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    "# Kaggle's Titanic Starter Competition: 2nd attempt\n",
    "\n",
    "A first attempt at Kaggle's [Titanic: Machine Learning from Disaster](https://www.kaggle.com/c/titanic) I got up to 73% accuracy by doing some preprocessing, training with scikit-learn models and submitting.\n",
    "\n",
    "From [reading around](https://www.quora.com/How-does-one-solve-the-titanic-problem-in-Kaggle) (but not peeking!) it seems that it should be possible to get closer to 80%, and many suspect that those who score much higher are cheating and looking at the full dataset.\n",
    "\n",
    "So my goal for this follow up notebook is to dig a bit deeper and get above 77% accuracy.\n",
    "\n",
    "## Candidates for improvement\n",
    "\n",
    "Some ways I could improve on the first attempt:\n",
    "- Explore the data to get better intuition\n",
    "- Include more previously skipped variables\n",
    "  - Cabin: we could group these by main cabin location (e.g A, B, C, D) in case general location had some influence on e.g how fast folks could get to the life boats.\n",
    "  - Embarked: could include this in case where people departed from is correlated in some way with survivorship; perhaps those from one location were close knit and had an in on the surival boats? \n",
    "- Hyperparameter tuning: we could experiment with some of the available parameters for each model to see if we get better performance against our 30% test set, for instance tree-depth, and regularization parameter C for logistic regression and SVMs\n",
    "\n",
    "In the first notebook, the fancier models, non-linear SVM and random forests, performed no better than logistic regression. One of the benefits of random forest classifier models is that they don't require much tuning; the randomization step of the ensemble of decision trees each looking at different portions of the variables and training samples provides some automatic tuning. As Python Machine Learning puts it, \"We typically don't need to prune the random forest since the ensemble model is quite robust to noise from the individual decision trees.\"\n",
    "\n",
    "So if a robust non-linear model performs no better than an untuned logistic regression model, my sense is that fussing with hyperparameter tuning won't get us very far looking at the same variables of the dataset. I think the next best step is to look more closely at the parameters via exploratory analysis and see if it's worth adding in 'Cabin' and/or 'Embarked' and perhaps whether some other parameters could be ignored completely.\n",
    "\n",
    "## Exploring variables\n",
    "\n",
    "The first step in exploring the variables of the dataset is to look at each individaully to see what their distributions look like. \n",
    "\n",
    "\n",
    "```\n",
    "VARIABLE DESCRIPTIONS:\n",
    "survival        Survival\n",
    "                (0 = No; 1 = Yes)\n",
    "pclass          Passenger Class\n",
    "                (1 = 1st; 2 = 2nd; 3 = 3rd)\n",
    "name            Name\n",
    "sex             Sex\n",
    "age             Age\n",
    "sibsp           Number of Siblings/Spouses Aboard\n",
    "parch           Number of Parents/Children Aboard\n",
    "ticket          Ticket Number\n",
    "fare            Passenger Fare\n",
    "cabin           Cabin\n",
    "embarked        Port of Embarkation\n",
    "                (C = Cherbourg; Q = Queenstown; S = Southampton)\n",
    "```"
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex  Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male   22      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "5                                   Moran, Mr. James    male  NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male   54      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male    2      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female   27      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female   14      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
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     "metadata": {},
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    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "training_data = pd.read_csv('train.csv')\n",
    "\n",
    "training_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dependent variable \"surivived\" is categorical. Let's do some C->C and Q->C analysis on the categorical output variable, 'survived'.\n",
    "\n",
    "### Side by side bar charts for categorical features \n",
    "\n",
    "For C->C analysis I like side by side bar charts. Let's look at \"Pclass\", \"Sex\", and \"Embarked\". For good measure, we'll intepret the first letter of \"Cabin\" as another categorical variable.\n"
   ]
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JkiRJkkqjyRJSwLlA4Vv1ngfGpJSWFbQtAi4jK2j+dt72HaCCbJXTuIjYJqW0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\nSZIq9f8BpJ1VMWsmVHUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10927ce80>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 488,
       "width": 594
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "td = training_data.copy()\n",
    "td[['Cabin_sector']] = td[['Cabin']].dropna().applymap(lambda cabin: cabin[0])\n",
    "\n",
    "def num_survived(var, val):\n",
    "    return td[var][(td['Survived'] == 1) & (td[var] == val)].count()\n",
    "\n",
    "def num_croaked(var, val):\n",
    "    return td[var][(td['Survived'] == 0) & (td[var] == val)].count()\n",
    "\n",
    "cvars = ['Pclass', 'Sex', 'Embarked', 'Cabin_sector']\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(10, 8))\n",
    "\n",
    "bar_width = 0.4\n",
    "\n",
    "for i, cvar in enumerate(cvars):\n",
    "    row = i // 2\n",
    "    col = i % 2\n",
    "    subplot = ax[row, col]\n",
    "    \n",
    "    all_labels = td[cvar].dropna().unique()\n",
    "    all_labels.sort()\n",
    "    bar_locations = np.arange(len(all_labels))\n",
    "    subplot.bar(\n",
    "        bar_locations - bar_width,\n",
    "        [num_survived(cvar, label) for label in all_labels],\n",
    "        bar_width,\n",
    "        color='b'\n",
    "    )\n",
    "    subplot.bar(\n",
    "        bar_locations,\n",
    "        [num_croaked(cvar, label) for label in all_labels],\n",
    "        bar_width,\n",
    "        color='r',\n",
    "        alpha=0.5\n",
    "    )\n",
    "    subplot.set_title(cvar)\n",
    "    subplot.legend(('Survived', 'Croaked'))\n",
    "    subplot.set_xticks(bar_locations)\n",
    "    subplot.set_xticklabels(all_labels)    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variables we used last time, Sex and Pclass both look to have some correlation with surviving, and likely play a role in the success the model had so far in predicting (particularly sex).\n",
    "\n",
    "It also appears that where the passenger embarked from and what cabin sector also have some correlation with surviving:\n",
    "- those who depart from Southampton croak disproportionately \n",
    "- those who stayed in cabin sectors b, c, d and e survived disproportionately\n",
    "\n",
    "So this is promising! Adding in 'Embarked' and 'Cabin_sector' should improve the model.\n",
    "\n",
    "\n",
    "### Histograms for quantitative features\n",
    "\n",
    "For Q->C analysis I like overlapping histograms as it gives an idea of . The quantitative variables we'll look at are \"Age\", \"SibSp\", \"Parch\"  and \"Fare\", and we're plotting each segmented by survivorship, so, for instance, the \"Q->C\" is \"Age->Survived\".\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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/1JmUY/zlii1JkiRJkiSVpTZPbEXE0RGxbCWfpUXafSUi7o+I2RGxMCKmRMQp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TSpfu5MktSYOAdzDrYt1NlbESP3MOo4YCHwIHBlkTptgbHk3rrTN6U0PV/+fXLfPJ4SEaellO6u\n0uZQYAjwBnBQSml5vnwU8DIwOiJ+m1LasgdVJamRK5s6taGHsF3593//dzp27MiFF17I7NmzOfvs\nswFo3bo1EbHBJGrvvfemf//+W9T/qaeeyiuvvMKIESO45ZZbuPXWWyktLWX58uWklIgIrr76as44\n44wN2h111FEMGDCABx54gKFDhzJ06FDatWtX+U3dt7/9bdauXUt5eXmNx9K+fXueeOIJ+vbty1//\n+leOPPJInnzySTp06EDTpk2ZOHEiAwYM4I9//CMXXHABF154Ie3atWP16tWVk7qI4Ktf/eoG/R52\n2GH8+Mc/5nvf+x533nknd955J+3ateMf//gH69ev54orruCll17i6aef3qLfnSRJjYlzsA05B3MO\nVt/qLNgCLgX6kVvifmQ1dU4BdgHKK0ItgJTSmogYRu7tO98B7q7S5qL8cURFqJVvMze/z8O15JbI\nl9XRfUhS9uXfzqINnXjiiRx11FGUl5fz6KOP8sorr7Bo0SIigq5du/KVr3yFAQMGMGDAAJo1a7bF\n/f/whz+kf//+/PSnP+X5559n6dKldOrUiZ49e3LppZdW+wrou+++m//5n/+hvLycN998k4igV69e\nDBo0iLPOOovzzz+/6NuANvWGoF122YUpU6bQt29fXnnlFb761a8yZcoUSktL6dSpE08//TR33303\n48ePZ/r06SxZsoTmzZuzzz770KNHD77+9a9z/PHHb9TvlVdeyb777stPfvITXn75ZdavX0+PHj24\n+OKL+da3vkW/fv02++YiSZIaLedgRTkHcw5Wn2JLNmartpOIfYDpwM9SSldERBnwfeDbKaVfVql3\nF3AmcEbVVVn5c02Af5AL29qklD7Jl78L7A50SSl9WNDmEGAa8IeUUp9NjC8BPHv++Rz22c/W9nZV\nxaQ5c3hhp504dtCgDZ5XllQ/Kv6yqov/dkvbuy39971K/R1zVrcdqpiDzR8yhN3btGno4WzWXxYs\n4L6FC/nXU0/lm9/8ZkMPR9J2wvmXdiRZnH/Veo+tiGgK/Ap4Gxi6mepfzB//VngipbQOeItcsPW5\nfN+tgS7AR4WhVt4b+WP3LR64JEmSJEmSMq0uHkX8PvBl4LCU0qZfcQCl5DaUX17N+eVA5OtR5bip\n+pB75bQkSZIkSZJ2ILVasRURBwP/BYxKKf2pboYkSZIkSZIkbd5Wr9jKP4J4J/A6cF111Qo+F67I\nKlRRvqxK/arlm6u/SYePG1ftuev69KHMjf4kSdqulZWVMXz48IYehiRJkrYTtXkUcWegW/7Pq6vZ\nfX9sRIwFbkopXU4uBDuQ3F5bM6pWzAdlXYG1wJsAKaWVETEf2D0iOqeUPijov+L6G+3ZVYybx0uS\nlG1lZWWUlZUVPbejvglIkiRpR1abYGs18H/k9swqdCCwP/AHcmHWtHz5FHJvRTwamFDQpjfQCng6\npbS2SvkU4Ox8mzsK2hyTPz65VXcgSZIkSZKkzNrqYCultBoYVOxcRJSRC7bKU0q/rHLqPuDHwOkR\ncXNK6eV8/ZbA9fk6Py/o7jZywdY1EfFQSmlZvs3ewMXkArbqnzGUJEmSJElSo1QXb0WssZTSiogY\nRC7gmhoRE4ClwAlAd+DelNI9BW2ei4gxwBBgVkTcDzQHTiP3NsRLUkrztuV9SJIkSZIkqeHVV7CV\nKP6IIimliRHRB7gGOBloCcwBLgd+Wk2bKyPiFXIrtAYB64Dp5N7GOKnuhy9JkiRJkqTtXb0EWyml\n4UC1ryxKKU0DjtvCPsuB8loOTZIkSZIkSY1ESUMPQJIkSZIkSdoaBluSJEmSJEnKpG26ebwkqW5E\nREMPQZIkaYfi/EvaPrliS5IkSZIkSZnkii1JypCUir5wVpIkSfXE+Ze0fXPFliRJkiRJkjLJYEuS\nJEmSJEmZZLAlSZIkSZKkTDLYkiRJkiRJUiYZbEmSJEmSJCmTDLYkSZIkSZKUSQZbkiRJkiRJyiSD\nLUmSJEmSJGWSwZYkSZIkSZIyyWBLkiRJkiRJmWSwJUmSJEmSpEwy2JIkSZIkSVImGWxJkiRJkiQp\nkwy2JEmSJEmSlEkGW5IkSZIkScokgy1JkiRJkiRlksGWJEmSJEmSMslgS5IkSZIkSZlksCVJkiRJ\nkqRMMtiSJEmSJElSJhlsSZIkSZIkKZMMtiRJkiRJkpRJBluSJEmSJEnKJIMtSZIkSZIkZZLBliRJ\nkiRJkjLJYEuSJEmSJEmZZLAlSZIkSZKkTDLYkiRJkiRJUiYZbEmSJEmSJCmTDLYkSZIkSZKUSQZb\nkiRJkiRJyiSDLUmSJEmSJGWSwZYkSZIkSZIyyWBLkiRJkiRJmWSwJUmS1AhExFkRsT7/M7CaOodG\nxKSIWBIRqyJiZkQMjohq54QRcW5EvBARKyJiWUQ8FRHH1d+dSJIk1ZzBliRJUsZFxGeAW4CP8kWp\nSJ0TgWeAw4H7gZuB5sANwIRq+h0NjAN2A34B3AXsCzwSERfX7V1IkiRtOYMtSZKkDIuIIBc+LQRu\nq6ZOW2AssBbom1IalFK6Cvgy8BxwSkScVtDmUGAI8AawX0rpipTSd4EDgSXA6IjYq55uS5IkqUYM\ntiRJkrLtUqAfcD6wqpo6pwC7ABNSStMrClNKa4Bh+Y/fKWhzUf44IqW0vEqbucCtQIv8NSVJkhqM\nwZYkSVJGRcQ+wEjgxpTSs5uo2j9/fKzIuWeAj4GeEdG8oE2qps3k/LHflo1YkiSpbhlsSZIkZVBE\nNAV+BbwNDN1M9S/mj38rPJFSWge8BTQFPpfvuzXQBfgopfRhkf7eyB+7b/HAJUmS6lDThh6AJEmS\ntsr3ye2RdVj+kcJNKSW3+mp5NeeXA5GvR5XjpuoDtKvZUCVJkuqHK7YkSZIyJiIOBv4LGJVS+lND\nj0eSJKmhuGJLkiQpQ/KPIN4JvA5cV121gs+FK7IKVZQvq1K/avnm6m9SlzFjqj13XZ8+lPXtW5Nu\nJElSAygrK2P48OENPYxqGWxJkiRly85At/yfV0cUZlgAjI2IscBNKaXLyYVgB5Lba2tG1Yr5oKwr\nsBZ4EyCltDIi5gO7R0TnlNIHBf1XXH+jPbuKmT9kCLu3aVOTqpIkaTtTVlZGWVlZ0XPVzEO2KYMt\nSZKkbFkN/B+5PbMKHQjsD/yBXJg1LV8+BTgTOBqYUNCmN9AKeDqltLZK+RTg7HybOwraHJM/PrlV\ndyBJklRHDLYkSZIyJKW0GhhU7FxElJELtspTSr+scuo+4MfA6RFxc0rp5Xz9lsD1+To/L+juNnLB\n1jUR8VBKaVm+zd7AxeQCtnF1cEuSJElbzWBLkiSpkUsprYiIQeQCrqkRMQFYCpwAdAfuTSndU9Dm\nuYgYAwwBZkXE/UBz4DRyb0O8JKU0b1vehyRJUiGDLUmSpMYjUfwRRVJKEyOiD3ANcDLQEpgDXA78\ntJo2V0bEK+RWaA0C1gHTyb2NcVLdD1+SJGnLGGxJkiQ1Eiml4UC1ry1KKU0DjtvCPsuB8loOTZIk\nqV6UNPQAJEmSJEmSpK1hsCVJkiRJkqRMMtiSJEmSJElSJhlsSZIkSZIkKZMMtiRJkiRJkpRJBluS\nJEmSJEnKJIMtSZIkSZIkZZLBliRJkiRJkjLJYEuSJEmSJEmZZLAlSZIkSZKkTDLYkiRJkiRJUiYZ\nbEmSJEmSJCmTah1sRcSPI2JKRLwTEasiYklEzIyI6yNit2raHBoRk/J1V+XrD46IascTEedGxAsR\nsSIilkXEUxFxXG3HL0mSJEmSpGyqixVblwGtgN8BNwK/AtYAQ4FXIqJb1coRcSLwDHA4cD9wM9Ac\nuAGYUOwCETEaGAfsBvwCuAvYF3gkIi6ug3uQJEmSJElSxjStgz7apJQ+KSyMiOvJhVtXAwPzZW2B\nscBaoG9KaXq+/PvAk8ApEXFaSunuKv0cCgwB3gAOSiktz5ePAl4GRkfEb1NKc+vgXiRJkiRJkpQR\ntV6xVSzUyrs3f+xSpewUYBdgQkWole9jDTAs//E7Bf1clD+OqAi18m3mArcCLYDzt270kiRJkiRJ\nyqr63Dz++PxxapWy/vnjY0XqPwN8DPSMiOYFbVI1bSbnj/22fpiSJEmSJEnKorp4FBGAiLgS2Bko\nBb4CHAzcDoypUu2L+ePfCtunlNZFxFvAPsDngNkR0Zrciq8VKaUPi1z2jfyxe53chCRJkiRJkjKj\nzoIt4Apym7tX+CO5Rw7XVikrJbf6ajnFLQciX48qx03VB2i3xaOVJEmSJElSptXZo4gppd1TSiXk\nwq0BQCfg9xFxVl1dQ5IkSZIkSapQlyu2AEgpLQQeiojp5B45/B/grvzpwhVZhSrKl1WpX7V8c/U3\n6fBx46o9d12fPpT17VuTbiRJUgMpKytj+PDhDT0MSZIkbSfqPNiqkFKaFxGvAftFxG75PbJeBw4k\nt9fWjKr1I6Ip0BVYC7yZ72NlRMwHdo+IzimlDwou0y1/3GjPrmKePf98DvvsZ7f6niRJUsMqKyuj\nrKys6LmI2LaDkSRJUoOrz7ciQm7j9wR8lP88JX88ukjd3kArYFrBvlxTyK3yKtbmmPzxydoPVZIk\nSZIkSVlSq2ArIrpFxEaPCUZESUSMILfP1hMppZX5U/cBi4DTI+LAKvVbAtfnP/68oLvb8sdrIqJd\nlTZ7AxcDq4HqnzGUJEmSJElSo1TbRxGPA/47Iv4AvA0sJrd5fB9yjxXOBS6qqJxSWhERg8gFXFMj\nYgKwFDgB6A7cm1K6p+oFUkrPRcQYYAgwKyLuB5oDp5F7G+IlKaV5tbwPSZIkSZIkZUxtg63Hgc8D\nhwP7kwuaVgCzgduBm1NKH1VtkFKaGBF9gGuAk4GWwBzgcuCnxS6SUroyIl4ht0JrELAOmA6MSilN\nquU9SJIkSZIkKYNqFWyllF4FLtmKdtPIrfbakjblQPmWXkuSJEmSJEmNU31vHi9JkiRJkiTVC4Mt\nSZIkSZIkZZLBliRJkiRJkjLJYEuSJEmSJEmZZLAlSZIkSZKkTDLYkiRJkiRJUiYZbEmSJEmSJCmT\nDLYkSZIkSZKUSQZbkiRJkiRJyiSDLUmSJEmSJGWSwZYkSZIkSZIyyWBLkiRJkiRJmWSwJUmSJEmS\npEwy2JIkSZIkSVImGWxJkiRJkiQpkwy2JEmSJEmSlEkGW5IkSZIkScokgy1JkiRJkiRlksGWJEmS\nJEmSMslgS5IkSZIkSZlksCVJkiRJkqRMMtiSJEmSJElSJhlsSZIkSZIkKZMMtiRJkiRJkpRJBluS\nJEmSJEnKJIMtSZIkSZIkZZLBliRJUgZFxI8jYkpEvBMRqyJiSUTMjIjrI2K3atocGhGT8nVX5esP\njohq54QRcW5EvBARKyJiWUQ8FRHH1d+dSZIk1ZzBliRJUjZdBrQCfgfcCPwKWAMMBV6JiG5VK0fE\nicAzwOHA/cDNQHPgBmBCsQtExGhgHLAb8AvgLmBf4JGIuLjub0mSJGnLNG3oAUiSJGmrtEkpfVJY\nGBHXkwu3rgYG5svaAmOBtUDflNL0fPn3gSeBUyLitJTS3VX6ORQYArwBHJRSWp4vHwW8DIyOiN+m\nlObW4z1KkiRtkiu2JEmSMqhYqJV3b/7YpUrZKcAuwISKUCvfxxpgWP7jdwr6uSh/HFERauXbzAVu\nBVoA52/d6CVJkuqGK7YkSZIal+Pzx6lVyvrnj48Vqf8M8DHQMyKaVwnM+gOpmjaTgWuBfkDZ5gY0\ne/ZsFrdqtdmBN7Q5y5czb8UKlk+bxvr16xt6OJtVWlrK0Ucf3dDDkCSpQRlsSZIkZVhEXAnsDJQC\nXwEOBm4HxlSp9sX88W+F7VNK6yLiLWAf4HPA7IhoTW7F14qU0odFLvtG/ti9JmP87aRJtKlJxQa2\nAHgeeO1Pf2roodTIvvvua7AlSdrhGWxJkiRl2xXkNnev8EdyjxyurVJWSm711XKKWw5Evh5Vjpuq\nD9CuZkP8LLnsbXu3GvgE+Ez+Z3u1DPh9Qw9CkqTtgsGWJElShqWUdgeIiE7AYUM3a8cAACAASURB\nVMBI4PcRcV5K6a4GHVyl3sDnG3oQNbAAWAicCnyzgceyKbMw2JIkKcdgS5IkqRFIKS0EHoqI6eQe\nOfwfoCLYKlyRVaiifFmV+lXLN1d/k8ZQfb7Whz70pW9NupEkSQ2grKyM4cOHN/QwqmWwJUmS1Iik\nlOZFxGvAfhGxW36PrNeBA8nttTWjav2IaAp0BdYCb+b7WBkR84HdI6JzSumDgst0yx832rOrmCGc\nRZtMrNiSJEmFysrKKCsrK3ouIrbtYIooaegBSJIkqc51Iben1kf5z1Pyx2I7jfcGWgHTCvblmkJu\nlVexNsfkj0/WfqiSJElbz2BLkiQpYyKiW0Rs9JhgRJRExAigE/BESmll/tR9wCLg9Ig4sEr9lsD1\n+Y8/L+jutvzxmohoV6XN3sDF5HZaH1f7u5EkSdp6PoooSZKUPccB/x0RfwDeBhaTezNiH3KPFc4F\nLqqonFJaERGDyAVcUyNiArAUOAHoDtybUrqn6gVSSs9FxBhgCDArIu4HmgOnkXsb4iUppXn1epeS\nJEmbYbAlSZKUPY+Te83g4cD+5IKmFcBs4Hbg5pTSR1UbpJQmRkQf4BrgZKAlMAe4HPhpsYuklK6M\niFfIrdAaBKwDpgOjUkqT6uG+JEmStojBliRJUsaklF4FLtmKdtPIrfbakjblQPmWXkuSJGlbcI8t\nSZIkSZIkZZLBliRJkiRJkjLJYEuSJEmSJEmZZLAlSZIkSZKkTDLYkiRJkiRJUibtUG9FfPnll1nz\nxhsNPYztVrNmzejVq1dDD0OSJEmSJKlGdqhg68+zZrGkoQexHWvVsqXBliRJkiRJyowdKtiCLkD3\nhh7Edmgt8MeGHoQkSZIkSdIW2QGDrT4NPYjt0McYbEmSJEmSpKxx83hJkiRJkiRlksGWJEmSJEmS\nMslgS5IkSZIkSZlksCVJkiRJkqRMMtiSJEmSJElSJhlsSZIkSZIkKZMMtiRJkiRJkpRJBluSJEmS\nJEnKJIMtSZIkSZIkZZLBliRJkiRJkjLJYEuSJEmSJEmZZLAlSZIkSZKkTDLYkiRJkiRJUiYZbEmS\nJEmSJCmTDLYkSZIkSZKUSQZbkiRJkiRJyqRaBVsR0SEivh0RD0bEGxGxKiKWRcQfIuLfIyKqaXdo\nREyKiCX5NjMjYnBEVDueiDg3Il6IiBX5azwVEcfVZvySJEmSJEnKrtqu2DoV+AVwEPAccANwP/D/\ngNuBewobRMSJwDPA4fm6NwPN820nFLtIRIwGxgG75a93F7Av8EhEXFzLe5AkSZIkSVIGNa1l+9eB\n41NKj1YtjIihwAvAyRExIKX0QL68LTAWWAv0TSlNz5d/H3gSOCUiTksp3V2lr0OBIcAbwEEppeX5\n8lHAy8DoiPhtSmluLe9FkiRJkiRJGVKrFVsppacKQ618+YfAbfmPfaqcOgXYBZhQEWrl668BhuU/\nfqegu4vyxxEVoVa+zVzgVqAFcH5t7kOSJEmSJEnZU5+bx39acATonz8+VqT+M8DHQM+IaF7QJlXT\nZnL+2K8W45QkSZIkSVIG1UuwFRFNgXPyH6sGUl/MH/9W2CaltA54i9zjkZ/L99Ma6AJ8lF8FVuiN\n/LF7HQxbkiRJkiRJGVJfK7ZGAv8KPJpSerxKeSm51VfLi7bKlUe+HlWOm6oP0G7rhypJkiRJkqQs\nqvNgKyIuJbfZ+2vA2XXdvyRJkiRJkgS1fyviBiLiu8CNwKvAESmlZQVVCldkFaoor2i3vKB8c/U3\naRwvAS8VPdeHPvSlb026kSRJDaSsrIzhw4c39DAkSZK0naizYCsiLgPGAK+QC7UWFan2OnAgub22\nZhS0bwp0BdYCbwKklFZGxHxg94jonFL6oKC/bvnjRnt2FXM+X+GzHFfDO5IkSdubsrIyysrKip6L\niG07GEmSJDW4OnkUMSKuIhdqzQD6VRNqAUzJH48ucq430AqYllJaW9AmqmlzTP745BYPWpIkSZIk\nSZlW62ArIq4F/pvcM35HpJSWbKL6fcAi4PSIOLBKHy2B6/Mff17Q5rb88ZqIaFelzd7AxcBqYFwt\nbkGSJEmSJEkZVKtHESPiXGA4sA54FrisyGMAb6WUygFSSisiYhC5gGtqREwAlgInAN2Be1NK91Rt\nnFJ6LiLGkNuQflZE3A80B04j9zbES1JK82pzH5IkSZIkScqe2u6xtXf+WAJcVk2dqUB5xYeU0sSI\n6ANcA5wMtATmAJcDPy3WQUrpyoh4hdwKrUHkgrTpwKiU0qRa3oMkSZIkSZIyqFbBVkppOLkVW1va\nbhps2S7u+VVf5ZutKEmSJEmSpB1CnWweL0mSJEmSJG1rBluSJEmSJEnKJIMtSZIkSZIkZZLBliRJ\nkiRJkjLJYEuSJEmSJEmZZLAlSZIkSZKkTDLYkiRJkiRJUiYZbEmSJEmSJCmTDLYkSZIkSZKUSQZb\nkiRJkiRJyiSDLUmSJEmSJGWSwZYkSZIkSZIyyWBLkiQpYyKiQ0R8OyIejIg3ImJVRCyLiD9ExL9H\nRFTT7tCImBQRS/JtZkbE4Iiodk4YEedGxAsRsSJ/jaci4rj6uztJkqSaM9iSJEnKnlOBXwAHAc8B\nNwD3A/8PuB24p7BBRJwIPAMcnq97M9A833ZCsYtExGhgHLBb/np3AfsCj0TExXV6R5IkSVuhaUMP\nQJIkSVvsdeD4lNKjVQsjYijwAnByRAxIKT2QL28LjAXWAn1TStPz5d8HngROiYjTUkp3V+nrUGAI\n8AZwUEppeb58FPAyMDoifptSmlvP9ypJklQtV2xJkiRlTErpqcJQK1/+IXBb/mOfKqdOAXYBJlSE\nWvn6a4Bh+Y/fKejuovxxREWolW8zF7gVaAGcX5v7kCRJqi2DLUmSpMbl04IjQP/88bEi9Z8BPgZ6\nRkTzgjapmjaT88d+tRinJElSrRlsSZIkNRIR0RQ4J/+xaiD1xfzxb4VtUkrrgLfIbVHxuXw/rYEu\nwEf5VWCF3sgfu9fBsCVJkraawZYkSVLjMRL4V+DRlNLjVcpLya2+Wl60Va488vWoctxUfYB2Wz9U\nSZKk2jPYkiRJagQi4lJym72/BpzdwMORJEnaJnwroiRJUsZFxHeBG4FXgSNSSssKqhSuyCpUUV7R\nbnlB+ebqb9IY7qr2XB/60Je+NelGkiQ1gLKyMoYPH97Qw6iWwZYkSVKGRcRlwBjgFXKh1qIi1V4H\nDiS319aMgvZNga7AWuBNgJTSyoiYD+weEZ1TSh8U9Nctf9xoz65ihnAWbfh8De9IkiRtT8rKyigr\nKyt6LiK27WCK8FFESZKkjIqIq8iFWjOAftWEWgBT8seji5zrDbQCpqWU1ha0iWraHJM/PrnFg5Yk\nSapDBluSJEkZFBHXAv8NvERupdaSTVS/D1gEnB4RB1bpoyVwff7jzwva3JY/XhMR7aq02Ru4GFgN\njKvFLUiSJNWajyJKkiRlTEScCwwH1gHPApcVeRTgrZRSOUBKaUVEDCIXcE2NiAnAUuAEoDtwb0rp\nnqqNU0rPRcQYchvSz4qI+4HmwGnk3oZ4SUppXn3doyRJUk0YbEmSJGXP3vljCXBZNXWmAuUVH1JK\nEyOiD3ANcDLQEpgDXA78tFgHKaUrI+IVciu0BpEL0qYDo1JKk2p9F5IkSbVksCVJkpQxKaXh5FZs\nbWm7acBxW9imnCoBmSRJ0vbEPbYkSZIkSZKUSQZbkiRJkiRJyiSDLUmSJEmSJGWSwZYkSZIkSZIy\nyWBLkiRJkiRJmWSwJUmSJEmSpEwy2JIkSZIkSVImGWxJkiRJkiQpkwy2JEmSJEmSlEkGW5IkSZIk\nScokgy39//buPliyujzw+PexZnkRcEiWAL5EEAJoXNEKbghjlrm4RUqXCMSwwB9ExEBWCggKUsbo\nhntTUpsYhFQo0PjCi1hbRGCVygqsG+AOJJC4UUSXjbzIDKijM5DhZUYYBObZP865cOnpvre7p6d/\n5/T9fqoOv+lz+vR9+uHe7qef/p1zJEmSJEmSWsnGliRJkiRJklrJxpYkSZIkSZJaycaWJEmSJEmS\nWsnGliRJkiRJklrJxpYkSZIkSZJaycaWJEmSJEmSWsnGliRJkiRJklrJxpYkSZIkSZJaycaWJEmS\nJEmSWsnGliRJkiRJklrJxpYkSZIkSZJaycaWJEmSJEmSWsnGliRJkiRJklrJxpYkSZIkSZJaycaW\nJEmSJEmSWsnGliRJkiRJklrJxpYkSZIkSZJaycaWJEmSJEmSWsnGliRJkiRJklrJxpYkSZIkSZJa\nycaWJEmSJEmSWsnGliRJkiRJklrJxpYkSZIkSZJaycaWJEmSJEmSWsnGliRJkiRJklrJxpYkSZIk\nSZJaycaWJEmSJEmSWsnGliRJkiRJklrJxpYkSZIkSZJaaZsbWxFxXERcEhF3RMRTEbElIq5eZJ8V\nEXFjRGyIiKcj4p6IODsiesYTESdHxDcjYmNEPBERt0XEUdsavyRJkiRJktppFDO2PgGcARwM/Khe\nl73uHBHHALcDvwlcD1wC7ABcDFzTY58LgSuAvYDPAV8G3gL8bUScMYLnIEmSJEmSpJYZRWPrQ8AB\nmbkcOH2hO0bEq4DPA88BU5l5WmZ+FHgbcBdwXESc0LHPCuAc4EHg4Mw8NzPPBA4BNgAXRsQ+I3ge\nkiRJkiRJapFtbmxl5mxm/qC+GYvc/ThgD+CazPz2vMd4lmrmF2zdHPtgPV6QmU/O2+dh4FJgR+CU\nIcOXJEmSJElSS4375PHvrMebu2y7HXgGOCwidujYJ3vsc1M9HjGyCCVJkiRJktQK425sHVSP93du\nyMwXgNXAMmA/gIjYBXgNsCkz13V5vAfr8cDRhypJkiRJkqQmG3djaznV7Ksne2x/kupwxuXz7j+3\nvtf9AXYfSXSSJEmSJElqjWWlAxinK/hn4J+7blvJSqaYGms8kiRpMNPT08zMzJQOQ5IkSQ0x7sZW\n54ysTnPrn5h3//nrF7v/gk7h7byeo/q5qyRJaqDp6Wmmp6e7botY7Bo2kiRJmjTjPhTxvno8qHND\nRCwD3gA8BzwEkJk/A9YCu0bE3l0e74B63OqcXZIkSZIkSZps425s3VKP7+qy7XBgZ+DOzHyuY5/o\nsc+76/HWkUUoSZIkSZKkVhh3Y+s64DHgxIg4ZG5lROwEfLK++ZmOfT5bjx+PiN3n7bMvcAawGbhi\nO8UrSZIkSZKkhtrmc2xFxLHAsfXNucMFV0TElfW/H83M8wAyc2NEnEbV4JqNiGuAx4GjgQOBazPz\nK/MfPzPvioiLgHOA70bE9cAOwAlUV0M8KzMf2dbnIUmSJEmSpHYZxYyttwLvA34POBJIqnNlva9e\nfnf+nTPzBmAlcHu97UzgWeDDwIndfkBmfgQ4BfgpcBpwEvA94D2ZedkInoMkSVJrRMRxEXFJRNwR\nEU9FxJaIuHqRfVZExI0RsSEino6IeyLi7IjoWQ9GxMkR8c2I2BgRT0TEbRHhlXgkSVJjbPOMrcyc\nAQa67nZm3gmDXZ4wM68CrhpkH0mSpAn1CeBgYCPwI+CNVF8udhURxwDXA08DfwNsoJoxfzHwDuD4\nLvtcSDVj/ofA54Adqb6E/NuIOCszLx3h85EkSRrKuM+xJUmSpG33IeCAzFwOnL7QHSPiVcDnqa48\nPZWZp2XmR4G3AXcBx0XECR37rKBqaj0IHJyZ52bmmcAhVE2xCyNin1E/KUmSpEHZ2JIkSWqZzJzN\nzB/UN2ORux8H7AFck5nfnvcYz1LN/IKtm2MfrMcLMvPJefs8DFxKNXvrlCHDlyRJGhkbW5IkSZPt\nnfV4c5dttwPPAIdFxA4d+2SPfW6qxyNGFqEkSdKQbGxJkiRNtoPq8f7ODZn5ArCa6ryr+wFExC7A\na4BNmbmuy+M9WI8Hjj5USZKkwdjYkiRJmmzLqWZfPdlj+5NUhzMun3f/ufW97g+w+0iikyRJ2gY2\ntiRJkiRJktRKy0oHIEmSpO2qc0ZWp7n1T8y7//z1i91/URfx5Z7bVrKSKab6fShJkjRm09PTzMzM\nlA6jJxtbkiRJk+0+4BCqc23dPX9DRCwD3gA8BzwEkJk/i4i1wKsjYu/M/GnH4x1Qj1uds6uXcziJ\n3dh/yPAlSVJJ09PTTE9Pd90WsdjFmbc/D0WUJEmabLfU47u6bDsc2Bm4MzOf69gneuzz7nq8dWQR\nSpIkDcnGliRJ0mS7DngMODEiDplbGRE7AZ+sb36mY5/P1uPHI2L3efvsC5wBbAau2E7xSpIk9c1D\nESVJklomIo4Fjq1v7l2PKyLiyvrfj2bmeQCZuTEiTqNqcM1GxDXA48DRwIHAtZn5lfmPn5l3RcRF\nwDnAdyPiemAH4ASqqyGelZmPbLcnKEmS1CcbW5IkSe3zVuB9QNa3k+pcWfvVt9cA583dOTNviIiV\nwMeB3wV2Ah4APgz8VbcfkJkfiYjvUc3QOg14Afg28BeZeeOIn48kSdJQbGxJkiS1TGbOAANdnigz\n7wSOGnCfq4CrBtlHkiRpnDzHliRJkiRJklrJxpYkSZIkSZJaycaWJEmSJEmSWsnGliRJkiRJklrJ\nxpYkSZIkSZJaycaWJEmSJEmSWsnGliRJkiRJklppWekA1BzPbN7M9MzMQPs8AMwCM1/4wvYIqXEy\ns3QIkiRJkiSp5owtSZIkSZIktZIzttTh/AHv/wDwSuA04NdHH05jROkAJEmSJElSB2dsSZIkSZIk\nqZVsbEmSJEmSJKmVbGxJkiRJkiSplWxsSZIkSZIkqZVsbEmSJEmSJKmVbGxJkiRJkiSplWxsSZIk\nSZIkqZVsbEmSJEmSJKmVbGxJkiRJkiSplWxsSZIkSZIkqZVsbEmSJEmSJKmVbGxJkiRJkiSplWxs\nSZIkSZIkqZVsbEmSJEmSJKmVbGxJkiRJkiSplWxsSZIkSZIkqZVsbEmSJEmSJKmVlpUOQO32Xb7L\nU/yb+taNRWMZh4goHULjZWbpECRJkiRJS4SNLW2zw/gJP+dbwAOlQ5k4q0oHIEmSJElSg9nY0khM\ncQjw2tJhbEdzLabzx/YTZ5kFpoDpsf3MbeNsNkmSJEnSeHmOLUmSJEmSJLWSjS1JkiRJkiS1ko0t\nSZIkSZIktZKNLUmSJEmSJLWSjS1JkiRJkiS1ko0tSZIkSZIktZKNLUmSJEmSJLWSjS1JkiRJkiS1\nko0tSZIkSZIktdKy0gFIkiRJTfEo9wJfAe4tHcoC1lX/XbeO6elpgBdHSZKWGhtbkiRJ0jwruRd4\ntHQYC9gEwC6bNsHsLExNFY1GkqSSbGxJkiRJHaaYKh3CAtYB32KvXXet525JkrR0eY4tSZIkSZIk\ntZIztiRJkqQWWrd+PbPr17Nq1SpmZmZKhzNRMrN0CJKkPjljS5IkSZIkSa3kjC1JkiSptVYCU8B0\n2TAmRpQOQJI0IGdsSZIkSZIkqZVsbEmSJEmSJKmVbGxJkiRJkiSplWxsSZIkSZIkqZVsbEmSJEmS\nJKmVbGxJkiRJkiSplZaVDkCSJEmSmiQiSocwsTKzdAiSJkxrZmxFxOsi4vKIWBsRmyNidURcHBG7\nl45tUsyWDqDhZksHoNabnp4uHULjmaOFmR+VYA3WPLP8n9IhTJjp0gFIC/L9f/TM6WSJNnTMI2J/\n4E7gl4CvAd8HDgWOAO4D3pGZGxbYPwFO4e28nqO2f8Ct8wzwKWaA8zl/oD2v53pezWOs4LeB126P\n4BpiZqj8bItZZlnFFO0ptqpvNtvwmlJKRJifRZijhZmfhc3NsMhMp1qMyKhqsHM4id3Yf/sHvI3W\ns55rmeXNwBTHlw6npxlmXqxIrmQfHmZfYKpcQEObLh1ALYCc92/m3dZw5ud0/jprxWH4/j965nR0\nmlB/teVQxMuoCqqzMvPSuZUR8Wngw8AFwOmFYpMkSZpU1mAtsJI1tG1u+apWNuIkSU3U+MZW/U3h\nkcDq+QVV7XzgvwAnRcS5mfn02AOU1Dolpx5P+rTn2dlZAKampoZ+jEnPkdQW1mDtMtWiRtFsy5pw\nzZlZNozp0gFI0nbX+MYW1VR3gG90bsjMTRHxD1RF128At44zMElba8vJVleW+sF142dirVnDvrBt\nz3PScyS1hzWYVFvZumacs+IkLR1taGwdVI/399j+AFVRdQAWVZoga148rGC6aByTbGrMP28VML0N\nM5naYOrKK4Hhn+fMqlUTn6NhTdvw0/hZg0nzOCtOTdWWL5abpt+8eS6u5mtDY2t5PT7ZY/vc+kWv\nzPMUa1nLLSMJarI8/+K/1rJ2oD2f47lRB6N52nbOjFXAynJzofpWNQ0B3j/GnzoDwPTMzBh/5vit\nqcdJf57SEjGyGuwx7mHji68QzbWRzcDj9a1vlQxFkiT1qfFXRYyIzwGnAqdm5uVdtl8AfAz4WGb+\neY/HaPaTlCRJI+NVEUfDGkySJPWrZP31ilI/eABz3wYu77F9bv0TY4hFkiRpqbAGkyRJjdeGQxG/\nX48H9dh+QD32Ov+D39xKkiQNzhpMkiQ1XhsORdwPeBBYDfxKzgs4InYDfgIksGdmPlMmSkmSpMli\nDSZJktqg8YciZuZDVJeZfgNwR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      "text/plain": [
       "<matplotlib.figure.Figure at 0x109644b00>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 488,
       "width": 603
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived = td[td['Survived'] == 1]\n",
    "croaked = td[td['Survived'] == 0]\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(10, 8))\n",
    "\n",
    "for i, (var, bins) in enumerate([(\"Age\", 15), (\"SibSp\", 8), (\"Parch\", 5), (\"Fare\", 5)]):\n",
    "    row = i // 2\n",
    "    col = i % 2\n",
    "    subplot = ax[row, col]\n",
    "    subplot.set_title(var)\n",
    "\n",
    "    # side by side\n",
    "#     subplot.hist(\n",
    "#         [survived[var].dropna().values, croaked[var].dropna().values], \n",
    "#         bins=bins, label=['Survived', 'Croaked'])\n",
    "\n",
    "    # overlapping\n",
    "    subplot.hist(survived[var].dropna().values, bins=bins, color='b', label='Survived')\n",
    "    subplot.hist(croaked[var].dropna().values, bins=bins, color='r', alpha=0.5, label='Croaked')\n",
    "    subplot.legend()\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I notice:\n",
    "- Young children survive disproportionately\n",
    "- There may be a correlation between fare and survivorship; those who pay more than the lowest value seem to survive disproportionately\n",
    "- Those with no family aboard (either childeren via 'Parch' or siblings via 'SibSp') perish disproprotinately\n",
    "\n",
    "So each of these variables seems worth keeping in our model.\n",
    "\n",
    "## Re-testing models against richer data\n",
    "\n",
    "Now that I'm confident adding in the previously skipped variables 'Embarked' and the computed 'Cabin_Sector\" could help, I think a good plan is to re-run the same models we tried last time is worth a shot. Here's the updated preprocessor helper function we'll use. Updates since last time:\n",
    "- We include 'Embarked' and 'Cabin_sector' \n",
    "- Since both multiple non-ordinal values, we'll use one-hot encoding to flatten them out into one binary variable for each possible value, e.g embarked becomes embarked_from_C, embarked_from_Q, embarked_from_S\n",
    "- We can handle missing values by setting the flattened out variable values to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# %load titanic.py\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import pandas as pd\n",
    "\n",
    "def make_preprocesser(training_data):\n",
    "    \"\"\"\n",
    "    Constructs a preprocessing function ready to apply to new dataframes.\n",
    "\n",
    "    Crucially, the interpolating that is done based on the training data set\n",
    "    is remembered so it can be applied to test datasets (e.g the mean age that\n",
    "    is used to fill in missing values for 'Age' will be fixed based on the mean\n",
    "    age within the training data set).\n",
    "\n",
    "    Summary by column:\n",
    "\n",
    "    ['PassengerId',\n",
    "     'Survived',    # this is our target, not a feature\n",
    "     'Pclass',      # keep as is: ordinal value should work, even though it's inverted (higher number is lower class cabin)\n",
    "     'Name',        # omit (could try some fancy stuff like inferring ethnicity, but skip for now)\n",
    "     'Sex',         # code to 0 / 1\n",
    "     'Age',         # replace missing with median\n",
    "     'SibSp',\n",
    "     'Parch',\n",
    "     'Ticket',      # omit (doesn't seem like low hanging fruit, could look more closely for pattern later)\n",
    "     'Fare',        # keep, as fare could be finer grained proxy for socio economic status, sense of entitlement / power in getting on boat\n",
    "     'Cabin',       # one hot encode using first letter as cabin as the cabin sector\n",
    "     'Embarked']    # one hot encode\n",
    "\n",
    "    Params:\n",
    "        df: pandas.DataFrame containing the training data\n",
    "    Returns:\n",
    "        fn: a function to preprocess a dataframe (either before training or fitting a new dataset)\n",
    "    \"\"\"\n",
    "\n",
    "    def pick_features(df):\n",
    "        return df[['PassengerId', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']]\n",
    "\n",
    "    # save median Age so we can use it to fill in missing data consistently\n",
    "    # on any dataset\n",
    "    median_age_series = training_data[['Age', 'Fare']].median()\n",
    "\n",
    "    def fix_missing(df):\n",
    "        return df.fillna(median_age_series)\n",
    "\n",
    "    def map_sex(df):\n",
    "        df['Sex'] = df['Sex'].map({'male': 0, 'female': 1})\n",
    "        return df\n",
    "\n",
    "    def one_hot_cabin(df):\n",
    "        def cabin_sector(cabin):\n",
    "            if isinstance(cabin, str):\n",
    "                return cabin[0].lower()\n",
    "            else:\n",
    "                return cabin\n",
    "\n",
    "        df[['cabin_sector']] = df[['Cabin']].applymap(cabin_sector)\n",
    "        one_hot = pd.get_dummies(df['cabin_sector'], prefix=\"cabin_sector\")\n",
    "\n",
    "        interesting_cabin_sectors = [\"cabin_sector_{}\".format(l) for l in 'bcde']\n",
    "\n",
    "        for column, _ in one_hot.iteritems():\n",
    "            if column.startswith('cabin_sector_') and column not in interesting_cabin_sectors:\n",
    "                one_hot = one_hot.drop(column, axis=1)\n",
    "\n",
    "        df = df.join(one_hot)\n",
    "\n",
    "        df = df.drop('Cabin', axis=1)\n",
    "        df = df.drop('cabin_sector', axis=1)\n",
    "        return df\n",
    "\n",
    "    def one_hot_embarked(df):\n",
    "        one_hot = pd.get_dummies(df['Embarked'], prefix=\"embarked\")\n",
    "        df = df.join(one_hot)\n",
    "        df = df.drop('Embarked', axis=1)\n",
    "        return df\n",
    "\n",
    "    # We want standard scaling fit on the training data, so we get a scaler ready\n",
    "    # for application now. It needs to be applied to data that already has the other\n",
    "    # pre-processing applied.\n",
    "    training_data_all_but_scaled = map_sex(fix_missing(pick_features(training_data)))\n",
    "    stdsc = StandardScaler()\n",
    "    stdsc.fit(training_data_all_but_scaled[['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']])\n",
    "\n",
    "    def scale_df(df):\n",
    "        df[['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']] = \\\n",
    "            stdsc.transform(df[['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']])\n",
    "        df[['Sex']] = df[['Sex']].applymap(lambda x: 1 if x == 1 else -1)\n",
    "        for column, _ in df.iteritems():\n",
    "            if column.startswith('cabin_sector_') or column.startswith('embarked_'):\n",
    "                df[[column]] = df[[column]].applymap(lambda x: 1 if x == 1 else -1)\n",
    "        return df\n",
    "\n",
    "    def preprocess(df, scale=True):\n",
    "        \"\"\"\n",
    "        Preprocesses a dataframe so it is ready for use with a model (either for training or prediction).\n",
    "\n",
    "        Params:\n",
    "            scale: whether to apply feature scaling. E.g with random forests feature scaling isn't necessary.\n",
    "        \"\"\"\n",
    "        all_but_scaled = one_hot_embarked(one_hot_cabin(map_sex(fix_missing(pick_features(df)))))\n",
    "        if scale:\n",
    "            return scale_df(all_but_scaled)\n",
    "        else:\n",
    "            return all_but_scaled\n",
    "\n",
    "    return preprocess\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  SibSp  Parch     Fare  cabin_sector_b  \\\n",
       "0            1       3    0   22      1      0   7.2500               0   \n",
       "1            2       1    1   38      1      0  71.2833               0   \n",
       "2            3       3    1   26      0      0   7.9250               0   \n",
       "3            4       1    1   35      1      0  53.1000               0   \n",
       "4            5       3    0   35      0      0   8.0500               0   \n",
       "\n",
       "   cabin_sector_c  cabin_sector_d  cabin_sector_e  embarked_C  embarked_Q  \\\n",
       "0               0               0               0           0           0   \n",
       "1               1               0               0           1           0   \n",
       "2               0               0               0           0           0   \n",
       "3               1               0               0           0           0   \n",
       "4               0               0               0           0           0   \n",
       "\n",
       "   embarked_S  \n",
       "0           1  \n",
       "1           0  \n",
       "2           1  \n",
       "3           1  \n",
       "4           1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocess = make_preprocesser(training_data)\n",
    "\n",
    "td_preprocessed_unscaled = preprocess(training_data, scale=False)\n",
    "\n",
    "td_preprocessed_unscaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "   PassengerId    Pclass  Sex       Age     SibSp     Parch      Fare  \\\n",
       "0            1  0.827377   -1 -0.565736  0.432793 -0.473674 -0.502445   \n",
       "1            2 -1.566107    1  0.663861  0.432793 -0.473674  0.786845   \n",
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       "4            5  0.827377   -1  0.433312 -0.474545 -0.473674 -0.486337   \n",
       "\n",
       "   cabin_sector_b  cabin_sector_c  cabin_sector_d  cabin_sector_e  embarked_C  \\\n",
       "0              -1              -1              -1              -1          -1   \n",
       "1              -1               1              -1              -1           1   \n",
       "2              -1              -1              -1              -1          -1   \n",
       "3              -1               1              -1              -1          -1   \n",
       "4              -1              -1              -1              -1          -1   \n",
       "\n",
       "   embarked_Q  embarked_S  \n",
       "0          -1           1  \n",
       "1          -1          -1  \n",
       "2          -1           1  \n",
       "3          -1           1  \n",
       "4          -1           1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "td_preprocessed = preprocess(training_data, scale=True)\n",
    "\n",
    "td_preprocessed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree training/test accuracy: 0.84 | 0.82\n",
      "Random forest training/test accuracy: 0.96 | 0.79\n",
      "Logistic Regression training/test accuracy: 0.81 | 0.80\n",
      "Linear SVM training/test accuracy: 0.79 | 0.78\n",
      "Kernel SVM training/test accuracy: 0.88 | 0.81\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "for name, should_scale, model in [\n",
    "        (\"Decision Tree\", False, DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)),\n",
    "        (\"Random forest\", False, RandomForestClassifier(criterion='entropy',\n",
    "                                    n_estimators=10, \n",
    "                                    random_state=1,\n",
    "                                    n_jobs=2)),\n",
    "        (\"Logistic Regression\", True, LogisticRegression(C=100.0, random_state=0)),\n",
    "        (\"Linear SVM\", True, SVC(kernel='linear', C=1.0, random_state=0)),\n",
    "        (\"Kernel SVM\", True, SVC(kernel='rbf', random_state=0, gamma=0.10, C=10.0))]:\n",
    "    pre_processed = td_preprocessed if should_scale else td_preprocessed_unscaled\n",
    "    X = pre_processed.loc[:,'Pclass':]\n",
    "    y = training_data['Survived']\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "    model.fit(X_train, y_train)\n",
    "    print(\"{} training/test accuracy: {:.2f} | {:.2f}\".format(\n",
    "            name, \n",
    "            accuracy_score(y_train, model.predict(X_train)),\n",
    "            accuracy_score(y_test, model.predict(X_test))))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bummer that this only seems to have added a modest boost. Let's try submitting an the LGR anyways."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression full training set accuracy: 0.81\n"
     ]
    },
    {
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       "      <td>-0.474005</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0.827377</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.565736</td>\n",
       "      <td>0.432793</td>\n",
       "      <td>0.767630</td>\n",
       "      <td>-0.401017</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId    Pclass  Sex       Age     SibSp     Parch      Fare  \\\n",
       "0          892  0.827377   -1  0.394887 -0.474545 -0.473674 -0.490783   \n",
       "1          893  0.827377    1  1.355510  0.432793 -0.473674 -0.507479   \n",
       "2          894 -0.369365   -1  2.508257 -0.474545 -0.473674 -0.453367   \n",
       "3          895  0.827377   -1 -0.181487 -0.474545 -0.473674 -0.474005   \n",
       "4          896  0.827377    1 -0.565736  0.432793  0.767630 -0.401017   \n",
       "\n",
       "   cabin_sector_b  cabin_sector_c  cabin_sector_d  cabin_sector_e  embarked_C  \\\n",
       "0              -1              -1              -1              -1          -1   \n",
       "1              -1              -1              -1              -1          -1   \n",
       "2              -1              -1              -1              -1          -1   \n",
       "3              -1              -1              -1              -1          -1   \n",
       "4              -1              -1              -1              -1          -1   \n",
       "\n",
       "   embarked_Q  embarked_S  \n",
       "0           1          -1  \n",
       "1          -1           1  \n",
       "2           1          -1  \n",
       "3          -1           1  \n",
       "4          -1           1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fully_trained_lgr = LogisticRegression(C=100.0, random_state=0)\n",
    "\n",
    "X = td_preprocessed.loc[:,'Pclass':]\n",
    "y = training_data['Survived']\n",
    "\n",
    "fully_trained_lgr.fit(X, y)\n",
    "print(\"Logistic Regression full training set accuracy: {:.2f}\".format(\n",
    "        accuracy_score(y, fully_trained_lgr.predict(X))))\n",
    "\n",
    "test_data = pd.read_csv('test.csv')\n",
    "test_data_preprocessed = preprocess(test_data, scale=True)\n",
    "test_data_preprocessed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_predict = fully_trained_lgr.predict(test_data_preprocessed.loc[:,'Pclass':])\n",
    "\n",
    "submission_df = test_data_preprocessed[['PassengerId']].copy()\n",
    "submission_df['Survived'] = test_data_predict\n",
    "\n",
    "submission_df.to_csv(\n",
    "    'titanic_submission_logistic_regression_2.csv',\n",
    "    index=False)\n",
    "\n",
    "submission_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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q9/Xv5BQHyf9IecKMLlxsRhl6W8PK5cznFQwO8x47nNspH/zt3b1bcvfsDq1i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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "image/png": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='kaggle-submission-logistic-regression2.png', width=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is just under a 2% boost from last time. *sigh*\n",
    "\n",
    "## Beefier trees and forests\n",
    "\n",
    "Well, let's try some different parameters for trees and forests to see how it looks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree depth 3 training/test accuracy: 0.84 | 0.82\n",
      "Decision Tree depth 6 training/test accuracy: 0.88 | 0.80\n",
      "Decision Tree depth 9 training/test accuracy: 0.92 | 0.78\n",
      "Decision Tree depth 12 training/test accuracy: 0.97 | 0.79\n",
      "Random forest training/test accuracy: 0.96 | 0.79\n",
      "Random forest 50 estimators training/test accuracy: 0.98 | 0.80\n",
      "Random forest 10 features training/test accuracy: 0.96 | 0.81\n",
      "Random forest 10 feature 50 estimator training/test accuracy: 0.98 | 0.82\n"
     ]
    }
   ],
   "source": [
    "for name, should_scale, model in [\n",
    "        (\"Decision Tree depth 3\", False, DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)),\n",
    "        (\"Decision Tree depth 6\", False, DecisionTreeClassifier(criterion='entropy', max_depth=6, random_state=0)),\n",
    "        (\"Decision Tree depth 9\", False, DecisionTreeClassifier(criterion='entropy', max_depth=9, random_state=0)),\n",
    "        (\"Decision Tree depth 12\", False, DecisionTreeClassifier(criterion='entropy', max_depth=12, random_state=0)),\n",
    "        (\"Random forest\", False, RandomForestClassifier(criterion='entropy',\n",
    "                                    n_estimators=10, \n",
    "                                    random_state=1,\n",
    "                                    n_jobs=2,\n",
    "                                    max_features='auto')),\n",
    "        (\"Random forest 50 estimators\", False, RandomForestClassifier(criterion='entropy',\n",
    "                                    n_estimators=50, \n",
    "                                    random_state=1,\n",
    "                                    n_jobs=2,\n",
    "                                    max_features='auto')),\n",
    "        (\"Random forest 10 features\", False, RandomForestClassifier(criterion='entropy',\n",
    "                                    n_estimators=10, \n",
    "                                    random_state=1,\n",
    "                                    n_jobs=2,\n",
    "                                    max_features=10)),\n",
    "        (\"Random forest 10 feature 50 estimator\", False, RandomForestClassifier(criterion='entropy',\n",
    "                                    n_estimators=50, \n",
    "                                    random_state=1,\n",
    "                                    n_jobs=2,\n",
    "                                    max_features=10)),]:\n",
    "    pre_processed = td_preprocessed if should_scale else td_preprocessed_unscaled\n",
    "    X = pre_processed.loc[:,'Pclass':]\n",
    "    y = training_data['Survived']\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "    model.fit(X_train, y_train)\n",
    "    print(\"{} training/test accuracy: {:.2f} | {:.2f}\".format(\n",
    "            name, \n",
    "            accuracy_score(y_train, model.predict(X_train)),\n",
    "            accuracy_score(y_test, model.predict(X_test))))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try submitting with the Random forest with 10 features (default is square root of the number of features which would be ~3) and 50 estimators\n",
    "\n",
    "### Submitting beefy forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beefy forest full training set accuracy: 0.98\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>cabin_sector_d</th>\n",
       "      <th>cabin_sector_e</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex   Age  SibSp  Parch     Fare  cabin_sector_b  \\\n",
       "0          892       3    0  34.5      0      0   7.8292               0   \n",
       "1          893       3    1  47.0      1      0   7.0000               0   \n",
       "2          894       2    0  62.0      0      0   9.6875               0   \n",
       "3          895       3    0  27.0      0      0   8.6625               0   \n",
       "4          896       3    1  22.0      1      1  12.2875               0   \n",
       "\n",
       "   cabin_sector_c  cabin_sector_d  cabin_sector_e  embarked_C  embarked_Q  \\\n",
       "0               0               0               0           0           1   \n",
       "1               0               0               0           0           0   \n",
       "2               0               0               0           0           1   \n",
       "3               0               0               0           0           0   \n",
       "4               0               0               0           0           0   \n",
       "\n",
       "   embarked_S  \n",
       "0           0  \n",
       "1           1  \n",
       "2           0  \n",
       "3           1  \n",
       "4           1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fully_trained_beefy_forest = RandomForestClassifier(criterion='entropy',\n",
    "                                    n_estimators=50, \n",
    "                                    random_state=1,\n",
    "                                    n_jobs=2,\n",
    "                                    max_features=10)\n",
    "\n",
    "X = td_preprocessed_unscaled.loc[:,'Pclass':]\n",
    "y = training_data['Survived']\n",
    "\n",
    "fully_trained_beefy_forest.fit(X, y)\n",
    "print(\"Beefy forest full training set accuracy: {:.2f}\".format(\n",
    "        accuracy_score(y, fully_trained_beefy_forest.predict(X))))\n",
    "\n",
    "test_data_preprocessed_unscaled = preprocess(test_data, scale=False)\n",
    "test_data_preprocessed_unscaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         1\n",
       "4          896         0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_predict = fully_trained_beefy_forest.predict(test_data_preprocessed_unscaled.loc[:,'Pclass':])\n",
    "\n",
    "submission_df = test_data_preprocessed[['PassengerId']].copy()\n",
    "submission_df['Survived'] = test_data_predict\n",
    "\n",
    "submission_df.to_csv(\n",
    "    'titanic_submission_decision_tree_2.csv',\n",
    "    index=False)\n",
    "\n",
    "submission_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VQHoHQbGi2In4aqwx0VgiJtHYo0EEI4ot0dhbomhiiSgaUBEVEQRpLkVQYCnC0vsCS933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\nq7iKLHqJBCjslQiuTi+rVWFP3wfWWqhOm7pFV1uLMtVisXf7UcPdnn0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f3tBWl9Gft9PT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\nnw5DsmEJ3I0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "image/png": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='kaggle-submission-random-forest2.png', width=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submitting various tree depths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing tree of depth 3 for submission\n",
      "deep_tree depth 3 full training set accuracy: 0.82\n",
      "preparing tree of depth 6 for submission\n",
      "deep_tree depth 6 full training set accuracy: 0.86\n",
      "preparing tree of depth 9 for submission\n",
      "deep_tree depth 9 full training set accuracy: 0.90\n"
     ]
    }
   ],
   "source": [
    "for depth in [3, 6, 9]:\n",
    "    print(\"preparing tree of depth {} for submission\".format(depth))\n",
    "    deep_tree = DecisionTreeClassifier(criterion='entropy', max_depth=depth, random_state=0)\n",
    "    X = td_preprocessed_unscaled.loc[:,'Pclass':]\n",
    "    y = training_data['Survived']\n",
    "    deep_tree.fit(X, y)\n",
    "    print(\"deep_tree depth {} full training set accuracy: {:.2f}\".format(\n",
    "            depth,\n",
    "            accuracy_score(y, deep_tree.predict(X))))\n",
    "    test_data_predict = deep_tree.predict(test_data_preprocessed_unscaled.loc[:,'Pclass':])\n",
    "\n",
    "    submission_df = test_data_preprocessed[['PassengerId']].copy()\n",
    "    submission_df['Survived'] = test_data_predict\n",
    "\n",
    "    submission_df.to_csv(\n",
    "        'titanic_submission_decision_tree_depth{}.csv'.format(depth),\n",
    "        index=False)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tHPz3qCm6CNOqUBVb6+eB43t4AeaSNRvk9k9GJQ0RP7d7a+nZvG7iuP7jZsqg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\nALoCPavz5Gc/JobLL543V+6/4jxnz8V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Cmb5yQ1bcAxeJAAIIIIAA\nAggggAACCCCAAAIlT4AAtOQ9c+4YAQQQQAABBBBAAAEEEEAAAQQQQACBEiNAAFpiHjU3igACCCCA\nAAIIIIAAAggggAACCCCAQMkTIAAtec+cO0YAAQQQQAABBBBAAAEEEEAAAQQQQKDECBCAlphHzY0i\ngAACCCCAAAIIIIAAAggggAACCCBQ8gQIQEveM+eOEUAAAQQQQAABBBBAAAEEEEAAAQQQKDECBKAl\n5lFzowgggAACCCCAAAIIIIAAAggggAACCJQ8AQLQkvfMuWMEEEAAAQQQQAABBBBAAAEEEEAAAQRK\njAABaIl51NwoAggggAACCCCAAAIIIIAAAggggAACJU+AALTkPXPuGAEEEEAAAQQQQAABBBBAAAEE\nEEAAgRIjQABaYh41N4oAAggggAACCCCAAAIIIIAAAggggEDJEyAALXnPnDtGAAEEEEAAAQQQQAAB\nBBBAAAEEEECgxAgQgJaYR82NIoAAAggggAACCCCAAAIIIIAAAgggUPIE/l8AAAAA///GZiwmAABA\nAElEQVTsfQe8XUXV/aT33ntCQm8fvRmKdKRK/xARLKCCgoCIIIIiKDaQ8seGFEHwk54QepUqSg1I\nSSEJCem9vbT/Xue+uW/f/eaUW97Lu++t+SXvnHvO1DXlzKzZs3erDeKccjNnzox+HfjcEvWUt0SA\nCBABIkAEiAARIAJEgAgQASJABIgAESACRIAIEIHqQ6AVCdDqqLQ9BnZxL3+2vDoyy1w2CgLb9eno\n/vz54a5Nq1autfz/0asz3cNTuXHRKOBXYSKd27Z29x82yvXv1Na1a93K3Tdpkbvstc+qsCTMcjUj\n8J3t+rmvbdXHrV2/wS1bs96d+uQn7pOlNdVcJOadCBABIkAEiAARIAJEgAgQgSpAoMkRoFv26ui+\nLosjWZ+7NvLnqelL3QNTFmeC8oiR3d1Bw7u7dbKwgljrtW/NrfqF1elb9nbnbd9fSC7nFqxe5770\nBBeLmRpDC/B08PBu7pd7DonaBtr7Va/Pdnd9tLAFlJxFLAUBEKBPHjXG9WjfOgr+2LSl7nsvflpK\nVAxDBEpG4Me7DHQnjOkZhV+1boM74dEpbtKS6iFAv71tX/f5od3cANlIkM9y5BbKt/nfc1e4H1d4\nQ+Enuw50O/br7Hp2aBOlhXH+sxVr3NMzlrmb3p1Xm3r6pSHzjM3Z727fz+EsEeZtD8l87c4PC79D\nF+3Y3w3q3C49owEfK9aud1fKtw3XkPvRzgPdLv07u94d27iV4qeTjHPwO2nxanfPx4vcs58uCwXj\nMyJABIgAESACRIAIEIEWiECTI0APFQLzmj0HRxNp1MfclWvdvg98nKlqsLgf1Llt5BcLhe++MMM9\nJQuFana/FCwOG9E9XyaSXNVcm5XN+96Du7ob9h6aJ0B/+q/PogVfZVNhbM0FgRwBOloI0DZRkUiA\nNpeara5yXLLzAPe/m/aKMg0C9IsTplTFRuU2vTu6G/cZ6vp2zM0xQqivlPJc85/Z7u9CvJXjTtq0\np/v+DgNcB+x8xrhZK9a6M5+ZlkgeN0ae9bwLWcWc6zsy9/IO485zx2zqOreNL4v3G7qKkLA7dsLk\neuX8/g793cmb9XLtwbomuLfmrXRfe2Z6LIGaEJSviAARIAJEgAgQASJABJoZAk2OAAW+tx8wwu3U\nr1Me6j++Nz+S5sw/CNx8c5u+7myRzPDuDZn0Qlqy2t3Pdh/kjh7VIyoGpfyqvTYrm38SoJXFs7nH\n1lwJUJTrtgOGu94d2rr7Ji9yN76TXTKuudd5UyxfNRKgvUUCc9zh2DzISU8n4SoHUNw5QgCWKnmI\n7/1PdhsUbWwlpYN3OBVynBDIs2Wj2LrGyPO1nxviDhzWrSBpu7GC/vnM0WNc13bp2BVEVPsjJCWs\n21AojH02Y/kad/BDk+xj/iYCRIAIEAEiQASIABFoYQg0SQIUug1vP2Ck6KnL1QYm+Zi8xh2BwgT7\nkcM3cf3kSBocJAa+/ORU9/b8VbkIqvgvFkOXyBEvSE9AT9rpT00LLnaquIjMeokIkAAtEbgWGqy5\nEqC6H1jpsxZa1U262Jq8qhYJ0LsOHOG271u3KYs5CXToPiEqerYQtT3HyZH+bUVC1Lu0OYv3Z6/o\no48dOVrI/JyUNt5PWlwjqk0WuLfmrXL7Denqjtmkhxvcpe44edxmb0PnGfn4ya6D8qd1fFksAYrn\nIEoHyhF4bOKmudXr1kfH/r3w64q1G9xxoibB64kdKmUfJ/M96DGGQ5yvzV4hm+RzojkfMDz/f/q7\noyR/nXwk4udvoh4GR+npiAARIAJEgAgQASJABFouAk2SAEV14GgvJvve3f3RIvfT18MGO3640wB3\nihyF8o6LYI8Er80ZAU38YBHII/DNubbLL1tzJUCvFGk5kDFwIfKlfOQYQyURqDYCdAchPm+VUyn+\nBDfU8hw2bnK9DVk7Z7ntvwvcNW/MKQq674qBqG9s3Scf5j9zV0ZGovIPam/GCwE4slv76BckTk97\n6hMHv941dJ6TpEvL7YMgOB/6wib54/92PnehHH3/yha9fVFjiU1spN+6/4h8PCBQUW90RIAIEAEi\nQASIABEgAi0XgSZLgMLAwPgjRud38JeKWOdR4yfXk360E3ErLdByq5Ylby4IjO7e3rVvkxOHxiLO\nS0JbI0gkQJtLjTdMObISoJv17OA+XLS6YTLRALFOkO/E8K45ibiHpy5xP3h5ZgOkwigrhYAlQJu6\nEaQrxBDRcaNzRpuw0XSxtC+0M+vQvx6VtthHjPHAwbDTkTJnKcbde8gokSjtEAVZJnOeEx6bmpd8\n1PFYgnPCtCXughfr2n1D51mrKUI+ccR8Cxk34MolQP/8+eFu9wGdo7hCp3l0+0mb7/1VJHeBFRwJ\n0AgG/iECRIAIEAEiQASIQItGoMkSoKgVLdmD3+Nk0XGRWdxaP/fKsbTLEiyxwmLp+f/Tz43p0TF/\nxB5xw80RyQ5YMP2tWI+Pc9A1CgMFYuvATZEFzlefnhbnNXqOhcIwWZzjtNbDU5a4X71ZnEQI0jpr\n675OTvW7xXLs7qxnpxeQwP59K4n/n7OWu0temeUuFSMTB4iV2r7KSq2cInPThTwb/8kS9/9qrcdi\nwfbzPQa7ncWCanfRbZY7UOZqy7ba/e7tubFGpID7WDHCs05Mv94kOvcekXiv3mOQ6G6ts1jrgZm+\nbI2744MF9SzD+ve43iE4DRWcVklGz3puerRYwSLuwGHdI71rqwVwGIT4QIgZGM2w7vQte7sTx/SK\n4vDlgB9f7j+JHtkHpG61O1h0l10k0sPrRISmvcQN3YFpxiv2kvZzhZR9g5QbYW55f4GDpI92IOWv\nGzvUbSVHIjuqI3jIy5Qlq93V/57tXpUje2kOC70viAEsb7TG+5+3ap27R47zvfzZctF9OKJiRpDK\n7Ruhtgjrw4ePzB3Z9BJUKAcWrm/NWxG115D+Ol9We0WbvVMWtT0EY0CLo48XvlS38Lf+8Rth7pAw\nvWrDvCGSUuf+89N6XtHeYN1ZHz+FJ7S99xauivLqj2HawEgDeij71BpI+eV/5jiQEnHuRDky+3Xp\n1xgXQv0aG0CR9JKAhn57mqi+QBrXjR0SHQ9Fu0J7AqZZST+Ef/KoOiNICAeDLb+R46mbyzHebrU6\nR9B/spRZl60SbR5j0R4DO0cYIg/e1Uj/nCdj8/sLV7sHRMfn07VWnSEth2OuwMo7HKmev6pOHyLG\njH/IN+H6t4vTC1psXnz6+hrXntD250keX5LxOu5UQynjmU7bjs/AAHGesllvOYrcVtTEyPgljW9x\nzTp38uOf1CPZjhjZ3Z0p7XOYSBnqfgsjP2+KtXN8Z4rptzpvlsA6fNwkd5pI9KHvDRLpP50exjrU\nuf0m23H4z+8tcLfLNybJQSLw13sNibxg7M7aLh46bBM3ukdO2jLNKKM2Woi2CD3k78vYkcWhDz0q\nx9+7SD+Fw/gO4z1xbpxISY6SzTE4q+OyIfN8znZ9ozmJzxe+rbBUv2Ot3vZyCFB7vD10vN+2H308\n3ufJXzWZSgLUo8IrESACRIAIEAEiQARaLgJNmgDFgkAbHrDK8PH+cbH87vU8pend+tVeg90hYmVe\nL65DVT9VCIczYnRtXi4kyfG10iDTlq5xh8riLc6BcHhalP97YqGUhYFOz5Yf6er3k4WQXSX6s7YS\nMiPJgfgAgYbFQZKVWeFXHAjlHwcIZS2p8sLM5W7L3h0SreMiP88IcXH283XWYX0eNU5IE4vrr27Z\nJ7/o9P5whWTaMYoARVgQT2llRtgXZUH5DbWgHDuoi1j1HZY3NvHOglXuJJG4SXL6mCPyepXoFLtL\nsPQOBPlZ8l8v4v07f0U4LL4vD+AKPyjT3QePdJD8THI48ripSN2gfSHOciRAK9030BYXCWHvF8Vx\n5QCJBxLuzg/rMIzzi+fARuv7BZkEAiWJjLG66l6SdvB11Q5A/ILw6lsruRWXPo6aguwObWLYNmzb\nhY1T99tQv9bSvf79Xw8a6bp7xcgqwtDGkHqdv0UeNQGKzYTRPToktlXUzw2yEQJDdHGu3DYPPceX\n7TIwcSzyaUMiDBahTxKVJ96SuH8Xd7VHaOP84XkpeYGkn3ZZ25PwY+6YRwqtW6OOSh3PdB70+Hz/\n5MXRZg02U6wLSdCFDNvYcCDIr5KNHIxjxTpNYIFQnSkbZJ5gjIsLbRVkopd+t3rC7XchFI82KIj3\nIMVvnphMjOf6zJi88SM7dth0sAl0yU4Do40NqV73G9nwxCZZFmdVmmBj8abazcpQeEu2eknahswz\nCMoHhXj1G3u+XrTe0lLmOb58uu1hvD33nzPqbcJqlUfAOG6sBQ46X6VI5Pp88UoEiAARIAJEgAgQ\nASLQPBBo0gQoILb6nvRiVpNR8JtkLf62/YdHko7w5x0WcZCA6SDHi61118U1692pT0yNjrF5/7jq\nxVuaREFuIVIncVXKwkCnByIE0o9aCk2/1/nEPcqGMP1EKg2SZtph4aAfwS+kgmBJWfvFIsTqF0M8\nIOi00QcdN+JqLSKpHQVXy9c8P3OZ++ZzhSSoxgn5gsSoP9Lq4wURA1JRY4hwD8tiDBJN2uFI3vK1\n64UYbCOEmS6li6Rxjnt0at77g4eNEmng3NE9TzRZQsN71vnEMysN9J1a/W06RSzw1wqIbQQPm5eQ\ntDLSuF/yhIWmdsAU5YIUI/xYB9xKJUAbom/Y/Pm+BqmznlIG7eLUW2g/+t62eUjuJhEFWgoI7Vlb\naAYJfv3ew+q1U2ym+P5g23BI4lK3jSx1ocsQ6teaDEEbWintGRs+3smjfD+NIwC8X3/VefTPEA8k\naZFnSKK2lvqBP91t8O56IUF/P7E+CVpumw/lCektEMk/XEHu600aP+Yi3a9t1SfqW/q9BImkV3GF\ng6GUez7OZvyk1LzkUsr9hVT5L/YcUq89oQ5FaLxgDLCEDNIvdzzzedHjM6QDh8h4osclP57aTbzf\n7zvMfU76hHbon3B2PEdfuviVmdHJDO0/7d63fd/2tH+McbnUXD2y39e996/LiCymGT6EJfL+tdLC\n+L4f8ODHeULVx2mvGB9ukE0y9Ae0xxtSSFP0Uahk8BbP9ffKxm1/67lOlvLojR3kzY8DDZlnLVkK\n8vykx6a4WSvWysZKHUlcTJk1BnZD2/YP7xcS3yA2vREk5ONSaYePiUEq7bQRqCx1p8PynggQASJA\nBIgAESACRKB5ItDkCVAsCrXEFxYGJz8+1S0VQkhbArVHwHR14Qg5jm15h8mwlcCD5M95YjlUS4GF\nJAL94g1x2QWZj99f7YK6lIWBTi9ElOj3Pl0s7n739hwHw1FwyEecVBGO/V8pxqVALMNhEQKDD1r6\n8DkhLb9lSEu9+IwCyh8shK7+d11ceH7TPkPdPnJU3jssmq1Uh8XJ+0U9PSjH1n8mUpaQ/AG58Kks\n5t8VSU24m2WxjsWed1g849i+liY8d/t+cvSzTwGhow1qgUg5UxmdSDJcAemeS3cemCcSNBGGRdkj\nsvD1kjHIO9Qp/FCkWb2DaoIT5Jg+CCe4EOGqJQPhB0QFyCccM/Tu+2IE4kub987Hg+dIrxQCtKH6\nhs8riE+oFfi5SHl6h76Gxb4mQnWdeH9xV4s1rCQfKZJ0IYf2jHrxUth2nHhCFtLaojL69KWvziow\nKIJj5/vL8VxPIKW14Sx1ofttqF9rAlSXC20cbdQTvoeKRDv6p5eM037tfVw/e3v+KnfuCzPyUrTw\nByJMS++GCCNbD6W0eWv05VmREodKA12eA6XfHyrSi9ALiLJrItaWqZQx1uNUbl4QD4igQWpDBkT6\nje/MzY/FGFdhMXzsoK7R2KYlaysxnvmyhMZnvPu3SI3/6o3ZkbXs3QRPbEi8IEfx4ez4BtL2ZpFA\n1GPPLXJqAOG8s/3JP0+66rbv/WGc+PP78yM1JP4Zjuyfs22/AgJcW/K2+U2ShEY/+cWeg/Njpt5I\n9emFrloSO0u/Lqc9alxCkrk2f3qM0HlrqDxbvaJeurWcMusyaXVGKI+PX/vx91ftPsgdJd8R7+B/\ngqjhwdiB8eJiUW2D8cm7iTJngD5VOiJABIgAESACRIAIEIGWjUCTJ0BRPSBpzhYC0xMQ0AGHe28l\nHpNfL/0A/9ZBWsBL1IG8gN61kK5HTOS19B38amkxxKsXKU2RAAVBEjKcgLJpAw0oi5VgxDM4K8Vi\npYTgxy6wIbV5yMNhdQDXyMJTH7+0us3sAgrxg+iGsYk4PYpWVxgIGhDjqBPrrDVYSFMe8OCkiGSx\nZY2TOkGc2qAC8qcljvRxRLRHkAbXBnTJaikfxPnkjKXuuy/k9FECB032h4g2hIHDIvcakTTzknpI\nsxQCtKH6BvIIUuMrYp0YBJt11ogH1ChA92tWd6tIdO8iumvhgFNIShnvzhAS5XuyseHHDk2gwJLw\nBULE+nfQ1aelgxHeOyvpaDdHdBvOUhd6HMlKgKK/niiL+KTj/j6/oavOo39v1UL457hqyVn8tkeG\nK9HmfyxH308QQhBuvkh97n3/R9F91j+5MpUvfYb0ys2LbU+hcTOuXJUaz3z8dnxGm0Tbx2ZSnNM6\nJTG+nfF0oWVxH04bwEG8vxJL57cKMZ3V6baPMGnjxF+kr3tpv89kk21/kdyEs+Nl3PcMfjW5HPqu\nw0/IxZGMIb94ZvtYMYS8xqUcArQh8qylSlFOrQqgnDIjLjgbh67nnI/6f0PSyvLJyZPcPgS+P5gb\n0BEBIkAEiAARIAJEgAgQgaogQFFN4w/fxI0Ugwwhl6T/Sy8GEPZfc1YIKRNvuAiSIiDsRDAmcta/\nXqQ0RQI0acGl9WuhcEkqA/QCOlRO/R6L4CRpDSxuNPlqF3d28YO8gUC0hi/w3DtdD3iWVBa8t/rf\ntH9N9GBxfIqoPrCkHYhSrW9WtzlbvhBeyAOc9atJEkhG/kQMLHkJUdv2cjHU/bV1UCwB2pB9A7kM\nSQ7X5V70yyqLxxoH7Sfu3mJlrSD7cBojK3Gr38XVu48HV60uwRJEug2jP6TVhW6/WQjQJJJX5zHp\nXucR/lAGEANxRlosKac3B2w7LrXNa9IRZfyhHGWFZHVWZ8uUNP6lxVluXvSR22LrS7cH5FOPT6F8\nJ41n8K/bNn6HjMnguXd2Q0JvzHg//mrJsGIxt2XVZJpPQ19vliPoYwfnJP3Rty4VqXpv0M5KDYb6\nnW0jxUit6jESaSdttCLPNq1isNG42LEKcVsXl7e45za8/52WZ7zXqhmsrvW08D6dpKvdGEw6iaHj\nsZur+h3ui8HfhuVvIkAEiAARIAJEgAgQgeaHQNUQoFrfla4GLDTtkWr9Xi8qsi6wtVScJWZ0fEmL\nfuShEgsDnV6IKNHv0xZo2nhA2gJLk6WhcuoFdlpcwELHZ/1bnOwCC+Gt0+nHSb3qMJbM0Qsj27b0\n0XYfhz4qD5y1LjgQAtqY0l8/WOiuVke+fRz+qqUXoTYAuujgNAGDNEKL+chj7R8tcZbFvw6Le912\nKt030toi0td1GGpj8JPk9PH1UJuxR7S11KZtc5rQjktTS6Lb8un4stSFxj7UrzWJgfykEVhxedbP\ndR7xPAsRpElfXUeVavO27wE7GEu78l+fZZJ0tWXS/VqXPct9OXmx+dBkcZa0dV8odzxDejq+LGSs\nbo/wf8bT06LNwri8P3/Mpq5PrdGwNALTxqHTQn0nbZ4hrK4X+NckpB3XQ/3EHpW/Q8ZnrZIDacQ5\n2w+hegI6h+OcbQfFtEeNC+ogtBGn09V5Ay5eElc/h/9y86x1rSNfVu9rOWX25XlW9LP2qz2ynlUn\nNFRWfFmk+L3aGR+Xvb4ye4X7qrRnOiJABIgAESACRIAIEAEiUDUEKKpKS4z5qgstePw7XPWiwkoe\nan/6Xi8eLUmg49OEgA7v7yuxMNDphYgS+x6WcuMkurTfNCy031A5NUaaxPNlt1ct4WEXsRanLEfW\ndPpZyAabhpVO1AYyQsfvNBFkF2h2wYl6gtEaf7RaYwGdnlrPrCaDNeZp9YM4NQkFTNMIU50P3Beb\nHsJo3JP6hlURgLDW6bhCbcz6t791/m2bgl/b5jxBgHe2PSTpD4R/OF3PSE+T4Dq+LHWh8x7q1zot\npK2P7uN3KU7nEeHTJIzhRx931vm0+Su1zSMNLTmJ33DAEHr7HhG9fpAGi3O2TMUQTqE4S82LzUeW\n9qTT132hEuOZji+kv1WnjXvdHvEbakJgtCnOaf29dpMwLox/rtPCeHi2qL7weki9H33da2AXd5Po\npPXqPmwd67Lq8dTHYduwt5bu3ydddTvP0q9tO7B5TUpL45Jl/Ld588SwfZ72XUjKM8hjb9UeeQ+1\n66TwSeX176yaklAa3q+/WgloPId+28lLVrvPD+mWJ+e9f3xfoN5E6xb273glAkSACBABIkAEiAAR\naDkIVBUBqgkfVFGS7jBfhVpZPkiZYydMrmfZ3fv1V30k2i5E9CIljbQpd2GA/Oj0NAHh86rf27x6\nP/6q/Ybi8v5w1X5D5dSLziwL9qRFmcUpywJIE5KWiNPl8PdI4yGxGO8NlNg866OUVsolJ2UEq7O5\n2Ky+Smtgw6eZ5aoX7DoPafWDuJMwzZJ2Q/aNtLaI/Ok2FGpjaWWwEp52M0RbLNZ6XxGv1f2axSgK\ndMnefsDIfDvQ7VS34SxEie5fobq2dauP/abhEvde5xF+dP7jwmipcV2nlWrzPl095vpn/oq6A5F0\nhUiFWmfLVAzhZOPyv0vJC9qTNrZVbD4qPZ7pvmXHOl9OfbXG1/S7tPti+65u+7pNxaWTVsdJEp5W\ndUmWzTWdD9sPyyETdbyhe41LaEywYeLyFvfchve/4/AFduMOH+16tM99+OLq2YaPU0fi07NXrd5I\nfw+tP//bGu7DPPCqf8+ODFt6Pxft2N/972a986Q5nr8nOp6PFxKUjggQASJABIgAESACRKDlIlBV\nBCiqSUuBZlnYWeMHMBAUJyHpm4E27gHpmWPEwrQ3PKIXKXELAh+PXRgUuyhGPDq90KIo7b3PS5a4\n4vyGyqkX2KH3Oi7ca/LaEkSl4KTVFOD489j70o2naCMfdjFkSU4tIaqPpltyFGWz1qPxDIuyNNdG\nFM0ulrwf/+iUqH3pthqqaxtfsQtdG16nh3JVsm9kyX+xbcjmH7+1OgG9eLZkpdVpaA1jZTkmCcL1\nYdFF3EXIdLh7Jy1yl72WI+V0G7btO/Js/qT123Lr1iQX/dR5xIMs41EcAVqpNq/zuYdI+n17275u\nuz6d8npw9XsYucFRVoz73pVSJh826VpsXmx7y4KtTr/S41mxfcuSvhi+1mJQSHEYw94XSd2TijAy\no9t+JQhQtAFtPE5viJ0jxhNBmMGhNF5KMnqQ4U9DWVQPJa2/M1lwsd9UX7ZK5VlLQwO768So30L5\nXnXwSqprC4FvHayu+6PokCy/56NFrreoSJgmkpdJ0r1avQGiy6JO4cmjxuQ3MjHmnx5jaA8E7oOy\n6YkrHMrwE9lECRnAjDzwDxEgAkSACBABIkAEiECzR6DqCNBiF3ZxC/ikmtVpgFw7bkKOoEIYvXhL\nI2ArsTjX6YVIpbT3upyl+g0RnBojveDU6el7TZjYhWgpOOn00+oB+cilUWctOiQJpMl1TXxrciJ0\n3N8uREuV1iu2rZZLkhWbHnDUuCf1jVBbRXjtdFyhNqb9xt1bScS7ZeH909c/c1qiDTzOOS/McM+K\nbknvbJvLIg2piQXEo4+l6/jQvtMkxdL6Yrl168uprzqPeK5Jfu1P32uSXBPMlWrzOi19/4MdB7hD\nhnfL6wX070CCHjZucv4oqy1TscSjjzfpmiUvNh9Z2pNOU/eFSoxnOr4sfcuOBYePm5Tf9NP5rMS9\nbvsgz05JUNuC9Cy5bDcz4EdLz+v+rsf0kJ5ghE1yIM8mHDHada0V/79/8mJ36auzYoNYY1LFtAM9\nlqEMaQbB7Df14pdzBsQqkedfiA5rLdEcW+CUF58uX+MOemhSrC9dPyjzaUJm/keOssc5PS7Cj96E\nCoWx/htifAily2dEgAgQASJABIgAESACTROBZk+A6sVWlgk2qkkfR7SLUR2fXYzbKraL4lIm3zq9\nEKmU9l7nqVS/oQW0XmCH8qXTxf0v9hzsDh/RPXpsrU+XgpNOP8vC1i4KQ8f09NE6kFiQqJm+rKbA\nwJEn2KKC1P6xxFjIj/Yfd68lgCxGoTB6cZeFdLNx6PZQ6b6RpU3oOgy1MZvfuN/aGJIn4zVpHYrb\ntjl7fD6UFqSVrth1UCSdaPHW8eHdn96b764Viak4pwmnEFbl1m0oXZ1HvA/hYsNpgkKT/5Vq8zY9\n+/vc7fu5L23e23WqlToDtte/Pdf9fuL8yKstUyljrE0z7ndSXnL5qNtgyaJfVaej+0IlxjMdX5Z6\nthsGSYYFdb5LudfjDurTSy7GxaWJQfjXund9GGsMCSotfiYbIRgH2omUKlwpbQP1qqVL04ylxZGS\nPp9JV0ueppGtWvpdS4xWIs/A72kxTNTN631JynjCuyT9sHoTBVHYUxmhaPW4iPc4+n7nhwtDXqNn\njTk+xGaCL4gAESACRIAIEAEiQASaDALNngC1E+a0RZCVNrELHr14CxEXumb1wg3P09LWYf19Wnpp\n7308uJbqN7SA1gtsLErTJN40SWVxK2WRosuSZRGtj0ICi5Cld5CkWufZy58tdzCIBOILDvkOGZlC\nuMflWJ4naTwJFwUq4s83t+nrzpYjwN6lEalX7DrQHTe6Z+Q9Sx34eP11Y/YN5EG3oVAb8/lMu2oy\nEcQxJKF+LoS7N5oCIzrXvDGnXjQ6fdsm63mWB/qYMPD2ElfwW2wb1sRiKG1dN6XULfJknc2jJk2s\nX/y27VqTGfZdqW0+lK59BmLoL/uPyOte1X3XlqmUMdaml/Q7KS+adA/VaVK8lR7PdNvO0ressZss\n0sFJ5Ul6p8sKf6HNKB1eq6RJGuv1kW2M2/dPXuQwpsJl3eDR6fp7bQU9rc/oPCyTwQhqRYB/Vqfb\nkN181XGg3cMv+iGcnaeUm2ds7v71wBFisK9tdHRcpx2614b9agRsHJVvL8Qz8nVGjAV2bZwq6xin\nN16S2oLPY2OPDz5dXokAESACRIAIEAEiQASaJgLNngDFBPhROcLWR/RRwWEBc9JjUwr0yOmqsSTH\nb96c4255v84KsZbSw6Lqghc/dY9NX6qjyN9jAYEFs3elLM71YjG0qE5779PGtVS/oQW0XmAj7tCR\ncjyH+8oWvd0FO/TPW0X/QBZFXxS1At6Vskix0jJJi0Wko/WGJUlX6oXjvFXrXM269W5wl3ZRVpMk\nVO45eKTbpnfHyB8WZrfHkG6Rh5g/llSaL+kf8vCk/HFfHcy2a6SZRkLr8Li3cZTbN4ppX0hft6FQ\nG4OfLA66OcdLHwcBDRwmL65xo3u0j4Kiz8RZfdbSWvD8qBjaOV/6c8jZjRFL+OXacJ0EoG3jOk4r\n+RTq141BgCJPd320UCTlZuvs5e+1VCAeWom0SrT5fGIJN3Z80PmwuGcxZpWQVOqrpLzoY9iIqJjx\nvtLjWbF9C2OP3vzBGHnG08nHkVPBivGgxwl4SbJSb/O1VDJ2YgypqElckHBzhMSDZChcOeOL1VMZ\nR9jqPos034FuVMmrdlv26ijGeXpF38L7hKC1x721dfOkMd1iqNVxIL1K5lnnP+7+pWM3FWNJuTmW\n3qCI8582nsaFw9h5wz7D8ptbIXUIOqyW2sdze2Q+rT50XLwnAkSACBABIkAEiAARqH4Emj0Biiq6\nUMg3kHDe4YghiMtXZ6/wj6LrH/Yb5vYSQxzeQQpi3wc+9j+jq5XSw7HQU8QAhDeS5D3buPC8mAWx\nj0cvdEJESdp7Hw+upfoNLR71Als4puhY8IsiMfmNZ6brJN3/btrLXSQGErw0HhZ1vxJpvFuFIPTO\nkgpZcdISJIgLBOVpT04rIAwR9z8OGelGdMsRYvCXdDxVS5jAr3fItyXD/TtcbTj4f1yI8e/9M0yo\nQa/gLv07R3ECN+/0sUY8myRkHgyMrFgrjEStQ5mA/+judWVCesUSoIiukn2jmPaFtHUbCrUx+Mnq\n9MaFDpN0tB046s0RhHvkkyXuwpdm6ijcwcO6uZ/uPihv/Agv/2iOuCOuh8TgxqDObaOwqI+75Ggm\njmhqd9SoHpHBEH20NNSvNZlSat3qdHGPPD55FKw654gKPEPcd36w0F0tOv+0Q7v4shw9rz09HEk/\nWyK5Em0e4/IJY3pGFpz1RpPOC9rVyTKO4CAz8muPQOvNDZD4X37yk1RDdzp+f19uXkDE6yPXiPcJ\nGQPODYwB+I5Idchx/nk+eVfJ8ayUvqU395Ap6Of88/vz3Y3v1OXRZ3aznh3cV7fq4wZ3bufOfHZ6\nwfjk/cRd9Tjh/aD/f/O5GQXSkmivkJQe3i1HYsJvEsEN//q4uo8b1zgpcO0n6V63MbTBP4oKhutE\nFYN32PyCvtxetRKZ8GO/cyBztT5R9Ht7osAa40N7/t4/ZxQYEkI7/d7/9M8bCkM8tm8iX5XIsy9f\n0jU3rtRt/mT5fuuNxlCfjksPaekxG2FBav641hidDgfyE6cDEAYOG9ZaF3SW+tDx8Z4IEAEiQASI\nABEgAkSg+hFoEQQoqknr9cRvTJwnioQGrJR2F0JgW7E83KN9bqLs34cIJTsBh19Zo7h35q90s0Th\nf285MrZtn44FZAn8wGVZGOR81v3Vi8UQUZL2vi6mhiNAkYYnQXHsD1jgCNwmPTq4LWShrF1IUjO3\ngKojZrLiZBcwSAeSP2/Ny9XFcCE9t5aFqdcBh/dpUo7wo48h4jdckpRSzodzN+491O07pKv/GV1R\nZx8tXu3WyLWHLICR557y35M5Vv+dlYxBJCsl7JtzVzhQoLBAvnXvTvnjwFEi8gftOdRe/fuka6X6\nRjFtEfkphaSJK4dVNwF/wMTia8NbCSG8xwYJ2vByacub9+wo7bh9XnoZ70NtGM8tmYxnM2VMADGP\nxTdIo5GKiMd7uFC/biwCNJcDF0nLvTN/lbS19W5b6TN6wwB+4qRjy23zWsoUhBvwmrVijVskdYCx\nFJihz3gHCcCjxk8u2HDSZAr8ob/AMvkaAX2AENKPTltSQDT6uOy1EnmxUqBIA2MijgJDjWlfIUlx\nrBiWtK2BmEqOZ6X2rdDYh/4wdUlNlH+MXb0k/91rdUNK0dyxEybHnqiwGOO3Hif0e/8dxTe5j6Sx\n64DO0TFq7yfL2K3r0IeLIwj9+yzXo2Xj4ie75fT/ev/YIJ0qeUU7HSX92m8W4H3otIDu0/ATNz6F\n2hDSQXpDu7bPb7IgDjgr1Zh76lwl8uzjSroW+/3WEvuIN4vOW52+1tXtn+P7/LZ891fJ+AW3hUja\nDutaR5zjmbUwn7U+EJaOCBABIkAEiAARIAJEoHkg0GIIUEzSHzhslBtSeyQuqfqwEIOhDRgyCbnT\ntxQJjO37Fyx4Qv4QzzxZtAyslQrLSuzpuPRiMbSQS3tfTFxxfkPSeXqBjXBYzOVMTehYCu9xbPjL\nYvHXSsvmFlDFSZD4mCGdd/UegyNCwT+Lu2KhBQnV94WQSnIaU+8vSfLI+8H12s8NcQdKnrK4uAUw\nFq5XyGLbS83GxYVFHxyI+7i44sLq55XqGxq3UFvVaeJet6FQG7P+035b8ibrwhrE5WkiVZXWfpH+\nf4XIOlapb7B5Gn/4JkGS0/qD5Hh/IcNAioWw0tKV5dStTlf3MxCyk5asdpvKJkWaC0l26zDltPkQ\nYaXj1vfA4Q8iefc7JXmH97lNA+gIDddg1r5bibwgP1eLtPAR0ofDuYGPnLNqFPC0UuNZqX0LbQSq\nWzY3G1e1Wa53KZcABQncq0NbkdJLRiskCVkvM/LAGkOCnyQp8FAccc+0ruE4P3gedyrEqr5I6tdW\nGjguPZxiidOxiTDl5jkuXf1cjyt4njbP0Tpd4T+OwMW7OPdb+c4elPE7izhChHQx9RGXDz4nAkSA\nCBABIkAEiAARqC4Eqo4A1UYGrOL/LNDDcMyBw7oXSHv6cFiQQCr0+3IEFoRMkgPJ9X0hTrx+SOsX\nkkw4Are5SCKcuXWf6PW4qUvcRWKgpRinpcpCRhXS3uu0tN8kXWoIo8ksHMM+8pHJOqoC8gr6Du/4\nYIH7znb9IlKnwKP8ABH8wsxl7uznZ9hX0W8soEAcgRCCKxYnhL9u7JDoSHmIBEH6z0v658SkHyWq\n/kBC5RHRKdkR7JQ4kEX66JzyGrzFMeez5IjrcCOBoj0vrlkXSapeLkf3LCEMf9BNdo0Y8tlEHXP3\n4dFOP1i42n3ruenuV3sNcTv26xQRoJe+Mss9MGWx91b0tdy+odtXqK3aDOlFfil92cZnF/tpC3Ed\nfg9RfXHRjv3dGCEEQ1QM+guMqvziP/WNKel4cP9rqZP9h3YNEnKQcgShiLb4/DGbRrqJQe4c9+iU\ngjEHqjhukiO14IVQ3+XWLfKl+xna3573fiRW7Qe6Q0d0D0qsI193i47QX4se5DRXapsH7hfIcd7R\ngnucwWmBzH0kY8w1b8yup7bE5wvj8cVy3BV917qs40ml8oL0gQcMmsV9HzAmPTMjfDy+EuNZuX0L\nFu+hmkCrS7C4Qsfms58uc1f86zP7KvG3Hidul+/GraJj+/eifiau72HT4VvPTg+Ok6GE9BwBfadU\nyfhQ3NgowRF0/63SfnD64NXPVrizZFwOOasiAd+VU56YGunPDvlP6psYj6Cu4ycZsC8nz6F82Wd6\nXME7raPX+sVvvVGV5TsRigPP0OfRxzB2hMZs+EEb/YcckQ+pcSi2PhAfHREgAkSACBABIkAEiEB1\nI1B1BGil4IaE1VZCMnUS8qy9EF3zV611f/9oUeZFls8HFs17DOwsZFnu+DyOqcF6+LtCpDZnFydh\nBL2WOLpYI6wFjnouW7vO/fm9BUXpiCsHNxjCGNKlfSQRCjIURweh/21jOGCxlxht6CLMzhyR+AOZ\nMHvlGvfG3JWZ2wfiOGBoN9damtcGWTCjfd0puiW1TtBKl61SfaPS+UqLTx+FBrlw2lPFG3HBYh76\nJkFwtJJVNf6DbMYiulh3hkiKDxQdiXDoD2iLpcRTbLql+Nd1jn4zRY48g5wq1pXT5qGOAIQhxlIc\nEYeDhOD/fbwoc3uH9PT2YngOx9+XrlnnPl5UE1kZL7YclcgL0sT3YZ/BXaPTAiCdO0nZ/rtoVbTJ\nkyVPG3s8Q7vYsa+M54InjhejXqYtXRORb5Ueg2zfQ595fPqSWIIwDj/o2fSbT1mlwOPiinuOet1N\nvnPIM/I5bVmN+7u00zSHjZGDZAMW48pjopoB0slpDmQ6NsIwV0EdvL9gdUltutQ8p+VvY7+37Qb5\nwUbTKzIP0/q1Q/kspT5C8fAZESACRIAIEAEiQASIQHUg0GIJ0OqonqabyzgCtOnmmDlrzghAf6K2\nYh2np7M5Y8CyEYGWjoDVBRxnsb2l48TyEwEiQASIABEgAkSACBCBlogACdCWWOsVKDMJ0AqAyCgq\nhsB3Rf3CN2pVTSBS6O/97Vt1VporlhAjIgJEoMkicOv+wyNVKMhgqVLgTbZwzBgRIAJEgAgQASJA\nBIgAESACZSFAArQs+FpuYBKgLbfum1rJIf05Xo69esvUIUvhTS3PzA8RIAKVRQBqC67Ytc5Se8jw\nTWVTZGxEgAgQASJABIgAESACRIAIVBMCJECrqbaaUF5JgDahymhhWYEuO5Cek0VPJXQUniQ6O7sp\nCzp3iy7fn75enGGWFgYhi0sEqh4B6MZcILq7oZ/0mE16usPEmFet6thI+vPcf87IpGOz6oFgAYgA\nESACRIAIEAEiQASIABHIhAAJ0Eww0ZNFgASoRYS/GwMBGLx4+ugxBYSnTneGGM05+KFJ+hHviQAR\naGYI7C2GpWD0zBOetnhPi3Ghc16YYR/zNxEgAkSACBABIkAEiAARIAItGAESoC248ssp+r2HjHJb\n9OoQRQGrwIeOI+lUDp4Mmw0BEKDPHbOpWF/OWQnXoWBl/YynprnZK9fqx7wnAkSgmSEAY0fX7DnY\nta4/DESWv7/xzPRmVmIWhwgQASJABIgAESACRIAIEIFyESABWi6CLTT8lbsNcnsO6uI2bHDu/YWr\n3NnPU9qmhTaFRi/2dWOHuFHdO7iOteJfS2rWuSemL3W/nzi/0fPCBIkAEWh8BLbs1dFdtGN/179T\nW9e2lgWdKdLfd3ywgMfeG786mCIRIAJEgAgQASJABIgAEagKBEiAVkU1MZNEgAgQASJABIgAESAC\nRIAIEAEiQASIABEgAkSACJSCAAnQUlBjGCJABIgAESACRIAIEAEiQASIABEgAkSACBABIkAEqgIB\nEqBVUU3MJBEgAkSACBABIkAEiAARIAJEgAgQASJABIgAESACpSBAArQU1BiGCBABIkAEiAARIAJE\ngAgQASJABIgAESACRIAIEIGqQIAEaFVUEzNJBIgAESACRIAIEAEiQASIABEgAkSACBABIkAEiEAp\nCJAALQU1hiECRIAIEAEiQASIABEgAkSACBABIkAEiAARIAJEoCoQIAFaFdXETBIBIkAEiAARIAJE\ngAgQASJABIgAESACRIAIEAEiUAoCJEBLQY1hiAARIAJEgAgQASJABIgAESACRIAIEAEiQASIABGo\nCgRIgFZFNTGTRIAIEAEiQASIABEgAkSACBABIkAEiAARIAJEgAiUggAJ0FJQYxgiQASIABEgAkSA\nCBABIkAEiAARIAJEgAgQASJABKoCARKgVVFNzCQRIAJEgAgQASJABIgAESACRIAIEAEiQASIABEg\nAqUgQAK0FNQYhggQASJABIgAESACRIAIEAEiQASIABEgAkSACBCBqkCABGhVVBMzSQSIABEgAkSA\nCBABIkAEiAARIAJEgAgQASJABIhAKQiQAC0FNYYhAkSACBABIkAEiAARIAJEgAgQASJABIgAESAC\nRKAqECABWhXVxEwSASJABIgAESACRIAIEAEiQASIABEgAkSACBABIlAKAiRAS0GNYYgAESACRIAI\nEAEiQASIABEgAkSACBABIkAEiAARqAoESIBWRTUxk0SACBABIkAEiAARIAJEgAgQASJABIgAESAC\nRIAIlIIACdBSUGMYIkAEiAARIAJEgAgQASJABIgAESACRIAIEAEiQASqAgESoFVRTcwkESACRIAI\nEAEiQASIABEgAkSACBABIkAEiAARIAKlIEACtBTUGIYIEAEiQASIABEgAkSACBABIkAEiAARIAJE\ngAgQgapAgARoVVQTM0kEiAARIAJEgAgQASJABIgAESACRIAIEAEiQASIQCkIkAAtBTWGIQJEgAgQ\nASJABIgAESACRIAIEAEiQASIABEgAkSgKhAgAVoV1cRMEgEiQASIABEgAkSACBABIkAEiAARIAJE\ngAgQASJQCgIkQEtBjWGIABEgAkSACBABIkAEiAARIAJEgAgQASJABIgAEagKBEiAVkU1MZNEgAgQ\nASJABIgAESACRIAIEAEiQASIABEgAkSACJSCAAnQUlBjGCJABIgAESACRIAIEAEiQASIABEgAkSA\nCBABIkAEqgIBEqBVUU3MJBEgAkSACBABIkAEiAARIAJEgAgQASJABIgAESACpSBAArQU1BiGCBAB\nIkAEiAARIAJEgAgQASJABIgAESACRIAIEIGqQIAEaFVUEzNJBIgAESACRIAIEAEiQASIQEMhsP/Q\nbu7UzXu5LXp2cN3at2moZBgvESACVYTAv+ascDe9O8+9NntFFeWaWSUCRCAOARKgccjwOREgAkSA\nCBABIkAEiAARIALNHgGQn78bO6TZl5MFJAJEoDQETn96GknQ0qBjKCLQpBAgAdqkqoOZIQJEgAgQ\nASJABIgAESACRKAxEbh1/+Ful/6dGzNJpkUEiEAVIQBJ0K88Na2KcsysEgEiEEKABGgIFT4jAkSA\nCBABIkAEiAARIAJEoEUgMPHkLVpEOVlIIkAESkNgac06t/u9H5UWmKGIABFoMgiQAG0yVcGMEAEi\nQASIABEgAkSACBABItDYCJAAbWzEmR4RqD4Etv7bf6sv08wxESACBQiQAC2Agz+IABEgAkSACBAB\nIkAEiAARaEkIkABtSbXNshKB0hAgAVoabgxFBJoSAiRAm1JtMC9EgAgQASJABIgAESACRIAINCoC\nJEAbFW4mRgSqEgESoFVZbcw0EShAgARoARz8QQSIABEgAkSACBABIkAEiEBLQoAEaEuqbZaVCJSG\nAAnQ0nBjKCLQlBAgAdqUaoN5IQJEgAgQASJABIgAESACRKBRESAB2qhwMzEiUJUIkACtympjpolA\nAQIkQAvg4A8iQASIABEgAkSACBABIkAEWhICJEBbUm2zrESgNARIgJaGG0MRgaaEAAnQplQbzAsR\nIAJEgAgQASJABIgAESACjYoACdBGhZuJEYGqRIAEaFVWGzNNBAoQIAFaAAd/EAEiQASIABEgAkSA\nCBABItCSECAB2pJqm2UlAqUhQAK0NNwYigg0JQRIgDal2mBeiAARIAJEgAgQASJABIgAEWhUBEiA\nNircTIwIVCUCJECrstqYaSJQgAAJ0AI4+IMIEAEiQASIABEgAkSACBCBloQACdCWVNssKxEoDQES\noKXhxlBEoCkhQAK0KdUG80IEiAARIAJEgAgQASJABIhAoyJAArRR4WZiRKAqESABWpXVxkwTgQIE\nSIAWwMEfRIAIEAEiQASIABEgAkSACLQkBEiAtqTaZlmJQGkIkAAtDTeGIgJNCQESoE2pNpgXIkAE\niAARIAJEgAgQASJABBoVARKgjQo3EyMCVYkACdCqrDZmmggUIEACtAAO/iACRIAIEAEiQASIABEg\nAkSgJSFAArQl1TbLSgRKQ4AEaGm4MRQRaEoIkABtSrXBvBABIkAEiAARIAJEgAgQASLQqAiQAG1U\nuJkYEahKBEiAVmW1MdNEoAABEqAFcPAHESACRIAIEAEiQASIABEgAi0JARKgLam2WVYiUBoCJEBL\nw42hiEBTQoAEaFOqDeaFCBABIkAEiAARIAJEgAgQgUZFgARoo8LNxIhAVSJAArQqq42ZJgIFCJAA\nLYCDP4gAESACRIAIEAEiQASIABFoSQiQAG1Jtc2yEoHSECABWhpuDEUEmhICJECbUm0wL0SACBAB\nIkAEiAARIAJEgAg0KgIkQBsVbiZGBKoSARKgVVltzDQRKECABGgBHPxBBIgAESACRIAIEAEiQASI\nQEtCgARoS6ptlpUIlIYACdDScGMoItCUECAB2pRqg3khAkSACBABIkAEiAARIAJEoFERaE4E6AZB\nrmbdBrdm/Qa3Vv63atXKtW/TyrVr5Vzb1vKHjggQgZIQIAFaEmwMRASaFAIkQJtUdTAzRIAIEAEi\nQASIABEgAkSACDQmAs2FAAX5uXzNejdzxTr36fK1bsHqda5j29ZuYKc2bliXNq5nhzauvSFB14Ek\nlWeyKIzI0sbEnWkRgWpCgARoNdUW80oEwgiQAA3jwqdEgAgQASJABIgAESACRIAItAAENiYBulaI\nR+Es3TqR2uzQViQ1DUGZFf4lNevd1KVr3MSFNW7qsnVu7ur1bsWaDZHUZ6/2rd2gTq3cFj3bu017\ntHN9hAhdtna9m7tyrVsmiQ/q0tYN79oua1L0RwRaJAIkQFtktbPQzQwBEqBVUqF7DOziXv5seZXk\nltmsZgS269PR/fnzw10bOTLVWv7/6NWZ7uGpS6q5SMw7EYgQ2LJXR/fJ0hq3QhZ9lXAclyuBYnXG\nocdJSFz9+LVZblwVjJPf2a6f+9pWfaJjsSA9Tn3yk6hPVGctFOa6s0i53XfoKDegc9uIQLpv0iJ3\n2WufFXraSL+Qt/sPG+X6d2p6edtIkJSU7M37DHN7DOoShf1g4Sp3wmNTS4onFGhjEqBLpC/OXrHW\nLVy13vWDpGa3dk540MwOY9Aq+a5NXLDGPT9ruXtlTo2btXK9W7OhLhI5Ae+ka7hte7R1O/fv4IZ3\naecWCTk6a/ka6S/Obde3g9u+T4do7pc5YXpsNgj89re/dY899pibOHFidN1qq63KLltNTY1r3759\n2fE0pQhIgDal2mBeiEBpCDQ5AhQL1K/L5Bybn23kz1PTl7oHpizOVLojRnZ3Bw3v7nCUA5OBa9+a\nW/UT+9O37O3O276/TEhcdIzlS080n8VKpkqlp0ZH4ODh3dwv9xwStTn0o6ten+3u+mhho+ejKSd4\n1tZ93X5DuzoZaiIi4XIhPyYtqcmU5c16dnBf2qyX27pPJ9ezfZu8Pq51IgGyYNVa98s35rhXZ6+o\nF9dPdh3ouov/LA56v254Z17Vj39ZyprVD4iRzQV7tOl7hRj5cRnECMflrKg3X3/VOk7+eJeB7oQx\nPaOKWSXSZic8OiXz2NXUaxMk45NHjXE9RNIN7rFpS933Xvy0SWS7KeetSQCUMRP3HDzSbdO7Y+Qb\nm1mHjZucMWS6t41JgGL+8Nrc1e71uTVu+94d3MFDO7o+HevmB2m5X7luvftQpD4nTF/hnpu5SiQ7\nRaJUnq1fX7fZJ/vZrnXrNq6zTCO6CrvaRfoJ5jD9hJQ/bHhnt6VIhvYR8rWL9KNSBFCnTJni5syZ\nk5bVxPedO3d22267baIfvmwYBL797W+7m266KYr8X//6l9t5551LTmiDzGe/9a1vuZtvvtntvvvu\n7oknnnBdu3YtOT4bULe1Hj16uC222MJ6abDfJEAbDFpGTAQaDYEmR4AeKgTmNXsOzn98cTRj3wc+\nzgQIJr6DsL0pDovc774wwz01Y1n0u1r//FKwOGxE9yj7KBPJqGqtyerJ996Du7ob9h6aJ0B/+q/P\n3D0fL6qeAjRwTiH5dfsBI/JH1LL2y95y3AyStSBA41xcXFg8P3fMpiK9ISuYDC4ungxBm6UXYP+4\nfB86YSdJ3DQ5InjouEkll5XjcsnQNZuA1TpOXrLzAPe/m/aK6gEE6BcnTGk2GyU5knG0EKC5jaKm\nR4A2zbxVU6e8WwjQbZshAfrGvFXusRkr3Uvz1rkBHVu5fQa0c7v37+iGyZH0DrXfraR6gvTog1OW\numeE/JyyspXbsHat27B+rSyGMBuoc61ayeZAa/kvbOiGDetdDxH9HNG9ndtFpD+3EeJ1lNxDArWU\nI/gnnnii+/vf/16XWAl3Y8eOdc8884xr0ybbZm8JSTBIDALf+c533PXXXx+9LZcAXbhwYUSgTp6c\n26AoNz6b5ZNPPtndfffd0ePGbjMkQG1t8DcRqD4EmhwBCghBLuzUr1MezT++Nz+S5sw/CNx8c5u+\n7uxt++bfvDFvpYO0ZLW7n+0+yB09qkdUjGogNUAO/UKkB+GuFOLsRR7bj7Copj/VurBvLIzHH76J\nG9mt7kgP+mUaSXzwsG7u6j0GBxcyIqgRkc2g5uL6OAi8R44Y7brhnFoGFxdPhqDN0gvwmyD4da3F\nL44AxXfkuNE93VyRxD3jqWmxR+WrbVxulpXaQIXK+g2r1nGSBGgDNZyUaJsyOZuS9Sb1urkSoK/O\nXunGTV/lXpO95hU1a9zw9uvcAcM6u537dnTD5Tg8pDLlX9Dh9Mh/F9S4G99d4CYuWudWrRNv64QA\ndeuF51SBaslQzA9gFb6VEKGQ/hwigiMDRCx0p77to7VX/0iFRDCpxIckQBPhyfzytddei46gH3LI\nIW6XXXbJHC7O4z333OM+/vhjd/rpp7vBgwfHeXOVJECXLFnidthhB9dQBKjO6wknnOD+9re/Ca+v\n2npsKct/QQK0fAwZAxHY2Ag0SQI0J2E1MtJJA4BgwfDghybFLkYxsXxESAl8yOGgSPzLT051b89f\nFf2u5j8gPy/ZeWAk+YXjPqfLony2SMU2VecXVyRgmmoNpeerWhf26SUr34eW/POxoa0nEaB7if7e\nG/cZWiBRsVIkrx79ZIm7W1QLvLsgN07hSO2YHh3cWDnGLAAAQABJREFUX95fUG+s08dtQZhOnL8y\nSh6LmJDD8aPr3p4bPEof8t8Snt0hG2s7ysYa8Lvjvwvcr96sf1TPL67xDTl2wuTYo8HVNi63hPqt\nVBmzfsOqdZz05QNelACtVKtJj4cEaDpGWXz4MRp+m9MR+H+J2ptHhAB9ef46t2SNc23X1bjeHVu7\nrXu2c7uJJOiO/Tq4AZDMbFOf5Fkqho/+PXelu+ndRW76qlZunehdzLl0ArRD2zZuZNe27qBhnaJ0\nRkS6R4UcDU8tauMNX9555x33ySef1NP52LZtW/fHP/4xL7H3la98xZ166qlurUipardmzRrXs2dP\nt9dee+nHLe7eH0X/zW9+484777yyyr9q1Sq30047uffee8+9/PLL0XH0uAg1qViuxCbmoFdffbW7\n5JJL3Fe/+lV3ww03uI4dc6or4tIv5rnHCGFIgBaDHP0SASIABJokAYqM4QjufkPq9IXc/dEi99PX\nP8Oreu6HOw1wp4hOPe9w7P07cvydrvEReFCU/IPEIQHa+NhXKsVqXdhXqvxx8WgSUvtJI0C1ag6E\nmyiE51cSpAt13P5e18kKYfAOl+PbTXkjxOe7mq5aSrS56UaspnrY2HnN+g3TfTJtDNjYZdLpkwDV\naDTePQnQymDdXAnQDxatdk/KEfjxM1a7hWtbC4m5KpLShB7QTbq3d5t1b+1GCzk5XI6oDxBhj54d\nWrv2tYo6l4iQyKtzVrub3lvkZq1u5dYL6QWXE/6sz2RCL2gkLSf6QEdJnLuLQSRImyKdrGp2iq3N\nhx56yB111FFRMJChX/va14qNokX415KTIA1B9JXj3n333bxO1TRSs5IEaDl5zhL2rrvucqecckrk\n9YgjjnD3339/o6lNoARolhqiHyLQtBFosgQoPvDj5cii19m2VERyjho/ud6iH4vWcYdDr1JuVxTk\nwHGi1B87w3SNiwDqDMd0O4q+IiwIz//np+4xMWJF1zQRGC2T3fa10gToL94ytib6qmlh35AoY/H6\nqLRtLEbgcIS6c7tWrm/HtlFbj5MAPUOMmJ3/P/3zWYNxozOenpb/nfVG1wnJuayoFefvpE17uktF\n2h7LRZLMxWHXXHwX8w2zBGi16OduLgQoDGbC4fjvh0IewTVlkjFr3qAj2pcnKlQL/ePrF8V/X6y9\ne9dcCdC5K9e414TEvPPjFW76yg1utScx27R1bWWe1rn1+khSc0vR0zm6e1s3tEtb11tIUMy3l4ol\n97fm10jYpW7WKjECW5PrD3EnRCICVHRsthICdL/BHd3RI8TwUN9ODUZ+ou7+8Y9/uOOPPz6qxkoQ\ne749NLcrSMpdd901KlYliOLf/e537rvf/W4U35tvvum23377WMiqiQDdmO2JBGhsE+ILIlA1CDRZ\nAhQIXrnbIHfMJjn9l/g9buoSd9HLM3Gbd9YPrPtelmDddw85jnr+//QTKcWO+SP2PrI5crT8IbE4\n/1uxHh/noCMOC2U5weqmiNXGr6aQGdBnCiXm2Kh9eMqS4LHLuLTwHGnB4jTsOC6WXd6znp1eQAI3\nRH4w8QRGW/XulCeWfR5BDMxescb9Z+4Kd6NYmYYUGib2l4ll2c+LVWzoKfIOqgtWrkXOcw5k2k+k\nbrLqBcWR1aGC3SpJ86znprvBXdq5727Xz20qCwRM+rwDOf6mHP/5sVji9lJxX9mid6TLb2jX9gX1\nDL/PfbqsXjvycdnrFWJ5+/NDu8lEs1Ah+2ppAO/JpPySV2bVI9uh7/EikUpeJ+Y120s+gdPfU4wI\n4Zj0FdLecWwEYW6RY9C3yTFd7Y4Y2d2dKW1hmOif1LZwcJz6TakP5MWXX4ez91gAf0EMa3lDEf79\nPFEcdY8cyX5Z9LbeJtgD4lIIULSH2w4YLmRhTiXFL/8zx02YtsQnU+96olgk/rqUC30k1MZBSty6\n/wjXQQo9XYja00R6ElaMIfU9TOpXGwgAFsj/RS/NzBO69RIs4QGMF+0+oHMUElZTQWJeN3ZIhGES\nRvceMspt0Stn9KiczRlNWqSpBCmhePkgDYk12sVv9hriduqPhVbdOIHE0S9fmrU8k8VmjAGHSvsd\nJOOB7gcyTETtZ/KS1e5pOQVw+weF/cePJ3Ys3qV/Z+lXfdyuA7pEbd6DMUuMSqyX/gjXVgItEL2g\nX34ypxc0bVz2ceBaiXL7vGMsPPv56dHGRWiMxncJ4/N9kxe7//fuPJ2Nevc/F520ewzsHPXTutHU\nuRpp4PNkXH9/4Wr3wORF7mkZL0t1p8sGwIljekXjuE4DdYW+/CfR7/2AfHPjnMcZxzH/Ke0DY9y3\nRdf34SN7RN8DXf/oX2/Nyz4O6jRRR8V+wywBer5YG8fY8NWt+ghBAUmqujaOcenjxavdb0XtAjZB\n0lylxvpQOnosyXIE/tfSZ8cO7lLwbUe8M5evERUei9yf358fSqbg2Y9kY2Fn6ffosx2EzPH1hnaA\nfjVB1IFc80Z9lRQFkcgP/60YKDoKdXtC3T89Y6m7QvSOP3lUdkND527fL9Kx3le+MTq+xTXrojHk\n0ldn2Szkf+s+ifkJNhExXzhwGL6trR3mCPg2QbIPhqbQHnTeHpb57DX/me1+87khbnOZc3n9zshH\n0vwinwFzU8pcxUQR/fyWzHH3kdNXQ6WuuooxKV9XGFuWrVnnXpH2+z3Z3A650PcD5ca3csd+naN5\nG+occaL8PzDzecw9f7rbQJnjdcyni3QwJr0jKq0w/71FvsXbiq55uOZ0BB74TpU1xV8/XOJen7/G\nzVm53m0QPZ6YhYHIbC3inB3biQX39m1dB7dOjBdtkA3YNnJMvq1rJ++ht/qNeavd4hohQNdDCWhO\nz2d0Y/5oAvQQkfw8blQXWRO1cx3VmGWClP2zGMLqgQcecG+//XZ0RB6kaZxV+Llz57obb7wxsi4O\nyckzzzzTDRkyJJhXSEJCTySOYW+11Vbu2GOPredv6dKlDpKFt956q3vllVfy7+H3m9/8pvv85z8f\n1UX+RcxNKfEg//fdd5+76KKL3Jw5ubGwf//+7oILLsinCT/QC7rnnnvGpFz3eMaMGe4vf/mLu+yy\ny/IPjznmmIKwFjNNgHqy9Pnnn49UF9x77735fMGqO9QYoG569+6dj1/fYC2DuoExJEgbn3/++cEj\n8Mgn4oZBI405dJ8C73322ScihPv06aOj36iEOgnQgqrgDyJQlQg0aQLUSndaySe815Z904iBX+01\n2B0iVub1RDdUa1NlIgsDGCEy6XKZ4B4vRjLg4gxp+Dgx8Xv66DH5iW0pFkl1erb8SEe/r0R+EB+M\ngKRhhLQ/lQXQQaKbVe/I43mck/ldZiv2GjuEAzGNRS9IuTi3TEiUEx6bGi1kPVkV53euLPBBpMVJ\nCoMoB0GACWaSw4IXRKXWJzh2EHQ+Dsvn9R058nyS5CvJaZUPIZyulUXSgUKsJjksmq7692z3D9kE\nCDlgirrC4jzJ/UfIZJDMWJAhL3HSjXFx2LpLk4zSbTjUxrX0Ixa6z3661B0mBFiSWyw6sS4UQiIr\n2Z4UF8j0C3bon+8TIJfuE4zTSGItTYb4y1HNoUmLSbJIOlKk4RvCNRTW2MQ5c5s++SN7cXkHEXqF\nbJKECHOM9/8nhDLIjzRn+5Bukwjrx2L7PCleyVpeL2ham/XxVKLcOo8oF/r30aN6Fmzs+PT0FTqw\nT358qn4U3UN/Kcg+vXFQz1PtA13mOD+h58gzNkG2qpXSC/nxz9BHv/HMdP+z4KpxniztfpFsqkGP\na5IDwQJi6c4PFyZ5K3hXyjfMEqD/nrNCyP3O+XGiIIHaH77+Lk/YpK3EWB9K2z/TY0kSAQpd7DfJ\nd6yX2fzz8fgrvhenPvmJ/1lwBZl2lvxP+m77ABjX8J30JxH8c3+9UdQi7avUIvnn+oo50FpZeOMo\nL5zv59oP7tE+/3rgCLe5fOeSHDZBsMlt5wm2T4KY/+qWQnz3qP9thTTnMQECFMTo6B4dCog+mxe0\n5RtElzMMgca5cuYqOk7U9x/2G56fr+p39h5zra8/M62enn39/fDf8r8eNNJ1DxjvswIN35GNra/J\n5kFSW0G6UCGzW+1mZHMiQBfK2DZV2i/606tzatyHi9cJEb5ObBmBzAQJ2tq1EqlNJ1KbsN7eWiy8\nox12aZ+zEr9a/C0QKdKaddD7GTNRrt3QwzgEA0iI8/ARXdwJm3SNLMFn+SbYtpD1dzEE6OWXX+6u\nuOKKKOqf/exn7oc//GEwGR0nPNx+++2RbtGQ51/84hfuBz/4QfQKpKCP3/t99dVXE/Vjwt8XvvAF\nd+edd7oePeoEc3x4fy0lHujp3HrrrfMGg3xcoWsWvaAfffSR22yzzULB6z3TekE1AQpS8pZbbnHj\nx4+vF0Y/GDduXISLfob75cuXR8QldI+CyP3vf//revWqU1UHgvSqq65yl156qQ1a73covK77xpYo\nJgFar4r4gAhUHQJNmgAFmhcK8QACwjtNImjSCO+TrMXftv9wkUDISXD5uEAYYacfEgn+CL1/BwLl\n1Cem1jOCoRcPaZMvTE70jn/cZNynGbrq9EKLFf2+3PxY4g75wc47Fp3tRAKqm5IGwLsnReLiuy98\n6n6/7zC3uxCGcF5aAPfY0V4LhrDW4Q5Smpj4pjmLnfaPRQGkRkL1hjT1BNrnHzvbdhL+uixYQYJa\nBxyu33tYPYIBBPsaKU/vDrLjXifcEwW30gxejxxe+oUAFnghZ8sKcnbfBz7OewW+n5M8aQeiCK6j\ntF2dF8B98Ssz62GMNO4X/ayQ6tAO7R+LCixy4cc61FkpBKhv91nC6zYcauOaaLD5Q/0iDKb7XoLG\n+8Hz05/6pN4izb/PcgXp9uiRo/PST5581Au9uDKC/Dp7u75R3lAv54he4mdLlKbTGPk8ZMl/sX4a\nAuvvyxh+mhrDkSf0U/RhLNSgVkAv14AV9D1bqem7hLDYXo7paYf2iz6Jtqvbr+1zeOfbJML7sRjP\n0S/6iwQYxjidD7St2vVilOQSSetEIWiwMabrI9RmEaBS5bZ5jzKj/gAD5CE0LoGs/5GSYgvFhfa7\nQKS/cUUf0ovgtG+Kykb+Fmk8/IVN6hHVGGeWy4mAbpBi0h8KCYkjrsc9OjUfh7/ROPtn/uq/39CD\n19OQdBgfQypzfFh7LeUbFuorwBBtCO0b3038DrXvc/85I9oQCeWj3LHexml/a0zj2i5OJFwvhKNu\nC/ju+hMddqyN2+SDCpDviQoQ3a+wiQVJwq7S1tBWtLOkmH93s3wD8V3WDlK1GENw6sTWv/fn+7n/\njSvSjPsWtpbxCPnS+cVc8JhHCtUv6X6EOp6+TKx2y2kV7YAXmrnPgw7j/aGdYL6COHD6obW0ZfjT\n3QPvrhcS9PcT65OglZir+LxgbjBOjIliHPQOYyAM7CCPFuPQd0j3CdQP2gu+od6hvD56vTGqv5Xe\nL/CbI9LsOBGD0yR1ufI+mpcE6AeLauQY+yo3V06vvy/XDxbWuGWC33p8EIXwhENbiJy00+hrBavX\nQogCG5CikeV3+WjFEqCi+xNuQy35iZMNR8nx95PGdHMDpf6BdUO5Ygirl156KW8IaezYse7pp592\nMKSkHQi0M844I5LW9M/jdEFC4nX//fd3zz77bOQVkoa77babDxY932+//fK/cQPDPZBcBIH34osv\n5t9tsskm7q233oqkTvMPa28QfynxwBgULLTPnz/fTZgwoSDaQw89NP8b0pSQED366KPzz0I3kKo8\n+OCD3YgRIwri69atm/vc5z6XD/LBBx+4Rx991G266abRM02A4gEkZVF+hEP++vbtG1mn13jAH+Kx\nhCtIXWAMSd4QgQkpX0ikeufT6N69e0QEQxLXu1C9ah2gJEA9UrwSASKQFYEmT4BiMmgtvEOqZaks\n+vRkbYZII8JSfMhhcnWOEBHeYRIBKRothQGpmPNkoq4l/kKTer14SFsc2gmvnwj7fGS56vRCixX9\nvtz82GO+t4pk46+NpWRIhx4uR7EhuQCpHa2bSU9+gfGlIhWRdLwxqfwWO+8X0kLnCunqpUTiJKyQ\n/iNyrO77chTaOxx3O0OkNPwcDwvoU574pKAM8PuEEF44bu8dcMVROOzMe4cjXfvL0Xg/XcQcVS9q\nIc2AY7XeQUo07ogfjnl63YPwr8lU+w6LipvlaCuOjnqHI2FeIgLPQn1BS1LBDxYXWFTpeEDYfGnz\n3nl84A84NkUCNFS/6MM/ENUDenEONQXHPzoVRSnJ3XPwSLdN79xxO/S/L9W2F9vWQxj9WKTscFQf\nDmEPe3iS20/URBwhkszDRY0BFppoizXyDsdjb5J6jTseq8k/SIo8P3OZHCtuHxEJUGWAhSri+JMs\nkkOS61kLr8vlw5SDdWhRjSOU5zw/I9+H/bFHrfPNkg47CPF5q6hl8MTApMXokzMLyG3ozsNxa6ji\ngJTQ2ZKGd3Y8CY3F2pheaKz1ceGqx92Q30qVG2nZvOMZHCTIrhSiOGlcsjhCfcA31LgEQv5Coy4C\nkuZQMQApeoxbIeIll4PwX0tWgfj8nYw1WiITY/HpMhb7+kRMIUOHGmefGsZtkOM/FylP79D3sVmq\nSZpQfN5/3FW3f7T7pG+Y9uvjQxhgiu+O/0ahXf9s90EF0oahuUWlxnqfl7irxjTUdhFuvBBhI2WM\n8u6teSvdeXLs2Y8tOIoOqXi0TTiU+1dyhB1zBu3wHrqTO0pFvzBzeSQ5r6XykRf0Wf9NDhHXh8qp\nnV/sOTjvB2nhyDzarXeh+se7UD+3EraQ8rxENg392Iv6gqqO4WIcxju98Y5ncX0SeXtQTqv87PXZ\nUf1DHQ5Oyrwr41FcGEhqnyubYx5b+AMhryWd0Y8PePDjfJvy+arEXMXHhStUNiFdYIFTNyiLd1BH\nhTES+YPDnOeHghvmK96F+gTeYQzAWIJvHBzq9Dn5hqGPgCCF7nj93QYmkLzVfejqPQa5TWXeqV3a\nnFf7zXI/8eQtsnhrED8o8zNiBOnVhTk1U9ABis29yNXuxNX+wtn2/NwT93kn/kAMphGgEWkq5F5n\n6XjHi/TniZt2k7GztZDTKq58pJW5KYYABdG3xRZbREeuQZ6BcBw4cGBBRubNmxdJTfrj4ngZ53fW\nrFlu8ODBUXhLxum04AHHukGowZ93jz/+eEQo+t8hKcxKxVMMTj4/SVdNahZjBMnHeccdd7gTTzzR\ntWtXNx5CyvXII4/MH4k/++yzHXSN6naXRICuWbMmIriRH7g//elP7rTTTisgudeJ9POkSXLCUI7G\ng7zea6+9fJai6xtvvOF23HHH6J4EaAE0/EEEiEAGBJo8AYoy2N1h6CTDZ9pbicekQO8mI4x2jwmh\n5SXfMJ8ISRfBPyZ2WjIAfq3Ull48pE2+7IQ3NBnX+Qzd6/RCixX9vtz86GOA/xLpSFiqLsZZqbik\nOkmL12IH/3HSIRcIcQ1dc96hPYDYuzagy1XrjA21G3vcOU4yCWmB5ASZ4KeMelGLSf0EmdRDmgQu\nJC0RvZA/OIoHggdO1gmiZ3BqntgZJ5JUo2qP8+HdGU9/UkB4RIHkDxYuO9UeDUW59GIUWOpNBLRr\nTdb6OHBFHV6z55A8MYG4QuSeDmPvdd1lCa/bcKiN20UV4vyNEPPQk2odcIfxNC/ta/G0/pN+a+lz\npKnblM5TXBl1f0L9Q8JHL2pt2ojHLuy9Hx2Xfxa6Ig4cxf3mc3UEY8hf3DNdLvhBfOVgbaX0saD+\noWyMhJyVdL7jg4V5kkuPLWi/p4h0PhaMWZ1ukwgTGottOzxBjOmh3kLO+oWOP4y/3lWq3IjP5h3P\nXpKNoK/HHBu/VU47QK8pHNo/Ngz9RpUm5eeL1Ofe938U+avUH0v8grhB+hobnxaO3Ea6fWvZL0iy\nHvDgpDzpAX8aZ/wG+fmVGKluS5KDcINuxmKcbmdo+0nfsFBf+YNsQIDstQ51qI2ogXQDoaVdJcZ6\nHV/cvcY0NN6C6PrJroPyknogw0KG2yzeafOPuPzovhLKj27PiONvoqf6SiEYrQPGmOtpiUPbz/FO\nq036TOphf1MPiBdxTThik8jIHX5DavWkx+rGg1CfRF+7WHRahtR3+Di1FDqeJal/0BvS8Hv92/Pc\nzRPr9PpWaq6CuLM6nSb6h50b2D6BeHGixUvOh9KxmzIY1zFmhNydMlf6H3UKoNQ2F4obzzYmATpj\n2VrZ2Fzp/jZllZu3Rgjm1aIrOInQxDtTkBx/6Wek5qX8BDkK16qNnHiQ00P9OrRyJ47u5r4oJGhD\nSn8izWKIPeQTRCSOYcM9+eSTEQkW/aj9A0nJww47TD+K7mFtHhKD2iH8gQceGD066aSTomPskO6E\n+/3vf+/OOuus6B4k2zPPPFNA9kUv5M8jjzySP+oNcnTixImRRKR/X6l4isHJp510hRX5m266KfJS\nLAH61FNPRXo4Q/Fr/ENSukkEqD4eD8lRYGklfENp6mf6mH9SPnWYSt3zCHylkGQ8RGDjIVAVBCjg\nsRIJGjKvY0k/8/d2QpZG7GFn+hqRNhDhrMhZ/3rxkDb5spNkOxn3eUy66vRCiwP9vtz8aGm3pIVr\nXH411qHJcVy40HOLHRYgxwkhgTJaZ3Ut2iPk2n9aHjXRlIVo0UfdLUGpFzBxcdkFmW7LdoHpVQ7o\n8vh7HIW7QfS1eYkq3dYgHfOT3QblJWhsm/Zx+KvGoJR61HWXJbxuw6E2rusMeQxJuPq846qJS/yO\nI87xLs6BnLn9gJEipZnz8YZIQEH60zudp7gyahx9OH+FBO7yNetkod0mn4Z/B8mYbwmBqV1SXNqf\nv4ek8HkiVfSCGI4pxulyIVw5WOt2gLiS+iXeoy/oTQP4P2zc5IgQ08QU/CYRqXhvnc2L7h/eb1o7\n9P5wTfJr0yqn3EjLxmdJTfjRTkvMoW1qEk8ToBiTrASXjqeUe40LwieppcF7SEZifPLO+rfxhfqG\nD4urNjqWphNbh/P3uv3H9euQXzwLkZreL65aCh/jnCbYKzXW6/Ti7jWmofFWE442nzZOvXmX5teG\n9b91e0Xb1huAoTFBq4fxcfirluLGM9vPNdlm+4aPw1/1d8T6tX0SYbBBlmRA04ZJ68d2M8Fuoupv\nQtz8wpcF16S5ivaXdI/68NKawAQGHrWxNd1/EA/ydZpsWGgpdRv/vYeKocBaXaxp5cA8R+tXT5vz\n2rTSfm9MAlT2R+Xkwmp3y38Xi8RwjVspv8UKkpx+l05R67SEnX9WzBVHweHatO/gOosqks26t3bH\nigGkA4cWqpcoJs6sfosl9mCICMeu4azOThCkkDoEsQcyEhKA+A1pUBxdh/V0jdXlSqeo1hNqJRHt\n0XhdNhB6e++9t/NSi5pMrFQ8SK9YnHQeQ/elSoCGSE0dvyYxrVQt/GUlQHH0HUfq4wxd6TT1vSZA\ndV1oPw11TwK0oZBlvESg8RCoGgLUSiV4iDBhipNmgx892YffLAs+LTFqF1E6vrTJl53w2sm4L0PS\nVacXWqzo9+Xmxy5GQaLcK0cNr1ZHDZPyqie/mBxb6YCksPadxS6pbNZvEs6aLLV5tPFoMtLmz//W\n0smIT5MNts3qo+0+vD4qj/A3KCkPXbdou5DCAXkZ554/ZtNI3xzeawkxTXrYMofiSpPyCIXRzzSO\nWdLT5Qy1cd2ukE5S/eK9ruMs/uHHOj0G2GPE8KvzFFdGvUD18c8TnXV/+3BRgSQPjG3BqJM/Bhqq\na2AKCckBYgBohuibm7Gsxs0TCT7oWBsiuucg8aePrCK9NKNwPk/6qsuF5+VgbeMKtX+dNu718WlN\nqGDh/diRY4QMrJNuwbFSLL6z6FXVbRLphMqV1g4Rzrskv5UsN9KzebffJJ8nf9Xp27ZpxyS8f0ZO\nVFwp1rP9EVwfTylX3eZx7BVG6TB2xzlL8th60Tgjr3p8DcWp00/6ZoTC4lkSdjaM9ot3SRtUeK/L\nYjf09LtQ/0d47eLGeu0n7l6nZcdb29ZgefukGGk8xK+/X5a8jEvfPgf5qw3K6Tq2Gx9pm1loTw/K\nqYmOtYOpbU9alQjG9T3v/dBmJ/97hKgAgC5bRIW2p08AWJyyjLU2TNrmEjKiSUvdnm1c5c5V8oVO\nubHp2jZv+4TdOLTR5+Ibk9fBD9UmR4q+1SSnv80ak6QwWd9tTAIUwpnzhQV97tPl7smZq0Uf6BrR\nJSwPxaJ7mlX3rOXLE6AdOove99Zuv0Ht3MFDO7vt+xSqFsgaXzH+iiX2YLXdk2JWShDHzXfeeedI\nVyTe4Uj2QQcdFJGTloyz5KTWV/nxxx/n9V9Ct+f777/v2revU/1hywcjSjCmBKelDisVD+ItFieE\nSXKlEqChY/46nSSCE/6S3oPAhiTu3//+93yUuD/88MNdp06503D5FzE3JEBjgOFjIkAEMiFQNQQo\nSqOlO3zp0iZYerJvFx0+DnvViyg7SdXxpU2+7GTRTsZtuqHfOj27WIF//b7c/IBkwOIBV+2Q7htz\nV7i/ilXdJLJBT36xYKgkAfr0jGWROgKdL39fDM7ar82jfoe40xZb8GPLrAlMvH/m6DGRgRXch47b\n6QWO1X+m6xbhcUQU8+E4p/XfaZJEx5OlD2gpC4tRXNr6ucYxS3idv1AbT8NYp417pK+PQqb1Cxv+\nlyIB7q3Mx+Xf5inU1rV0FNLQdWLTBOl8vqhy8JLnaeOaDY/f0N0Isltbbb5XdB1flmBx2sZjy2Xb\ns/WfhLWNK0mfoo9XtwWro1dL0Hn/uE4TQvhpMcj2S9FBGOd0m4Sf0Fis0w61Qx13kt9Kl9vmXW9u\n6Dz5e5u+bZuaBPJh0M6hNxV6k6Grr1Snv51WYi0Upy2blfDUOGch2HT6xfZ75C8NO10G69dKw2m/\nuNdlse1Lv4PfUsd6hE1zOi2bj1x91BFSaBdQlaD2HfLRy7QgGmv8mAW/mrzMe5Qb6Of90ma93Oai\nX7OvGLSB8RUQi/6KdOEQh26vFuO0McS2J9vPdftAeiib33jCb+1gONETqXiuyT6bTtKxbR+nDZN2\nEgPhtGobXVc2rkrMVXQ+z962b3TUvJcYfOwgld9GzlbjiDS2n7SRKIuvri/EF6euQKel1QJkKYeu\nw1L6uE87dN2YBCjyA6OhM5evdf+eV+NembPaTVmy1s1bWeOW16xN1u0ZKox9JpNH9C+41u06uCGd\n27hTN+vi9hjQMdo0zr1puL/FEntawhBSgjgmPWzYsCiDzz33nNt3332jey8dqslJvIe0JhwMAvlw\nIDlBrHqSTZNo8HvOOee4Nm0K1z947t21117rbyOpUxwvh6tUPIirWJwQJsmVSoBqgjcUfxLBCf9p\n72EVfssttyyIGvV85ZVXRioMRo0aVfDO/tDhKQFq0eFvIkAE0hCoKgJUEzMoWJI+MF/wq+SI3VG1\nR+ywgDp2wuRYvW4+jD66bAkjvXhIm3zZSaqdLPr0kq46PT0B9mH0+0rkB+QniBtIP4QcrJ3e8/FC\n95eA/kU9+bULmVBcSc+Kwa5UvzaPKLs+gmsNH4Tya49K2wm81jkKyR5toT0n/TQ6fwTa6qyLI3xC\n+bDPdFvQeQi1IRu23HrU9WExtmnht27DofyVkp9SF0n6OCbyphe9+O2dHotQxtDCXBNNKJc+8urj\n0VfoCBxWa0lY15/2k3aPfGlVCMXGU0mstbQhMIrTJarLZI+oajIE/tBWtNEUHRaGoF4R3Zgwbmel\nGXWbRJjQWJzWDnVaSX4rXe4sedd5y1KH+hunw+Ie5BvwuUKkQot1ekPHbh6G4kLZHpJNt0Ei2Qxn\nSVONs/0Wh+Irtd/7uLJgV4pfhNFlseNcpcZ6n7eka1I+8A0cd/jovEReUjz2Hfq4JUD3EGvyF+3Y\n340R4zUgz9Kc/V6gL10h+ki9FKYdD2x8aX0ltIlu44j7rccMm4797ofiKCWMPtKv239DzFWQv9+J\nccddB3SJJYVtuTQmeGf7T+i7qOOw5bDxab/+vtw+7uMJXTc2AerzNGXJGvfK7JVCgta4j8Q6/IJV\na3JH4cswUoSj9K2g91L+i/Cn27pXe/etbXqLcbaGtf7uy1QKsafJO+icPOSQQ6LoNNn58ssvu913\n3z2SyDzggAOi9xdeeKG75ppronutuxMW1H/+85/7LDlNouUfZrzRhncqFQ+SLgWnpCxrDNOIwmL8\nphGcae+RZ0jOHnvssZGleFsGWLG/5JJLIqv1Wp2B9/fZZ5+57bffPlJ7kFYuH6ZSVx6BrxSSjIcI\nbDwEqooABUx6AmsXSyEY9ZFKEFA4kucNQoT845mWALPHX/XiIY1csBPeLJM7myednl00wa9+X8n8\nQCINFt9HigGe0MIFVmH/V+lERF7s5DdtsYIwca4Y7Er1axdb1igHrFXDEmmSw3Hrh8VibpdaCRYr\ncWdJTi3hpI+mW3IUaVqSAhI3kBBIc21EuuZ9kebyRxd1Hwi1IRtfufWo68NibNPCb92GQ/krJT+a\nfEzrFz5PWIxpCWgsOH/w8qciHdGuwNAqNl5AVH51qzoDWJB0gQQdyo6+gePZxS7UNFGdpk/Q5zl0\nLTZdHUclsdbqIZAGJOS8FWCdpr6HJexLdhoYScKG+gT8AmOoDdh9YOd8v9NxoH5gIRyWwr3TbRLP\nQmNxWjv0ceGa5LfS5c6Sd523rHUIcurbIum1XZ9OQcIDuksx/uE7m9Xp46k4Fjz2vnQjS9r4z3sL\nV7njH52aTy4J57wndVNO20c0WbEr1i/8J5WlUmM90klzSfmwG3qIC/0pi8N3B9bUQQbCWd3TPg5Y\ntl4ix89XrVvv5J9YZHFueO3Gj/1e6L5k3/n49DWpr+CdJtsRDpsmSacqfNyQfsS3/ce10vRJ6fgw\n9lpKmDgCtNJzlTjiG5gvkQ0RqLNAM1glKlc2FWlePye042gx/Qf45NrbCNkEzsVo47MY4ne5fTwU\np3/WVAjQ14T8fHLGCvfh0vXuU1EhskwkQNfjg+iB9xku4pojQNu4VnLEe1SXNm7vAe3dMZt0k/lN\n6wJ9mUVEWZTXUog9GDQ66qijonQ8eblkyRK3xx57uPfee89BovPNN990kBzUVtjxHJbju3bt6jRZ\nao0paeNISGS77bZzQ4YMSS3X7NmzI3Lui1/8YuS3UvEgslJwSspwMaRmMX7TCM609zrP0L168803\nu9tuu00/ju6/8IUvuL/+9a+uZ8+eBe98/FOmTInqOk1itCBwmT9IgJYJIIMTgSaAQNURoMVOfuIm\nkEnY6zSwiDtOLPx6iSK9eEgjYEuZ8Np86fRC5JB+3xD5wdE1LEL2HtLVdTJnxewRrmInv7as+ncx\n2JXq1y6obDxZpDqsjrLQkS9N2mtCXZMFIcLLtt3Dx03Kt0ONVdq9jSfOmJSPp9x61DhajH0a+qrb\ncKiN2/xkOZatrd4nHT3X+bDSn/pdMfePTV/qvvfPT4verNH1BBzSJEbj8qTHL6hVgAVekMBZXCWx\nziIla/NkNwXOEUNOSWo3Tt+yd0S0QMJMO6wVtfEN3SbhL7TQTmuHOv4kv5Uud5a867zZOsyyEfWD\nHQe4Q4Z3c/1kQ0c7bYhKP4+7120v7XuEOHJlqztybY8SJ+EcyoNOP+vGh46nGOyK8Ys0ksqi+z42\nXkod63VZ4u6T8mHbWha9vaF0QoTax4tXu/smL66nYsHq2tTt1fYl/S6Urs2/7ed6YyxL+wylgWdp\n6YTC2TB6MzTkH8/s5qX/Jti4yp2r6DkK0oWu6gekrn4/cX5khA7PvNP6Zy2+xfYJW44J05a4C16c\n6ZMKXsvt48FIax82FQJ0oujefUYswj/z2WpRnbTerV0nFpHkPw6xh6ThksrkLb9jF7d16zauQ7u2\nbt9BHdzhwzu7rXu3j9pyUvhKvSuF2LPH1yFp+frrr7s999wzypaW9EQ5zzjjDAfjSXCQCtxxxx3d\nDjvsEEkYQjcoSNGBAwdG7/Fn0qRJbsyYMdFv6KS86667isa3kvEgrlJwigoQ86cYUrMYv56AfPvt\ntyNDVKibXr165XOR9j7vUd3MnTvXjRs3LqpH9dhBzytUF7Rr1y7/2KtImDdvXiTJq9POe2qgGxKg\nDQQsoyUCjYhAsydA9WTfLorjcNbH+OwkWceXtji0kzs7WYxLXz/X6YXIIf2+ofPz672GuINEz2Dt\nZr2DSoGTxUCCl6gtdvKry2nvi8GuVL+WnLPxZNHDmOWIHgjkc7brGxURaeKY4HQxYqOtmd790SL3\n09cLj5zqY5Fou0nGvix++rcmlWydaX/+vtx61DiivNp4hE9DX/XiP9TGdX4QLq0fYfH96JGj89KB\nVrWATlvfWzJbvyvm3udPGxWz+l1D8WnJ86ykbSiechb5lcTaxgXdktck6OlEWbQUrD7yGSqnfnbE\nyO7uPNGhCols73S96zaJ976OvF9c9VgaaodZ/Va63FnyrvOm07djnPYXuj93+37uS5v3zm92Ifz1\nb8+NiJCQf/tMkxNZDMOgr2q1I5YAKaZOkBedfjURoJUa6219hH4nYYr68Fa+EdZK5IbiCz2zm0mv\nymkKGPELuSS1F3pMRlvMsvmldUrafq6JPr0ZGcpX0rNi+yTismGytE+dX71JauMqZ65i+yDSOXL8\n5HrEJ8oAg1W3HjAirxPW4lvs2JMrR90GSJp+Y+Sh3D6OOOJcUyFAFwgB/ca81e4fU5a7ycs2RBKg\na9asFSW5QoDmNXlKKVKOxOfJT3ht0851bNfKDe3Yyh29SVd36PCu0RwpJYo4qIp+Xgqxt3bt2kjX\nJ6yEQ8oTJBiM5YD4hNO6PvFbS4zCiM8JJ5zghg4dileRXsn77rvPtW1bN0fQR9etftAoUMY/lYoH\nyZWCU1I2iyE1i/GbRnCmvU/K89KlS911113nfvSjH+W92bpevXp15Kempsadd955rkuXLnm/DX1D\nArShEWb8RKDhEWj2BKiekAFOO2GzENsjYNa6ZtLiwcZlFwFpadvw+J2WXtp7HWcl8oPFyte3zh39\nxYLk4pdnOkiJwGmsi114RxGoP3aCn4RdqX5DedST6zQSBNnVRxctHr44WGBovWovi55CGEQCeQqH\ndL4k6gQ8kezD6ePAeJZFYsSH1ddvbtPXwaiBdyGy1b/D9YpdB0bqD3AfwgjPk1wx9YF49CIvhLlu\nV/BvNyXwTDuob7hwh/75R0ltJ+9JbtD3rx07NDL4oJ+H7nEksns7UaRV65bL0UBYZG8n+rXGf7LY\n/UxIbkgnwrARTq1lwRFEkD8KWioBCuzHi0qG/rVEYBpWPv/+WkmsbTvIsuB/VoyGeSnELFbEfb5x\ntWXXY7fNS6hN6LE0jXzVfm2btWmVW24bXyjvGgddh1nanQ6Le5Acf9kfx1Jzb4qRAtS4IG2rEzIX\nY91fbAxhg8g7m5aOz+Lsw+irHr+z4K7D4r4Y7Irxi7iTylKpsR7ppLmkfCCsVkmA4++nBL5NxaSR\n1g5+IUbnDh/RPYrStlc7H0sjZNPmOHqTCWll0UscKmuxfRJx2DBpYwzmDY8fNSa/GWG/CbqtZ+kb\ncXMVTTIjn/4EA+6t0/qN8c6ORcX2CcTxpJTR6wBO2yi0c6lS+jjSjHNNhQCFqqOFcvrsTSFBX5y9\nWowirXLzRSWJTDFcK7EKn3cp7GWeAG0lx9yF+BsA8nNEFxnnOrlNRL2VF2bIx9eAN6USe9DZefHF\nF0c5u/fee90tt9zixo8fHxGbkD7Ukn+zZs1ygwcPjvwef/zx7rjjjnMnnnhi9Fvr7PTF1Nbk8ez5\n5593Y8eO9a8zXysVDxIsFae4zBZDahbjN43gTHsfl1/9/IorrnCXX3559AjH47/85S/r1xvtngTo\nRoOeCROBiiHQ7AlQTDphXKRPx5xlP0w6T3psSqxeMztJtBNkLU0HqbwLXvw0mjCGasRagbaTxVAY\n+yxtsdLY+cHi+DaRAMBpeJT/h6LzyxOgeiJdzuICGNjFQhJ2pfpFHu2ROi2Ngnw8KsZAzpc6Djm7\nOEsy+nHD3kPdfqJGAG6eWJ6tEeVng7vkjnPELersZB/Sm2c8/Yn7z9yVoezEPrMLKVi+PeThSUEJ\nD9tfQhjFJlT7IlcfdVIdHyxa7b4oaiRCTh9xxPvQQk4vquAH7Q7SRFDBEHJatYBtoyH/pTzT/QAY\nnS9H3rFw1M5imSQRZ/XlWfUSOt6key1BCX9Zjkbq+CqN9a37D3e79O8cJZHWlkCGnSlkmFdzVgrh\nr8fcYglQPZaiHfrjphoff582Lley3MWMb8ifrsM0zH159NWmd78chb301VnaS+y9lRBLI+A1+RGS\nTk/D2WZEk0KlkCPFfMOKxTmpLJUa6y0eod9J+YB/3Q/wu5SxSMeBNhhHhENiGxKn3tp6qL1qQhbj\nuVZtgfxpp62m47mdN+j6xfu0UzPwE3K2j9h0soSBn7tEdzQ2y0JOSwXjve2HlZqrWEySjD9ic22k\nMpBpy11sn0C5NCmN30knBbCheIZsLHoX18ex6TtU9Mp+tmKNSLDP895Tr02FAEVG14u056LV692U\npWvcB4vXuEmLa9yHC2vcNDk9tBrCoPC0QSaFcIYIzROf8jVtJVbNW7VuKyRza7dL33buSCFAR/do\n7zq19V/aXBQN/bdUYu+ll15ye+21V73snX322e76668veI5yH3PMMe7BBx8seI4f0BUKoznawT/i\nuemmm6LHSOeZZ54pOGqt/cfdVyoexP9///d/keQq7iHFCunGchws1fvyeYNRcfE1NQJUS/RaAhTS\nwR9++KEIRW+Ijsjr4/Fx5avUcxKglUKS8RCBjYdAsydAAS2kwSAV5h2ICBCXOJal3R/2G+b2EsMQ\n3mFyvO8DH/uf0dVK0+G40ClyDNzrCPWebVx4bieL3m/SNW2xUsn8XCOSGMNlcnuHHFUd/0lOqtPm\nTS8w7II1RwjWKbSPm5zaOEO/i1lglOo3tNhCXJowR94eESwufKlQL9XBogrgp7sPyh+zhr8/vjff\nXfvWXNzWc3aR4T0gD5Zk9+9w1QtJ/IZEzp/fnx8ZlMFv7aCvFYZ5Bndu5858dnoBwakJGYTBZBpG\nklZApKDWoewgEEaLZIB3IYz8u7gr4tHGJhDHXR8udFf9u3Chd9SoHu7inQa4bl7UTPxlIUCRLqQD\nYe0bR2a1u0fyv03vjvlHSaR03lMJN7o+Ub64Bb6WpkUyIITOeX5GgU5O4HDpzgOF9M8tSLDI17ov\nQRJfJ5KpOB74h4nzHPQkWgfMrxMLvnuq8SuNxLNx4LdewPr35WCN8fSmfYflj0zK/lN0pBpqEbTD\nGH2qHL32aoaBwSlPTM2XFXV6tRg+el1I71/KMXrdbn08IJEv22Wg61AbSbFH4LXBFcSZRASkjcuV\nKjfygbpNOtYLP9rpOkTb1Js8+A6eMKan+4cYdLnl/QU6WP4eZTt50155yeW0Y8f5gLU3+huBR9jg\nOe3JaQV1hjL945CRDvofvQsRbWk4+7D+Wi4BWsw3LAlnnx99TStLpcZ6nWboPi0floxFHDDwhnHL\nznPw7pTNesmR2h7ucdksxDcQzkoKviNjljfKF3mQPzDCBWNmfWs3p/Hctlc8s0QfxiPoWH5RxkPt\nbpRNxn1rNxn989Ccy34jMM+D+hn0d+vwnT9R+sIimTN+T22EFtsnEa8Ng2co750fLHRXi9E27TAe\nflnGQy+hFxrLEV8l5iqob62GAvNjnEjB/M07pHXbAcPdVr3qvq14Z/Ettk8gDrtpgrH/D+/Nq0dc\noh1gfuO/EQgbmmPaTcBiNtKaEgGK8oEExTx7/qr1Ig26yj07c4X7j1yXr5F3YtW9gAAVv2hPeSek\naCvR+dmuTWs5rdLG7SFGjw4a2sltK9/SznIUvrFdqQSoN27Ut2/fyPCRz7c1aOSfw2DOqaee6n9G\nVxyDh9EkHKO3Th9fx7tDDz3U/elPf8pLkmr/60QP6zvvvOOmT58eHanX7yoVD45677vvvlHU0FuK\nY//9+vXTSRV1ryVooRLgzjvvLFADoCNrLAIU5OVf/vIXt/fee7vNN99cZyF/D2NX0Ms6YcKE6Jk+\nAg/SU+t7PeKII5xVb5CPqAFuSIA2AKiMkgg0MgItggAFplqvJ35jooBJ/TSZ5HVv38ZtK5Zwe7Sv\nO9KK93rhiDBwoUknFvXvzF/pZsmspHfHthJXxwJiLBey/mTRP0+6pi1WKpkffRQZiwzoqZwji4Pl\ncj9EpBU3l8mvl9RAnkOSfVr6Dn6wuJi+bI1bKx8sSHtcLSTYC7MKFy7wZx3KlXXRX6rfuDrWej19\nvrAoQB0Di817dnSbyA66nkKmSTohHosNnkEP2QEPflxADuC5dqFwyM9UIdOwGOgpC5he0u78kWxM\nmI+dMLlAytlKqyL+lUKmvjl3hQMFCiv2W/fulD/26tOPw8i/j7vaTQf4myn9A2QIFjgga7UkiY8n\nKwHq/YPI/WBRzvr6ziJp2FWRqaXm3ceddM260EPbvP+wUW5orbQv4sR4MWXJ/2/vXmOsKO84jj/I\nssgdLAIFJaX1QqGpkVC1XmLV0nhJSSpJEzUWiJcSm/QFEpumRuObJq2gjalNrWliNE2Jra3pvS9E\nsG8KNNZWCVoVUctVRKkLK7ss9P+b5Tk8OzPnnDm755ydnflOAmfPnLk8z+eZmTPnP8/lqPvIyn6K\nlV0YcNbn8UBQuC99rmaCOq/0o1yjGKvJ+DwLJPkfy1pG05OvHXQ/eHF//5uM/8f3Fa42WOvv24MC\nBXnDSdcEXX91/H7Orr2+CaRfJl7zOgw422Hr9lnNnl12PMlhnBlrZOLQWMdYWDs9yzVC6//Bajj5\nEYmVFuX5fatuM9Zw1e3Btzb1B4HqXZe1bjPyre1kSbuW81NYhvFzIKxVpocpOif3mKWOJX136bxU\nQMRP9Zqk+uXC13hARZ/pOP3Xgf7vSD1kW2g/wkPnai0zsjiH+x5qAFTbil9vq32H1XIO0+T/zpKX\n+L61bqPXer+/aq9Z0hF/GKBt6Vh60wYy0gjuE+w6q++dM8Z2VL4zwgcG+q7/o9XsDAdOVD5etet/\njx13Z0/sTHyH+n2k3XeFNYX9cv+246nr5AO8C+waEl77tYymeIBO8/Rd+IR18eAflGieJp0Luqbo\nfNN1ebqdD/5+J/4grdFzUtuPr6N5ftJ9loLE3dYyRAGq8MGAlolfD/16zbpXCWvZatv+fNWDpil2\nf5x2b6Dl4r6NnhPahqZwsKf+Oc7ttHtzlYeag+uea9b4U303+mXSAqDhNUDLxbsO8OumveYpAGq3\nzO5Qb58dl33WPVKP+8d7R+386b8nP2bHSSX4qYyoBmgYAFXws2OMfb+OcjNOP81dOWusu3zW6W7B\nGWOjmp+nafk2T4MNgCrYpebsav6uUdqrDbrjs7Nz5043b948/zZ6veOOO9xjjz1mTOn5fvjhh93q\n1asHrKN+RtUcXkHPXbt2RYFIjVSuSfNVU3S01a4Np2ZsR4P6LFy40O3f33/vpiCoanAqeKt56qvU\nDwIV7rva3/EatMuWLXPLly93Y8eOjQaBWrp0aWXk+3YFQP0ARgpKq0xvueWW6FV57e7udps3b3Z3\n3313JUvKs2rw+gB22LxeC2k9BaDD7hAqK7fgDwKgLUBlkwi0WaA0AVDdfD5rgQgF8upN1Wop+fXU\nt9/qC2YkAg7+c/+q7RywG1t/4xa/WfTL1XrN8mOlWel55rp5br79AM4y6cfziufertTQ8uuEA/74\nef5VP6Cq1ZTzy/jX/h8Lp5pR17Ib7LK10qMA3nKrLZV+u+RT2f/6qjXxXlaliXe4ZFiWfn74o9HP\ni78qf2rae37GskkLgGqbqiH3wMWfrNTGi+/Hv1dQVpMeCNQy8stXe403l6u2nGpRq89KBcPSarqE\nP6q0Df0464xH+2IbV7qfsBpua19qLAAY20zVt2FArp6Ryk8PF+ZOqn/tSesOQf3aqXZ2nSxX0qrg\n3y+taWW8xm1lgRp/tMo67Fqkxu6jj+ID4WhmowaqHXbn8+9WdpX1GpH2Q9xvJDyvwnM57Zj16ww1\n39pO1rT7fdY6NsMAqF++2quO65/ZKNCP2CBIjU6qOacau/EgU9p2FBhTWcX7QNayWZ39dsPgR1pw\nxC9X6zXrd1gt57TtZ8lLs671afv387KkQ8uqdckq+5e1pWz8u+yek9+hfr/VXnXNmzFuTFQTtNq1\nVEF1PUhSULLWlPWeS7VP19o1VUHcLJMCcV/53ZuVRRs9J7ViuI6u0W/aQ7Bzp9S/34pfyyqJOPlH\nM+5V1Mrgx1eeqqkf34d/r9HhD3T3ufnT+tMdvy9r9Jzw29VrOHhfOD/+t85rPUTRvVnaOR7fTt4D\noKrlaZdAt9/ugxQIP2L31upPXA+FVPNzb/dxt8vMd1lT+A8+7o26T1Kwc8CkgKf6+VRAzl5P9B1z\n0+ye6hxrzfP5M8a4i8/sdPMmj4kqewxYr41vBhsAVRI1svvKlSsrqb3tttvc448/nhrQDAdO8ito\n3wr81ZqeeuqpzH1MVguAavvN2E5aINWnvdFm8fJYsmSJ27hxo9/EgNewWXzYXH7r1q1u8eLFA5YN\n34RBzLQAZK3P9ZkCnzt27Ag3WfXvDRs2uKuuuqryOQHQCgV/IIDAIAVGXAA0vLkJ+3jLmn81SV1y\n9uQBtT39urqlUK2ke6y5s26sak1L7AeebvB9P47xZVWbYK011VStyW/aoEGaGu2PT+vEf6yoL8W0\ntDUjPQqk3nLeGVHAVjeXaZMCn1us64D7t+xJbQ6nddRU6VYLHoa1PzRfvmm1O/RZfNKPhXAwl1p2\nWjZsChbvKyvctpZVLRv9oKqXHv1I+s6iGe4c+5GS5qHaUb/d8WHmWnbx/s70Iyhs6hymM+1vjdCs\n5quqkVFt0g30xl1d7oGtA0eU98t/1o5HBdPUAX58ksdrHxx1d2161629bI5bdOa4yOjev+9xz751\nKL54pvfrbDvXnDVxQG0vv6KOJf24U9PKF752btRPb9rAEGFQTmn8nqXnBhv1+6IZEyq1j/w29aoa\nW6ppHO+TM1xmqH+r2Z4fKEblGNY2rLbtH10+x1191qQo0BtfRjWuf1PjWFp72Wxr3j4x9brlt+Vr\n7dxvXQOkXSP8crVeW2mtvttututLvLanT48C4Y++/F7U152f51913qqJ/4Vnjk9cV/wyetV1V4N8\nqZuIcGrkeqL9XD1nUiLgHB6bYQ1nld3X/7qzqvlQ8q08NJJ2LR8GM+LHpq5pa6wfvc/YNS2oLK3V\nKpOdlu51e6jzw3/uS3QTU1kowx++zNQHbFjb06+qYNULu7ui89/Pi7824qx1w+b3g7k/8PvP8h0W\ndnOg61K962QjeWnGtd7nJf7aSDr0PfmoBcbUBUW1BzBqSfCG1Qz96SsHou+ecH8KJn9j/rTU7yxd\nr5637yo1Z/eBax2v37V+xfV9nzY9bt0UXTJzQmpadO4/9NJ7UW3wrPdcuiZfMXtipaZnfJ86Rt+1\ne0HdU4TXlEbPSW03XOdQT5+79JnXowEHr7MBoNQCIz7perPeHmSty/AQrxn3KtqGuhDxA/GF6dHx\nrXvjb9t3tZqhq9sDTc9YVxr32feNnxo9J/x6/lUPaL76qSmp5eHvt3R9//0Nn46+R1VDf+mfBgZR\nVKa6H/ZTlpHl/bLDUQO0zw76rt4TVru6v4XMriN9Ua38/fZ6wAKgh4+Pcn2j7J7PBj86YbURT5zs\n9zOqzaiCsUmN3zss+Nk5piNqETHZnhOcO6U/8Llo+lg3w2rPpl2D+9duz/9qynz99ddHO1MTcwUx\ns07bt293CxYsqCyubV177bWV9/E/VNtz1apV0Ww/evzcuXPjiyXeq2n7gw8+mOhb1C+oba1ZsyYa\nWKla020t24ztrF+/3t10001+15VXBX5vv/32yvssfxw5csStW7fO3XfffYnFw75Rw0GHwvmJlWyG\ngpBqwq5AabyGplFmm/8AAA3TSURBVJbXKO0KFOtzdUGwbds2N3ly/2B3+lxBTdVsVc3etEnW9957\nr1uxYkVUwzNcJt4E/rzzzou232EDfbVjogZoO5TZBwKtFRhxAdBmcehJtfozUvNJNW18355sP203\nVml9XNXap24avzhrvN2w9d/AKviiUb5fsZvFZkxZA6B+X81Kz83W79Unxo12EzusA/WT0T/9oFTf\ncVkn/fBXU7deu8GTy+Z9hxM1RrNuaziX0w8X9YmnWoqy0D8FChuxaGb6dewumj4+qgn5sTWFUi0r\n1XJQX6VpfSOm7VtBiS9bMM4GLY8qE6h8fmH9dGZdP22btebpWJhlfZNqUhNINW/L6hcPyoW1iDV6\nsh5CKJiq2jwv2QBR1fqvrZW+dn52l9WqUrN1nRc6lhRsevqNbOeVmstfbAGA8NzUNtQdgspvqFM7\nrHXsXTRzfBQU0Y8ydSuxxfr2VDAsy6Qft3PtuqLy9jUMP7Kmgs9bH37Nuu7qnL/THlypqe9RC0Ts\n6+61H/uHhnR+DDXfWWwaWUbNZ3Xu6LvLO6qm26/sWGz2dUDn6ZwJndF+VOY6/zXYSd6n4f4Oa8a1\nvhnGOh/Uf6yuW2/ZtUbHje6Z1KQ9S5c2N9qxpmbMulY1ev2Pp9+fm3oQqGuozv1NFkhN6xs5vm61\n9zo+lb599hBG/SNquzssuNau75LwflTnh4zVhUmjUzPuVXR91cNPNZ/WpK6QhnrtazQfOu9Uy1PH\niu5xth88mujvu9Y2dd8sC5Wj+grPOg1HAFRps2xa7c4+93bXMQs0H3Xb7P5SAVHVuP34uGp2WmDH\nan2q389KANRu3qzep80/7jqsI6PJ1mJntpmdb9HPBVZD9/ypnXa+jrbg+ijXUe3pRVaYki2nGop7\n9+6N+srU4Dq9vb1u6lSrfDBlYFc+9ViGup2enp4omKqAn9IxmDSEaVS/mnv27Ima7o8bNy7a3oQJ\np8a9CJdt198KznZ1dUVN3+WsSWmbOXNm1b5KtYwCrGr2rkkBUK3TrokAaLuk2Q8CrRMobQC0daTN\n3XKjAdDm7p2tITD8AvGgXNZaxMOf8pGXAqxHXpmRYgQQQACBoQsMVwBUKVdtY/ULvufwMasBesxG\nsLfuBiwo+mGP1RC1Dw/a32pt0GM1QdWH58RO9fs+2k2xgP3UzlHWguY0N2tCh5ttQc/Z9qrBxar1\neTl0KbaAQHkFCICWt+zJeXEECIDmvCzDAGjYBDPnySZ5CDRNgKBc0yjrbgjrukQsgAACCCBQQIHh\nDIDGOVV79YOjx6Oa1up39b8WGD1oA/J1WzBUtYSn2gBkMy3Yqa6VptvrNKsBqhYLJyvuxjfHewQQ\naJIAAdAmQbIZBIZRgADoMOJn2fVD1p+RBpTQVGuwjSzbYhkERqIAQbn2lRrW7bNmTwgggAAC+RHI\nUwBUKscsCHrMmj9r0HeNTan31gg+avquQSMVCNU/a+0fNXPv77QgP56kBIEiChAALWKpkqeyCRAA\nzVmJayCiifYU98X93e7y2ROiQYl0o6NJ/TR+6dk3+t/wPwIlESAo176Cxrp91uwJAQQQQCA/AnkL\ngOZHhpQggIAXIADqJXhFYOQKEADNUdmp0/ZNNhr2eOu0PG2qNbp52vLMQ6AIAgTl2leKWLfPmj0h\ngAACCORHgABofsqClCCQVwECoHktGdKFQHYBAqDZrVq+ZK0A6Gs2UvSNf36r5WlgBwjkTUAj5K69\ndI51/G8DnlriwlHg85bWkZ4erEd6CZJ+BBBAAIHBCBAAHYwa6yBQLgECoOUqb3JbTAECoDkr1wcu\nmuUunD4+6shcTd/7LOLzl3f+5x59+UDOUkpyEGiPgDr5//nVc13nyb4gNAr83/Ycbs/OS7YXrEtW\n4GQXAQQQQCASIADKgYAAAvUECIDWE+JzBPIvQAA0/2VEChFAAAEEEEAAAQQQQKBFAgRAWwTLZhEo\nkAAB0AIVJlkprQAB0NIWPRlHAAEEEEAAAQQQQAABAqAcAwggUE+AAGg9IT5HIP8CBEDzX0akEAEE\nEEAAAQQQQAABBFokQAC0RbBsFoECCRAALVBhkpXSChAALW3Rk3EEEEAAAQQQQAABBBAgAMoxgAAC\n9QQIgNYT4nME8i9AADT/ZUQKEUAAAQQQQAABBBBAoEUCBEBbBMtmESiQAAHQAhUmWSmtAAHQ0hY9\nGUcAAQQQQAABBBBAAAECoBwDCCBQT4AAaD0hPkcg/wIEQPNfRqQQAQQQQAABBBBAAAEEWiRAALRF\nsGwWgQIJEAAtUGGSldIKEAAtbdGTcQQQQAABBBBAAAEEECAAyjGAAAL1BAiA1hPicwTyL0AANP9l\nRAoRQAABBBBAAAEEEECgRQIEQFsEy2YRKJAAAdACFSZZKa0AAdDSFj0ZRwABBBBAAAEEEEAAAQKg\nHAMIIFBPgABoPSE+RyD/AgRA819GpBABBBBAAAEEEEAAAQRaJEAAtEWwbBaBAgkQAC1QYZKV0goQ\nAC1t0ZNxBBBAAAEEEEAAAQQQIADKMYAAAvUECIDWE+JzBPIvQAA0/2VEChFAAAEEEEAAAQQQQKBF\nAgRAWwTLZhEokAAB0AIVJlkprQAB0NIWPRlHAAEEEEAAAQQQQAABAqAcAwggUE+AAGg9IT5HIP8C\nBEDzX0akEAEEEEAAAQQQQAABBFokQAC0RbBsFoECCRAALVBhkpXSChAALW3Rk3EEEEAAAQQQQAAB\nBBAgAMoxgAAC9QQIgNYT4nME8i9AADT/ZUQKEUAAAQQQQAABBBBAoEUCBEBbBMtmESiIwEe9x90l\nv/5PQXJDNhAorwAB0PKWPTlHAAEEEEAAAQQQQKD0Ak9cM9d9Ycb40jsAgAAC6QJb9x9xK557J/1D\n5iKAwIgRIAA6YoqKhCKAAAIIIIAAAggggECzBa45a5J75Io5zd4s20MAgYIIrNzwjtuy70hBckM2\nECivAAHQ8pY9OUcAAQQQQAABBBBAAAETUBD01vOnufnTTneTxpyGCQIIIOBU8/Mnrxwg+MmxgEBB\nBAiAFqQgyQYCCCCAAAIIIIAAAggggAACCCCAAAIIJAUIgCZNmIMAAggggAACCCCAAAIIIIAAAggg\ngAACBREgAFqQgiQbCCCAAAIIIIAAAggggAACCCCAAAIIIJAUIACaNGEOAggggAACCCCAAAIIIIAA\nAggggAACCBREgABoQQqSbCCAAAIIIIAAAggggAACCCCAAAIIIIBAUoAAaNKEOQgggAACCCCAAAII\nIIAAAggggAACCCBQEAECoAUpSLKBAAIIIIAAAggggAACCCCAAAIIIIAAAkkBAqBJE+YggAACCCCA\nAAIIIIAAAggggAACCCCAQEEECIAWpCDJBgIIIIAAAggggAACCCCAAAIIIIAAAggkBQiAJk2YgwAC\nCCCAAAIIIIAAAggggAACCCCAAAIFESAAWpCCJBsIIIAAAggggAACCCCAAAIIIIAAAgggkBQgAJo0\nYQ4CCCCAAAIIIIAAAggggAACCCCAAAIIFESAAGhBCpJsIIAAAggggAACCCCAAAIIIIAAAggggEBS\ngABo0oQ5CCCAAAIIIIAAAggggAACCCCAAAIIIFAQAQKgBSlIsoEAAggggAACCCCAAAIIIIAAAggg\ngAACSQECoEkT5iCAAAIIIIAAAggggAACCCCAAAIIIIBAQQQIgBakIMkGAggggAACCCCAAAIIIIAA\nAggggAACCCQFCIAmTZiDAAIIIIAAAggggAACCCCAAAIIIIAAAgURIABakIIkGwgggAACCCCAAAII\nIIAAAggggAACCCCQFCAAmjRhDgIIIIAAAggggAACCCCAAAIIIIAAAggURIAAaEEKkmwggAACCCCA\nAAIIIIAAAggggAACCCCAQFKAAGjShDkIIIAAAggggAACCCCAAAIIIIAAAgggUBABAqAFKUiygQAC\nCCCAAAIIIIAAAggggAACCCCAAAJJAQKgSRPmIIAAAggggAACCCCAAAIIIIAAAggggEBBBAiAFqQg\nyQYCCCCAAAIIIIAAAggggAACCCCAAAIIJAUIgCZNmIMAAggggAACCCCAAAIIIIAAAggggAACBREg\nAFqQgiQbCCCAAAIIIIAAAggggAACCCCAAAIIIJAUIACaNGEOAggggAACCCCAAAIIIIAAAggggAAC\nCBREgABoQQqSbCCAAAIIIIAAAggggAACCCCAAAIIIIBAUoAAaNKEOQgggAACCCCAAAIIIIAAAggg\ngAACCCBQEAECoAUpSLKBAAIIIIAAAggggAACCCCAAAIIIIAAAkkBAqBJE+YggAACCCCAAAIIIIAA\nAggggAACCCCAQEEEEgHQrq6ugmSNbCCAAAIIIIAAAggggAACCCCAAAIIIIBA2QUIgJb9CCD/CCCA\nAAIIIIAAAggggAACCCCAAAIIFFggEQDdvXt3lN0nn3yywNkuT9YWL15cnsySUwQQQAABBBBAAAEE\nEEAAAQQQQAABBGICBEBjIEV7SwC0aCVKfhBAAAEEEEAAAQQQQAABBBBAAAEEGhEgANqI1ghclgDo\nCCw0kowAAggggAACCCCAAAIIIIAAAggg0DQBAqBNo8znhgiA5rNcSBUCCCCAAAIIIIAAAggggAAC\nCCCAQHsECIC2x3nY9kIAdNjo2TECCCCAAAIIIIAAAggggAACCCCAQA4ECIDmoBBamQQCoK3UZdsI\nIIAAAggggAACCCCAAAIIIIAAAnkX+D9trdAgzvEI6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "image/png": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='kaggle-submission-tree-depth3.png', width=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pcHRpb24+CiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE+Chn5ytgAADIUSURBVHgB7Z0H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\nTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "image/png": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='kaggle-submission-tree-depth6.png', width=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lQe66YIxM6Ne1cIeET5JmxSfsLknjYrPTv6E11dNuMf+0x9p+fk38cr38e7Mp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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "image/png": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='kaggle-submission-tree-depth9.png', width=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reaching my goal with tree depth 3\n",
    "\n",
    "Interesting, so the performance on the local test dataset was pretty spot on for how well it generalizes to the unseen kaggle test set: the tree depth of 3 performed best on kaggle. For some reason the plain tree does better than a random forest in this case even though they looked like they'd perform about the same.\n",
    "\n",
    "In any case, my tree depth 3 trained against all of the features has reached a 78% accuracy, meeting my goal!\n",
    "\n",
    "### So would tree depth 3 work skipping the variables like we did in our first attempt?\n",
    "\n",
    "Let's find out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  SibSp  Parch     Fare\n",
       "0       3    0   22      1      0   7.2500\n",
       "1       1    1   38      1      0  71.2833\n",
       "2       3    1   26      0      0   7.9250\n",
       "3       1    1   35      1      0  53.1000\n",
       "4       3    0   35      0      0   8.0500"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree3 = DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)\n",
    "to_drop = [col for col, _ in td_preprocessed_unscaled.iteritems() if col.startswith('cabin_sector') or col.startswith('embarked')]\n",
    "\n",
    "X_orig = td_preprocessed_unscaled.drop(to_drop, axis=1).loc[:,'Pclass':]\n",
    "\n",
    "X_orig.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deep_tree depth 3 full training set accuracy: 0.82\n"
     ]
    }
   ],
   "source": [
    "y = training_data['Survived']\n",
    "tree3.fit(X_orig, y)\n",
    "print(\"deep_tree depth 3 full training set accuracy: {:.2f}\".format(\n",
    "        accuracy_score(y, tree3.predict(X_orig))))\n",
    "test_data_predict = tree3.predict(test_data_preprocessed_unscaled.drop(to_drop, axis=1).loc[:,'Pclass':])\n",
    "\n",
    "submission_df = test_data_preprocessed[['PassengerId']].copy()\n",
    "submission_df['Survived'] = test_data_predict\n",
    "\n",
    "submission_df.to_csv(\n",
    "    'titanic_submission_decision_tree_depth3_orig.csv'.format(depth),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diffing the file locally, it is the same as with the full feature set.\n",
    "\n",
    "So, yep, all the work above exploring features and reasoning that the cabin sector and departure terminal was for naught, the real fix was to submit using a decision tree!\n",
    "\n",
    "Nonetheless, using the additional features *did* help with the logistic regression model, and it didn't hurt the decision tree."
   ]
  }
 ],
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