NLP_version_2.0.ipynb

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      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the \"../input/\" directory.\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
        "\n",
        "\n",
        "import re\n",
        "import string\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from wordcloud import WordCloud, STOPWORDS\n",
        "import matplotlib as plty\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "import sklearn\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from subprocess import check_output\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "import plotly.offline as py\n",
        "py.init_notebook_mode(connected=True)\n",
        "import plotly.graph_objs as go\n",
        "import plotly.tools as tls\n",
        "\n",
        "import plotly.graph_objs as go\n",
        "from sklearn import preprocessing\n",
        "Encode = preprocessing.LabelEncoder()\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "tfid = TfidfVectorizer()\n",
        "vect = CountVectorizer()\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.manifold import TSNE\n",
        "NB = MultinomialNB()\n",
        "\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from sklearn import metrics\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.tokenize import word_tokenize"
      ],
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          "height": 17
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        "id": "IqjpVXFf9VJ6",
        "outputId": "fee5c855-a03e-4d56-bd40-3d7aba26e9ff"
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      "outputs": [
        {
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              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-2.24.1.min']\n",
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    {
      "cell_type": "markdown",
      "source": [
        "# import Data"
      ],
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        "id": "wS2WtudY9llP"
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    {
      "cell_type": "code",
      "source": [
        "#load the data\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YpdHPTE17UUr",
        "outputId": "cf2c7c23-9c50-46ec-b6f3-7b0a12856f11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(r'/content/drive/MyDrive/Sheet_1.csv',usecols=['response_id','class','response_text'],encoding='latin-1')\n"
      ],
      "metadata": {
        "id": "CED3yUaL77R2"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
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            "text/plain": [
              "    response_id        class  \\\n",
              "0    response_1  not_flagged   \n",
              "1    response_2      flagged   \n",
              "2    response_3      flagged   \n",
              "3    response_4  not_flagged   \n",
              "4    response_5  not_flagged   \n",
              "..          ...          ...   \n",
              "75  response_76  not_flagged   \n",
              "76  response_77      flagged   \n",
              "77  response_78  not_flagged   \n",
              "78  response_79      flagged   \n",
              "79  response_80  not_flagged   \n",
              "\n",
              "                                        response_text  \n",
              "0               I try and avoid this sort of conflict  \n",
              "1   Had a friend open up to me about his mental ad...  \n",
              "2   I saved a girl from suicide once. She was goin...  \n",
              "3   i cant think of one really...i think i may hav...  \n",
              "4   Only really one friend who doesn't fit into th...  \n",
              "..                                                ...  \n",
              "75  Now that I've been through it, although i'm no...  \n",
              "76  when my best friends mom past away from od'ing...  \n",
              "77  As a camp counselor I provide stability in kid...  \n",
              "78  My now girlfriend used to have serious addicti...  \n",
              "79  The one person I ever talked to it was because...  \n",
              "\n",
              "[80 rows x 3 columns]"
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              "      <th></th>\n",
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              "      <td>when my best friends mom past away from od'ing...</td>\n",
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              "      <th>77</th>\n",
              "      <td>response_78</td>\n",
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              "      <td>As a camp counselor I provide stability in kid...</td>\n",
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              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>response_79</td>\n",
              "      <td>flagged</td>\n",
              "      <td>My now girlfriend used to have serious addicti...</td>\n",
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              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>response_80</td>\n",
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              "      <td>The one person I ever talked to it was because...</td>\n",
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              "<p>80 rows × 3 columns</p>\n",
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          "metadata": {},
          "execution_count": 13
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[:,\"response_text\"] = df.response_text.apply(lambda x : str.lower(x))  # convert the words into lowercase  for stopwords\n"
      ],
      "metadata": {
        "id": "nPOmbwRN94Sm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"response_text\"]\n"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WMT0h1-G9_wM",
        "outputId": "50a1ca69-ce7f-445f-f50f-6e5919eb9dc2"
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                 i try and avoid this sort of conflict\n",
              "1     had a friend open up to me about his mental ad...\n",
              "2     i saved a girl from suicide once. she was goin...\n",
              "3     i cant think of one really...i think i may hav...\n",
              "4     only really one friend who doesn't fit into th...\n",
              "                            ...                        \n",
              "75    now that i've been through it, although i'm no...\n",
              "76    when my best friends mom past away from od'ing...\n",
              "77    as a camp counselor i provide stability in kid...\n",
              "78    my now girlfriend used to have serious addicti...\n",
              "79    the one person i ever talked to it was because...\n",
              "Name: response_text, Length: 80, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aINxCoGu-MRh",
        "outputId": "1aa5b43f-bdd4-4451-fdca-d3b60694adc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_tags(string):\n",
        "    result = re.sub(r'<[^>]+>', '', string)  # remove HTML tags\n",
        "    result = re.sub(r'https?://\\S+', '', result)  # remove URLs\n",
        "    return result\n",
        "\n",
        "def remove_special_characters(text, remove_digits=True):\n",
        "    pattern = r'[^a-zA-Z0-9_\\s]'  # include underscore and remove non-alphanumeric characters\n",
        "    if remove_digits:\n",
        "        pattern = r'[^a-zA-Z_\\s]'  # include underscore and remove non-alphanumeric characters and digits\n",
        "    text = re.sub(pattern, '', text)\n",
        "    return text\n",
        "\n",
        "\n",
        "def tokenize(text):\n",
        "    tokens = word_tokenize(text)\n",
        "    stemmer = PorterStemmer()\n",
        "    stemmed_tokens = [stemmer.stem(word) for word in tokens]\n",
        "    return stemmed_tokens\n",
        "\n",
        "def remove_stopwords(tokens):\n",
        "    stop_words = set(stopwords.words('english'))\n",
        "    filtered_tokens = [word for word in tokens if not word in stop_words]\n",
        "    return filtered_tokens\n",
        "\n",
        "def lemmatize(tokens):\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "    lemmatized_tokens = [lemmatizer.lemmatize(word) for word in tokens]\n",
        "    return lemmatized_tokens"
      ],
      "metadata": {
        "id": "g2y6yzVy_jBk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['new_response_text'] = df['response_text'].apply(remove_tags)\n",
        "df['new_response_text'] = df['response_text'].apply(remove_special_characters)\n",
        "df['new_response_text'] = df['response_text'].apply(lambda x: x.lower())\n",
        "\n",
        "df"
      ],
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          "height": 424
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        "id": "T2MBnzk7_sOn",
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              "    response_id        class  \\\n",
              "0    response_1  not_flagged   \n",
              "1    response_2      flagged   \n",
              "2    response_3      flagged   \n",
              "3    response_4  not_flagged   \n",
              "4    response_5  not_flagged   \n",
              "..          ...          ...   \n",
              "75  response_76  not_flagged   \n",
              "76  response_77      flagged   \n",
              "77  response_78  not_flagged   \n",
              "78  response_79      flagged   \n",
              "79  response_80  not_flagged   \n",
              "\n",
              "                                        response_text  \\\n",
              "0               i try and avoid this sort of conflict   \n",
              "1   had a friend open up to me about his mental ad...   \n",
              "2   i saved a girl from suicide once. she was goin...   \n",
              "3   i cant think of one really...i think i may hav...   \n",
              "4   only really one friend who doesn't fit into th...   \n",
              "..                                                ...   \n",
              "75  now that i've been through it, although i'm no...   \n",
              "76  when my best friends mom past away from od'ing...   \n",
              "77  as a camp counselor i provide stability in kid...   \n",
              "78  my now girlfriend used to have serious addicti...   \n",
              "79  the one person i ever talked to it was because...   \n",
              "\n",
              "                                    new_response_text  \n",
              "0               i try and avoid this sort of conflict  \n",
              "1   had a friend open up to me about his mental ad...  \n",
              "2   i saved a girl from suicide once. she was goin...  \n",
              "3   i cant think of one really...i think i may hav...  \n",
              "4   only really one friend who doesn't fit into th...  \n",
              "..                                                ...  \n",
              "75  now that i've been through it, although i'm no...  \n",
              "76  when my best friends mom past away from od'ing...  \n",
              "77  as a camp counselor i provide stability in kid...  \n",
              "78  my now girlfriend used to have serious addicti...  \n",
              "79  the one person i ever talked to it was because...  \n",
              "\n",
              "[80 rows x 4 columns]"
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              "      <th>79</th>\n",
              "      <td>response_80</td>\n",
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              "      <td>the one person i ever talked to it was because...</td>\n",
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              "<p>80 rows × 4 columns</p>\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "\n",
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              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  }\n",
              "</style>\n",
              "\n",
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              "</div>\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "wnl = WordNetLemmatizer()"
      ],
      "metadata": {
        "id": "Pf3-uHqEALuh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk import pos_tag, word_tokenize\n"
      ],
      "metadata": {
        "id": "VIlXUUz2ATbg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "w_tokenizer = nltk.tokenize.WhitespaceTokenizer()\n",
        "lemmatizer = nltk.stem.WordNetLemmatizer()\n",
        "\n",
        "def lemmatize_text(text):\n",
        "\n",
        "    for word, tag in pos_tag(word_tokenize(text)):  # for lemmatizer we have to explicitly mention the  pos tags\n",
        "        doc=[]\n",
        "        wntag = tag[0].lower()\n",
        "        if wntag in ['a', 'r', 'n', 'v']:\n",
        "            lemma = wnl.lemmatize(word, wntag)\n",
        "\n",
        "        else:\n",
        "            lemma=word\n",
        "\n",
        "        doc=\"\".join(lemma)\n",
        "\n",
        "\n",
        "\n",
        "        print(doc)"
      ],
      "metadata": {
        "id": "f2u1untUAWfX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Nc_8Dd1sAZSA",
        "outputId": "23926136-b41d-4bbd-980d-1c02d2cf2ece"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  response_id        class                                      response_text  \\\n",
              "0  response_1  not_flagged              i try and avoid this sort of conflict   \n",
              "1  response_2      flagged  had a friend open up to me about his mental ad...   \n",
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              "4  response_5  not_flagged  only really one friend who doesn't fit into th...   \n",
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              "                                   new_response_text  \n",
              "0              i try and avoid this sort of conflict  \n",
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              "2  i saved a girl from suicide once. she was goin...  \n",
              "3  i cant think of one really...i think i may hav...  \n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-17421b5d-9577-4276-8374-173852d92f70 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stop = set(stopwords.words('english'))\n"
      ],
      "metadata": {
        "id": "ZLX8Y5DgBBNP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfTransformer"
      ],
      "metadata": {
        "id": "SMDnY4lBBMwg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "vect = CountVectorizer()"
      ],
      "metadata": {
        "id": "6sjkx3mABPmU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.new_response_text  #count vectorizer is bag of words , converting text into numerical to developing model\n",
        "y = df[\"class\"]\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,random_state=1)\n",
        "x_train_dtm = vect.fit_transform(x_train).toarray()\n",
        "x_test_dtm = vect.transform(x_test)"
      ],
      "metadata": {
        "id": "CyTpgjUZBSJG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Tf-Idf\n",
        "#The TFIDF Vectorizer should expect an array of strings. So if you pass him an array of arrays of tokenz, it crashes.\n",
        "#https://newbedev.com/train-model-fails-because-list-object-has-no-attribute-lower\n",
        "tfidf_vectorizer = TfidfVectorizer(use_idf=True)\n",
        "X_train_vectors_tfidf = tfidf_vectorizer.fit_transform(x_train)\n",
        "X_test_vectors_tfidf = tfidf_vectorizer.transform(x_test)"
      ],
      "metadata": {
        "id": "u38rlX0XBV0O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def cloud(text): # function to create wordcloud\n",
        "    wordcloud = WordCloud(background_color=\"white\").generate(\" \".join([i for i in text.str.upper()]))\n",
        "    #generate(text) is syntax , but\n",
        "    #text = \" \".join(review for review in df.country_description)  where df is dataframe , country_Dessc is column\n",
        "    plt.imshow(wordcloud) # Display the generated image:\n",
        "    plt.axis(\"off\")  # create a border  around the wordcloud\n",
        "    plt.title(\"Claud Response\")\n",
        "\n",
        "\n",
        "cloud(df['new_response_text'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "AnJbwmi2BZt_",
        "outputId": "56afc914-2ccb-49f0-fb53-fd09836f5c18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NB.fit(x_train_dtm,y_train)\n",
        "y_predict = NB.predict(x_test_dtm)\n",
        "metrics.accuracy_score(y_test,y_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QBiGEybdBhQQ",
        "outputId": "0cba2ba0-3e1c-4f01-cd24-2846cd2134b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(max_depth=10,max_features=10)\n",
        "rf.fit(x_train_dtm,y_train)\n",
        "rf_predict = rf.predict(x_test_dtm)\n",
        "metrics.accuracy_score(y_test,rf_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ID2BB3szBp-E",
        "outputId": "96b7a62e-51fa-48b2-a446-7e51d228a581"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.75"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf = ExtraTreesClassifier()\n",
        "rf.fit(x_train_dtm,y_train)\n",
        "rf_predict = rf.predict(x_test_dtm)\n",
        "metrics.accuracy_score(y_test,rf_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xWZ9tVRRBslh",
        "outputId": "95e35147-7fb5-40d4-84f0-9e0571804d93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.85"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf = DecisionTreeClassifier()\n",
        "rf.fit(x_train_dtm,y_train)\n",
        "rf_predict = rf.predict(x_test_dtm)\n",
        "metrics.accuracy_score(y_test,rf_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZcksngTHBvKW",
        "outputId": "a9a38081-502d-4d63-eee4-8cbc253fe7c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.75"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Apply model"
      ],
      "metadata": {
        "id": "rU9SBI00CTvh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.replace(('flagged','not_flagged'),(1,0),inplace=True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VFQW_itACXZo",
        "outputId": "3e99f7ee-753d-4596-ae7c-e3c2f6d35d8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  response_id  class                                      response_text  \\\n",
              "0  response_1      0              i try and avoid this sort of conflict   \n",
              "1  response_2      1  had a friend open up to me about his mental ad...   \n",
              "2  response_3      1  i saved a girl from suicide once. she was goin...   \n",
              "3  response_4      0  i cant think of one really...i think i may hav...   \n",
              "4  response_5      0  only really one friend who doesn't fit into th...   \n",
              "\n",
              "                                   new_response_text  \n",
              "0              i try and avoid this sort of conflict  \n",
              "1  had a friend open up to me about his mental ad...  \n",
              "2  i saved a girl from suicide once. she was goin...  \n",
              "3  i cant think of one really...i think i may hav...  \n",
              "4  only really one friend who doesn't fit into th...  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>response_id</th>\n",
              "      <th>class</th>\n",
              "      <th>response_text</th>\n",
              "      <th>new_response_text</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>response_1</td>\n",
              "      <td>0</td>\n",
              "      <td>i try and avoid this sort of conflict</td>\n",
              "      <td>i try and avoid this sort of conflict</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>response_2</td>\n",
              "      <td>1</td>\n",
              "      <td>had a friend open up to me about his mental ad...</td>\n",
              "      <td>had a friend open up to me about his mental ad...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>response_3</td>\n",
              "      <td>1</td>\n",
              "      <td>i saved a girl from suicide once. she was goin...</td>\n",
              "      <td>i saved a girl from suicide once. she was goin...</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = df[\"new_response_text\"]\n"
      ],
      "metadata": {
        "id": "FTD2ZZP2E7tA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Tf_idf = CountVectorizer(max_features=256).fit_transform(text.values)\n"
      ],
      "metadata": {
        "id": "JHSBMPSWEyr9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tsne = TSNE(\n",
        "    n_components=2,\n",
        "    init='random', # pca\n",
        "    random_state=34,\n",
        "    method='barnes_hut',\n",
        "    n_iter=250,\n",
        "    verbose=2,\n",
        "    angle=0.5\n",
        ").fit_transform(Tf_idf.toarray())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1KaLLbnrFChP",
        "outputId": "9218ef5a-bbaa-4e7b-d7ef-179d282bc90b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[t-SNE] Computing 79 nearest neighbors...\n",
            "[t-SNE] Indexed 80 samples in 0.001s...\n",
            "[t-SNE] Computed neighbors for 80 samples in 0.090s...\n",
            "[t-SNE] Computed conditional probabilities for sample 80 / 80\n",
            "[t-SNE] Mean sigma: 2.119036\n",
            "[t-SNE] Computed conditional probabilities in 0.005s\n",
            "[t-SNE] Iteration 50: error = 67.7453232, gradient norm = 0.6631832 (50 iterations in 0.037s)\n",
            "[t-SNE] Iteration 100: error = 65.0064545, gradient norm = 0.7964350 (50 iterations in 0.054s)\n",
            "[t-SNE] Iteration 150: error = 73.0126953, gradient norm = 0.3499564 (50 iterations in 0.054s)\n",
            "[t-SNE] Iteration 200: error = 71.5092773, gradient norm = 0.3677813 (50 iterations in 0.074s)\n",
            "[t-SNE] Iteration 250: error = 65.5285492, gradient norm = 1.0221703 (50 iterations in 0.046s)\n",
            "[t-SNE] KL divergence after 250 iterations with early exaggeration: 65.528549\n",
            "[t-SNE] KL divergence after 251 iterations: 179769313486231570814527423731704356798070567525844996598917476803157260780028538760589558632766878171540458953514382464234321326889464182768467546703537516986049910576551282076245490090389328944075868508455133942304583236903222948165808559332123348274797826204144723168738177180919299881250404026184124858368.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['label'] = Encode.fit_transform(df['class'])\n"
      ],
      "metadata": {
        "id": "QF555RA4Caeh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "sentences=df['label'].tolist()\n",
        "labels=df['class'].tolist()"
      ],
      "metadata": {
        "id": "Su6B2jzeEmfd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_size = int(len(sentences) * 0.8)\n",
        "\n",
        "training_sentences = sentences[0:training_size]\n",
        "testing_sentences = sentences[training_size:]\n",
        "training_labels = labels[0:training_size]\n",
        "testing_labels = labels[training_size:]\n",
        "\n",
        "# Make labels into numpy arrays for use with the network later\n",
        "training_labels_final = np.array(training_labels)\n",
        "testing_labels_final = np.array(testing_labels)"
      ],
      "metadata": {
        "id": "8YcWuxAJFjGH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_size = 300\n",
        "embedding_dim = 16\n",
        "max_length = 25\n",
        "trunc_type='post'\n",
        "padding_type='post'\n",
        "oov_tok = \"<OOV>\"\n",
        "training_sentences = [str (item) for item in training_sentences]\n",
        "testing_sentences = [str (item) for item in testing_sentences]\n",
        "\n",
        "tokenizer = Tokenizer(num_words = vocab_size, oov_token=oov_tok)\n",
        "tokenizer.fit_on_texts(training_sentences)\n",
        "word_index = tokenizer.word_index\n",
        "training_sequences = tokenizer.texts_to_sequences(training_sentences)\n",
        "training_padded = pad_sequences(training_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "\n",
        "testing_sequences = tokenizer.texts_to_sequences(testing_sentences)\n",
        "testing_padded = pad_sequences(testing_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.GlobalAveragePooling1D(),\n",
        "    tf.keras.layers.Dense(6, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_y86PepJFpnV",
        "outputId": "2c393961-574d-469c-aabb-8c81ad0f6bd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 25, 16)            4800      \n",
            "                                                                 \n",
            " global_average_pooling1d (G  (None, 16)               0         \n",
            " lobalAveragePooling1D)                                          \n",
            "                                                                 \n",
            " dense (Dense)               (None, 6)                 102       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 7         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,909\n",
            "Trainable params: 4,909\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Define a list of epoch values to try\n",
        "epochs_to_try = [ 30, 40, 50, 80, 100]\n",
        "\n",
        "for num_epochs in epochs_to_try:\n",
        "    model = tf.keras.Sequential([\n",
        "        tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "        tf.keras.layers.GlobalAveragePooling1D(),\n",
        "        tf.keras.layers.Dense(6, activation='relu'),\n",
        "        tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "    ])\n",
        "    model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "    history = model.fit(training_padded, training_labels_final, epochs=num_epochs, validation_data=(testing_padded, testing_labels_final), verbose=0)\n",
        "\n",
        "    # Plotting accuracy over epochs for this iteration\n",
        "    plt.plot(history.history['accuracy'], label=f'training_accuracy (epochs={num_epochs})')\n",
        "    plt.plot(history.history['val_accuracy'], label=f'val_accuracy (epochs={num_epochs})')\n",
        "\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "1W-xe2BOFs8W",
        "outputId": "49e75955-a3c7-45d3-8b9e-596cb2790711"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LSTM"
      ],
      "metadata": {
        "id": "oABPIiSvHoJ8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM, Embedding, Dense, Conv1D, MaxPooling1D, Flatten, Bidirectional\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "rd0BKHnrH3IA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "all_words = df['new_response_text'].str.split(expand=True).unstack().value_counts()\n",
        "\n",
        "plt.bar(all_words.index.values[2:50], all_words.values[2:50], color='skyblue')\n",
        "plt.xlabel('Words')\n",
        "plt.ylabel('Word Counts')\n",
        "plt.title('Top 50 Word Frequencies in the Dataset')\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "p9TWbexJUiYy",
        "outputId": "f637c501-ddef-4856-ef21-16d10989ade6"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_features = 100\n",
        "count_vectorizer = CountVectorizer(max_features=max_features)\n",
        "sparce_matrix = count_vectorizer.fit_transform(df['new_response_text']).toarray()\n",
        "print(\"Top {} Most Used Words: {}\".format(max_features, count_vectorizer.get_feature_names_out()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Di8ZxxwOJOMz",
        "outputId": "23c31091-1874-4e2d-de38-ba05b5cdf0e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top 100 Most Used Words: ['about' 'addiction' 'advice' 'all' 'always' 'and' 'are' 'as' 'at' 'back'\n",
            " 'be' 'because' 'been' 'before' 'being' 'best' 'better' 'but' 'can' 'come'\n",
            " 'depression' 'even' 'feel' 'for' 'friend' 'friends' 'from' 'get' 'girl'\n",
            " 'go' 'going' 'had' 'have' 'he' 'help' 'helped' 'her' 'him' 'his' 'how'\n",
            " 'if' 'in' 'is' 'issues' 'it' 'just' 'know' 'life' 'listen' 'll' 'lot'\n",
            " 'me' 'more' 'much' 'my' 'not' 'of' 'on' 'one' 'open' 'or' 'other' 'out'\n",
            " 'over' 'people' 'problems' 're' 'really' 'same' 'school' 'she' 'shit'\n",
            " 'so' 'some' 'someone' 'talk' 'talked' 'that' 'the' 'their' 'them' 'there'\n",
            " 'they' 'think' 'this' 'through' 'to' 'too' 'try' 'up' 've' 'was' 'what'\n",
            " 'when' 'while' 'who' 'with' 'would' 'years' 'you']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['class'].values\n",
        "x = sparce_matrix"
      ],
      "metadata": {
        "id": "tkhNHo4zJ5u9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "UFUpxXUFKGl4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Support Vector Machine (SVM)\n",
        "svm = SVC()\n",
        "svm.fit(x_train, y_train)\n",
        "accuracy_svm = svm.score(x_test, y_test)\n",
        "print(\"SVM Accuracy: {:.2f}\".format(accuracy_svm))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EMF9otK4JAP4",
        "outputId": "e032ef59-4d5c-4b38-e68b-7842961ee9d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM Accuracy: 0.69\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LSTM Model\n",
        "lstm_model = Sequential()\n",
        "lstm_model.add(LSTM(units=64, input_shape=(x_train.shape[1], 1)))  # Assuming 1D input\n",
        "lstm_model.add(Dense(1, activation='sigmoid'))\n",
        "lstm_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "lstm_model.fit(np.expand_dims(x_train, axis=-1), y_train, epochs=10, batch_size=32, verbose=0)\n",
        "accuracy_lstm = lstm_model.evaluate(np.expand_dims(x_test, axis=-1), y_test)[1]\n",
        "print(\"LSTM Model Accuracy: {:.2f}\".format(accuracy_lstm))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LqLvtlL7H4lO",
        "outputId": "b4b27e39-4c80-4e77-ef8e-dfbfa89ee555"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 500ms/step - loss: 0.5873 - accuracy: 0.8125\n",
            "LSTM Model Accuracy: 0.81\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_sizes = [16, 32, 64]\n",
        "epochs = [10, 20, 30]\n",
        "accuracy_results = []\n",
        "\n",
        "for batch_size in batch_sizes:\n",
        "    batch_accuracy = []\n",
        "    for num_epochs in epochs:\n",
        "        lstm_model = Sequential()\n",
        "        lstm_model.add(LSTM(units=64, input_shape=(x_train.shape[1], 1)))\n",
        "        lstm_model.add(Dense(1, activation='sigmoid'))\n",
        "        lstm_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "        lstm_model.fit(np.expand_dims(x_train, axis=-1), y_train, epochs=num_epochs, batch_size=batch_size, verbose=0)\n",
        "        accuracy = lstm_model.evaluate(np.expand_dims(x_test, axis=-1), y_test)[1]\n",
        "        batch_accuracy.append(accuracy)\n",
        "    accuracy_results.append(batch_accuracy)\n",
        "\n",
        "# Plotting the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i, batch_size in enumerate(batch_sizes):\n",
        "    plt.plot(epochs, accuracy_results[i], marker='o', label=f'Batch Size {batch_size}')\n",
        "plt.xlabel('Number of Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Accuracy vs. Batch Size and Epochs')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 780
        },
        "id": "7H_9rHHPKitS",
        "outputId": "e13a23d2-e4c0-4523-9785-8baf997eae2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 494ms/step - loss: 0.5285 - accuracy: 0.8750\n",
            "1/1 [==============================] - 1s 502ms/step - loss: 0.5124 - accuracy: 0.7500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 104 calls to <function Model.make_test_function.<locals>.test_function at 0x7bef2b685750> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 508ms/step - loss: 0.4721 - accuracy: 0.8125\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:6 out of the last 105 calls to <function Model.make_test_function.<locals>.test_function at 0x7bef2b7bdfc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 756ms/step - loss: 0.4626 - accuracy: 0.8750\n",
            "1/1 [==============================] - 1s 515ms/step - loss: 0.5022 - accuracy: 0.9375\n",
            "1/1 [==============================] - 0s 494ms/step - loss: 0.4547 - accuracy: 0.8125\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.6735 - accuracy: 0.6875\n",
            "1/1 [==============================] - 1s 515ms/step - loss: 0.4085 - accuracy: 0.8750\n",
            "1/1 [==============================] - 0s 498ms/step - loss: 0.5024 - accuracy: 0.8750\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_sizes = [16, 32, 64]\n",
        "epochs = [10, 20, 30]\n",
        "accuracy_results = []\n",
        "best_accuracy = 0\n",
        "best_config = (0, 0)  # To store batch size and epoch with best accuracy\n",
        "\n",
        "for batch_size in batch_sizes:\n",
        "    batch_accuracy = []\n",
        "    for num_epochs in epochs:\n",
        "        lstm_model = Sequential()\n",
        "        lstm_model.add(LSTM(units=64, input_shape=(x_train.shape[1], 1)))\n",
        "        lstm_model.add(Dense(1, activation='sigmoid'))\n",
        "        lstm_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "        lstm_model.fit(np.expand_dims(x_train, axis=-1), y_train, epochs=num_epochs, batch_size=batch_size, verbose=0)\n",
        "        accuracy = lstm_model.evaluate(np.expand_dims(x_test, axis=-1), y_test)[1]\n",
        "        batch_accuracy.append(accuracy)\n",
        "\n",
        "        if accuracy > best_accuracy:\n",
        "            best_accuracy = accuracy\n",
        "            best_config = (batch_size, num_epochs)\n",
        "\n",
        "    accuracy_results.append(batch_accuracy)\n",
        "\n",
        "# Plotting the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i, batch_size in enumerate(batch_sizes):\n",
        "    plt.plot(epochs, accuracy_results[i], marker='o', label=f'Batch Size {batch_size}')\n",
        "plt.xlabel('Number of Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Accuracy vs. Batch Size and Epochs')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "print(f\"Best Accuracy: {best_accuracy:.2f}\")\n",
        "print(f\"Best Configuration - Batch Size: {best_config[0]}, Epochs: {best_config[1]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "id": "7gfoC89FMylN",
        "outputId": "19e17b81-dbfe-4c68-8229-3a130b11d368"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 503ms/step - loss: 0.5222 - accuracy: 0.8125\n",
            "1/1 [==============================] - 1s 931ms/step - loss: 0.4112 - accuracy: 0.7500\n",
            "1/1 [==============================] - 1s 505ms/step - loss: 0.4639 - accuracy: 0.7500\n",
            "1/1 [==============================] - 1s 658ms/step - loss: 0.4496 - accuracy: 0.9375\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.4734 - accuracy: 0.8750\n",
            "1/1 [==============================] - 0s 493ms/step - loss: 0.4541 - accuracy: 0.7500\n",
            "1/1 [==============================] - 0s 492ms/step - loss: 0.6624 - accuracy: 0.6875\n",
            "1/1 [==============================] - 1s 544ms/step - loss: 0.5812 - accuracy: 0.8125\n",
            "1/1 [==============================] - 1s 509ms/step - loss: 0.5247 - accuracy: 0.8750\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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jdgEBAQC3jBvgscceY9SoUfzvf//j8uXLmJubG03ru5lBgwYxefJklixZgru7O9nZ2Tz22GOl2nl6evLSSy/x0ksvkZaWRosWLfjoo48qNLkqLi4GMGzwEBMTw8WLF1m6dCmhoaGGdqdOnSr13hslvrfz51pZWrVqBUBycvJN202ZMoXff/+dpUuXUr9+faM6V1dX7O3tKSkpuWWCVpbly5dTWFjIn3/+aTQSs3HjxnLf6xo/Pz+OHz9e6vrRo0fv+J438+/PUhSFEydOGJLKa7s0Hj16tNSGIkePHjXUl+d76nZptVq6d+9O9+7dmT59Oh9//DHjx49n48aNd/TnJYS4f8i0QCFEtXH58mWWLl3Kgw8+yMCBA0uVkSNHkpOTw59//gnAgAED2LdvX5lbll/7LfSAAQPIyMgoc8TnWhs/Pz90Oh1xcXFG9d9+++1tx37tt9n/HAlQFIUvv/zSqJ2rqyuhoaHMnj2bs2fPlhnPNS4uLvTq1Yv58+ezYMECIiMjb/v8oAYNGtCkSRMWL17M4sWL8fT0NEpkSkpKSk0dc3Nzw8vLi8LCQsO1jIwMjhw5Umqr6vJYvnw5oE5XhLL76sqVK2X2t62tbZnTBG/nz/Vubdq0iaKiolLXr60Jutl0ufXr1zNhwgTGjx/Pww8/XKpep9MxYMAAlixZUmZSkJ6eftPYyurDS5culbm26Xb17t2bbdu2kZCQYBTHjY5BuFs///wzOTk5hte//fYbycnJhsS+VatWuLm5MWvWLKOvydWrV3P48GH69OkDlO976naUtdths2bNAIziEEJUTzJyJYSoNv78809ycnLo169fmfXt2rXD1dWVBQsWMGjQIN566y1+++03/vOf/zB8+HBatmxJZmYmf/75J7NmzaJp06YMGTKEn3/+mVGjRpGQkEDnzp3Jy8tj/fr1vPTSSzz00EM4Ojryn//8h//+979oNBoCAwNZsWLFLde8/FP9+vUJDAxk9OjRnD9/HgcHB5YsWVLmup6vvvqKTp060aJFC5577jnq1q3L6dOnWblyJXv37jVqO2TIEAYOHAjABx98cPudiTp69d5772FlZcUzzzxjNJUxJycHHx8fBg4cSNOmTbGzs2P9+vXs2LGDzz//3NDu66+/ZvLkyWzcuNHoHKcb2b17N/Pnzzd8RnR0NEuWLKFDhw6G6ZwdOnSgVq1aPP3007z66qtoNBrmzZtX5g/CLVu2ZPHixYwaNYrWrVtjZ2dH3759b+vP9W5NnTqVXbt28cgjjxhGU3bv3s3PP/9M7dq1b7pV+uDBg3F1daVevXqG/rimR48euLu7M2XKFDZu3Ejbtm0ZMWIEDRs2JDMzk927d7N+/foyf8i/pmfPnlhYWNC3b1+ef/55cnNz+eGHH3Bzc7vliNqNjBkzhnnz5hEZGclrr71m2Irdz8+P/fv33/Z9fvvtN8OW+WU99zW1a9emU6dODBs2jNTUVGbMmEFQUBAjRowAwNzcnKlTpzJs2DDCwsIYPHiwYSt2f39/3njjDcO9yvM9dSvvv/8+cXFx9OnTBz8/P9LS0vj222/x8fGhU6dO5bqXEOI+dM/3JxRCiErSt29fxcrKqsytrq8ZOnSoYm5ubti++uLFi8rIkSMVb29vxcLCQvHx8VGefvppo+2t8/PzlfHjxyt169ZVzM3NFQ8PD2XgwIFKYmKioU16eroyYMAAxcbGRqlVq5by/PPPKwcPHixzK3ZbW9syYzt06JASHh6u2NnZKS4uLsqIESOUffv2lbqHoqjbfPfv319xcnJSrKyslJCQEOXdd98tdc/CwkKlVq1aiqOjo9F21Lfj+PHjhq2wN2/eXOq+b731ltK0aVPF3t5esbW1VZo2bap8++23Ru2ubV/9723q/62srdjNzMyUgIAA5a233lJycnKM2m/ZskVp166dYm1trXh5eSljxoxR1qxZU+qzcnNzlccff1xxcnJSAKNt2W/153otps8++6xUvMAtt7TfsmWL8vLLLyuNGzdWHB0dFXNzc6VOnTrK0KFDjb52FKX0Vuz/7ot/ln8+X2pqqvLyyy8rvr6+hmfo3r278v333980NkVRlD///FN54IEHFCsrK8Xf31+ZOnWqYYv6f26b7ufnp/Tp06fU+8vaPn7//v1KWFiYYmVlpXh7eysffPCB8n//9393vRX7P5/72lbs//vf/5S3335bcXNzU6ytrZU+ffqU2kpdURRl8eLFSvPmzRVLS0uldu3ayhNPPKEkJSWVaner76lr8f17i/U5c+YYPV90dLTy0EMPKV5eXoqFhYXi5eWlDB48WDl27NhNn18IUT1oFKUGrkYVQogaori4GC8vL/r27cv//d//mTocIe5aTEwMXbt25ddffzWMygohRFUha66EEKIa+/3330lPTzfaJEMIIYQQlUPWXAkhRDW0fft29u/fzwcffEDz5s0N52UJIYQQovLIyJUQQlRDM2fO5MUXX8TNzY2ff/7Z1OEIIYQQNYLJk6tvvvkGf39/rKysaNu2rdEWrv9WVFRkdGBk06ZNiYqKMmozadIkNBqNUfn3+SBCCFHdzZ07l+LiYnbu3Enjxo1NHY4QFaZLly4oiiLrrYQQVZJJk6tr2+NOnDiR3bt307RpUyIiIm64ffGECRP47rvv+O9//8uhQ4d44YUX6N+/P3v27DFq16hRI5KTkw1l8+bN9+JxhBBCCCGEEDWYSXcLbNu2La1btzYc4qjX6/H19eWVV15h3Lhxpdp7eXkxfvx4Xn75ZcO1AQMGYG1tbTgHZNKkSfz+++/lPpdCCCGEEEIIIe6GyTa0uHLlCrt27eLtt982XNNqtYSHhxMfH1/mewoLC7GysjK6Zm1tXWpk6vjx43h5eWFlZUX79u355JNPqFOnzg1jKSwsNDo1Xa/Xk5mZibOzMxqN5k4eTwghhBBCCFENKIpCTk4OXl5eaLU3n/hnsuQqIyODkpISo9PWAdzd3Tly5EiZ74mIiGD69OmEhoYSGBhIdHQ0S5cupaSkxNCmbdu2zJ07l5CQEJKTk5k8eTKdO3fm4MGD2Nvbl3nfTz75hMmTJ1fcwwkhhBBCCCGqlXPnzuHj43PTNiabFnjhwgW8vb3ZunUr7du3N1wfM2YMsbGxbN++vdR70tPTGTFiBMuXL0ej0RAYGEh4eDizZ8/m8uXLZX5OVlYWfn5+TJ8+nWeeeabMNv8eubp06RJ16tTh1KlTN0zI7pWioiI2btxI165dMTc3N2ks1ZH0b+WS/q1c0r+VT/q4ckn/Vi7p38ol/Vu5qlL/5uTkULduXbKysnB0dLxpW5ONXLm4uKDT6UhNTTW6npqaioeHR5nvcXV15ffff6egoICLFy/i5eXFuHHjCAgIuOHnODk5ERwczIkTJ27YxtLSEktLy1LXa9eujYODw20+UeUoKirCxsYGZ2dnk39hVUfSv5VL+rdySf9WPunjyiX9W7mkfyuX9G/lqkr9e+3zb2e5kMl2C7SwsKBly5ZER0cbrun1eqKjo41GsspiZWWFt7c3xcXFLFmyhIceeuiGbXNzc0lMTMTT07PCYhdCCCGEEEKIfzPpVuyjRo3ihx9+4KeffuLw4cO8+OKL5OXlMWzYMACGDBlitOHF9u3bWbp0KSdPnmTTpk1ERkai1+sZM2aMoc3o0aOJjY3l9OnTbN26lf79+6PT6Rg8ePA9fz4hhBBCCCFEzWGyaYEAgwYNIj09nffee4+UlBSaNWtGVFSUYZOLs2fPGu3IUVBQwIQJEzh58iR2dnb07t2befPm4eTkZGiTlJTE4MGDuXjxIq6urnTq1Ilt27bh6up6rx9PCCGEEEIIUYOYNLkCGDlyJCNHjiyzLiYmxuh1WFgYhw4duun9Fi1aVFGhCSGEEEKI+4iiKBQXFxvtJF1ZioqKMDMzo6Cg4J58Xk1zL/tXp9NhZmZWIUcwmTy5EkIIIYQQ4m5duXKF5ORk8vPz78nnKYqCh4cH586dk3NRK8G97l8bGxs8PT2xsLC4q/tIciWEEEIIIe5rer2eU6dOodPp8PLywsLCotJ/INfr9eTm5mJnZ3fLg2VF+d2r/lUUhStXrpCens6pU6eoV6/eXX2eJFdCCCGEEOK+duXKFfR6Pb6+vtjY2NyTz9Tr9Vy5cgUrKytJrirBvexfa2trzM3NOXPmjOEz75R8JQghhBBCiGpBkhxxpyrqa0e+AoUQQgghhBCiAkhyJYQQQgghhBAVQJIrIYQQQgghgBK9QnziRf7Ye574xIuU6BVTh1Rp5s6da3RWbEWZNGkSzZo1q/D73i8kuRJCCCGEEDVe1MFkOk3dwOAftvHaor0M/mEbnaZuIOpgcqV95tChQ9FoNIbi7OxMZGQk+/fvL9d97mVCs2zZMtq1a4ejoyP29vY0atSI119/3VA/evRooqOjK+3zCwoKGDp0KE2aNMHMzIyHH364zHaFhYWMHz8ePz8/LC0t8ff3Z/bs2ZUW1zWSXAkhhBBCiBot6mAyL87fTfKlAqPrKZcKeHH+7kpNsCIjI0lOTiY5OZno6GjMzMx48MEHK+3z7kZ0dDSDBg1iwIABJCQksGvXLj766COKiooMbezs7HB2dq60GEpKSrC2tubVV18lPDz8hu0effRRoqOj+b//+z+OHj3K//73P0JCQiotrmskuarK9CVozmzGOzMezZnNoJfTv4UQQgghboeiKORfKb5lySkoYuKff1HWBMBr1yb9eYicgqJS7718paTUNUUp31RCS0tLPDw88PDwoFmzZowbN45z586Rnp5uaDN27FiCg4OxsbEhICCAd99915DQzJ07l8mTJ7Nv3z7DCNjcuXMByMrK4vnnn8fd3R0rKysaN27MihUrjD5/zZo1NGjQADs7O0OidyPLly+nY8eOvPXWW4SEhBAcHMzDDz/MN998Y2jz71G0f47MXSv+/v6G+oMHD9KrVy/s7Oxwd3fnqaeeIiMj44Yx2NraMnPmTEaMGIGHh0eZbaKiooiNjWXVqlWEh4fj7+9P+/bt6dix4w3vW1HknKuq6tCfEDUWs+wLtAI4MxMcvCByKjTsZ+rohBBCCCGqtMtFJTR8b81d30cBUrILaDJp7W21P/R+BDYWd/Yjdm5uLvPnzycoKMho9Mfe3p65c+fi5eXFgQMHGDFiBPb29owZM4ZBgwZx8OBBoqKiWL9+PQCOjo7o9Xp69epFTk4O8+fPJzAwkEOHDqHT6Qz3zc/PZ9q0acybNw+tVsuTTz7J6NGjWbBgQZnxeXh4sHDhQg4ePEjjxo1v65n+mazl5eURGRlJ+/btATX569atG88++yxffPEFly9fZuzYsTz66KOGZ7kTf/75J61ateLTTz9l3rx52Nra0q9fPz744AOsra3v+L63Q5KrqujQn/DLEPj371Cyk9Xrj/4sCZYQQgghRDWwYsUK7OzsADX58PT0ZMWKFUbnLk2YMMHw//39/Rk9ejSLFi1izJgxWFtbY2dnh5mZmdFIztq1a0lISODw4cMEBwcDEBAQYPTZRUVFzJo1i8DAQABGjhzJ+++/f8NYX3nlFTZt2kSTJk3w8/OjXbt29OzZkyeeeAJLS8sy33MtJkVRGDBgAI6Ojnz33XcAfP311zRv3pyPP/7Y0H727Nn4+vpy7NixG45M3crJkyfZvHkzVlZWLFu2jIyMDF566SUuXrzInDlz7uiet0uSq6pGXwJRYymVWMHVaxqIGgf1+4BWV0YbIYQQQghhba7j0PsRt2yXcCqToXN23LLd3GGtaVO3tuG1Xq8nJzsHewd7o0TI2rx8P5917dqVmTNnAvD333/z7bff0qtXLxISEvDz8wNg8eLFfPXVVyQmJpKbm0txcTEODg43ve/evXvx8fExJFZlsbGxMSRWAJ6enqSlpd2wva2tLStXriQxMZGNGzeybds23nzzTb788kvi4+OxsbG54Xvfeecd4uPj2blzp2H0aN++fWzcuNGQXP5TYmLiHSdXer0ejUbDggULcHR0BGD69OkMHDiQb7/9tlJHr2TNVVVzZitkX7hJAwWyz6vthBBCCCFEmTQaDTYWZrcsneu54uloheZG9wE8Ha3oXM+11HutLXSlrmk0N7pT2WxtbQkKCiIoKIjWrVvz448/kpeXxw8//ABAfHw8TzzxBL1792bFihXs2bOH8ePHc+XKlZve93YSCHNzc+Nn1Whua81YYGAgzz77LD/++CO7d+/m0KFDLF68+Ibt58+fzxdffMGyZcvw9vY2XM/NzaVv377s3bvXqBw/fpzQ0NBbxnEjnp6eeHt7GxIrgAYNGqAoCklJSXd839shyVVVk5t6e+1+fxE2fATnd4FeX7kxCSGEEEJUUzqthol9GwKUSrCuvZ7YtyE6bfmSpjul0WjQarVcvnwZgK1bt+Ln58f48eNp1aoV9erV48yZM0bvsbCwoKTEeOOzBx54gKSkJI4dO1ap8fr7+2NjY0NeXl6Z9fHx8Tz77LN89913tGvXzqiuRYsW/PXXX/j7+xsSzGvF1tb2jmPq2LEjFy5cIDc313Dt2LFjaLVafHx87vi+t0OSq6rGzv322l06B3Gfwg/d4PMQ+ONlOLwCCnNv/V4hhBBCCGEQ2diTmU+2wMPRyui6h6MVM59sQWRjz0r77MLCQlJSUkhJSeHw4cO88sorhhEdgHr16nH27FkWLVpEYmIiX331FcuWLTO6h7+/P6dOnWLv3r1kZGRQWFhIWFgYoaGhDBgwgHXr1nHq1ClWr15NVFTUHcc6adIkxowZQ0xMDKdOnWLPnj0MHz6coqIievToUap9SkoK/fv357HHHiMiIsLwnNd2Qnz55ZfJzMxk8ODB7Nixg8TERNasWcOwYcNKJYv/dOjQIfbu3UtmZiaXLl0yjHhd8/jjj+Ps7MywYcM4dOgQcXFxvPXWWwwfPlw2tKhx/DqouwJmJ1P2uisN2HtAtwlwfC2c2AB5abBnvlp0FuDfGUJ6QXAEONW5108ghBBCCHHfiWzsSY+GHiScyiQtpwA3eyva1K1d6SNWUVFReHqqyZu9vT3169fn119/pUuXLgD069ePN954g5EjR1JYWEifPn149913mTRpkuEeAwYMYOnSpXTt2pWsrCzmzJnD0KFDWbJkCaNHj2bw4MHk5eURFBTElClT7jjWsLAwvvnmG4YMGUJqaiq1atWiefPmrF27tswzpI4cOUJqaio//fQTP/30k+G6n58fp0+fxsvLiy1btjB27Fh69uxJYWEhfn5+REZGGq1j+7fevXsbjd41b94cwDCl0c7OjnXr1vHKK6/QqlUrnJ2defTRR/nwww/v+Nlvl0Yp72b8NUB2djaOjo5cunTplosFK4Vht0AwTrCufnP/c7fA4itwZgscWwPHVsPfp43v5dYQgiPV4tNKNsH4l6KiIlatWkXv3r1LzTsWd0/6t3JJ/1Y+6ePKJf1buWpS/xYUFHDq1Cnq1q2LlZXVrd9QAfR6PdnZ2Tg4ONw0ERB35l73782+hsqTG8jIVVXUsJ+aQEWNNd7cwsELIqcYb8NuZgGBXdUS+QlkHINjUWqydTYe0g6pZfN0sK4N9XpCSCQEdgMrx9KfLYQQQgghhLgjklxVVQ37Qf0+FJ+MY++mNTTrHIFZQOjNR540GnANUUvH1yA/E05Eq8nWiXVwORP2L1KL1kydghh8dfqgc+CN7yuEEEIIIYS4JUmuqjKtDsWvE+f/yqapX6fyT+mzqQ0P/EctJUVwbvv1Ua2MY3AqTi1r3gbnemqSFdILfNuCrnpPHxBCCCGEEKKiSXJVU+jMwb+TWnp+CBcTr67TilLXbF08DvHHIf5rdbpgULg6qhXUXU3ShBBCCCGEEDclyVVN5RwI7V9SS8ElSNygJlvH10L+RTi4RC0aLfi2U9dpBUeCS7A6/VAIIYQQQghhRJIroY5UNeqvFn0JJO28Pn0w7S84u1Ut696DWv7X12n5dVQ31BBCCCGEEEJIciX+RauDOm3VEj4R/j6jjmYdi1LXZ/19GrbPVIuFPQR1U0e0gnqAnaupoxdCCCGEEMJkJLkSN1fLD9qMUEthLpyMuT6qlZcGh/5QCxr1HK1rZ2q5N5Lpg0IIIYQQokaR5ErcPks7aPCgWvR6SN6jJllHV0PKfkjaoZYNH4Cjrzp1MDgS/DuD+b050E8IIYQQQghTkeOkxZ3RasG7JXR9B17YBKMOw4Mz1PVYZtZw6Rzs+BEWDIRP68L/HoddP0FOiqkjF0IIIYQom74ETm2CA7+p/9WXmDqiSjN37lycnJwq/L6TJk2iWbNmFX7f+4UkV6JiOHhBq2Hw+CIYcxIe/wVaDQcHbyjKh6MrYfmr8HkIfBcGMVPgwh51BEwIIYQQwtQO/QkzGsNPD8KSZ9T/zmisXq8kQ4cORaPRGIqzszORkZHs37+/XPe5lwnNsmXLaNeuHY6Ojtjb29OoUSNef/11Q/3o0aOJjo6utM8/evQoXbt2xd3dHSsrKwICApgwYQJFRUWGNj/88AOdO3emVq1a1KpVi/DwcBISEiotpn+SaYGi4lnYXJ0SGAGKAikHrp6ptRrO74LkvWqJ+QTsPK5PHwwIAwtbU0cvhBBCiJrm0J/wyxBAMb6enaxef/RnaNivUj46MjKSOXPmAJCSksKECRN48MEHOXv2bKV83t2Ijo5m0KBBfPTRR/Tr1w+NRsOhQ4dYt26doY2dnR12dnaVFoO5uTlDhgyhRYsWODk5sW/fPkaMGIFer+fjjz8GICYmhsGDB9OhQwesrKyYOnUqPXv25K+//sLb27vSYgMZuRKVTaMBzwcg7C0YsQFGH4eHvoEGfcHCDnJTYPdPsGgwTK0L8weq0wmzzpk6ciGEEELczxQFruTduhRkw+oxlEqs1Juo/4kaq7b793uL8ktfU8q6z41ZWlri4eGBh4cHzZo1Y9y4cZw7d4709HRDm7FjxxIcHIyNjQ0BAQG8++67hpGauXPnMnnyZPbt22cYAZs7dy4AWVlZPP/884ZRnsaNG7NixQqjz1+zZg0NGjTAzs6OyMhIkpOTbxjr8uXL6dixI2+99RYhISEEBwfz8MMP88033xja/HsU7Z8jc9eKv7+/of7gwYP06tULOzs73N3deeqpp8jIyLhhDAEBAQwbNoymTZvi5+dHv379eOKJJ9i0aZOhzYIFC3jppZdo1qwZ9evX58cff0Sv11fqiNo1MnIl7i07N2j+pFqKC+H05uujWlln4cQ6tfAmuDe5Pqrl3VJd5yWEEEIIcTuK8uFjrwq4kQLZF2CKr9FVLeBUVvN3LtzxTJzc3Fzmz59PUFAQzs7Ohuv29vbMnTsXLy8vDhw4wIgRI7C3t2fMmDEMGjSIgwcPEhUVxfr16wFwdHREr9fTq1cvcnJymD9/PoGBgRw6dAidTme4b35+PtOmTWPevHlotVqefPJJRo8ezYIFC8qMz8PDg4ULF3Lw4EEaN258W8/0z2QtLy+PyMhI2rdvD6jJX7du3Xj22Wf54osvuHz5MmPHjuXRRx81PMutnDhxgqioKB555JEbtsnPz6eoqIjatWvf1j3vhiRXwnTMLCGou1p6TYX0I+o270ejICkBUg+oZdM0sHG5PtUwoCtYOZg6eiGEEEKIu7ZixQrDNLq8vDw8PT1ZsWIF2n/8UnnChAmG/+/v78/o0aNZtGgRY8aMwdraGjs7O8zMzPDw8DC0W7t2LQkJCRw+fJjg4GBAHfX5p6KiImbNmkVgYCAAI0eO5P33379hrK+88gqbNm2iSZMm+Pn50a5dO3r27MkTTzyBpaVlme+5FpOiKAwYMABHR0e+++47AL7++muaN29umM4HMHv2bHx9fTl27JjR8/xbhw4d2L17N4WFhTz33HM3jXvs2LF4eXkRHh5+wzYVRZIrUTVoNODWQC2d3oC8i3BivTqidSIa8jNg7wK1aM3Bv9PVM7UioHZdU0cvhBBCiKrG3EYdRbqVM1vV3Y1v5YnfwK+D4aVeryc7JwcHe3ujRAhzm3KF2bVrV2bOnAnA33//zbfffkuvXr1ISEjAz88PgMWLF/PVV1+RmJhIbm4uxcXFODjc/BfNe/fuxcfHx5BYlcXGxsaQWAF4enqSlpZ2w/a2trasXLmSxMRENm7cyLZt23jzzTf58ssviY+Px8bmxs/+zjvvEB8fz86dO7G2tgZg3759bNy4scw1WomJiTdNrhYvXkxOTg779u3jrbfeYtq0aYwZM6ZUuylTprBo0SJiYmKwsqr8o4EkuRJVk60zNB2klpIiOBuvjmgdWw2ZJ+HkRrVEjQXX+tenD/q0AZ18WQshhBA1nkZze9PzArupux5nJ1P2uiuNWh/YDbTXp9Sh14N5ifoZd7F0wdbWlqCgIMPrH3/8EUdHR3744Qc+/PBD4uPjeeKJJ5g8eTIRERE4OjqyaNEiPv/885ve91oCczPm5uZGrzUaDcptrBkLDAwkMDCQZ599lvHjxxMcHMzixYsZNmxYme3nz5/PF198QUxMjNGGErm5ufTt25epU6eWeo+7uzslJTfeCt/XV52m2bBhQ0pKSnjuued48803jaY9Tps2jSlTprB+/XoeeOCBWz5XRZCfQkXVpzOHuqFqifwYMk6o0wePRam/bUo/opYtX4J1LQjqoSZbQd3V10IIIYQQN6LVQeTUq7sFajBOsDTqfyKnGCdWlUij0aDVarl8+TIAW7duxc/Pj/HjxxvanDlzxug9FhYWpRKRBx54gKSkJI4dO3bT0au75e/vj42NDXl5eWXWx8fH8+yzz/Ldd9/Rrl07o7oWLVqwZMkS/P39MTMzTkv0ej3Z2dm3FYNer6eoqAi9Xm9Irj799FM++ugj1qxZQ6tWre7gye6MJFfi/uMSBC4jocNIuJwFidHqqNaJdXD5bzjwi1o0OnX4/tqolks9U0cuhBBCiKqoYT91u/WosermFdc4eKmJVSVtww5QWFhISkoKoE4L/Prrrw0jOgD16tXj7NmzLFq0iNatW7Ny5UqWLVtmdA9/f39OnTplmApob29PWFgYoaGhDBgwgOnTpxMUFMSRI0fQaDRERkbeUayTJk0iPz+f3r174+fnR1ZWFl999RVFRUX06NGjVPuUlBT69+/PY489RkREhOE5dTodrq6uvPzyy/zwww8MHjyYMWPGULt2bU6cOMGiRYv4/vvvy4xhwYIFmJub06RJEywtLdm5cydvv/02gwYNMozETZ06lffee4+FCxfi7+9v+NzK3iYeJLkS9ztrJ2g8QC0lxZC04/qoVvoROL1JLWsnQO3A6+u0/DqoI2JCCCGEEKAmUPX7qLNiclPBzl39eaGSR6yioqLw9PQE1F0B69evz6+//kqXLl0A6NevH2+88QYjR46ksLCQPn368O677zJp0iTDPQYMGMDSpUvp2rUrWVlZzJkzh6FDh7JkyRJGjx7N4MGDycvLIygoiClTptxxrGFhYXzzzTcMGTKE1NRUatWqRfPmzVm7di0hISGl2h85coTU1FR++uknfvrpJ8N1Pz8/Tp8+jZeXF1u2bGHs2LH07NmTwsJC/Pz8iIyMNF7H9g9mZmZMnTqVY8eOoSgKfn5+jBw5kjfeeMPQZubMmVy5coWBA43X0k2cONGo3yqDRrmdiZU1THZ2No6Ojly6dOmWiwUrW1FREatWraJ3796l5sWKW8g8BcfXwtHV6pbv+usnd2PpAEHdKQ7swbpTCuH9Bkn/VgL5+q1c0r+VT/q4ckn/Vq6a1L8FBQWcOnWKunXr3pNNC+D6tDUHB4cbJgLizt3r/r3Z11B5cgMZuRLVV+260PZ5tRTmQOJG9Uyt42sgLx3+WobZX8uIRIPy988QEqmObLk1UBfBCiGEEEIIUQ6SXImawdJeHe5v2E/d3efCbji6GuVoFJq0g2iStkPSdoieDE51rk8f9O+snsclhBBCCCHELUhyJWoerRZ8WoFPK4pDx7Hh93mE+xahS1wPJ2Mh6ywkfK8Wc1sI7KomW/V6gr27qaMXQgghhBBVlCRXosYrsHBG37I3unbPw5U8OBWnrtM6tgZyU+DICrUAeLe8Pqrl8YBMHxRCCCGEEAaSXAnxTxa2ENJLLYoCyfuu7z54YQ+c36WWjR+Bvdf1bd4DwsD81of1CSGEEEKI6kuSKyFuRKMBr2Zq6TIOclKu7j4YBSc3Qs4F2DVHLWbWaoIVHAH1IsDR+1Z3F0IIIYQQ1YwkV0LcLnsPaDFELUUF6vbux65OH7x07voIF6hTBoOv7j7o1Vxd5yWEEEIIIao1Sa6EuBPmVlAvXC29p0HaoevrtJJ2QMp+tcR9CrZuENzz6vTBrmBZuSeDCyGEEEII05DkSoi7pdGAeyO1hI6GvAx1+uCxKDixAfLSYM98tegs1O3dr22KUcvP1NELIYQQQogKInOVhKhoti7Q7HF49GcYcxKe+h3avgi1/KHkCiRGw+q34MsH4Nv2sH4SnN0O+hITBy6EEELUbCX6Enak7GDVyVXsSNlBSTX+t3nu3Lk4OTlV+H0nTZpEs2bNKvy+9wtJroSoTGYW6jlZvabAq3vh5R3Q433w6wganTqdcPMXMLsnfBYES5+Hg0uh4JKpIxdCCCFqlPVn1hOxJILha4YzdtNYhq8ZTsSSCNafWV9pnzl06FA0Go2hODs7ExkZyf79+8t1n3uZ0Cxbtox27drh6OiIvb09jRo14vXXXzfUjx49mujo6EqNQVEUpk2bRnBwMJaWlnh7e/PRRx+V2XbLli2YmZnds/6RaYFC3CsaDbgGq6Xja5CfCSeir04fXAeXM2H/IrVozcCvw/VNMZwDTR29EEIIUW2tP7OeUTGjUFCMrqflpzEqZhTTu0wn3C+8Uj47MjKSOXPmAJCSksKECRN48MEHOXv2bKV83t2Ijo5m0KBBfPTRR/Tr1w+NRsOhQ4dYt26doY2dnR12dpW7vvy1115j7dq1TJs2jSZNmpCZmUlmZmapdllZWQwZMoTu3buTmppaqTFdIyNXQpiKTW144D8w8P/grZMwdCV0eAVcgkFfrB5mvOYd+G8L+G8rWDMeTm2CkiJTRy6EEEJUeYqikF+Uf8uSU5jDJwmflEqsAJSr/5uSMIWcwpxS771cfLnUNUUpfZ+bsbS0xMPDAw8PD5o1a8a4ceM4d+4c6enphjZjx44lODgYGxsbAgICePfddykqUn8emDt3LpMnT2bfvn2GEbC5c+cCanLx/PPP4+7ujpWVFY0bN2bFihVGn79mzRoaNGiAnZ0dkZGRJCcn3zDW5cuX07FjR9566y1CQkIIDg7m4Ycf5ptvvjG0+fco2j9H5q4Vf39/Q/3Bgwfp1asXdnZ2uLu789RTT5GRkXHDGA4fPszMmTP5448/6NevH3Xr1qVly5b06NGjVNsXXniBxx9/nPbt29/wfhVNRq6EqAp0ZuDfSS09P4SLierOg8ei4MwWuHgc4o9D/Ndg5QhB4eqIVlC4mqQJIYQQwsjl4su0Xdi2Qu6Vmp9Kh0Udbqvt9se3Y2Nuc0efk5uby/z58wkKCsLZ2dlw3d7enrlz5+Ll5cWBAwcYMWIE9vb2jBkzhkGDBnHw4EGioqJYv16dwujo6Iher6dXr17k5OQwf/58AgMDOXToEDqdznDf/Px8pk2bxrx589BqtTz55JOMHj2aBQsWlBmfh4cHCxcu5ODBgzRu3Pi2numfyVpeXh6RkZGGZCcrK4tu3brx7LPP8sUXX3D58mXGjh3Lo48+aniWf1u+fDkBAQGsWLGCyMhIFEUhPDycTz/9lNq1r/9MNGfOHE6ePMn8+fP58MMPbyvWiiDJlRBVkXMgtH9JLQWXIHGDmmwdXwv5F+HgErVotODbTt15MKSXOuql0Zg6eiGEEELcphUrVhim0eXl5eHp6cmKFSvQ/uOMzAkTJhj+v7+/P6NHj2bRokWMGTMGa2tr7OzsMDMzw8PDw9Bu7dq1JCQkcPjwYYKDgwEICAgw+uyioiJmzZpFYKC6/GDkyJG8//77N4z1lVdeYdOmTTRp0gQ/Pz/atWtHz549eeKJJ7C0tCzzPddiUhSFAQMG4OjoyHfffQfA119/TfPmzfn4448N7WfPno2vry/Hjh0zep5rTp48yZkzZ/j111/5+eefKSkp4Y033mDgwIFs2LABgOPHjzNu3Dg2bdqEmdm9TXckuRKiqrNyhEb91aIvgaSdVw8sXgNpf8HZrWpZP1HdkfDaOi2/juqGGkIIIUQNZG1mzfbHt9+y3a7UXbwU/dIt233b/Vtaurc0vNbr9eTk5GBvb2+UCFmbWZcrzq5duzJz5kwA/v77b7799lt69epFQkICfn7qkS2LFy/mq6++IjExkdzcXIqLi3FwcLjpfffu3YuPj48hsSqLjY2NIbEC8PT0JC0t7YbtbW1tWblyJYmJiWzcuJFt27bx5ptv8uWXXxIfH4+NzY1H7N555x3i4+PZuXMn1tZqH+3bt4+NGzeWuUYrMTGxzORKr9dTWFjIzz//bHi2//u//6Nly5YcPXqUoKAgHn/8cSZPnnzTZ68sklwJcT/R6qBOW7WET4S/z1w/U+tUHPx9GrbPUouFvbpTYUgvCOoBdq6mjl4IIYS4ZzQazW1Nz+vg1QF3G3fS8tPKXHelQYO7jTsdvDqg016fUqfX6yk2K8bG3MYouSovW1tbgoKCDK9//PFHHB0d+eGHH/jwww+Jj4/niSeeYPLkyURERODo6MiiRYv4/PPPb3rfawnMzZibmxu91mg0t7VmLDAwkMDAQJ599lnGjx9PcHAwixcvZtiwYWW2nz9/Pl988QUxMTF4e3sbrufm5tK3b1+mTp1a6j3u7u6UlJTeCt/T0xMzMzOjxKlBgwYAnD17Fnd3d3bu3MmePXsYOXIkoP5ZKYqCmZkZa9eupVu3brd8xjslyZUQ97NaftBmhFoKc+FkzPVRrbw0OPynWtCATyt1+mBwL/XAY5k+KIQQQqDT6hjXZhyjYkahQWOUYGlQ/60c22asUWJVmTQaDVqtlsuXLwOwdetW/Pz8GD9+vKHNmTNnjN5jYWFRKhF54IEHSEpK4tixY5U6guPv74+NjQ15eXll1sfHx/Pss8/y3Xff0a5dO6O6Fi1asGTJEvz9/UtN39Pr9WRnZ5e6X8eOHSkuLiYxMdEw6nbs2DEA/Pz8cHBw4MCBA0bv+fbbb9mwYQO//fYbdevWveNnvR2SXAlRXVjaQYMH1aLXQ/Ke65tiJO+DpB1q2fAhOPhcX6fl3xnMrUwdvRBCCGEy4X7hTO8ynSkJU0jNv75lt7uNO2PbjK20bdgBCgsLSUlJAdRpgV9//bVhRAegXr16nD17lkWLFtG6dWtWrlzJsmXLjO7h7+/PqVOnDFMB7e3tCQsLIzQ0lAEDBjB9+nSCgoI4cuQIGo2GyMjIO4p10qRJ5Ofn07t3b/z8/MjKyuKrr76iqKiozN36UlJS6N+/P4899hgRERGG59TpdLi6uvLyyy/zww8/MHjwYMaMGUPt2rU5ceIEixYt4vvvvy8zhvDwcFq0aMHw4cOZMWMGer2el19+mR49ehiSyH9vtuHm5mbYLbGySXIlRHWk1YJ3S7V0fQeyL1xNtNaoo1vZSbDz/9RibgMBXdR1WvV6goOnqaMXQggh7rlwv3C6+nZld9pu0vPTcbVxpYVbi0ofsYqKisLTU/23197envr16/Prr7/SpUsXAPr168cbb7zByJEjKSwspE+fPrz77rtMmjTJcI8BAwawdOlSunbtSlZWFnPmzGHo0KEsWbKE0aNHM3jwYPLy8ggKCmLKlCl3HGtYWBjffPMNQ4YMITU1lVq1atG8eXPWrl1LSEhIqfZHjhwhNTWVn376iZ9++slw3c/Pj9OnT+Pl5cWWLVsYO3YsPXv2pLCwED8/PyIjI2841VKr1bJ8+XJeeeUVQkNDsbW1pVevXrecJnmvaJTybsZfA2RnZ+Po6MilS5duuViwshUVFbFq1Sp69+5dal6suHs1sn+LLqvrs65NH8w+b1zv2UxNtEIiwaOpmqjd6UfVxP69h6R/K5/0ceWS/q1cNal/CwoKOHXqFHXr1sXK6t7Mxrg2bc3BweGu1lyJst3r/r3Z11B5cgMZuRKipjG3vrr2KgIUBVIOXJ8+eH4XJO9VS+wUsPOA4J7qOq2AMLCwNXX0QgghhBBVliRXQtRkGg14PqCWsLcgN+367oOJGyE3BXb/rBadJdQNvZqYRYKTr6mjF0IIIYSoUiS5EkJcZ+cGzZ9US3EhnN58dVRrNWSdhRPr1LJqNLg3vr77oHcLdZt4IYQQQogaTJIrIUTZzCwhqLtaek2F9CPX12md2w6pB9Wy6XOwcVE3wwiJhICuYGXatYpCCCGEEKYgyZUQ4tY0GnBroJZOb0DeRTixXk22TkRDfgbsW6gWrTn4d7y6Tqu7qSMXQghRg8g+beJOVdTXjiRXQojys3WGpoPUUlIEZ+PVEa2jqyEzUd3u/WQM5kA3Ky+0Vjugfm/waQM6+WtHCCFExbq2G2J+fj7W1tYmjkbcj/Lz8wHuemdN+SlHCHF3dObqRhd1QyHiI8g4cXX6YBTKma3YF1yA+P+qxcoJ6vVQN8QI6g7WtUwdvRBCiGpAp9Ph5OREWloaADY2Nmg0mkr9TL1ez5UrVygoKJCt2CvBvepfRVHIz88nLS0NJycndLq7W0MuyZUQomK5BIHLSOgwkuKcDPYu+ZyWduloE9fD5b/hwK9q0eigTnt1nVZwJDgHqdMPhRBCiDvg4eEBYEiwKpuiKFy+fBlra+tKT+Rqonvdv05OToavobshyZUQovJYOXKhVjua9e6NVquBpB2GUS3Sj8CZzWpZOwFqB6hJVnCkmnSZWZg6eiGEEPcRjUaDp6cnbm5uFBUVVfrnFRUVERcXR2hoaLU/pNkU7mX/mpub3/WI1TWSXAkh7g2dGfi1V0uPyZB5Sj1T6+hqdcv3zJOw7Vu1WDpAYDcI6QVBPdQ1XkIIIcRt0Ol0FfaD8q0+p7i4GCsrK0muKsH92r+SXAkhTKN2XWj7vFoKc9RDi4+tgeNrIC8dDv2uFjTg2+b6qJZbA5k+KIQQQogqSZIrIYTpWdpDw35q0evhwm516uDRKEg9oJ6rdW47RE8GpzpXE60I8O+snsclhBBCCFEFSHIlhKhatFrwaaWWbhPgUpI6onUsCk7GQtZZSPheLea2ENhVTbbq9QR7d1NHL4QQQogazOT7Rn7zzTf4+/tjZWVF27ZtSUhIuGHboqIi3n//fQIDA7GysqJp06ZERUXd1T2FEFWcow+0fgae+BXGnoLBi6DlULD3hKI8OLIC/hwJnwfD910h9lNI3gdykKQQQggh7jGTJleLFy9m1KhRTJw4kd27d9O0aVMiIiJuuIXmhAkT+O677/jvf//LoUOHeOGFF+jfvz979uy543sKIe4jFrbqJhd9v4RRh+G5WOjyDng1V+sv7IaNH8F3oTC9ISx/XZ1aeCXfpGELIYQQomYwaXI1ffp0RowYwbBhw2jYsCGzZs3CxsaG2bNnl9l+3rx5vPPOO/Tu3ZuAgABefPFFevfuzeeff37H9xRC3Kc0GvBqBl3GwnMx8OZR6PdfCOkD5jaQcwF2zYH/DYJP68LCQbBzNlw6b+rIhRBCCFFNmWzN1ZUrV9i1axdvv/224ZpWqyU8PJz4+Pgy31NYWIiVlZXRNWtrazZv3nzH97x238LCQsPr7OxsQJ2GeC/OSbiZa59v6jiqK+nfynVP+9fKGZoMVktxAZozW9AcX4P2+Fo02UnXz9cCFPcm6Ov1RAmKQPFqBhqTz5C+I/L1W/mkjyuX9G/lkv6tXNK/lasq9W95YtAoimkWJly4cAFvb2+2bt1K+/btDdfHjBlDbGws27dvL/Wexx9/nH379vH7778TGBhIdHQ0Dz30ECUlJRQWFt7RPQEmTZrE5MmTS11fuHAhNjY2FfC0QgiTURTsC5LwuLQHj+y91MpLRMP1v/YKzBxJdWhKqmMz0uwbU6KzusnNhBBCCFHT5Ofn8/jjj3Pp0iUcHBxu2va+2i3wyy+/ZMSIEdSvXx+NRkNgYCDDhg276yl/b7/9NqNGjTK8zs7OxtfXl549e96yAytbUVER69ato0ePHvfVAWr3C+nfylUV+7c4LwNNYjTa42vQnNyA1ZVL+GXG4ZcZh6KzQPHriBIUgb5eT3Xb9yqsKvZvdSN9XLmkfyuX9G/lkv6tXFWpf6/NarsdJkuuXFxc0Ol0pKamGl1PTU3Fw8OjzPe4urry+++/U1BQwMWLF/Hy8mLcuHEEBATc8T0BLC0tsbQsfVaOubm5yf8wr6lKsVRH0r+Vq0r1r5MntHxSLcVX4OxWddOLY6vR/H0azcmNcHIjurXjwLUBhFw9vNinNWh1po6+TFWqf6sp6ePKJf1buaR/K5f0b+WqCv1bns832UIDCwsLWrZsSXR0tOGaXq8nOjraaEpfWaysrPD29qa4uJglS5bw0EMP3fU9hRA1kJkFBHSBXlPg1b3w8g7o8QH4dQSNDtIPw+YvYHYEfBYES5+Hg0uh4JKpIxdCCCFEFWTSaYGjRo3i6aefplWrVrRp04YZM2aQl5fHsGHDABgyZAje3t588sknAGzfvp3z58/TrFkzzp8/z6RJk9Dr9YwZM+a27ymEEGXSaMA1WC0dX4X8TEjcAEdXw4l1cDkT9i9Si9YM/DqoI1rBkeAcaOrohRBCCFEFmDS5GjRoEOnp6bz33nukpKTQrFkzoqKicHd3B+Ds2bNotdcH1woKCpgwYQInT57Ezs6O3r17M2/ePJycnG77nkIIcVtsakOTgWopKYZz2+HYaji2BjKOwak4tax5B5zrQXCEmmjVaQc6mR4ihBBC1EQm39Bi5MiRjBw5ssy6mJgYo9dhYWEcOnToru4phBDlpjMD/45q6fkhXExUk6xjUXBmC1w8DvHHIf5rsHKEoHA10QoKV5M0IYQQQtQIJk+uhBDivuMcCO1fUkvBJXX64LE1cHwt5F+Eg0vUotGCb7vro1quIer0QyGEEEJUS5JcCSHE3bByhEb91aIvgfO71HVax9ZA2l/qboRnt8L6iVDL/+o6rQh10wyz0ruUCiGEEOL+JcmVEEJUFK0OfNuoJXwiZJ29Pn3wVBz8fRq2z1KLhR0EdlOTrXo9wc7V1NELIYQQ4i5JciWEEJXFqQ60GaGWwlw4GaMmWsfXQm4qHP5TLWjAp9X16YPujWX6oBBCCHEfkuRKCCHuBUs7aPCgWvR6SN6rJlrHoiB5HyTtUMuGD8HB53qiVTcUzK1MHb0QQgghboMkV0IIca9pteDdQi1d34HsC1enD65RR7eyk2Dn/6nF3EY96Dg4AupFgIOnqaMXQgghxA1IciWEEKbm4AWthqml6LK6PutYlJpsZZ+Ho6vUAuDZ7PqmGK6NTBq2EEIIIYxJciWEEFWJufXVKYERoCiQcuD6phjnd6nTCZP3QuwUzOzcaWpZH81RIDgcLGxNHLwQQghRs0lyJYQQVZVGA54PqCXsLchNUzfDOBYFiRvR5Kbin5sKv8WCzlJdn3VtrZaTr6mjF0IIIWocSa6EEOJ+YecGzZ9US3EhxYmxnF3/PXWvHEVz6SycWKeWVaPVHQevJVreLdVt4oUQQghRqSS5EkKI+5GZJUpAVw74XMa3Vy/MsxKvr9M6tx1SD6pl0+dg46KepRUcoZ6tZeVg6uiFEEKIakmSKyGEuN9pNODWQC2d3oD8TDi+Tk22TkRDfgbsW6gWrTn4d7y6KUYk1K5r6uiFEEKIakOSKyGEqG5sakPTQWopKYKz8eqI1tHVkJmobvd+MgaixoFLiDqiFdILfNqATv5ZEEIIIe6U/CsqhBDVmc5c3eiibihEfAQZJ64fXnxmK2QcVcvWr8DKCer1UEe0grqDdS1TRy+EEELcVyS5EkKImsQlCFxGQoeRcDkLEqPVUa3ja+Hy33DgV7VodFCn/fVRLecgdfqhEEIIIW5IkishhKiprJ2g8QC1lBRD0o7rm2KkH4Yzm9Wy7l2oHXD98OI6HcDMwtTRCyGEEFWOJFdCCCHUtVZ+7dXSYzJknrp+ptapTZB5ErZ9qxZLB3XXweBIdRdCW2dTRy+EEEJUCZJcCSGEKK12XWj7vFoKcyBx49Xpg2sgLx0O/a4WNODb5uqZWr3UHQtl+qAQQogaSpIrIYQQN2dpDw37qUWvhwu7r2+KkXJAPVfr3HaIfh8c61w/vNi/E5hbmTp6IYQQ4p6R5EoIIcTt02rBp5Vauk2AS0nqiNaxNXAqFi6dhR0/qMXcFgK7qslWvQiwdzd19EIIIUSlkuRKCCHEnXP0gdbPqOVKvppgXdsUIycZjqxQC4BXC3VEKyQSPB6Q6YNCCCGqHUmuhBBCVAwLG3Xb9pBeoCiQvO/qqNZquLBHnU54YTfEfAz2XhDcU12nVTdUfa8QQghxn5PkSgghRMXTaMCrmVq6jIWclKu7D66BxA2QcwF2zVWLmRXUDVNHtOpFgKO3aWMXQggh7pAkV0IIISqfvQe0GKKWogI4vfn6phiXzqm7EB5fo7b1aHL1TK1e4NVcXeclhBBC3AckuRJCCHFvmVtBvXC19P4M0g6pSdbRKPUg45QDaon7DGxd1dGskEgI6KLuXCiEEEJUUZJcCSGEMB2NBtwbqaXzm5CXAcfXqcnWiWj1TK2989Wis1C3dw+OVEstP1NHL4QQQhiR5EoIIUTVYesCzQarpfgKnN2qrtM6uhr+PqWu10rcAKvHgGsDdZv3kF7g0xq0OlNHL4QQooaT5EoIIUTVZGahTgUM6AIRH0PG8evrtM5ug/TDatkyA6xrQ70e6ohWUHewcjRx8EIIIWoiSa6EEEJUfRoNuAarpeOrkJ+pjmAdXQ0n1sHlTNi/WC1aM6jTXh3RCo4E50BTRy+EEKKGkORKCCHE/cemNjQZqJaSYji3XT1P69gayDgGpzepZc074Bx0fZ1WnXagMzd19EIIIaopSa6EEELc33Rm4N9RLT0/hIuJ6plaR1fDmS1w8QTEf60WS0d12mBILwgKV5O0G9GXoDmzGe/MeDRnHCAgVNZ1CSHEPVCiL2Fn6k72XdmHW6obbbzaoLtP/v6V5EoIIUT14hwIzi9Cuxeh4BIkblTXaR1fC/kX4a+latFowbft9VEt1xB1+iHAoT8haixm2RdoBXBmJjh4QeRUaNjPlE8nhBDV2voz65mSMIXU/FQAfo3+FXcbd8a1GUe4X7iJo7s1Sa6EEEJUX1aO0OhhtehL4Pyu62dqpf0FZ+PVsn4iOPmpI1pWDhD7GaAY3ys7GX4ZAo/+LAmWEEJUgvVn1jMqZhTKv/7+TctPY1TMKKZ3mV7lEyxJroQQQtQMWh34tlFL9/cg66y6RutYFJyKg6wzsH3WTW6gABqIGgf1+8gUQSGEqEAl+hKmJEwplVgBKCho0DA1YSpdfbtW6SmCWlMHIIQQQpiEUx1oMwKeXAJjTsFjCyGoxy3epED2eTiz9Z6EKIQQNcXGcxsNUwHLoqCQkp/C7rTd9zCq8pORKyGEEMLSTh2NKrqsbu1+K7k3/gFACCHErSmKwpHMI8QkxRB3Lo6DFw/e1vvS89MrObK7I8mVEEIIcY2d++21s3So3DiEEKIaulx8mYTkBDWhSoojLT+t3PdwtXGthMgqjiRXQgghxDV+HdRdAbOTKbWhxT8tfw0iPoJG/a/vMCiEEKKUlLwU4pLiiE2KZXvydgpLCg111mbWtPdsT5hvGB28OvDkqidJy08rc92VBg3uNu60cGtxL8MvN0muhBBCiGu0OnW79V+GABqME6yrr21cIecC/DYMds2BXp+BW33TxCuEEFWMXtHzV8ZfhtGpI5lHjOo9bT0J8wkjzDeM1h6tsdRZGurGtRnHqJhRaNAYJVga1F9ijW0ztkpvZgGSXAkhhBDGGvZTt1uPGgvZF65fd/CCyClQrwds+RI2f6HuMjirI7R9AcLGqtu4CyFEDZNXlMe2C9sMCVVmQaahToOGpq5NCfMNI9QnlHpO9dDcYMQ/3C+c6V2mG51zBeBu487YNmOr/DbsIMmVEEIIUVrDflC/D8Un49i7aQ3NOkdgFhB6ffv1LuOg6WMQ9Q4cXQnxX8OB36DnB9DkPzJVUAhR7Z3PPU/MOTWZ2pGygyJ9kaHO1tyWjl4dCfMNo5N3J2pb1b7t+4b7hdPVtysJFxJYF7+OHu170MarTZUfsbpGkishhBCiLFodil8nzv+VTVO/TqXPtarlD4MXwvF1sHoMZJ6EpSNg5xzo/Rl4NDZJ2EIIURlK9CXsz9hvSKhOZJ0wqve19zVM92vp1hJznfkdf5ZOq6OVeyvSLNJo5d7qvkmsQJIrIYQQ4u7U6wF1t8HW/0LcNDi7Fb4LVc/Q6vI2WDuZOkIhhLgjOVdy2HJhC7HnYtl8fjNZhVmGOp1GR3O35oT5hBHqG0pdh7o3nO5Xk0hyJYQQQtwtM0sIHQ0PDIK14+HQH7B9FhxcAuGToelg0GpNHaUQQtzSmewzxJ6LJTYplt2puylWig11DhYOdPLuRJhPGB29O+Jo6WjCSKsmSa6EEEKIiuLkq26GkbgBVo2Bi8fhj5dg11x1qqBXM1NHKIQQRor0RexN22tIqE5nnzaqr+tYly4+XQj1CaWZWzPMtJI+3Iz0jhBCCFHRArvBi1th+0yImQpJCfB9F2g1HLpNAJvbX9wthBAVLasgi80XNhN7LpYt57eQU5RjqDPTmtHKvZU63c8nlDoOdUwY6f1HkishhBCiMphZQMfX1N0D174LB3+Dnf8Hfy2D8InQfIhMFRRC3BOKonDy0klik2KJPRfL3vS96BW9ob6WZS06+3QmzEc9zNfOws6E0d7fJLkSQgghKpODFwz8P2g5FFa9BemHYflrsOsn6D0NfFqaOkIhRDV0peQKO1N3EpcUR8y5GM7nnjeqr1ernmG6XxOXJvfVjnxVmSRXQgghxL1QtzO8sAkSfoCYT+DCbvixO7R4CrpPBFsXU0cohLjPXbx8kU3nNxF7LpatF7aSX5xvqDPXmtPGs40hofKy8zJhpNWXJFdCCCHEvaIzh/YvQeMBsH4i7Psf7P4ZDv2prsVqNbz0eVpCCHEDiqJw7O9jhul+BzIOoKAY6l2sXQj1CSXMJ4x2nu2wMbcxYbQ1gyRXQgghxL1m7w79Z6lTBVeOhtQDsGo07P4Jen8OddqaOkIhRBVVUFxAQkqCYXe/1PxUo/oGtRvQxbcLYT5hNHBugFYjazvvJUmuhBBCCFOp0w6ei4Fdc2DDB5ByAGb3hKaPQ4/JYOdm6giFEFVAWn4acUlxxJ6LZVvyNgpKCgx1Vjor2nm2I8w3jM7enXG3dTdhpEKSKyGEEMKUdGbQZgQ0fBiiJ8OeebBvIRxZAV3fgdYj1DZCiBpDr+g5fPGwOt0vKZZDFw8Z1bvbuNPFV1071cajDVZmViaKVPyb/G0thBBCVAV2rvDQ11enCr4JyXshapy6Jqv3NPDvaOoIhRCVKL8on23J24hLiiMuKY70y+mGOg0amrg0Icw3jDCfMIJrBaPRaEwYrbgRSa6EEEKIqsSnFYzYoCZV0ZMh7RDM7a2el9XjA3DwNHWEQogKciH3gjrdLymWhOQEruivGOpszGzo6N2RUJ9QOnl3wsVadhS9H0hyJYQQQlQ1Wh20GgYNH1LXYu2cAwd+haOrIWwstHtR3XlQCHFfKdGXcCDjgHr2VFIMx/8+blTvbedNmE8YYb5htHJvhYXOwkSRijslyZUQQghRVdnUhge/gBZD1F0Fz++Ede/CnvnQ+1MI6GLqCIUQt5B7JZetF7YSmxTL5vObySzINNRpNVqauTYj1CeULr5dCHAMkOl+9zlJroQQQoiqzqs5PLNO3ehi3UTIOAo/P6RughHxETj6mDpCIcQ/nMs+R2xSLDFJMexK3UWxvthQZ29uT0fvjoT5htHJqxNOVk6mC1RUOEmuhBBCiPuBVgvNn4T6fWDjx7DjRzj0OxxfC6Gjof1IMLM0dZRC1EjF+mL2pu01TPc7demUUb2/g79hdKqZWzPMtTKtt7qS5EoIIYS4n1jXgt6fqVMFV70FZ+Mh+n3YswB6fQr1wk0doRA1QvaVbPZd2ceWLVvYkryF7CvZhjozjRkt3FsQ5hNGqE8o/o7+pgtU3FOSXAkhhBD3I48mMGw17P9FXYeVmQgLBkD9ByHiY6jlZ+oIhahWFEXhVPYp4s6pu/vtSdtDiVICZ9R6R0tHOnt3Jsw3jA5eHXCwcDBtwMIkJLkSQggh7lcaDTQdBCG9IHYqbJupHj58Yj10GgUdXwNzOVxUiDtVVFLErrRdxJ6LJS4pjrM5Z43q3bRu9K7fm25+3XjA5QF0Wp2JIhVVhSRXQgghxP3OykHd2KL5k+pUwdObIOZjdQOMyClq8iWEuC2ZBZlsPr+Z2HOxbL2wldyiXEOdudac1h6tCfUJpaNHR/bF7aN3s96Ym8saKqGS5EoIIYSoLtwawNPL4a+lsGYC/H0a/vcY1IuAXlOgdoCpIxSiylEUheNZx9XDfM/Fsi99HwqKob62VW1CfUIJ8wmjvVd7bM1tASgqKmIf+0wVtqiiJLkSQgghqhONBhoPUBOquE8h/ls4vgZOblSnCXYaBRY2po5SCJMqLClkR8oOw3S/C3kXjOrr166v7u7n04VGLo3QarQmilTcbyS5EkIIIaojSzvo8T40exJWj1GTq7jPYN9iiPxY3fhCDisVNUh6fjqbzm8i9lws8cnxXC6+bKiz1FnS1rOtYXc/D1sPE0Yq7meSXAkhhBDVmWswPLUMDi+HNe/ApbOw+EkI7KZu3e5Sz9QRClEpFEXhcOZhYpNiiTsXx8GLB43q3azdCPVVp/u19WyLtZm1iSIV1YkkV0IIIUR1p9FAw34QFA6bPoetX0HiBvi2PbR/GULfUke6hLjPXS6+zPbk7YaEKu1ymlF9Y+fGhPqq0/3q166PRkZvRQWT5EoIIYSoKSxsoPu70OxxiBoHx9fClhlw4Ffo+SE06i9TBcV9JyUvRd2MIimW7cnbKSwpNNRZm1nT3rM9Yb7qdD8XaxcTRipqAkmuhBBCiJrGORAe/wWORcHqsZB1Bn4bBrvmQK/PwK2+qSMU4ob0ip6DGQfV0amkOI5kHjGq97T1JMwnjC6+XWjl0QpLnaWJIhU1kSRXQgghRE2k0ajnXwV0gS1fwuYv4FQczOoIbV+AsLHq+VlCVAF5RXnEX4g3JFSZBZmGOg0amro2Jcw3jDCfMIKcgmS6nzAZSa6EEEKImszcGrqMg6aPQdQ7cHQlxH+tThXs8QE88KhMFRQmkZSTZEimdqTsoEhfZKizNbelo1dHwnzD6OTdidpWtU0YqRDXSXIlhBBCCKjlD4MXwvF16tbtmSdh2XOway70/gw8Gps6QlHNlehL2Je+z5BQncg6YVTva+9LmE8YYb5htHRribnO3ESRCnFjJj8R7ZtvvsHf3x8rKyvatm1LQkLCTdvPmDGDkJAQrK2t8fX15Y033qCgoMBQP2nSJDQajVGpX1/mjgshhBC3pV4PeGkbdHsXzKzh7Fb4rjOsGgOXs0wdnahmsq9kE3Uqirc3vU3YL2E8HfU0sw/O5kTWCXQaHa3cW/Fmyzf54+E/WNl/JWPbjKWdZztJrESVZdKRq8WLFzNq1ChmzZpF27ZtmTFjBhERERw9ehQ3N7dS7RcuXMi4ceOYPXs2HTp04NixYwwdOhSNRsP06dMN7Ro1asT69esNr83MZIBOCCGEuG1mlhA6Gh4YBGvHw6E/IOE7+GsphE+GpoNBa/Lfz4r71OlLpw2jU7tTd1OsFBvqHCwc6OTdiTCfMDp6d8TR0tGEkQpRfibNOqZPn86IESMYNmwYALNmzWLlypXMnj2bcePGlWq/detWOnbsyOOPPw6Av78/gwcPZvv27UbtzMzM8PCQk7WFEEKIu+LkC4/+DIkb1amCGcfgj5euTxX0ambqCMV9oEhfxJ7UPYaE6nT2aaP6AMcAwnzUrdKbuTXDTCu/FBf3L5N99V65coVdu3bx9ttvG65ptVrCw8OJj48v8z0dOnRg/vz5JCQk0KZNG06ePMmqVat46qmnjNodP34cLy8vrKysaN++PZ988gl16tS5YSyFhYUUFl4/EyE7OxuAoqIiioqKbvS2e+La55s6jupK+rdySf9WLunfyid9fFWdTvBsDNqE79BumoYmKQHl+y7oWzyNvst4sK51R7eV/q1cpuzfrMIstlzYwqbzm9iavJXcolxDnZnWjJZuLens1ZnO3p3xtfc11CklCkUl98fXg3z9Vq6q1L/liUGjKIpSibHc0IULF/D29mbr1q20b9/ecH3MmDHExsaWGo265quvvmL06NEoikJxcTEvvPACM2fONNSvXr2a3NxcQkJCSE5OZvLkyZw/f56DBw9ib29f5j0nTZrE5MmTS11fuHAhNjY2d/mkQgghRPVhdSWTRhcW4fP3NgAKdXYc9voPZ5zDQCNTBWsqRVFI16dzpOgIR4uOcrbkLArXf8S00dgQYhZCiHkIQeZBWGmsTBitEOWTn5/P448/zqVLl3BwuPkRFfdVchUTE8Njjz3Ghx9+SNu2bTlx4gSvvfYaI0aM4N133y3zc7KysvDz82P69Ok888wzZbYpa+TK19eXjIyMW3ZgZSsqKmLdunX06NEDc3NZvFnRpH8rl/Rv5ZL+rXzSxzemObMZ3ZpxaNLVA1z1ns3QR3yK4t3itu8h/Vu5Krt/r5RcYXfabuLOx7Hp/CbO5503qq/nVI/O3p3p7NWZxs6N0Wl1FR6DKcnXb+WqSv2bnZ2Ni4vLbSVXJpsW6OLigk6nIzU11eh6amrqDddLvfvuuzz11FM8++yzADRp0oS8vDyee+45xo8fj7aMxbVOTk4EBwdz4sSJUnXXWFpaYmlZ+vRuc3Nzk/9hXlOVYqmOpH8rl/Rv5ZL+rXzSx2UI6gp1N0PCDxDzCdrkvWjnRkCLp6D7RLB1ue1bSf9Wrors34uXLxKXFEdcUhxbL2wlvzjfUGehtaCNZxvD+ikvO68K+cyqTr5+K1dV6N/yfL7JkisLCwtatmxJdHQ0Dz/8MAB6vZ7o6GhGjhxZ5nvy8/NLJVA6nfpbkBsNwOXm5pKYmFhqXZYQQggh7pLOHNq/BI0HwPqJsO9/sPtndXfBbu9Cq+FQzUYrahpFUTj29zFizsUQlxTHgYwDRtP9XKxdDMlUO8922JjLcgpRs5l0O5ZRo0bx9NNP06pVK9q0acOMGTPIy8sz7B44ZMgQvL29+eSTTwDo27cv06dPp3nz5oZpge+++y59+/Y1JFmjR4+mb9+++Pn5ceHCBSZOnIhOp2Pw4MEme04hhBCiWrN3h/6zoOVQWDUaUg6o/939E/T+HOq0NXWEohwKigtISEkg9lwssUmxpOYbzzJq6NxQPczXJ4wGzg3Qylo7IQxMmlwNGjSI9PR03nvvPVJSUmjWrBlRUVG4u7sDcPbsWaORqgkTJqDRaJgwYQLnz5/H1dWVvn378tFHHxnaJCUlMXjwYC5evIirqyudOnVi27ZtuLq63vPnE0IIIWqUOu3guVjYORs2fKAmWbN7qudi9Xgf7EqfYSmqhrT8NHWr9HNxbEveRkFJgaHOSmdFO692hPmE0dm7M+627iaMVIiqzeQHCYwcOfKG0wBjYmKMXpuZmTFx4kQmTpx4w/stWrSoIsMTQgghRHloddBmBDTqD+snwZ556nTBIyuh6zvQegToTP7jR42nV/QcuniI2KRYYs/FcjjzsFG9h62HYbpfG482WJnJ7n5C3A75200IIYQQFc/WBR76Wp0quPJNSN4LUePUNVm9PwP/TqaOsMbJL8pnW/I2w2G+GZczDHUaNDRxbWKY7hdcKxiNRmPCaIW4P0lyJYQQQojK49MKRmxQk6royZB2COb2gcYDoduNZ6KIivG3/m9+OfYLm5I3sSN5B1f0Vwx1NmY2dPTuSKhPKJ28O+Fiffs7PAohyibJlRBCCCEql1YHrYZBw4fUtVg758DB3zA7tppAl75QEg6ylXWFKNGXcCDjALFJscSci+FE9gnYeb3e286bLr5dCPUJpZV7Kyx0FiaLVYjqSJIrIYQQQtwbNrXhwS+gxRBYORrN+Z00vrAI5Ydd0GcaBHQxdYT3pdwruWy9sJXYpFg2JW3i78K/DXUaNDR1bUrXOl0J8wkjwDFApvsJUYkkuRJCCCHEveXVHJ5ZR/HueZREjcfy4nH4+SFo+DBEfASOPqaOsMo7l32OmKQYYpNi2ZW6i2J9saHO3tyeTt6d6OjZkfy/8hnYY6DJD2EVoqaQ5EoIIYQQ955Wi9L0cdafMSfScje6Xf8Hh36H42shdDS0HwlmlqaOssoo1hezN22vurtfUiynLp0yqvd38Fc3o/ANo5lbM8y15hQVFbHq8CoTRSxEzSTJlRBCCCFMptjMFn3EJ+haPQ2r3oKz8RD9PuxZAL0+hXrhpg7RZC4VXmLL+S3EJMWw+fxmcq7kGOrMNGa0dG9JqE8ooT6h+Dv6my5QIYSBJFdCCCGEMD2PJjBsNez/Bda9C5mJsGAAhPSByE+glp+pI6x0iqJwKvsUsefU0am9aXspUUoM9U6WTnT27kyobygdvDrgYOFgwmiFEGWR5EoIIYQQVYNGA00HQUgviJ0K22bC0ZWQGA2dRkHH18C8eh1mW1RSxK60XYaE6lzOOaP6IKcgw3S/B1weQKfVmShSIcTtkORKCCGEEFWLlYO6sUXzJ9Wpgqc3QczHsG8hRE5Rk6/7WGZBJpvPbybmXAxbL2wlryjPUGeuNae1R2vCfMII9QnFx1429xDifiLJlRBCCCGqJrcG8PRy+GsprJkAf5+G/z0G9SLUqYLOgaaO8LYoisLxrOPEJcURcy6G/en7UVAM9bWtahPqE0oXny6082qHrbmt6YIVQtwVSa6EEEIIUXVpNNB4gJpQxX0G8d/A8TVwcqM6TbDTKLCwMXWUpRSWFLIjZQex52KJS4rjQt4Fo/r6teur0/18wmjk0gitRmuiSIUQFUmSKyGEEEJUfZZ20GPy9amCJzeqyda+RRDxMTToqyZiJpSen86m85uIORfDtuRtXC6+bKiz1FnS1rOtYbqfh62H6QIVQlQaSa6EEEIIcf9wqQdPLYPDy2HNO3DpHPzyFAR2U7dud6l3z0JRFIXDmYfVs6fOxfLXxb+M6t2s3Qj1Vaf7tfFsg7WZ9T2LTQhhGpJcCSGEEOL+otFAw34QFA6bp8OWLyFxA3zbHtq/DKFvqSNdleBy8WW2J28n5lwMm5I2kXY5zai+sXNjwnzV6X71a9dHY+LRNCHEvSXJlRBCCCHuTxY20G0CNB0MUePg+FrYMkM9KyviQ2j0SIVMFUzJSzFsRpGQkkBhSaGhztrMmvae7eni24XOPp1xsXa5688TQty/JLkSQgghxP3NORCe+BWOrobVYyHrDPw2HHbOgd6fqbsOloNe0XMw46Bhut/Rv48a1XvZehlGp1p5tMJSZ1mRTyOEuI9JciWEEEKI6iGkFwR0gS1fqdMFT2+CWZ2g7QsQNlY9P+sG8oryiL8QT2ySurtfZkGmoU6DhqauTQ0JVZBTkEz3E0KUSZIrIYQQQlQf5tbQZSw0HQRrxsORFRD/NRz4FXp8AA88apgqmJSTZEimdqTsoEhfZLiNnbkdHbw6EOYbRifvTtS2qm2qJxJC3EckuRJCCCFE9VPLHx5bAMfXw+q3IPMkxcueY/+uWcQGtSc28xCJlxKN3uJr70uYTxhdfLvQwq0F5jpz08QuhLhvSXIlhBBCiGor268NW3u/T+y+H9l06QSXdBlwajkAOo2O5m7N1cN8fcPwd/CX6X5CiLsiyZUQQgghqpXTl04bpvvtTt1NsVKsVui0OKCjU242XfIv0wEbHJt0h4aPg1Zr2qCFENWCJFdCCCGEuK8V6YvYk7pH3d0vKZYz2WeM6gMcAwyjU01dm2J2ahOsHgMZx+CPl2HXXHVXQa/mpnkAIUS1IcmVEEIIIe47WQVZbDq/ibikOLac30JOUY6hzkxrRiv3VmpC5ROGr4Ov8ZsDu8ILW2D7LIidCkk74Puu0GoYdHsXbGTzCiHEnZHkSgghhBBVnqIoJGYlGkan9qXvQ6/oDfW1LGvR2aczYT5hdPDqgJ2F3c1vaGYBHV+FJgNh7btw8DfYORv++h26vwcthoBWV7kPJYSodiS5EkIIIUSVdKXkCjtTdhoSqvO5543qg2sFE+YTRqhPKE1cmqC7k2TIwQsG/p86arXqLUg7BCteh90/Qe/PwadlxTyMEKJGkORKCCGEEFVGxuUMNiWp0/22XthKfnG+oc5Ca0EbzzaG6X6edp4V98H+neD5ONjxI2z8GC7sgR+7QfOnIHwS2LpU3GcJIaqtcidX/v7+DB8+nKFDh1KnTp3KiEkIIYQQNYSiKBz9+yhbkrcQlxTHgYwDKCiGehdrF8PoVDvPdtiY21ReMDpzaPciNHoE1k+CfQthzzw4/Ke6FqvVcJkqKIS4qXInV6+//jpz587l/fffp2vXrjzzzDP0798fS0vLyohPCCGEENVMQXEBCSkJbDyzkbXZa8lenW1U39C5oWF0qoFzA7Sae7xNur079J8JLZ+GVaMh5YD6390/Qe9pUKfdvY1HCHHfuKPk6vXXX2f37t3MnTuXV155hZdeeonHH3+c4cOH06JFi8qIUwghhBD3sdS8VOLOxxF3Lo5tydsoKCkw1FnprGjn1c4wQuVm42bCSP+hTjt4Llbd6GLDB2qSNTsCmg6G8MlqEiaEEP9wx2uuWrRoQYsWLfj888/59ttvGTt2LDNnzqRJkya8+uqrDBs2TE45F0IIIWoovaLn0MVD6mYU52I5nHnYqN7D1oPOXp2xumDFi31exN7a3kSR3oJWB21GQKP+6lTBPfNg3//gyEro8ja0eQ50soRdCKG6478NioqKWLZsGXPmzGHdunW0a9eOZ555hqSkJN555x3Wr1/PwoULKzJWIYQQQlRh+UX5xCfHE5cUR1xSHBmXMwx1GjQ0cW1imO4XXCuY4uJiVq1ahZWZlQmjvk22LvDQ19ByGKx6U93wYs3barLV+zN1QwwhRI1X7uRq9+7dzJkzh//9739otVqGDBnCF198Qf369Q1t+vfvT+vWrSs0UCGEEEJUPRdyLxi2St+RvIMr+iuGOhszGzp6dyTUJ5TO3p1xtnY2YaQVxKclPButJlXrJ6tbt8/tA40HQs8PwaECdzAUQtx3yp1ctW7dmh49ejBz5kwefvhhzM3NS7WpW7cujz32WIUEKIQQQoiqo0RfwoGMA4aE6vjfx43qve286eLbhVCfUFq5t8JCZ2GiSCuRVgcth0KDfuparJ1z1EOIj0VB2Bho+6J6SLEQosYpd3J18uRJ/Pz8btrG1taWOXPm3HFQQgghhKg6cq/ksuWCulX6pqRN/F34t6FOq9HSzLUZYb7qdL8Ax4Cas+bapjY8+AW0uLqrYNIOWPce7JkPvT6FwK6mjlAIcY+VO7lKS0sjJSWFtm3bGl3fvn07Op2OVq1aVVhwQgghhDCNs9lnDaNTu1J2UawUG+rsze3p5N2JUN9QOnl1wsnKyXSBVgVezWD4WvVcrHUTIeMYzHsYGj4EER+Do4+pIxRC3CPlTq5efvllxowZUyq5On/+PFOnTmX79u0VFpwQQggh7o1ifTF70vYQlxRHbFIspy6dMqr3d/BXN6PwDaOZWzPMtaWXBdRoWi00fxLqPwgbP4YdP8ChP+D4Ouj8JnR4BczkTFAhqrtyJ1eHDh0q8yyr5s2bc+jQoQoJSgghhBCV71LhJTaf30xsUiybz28m50qOoc5MY0ZL95aE+oQS5huGn8PNlwSIq6ydoPen0GKIOlXwbLy6LmvvQnWqYL1wU0cohKhE5U6uLC0tSU1NJSAgwOh6cnIyZmZyzoMQQghRVSmKwqlLpwzT/fam7aVEKTHUO1k60dm7M6G+oXT06oi9RRU9e+p+4NEYhq2GA7/C2gmQmQgLBkBIH4j8GGr5mzpCIUQlKHc21LNnT95++23++OMPHB0dAcjKyuKdd96hR48eFR6gEEIIIe5cUUkRO1N3Gqb7ncs5Z1Qf5BRkmO73gMsD6LQ6E0VaDWk08MCjEBwJsVNh20w4uhISo6HTG9DxNTC3NnWUQogKVO7katq0aYSGhuLn50fz5s0B2Lt3L+7u7sybN6/CAxRCCCFE+WQWZLIpaROxSbFsvbCVvKI8Q5251pw2Hm0I9Qkl1CcUH3vZbKHSWTlAxEfqmqxVb8HpTRDziTpVMHIKhPRSEzEhxH2v3MmVt7c3+/fvZ8GCBezbtw9ra2uGDRvG4MGDyzzzSgghhBCVS1EUjmcdJ/acOt1vf/p+FBRDvbOVs7p2yieMdl7tsDW3NWG0NZhbA3h6Ofy1DNaMh6wzsGgw1OupJlnOgaaOUAhxl+5okZStrS3PPfdcRccihBBCiNtUWFLIjpQdxJyLIS4pjuS8ZKP6BrUbGBKqRi6N0Gq0pglUGNNooPEjakIV9xnEfwPH18LJGOjwqrqzoIWNqaMUQtyhO96B4tChQ5w9e5YrV64YXe/Xr99dByWEEEKI0tLz0w1rp7Ylb+Ny8WVDnaXOknae7QzT/TxsPUwYqbglSzvoMVmdKrh6DCRugE3TYP9i9WysBn1lqqAQ96FyJ1cnT56kf//+HDhwAI1Gg6Ko0w6uncZeUlJys7cLIYQQ4jYpisLhzMOG6X5/XfzLqN7Nxk3djMInjDaebbA2k80R7jsu9eDJpXB4Oax5By6dg1+egoCu0PsztV4Icd8od3L12muvUbduXaKjo6lbty4JCQlcvHiRN998k2nTplVGjEIIIUSNcbn4MtsubCM2KZZNSZtIu5xmVN/EpYlhul/92vUNv9wU9zGNBhr2g6Bw2DwdtnwJJzfCt+2h/csQ+pY60iWEqPLKnVzFx8ezYcMGXFxc0Gq1aLVaOnXqxCeffMKrr77Knj17KiNOIYQQotpKyUsxjE4lpCRQWFJoqLM2s6aDVwfCfMLo7NMZF2sXE0YqKpWFDXSbAE0HQ9TbcHwNbJkB+3+BiA+h0SMyVVCIKq7cyVVJSQn29uqhgi4uLly4cIGQkBD8/Pw4evRohQcohBBCVDd6Rc/BjIOGzSiO/m3876eXrRdhvup0v1YerbDUWZomUGEazoHwxC9wdDWsHqvuKvjbcNg5R50q6NbA1BEKIW6g3MlV48aN2bdvH3Xr1qVt27Z8+umnWFhY8P333xMQEFAZMQohhBD3vbyiPOIvxBNzLoZN5zeRWZBpqNOgoalrU0NCFeQUJNP9hHr+VUBXdZrg5unq+VgzO0LbF6DLOPX8LCFElVLu5GrChAnk5amHEb7//vs8+OCDdO7cGWdnZxYvXlzhAQohhBD3q6ScJGKTYok9F8uO1B0U64sNdXbmdnTw6kAX3y508u5ELataJoxUVFnmVtBlLDQdpJ6NdWQFbPsGDv4GPT6ABx6VqYJCVCHlTq4iIiIM/z8oKIgjR46QmZlJrVq15LdsQggharRifTH70/cTkxRD3Lk4Ei8lGtXXsa9jGJ1q4dYCc525iSIV951a/vDYAji+Xt26PTMRlj0Hu65OFfRoYuoIhRCUM7kqKirC2tqavXv30rhxY8P12rVrV3hgAkr0CttPZbIrQ4PzqUzaB7mh00oCK4QQ90KJvoSdqTvZd2UfbqlutPFqg06rK9Uu+0o2W89vJSYphs3nN3Op8JKhTqfR0dytOV18uxDqE4q/g7/8IlLcnXrhUDce4r+GuGlwNh6+C4XWI6DrO2DtZOoIhajRypVcmZubU6dOHTnL6h6IOpjM5OWHSL5UAOj4+fhOPB2tmNi3IZGNPU0dnhBCVGvrz6xnSsIUUvNTAfg1+lfcbdwZ12Yc4X7hnL50Wp3ulxTL7tTdlCjX/110sHCgs09nwnzC6ODVAUdLR1M9hqiuzCyh85vQ5FFYOwEO/Q4J38HBJerBxE0fB63W1FEKUSOVe1rg+PHjeeedd5g3b56MWFWSqIPJvDh/N8q/rqdcKuDF+buZ+WQLSbCEEKKSrD+znlExo1D+9bdwan4qb8S8gau1K+mX043qAhwDDNP9mro2xUxb7n9ehSg/J1949CdI3KhOFcw4Bn+8DLvmqlMFXRvf8hZCiIpV7r/9v/76a06cOIGXlxd+fn7Y2toa1e/evbvCgquJSvQKk5cfKpVYASiABpi8/BA9GnrIFEEhhKhgJfoSpiRMKZVY/VP65XR0Gh2tPVqr0/28Q/F18L2HUQrxL4Fd4YUtsH0WxE6FpB3wfVe0LZ7GvLi1qaMTokYpd3L18MMPV0IY4pqEU5lXpwKWTQGSLxWQcCqT9oHO9y4wIYSoAXan7TZMBbyZr7p+Rahv6D2ISIjbZGYBHV+FJv+Bde/CgV/R7Z5LuO5XND4F0HoYlLFmUAhRscqdXE2cOLEy4hBXpeXcOLH6p7d+3ccjLbzp3sCdJt6OaGUUSwgh7trRzKO3bgTkFuVWciRC3CEHTxjwI7QcirJyNBbph2H1m7BvPvSeBj6tTB2hENWarHasYtzsrW6rXVLWZb7acIKHvtlCm4+jGfPbPtb8lUJeYfGt3yyEEMJIQXEBM/fOZPqu6bfV3tXGtZIjEuIu+Xei+NmNHPB+AsXSHi7sgR+7q2uy8jJMHZ0Q1Va5R660Wu1Nt5GVnQTvTpu6tfF0tCLlUkGZM/41gJuDJW/2DCHmaBpxxzLIyC3kl51J/LIzCQudlnaBzoQ3cKNbfTd8atnc60cQQoj7hqIoxJyLYeqOqZzPPQ+AhdaCK/orZbbXoMHdxp0Wbi3uYZRC3CGtGSfdIqg/8B3MYz6CfQthz3w4vBy6vQsth4FONl8RoiKV+ztq2bJlRq+LiorYs2cPP/30E5MnT66wwGoqnVbDxL4NeXH+bjRglGBdS2kn92tEZGNPHm3ly5ViPQmnMok+kkr04TTOZuYTdyyduGPpvPfHX4S429O9gRvdG7jRzLeWbIIhhBBXnc0+y5SEKWw6vwkAdxt33mr9Flq0vBn7JoDRxhaaq38Lj20ztszzroSosuzcof9MaPk0rBoNKQfU/+76CfpMgzrtTB2hENVGuZOrhx56qNS1gQMH0qhRIxYvXswzzzxTIYHVZJGNPZn5ZIt/nHOl8ijjnCsLMy2d6rnQqZ4L7z3YkMT0PKIPpxJ9JI1dZ/7maGoOR1Nz+DYmkVo25nQNcaN7A3c6B7vgYGVuiscTQgiTulx8mR/2/8Dcv+ZSpC/CTGvG0w2f5rkHnsPGXB3tn66ZbnTOFajJ19g2Ywn3CzdV6ELcnTrt4LlY2DkbNnwAqQdgdgQ0HQzhk8He3dQRCnHfq7Cx4Hbt2vHcc89V1O1qvMjGnvRo6EH8iTTWbtpOz85taR/kdtORJ41GQ5CbHUFudjwfFkhW/hVij6UTfTiNmKNp/J1fxNI951m65zxmWg1t6tamewN3utd3w9/F9ob3FUKI6kBRFNafXc9nOz4jOS8ZgI5eHRnXZhz+jv5GbcP9wunq25WECwmsi19Hj/Y9aOPVRkasxP1Pq4M2I6BRf4ieDLvnwb7/wZGV0OVttU4nv3wV4k5VSHJ1+fJlvvrqK7y9vSviduIqnVZD27q1uXhYoW3d2uWe0udkY8FDzbx5qJk3RSV6dp35mw1H0og+nEpieh5bEy+yNfEiH6w4RICrLd3rq6NaLf1qYa6TvU6EENXHqUun+GT7J8QnxwPgZevFmDZj6Obb7YbriHVaHa3cW5FmkUYr91aSWInqxdYF+v0XWgyFVW+qG16seRv2zFMPIPbvZOoIhbgvlTu5qlWrltE/RIqikJOTg42NDfPnz6/Q4ETFMddpaRfgTLsAZ97p3YDTGXlEH0ljw5FUtp/M5GR6HifTT/HDplM4WJkRFuJGeAM3woJdcbKxMHX4QghxR/KL8pm1fxbzDs2jWF+MhdaCYY2H8UyTZ7A2szZ1eEKYnk9LeDZaTarWT4a0QzC3DzQeCD0/AAcvU0coxH2l3MnVF198YZRcabVaXF1dadu2LbVq1arQ4ETl8Xex5ZlOdXmmU12yC4rYdCyD6COpxBxNJzPvCsv3XWD5vgtoNdDKr7ZhU4xAV7ub7hYphBBVgaIoRJ2OYtrOaaTlpwEQ5hPG2NZj8XXwNXF0QlQxWh20HAoN+sGGD9U1WQd/g2NREDYG2r6oHlIshLilcidXQ4cOrYQwhCk5WJnT5wFP+jzgSYleYe+5v4k+nMaGI2kcSckh4XQmCacz+WT1EerUtlETrfrutKlbGwszmT4ohKhaTvx9gk8SPiEhJQEAHzsfxrUZR5hvmIkjE6KKs6kND06HFkPU3QSTdsC699Tt23t9CoFdTR2hEFVeuZOrOXPmYGdnx3/+8x+j67/++iv5+fk8/fTTFRacuPd0Wg0t/WrT0q82YyLrcy4zn41H04g+nEZ84kXOZuYzZ8tp5mw5jZ2lGaHBLnSr706XEFdc7CxNHb4QogbLvZLLt/u+ZeHhhZQoJVjqLHm2ybMMazwMS538/STEbfNqBsPXqhtdrHsPMo7BvIfVka2Ij8FJRn+FuJFyJ1effPIJ3333Xanrbm5uPPfcc5JcVTO+tW0Y0t6fIe39ySssZvOJDDYcTiP6SBoZuYWsOpDCqgMpaDTQzNeJ8AbudKvvRn0Pe5k+KIS4JxRFYcXJFXy+83MuFlwEoHud7rzV+i287WSjJSHuiFYLzZ+A+n0g5hNI+B4O/wkn1kPnN6HDK2Amv7QQ4t/KnVydPXuWunXrlrru5+fH2bNnKyQoUTXZWpoR0ciDiEYe6PUKB85fMmyKcfB8NnvOZrHnbBafrTmKt5M13eq70a2BG+0DnLEyl122hBAV72jmUT7e/jG703YD4Ofgx9tt3qajd0cTRyZENWHtBL2mQvOnYNVbcHarekbW3gXqVMF6PUwdoRBVSrmTKzc3N/bv34+/v7/R9X379uHs7FxRcYkqTqvV0NTXiaa+TozqEUzKpQI2XE20Np/I4HzWZeZtO8O8bWewNtfRqZ4L3eu70a2+G24OVqYOXwhxn8u+ks3Xe75m8dHF6BU91mbWPPfAcwxpOAQLnSy8F6LCeTSGYavgwK+wdgJknoQFAyGkD0R+DLX8TR2hEFVCuZOrwYMH8+qrr2Jvb09oaCgAsbGxvPbaazz22GMVHqC4P3g4WvF42zo83rYOl6+UEH8yw7ApRvKlAtYdSmXdoVQAmng7GjbFaOTlgLac53cJIWouvaLnjxN/MGP3DDILMgGI8I9gdKvReNh6mDg6Iao5jQYeeBSCIyF2KmyfBUdXQmI0dHoDOr4G5nLEgajZyp1cffDBB5w+fZru3btjZqa+Xa/XM2TIED7++OMKD1Dcf6wtdHSr7063+u4oisKh5Gw2HE5j/ZE09p3L4sD5Sxw4f4kZ64/jZm9J9wZudKvvTscgZ2wsKuRcayFENfTXxb/4ePvH7E/fD0CAYwBvt32bdp7tTByZEDWMlQNEfHR1quBoOL1JXZe1dyFEToGQXmoiJkQNVO6fZC0sLFi8eDEffvghe/fuxdramiZNmuDn51cZ8Yn7nEajoZGXI428HHmlez3ScwrZeDSNDYfT2HQ8nbScQv6XcI7/JZzDwkxLh0Bnul/dFMPbSX77JYSArIIsvtrzFb8d+w0FBRszG15q9hKPN3gcc625qcMTouZyqw9PL4e/lsGa8ZB1BhYNhno91STLOdDUEQpxz93xIUX16tXjP//5Dw8++OBdJVbffPMN/v7+WFlZ0bZtWxISEm7afsaMGYSEhGBtbY2vry9vvPEGBQUFd3VPce+42lvyaCtfZj3Vkt3v9eDn4W0Y2sEfn1rWXCnWE3M0nXd/P0jHKRvo9eUmpq05yu6zf6PXK6YOXQhxj5XoS/jl6C88+PuD/HrsVxQU+gT0YXn/5Tzd6GlJrISoCjQaaPwIjNyhTg3UmsPxtfBtO4j+AK7kmzpCIe6pco9cDRgwgDZt2jB27Fij659++ik7duzg119/ve17LV68mFGjRjFr1izatm3LjBkziIiI4OjRo7i5uZVqv3DhQsaNG8fs2bPp0KEDx44dY+jQoWg0GqZPn35H9xSmY2mmIzTYldBgVyb2bcjxtNyr67RS2XXmbw4nZ3M4OZuvN57A2daCrvXd6F7fjU71XLC3kh+qhKjO9qfv56PtH3Ho4iEA6tWqxztt3qGVRysTRyaEKJOlHYRPgmZPwOoxkLgBNk2D/YvVKYQN+slUQVEjlDu5iouLY9KkSaWu9+rVi88//7xc95o+fTojRoxg2LBhAMyaNYuVK1cye/Zsxo0bV6r91q1b6dixI48//jgA/v7+DB48mO3bt9/xPUXVoNFoCHa3J9jdnhe7BJKZd4XYY+rhxbHH0rmYd4XfdiXx264kzHUa2gU4062+uilGHWcbU4cvhKggmQWZzNg1g2UnlgFgZ27HyOYjGRQyCDOtrMkUospzqQdPLoUjKyDqbbh0Dn4ZAgFd1a3bXYNNHaEQlarc/1Ll5uZiYVF6m1tzc3Oys7Nv+z5Xrlxh165dvP3224ZrWq2W8PBw4uPjy3xPhw4dmD9/PgkJCbRp04aTJ0+yatUqnnrqqTu+J0BhYSGFhYWG19eeo6ioiKKiott+pspw7fNNHce9Zm+h4cHG7jzY2J2iEj27zmSx8Wg6G46mc/piPpuOZ7DpeAaTlx8iyNWWriGudA1xpbmvI2a625/tWlP7916R/q1c1al/i/XF/Hb8N2bun0lOUQ4AfQP68mrTV3G2dkYpUSgquffPWZ36uCqS/q1cJu3foEh4PhTtli/RbvsvmpMbUWZ2QN/2BfSd3gQLu3sfUwWTr9/KVZX6tzwxaBRFKddiljZt2vDggw/y3nvvGV2fNGkSy5cvZ9euXbd1nwsXLuDt7c3WrVtp37694fqYMf/f3p3H13Tnfxx/3XuzRxLZF2JfElsQhGqV2LVK69d9oaY7XYYyaFWXKUprVEeZaal2Ot20pSstIVRLEDtJiH3LKpFIJLKc3x+pTFM7iZPl/Xw87uPhnHvO937OJ18n+dzzPd8zllWrVpW5GvVHs2bN4vnnn8cwDAoLC3niiSeYM2fONbX58ssv88orr5yz/pNPPsHFRVdFKpuU07Azw8KODAv7siwU879hBi52BqG1DVp5GoTUNnDRF90ild7BwoN8l/sdScVJAATaAhnoPJB6dvVMjkxEyoNrfjKtjnxMQNZWAE7be7Kzzr0crR2hoYJSJeTm5nLfffdx8uRJ3N3dL7rtFf/pOXHiRO644w727t1LZGQkAFFRUXzyySd8+eWXVxfxZYqOjmby5Mm8++67REREkJiYyLPPPstrr73GxIkTr7rd8ePHM2rUqNLlrKwsgoOD6dOnzyUTWNEKCgpYtmwZvXv3xt5e9xn9WdbpAn5JTGdFfCqr96SRebqA2DQLsWlgs1roUL92yVWtZr408nU9Z3/lt2IpvxWrquc37XQas7bM4vv93wPg7uDOiLAR3NH4DmxWm8nRlajqOa7slN+KVbny+zCFe37C9vMLOGceoMOBd2lffxtFfaeCb4jJsV2dypXf6qcy5fdKRuddcXE1cOBAFi9ezOTJk/nyyy9xdnYmLCyMFStW4OXlddnt+Pj4YLPZSE5OLrM+OTmZgIDzPwhy4sSJPPjggzzyyCMAtG7dmpycHB577DFeeOGFq2oTwNHREUdHx3PW29vbm/7DPKsyxVKZeNvbM7i9C4PbB1NYVMzmw5lExaUQFZfMnpRTxOzPIGZ/BlOX7qahj+vv92n50bGhF/Z/GD6o/FYs5bdiVbX8FhQX8Fn8Z7y75V1OFZzCgoU7mt7Bs+2fxdPJ0+zwzquq5biqUX4rVqXJb4tboWkv+G0W/PIW1oNrsL53M0Q8Ad3HlTw/qwqqNPmtpipDfq/k869q0NQtt9zCLbfcApRUcp9++inPP/88sbGxFBUVXVYbDg4OhIeHExUVxeDBg4GShxFHRUUxcuTI8+6Tm5uL1Vr2fhqbreTbTcMwrqpNqT7sbFY6NvCiYwMvxvUP4VB6Livik4mKT2HdvnT2p+Uwb81+5q3Zj5ujHd2a+9K9qTdnzB/KK1JjbEjawOSYySRmJgLQyrsVEyIm0Nq3tcmRich1Ye8EN4+FNnfDTxNKJr5YNxu2L4Q+r5Ws11BBqcKu+o6U1atXM2/ePL766iuCgoK44447mD179hW1MWrUKIYOHUqHDh3o1KkTM2fOJCcnp3Smv4ceeog6deowZcoUoOSq2YwZM2jXrl3psMCJEycycODA0iLrUm1KzVHP24VhXRsyrGtDTuUXsmZPKlFxKaxMSCHt1Bl+2HacH7Ydx4KNr1PW07OFPz1D/GnmXwuLTuwi5So5J5m3Nr7FkgNLAKjtWJvn2j/H7U1vx2q56kcuikhV5Vkf7vkv7FleMnX7ib2w6HGIXQADpkOAvnCRqumKiqukpCQWLFjAvHnzyMrK4q677iI/P5/FixfTokWLK/7wu+++m9TUVF566SWSkpJo27YtS5cuxd/fH4BDhw6VuVL14osvYrFYePHFFzl69Ci+vr4MHDiQ119//bLblJqplqMd/VoF0q9VIMXFBluPZLIiPoVlu5KJT8om9lAmsYcymbY0gbqezvQM8SMy1J/OjbxwtKsc936IVEUFRQV8HPcxc7fOJbcwF6vFyp3N7uTpdk/j4ehhdngiYramvaDhWlg7G1ZPh0Nr4V/doOMj0OMFcK5tdoQiV+Syi6uBAweyevVqbrnlFmbOnEm/fv2w2WzMnTv3mgIYOXLkBYfsRUdHlw3Wzo5JkyYxadKkq25TxGq10K6eJ+3qefJMj0b8d9GPWOq0Jnp3Gr/uTedIxmk+XHuQD9cexMXBxk1NfegZ4k/3EF/83JzMDl+kylh7bC1T1k9h/8n9AIT5hvFCxAuEeoeaHJmIVCp2jnDTKGhzF/z0AuxaDOv/DTu+/t+Dia26wi1Vw2UXV0uWLOGZZ57hySefpGnTphUZk8h15ekIAzoFM7RrI3LPFPJbYjpR8SmsiE8mOSufn3Ym89POkklSwoJrl1zVCvGjZZC7hg+KnMfxU8eZvnE6yw4uA8DLyYtR4aMY2HighgCKyIV51IW7PoR90fDjWEhLgG9HlgwVvOVNCGpndoQil3TZxdWaNWuYN28e4eHhhIaG8uCDD3LPPfdUZGwi152Lgx29WvjTq4U/htGKnceySmYfjE9m25GTbD2cydbDmcxYtpsAdyciQ0tmH+zaxAcnew0flJrtTNEZFuxcwHvb3iOvKA+bxca9IffyZNsncXeomrOAiYgJGnWHJ9ZAzFxY9QYc3Qj/7gHhw6DnS+By+bNTi1xvl11cde7cmc6dOzNz5kw+//xz5s+fz6hRoyguLmbZsmUEBwfj5uZWkbGKXFcWi4VWdTxoVceDZ3s1JSUrj5UJKSyPS2HNnjSSsvL4JOYQn8QcwsneStfGPkSGllzVCvRwNjt8kevqlyO/MHX9VA5lHwIg3D+c8Z3G09yrucmRiUiVZOcAXZ+B1nfCsoklswnGflAyZLDnS9B+KFSS5+GJ/NEVzxbo6urK8OHDGT58OAkJCcybN4+pU6cybtw4evfuzbffflsRcYqYzs/dibs71uPujvXIKyhi3b50ouJSWBGfwtHM00TFpxAVnwJAyyD30kkx2tTxwGrV8EGpno5kH2HahmmsPLwSAF9nX0Z3GM2AhgM0bFZErp17IAx5H8Ifhh/HQMpO+P6vEPsh3PIW1O1gdoQiZVz1VOwAzZs3Z9q0aUyZMoXvvvuO+fPnl1dcIpWak72N7s396N7cj1cNg4Tk7NKHF28+nMnOY1nsPJbFrBWJ+NRyJDLEl8gQf25q6oOr4zX9txOpFPIK8/hgxwfM2zGP/KJ87Cx23B96P0+EPUEth1pmhyci1U2DrvD4atjwPqx8HY5vgfd7QrsHoNcr4OpjdoQiwDUWV2fZbDYGDx5c+uBekZrEYrEQEuBOSIA7I3o0If1UPtEJqUTFJ7N6dxppp/L5YuMRvth4BAeblc6NvUsnxQj2cjE7fJErYhgG0YejeWPDGxw9dRSAiIAIxkeMp3HtxuYGJyLVm80OOj8Bre6AZZNg6yew+WOI+w56vAgdhpdsI2Ii9UCRcuZdy5Eh4XUZEl6XM4XFbDhwguVxyUTFpXDoRC6rd6eyencqk77dSXN/NyJD/egV6kfbYE9sGj4oldihrENMWT+FNUfXAODv4s+YjmPoU7+PhgCKyPVTyw9un1MywcWPz0PSNlgyBjZ9VDKrYL3OZkcoNZiKK5EK5GBnpWsTH7o28eGlW1uwNzWHFfHJLI9LIfZgBgnJ2SQkZzMnei+eLvb0aO5HZKgf3Zr54u5kb3b4IgDkFuTy/vb3WbBzAQXFBdhZ7RjWchiPtn4UF3tdfRURk9SLgMeiYeN8WPEaJG+H+X2hzT3Q+1Vw8zc7QqmBVFyJXCcWi4UmfrVo4leLx7o1JjP3DKt2pxIVl0J0QgoZuQV8vfkoX28+ip3VQqeGXkSG+NEr1J8GPq5mhy81kGEYLD+0nGkbppGUkwRA16CujOs0jgYeDcwNTkQESmYM7PQotLwdol6BTf+BbZ9B/A/QYzx0egxs+rJSrh8VVyImqe3iwKC2dRjUtg6FRcXEHswomXEwLpm9qTn8tjed3/am8/cf4mjk6/r7fVr+dGjgib1ND2KVirXv5D6mxkxl7fG1AAS5BjG201gigyM1BFBEKh9XH7jtHWg/DH4cDcc2w08TSoqtAdOh4U1mRyg1hIorkUrAzmYlopE3EY28mTAglANpOUTFp7AiPpmYfSfYl5rDvtT9vPfLftyd7Li5ecnDi7s396W2i4PZ4Us1klOQw7+2/ov/7PoPhUYhDlYHhrcezvBWw3G20/PbRKSSqxsOj6yAzR/B8lcgNQ4+vBVaDYE+fwf3ILMjlGpOxZVIJdTAx5W/3NiQv9zYkKy8An7ZnUZUfDLRCamcyDnDd1uP8d3WY1gt0KG+V+mkGI19a+mqglwVwzBYemApb254k5TTJc9r6163O2M7jiXYPdjk6EREroDVWjLZRehtsOLvJfdk7fgKdv8EN4+FiCdLHlIsUgFUXIlUcu5O9tzSJpBb2gRSVGyw5XAmUXHJrIhPIT4pm/UHTrD+wAmmLomnnpdL6X1anRp64WCn4YNyaXsy9jBl/RQ2JG0AoG6tuoyPGE+3ut1MjkxE5Bq4eMGtM6D9QyUPID6yHpa9VDJ9e/9p0LiH2RFKNaTiSqQKsVkthNf3JLy+J2P7hXAkI5cV8SlExaWwdm86h07ksuC3Ayz47QC1HO24qakPPUP96d7cF59ajmaHL5VM9pls5mydwydxn1BkFOFoc+SR1o/wcKuHcbSpv4hINRHUFob/BFs/heWTIG03/GdwyZWtvpOhtq7OS/lRcSVShdX1dOGhLg14qEsDcvILWZOYxoq4FFYkpJCanc+SHUks2ZGExQJtg2vTM8SPnqH+hAS4afhgDWYYBt/v+563Nr5Fel46AD3r9WRMxzHUqVXH5OhERCqA1Qrt7oeQWyB6Cqz/N8R9C3uWQbfRcMMzYKcvleTaqbgSqSZcHe3o2zKAvi0DKC422HHsJMvjSibF2HE0i82HMtl8KJM3f95NkIcTkaElhVaXRt442dvMDl+uk4QTCbwe8zqbUzYD0MC9AeM6jaNrna4mRyYich0414b+b0C7B0uGCh76reS+rC2flAwVbNrb7AililNxJVINWa0W2tStTZu6tRnVuxlJJ/NY8fvsg2sS0zh2Mo+P1x3i43WHcLa30bWJD71C/egR4oe/u5PZ4UsFOJl/ktlbZvN5wucUG8U42znzeJvHebDFgzjYdGO3iNQwAa3g4R9h+0L4+UU4sQ/++3/QfAD0mwKeDcyOUKooFVciNUCAhxP3RdTjvoh65BUU8dveNKLiUlgRn8Lxk3ksj0tmeVwyAK3reJROitEyyB2rVcMHq7Jio5hvEr9h5qaZnMg7AUDfBn15vsPzBLgGmBydiIiJLBZocxc06wer3oCYuZDwI+xdAV2fgxufA3s9gkKujIorkRrGyd5GZIg/kSH+GIbBruNZrIhLISo+ha1HMtl+9CTbj57k7ag9+Lk5Evn7fVpdm3jj4qBTRlWyM20nk2Mmsy1tGwCNPRozPmI8EYERJkcmIlKJOLlD39dLhgouGQP7V8OqqSUTYPSbCs37lxRiIpdBfymJ1GAWi4WWQR60DPLg6Z5NSc3OZ2VCCiviUvhlTyop2fl8tuEwn204jIOdlRsae9MzxI/IUH/q1Na3eZVVZl4mszbP4svdX2Jg4GrvypNhT3Jf6H3YW+3NDk9EpHLyC4GHvoWdi+CnFyDzIHx2LzTpXXKflndjsyOUKkDFlYiU8nVz5K4OwdzVIZj8wiJi9p1gRXwKy+OSOZJxmuiEVKITUpn4zU5CAtzo+fukGGF1a2PT8EHTFRUX8dWer5i1eRYn808CcEujWxgdPhpfF1+ToxMRqQIsFmh1BzTtA7+8Cb/9ExKXwburSmYUvGkUOLiaHaVUYiquROS8HO1sdGvmS7dmvkwa2II9Kad+v08rmdiDGcQnZROflM3slXvxdnWgR4gfPUP8uLGpD25OujpyvW1N3crkmMnsSt8FQFPPpkzoNIEOAR1MjkxEpApyrAW9Xoa298OSsSX3Yf3yJmz9DPpNLnlGlsh5qLgSkUuyWCw083ejmb8bT3ZvTEbOGaJ3lzy8eNXuVNJzzvBl7BG+jD2Cvc1CRENveob60a2Jl9mhV3s5xTm8su4Vvtn3DQC17Gsxst1I7m5+N3ZWneJFRK6JT1N44GuI/x6WToCTh+CLh6BRD+g92ezopBLSb14RuWKerg7c3q4ut7erS0FRMRsOnCh5eHF8CvvScliTmMaaxDQA/J1t7LDtpnfLQNrXq42dzWpy9NVDYXEhnyV8xqzsWeRl5QEwqPEgngt/Dh9nH5OjExGpRiwWCB0IjXvCmn/Ar2/DvpXYvXcTLXz6QP5NYK8vE6WEiisRuSb2Nis3NPbhhsY+vHhrC/alnmJFfMlVrQ0HTpB8Gt5bc4D31hzAw9me7s196Rnqz81NffFw0fDBq7EpeROTYyaTkJEAQIhnCC90foG2fm3NDUxEpDpzcIHIF6DtvbBkHJY9P9E05UeMuV1KZhtsNUSzCoqKKxEpX418a9HItxaP3NSI9KxcZi1cTqZLXVbtSSMzt4Bvthzjmy3HsFktdKjvSa9QfyJD/Wjk44pFv5QuKjU3lX/E/oPv9n0HgLuDO91t3ZnYdyJOjnr4s4jIdeHVCO7/gsJd35O/+K+4nkqCr/4CsQug/zTwb2F2hGIiFVciUmHcne1p72MwYEBrLFYbmw9nlk6KsTv5FDH7TxCz/wSv/xhHA28XIkP86RXqR4cGXjjYafjgWQXFBXwa9ynvbn2XnIIcLFi4o+kdPNX6KdauWIvNajM7RBGRGsdo2pcVoZMZ4JGI7beZcOAXmHsjRDwB3f8GTh5mhygmUHElIteFnc1KxwZedGzgxbj+IRxKz2VFfDJR8Sms25fOgfRc5v+6n/m/7sfN0Y5uzXzpGepH9+Z+eLk6mB2+aTYkbWByzGQSMxMBaO3TmgkRE2jl04qCggKToxMRqdmKrQ4U3/Q8tnb3wU8TSia+WDcbti+EPq9Bm7s1VLCGUXElIqao5+3CsK4NGda1IafyC1mzJ5WouBRWJqSQduoMP2w/zg/bj2OxQPt6niXP1Arxp5l/rRoxfDA5J5m3Nr7FkgNLAKjtWJvn2j/H7U1vx2rRVT0RkUrFsz7c819IXA4/joUTe2HR47DxA7jlTQhobXaEcp2ouBIR09VytKNfq0D6tQqkuNhg65HM0kkxdh3PIvZgBrEHM5i2NIG6ns70DPEjMtSfzo28cLSrXkPiCooK+E/cf5i7dS6nC09jtVi5s9mdPN3uaTwcNcRERKRSa9ILnloLa2fD6ulweB38qxt0fAR6TABnT7MjlAqm4kpEKhWr1UK7ep60q+fJ6D7NOZZ5mhXxJdO8/5qYxpGM03y49iAfrj2Ii4ONm5r60DPEn+4hvvi5Ve1JHX479htTYqZwIOsAAGG+YbwQ8QKh3qHmBiYiIpfPzhFuGgVt7oKfX4Sdi2D9v2HH1/97MLFVIxCqKxVXIlKpBdV25oHO9Xmgc31Onyni18Q0ouJLJsVIzsrnp53J/LQzGYCwuh70DPUnMsSPlkHuVWb44PFTx5m+cTrLDi4DwMvJi1HhoxjYeKCGAIqIVFUedeHOBRA+rGSoYFoCfDuyZFbBAdOhTnuTA5SKoOJKRKoMZwcbvVr406uFP4bRip3HskpnH9x65GTpa8ay3QS4OxEZ6kfPED9uaOyDs0PlGz54pugMC3Yu4L1t75FXlIfNYuPekHt5qu1TuDm4mR2eiIiUh0bd4clfIWYuRE+FoxvhvciSoqvnS+CiBxBXJyquRKRKslgstKrjQas6HjzbqykpWXmsTCi5T+uXPWkkZeXxScwhPok5hKOdlRub+BAZ6kdkiB+BHs5mh8/qI6t5Y/0bHMo+BEC4fzgTIibQzLOZyZGJiEi5s9nDDU9Dq/+DZRNLZhOM/QB2LS4psNoPBT1Wo1pQcSUi1YKfuxN3d6zH3R3rkVdQxLp96aWTYhzNPE1UfApR8SkAtAh0p1doyaQYbep4YLVev+GDh7MPM23DNKIPRwPg6+zL6A6jGdBwQJUZxigiIlfJPRCGvA/hD8OPYyBlJ3z/V4j9EAa8CcEdzY5QrpGKKxGpdpzsbXRvXvKMrFduM0hIziYqLoWouGQ2H85k1/Esdh3PYtaKRHxqORIZ4ktkiD83NfXB1bFiTot5hXnM3zGfedvncab4DHYWOx5o8QBPhD2Bq71rhXymiIhUUg26wuOrYcP7sPJ1OL4F5vWCdg9Ar1fA1cfsCOUqqbgSkWrNYrEQEuBOSIA7I3o0If1UPtEJqayIT2HV7lTSTuXzxcYjfLHxCA42K50be5dM9R7iR7CXyzV/vmEYRB+O5o0Nb3D01FEAIgIjmNBpAo1qN7rm9kVEpIqy2UHnJ6DVHbD8ZdjyX9j8McR9Bz1ehA7DS7aRKkU/MRGpUbxrOTIkvC5DwutyprCYDQdOlFzVik/mYHouq3ensnp3KpO+3Ulzf7fSSTHa1fPEdoXDBw9mHWTq+qmsOboGAH8Xf8Z0HEOf+n00BFBERErU8oPB75bcd/Xj85C0DZaMgU0flcwqWL+L2RHKFVBxJSI1loOdla5NfOjaxIeJt4ayNzWHFfHJRMWlsPFgBgnJ2SQkZzMnei+eLvb0aO5HZKgf3Zr54u5kf8F2cwtyeX/7+yzYuYCC4gLsrHYMazmMR1s/iov9tV8NExGRaqheBDwWXTLRRdRrkLwdPugHbe6B3q+AW4DZEcplUHElIkLJ8MEmfrVo4leLx7o1JjP3DKt2lwwfjE5IJSO3gK83H+XrzUexs1ro1NCLyBA/eob609Cn5J4pwzBYfmg50zZMIyknCYCuQV0Z12kcDTwamHh0IiJSJVht0PERaHE7RL1ScvVq22cQ/wP0GA+dHiuZeVAqLRVXIiLnUdvFgUFt6zCobR0Ki4qJPZjBivgUlsclszc1h9/2pvPb3nT+/kMcjXxd6dikgP38l7jMWACCXIMY22kskcGRGgIoIiJXxtUbbpv1v6GCxzbBTxNg039Khgo2vMnsCOUCVFyJiFyCnc1KRCNvIhp5M35AKAfSclgRn8KK+BRiDhzjqOVHUjLWYLEUQ7Ed9e1u5aGGw2jvHazCSkRErl7dcHgkCjb/p2TSi9Q4+PBWaDUE+vwd3IPMjlD+RMWViMgVauDjysNdGxAQFMfRje+QdjoVAEtuS7KPDWBHgTdjd8ZjtcTTob5X6aQYTfxqqdgSEZErY7VC+FAIHQgr/g4b58OOryBhKdw8Fjo/BXYOZkcpv1NxJSJyhfZk7GHK+ilsSNoAQLBbMOM6jaNr0E1sOZxZOilGfFI26w+cYP2BE0xdEk89L5ff79Pyo1NDLxztbCYfiYiIVBkuXnDrDGj/UMkDiI+sh+WTSqZvHzANGkeaHaGg4kpE5LJln8nm3S3v8mn8pxQZRTjZnHik9SMMazUMR5sjAOH1PQmv78mYviEcychlZXwKy+NSWLs3nUMnclnw2wEW/HYAVwcb3Zr5EhniR48QP3xqOZp8dCIiUiUEtYXhP5VMdLHsJUjfA/+5HUJvg76ToXaw2RHWaCquREQuwTAMvt/3PW9tfIv0vHQAetbrydiOYwmqdeHx7nU9XXiwSwMe7NKAnPxCfk1MIyouhRUJKaRm57NkRxJLdiRhsUDb4Nq/P7zYn9BANw0fFBGRC7Naoe190HwARE+F9f+GuG9hzzLoNhpueAbs9KWdGVRciYhcRPyJeCbHTGZzymYAGrg3KBkCWKfrFbXj6mhHn5YB9GkZQHGxwY5jJ0sfXrzjaBabD2Wy+VAmb/68myAPp9/v0/KnS2NvnOw1fFBERM7DuTb0nwrtHigZKnjot5L7srZ8Av3egGZ9zI6wxlFxJSJyHifzT/LPzf/ki91fUGwU42znzONtHuehFg9hf43PGLFaLbSpW5s2dWvz197NSDqZx8qEFKLiklmTmMaxk3l8vO4QH687hLO9ja5NfOgZ6kdkiB/+7k7ldIQiIlJtBLSCh3+E7V/Czy/CiX3wyZ0lV7b6TgavhmZHWGOouBIR+YNio5hvEr9h5qaZnMg7AUC/Bv0Y3WE0Aa4BFfKZAR5O3NupHvd2qkdeQRFr96YT9fukGMdP5rE8LpnlcckAtK7jUTopRnNflwqJR0REqiCLBdrcCc36wqo3IGYuJPwIiVFw41/hxufA3tnsKKs9FVciIr/bmbaTyTGT2Za2DYDGHo0ZHzGeiMCI6xaDk72NHr9PcvHaIIO449msiE9meVwKW49ksv3oSbYfPcnbUXvwc3OksbMVh7gUbg7xx8VBp3QRkRrPyR36vg7tHoQlY2D/alg1FbZ+Av2mllzN0n29FUa/iUWkxsvMy+TtzW/z1e6vMDBwtXflybAnuS/0Puyt1zYE8FpYLBZaBLnTIsidkZFNSc3OJzohhai4FH7Zk0pKdj4p2VbWfrIFBzsrNzT2LpkUI9SfOrX17aSISI3mFwIPfQu7FsNPL0DmIfjsPmjSG/q/Ad6NzY6wWlJxJSI1VlFxEV/t+YpZm2dxMv8kALc2upVR4aPwdfE1Obpz+bo5cmeHYO7sEEx+YRG/7Unlg582sC/PhSOZeUQnpBKdkMrEb3YSEuD2+31a/rQNro3Nqm8pRURqHIsFWt5eUlD98ib89k9IXAbvroIbnoabRoODq9lRVisqrkSkRtqaupXJMZPZlb4LgGaezZgQMYFw/3CTI7s8jnY2bmziTdbuYvr3v4mDGfksj0thRXwysQcziE/KJj4pm9kr9+Lt6kD35iX3ad3U1Ac3J/OuxomIiAkca0Gvl6HtA7BkLOyNgl/egq2flwwhbDFIQwXLiYorEalR0k+nM3PTTBYnLgbAzd6NEe1GcHfzu7GzVs1TosVioam/G0393Xiye2Mycs6wancqy+OSWbU7lfScM3y16QhfbTqCvc1CRENvIkP86BXqTz1vTYohIlJj+DSBB76C+B9g6Xg4eQgWDoVG3aH/dPBtZnaEVV7V/EtCROQKFRYX8kXCF/xzyz/JPpMNwKDGg3gu/Dl8nH1Mjq58ebo6MLhdHQa3q0NBUTEbD2QQFZfMivgU9qXlsCYxjTWJabz6/S6a+NWiZ4gfPUP9aV+vNnY2q9nhi4hIRbJYIPRWaBwJv86ENTNhXzTM6QKdn4Kbx4Kjm8lBVl0qrkSk2tuUvInJMZNJyEgAINQrlAkRE2jr19bcwK4De5uVLo296dLYmxdvbcG+1FOsiC+ZFGPDgRMkppwiMeUU/1q9Dw9ne7o39yUyxI/uzfzwcNHwQRGRasvBBXpMgLB7Sq5i7V4Kv82C7Quhz9+h1RANFbwKKq5EpNpKzU1lRuwMvt/3PQDuDu482/5ZhjQdgs1qMzk6czTyrUUj31o8clMjTp4u4Jc9qUTFpbAyIYXM3AK+2XKMb7Ycw2a10KG+Jz1DS65qNfJxxaJfsiIi1Y9XI7jvc0hYCkv/BhkH4Ku/QOwC6D8N/FuYHWGVouJKRKqdguICPo37lHe3vktOQQ4WLAxpNoRn2j2Dp5On2eFVGh7O9tzaJohb2wRRVGyw+VBG6aQYu5NPEbP/BDH7TzD5x3gaeLsQGeJPz1A/OjbwwsFOwwdFRKqV5v1K7r36bVbJZBcHfoG5N0LE49B9HDh5mB1hlaDiSkSqlQ1JG5gcM5nEzEQAWvu0ZkLEBFr5tDI5ssrNZrXQoYEXHRp4Ma5/CIdP5BIVl0xUfAox+05wID2X+b/uZ/6v+3FztKNbs5Lhgz1C/PBydTA7fBERKQ/2TiX3XLW5G36aAPHfw7p3YfuX0PvVkiGEGsVwUSquRKRaSMpJYsbGGSw5sAQAT0dPngt/jsFNBmO16CrLlQr2cmFY14YM69qQU/mFrPnD8MG0U2f4Yftxfth+HIsF2tfzLJ19sJl/LQ0fFBGp6jzrwz3/hcTl8ONYOLEXFj9RMlRwwHQIbGN2hJWWiisRqdIKigr4T9x/mLt1LqcLT2O1WLmr2V2MbDcSD0cNYSgPtRzt6NcqkH6tAikuNth29GTJVa24FHYdzyL2YAaxBzOY/lMCdWo7l96nFdHQCyf7mnlvm4hItdCkFzy1FtbOhtXT4fA6+PfN0OEvEPkCOGuo/Z+puBKRKuu3Y78xJWYKB7IOANDWty0TIiYQ6h1qbmDVmNVqoW1wbdoG12Z0n+YcyzzNivgUVsSn8GtiGkczT/PR2oN8tPYgLg42bmziQ69Qf7qH+OLn5mR2+CIicqXsHOGmUdDmLvj5Rdi5CDa8Bzu//t+Dia0aIXKWiisRqXKOnzrOtA3TWH5oOQDeTt6M6jCKgY0GakjadRZU25kHOtfngc71OX2miF8T04iKL5kUIzkrn593JfPzrmQAwup6lE6K0TLIXT8rEZGqxKMu3LkAwoeVDBVMS4Bvn4bYD0uGCtZpb3aElYKKKxGpMs4UnWHBzgW8t+098orysFls3BtyL0+1fQo3Bz3w0GzODjZ6tfCnVwt/DKMVO49lEfX77INbj5wsff1j+W4C3J3oEeJHr1A/bmjsg7ODhg+KiFQJjbrDk79CzFyIngpHN8J7kRA+FHpOAhcvsyM0lYorEakSVh9ZzRvr3+BQ9iEAwv3DmRAxgWaezUyOTM7HYrHQqo4Hrep48GyvpqRk57Hy94cXr0lMIykrj0/XH+LT9YdwtLPStYkPPUP9iAzxI9DD2ezwRUTkYmz2cMPT0PpO+HkibP+iZLKLXd9A5MSSq1s19HmSKq5EpFI7nH2YaeunEX0kGgBfZ1+e7/A8/Rv217CyKsTPzYm7O9bj7o71yCsoImb/idJJMY7+4b4tgBaB7qWTYrSp44HVqp+ziEil5BYAQ977fajgGEjZCT+Mgk0fwYA3Ibij2RFedyquRKRSyivMY/6O+czbPo8zxWews9jxYIsHeTzscVztXc0OT66Bk72Nm5v5cnMzX165zWB38imWxyWzIj6FTYcy2HU8i13Hs3hnRSI+tRzo0byk0LqxqQ+1HPVrS0Sk0mnQFR5fDRveh5Wvw/EtMK9XyWQXvV6GWr5mR3jd6LeUiFQqhmGw8vBKpm2YxtFTRwGICIxgQqcJNKrdyOTopLxZLBaaB7jRPMCNET2akH4qn+iEVFbEp7Bqdyppp86wMPYIC2OP4GCzEtHIi54hJcVWsJeL2eGLiMhZNjvo/AS0ugOWvwxb/gtbPob476DHi9BheMk21Vz1P0IRqTIOZh1k6vqprDm6BoAA1wDGdBhD7/q9NQSwhvCu5ciQ8LoMCa/LmcJiNhw4QVRcClHxyRxMz+WXPWn8sieNl7/bRTP/WkSG+NMr1I929TyxafigiIj5avnB4Heh/VD48XlI2gZLxsCmD0uGCtbvYnaEFUrFlYiYLrcgl/e3v8+CnQsoKC7AzmrHsJbDeLT1o7jY6+pETeXw+0QXXZv4MPHWUPam5rAivuQ+rY0HM9idfIrdyaeYu2ovni729GjuR2SoH92a+eLuZG92+CIiNVu9CHgsGmI/gKjXIHkHfNAP2twNvV8tuV+rGlJxJSKmMQyDZQeXMX3jdJJykgDoWqcr4zqOo4FHA3ODk0rFYrHQxK8WTfxq8Vi3xpzMLSB6d8kkGNEJqWTkFvD15qN8vfkodlYLHRt4lU6K0dBH9+iJiJjCaoOOj0CL2yHqlZKJLrZ9DvE/QvdxEPF4ycyD1YiKKxExxb7MfUxZP4V1x9cBUKdWHcZ2HEuP4B4aAiiX5OFiz6C2dRjUtg6FRcXEHsxgRXwKUfEpJKacYu2+dNbuS+fvP8TRyMf192ne/enQwBN7m9Xs8EVEahZXb7htVsmzsH54Ho5tgp9fgM0flzyAuOFNZkdYblRcich1lVOQw7+2/ov/7PoPhUYhDlYHhrcezl9a/QUnOyezw5MqyM5mJaKRNxGNvBk/IJSD6Tm/P7w4hZj96exLy2HfL/t575f9uDnZcXMzX3qF+nNzM188XR0u2G5RsUHM/hPEplnw3n+CLk38dF+XiMi1qBMOj0TB5v+UTHqRGgcf3gqthkDv18CjDgBFhYXEr1vCmYPriF9noUWXAdjsqkbZUiminD17NtOnTycpKYmwsDDeeecdOnXqdN5tu3fvzqpVq85ZP2DAAH744QcAhg0bxocffljm/b59+7J06dLyD15ELothGCzZv4S3Nr5FyumS5xl1r9udsZ3GEuwWbHJ0Up3U93Zl+I0NGX5jQ7LzCvhlTxpRcSmsTEjhRM4Zvt92nO+3HcdqgfD6nvQM9adniB9N/GqVXjVduuM4r3y3i+Mn8wAbH+3ZSKCHE5MGtqBfq0BzD1BEpCqzWkuuYIUOLJm2feN82PEVJCyFm8eyOduNoJjXaU06rQGi3iU5yptjXSbRru9Qs6O/JNOLq88//5xRo0Yxd+5cIiIimDlzJn379iUhIQE/P79ztv/66685c+ZM6XJ6ejphYWHceeedZbbr168fH3zwQemyo6NjxR2EiFxUYmYi02KnsTF5IwDBbsGM6zSObnW7mRyZVHduTvYMaB3IgNaBFBUbbDmcWTopRnxSNhsOZLDhQAZTl8QT7OVMzxB/3J3teCcqEeNPbSWdzOPJjzcx54H2KrBERK6Vixfc8ha0f6hkqOCR9bB8Em3Pnnz/MFDA10jH97dn2AyVvsAyvbiaMWMGjz76KA8//DAAc+fO5YcffmD+/PmMGzfunO29vLzKLH/22We4uLicU1w5OjoSEFA9ZyERqSqyz2Tz4+kfmbRkEkVGEU42Jx5p/QjDWg3D0aYvPOT6slkthNf3JLy+J2P6hnAkI5eVv9+n9dvedA6fOM2C3w5ccP+zv+/Hf72d4mIDq4YIXrPCwiK2pluw7UzGzs5mdjjVjvJbsZTf8uIHEQsI9FtEq00vYrP8+astsFqg2IDAta9Q1PP+Sj1E0NTIzpw5Q2xsLOPHjy9dZ7Va6dWrF2vXrr2sNubNm8c999yDq2vZ2aCio6Px8/PD09OTyMhI/v73v+Pt7X3eNvLz88nPzy9dzsrKAqCgoICCgoIrPaxydfbzzY6julJ+K4ZhGPyw/wdmbp7JifwTAEQGRzKq/SiCXIOgGAqKlfNrpf57bfxr2XNPhzrc06EOOfmFrN13gs83HiF6d9pF98vILeCpTzZfpyhrAhvzd281O4hqTPmtWMpveelsPc1nDucWVmdZLRBAOtvX/khI5/7XMbIr+z1ranGVlpZGUVER/v7+Zdb7+/sTHx9/yf3Xr1/Pjh07mDdvXpn1/fr144477qBhw4bs3buXCRMm0L9/f9auXYvNdu43C1OmTOGVV145Z/3PP/+Mi0vleMbOsmXLzA6hWlN+y8/xwuN8d/o7DhUdAsDH6sMtzrfQNLspW1ZtYQtbzA2wGlL/LT/1DAtw6W+gfZ0MalWv2YNFREzVuCDjsraL37yWfScuXIRVhNzc3MvetvJeU7sM8+bNo3Xr1udMfnHPPfeU/rt169a0adOGxo0bEx0dTc+ePc9pZ/z48YwaNap0OSsri+DgYPr06YO7u3vFHcBlKCgoYNmyZfTu3Rt7e/0mL2/Kb/nJOpPFu1vf5cvELyk2inG2c2Z46HB8D/nSv09/5bcCqP+WP+/9J/hoz8ZLbveP+zoS0dDrktvJxakPVyzlt2Ipv+Urfl0xRM2+5HYh7bpc9ytXZ0e1XQ5TiysfHx9sNhvJycll1icnJ1/yfqmcnBw+++wzXn311Ut+TqNGjfDx8SExMfG8xZWjo+N5J7ywt7evNP9ZKlMs1ZHye/WKjWIWJy5mZuxMMvJLvnXq16AfozuMxtvBmx8P/6j8VjDlt/x0aeJHoIcTSSfzzpnQAkrurw7wcNK07OVMfbhiKb8VS/ktHy26DCA5yhtfI53znV6LDUixeJsyLfuV/HxNfZKig4MD4eHhREVFla4rLi4mKiqKLl26XHTfhQsXkp+fzwMPPHDJzzly5Ajp6ekEBmp2J5HytDNtJw/8+ACTfptERn4GjT0a836f95l+83QCXDWhjFQ9NquFSQNbAGUmqiqzPGlgCxVWIiLlzGZnx7Euk4CSQuqPzi4f7zKpUk9mASYXVwCjRo3ivffe48MPPyQuLo4nn3ySnJyc0tkDH3rooTITXpw1b948Bg8efM4kFadOnWLMmDGsW7eOAwcOEBUVxaBBg2jSpAl9+/a9LsckUt1l5mXyytpXuPeHe9meth1Xe1ee7/A8C29bSERghNnhiVyTfq0CmfNAewI8yj7UOsDDSdOwi4hUoHZ9h7L1hlmkWsr+fZ9i8WbrDbMq/TTsUAnuubr77rtJTU3lpZdeIikpibZt27J06dLSSS4OHTqE1Vq2BkxISGDNmjX8/PPP57Rns9nYtm0bH374IZmZmQQFBdGnTx9ee+01PetK5BoVFRfx1Z6vmLV5FifzTwJwa6NbGRU+Cl8XX5OjEyk//VoF0rtFAGsTU/j5lxj63BShoYAiItdBu75DKep5P9vX/kj85rWEtOtCiy4DCKjkV6zOqhRRjhw5kpEjR573vejo6HPWNW/eHMM4/ywhzs7O/PTTT+UZnogAW1O38vq614k7EQdAM89mTIiYQLh/uMmRiVQMm9VCREMv0uMMIhp6qbASEblObHZ2hHTuz74TBiGd+1f6oYB/VHUiFRFTpJ9OZ+ammSxOXAyAm70bI9qN4O7md2Nn1SlERERE5Cz9ZSQi51VYXMjnCZ8ze/NssguyARjcZDDPtX8Ob+fzP5BbREREpCZTcSUi54hNjmVyzGR2Z+wGINQrlBc6v0CYb5jJkYmIiIhUXiquRKRUam4qM2Jn8P2+7wFwd3Dn2fbPMqTpEGxWm8nRiYiIiFRuKq5EhILiAj6J+4Q5W+eQU5CDBQtDmg3hmXbP4OnkaXZ4IiIiIlWCiiuRGm798fVMWT+FxMxEAFr7tGZCxARa+bQyOTIRERGRqkXFlUgNlZSTxFsb32LpgaUAeDp68lz4cwxuMhirxfTni4uIiIhUOSquRGqYgqICPtr1Ef/a9i9OF57GarFyV7O7GNluJB6OHmaHJyIiIlJlqbgSqUF+O/obU9ZP4UDWAQDa+rblhc4vEOIVYm5gIiIiItWAiiuRGuDYqWNM3zCd5YeWA+Dt5M2oDqMY2GggFovF5OhEREREqgcVVyLVWH5RPgt2LOD97e+TV5SHzWLj3pB7eartU7g5uJkdnoiIiEi1ouJKpJpafWQ1U9dP5XD2YQDC/cOZEDGBZp7NTI5MREREpHpScSVSzRzOPsy09dOIPhINgJ+zH6M7jKZ/w/4aAigiIiJSgVRciVQTeYV5zNsxj/nb53Om+Ax2FjsebPEgj4c9jqu9q9nhiYiIiFR7Kq5EqjjDMFh5eCXTNkzj6KmjAEQERjCh0wQa1W5kcnQiIiIiNYeKK5Eq7GDWQaasn8KvR38FIMA1gDEdxtC7fm8NARQRERG5zlRciVRBuQW5vL/9fRbsXEBBcQH2VnuGtRzGI60fwcXexezwRERERGokFVciVYhhGCw7uIzpG6eTlJMEQNc6XRnfaTz13eubHJ2IiIhIzabiSqSK2Je5jynrp7Du+DoA6tSqw9iOY+kR3ENDAEVEREQqARVXIpVcTkEOc7fO5eNdH1NoFOJgdeAvrf/C8FbDcbJzMjs8EREREfmdiiuRSsowDJbsX8JbG98i5XQKAN3rdmdsp7EEuwWbHJ2IiIiI/JmKK5FKaE/GHibHTGZj8kYAgt2CGddpHN3qdjM5MhERERG5EBVXIpVI9pls3t3yLp/Gf0qRUYSTzYlH2zzK0JZDcbQ5mh2eiIiIiFyEiiuRSqDYKOb7fd8zY+MM0vPSAehdvzfPd3ieoFpBJkcnIiIiIpdDxZWIyeJPxPP6utfZkroFgAbuDRjfaTw31LnB3MBERERE5IqouBIxycn8k/xz8z/5YvcXFBvFONs580TYEzwY+iD2NnuzwxMRERGRK6TiSuQ6KzaKWZy4mJmxM8nIzwCgX4N+jO4wmgDXAJOjExEREZGrpeJK5DrambaT12NeZ3vadgAaezRmQsQEOgV2MjkyEREREblWKq5EroOMvAze3vQ2X+/5GgMDV3tXngp7intD78XeqiGAIiIiItWBiiuRClRUXMRXe75i1uZZnMw/CcCtjW5lVPgofF18TY5ORERERMqTiiuRCrIlZQuTYyYTdyIOgGaezZgQMYFw/3CTIxMRERGRiqDiSqScpZ9OZ+ammSxOXAyAm70bI9uN5K7md2Fn1X85ERERkepKf+mJlJPC4kI+T/ic2Ztnk12QDcDtTW7n2fbP4u3sbXJ0IiIiIlLRVFyJlIPY5Fgmx0xmd8ZuAEK9Qnmh8wuE+YaZHJmIiIiIXC8qrkSuQWpuKjNiZ/D9vu8B8HD04Jl2zzCk6RBsVpvJ0YmIiIjI9aTiSuQqFBQX8EncJ8zZOoecghwsWBjSbAjPtnuW2k61zQ5PREREREyg4krkCq0/vp7JMZPZe3IvAK19WvNCxAu09GlpcmQiIiIiYiYVVyKXKSkniTc3vslPB34CwNPRk+fCn2Nwk8FYLVaToxMRERERs6m4ErmEgqICPtr1Ef/a9i9OF57GarFyV7O7GNluJB6OHmaHJyIiIiKVhIorkYv47ehvTFk/hQNZBwBo59eOCRETCPEKMTcwEREREal0VFyJnMexU8eYvmE6yw8tB8DbyZtRHUYxsNFALBaLydGJiIiISGWk4krkD/KL8lmwYwHvb3+fvKI8bBYb94bcy1Ntn8LNwc3s8ERERESkElNxJfK71UdWM3X9VA5nHwagg38HJkRMoKlnU5MjExEREZGqQMWV1Hgnik7w3KrnWH10NQB+zn483/F5+jXopyGAIiIiInLZVFxJjZVXmMe/t/2bD7I/oDC7EDuLHQ+2eJDHwx7H1d7V7PBEREREpIpRcSU1jmEYrDi8gukbpnP01FEAIgIimNB5Ao08GpkcnYiIiIhUVSqupEY5cPIAUzdM5dejvwIQ4BJAd7ozpscYHBwcTI5ORERERKoyFVdSI+QW5PLe9vf4cOeHFBQXYG+1Z1jLYQwLHcbKn1fq3ioRERERuWYqrqRaMwyDnw/+zPQN00nOTQbgxjo3Mq7TOOq716egoMDkCEVERESkulBxJdXWvsx9TF4/mZjjMQDUqVWHsR3H0iO4h65UiYiIiEi5U3El1U5OQQ5zt87l410fU2gU4mB14C+t/8LwVsNxsnMyOzwRERERqaZUXEm1YRgGP+7/kbc2vkXq6VQAugd3Z2zHsQS7BZscnYiIiIhUdyqupFrYnbGbyTGTiU2OBSDYLZhxncbRrW43kyMTERERkZpCxZVUadlnsnl3y7t8Gv8pRUYRTjYnHm3zKENbDsXR5mh2eCIiIiJSg6i4kiqp2Cjmu73fMSN2BifyTgDQu35vnu/wPEG1gkyOTkRERERqIhVXUuXEpccxOWYyW1K3ANDAvQHjO43nhjo3mBuYiIiIiNRoKq6kyjiZf5J3Nr/Dwt0LKTaKcbZz5omwJ3gw9EHsbfZmhyciIiIiNZyKK6n0io1iFu1ZxNub3iYjPwOA/g36M6rDKAJcA0yOTkRERESkhIorqdR2pu3k9ZjX2Z62HYDGHo2ZEDGBToGdTI5MRERERKQsFVdSKWXkZfD2prf5es/XGBi42rvyVNhT3Bt6L/ZWDQEUERERkcpHxZVUKkXFRXy5+0tmbZ5F1pksAAY2GsioDqPwcfYxOToRERERkQtTcSWVxpaULUyOmUzciTgAmnk244WIF2jv397kyERERERELk3FlZgu/XQ6/4j9B9/s/QYAN3s3RrYbyV3N78LOqi4qIiIiIlWD/nIV0xQWF/J5wufM3jyb7IJsAG5vcjvPtn8Wb2dvk6MTEREREbkyKq7EFBuTNjJ5/WT2ZOwBINQrlBc6v0CYb5jJkYmIiIiIXB0VV3Jdpeam8lbsW/yw7wcAPBw9eKbdMwxpOgSb1WZydCIiIiIiV0/FlVwXBcUFfBL3CXO2ziGnIAcLFv6v2f/xTLtnqO1U2+zwRERERESumYorqXDrj69ncsxk9p7cC0AbnzZMiJhAS5+WJkcmIiIiIlJ+VFxJhUnKSeLNjW/y04GfAPB09OSv4X9lUJNBWC1Wk6MTERERESlfKq6k3BUUFfDhrg/597Z/c7rwNFaLlbub382ItiPwcPQwOzwRERERkQqh4krK1W9Hf2PK+ikcyDoAQDu/dkyImECIV4i5gYmIiIiIVDAVV1Iujp06xrQN04g6FAWAt5M3ozuM5tZGt2KxWEyOTkRERESk4qm4kmuSX5TPBzs+YN72eeQV5WGz2Lg35F6eavsUbg5uZocnIiIiInLdVIpZBWbPnk2DBg1wcnIiIiKC9evXX3Db7t27Y7FYznndcsstpdsYhsFLL71EYGAgzs7O9OrViz179lyPQ6lRVh9Zze3f3M7sLbPJK8qjg38HFg5cyN86/U2FlYiIiIjUOKYXV59//jmjRo1i0qRJbNq0ibCwMPr27UtKSsp5t//66685fvx46WvHjh3YbDbuvPPO0m2mTZvGrFmzmDt3LjExMbi6utK3b1/y8vKu12FVa4ezDzMyaiQjokZwOPswfs5+TOs2jfl959PUs6nZ4YmIiIiImML04mrGjBk8+uijPPzww7Ro0YK5c+fi4uLC/Pnzz7u9l5cXAQEBpa9ly5bh4uJSWlwZhsHMmTN58cUXGTRoEG3atOGjjz7i2LFjLF68+DoeWfVzuvA0s7fMZvDiwaw6sgo7ix0Pt3qYb2//lv4N++veKhERERGp0Uy95+rMmTPExsYyfvz40nVWq5VevXqxdu3ay2pj3rx53HPPPbi6ugKwf/9+kpKS6NWrV+k2Hh4eREREsHbtWu65555z2sjPzyc/P790OSsrC4CCggIKCgqu6tjKy9nPNzMOwzCIPhLNm5ve5HjOcQAiAiIYGz6Whh4NTY/vWlSG/FZnym/FUn4rnnJcsZTfiqX8Vizlt2JVpvxeSQymFldpaWkUFRXh7+9fZr2/vz/x8fGX3H/9+vXs2LGDefPmla5LSkoqbePPbZ5978+mTJnCK6+8cs76n3/+GRcXl0vGcT0sW7bMlM9NK0rjh9M/sKew5J41D4sHA5wH0OJ0C+J+jSOOOFPiKm9m5bemUH4rlvJb8ZTjiqX8Vizlt2IpvxWrMuQ3Nzf3sret0rMFzps3j9atW9OpU6dramf8+PGMGjWqdDkrK4vg4GD69OmDu7v7tYZ5TQoKCli2bBm9e/fG3t7+un3u6cLTvL/jff4T/x8Kiwuxt9rzUOhDDG85HGc75+sWR0UzK781hfJbsZTfiqccVyzlt2IpvxVL+a1YlSm/Z0e1XQ5TiysfHx9sNhvJycll1icnJxMQEHDRfXNycvjss8949dVXy6w/u19ycjKBgYFl2mzbtu1523J0dMTR0fGc9fb29qb/MM+6XrEYhsHPB39m+obpJOeW/FxurHMj4zqNo757/Qr/fLNUpp91daT8Vizlt+IpxxVL+a1Yym/FUn4rVmXI75V8vqkTWjg4OBAeHk5UVFTpuuLiYqKioujSpctF9124cCH5+fk88MADZdY3bNiQgICAMm1mZWURExNzyTZrun2Z+3h02aM8v+p5knOTqVOrDrN6zOLdnu9W68JKRERERKQ8mD4scNSoUQwdOpQOHTrQqVMnZs6cSU5ODg8//DAADz30EHXq1GHKlCll9ps3bx6DBw/G29u7zHqLxcJzzz3H3//+d5o2bUrDhg2ZOHEiQUFBDB48+HodVpWSU5DD3K1z+XjXxxQahThYHfhL678wvNVwnOyczA5PRERERKRKML24uvvuu0lNTeWll14iKSmJtm3bsnTp0tIJKQ4dOoTVWvYCW0JCAmvWrOHnn38+b5tjx44lJyeHxx57jMzMTG688UaWLl2Kk5MKhT8yDIMf9//IWxvfIvV0KgDdg7sztuNYgt2CTY5ORERERKRqMb24Ahg5ciQjR44873vR0dHnrGvevDmGYVywPYvFwquvvnrO/VjyP7szdjM5ZjKxybEA1HOrx986/Y1udbuZHJmIiIiISNVUKYoruX6yzmQxZ8scPo3/lCKjCCebE4+1eYyHWj6Eo+3cST1EREREROTyqLiqIYqNYr7b+x0zYmdwIu8EAL3r92ZMhzEE1gq8xN4iIiIiInIpKq5qgLj0OCbHTGZL6hYAGrg3YHzEeG4IusHcwEREREREqhEVV9XYyfyTvLP5HRbuXkixUYyznTNPhD3Bg6EPYm/T8xhERERERMqTiqtqqNgoZtGeRby96W0y8jMA6N+gP6M7jMbf1d/k6EREREREqicVV9XMjrQdvL7udXak7wCgSe0mTIiYQMeAjiZHJiIiIiJSvam4qiYy8jJ4e9PbfL3nawwMXO1deSrsKe4NvRd7q4YAioiIiIhUNBVXVVxRcRFf7v6SWZtnkXUmC4CBjQYyqsMofJx9TI5ORERERKTmUHFVhW1J2cLkmMnEnYgDoLlncyZETKC9f3uTIxMRERERqXlUXFViRcVFbEzeyNYzW/FL9qNTUCdsVhtpp9OYGTuTb/Z+A4CbgxtPt3uaO5vdiZ1VP1IRERERETPoL/FKavnB5UxdP5Xk3GQAFkYtxN/FnxuCbmD5weVkF2QDcHuT23m2/bN4O3ubGa6IiIiISI2n4qoSWn5wOaOiR2FglFmfnJvMosRFAIR6hfJC5xcI8w0zI0QREREREfkTFVeVTFFxEVPXTz2nsPojdwd3Pu7/MQ52DtcxMhERERERuRir2QFIWZtSNpUOBbyQrDNZbE3bep0iEhERERGRy6HiqpJJzU0t1+1EREREROT6UHFVyfi6+JbrdiIiIiIicn2ouKpk2vu1x9/FHwuW875vwUKASwDt/fQsKxERERGRykTFVSVjs9oY12kcwDkF1tnlv3X6Gzar7brHJiIiIiIiF6biqhLqVb8XM7rPwM/Fr8x6fxd/ZnSfQa/6vUyKTERERERELkRTsVdSver3okdwD9YfW8+ytcvo3aU3nYI66YqViIiIiEglpeKqErNZbXTw70CKQwod/DuosBIRERERqcQ0LFBERERERKQcqLgSEREREREpByquREREREREyoGKKxERERERkXKg4kpERERERKQcqLgSEREREREpByquREREREREyoGKKxERERERkXKg4kpERERERKQcqLgSEREREREpByquREREREREyoGKKxERERERkXKg4kpERERERKQc2JkdQGVkGAYAWVlZJkcCBQUF5ObmkpWVhb29vdnhVDvKb8VSfiuW8lvxlOOKpfxWLOW3Yim/Fasy5fdsTXC2RrgYFVfnkZ2dDUBwcLDJkYiIiIiISGWQnZ2Nh4fHRbexGJdTgtUwxcXFHDt2DDc3NywWi6mxZGVlERwczOHDh3F3dzc1lupI+a1Yym/FUn4rnnJcsZTfiqX8Vizlt2JVpvwahkF2djZBQUFYrRe/q0pXrs7DarVSt25ds8Mow93d3fSOVZ0pvxVL+a1Yym/FU44rlvJbsZTfiqX8VqzKkt9LXbE6SxNaiIiIiIiIlAMVVyIiIiIiIuVAxVUl5+joyKRJk3B0dDQ7lGpJ+a1Yym/FUn4rnnJcsZTfiqX8Vizlt2JV1fxqQgsREREREZFyoCtXIiIiIiIi5UDFlYiIiIiISDlQcSUiIiIiIlIOVFyJiIiIiIiUAxVX19Hq1asZOHAgQUFBWCwWFi9eXOZ9wzB46aWXCAwMxNnZmV69erFnz55Ltjt79mwaNGiAk5MTERERrF+/voKOoPK7WI4LCgr429/+RuvWrXF1dSUoKIiHHnqIY8eOXbTNl19+GYvFUuYVEhJSwUdSOV2qDw8bNuycXPXr1++S7aoPl7hUfv+c27Ov6dOnX7BN9d8SU6ZMoWPHjri5ueHn58fgwYNJSEgos01eXh4jRozA29ubWrVqMWTIEJKTky/a7tWet6ubS+X3xIkTPP300zRv3hxnZ2fq1avHM888w8mTJy/a7tWeU6qby+m/3bt3PydXTzzxxEXbVf8tcan8Hjhw4ILn34ULF16wXfXf/5kzZw5t2rQpfSBwly5dWLJkSen71en8q+LqOsrJySEsLIzZs2ef9/1p06Yxa9Ys5s6dS0xMDK6urvTt25e8vLwLtvn5558zatQoJk2axKZNmwgLC6Nv376kpKRU1GFUahfLcW5uLps2bWLixIls2rSJr7/+moSEBG677bZLttuyZUuOHz9e+lqzZk1FhF/pXaoPA/Tr169Mrj799NOLtqk+/D+Xyu8f83r8+HHmz5+PxWJhyJAhF21X/RdWrVrFiBEjWLduHcuWLaOgoIA+ffqQk5NTus1f//pXvvvuOxYuXMiqVas4duwYd9xxx0XbvZrzdnV0qfweO3aMY8eO8eabb7Jjxw4WLFjA0qVL+ctf/nLJtq/0nFIdXU7/BXj00UfL5GratGkXbVf9t8Sl8hscHHzO+feVV16hVq1a9O/f/6Jtq/+WqFu3LlOnTiU2NpaNGzcSGRnJoEGD2LlzJ1DNzr+GmAIwFi1aVLpcXFxsBAQEGNOnTy9dl5mZaTg6OhqffvrpBdvp1KmTMWLEiNLloqIiIygoyJgyZUqFxF2V/DnH57N+/XoDMA4ePHjBbSZNmmSEhYWVb3DVwPnyO3ToUGPQoEFX1I768PldTv8dNGiQERkZedFt1H/PLyUlxQCMVatWGYZRcr61t7c3Fi5cWLpNXFycARhr1649bxtXe96uCf6c3/P54osvDAcHB6OgoOCC21zNOaUmOF9+b775ZuPZZ5+97DbUfy/scvpv27ZtjeHDh1+0HfXfi/P09DTef//9anf+1ZWrSmL//v0kJSXRq1ev0nUeHh5ERESwdu3a8+5z5swZYmNjy+xjtVrp1avXBfeRsk6ePInFYqF27doX3W7Pnj0EBQXRqFEj7r//fg4dOnR9AqyCoqOj8fPzo3nz5jz55JOkp6dfcFv14auXnJzMDz/8cFnf/Kv/nuvscDQvLy8AYmNjKSgoKNMXQ0JCqFev3gX74tWct2uKP+f3Qtu4u7tjZ2d30bau5JxSU1wov//973/x8fGhVatWjB8/ntzc3Au2of57YZfqv7GxsWzZsuWyzr/qv+cqKiris88+Iycnhy5dulS78+/Fz2hy3SQlJQHg7+9fZr2/v3/pe3+WlpZGUVHRefeJj4+vmECrkby8PP72t79x77334u7ufsHtIiIiWLBgAc2bNy8dCnDTTTexY8cO3NzcrmPElV+/fv244447aNiwIXv37mXChAn079+ftWvXYrPZztleffjqffjhh7i5uV1y2IT677mKi4t57rnn6Nq1K61atQJKzsEODg7nfNFysXPw1Zy3a4Lz5ffP0tLSeO2113jssccu2taVnlNqggvl97777qN+/foEBQWxbds2/va3v5GQkMDXX3993nbUf8/vcvrvvHnzCA0N5YYbbrhoW+q/ZW3fvp0uXbqQl5dHrVq1WLRoES1atGDLli3V6vyr4kpqpIKCAu666y4Mw2DOnDkX3faP46nbtGlDREQE9evX54svvrisb61qknvuuaf0361bt6ZNmzY0btyY6OhoevbsaWJk1c/8+fO5//77cXJyuuh26r/nGjFiBDt27KiR955dD5fKb1ZWFrfccgstWrTg5ZdfvmhbOqec60L5/WOh2rp1awIDA+nZsyd79+6lcePG1zvMKutS/ff06dN88sknTJw48ZJtqf+W1bx5c7Zs2cLJkyf58ssvGTp0KKtWrTI7rHKnYYGVREBAAMA5M6MkJyeXvvdnPj4+2Gy2K9pH/ldYHTx4kGXLll30qtX51K5dm2bNmpGYmFhBEVYfjRo1wsfH54K5Uh++Or/88gsJCQk88sgjV7xvTe+/I0eO5Pvvv2flypXUrVu3dH1AQABnzpwhMzOzzPYX64tXc96u7i6U37Oys7Pp168fbm5uLFq0CHt7+ytq/1LnlOruUvn9o4iICIAL5kr991yXk98vv/yS3NxcHnrooStuv6b3XwcHB5o0aUJ4eDhTpkwhLCyMt99+u9qdf1VcVRINGzYkICCAqKio0nVZWVnExMTQpUuX8+7j4OBAeHh4mX2Ki4uJioq64D413dnCas+ePSxfvhxvb+8rbuPUqVPs3buXwMDACoiwejly5Ajp6ekXzJX68NWZN28e4eHhhIWFXfG+NbX/GobByJEjWbRoEStWrKBhw4Zl3g8PD8fe3r5MX0xISODQoUMX7ItXc96uri6VXyjJTZ8+fXBwcODbb7+95FXX87nUOaW6upz8/tmWLVsALpgr9d//uZL8zps3j9tuuw1fX98r/pya2n8vpLi4mPz8/Op3/jV1Oo0aJjs729i8ebOxefNmAzBmzJhhbN68uXSmuqlTpxq1a9c2vvnmG2Pbtm3GoEGDjIYNGxqnT58ubSMyMtJ45513Spc/++wzw9HR0ViwYIGxa9cu47HHHjNq165tJCUlXffjqwwuluMzZ84Yt912m1G3bl1jy5YtxvHjx0tf+fn5pW38OcejR482oqOjjf379xu//vqr0atXL8PHx8dISUkx4xBNdbH8ZmdnG88//7yxdu1aY//+/cby5cuN9u3bG02bNjXy8vJK21AfvrBLnSMMwzBOnjxpuLi4GHPmzDlvG+q/5/fkk08aHh4eRnR0dJn/+7m5uaXbPPHEE0a9evWMFStWGBs3bjS6dOlidOnSpUw7zZs3N77++uvS5cs5b9cEl8rvyZMnjYiICKN169ZGYmJimW0KCwtL2/ljfi/3nFITXCq/iYmJxquvvmps3LjR2L9/v/HNN98YjRo1Mrp161amHfXf87uc84NhGMaePXsMi8ViLFmy5LztqP9e2Lhx44xVq1YZ+/fvN7Zt22aMGzfOsFgsxs8//2wYRvU6/6q4uo5WrlxpAOe8hg4dahhGybSSEydONPz9/Q1HR0ejZ8+eRkJCQpk26tevb0yaNKnMunfeeceoV6+e4eDgYHTq1MlYt27ddTqiyudiOd6/f/953wOMlStXlrbx5xzffffdRmBgoOHg4GDUqVPHuPvuu43ExMTrf3CVwMXym5uba/Tp08fw9fU17O3tjfr16xuPPvroOUWS+vCFXeocYRiG8a9//ctwdnY2MjMzz9uG+u/5Xej//gcffFC6zenTp42nnnrK8PT0NFxcXIzbb7/dOH78+Dnt/HGfyzlv1wSXyu+F+jZg7N+/v0w7Z/e53HNKTXCp/B46dMjo1q2b4eXlZTg6OhpNmjQxxowZY5w8efKcdtR/z3U55wfDMIzx48cbwcHBRlFR0QXbUf89v+HDhxv169c3HBwcDF9fX6Nnz56lhZVhVK/zr8UwDOOaL3+JiIiIiIjUcLrnSkREREREpByouBIRERERESkHKq5ERERERETKgYorERERERGRcqDiSkREREREpByouBIRERERESkHKq5ERERERETKgYorERERERGRcqDiSkREKr0DBw5gsVjYsmWL2aGUio+Pp3Pnzjg5OdG2bVuzw7koi8XC4sWLzQ5DRKTaU3ElIiKXNGzYMCwWC1OnTi2zfvHixVgsFpOiMtekSZNwdXUlISGBqKio825zNm9/fvXr1+86RysiIteDiisREbksTk5OvPHGG2RkZJgdSrk5c+bMVe+7d+9ebrzxRurXr4+3t/cFt+vXrx/Hjx8v8/r000+v+nNFRKTyUnElIiKXpVevXgQEBDBlypQLbvPyyy+fM0Ru5syZNGjQoHR52LBhDB48mMmTJ+Pv70/t2rV59dVXKSwsZMyYMXh5eVG3bl0++OCDc9qPj4/nhhtuwMnJiVatWrFq1aoy7+/YsYP+/ftTq1Yt/P39efDBB0lLSyt9v3v37owcOZLnnnsOHx8f+vbte97jKC4u5tVXX6Vu3bo4OjrStm1bli5dWvq+xWIhNjaWV199FYvFwssvv3zBnDg6OhIQEFDm5enpWaatOXPm0L9/f5ydnWnUqBFffvllmTa2b99OZGQkzs7OeHt789hjj3Hq1Kky28yfP5+WLVvi6OhIYGAgI0eOLPN+Wloat99+Oy4uLjRt2pRvv/229L2MjAzuv/9+fH19cXZ2pmnTpufNv4iIXJyKKxERuSw2m43JkyfzzjvvcOTIkWtqa8WKFRw7dozVq1czY8YMJk2axK233oqnpycxMTE88cQTPP744+d8zpgxYxg9ejSbN2+mS5cuDBw4kPT0dAAyMzOJjIykXbt2bNy4kaVLl5KcnMxdd91Vpo0PP/wQBwcHfv31V+bOnXve+N5++23eeust3nzzTbZt20bfvn257bbb2LNnDwDHjx+nZcuWjB49muPHj/P8889fUz4mTpzIkCFD2Lp1K/fffz/33HMPcXFxAOTk5NC3b188PT3ZsGEDCxcuZPny5WWKpzlz5jBixAgee+wxtm/fzrfffkuTJk3KfMYrr7zCXXfdxbZt2xgwYAD3338/J06cKP38Xbt2sWTJEuLi4pgzZw4+Pj7XdEwiIjWSISIicglDhw41Bg0aZBiGYXTu3NkYPny4YRiGsWjRIuOPv0omTZpkhIWFldn3H//4h1G/fv0ybdWvX98oKioqXde8eXPjpptuKl0uLCw0XF1djU8//dQwDMPYv3+/ARhTp04t3aagoMCoW7eu8cYbbxiGYRivvfaa0adPnzKfffjwYQMwEhISDMMwjJtvvtlo167dJY83KCjIeP3118us69ixo/HUU0+VLoeFhRmTJk26aDtDhw41bDab4erqWub1x7YB44knniizX0REhPHkk08ahmEY//73vw1PT0/j1KlTpe//8MMPhtVqNZKSkkrjfeGFFy4YB2C8+OKLpcunTp0yAGPJkiWGYRjGwIEDjYcffviixyIiIpdmZ2ZhJyIiVc8bb7xBZGTkNV2tadmyJVbr/wZP+Pv706pVq9Jlm82Gt7c3KSkpZfbr0qVL6b/t7Ozo0KFD6RWerVu3snLlSmrVqnXO5+3du5dmzZoBEB4eftHYsrKyOHbsGF27di2zvmvXrmzduvUyj/B/evTowZw5c8qs8/LyKrP8x+M6u3x2ZsS4uDjCwsJwdXUtE0txcTEJCQlYLBaOHTtGz549LxpHmzZtSv/t6uqKu7t7aX6ffPJJhgwZwqZNm+jTpw+DBw/mhhtuuOJjFRGp6VRciYjIFenWrRt9+/Zl/PjxDBs2rMx7VqsVwzDKrCsoKDinDXt7+zLLFovlvOuKi4svO65Tp04xcOBA3njjjXPeCwwMLP33H4uU68HV1fWcIXrlydnZ+bK2u1h++/fvz8GDB/nxxx9ZtmwZPXv2ZMSIEbz55pvlHq+ISHWme65EROSKTZ06le+++461a9eWWe/r60tSUlKZAqs8n021bt260n8XFhYSGxtLaGgoAO3bt2fnzp00aNCAJk2alHldSUHl7u5OUFAQv/76a5n1v/76Ky1atCifA/mTPx7X2eWzxxUaGsrWrVvJyckpE4vVaqV58+a4ubnRoEGDC04Hf7l8fX0ZOnQoH3/8MTNnzuTf//73NbUnIlITqbgSEZEr1rp1a+6//35mzZpVZn337t1JTU1l2rRp7N27l9mzZ7NkyZJy+9zZs2ezaNEi4uPjGTFiBBkZGQwfPhyAESNGcOLECe699142bNjA3r17+emnn3j44YcpKiq6os8ZM2YMb7zxBp9//jkJCQmMGzeOLVu28Oyzz15xzPn5+SQlJZV5/XEGQ4CFCxcyf/58du/ezaRJk1i/fn3phBX3338/Tk5ODB06lB07drBy5UqefvppHnzwQfz9/YGSWRrfeustZs2axZ49e9i0aRPvvPPOZcf40ksv8c0335CYmMjOnTv5/vvvS4s7ERG5fCquRETkqrz66qvnDNsLDQ3l3XffZfbs2YSFhbF+/fprnknvj6ZOncrUqVMJCwtjzZo1fPvtt6Wz2p292lRUVESfPn1o3bo1zz33HLVr1y5zf9fleOaZZxg1ahSjR4+mdevWLF26lG+//ZamTZteccxLly4lMDCwzOvGG28ss80rr7zCZ599Rps2bfjoo4/49NNPS6+Subi48NNPP3HixAk6duzI//3f/9GzZ0/++c9/lu4/dOhQZs6cybvvvkvLli259dZbS2c2vBwODg6MHz+eNm3a0K1bN2w2G5999tkVH6uISE1nMf48OF5ERESuG4vFwqJFixg8eLDZoYiIyDXSlSsREREREZFyoOJKRERERESkHGgqdhERERNpdL6ISPWhK1ciIiIiIiLlQMWViIiIiIhIOVBxJSIiIiIiUg5UXImIiIiIiJQDFVciIiIiIiLlQMWViIiIiIhIOVBxJSIiIiIiUg5UXImIiIiIiJSD/wfEuA/lptr3OAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Accuracy: 0.94\n",
            "Best Configuration - Batch Size: 32, Epochs: 10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "cnn_model = Sequential()\n",
        "cnn_model.add(Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(x_train.shape[1], 1)))\n",
        "cnn_model.add(MaxPooling1D(pool_size=2))\n",
        "cnn_model.add(Flatten())\n",
        "cnn_model.add(Dense(1, activation='sigmoid'))\n",
        "cnn_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "cnn_model.fit(np.expand_dims(x_train, axis=-1), y_train, epochs=10, batch_size=32, verbose=0)\n",
        "accuracy_cnn = cnn_model.evaluate(np.expand_dims(x_test, axis=-1), y_test)[1]\n",
        "print(\"CNN Model Accuracy: {:.2f}\".format(accuracy_cnn))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YTOY1TwSK6RY",
        "outputId": "a7d90a63-905f-46ed-b9e0-ca8e30fe7f3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 150ms/step - loss: 0.7263 - accuracy: 0.6875\n",
            "CNN Model Accuracy: 0.69\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_sizes = [16, 32, 64]\n",
        "epochs = [10, 20, 30,50]\n",
        "accuracy_results = []\n",
        "\n",
        "for batch_size in batch_sizes:\n",
        "    batch_accuracy = []\n",
        "    for num_epochs in epochs:\n",
        "        cnn_model = Sequential()\n",
        "        cnn_model.add(Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(x_train.shape[1], 1)))\n",
        "        cnn_model.add(MaxPooling1D(pool_size=2))\n",
        "        cnn_model.add(Flatten())\n",
        "        cnn_model.add(Dense(1, activation='sigmoid'))\n",
        "        cnn_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "        cnn_model.fit(np.expand_dims(x_train, axis=-1), y_train, epochs=num_epochs, batch_size=batch_size, verbose=0)\n",
        "        accuracy_cnn = cnn_model.evaluate(np.expand_dims(x_test, axis=-1), y_test)[1]\n",
        "        batch_accuracy.append(accuracy_cnn)\n",
        "    accuracy_results.append(batch_accuracy)\n",
        "# Plotting the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i, batch_size in enumerate(batch_sizes):\n",
        "    plt.plot(epochs, accuracy_results[i], marker='o', label=f'Batch Size {batch_size}')\n",
        "plt.xlabel('Number of Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Accuracy CNN vs. Batch Size and Epochs')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 778
        },
        "id": "-eIFxXoyLCq2",
        "outputId": "d4696c01-8f18-4d12-88de-5785e2aba094"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 478ms/step - loss: 0.6382 - accuracy: 0.7500\n",
            "1/1 [==============================] - 0s 153ms/step - loss: 0.6113 - accuracy: 0.6875\n",
            "1/1 [==============================] - 0s 148ms/step - loss: 0.6166 - accuracy: 0.6250\n",
            "1/1 [==============================] - 0s 225ms/step - loss: 0.6932 - accuracy: 0.6875\n",
            "1/1 [==============================] - 0s 160ms/step - loss: 0.6936 - accuracy: 0.6250\n",
            "1/1 [==============================] - 0s 150ms/step - loss: 0.6136 - accuracy: 0.6875\n",
            "1/1 [==============================] - 0s 150ms/step - loss: 0.6444 - accuracy: 0.6250\n",
            "1/1 [==============================] - 0s 143ms/step - loss: 0.6240 - accuracy: 0.6250\n",
            "1/1 [==============================] - 0s 145ms/step - loss: 0.7285 - accuracy: 0.6250\n",
            "1/1 [==============================] - 0s 146ms/step - loss: 0.6500 - accuracy: 0.6875\n",
            "1/1 [==============================] - 0s 145ms/step - loss: 0.6735 - accuracy: 0.6250\n",
            "1/1 [==============================] - 0s 144ms/step - loss: 0.6901 - accuracy: 0.6250\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# CNN-BiLSTM Model\n",
        "cnn_bilstm_model = Sequential()\n",
        "cnn_bilstm_model.add(Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(x_train.shape[1], 1)))\n",
        "cnn_bilstm_model.add(MaxPooling1D(pool_size=2))\n",
        "cnn_bilstm_model.add(Bidirectional(LSTM(64)))\n",
        "cnn_bilstm_model.add(Dense(1, activation='sigmoid'))\n",
        "cnn_bilstm_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "cnn_bilstm_model.fit(np.expand_dims(x_train, axis=-1), y_train, epochs=10, batch_size=32, verbose=0)\n",
        "accuracy_cnn_bilstm = cnn_bilstm_model.evaluate(np.expand_dims(x_test, axis=-1), y_test)[1]\n",
        "print(\"CNN-BiLSTM Model Accuracy: {:.2f}\".format(accuracy_cnn_bilstm))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CMdU1pS2Hp-n",
        "outputId": "335cfd8b-fb1c-477e-8ff0-07d7a9221d2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step - loss: 0.5241 - accuracy: 0.7500\n",
            "CNN-BiLSTM Model Accuracy: 0.75\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_sizes = [16, 32, 64]\n",
        "epochs = [10, 20, 30]\n",
        "accuracy_results = []\n",
        "best_accuracy = 0\n",
        "best_config = (0, 0)  # To store batch size and epoch with best accuracy\n",
        "\n",
        "for batch_size in batch_sizes:\n",
        "    batch_accuracy = []\n",
        "    for num_epochs in epochs:\n",
        "        cnn_bilstm_model = Sequential()\n",
        "        cnn_bilstm_model.add(Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(x_train.shape[1], 1)))\n",
        "        cnn_bilstm_model.add(MaxPooling1D(pool_size=2))\n",
        "        cnn_bilstm_model.add(Bidirectional(LSTM(64)))\n",
        "        cnn_bilstm_model.add(Dense(1, activation='sigmoid'))\n",
        "        cnn_bilstm_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "        cnn_bilstm_model.fit(np.expand_dims(x_train, axis=-1), y_train, epochs=num_epochs, batch_size=batch_size, verbose=0)\n",
        "        accuracy_cnn_bilstm = cnn_bilstm_model.evaluate(np.expand_dims(x_test, axis=-1), y_test)[1]\n",
        "        batch_accuracy.append(accuracy_cnn_bilstm)\n",
        "\n",
        "        if accuracy > best_accuracy:\n",
        "            best_accuracy = accuracy\n",
        "            best_config = (batch_size, num_epochs)\n",
        "\n",
        "    accuracy_results.append(batch_accuracy)\n",
        "\n",
        "# Plotting the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i, batch_size in enumerate(batch_sizes):\n",
        "    plt.plot(epochs, accuracy_results[i], marker='o', label=f'Batch Size {batch_size}')\n",
        "plt.xlabel('Number of Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Accuracy vs. Batch Size and Epochs')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "print(f\"Best Accuracy: {best_accuracy:.2f}\")\n",
        "print(f\"Best Configuration - Batch Size: {best_config[0]}, Epochs: {best_config[1]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "id": "LGxK4zbSNRJb",
        "outputId": "67e67138-faf9-4275-a30c-e7e0d271970d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step - loss: 0.5351 - accuracy: 0.7500\n",
            "1/1 [==============================] - 1s 1s/step - loss: 0.4513 - accuracy: 0.7500\n",
            "1/1 [==============================] - 1s 861ms/step - loss: 0.5208 - accuracy: 0.8125\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.4895 - accuracy: 0.8750\n",
            "1/1 [==============================] - 1s 1s/step - loss: 0.6934 - accuracy: 0.6875\n",
            "1/1 [==============================] - 1s 876ms/step - loss: 0.4654 - accuracy: 0.8125\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.5829 - accuracy: 0.8750\n",
            "1/1 [==============================] - 1s 913ms/step - loss: 0.5720 - accuracy: 0.6250\n",
            "1/1 [==============================] - 1s 1s/step - loss: 0.5698 - accuracy: 0.6250\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Accuracy: 0.88\n",
            "Best Configuration - Batch Size: 16, Epochs: 10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LDA"
      ],
      "metadata": {
        "id": "GvTUjc7XXGWj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " Latent Dirichlet Allocation\n",
        "Finally we arrive on the subject of topic modelling and the implementation of a couple of unsupervised learning algorithms. The first method that I will touch upon is Latent Dirichlet Allocation. Now there are a couple of different implements of this LDA algorithm but in this notebook, I will be using Sklearn's implementation. Another very well-known LDA implementation is Radim Rehurek's gensim, so check it out as well.\n",
        "\n",
        "Corpus - Document - Word : Topic Generation\n",
        "\n",
        "In LDA, the modelling process revolves around three things: the text corpus, its collection of documents, D and the words W in the documents. Therefore the algorithm attempts to uncover K topics from this corpus via the following way (illustrated by the diagram)\n",
        "\n",
        "Three_Level Bayesian Model\n",
        "\n",
        "Model each topic, $\\kappa$ via a Dirichlet prior distribution given by $\\beta_{k}$:\n",
        "\n",
        "\n",
        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)\n",
        "Model each document d by another Dirichlet distribution parameterized by $\\alpha$:\n",
        "\n",
        "\n",
        "\n",
        "Subsequently for document d, we generate a topic via a multinomial distribution which we then backtrack and use to generate the correspondings words related to that topic via another multinomial distribution:\n",
        "\n",
        "\n",
        "\n",
        "(Image source: http://scikit-learn.org/stable/modules/decomposition.html#latentdirichletallocation)\n",
        "\n",
        "The LDA algorithm first models documents via a mixture model of topics. From these topics, words are then assigned weights based on the probability distribution of these topics. It is this probabilistic assignment over words that allow a user of LDA to say how likely a particular word falls into a topic. Subsequently from the collection of words assigned to a particular topic, are we thus able to gain an insight as to what that topic may actually represent from a lexical point of view.\n",
        "\n",
        "From a standard LDA model, there are really a few key parameters that we have to keep in mind and consider programmatically tuning before we invoke the model:\n",
        "\n",
        "n_components: The number of topics that you specify to the model\n",
        "$\\alpha$ parameter: This is the dirichlet parameter that can be linked to the document topic prior\n",
        "$\\beta$ parameter: This is the dirichlet parameter linked to the topic word prior\n",
        "To invoke the algorithm, we simply create an LDA instance through the Sklearn's LatentDirichletAllocation function. The various parameters would ideally have been obtained through some sort of validation scheme. In this instance, the optimal value of n_components (or topic number) was found by conducting a KMeans + Latent Semantic Analysis Scheme (as shown in this paper here) whereby the number of Kmeans clusters and number of LSA dimensions were iterated through and the best silhouette mean score."
      ],
      "metadata": {
        "id": "Jvmk7udrXLge"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import NMF, LatentDirichletAllocation\n",
        "\n",
        "lda = LatentDirichletAllocation(n_components=11, max_iter=5,\n",
        "                                learning_method = 'online',\n",
        "                                learning_offset = 50.,\n",
        "                                random_state = 0)"
      ],
      "metadata": {
        "id": "L4mST39JXc13"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lda.fit(Tf_idf)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "RwR6nXmrXPVA",
        "outputId": "aa32c869-2a6d-4a81-cba6-f969b5891e35"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LatentDirichletAllocation(learning_method='online', learning_offset=50.0,\n",
              "                          max_iter=5, n_components=11, random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LatentDirichletAllocation(learning_method=&#x27;online&#x27;, learning_offset=50.0,\n",
              "                          max_iter=5, n_components=11, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LatentDirichletAllocation</label><div class=\"sk-toggleable__content\"><pre>LatentDirichletAllocation(learning_method=&#x27;online&#x27;, learning_offset=50.0,\n",
              "                          max_iter=5, n_components=11, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_top_words = 40\n",
        "print(\"\\nTopics in LDA model: \")\n",
        "tf_feature_names = count_vectorizer.get_feature_names_out()\n",
        "print(lda, tf_feature_names, n_top_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tbDsR4LpYLCg",
        "outputId": "f19427c7-ec25-41eb-c011-4c88a73221a7"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Topics in LDA model: \n",
            "LatentDirichletAllocation(learning_method='online', learning_offset=50.0,\n",
            "                          max_iter=5, n_components=11, random_state=0) ['about' 'addiction' 'advice' 'all' 'always' 'and' 'are' 'as' 'at' 'back'\n",
            " 'be' 'because' 'been' 'before' 'being' 'best' 'better' 'but' 'can' 'come'\n",
            " 'depression' 'even' 'feel' 'for' 'friend' 'friends' 'from' 'get' 'girl'\n",
            " 'go' 'going' 'had' 'have' 'he' 'help' 'helped' 'her' 'him' 'his' 'how'\n",
            " 'if' 'in' 'is' 'issues' 'it' 'just' 'know' 'life' 'listen' 'll' 'lot'\n",
            " 'me' 'more' 'much' 'my' 'not' 'of' 'on' 'one' 'open' 'or' 'other' 'out'\n",
            " 'over' 'people' 'problems' 're' 'really' 'same' 'school' 'she' 'shit'\n",
            " 'so' 'some' 'someone' 'talk' 'talked' 'that' 'the' 'their' 'them' 'there'\n",
            " 'they' 'think' 'this' 'through' 'to' 'too' 'try' 'up' 've' 'was' 'what'\n",
            " 'when' 'while' 'who' 'with' 'would' 'years' 'you'] 40\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TDR5yQE8ZRGC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ANN model"
      ],
      "metadata": {
        "id": "GBnwddd3ZSvO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "10vImlSHaQ5f",
        "outputId": "191a8919-7f2a-4cd3-e7a8-6688d1d73bbe"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    response_id  class                                      response_text  \\\n",
              "0    response_1      0              i try and avoid this sort of conflict   \n",
              "1    response_2      1  had a friend open up to me about his mental ad...   \n",
              "2    response_3      1  i saved a girl from suicide once. she was goin...   \n",
              "3    response_4      0  i cant think of one really...i think i may hav...   \n",
              "4    response_5      0  only really one friend who doesn't fit into th...   \n",
              "..          ...    ...                                                ...   \n",
              "75  response_76      0  now that i've been through it, although i'm no...   \n",
              "76  response_77      1  when my best friends mom past away from od'ing...   \n",
              "77  response_78      0  as a camp counselor i provide stability in kid...   \n",
              "78  response_79      1  my now girlfriend used to have serious addicti...   \n",
              "79  response_80      0  the one person i ever talked to it was because...   \n",
              "\n",
              "                                    new_response_text  label  \n",
              "0               i try and avoid this sort of conflict      0  \n",
              "1   had a friend open up to me about his mental ad...      1  \n",
              "2   i saved a girl from suicide once. she was goin...      1  \n",
              "3   i cant think of one really...i think i may hav...      0  \n",
              "4   only really one friend who doesn't fit into th...      0  \n",
              "..                                                ...    ...  \n",
              "75  now that i've been through it, although i'm no...      0  \n",
              "76  when my best friends mom past away from od'ing...      1  \n",
              "77  as a camp counselor i provide stability in kid...      0  \n",
              "78  my now girlfriend used to have serious addicti...      1  \n",
              "79  the one person i ever talked to it was because...      0  \n",
              "\n",
              "[80 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-23516276-72b0-4bec-aaf9-264b07b2c4f0\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>response_id</th>\n",
              "      <th>class</th>\n",
              "      <th>response_text</th>\n",
              "      <th>new_response_text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>response_1</td>\n",
              "      <td>0</td>\n",
              "      <td>i try and avoid this sort of conflict</td>\n",
              "      <td>i try and avoid this sort of conflict</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>response_2</td>\n",
              "      <td>1</td>\n",
              "      <td>had a friend open up to me about his mental ad...</td>\n",
              "      <td>had a friend open up to me about his mental ad...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>response_3</td>\n",
              "      <td>1</td>\n",
              "      <td>i saved a girl from suicide once. she was goin...</td>\n",
              "      <td>i saved a girl from suicide once. she was goin...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>response_4</td>\n",
              "      <td>0</td>\n",
              "      <td>i cant think of one really...i think i may hav...</td>\n",
              "      <td>i cant think of one really...i think i may hav...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>response_5</td>\n",
              "      <td>0</td>\n",
              "      <td>only really one friend who doesn't fit into th...</td>\n",
              "      <td>only really one friend who doesn't fit into th...</td>\n",
              "      <td>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>response_76</td>\n",
              "      <td>0</td>\n",
              "      <td>now that i've been through it, although i'm no...</td>\n",
              "      <td>now that i've been through it, although i'm no...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>response_77</td>\n",
              "      <td>1</td>\n",
              "      <td>when my best friends mom past away from od'ing...</td>\n",
              "      <td>when my best friends mom past away from od'ing...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>response_78</td>\n",
              "      <td>0</td>\n",
              "      <td>as a camp counselor i provide stability in kid...</td>\n",
              "      <td>as a camp counselor i provide stability in kid...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>response_79</td>\n",
              "      <td>1</td>\n",
              "      <td>my now girlfriend used to have serious addicti...</td>\n",
              "      <td>my now girlfriend used to have serious addicti...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>response_80</td>\n",
              "      <td>0</td>\n",
              "      <td>the one person i ever talked to it was because...</td>\n",
              "      <td>the one person i ever talked to it was because...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23516276-72b0-4bec-aaf9-264b07b2c4f0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-23516276-72b0-4bec-aaf9-264b07b2c4f0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-23516276-72b0-4bec-aaf9-264b07b2c4f0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-98388e00-8620-4ea5-887a-1feb5f804526\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-98388e00-8620-4ea5-887a-1feb5f804526')\"\n",
              "            title=\"Suggest charts.\"\n",
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              "\n",
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              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-98388e00-8620-4ea5-887a-1feb5f804526 button');\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.tokenize import TweetTokenizer\n"
      ],
      "metadata": {
        "id": "VGWGHYsRcj2X"
      },
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "contractions_dict = {\n",
        "    \"ain't\": \"am not / are not / is not / has not / have not\",\n",
        "    \"aren't\": \"are not\",\n",
        "    \"can't\": \"cannot\",\n",
        "    \"could've\": \"could have\",\n",
        "    \"couldn't\": \"could not\",\n",
        "    \"didn't\": \"did not\",\n",
        "    \"doesn't\": \"does not\",\n",
        "    \"don't\": \"do not\",\n",
        "    \"hadn't\": \"had not\",\n",
        "    \"hasn't\": \"has not\",\n",
        "    \"haven't\": \"have not\",\n",
        "    \"he'd\": \"he had / he would\",\n",
        "    \"he'll\": \"he shall / he will\",\n",
        "    \"he's\": \"he is\",\n",
        "    \"I'd\": \"I had / I would\",\n",
        "    \"I'll\": \"I shall / I will\",\n",
        "    \"I'm\": \"I am\",\n",
        "    \"I've\": \"I have\",\n",
        "    \"isn't\": \"is not\",\n",
        "    \"it'd\": \"it had / it would\",\n",
        "    \"it'll\": \"it shall / it will\",\n",
        "    \"it's\": \"it is\",\n",
        "    \"let's\": \"let us\",\n",
        "    \"might've\": \"might have\",\n",
        "    \"mightn't\": \"might not\",\n",
        "    \"must've\": \"must have\",\n",
        "    \"mustn't\": \"must not\",\n",
        "    \"shan't\": \"shall not\",\n",
        "    \"she'd\": \"she had / she would\",\n",
        "    \"she'll\": \"she shall / she will\",\n",
        "    \"she's\": \"she is\",\n",
        "    \"should've\": \"should have\",\n",
        "    \"shouldn't\": \"should not\",\n",
        "    \"that's\": \"that is\",\n",
        "    \"there's\": \"there is\",\n",
        "    \"they'd\": \"they had / they would\",\n",
        "    \"they'll\": \"they shall / they will\",\n",
        "    \"they're\": \"they are\",\n",
        "    \"they've\": \"they have\",\n",
        "    \"wasn't\": \"was not\",\n",
        "    \"we'd\": \"we had / we would\",\n",
        "    \"we'll\": \"we shall / we will\",\n",
        "    \"we're\": \"we are\",\n",
        "    \"we've\": \"we have\",\n",
        "    \"weren't\": \"were not\",\n",
        "    \"what'll\": \"what shall / what will\",\n",
        "    \"what're\": \"what are\",\n",
        "    \"what's\": \"what is\",\n",
        "    \"what've\": \"what have\",\n",
        "    \"where's\": \"where is\",\n",
        "    \"who'd\": \"who had / who would\",\n",
        "    \"who'll\": \"who shall / who will\",\n",
        "    \"who're\": \"who are\",\n",
        "    \"who's\": \"who is\",\n",
        "    \"who've\": \"who have\",\n",
        "    \"won't\": \"will not\",\n",
        "    \"would've\": \"would have\",\n",
        "    \"wouldn't\": \"would not\",\n",
        "    \"you'd\": \"you had / you would\",\n",
        "    \"you'll\": \"you shall / you will\",\n",
        "    \"you're\": \"you are\",\n",
        "    \"you've\": \"you have\"\n",
        "}"
      ],
      "metadata": {
        "id": "du-I91S0cVEk"
      },
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def cleaning_with_re(tweet):\n",
        "    tweet = re.sub(r'^RT[\\s]+', '', tweet)\n",
        "\n",
        "    # remove hyperlinks\n",
        "    tweet = re.sub(r'https?://(?:www\\.[^\\s\\n\\r]+|[^\\s\\n\\r]+)', '', tweet)\n",
        "\n",
        "    # remove hashtag (#)\n",
        "    tweet = re.sub(r'#', '', tweet)\n",
        "\n",
        "    #replaces newline (\\n) and carriage return (\\r) characters in a tweet with an empty string\n",
        "    tweet = re.sub(r'[\\n\\r]', '', tweet)\n",
        "\n",
        "    #replaces the numbers with an empty string\n",
        "    tweet = re.sub(r'\\d+', '', tweet)\n",
        "\n",
        "    return tweet\n",
        "\n",
        "#----------------------------------------------------------------\n",
        "def expanding_words(tweet):\n",
        "\n",
        "    words = tweet.split()\n",
        "    expanded_words = []\n",
        "    for word in words:\n",
        "        if word.lower() in contractions_dict:\n",
        "            expanded_words.extend(contractions_dict[word.lower()].split(\"/\"))\n",
        "        else:\n",
        "            expanded_words.append(word)\n",
        "    return \" \".join(expanded_words)\n",
        "\n",
        "#----------------------------------------------------------------\n",
        "def tokenizing(tweet):\n",
        "\n",
        "    stopwords_english = stopwords.words('english')\n",
        "    tokenizer = TweetTokenizer(preserve_case=False, strip_handles=True,reduce_len=True)\n",
        "    new_tweet = tokenizer.tokenize(tweet)\n",
        "\n",
        "    clean_tweet=[]\n",
        "\n",
        "    for word in new_tweet: # Going through every word in tokens list\n",
        "        if (word not in stopwords_english and word not in string.punctuation):  # remove punctuation and stopwords\n",
        "            clean_tweet.append(word)\n",
        "\n",
        "    return clean_tweet\n",
        "\n",
        "#----------------------------------------------------------------\n",
        "def stemming(tweet):\n",
        "    # Instantiate stemming class\n",
        "    stemmer = PorterStemmer()\n",
        "\n",
        "    # Create an empty list to store the stems\n",
        "    stemmed_tweets = []\n",
        "\n",
        "    for word in tweet:\n",
        "        stem_word = stemmer.stem(word)  # stemming word\n",
        "        stemmed_tweets.append(stem_word)  # append to the list\n",
        "\n",
        "    return stemmed_tweets"
      ],
      "metadata": {
        "id": "2EmzkatRb_BX"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocessing(tweet):\n",
        "\n",
        "    tweet=cleaning_with_re(tweet)\n",
        "    tweet=expanding_words(tweet)\n",
        "    tweet=tokenizing(tweet)\n",
        "    tweet=stemming(tweet)\n",
        "\n",
        "    return tweet"
      ],
      "metadata": {
        "id": "zIjYkmknbzHq"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def build_freqs(listoftweets, labels):\n",
        "\n",
        "    labelslist = np.squeeze(labels).tolist()\n",
        "\n",
        "    # Start with an empty dictionary and populate it by looping over all tweets and all the words in them\n",
        "    freqs = {}\n",
        "    for y, tweet in zip(labels, listoftweets):\n",
        "        t=preprocessing(tweet)\n",
        "        for word in t:\n",
        "            pair = (word, y)\n",
        "            if pair in freqs:\n",
        "                freqs[pair] += 1\n",
        "            else:\n",
        "                freqs[pair] = 1\n",
        "\n",
        "    # Calculate the sum of frequencies for each word\n",
        "    word_sums = {}\n",
        "    for (word, y), count in freqs.items():\n",
        "        if word in word_sums:\n",
        "            word_sums[word] += count\n",
        "        else:\n",
        "            word_sums[word] = count\n",
        "\n",
        "    # Divide each key of word and class by the sum\n",
        "    for (word, y), count in freqs.items():\n",
        "        freqs[(word, y)] /= word_sums[word]\n",
        "\n",
        "    return freqs\n",
        "\n",
        "#------------------------------------------------------------------\n",
        "def extract_features(tweet, freqs, preprocessing=preprocessing):\n",
        "\n",
        "    # process_tweet tokenizes, stems, and removes stopwords\n",
        "    word_list = preprocessing(tweet)\n",
        "\n",
        "    # 2 elements for [ positive, negative] counts\n",
        "    features = np.zeros(2)\n",
        "\n",
        "    # loop through each word in the list of words\n",
        "    for word in word_list:\n",
        "\n",
        "        # increment the word count for the positive label 1\n",
        "        features[0] += freqs.get((word, 1.0),0)\n",
        "\n",
        "        # increment the word count for the negative label 0\n",
        "        features[1] += freqs.get((word, 0),0)\n",
        "\n",
        "    features = features[None, :]\n",
        "    assert(features.shape == (1, 2))\n",
        "    return features"
      ],
      "metadata": {
        "id": "T2KKsVfMa_Kp"
      },
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word_frequencies= build_freqs(df['response_text'].tolist(), df['class'].tolist())\n"
      ],
      "metadata": {
        "id": "O5H2PbJgbXwC"
      },
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.zeros((len(df['class']), 2))\n",
        "\n",
        "for i in range(len(df['class'])):\n",
        "    X[i, :]= extract_features(df['new_response_text'][i], word_frequencies)\n",
        "\n",
        "print(X)\n",
        "\n",
        "# training labels corresponding to X\n",
        "Y = np.array(df['class'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4UbJ5WvqaLTL",
        "outputId": "9563969e-8625-4c3d-f5de-b1727e87a972"
      },
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  0.57142857   3.42857143]\n",
            " [  6.79333333   2.20666667]\n",
            " [  8.50323529   2.49676471]\n",
            " [  1.55555556   6.44444444]\n",
            " [  7.50256854  22.49743146]\n",
            " [  6.02233439   6.97766561]\n",
            " [  6.67156863   1.32843137]\n",
            " [ 12.26116259   6.73883741]\n",
            " [  3.63505495   5.36494505]\n",
            " [ 14.53076923   4.46923077]\n",
            " [  5.71465465  21.28534535]\n",
            " [  6.42666667   1.57333333]\n",
            " [  1.51576577   3.48423423]\n",
            " [  0.25         0.75      ]\n",
            " [  0.5          0.5       ]\n",
            " [  7.76666667   1.23333333]\n",
            " [  1.72576577   5.27423423]\n",
            " [  1.22576577   1.77423423]\n",
            " [  2.54166667   4.45833333]\n",
            " [  1.87666667   9.12333333]\n",
            " [  0.45299145   2.54700855]\n",
            " [  3.9          2.1       ]\n",
            " [ 19.35991981  10.64008019]\n",
            " [  4.73052767  11.26947233]\n",
            " [  2.30793226   5.69206774]\n",
            " [ 14.21         2.79      ]\n",
            " [  9.43243243   1.56756757]\n",
            " [  1.36410256   6.63589744]\n",
            " [  3.39243243   1.60756757]\n",
            " [  1.96576577  12.03423423]\n",
            " [  1.625        3.375     ]\n",
            " [  2.68571429   4.31428571]\n",
            " [  1.48076923   7.51923077]\n",
            " [  3.46904762  17.53095238]\n",
            " [  1.77333333   5.22666667]\n",
            " [  7.28095238  10.71904762]\n",
            " [ 29.18859999   8.81140001]\n",
            " [  4.56410256  23.43589744]\n",
            " [ 15.47417846   7.52582154]\n",
            " [  1.5          5.5       ]\n",
            " [  6.99055556   4.00944444]\n",
            " [  5.11007937  11.88992063]\n",
            " [  1.16238095   3.83761905]\n",
            " [  0.66666667   1.33333333]\n",
            " [  1.73243243   7.26756757]\n",
            " [  2.51833333   8.48166667]\n",
            " [  2.26         2.74      ]\n",
            " [  2.0990991    3.9009009 ]\n",
            " [100.55184118  37.44815882]\n",
            " [  2.89333333   4.10666667]\n",
            " [  1.25743243   5.74256757]\n",
            " [  6.29823529   1.70176471]\n",
            " [  3.55100386   5.44899614]\n",
            " [  0.9          2.1       ]\n",
            " [  2.09177309   3.90822691]\n",
            " [  8.42222222  37.57777778]\n",
            " [  3.13142857   5.86857143]\n",
            " [ 53.7309399   19.2690601 ]\n",
            " [  1.41         2.59      ]\n",
            " [ 18.15214286   5.84785714]\n",
            " [  1.36857143   6.63142857]\n",
            " [  7.39333333   1.60666667]\n",
            " [  1.01555556   1.98444444]\n",
            " [  4.55613046  10.44386954]\n",
            " [  7.753861     4.246139  ]\n",
            " [  1.29243243   1.70756757]\n",
            " [  2.26699251   8.73300749]\n",
            " [  1.43243243   7.56756757]\n",
            " [  2.296535    10.703465  ]\n",
            " [  2.18632479   5.81367521]\n",
            " [  0.25         0.75      ]\n",
            " [  1.67142857   3.32857143]\n",
            " [  6.78567119   2.21432881]\n",
            " [  3.11727717   7.88272283]\n",
            " [ 12.46338481   3.53661519]\n",
            " [  8.75133545  22.24866455]\n",
            " [  4.50761905   2.49238095]\n",
            " [  1.53333333   8.46666667]\n",
            " [ 22.8422737    8.1577263 ]\n",
            " [  4.83717566   8.16282434]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Model\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Dropout\n",
        "from keras.optimizers import Adam\n",
        "\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "from sklearn.metrics import roc_curve, roc_auc_score, confusion_matrix\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(4, input_dim=2, activation='relu'))  # Hidden layer with 4 neurons\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(1, activation='sigmoid'))  # Output layer with logistic regression\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.001), metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "model.fit(X, Y, epochs=60, batch_size=32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uLlRqzgTZVGy",
        "outputId": "53902866-b782-4190-c3e1-6e72b04512e4"
      },
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/optimizers/legacy/adam.py:117: UserWarning:\n",
            "\n",
            "The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/60\n",
            "3/3 [==============================] - 3s 7ms/step - loss: 2.3036 - accuracy: 0.6750\n",
            "Epoch 2/60\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 2.3615 - accuracy: 0.6375\n",
            "Epoch 3/60\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 3.1344 - accuracy: 0.6125\n",
            "Epoch 4/60\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 3.1098 - accuracy: 0.6250\n",
            "Epoch 5/60\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 2.9902 - accuracy: 0.6625\n",
            "Epoch 6/60\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 2.6319 - accuracy: 0.7000\n",
            "Epoch 7/60\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 2.7480 - accuracy: 0.7125\n",
            "Epoch 8/60\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 2.8455 - accuracy: 0.6875\n",
            "Epoch 9/60\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 2.7243 - accuracy: 0.7000\n",
            "Epoch 10/60\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 2.3476 - accuracy: 0.7000\n",
            "Epoch 11/60\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 2.2536 - accuracy: 0.6875\n",
            "Epoch 12/60\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 2.5618 - accuracy: 0.6625\n",
            "Epoch 13/60\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 2.4791 - accuracy: 0.6750\n",
            "Epoch 14/60\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 2.5447 - accuracy: 0.6500\n",
            "Epoch 15/60\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 2.4587 - accuracy: 0.6875\n",
            "Epoch 16/60\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 2.1459 - accuracy: 0.6875\n",
            "Epoch 17/60\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 2.4623 - accuracy: 0.6750\n",
            "Epoch 18/60\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 2.4762 - accuracy: 0.6375\n",
            "Epoch 19/60\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 2.2785 - accuracy: 0.6625\n",
            "Epoch 20/60\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 2.3361 - accuracy: 0.7000\n",
            "Epoch 21/60\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 2.2296 - accuracy: 0.6250\n",
            "Epoch 22/60\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 1.6280 - accuracy: 0.6375\n",
            "Epoch 23/60\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 2.0719 - accuracy: 0.6750\n",
            "Epoch 24/60\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 2.1879 - accuracy: 0.6875\n",
            "Epoch 25/60\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 2.2614 - accuracy: 0.6625\n",
            "Epoch 26/60\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 2.0585 - accuracy: 0.7250\n",
            "Epoch 27/60\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 2.2486 - accuracy: 0.6500\n",
            "Epoch 28/60\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 2.1130 - accuracy: 0.6250\n",
            "Epoch 29/60\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 1.9448 - accuracy: 0.6625\n",
            "Epoch 30/60\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 1.7796 - accuracy: 0.7125\n",
            "Epoch 31/60\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.5931 - accuracy: 0.6375\n",
            "Epoch 32/60\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 1.8775 - accuracy: 0.6750\n",
            "Epoch 33/60\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 2.1076 - accuracy: 0.6625\n",
            "Epoch 34/60\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 1.8671 - accuracy: 0.6375\n",
            "Epoch 35/60\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 1.3661 - accuracy: 0.6750\n",
            "Epoch 36/60\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 1.9582 - accuracy: 0.6625\n",
            "Epoch 37/60\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 1.8113 - accuracy: 0.6875\n",
            "Epoch 38/60\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 1.3593 - accuracy: 0.6875\n",
            "Epoch 39/60\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 1.6558 - accuracy: 0.6750\n",
            "Epoch 40/60\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 1.7670 - accuracy: 0.6500\n",
            "Epoch 41/60\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.7522 - accuracy: 0.6375\n",
            "Epoch 42/60\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 1.7273 - accuracy: 0.6625\n",
            "Epoch 43/60\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 1.6721 - accuracy: 0.6625\n",
            "Epoch 44/60\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 1.5178 - accuracy: 0.6875\n",
            "Epoch 45/60\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 1.0275 - accuracy: 0.6750\n",
            "Epoch 46/60\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 1.5745 - accuracy: 0.6375\n",
            "Epoch 47/60\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 1.5944 - accuracy: 0.6625\n",
            "Epoch 48/60\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.5280 - accuracy: 0.6250\n",
            "Epoch 49/60\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.5690 - accuracy: 0.7000\n",
            "Epoch 50/60\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.5029 - accuracy: 0.6625\n",
            "Epoch 51/60\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 1.3953 - accuracy: 0.6875\n",
            "Epoch 52/60\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 1.3187 - accuracy: 0.6750\n",
            "Epoch 53/60\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 1.0883 - accuracy: 0.6875\n",
            "Epoch 54/60\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.0722 - accuracy: 0.7125\n",
            "Epoch 55/60\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 1.3961 - accuracy: 0.6125\n",
            "Epoch 56/60\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 1.3510 - accuracy: 0.6625\n",
            "Epoch 57/60\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 1.4158 - accuracy: 0.6125\n",
            "Epoch 58/60\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 1.3443 - accuracy: 0.6500\n",
            "Epoch 59/60\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.1551 - accuracy: 0.6750\n",
            "Epoch 60/60\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 1.3166 - accuracy: 0.6500\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7bef293034f0>"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = np.zeros((len(df['class']), 2))\n",
        "for i in range(len(df['class'])):\n",
        "    X_test[i, :]= extract_features(df['response_text'][i], word_frequencies)\n",
        "print(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cpqfZLeEeCcq",
        "outputId": "910e0ab0-1cea-4533-a098-c977f27502d3"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  0.57142857   3.42857143]\n",
            " [  6.79333333   2.20666667]\n",
            " [  8.50323529   2.49676471]\n",
            " [  1.55555556   6.44444444]\n",
            " [  7.50256854  22.49743146]\n",
            " [  6.02233439   6.97766561]\n",
            " [  6.67156863   1.32843137]\n",
            " [ 12.26116259   6.73883741]\n",
            " [  3.63505495   5.36494505]\n",
            " [ 14.53076923   4.46923077]\n",
            " [  5.71465465  21.28534535]\n",
            " [  6.42666667   1.57333333]\n",
            " [  1.51576577   3.48423423]\n",
            " [  0.25         0.75      ]\n",
            " [  0.5          0.5       ]\n",
            " [  7.76666667   1.23333333]\n",
            " [  1.72576577   5.27423423]\n",
            " [  1.22576577   1.77423423]\n",
            " [  2.54166667   4.45833333]\n",
            " [  1.87666667   9.12333333]\n",
            " [  0.45299145   2.54700855]\n",
            " [  3.9          2.1       ]\n",
            " [ 19.35991981  10.64008019]\n",
            " [  4.73052767  11.26947233]\n",
            " [  2.30793226   5.69206774]\n",
            " [ 14.21         2.79      ]\n",
            " [  9.43243243   1.56756757]\n",
            " [  1.36410256   6.63589744]\n",
            " [  3.39243243   1.60756757]\n",
            " [  1.96576577  12.03423423]\n",
            " [  1.625        3.375     ]\n",
            " [  2.68571429   4.31428571]\n",
            " [  1.48076923   7.51923077]\n",
            " [  3.46904762  17.53095238]\n",
            " [  1.77333333   5.22666667]\n",
            " [  7.28095238  10.71904762]\n",
            " [ 29.18859999   8.81140001]\n",
            " [  4.56410256  23.43589744]\n",
            " [ 15.47417846   7.52582154]\n",
            " [  1.5          5.5       ]\n",
            " [  6.99055556   4.00944444]\n",
            " [  5.11007937  11.88992063]\n",
            " [  1.16238095   3.83761905]\n",
            " [  0.66666667   1.33333333]\n",
            " [  1.73243243   7.26756757]\n",
            " [  2.51833333   8.48166667]\n",
            " [  2.26         2.74      ]\n",
            " [  2.0990991    3.9009009 ]\n",
            " [100.55184118  37.44815882]\n",
            " [  2.89333333   4.10666667]\n",
            " [  1.25743243   5.74256757]\n",
            " [  6.29823529   1.70176471]\n",
            " [  3.55100386   5.44899614]\n",
            " [  0.9          2.1       ]\n",
            " [  2.09177309   3.90822691]\n",
            " [  8.42222222  37.57777778]\n",
            " [  3.13142857   5.86857143]\n",
            " [ 53.7309399   19.2690601 ]\n",
            " [  1.41         2.59      ]\n",
            " [ 18.15214286   5.84785714]\n",
            " [  1.36857143   6.63142857]\n",
            " [  7.39333333   1.60666667]\n",
            " [  1.01555556   1.98444444]\n",
            " [  4.55613046  10.44386954]\n",
            " [  7.753861     4.246139  ]\n",
            " [  1.29243243   1.70756757]\n",
            " [  2.26699251   8.73300749]\n",
            " [  1.43243243   7.56756757]\n",
            " [  2.296535    10.703465  ]\n",
            " [  2.18632479   5.81367521]\n",
            " [  0.25         0.75      ]\n",
            " [  1.67142857   3.32857143]\n",
            " [  6.78567119   2.21432881]\n",
            " [  3.11727717   7.88272283]\n",
            " [ 12.46338481   3.53661519]\n",
            " [  8.75133545  22.24866455]\n",
            " [  4.50761905   2.49238095]\n",
            " [  1.53333333   8.46666667]\n",
            " [ 22.8422737    8.1577263 ]\n",
            " [  4.83717566   8.16282434]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prob = model.predict(X_test)\n",
        "y_pred = np.round(y_prob).flatten()\n",
        "Y_test = df['class'].tolist()\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "accuracy = accuracy_score(Y_test, y_pred)\n",
        "precision = precision_score(Y_test, y_pred)\n",
        "recall = recall_score(Y_test, y_pred)\n",
        "f1 = f1_score(Y_test, y_pred)\n",
        "\n",
        "print(f\"Test set accuracy = {accuracy}\")\n",
        "print(f\"Test set precision = {precision}\")\n",
        "print(f\"Test set recall = {recall}\")\n",
        "print(f\"Test set F1 score = {f1}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r45XE-ctdI3V",
        "outputId": "9fbc34eb-a5d5-4e4f-e126-ab4f72b9fe1b"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 11ms/step\n",
            "Test set accuracy = 0.65\n",
            "Test set precision = 0.0\n",
            "Test set recall = 0.0\n",
            "Test set F1 score = 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate the confusion matrix\n",
        "cm = confusion_matrix(Y_test, y_pred)\n",
        "\n",
        "# Normalize the confusion matrix\n",
        "cm_norm = cm / np.sum(cm)\n",
        "\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "# Create the heatmap\n",
        "plt.imshow(cm_norm, cmap=plt.cm.Blues)\n",
        "plt.xticks([0, 1], ['Negative', 'Positive'])\n",
        "plt.yticks([0, 1], ['Negative', 'Positive'])\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.colorbar()\n",
        "\n",
        "# Display the percentages in the plot\n",
        "for i in range(cm_norm.shape[0]):\n",
        "    for j in range(cm_norm.shape[1]):\n",
        "        plt.text(j, i, '{:.1%}'.format(cm_norm[i, j]), ha='center', va='center')\n",
        "\n",
        "# Remove the grid lines\n",
        "plt.grid(False)\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "t3a8HB3WfBWF",
        "outputId": "9f054eb7-6148-4d23-879f-da35763debfb"
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the false positive rate and true positive rate\n",
        "fpr, tpr, thresholds = roc_curve(Y_test, y_prob)\n",
        "\n",
        "# Compute the AUC score\n",
        "auc_score = roc_auc_score(Y_test, y_prob)\n",
        "\n",
        "# Plot the ROC curve\n",
        "plt.plot(fpr, tpr, label='ROC Curve (AUC = {:.2f})'.format(auc_score))\n",
        "plt.plot([0, 1], [0, 1], 'k--', label='Random Guess')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "oYw4j2PPdlAT",
        "outputId": "43c54320-d700-428a-a3bd-c7a2ed97fd5d"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}