Compare_Knn_DTree_LRegresion_NBayes_MLP.ipynb

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    "# Author : Amir Shokri\n",
    "# github link : https://github.com/amirshnll/COVID-19-Surveillance\n",
    "# dataset link : http://archive.ics.uci.edu/ml/datasets/COVID-19+Surveillance\n",
    "# email : amirsh.nll@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <p style=color:blue>Compare different models for predicting whether a couple will get divorced </p>\n",
    "<b>-Decision Tree<br>-Logistic Regression<br>-Naive Bayes<br>-KNN<br>-MLP "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Dataset\n",
    "The Dataset is from UCIMachinelearning and it provides you all the relevant information needed for the prediction of Divorce. It contains 54 features and on the basis of these features we have to predict that the couple has been divorced or not. Value 1 represent Divorced and value 0 represent not divorced. Features are as follows:\n",
    "1. If one of us apologizes when our discussion deteriorates, the discussion ends.\n",
    "2. I know we can ignore our differences, even if things get hard sometimes.\n",
    "3. When we need it, we can take our discussions with my spouse from the beginning and correct it.\n",
    "4. When I discuss with my spouse, to contact him will eventually work.\n",
    "5. The time I spent with my wife is special for us.\n",
    "6. We don't have time at home as partners.\n",
    "7. We are like two strangers who share the same environment at home rather than family.\n",
    "8. I enjoy our holidays with my wife.\n",
    "9. I enjoy traveling with my wife.\n",
    "10. Most of our goals are common to my spouse.\n",
    "11. I think that one day in the future, when I look back, I see that my spouse and I have been in harmony with each other.\n",
    "12. My spouse and I have similar values in terms of personal freedom.\n",
    "13. My spouse and I have similar sense of entertainment.\n",
    "14. Most of our goals for people (children, friends, etc.) are the same.\n",
    "15. Our dreams with my spouse are similar and harmonious.\n",
    "16. We're compatible with my spouse about what love should be.\n",
    "17. We share the same views about being happy in our life with my spouse\n",
    "18. My spouse and I have similar ideas about how marriage should be\n",
    "19. My spouse and I have similar ideas about how roles should be in marriage\n",
    "20. My spouse and I have similar values in trust.\n",
    "21. I know exactly what my wife likes.\n",
    "22. I know how my spouse wants to be taken care of when she/he sick.\n",
    "23. I know my spouse's favorite food.\n",
    "24. I can tell you what kind of stress my spouse is facing in her/his life.\n",
    "25. I have knowledge of my spouse's inner world.\n",
    "26. I know my spouse's basic anxieties.\n",
    "27. I know what my spouse's current sources of stress are.\n",
    "28. I know my spouse's hopes and wishes.\n",
    "29. I know my spouse very well.\n",
    "30. I know my spouse's friends and their social relationships.\n",
    "31. I feel aggressive when I argue with my spouse.\n",
    "32. When discussing with my spouse, I usually use expressions such as ‘you always’ or ‘you never’ .\n",
    "33. I can use negative statements about my spouse's personality during our discussions.\n",
    "34. I can use offensive expressions during our discussions.\n",
    "35. I can insult my spouse during our discussions.\n",
    "36. I can be humiliating when we discussions.\n",
    "37. My discussion with my spouse is not calm.\n",
    "38. I hate my spouse's way of open a subject.\n",
    "39. Our discussions often occur suddenly.\n",
    "40. We're just starting a discussion before I know what's going on.\n",
    "41. When I talk to my spouse about something, my calm suddenly breaks.\n",
    "42. When I argue with my spouse, ı only go out and I don't say a word.\n",
    "43. I mostly stay silent to calm the environment a little bit.\n",
    "44. Sometimes I think it's good for me to leave home for a while.\n",
    "45. I'd rather stay silent than discuss with my spouse.\n",
    "46. Even if I'm right in the discussion, I stay silent to hurt my spouse.\n",
    "47. When I discuss with my spouse, I stay silent because I am afraid of not being able to control my anger.\n",
    "48. I feel right in our discussions.\n",
    "49. I have nothing to do with what I've been accused of.\n",
    "50. I'm not actually the one who's guilty about what I'm accused of.\n",
    "51. I'm not the one who's wrong about problems at home.\n",
    "52. I wouldn't hesitate to tell my spouse about her/his inadequacy.\n",
    "53. When I discuss, I remind my spouse of her/his inadequacy.\n",
    "54. I'm not afraid to tell my spouse about her/his incompetence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally, logistic Machine Learning in Python has a straightforward and user-friendly implementation. It usually consists of these steps:<br>\n",
    "1. Import packages, functions, and classes<br>\n",
    "2. Get data to work with and, if appropriate, transform it<br>\n",
    "3. Create a classification model and train (or fit) it with existing data<br>\n",
    "4. Evaluate your model to see if its performance is satisfactory<br>\n",
    "5. Apply your model to make predictions<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages, functions, and classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix as cm\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import accuracy_score \n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get data to work with and, if appropriate, transform it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "   Atr1  Atr2  Atr3  Atr4  Atr5  Atr6  Atr7  Atr8  Atr9  Atr10  ...  Atr46  \\\n",
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df = pd.read_csv('divorce.csv',sep=';')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 170 entries, 0 to 169\n",
      "Data columns (total 55 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
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      " 1   Atr2    170 non-null    int64\n",
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      " 54  Class   170 non-null    int64\n",
      "dtypes: int64(55)\n",
      "memory usage: 73.2 KB\n"
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    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.Class\n",
    "x_data=df.drop(columns=['Class'])\n",
    "# print(x_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of no divorce is 50.588235294117645\n",
      "percentage of divorce 49.411764705882355\n"
     ]
    }
   ],
   "source": [
    "sns.countplot(x='Class',data=df,palette='hls')\n",
    "plt.show()\n",
    "\n",
    "count_no_sub = len(df[df['Class']==0])\n",
    "count_sub = len(df[df['Class']==1])\n",
    "pct_of_no_sub = count_no_sub/(count_no_sub+count_sub)\n",
    "print(\"percentage of no divorce is\", pct_of_no_sub*100)\n",
    "pct_of_sub = count_sub/(count_no_sub+count_sub)\n",
    "print(\"percentage of divorce\", pct_of_sub*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Atr1</th>\n",
       "      <th>Atr2</th>\n",
       "      <th>Atr3</th>\n",
       "      <th>Atr4</th>\n",
       "      <th>Atr5</th>\n",
       "      <th>Atr6</th>\n",
       "      <th>Atr7</th>\n",
       "      <th>Atr8</th>\n",
       "      <th>Atr9</th>\n",
       "      <th>Atr10</th>\n",
       "      <th>...</th>\n",
       "      <th>Atr45</th>\n",
       "      <th>Atr46</th>\n",
       "      <th>Atr47</th>\n",
       "      <th>Atr48</th>\n",
       "      <th>Atr49</th>\n",
       "      <th>Atr50</th>\n",
       "      <th>Atr51</th>\n",
       "      <th>Atr52</th>\n",
       "      <th>Atr53</th>\n",
       "      <th>Atr54</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>...</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Atr1  Atr2  Atr3  Atr4  Atr5  Atr6  Atr7  Atr8  Atr9  Atr10  ...  Atr45  \\\n",
       "0  0.50   0.5  1.00  0.25  0.00  0.00  0.00  0.00  0.00   0.00  ...   0.75   \n",
       "1  1.00   1.0  1.00  1.00  1.00  0.00  0.00  1.00  1.00   1.00  ...   0.50   \n",
       "2  0.50   0.5  0.50  0.50  0.25  0.75  0.50  0.25  0.25   0.50  ...   0.50   \n",
       "3  0.75   0.5  0.75  0.50  0.75  0.75  0.75  0.75  0.75   0.75  ...   0.75   \n",
       "4  0.50   0.5  0.25  0.25  0.25  0.25  0.00  0.00  0.00   0.00  ...   0.50   \n",
       "\n",
       "   Atr46  Atr47  Atr48  Atr49  Atr50  Atr51  Atr52  Atr53  Atr54  \n",
       "0   0.50   0.25   0.75   0.75   0.75   0.50   0.75   0.50   0.25  \n",
       "1   0.50   0.50   0.75   1.00   1.00   1.00   1.00   0.50   0.50  \n",
       "2   0.75   0.50   0.75   0.25   0.25   0.25   0.50   0.50   0.50  \n",
       "3   0.50   0.50   0.75   0.75   0.75   0.75   0.50   0.50   0.50  \n",
       "4   0.50   0.25   0.50   0.75   0.50   0.50   0.50   0.25   0.00  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = (x_data - np.min(x_data)) / (np.max(x_data) - np.min(x_data)).values\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(df.corr(), cmap='viridis');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split dataset to data train & data test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train:  (102, 54)\n",
      "x_test:  (68, 54)\n",
      "y_train:  (102,)\n",
      "y_test:  (68,)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.4,random_state=400)\n",
    "print(\"x_train: \",x_train.shape)\n",
    "print(\"x_test: \",x_test.shape)\n",
    "print(\"y_train: \",y_train.shape)\n",
    "print(\"y_test: \",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train & Score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1. Import the model you want to use<br>\n",
    "Step 2. Make an instance of the Model<br>\n",
    "Step 3. Training the model on the data, storing the information learned from the data<br>\n",
    "Step 4. Predict labels for new data <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.94      0.95        33\n",
      "           1       0.94      0.97      0.96        35\n",
      "\n",
      "    accuracy                           0.96        68\n",
      "   macro avg       0.96      0.96      0.96        68\n",
      "weighted avg       0.96      0.96      0.96        68\n",
      "\n",
      "Accuracy of Decision Tree classifier on test set: 0.96\n"
     ]
    },
    {
     "data": {
      "image/png": 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9r1evXk3VqlWpVKnSJe1P5GwqeERECmjDhg18/vnnpKenExISQmhoKEFBQUDWZZjIyEh8Ph/r16/nz3/+8yXvb+TIkWRkZLB8+XIWLVpEt27dzlvmgQceYOLEiXz77beYGUePHuXDDz8kJSUlz7znGjZsWLYnqs5tuTl58iRpaWlkZmZy6tQp0tLSOHHiRLZlZs2aRcuWLc+7nNa3b1/eeecd1q1bx6FDh3j22Wfp379/wb9RInlQwSMiUkDp6ekMHTqUmJgYYmNj2bdvH2PGjAHg5ZdfZu7cuURERPDAAw/Qo0ePS9pXbGwsFStWpFq1avTu3ZuJEyfSoEGD85Zr1qwZkydP5pFHHqFixYpcccUV/ied8spbWGbNmkVoaCi/+c1vWL58OaGhoTzwwAPZlpk5cyb9+vU7b9327dvzhz/8gV/+8pfEx8cTHx/P6NGjCzWfiObSEhE5R0mZSysxMZE+ffqwa9cur6OUSJpLSwpCPTwiIiIS8FTwiIiISMDTJS0RkXOUlEtakjdd0pKCUA+PiIiIBDwVPCIiRejMfFglRWJiIuXKlcPn87F06VKv4+Rp48aN+Hw+goKCmDJlitdxpJRTwSMiUsZUq1aN1NRU/2zlAG+++Sa1a9cmMjKSZs2a8dVXX2Vb59NPP6VJkyaEh4dTs2bNHKe3yImZMWLECKpXr05UVBStW7dm7dq15y23adMmQkJC6NOnj/+9evXqkZqayi233HKRRyryXyp4RETKuG+//ZahQ4cyf/58jhw5wsCBA7nnnns4efIkAOvWraNXr14899xzHDlyhFWrVtG0adN8bftvf/sbU6dOZfny5Rw8eJAWLVrw61//+rzlHn74Ya6//vpCPS6Rs6ngERG5gLFjx9K1a9ds7z322GMMGTIEgGnTpnHVVVcRERFBnTp1mDRpUq7bcs6xefNm/+v+/fszYsQI/+tFixbRuHFjoqOjadmyJUlJSYV8NOfbvn07CQkJNG3aFOccffv2Zf/+/ezbtw+AZ599lkGDBtGhQwfKly9PpUqVcp189Fzbtm3j5ptvpk6dOgQFBdGnTx/WrVuXbZm//OUvREdHc9tttxX6sYmcoYJHROQCevbsyeLFi/2TWp48eZJ58+bRq1cvAKpUqcKiRYtITk5m2rRpPPHEE6xcubLA+1m5ciX3338/kyZN4sCBAwwaNIg777yT9PT0HJdv2LBhtslCz24PPfRQvvfboUMHTp48ybfffsvJkyeZOnUqjRs3JjY2FoAVK1YAcO211xIXF0efPn04ePBgvrZ93333sXnzZjZu3MiJEyeYMWNGtktpycnJPP3007zyyiv5zityMcp7HUBEpKSLj4+nSZMmLFy4kL59+/L5558TFhZG8+bNAejYsaN/2VatWtG2bVuWL19OkyZNCrSfyZMnM2jQIG688UYA+vXrx5gxY1ixYgWtWrU6b/nC6v2JiIigS5cu3HzzzZgZ0dHRLFmyxD+j+a5du5g1axYff/wx1apVo1+/fjz66KPMmTPngtuOi4vjlltuoX79+gQFBVGzZk0+//xz/+dPPfUUAwcOpGbNmoVyLCK5UQ+PiEg+9OrVi3fffReAuXPn+nt3AJYsWULz5s257LLLiI6OZvHixezfv7/A+9ixYwevvPJKtp6anTt3snv37kI7jpxMmTKFqVOnsnbtWjIyMpg9ezadOnXy7zc0NJQBAwZQr149fD4fw4YNY/Hixfna9ujRo/nnP//Jzp07SUtLY+TIkbRp04Zjx46xatUqPv30U5544omiPDwRQAWPiEi+dOvWjcTERHbt2sX777/vL3jS09Pp0qULv/vd79i7dy+HDx/mjjvuILeBC8PCwjh27Jj/9Z49e/xf16xZk+HDh3P48GF/O3bsGD179sxxWwkJCfh8vhzb4MGD831sq1evpnPnztSrV49y5crRvn174uLi+Prrr4GsS2dnensKavXq1fTo0YMaNWpQvnx5+vfvz6FDh1i3bh2JiYls376dyy+/nNjYWF5++WUWLFhQ4J4xkfxQwSMikg+VK1emdevWDBgwgNq1a3PVVVcBkJGRQXp6OpUrV6Z8+fIsWbKEjz/+ONftNG7cmLlz53Ly5EmWLl3KF1984f/sgQceYOLEiXz77beYGUePHuXDDz8kJSUlx22tXbuW1NTUHNvEiRPzfWzXX389H374IVu3bsXM+OSTT9i4cSPXXHMNAAMGDGDatGls3bqVY8eO8cILL9CpUyf/+rVq1fLPzJ7Ttv/2t7+xd+9eTp06xaxZszhx4gRXXHEFDz74IFu2bGHVqlWsWrWKwYMH07FjRz766KN8ZxfJL93DIyKST7169aJv3768+OKL/vciIiIYN24c3bt3Jz09nc6dO3PnnXfmuo033niDfv36MWHCBO6++27uvvtu/2fNmjVj8uTJPPLII2zatInQ0FBuvvlmbr311qI8LPr27cuWLVto3bo1hw4dokaNGkyaNIkGDRoAcP/997Njxw7/vUXt27dn3LhxQFbBd+DAAf/9TOd68skn2bdvH40bN+bo0aNcccUVLFiwgOjoaCCrx+sMn89HSEgIlStXLsKjlbJKc2mJiJwjkOfS+vLLL2nXrh3BwcH89a9/pV27dpe0va+++ooJEyb4728qTJs2beL6668nIyODt956i/79+2f7XHNpSUGo4BEROUcgFzyBRAWPFITu4REREZGAp4JHREREAp4KHhEREQl4KnhEREQk4KngERERkYCncXhERM4REhKy1zlX1esckreQkJC9XmeQ0kOPpYuIeMw5FwGsBh4zsw+8zlMQzjkfsAr4nZkt9DaNSO5U8IiIeMw59zZQ3szu9zrLxXDO3QTMBxqZ2T6v84jkRAWPiIiHnHN3AG8BDc0s2es8F8s59wJwJdBFozZKSaSblkVEPOKcqwRMBgaU5mLntKfJKnh+7XUQkZyoh0dExCPOub8Ae8zsca+zFAbn3HXAx0ATM9vpdR6Rs6mHR0TEA865HkBj4I8eRyk0ZvYj8AYw1Tmn3y9SouiEFBEpZs65OGAc0NfMjnudp5CNBSKB33gdRORsuqQlIlKMnHMOWASsNLOnvM5TFJxz9YF/AC3MbJPXeURAPTwiIsVtIBAHPON1kKJiZhuAPwEznXMa4FZKBPXwiIgUE+dcbeCfQGszW+N1nqJ0+h6eT4FPzOx5r/OIqOARESkGpwuAZcAiM3vJ6zzFwTkXD3wP3G5mq73OI2WbLmmJiBSPx8n6mfuqxzmKjZntAH5P1qWtYK/zSNmmHh4RkSLmnLsa+BK40cy2eJ2nOJ2+SXshsNbMhnkcR8owFTwiIkXIOfcL4BtgiplN9DqPF07PPL8auMfMvvE6j5RNuqQlIlK0hgP7gUleB/GKme0FHibr0la413mkbFIPj4hIEXHONQMWA9eZ2c9e5/Gac242cNjMHvE6i5Q96uERESkCzrkQYCbwuIodv0eBu5xzt3sdRMoe9fCIiBQB59wrwOVAd9MPWj/nXFtgCtDQzA57HEfKEBU8IiKFzDnXCniXrF/q+73OU9I4594Cws2sn9dZpOzQJS0RkULknIsApgODVOzk6vfATc65e7wOImWHenhERAqRc24yUM7MBnqdpSRzzt0ELCCrF2yf13kk8KngEREpJM65jsAEsn6JJ3udp6Rzzo0F6gP36j4nKWq6pCUiUgicc5WAt4EBKnbybSRQF/i110Ek8KmHR0SkEDjn/gLsMbPHvc5SmjjnGgMfA03NbKfHcSSAqYdHROQSOefuAxoDf/Q4SqljZquAN4Bpp2eUFykSOrlERC6Bc64aWb+w+5rZca/zlFIvAD7gIa+DSODSJS0RkYt0eibwD4Hvzexpr/OUZs65+sA/gJZmttHrPBJ41MMjInLx/geIBZ71OkhpZ2YbgNHADOdcea/zSOBRD4+IyEVwztUG/gm0NrM1XucJBKfv4fkE+NTMnvc6jwQWFTwiIgXknAsClgEfmNlLXucJJM65y4EfgNvNbLXXeSRw6JKWiEjBPQ444FWPcwQcM/uJrKknZjnngr3OI4FDPTwiIgXgnEsAvgBuMLOtXucJRKdvBl8IrDMzPeovhUIFj4hIPjnnfgGsAN42s0le5wlkzrmqwGqypp342us8UvrpkpaISP4NB/aRNYWEFCEz20vWuDwznHPhXueR0k89PCIi+eCcawYsBq4zs5+9zlNWOOdmAUfM7BGvs0jpph4eEZELcM6FArOAx1XsFLtHgbucc7/yOoiUburhERG5AOfcq0ANoIfph2axc861BaYADc3ssMdxpJRSwSMikgfnXCvgXbJ+2e73Ok9Z5ZybAESYWV+vs0jppEtaIiK5cM5FANOBB1XseO4PQAvn3D1eB5HSST08IiK5cM5NBsqZ2UCvswg451oCC4BGZrbP6zxSuqjgERHJgXOuIzCBrEtZyV7nkSzOubFAA+Ae3U8lBaFLWiIi53DOVSJrrJ3+KnZKnJFAHUD38kiBqIdHROQczrm/ArvN7Amvs8j5nHONyZpVvenpubdELkg9PCIiZ3HO3Qc0BIZ5nUVyZmargNeAqc45/R6TfNGJIiJymnOuGvAG0NfMjnudR/L0IhBO1vQTIhekS1oiIvhn6P4Q+N7MnvY6j1yYc64e8DXQ0sw2ep1HSjb18IiIZHkAqAo863UQyZ/TRc5oYKZzrrzXeaRkUw+PiJR5zrk6wHdAKzNb63Ueyb/T9/B8AnxmZmO8ziMllwoeESnTnHNBwDLg/5nZy17nkYJzzl0O/ADcbmarvc4jJZMuaYlIWff46f++5mUIuXinH03/HTDLORfsdR4pmdTDIyJllnMuAfgCuMHMtnqdRy7e6ZvO3wf+ZWZ/9DqPlDwqeESkTHLO/QJYAbxtZpO8ziOXzjlXBUgC7jWzr73OIyWLLmmJSFk1AthH1hQSEgBOTyj6EDDDORfudR4pWdTDIyJljnPuerLG3GlsZru9ziOFyzk3E0gxs4e9ziIlh3p4RKRMcc6FAjOBx1TsBKwhQGfn3K+8DiIlh3p4RKRMcc69CtQAeph+AAas08XOVKChmR3yOo94TwWPiJQZzrnWwFyyfgnu9zaNFDXn3AQg0sx+7XUW8Z4uaYlImeCciwSmAQ+q2Ckz/gA0d87d63UQ8Z56eESkTHDOTQEws//xOosUH+dcS+A9oJGZ7fU6j3hHBY+IBDznXEdgPFm/9JK9ziPFyzn3PHAVcI/u2yq7dElLRAKSc+5t51wF51wlssbaGaBip8waBdQG+nqcQzykHh4RCTinHz0/BIQD7wI/m9kT3qYSLznnGgGfAk1Pz70lZYx6eEQkENUBtgPdgWuBYadnRZcy6vQs6q8B05xz+t1XBukfXUQC0RXALuAN4FHgJWDj6Qkmpex6EQgDNAJzGaSCR0QCUV2gHlkTSf4FOEnWjOi6hl+GmVkmWffxjHTO1fM6jxQvFTwiEog6AjWBZKCFmT1mZgc8ziQlgJltIusm5pnOufIex5FipJuWRSTgOOceAvab2Tyvs0jJc/oeno+Bz81sjNd5pHio4BERkTLHOVcTWAn8ysxWOeeqAulmdtjbZFJUdElLRETKHDPbCfwOmOWcCwYeIusGdwlQ6uERKSVCQ0P3pKWlVfU6h5wvJCRk7/Hjx2O9ziEFc/qpvfeADcAm4FYz6+dtKikqumFLpJRIS0urqj9QSqbTl0OkFHHO3QhcCfwG+BF4lqyn+yRAqYdHpJRwzump6hLKOYeZaYyfUuT0Y+nTgVDgfWAAEGpm6qkLUCp4REoJFTwllwqe0un0Ja0uwAtACFAN8JnZUU+DSZHQTcsiIlImWZb5wNXAeCCTrPGbJACp4BEJYK1btyYkJIRbb721ULY3cuRIwsPDcc6RmZlZKNsU8ZqZpZvZ80AFM1vvdR4pGip4REqp1NRUatWqxdy5c/3vpaSkcPnllzN//nz/e+PHj+fLL78EID09nYEDBxIfH09ERATXXXcdS5Ys8S+7fft2nHP4fD5/e+aZZ/yfjx49mrVr1xbD0eVu1apVNG3alLCwMJo2bcqqVatyXfYPf/gDNWvWJDIykvj4eJ577jn/Z8uXL892nD6fD+ccCxYsAGD69OkEBQVl+zwxMbGIj068pGvGAc7M1NTUSkHD3wP/Xx999JHFxMTYvn37zMxs8ODBds899/g/b9WqlU2ePNn/OjU11UaOHGnbtm2zkydP2gcffGA+n8+2bdtmZmbbtm0zwE6cOHHevs7IzzK52bNnT4HXOVt6erpdfvnl9uqrr1paWpq98cYbdvnll1t6enqOy69fv95SU1PNzGzXrl129dVX24IFC3JcdtmyZebz+fzLT5s2zW666aZ85Tr9b+P5OaKmppZ7Uw+PSCnWtm1bOnbsyJAhQ0hMTGTevHlMmDAh1+XDw8MZNWoUtWrVoly5cnTq1InatWvzww8/FFnGPXv28NJLL3H11VczatSoS9pWYmIimZmZPP744wQHBzNkyBDMjM8//zzH5evXr094eLj/dbly5di8eXOOy86YMYOuXbtmW168Exoausc5Z2ret9DQ0D1enw+FQQWPSCn32muvkZiYSNeuXXn55ZeJi4vL97p79+5l48aNJCQkZHs/Pj6eGjVqMGDAAPbv31/gTCdOnOC9996jc+fO1K9fn6SkJMaNG5etGGvYsCHR0dE5toceeijH7a5du5aGDRuS9XDNf7eT12W2sWPH4vP5qFGjBkePHqVXr17nLXPs2DHmz59Pv37Zx5z78ccfiYmJoV69ejzzzDO6b6kYnRl3Ss37FigDnmrgQZFSrmLFiiQkJPD1119z77335nu9EydO0Lt3b/r160eDBg0AiImJ4Z///CeNGzfmwIEDPPzww/Tu3ZuPPvoo39t96qmnmDRpEg0aNKB///7MnTuXiIiI85ZLSkrK9zbPSE1NJSoqKtt7UVFRpKSk5LrO0KFDefLJJ1m1ahULFy48b32ABQsWEBMTQ6tWrfzv3XrrraxZs4b4+HjWrl1Ljx49KF++PH/84x8LnFtEvKceHpFSbvbs2Wzfvp3bb7+dJ598Ml/rnDp1il//+tdUqFCB8ePH+9/3+Xw0a9aM8uXLU7VqVcaPH8/HH39McnJyvvNs2LCBEydO0LhxYxo2bJhjsXOxfD7feVmSk5MvuA/nHNdddx2hoaGMHDnyvM9nzJhB3759s/Uc1alTh9q1a1OuXDmuvfZann766Ww3g4tI6aKCR6QU27dvH0888QSTJ09m0qRJzJs3z/9EVm7MjIEDB7J3714WLFjAL37xi1yXPVMAmOX/4ZV58+axatUqYmJi6NGjBwkJCYwdO5Zdu3ZlWy4hIeG8p6TOtMGDB+e47YSEBJKSkrLlSUpKOu+SXG4yMzPZsmVLtvd27txJYmIiffv2zXPd04ML5ms/UvosX76c+vXrF/qyUoJ4fW1QTU0tf40cntLq1q2b/c///I//9eTJk61evXqWlpZmZuc/pWVmNmjQILvxxhstJSXlvO2tWLHC1q9fbydPnrT9+/db9+7drXXr1tmWKchTWqdOnbLExETr37+/RUZG2siRIy+4Tl7OPKX1+uuvW1pamr355pu5PqV18uRJmzhxoh08eNBOnTpl3377rcXGxtobb7yRbbnnnnvObrnllvPWX7x4sf+psn/961+WkJBgo0aNyjEXekqrWM73QPfjjz9akyZNLDQ01Jo0aWI//vhjrsumpaXZgAEDLCIiwqpWrWqvvPJKkeUKlPPb8wBqamr5a+f+Anj//fctLi7ODh06lO39Nm3a2LBhw8zs/IJn+/btBlhwcLCFh4f72+zZs83MbO7cuVarVi0LCwuz2NhY+/Wvf23//ve/s23/Yh9LT01NzfMHeH6tXLnSmjRpYiEhIXbdddfZypUr/Z/Nnj3brr76ajPLKnjatWtnFStWtPDwcLvyyivtueees1OnTmXbXv369W3KlCnn7ee3v/2tValSxcLCwqx27dr21FNPWUZGRo6ZAuUXQklqZa3gKeiQC0OHDrWbb77ZDh48aOvWrbOqVavakiVLiiRboJzfngdQU1PLX7uYXwC/+tWvzOfznddLc7FGjRplkZGRFhwcbJmZmYWyzUAQKL8QSlIrioLnhx9+sMaNG5vP57OuXbta9+7dbfjw4WaWNQ5T9erV/cvGx8fbSy+9ZNdee61FRkZa9+7d7fjx4zkuWxg++ugjq1atWraCvGbNmrkWMdWqVbOPPvrI/3rEiBHWo0ePQs10RqCc37qHRySAffzxx6SkpLBs2bJC2d7IkSM5cuQIaWlpBAUFFco2RYpDRkYG99xzD/379+fgwYP07NmT999/P8915s2bx9KlS9m2bRtJSUlMnz49X/sq6iEXDh06xO7du2nUqJH/vUaNGnk+CnpJp8fSRUQk4K1YsYLMzEyGDBmCc457772XG264Ic91hgwZQrVq1QDo3LlzntOYnK2oh1xITU31f36hZeW/1MMjIiIBb/fu3VSvXj1bD0rNmnlPjB4bG+v/OiwszF9oFIWCDLng8/n8n19oWfkvFTwiZZwex5WyIC4ujp9//vnM/UFA1pAERaGoh1yoWLEicXFxrF692v/e6tWr8z08Q1mlgkekjLvlllvYsGFDoS9bEAWZAT09PZ3777+fyMhIYmNjefXVV7N9fvLkSUaMGEG1atX8M8IfPnwY0AzoZVmLFi0ICgpi/PjxZGZm8ve//53vvvuuSPa1du1aUlNTc2wTJ07McZ3WrVsTFBTEuHHjSE9P9w8I2qZNmxyX79u3L88++yyHDh1i/fr1TJ48mf79+xfJ8QQKFTwi4qmMjAzuuusu+vTpw6FDh+jXrx933XUXGRkZOS4/atQoNm3axI4dO1i2bBkvvvgiS5cu9X8+cuRIvv76a7755huSk5OZNWsWISEh/s9btGiR7RdQ69ati/oQpQSoUKEC7733Hu+88w7R0dHMnj2bTp06ERwc7HU0ICvfwoULmTlzJtHR0UydOpWFCxdSoUIFAObMmZOtB2f06NHUrVuX+Ph4WrVqxe9//3vat2/vVfzSwevHxNTU1PLXuITHdMvK47gHDx608PBw27x5c47rTps2zW666aZCTJ+FAHlstyS1Sznf8+uGG26wqVOnFvl+SrtAOb/VwyMS4MrS47j/93//R/ny5Zk/fz6xsbHUq1cv2wztoBnQy7IvvviCPXv2kJmZyYwZM0hKSlKvSBmix9JFAlxZehx3165dHDlyhI0bN7Jt2zY2bdrEbbfdRr169fjVr36lGdDLuA0bNtC9e3dSU1OpW7cu8+fPJy4uzutYUkzUwyMS4MrS47ihoaEAPP3004SGhtKwYUPuu+8+Fi9eDGgG9LLuwQcfZO/evRw9epSkpCQ6duzodSQpRip4RAJcWXoct2HDhgDZiru8aAZ0kbJDBY9IgCtLj+PWrVuXW265heeee4709HT+9a9/8de//pVOnToBsGTJEvbu3QvA+vXreeaZZ7jrrrsK+bsgZUn//v0ZMWKE1zEkH1TwiAS4svY47rvvvsuOHTuoVKkSHTt25JlnnuG2224D4LPPPqNhw4aEh4dzxx13cO+99zJs2LDiPWCRInLw4EF69OhBTEwMMTEx9O7dO9vl3wcffJD69etTrly5fD+IEEicunNFSgfnnBXW/6833ngjgwcPZsCAAYWyvbLu9KWx/F1Hk3wpzPO9KPXv358aNWrw7LPPeh2Fhx56iM2bNzN//nzMjC5dutCwYUP/4JwTJkygQYMGPPnkkzzyyCP5HqgwUM5v9fCIlAF6HFcC0QsvvED16tWJiIigfv36fPbZZwB89913tGjRgujoaOLi4njkkUeyDWTpnOOtt97iyiuvJCIigqeeeootW7bQokULIiMj6d69u3/5xMREatSowZgxY4iJiaFWrVrMmTMn10yLFi2icePGREdH07Jly2xPLuaWt7Bs27aNu+++m8jISKKiorjnnnuyDe/w8MMPc9ttt2UbiLMsUcEjUgZs2LCBRo0aERUVxSuvvKLHcaXU27BhA+PHj+ef//wnKSkpfPTRR9SqVQuAoKAgXnvtNfbv388333zDZ599xltvvZVt/aVLl/LDDz+wYsUKXnzxRR588EHmzJnDzp07WbNmDe+++65/2T179rB//35+/vlnZsyYwYMPPpjjFCsrV67k/vvvZ9KkSRw4cIBBgwZx5513kp6enmfec40dOzbX8ayio6Nz/Z48/PDDLFq0iEOHDnHo0CEWLFhAhw4dCvy9DVQqeETKAD2OK4EmKCiI9PR01q1bx4kTJ6hVqxZ169YFoGnTpjRv3pzy5ctTq1YtBg0axBdffJFt/SeffJLIyEgSEhK45ppraNu2LXXq1CEqKooOHTrw448/Zlv+mWeeITg4mFatWtGxY0fmzZt3XqbJkyczaNAgbrzxRoKCgujXrx/BwcGsWLEiz7znGjp0KIcPH8615aZJkyZkZGRQqVIlKlWqRFBQUK4DfpZFKnhERKTUueKKK3j99dcZNWoUVapU4b777mP37t0AbNy4kU6dOhEbG0tkZCTDhg1j//792davWrWq/+vQ0NDzXp899lTFihUJDw/3v46Pj/fv62w7duzglVdeydYbs3PnTnbv3p1n3sLSrVs36tWrR0pKCsnJydStW5c+ffoU6j5KMxU8IlIk9LiuFLVevXrx1VdfsWPHDpxzPPnkkwD85je/oUGDBmzatInk5GTGjBlzSeMtHTp0iKNHj/pf//TTT/6RyM9Ws2ZNhg8fnq035tixY/Ts2TPPvOcaM2ZMruNZnRl8MyerV69m0KBBhIeH+8e+OjPopqjgEZEy4EKP637wwQdcc801+Hw+WrZsybp16zxMK/mxYcMGPv/8c9LT0wkJCSE0NJSgoCAAUlJSiIyMxOfzsX79ev785z9f8v5GjhxJRkYGy5cvZ9GiRXTr1u28ZR544AEmTpzIt99+i5lx9OhRPvzwQ1JSUvLMe65hw4blOp5VXqOeX3/99UyZMoXjx49z/Phx3n777WzzzmVkZJCWloaZceLECdLS0jh16tQlf29KCxU8IhLwRowYwaFDh9i6dStbtmxh7969jBo1CoBNmzbRu3dvJk6cyOHDh+ncuTN33nmnJhUt4dLT0xk6dCgxMTHExsayb98+xowZA8DLL7/M3LlziYiI4IEHHqBHjx6XtK/Y2FgqVqxItWrV/OdKgwYNzluuWbNmTJ48mUceeYSKFStyxRVX+Me7yStvYZk6dSrbt2+nRo0aVK9ena1bt2Ybb6dt27aEhoby9ddf8+CDDxIaGsqXX35ZqBlKNK+na1dTU8tfy/rfNX/Gjh1r1apVM5/PZ/Xq1bNPP/3UzMy+/fZba968uUVFRVlsbKw9/PDDlp6e7l8PsAkTJtgVV1xhPp/PRowYYZs3b7bmzZtbRESEdevWzb/8smXLrHr16vbcc89ZpUqVLD4+3mbPnu3fVr9+/Wz48OH+1x988IE1atTIoqKirEWLFrZ69eoL5i0s7du3twkTJvhfjx8/3tq2bWtmZm+++abdcccd/s9OnjxpISEhBcpw+t/G83MkkFpBzveidOY8L8sC5fxWD49IgNHjuufL63HdMz8Mzzjzes2aNfn8jotIaaCCRyTA6HHd8+X1uO6vfvUrvvjiCxITE8nIyGDMmDFkZGRw7NixgnzbRaSEU8EjEmD0uO758npct0GDBsyYMYNHHnmEuLg49u/fz9VXX02NGjUKNYOUTq1bt2bXrl1ex5BCoIJHJADpcd3sLvS4bteuXVmzZg0HDhxg9OjR7Nixg+uvv/5ivy0iUgKp4BEJMHpc93wXelz3hx9+4OTJk/znP/9h0KBBdO7cOcencKR0OjMfVkmRmJhIuXLl8Pl8LF261Os4tGnThpCQEG6++WavoxQpFTwiAUaP657vQo/rPvbYY0RHR1O/fn2io6OZPHlyoe5f5FzVqlUjNTU12yS+c+fOJT4+nvDwcO6++24OHjyY7+19/vnnNGnShMjISOrUqcPbb7/t/2zNmjW0a9eOmJgYnDt/0vPPP/+ciRMnXtoBlQZePyampqaWv0YJeUz3DD2u+18EyGO7JakV5vle0s7VnPKsWbPGfD6fffHFF5aSkmI9e/a0Hj165Gt7GRkZFhkZaRMnTrRTp07Zd999Z+Hh4bZq1SozM1u/fr1NmTLFFi5caLl9X6dNm2Y33XRTjp8FyvmtHh4RESnxxo4dS9euXbO999hjjzFkyBAApk2bxlVXXUVERAR16tRh0qRJuW7LOcfmzZv9r8+dBmXRokU0btyY6OhoWrZsSVJSUiEfzfnmzJlD586dufXWW/H5fDzzzDO89957pKSkXHDdgwcPkpyczK9//Wucc1x//fVcddVV/hHD69evz8CBA0lISCjqwyjRVPCIiEiJ17NnTxYvXuyfEuTkyZPMmzePXr16AVClShUWLVpEcnIy06ZN44knnmDlypUF3k9eY0blpGHDhrmOC1WQmcrXrl2b7b6yunXrUqFCBTZu3HjBdatWrUrPnj2ZNm0aJ0+e5JtvvmHHjh0Bf09OQangEZGLosd1pTjFx8fTpEkTFi5cCGTddxIWFkbz5s0B6NixI3Xr1sU5R6tWrWjbti3Lly8v8H7yGjMqJ0lJSbmOC3XuoJ55SU1NJSoqKtt7UVFR+erhgayC8E9/+hPBwcHccsstPPfcc9SsWTPf+y8LVPCIiEip0KtXL/9I33PnzvX37gAsWbKE5s2bc9lllxEdHc3ixYvPG2MqP/IaM6oo+Xy+bBPaAiQnJxMREXHBddevX0+PHj2YOXMmGRkZrF27lhdffJEPP/ywqOKWSip4RMowPa6bt7LyuG5p0a1bNxITE9m1axfvv/++v+BJT0+nS5cu/O53v2Pv3r0cPnyYO+6448zNz+cJCwvLNpL2nj17/F9faMyocyUkJOQ6LtTgwYPzfWwJCQmsXr3a/3rr1q2kp6dTr169C667Zs0a6tevT7t27ShXrhz169enY8eOLFmyJN/7LwtU8IhIiZLT47pvvvkmtWvXJjIykmbNmvHVV19lW+fTTz+lSZMmhIeHU7NmzRynt8jJX/7yF+rXr09UVBRVqlShX79+2f7KLjOP65YSlStXpnXr1gwYMIDatWtz1VVXAZCRkUF6ejqVK1emfPnyLFmyhI8//jjX7TRu3Ji5c+dy8uRJli5dmm16lbzGjMrJ2rVrcx0XqiDnTu/evfnggw9Yvnw5R48e5emnn+bee+/19/CMGjWK1q1b57juddddx6ZNm/j8888xM7Zs2cKiRYv89wSZGWlpaWRkZACQlpaW6z1JgUwFj4iUaN9++y1Dhw5l/vz5HDlyhIEDB3LPPfdw8uRJANatW0evXr147rnnOHLkCKtWraJp06b52vZNN93EP/7xD44cOcLWrVvJzMzM9rSOlDy9evXi008/zXY5KyIignHjxtG9e3cqVqzI3LlzufPOO3PdxhtvvMEHH3xAdHQ0c+bM4e677/Z/lteYUUUpISGBiRMn0rt3b6pUqUJKSkq2e4B27tzJTTfdlOO6devWZerUqQwZMoTIyEhatWpFly5dGDhwIJB1mS40NNT/lFZoaCj169cv8mMqcbx+Ll5NTS1/jVzGz3j++eetS5cu2d4bMmSIPfroo2ZmNnXqVGvQoIH5fD6rXbu2TZw40b/cueOBALZp0yb/6379+tnw4cP9rz/44ANr1KiRRUVFWYsWLWz16tU5ZrpYOY1P8pe//MWuv/56/+vU1FQDbPfu3WZm1rNnTxsxYsQl7zslJcV+/etfW4cOHbK9n9f4JGcQIOOUlKSW2/keCL744gsLCQmxqKgoW7p0ab7WadSoke3fv79I8tx+++3m8/msTZs2OX4eKOe3enhESrlAf1y3Q4cOnDx5km+//ZaTJ08ydepUGjduTGxsLID/6Zlrr72WuLg4+vTpU6ARar/66iuioqKIiIhgwYIFPP744/leV+Ri3HrrrRw/fpzDhw/Trl27fK2zatUqKlWqVCR5PvnkE1JSUvjss8+KZPslhQoekVIu0B/XjYiIoEuXLtx8880EBwczevRo3n77bf8Q+bt27WLWrFksWLCATZs2cfz4cR599NF8b//mm2/myJEj7Nq1i9///vfUqlUr3+uKSOmhgkckAATy47pTpkxh6tSprF27loyMDGbPnk2nTp38+w0NDWXAgAHUq1cPn8/HsGHDss2Enl/Vq1enffv23HfffYV9CCJSAqjgEQkAgfy47urVq+ncuTP16tWjXLlytG/fnri4OL7++msg69JZThMiXozMzEy2bNlSKNsSkZJFBY9IAAjkx3Wvv/56PvzwQ7Zu3YqZ8cknn7Bx40auueYaAAYMGMC0adPYunUrx44d44UXXqBTp07+9WvVqpXrUzZz5szhp59+wszYsWMHw4cP57bbbst3NhEpPVTwiASIQH1ct2/fvtx33320bt2ayMhIhgwZwqRJk2jQoAEA999/P3379uXGG28kPj6e4OBgxo0bB2QVfAcOHPDfz3SudevW0bJlS3w+HzfddBP169dn8uTJRX5MIlL8XG5d2yJSsjjnLND/f/3yyy9p164dwcHB/PWvf833Eyy5+eqrr5gwYYL//qaC+tWvfsWKFSu44YYb8nyCxTmHmRXOdTUBIDQ0dE9aWlpVr3MIhISE7D1+/His1zkulQoekVKiLBQ8pZUKHpGST5e0REREJOCp4BEREZGAp4JHREREAp4KHhEREQl4KnhEREQk4JX3OoCI5E9ISMhe55we0y2BQkJC9nqdQUTypsfSRSQb51xlYDXQw8wKPsuoh5xzFYDvgHFmNtXrPCJScqjgERE/lzUp1d+A7Wb2O6/zXAznXEPgM+B6M9vucRwRKSF0D4+InK0XcBUwwusgF8vMkoBXgGnOOf2MExFAPTwicppzrjrwI9DBzH7wOs+lcM4FAcuBv5rZG17nERHvqeARkTOXspYC/zCzP3mdpzA4564EvgFuNrP1XucREW+pu1dEAAYDlwHPex2ksJjZJuBpYKZzTk+kipRx6uERKeOcc1cAK4BbzOxfXucpTKd7rj4ClpvZM17nERHvqOARKcNO3+vyBbDAzF7zOk9RcM7VAFYC7c1spdd5RMQbuqQlUrb9FsgEAvbGXjPbRdZxznTOhXidR0S8oR4ekTLKOXctsIys8Wq2eZ2nKJ2+tDUf2GJmf/A6j4gUPxU8ImXQWSMSv2lm73idpzicHkE6Cehe2kaQFpFLp0taImXT08AuoMxMv2Bm/yHrabTpzjmf13lEpHiph0ekjHHO3Qj8P6Cxmf3b6zzFzTk3HUgzs8FeZxGR4qMeHpEyxDkXBswEHi2Lxc5pjwEdnHPtvQ4iIsVHPTwiZYhz7g2gipn19DqLl5xztwEzgIZmdtDrPCJS9FTwiJQR+iWfnXNuHBBjZr28ziIiRU+XtETKAOdcFFk3KD+gYsdvKNDUOdfN6yAiUvTUwyNSBjjnpgEZZjbI6ywliXOuOfB3oJGZ7fE6j4gUHRU8IgHOOXcn8DpZv9RTPI5T4jjnngMaAneafiCKBCxd0hIJYKcH25sI9FOxk6vRQE1ggNdBRKToqIdHJECdNZ3CNjP7ndd5SjLnXEPgc6CZmW33OI6IFAH18IgErt5AA2CE10FKOjNLAl4iaxRm/VwUCUDq4REJQM65GsBKoL2ZrfQ6T2ngnAsCvgT+ZmavexxHRAqZCh6RAHP6UtZS4B9m9iev85QmzrkrgBXAzWa23us8IlJ41HUrEngGA5cBz3sdpLQxs81kTaw60zlX3us8IlJ41MMjEkDO6qG4xcz+5XWe0uh0D9lHwHIze8brPCJSOFTwiAQI3YNSeM66B6qDmf3gdR4RuXS6pCUSOH4HZADjvA5S2pnZLuB/ybq0FeJ1HhG5dOrhEQkAzrlryRpH5nqNI1M4zhrHaKuZ/d7rPCJyaVTwiJRyzrkKwHfAm2b2jtd5AsnpkaqTgO5mttzrPCJy8XRJS6T0exrYRdZs6FKIzOw/ZD31Nt05F+F1HhG5eOrhESnFTs/2vRBorNm+i45mmxcp/dTDI1JKOefCgBnAoyp2itzjQHvnXAevg4jIxVEPj0gp5ZwbB8SYWS+vs5QFzrk2wEygoZkd9DqPiBSMCh6RUsg5dxtZvTv65VuMVGSKlF66pCVSyjjnosi6QfkBFTvFbijQ1DnX3esgIlIw6uERKWV0A623nHM3Av+PrBvF/+11HhHJHxU8IqWIc+4u4FWgkZmlep2nrHLOPQs0BjqbfoiKlAq6pCVSSpweBG8i0F/Fjuf+BFQH7vc6iIjkj3p4REoBTXNQ8mg6D5HSRT08IqVDb6A+8JTXQSSLmf0f8DJZozDrZ6lICaceHpESzjlXA1gJtDezlV7nkf9yzgUBXwLzzew1r/OISO5U8IiUYKcvZS0FvjKzZ7zOI+dzzl0BrABuMbN/eZ1HRHKmbliRkm0wUBF43usgkjMz20zWpcaZzrlfeJ1HRHKmHh6REuqsnoObzWy913kkd2f1xP3DzP7kdR4ROZ8KHpES6Kx7Q/5mZq97HEfy4ax7rTqY2Q9e5xGR7HRJS6Rk+h2QAYzzOojkj5ntAv6XrEtbIV7nEZHs1MMjUsJofJfS6/Slrb8B283sd17nEZH/UsEjUoI45yoA3wHjzGyq13mk4E6PiL0auM/MvvQ6j4hk0SUtkZLlaWAnMM3rIHJxzOw/ZD1dN905F+F1HhHJoh4ekRLCOdcc+DtZE4Pu8TqPXBrNai9SsqiHR6QEcM6FATOAR1TsBIzHgfbOuQ5eBxER9fCIlAjOuXFAjJn18jqLFB7nXBtgFnCtmR30Oo9IWaaCR8RjzrnbyOrdaahfioHHOfcGUMXMenqdRaQs0yUtEQ8556KAqcD/qNgJWH8EmjjnunsdRKQsUw+PiIecc9OBNDMb7HUWKTrOuRuB/wc0NrN/e51HpCxSwSPiEefcXcCrZD2Vlep1HilazrlngcZAZ9MPXpFip0taIh44PTjdRKC/ip0y409AdWCg10FEyiL18IgUs9PTD8wHtpjZH7zOI8Xn9LQhy8iaNmSb13lEyhL18IgUv95AfbJGVZYyxMz+D3gRmOac089fkWKk/+FEioFz7gPnXIhzrgZZ9+382szSvM4lnngFKA885nUQkbJEl7REitjp+ZT2Aj5gKbDczJ7xNpV4yTl3BbACuMXM/uV1HpGyQD08IkWvDrCNrAklo4HnnXMxniYST5nZZuApYKZz7hde5xEpC1TwiBS9usC/yXpK5yngr8Dy0zcvS9k1ETgADPM6iEhZoIJHpOhdATQE1gJzgH8CTTQWS9l2+t9/IPCwc66p13lEAl15rwOIlAGdgRjg/4CuZvYfj/NICWFmPzvnniDr0lZT3cguUnR007JIEXPO3Qv828y+8TqLlDynL23+DdhuZr/zOo9IoFLBIyLisdMjb68G7jOzL51zdYDjmndLpPDoHh4REY+dvsw5GJh+ehiDvqdfi0ghUQ+PXJTQ0NA9aWlpVb3OIecLCQnZe/z48Vivc0jBOeemApnAP4BfmVkfjyOJBAzdtCwXJS0traqK5ZLJOadCtJRxzrUBmgFDge+AjWQ93ScihUSXtEREvLcBuAH4HpgL/BYVPCKFSpe05KI45zSMTAnlnMPMNKhhKeScu4msubbqAJWBKDNL9jaVSGBQD4+ISAlhZv8AWgD/C6QAujwpUkhU8Eixad26NSEhIdx6662Fsr2RI0cSHh6Oc47MzMxC2aaI1yzLbDOLNLNNXucRCRQqeKRQpKamUqtWLebOnet/LyUlhcsvv5z58+f73xs/fjxffvml/3WfPn2Ii4sjMjKSevXqMWXKlGzbnTJlCldccQU+n4/27duze/du/2ejR49m7dq1RXhUF7Zq1SqaNm1KWFgYTZs2ZdWqVXku/+mnn9KkSRPCw8OpWbMm8+bNA2Djxo3cddddVK5cmcsuu4x27dqxYcMG/3rTp08nKCgIn8/nb4mJiUV4ZCIiAcbM1NQK3PD/IfpfH330kcXExNi+ffvMzGzw4MF2zz33+D9v1aqVTZ48Ods6a9assbS0NDMz+9e//mVVq1a177//3szMEhMTrXLlyrZmzRpLT0+3wYMH26233ppt/W3bthlgJ06cOC/PhezZs6fA65wtPT3dLr/8cnv11VctLS3N3njjDbv88sstPT09x+XXrl1rlStXtsWLF9uJEyds//79tnnzZjMz+/bbb23KlCl24MABy8jIsBEjRlj9+vX9606bNs1uuummfOU6/W/j+TkSyC0kJGQPYGpF30JCQvZ4/e+tFhjN8wBqpbPlVPCYmfXr18/uu+8+W7ZsmV122WW2e/du/2c5FTxnW79+vcXGxtpf//pXMzP77W9/aw899JD/859//tkAf5FgVvCC59///re9+OKLdtVVV9ngwYPztU5uPvroI6tWrZqdOnXK/17NmjVtyZIlOS7fs2dPGzFiRL62feDAAQNs//79ZqaCp6S13M5/KXw6n9UKq+mSlhSq1157jcTERLp27crLL79MXFzcBdd56KGHCAsLo0GDBsTFxXHHHXcA/y3Gzzjz9Zo1awqU6cSJE7z33nt07tyZ+vXrk5SUxLhx45gwYYJ/mYYNGxIdHZ1je+ihh3Lc7tq1a2nYsCFZUyH9dzu5XWZbsWIFANdeey1xcXH06dOHgwcP5rjsl19+SWxsLJUqVfK/9+OPPxITE0O9evV45plndN+SiEgBqOCRQlWxYkUSEhI4duwY9957b77Weeutt0hJSWH58uXce++9BAcHA3DHHXcwb948kpKSOH78OH/6059wznHs2LF853nqqaeoXr06r7/+Ovfccw+7du1i1qxZ3H777ZQr99/TPykpicOHD+fY3nrrrRy3nZqaSlRUVLb3oqKiSElJyXH5M/tesGABmzZt4vjx4zz66KM5Lvfwww/z6quv+t+79dZbWbNmDfv27WPBggW8++67vPTSS/n+PoiIlHUqeKRQzZ49m+3bt3P77bfz5JNP5nu9oKAgbr75Znbt2sWf//xnAG677TZGjx5Nly5diI+Pp1atWkRERFCjRo18b3fDhg2cOHGCxo0b07BhQyIiIgp8TLnx+XwkJ2cfIiU5OTnXfYSGhjJgwADq1auHz+dj2LBhLF68ONsy//nPf2jbti0PPfQQPXv29L9fp04dateuTbly5bj22mt5+umns90MLoFl+fLl1K9fv9CXFSnLVPBIodm3bx9PPPEEkydPZtKkScybNy/bE1n5kZmZyZYtW/yvH374YTZt2sS+ffvo0qULmZmZXHPNNfne3rx581i1ahUxMTH06NGDhIQExo4dy65du7Itl5CQkO0JqLPb4ME5z+GYkJBAUlJStstuSUlJJCQk5Lj8uZe/znXo0CHatm3LnXfeyfDhw/M8rtODC+a5jJRet9xyS7an9Apr2YIoyBOI6enp3H///URGRhIbG5utd1KkxPD6JiK10tnI4abNbt262f/8z//4X0+ePNnq1avnfwrr3JuW9+7da++++66lpKRYZmamLV261MLCwmzhwoVmZnb8+HH7v//7Pzt16pTt2LHDWrVqZX/84x+z7bMgNy2fOnXKEhMTrX///hYZGWkjR4684Dp5OfOU1uuvv25paWn25ptv5vmU1jvvvGO1atWyLVu22NGjR61bt27Wp08fMzM7cuSIXX/99fbwww/nuO7ixYv9T5X961//soSEBBs1alSOy6KbPD05/wNJQZ9AHDp0qN1888128OBBW7dunVWtWjXXm/cLSuezWmE1zwOolc527g/8999/3+Li4uzQoUPZ3m/Tpo0NGzbMzM4vePbt22e33nqrRUVFWUREhF1zzTX29ttv+z8/dOiQXXvttRYWFmZVq1a1oUOHWmZmZrbtX+xj6ampqfbjjz8WaJ2crFy50po0aWIhISF23XXX2cqVK/2fzZ49266++upsyz/99NMWExNjMTEx1qdPHzt48KCZmU2fPt0ACwsLs/DwcH/bsWOHmWU9sValShULCwuz2rVr21NPPWUZGRk5ZtIviOI//y/GDz/8YI0bNzafz2ddu3a17t272/Dhw83MbNmyZVa9enX/svHx8fbSSy/Ztddea5GRkda9e3c7fvx4jssWhoI+gVitWjX76KOP/K9HjBhhPXr0KJQsOp/VCqt5HkCtdLaL+YH/q1/9ynw+n7Vu3brA6+Zk1KhRFhkZacHBwecVQmWZfkGUzPP/bGf3DmZkZNiCBQvsF7/4RZ4Fz/XXX28///yzHThwwBo0aGB//vOfc1z2XNdee61FRUXl2H7zm9/kuM6rr75q7du3z/Zex44d7eWXXz5v2YMHDxqQbVyrv/3tb3bNNdfk/xuSB53PaoXVyhfn5TMp2z7++ONC3d7IkSMZOXJkoW5TpDisWLGCzMxMhgwZgnOOe++9lxtuuCHPdYYMGUK1atUA6Ny58wVH9T4jKSmpwPkK8gRiamqq//MLLSviJd20LCJSzHbv3k316tWz3cRes2bNPNeJjY31fx0WFuYvNIpCQZ5A9Pl8/s8vtKyIl1TwiIgUs7i4OH7++WfM/vuk3c6dO4tkX0X9BGLFihWJi4tj9erV/vdWr16d69OKIl5RwSMlisYfkbKgRYsWBAUFMX78eDIzM/n73//Od999VyT7Wrt2LampqTm2iRMn5rhO69atCQoKYty4caSnpzN+/HgA2rRpk+Pyffv25dlnn+XQoUOsX7+eyZMn079//yI5HpGLpYJHSpRAGn/kQjOg/+Uvf6F+/fpERUVRpUoV+vXrd95lBAlMFSpU4L333uOdd94hOjqa2bNn06lTJ/8o416rUKECCxcuZObMmURHRzN16lQWLlxIhQoVAJgzZ062HpzRo0dTt25d4uPjadWqFb///e9p3769V/FFcub1XdNqpbMRoOOQFOb4IxeaAf2nn36y//znP2ZmlpKSYr169bJHH330ko8BPdVSKs//G264waZOnVro2y3tdD6rFVbzPIBa6WyX8gO/rI4/cu4M6GdLSUmxX//619ahQ4dLPALTL4gSfv6fkZiYaP/+97/txIkTNn36dAsJCbHdu3df8nYDjc5ntcJquqQlxSojI4N77rmH/v37c/DgQXr27Mn777+f5zrz5s1j6dKlbNu2jaSkJKZPn56vfRX1DOiHDh1i9+7dNGrUyP9eo0aNcp0tPacZ0L/66iuioqKIiIhgwYIFPP744/k6Nin9NmzYQKNGjYiKiuKVV15h/vz5xMXFeR1LJGBpHB4pVmV1/JGcZkAHuPnmmzly5Ag///wzkydPplatWgXOLKXTgw8+yIMPPuh1DJEyQz08UqzK4vgjuc2Afrbq1avTvn177rvvvks9BBERyYEKHilWZW38kYLMgH7uTPEil6J///6MGDHC6xgiJYYKHilWZWn8keTkZNq1a8dNN93E2LFjz1t3zpw5/PTTT5gZO3bsYPjw4dx2222Fc/AiJcjvfvc7rrzySiIiImjQoAEzZ870OpKUQSp4pFiVpfFH3n//ff75z38ybdq0bL1LP/30EwDr1q2jZcuW+Hw+brrpJurXr8/kyZOL/6BFilh4eDgffPABR44cYcaMGTz22GN8/fXXXseSssbrx8TUSmejEMch0fgjhQs9xuv5+T927FirVq2a+Xw+q1evnn366admljU2U/PmzS0qKspiY2Pt4YcfzjbGE2ATJkywK664wnw+n40YMcI2b95szZs3t4iICOvWrZt/+TPDMjz33HNWqVIli4+Pt9mzZ/u31a9fP/9wD2ZmH3zwgTVq1MiioqKsRYsWtnr16gvmLSqdO3fOceb1nOh8Vius5nkAtdLZLqXg0fgjRUu/ILw9/9evX281atSwn3/+2czMtm3bZps3bzYzs++//96++eYbO3HihG3bts0aNGhgr732WrZ/u86dO9uRI0dszZo1VqFCBWvTpo1t2bLFDh8+bFdddZVNnz7dzLIKnqCgIHviiScsLS3NEhMTLSwszNavX29m2QueH374wSpXrmwrVqywzMxMmz59usXHx1taWlqeec/1/PPPW1RUVK4tP44dO2axsbG5jm11Lp3PaoXVdElLip3GH5FAFhQURHp6OuvWrePEiRPUqlWLunXrAtC0aVOaN29O+fLlqVWrFoMGDeKLL77Itv6TTz5JZGQkCQkJXHPNNbRt25Y6deoQFRVFhw4d+PHHH7Mt/8wzzxAcHEyrVq3o2LEj8+bNOy/T5MmTGTRoEDfeeCNBQUH069eP4OBgVqxYkWfecw0dOpTDhw/n2vJj8ODBNGrUiHbt2uVreZHCooJHit2DDz7I3r17OXr0KElJSXTs2NHrSCKF5oorruD1119n1KhRVKlShfvuu4/du3cDWfOrderUidjYWCIjIxk2bBj79+/Ptn7VqlX9X4eGhp73+uxhGSpWrEh4eLj/dXx8vH9fZ9uxYwevvPJKtsE3d+7cye7du/PMW9h+//vfs2bNGubNm5dtaAqR4qCCR0SkkPXq1YuvvvqKHTt24JzjySefBOA3v/kNDRo0YNOmTSQnJzNmzBjM7AJby92hQ4c4evSo//VPP/3kH6TzbDVr1mT48OHZemOOHTvmHxcqt7znGjNmTK5DPZwZlyo3I0eOZMmSJXz88cdERkZe9DGLXCwVPFImaEwSKS4bNmzg888/Jz09nZCQEEJDQwkKCgIgJSWFyMhIfD4f69ev589//vMl72/kyJFkZGSwfPlyFi1aRLdu3c5b5oEHHmDixIl8++23mBlHjx7lww8/JCUlJc+85xo2bFiuQz3kNSDo888/z9y5c/nkk0+yTa0iUpxU8IgUs4MHD9KjRw9iYmKIiYmhd+/e/tGaly9fft5fzc45FixY4HFqya/09HSGDh1KTEwMsbGx7Nu3jzFjxgDw8ssvM3fuXCIiInjggQfo0aPHJe0rNjaWihUrUq1aNXr37s3EiRNp0KDBecs1a9aMyZMn88gjj1CxYkWuuOIK/5x0eeUtLMOGDeOnn37iyiuv9J/Xhb0PkQtxl9KdKmWXc85K07nTv39/atSowbPPPut1FB566CE2b97M/PnzMTO6dOlCw4YNz5tnCyAxMZHOnTuzZ8+ebPdq5MU5h5npBokiVBLO/8TERPr06cOuXbs8zVHUdD5LYVEPjxS5F154gerVqxMREUH9+vX57LPPAPjuu+9o0aIF0dHRxMXF8cgjj5CRkeFfzznHW2+95R+h9amnnmLLli20aNGCyMhIunfv7l8+MTGRGjVqMGbMGGJiYqhVqxZz5szJNdOiRYto3Lgx0dHRtGzZMttEo7nlLSzbtm3j7rvvJjIykqioKO65555cZ1ifMWMGXbt2zXexIyIiufD6uXi10tnI5zg8GpPkfB988IF16NDBDh48aAcPHrRf/vKX2Y77jKNHj5rP57Nly5bl63t99vfNSsA5Esgtv+d/UToz8GCg0/msVlhNPTxSpDQmyfmaNGlCRkYGlSpVolKlSgQFBfHQQw+dt9yCBQuIiYmhVatWF/o2SxnUunXrgL+cJVKYVPBIkdKYJOfr1q0b9erVIyUlheTkZOrWrUufPn3OW27GjBn07dtX45WIiBQCFTxS5DQmSXarV69m0KBBhIeH4/P5GDx4MIsXL862zM6dO0lMTKRv374X862QUuTM/WclRWJiIuXKlcPn87F06dIi398777zjfxpx8+bNRb4/KbtU8EiR0pgk57v++uuZMmUKx48f5/jx47z99ts0atQo2zKzZs2iZcuWuV5OEylK1apVIzU1lfbt2/vfmzt3LvHx8YSHh3P33Xdz8ODBfG1r48aN3HXXXVSuXJnLLruMdu3asWHDBv/nAwcOzPP/F5HCooJHipTGJDnf1KlT2b59OzVq1KB69eps3brVv/8zZs6cSb9+/Qp1vyIXa+3atQwaNIhZs2axd+9ewsLCcrzvLCeHDx/mzjvvZMOGDezdu5cbbriBu+66q4gTi+TA67um1UpnowQ8pXK2svLESn6gp1qK/fx//vnnrUuXLtneGzJkiD366KNmZjZ16lRr0KCB+Xw+q127tk2cONG/3LnnLmCbNm3yvz77CUOzrKf8GjVqZFFRUdaiRQtbvXq1Faac/l/64x//aD179vS/3rx5s/3iF7+w5OTkAm//wIEDBtj+/fuzvX/ucZ/9vpWAf3O10t/UwyMicol69uzJ4sWL/SNmnzx5knnz5tGrVy8AqlSpwqJFi0hOTmbatGk88cQTrFy5ssD7WblyJffffz+TJk3iwIEDDBo0iDvvvJP09PQcl2/YsGG2m/PPbvntoYGsHp6zL7vWrVuXChUqsHHjxgIfw5dffklsbKymmJBip4JHROQSxcfH06RJExYuXAjA559/TlhYGM2bNwegY8eO1K1bF+ccrVq1om3btixfvrzA+8lrSIWcJCUl5TpswltvvZXv/aamphIVFZXtvaioKFJSUgqUf9euXTz88MM5jiouUtRU8EhA0Jgk4rVevXrx7rvvAlk3+J7p3QFYsmQJzZs357LLLiM6OprFixefNwRDfuQ1pEJR8vl8/t6rM5KTk4mIiMj3Nv7zn//Qtm1bHnroIf8TkSLFSQWPiEgh6NatG4mJiezatYv333/fX/Ckp6fTpUsXfve737F3714OHz7MHXfcgVnOQzCEhYVx7Ngx/+s9e/b4v77QkArnSkhIyHXYhMGDB+f72BISEli9erX/9datW0lPT6devXr5Wv/QoUO0bduWO++8k+HDh+d7vyKFSQWPlBhlfTySC2nTpg0hISHcfPPNXkeRHFSuXJnWrVszYMAAateuzVVXXQVARkYG6enpVK5cmfLly7NkyRI+/vjjXLfTuHFj5s6dy8mTJ1m6dGm20cfzGlIhJ2vXrs112ISJEyfm+9h69+7NBx98wPLlyzl69ChPP/009957r7+HZ9SoUbRu3TrHdZOTk2nXrh033XQTY8eOzfc+RQqbCh6RPOQ0Hsmbb75J7dq1iYyMpFmzZnz11VfZ1vn0009p0qQJ4eHh1KxZM8fpLXKyZs0a2rVrR0xMTI6jK3/++ecF+iUlxa9Xr158+umn2S5nRUREMG7cOLp3707FihWZO3cud955Z67beOONN/jggw+Ijo5mzpw53H333f7P8hpSoSglJCQwceJEevfuTZUqVUhJScl2D9DOnTu56aabclz3/fff55///CfTpk3L1sP0008/FXlukWy8fkxMrXQ2iuCx9JL2aHlOeVasWGFhYWH2/fff26lTp+ytt96ymJgYy8zMNDOztWvXWuXKlW3x4sV24sQJ279/f66Tj55r/fr1NmXKFFu4cKHl9v2dNm2a3XTTTXluBz3GWyrP/5Liiy++sJCQEIuKirKlS5fma51GjRqd95h5fk2dOtWioqIsODjYtmzZct7nOp/VCquph0cK1dixY+natWu29x577DGGDBkCwLRp07jqqquIiIigTp06TJo0KddtnTvUfP/+/RkxYoT/9aJFi2jcuDHR0dG0bNmSpKSkQj6a823fvp2EhASaNm2Kc46+ffuyf/9+9u3bB8Czzz7LoEGD6NChA+XLl6dSpUr5Hi25fv36DBw4kISEhKI8BJE83XrrrRw/fpzDhw/Trl27fK2zatWqi37MfMCAARw+fJi0tDTq1KlzUdsQyQ8VPFKoAn08kg4dOnDy5Em+/fZbTp48ydSpU2ncuDGxsbEA/seDr732WuLi4ujTp0++h+AXEZGio4JHClWgj0cSERFBly5duPnmmwkODmb06NG8/fbb/ntudu3axaxZs1iwYAGbNm3i+PHjPProowU+PhERKVwqeKTQBfJ4JFOmTGHq1KmsXbuWjIwMZs+eTadOnfz7DQ0NZcCAAdSrVw+fz8ewYcPOmwldRESKnwoeKXSBPB7J6tWr6dy5M/Xq1aNcuXK0b9+euLg4vv76ayDr0llOT1iJiIi3VPBIoQvk8Uiuv/56PvzwQ7Zu3YqZ8cknn7Bx40auueYaIOsGzGnTprF161aOHTvGCy+8QKdOnfzr16pVK9fHiM2MtLQ0MjIyAEhLS8v1niQRESmY8l4HkMDUq1cv+vbty4svvuh/7+zxSNLT0+ncufMFxyPp168fEyZM4O677851PJJNmzYRGhrKzTffzK233lqUh0Xfvn3ZsmULrVu35tChQ9SoUYNJkybRoEEDAO6//3527NjBjTfeCED79u0ZN24ckFXwHThwwH8/07l27NhB7dq1/a9DQ0OJj49n+/btRXpMUnAhISF7nXNVvc5RFoSEhOz1OoMEBpfb5QSRvDjnLNDPnS+//JJ27doRHBzMX//613w/opubr776igkTJvjvbyqoX/3qV6xYsYIbbriBzz77LNflnHOYma6riYicRQWPXJSyUPCUVip4RETOp3t4REREJOCp4BEREZGAp4JHREREAp4KHhEREQl4KnhEREQk4GkcHrkoGoek5NK4JSIi59Nj6VKiOedqAj8Abc1slcdxCsQ5FwQsA/5uZq94nUdEpCxTwSMllnOuHPAx8LmZjfE6z8VwztUBvgNamdlar/OIiJRVuodHSrKHgHDgxQstWFKZ2VZgGDDTOfcLr/OIiJRV6uGREsk5Vw/4GmhpZhu9znMpXNb06R8C/zSzkV7nEREpi1TwSInjnCsPLAfmmtmbXucpDM65asAqoKOZ/dPjOCIiZY4uaUlJ9HvgGDDB6yCFxcx2A4+RdWkr1Os8IiJljXp4pERxzjUCPgWamtlPXucpTKcvbf0F2G1mT3idR0SkLFHBIyWGcy4Y+CfwipnN8DpPUXDOVQKSgN5mluhxHBGRMkOXtKQkGQVsBWZ6nKPImNkB4EFgmnMu0us8IiJlhXp4pERwzrUEFgCNzGyf13mKmnNuCoCZ/Y/XWUREygL18IjnnHPhwAzg4bJQ7Jz2v8BtzrlOXgcRESkL1MMjnnPOTQAizKyv11mKk3OuFfAu0NDM9nudR0QkkKngEU85534FvEPWL/3DHscpds65V4EaQA/T/4wiIkVGl7TEM865aLKKnYFlsdg5bThwDXCf10FERAKZenjEM865mUCKmT3sdRYvOeeaAYuBxqcHKBQRkUKmHh7xhHPuXqAF8Aevs3jNzL4H/gy8c3pwQhERKWQqeKTYOeeqAm8B/czsqNd5SohngSpkjdEjIiKFTJe0pFid7sF4H/iXmf3R6zwliXPuauBL4EYz2+J1HhGRQKIeHilufYHaZI2qLGcxs3XA88B051yQ13lERAKJenik2DjnLgd+AG43s9Ve5ymJThc6y4D/Z2Yve51HRCRQqOCRYuGcKwd8DHxuZmO8zlOSOefqAN8Brc1sjdd5REQCgS5pSXF5GAgHXvQ6SElnZluBYcBM51wFr/OIiAQC9fBIkXPO1QO+Blqa2Uav85QGp2/u/hD43sye9jqPiEhpp4JHipRzrjzwFTDbzMZ7nac0cc5VA34EOpnZP73OIyJSmumSlhS1PwCpZI27IwVwetTlx8i6tBXqdR4RkdJMPTxSZJxzjYBPgSZmttPrPKWVc+6vwG4ze8LrLCIipZUKHikSzrlg4J/AK2Y2w+s8pZlzrhKQBPQxs2Ve5xERKY10SUuKymhgCzDT6yClnZkdIGvKiWnOuUiv84iIlEbq4ZFC55xrCSwAGpnZPq/zBArn3GSgnJkN9DqLiEhpox4eKVTOuXBgBvCQip1C979AG+dcZ6+DiIiUNurhkULlnJsARJhZX6+zBCLnXCvgXaChme33Oo+ISGmhgkcKjXPuV8A7ZP0yPuxxnIDlnHsVqAH0MP0PLCKSL7qkJYXCOVeRrGJnoIqdIjccuAa4z+sgIiKlhXp4pFA452YBR8zsEa+zlAXOuWbAYuA6M/vZ6zwiIiWdenjkkjnn7gVuBJ70OktZYWbfkzV69ZTT826JiEgeVPDIJXHOVSXrF28/MzvqdZ4y5jmgMllj9IiISB50SUsu2umehfeBf5nZH73OUxY5564GvgRuNLMtXucRESmp1MMjl6IvUBsY5XGOMsvM1gFjgOnOuSCv84iIlFTq4ZGL4py7HPgBuM3MkrzOU5Y558oBy4BFZvaS13lEREoiFTxSYKd/wX4CfGpmz3udR8A5Vxv4Dvilma3xOo+ISEmjS1pyMR4GwgD1JpQQZrYNGAbMdM5V8DqPiEhJox4eKRDnXD3ga6CFmW3yOo/81+mbyD8Evjezp73OIyJSkqjgkXxzzpUHvgJmm9l4r/PI+Zxz1YAfgU5m9k+v84iIlBS6pCUF8Qcghaxxd6QEMrPdwBBglnMu1Os8IiIlhXp4JF+cc42Bj4GmZrbT4zhyAc65vwB7zOxxr7OIiJQEKnjkgpxzwcD3wEtmNtPrPHJhzrlKQBLQx8yWeZ1HRMRruqQl+TEa2AzM8jqI5I+ZHQAeAKY55yK9ziMi4jX18EienHMtgQVAIzPb53UeKRjn3GSgnJkN9DqLiIiX1MMjuXLOhQMzgIdU7JRa/wv80jnX2esgIiJeUg+P5Mo5NwHwmVk/r7PIxXPO3Qr8BWhoZvu9ziMi4gUVPJIj51xbYApZvyQPexxHLpFz7hXgcqC76X96ESmDdElLzuOcqwi8A9yvYidgDAcSgJ5eBxER8YJ6eOQ8zrlZwBEze8TrLFJ4nHPNgMXAdWb2s9d5RESKk3p4JBvn3L3AjcCTXmeRwmVm3wMTgCmn590SESkzVPCIn3OuKlm/EPua2VGv80iRGANUBh70OoiISHHSJS0B/DNtvw+sM7NhXueRouOcuxr4AmhuZlu8ziMiUhzUwyNn9ANqkzWqsgQwM1sHPA/McM4FeZ1HRKQ4qIdHcM5dDvwA3GZmSV7nkaLnnCsHLAMWmdlLXucRESlqKnjKuNO/+D4BPjWz573OI8XHOVcb+A74pZmt8TqPiEhR0iUteRgIBfRXfhljZtuAPwIznXMVvM4jIlKU1MNThjnn6gNfAS3NbJPXeaT4nb5Z/QNgpZk97XUeEZGiooKnjHLOlQf+Acw0swle5xHvOOfigFVAZzP7zuM4IiJFQpe0yq4ngWTgz14HEW+Z2b+BIWRd2gr1Oo+ISFFQD08Z5JxrDHwMNDWznR7HkRLCOfcXYI+ZPe51FhGRwqYenjLGORcMzAJ+q2JHzvEw0NU590uvg4iIFDYVPGXPaGATMNvrIFKymNkBsqacmOaci/Q6j4hIYdIlrTLEOXcTMB9oaGb/8TqPlEzOubeBIDMb6HUWEZHCoh6eAOacK3d69nOccz5gBvAbFTtyAb8Ffumc6wzgnGvknLvS40wiIpdEPTwBzDlXC/jKzGo4594Cws2sn8expBRwzt0K/AVoCDwEBJvZcG9TiYhcPPXwBLa6wBbnXFugI/CYx3mklDCzL4G5ZA1bsJWsc0lEpNRSwRPY6gI7gXeAgcAdzrk3vY0kJZ1zrpNz7jXgNeBqoA4qeESklCvvdQApUnWBBLImiDwzMejjnqWR0uIfQDuyRl+eS9aghEFeBhIRuVS6hyeAOee+AZoBu8maJPIvZnbK21RSWpyea20s0BqIBiqZ2UEvM4mIXCxd0gpsJ4CJQH0zm6tiRwrCzDaY2T3AvcAuIMrjSCIiF009PCIiIhLw1MMjIiIiAS+gbloODQ3dk5aWVtXrHAIhISF7jx8/Hut1jrJA533x0XktUnoF1CUt55wF0vGUZs45zMx5naMs0HlffHRei5ReuqQlIiIiAU8Fj4iIiAQ8FTyXYPny5dSvX7/QlxUpSXSei0ggUMFzCW655RY2bNhQ6MsWxKpVq2jatClhYWE0bdqUVatW5bpseno6999/P5GRkcTGxvLqq68Weh4JPDrPRSQQqOApxTIyMrjrrrvo06cPhw4dol+/ftx1111kZGTkuPyoUaPYtGkTO3bsYNmyZbz44ossXbq0mFOLFIzOcxEpFGYWMC3rcArXDz/8YI0bNzafz2ddu3a17t272/Dhw83MbNmyZVa9enX/svHx8fbSSy/Ztddea5GRkda9e3c7fvx4jssWho8++siqVatmp06d8r9Xs2ZNW7JkSY7LV6tWzT766CP/6xEjRliPHj0KNdMZp/8tPD8nykIrjPNe53n+6LxWUyu9TT08ecjIyOCee+6hf//+HDx4kJ49e/L+++/nuc68efNYunQp27ZtIykpienTp+drXw0bNiQ6OjrH9tBDD+W4ztq1a2nYsCHOuWzbWbt27XnLHjp0iN27d9OoUSP/e40aNcpxWSlbdJ6LSFkQUAMPFrYVK1aQmZnJkCFDcM5x7733csMNN+S5zpAhQ6hWrRoAnTt3zvNeg7MlJSUVOF9qaipRUdmnN4qKiiIlJSXHZc98fqFlpWzReS4iZYF6ePKwe/duqlevnu0vy5o1a+a5TmzsfwdhDQsL8/8ALgo+n4/k5ORs7yUnJxMREZHjsmc+v9CyUrboPBeRskAFTx7i4uL4+eefMfvvKLY7d+4skn0lJCTg8/lybIMHD851naSkpGz5kpKSSEhIOG/ZihUrEhcXx+rVq/3vrV69OsdlpWzReS4iZYEKnjy0aNGCoKAgxo8fT2ZmJn//+9/57rvvimRfa9euJTU1Ncc2ceLEHNdp3bo1QUFBjBs3jvT0dMaPHw9AmzZtcly+b9++PPvssxw6dIj169czefJk+vfvXyTHI6WHznMRKQtU8OShQoUKvPfee7zzzjtER0cze/ZsOnXqRHBwsNfRgKx8CxcuZObMmURHRzN16lQWLlxIhQoVAJgzZ062v2xHjx5N3bp1iY+Pp1WrVvz+97+nffv2XsWXEkLnuYiUBZo8tIBuvPFGBg8ezIABA4p0P6WdJlksPkVx3us8z5nOa5HSSz08F/DFF1+wZ88eMjMzmTFjBklJSfprUQKOznMRCXR6LP0CNmzYQPfu3UlNTaVu3brMnz+fuLg4r2OJFCqd5yIS6HRJS4qEuv6Lj8774qPzWqT00iUtERERCXgqeDzQv39/RowY4XUMkWKjc15EvKaCp4w7ePAgPXr0ICYmhpiYGHr37p1tlNqTJ08yYsQIqlWrRkREBNdddx2HDx/2LrDIJZo3bx4tW7YkLCyM1q1bex1HRIqJCp4ybsSIERw6dIitW7eyZcsW9u7dy6hRo/yfjxw5kq+//ppvvvmG5ORkZs2aRUhIiHeBRS7RZZddxuOPP87QoUO9jiIixajMFTwvvPAC1atXJyIigvr16/PZZ58B8N1339GiRQuio6OJi4vjkUceISMjw7+ec4633nqLK6+8koiICJ566im2bNlCixYtiIyMpHv37v7lExMTqVGjBmPGjCEmJoZatWoxZ86cXDMtWrSIxo0bEx0dTcuWLbNNsJhb3sKybds27r77biIjI4mKiuKee+7xzyx96NAhXn/9dSZPnkx8fDzOOa655hoVPKWMzvnsbr/9drp37+6f/FREyggzC5iWdTi5W79+vdWoUcN+/vlnMzPbtm2bbd682czMvv/+e/vmm2/sxIkTtm3bNmvQoIG99tpr/nUB69y5sx05csTWrFljFSpUsDZt2tiWLVvs8OHDdtVVV9n06dPNzGzZsmUWFBRkTzzxhKWlpVliYqKFhYXZ+vXrzcysX79+Nnz4cDMz++GHH6xy5cq2YsUKy8zMtOnTp1t8fLylpaXlmfdczz//vEVFReXacvPBBx9Yhw4d7ODBg3bw4EH75S9/6T/uL774wqKiomzs2LFWtWpVu/LKK238+PF5fo/P/n5ZCTgnykLL67zXOZ+7yZMnW6tWrS643Nl0Xqupld5Wpnp4goKCSE9PZ926dZw4cYJatWpRt25dAJo2bUrz5s0pX748tWrVYtCgQXzxxRfZ1n/yySeJjIwkISGBa665hrZt21KnTh2ioqLo0KEDP/74Y7bln3nmGYKDg2nVqhUdO3Zk3rx552WaPHkygwYN4sYbbyQoKIh+/foRHBzMihUr8sx7rqFDh3L48OFcW26aNGlCRkYGlSpVolKlSgQFBfHQQw8BsGvXLo4cOcLGjRvZtm0b8+fPZ9SoUXzyyScF+baLh3TOi4hkKVMFzxVXXMHrr7/OqFGjqFKlCvfddx+7d+8GYOPGjXTq1InY2FgiIyMZNmwY+/fvz7Z+1apV/V+Hhoae9zo1NdX/umLFioSHh/tfx8fH+/d1th07dvDKK68QHR3tbzt37mT37t155i0s3bp1o169eqSkpJCcnEzdunXp06eP/5gAnn76aUJDQ2nYsCH33XcfixcvLtQMUnR0zouIZClTBQ9Ar169+Oqrr9ixYwfOOZ588kkAfvOb39CgQQM2bdpEcnIyY8aMweziB3M7dOgQR48e9b/+6aefcrxnoGbNmgwfPjzbX6bHjh2jZ8+eeeY915gxY/D5fLm23KxevZpBgwYRHh6Oz+dj8ODB/oKmYcOGQNa9HFJ66ZwXESljBc+GDRv4/PPPSU9PJyQkhNDQUIKCggBISUkhMjISn8/H+vXr+fOf/3zJ+xs5ciQZGRksX76cRYsW0a1bt/OWeeCBB5g4cSLffvstZsbRo0f58MMPSUlJyTPvuYYNG0ZqamquLTfXX389U6ZM4fjx4xw/fpy3336bRo0aAVC3bl1uueUWnnvuOdLT0/nXv/7FX//6Vzp16nTJ3xspHjrnz3fy5EnS0tLIzMzk1KlTpKWlceLEiUs+dhEp2cpUwZOens7QoUOJiYkhNjaWffv2MWbMGABefvll5s6dS0REBA888AA9evS4pH3FxsZSsWJFqlWrRu/evZk4cSINGjQ4b7lmzZoxefJkHnnkESpWrMgVV1zB9OnTL5i3sEydOpXt27dTo0YNqlevztatW/37B3j33XfZsWMHlSpVomPHjjzzzDPcdttthZpBio7O+fPNmjWL0NBQfvOb37B8+XJCQ0N54IEHCnUfIlLyaC6tIpCYmEifPn3YtWuX11E8ozmHik9JOO/Lyjmv81qk9CpTPTwiIiJSNqngERERkYCnS1pSJNT1X3x03hcfndcipZd6eERERCTgqeDJxZm5gUqKxMREypUrh8/nY+nSpV7HoU2bNoSEhHDzzTd7HUUKUVk/79955x18Ph/OOTZv3lzk+xOR4qOCpxSpVq0aqamptG/f3v/em2++Se3atYmMjKRZs2Z89dVX2db59NNPadKkCeHh4dSsWTPHof5z89prrxEbG0tUVBT3338/6enp/s8+//xzJk6ceOkHJXIBOZ33c+fOJT4+nvDwcO6++24OHjyY7+09+OCD1K9fn3LlymUbggFg4MCBeY7hIyKllwqeUuzbb79l6NChzJ8/nyNHjjBw4EDuueceTp48CcC6devo1asXzz33HEeOHGHVqlU0bdo0X9v+6KOPGDt2LJ999hnbt29n69atjBw5sigPRyRf1q5dy6BBg5g1axZ79+4lLCzMP/9bfjRq1Ii33nqLJk2aFGFKESlpArrgGTt2LF27ds323mOPPcaQIUMAmDZtGldddRURERHUqVOHSZMm5bqtc7u4+/fvz4gRI/yvFy1aROPGjYmOjqZly5YkJSUV8tGcb/v27SQkJNC0aVOcc/Tt25f9+/ezb98+AJ599lkGDRpEhw4dKF++PJUqVcp1IsZzzZgxg4EDB5KQkEDFihV56qmnzvtrWEqmQD/v58yZQ+fOnbn11lvx+Xw888wzvPfee6SkpORr/YcffpjbbruNkJCQIk4qIiVJQBc8PXv2ZPHixSQnJwNZQ8rPmzePXr16AVClShUWLVpEcnIy06ZN44knnmDlypUF3s/KlSu5//77mTRpEgcOHGDQoEHceeed2S4Bna1hw4bZJk48uxXkL9UOHTpw8uRJvv32W06ePMnUqVNp3LgxsbGxAKxYsQKAa6+9lri4OPr06ZPvrv+1a9f6p5iArL+K9+7dy4EDB/KdT7wR6Of9uedm3bp1qVChAhs3bizwMYhI2RHQBU98fDxNmjRh4cKFQNZ9J2FhYTRv3hyAjh07UrduXZxztGrVirZt27J8+fIC72fy5MkMGjSIG2+8kaCgIPr160dwcLC/4DhXUlJStokTz25vvfVWvvcbERFBly5duPnmmwkODmb06NG8/fbb/sk+d+3axaxZs1iwYAGbNm3i+PHjPProo/nadmpqKlFRUf7XZ77O71/R4p1AP+/PPTch6/zUuSkieQnoggeyZl5+9913gawbHc/8lQuwZMkSmjdvzmWXXUZ0dDSLFy9m//79Bd7Hjh07eOWVV7L9xbpz5052795daMeRkylTpjB16lTWrl1LRkYGs2fPplOnTv79hoaGMmDAAOrVq4fP52PYsGH+mdAvxOfz+XsIAP/XERERhX8gUugC+bw/99yErPNT56aI5CXgC55u3bqRmJjIrl27eP/99/0/+NPT0+nSpQu/+93v2Lt3L4cPH+aOO+4gtwHcwsLCOHbsmP/1nj17/F/XrFmT4cOHZ/uL9dixY/Ts2TPHbSUkJODz+XJsgwcPzvexrV69ms6dO1OvXj3KlStH+/btiYuL4+uvvwayLiGc6e0pqISEBFavXp1tX1WrVqVSpUoXtT0pXoF83p97bm7dupX09HTq1auX722ISNkT8AVP5cqVad26NQMGDKB27dpcddVVAGRkZJCenk7lypUpX748S5Ys4eOPP851O40bN2bu3LmcPHmSpUuX8sUXX/g/e+CBB5g4cSLffvstZsbRo0f58MMPc+1iX7t2LampqTm2gjzqff311/Phhx+ydetWzIxPPvmEjRs3cs011wAwYMAApk2bxtatWzl27BgvvPACnTp18q9fq1atXG9E7tu3L++88w7r1q3j0KFDPPvss/Tv3z/f2cRbgXze9+7dmw8++IDly5dz9OhRnn76ae69915/D8+oUaNo3bp1rutnZGSQlpaGmXHixAnS0tI4depUvvcvIqVTwBc8kNW9/+mnn2br1o+IiGDcuHF0796dihUrMnfuXO68885ct/HGG2/wwQcfEB0dzZw5c7j77rv9nzVr1ozJkyfzyCOPULFiRa644opieaKpb9++3HfffbRu3ZrIyEiGDBnCpEmTaNCgAQD3338/ffv25cYbbyQ+Pp7g4GDGjRsHZP3QP3DggP++jnO1b9+eP/zhD/zyl78kPj6e+Ph4Ro8eXeTHJIUnUM/7hIQEJk6cSO/evalSpQopKSnZ7gHauXMnN910U67rt23bltDQUL7++msefPBBQkND+fLLL4s8t4h4zMwCpmUdTmD64osvLCQkxKKiomzp0qWXvL3ly5fbfffdd9Hr33777ebz+axNmzY5fn7638Lzc6IsNJ332TVq1Mj2799/UfubOnWqRUVFWXBwsG3ZsuW8z3Veq6mV3qbJQ6VIaJLF4qPzvvjovBYpvcrEJS0REREp21TwiIiISMBTwSMiIiIBTwWPiIiIBDwVPCIiIhLwynsdoDCFhITsdc5V9TqHZP1beJ2hrNB5X3x0XouUXgH1WLqIiIhITnRJS0RERAKeCh4REREJeCp4REREJOCp4BEREZGAp4JHREREAp4KHhEREQl4KnhEREQk4KngERERkYCngkdEREQCngoeERERCXgqeERERCTgqeARERGRgKeCR0RERAKeCh4REREJeCp4REREJOCp4BEREZGAp4JHREREAp4KHhEREQl4KnhEREQk4KngERERkYCngkdEREQCngoeERERCXgqeERERCTgqeARERGRgKeCR0RERAKeCh4REREJeCp4REREJOCp4BEREZGAp4JHREREAp4KHhEREQl4KnhEREQk4KngERERkYCngkdEREQCngoeERERCXgqeERERCTgqeARERGRgKeCR0RERAKeCh4REREJeCp4REREJOCp4BEREZGAp4JHREREAp4KHhEREQl4KnhEREQk4KngERERkYCngkdEREQCngoeERERCXgqeERERCTgqeARERGRgPf/AWXsCWyiGFXCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clft = DecisionTreeClassifier()\n",
    "clft = clft.fit(x_train,y_train)\n",
    "y_predt = clft.predict(x_test)# step 4\n",
    "print(classification_report(y_test, clft.predict(x_test)))\n",
    "print('Accuracy of Decision Tree classifier on test set: {:.2f}'.format(clft.score(x_test, y_test)))\n",
    "from sklearn import tree\n",
    "plt.figure(figsize=(10,10))\n",
    "temp = tree.plot_tree(clft.fit(x,y), fontsize=12)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regreession Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "clfr = LogisticRegression(solver='lbfgs')# step 2\n",
    "clfr.fit(x_train, y_train.ravel())# step 3\n",
    "y_predr = clfr.predict(x_test)# step 4\n",
    "# model = LogisticRegression(solver='liblinear', random_state=0).fit(x_train, y_train.ravel())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99        33\n",
      "           1       1.00      0.97      0.99        35\n",
      "\n",
      "    accuracy                           0.99        68\n",
      "   macro avg       0.99      0.99      0.99        68\n",
      "weighted avg       0.99      0.99      0.99        68\n",
      "\n",
      "Accuracy of logistic regression classifier on test set: 0.99\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, clfr.predict(x_test)))\n",
    "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(clfr.score(x_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "logit_roc_auc = roc_auc_score(y_test, clfr.predict(x_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, clfr.predict_proba(x_test)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating divorce')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        33\n",
      "           1       1.00      1.00      1.00        35\n",
      "\n",
      "    accuracy                           1.00        68\n",
      "   macro avg       1.00      1.00      1.00        68\n",
      "weighted avg       1.00      1.00      1.00        68\n",
      "\n",
      "Naive Bayes test accuracy:  1.0\n"
     ]
    }
   ],
   "source": [
    "clfb = GaussianNB()\n",
    "clfb.fit(x_train, y_train.ravel())\n",
    "y_predb = clfb.predict(x_test)# step 4\n",
    "print(classification_report(y_test, clfb.predict(x_test)))\n",
    "print(\"Naive Bayes test accuracy: \", clfb.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "When K = 5 neighnors , KNN test accuracy: 0.9852941176470589\n",
      "When K = 5 neighnors , KNN train accuracy: 0.9705882352941176\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99        33\n",
      "           1       1.00      0.97      0.99        35\n",
      "\n",
      "    accuracy                           0.99        68\n",
      "   macro avg       0.99      0.99      0.99        68\n",
      "weighted avg       0.99      0.99      0.99        68\n",
      "\n",
      "Knn(k=5) test accuracy:  0.9852941176470589\n",
      "Best test score is 1.0 , K = 1\n",
      "Best train score is 1.0 , K = 1\n"
     ]
    }
   ],
   "source": [
    "K = 5\n",
    "clfk = KNeighborsClassifier(n_neighbors=K)\n",
    "clfk.fit(x_train, y_train.ravel())\n",
    "y_predk=clfk.predict(x_test)\n",
    "\n",
    "print(\"When K = {} neighnors , KNN test accuracy: {}\".format(K, clfk.score(x_test, y_test)))\n",
    "print(\"When K = {} neighnors , KNN train accuracy: {}\".format(K, clfk.score(x_train, y_train)))\n",
    "print(classification_report(y_test, clfk.predict(x_test)))\n",
    "print(\"Knn(k=5) test accuracy: \", clfk.score(x_test, y_test))\n",
    "\n",
    "ran = np.arange(1,30)\n",
    "train_list = []\n",
    "test_list = []\n",
    "for i,each in enumerate(ran):\n",
    "    clfk = KNeighborsClassifier(n_neighbors=each)\n",
    "    clfk.fit(x_train, y_train.ravel())\n",
    "    test_list.append(clfk.score(x_test, y_test))\n",
    "    train_list.append(clfk.score(x_train, y_train))\n",
    "    \n",
    "\n",
    "print(\"Best test score is {} , K = {}\".format(np.max(test_list), test_list.index(np.max(test_list))+1))\n",
    "print(\"Best train score is {} , K = {}\".format(np.max(train_list), train_list.index(np.max(train_list))+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test score is 1.0 , K = 1\n",
      "Best train score is 1.0 , K = 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15,10])\n",
    "plt.plot(ran,test_list,label='Test Score')\n",
    "plt.plot(ran,train_list,label = 'Train Score')\n",
    "plt.xlabel('Number of Neighbers')\n",
    "plt.ylabel('fav_number/retweet_count')\n",
    "plt.xticks(ran)\n",
    "plt.legend()\n",
    "print(\"Best test score is {} , K = {}\".format(np.max(test_list), test_list.index(np.max(test_list))+1))\n",
    "print(\"Best train score is {} , K = {}\".format(np.max(train_list), train_list.index(np.max(train_list))+1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MLP Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9852941176470589\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99        33\n",
      "           1       1.00      0.97      0.99        35\n",
      "\n",
      "    accuracy                           0.99        68\n",
      "   macro avg       0.99      0.99      0.99        68\n",
      "weighted avg       0.99      0.99      0.99        68\n",
      "\n",
      "MLP test accuracy:  0.9852941176470589\n"
     ]
    }
   ],
   "source": [
    "clfm = MLPClassifier(hidden_layer_sizes=(5,), max_iter=2000)\n",
    "clfm.fit(x_train, y_train.ravel())\n",
    "y_predm = clfm.predict(x_test)\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_predm))\n",
    "print(classification_report(y_test, clfm.predict(x_test)))\n",
    "print(\"MLP test accuracy: \", clfm.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def confusionMatrix(y_pred,title,n):\n",
    "    plt.subplot(5,5,n)\n",
    "    ax=sns.heatmap(cm(y_test, y_pred)/sum(sum(cm(y_test, y_pred))), annot=True \n",
    "                   ,cmap='RdBu_r', vmin=0, vmax=0.52,cbar=False, linewidths=.5)\n",
    "    plt.title(title)\n",
    "    plt.ylabel('Actual outputs')\n",
    "    plt.xlabel('Prediction')\n",
    "    b, t=ax.get_ylim()\n",
    "    ax.set_ylim(b+.5, t-.5)\n",
    "    plt.subplot(5,5,n+1)\n",
    "    axx=sns.heatmap(cm(y_test, y_pred), annot=True \n",
    "                   ,cmap='plasma', vmin=0, vmax=40,cbar=False, linewidths=.5)\n",
    "    b, t=axx.get_ylim()\n",
    "    axx.set_ylim(b+.5, t-.5)\n",
    "    return\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "# figure, axes = plt.subplots(nrows=1, ncols=1)\n",
    "confusionMatrix(y_predt,'Decision Tree',1)\n",
    "confusionMatrix(y_predr,'Logistic Regression',4)\n",
    "confusionMatrix(y_predb,'Naive Bayes',11)\n",
    "confusionMatrix(y_predk,'KNN',14)\n",
    "confusionMatrix(y_predm,'MLP',21)\n",
    "# plt.subplots_adjust(bottom=0.25, top=0.75)\n",
    "# figure.tight_layout()\n",
    "plt.savefig('Compare Confusion Matrix')\n",
    "plt.show"
   ]
  },
  {
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    "#### Result:\n",
    "So we have successfully trained our dataset into different models for predicting and compare whether a couple will get divorced or not in divorce data set. And also got the accuracy & confusion matrix for each model as well."
   ]
  }
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