAbp0KFDMDMzQ2hoqNSlEBER6SUO4hERETVBUFAQhBD46aefpC6FiIiIiIjIIB0+fBgDBw6Ek5OT1KUQERHpJQ7iERERNUG7du3QpUsXzotHRERERETURJwPj4iIqGEcxCMiImqioKAgDuIRERERERE1QV5eHn7//XfOh0dERNQADuIRERE1UWBgILKysiCEkLoUIiIiIiIig3Lo0CHY2toiKChI6lKIiIj0FgfxiIiImigwMBD379/HlStXpC6FiIiIiIjIoBw+fBihoaGwtraWuhQiIiK9xUE8IiKiJurfvz+sra15S00iIiIiIiIdKJVKfPfdd7yVJhERUSM4iEdERNRE1tbWeO655ziIR0REREREpIOzZ8+isLAQI0aMkLoUIiIivcZBPCIiomYICgpCVlaW1GUQEREREREZjEOHDqFt27bw9/eXuhQiIiK9xkE8IiKiZggMDMS5c+dQUVEhdSlEREREREQG4fDhwxgxYgTMzPinSSIiooYwKYmIiJohMDAQ1dXVOHPmjNSlEBERERER6b2qqiocP36c8+ERERFpgYN4REREzeDj44N27drxlppERERERERaOH78OMrLyzmIR0REpAUO4hERETXToEGDcOLECanLICIiIiIi0nuHDx+Gr68vvL29pS6FiIhI73EQj4iIqJkCAwM5iEdERERERKSFQ4cOYcSIEVKXQUREZBA4iEdERNRMgYGBuHr1Km7evCl1KURERERERHqruLgYp06d4q00iYiItMRBPCIiomYaNGgQzMzM8NNPP0ldChERERERkd767rvvIITA0KFDpS6FiIjIIHAQj4iIqJmcnJzQs2dP3lKTiIiIiIioAYcPH0a/fv3Qtm1bqUshIiIyCBzEIyIiagGcF4+IiIiIiKhhnA+PiIhINxzEIyIiagGBgYH46aefUFNTI3UpREREREREeufGjRu4ePEi58MjIiLSAQfxiIiIWkBQUBDKy8tx4cIFqUshIiIiIiLSO//3f/8HGxsbDBkyROpSiIiIDAYH8YiIiFqAn58f7O3teUtNIiIiIiKiOhw+fBiDBw+GXC6XuhQiIiKDwUE8IiKiFmBubo7nn3+eg3hERERERERPEULg8OHDvJUmERGRjjiIR0RE1EKCgoKQlZUldRlERERERER6JScnB7du3cKIESOkLoWIiMigcBCPiIiohQQGBuLXX3/Fw4cPpS6FiIiIiIhIbxw6dAjOzs7o37+/1KUQEREZFA7iERERtZCgoCAolUqcOnVK6lKIiIiIiIj0xuHDh/HSSy/B3Nxc6lKIiIgMioXUBRARERkLd3d3eHp6IisrSzU/3okTJ5CVlYWIiAhMnTpV6hKJiMiAFRcXQwih0V5eXo4HDx6otdnb28PS0vJZlUZERKQyYMAA9OrVCyNHjsSIESPQrl07HD16FB999JHUpRERERkcmajrLJCIiIi0VlNTg+zsbGRlZeGTTz5BUVERSkpKIISAlZUVqqqqcPDgQYwcOVLqUomIyIC99NJL+O677xrtZ25ujhs3bqB9+/bPoCoiIiJ1PXv2xKVLlyCTyaBUKuHh4YGioiJ88skneP311+Hk5CR1iURERAaDt9MkIiJqohMnTiAkJAT29vbo378/5s+fj2vXruHhw4eqX0pUVVUBALp06SJhpUREZAxee+01yGSyBvuYmZnhxRdf5AAeERFJpl27dhBCQKlUAgAKCgpQU1ODefPmoU2bNhgwYABWrFiBH3/8UeJKiYiI9B8H8YiIiJooICAAd+7cQXV1NYAnv8irqanR6CeTyeDl5fWsyyMiIiMTFhYGC4uGZ0SQyWT405/+9IwqIiIi0tShQweNi06qq6tVA3s///wz1q1bhx9++EGiComIiAwHB/GIiIiayNraGp9//nmd8xP9kaurK6ytrZ9RVUREZKxcXFwwatQomJub19vHzMwMkyZNeoZVERERqXNzc2vwohMLCwsMGDAA77333jOsioiIyDBxEI+IiKgZQkNDMXXqVFhaWtbbh7fSJCKiljJjxgzV7cmeZmFhgbFjx3KuISIiklTbtm1hZlb/nxzNzc3x1VdfNXhRChERET3BQTwiIqJm+u///u96f2lnZmaGbt26PeOKiIjIWI0fP77ezHn8+DFmzJjxjCsiIiJS5+rqWu/dSszMzLBx40Z07979GVdFRERkmDiIR0RE1Ezt27fHhg0b6rza1NLSEt7e3hJURURExsjW1haTJk2q8xfgcrkcY8aMkaAqIiKi/2jbtm2dc4VbWlpi0KBBmDdvngRVERERGSYO4hEREbWAyMhIBAQEaMz98PjxY3Tu3FmiqoiIyBhNnz4d1dXVam2WlpYICwuDXC6XqCoiIqIn2rZtW+etn83NzbFz584Gb7VJRERE6piaRERELcDMzAzbtm3TOFmtqanhIB4REbWol19+WWPeu+rqakyfPl2iioiIiP6jbdu2Gm0ymQxbtmyBj4+PBBUREREZLg7iERERtZDnn38ec+fO1fg1XpcuXaQpiIiIjJKlpSXCw8NhZWWlanN2dsbw4cMlrIqIiOiJpwfxLC0tMXToUMyaNUuiioiIiAwXB/GIiIha0EcffQQXFxfIZDIAT6449fLykrgqIiIyNq+99hqqqqoAPPnj6IwZMzQuIiEiIpKCq6ur6r9lMhmsrKzwv//7v6pzJCIiItIeB/GIiIhakKOjI+Li4lT/7+LiwvmJiIioxb3wwgto3749gCe30gwPD5e4IiIioifs7OzUfi2+detWeHp6SlgRERGR4eIgHhERUQubPn06XnzxRQC8lSYREbUOMzMzvPHGGwAAd3d3hISESFwRERHRf7i4uAAAxowZo8orIiIi0h3vt0JEBiUzMxPXr1+XugyiRk2cOBHHjh2DtbU1UlJSpC6HqFVNnTpV6hJ0xjwhY1A751BgYCBSU1Mlroao+QwxTxrCfwOSKbO0tISdnR0mTJjAzwIZpJCQEHh4eEhdBhERZEIIIXURRETamjJlCr7++mupyyAioj8wxH9OMk+IiPSPIeZJQzj/FxGR4UpOTja6i0uIyDDxdppEZHDCwsIghODyjBfgyT9ipa5Dn5awsLAG348KhQIHDhyQvE59WpKTkwFA8jq4tOzxNFTME2kWgHny9NJYnjS0pKamSl6/FAvzxLgWQ8+ThvD7Trr3k9R16NvyrN+P6enpkj/n5i7NyWcuhr0QEekTDuIRERG1ErlcjpdfflnqMoiIyIiFhYVJXQIREZGG8ePHS10CERGRUeAgHhEREREREREREREREZGe4SAeERERERERERERERERkZ7hIB4RERERERERERERERGRnuEgHhEREREREREREREREZGe4SAeERERERERERERERERkZ7hIB4REZHE/v3vf8PJyQn/+te/pC5F7x06dAhLly7F7t270bVrV8hkMshkMrzxxhsafUeNGgUHBweYm5ujT58+OH36tAQVa2/NmjXo3bs3HB0dYW1tDV9fX3zwwQcoKytT67du3TrV8/7j4ufnp7HN6upqxMTEwNfXF1ZWVnB2doafnx/y8/MBAHv37sXHH3+Mx48fP4unSEStjHmiPWPOE11yAgCUSiViY2MREhJS5+Pa5BPzhMh0MGu0Z8xZU6uxDAGAY8eOYfDgwbC1tYW7uzuioqLw6NEjtT7angs9rbKyEj179sTy5ctVbcwkIjI2HMQjIiKSmBBC6hIMwsqVK7FlyxYsW7YMkydPRl5eHnx8fODq6oqdO3di3759av0PHjyI1NRUjBs3Djk5Oejfv79ElWsnIyMD8+bNQ35+Pu7du4eYmBjExcVhypQpTd7mtGnT8Pe//x1fffUVFAoFfv31V/j4+KhOhsePHw8bGxsMHz4cxcXFLfVUiEgizBPtGHue6OLy5ct48cUXsWjRIigUijr7aJNPzBMi08Gs0Y4pZI02GZKTk4NRo0Zh+PDhKCwsxJ49e/D5558jMjJSrV9Tz4Wio6Nx8eJFtTZmEhEZGw7iERERSWzs2LF4+PAhxo0bJ3UpqKioaPAqSqls2LABSUlJSElJgYODg9pjW7ZsgZmZGebMmYOHDx9KVGHz2dvbY86cOWjTpg0cHBwwdepUTJo0CQcOHMD169fV+n755ZcQQqgtv/zyi1qfpKQkpKWlITU1FYGBgbCwsIC7uzvS09PVfo3x7rvv4rnnnsOYMWNQU1PzTJ4rEbUO5knjTCFPAO1y4ty5c1iyZAkiIyMREBBQ77a0zSfmCZFpYNY0zhSyRtsMWbt2LTp06IDVq1fDzs4OwcHBiIqKwhdffIHffvtN1U+Xc6FaP/74o0a21WImEZEx4SAeERERqezYsQN3796Vugw1V65cwYoVK7B69WrY2NhoPB4SEoIFCxbgxo0beO+99ySosGV88803MDc3V2tr27YtANR7ZWtDtm7div79+8Pf37/RvqtWrcLZs2cRFxen836IiOrCPNF/zz33HHbv3o3XX38d1tbW9fbTJZ+YJ0T0LDFrpKNNhtTU1GDfvn0IDQ2FTCZTtY8ePRpCCKSnp6vadD0XqqiowPvvv99g3jCTiMhYcBCPiIhIQseOHYOXlxdkMhn++te/AgASEhJgZ2cHW1tbpKenY/To0XB0dISHhwd27dqlWnfLli2wsbFBu3btMHfuXLi7u8PGxgYhISE4ceKEqt/8+fNhZWWFDh06qNreeecd2NnZQSaT4d69ewCABQsWYPHixcjNzYVMJoOvry8A4MCBA3B0dMT69eufxUuiYcuWLRBCYPz48fX2WbduHbp3746//e1vOHToUIPbE0Jg8+bN6NWrF6ytreHi4oKJEyeqXQmq7TEAgMePH+PDDz+El5cX5HI5+vbti+Tk5OY96f/vxo0bkMvl8Pb21mm9qqoqZGVlNXhV7B+5uLggNDQUcXFxvEUSkYFinjTOlPOkpdWXT8wTIuPGrGkcs+Y/8vLyUFZWBi8vL7V2Hx8fAMD58+cbXL+hc6Ho6Gi88847cHNzq3d9ZhIRGQsO4hEREUloyJAh+PHHH9Xa3n77bSxcuBAVFRVwcHBAcnIycnNz0bVrV8yePRvV1dUAnpzgRkREQKFQ4N1330V+fj5Onz6NmpoajBw5UnXbkS1btmDq1Klq+4iPj8fq1avV2uLi4jBu3Dj4+PhACIErV64AgGpCcKVS2SqvQWP27duHHj16wNbWtt4+crkcX3zxBczMzDB79myUl5fX23fVqlVYunQpoqOjcffuXRw9ehTXr1/HCy+8gDt37gDQ/hgAwJIlS7Bx40bExsbi1q1bGDduHKZPn45Tp04163krFApkZGRg9uzZsLKyUnts6dKlcHFxgZWVFby9vTFx4kScPHlS9fjNmzdRVVWFn3/+GcOGDVP9kaRXr16Ij4+v8yS2X79+uHHjBs6dO9esuolIGsyTxplSnjSWE83RUD4BzBMiY8asaZwpZU1jbt++DQAatxS1sbGBXC5X1V+XhrLm+PHjyM3NxfTp0xutgZlERMaAg3hERER6LCQkBI6OjnBzc0N4eDjKy8tx7do1tT4WFhaqKzN79+6NhIQElJaWIjExsUVqGDt2LEpKSrBixYoW2Z4uysvL8fvvv6uu1mxIcHAwFi5ciPz8fCxZsqTOPhUVFdi8eTNeffVVzJgxA05OTvD398dnn32Ge/fuYdu2bRrrNHQMKisrkZCQgEmTJmHy5MlwdnbG8uXLYWlp2ezXPyYmBu7u7li3bp1a+5tvvom9e/fi+vXrKCsrw65du3Dt2jWEhoYiJycHAFBWVgYAcHNzw/r165GTk4M7d+5g4sSJmDdvHv7xj39o7K9bt24AgOzs7GbVTUT6iXliOnmiTU40R335VIt5QmS6mDWmkzXaePToEQBo3CYTACwtLVFRUVHvuvVlTUVFBRYsWICEhAStamAmEZEx4CAeERGRgai9AvGPV1LWZcCAAbC1tVW7xYqhunv3LoQQDV7J+kfr1q1Djx49EB8fj2PHjmk8npOTg7KyMgwYMECtfeDAgbCyslK7lU9dnj4GFy9ehEKhgJ+fn6qPXC5Hhw4dmvX679mzBykpKfj22281rlz19PREv379YG9vDysrKwQFBSExMREVFRWIj48HANW8FH369EFISAjatGkDJycnrF69Gk5OTnWe8Ne+xg1dEUtExoF50jhDzhNtcqKpGsqnWswTIgKYNdow5KzRRu2cgDU1NRqPVVVVQS6X17leQ1mzbNky/PnPf0anTp20qoGZRETGgIN4RERERsja2hqFhYVSl9FslZWVAFDvZOlPs7GxQWJiImQyGWbOnKlxdWdxcTEAwN7eXmNdZ2dnlJaW6lRf7a1vli9fDplMplquXr1a5wTs2khKSsKGDRtw5MgRdOnSRat1/P39YW5ujkuXLgEA3N3dAUA1Z0gtKysrdO7cGbm5uRrbqD2Jrn3NiYgA5okh58kfPZ0TTaFtPjFPiEhXzBrjyJqn1c5rWFJSotauUChQWVmpOmf5o4ay5tixY8jOzsasWbO0roGZRETGgIN4RERERqa6uhrFxcXw8PCQupRmqz3pqp3bQhvBwcFYtGgRLl++jLVr16o95uzsDAB1nvA25TWrnUg9NjYWQgi1JTMzU6dtAcCnn36KnTt3IiMjAx07dtR6PaVSCaVSqfqDgb29Pbp164YLFy5o9K2pqYGTk5NGe1VVFQDUe0UsEZke5onh5snTns4JXemST8wTItIFs8Z4suZp3t7ecHBwwNWrV9Xaa+cv7Nu3r1p7Y1mzY8cOHD58GGZmZqoByNrntH79eshkMo25/ZhJRGQMOIhHRERkZI4cOQIhBIKCglRtFhYWjd7KRh+1a9cOMpkMDx8+1Gm9tWvXomfPnjhz5oxau5+fH+zt7TVO7k6cOIGqqio8//zzOu3H09MTNjY2OHv2rE7rPU0IgaioKGRnZyMtLa3Oq21rvfzyyxptJ0+ehBACwcHBqrZp06bhzJkzyMvLU7UpFApcvXoV/v7+GtuofY3bt2/fnKdCREaEeWJ4eQJonxPa0CWfajFPiEgXzBrDzBptWFhYYMyYMTh69CiUSqWqff/+/ZDJZBg/fjwA7bMmMTFRY/Cx9hec0dHREEJo3HqUmURExoCDeERERAZOqVTiwYMHqKmpwfnz57FgwQJ4eXkhIiJC1cfX1xf3799HWloaqqurUVhYqHFFJAC0adMGN2/eRH5+PkpLS1FdXY39+/fD0dER69evf4bP6glbW1t07doVBQUFOq1XfR9TsAAAIABJREFUe2uapydRt7GxweLFi7Fnzx7s3LkTJSUlyM7ORmRkJNzd3TFnzhyd9/PWW29h165dSEhIQElJCR4/foyCggLcunULABAeHo727dvj9OnT9W7nwoUL2LhxI7Zv3w5LS0u129vIZDJ88sknqr43btxAUlISiouLUV1djczMTMyaNQteXl6IjIxU9Vu0aBE6d+6MiIgIXLt2DUVFRYiKikJFRQWWLFmiUUPta1zXAB8RmQbmiSZDyxNA+5zQhi75VIt5QkQNYdZoMsSs0daKFStw584drFy5EuXl5cjMzMSmTZsQERGBHj16AGha1miLmURExoCDeERERBL661//ioEDBwIAoqKiMGHCBCQkJCA2NhbAk1uM5OXlYfv27Vi8eDEA4JVXXsHly5dV26isrIS/vz/kcjleeOEFdO/eHd99953aLbPefvttDBs2DK+99hp69OiBtWvXqm4pEhwcjOvXrwMAIiMj0a5dO/Tu3RtjxozB/fv3n8nr0JCxY8ciJydHbY6If/7zn/D19UVubi4GDhyIv/zlLxrrBQUFYdGiRRrtK1euRExMDNasWYO2bdsiNDQUXbp0wZEjR2BnZwcAOh2DuLg4LFy4EB9//DFcXV3h7u6OBQsW4MGDBwCe3MLl7t27SE9Pr/c5CiG0fj1eeeUVLF++HB4eHrC1tcXUqVMxePBgZGVlwdXVVdXPxcUFP/zwAzw8PBAQEIBOnTrhp59+wr59+xAQEKCx3ZMnT6JTp04at7UhIsPAPGmcKeRJ7Ta1yYmsrCwMGTIEHTt2xIkTJ3Du3Dm4u7tj8ODBOHr0KADd8qkW84TIeDFrGmcqWaNNhgBAnz598O233+LgwYNwdXXF5MmTMXPmTGzdulXVpylZoy1mEhEZA5lozW9KIqIWNmXKFABAamqqxJWYHplMhuTkZEydOlXqUvSGPrwf586di9TUVBQVFUlWgy5SUlIwbdo0nU7Urly5gl69eiExMREzZsxoxepah1KpxNChQxEREYGZM2dKXU6dioqK4OHhgXXr1qlO9rXRlOOpL/Th82uqmCea9OH9yDzRf8wTw8PvO2kY6/upufTh/WhoWdOUfGbW6I+mZhKgH58XIqJa/CUeERGRgdNl4nRD5OvrizVr1mDNmjUoKyuTuhydPH78GGlpaSgtLUV4eLjU5dRr1apVCAgIwPz586UuhYgkxDzRX8wTIjIWzBr9ZShZoy1mEhEZCw7iEZHJmTVrFhwcHCCTyZ7ZhM6trbKyEj179sTy5cvV2tesWYPevXvD0dER1tbW8PX1xQcffNDqJxO7d+9G165dNe5lb2VlhXbt2mHo0KHYtGmT6pYdRI1ZunQppkyZgvDwcJ0nipfSkSNHsHv3buzfvx+2trZSl1OnzZs34+zZs/j3v/8NS0tLqcsxKKaUJwBQXV2NmJgY+Pr6wsrKCs7OzvDz80N+fn6r1cM8oZbGPGk9zJOWZww5s27dOo3vcJlMBj8/P42+x44dw+DBg2Frawt3d3dERUXh0aNHrV4js4ZaGrNGeswkIjImHMQjIpPzt7/9Ddu3b5e6jBYVHR2NixcvarRnZGRg3rx5yM/Px7179xATE4O4uDjVbUFay+TJk5GXlwcfHx84OTlBCAGlUom7d+8iJSUF3t7eiIqKQp8+fXDq1KlWrcWYLVu2DImJiXj48CG8vb3x9ddfS11Sq1q/fj3mz5+Pjz76SOpStDZ8+HB89dVX6NChg9Sl1Ck9PR2PHj3CkSNH4OLiInU5BseU8gQApk2bhr///e/46quvoFAo8Ouvv8LHx6dVLwxhnjwbzBP9xzwxTcaYM/XJycnBqFGjMHz4cBQWFmLPnj34/PPPERkZ2er7ZtY8G8wa/afvWaMtZhIRGRsLqQsgIqLm+fHHH/HLL7/U+Zi9vT3mzJkDc3NzAMDUqVOxe/dupKSk4Pr16/D09HxmdcpkMjg7O2Po0KEYOnQoxo4di2nTpmHs2LG4dOkSnJycnlktxiImJgYxMTFSl/FMjRo1CqNGjZK6DKMxYcIETJgwQeoySE80lCdJSUlIS0vDuXPn4O/vDwBwd3dHenr6sywRAPOkNTBPqLmYJ9SQL7/8stG5wdauXYsOHTpg9erVkMlkCA4ORlRUFD744AN88MEH6Nmz5zOq9glmTctj1tCzwkwiImPDX+IRkUmSyWRSl9AiKioq8P777yMuLq7Ox7/55hvVAF6ttm3bAgAUCkWr19eQsLAwRERE4O7du/jss88krYWIqKlMJU+2bt2K/v37qwbw9AnzhIiMmbHkTENqamqwb98+hIaGqj3f0aNHQwghyQUjT2PWEBERkVQ4iEdERk8IgU2bNqFHjx6wtraGk5MT3n//fY1+jx8/xocffggvLy/I5XL07dsXycnJAICEhATY2dnB1tYW6enpGD16NBwdHeHh4YFdu3apbef777/HoEGDYGtrC0dHR/j7+6OkpKTRfTRFdHQ03nnnHbi5uWm9zo0bNyCXy+Ht7d3k/baUiIgIAMD+/ftVbYZ4HIjINJhqnlRVVSErKwsBAQFN3n5rY54QkTEw5pxpSF5eHsrKyuDl5aXW7uPjAwA4f/58q+xXV8waIiIikgIH8YjI6K1YsQJRUVGYM2cO7ty5g9u3b2PJkiUa/ZYsWYKNGzciNjYWt27dwrhx4zB9+nScOnUKb7/9NhYuXIiKigo4ODggOTkZubm56Nq1K2bPno3q6moAQHl5OcaPH4+wsDDcv38fly9fRvfu3VFVVdXoPnR1/Phx5ObmYvr06Vqvo1AokJGRgdmzZ8PKykrnfba02j8I5+XlqdoM7TgQkekw1Ty5efMmqqqq8PPPP2PYsGFwd3eHjY0NevXqhfj4eAghdN5nS2OeEJExMNacWbp0KVxcXGBlZQVvb29MnDgRJ0+eVD1++/ZtAICDg4PaejY2NpDL5bhz547O+2wNzBoiIiKShCAiMiBhYWEiLCxM6/4KhULY2tqKkSNHqrXv2rVLABBnzpwRQghRUVEhbG1tRXh4uNq61tbW4u233xZCCBEdHS0AiIqKClWf+Ph4AUBcuXJFCCHEL7/8IgCIb775RqMWbfahy/MaMGCAKCgoEEIIUVhYKACI6OjoBteLjo4W3bt3FyUlJTrtTwghAIjk5GSd1vHx8RFOTk4N9pHJZMLZ2VkIYXjHQdf3IwmRnJws+M8P42HIx5N58p/1GsuT7OxsAUCMHDlSHD9+XBQVFYni4mKxZMkSAUDs3LlTp30yTzQxT3RnyN8/pMlYj6cu33fGmjPXrl0Tp0+fFqWlpeLRo0ciMzNT9OvXT8jlcvHLL78IIYQ4ePCgACA2b96ssb6jo6MICQnRaZ9NfT8Ze9Y0JX9NHfPZdPHzQkT6xKJ1hwiJiKR15coVKBQKDB8+vMF+Fy9ehEKhgJ+fn6pNLpejQ4cO+O233+pdr/bXbLVXUXbt2hXt2rXDjBkz8O677yIiIgJdunRp1j7qsmzZMvz5z39Gp06dtF5nz549SElJwcGDBzWucpVKeXk5hBBwdHQEYHjHAQCysrIwZcoUndczVQUFBQDA18xI1B5PU2DKeWJtbQ0A6NOnD0JCQlTtq1evxtatW7Ft2za8/vrrOu23pTFPTA/zxLiYUp7Ux1hzxtPTE56enqr/DwoKQmJiIgICAhAfH4+EhATY2NgAeDI33tOqqqogl8t12mdrMYasiY2NRWpqqs7rmaqsrCwAzBoiIpIWb6dJREat9g8Cjc0ZV15eDgBYvnw5ZDKZarl69SoUCoXW+5PL5cjIyMCQIUOwfv16dO3aFeHh4aioqGixfRw7dgzZ2dmYNWuW1uskJSVhw4YNOHLkiOqEUB9cunQJANCzZ08AhnUciMi0mHKeuLu7AwDu3bun1m5lZYXOnTsjNzdX6322FuYJERk6Y8yZ+vj7+8Pc3Fz13d2hQwcAUM0BV0uhUKCyslKVQ1Jj1hAREZEU+Es8IjJqtVd1Pnr0qMF+tSfLsbGxWLBgQbP22adPH/zrX/9CYWEhNm/ejA0bNqBPnz4IDw9vkX3s2LEDhw8fhpmZ5nUY69evx/r163Hy5EkMGDAAAPDpp5/i22+/RUZGBuzt7Zu839Zw4MABAMDo0aMBGNZxqBUUFMSrWXWQkpKCadOm8TUzErXH0xSYep5069YNFy5c0OhXU1MDJyenJtfQUpgnpod5YlxMKU/qY4w5Ux+lUgmlUqn6pbe3tzccHBxw9epVtX5XrlwBAPTt27fFa2gKY8iahQsXYurUqc3ejqmo/QUes8b0yGQyqUsgIlLhL/GIyKj5+fnBzMwM33//fYP9PD09YWNjg7NnzzZrfzdv3lT9kdPNzQ0fffQR+vfvjwsXLrTYPhITEyGEUFsKCwsBANHR0RBCYMCAARBCICoqCtnZ2UhLS9O7Abzbt28jNjYWHh4emDlzJgDDOg5EZFpMOU8AYNq0aThz5gzy8vJU6ysUCly9ehX+/v7NqqO5mCdEZAyMMWcA4OWXX9ZoO3nyJIQQCA4OBgBYWFhgzJgxOHr0KJRKparf/v37IZPJMH78+GbX0VzMGiIiIpIKB/GIyKi5ublh8uTJ+Prrr7Fjxw6UlJTg/Pnz2LZtm1o/GxsbvPXWW9i1axcSEhJQUlKCx48fo6CgALdu3dJ6fzdv3sTcuXPx22+/oaqqCmfOnMHVq1cRFBTUYvvQ1oULF7Bx40Zs374dlpaWardfkclk+OSTT1p8n3URQqCsrAxKpVL1B+Lk5GQMHjwY5ubmSEtLU80rYYzHgYiMgynnCQAsWrQInTt3RkREBK5du4aioiJERUWhoqICS5YsaZV9Po15QkTGzFhz5saNG0hKSkJxcTGqq6uRmZmJWbNmwcvLC5GRkap+K1aswJ07d7By5UqUl5cjMzMTmzZtQkREBHr06KHTPpuDWUNERER6RxARGZCwsDARFham0zqlpaVi1qxZwtXVVdjb24shQ4aIDz/8UAAQHh4e4ty5c0IIIR49eiSioqKEl5eXsLCwEG5ubmLy5MkiJydHxMfHC1tbWwFAdOvWTeTm5opt27YJR0dHAUB07txZXLp0SeTn54uQkBDh4uIizM3NRceOHUV0dLSoqalpdB/NUVhYKACI6OhoVVt2drYAUO+yadMmnfYBQCQnJ2vVd+/evaJv377C1tZWWFlZCTMzMwFAyGQy4ezsLAYNGiTWrFkjioqKNNY1pOPQlPejqUtOThb854fxMOTjyTypW115Uuv69evitddeEy4uLsLa2loMGjRI7N+/X+d9ME80MU90Z8jfP6TJWI+nLt93QhhnzixevFj4+PgIOzs7YWFhITw8PMTs2bPFzZs3Nfp+//33YtCgQcLa2lq4u7uL999/X1RWVuq0PyF0fz+ZStbo+n4k5rMp4+eFiPSJTAghWnWUkIioBfGe9NKRyWRITk7mHAp/wPej7mrnvOE/P4yDIR9Pfn6lwzzRxPej7gz5+4c0Gevx5PedNIz1/dRcfD/qjvlsuvh5ISJ9wttpEhEREREREREREREREekZDuIREemB3377TWPOurqW8PBwqUslktShQ4ewdOlS7N69G127dlV9Nt544w2NvqNGjYKDgwPMzc3Rp08fnD59WoKKdadUKhEbG4uQkJA6Hx86dGi93xH29vZqff/xj39g4MCBcHBwQOfOnfHWW2/h9u3bqsf37t2Ljz/+GI8fP27V50TPDvOESDvME+YJNQ1zhqh5mD/MHyIiXXEQj4hID/Ts2RNCiEaXpKQkqUslkszKlSuxZcsWLFu2DJMnT0ZeXh58fHzg6uqKnTt3Yt++fWr9Dx48iNTUVIwbNw45OTno37+/RJVr7/Lly3jxxRexaNEiKBQKndcfMmSI6r+Tk5Px+uuvY8qUKSgoKEB6ejqOHj2K0aNHo6amBgAwfvx42NjYYPjw4SguLm6x50HSYZ4QNY550jjmCdWHOUPUdMyfxjF/iIg0cRCPiIjIgFVUVNR7haMh7aMxGzZsQFJSElJSUuDg4KD22JYtW2BmZoY5c+bg4cOHElXYfOfOncOSJUsQGRmJgICAevvZ2NigpKRE449lc+bMwQcffKDq9z//8z/4f+zdeXRUVbr38V+FDJWRJBIGCQFCcEAQUHglAQQ0zERAxqu0ojYiDhBAVAZtRLBFVwMXhesFle7lgAGlQSaBAhFR4IqAQlwiIIQwhsGQhAQy7fcPV6opE0IKkpwM389aWUvP2Wfv55x9qnZRT529b775Zk2YMEE1a9ZUq1atNG7cOO3Zs0c7duxwlhszZoxatmypXr16Of8xDKD6YTxhPGE8AVCWqss4czWMP//B+AMA7iGJBwBAJfb+++8rJSWl0rdRnIMHD+rll1/Wq6++KrvdXmh/TEyM4uPjdfz4cT3//PMWRFg6WrZsqc8//1wPP/ywfHx8rlruyy+/LPQP/+TkZO3bt0/33Xefy7Z69erJZrM5tzVo0ECSlJSU5HL81KlTtWfPHs2ZM6c0TgVAJcR4wngiMZ4AKDvVYZy5GsYfV4w/AOAekngAAJQjY4xmzZql22+/XT4+PgoJCVG/fv30yy+/OMuMHj1a3t7eqlu3rnPbM888I39/f9lsNp09e1aSFB8fr/Hjx+vQoUOy2WyKiorS3LlzZbfbVbt2bT311FOqV6+e7Ha7YmJiXH6teCNtSH/8wysoKEgzZswo0+sl/fHLVGOMHnjggauWmT59um655Ra99957cjgcxdZXkj6YP3++/P395efnpxUrVqhnz54KCgpSeHi4Fi9e7FJfXl6eXnnlFUVERMjX11d33nmnEhISbuyk3fTGG29ozJgxLtsiIyMLfYlRsH5EZGSky/aQkBB16tRJc+bMkTGmbIMFUCoYT9zHeHJtjCcACjDOlB7Gn2tj/AGAYhgAqEQGDhxoBg4caHUY1ZIkk5CQYHUYFcr13I+vvPKK8fb2Nh9++KFJTU01P/30k7nrrrtMrVq1zKlTp5zlHn74YVOnTh2XY9966y0jyZw5c8a5bcCAAaZJkyYu5UaOHGn8/f3Nzz//bC5dumQSExNN27ZtTWBgoDl69GiptLFq1SoTGBhopk2b5tb5JyQkGHc/fkRGRppmzZoVua9Jkybm8OHDxhhjvvvuO+Ph4WEaNWpkMjIyjDHGrF271vTt29flmJL2weTJk40ks3HjRnPhwgWTkpJiOnbsaPz9/U12draz3PPPP298fHzMZ599Zn7//XczadIk4+HhYb7//nu3zvNK99xzj2nZsmWJyh47dsw0a9bM5OXluWzfvHmz8fLyMnPnzjVpaWlm37595vbbbzfdu3cvsp6JEycaSWb37t0ljvN6+rOiYDyxDuNJYYwnjCfGMJ5U1vGkOLzfWaOq3k83yt37sbqPM8aU3udFxp/iWTn+XA3v3wAqEp7EAwCgnGRlZWnWrFl68MEHNWzYMNWsWVMtWrTQu+++q7Nnz2rBggWl1panp6fzl5nNmjXT/PnzlZ6erkWLFpVK/b1791ZaWppefvnlUqnvai5evKjDhw+rSZMm1ywbHR2tsWPH6siRI3rppZeKLHM9fRATE6OgoCCFhYVp6NChunjxoo4ePSpJunTpkubPn6/+/ftrwIABCg4O1pQpU+Tl5VVq1/pa3njjDT333HPy8HD9WNepUye9+OKLGj16tIKCgtS8eXOlp6frvffeK7Kepk2bSpL27t1b5jEDuDGMJ+5jPLk2xhMABRhnSg/jz7Ux/gBA8UjiAQBQThITE5WRkaE2bdq4bG/btq28vb1dpo0pbW3atJGfn5/LFCuVQUpKiowx8vPzK1H56dOn69Zbb9W8efO0devWQvtvtA+8vb0lSTk5OZKk/fv3KzMzU82bN3eW8fX1Vd26dcvlWp84cUJffPGFhg8fXmjf5MmTtWDBAm3cuFEZGRn67bffFBMTo+joaCUnJxcqX3CNT58+XdZhA7hBjCfuYzwpHuMJgCsxzpQexp/iMf4AwLWRxAMAoJykpqZKkgICAgrtCw4OVnp6epm27+PjozNnzpRpG6Xt0qVLklTswuhXstvtWrRokWw2mx5//HFlZWW57C/tPrh48aIkacqUKbLZbM6/pKQkZWZmulXX9Zg5c6ZGjBghu93usv3kyZOaOXOmnnzySd13333y9/dX48aNtXDhQp04cUJvvfVWobp8fX0l/eeaA6i4GE/cx3hSPMYTAFdinCk9jD/FY/wBgGsjiQcAQDkJDg6WpCL/YZWamqrw8PAyazsnJ6fM2ygLBf8Qy8vLK/Ex0dHRGjdunA4cOKDXXnvNZV9p90FYWJgkafbs2TLGuPxt27bNrbrcderUKX3yySd6+umnC+07cOCA8vLydPPNN7tsDwoKUmhoqBITEwsdk52dLek/1xxAxcV44j7Gk6tjPAHwZ4wzpYfx5+oYfwCgZEjiAQBQTpo3b66AgADt3LnTZfuOHTuUnZ2tu+++27nN09PTOcVJadi8ebOMMWrXrl2ZtVEWateuLZvNpgsXLrh13GuvvabbbrtNu3fvdtnuTh+URIMGDWS327Vnzx63jisNM2fO1LBhwxQaGlpoX8E/3k+ePOmyPT09XefPn1eDBg0KHVNwjevUqVMG0QIoTYwn7mM8uTrGEwB/xjhTehh/ro7xBwBKhiQeAADlxG63a/z48Vq2bJk++ugjpaWlae/evRo1apTq1aunkSNHOstGRUXp/PnzWr58uXJycnTmzBklJSUVqjM0NFQnTpzQkSNHlJ6e7vzHbX5+vn7//Xfl5ubqp59+Unx8vCIiIlzWGriRNtauXaugoCDNmDGj9C/UFfz8/BQZGaljx465dVzBNDQ1atQotL2kfVDSdh577DEtXrxY8+fPV1pamvLy8nTs2DHnPziHDh2qOnXqaNeuXW7VXZzTp0/rgw8+0NixY4vc37hxY3Xp0kULFy7Uli1blJWVpeTkZOf5PfHEE4WOKbjGLVq0KLU4AZQNxhP3MZ4UjfEEQFEYZ0oP40/RGH8AwA0GACqRgQMHmoEDB1odRrUkySQkJFgdRoVyPfdjfn6+eeutt0zTpk2Nl5eXCQkJMf379zf79+93KXfu3DnTpUsXY7fbTePGjc1zzz1nJkyYYCSZqKgoc/ToUWOMMbt27TINGzY0vr6+pkOHDubUqVNm5MiRxsvLy9SvX994enqaoKAg069fP3Po0KFSa2PNmjUmMDDQTJ8+3a3zT0hIMO5+/Bg9erTx8vIymZmZzm3Lli0zTZo0MZJMrVq1zLPPPlvksRMmTDB9+/Z12VaSPpg3b57x8/MzkkzTpk3NoUOHzIIFC0xQUJCRZBo2bGh+/fVXY4wxly9fNi+++KKJiIgwnp6eJiwszAwYMMAkJiYaY4zp37+/kWReeeWVYs9z27Ztpn379qZevXpGkpFk6tata2JiYszXX3/tUnbcuHFm2LBhxdZ39uxZEx8fb6KiooyPj48JCAgw7du3N//+97+LLN+7d29Tv359k5+fX2y9V7qe/qwoGE+sw3hSGOMJ44kxjCeVdTwpDu931qiq99ONcvd+rO7jjDGl93mR8afijj9Xw/s3gIrEZowxZZ0oBIDSMmjQIEnS0qVLLY6k+rHZbEpISNDgwYOtDqXCqKj341NPPaWlS5fq3LlzVodSyJIlSzRkyBC58/Hj4MGDuv3227Vo0SINGzasDKMrG/n5+ercubOGDx+uxx9/3OpwinTu3DmFh4dr+vTpGj9+fImPu57+rCgq6uu3OmA8Kayi3o+MJxUL40nlw/udNarq/XSjKuL9WJHHGan0xmfGn7J3vePP1VTE1wuA6ovpNAEAqILcWTi9oouKitK0adM0bdo0ZWRkWB2OW/Ly8rR8+XKlp6dr6NChVodzVVOnTlWrVq00evRoq0MBUMEwnlQMjCcAqqqqNM5cDeNP2WP8AVCVkcQDAAAV3sSJEzVo0CANHTrU7UXhrbR582Z9/vnnWrt2rfz8/KwOp0izZs3Snj17tGbNGnl5eVkdDgCUKcaTssN4AgBXx/hTdhh/AFR1JPEAAKhCJk2apEWLFunChQtq3LixPvvsM6tDKjUzZszQ6NGj9fe//93qUErs/vvv18cff6y6detaHUqRVqxYocuXL2vz5s0KCQmxOhwAFQjjScXCeAKgqqnK48zVMP6UPsYfANWBp9UBAACA0vP666/r9ddftzqMMtOtWzd169bN6jCqjL59+6pv375WhwGgAmI8gTsYTwC4q6qPM1fD+FO6GH8AVAc8iQcAAAAAAAAAAABUMCTxAAAAAAAAAAAAgAqGJB4AAAAAAAAAAABQwZDEAwAAAAAAAAAAACoYkngAAAAAAAAAAABABWMzxhirgwCAkho0aJA+++wzq8MAAFyhMn6cZDwBgIqnMo4nxbHZbFaHAAC4TgkJCRo8eLDVYQAASTwAlcu2bduUnJxsdRgAqrmkpCStX79eP/74o86cOSNfX1/dcccdatmype68807VrVvX6hDLVWX8xy3jCaqKIUOGKD4+XtHR0VaHAtywyjieFGfJkiVWh1DujDFKSkrSTz/9pH379umXX37R5cuXVbduXbVo0UKxsbFq1KiR1WECwDXFxMQoPDzc6jAAgCQeAADAjfjtt9/kcDjkcDi0YcMGpaamqnHjxuratatiY2MVGxurkJAQq8MEUEXZbDZ+KQ7AUidPntTWrVvlcDi0evVqHT9+XLVq1VKXLl0UGxurrl27qnHjxlaHCQAAUCmRxAMAACgleXl52rNnjzOpt3nzZhlj1KpVK2dCr1OnTvLy8rI6VABVBEk8AOUtPT1dO3bscH7e+eGHH+Tr66v27ds7P++0bt1aHh4eVocKAABQ6ZHEAwAAKCPnzp3Tpk2b5HA4tG7dOiUlJSkgIEDt2rVTnz599MADD/DLdAA3hCQegLKWm5urH3/8sdgfKXXo0EF2u93qUAGxni1yAAAgAElEQVQAAKockngAAADl5MqpN9etW6e0tDRFRkY6vwDr2rWrgoODrQ4TQCVCEg9AacvPz9fu3bu1detWffvtt/ryyy+Vnp7u8pnl/vvvV2hoqNWhAgAAVHkk8QAAACxQ8Kv2lStXatWqVdq9e7dsNhtTbwJwC0k8AKXhyh8abdq0SefOnVNYWJg6d+6s2NhYdevWTY0aNbI6TAAAgGqHJB4AAEAFcPbsWX311VdyOBxau3atkpOTFRAQoM6dOysuLk7du3dXw4YNrQ4TQAVDEg/A9bjyc8eGDRt0+PBh+fn5KSYmxvljorvuuks2m83qUAEAAKo1kngAAAAV0JW/iC9qGqtu3bqpZs2aVocJwGIk8QCURFZWlr799lvnZ4uiZgDo2LGjfHx8rA4VAAAAVyCJBwAAUMHl5uZq+/btWrVqlRwOh3bt2qUaNWqoZcuWio2NVZ8+fRQTEyMPDw+rQwVQzkjiAShKwbp2BUm7rVu36tKlSy4/CIqNjVVISIjVoQIAAKAYJPEAAAAqmTNnzmjz5s1yOBxas2aNjh07plq1aqlLly6KjY1Vjx49FBERYXWYAMoBSTwABa58in/jxo06f/68ateurU6dOik2NpapuQEAACohkngAAACVXMGXditXrpTD4Sj0S/sePXooMDDQ6jABlAGSeED1deWPetavX68jR47I399f0dHRrGsHAABQRZDEAwAAqEL+vOZNwdSb99xzj+Li4vhCD6hiSOIB1UdmZqa+++67Yte1u/fee+Xt7W11qAAAACglJPEAAACqsNOnT2vLli1yOBxatWqVTpw4obCwMHXu3FmxsbHq1auXwsPDrQ4TwHUiiQdUXXl5edqzZ48zaffNN9/o8uXLLk/bd+3aVcHBwVaHCgAAgDJCEg8AAKAaSUxM1KpVq4r8MrBPnz7q2rWr7Ha71WECKCGSeEDVcuW6dg6HQ7///rvq1Kmje++9V7GxserZs6caNGhgdZgAAAAoJyTxAAAAqqk/T8u1a9cu2e12tW/fnrV0gEqCJB5QuaWkpOjrr7+Ww+HQunXrlJSUxLp2AAAAcCKJBwAAAEnSqVOn9M0332jlypVavXq1zp8/r9q1a6tTp07OJ/Vuvvlmq8MEcAWSeEDlUtQPaGrUqKGWLVuyrh0AAAAKIYkHAACAQvLz87V7927nl4xbtmxRdna2mjVrpri4OMXGxqpjx47y8fGxOlSgWiOJB1RsJVnXrlu3bqpZs6bVoQIAAKACIokHAACAa7p48aK2bdvm/BLyhx9+kJ+fn2JiYpjuC7AQSTyg4rlyXbsNGzYoNTVVdevWVceOHRUbG6tevXopPDzc6jABAABQCZDEAwAAgNsOHz6sDRs2OL+k/P33351fUPbp00e9e/fWTTfdZHWYQJVHEg+w3unTp7VlyxY5HA59+eWXOnr0qAICAtSuXTt+6AIAAIAbQhIPAAAAN+TPU4V9/fXXysvLU+vWrVnfByhjJPGA8vfnp9OLWteuU6dO8vLysjpUAAAAVHIk8QAAAFCqMjIytH37dq1cuVIrV67U4cOH5e/vr+joaOeXm3fffbfVYQJVAkk8oOz9+ccqBevEXrmuXffu3RUUFGR1qAAAAKhiSOIBAACgTBW1NlC9evUUGxuruLg4xcbGKiQkxOowgUqJJB5QNq4cu9avX68LFy6oXr166tChg2JjY9W7d2/Vr1/f6jABAABQxZHEAwAAQLkpaurN/Px8tWrViqk3getAEg8oHVeua7d27VolJycXWteOp8gBAABQ3kjiAQAAwDIZGRn66quvtGrVKq1bt05JSUkuU2/GxcWpWbNmVocJVFgk8YDrw7p2AAAAqAxI4gEAAKDCKGr6sivXHOratauCg4OtDhOoMEjiASWTm5urH3/8sdh17Xr06KHAwECrQwUAAACcSOIBAACgQrryC9eVK1dq27ZtstlsLlNv8pQEqjuSeMDVXfnDkHXr1iktLU2RkZFq3769OnTooD59+ujmm2+2OkwAAADgqkjiAQAAoFI4e/asvvrqKzkcDn355Zc6evSoAgIC1LlzZ8XFxalbt25q1KiR1WEC5YokHvAfp06d0jfffCOHw6HVq1fr+PHjqlWrlrp06eL88UdkZKTVYQIAAAAlRhIPAAAAldKVT1h8+eWXSk9Pd5kWrVu3bqpZs6bVYQJliiQeqrOMjAxt377dORb88MMP8vX1Vfv27Z1jQevWreXh4WF1qAAAAMB1IYkHAACASi83N1fbt2/XqlWr5HA4tGvXLtWoUUMtW7ZUbGys+vTpo5iYGL7IRZVDEg/VyZ/Xtfv666+Vl5en1q1bO5N2HTp0kN1utzpUAAAAoFSQxAMAAECVc+bMGW3evFkOh0Nr1qzRsWPHXKZU69GjhyIiIqwOE7hhJPFQ1V3rqev7779foaGhVocJAAAAlAmSeAAAAKjyCr4EXrlypRwOhy5duuTyJXD37t0VFBRkdZiA20jioao5efKktm7dKofDoVWrVunEiRMuP8Lo2rWrGjdubHWYAAAAQLkgiQcAAIBqJSsrS99++63zyY6CqTfvuecexcXFsYYSKhWSeKjs0tPTtWPHDta1AwAAAIpAEg8AAADVWkpKir7++ms5HA6tXr1ax48fV1hYmDp37qzY2Fj16tVL4eHhVocJFIkkHiqbK39IsXXrVu3YsUPGGLVq1Yp17QAAAIA/IYkHAAAAXCExMVGrVq2Sw+HQN998o8uXL7tMvdmzZ08FBARYHSYgiSQeKr78/Hzt3r3b+aTd1q1bC01pHBsbq5CQEKtDBQAAACockngAAADAVWRmZuq7775zmXrTbre7TPN21113yWazWR0qqoHFixcrPT3dZdvIkSM1YsQItWnTxmV7//79FRYWVp7hAU4F65A6HA5t2rRJ586dc3nCuXv37mrYsKHVYQIAAAAVHkk8AAAAoIROnz6tLVu2yOFwaOXKlTp58qRq166tTp06KTY2Vr1791b9+vWtDhNV1PDhw/Wvf/1LXl5ezm0F/5wrSCTn5eUpICBAKSkp8vHxsSROVD9nz57VV199JYfDoQ0bNujw4cPy8/NTTEwMP3gAAAAAbgBJPAAAAOA6/HmKuCun3uzTp4/i4uJY1wmlat26derRo0exZby8vPTII4/ovffeK6eoUB1dua6dw+HQ7t27ZbPZXNa169ixI4lkAAAA4AaRxAMAAABKwZ+n3vzhhx/k6+vL1JsoNbm5uapTp47Onz9fbLmNGzfqvvvuK6eoUB3k5eVpz549xa5r17VrVwUHB1sdKgAAAFClkMQDAAAAysDJkye1YcMGrVq1Sg6HQ7///rvq1Kmje++9V7GxsYqLi1O9evWsDhOVzLPPPqsFCxYoJyenyP21atXSqVOnVKNGjXKODFXNlevabdy4UefPn3eZPrhHjx6KiIiwOkwAAACgSiOJBwAAAJSxPz/FsmXLFuXm5qp169alPvXc5cuXmcKuCvv222/VoUOHIvd5eXnp2Wef1axZs8o5KlglPz9fHh4epVLXmTNntHnzZjkcDq1fv15HjhyRv7+/oqOjeZoYAAAAsAhJPAAAAKCcXbx4Udu2bZPD4dDKlSv1888/y8/PTzExMc4vy+++++7rqrtr167q06ePRo8ezZftVZAxRhERETp27FiR+//v//5Pbdu2LeeoUN4uXbqkqVOnKiwsTOPHj7+uOv48BXBR69rde++98vb2LuXoAQAAAJQUSTwAAADAYldOW7dhwwalpqaqbt266tq1q+Li4nT//fcrNDT0mvWkpaUpNDRUeXl5uvfee/Xhhx8y3V0VNHHiRP3jH/8oNKVmgwYNlJSURPK2itu5c6ceeughHThwQF26dNGmTZtKdNyfnwj+5ptvdPnyZda1AwAAACowkngAAABABfLnL9q//vpr5efnl+jpmOXLl+vBBx+UMUbe3t7y9PTU7Nmz9eSTT1pwJigrP/30k1q2bOmyzcvLSy+99JKmTZtmUVQoazk5OZo+fbpmzJghm82m3NxceXt768KFC7Lb7UUec+UPBIpam7Nnz55q0KBBOZ8JAAAAgJIiiQcAAABUYBkZGfrqq6+0atWqItepiouLU7NmzSRJzzzzjN577z1lZ2c7j7fZbOrbt68WLlyoWrVqWXUaKGW33Xab9u/f77Jt3759uuOOOyyKCGUpMTFRDz30kBITE5WXl+eyb+PGjbrvvvskSSkpKfr666/lcDi0bt06JSUlsa4dAAAAUImRxAMAAAAqkf3792v9+vVav369Nm/erIyMDDVp0kTdunXTsmXLdPr06ULHeHt7KyAgQB988IH69u1rQdQobTNmzNCrr77qnFKzWbNmSkxMtDgqlLb8/Hy9/fbbmjBhgowxys3Nddnv7e2tAQMGqE6dOnI4HNq3b5+8vb2dSbv7779fbdu2laenp0VnAAAAAOBGkMQDAAAAKqns7Gx999132rBhg1auXKm9e/detayHh4fy8/P18MMP63/+538UGBhYjpGitCUlJalx48YyxsjLy0vTp0/XCy+8YHVYKEW//fab/vKXv2j79u3Kz8+/arnAwECFhYU5n7Tr1q2batasWY6RAgAAACgrJPEAAACAKuDdd9/Vs88+W2iqvT/z8vJS7dq19fHHH6tTp07lFB3KQps2bfTDDz/IZrPpyJEjioiIsDoklAJjjBYuXKgxY8YoLy/P+bTl1Xh4eOjMmTMKDQ0tpwgBAAAAlBcPqwMAAAAAcOPWrVtXonI5OTk6deqUunTpotGjR+vy5ctlHBnKyiOPPCJJuueee0jgVRFJSUnq3LmzRo0apUuXLl0zgSf9kfT76quvyiE6AAAAAOWNJ/EAAECFtW3bNs2aNcvqMIAKzxijFStWFFovqySCgoJ0zz33MP1eJXTp0iWtXr1arVq1UpMmTawOBzfo8OHD+vHHH91+HXt4eKhRo0a66667yigyoHKLjo7WuHHjrA4DAADgurC6NQAAqLCSk5P12WefaeDAgVaHAott375dktSuXTuLI6mYzp8/r9zcXNlsNtlsNhljVNRv9Tw9PeXl5SVvb2/5+PjIx8dH3t7eOn78uPz8/OTl5VXmsX722Wdq166dwsPDy7ytquJq97/dbledOnW4llVAamqq0tPTFR4ersuXL+vSpUvKzs5WdnZ2kU/jFbzWJSk/P1+nTp0q75CBSqHg/RMAAKCyIokHAAAqvKVLl1odAiw2aNAgSdwLV7NhwwbdeeedCg0NVUhIiEJDQ51/V/6/h4f1s+nbbDaNHTtWgwcPtjqUSqO4+//gwYOKiooq75BQjowxOnfunM6dO6ezZ886//vcuXM6c+aMc9vChQsVHBxsdbhAhVLw/gkAAFBZkcQDAAAAKrmuXbuqa9euVocBC5DAq/psNptq1aqlWrVq6dZbb7U6HAAAAADlyPqf4gIAAAAAAAAAAABwQRIPAAAAAAAAAAAAqGBI4gEAAAAAAAAAAAAVDEk8AAAAAAAAAAAAoIIhiQcAAACg0lmzZo1q1qyplStXWh1KhfTUU0/JZrM5/4YNG1aojMPh0MSJE/X5558rMjLSWfYvf/lLobLdunVTYGCgatSooTvuuEO7du0qj9O4Yfn5+Zo9e7ZiYmKK3N+5c2eX63TlX0BAgEvZTz75RG3btlVgYKAaNmyoxx57TKdOnXLu/+KLLzRz5kzl5eWVSuz0D/1jtYrQP8uXL3ept1atWqV/ogAAABUYSTwAAAAAlY4xxuoQKrzQ0FCtXbtW+/fv1/vvv++y729/+5vmzp2rSZMmacCAAfrtt9/UpEkT3XTTTfroo4+0evVql/Lr16/X0qVLFRcXp8TERN11113leSrX5cCBA7r33ns1btw4ZWZmun18hw4dnP+dkJCghx9+WIMGDdKxY8e0YsUKbdmyRT179lRubq4k6YEHHpDdbtf999+v1NTUG4qd/rk2+qdsVZT+6du3r44dO6YtW7aoV69eN35iAAAAlQxJPAAAAACVTu/evXXhwgXFxcVZHYqysrKu+qSKlXx9fdWjRw/dcsst8vHxcW5/44039Omnn2rJkiUKDAx0OWbu3Lny8PDQyJEjdeHChfIOudT8+OOPeumllzRq1Ci1atXqquXsdrvS0tJkjHH5GzlypF544QVnuf/93//VzTffrAkTJqhmzZpq1aqVxo0bpz179mjHjh3OcmPGjFHLli3Vq1cvZ3LCXfTPf9A/1qhI/WOz2VS/fn117NhRTZs2LbuTBgAAqKBI4gEAAADADXj//feVkpJidRglcvDgQb388st69dVXZbfbC+2PiYlRfHy8jh8/rueff96CCEtHy5Yt9fnnn+vhhx92SWD+2ZdfflkoEZOcnKx9+/bpvvvuc9lWr1492Ww257YGDRpIkpKSklyOnzp1qvbs2aM5c+a4HTf944r+sUZl7R8AAICqiCQeAAAAgEpl69atioiIkM1m0zvvvCNJmj9/vvz9/eXn56cVK1aoZ8+eCgoKUnh4uBYvXuw8du7cubLb7apdu7aeeuop1atXT3a7XTExMS5PhIwePVre3t6qW7euc9szzzwjf39/2Ww2nT17VpIUHx+v8ePH69ChQ7LZbIqKipL0x5fbQUFBmjFjRnlckhKbO3eujDF64IEHrlpm+vTpuuWWW/Tee+/J4XAUW58xRrNmzdLtt98uHx8fhYSEqF+/fvrll1+cZUraN5KUl5enV155RREREfL19dWdd96phISEGztpN73xxhsaM2aMy7bIyMhCidqC9bwiIyNdtoeEhKhTp06aM2eO29O+0j/XRv/QPwAAANUJSTwAAAAAlUqHDh303XffuWx7+umnNXbsWGVlZSkwMFAJCQk6dOiQIiMjNWLECOXk5Ej6Izk3fPhwZWZmasyYMTpy5Ih27dql3Nxcde3aVcnJyZL++LJ+8ODBLm3MmzdPr776qsu2OXPmKC4uTk2aNJExRgcPHpT0x5fpkpSfn18m1+B6rV69Wrfeeqv8/PyuWsbX11f//Oc/5eHhoREjRujixYtXLTt16lRNnDhRkydPVkpKirZs2aLk5GR17NhRp0+fllTyvpGkl156SW+++aZmz56tkydPKi4uTg899JB27txZehehGMePH9fmzZs1YMAAl+2TJk3SqVOn9Pbbbys9PV2JiYmaM2eOunfvrnbt2hWqp3Xr1jp+/Lh+/PFHt9qnf4pH/9A/AAAA1Q1JPAAAAABVSkxMjIKCghQWFqahQ4fq4sWLOnr0qEsZT09P59MvzZo10/z585Wenq5FixaVSgy9e/dWWlqaXn755VKprzRcvHhRhw8fVpMmTa5ZNjo6WmPHjtWRI0f00ksvFVkmKytLs2bN0oMPPqhhw4apZs2aatGihd59912dPXtWCxYsKHRMcX1z6dIlzZ8/X/3799eAAQMUHBysKVOmyMvLq9T65VreeOMNPffcc/LwcP2ncqdOnfTiiy9q9OjRCgoKUvPmzZWenq733nuvyHoK1u7au3dvidumf66N/qF/AAAAqhuSeAAAAACqLG9vb0lyeVqlKG3atJGfn5/LNHZVTUpKiowxxT5FdKXp06fr1ltv1bx587R169ZC+xMTE5WRkaE2bdq4bG/btq28vb1dpictyp/7Zv/+/crMzFTz5s2dZXx9fVW3bt1y6ZcTJ07oiy++0PDhwwvtmzx5shYsWKCNGzcqIyNDv/32m2JiYhQdHe18evNKBde44GmqkqB/ikf/uKJ/AAAAqgeSeAAAAAAgycfHR2fOnLE6jDJz6dIlSX+cZ0nY7XYtWrRINptNjz/+uLKyslz2p6amSpICAgIKHRscHKz09HS34iuYdnDKlCmy2WzOv6SkJGVmZrpV1/WYOXOmRowYIbvd7rL95MmTmjlzpp588kndd9998vf3V+PGjbVw4UKdOHFCb731VqG6fH19Jf3nmpcE/VM8+qd41b1/AAAAqiqSeAAAAACqvZycHKWmpio8PNzqUMpMwRfjBev1lUR0dLTGjRunAwcO6LXXXnPZFxwcLElFJhuu51qGhYVJkmbPni1jjMvftm3b3KrLXadOndInn3yip59+utC+AwcOKC8vTzfffLPL9qCgIIWGhioxMbHQMdnZ2ZL+c81Lgv65Ovrn2qp7/wAAAFRVJPEAAAAAVHubN2+WMUbt2rVzbvP09LzmNJyVSe3atWWz2XThwgW3jnvttdd02223affu3S7bmzdvroCAAO3cudNl+44dO5Sdna27777brXYaNGggu92uPXv2uHVcaZg5c6aGDRum0NDQQvsKkiknT5502Z6enq7z58+rQYMGhY4puMZ16tQpcQz0z9XRP9dW3fsHAACgqiKJBwAAAKDayc/P1++//67c3Fz99NNPio+PV0REhMt6TlFRUTp//ryWL1+unJwcnTlzRklJSYXqCg0N1YkTJ3TkyBGlp6crJydHa9euVVBQkGbMmFGOZ1U8Pz8/RUZG6tixY24dVzAtYI0aNQptHz9+vJYtW6aPPvpIaWlp2rt3r0aNGqV69epp5MiRbrfz2GOPafHixZo/f77S0tKUl5enY8eOORMAQ4cOVZ06dbRr1y636i7O6dOn9cEHH2js2LFF7m/cuLG6dOmihQsXasuWLcrKylJycrLz/J544olCxxRc4xYtWpQ4bvqnaPRPydupqv0DAABQrRkAAIAKKiEhwfBxBcYYM3DgQDNw4ECrw0ApkGQSEhJuqI63337b1K1b10gyfn5+5oEHHjDz5s0zfn5+RpJp2rSpOXTokFmwYIEJCgoykkzDhg3Nr7/+aowxZuTIkcbLy8vUr1/feHp6mqCgINOvXz9z6NAhl3bOnTtnunTpYux2u2ncuLF57rnnzIQJE4wkExUVZY4ePWqMMWbXrl2mYcOGxtfX13To0MGcOnXKrFmzxgQGBprp06ff0Lkac333/8iRI039+vULbR89erTx8vIymZmZzm3Lli0zTZo0MZJMrVq1zLPPPltknRMmTDB9+/Z12Zafn2/eeust07RpU+Pl5WVCQkJM//79zf79+51l3Omby5cvmxdffNFEREQYT09PExYWZgYMGGASExONMcb079/fSDKvvPJKsee/bds20759e1OvXj0jyUgydevWNTExMebrr792KTtu3DgzbNiwYus7e/asiY+PN1FRUcbHx8cEBASY9u3bm3//+99Flu/du7epX7++yc/Pdytu+of+oX8K90+BMWPGmJtuuqnYtv6Mzw8AAKCysxljTHklDAEAANyxZMkSDRkyRHxcwaBBgyRJS5cutTgS3CibzaaEhAQNHjzYshieeuopLV26VOfOnbMsBndcz/3/1FNPadWqVYWeGjp48KBuv/12LVq0SMOGDSvVOMtDfn6+OnfurOHDh+vxxx+3OpwinTt3TuHh4Zo+fbrGjx8vqeRx0z9lj/6pfP1TID4+Xh999JHOnj1b4vr4/AAAACo7ptMEAAAAUO3k5eVZHUKZy8rK0rp163TgwAFlZ2dL+mOK0GnTpmnatGnKyMiwOEL35OXlafny5UpPT9fQoUOtDueqpk6dqlatWmn06NGS3Iub/il79E/l6h9jjE6cOKGtW7fq4MGDFkcHAABQ/kjiAQAAAEAVdP78efXo0UO33HKLy1M3EydO1KBBgzR06FBduHDBwgjds3nzZn3++edau3at/Pz8rA6nSLNmzdKePXu0Zs0aeXl5SXI/bvqn7NA/la9/VqxYofr166tjx45avXq1xRECAACUP6bTBAAAFRbTaVZeM2fO1KJFi5SUlCQPDw81aNBAgwYN0oQJExQUFOR2fTcyHdb+/fv1zjvvaNOmTTp69KiysrLk7++vOnXqqGnTppoyZYqio6PdrhfXx+rpNCdNmqR//OMfys7OVqNGjfTWW29p4MCBlsRSUmU1Hdz69eu1adMmvfHGG6Vab3W1YsUK/fzzz3rhhRdUo0aNG66P/ild9E/FVtr9U4DpNAEAQGVHEg8AAFRYJPEqrz59+qhLly565JFH5Ovrq7Vr1+rhhx9W586dtX79erfru94v4d5//32NGjVK0dHRmjRpku655x75+vrq+PHj+v777zV37lw9+uijevLJJ92OCdfH6iReZcSX0ABwfXj/BAAAlR3TaQIAAFgoKytLMTExVa5tb29vPfPMMwoLC1NAQIAGDRqkfv36acOGDTp58mSZtPln27dv18iRI9WxY0dt3LhR3bt3V3BwsHx8fBQZGakhQ4bolVdeca4VVhFV1fsDAAAAAABcm6fVAQAAAFRn77//vlJSUqpc28uWLSu0rX79+pKkjIyMMmnzz6ZPn668vDz9/e9/l6dn0R97u3fvru7du5dLPNejqt4fAAAAAADg2ngSDwAAVDkffvih2rRpI7vdLn9/fzVq1EivvfaaJMkYo1mzZun222+Xj4+PQkJC1K9fP/3yyy/O4+fPny9/f3/5+flpxYoV6tmzp4KCghQeHq7Fixe71d4333yjZs2aqWbNmrLb7WrRooXWrVsnSYqPj9f48eN16NAh2Ww2RUVFSZLy8vL0yiuvKCIiQr6+vrrzzjuVkJDgdmyl3faNOnDggIKDg9WwYcNSqa842dnZ2rhxo2666Sb9v//3/0p8HPeHdfcHAAAAAABwRRIPAABUKXPmzNEjjzyigQMH6sSJEzp27JgmTZqk/fv3S5KmTp2qiRMnavLkyUpJSdGWLVuUnJysjh076vTp05Kkp59+WmPHjlVWVpYCAwOVkJCgQ4cOKTIyUiNGjFBOTk6J2zt9+rSGDBmiI0eO6MSJEwoICNDDDz/sPDYuLk5NmjSRMUYHDx6UJL300kt68803NXv2bJ08eVJxcXF66KGHtHPnTrd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",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 121,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# แสดง Plot จุดเชื่อมโยงต่างๆ ของ Architecture ที่เราสร้างขึ้นเพื่อ Train Model ของเรา\n",
        "\n",
        "tf.keras.utils.plot_model(model, show_shapes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k6HgyXJLZpOE",
        "outputId": "a83b0ec9-5f49-47cc-b36d-59f24a4b763c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model_3\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input (InputLayer)             [(None, 178)]        0           []                               \n",
            "                                                                                                  \n",
            " batch_normalization_9 (BatchNo  (None, 178)         712         ['input[0][0]']                  \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " batch_normalization_10 (BatchN  (None, 178)         712         ['input[0][0]']                  \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_11 (BatchN  (None, 178)         712         ['input[0][0]']                  \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " dense_39 (Dense)               (None, 256)          45824       ['batch_normalization_9[0][0]']  \n",
            "                                                                                                  \n",
            " dense_43 (Dense)               (None, 512)          91648       ['batch_normalization_10[0][0]'] \n",
            "                                                                                                  \n",
            " dense_47 (Dense)               (None, 1024)         183296      ['batch_normalization_11[0][0]'] \n",
            "                                                                                                  \n",
            " dropout_27 (Dropout)           (None, 256)          0           ['dense_39[0][0]']               \n",
            "                                                                                                  \n",
            " dropout_30 (Dropout)           (None, 512)          0           ['dense_43[0][0]']               \n",
            "                                                                                                  \n",
            " dropout_33 (Dropout)           (None, 1024)         0           ['dense_47[0][0]']               \n",
            "                                                                                                  \n",
            " dense_40 (Dense)               (None, 256)          65792       ['dropout_27[0][0]']             \n",
            "                                                                                                  \n",
            " dense_44 (Dense)               (None, 512)          262656      ['dropout_30[0][0]']             \n",
            "                                                                                                  \n",
            " dense_48 (Dense)               (None, 1024)         1049600     ['dropout_33[0][0]']             \n",
            "                                                                                                  \n",
            " dropout_28 (Dropout)           (None, 256)          0           ['dense_40[0][0]']               \n",
            "                                                                                                  \n",
            " dropout_31 (Dropout)           (None, 512)          0           ['dense_44[0][0]']               \n",
            "                                                                                                  \n",
            " dropout_34 (Dropout)           (None, 1024)         0           ['dense_48[0][0]']               \n",
            "                                                                                                  \n",
            " dense_41 (Dense)               (None, 256)          65792       ['dropout_28[0][0]']             \n",
            "                                                                                                  \n",
            " dense_45 (Dense)               (None, 512)          262656      ['dropout_31[0][0]']             \n",
            "                                                                                                  \n",
            " dense_49 (Dense)               (None, 1024)         1049600     ['dropout_34[0][0]']             \n",
            "                                                                                                  \n",
            " dropout_29 (Dropout)           (None, 256)          0           ['dense_41[0][0]']               \n",
            "                                                                                                  \n",
            " dropout_32 (Dropout)           (None, 512)          0           ['dense_45[0][0]']               \n",
            "                                                                                                  \n",
            " dropout_35 (Dropout)           (None, 1024)         0           ['dense_49[0][0]']               \n",
            "                                                                                                  \n",
            " dense_42 (Dense)               (None, 178)          45746       ['dropout_29[0][0]']             \n",
            "                                                                                                  \n",
            " dense_46 (Dense)               (None, 178)          91314       ['dropout_32[0][0]']             \n",
            "                                                                                                  \n",
            " dense_50 (Dense)               (None, 178)          182450      ['dropout_35[0][0]']             \n",
            "                                                                                                  \n",
            " concatenate_3 (Concatenate)    (None, 534)          0           ['dense_42[0][0]',               \n",
            "                                                                  'dense_46[0][0]',               \n",
            "                                                                  'dense_50[0][0]']               \n",
            "                                                                                                  \n",
            " dense_51 (Dense)               (None, 128)          68480       ['concatenate_3[0][0]']          \n",
            "                                                                                                  \n",
            " output (Dense)                 (None, 1)            129         ['dense_51[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 3,467,119\n",
            "Trainable params: 3,466,051\n",
            "Non-trainable params: 1,068\n",
            "__________________________________________________________________________________________________\n",
            "Epoch 1/10\n",
            "321/322 [============================>.] - ETA: 0s - loss: 0.5506 - accuracy: 0.6962WARNING:tensorflow:Can save best model only with val_accuarcy available, skipping.\n",
            "322/322 [==============================] - 17s 47ms/step - loss: 0.5502 - accuracy: 0.6966 - val_loss: 0.4974 - val_accuracy: 0.8090\n",
            "Epoch 2/10\n",
            "322/322 [==============================] - ETA: 0s - loss: 0.4422 - accuracy: 0.7866WARNING:tensorflow:Can save best model only with val_accuarcy available, skipping.\n",
            "322/322 [==============================] - 15s 46ms/step - loss: 0.4422 - accuracy: 0.7866 - val_loss: 0.3971 - val_accuracy: 0.8335\n",
            "Epoch 3/10\n",
            "322/322 [==============================] - ETA: 0s - loss: 0.4155 - accuracy: 0.8070WARNING:tensorflow:Can save best model only with val_accuarcy available, skipping.\n",
            "322/322 [==============================] - 16s 49ms/step - loss: 0.4155 - accuracy: 0.8070 - val_loss: 0.3777 - val_accuracy: 0.8331\n",
            "Epoch 4/10\n",
            "322/322 [==============================] - ETA: 0s - loss: 0.3919 - accuracy: 0.8230WARNING:tensorflow:Can save best model only with val_accuarcy available, skipping.\n",
            "322/322 [==============================] - 16s 51ms/step - loss: 0.3919 - accuracy: 0.8230 - val_loss: 0.3692 - val_accuracy: 0.8350\n",
            "Epoch 5/10\n",
            "322/322 [==============================] - ETA: 0s - loss: 0.3781 - accuracy: 0.8292WARNING:tensorflow:Can save best model only with val_accuarcy available, skipping.\n",
            "322/322 [==============================] - 15s 46ms/step - loss: 0.3781 - accuracy: 0.8292 - val_loss: 0.3358 - val_accuracy: 0.8401\n",
            "Epoch 6/10\n",
            "322/322 [==============================] - ETA: 0s - loss: 0.3602 - accuracy: 0.8381WARNING:tensorflow:Can save best model only with val_accuarcy available, skipping.\n",
            "322/322 [==============================] - 16s 49ms/step - loss: 0.3602 - accuracy: 0.8381 - val_loss: 0.3205 - val_accuracy: 0.8626\n",
            "Epoch 7/10\n",
            "322/322 [==============================] - ETA: 0s - loss: 0.3469 - accuracy: 0.8445WARNING:tensorflow:Can save best model only with val_accuarcy available, skipping.\n",
            "322/322 [==============================] - 15s 47ms/step - loss: 0.3469 - accuracy: 0.8445 - val_loss: 0.3043 - val_accuracy: 0.8665\n",
            "Epoch 8/10\n",
            "322/322 [==============================] - ETA: 0s - loss: 0.3310 - accuracy: 0.8535WARNING:tensorflow:Can save best model only with val_accuarcy available, skipping.\n",
            "322/322 [==============================] - 15s 46ms/step - loss: 0.3310 - accuracy: 0.8535 - val_loss: 0.2985 - val_accuracy: 0.8672\n",
            "Epoch 9/10\n",
            "321/322 [============================>.] - ETA: 0s - loss: 0.3196 - accuracy: 0.8591WARNING:tensorflow:Can save best model only with val_accuarcy available, skipping.\n",
            "322/322 [==============================] - 15s 47ms/step - loss: 0.3197 - accuracy: 0.8590 - val_loss: 0.2922 - val_accuracy: 0.8797\n",
            "Epoch 10/10\n",
            "321/322 [============================>.] - ETA: 0s - loss: 0.3113 - accuracy: 0.8647WARNING:tensorflow:Can save best model only with val_accuarcy available, skipping.\n",
            "322/322 [==============================] - 16s 48ms/step - loss: 0.3112 - accuracy: 0.8646 - val_loss: 0.2888 - val_accuracy: 0.8777\n"
          ]
        }
      ],
      "source": [
        "# ทําการ Compile ตัว Model และเลือก Loss Method เป็น Binary เพราะเนื่องจากเราได้ทําการแปลงการบันทึก EEG ของเราเป็น Binary แล้วต่อไปเราก็รับ optimizer เป็น Adam \n",
        "# จากนั้นก็ทําการ summary() เพื่อเช็คค่าต่างๆ และความถูกต้องใน Architecture ของเราที่สร้างขึ้นมา Train Model \n",
        "# และอย่างต่อไปคือ modelCheckpoint คือการ checkpoint ตามชื่อเลย เมื่อ val_accuracy เต็มที่ในการ Prediction แล้ว การ fit ข้อมูลจะทําการหยุดและ Save จะไม่มีการ Prediction ต่อเพราะเนื่องจากการ เรียนรู้นั้นเต็มที่แล้ว\n",
        "# model.fit เป็นการนําเข้าข้อมูลทุกอย่างมาประมวลผลและมี option เข้ามาอีกนั่นก็คือ epochs คือรอบในการเรียนรู้ของ ML Model และ Batch Size เป็น Hyperparameter ไว้สําหรับปรับจูนในการ Train Model ของเราซึ่งแนะนําว่าอย่าปรับสูงกว่า 32 (https://www.bualabs.com/archives/729/what-is-batch-size-in-deep-neural-networks-how-to-adjust-machine-learning-model-accuracy-deep-learning-hyperparameter-tuning-ep-2/)\n",
        "# validation_split คือการ แบ่งการ prediction และการ Validation ซึ่งในที่นี้คือเราปรับเป็น 0.2 นั่นก็คือ Predict 80% และ Valid 20%\n",
        "# callbacks เป็นตัวส่งข้อมูลให้ ตัวแปร model_checkpoint_callback ที่ทํางานอยู่เพื่อป้องกัน การ Train เกินทรัพยากรณ์\n",
        "\n",
        "model.compile(\n",
        "    loss='binary_crossentropy',\n",
        "    optimizer=tf.keras.optimizers.Adam(1e-4),\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "model.summary()\n",
        "\n",
        "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
        "    filepath='/tmp/checkpoint',\n",
        "    save_weight_only=True,\n",
        "    monitor='val_accuarcy',\n",
        "    mode='max',\n",
        "    save_best_only=True\n",
        ")\n",
        "\n",
        "history = model.fit(x_train, y_train, epochs=200, batch_size=32, validation_split=0.2, verbose=1, callbacks=[model_checkpoint_callback])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "KoKZcc4vZycM",
        "outputId": "3458499b-f91a-4923-ff71-2a850a5dfcc3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "173/173 [==============================] - 2s 11ms/step - loss: 0.2890 - accuracy: 0.8757\n",
            "Evaluate on the test data\n",
            "Test-loss, Test-accuracy:  [0.28897732496261597, 0.8757246136665344]\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0f60ba4cd0>]"
            ]
          },
          "execution_count": 123,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ทําการ Save Model เพื่อสามารถนําไปใช้ต่อได้ในงานอื่นๆ เช่นนําไปใช้งานในการตรวจจับลมบ้าหมู ในนาฬิกาหรือในรถยนต์ เป็นต้น ฯลฯ ขึ้นอยู่กับผู้ที่จะนําไปใช้งาน\n",
        "# และทําการสรุปผลรวมของการ Train Model และแสดงออกมาเป็นความแม่นยําในการ Train ซึ่งถ้าสูงกว่า 90% ถือว่าดี\n",
        "\n",
        "model.save('epileptic_seizures.h5')\n",
        "results = model.evaluate(x_test, y_test, batch_size=32)\n",
        "\n",
        "print('\\nEvaluate on the test data')\n",
        "print(\"Test-loss, Test-accuracy: \", results)\n",
        "\n",
        "loss_train = np.array(history.history['loss'])\n",
        "loss_test = np.array(history.history['val_loss'])\n",
        "\n",
        "x = np.arange(0, loss_train.shape[0])\n",
        "plt.plot(x, loss_train, label=\"Training loss\")\n",
        "plt.plot(x, loss_test, label=\"Validation loss\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "epileptic_seizures.ipynb",
      "provenance": []
    },
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