ass1_basic_titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "#libraries preparetion\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") #dont wanna se warning\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\",context=\"notebook\",palette=\"muted\")\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "train = pd.read_csv(\"data/train.csv\")\n",
    "test = pd.read_csv(\"data/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape train: (891, 12)\n",
      "shape train: (418, 11)\n"
     ]
    }
   ],
   "source": [
    "#data info in general\n",
    "#print(\"describe train:\\n\",train.describe())\n",
    "print(\"shape train:\",train.shape)\n",
    "print(\"shape train:\",test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1046.000000</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.881138</td>\n",
       "      <td>33.295479</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.413493</td>\n",
       "      <td>51.758668</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>378.020061</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.486592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>982.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age         Fare        Parch  PassengerId       Pclass  \\\n",
       "count  1046.000000  1308.000000  1309.000000  1309.000000  1309.000000   \n",
       "mean     29.881138    33.295479     0.385027   655.000000     2.294882   \n",
       "std      14.413493    51.758668     0.865560   378.020061     0.837836   \n",
       "min       0.170000     0.000000     0.000000     1.000000     1.000000   \n",
       "25%      21.000000     7.895800     0.000000   328.000000     2.000000   \n",
       "50%      28.000000    14.454200     0.000000   655.000000     3.000000   \n",
       "75%      39.000000    31.275000     0.000000   982.000000     3.000000   \n",
       "max      80.000000   512.329200     9.000000  1309.000000     3.000000   \n",
       "\n",
       "             SibSp    Survived  \n",
       "count  1309.000000  891.000000  \n",
       "mean      0.498854    0.383838  \n",
       "std       1.041658    0.486592  \n",
       "min       0.000000    0.000000  \n",
       "25%       0.000000    0.000000  \n",
       "50%       0.000000    0.000000  \n",
       "75%       1.000000    1.000000  \n",
       "max       8.000000    1.000000  "
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#null values in data\n",
    "full = train.append(test,ignore_index=True)\n",
    "full.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 12 columns):\n",
      "Age            1046 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Name           1309 non-null object\n",
      "Parch          1309 non-null int64\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Sex            1309 non-null object\n",
      "SibSp          1309 non-null int64\n",
      "Survived       891 non-null float64\n",
      "Ticket         1309 non-null object\n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 122.8+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'There are 4 types of data having missing values, Cabin 1000+, age 200+, Embarked 2, Fare 1'"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#null values in data\n",
    "full.info()\n",
    "\"\"\"There are 4 types of data having missing values, Cabin 1000+, age 200+, Embarked 2, Fare 1\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survivate rate: 0.33695652173913043 of passengers from embarkation : S\n",
      "Survivate rate: 0.5535714285714286 of passengers from embarkation : C\n",
      "Survivate rate: 0.38961038961038963 of passengers from embarkation : Q\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'High survivel rate for passengers from C,cherbourg'"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation from features to result\n",
    "sns.barplot(data = train, x = \"Embarked\",y=\"Survived\" )\n",
    "for e in [\"S\",\"C\",\"Q\"]:\n",
    "    print(\"Survivate rate: {} of passengers from embarkation : {}\".format(full[\"Survived\"][full[\"Embarked\"]==e].value_counts(normalize=True)[1],e))\n",
    "\n",
    "\"\"\"High survivel rate for passengers from C,cherbourg\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'More C passengers stays in 1st class '"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data = train,  x=\"Pclass\",col=\"Embarked\", kind=\"count\")\n",
    "\"\"\"More C passengers stays in 1st class \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c05bfa25f8>"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=train,x = \"Parch\",y=\"Survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c05c15ec18>"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=train,x = \"SibSp\",y=\"Survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'females are more likely to survive'"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=train,x='Sex',y='Survived')\n",
    "\"\"\"females are more likely to survive\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c05c41bb38>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=train,x='Pclass',y='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0-10 has higher serviva rate'"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fxz9fovrLP6A5KpqhaiGEEK2Rouv6mb9h/k1hYSFTpkxhwYIFWCwWJk+ezOuvv058fHz9OWPHjuXFF18kOTmZp59+mh49enD77bdzww038N5775GQkMAXX3zBihUrmDNnDh999BEej4fp06dfULF5eXkMHz6cFStWEBsbe3FXLK46732Vx9frT/HrCTG0s1+ZrU0Vt4PQnZ/hd+A7dGsgVZ3TcUUktNp2WFfa3iIDn2SbMRkVnhwJPdqc/f+Trnnx7FmDe+cKFKsffiMfwJw4RPrHCiGEOK8GR2CzsrJITU0lJCQEPz8/0tPTWbp0af3x/Px8nE4nycnJAGRkZLB06VLcbjePPPIICQkJAHTt2pWCggIAdu7cydq1axk3bhwPPPBA/feFuFA5Rx18vf4Ug3oEX7Hw6pufTfTiR/E78B3OtgM41f+/cNm6SXg9jwS7xqOD3fiaNX6XqbN0z9nfJysGI5Ye1+M7+kEUv2Aci17F8c8X0apLm7hiIYQQrUmDAbaoqAibzVZ/2263U1hYeM7jNpuNwsJCLBYLEyZMAEDTNN555x1GjBgBQGBgIHfeeSeZmZkMHTqURx999LJdkLh2eFSN2QuOExJgYmTfsMv++IrqImzzn4lY8yq62Y+ylPuoih95VbbFuhJsATr/fZ2brnaN91bD+2t0VO/Zg6wxJBLfkdOx9B6D5+BmKv/yEJ7D25q4YiGEEK1FgwFW07TTPs7Tdf202w0dd7vdPP7446iqWj9l4Pnnn2fkyJEATJkyhdzcXKqqqi79asQ15YvVxRwrcjFhUARWy+VtkG8uPUTU8ifxz/2WmvZplPa5BzUw+rI+x7XA1wz39fdwQ7zK4t3w+2+g0nme0djEwXVzY01Wqj+bSe2aT9E1bxNXLYQQoqVr8Ld+VFQUxcXF9beLi4ux2+3nPF5SUlJ/3OFwcN9996GqKnPmzMFsNqNpGnPmzMHrPf2XktEoW02Kxjte5OTTlYUkdwqgWzv/y/fAukbQ3q+I/G4mBlc1Zb3uoLrjiGuip+uVYlBgfKLKHX3c5JzUeeyfcKz03FPvjSGR+I76NaaOfXCumUv1ZzNlSoEQQojTNBhg09LSWL9+PaWlpdTW1rJ8+XKGDBlSfzwmJgar1Up2djYAixYtqj8+Y8YM2rdvz+zZs7FY6lobGQwGvv32W5YtWwbAwoUL6dWrF35+Tbtrkmi9dF3nrS/zsJgUxqZevg0LDLVl2Fe9RPC2ubgjunKq3/14wjpetse/1qXEajw4yE2tR2fGlzobj5w7xComCz4DJ2FNnYSal1M3peDI9iasVgghREvWYBcCqGuj9cEHH+DxeJg0aRLTpk1j2rRpPPzwwyQlJbF3715mzpxJdXU13bt3Z9asWeTm5jJx4kTi4+MxmepGr+x2O3/60584cOAAv/vd76iqqiIsLIxXX32V6OiGP56VLgQCYMWWUl77x3EmDbHRr2vQZXlMS8l+Itb9EYO7huou6dRGJssirSukvBY+2mTheLnCnQMUbknmvF0HvOWFuNZ+hlZRhM/QO/BJ+4V0KRBCiGtcowJsSyEBVjicXqb9cS9B/kYeGBdTv9vTpfA/tJLQzX9B8wmivPuteAMiL0Ol4nzcXvh8u5kteUaGxsOD14PVdO6fpa66cW1ciHp4K+bEIfjf9N8oZllMJ4QQ1yqZ2Cdalc9WFlJerTJ1ROSlh1evSti2v+F/YDme8E6Ud8uQDQmaiMUId/b2EB2o8U2OmRMV8HS6TnjA2X+mismCdeCtGEKicG9dSlXpCQJufRZD4OWbQiKEEKL1uLxLt4W4go4VOVm4rph+CYG0tV1az1eDs4LIVS/gf2A5Ne3TKOsxRcJrE1MUuLGzl/v6uTlWpvPbBbC/8DzzYhUFS+IQfIbeiffUcSo//m/UggNNWLEQQoiWQgKsaBV0Xef9zHwsZgPpKZc26mYuO0zU8icxnzpIZWLGT10G5J9Cc+kRrfHIdW4M6Dz1lc6q/eef1WSK7YbvjQ8AUPV/T+Des7opyhRCCNGCyG9t0Spk7alka241I/uGEeB78S3XrCd3YF/x/1A0jbI+v8QZ2eMyVikuVpsgnUcHu2gfqvP6SvjrBh2vdp5WW6FR+KX/F4awGBwLX6F29Vxa0XR+IYQQl0gCrGjxnG6ND7/OJzrcwoBuF991wO/oGmw//AHNN4TSPr+UjQlamAArPJDqZlCcyoJt8P/Os+kBgOITgO8N92LqlIJz7afULH4T3as2YcVCCCGaiwRY0eJ9sbqIonIP4wdGYDRcxMItXSdobybh699BDW1HWa+70KyXp/2WuLxMBri1p8qUZA+7C3Qe/QIOFp8nxBpNWAdkYE4ajnv7t1R/8QK629mEFQshhGgOEmBFi3ay1MX8VUUkxwfQMfoiFlnpGqHbPiF4299xRfagLGkKuvnSFoCJK29AOy8PD3KjenWeWKizct/5F3dZe47AOmAi6qFsquY+ieYob8JqhRBCNDUJsKJF+9M3J1AUGN3vIhZueT1ErH+bgH2LqW2bSkW3ibIlbCvSLlTnt0NcdAjVmf09vL9Gx+M9d5A1x/fHZ8ideIuOUPW3x/CWnmjCaoUQQjQlCbCixdp+sJqsPZXckBxKSMCFBU9FdWNf8yq+x7Kojr+Rqk43ys5arVCgFaanurkhXmXxbnh6ERRXnTvEmmK74Tv8V+i1VVR98hjqif1NWK0QQoimIgFWtEheTeeDr/MJCzRxXVLwBd1XUZ3Y1ryC5eROKruNp6btQAmvrZjRAOMTVe5JcXO0VOeRL2Dz0fN0KLC1x3fkdDCYqJr7FJ7DW5uwWiGEEE1BAqxokb7NLuXwSSej+4VjNjX+r6nicWJf/QcshbupSrwZZ1TyFaxSNKVebTQeG+ImxEfj+SXwtx/P3WrLEGTDd+R0DAGhVM//f7hz1jZxtUIIIa4kCbCixXE4vfx12Uk6RPmQ1NG/0fdTPDXYV7+MuXgfVd0zcEYmXcEqRXOwBeg8fJ2btDiVf26FmZlwynGOEOsbhO/waRjCYnF8+QdcW5c2cbVCCCGuFAmwosWZt6qQCofK2NRwlEZ+9K+4HdhXvYS55ACV3W/Bae9+hasUzcVihNt6qtzZx01usc4j/zj3lALF6ovvDfdgbNOFmiVv48yaLxseCCHEVUACrGhRCkpdfLm2hJQugcTaGtfuyuCqJnLVi5hLD1PR41Zctm5XuErREvSN1Xh0sJtAa92Ugg/X6rjVM8OpYrLgM+QOTHHJ1K76G7UrP5IQK4QQrVyjAmxmZiZjxoxh5MiRzJ0794zjOTk5ZGRkkJ6ezjPPPIOq1u2Gk52dzaRJk5gwYQJ33303+fn5AFRWVnL//fczevRopk6dSnFx8WW8JNGa/WVxAQYF0lPCGnW+4qnB/sNLmMqPUpF0G+6Irle4QtGSRAXq/Pd1bq7vpPL1LvjtP+Fo6VlCrNGENe1WzF3TcP24gJpv3kTXvM1QsRBCiMuhwQBbWFjIG2+8waeffsrChQuZN28eubm5p50zY8YMnn32WZYtW4au68yfP7/++y+++CKLFi1i3LhxvPjiiwDMnj2blJQUlixZwq233spLL710BS5NtDY7DlWzbncFw5JDCfJvuG2Worqwr34FU9kRKnrchju8cxNUKVoasxFu7q4yPdVNeY3Ob/+p8/VO/YxRVkUxYOk7FnPSCNw7vsWxYBa66m6mqoUQQlyKBgNsVlYWqamphISE4OfnR3p6OkuX/msxRH5+Pk6nk+TkutXeGRkZLF26FLfbzSOPPEJCQgIAXbt2paCgAIBVq1Yxbtw4AMaOHcvq1avxeDyX/eJE6+HVdD78Op/QABODG9M2y+vBtva1ugVbiRkSXgXd7BozrnfRJULjw3Xw/GI4Vf2fIVbB2nM4lpTxePavp3re79FdNc1UsRBCiIvVYIAtKirCZrPV37bb7RQWFp7zuM1mo7CwEIvFwoQJEwDQNI133nmHESNGnHEfk8lEQEAApaWll+eKRKv03ZZSDhY4Gd0/rOG2WZqKbf2bWE/uoKrbOJz2xKYpUrR4gVa4r7+HST097Dyh85v5sGLfmaOxlq4DsQ76BeqxXVTNfQrNUdFMFQshhLgYDQZYTdNOWwmu6/pptxs67na7efzxx1FVlenTp5/1OXRdx2CQ9WTXqp/bZsVF+tCzY8D5T9Y1wjfOwSdvE1VdRkufV3EGRYHr4rzMGOomOlDjze/hxSVnttsyxyXjM/QuvMVHqfq/GWgVRc1UsRBCiAvVYGqMioo6bZFVcXExdrv9nMdLSkrqjzscDu677z5UVWXOnDmYzWagbhS3pKQEAFVVcTgchISEXJ4rEq3O/FVFlFc3om2WrhOW/Rf8jqylutNwamP6NV2RotWxBej8Js3NxB4etuXrPDQfvt9/+misKaYrvjfci1Z9ispPZuAtOd6MFQshhGisBgNsWloa69evp7S0lNraWpYvX86QIUPqj8fExGC1WsnOzgZg0aJF9cdnzJhB+/btmT17NhaLpf4+Q4cOZeHChQAsXryYlJSU+nArri0nS10sWFtM386BtLWfv21WyI65+Od+R03cYGraDWqiCkVrZlBgaMe60Vibv8YbK+GFJVBU9a8Qa7TH4Tt8Gqhuqv5vBmrBgWasWAghRGMoeiMaImZmZvLBBx/g8XiYNGkS06ZNY9q0aTz88MMkJSWxd+9eZs6cSXV1Nd27d2fWrFnk5uYyceJE4uPjMZnqVpTb7Xb+9Kc/UV5ezpNPPsnx48cJDAzktddeIzY2tsFi8/LyGD58OCtWrGjU+aLle2nuETbureTx29oRfJ7OA4H7viFk6yfUxvanKj697nNiIS6ApsPqQ0YW7zWhKHB7P4XxSWA01P1d0qpKqF35V3S3g4BJz2KO69XMFQshhDiXRgXYlkIC7NVl5+FqnvjwICNTQhne+9x9X/2OrSM86y1c9u5UJE4EReZLi4tXWgMLdpnZddJIh3B4cCh0tv8UYmsqcX7/V7SqYvxv/h8sXdOauVohhBBnI0lANAuvpvNB5s9ts849/9lauIuwDe/hCY2jImGChFdxycL84Ff9PNyb4qbUofP4Ap0P1+pUu3QMfkH4jrgPQ1gsjgWzcG1f3tzlCiGEOAtJA6JZrNhSxsECJ6P6hWE5R9ssc9kRIta+htcvnPLut4Kx4c0NhGgMRYGebTSeGuZicEcv3+zSeeAzWJ6jo5l98b3hHoxtulDzzZs4N/yzucsVQgjxHyTAiiZX4/Ly12UFtI+00qvT2dtmGR3F2FbPAqOZ8qTJ6GbfJq5SXAt8zJDRQ+WxnxZ5vfMDPLYAcorN+AyeiikumdqVH1Gz8qMzeskKIYRoPhJgRZObv6qIsmqVsakRZ22bZXBVY//hZRTVRVnS7Wg+jdiZS4hLEBus82Cam7v61E0reHIRvL7KiKPnJExdBuLa8E9qlryNrnmbu1QhhBCAfCYrmlRBqYsFa+raZrU7S9ssRXVjW/MKxuoiyntNxRtgP8ujCHH5KQr0idXoHuViRa6JlblG1h9SuLnnWG5O9MO9bRl6bRX+E55AMUnbPyGEaE4yAiua1Idfn8BggPR+Z+k6oGlEbHgLc8kBKhMn4glp3/QFimue1QRjElSeHOamR7TG/K0KD2wfTm6bsXj2ZVE9//forprmLlMIIS7ZggULuPnmmxk3bhy33347OTk5F/1Yn332GYsXL77o+zscDm644YZGny8jsKLJbNpXyYacSsb0Dzuz56uuE7blo7otYjuPwmXr1jxFCvGTCH+du/p4uL6jytd7zDy7L42xgT5MObqAqk+fJuAXz2Hwk+ktQojWqaCggI8//ph//OMf+Pj4sHXrVh599FGWLl16UY83ZcqUy1zh+UmAFU3CrWq8n5mPPcTMoB5nts0KyvkS/9xvqWk/iNrY/s1QoRBn1y5E578GuskpMvB1TjInHL48UvAZNX95HNtdL2MMtjV3iUIIccFqamrweDzU1NTg4+ND7969efrpp3nyySdJT09n2LBh/Pjjj8ydO5e33nqLYcOGER0dTXR0NJs3b2b58uVYrVYyMzPZs2cPfn5+hIaGkpubS58+fRg/fjxut5ubbrqJpUuXsnLlSubMmSV6jUAAACAASURBVIOqqowYMYKHH36YmpoaHnvsMfLy8ujRo8cF1S9TCESTWLi2mBOn3IxNjcBkPH3hlv/h7wneMQ9ndC+qOzT+4wMhmoqiQGKkxuND3XTr2Yk52i/xVp7i2HuPsmPTbulQIIRodTp16kS/fv0YMmQId999Nx999BF9+vQ55/knTpzg+eef549//CMDBw4kKysLgOXLlzNq1Kj680aPHs13330HwNq1a0lLS6O8vJxPPvmEzz//nIULF7Jv3z42bNjA3//+d+Li4sjMzKR//wsbvJIAK6644go3n31fRPc4f7q29TvtmO+JrYRu/BB3eDyVXcbKFrGiRTMo0K+txq3D2rCx7T0omkrE8pm8//Yith2skiArhGhVXnjhBRYtWsTgwYNZunQpt912Gx6P56zn+vr6Eh8fD/wrpNbW1pKbm0vPnj3rz+vXrx979+7F5XLVh9vt27dz4MABbr31ViZOnMiBAwc4ePAg2dnZjBw5EoAxY8ZcUO0SYMUV95fFBahenbEDwk/7vuVULuHrXscbGEVF4i1gMDZThUJcGKMBEjvbqR34K1w+EdxW9WfW/O1vPDbnABv3VkqQFUK0eD/88AMrV66kU6dO3HfffcybNw8fHx+KiorqX8NUVa0/32q11v93Wloa2dnZrFq1iuuvv/60lpgGg4HrrruO1atXs23bNvr374+maQwbNoxFixaxaNEi5s+fzy233HJaPYqinLW15rlIgBVX1I5D1fywo5xhvUIIC/pX6yFTVQG21X9AswRQ3mMyusl6nkcRomUy+AbhHnA37shE7vBZwojyuTz/twM89PZ+1uwsR9MkyAohWiar1cobb7xBSUkJAKWlpTgcDtq1a8fBgwcBWLVq1Vnvazab6d27N3PmzDlt+sDPRo8ezdtvv82AAQMwGo0kJSWRlZVFSUkJHo+H6dOns3XrVlJSUuo7F3z77bcX9OZfFnGJK8br1Xnvq3zCAk0M7fWvhVsGZzn2H14GXaO85xQ069l34xKiVTCaqUzMQPOPYODhH+gSXcEfHbfz8qdOYm1WfnG9naE9QzCfY8tkIYRoDqmpqUyePJmpU6diNBqxWCw89dRTxMXF8dBDD/HNN9+QlpZ2zvuPGTOGDRs2nDZ94Gd9+/alrKysPtxGRkby2GOPcc8996CqKiNHjmTgwIH07t2b//mf/2Hs2LH06dMHo7Hxn8Qqeiv6rCsvL4/hw4ezYsUKYmNjm7sc0YCF64r54OsT3DUiku4d6kKq4nES+f1zmCryKOt1B2qw/BzF1cOncBeBe7/C6xtKVtyv+epAICdOuQkLNHHzIBuj+4cT4CtTZYQQ4lI1akggMzOTMWPGMHLkSObOnXvG8ZycHDIyMkhPT+eZZ545bc4EwOzZs3n77bfrb2/cuJEBAwYwYcIEJkyYwFNPPXWJlyFamuIKN598e5Kusb4kxvnXfVNTich6HVPZESq63yLhVVx1nJE9KOt9NwbVxeCcl3m6517uTY8iPMjMR0sLuPMPe/jg63wKy9zNXaoQQrRqDQbYwsJC3njjDT799FMWLlzIvHnzyM3NPe2cGTNm8Oyzz7Js2TJ0XWf+/PkAVFVV8fTTT/Pxxx+fdv6uXbu499576yfzzpo16zJekmhuuq7z3qJ8VK/OhEG2uknZuk74pg/wKdhOdcJNuMM7N3eZQlwRalAMp1KmoYa2J2zzn0k78RHTbgzjkYxYurf3J3N9Cff+bw6zPjvK/jzZ0UsIIS5GgwE2KyuL1NRUQkJC8PPzIz09/bRdGvLz83E6nSQnJwOQkZFRf3zFihXExcVxzz33nPaYO3fuZO3atYwbN44HHniAgoKCy3lNopmt3VXBhpxKRvYNJfynhVshOz/H7/BqHB2upzaqdzNXKMSVpVv8KUuajKPjMHyPrCPq26doZyrituvtPPGL9gzpGcLGvZU88u4BZnyQy4acClnwJYQQF6DBAFtUVITN9q+dZux2O4WFhec8brPZ6o/ffPPN3H///WdMyg0MDOTOO+8kMzOToUOH8uijj17yhYiWoapWZc5X+cRGWOt33Ao4sIzAPQupjemLo/3gZq5QiCaiGHC0H0x58h0YXJVELX8a/8M/EBJgYnT/cJ6a0p5xqeHkn3Lx3CdHmP7GPhZvPIXLozV35UII0eI1GGA1TTutL5eu66fdbuj42Tz//PP1jWunTJlCbm4uVVVVF1y8aHk+WlJAhUMlY7ANo0HB7+g6QrI/xm1LoCp+tGxUIK45ntAOlPa9HzU4hrAf3yNi/ZsYXJX4WAxclxTCjNvaMWWYHRR4+8s87vzDHv62vIBTlWdvJi6EEKIRATYqKori4uL628XFxdjt9nMeLykpOe34f9I0jTlz5uD1ek/7/oW0ThAt045D1SzdVMrgpGBiIqz4nthC2IZ3UUPbU95tIhikjZC4NmnWAMqSpuLoOAyfYz8SveQxfPM2AmA0KCTHB/LghBimj21De7sP874v4u5X9vDqPJknK4QQZ9NgokhLS2P9+vWUlpZSW1vL8uXLGTJkSP3xmJgYrFYr2dnZACxatOi042c8ocHAt99+y7JlywBYuHAhvXr1ws/P75z3ES2f26Px1pfHCQ8yMaJvGNaiPfW7bJX3+AUYzQ0/iBBXM0PdlILSlPvQLAFErP0j4evfwuCq+/RJURQ6Rvty541RzPhFO9K6B7N+T9082cfeP8CaneV4vTJPVghx9Wioy9X5NLiRQWRkJI8++ih33XUXHo+HSZMm0bNnT6ZNm8bDDz9MUlISr732GjNnzqS6upru3btz1113nfcxX3nlFX73u9/x7rvvEhYWxquvvnpBRYuW5/PvC8kvcfOr0dH4Vx4hYs2raD4hlCVNkV22hPg33oBISnvfg//xLPwO/4C1cBdlKdOoje1Xf054kJmxqRGM6BNG9v5K1u2u4OVPj2ILNjM+LYJR/aSfrBCidfu5y9WCBQuwWCxMnjyZAQMGEB8f36j7y0YG4pIdOVnLg2/vJ7lTILf38RC54vfoBhNlyXej+QQ1d3lCtFim6pME7c3EVFWAM7Yfpcl34g2IPOM8TdPJOVbDul3lHCxw4mM2cGPfUMan2Yi1yRtEIUTr8+WXX7Jp0yZefvllAN599110XefBBx9s1P1lK1lxSdyqxivzjuFrNTKhp4Z91YuATnnP2yW8CtEANSCK0j734pe3Af8ja4g+sZXqhJuo6DYR3exbf57BoNA9zp/ucf7kl7jI2l3Bkk2lZG44Rf+EQG4eZCO5U0CDC2iFEALguy2lLN9cekUee2RKGCP6hDV43tm6XO3YsaPRzyMBVlySvy0/yZGTTqYPtRK3fhaKp5by5Dvx+oU3d2lCtA4GIzXtBuGM7EnA4e8J3LMIv0OrqOg5BUeHoaCcvlQhJsLKrUPtjOoXxo85lazPqWTj3kO0j/Rh4qAIrk8OxWqWBZNCiJbtYrpY/TsJsOKibc2tYsGaYkZ2URm0/02MznLKet6OGhDV3KUJ0epo1kAqE8ZT06YvgQe/JWzj+wQeWEZZr9txRSad0YIu0K9uweTQXiHsOFTN2l0VzF6Qx0dLC7hpQDhjUyMIC5LFk0KIM43o07hR0ispKiqKzZs319/+zy5XDZG36eKiVDpUXpt/jG4hVdxR8TZGZwVlPaeiBrdt7tKEaNXUoBjKku+mMjEDY20p9lUvEfntM/jkbQL9zE0OzCYDfbsE8fDEWO6/qQ1t7T58/n0Rd7+Sw//OO8aBfGnDJYRoeRrqctUQGYEVF0zXdd76Mg9rTSH/E/YxBk8tZcl3oAa2ae7ShLg6KArOyB44bQn4Fu7A71gWtrWv4QluS2XiRGraDjyjr7KiKHRq40unNr6cqvSQtbuCdbvLWbmtjO7t/bj5OhsDE4MxGmSerBCi+Z2ry1VjSRcCccGWbT7F/C+38ELwx1gNKuW9psq0ASGuJE3Dp3g3/sfWYawuQg2MoqrLGBztr0O3+J/zbrVuL5v3VZG1u4LSKpXocAsZ19m4sW+YzJMVQrRqEmDFBTlR4uKld9bwpPXP+Jl1ynrdgTeg8XNWhBCXQNexluzD//g6TBX56EYLte1Sqeo4HHdE13Nu1axpOruPOFi9s5xjRS6C/IyMT4tgbGoEwf7yQZwQovWRACsazaNqvPned9xW9Wd8rQYqku/E6x/R3GUJcU0yVZ3Ar2Ab1sKdKKoLNSiG6k434Gg/GM0n+Kz30XWdwyedrN5RTs6xGiwmhfSUMCYOthEdJv1khRCthwRY0WiZf/sn/fM+QbUGUdt7irTKEqIFUFQ3PsV78D25FVP5cXRFwWNLoCZ2ADUx/c75JvNkqZs1O8vZmluFpsPgHsHcMsROl1jZ1lsI0fJJgBUN0nWNnZ/9ibZHvqLA0gFT/1vQzfJLToiWxugowrc4B0vxXkzVhQB4wjvVhdk2fVGDYs6YZlDhUMnaXcGGnEqcbo2kDv5MGmKnX9dA2RhBCNFiSYAV56V7nJz4/FX8jv/INlNfotJGYTDKHuxCtHTGmhKsJfvwKdmHqSIPAK9fGK6ontTae+CMSkLzCak/3+nW2Li3knW7Kih3qMRF+nDrUDtDeoZgMkqQFUK0LBJgxTlpVSWUffYcFB/mK2UUvQel4GuRX2RCtDYGZwXWsoNYyg5jLj2MwVPXG9YT0g5XZA+cEQm4Irqi+YagenW2H6pi9fYKTpa5sYeYyRhsIz0lDB+LvHkVQrQMEmD/g17fKFy5pj8+8xzZTvWi13A5qnnf/QtuGNSRyIBW81dFCHEuuoap+iSWsiNYyw9jKjuGonkAUAMicdsTcEZ0pTa8K7vKQli9s4LDJ50E+hmZMDCCcQMjCJLOBUKIZnbNBljd60E7lY+3+GjdV8kxvMVH0cpP/ttuN0rdfDGDARQjhmAbxoh2GMPbYoiIxRjeFmN4WxSLz6VfXAuhu2upXfkRri2LKTdG8IfKKYzqF05i5Jk7AAkhrgKaF1NVAZbKPCyVxzGVH8fgrq47ZA3AbUugwNKRH4qjWXkiHKPZzKh+4Uy8zkZkqKWZixdCtGbV1dVMnjyZ999//4JzXaPeRmdmZjJnzhxUVeXuu+9m6tSppx3PycnhmWeeweFwkJKSwnPPPYfJ9K+Hnj17NkajkYceegiAyspKHn/8cY4fP05YWBizZ8/GZrNdUOEXQ/c48ez/Edfu71EPbQVNrTugGDAE2VBCIjHHJKAYjNTleh30n740Fa26FLUgF8/+Dadt6Wiwd8DcIRlzXDKmtj1abaD1HNlOzTez0SqKORR2Hc8fHcGoRIXESG9zlyaEuFIMRtTgWNTgWGpIBV3HWFuKpfI45so8zGVH6ODYTAfgrmATBaZ2bMluy1sb2xOZ2JPxw+KIi/Jt7qsQQrQy27dvZ+bMmRw5cuSi7t9ggC0sLOSNN95gwYIFWCwWJk+ezIABA4iPj68/Z8aMGbz44oskJyfz9NNPM3/+fG6//XaqqqqYNWsW33zzDffdd1/9+bNnzyYlJYUPP/yQhQsX8tJLLzF79uyLuoCG6JoX9cg23LtW4d6XBR4nil8w5q4DMYTFYgi2YwiKQDE2/iMx3auiV59CqyhGqyjCW3QY16ZMXD9+CQYjxpgEzHHJmDulYIyOR1Fa9o43daOuH+Pa8g1KYARbO03jf7fF0a+tl2GdPM1dnhCiKSkKXr9wav3CqY1KrvuW24G54jiWyjwiK48z1rOGcfoPcBjycu2sDuxMTM9k4lL6YQy68oMRQohL49q5Avf2b6/IY1t63Yg1aXiD582fP5/f//73PPHEExf1PA2mtqysLFJTUwkJqVutmp6eztKlS3nwwQcByM/Px+l0kpxc90KXkZHBW2+9xe23386KFSuIi4vjnnvuOe0xV61axdy5cwEYO3Yszz//PB6PB7PZfFEXcTa6x4lrcybOjQvRHeVg8cXUviemuF4Y7XGXFCoVowklOBJDcOS/nk91101FKDyE92QuzjWf4lwzFyUgDHPnAVg6D8AU1wvF1HI+ctM1L56cNdSu+gStoghzwnWsto7g7bUWekZ7mdzLc66NfYQQ1xDd4o/bloDbllD3Da8Hc2U+lOdhOJlHJ0c2fhvWUbkBXAFtCOzSB3OHZEztkzD4BDRv8UKIFumll166pPs3GGCLiopO+3jfbrezY8eOcx632WwUFtb1H7z55psBePvtt8/5mCaTiYCAAEpLS4mMjORS6V4V9/bl1K75FN1RhrFNV8x9x2Js0/WCRlkvlGKyYIrujCm6M5CO7nKg5u9HPbEX966VuLcuAbMP5g69MXcZgLlTfwz+Z98t50rTVTfuHd/h3PBPtPKTGEIi8b1xGj+UxfHO9zqJkV7u6uPB2LIHjoUQzcVoxhMaB6FxmDpAqUdj/cESKvOP0qH8IIlblmHZ8jUoCsboLpg7pWDuPABjZMdrenGsEC2FNWl4o0ZJW7IGE52maae94Oi6ftrtho43hq7rGAyXlpZ0XcOzZzW1P/xfXSizd8An7TaM9rhLetyLpVj9MXfsjbljb3SvirfwEGr+XtS8HDz719e9sMd0w9Ilte6FPfzKtwXTXTW4ti7B+eOX6I4yDBHt8BlyB8bYbqw9qPDmKp0uNp17UjyYpFuOEKKRLGYDSQl2vF3sbC8YwLwD4OvIp5/vQQaW5xJS/4lUOObO/THH98cc1wvFLNvXCiEuToMBNioqis2bN9ffLi4uxm63n3a8uLi4/nZJSclpx8/GbrdTUlJCVFQUqqricDjqpyhcDM+xXdQsfx+t6DCG0Db4XH933YhrC3mnrxhNmNp0wdSmC3rKOLSyE6j5e/Hm5VC78iNqV36EIawN5o59McV0wxjbrW5R2WWoX6suw3NkG+rhrXj2b0B3OTBGdcacekv9aMiGwzp/XKHTMUzn3n5uzBJehRAXwWiAPjEavdvAvuIYVuS25/8KRxBlqeaOdvtINu1D//kTKZMFc6d+WBIHY47vh2JunYtfhRDNo8EAm5aWxttvv01paSm+vr4sX76cF154of54TEwMVquV7Oxs+vbty6JFixgyZMh5H3Po0KEsXLiQBx54gMWLF5OSknJR8191j5PaVZ/g2vQVSkAo1rTJmOKSWvSiKUVRMIbFYAyLgaThaI7y+jDr2rYM1+bMuvP8QzHFJmKKTcBo74DBPxQlIAzF9+zbO+q6Du5atNoqtNI8PIe34jm0Fa34SN3jWf3rPspLGIgxvG39/dYf1nn1W512oTrTBrixSntHIcQlUhRIsGsk2N0cLVNYedCXP+b2wWTsw8guXm5uc5iQ8r2ox3bg2bcOTNa6tQKJgzF3SmlRawWEEC1To/rAZmZm8sEHH+DxeJg0aRLTpk1j2rRpPPzwwyQlJbF3715mzpxJdXU13bt3Z9asWVgs/3oB+nkO7M9ttMrLy3nyySc5fvw4gYGBvPbaa43q//XvfWCjqMLx9etopScwd03Dkpze6l/0dM2LVn4Sb8lxtJJjeIuPoVefOv0kgwnFPxiDfyiYrei1Vei1lei11f9qC/bTeUZ7HMaoeIxR8RjCok8L9rqus2AbfPKjTvtQnftT3fhdvjV0QghxmsJqhVUHTWw6bkDVFPq1g/FJGt3Nh1GP7cJ7fDe6sxosvlgSBmFJGo6pXY8WPSAhhGg+rXIjg29efIDw3BUofiFYUzMwRcU3fOdWSqutQq8sQXNWozur0Gt//rMK3auiWP1QLH51f1p96/7bLxijrd05A73Hq/PealixD/rEeJmc7EF2iBRCNIUqJ6w7amTdERNVLoX2YTC+Jwzp6MV46jDq0Z2ox3bWtTwMsmFNGo4laTjGsDbNXboQogVplQF2wS3taZc8CGuf0TJv6gJVOnX+sAx2FUB6V5X0LiqGljFVWAhxDfF4YesJIz8cMpJfYSDIB25MgFGJYPfzoB7PQT28Be/JA3WbK8R0w5o8Eku3Ia12sxghxOXTKgPsN//7GO17pTZ3Oa1OXrnOC4uhuFpnSrKHvrGyPawQonnpOuSeMrD2iJGdBQZ0HVLaKYzpAb3bArWVqEe2ox7KRqsoAosf1h7DsPQZjcneobnLF0I0k1a5ZMcU2bG5S2hVdF1ndS68v0bHoMBv0tx0CGs171uEEFcxRYHOERqdIzTKayHrqIkNR41sOqYQFQTp3YK4oetgQroNRis+iid3E67ty3Bt+QZjm65Y+4zG0m2wfBonxDWmVY7ALvvkXWKizt+qS9Qpq9GZsxo2HIG4MI07e3sI9281P3IhxDVI1WBngYG1R0wcPGXAqOj0i1MYmVA3Kmvw1NR1WsndhF5RBFY/rD1uwNp7dLP1/hZCNK1WOQIrGmdtrs6cNVDr0RmfqHJ9J6/MdxVCtHgmA/SO0egd46awSmHjcSMbjxvZcFghzB9GdPVjWJdBtOk6CK3oCJ7cjbi2LsWV/TXGmASsvX8elZWNEoS4WskI7FWoolbn/TWw7hC0D9WYkuwhKrDV/JiFEOIMqga7Txr48biRnEIDOgqdbTCsKwzuBEHKz6OyG9Eri1Gs/liSfhqVtbVv7vKFEJeZBNirSK1HJ3MnfLkNXKrOqK4qwzp5MUobRSHEVaS8tq6DQXaekbyKuikGvdsqDO0MKe10rOVHUHM3oR7bBZqKMbZb3ahswnUyKivEVUIC7FXA49VZugfmZ0OFE3pEebkpQSU6qNX8aIUQ4qKcqFTYkl8XZstqFczGujA7qCP0i6rGkr+9bq5sZdFPo7LDsfYeJaOyQrRyMge2FVO9Ot8fgM82Q0l13UreX6Z4pMOAEOKa0SZIp02QypgElSOlCjtOGtl+wsjGIwpGgz/JsYNI7ZhGP//D+OVvxrXlG1ybv8IYm/jTqOwgGZUVohWSEdhWqKhKZ3kOfLsXymrq5rmOSVDpEqGhyCItIcQ1TtPhWLnCjoK6MHuqpu6FsWMEXBdTTZppG8EnN9XNlfUJqBuVTR6F0daumSsXQjSWBNhWwqvpbDoKy3Jgy7G6H1lipEZaey+JkRJchRDibHQdTlYp7CkysKfQyKFSBV1XCPHVGW07RJqyibDy3Siat263r1431nUwsPo1d+lCiPOQANuCqV6dXQXw4xFYfwhKayDYR2dAOy+p7VTC5PVVCCEuSLUL9hYZyCk2sr/YQJVLIVCpZlzgFgabthDsKQKTBUvXNCw9R2CK64WiyEpYIVoamQPbwtS4dbYcrwutm4/qONx1ixISbBo3d/fSPVKTrgJCCHGRAqyQ0lYjpa2GpsPJSoX9JVa2lQzii5LraKvnM9SczaDdG/DdvQq3TxjGxGGE9h0hUwyEaEFkBLaZVbt0ck7CrhN1XwdLdDRdwd+i0yNKo3ukl642Dau81RBCiCtK9dbNnT1UauDYKS/BFfsYaNhKT+MBDIpOsTmGU9Fp+PYYSqcubQn0kxdmIZpLo/71ZWZmMmfOHFRV5e6772bq1KmnHc/JyeGZZ57B4XCQkpLCc889h8lk4sSJE8yYMYNTp07RoUMHXnvtNfz9/dm4cSMPPfQQUVFRACQmJjJr1qzLf3UtjMerc6wMDhbDoRLYexIOn9LRUTAZdNqH6ozorNHV5iUuVJeRViGEaEImI3QM1+kY7oXOoOldOVGZwDdFDgJO5dChdgcJx/4Bx/7Bzq/i2G3pQ01sf9rFRdE51pf4Nr74WIzNfRlCXBMaHIEtLCxkypQpLFiwAIvFwuTJk3n99deJj4+vP2fs2LG8+OKLJCcn8/TTT9OjRw9uv/12pk+fzvjx47npppt49913qampYcaMGXz00Ud4PB6mT59+QcW2phFYt6pz5BQcLPnpqxiOluqoWt1qKx+zTrtgnY4RXjqF6bQP1ZDXPSGEaNnclaV48/YQWrqTIE8xmq6wz9ueTWp3sr2J+Nui6RzrS4coXzpE+9AxypcgfxmpFeJy+//t3X9wVNX9//Hn3d/5nWySzS4BQdCKBiR8Ta1QvmRwKr8CQjNYAZWOjFintXT4TmkpYm3RFkWKlMq3o99xdDpSqFpKCh9KsVDmgwQV+CDoR0B+RUIIIT82yWZ3s3v33vP9Y8OSRDDIR10S3o+ZO7v33t3l3EPu3VdOzj2nx7OqsrKSu+66i+zsbAAmTJjA1q1befzxxwGoqamhvb2d4uJiAMrLy1m9ejX33Xcfe/fuZc2aNYntDz74IAsXLuTDDz+koaGBzZs3U1hYyFNPPYXP5/uqjvErF4rGw+rJTmH1tD/eFQAgzaHon60oHWIyINOkMEuRm6awyMgBQgjRqzgy3XDbGNrVt9GD53E1HmVI3RFuDf4Hc/gPzkYG8P7h29h84FbOmvGGFneGjcG+eKC90ZvCYF8KhXlObFb5EhDiavUYYM+fP09+fn5i3ePxcOjQocvuz8/Pp66uDr/fT3p6Ojabrct2gIyMDCZNmsT48eNZt24dCxYsYP369V/aQX2V/CHFyQa6LLWtF/dnOBUDsk2+c7Oif6ZJ/2yTnBRkmCshhOhLNA0jvYBgegEMHIs11ISr8SiehqNMb/4n09P+STDFx6m0kRxgGAcb8jlwPIBhxt9us2rc4HFyozeFQV4XgwpcDPS6yMu0o8kXhhA96jHAmqbZ5WRSSnVZv9z+7q8DEutLly5NbJs1axa/+93vCAQCZGRkXP2RfMmUUjQE4ZO6i0H1REN8Du4LclMV/bNNRvaLt6oWZppkuSSsCiHE9cZIdRNMHUVwwCgskVacDZ/gajhCUcM/GMYWZqflExz5TWoybue4GkRts0ltY4S9R1vZfsCf+JxUl4VBBS4GFaQwsMDFIK+LgQUusqQbghBd9HhGeL1e9u3bl1ivr6/H4/F02V9fX59Yb2howOPx4Ha7CQQCGIaB1WpNvM80TV566SUeffRRrNaLnT47P0+GiK44Vg9H6+Do+fijPxTfZ9EU3gzFN/IUhVkmhZkm/bIUqfakFlkIIcQ1yHRmEi4sIVxYY7skIQAAFaVJREFUghYN4Wr6BGfDUTKO/5NbzS3cYk8h4htB+JaRhPsVEyCDOn80sZzzR/n3QT/hiJn4zOx0W7yVtlOoHVjgItUpN0+I61OPAXb06NH84Q9/oKmpiZSUFLZt28bTTz+d2F9YWIjT6WT//v3ccccdVFRUMHbsWOx2OyUlJWzZsoWpU6eyceNGxo4di8Vi4e2332bgwIFMnjyZjRs3MmLECFJTv95R+YMXhq+qhf8+C8frFUZHn9X8dMWQXJOBQ0wGZpv4MhV2uUYIIYT4gpQjlbC3mLC3GC0Wxd58ClfTcZx1H5Ny+l0AdPdg+vX7X4R9I4neOhg0C0opAiGDc52CbZ0/ysefBonGLt577cm2J7ofXGi1HZDvxGGXYWxE33ZF48Bu2rSJl156CV3XmTFjBvPmzWPevHnMnz+f4cOHc+TIEZYsWUJbWxtFRUUsW7YMh8NBTU0NixYtorGxEZ/Px8qVK8nKyuLYsWM8+eSTBAIB3G43y5cvv6KbuP4noxBEYoqPa+GDM3CwpmP4KqVh7Ri+arDb5EZ3PLCmO7/QRwshhBBfjFLY2upw+o/jaDyOrbkaDYXhyiLSbyQh30javbej7F0bd0yl8AdiXULtheVC/1qLBv1ynR2htqPVtsBFv1wnVrlxTPQRfXYiA6UUVU3wQTUcOAP/XavQjfh4q4PciptyTQa7TQblmDika5EQQogk0qJBnE0ncPqPY288gUUPozQrUc8ttHuLCfuK0bNuuOxNFoapaGjRPxNq65t1LnzJX7hx7EKgHVgQv4EsP8uORYbFEb1MnwqwkZjiUA3s/TS+NAbj232ZJrfkm3wjz2RIrsxqJYQQ4hpmmthbq3E2ncDRdAJboBYAIyWHdl8xYe8I2r3DUY70Hj8qGjOpb9Y/08fWH4glXpPisHBDgeszfWxz0m0yIoK4ZvX6AOsPKd6rigfWg2cUUUPDaVMM9Zjc5onPapWdktxyCyGEEFfLEgngaDqB038Se9OF1lkNPe8btHtHEPIVo+fcCNqV93sNRw3q/DrnO0LteX+U2qYogbCReE1mqvVia22nG8cyUqQVSCRfrwywr//fF6kKenj3FBw+F5+KNTdVUeQ1KPLEW1ltctOVEEKIvsY0sQVqcPlP4mg6jq2lJr7ZmUm7b0RH6+wITFfmVX18WzhGXXM82J67EGwbo4SjF0dEyM20dQTblESoHVjglGl0xdeqVwbYARNexJ7moX+WyXCfwXCviS9DyfirQgghritaNIjTfxKn/0S872w0iEJDzx3c0d2gmKj7JrBc/agESilaggZ1/gh1/o7uCM1RzjVF0TtGRNA0KMhxdLlpbKDXRf88J3abjIggvny9MsDO/fEqxgzNJS+t1xRdCCGE+GophS1wFqf/JI6mk9iaT6OhMB1p8XFnC0YQ9hVjpmR/Kf+caSqaAp371+qJVluz4+vZaomPiFCY56RfnpPCXCe+XAeFeU7yMuXmMXH1emVHllE3GBJehRBCiM40jVhmIbHMQoID/zeaHsbpP4XDfxLnuY9I+bQSAD1nUEd3g2Iied8Ay9VFAYtFIy/LQV6Wg6JBF7fHDEV9y8W+tXV+nU/r2tn/SQDduPjd7bBpeN3xMFuY58SXGw+4/fIc5GZIuBWfr1cGWCGEEEJ8PmVPod1zG+2e2+Kts8G6jtbZE6Qf3kTGxxWYNhdRz220FwyjvWA4etaA//F86Darhs/txOfuOqi6qRStQYOGliiNrXrHEuPUuXb2Hg0Q6xxu7Rr93PFW234dLbb9cuNLbqaMjiAkwAohhBB9n6YRS/cSS/cSHDAaLRaJzwrmr8LuP4nr7H8BYLiyiBYMI1wwnPaCYRhp+V9aESyaRna6jex0GzcVdt1nmoqWYIyGRLDVaWzROXE2xPtHWruEW6dd69JaeyHYFuY5cWdIuL1eSIAVQgghrjPK5iSaN5Ro3lAALO0tOJqrcDafwnnuECmf7gYgluEl0hFoI54iTGfGV1Iei0UjJ8NOToadmy8RbpvbLobbpladhladYzUh3j3ckpiBDDrCrdtJYX5Hy21uR9cECbd9jgRYIYQQ4jpnurJo7xiCC6WwhupxNlfh8FeRWrWLtOP/QqERyxlIJH8o7XlDieTfgpni/srLZrFouDPtuDPtny13t3B7YTl2JsS7H18+3BbmxgNuv46uCRJuex8JsEIIIYS4SNMw0jyE0jyECu9MjD3rbK7C0XKatBM7SP9kKwBGuodI/lAiebcQcd8U70Nr+frGg/28cGuYipZu4bah5dLh1mW34OsItJ3Drc8dD7dyQ9m1RwKsEEIIIS7PYiGWNYBY1gCCAKaBre0cjtYzOFpO46rZT+qp/wRAWR3o7huJum8i4h5CxD0EI93zhWYJ+7JYv2C4bbxMuHXYNbw58RvJfO74MGD9OoYD82Q5sFol3CaDBFghhBBCXDmLNTFcV6j/t+JdDsJ+bIGzONpqsbfWkHZsG+mmDoBpcxHLHoCePYho9g1Es25Az7oB5UhN2iH0FG6b22Lx/raBC/1uY1Sda2ff0a5DgVkt4MlxJMa39bmdiXDrzXHgsMskDl8VCbBCCCGEuHqahpHqxkh1EykYFt9mGtiC57G31WIL1mNrqyPl03dIOx5OvM1wZWNk+ohl+NAzfETT48+NtDyUzZWkg4mH29xMO7mX6nOrFIGQkbiZLB5yY5zzR/moKkh7pyl3NSA7w0Z+lp38bEf8MctBfradvI7nORk2rNI94apcUYDdtGkTf/zjH4nFYnz/+9/ngQce6LL/8OHDPPHEEwSDQUpKSvj1r3+NzWbj7NmzLFy4kMbGRm688UZWrFhBWloara2t/PSnP6W6uhq3282qVavIz//yhuoQQgghRBJZrMQy4oE0QSkskVZsbXXYww1YQ43Ywk24qt8jNRrs8nbTmY6RmoeRlkcsNQ8jNRfDmU3MlYXpysJwZWI6M696EoarPixNIyvNRlaajcG+lC77lFKEIiYNLRdbbluCMZrbYhyvCbHvaIyI3nUSJqsF3Bn2eKjNtMc/Oz3++dkdj1lpNrLTbKSnWKUvbic9TiVbV1fHrFmz2LBhAw6Hg5kzZ7Jy5UpuuummxGumTJnCM888Q3FxMYsXL2bYsGHMnj2bH/zgB9x7772UlZWxZs0aQqEQCxcuZOnSpXi9Xh599FE2btzIzp07WbVqVY+FvTCV7P97/ncUSOAVQggh+gRND2MNN2ELN2GNtGKNtGJpb8HS3oK1vQUt1n7J95mONExHOsqeiulMiz92LMrmwrQ6UFYHyhZ/NC0OlMUW75OrWUDTUGiJ52gWVKfn8XZUQJlgGmjKBHXhsfM2E02ZaKYBGGhmLL7P7Hiu4o8xPUY0ohONRNH1GLFojJiuE4vFMGMxlBEDM4YNEytGfNFMbBhYMXFYDOwWE6sGmqawaGDRQEN1PMa3axp4/s/ar+u/Lyl6/NWlsrKSu+66i+zs+NzJEyZMYOvWrTz++OMA1NTU0N7eTnFxMQDl5eWsXr2a++67j71797JmzZrE9gcffJCFCxeyc+dO1q6NV+yUKVNYunQpuq5jt3+2ub4zwzAAaGppxmKVfiVCCCFE3+ECe7/4kt51j2ZE0KKheNDVQ1hiYSx6GE0PocUiaKEIWst5NCMaX4+1g6ED19q08/HgbLNYsGpWsFhAs8aDc8dzZbNgEF9iyoquLIRMK7pyoBsaumklamiYSiOmwDQ1YiaYaInDVcDEM2fwer3YbH2zt2iPR3X+/Pkuf973eDwcOnTosvvz8/Opq6vD7/eTnp6eqLgL27u/x2azkZ6eTlNTEwUFBZ9blvr6egB+/szTV3p8QgghhBDXnec3bWb79u30798/2UX5SvQYYE3T7DK4r1Kqy/rl9nd/HXDZQYKVUlgsPbeoDhs2jLVr15Kfn4/V+vWNMyeEEEII0dt4vd5kF+Er02OA9Xq97Nu3L7FeX1+Px+Ppsv9CyyhAQ0MDHo8Ht9tNIBDAMAysVmuX93k8HhoaGvB6vcRiMYLBYKKLwudxuVyUlJR8oQMUQgghhBB9S4/NnqNHj2bPnj00NTURDofZtm0bY8eOTewvLCzE6XSyf/9+ACoqKhg7dix2u52SkhK2bNkCwMaNGxPvKy0tZePGjQBs2bKFkpKSHvu/CiGEEEIIAVcwCgHEh9F66aWX0HWdGTNmMG/ePObNm8f8+fMZPnw4R44cYcmSJbS1tVFUVMSyZctwOBzU1NSwaNEiGhsb8fl8rFy5kqysLJqbm1m0aBHV1dVkZGSwYsWKPttHQwghhBBCfLmuKMAKIYQQQghxrZCxqIQQQgghRK8iAVYIIYQQQvQqEmCFEEIIIUSvIgFWCCGEEEL0Kr0qwG7atInJkyczfvz4xFS0Iq6trY0pU6Zw5swZID4F8NSpUxk/fjwvvPBCkkuXfC+++CJlZWWUlZWxfPlyQOroUn7/+98zefJkysrKePXVVwGpp8t57rnnWLRoEQCHDx+mvLycCRMm8MQTTxCLxZJcuuR76KGHKCsrY9q0aUybNo2DBw/KNbybHTt2UF5ezqRJk3jmmWcAOd+6e/PNNxM/Q9OmTeOOO+5g6dKlUk/dVFRUJL7jnnvuOeA6uC6pXuLcuXNq3Lhxyu/3q2AwqKZOnaqOHTuW7GJdEz744AM1ZcoUVVRUpKqrq1U4HFalpaXq9OnTStd1NXfuXLVz585kFzNpdu/ere6//34ViURUNBpVc+bMUZs2bZI66ua9995TM2fOVLquq3A4rMaNG6cOHz4s9XQJlZWV6lvf+pb6+c9/rpRSqqysTB04cEAppdQvfvELtXbt2mQWL+lM01RjxoxRuq4ntsk1vKvTp0+rMWPGqNraWhWNRtWsWbPUzp075Xz7HJ988om655571NmzZ6WeOgmFQuqb3/ymamxsVLquqxkzZqjdu3f3+etSr2mBrays5K677iI7O5vU1FQmTJjA1q1bk12sa8Ibb7zBU089lZjp7NChQwwcOJABAwZgs9mYOnXqdV1X+fn5LFq0CIfDgd1uZ8iQIVRVVUkddXPnnXfypz/9CZvNRmNjI4Zh0NraKvXUTXNzMy+88AKPPfYYADU1NbS3t1NcXAxAeXn5dV9HJ0+eBGDu3Lnce++9vP7663IN7+btt99m8uTJeL1e7HY7L7zwAikpKXK+fY5f/epXLFiwgOrqaqmnTgzDwDRNwuEwsViMWCyGzWbr89elXhNgz58/T35+fmLd4/FQV1eXxBJdO37zm990mWJX6qqrm2++OXESV1VV8Y9//ANN06SOLsFut7N69WrKysoYNWqU/Cxdwi9/+UsWLFhAZmYm8NnzLT8//7qvo9bWVkaNGsWaNWt47bXXWL9+PWfPnpWfpU4+/fRTDMPgscceY9q0afz5z3+W8+1zVFZW0t7ezqRJk6SeuklPT+cnP/kJkyZNorS0lMLCQux2e5+/LvWaAGuaJpqmJdaVUl3WxUVSV5d27Ngx5s6dy89+9jMGDBggdXQZ8+fPZ8+ePdTW1lJVVSX11Mmbb76Jz+dj1KhRiW1yvn3WyJEjWb58ORkZGbjdbmbMmMHq1aulnjoxDIM9e/bw29/+lr/85S8cOnSI6upqqaPLWL9+PQ8//DAg51x3R44c4a9//Sv//ve/2bVrFxaLhd27d/f5OrIluwBXyuv1sm/fvsR6fX194k/moiuv10t9fX1iXeoK9u/fz/z581m8eDFlZWW8//77UkfdnDhxgmg0yq233kpKSgrjx49n69atWK3WxGuu93rasmUL9fX1TJs2jZaWFkKhEJqmdflZamhouK7rCGDfvn3oup4I+kopCgsL5ZzrJC8vj1GjRuF2uwH4zne+I+fbZUSjUfbu3cuzzz4LyHdcd++88w6jRo0iNzcXiHcXeOWVV/r8danXtMCOHj2aPXv20NTURDgcZtu2bYwdOzbZxbomjRgxglOnTiX+RLV58+bruq5qa2v50Y9+xIoVKygrKwOkji7lzJkzLFmyhGg0SjQaZfv27cycOVPqqZNXX32VzZs3U1FRwfz587n77rtZtmwZTqeT/fv3A/G7ga/nOgIIBAIsX76cSCRCW1sbf/vb33j++eflGt7JuHHjeOedd2htbcUwDHbt2sXEiRPlfLuEo0ePMmjQIFJTUwG5fnc3dOhQKisrCYVCKKXYsWMHd955Z5+/LvWaFtiCggIWLFjAnDlz0HWdGTNmcPvttye7WNckp9PJs88+y49//GMikQilpaVMnDgx2cVKmldeeYVIJJL47R1g5syZUkfdlJaWcujQIaZPn47VamX8+PGUlZXhdrulnnqwYsUKlixZQltbG0VFRcyZMyfZRUqqcePGcfDgQaZPn45pmsyePZs77rhDruGdjBgxgkceeYTZs2ej6zrf/va3mTVrFoMHD5bzrZvq6mq8Xm9iXb7juhozZgwff/wx5eXl2O12hg8fzqOPPso999zTp69LmlJKJbsQQgghhBBCXKle04VACCGEEEIIkAArhBBCCCF6GQmwQgghhBCiV5EAK4QQQgghehUJsEIIIYQQoleRACuEED3QdZ0xY8bwyCOPJLsoQgghkAArhBA9evvttxk6dCgfffQRJ06cSHZxhBDiuifjwAohRA8eeughJk+ezLFjx4jFYixduhSAl19+mbfeeou0tDRKSkrYvn07O3bsIBqNsmLFCvbu3YthGNx2220sWbKE9PT0JB+JEEL0DdICK4QQn+P48eMcOHCAiRMnMn36dCoqKvD7/ezatYsNGzbw1ltvsWHDBoLBYOI9L7/8MlarlQ0bNvD3v/8dj8fDihUrkngUQgjRt/SaqWSFECIZ1q1bx7hx48jJySEnJ4f+/fvzxhtvUF9fz8SJE8nMzATggQce4N133wVg586dBAIBKisrgXgf2tzc3KQdgxBC9DUSYIUQ4jJCoRAVFRU4HA7uvvtuANra2nj99dcpKyujcw8sq9WaeG6aJosXL6a0tBSAYDBIJBL5egsvhBB9mHQhEEKIy9i0aRPZ2dns2rWLHTt2sGPHDv71r38RCoUoKipi27ZtBAIBAN56663E+8aMGcPatWuJRqOYpsmTTz7JypUrk3UYQgjR50iAFUKIy1i3bh0PP/xwl9bVzMxMHnroIV577TW+973vcf/991NeXk4gECAlJQWAH/7whxQWFvLd736XyZMno5Ri0aJFyToMIYToc2QUAiGEuAoffvghBw4cYM6cOQC8+uqrHDx4kFWrViW5ZEII0fdJgBVCiKvQ1tbG4sWLOXnyJJqm4fP5ePrppykoKEh20YQQos+TACuEEEIIIXoV6QMrhBBCCCF6FQmwQgghhBCiV5EAK4QQQgghehUJsEIIIYQQoleRACuEEEIIIXoVCbBCCCGEEKJX+f9Vt8cbaJ0+OAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 696.425x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ageFacet=sns.FacetGrid(train,hue='Survived',aspect=3)\n",
    "\n",
    "ageFacet.map(sns.kdeplot,'Age',shade=True)\n",
    "\n",
    "ageFacet.set(xlim=(0,train['Age'].max()))\n",
    "ageFacet.add_legend()\n",
    "\"\"\"0-10 has higher serviva rate\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Lower fare 0-20 has lower survive rate'"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 696.425x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ageFacet=sns.FacetGrid(train,hue='Survived',aspect=3)\n",
    "ageFacet.map(sns.kdeplot,'Fare',shade=True)\n",
    "ageFacet.set(xlim=(0,150))\n",
    "ageFacet.add_legend()\n",
    "\"\"\"Lower fare 0-20 has lower survive rate\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2c05d4e84a8>"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "farePlot=sns.distplot(full['Fare'][full['Fare'].notnull()],label='skewness:%.2f'%(full['Fare'].skew()))\n",
    "farePlot.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2c05d4f5f60>"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#due to high skew to left, we logrithmic it\n",
    "full['Fare']=full['Fare'].map(lambda x: np.log(x) if x>0 else 0)\n",
    "farePlot=sns.distplot(full['Fare'][full['Fare'].notnull()],label='skewness:%.2f'%(full['Fare'].skew()))\n",
    "farePlot.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data preprocessing\n",
    "#ETL \n",
    "#U stands for unknown\n",
    "full[\"Cabin\"] = full[\"Cabin\"].fillna(\"U\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20657995409334354"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full[full['Embarked'].isnull()]\n",
    "full['Embarked'].value_counts()\n",
    "\n",
    "270/(914+270+123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "full['Embarked']=full['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3rd class from C, find mean value\n",
    "full['Fare']=full['Fare'].fillna(full[(full['Pclass']==3)&(full['Embarked']=='C')&(full['Cabin']=='U')]['Fare'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr         757\n",
       "Miss       262\n",
       "Mrs        200\n",
       "Master      62\n",
       "Officer     23\n",
       "Royalty      5\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#feature engineering\n",
    "#New feature, title\n",
    "full[\"Title\"]=full[\"Name\"].map(lambda x:x.split(\",\")[1].split('.')[0].strip())\n",
    "full[\"Title\"].value_counts()\n",
    "\n",
    "TitleDict={}\n",
    "TitleDict['Mr']='Mr'\n",
    "TitleDict['Mlle']='Miss'\n",
    "TitleDict['Miss']='Miss'\n",
    "TitleDict['Master']='Master'\n",
    "TitleDict['Jonkheer']='Master'\n",
    "TitleDict['Mme']='Mrs'\n",
    "TitleDict['Ms']='Mrs'\n",
    "TitleDict['Mrs']='Mrs'\n",
    "TitleDict['Don']='Royalty'\n",
    "TitleDict['Sir']='Royalty'\n",
    "TitleDict['the Countess']='Royalty'\n",
    "TitleDict['Dona']='Royalty'\n",
    "TitleDict['Lady']='Royalty'\n",
    "TitleDict['Capt']='Officer'\n",
    "TitleDict['Col']='Officer'\n",
    "TitleDict['Major']='Officer'\n",
    "TitleDict['Dr']='Officer'\n",
    "TitleDict['Rev']='Officer'\n",
    "\n",
    "full['Title']=full['Title'].map(TitleDict)\n",
    "full['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mr/Officer has lower survival rate'"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kDvo0i4iIACoEERGxUCGIiAigQhAREQsVgoiIACoEERGxUCGIiAigQhAREQsVgoiIACoEERGxUCGIiAigQhAREQsVgoiIACoEERGxUCGIiAigQhAREQsVgoiIACoEERGxUCGIiAigQhAREQsVgoiIDc5XnnN0hCtyNXldDMghItLguLi68drTYXY9ZknRecvt93Y/9ssL86/4OZohiIgIoEIQERELFYKIiAAqBBG5hrk4u9a4FWOpEETkmnVPy9/ToklL7mn5e0dHuSHor4xE5Jp1a/NW3Nq8laNj3DA0QxAREUCFICIiFioEEREBVAgiImJhaCHk5OQQHh5Or169yMjIuGj/xx9/TFRUFH379mXUqFH89NNPRsYREZFaGFYIRUVFzJkzhyVLlpCVlcWyZcs4cuSIdX9paSmvvPIKaWlprFmzhoCAAObNm2dUHBERqYNhhbBlyxZCQkLw8vLC09OTsLAw8vLyrPsrKyuZMmUKvr6+AAQEBHD8+HGj4oiISB0MK4Ti4mK8vb2t2z4+PhQVFVm3b775Zh577DEAKioqSEtLo2fPnkbFERGROhhWCNXV1Tg5OVm3zWZzje0LTp8+TWJiIu3atSM6OtqoOCIiUgfDCsHPzw+TyWTdNplM+Pj41HhMcXExcXFxBAQEMG3aNKOi/GaFhYUkJydTWFjo6CgiIoYxrBA6d+5MQUEBJSUllJeXs379ekJDQ637q6qqGDFiBL1792bixImXnD1cKxYtWsSuXbtYtGiRo6OIiBjGsLWMfH19GTt2LEOHDqWyspIBAwYQHBxMQkICSUlJnDhxgv3791NVVUV+/i9X9gkMDLwmZwplZWU1bkVEGiJDF7eLjIwkMjKyxn3p6ekABAUFcfDgQSO/vYiIXAF9UllERAAVgoiIWKgQREQEUCGIiIhFgyuEc5VVjo5wRa63vCLScDW4S2i6uToTN/lTux7z5A/lAJz4odzux17yaje7Hk9E5Go1uBmCiIhcHRWCiIgAKgQREbFQIYiICKBCsImTs1uNWxGRhkiFYIMmd3TDtVkrmtzRzdFRREQM0+D+7NQI7rfcjfstdzs6hoiIoTRDEBERQIUgIuIwzo1q3jraNRJDROTG09qrEV4eTrT2ujZ+FOscgoiIg/zOsxG/83R0il9dG7UkIiIOp0IQERFAhSAiIhYqBBERAVQIIiJioUIQERFAhSAiIhYqBBERAVQIIiJioUIQERFAhSAiIhYqBBERAVQIIiJioUIQERFAhSAiIhYqBBERAQwuhJycHMLDw+nVqxcZGRkX7T9w4AAxMTGEhYUxceJEzp8/b2QcERGphWGFUFRUxJw5c1iyZAlZWVksW7aMI0eO1HhMSkoKkydPJj8/H7PZTGZmplFxRESkDoZdQnPLli2EhITg5eUFQFhYGHl5eYwePRqA77//noqKCjp27AhATEwMc+fOJS4urs5jV1VVAXDixIlL7j975qQ9hlAvjh49esXPKTpdYUASY3he4fjKTpYZlMQYV/r6mcoa9vh+LvvRoCTGuNLxlVZUGpTE/i41tgs/My/8DP1vhhVCcXEx3t7e1m0fHx9279592f3e3t4UFRXZdGyTyQTAkCFD7JTWcXqsdXQCgy3t4egEhvoHnzg6grF6NOzXb9E/Zjs6gmGyanntTCYTrVq1uuh+wwqhuroaJycn67bZbK6xXdf+2gQGBpKRkYG3tzfOzs72Cy0i0oBVVVVhMpkIDAy85H7DCsHPz4/PPvvMum0ymfDx8amx/8Jv+gAnT56ssb82Hh4ePPDAA/YLKyJyg7jUzOACw04qd+7cmYKCAkpKSigvL2f9+vWEhoZa9/v7++Pu7s6OHTsAyM7OrrFfRETql5PZbDYbdfCcnBzeffddKisrGTBgAAkJCSQkJJCUlERQUBAHDx5k0qRJlJaWcu+99zJ9+nTc3NyMiiMiIrUwtBBEROT6oU8qi4gIoEIQERELFYKIiAAqBBERsVAh/JejR48SEBDA5MmTa9x/4MABAgICWLVqlYOS/TYNcVy2jCkqKspB6Wxn79dm9+7dzJo1y54Rr9rRo0cJDAwkKiqKqKgoIiMj6d69O3PnzrXb95g3bx7z5s0DYMKECXz//fd2O7atzpw5w9SpU3nsscfo27cvcXFxFBQUALBv3z66devGkCFDOHbsGGFhYURFRfH+++/z4Ycf1nvW2hj2wbTrmZeXF//617+oqqqyfhJ63bp13HLLLQ5O9ts0xHHVNabs7GxHxrOZPV+bI0eO8MMPP9g74lXz8fGp8ToUFRURFhZGnz59aNOmjV2/19atW3n22Wftesy6mM1mRowYwT333ENubi5ubm7s37+fxMREUlNT2b59O3379iU5OZmsrCwCAwNJTU2t14y2UiFcQuPGjWnXrh3bt28nJCQEgM2bN9O5c2cAQkJCCAwMxGQysWLFClxdXR0Z12ZXMq533nmHlJQUysrKaNSoEZMmTbIuRHgtqWtMAQEBHDp0iIKCAutvzc2bNyc1NRU3NzeSk5M5efKXxRCfffZZejho7Z66xvHBBx+QnZ1NeXk5rq6upKamctdddzFjxgw2b95Mo0aN6NmzJ0OHDmXu3LmUlZWxYMECEhMTmTlzJtu2baOqqoqYmBji4+PZunUrs2bNorq6mrvvvpsZM2bU21hNJhNms5nGjRvzzjvvsGbNGpydnXnkkUdISUlh3rx5mM1mxo4dC8D48eMJDQ2lbdu2vPbaa5SVlVFSUkJiYiKDBw+2HjctLY3i4mISExN5/vnnee+991i6dCkAq1atYteuXUydOtXu49m2bRvHjh1j8eLF1uV32rdvz8iRIxk6dCgtWrQAwM3NjRUrVlBWVsbkyZOta7k999xz5OTksGDBApycnAgKCuK1117j3LlzvPrqqxw+fJiqqioSEhKIiIhg1apVrF69mlOnTvHoo4+SnJxst7GoEC6jd+/e5OfnExISwu7duwkICODCRzZ+/PFHEhISeOihhxyc8srZOq758+fTrVs3hg8fzqZNm9ixY8c1WQhQ+5guePvtt3nllVcIDg4mPT2d/fv3YzKZ8Pf3Jy0tjQMHDrBmzRqHFQJcfhylpaVs3LiR999/Hw8PD958800yMjJ46qmn2LRpE7m5uZSXlzNhwgTc3d1JSkpi27ZtjBw50vqWxOrVqzl37hxPP/20dR2bb775hk8++YSmTZsaOq7i4mKioqI4e/YsP/74I0FBQcyfP59Dhw6xceNGVq5ciaurK8899xxLly6lf//+DBs2jDFjxlBRUUFhYSFTp05l9uzZjBo1iocffpjvvvuOvn371iiExMREli5dSlpaGv7+/sycOZNvv/2WO+64g6ysLF544QVDxrdnzx4CAwMvWovtwQcfpHHjxsTGxgIwevRoWrZsybZt23j11Vetb3MVFRUxffp0Vq1ahZ+fHykpKfzzn/9k586d3HvvvcyYMYPS0lJiY2Pp0KGD9Tnr1q3DxcW+P8J1DuEyunfvzqZNm6iuruajjz6id+/eNfZfeGGuN7aO6+GHH+Zvf/sbL7zwAqdOneLJJ590RFyb1DUmgB49ejB69GheffVV2rdvT5cuXejUqRMff/wxo0aNYs+ePfX+VsN/u9w4mjRpQmpqKrm5uaSmpvLJJ59QVlaGr68v7u7uxMbGsnjxYv70pz/h7u5e45gFBQVs3LiRqKgoBg4cyIkTJzh06BAArVu3NrwM4Ne3jNatW0dUVBRms5lHHnmEwsJC+vTpw0033YSLiwv9+/enoKCA22+/HX9/f7Zv305+fj5du3bF3d2d8ePHc/bsWd59913eeOMNympZStzJyYno6GjWrFnDsWPH+OGHHwz7f9bJyemSy0lXVlbatGDn559/zn333Yefnx8As2bNomfPnmzZsoWlS5cSFRXFkCFDKCsr4/Dhw8AvMxB7lwGoEC7rwhR+x44dFBYWWqfuF3h4eDgo2W9j67juv/9+cnNz6dKlC+vWrWPEiBGOiGuTusYEEB8fz/vvv88dd9zBrFmzWLBgAXfeeScfffQRkZGRfPbZZwwYMIDq6moHjOAXlxvH8ePHGTRoEKdPnyY0NJTo6GjMZjMuLi4sX76c559/nlOnThEbG8vXX39d45hVVVWkpKSQnZ1NdnY2y5YtY8CAAUD9/zfcqFEjxo0bR1FREQsXLrzkv/WFqyb279+ftWvXsnbtWmJiYgAYM2YM//jHP2jTpg1jxoyp8/tFR0eTm5vL2rVrDf3jgg4dOrB3714qK2teK2Hnzp2XXVX0/3NxcalRHCUlJZSUlFBdXc2sWbOsr11mZiZ/+MMfAONeOxVCLXr37k1qaiqBgYGGtLGj2DKumTNnsmbNGqKjo5k8eTL79++v55RXpq4xDRw4kDNnzhAfH098fDz79+/ngw8+YN68efTu3ZspU6ZQUlJCaWmpA9L/6lLj8PT0pFWrVsTHxxMUFMTHH39MVVUV+/fv58knn+TBBx/kxRdfpE2bNnz99dc4Oztbf7CGhISQmZlJZWUlZ86cIS4ujp07dzpsfC4uLowbN463336b9u3bk5ubS0VFBefPn2flypXW8yePP/44BQUFnDx50vqb/ebNm0lKSqJnz55s2rQJuPhCL87Oztb7/P398fPzs/6WbZQHHniAtm3b8vrrr1tLYe/evSxYsIBRo0bV+fygoCB27txpXf359ddfZ8OGDYSEhFjf8isuLqZv374cP37csHGACqFWjz76KAcOHCA8PNzRUezKlnH98Y9/JD8/n6ioKEaPHl2vJx2vRl1jSk5OZvz48cTExLBixQpeeOEF+vXrx9dff01kZCRDhgwhJSWFZs2a1XPymi41DldXV6qrqwkPDyc6OprWrVtz9OhR2rdvT8eOHYmIiCA6Opq2bdsSGhpKcHAwu3btYvbs2cTGxnLnnXcSHR1N//79iYmJcfi5r9DQUDp16sT27dvp1q0b/fv3p0+fPrRs2dL61qSHhwcdO3akT58+1uc999xzxMXFER4ezo4dO/D397/oqmDdunUjMTGR7777DoDw8HDatGmDr6+voWOaP38+bm5uREREEB4ezrRp05g1a5ZN/9a+vr5MnDiRp59+moiICDw8PIiJiWH06NFUVFQQERHBsGHDSElJ4Y477jB0HFrcTkSuKWazmTNnzjBo0CAWLVpU48qKV+L8+fOMGzeOxx9/nF69etk5ZcOkGYKIXFP27NlD9+7deeKJJ666DMxmM3/4wx9wcnKiZ8+edk7YcGmGICIigGYIIiJioUIQERFAhSAiIhYN54/rRQz25z//me3btwPw5Zdf4u/vb/2AUEVFBcuXL8fJyYlnn32WxYsXA7+spVRQUHBdLyAoNw4VgoiNJk2aZP26e/fuzJ49m6CgoBqPOXr0KHv27KnvaCJ2obeMROwgICCAkpISJkyYQEVFBVFRURd9inb58uXExMTQr18/4uPj+fLLLx2UVuTSVAgidjR9+nQ8PDzIzs62XtcAflkiOSsri4yMDLKyshg+fDijR492YFKRi+ktI5F68Omnn/K///u/1qWQAX7++WdOnTqFl5eXA5OJ/EqFIFIPqquriYqKIiUlxbpdXFxM8+bNHZxM5Fd6y0jEjlxcXKiqqrroAj1dunQhNzeX4uJiAD788EOGDRvmiIgil6UZgogdeXt7ExwcTJ8+fcjIyLDe36VLFxISEnjqqadwcnKiSZMmzJ8/36YLqIjUF61lJCIigN4yEhERCxWCiIgAKgQREbFQIYiICKBCEBERCxWCiIgAKgQREbFQIYiICAD/B1xGIMHX6vXSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=full,x='Title',y='Survived')\n",
    "\"\"\"Mr/Officer has lower survival rate\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c05d6f98d0>"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#family size\n",
    "full['familyNum']=full['Parch']+full['SibSp']+1\n",
    "sns.barplot(data=full,x='familyNum',y='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c05d784588>"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def familysize(familyNum):\n",
    "    if familyNum==1:\n",
    "        return 0\n",
    "    elif (familyNum>=2)&(familyNum<=4):\n",
    "        return 1\n",
    "    else:\n",
    "        return 2\n",
    "\n",
    "full['familySize']=full['familyNum'].map(familysize)\n",
    "full['familySize'].value_counts()\n",
    "sns.barplot(data=full,x='familySize',y='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c05d7dec18>"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full['Deck']=full['Cabin'].map(lambda x:x[0])\n",
    "#deck vs survival rate\n",
    "sns.barplot(data=full,x='Deck',y='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c05d866c50>"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#提取各票号的乘客数量\n",
    "TickCountDict={}\n",
    "TickCountDict=full['Ticket'].value_counts()\n",
    "TickCountDict.head()\n",
    "\n",
    "full['TickCot']=full['Ticket'].map(TickCountDict)\n",
    "full['TickCot'].head()\n",
    "\n",
    "def TickCountGroup(num):\n",
    "    if (num>=2)&(num<=4):\n",
    "        return 0\n",
    "    elif (num==1)|((num>=5)&(num<=8)):\n",
    "        return 1\n",
    "    else :\n",
    "        return 2\n",
    "full['TickGroup']=full['TickCot'].map(TickCountGroup)\n",
    "\n",
    "sns.barplot(data=full,x='TickCot',y='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>familyNum</th>\n",
       "      <th>TickCot</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>...</th>\n",
       "      <th>SibSp_0</th>\n",
       "      <th>SibSp_1</th>\n",
       "      <th>SibSp_2</th>\n",
       "      <th>SibSp_3</th>\n",
       "      <th>SibSp_4</th>\n",
       "      <th>SibSp_5</th>\n",
       "      <th>SibSp_8</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Parch  Pclass  SibSp  familyNum  TickCot  Title_Master  Title_Miss  \\\n",
       "0  22.0      0       3      1          2        1             0           0   \n",
       "1  38.0      0       1      1          2        2             0           0   \n",
       "2  26.0      0       3      0          1        1             0           1   \n",
       "3  35.0      0       1      1          2        2             0           0   \n",
       "4  35.0      0       3      0          1        1             0           0   \n",
       "\n",
       "   Title_Mr  Title_Mrs    ...     SibSp_0  SibSp_1  SibSp_2  SibSp_3  SibSp_4  \\\n",
       "0         1          0    ...           0        1        0        0        0   \n",
       "1         0          1    ...           0        1        0        0        0   \n",
       "2         0          0    ...           1        0        0        0        0   \n",
       "3         0          1    ...           0        1        0        0        0   \n",
       "4         1          0    ...           1        0        0        0        0   \n",
       "\n",
       "   SibSp_5  SibSp_8  Pclass_1  Pclass_2  Pclass_3  \n",
       "0        0        0         0         0         1  \n",
       "1        0        0         1         0         0  \n",
       "2        0        0         0         0         1  \n",
       "3        0        0         1         0         0  \n",
       "4        0        0         0         0         1  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Random forest for age filling\n",
    "AgePre=full[['Age','Parch','Pclass','SibSp','Title','familyNum','TickCot']]\n",
    "\n",
    "#进行one-hot编码\n",
    "AgePre=pd.get_dummies(AgePre)\n",
    "ParAge=pd.get_dummies(AgePre['Parch'],prefix='Parch')\n",
    "SibAge=pd.get_dummies(AgePre['SibSp'],prefix='SibSp')\n",
    "PclAge=pd.get_dummies(AgePre['Pclass'],prefix='Pclass')\n",
    "\n",
    "#查看变量间相关性\n",
    "AgeCorrDf=pd.DataFrame()\n",
    "AgeCorrDf=AgePre.corr()\n",
    "AgeCorrDf['Age'].sort_values()\n",
    "\n",
    "AgePre=pd.concat([AgePre,ParAge,SibAge,PclAge],axis=1)\n",
    "AgePre.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=-1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#拆分实验集和预测集\n",
    "AgeKnown=AgePre[AgePre['Age'].notnull()]\n",
    "AgeUnKnown=AgePre[AgePre['Age'].isnull()]\n",
    "\n",
    "#生成实验数据的特征和标签\n",
    "AgeKnown_X=AgeKnown.drop(['Age'],axis=1)\n",
    "AgeKnown_y=AgeKnown['Age']\n",
    "#生成预测数据的特征\n",
    "AgeUnKnown_X=AgeUnKnown.drop(['Age'],axis=1)\n",
    "\n",
    "#利用随机森林构建模型\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rfr=RandomForestRegressor(random_state=None,n_estimators=500,n_jobs=-1)\n",
    "rfr.fit(AgeKnown_X,AgeKnown_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfr.score(AgeKnown_X,AgeKnown_y)\n",
    "#age prediction\n",
    "AgeUnKnown_y=rfr.predict(AgeUnKnown_X)\n",
    "#fill pred to null values\n",
    "full.loc[full['Age'].isnull(),['Age']]=AgeUnKnown_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "#peer effect\n",
    "\n",
    "full['Surname']=full['Name'].map(lambda x:x.split(',')[0].strip())\n",
    "SurNameDict={}\n",
    "SurNameDict=full['Surname'].value_counts()\n",
    "full['SurnameNum']=full['Surname'].map(SurNameDict)\n",
    "\n",
    "MaleDf=full[(full['Sex']=='male')&(full['Age']>12)&(full['familyNum']>=2)]\n",
    "FemChildDf=full[((full['Sex']=='female')|(full['Age']<=12))&(full['familyNum']>=2)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "MSurNamDf=MaleDf['Survived'].groupby(MaleDf['Surname']).mean()\n",
    "MSurNamDf.head()\n",
    "MSurNamDf.value_counts()\n",
    "\n",
    "MSurNamDict={}\n",
    "MSurNamDict=MSurNamDf[MSurNamDf.values==1].index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "FCSurNamDf=FemChildDf['Survived'].groupby(FemChildDf['Surname']).mean()\n",
    "FCSurNamDf.head()\n",
    "FCSurNamDf.value_counts()\n",
    "\n",
    "FCSurNamDict={}\n",
    "FCSurNamDict=FCSurNamDf[FCSurNamDf.values==0].index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "full.loc[(full['Survived'].isnull())&(full['Surname'].isin(MSurNamDict))&(full['Sex']=='male'),'Age']=5\n",
    "full.loc[(full['Survived'].isnull())&(full['Surname'].isin(MSurNamDict))&(full['Sex']=='male'),'Sex']='female'\n",
    "\n",
    "#对数据集中这些姓氏的女性及儿童的数据进行修正：1、性别改为男；2、年龄改为60。\n",
    "full.loc[(full['Survived'].isnull())&(full['Surname'].isin(FCSurNamDict))&((full['Sex']=='female')|(full['Age']<=12)),'Age']=60\n",
    "full.loc[(full['Survived'].isnull())&(full['Surname'].isin(FCSurNamDict))&((full['Sex']=='female')|(full['Age']<=12)),'Sex']='male'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5]),\n",
       " <a list of 9 Text xticklabel objects>)"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FCc/w4cPZvHkzxYsXp0mTJvTv358sWbKkdWwiIiIiqcKihCdv3rysWrUKLy+vtI5HREREJNUlm/AsW7aM9957j7CwMBYvXpzg/i5duqRZYCIiIvKctGg5ScmOjAVfpC4iIiKS4SVb4WnZsiUAbm5uNGrUCG9vb6sEJSIiIi9AFZ4kWTQy/1yH5+OPP2bt2rVERUWldVwiIiIiqUbX4RERERGbp+vwiIiI2Ao7fXloUnQdHhEREbF5FiU8Xl5eug6PiIhIRqdFy0myaGTWrVunZEdERET+syyq8BQqVIhJkyZRqlQpsmbNGnd72bJl0ywwERERkdRiUcITGhrKgQMHOHDgQNxtdnZ2zJ8/P80CExERkeekLw9NkkUJz4IFC9I6DhEREZE0Y1HC06ZNG+wS+aibKjwiIiIZiBYtJ8mihKdr165xvxuNRrZu3Yq7u3uaBSUiIiKSmixKeMqVKxdvu1KlSrRo0YIvvvgiTYISERERSU0WJTy3bt2K+91sNnPx4kVCQ0PTLCgRERF5AZrSSpJFCc8HH3wQt4bHzs4OT09P+vfvn6aBiYiIiKSWZyY827dvZ+7cueTJk4fNmzezYsUKXnrpJSpVqmSN+ERERMRCiX3ASGIlW/uaNWsWkyZNIjo6mrNnz9KrVy9q165NWFgYo0ePtlaMIiIiIimSbIVnzZo1LFu2DCcnJ8aOHcsbb7xBixYtMJvNNGjQwFoxioiIiA1at24dU6dOxWg00q5dO1q3bh3v/p07dzJ27FgAihQpwtChQ3FxcXmhYyVb4bGzs8PJyQmAAwcOULVq1bjbRUREJIOxt0//HwsFBgYyfvx4Fi9ezOrVq1m2bBkXL16Muz88PJy+ffsyfvx41q1bR7FixRg/fvyLD01ydzo4OBAeHs6dO3c4c+YMlStXBuDmzZtkymTRemcRERH5fyQ8PJwbN24k+AkPD4/Xbt++fVSoUAEPDw+cnZ2pW7cuGzZsiLv/ypUr5MyZk0KFCgFQs2ZNtmzZ8sJxJZu1dOzYkbfeeguj0Ujz5s3x9fVl/fr1jB8/ns6dO7/wQUVERMQ2zZs3j0mTJiW4vUuXLvEuZHz37l18fHzitn19fTl+/Hjcdr58+bhz5w5nz56lWLFi/P7779y7d++F40o24alXrx6lS5fm/v37FCtWDAAXFxeGDx9O+fLlX/igIiIikgYywHV42rVrR7NmzRLc/vQ3NJhMpnhLZMxmc7xtd3d3Ro0axYABAzCZTLz77rs4Ojq+cFzPnJfy8/PDz88vbrt69eovfDDz7qMvvG9Gki1/0/QOIVVU88ud3iGkmHO2vOkdQqqo+vXh9A4hxVwDaqd3CKnCKftr6R1CqnB283t2owzOybNQeocgL8Dd3d2ir5/y9/fn8OF/X/uCgoLw9fWN246JicHf35+ff/4ZgOPHj5M794v/37LqQpywy2usebg0kS1/U+yqlk7vMFLkn8Qz5OGDdI4kZbxc3Qg+tzi9w0gR76KtAFjb67/9wt5kzEUubuqZ3mGkWKE3x3Flz6D0DiNF8lUZAsDNQ+PSOZKUCSjbk1t/TkjvMFIk5+tfWv+gdulf4bFUpUqVmDhxIiEhITg5ObFp0yaGDRsWd7+dnR3t27fn559/xtfXl7lz56boE+L/nZERERERm+Hn50f37t1p27Ytb731Fo0aNaJkyZJ88sknnDhxAnt7e4YOHcrHH39MvXr1cHd3p0OHDi98PH3USkRERNJF48aNady4cbzbZsyYEfd7jRo1qFGjRqocSwmPiIiIrcgAi5YzKo2MiIiI2DxVeERERGyFvb4JISmq8IiIiIjNU8IjIiIiNk9TWiIiIrbiP3QdHmvTyIiIiIjNU4VHRETEVuhj6UnSyIiIiIjNU8IjIiIiNk9TWiIiIrZCU1pJ0siIiIiIzVOFR0RExFboY+lJ0siIiIiIzVPCIyIiIjZPU1oiIiK2Ql8emiRVeERERMTmKeERERERm6cpLREREVuh6/AkSSMjIiIiNk8VHhEREVuh6/AkSSMjIiIiNk8Jj4iIiNg8TWmJiIjYCDstWk6SRkZERERsnio8IiIiNsJeFZ4kaWRERETE5inhEREREZunKS0REREboSmtpGlkRERExOapwiMiImIjVOFJmkZGREREbJ4SHhEREbF5/5kprT0HzjBlzu9EG4wUyp+D/t1b4OqS9bnarVi3jzUbDvI42kixQgH0796CzJkz3hDM7TeUE5cuMG7pgvQOJVF7d+9h6qRJGAzRFCxUmG8GDsDF1TVBO7PZzLBBgylYqBCt27YBICYmhnGjRnP0yBEAKlauTNcvv8DOzs46sR86z0/zt2IwxlAwrx/9ujXBxTmLxe1iYkx8P209R09djY3/9cJ0+agOV67fY/C4X+L2jzGZuXT1Lt/1fZcalYpbpW8AvsVq8FKDr7B3yEz47XP89fPXGB8/TLK9/8u1ea3lWNYPeNVqMSbm4MlA5q07i8FoIl9Od75sVRJnJ8cE7bYdusHKLX+DnR1ZMjvQqfnLFM7jEXf/w0gDfX7Yx5etS8W73VoOHLvFnJXHMBhM5M/lQfePyuGSSD8g9vkxdvYB8gV40KJeMQBiTCYmLzrCiXN3AShbIgefvPuq1Z4fAPuPXmPm8oNEG2IokMeLXh9Xx8U5c5J9GDVtB/lze/Few1Jxt7/VaR4+Xv++JrzXsCS1KxdO89if9MfRq8xcuh+DMYYCub3p1bFmsv0Y+dM2CuT25r1GCZ8LA8dvwNvDhS8+qprWYaeYprSSZvHIhIWFpWUcybof+pBh3y9n5IA2rJjVm4Ac3kye8/tztdu+5wTL1+5j0siOLJ3Wg8fRBpas2m3triSrWN78bJ0wjeY1aqd3KEm6f/8+3w4Zwogxo1m2ciUBuQKYMnFSgnZXLl+ma6fP2L51a7zbN/y2nmtXr7Jw2VIWLFnCX0eOsG3L1gT7p0nsYRF8++Mavvv6XZZO7UJOfw+mzNvyXO027DjO1ZvBLPjxM+b/0ImjJ6+yfe9p8ufxYd4PneJ+yr1agDrVXrFqspPZxYvS743i0PzObBvzJhEh1yjeoFeS7V2y5+XlRl+DFf+ZJibswWMmLDpGvw6vM31ATfyzOzNn7dkE7W4EPmT26jMM/bw8k/pWo2XdQnw783Dc/YdOBdJj3B5u3I2wZvhxQh88YtycAwz4vAqzvmuIv48Ls1ccS7TttVth9Bm7nd2Hr8e7feu+K9y4E85PQ+sxdXA9TpwPStAmLYWGRzF6xg4Gf1GH+WPfI6evOzOWHUy07dWb9+k54jd2Hboc7/Zrt0Jxd83KjO/eifuxdrITGh7F6GnbGPJlXeaPa0UOP3emL92faNurN+/T89u17Dp4KdH7l6w7yvGzt9MyXLGSZyY8Z86coV69ejRt2pTAwEDq1KnDqVOnrBFbnANHzvNSkdzkCfAB4J2GFdiw7Shms9nidr9tPUKrt6uSzc0Ze3t7+nZ9m/q1XrNqP56lc7N3mfnrKn7esTm9Q0nSwT/2U/yll8idJw8Abzdvzsbff0/wWKxYvpzGb73FG7XjJ28xphiioqIwRBuINkRjMBjInCXxd12pHvvRvyleOIDcOb1jY69flk07TySIPbl2phgTjx4bMBhjiDbEYDTGJKgS/nXqKtv3naH3542s0q9/+BSpQuj140Tci60+XfljMblKN0m0rYNjVl57fxwn131nzRATdeRsEIXzeBDgG1sRaFglLzsO30zwuDhmsqfb+yXxyhZbsS2cx4P74Y8xGE0ArN15ha/alsbLPWHFzhqOnLpD0XxeBPi5AdCoZiG2HbiaoB8Aa7dfpF7VAlQrkzve7SazmUePjRgMJgzGGAxGE5kdHawSP8DhEzcomt+HXP7ZAGhS6yW27ruQaB9WbzlFwxrFqF6uQLzbT10IxN7eji+GruXjr1cwf9WfxJhMVon/H4eOX6doAV9y5Yit8jWt/TJb9ybRj00naVCzONXLF0xw31+nb3Lo2DWa1Ho5zWNOLfb29un+k1E9M7Lhw4czefJkPDw88PPzY/DgwQwaNMgascUJDArD1ydb3LavTzYiIh8REfnY4nbXbwZxPyyCbt/MpFWn75mxcDNurk5W64Mluk4YxZItG9I7jGQFBgbi6+8Xt+3j60tERASREfHfVX/Vpw9169dLsH/Dxo1xc3enSf36NK5bj1y5c1G1WrU0jxsg8F44ftnd47Z9srsTEfmYyKhoi9s1qPUqbi5Zafrh9zT5cBy5cnhRpVzRePtPnrOZTz94I9GpsrTk5JGDqNB/34k+CruDo5MbmbIknG4s+c5wruxfSvjthJUUawu6/wgfz3+np7N7ZCXykZGoR8Z47fy8nSn3Suy5ZzabmbHyNOVL+OGYKfZlbNjn5SmS1/rTWP8ICokku5dz3LaPpzORUQYin+oHQJfWr/NGhXwJbq9TOT+uLplp/dUa3u+xhpy+rlR4NSAtw47nbnAEvt7/ni8+Xi5ERBmIjDIkaPtFuyrUqlQowe0xJhOvvRzAyN71mdC/MYeO32DVJuu+SQ4KefhUP1yJiIpOvB8fVaV25SIJbr93P4KJ8/fyTefa2NunbxVUUsczE56oqCgKFvw3861cuTLR0dHJ7JH6TGZzonPYDg72FrczGmM4eOQ83/X7gHkTuxH+IJKpczN2cpERmcwm7Eg4xvYOlr0LnTV9Bp6eHvy2eRNr1q8nPCycxQsWpnaYiTKbzIlEToIXs+TazV66E49sLvw6/ytWz+5O+MMoFq/aF9fmxJnr3A+P5M3qJVI3eAvY2dkDCd/Bmk0x8bbzVWyN2WTk+qEVVooseeYknrdJ/ZN59NjIiNlHuH0vgm7vl0q0TXpI8vXnOf5ZLlx7Cg/XLCwd/xaLxjbhQUQ0KzZaLyk1m80kdvI/zz/8RjWL061dZZyyOuLqkoUW9Uuw5/CV1AvSAiYLn+tJMRpjGDZxM53bVMLb0yV1g5N088wVux4eHpw9ezbuibx27VqyZcv2jL1Sbtr8jezafxqAiMjHFMrnH3df0L1w3F2dcMoafyrE38eDU2evJdouu7c7NSqXiFvAXO+N15i1OOH6DUmev78/p0+ejNsOCgrCzd0dJyfLqmU7t2+jR6/eODo64ujoSINGjdi2dSut2nyQJvHOWLSdPQfPAbHnUYG8/1angoLDcXPNmuA88vPJxqnzNxNtt+OPM/ToWB9HRwccHR2o/0Yptu89TatmlQDYsuck9WuWtNo7wqJvfoH/y7UAyJTFlfA75+Puy+ruR3RkKDGGqHj75C7zNg6ZnajefS32DplxcMxK9e5r2T/rYx6H37VK3At+O8eBE4EARD4yki+nW9x9wWGPcHV2JGuWhC9Pd0OiGDrtILn93RjRtSJZMltvuicx81afYP9fsedKZJSBfLn+rTDdux+Fq3PmRPuRlL1/3uDz1q/hmMkBx0wO1KmUj91/3qB53WKpHvs/5qw4zL4jsdOgkVHR5M/tFXdf0P0I3Fyy4JQ18YXXidm05zwF83hTME/slLAZyOSQ9tMcs38+yL4jVwCIjIwm//+ODxAU8nz9OHcpiNt3w5myMPbNTEhoJCaTmWiDkV4da6Z67KnJPp3X5GVkz3wmDh48mD59+nDhwgXKlClD3rx5GTNmTJoH9mnbunzati4AIaEPadXpe67dDCJPgA8rf9tPtYoJ51TLv16EH2b8mmi7N6qUZMuuYzStV44smTOx849TvFQkd4K/IckrV6ECP46fwPVr18idJw+rVvxCterVLd6/SLFibN28mdfLlsFoMLJ71y5eKfFKmsX7SeuafNI69gUqJDSCNl2ncv1WMLlzerP698NULZ/wH0m50gWZOHtTou2KFszBtj2neL1kfozGGPYcOMfLRXPF7fvXyav0+LRBmvXnaec2/cC5TT8AsYuWa/Zcj0v2vETcu0q+iq24cyphUr974jtxvzt5BlCz53p2jk98rU9aadOwKG0axk4Fhj54TOcRO7l59yEBvq6s33OVCiX8EuwT+chI3x//oHa5XLRqkHAKIj20e6sE7d6KreaFhj/i00G/czPwAQF+bvy28yIVSz/fdFShvJ7sOnSdV4v5YTSa2H/sFsULeD97xxT4qHkZPmpeBtNyXRMAACAASURBVID7YVF8/PUKbtwJI5d/NtZtPUOl1/I+19+7cuM+uw9dZvAXdTAaTazedIpalRNOfaW29i3K0b5FOQDuh0XSoc9ybtwOJVcOD9ZtPUXl1/NZ/LdeLuLP8klt47bnrjhE2INH/4lPaUnSnpnw5MmThyVLlhAZGYnJZMI1kY8fpzUvD1cG9GhB3+ELMRpjCMjhxeBeLQE4ff46305YwaIp3ZNt17xRRcIfRNKu6w/ExJgoViiALz6x7qJSW+Dl5UX/QQPp17sPBoOBgFy5GDh0CGdOn2bEsOHMX7I42f2/7NGDcaNH897b7+Dg4ECZsmX5oF0768Tu4cI3XzTlm5E/YzDGEODvycDuzQA4c+EWIyetZd4PnZJt90WHuoyb9jstP5uEg709r5fKzwdvV447xvVbIeTwTZ91JNERIRxd3ocybSZh7+BIRPA1ji6N/ZRWtlyv8GqL76ye2FjCwy0LX7YuxYhZf2KIMZMjuzM928R+NPjCtVB+WHycSX2r8euuKwSFRLLv+B32Hb8Tt/93XSvg7mKdhe/J8XDPSs+PyjNsyl6MMSZy+LjSq0N5AM5fCWH83INMHZxwXduTOrUszeRFf9Lhm9+wt7ejdHE/WtRPu+rO0zyzOdGrY3UG/7gZo9FETl93+naqAcRWPcbO3MWM795J9m+0bfY6P87bw8d9V2CMMVG9XAEa1rBeHwA8sznT+9OaDPphE0ZjDDn9svH1Z28AcO7SXcbM2MHMEe9aNSZryciLhtObnTmxZetPaNOmTbx5aTs7O7JmzUqBAgXo1KnTc01vhV1e8+KRZhDZ8jfFrmrp9A4jRcy7jwIQ8vBBOkeSMl6ubgSfSz7Byui8i7YCYG2vtH8HnJaajLnIxU090zuMFCv05jiu7LHuhzJSW74qQwC4eWhcOkeSMgFle3LrzwnpHUaK5Hz9S6sf07NXV6sf82n3x0xM7xAS9cwKT6FChciUKRPvvBOb1f/666/cuXMHPz8/vvnmGyZNSngNFhEREZGM5JkJz7Fjx1i5cmXcdrFixXjnnXcYO3Ysq1evTtPgRERExHKa0kraM0fGYDBw4cKFuO3z589jMpl49OgRBkPCaxqIiIiIZDTPrPD079+fTz75BG9vb8xmM2FhYYwZM4aJEyfStGlTa8QoIiIikiLPTHjKly/Pli1bOH36NLt27WLPnj106NCBo0ePWiM+ERERsZCmtJL2zITn+vXrLF++nF9++YXw8HA6derElClTrBGbiIiISKpIMhXcvHkzHTp0oEWLFoSGhjJmzBh8fX3p0qULXl5eSe0mIiIi6SS9vzg0I1eYkqzwdO3alfr167Ns2TLy5o290mZi3xMjIiIiktElmfCsXbuWlStX0qpVKwICAmjYsCExMTFJNRcRERHJsJKsPRUpUoS+ffuyc+dOOnbsyIEDB7h37x4dO3Zk586d1oxRRERELJDe01kZeUrrmZFlypSJ2rVrM2XKFHbt2kWFChUYN+6/fclyERER+f/lmZ/SepKXlxft27enffv2aRWPiIiIvKCMXGFJbxoZERERsXlKeERERMTmPdeUloiIiGRcDprSSpJGRkRERGyeKjwiIiI2QouWk6aREREREZunhEdERERsnqa0REREbISmtJKmkRERERGbpwqPiIiIjVCFJ2kaGREREbF5SnhERETE5mlKS0RExEZoSitpGhkRERGxearwiIiI2AhVeJKmkRERERGbp4RHREREbJ6mtERERGyEvZ1deoeQYanCIyIiIjZPCY+IiIjYPE1piYiI2Ah9SitpGhkRERGxearwiIiI2AhVeJJmZzabzekdhIiIiKRcyQkj0zsEjn/ZN71DSJRSQREREbF5Vp3S2hV43ZqHSxPV/HIT8vBBeoeRIl6ubgDYVS2dzpGkjHn3UfbevZneYaRIZd8AAFqUzpzOkaTMz0ejORh0O73DSLFyPjlYceVseoeRIs3zFQNg5LF96RxJyvQtVYl3Vy1O7zBSZHmzVlY/pqa0kqaREREREZunRcsiIiI2QhWepGlkRERExOYp4RERERGbpyktERERG6EpraRpZERERMTmqcIjIiJiI1ThSZpGRkRERGyeEh4RERGxeZrSEhERsREOmtJKkkZGREREbJ4qPCIiIjZCFZ6kaWRERETE5inhEREREZunKS0REREboSmtpGlkRERExOYp4RERERGbpyktERERG6EpraRpZERERMTmqcIjIiJiIxwcVMdIikZGREREbJ4SHhEREbF5mtISERGxEVq0nDSNjIiIiNg8VXhERERshCo8SdPIiIiIiM1TwiMiIiLpYt26dTRo0IA333yTRYsWJbj/0qVLtGnThiZNmtChQwfCwsJe+FhKeERERGyEg719uv9YKjAwkPHjx7N48WJWr17NsmXLuHjxYtz9ZrOZzz77jE8++YS1a9dSvHhxpk+f/sJjo4RHRERErG7fvn1UqFABDw8PnJ2dqVu3Lhs2bIi7/9SpUzg7O1OtWjUAOnXqROvWrV/4eFq0LCIiYiPsM8Ci5fDwcMLDwxPc7u7ujru7e9z23bt38fHxidv29fXl+PHjcdvXrl0je/bs9OvXjzNnzlCgQAEGDBjwwnGl/8iIiIiIzZg3bx61atVK8DNv3rx47UwmE3Z2dnHbZrM53rbRaOTgwYO8//77rFq1ity5czNy5MgXjksVHhEREUk17dq1o1mzZgluf7K6A+Dv78/hw4fjtoOCgvD19Y3b9vHxIW/evJQoUQKARo0a0a1btxeOSwmPiIiIjcgI1+F5euoqKZUqVWLixImEhITg5OTEpk2bGDZsWNz9pUuXJiQkhLNnz1KsWDG2bdvGyy+//MJxKeERERERq/Pz86N79+60bdsWg8FA8+bNKVmyJJ988gndunWjRIkSTJ48mf79+xMVFYW/vz+jR49+4eP9JxKe43/sZ+W0WRgNBnIVLEC7Pj1xcnGJ12bbL6vZsWYddnZ2+OTMQdvePXD39CTy4UPmjRrHnWvXMZtMVKz3JvVbt0yXfuzdvYepkyZhMERTsFBhvhk4ABdX1wTtzGYzwwYNpmChQrRu2waAmJgYxo0azdEjRwCoWLkyXb/8It58Z0Yzt99QTly6wLilC9I7lASO7dvPL9NmYjBEk7tgAT7q2yvBObX1l1VsX7029pwKyMmHvXvGnVNzRo7lzrVrmExmKtd/kwat30+nnsBrVerTqutwHDNn4eqFE0wd0pGoiAcJ2uUp9Art+4zH2TUbJlMM04d/zqUzR8nkmJn2vcfzSrkaPIqM4M9dv7H8p6GYzWarxP/Xvj9YPm0GhmgDuQsW4JOveyd4LDb/spKtq9aCHfgFBNC+z1dk8/Qk+vFj5o2bwN9nzoAZCr5UnHY9vyRzlixWif1JZw8cZtOc+cQYDPjnz0ez7l3J6uIcr80fa37j4K+/g50dXjn8ada9M64eHkSGP2DtxKncvnQZx6xZef3NWlRs2sjqfQC4fuQYfy5eQYzBiFfeXFTu1J7Mzk6Jtr168Ai7Js2gzfypAOyfvYjAM+fi7o8IuY+zpwdvjR2W6P5ppbRfTlq9XApHeweuhofy05H9RBmN8dq0eaU0FQPy8DA6GoBbD8OZcGgvAC2KlaBirjyYzGYuh4Yw/ehBDCaTVfvwojJChed5NG7cmMaNG8e7bcaMGXG/lypVihUrVqTKsTL8yDwIDWXuiLF8NmwQwxfNJXuOHKycNjNem6vnzrNp2c/0nfIDQ+bNxC9XLtbMnAvAmllz8fTxYci8mXwzfTI716zj75Onrd6P+/fv8+2QIYwYM5plK1cSkCuAKRMnJWh35fJlunb6jO1bt8a7fcNv67l29SoLly1lwZIl/HXkCNu2bE2wf0ZQLG9+tk6YRvMatdM7lESF3w9l9ojRdB4+mBGL5+OTMycrfpoRr82Vc+fZsHQ5/aZOZNj82fjlCmDVzDkArJo5B0/f7AybP5uBM6awffVaLp48lQ49AXfP7Hw+ZAZje73HF81eIfDGZVp3+zZBu8xZneg/5TfWzBtH7/fLsWLGd3T7dj4Ab3foi0+OPPRs8Rq93y+HR3Z/6r7bySrxh98PZfp3o+g2fChjlizAN2dOlk2Nf52Ny2fPsX7JMgb+NImRC+bilyuAX2bMBmDNvAXExMTw3bzZfDdvFtGPH7NuQcKLl6W1iNAwVo77kVYD+tJ91lQ8/f3ZOHt+vDY3L1xkzy+r+XTCKL6YPpHsATnZMm8xAOunzSKzkxNfTJ9EpwmjOX/oCGf3H7J6Px6Fh7Nnyixq9uzMOz+MwNXXhz8X/5xo27Dbdzi0YBk8kRhXaN+apmOG0nTMUN7o1Q2HzI5U7fKJtcIHwC1zFj5/vQLjDuzhyy2/cjfiIa1efjVBu6LePkw4tJfe23+n9/bf45Kdl7L7UilXHvps28BXW9fjlMmRegWLWrUPkjYyfMJz6uCf5CtWBL/cuQCo8VZjDmzeGu/dZ96iRRi+eB7Orq4YHkdz/949XLLFzh+27NaZFp9/CkBYcAjGaANOri4JD5TGDv6xn+IvvUTuPHkAeLt5czb+/nuCd9Erli+n8Vtv8Ubt+MlCjCmGqKgoDNEGog3RGAwGMmfJbLX4n0fnZu8y89dV/Lxjc3qHkqhThw6Tv1jRuHOq5ltN2P/UOZWvaBFGLFnw7zkVdA/X/81Jt/qiC+99/hkAof+cUy7WP6cASlaow9+nDnPnWuzFujb9PI2q9RNWm0pVqEPgjUsc3RN7jYvDO9bxfZ9WABQoXpq9G5djiH4MwKHta6lQ+22rxH/i0CEKFC+G//8ei1rNmrBv85Z4j0X+YkUZs3QRzq6uRD9+HPtY/O/5XezVUjRt1wZ7e3vsHRzIW6Qw9+4EWiX2J104cpSAooXIHpATgPKN6nFs2854/QgoXIges6eS1cUFQ3Q04cHBOLu5AXDzwt+8WqsG9g4OZHJ0pGi51zm5Z5/V+3Hz2CmyF8xPthz+ABR78w3+3r0/weuU8fFjdk2cQbl2SVfL902bwysN6+KdL0+axvy0Ur45+Pt+MHf+V+XcdPkCVXPni9cmk709+bJ50rRwcca+0YCe5arg7RRbjbO3s8PR3oHMDg442NnjaO+AISbGqn2QtPFcU1phYWFky5YtrWJJ1P27d/F8YtW2p48PURGRPIqMjPdPJlOmTBzdvZf5o8eRydGRpu3bAWBnZ4dDJgdmDhvBnzt3UbpqlbgXV2sKDAzE198vbtvH15eIiAgiIyLiTWt91acPAAf374+3f8PGjdm2ZStN6tcnJiaGchXKU/V/F2PKaLpOGAXAm+UqpnMkiQu5excvv6fPqYhEz6kju/Ywd/RYMjk60qzDR8C/59T0od9xeOdOXqtahRx5clu9HwDZ/XNxL/BG3Hbw3Rs4u2XDycUt3rRWjryFCQ0O5LNB08hbpCQRD0JZOOFrAC6cPESlui3Yv2UlRkM0Veq3xDN7DqvEHxJ4F2/ff6/D4ZXMY3F4125mjRqDo2Nm3vm4PQAlypWNa3Pvzh02Ll9B+949rRL7k8KC7pEte/a4bXef7DyOjORxZFS8aS2HTJk4vW8/q8ZPwsHRkVptY5PO3MWK8NfWHeR9uThGg4FTe/7APpOD1fsRERyCi7dX3LaLtyeGqCgMUY/iTWvtmz6PorWr45nEeX/j6HEe3guheIM6aR7z07ydnQmOiozbDo6KxNkxM06ZMsVNa3lmdeJkUCBLTx/n+oMwGhcuTu8K1eizfQMngwI5EXSHqfWaYjSZuPXwAZsvX0zqcBnOf21Ky5osGpkzZ85Qr149mjZtSmBgIHXq1OHUKeuU8E1mM4ktU0ns4kqlq1Zm/LqVNP6oLRO+6ovpiTnXjwd8zfi1K4kID2fdvIVpGXKiTGYTdiTsiL2DZS9qs6bPwNPTg982b2LN+vWEh4WzeIH1+2ELzKbE16Ykdk69Vq0KP/66mqYftWNczz7xzqmOA/vx47rVRIQ/YO3c9FmnZGdnH29K4R+mp96RZsrkSOnK9dj8y0z6tq7I70un0G/iWjI5ZmbNnDFc//s0387bzYBpGzh37A+MhmirxG82m0nsCW6XyGNRplpVpv62lmbtP2R0j17xHovLZ88x/PNu1HmnGaUrV0rTmBNjNpkTXU9n75CwHy9VqsA3Py+k1gfvM7ffYEwmE/U7fgR2MOnz7iwa/B2FXnsVh0yO1gg9HrPp2Y/HmY3bsHNwoMgbSb/hOvXbJko2a5guF8GzJ9GnBKYnbgyKjGDkHzu4/iD2e5nWXTiDn4sbPs4u1MxbAB9nVzr+voqOv6/ibsRD2pYobaXoJS1ZdDYOHz6cyZMn4+HhgZ+fH4MHD2bQoEFpHRsA3n6+hN4LjtsOvXcPZzc3sjj9+27j7o2bXDh+Im67SoN6BAfeJfLBA04ePETovXsAZHV2olztN7h2/oJVYn+Sv78/9+4FxW0HBQXh5u6Ok1PiiwGftnP7Nho1aYqjoyOubq40aNSIP5+4foFYztvPl9Dgf8+p+/eCcHnqnAq8cZPzT5xTVRvWJzgwMPacOnCI+0+cU+Vrv8HV8+etFv97nw1izNJDjFl6iFrNPsLTJ2fcfV6+ATwMC+Hxo8h4+4QE3eLm5bNcPBm7LuTwjnXYOzjgl6sArtm8WLdgAj3ffY1BHWoRER7Knet/W6UvTz+/79+7h4ubG1njPRY3OHfs36uvVm9Yn3uBgUQ8iK1g/bFlK6O6f8W7nTrSpO0HVon7aR6+PoQHh8Rth98LxsnVlcxZs8bdFnzzNleeWD/4et1ahN4N4tHDhzyOjKJehw/5YvpE2o8ahtlsxjunvzW7AIBrdi8i79+P244MuU9mFxccs/67CPzijj3c+/sya3oNZPOI8cRER7Om10AiQ2L3exQeTtCFS+SrUDbB37eGe1GReD5x/nhldeJh9GMeP/EmII+7R4JpLjsgxmSiXM7c7Ll+hUdGI0aTiS1XLvKyjx//FQ4Odun+k1FZlPBERUVRsGDBuO3KlSsTHW2dd4AvlX2dS6fPEHg9tmy/c806Xq0S/x1caHAI04d8y4PQ2Gx9/+atBOTPh2u2bBzetpN1cxZgNpsxREdzeNtOir2WcAFbWitXoQInT5zk+rVrAKxa8QvVqle3eP8ixYqxdXPsmhijwcjuXbt4pcQraRKrrXu5XBkunfr3nNqxOuE5FRYczLTBw+LOqT+eOKcObt/B2jnz486pQ9t3UPw1670DXDZ1CL1alqVXy7L0a1uVwiXK4Z+nEABvNu/IoR3rEuzz196N+Abko0Dx2DiLv1YFs9nM3ZuXKVO9EZ/2nwJAVicXGn7Qjd2/L7FKX14pV5aLp05z53+PxdbVa3mtauV4bUKDQ5g8eCgPQkMB2LdpC7ny58ctWzaO7NnHggkT6T1+DJXeTL9F8oVef5XrZ89x7+YtAA7+toHiFcvFa/MgJIRlI8YSERZ7yf1j23bilzcPzu7uHPx1A1vmxy5gfng/lMMbNlOqpuWvD6klZ6lXCLpwibDbdwA4u3k7ecrGP7cbjxhIs3HDaTpmKHW+7o5D5sw0HTMUZy9PAALPXiR7ofzxkiRrOhZ4m8Ke3vi7xK6PqpO/MIdu34jXxmw281HJMvg4x06bvpm/MFfDQwl5FMXl0BDK58yN/f8qXeVz5uZCyD3rdkLShEVreDw8PDh79mxcyXbt2rVWW8vj7unJR3178dPAoRgNRnwCctDhmz5cOXuOeaO/Z9DsaRQpVYKGbVox9oue2Ds44OHtzeffDgHg3c6dWDhuAoM/jP2kQOmqlanV3DoLMp/k5eVF/0ED6de7DwaDgYBcuRg4dAhnTp9mxLDhzF+yONn9v+zRg3GjR/Pe2+/g4OBAmbJl+aBdOytFb1vcPT1p/3UvJg8YTIzRiE/OnHzcvy+Xz55j7qixDJkzgyKlStKoTWtGd+see05l96brd7EfrW3Z+TPmjx3PwHYdAChdrQq1W7yTLn0Jvx/ElMGf0HPMUjJlykzgjb+ZNCB2fUuBl17js4HT6NWyLKHBgYzu0ZyPv55IFicXjNGPGdvzXQzRj9m2Zi6FS5Tj+xV/YW/vwJZVs9i/ZaVV4s/m6ckn/frwY/9BxBgN+Abk5NP+/bh09iyzRo7h27mzKFqqJE3atuHbrl/i4OCAR/bsfDliOABLJk8FzMwaOSbubxYuUYIPe35plfj/4erhwTs9u7Fk2ChijEa8cvjTvNeX3Dh/gVXjJ9N16gTylXiZGi1bMLPXN9g7OODu7UXrwf0AqN7yHX4ePYEfOnYFs5nabVuRq2hhq/YBwCmbO1U+a8/276dgMhpx8/OlWpePuff3Zfb+NIemY4Y+82+E3wnE1cfbCtEmcfzox0w9coAe5auQyd6ewIiHTDr8BwU8vOhUujy9t//O9QdhzDl+mD4Vq2NvZ0dIVCQ//O9TWqvOnaJtidf4vnZDjDEmrobdZ+YxVdNtgZ3ZgottXLt2jT59+nDixAmyZs1K3rx5GTt2LPnz53+ug+0KvP7CgWYU1fxyE/Iw4TVO/ku8XGPf+dhV/W/PS5t3H2Xv3ZvpHUaKVPYNAKBF6Yz5iTtL/Xw0moNBt9M7jBQr55ODFVfOpncYKdI8XzEARh6z/qe8UlPfUpV4d1XybwQzuuXNWln9mO3WJ34ZAWua16BFeoeQKIsqPPv27WPJkiVERkZiMplwTeRieSIiIiIZlUVreBYujP00kLOzs5IdERER+c+xqMLj7+9P27ZtKVWqFFmeuGR7ly5d0iwwEREReT66Dk/SLEp4Xn3V+p9qEhEREUktFiU8T1dyzGYzN27cSKK1iIiIpAdVeJJmUcKzbNkyRo0aRVRUVNxtuXLlYvPmjPldSSIiIiJPsigVnDZtGmvWrKFBgwZs3ryZ/v37U7JkybSOTURERCRVWFTh8fb2Jnfu3BQtWpTz58/TunVrliyxzpVYRURExDKa0kqaRSPj5OTE/v37KVq0KNu3bycoKIhHjx6ldWwiIiIiqSLZhCcwMBCAAQMGsH37dqpWrUpoaCj169fngw/S50v6REREJHEO9vbp/pNRJTul1alTJ1atWkXhwoXx8/PD3t6eiRMnWis2ERERkVSRbCr25NdsrVuX8BuYRURERP4Lkq3w/PPt6BA/+REREZGMJyNPKaU3i0fmyeRHRERE5L8k2QrPhQsXqFWrFhC7gPmf381mM3Z2dmzdujXtIxQRERGLqMKTtGQTno0bN1orDhEREZE0k2zCExAQYK04RERERNKMRVdaFhERkYxPU1pJ08iIiIiIzVOFR0RExEY4OKiOkRSNjIiIiNg8JTwiIiJi8zSlJSIiYiO0aDlpGhkRERGxearwiIiI2AhVeJKmkRERERGbp4RHREREbJ6mtERERGyEprSSppERERERm6eER0RERGyeprRERERshL2mtJKkkRERERGbpwqPiIiIjXCws0vvEDIsVXhERETE5inhEREREZunKS0REREb4WCnOkZS7Mxmszm9gxAREZGUG3lsX3qHQN9SldI7hERZtcITGXbVmodLE87Z8hJ8bnF6h5Ei3kVbAbD37s10jiRlKvsGYFe1dHqHkSLm3UcBiLp3JJ0jSRmn7K+x9tqF9A4jxZrkKYxbj8/TO4wUefD9FABKThiZzpGkzPEv+1Jl5qT0DiNF9nzcxerH1KLlpKn2JSIiIjZPCY+IiIjYPC1aFhERsRGa0kqaKjwiIiJi81ThERERsRH2+lh6kjQyIiIiYvOU8IiIiIjN05SWiIiIjdCi5aSpwiMiIiI2TxUeERERG+FgrzpGUjQyIiIiYvOU8IiIiIjN05SWiIiIjdCi5aSpwiMiIiI2TwmPiIiI2DxNaYmIiNgIe01pJUkVHhEREbF5qvCIiIjYCAd9eWiSNDIiIiJi85TwiIiIiM3TlJaIiIiN0HV4kqYKj4iIiNg8VXhERERshBYtJ00jIyIiIjZPCY+IiIjYPE1piYiI2AgtWk6aKjwiIiJi81ThERERsRH6Lq2kqcIjIiIiNk8Jj4iIiNg8TWmJiIjYCAd71TGSopERERERm6cKj4iIiI3Qx9KTpgqPiIiI2DwlPCIiImLzMuyU1u49B5g4ZTbR0QYKF8rPoP49cHV1sahNTEwMI8dM5s+jxwGoUqkc3bt9gp2dHadOn2PM91OJinqEyWTiw7bv0rB+7TTty95D5/lp/lYMxhgK5vWjX7cmuDhnsbhdTIyJ76et5+ipqwBUfL0wXT6qw5Xr9xg87pe4/WNMZi5dvct3fd+lRqXiadafY/v288u0mRgM0eQuWICP+vbCySX+Y7P1l1VsX70WOzs7fAJy8mHvnrh7ehL58CFzRo7lzrVrmExmKtd/kwat30+zWFNqbr+hnLh0gXFLF6R3KADs2neEiT8tJTraSOFCeRj8dUdcXZyfq82dwGDadBzA8nkj8fRwB+DvyzcYNnomkZGPsLODLz57n0rlS1mlT2cOHGL9rHnEGAzkyJ+PFj2/IOtTfdq7eh1//Po72IF3jhy06N4VV08P5g/9jns3b8e1u38nkAIlX+GjYQOtEvuT6hZ/hcENm5IlUyZO3rpJ52ULefD4Udz975cpT5fqb8Rtu2d1IsDDk6JD+mE0xTD+nfcpGZCLiOjHLDy4n2l7dli9DwBV8xXki8rVyezgwPl7QQzasp6I6Oh4bQp7+9C3Zh1cM2fBZDYxdOsGztwNxNHBgb41alMuV14iDQZ2XrrI1P27MVu5DxVz5+XTshXJbO/A3yHBjNi9lUiDIV6bAp7edK9UDRfHzJjMZsbs2c654CAAZr31LpkdMmE0xQCw6eJ5lpw4auVevBh9eWjSMuTIhNwPZdCwsYwZOZDVK2aTKyAHP06eZXGb337fytWr1/l58TSWLfqJP48cZ8vW3ZjNZr7qM5ROHduybNFPTJrwLeMmTOPqtZtp1pf7YRF8++MaFbvwvQAAIABJREFUvvv6XZZO7UJOfw+mzNvyXO027DjO1ZvBLPjxM+b/0ImjJ6+yfe9p8ufxYd4PneJ+yr1agDrVXknTZCf8fiizR4ym8/DBjFg8H5+cOVnx04x4ba6cO8+GpcvpN3Uiw+bPxi9XAKtmzgFg1cw5ePpmZ9j82QycMYXtq9dy8eSpNIv3RRXLm5+tE6bRvEbaJsPPI+R+OIO+ncbYb7uzZun35Mrpyw9TlzxXm3W/76J95yEE3bsfb7/vxs3mrYbVWT5vJEP6fUrvAT9gNMakeZ8ehoaxbOwE2g78mt5zpuGVw5/1s+bGa3Pj/EV2rlhF5x/G8NWMKWQPyMmGeQsBaDuwHz2mTaTHtIm06NGVrK4uNOv6WZrH/bTsLq5MbdmGD+ZO57WRQ7gSco8hjd6K12bJ4QNUHjeCyuNGUH38KO4+CKfnymUEPXzAyKbNiYh+TJlRQ3njhzHUKf4S9V56xer98HT6P/buO7zJ8vvj+DtdtHRS6ICWKXuD7C0gCAgyBFRwT5RhQZSpoAwFZAiKiCDDAYpMBUH23hsBmbKku5TutMnvj/oNlNJaKElrfp/XdeWCJ89Jcu4mTU7OfT9P3fioTXsG/rqMTgtmcyU2hrcbt8gQ4+rkxJdde/LN/t30/P4bZu3ZycePdQLg1boNKebpTbdv59Dz+2/wc3enZ43aNh2Dj6srw5q1YsT6NTyz5Duu3bxBn7qNMsQUcHRicrtOfHfkIC8tX8y8Q/t4/5E2lvEV8/TmhaWLeHHZYl5ctvg/U+xI9vJlwbN7zwGqVK5AyRJBAHTv9jhrftuI2WzOUUxaWhqJSUmkGI0YU4wYjam4FHAmJcXIa6/0pkG99F/AgAA/Cvn4EBYWbrWx7D10jkrlgiherDAAXdvVZd2WYxnG8m9xpjQTSclGjKlppBjTSE1Nw8UlY3Pu8Im/2LTzJO+++bjVxgJwYt9+SlesQEDxYAAe6dyJ3b9vyDCeUhXKM/6HhRT08MCYnEJ0eAQeXumdhGcG9KXnm+kfSDGRUaSmGDN1h/KDt7r04OtflvHT5t/zOhWLXXuPUqVSGUoWLwpA9y6Psmbdjgw/++xiwsKj2LR1PzMnD81036Y0E7E34wGIT0jCxcXZBiOCPw8cpHj5cvgFp/8eN+zYnkMbNmcYU3D5srw37yvc3N0xpqQQGxGJu6dnhvtJNRpZNGEKnfq8io+/n01yv13LCpU4ePkvzkWkv5d8vWMrPWrXzTI+pGUbwuNu8s2u7QDUDC7BD/v3YDKbMaalsfaP43SuXssmud+uYYnSHA/9m0sx6QXxj0cP0b5i5YwxJUtzOSaa7RfPA7D5/BneWb0cgEr+gaz58yQpaenF8sZzZ3i0XEUbjgDqBpXgZHgYV2JvALDs5HEeLVs+Q0y94OJci73B7ivpXfPtly7w/sbfAKjkF0BiqpFPH+vI/K5P069+E1wcHW06htxwNBjy/JJf5XhKKyEhgRs3bmR4IypWrJhVkroeGk7AbW9a/v5+xMUnEB+fYJnWyi6m0+NtWL9hG207PENaWhoN6j9M86YNAejyRDvLbX5e9isJCQlUq2q9jkhoRCwBRbws235FvIhPSCYhMSXDtFZ2ce1b1WTjjj944oXJpJlM1Kv5EE3qVcjwOJ9/8zuv925516myBykqLAzfAH/LdiE/PxLj40lKSMhQuDg5OXFw63bmTZiEk7MzXV5+EQCDwYCjkyNffTiO/Vu2ULtpE4qWKG7VnO9Hv6mfANCmXsM8zuSW0LBIAv0LW7YD/HyJi08kPiHRMmWVXYy/ny+Txw+8630PHfQir/Ufw7eL1xAVfYNPRvfHycn6b/Ix4RH4+BWxbHv7FSEpIYHkhMQM01qOTk4c37GLnyZPx8nZiTbP98pwP3t/+x2vwr5Ua5Lxm7ytBPsU4krMra7Z1RsxeLu54VnANcO0FkBhd3f6tWhNs8njLdftv3SRp+vUZ/eFcxRwcuaJ6rUwplm/w3anQE8vrt+8adkOvRmLZwFX3F1cLNNapXx8iUyIZ1TrdlTw8+dmcjJTtm0C4Nj1azxWviK/nzmFMS2N9hUr41fQtl9oAtw9CIuPs2yHx8fh4VKAgs7Olmmt4t4+RCYmMKRpS8r6FiEuJZkv9u4EoKCzMwevXWHa7m0kp6byfos2vFG3IZ/t3m7TcciDl6MOz4wZM2jcuDG9evWid+/e9O7dm2effdZqSZlNZgx3qRIdHR1yFDPr628pVMibDb8t5rdfvic29iYLvluSIW7u/EV8+dVCpn76Ia6u1isSzCYzd6t3HRwMOY6bu2gLPt7u/LLgHZbPDSE2LpHvl+20xBw7eZno2ATaNK/2YJO/C7Pp7rPxDnc52VXtZk347JflPPHi83w66D1MJpNl32vvD+OzVcuJj73Jynn5Y31MfmfK6jV/288+JzF3Sk5O4b33P+PD4X1Yt/xz5n7+AWMmfs310MgHk3g2zCYz3CXfu72eqjZuyOifv6fNc8/w9dD3M7yetv28nNa9elo11+w4GAyZurYAaWZTputebNCE1cePcDHq1s932IqfMZvN7Bg0jB9eep1Nf57Kk4In/e8wZR6H6bbfeycHB5qUeoifjx/m6R/m8/3hA3zeuTvOjo7M3b+bc5ERfNvzWWZ3fZrD165iNNl2HAaDAfPdxmDOOIaGxUuy8tQJXlnxI0tOHGVi28dxdnBgx6WLjNmynpvJyaSkpbHw8H6alSxjyyGIleSo4Fm6dCkbN27McNmwYYPVkgoM9CM84tabQVh4BF5enri5ueUoZuOm7TzRsS3Ozs54erjTscOj7N9/BICUlBSGjBjHb2s3M3/OVCqUf+iB5z/7u008P+BLnh/wJat+P0h41G3fNiJj8fRwxc3VJcNtAvy8s4zbvOskj7euibOzIx7urrRrWYODxy5aYtdvP067R6pnKqKsoXCAPzGRt37u0RHhuHt6UuC25yb0ylX+PHrMst20QzsiQ0NJuHmT43v2ER0RAYBrQTfqt27JX3/+afW87UHRwMIZ1t6ERUTh5emOm5vrPcXc6ez5yyQlpdCscfpUb/Wq5ShTOphjf5y1wigy8vH3IzYyyrIdGxGJm6cHLrflG3H1GhduW+dVt+2jRIeFkxiX/vty9ew5TGlplKlu/YI/K5djoinq7W3ZLubtQ1RCPAl3LPYF6FrrYRbu253hOk9XV0b+soz6E8fQ6cvPMADnIsKsnXYmf9+Mxc/91nShv4cnN5ISSUy9teA3LD6OC1GRHLuevlh88/kzOBgcCPbywdvVjfkH9tLt27m8uOQ7biYncSkmxqZjCI2/SZHbukpF3D2ITUoiKTXVcl1EfAJ/xUTzR3gokD6l5WBwoJiXN41LlKJG4G2zFwYDqabMhWt+5WBwyPNLfpWjzPz9/fG8Y87cmhrWf5hjx09aFhMvWfoLLZo1zHFMxQrlWLd+KwDG1FS2bN1Ftarp88jD3/+E+PgE5s+ZQrFigVbJ/9Vej1gWEn818RVOnL7C5WvpRcLyNftpWj/znHa9Wg9lGVfhoaJs3J7+hp+amsb2PaepUiHYctvDx/+iTg3bfAOpUq8O50+cJPTyFQA2L19FzTumEW5ERjJr1EfcjEmfQ9/1+waCSpfCw9ubvZs2s/KbBZjNZowpKezbtJlKtW2/VuG/qGG96hw9cYa/Lqd/0CxZtp4WTevcc8ydigcHcjM+gcPH0gvPy1dCOX/hKhXLlXrwg7hDhYdrcenkacKvpP8e7/plNVUaNsgQExsVxXdjJxB/I/31dHDjZgJLlcD9n3Vh548ep2zNGnftbNnKhtN/ULdkaR4qkj7N/nKjpqw+fjRTnI+bG2UK+7HnwrkM17/cqCnDH0tff+fn4clzDRrz08H91k/8Drv+ukD1wGKU8CkEQPfqtdh07kyGmO0XzxPk7U0l/wAAHg4qDpi5GhtDizJleb/VYwC4OTvTu1ZdVp+y7UEJe69cpop/IMFe6QVo54pV2XbpQoaY3Vf+oqiHFxUKpz9f6QWO+Z+Cz4O36jXGxdERB4OBp6rWZOMF6xf/Yn0G8936sP+YMWMGAEePHiUyMpJmzZrheNvirb59+97TgyXc+CvHsdt27GX653NJTTUSHFSMj0YN5srV63w4djKLv/syyxhvby9iYmL5eNIMTp8+i4ODI/Xq1mTggNf44+QZXnjlbUqWCKZAgVsdlgF9X6FRw+w/FP6noHdJIk9/f0/j3rn/jOVw86DAQrwf0gUvTzdOnrnGxzNWMn/aG9nG3YhN4NNZa/jz/N84OjjwcI3S9HuxDc7O6c9Fy+7jWDSzL/63rQHKTuEKzwCwI+z+jk47ums3S2Z9TVpqKn7FivHKiCGEX/ubeZ9MYvQ36UdsbVq2go3LVuDg6IhPkcL0DhmAX7GiJNyMY8GkKVy9kP4GVKtZEzq/9MJdpzD+TWP/IAxNrVssfTNsNMfPn7XaYenmbelHfyRGHMxR/Ladh5g+axFGYyrBQQGMGfkmV66GMvrj2fw4/+MsY7y9PDLcT83GT7Pp11mWw9L3HTjBlC++JyXFiKOjA6+/1I2WzbJedHsntyK1WXnpzL8H3sXJPftYM3c+acZUChcrylPvDiTy7+v8NPkzBs6aDsDOVavZufIXHBwc8S7sS5d+ffAtmv6FZelnM/EqXIjWvZ66r8e/XacS5fAc+OZ93bZNpSqM6vAELo5OXIgI57Uf5lPKtwgzevai8afp63VqFy/J3GdfpOa4URlu61GgALOfeYEyRfwwGODTDetYfGDvfeVxc/IXAFSf+vF93b5JqTIMaNwCZ0cHLsfEMHztLwR7+zDq0Xb0+C79aMuHg4ozsOkjuDk5k5KWxidb1nPo2hUcDQZGtnqM6kWL4Whw4OfjR1hw8P7GcfTtITT5esZ93bZBcEneqNsQJ0cHrsbGMmbL7xTz9GZI00d4cdliIL3IeateI1ydnDGa0pi2axtHQ//GAPSp14hGJUrhaHDg0N9XmbJzC8b76PJsf+XePiMfhDVXztv8Me/ULjh/TgHmqODJijULnvzqfgqe/Ca3BU9+YYuCx9ruteDJr3JT8OQnuSl48ovcFjz5RW4KnvwiLwqedVcv/HuQlbUJKp3XKdxVtkdp/a+gSU1NZcuWLbRq1YqoqCg2btxIt27dbJKgiIiISG7l6LD0kSNHYjKZaNWqFQB79uzh6NGjfPjhh1ZNTkRERHIuP58HJ6/lqOA5fvw4q1atAsDX15eJEyfSsWNHqyYmIiIi8qDkaKWoyWQiLOzWIZKRkZH3tchUREREJC/kqMPzxhtv0KVLFx5++GEAjhw5wvDhw62amIiIiNyb7E4y+v9djgqecuXKsXTpUg4fPoyTkxMjRozA39//328oIiIikg/kqOAJCQlhzZo1tG3b1tr5iIiIyH1y0KLlLOWo4ClbtiwzZsygRo0auLreOuV73bo5PzGZiIiISF7JUcETExPDnj172LNnj+U6g8HAggULrJaYiIiIyIOSo4Jn4UL9NWsREZH8zjEf//HOvJajgufw4cPMmjWLhIQEzGYzJpOJa9eusXHjRmvnJyIiIpJrOSoFhw0bRuvWrUlLS6NXr14EBATQunVra+cmIiIi98DRYMjzS36Vow6Pi4sL3bp14+rVq3h5eTFhwgSdaVlERET+M3LU4SlQoAAxMTGULl2aI0eO4OjoSFpamrVzExEREXkgclTwvPjii4SEhPDII4+wYsUKOnToQNWqVa2dm4iIiNwDBwx5fsmvsp3SCg0NZcKECZw5c4aaNWtiMpn4+eefuXjxIhUrVrRVjiIiIiK5km2HZ9iwYfj7+zNw4ECMRiPjx4+nYMGCVK5cWX88VEREJJ9xMOT9Jb/61w7PnDlzAGjcuDGdO3e2SVIiIiJi/1atWsXMmTNJTU3l+eefp1evXhn2//7773z22WeYTCaqVavGhx9+iIuLy309VrZtGmdn5wz/v31bRERE5H6FhoYyZcoUvv/+e5YvX87ixYs5e/asZX9CQgIffvgh33zzDb/++ivJycksW7bsvh8vR4el/48hHx9fLyIi8v+dIR8sGo6NjSU2NjbT9V5eXnh5eVm2d+7cSYMGDfDx8QGgbdu2/Pbbb/Tt2xeAggULsnHjRpydnUlMTCQyMjLD7e9VtgXPmTNnaNWqlWU7NDSUVq1aYTabMRgMbNiw4b4fWEREROzP/PnzmTFjRqbr+/btS79+/SzbYWFh+Pn5Wbb9/f05evRohts4OzuzZcsW3n33Xfz9/WnSpMl955VtwbN27dr7vmMRERGxLYd8MBPz/PPP06VLl0zX39mdMZlMGWaO/tdMuVPz5s3Zs2cPkydPZtSoUXz66af3lVe2BU9QUNB93amIiIj8/3Tn1FVWAgMD2b9/v2U7PDwcf39/y3ZMTAzHjx+3dHU6duxISEjIfeelY8tFRETE5ho1asSuXbuIiooiMTGRdevW0axZM8t+s9nM4MGDuXbtGgC//fYbtWvXvu/Hu6dFyyIiIpJ//Ze6GAEBAYSEhPDcc89hNBp58sknqV69Oq+++ir9+/enWrVqfPTRR7z++usYDAbKli3L6NGj7/vxVPCIiIhInujYsWOmP0Y+e/Zsy/9bt25N69atH8hj/ZeKQREREZH7og6PiIiIncgPR2nlV+rwiIiIiN1Th0dERMROqIuRNf1sRERExO6p4BERERG7pyktERERO5Ef/nhofqUOj4iIiNg9dXhERETshA5Lz5o6PCIiImL3DGaz2ZzXSYiIiEjunYqOyOsUqFioSF6ncFc2ndLaNr6OLR/OKpoO3c/KwWXzOo1c6TTxLADda7nkcSa589OhFBIjDuZ1GrniViT9L/8amtbK40xyx7ztEInRZ/M6jVxzK1SWxKhTeZ1Grrj5VgQg5tzSPM4kd3we6krUn4vzOo1c8S3f0+aPqWmbrOlnIyIiInZPi5ZFRETshBYtZ00dHhEREbF7KnhERETE7mlKS0RExE446EzLWVKHR0REROyeOjwiIiJ2QmuWs6YOj4iIiNg9FTwiIiJi9zSlJSIiYie0aDlr6vCIiIiI3VPBIyIiInZPU1oiIiJ2Ql2MrOlnIyIiInZPHR4RERE7oT8emjV1eERERMTuqeARERERu6cpLRERETuh8/BkTR0eERERsXvq8IiIiNgJrVnOmjo8IiIiYvdU8IiIiIjd05SWiIiIndCi5aypwyMiIiJ2Tx0eERERO6EzLWdNHR4RERGxeyp4RERExO5pSktERMROqIuRNf1sRERExO6pwyMiImIndFh61tThEREREbungkdERETsnqa0RERE7IROw5M1dXhERETE7v1nOjyFHmpM6RZ9MTi6EB92hjOrPyItJT5TXNGHe1C0VjcAkqKvcGbNGIwJ0RliKnWdQEpcBOfWTbBJ7lnxr9iCyu3fwcHRhdi/T3P4p6GkJsdlGR9YpTW1n5rE6pE1bZhlZrWbtOOZfmNwdinAX2eOMXP0ayTG38wUV6JsVV56bwoFPbwxmdL4asybnD95CCdnF156dwpV67UgKSGeA1t/5ccvP8RsNls17607DzL9y0WkpKRSrmwJRg19DQ/3gvcUcz00kmdfG8mP8z+mkI8XAOcuXOGjCV+TkJCEwQAD+jxNo/o1rDqWezVv2IccO3+GTxctzOtUANi6Yy/Tv5hPitFIubKlGDX87czPRTYxi5f8wrKV60hOTqZSxbKMGv42Li7Oltvu3HOQqTPm8uPCGTYc036mz1yQnu9DpRg1vN9dxpR1TIvHeuPvX8QS+3yvznRo28ImuW/fe4qZ89aSYkylbOlAhr/dDY+CrvcUt+SXXaxcu5/kFCMVywYx/O1uJCcb6TNkdob7OHfxOv1easczXZs+8HHs2HeamQvWYzSm8lCpQIb3fwL3u4wjq7hh4xdx5e8oS9y10GhqVS3FxJG9OHD0PJ/NWUuayYS3pxtvv9qecqUDH/gYckuLlrP2n+jwOLv5UL7DB/yx9F0OfNWNpJirlHqkb6Y4j8CKBNfrzZGFL3Hw654kRl+iZLM+GWKC6z+Hd/Fatko9Sy7uvtTq+Qn7FrzFxoltiI+6RKX2g7OMdy9SkiqPD83zfqVXoSK8OXo2kwb3ZECXqoReuUCv/mMzxbm4ujHii19ZMf9T3n26Hktmj6P/2AUAdH15CH5FSzCoe23efboePkUCadvjDavmHRUdywdjZzFpbAgrFk0muJg/02b+cE8xq9Zs5aW3RhMekbGAHvfpXDp3aM6P8z9m9LDXeXfkNFJT06w6npyqWLI0G6bO4skWrfM6FYuo6Bt8MGYqk8YPY8WPXxFcLJBpn3+T45gNm3aw6KdVzJo+lp9/mElycgrfLloGQFJSMjO+XMB7Iz4mLc1k2zGN/YxJ44ewYvFMgoMCmfbFghzHXPzrCl5envy4YKrlYqtiJ/pGHGOmLGH88F78NHsQQYG+fPHNb/cUt2nHcX5atYvp417mh5lvk5Rs5Idl2/H0cOPbGf0tl05t6lCjcil6dGpkhXHEM3bacsYPfYrFXw4gKLAQX8z7/Z7ixg19igWfvcmCz95kSN8n8HR35Z03HicuPomh4xbR96U2fDv9LQb36ciITxaTYkx94OMQ6/lPFDw+ZRoQ9/cfJEVfBuDvQ0vwr9wuU1zc9VPsn9WFtOR4DI4uuHj6Y0yMsez3LvEwhco05O9DP9ss96z4lW9CzOWjxEf8BcDFXd8TXKvTXWMdnV2p/fSnHF81zpYp3lX1Bo9y7sR+rl86C8C6n2bRtN3TmeJqNHiU0CvnObQ9/Q1x/+ZVTH7vGQDKVKrFjrU/YkxJBmDfppU0aN3Vqnnv2nuUKpXKULJ4UQC6d3mUNet2ZOgqZRcTFh7Fpq37mTl5aKb7NqWZiL2Z3m2MT0jK0GnIa2916cHXvyzjp82Z3/jzyq49B6lSqRwlSwQB0L1rB9as3ZzxucgmZtWajTz7TFe8vT1xcHBg+Ht96fBYSyC9s5OYlMRHIwfadkx7D1GlUllKFi/2T76PsWbtljteX1nHHD52CkcHB158Yyjde/dn1pxFpKXZpmjec/AMlcoHUyIovbvUtUMDftt0OFPHNbu41RsO8UyXpnh7FsTBwYEh/TrTrmXGL5aXr0Uwd9EmRr3TAycnxwc+jr2HzlKpXDGKFyucnl+7uqzdcjTTOHISZzSm8tHUpQx4tR0Bft5cvhaJu7srdWs8BECp4n64uxXg+KnLD3wcYj3/iSmtAp4BJMeGWraTY8NwcvXA0cU907SW2ZRG4XLNKdd+JKa0FP7a+iUALh5FKNN6EMcX97NMeeUlN5+iJMb8bdlOunEdZzdPnAp4ZJrWqt5tDBd3LyL271O2TjOTIoHBRIResWxHhl2hoKc3bu6eGaa1ipYsR0xkKH0+mEXJ8tWJvxnDt1PTi4Uzx/fRqG13dq9fSqoxhSbtnqJQkaJWzTs0LJJA/8KW7QA/X+LiE4lPSLRMKWQX4+/ny+Txd/8QHTroRV7rP4ZvF68hKvoGn4zub5U39PvRb+onALSp1zCPM7klNCycwAA/y3aAfxHi4hPueC6yjrl06SpRlWN48+2RhIdHUatmFUL6vgRAy+YNadm8IfsOHLXtmEIjCLxtOirA7y5jyiYmLc1E/bo1GPDmcxhTU+k36CPc3QvS+6m7fwl6oLmH3yCgiLdl27+IF/EJycQnJmeY1sou7tLVCKJvxDFg5FwiIm9So0op+r2c8Uvpl/PX0aNjQwL9faw2Dv/b8vP7J7+ExOQM01o5iVv1+0GK+HrSomFlAEoEFSYpKYU9B89Sv3ZZ/vjzKucvhRMRlXkqP6/pj4dmLUcdnkuXLrFy5UrMZjMjR46kW7duHDt2zNq5WRgMDkDm9R1m892/AUWe2cLuaa25tO0rqvacjsHRmYpPjOX8hskY4yOtnG3OZDkmU8YxlWrYC7Mplcv7ltgos+wZDA5wl7U2pju+jTo5OVOr8WP8/vPXDOnVkDWLvmDY9JU4Obuw4puJXD73B2Pnb2PkrN84fWQXqcYUq+ZtMpkx3OWNwNHB4Z5i7pScnMJ773/Gh8P7sG7558z9/APGTPya66H543WWH5lMZgx3WWeQ6bnIIsaYmsbuvYeYMHYo38+bSmzsTaZ/uSBTrC3l9vXV7Yk2DBn0Gm5urnh5evDs00+wactuq+b8P2Zzzl732cWlpqWx59BZxg59hnnT3iI2LoGZ89daYkLDY9h98Aw9n2j84AfwD1MW+TncMY6cxC1asYsXeza3bLsXdOXj4U8z/6etPNvvc9ZsOszD1UvjnE++2EjO5KjDM3ToULp3786GDRu4ePEiQ4cOZezYsSxatMhqiZVs+jq+5ZoB4OjiTkL4Ocu+Ap5+GBNvYDImZbiNa6FgXNwLE3vlCADXj66k7GND8QysjKtPEGVahQDg4l4Yg4MjDo4unFkzxmpjuFOFNgMIrNIKAKcCHsRe//NW7l4BpCTEkGZMzHCb4nW64ujiRvOQlTg4uuDo7ErzkJXsnvMKybFhNsm7Z58PqNP8cQDc3D25dPaEZZ+vfxBxN6JITkrIcJuo8GtcvXCKs8f3AelTWn3e/5KA4DLEx0azauFUFk4ZAkCTx57i+uVzWFPRwMIc/+OsZTssIgovT3fc3FzvKeZOZ89fJikphWaNawNQvWo5ypQO5tgfZwkMKJzl7f4/Kxrgx/ETpy3bYeGReHl5ZHwusonx9/OlVYtGls5J+8ce4as5Gddj2VrRQD+O/3Hr9zksPBIvzzvGlE3ML2s2Ub5cacqXLQWkFxfW7BLOWvg72/acBNKnYR8qdWvxbXhELF4ebri5umS4TYCfD8dPX75rnJ+vF480qmLpCD32SC3mfr/BErtx+3GaN6yCe8ECD3QcX327ge17T/8zjmQeKhVwK7/Im3jeZRyBfj788efVLONOn/ubtDQTtaqWssSYTCYKuhV87XqGAAAgAElEQVTgi/EvWa7r8fo0govpd/y/JEcdnuTkZDp37symTZvo2LEjderUISXFut/I/9o2i0Nze3Fobi+OLHgRz6CquBYqDkDRWt2IPLMl021c3ItQ8YlxOLmltyv9q7QjPvwcsVePsPfzxy339/ehpYSf/N2mxQ7A6XXT2DKlE1umdGLb9CfxLVET9yIlASjV8Bmun1if6Tbbpndj86ft2TKlE7vnvEyaMYktUzrZrNgBWDxzNIOfqsvgp+oy7LmmlKtWj8ASZQFo8+Rr7Nu8KtNtDu9Yi39QKcpUSp/Hr1S7SfpamKsXqNP8cV4f8QUArm7udOjdn21rrPuB1bBedY6eOMNfl9OnEZcsW0+LpnXuOeZOxYMDuRmfwOFj6R9kl6+Ecv7CVSqWK/XgB2EnGtavzdHjp/nrUvqHzpJlq2nRtEGOY1o/0ph1G7aTlJSM2Wxm05bdVKlc3raDuEPDejXT8718DYAly36jRbN6OY45e/4SX8z+nrS0NJKSklm0ZDVtWj/4o5j+5/VnH7UsJJ4z+U2On7rMpasRACxdvYemDSpnuk392uWyjGvZpCrrtx0jKdmI2Wxm664/qFQ+2HLbg8cuULfmQw98HK/1bmVZZDx70qscP32Zy9fSu6vL1uyjWf2KmW5Tr9ZD2cYdOn6Bh6uXztAFMhgMDBy1kJNn0l+P67cdw8XFibK3FVj5hQFznl/yqxx1eBwdHVm7di2bN29mwIABrF+/PlOb0JqMCdH8+euHVOryCQ6OziTGXOHPVR8A4BFYiXLtR3Bobi9irxzm8s65VO/1FWZTKilxEfzx8zs2y/NepMRHcejH96jz7AwcHJ2Jj7zEoUXpR2l5B1elZvdxbJli/fn7exUbHc4Xo15l0MRFODm5EHrlHDNGpn/rKVO5Nn3en8Xgp+oSExnKhIFP8srQ6RRwcyc1JZlJg3pgTElm44p5lKtWj8lLDuPg4Mj6ZXPYvX6pVfP2LeTN6GFvMHjEVIzGVIKDAhgz8k1OnDzH6I9n8+P8j7OMyY6XpztTxg1kwtT5pKQYcXR0YOR7r1A8OP+9EeYXvr4+jB75NoOHjcdoNBIcXJQx7w/ixMkzjB43jR8XzsgyBqBHtw7ciI3jmRcGkGYyUanCQwwa8Erej2lEfwYP++Sf104gY95/O31M4z/nxwVTs4wBeP3lp/h40iy69+6PMTWNR1s2pmunR22Tu48HI0O6MXTcd6SmphEU6MsH7/QA4OSfVxj72VK+ndE/27huHRoQezOB5/vPwGQyUaFsMYa82sXyGJevRVA0oJDVxzFiQBeGjV+E8Z/83h+YfjDEyTNXGT99BQs+ezPbuPRcoygakHGdkcFgYPQ7TzJ+xgpSjWkU9vXkk+FP33VqTPIvgzkHJz85ffo08+bNo0WLFrRt25aQkBBef/11KlbMXD1nZ9v47L8t/xc0HbqflYPL5nUaudJpYvq0TfdaLv8Smb/9dCiFxIiDeZ1GrrgVSZ8KMzTN+1Ml5IZ52yESo8/+e2A+51aoLIlReX9wQG64+aa/L8ecs+6XCGvzeagrUX8uzus0csW3fE+bP2bCXc6JZmsF3T3zOoW7ylGHp0KFCoSEhODv78/+/fupU6cOpUqVsnJqIiIiIg9GjualPvjgA6ZOncrZs2cZNGgQJ06cYMSIEdbOTUREROSByFHBc+zYMcaOHcuaNWt48sknGTduHBcuXLB2biIiInJPTPngkj/lqOBJS0vDZDKxYcMGmjVrRmJiIomJif9+QxEREZF8IEdreDp37kyTJk2oXbs2NWrUoH379vTo0cPauYmIiMi9MOffDktey1HB8+KLL/L8889bDkX/9ttv8fX1tWpiIiIiIg9Kjgqew4cPM2vWLBISEjCbzZhMJq5du8bGjRutnZ+IiIhIruVoDc+wYcNo3bo1aWlp9OrVi4CAAFq3bm3t3EREROSe5PWC5fw7pZajDo+LiwvdunXj6tWreHl5MWHCBDp27Gjt3EREREQeiBx1eAoUKEBMTAylS5fmyJEjODo6kpZ2979ULiIiInnEbMr7Sz6Vo4LnhRdeICQkhEceeYQVK1bQoUMHqlatau3cRERERB6IHE1ptWvXjsceewyDwcDPP//MxYsXqVSpkrVzExEREXkgsi14hg4dmu2Nx48f/0CTERERkdzIv1NKeS3bgqdevXq2ykNERETEarIteLp06QJAXFwcK1asoFevXoSGhrJo0SJee+01myQoIiIiOZSPFw3ntRwtWn7nnXcICwsDwN3dHZPJxLvvvmvVxEREREQelBwVPNeuXSMkJAQADw8PQkJCuHTpklUTExEREXlQclTwGAwGTp8+bdk+d+4cTk45OsBLREREbCavz7Kcf6fUclS1DBkyhJdeeomAgAAAoqOjmThxolUTExEREXlQsi14QkNDmTBhAmfOnKF58+Y89dRTuLi4UKZMGVxcXGyVo4iIiOSEFi1nKdsprWHDhuHv78/AgQMxm8388MMPVKxYUcWOiIiI/Kf8a4dnzpw5ADRu3JjOnTvbJCkRERGRBynbgsfZ2TnD/2/fFhERkfxGU1pZydFRWv9jMBislYeIiIiI1WTb4Tlz5gytWrWybIeGhtKqVSvMZjMGg4ENGzZYPUERERGR3Mq24Fm7dq2t8hAREZHc0lFaWcq24AkKCrJVHiIiIiJWo9Mli4iI2A11eLJyT4uWRURERP6LVPCIiIiI3dOUloiIiJ0wmM15nUK+pQ6PiIiI2D11eEREROyGFi1nRR0eERERsXsqeERERMTuaUpLRETEXuhMy1kymM1a0i0iImIPEqNO5XUKuPlWzOsU7kodHhEREbuhDk9WbFrwxF1db8uHswqPoNacXTcor9PIlbJtPgVgb/jfeZxJ7tTzK8rKS2fyOo1c6VSiHACJ0WfzOJPccStUFkPTWnmdRq6Ztx3C4bnueZ1GrpgW/ARA0ZGD8ziT3Pn7o4nU/GxCXqeRK4f7v5vXKchttGhZRERE7J6mtEREROyFFi1nSR0eERERsXvq8IiIiNgNdXiyog6PiIiI2D0VPCIiImL3NKUlIiJiL7RoOUvq8IiIiIjdU4dHRETEbqjDkxV1eERERMTuqeARERERu6cpLREREXuhRctZUodHRERE8sSqVato3749bdq04bvvvsu0/+TJk3Tt2pW2bdsyfPhwUlNT7/uxVPCIiIiIzYWGhjJlyhS+//57li9fzuLFizl79myGmMGDB/P++++zdu1azGYzP/74430/ngoeERERu2HKB5ec2blzJw0aNMDHx4eCBQvStm1bfvvtN8v+q1evkpSURM2aNQHo2rVrhv33Smt4RERE5IGJjY0lNjY20/VeXl54eXlZtsPCwvDz87Ns+/v7c/To0Sz3+/n5ERoaet95qeARERGxF/lg0fL8+fOZMWNGpuv79u1Lv379LNsmkwmDwWDZNpvNGbb/bf+9UsEjIiIiD8zzzz9Ply5dMl1/e3cHIDAwkP3791u2w8PD8ff3z7A/PDzcsh0REZFh/73SGh4RERF5YLy8vAgODs50ubPgadSoEbt27SIqKorExETWrVtHs2bNLPuDgoIoUKAABw4cAGDFihUZ9t8rdXhERETsRt5PaeVUQEAAISEhPPfccxiNRp588kmqV6/Oq6++Sv/+/alWrRqTJk1ixIgRxMXFUaVKFZ577rn7fjwVPCIiIpInOnbsSMeOHTNcN3v2bMv/K1asyJIlSx7IY6ngERERsRf5YNFyfqU1PCIiImL3VPCIiIiI3dOUloiIiJ0wm9PyOoV8Sx0eERERsXvq8IiIiNgJs0mLlrOiDo+IiIjYPRU8IiIiYvc0pSUiImIntGg5a+rwiIiIiN1Th0dERMROmE3q8GRFHR4RERGxeyp4RERExO7l6ymtbbuPM+PrFRhTUilbJoj3B/fCw90tx3E34xL5aNK3XLwUisls5vE29Xnh6TYAbN15jA8+WUCgfyHL/Xw9bSDuBV2tOqa9x0OZv+oUxlQTpYp58fYz1Sno5pwpbuO+Kyxdfw4MBgq4OPLGk1UoV8LHsj8uwch703bydq8aGa63hcM7d/HjrNkYU4wUf6gMrw59Fzd39wwxv/+8lA3LVoIBAoKCeOm9d/AuVIiU5GTmfzqVcydPghkeqlyJ5we9jUuBAjYdw8k9+1g9Zz5pRiNFS5ei+6ABuLoXzBCzY/kqdv2yBgxQuGhRuof0w6OQDws+HEfE1b8tcdHXQylTvSovfvS+1fPeumMv07+YT4rRSLmypRg1/G087sg7u5jFS35h2cp1JCcnU6liWUYNfxsXl1uvv517DjJ1xlx+XDjD6mO5V/OGfcix82f4dNHCvE7lrtrXqM247s9QwNmZo5f/4pWvZ3IzKTFDTOeH6zGqSw9MZjNR8XG8NvdLzoeFAtCnVRtebt4KNxcXDlw4zytzZpKSmmrzcbQqX5Fhj7bHxcmRk9f/ZuDyn4hLTrbs717zYV5r1Myy7eXqSlEvb2pPHENEfBwnhoziWuwNy/6Z2zez9Oghm46haaky9GvUDBdHJ85EhDFqw2/Ep6RkiClbuAhDmrfGo0AB0kxmxmxcy8nw9OfijfqNaVOuIiaziZNhoXy0cS0paf+NqSItWs5avu3wRMfcZPSEhUwc9SpLF3xAcLEiTJ+94p7iZn6zCv8ihfhx7ggWfvEuS1Zu4+iJ8wAcOXGeZ3u04ofZwywXaxc7N24mM/W7Iwx7+WG+GvkIgUUK8s3KU5niroTGMXf5ST58sz4zhjTjqbZlGfv1fsv+fSdCGfjpdq6ExVs137uJjY7hq3Gf0H/Mh0z8YSH+xYqxeOZXGWIunDrN6h8W8/6XM/h44TwCgoP4efZcAFbMX0haWhrj5s9l3Pw5pCQns2rhdzYdQ1zMDRZPmspz7w/l3W9m4Vs0kNVz5mWIufLnWbYsWcZb0ybyzuwvKBJUjN/mfwvAc+8PY+Cs6QycNZ3uA/vh6uFOl359rJ53VPQNPhgzlUnjh7Hix68ILhbItM+/yXHMhk07WPTTKmZNH8vPP8wkOTmFbxctAyApKZkZXy7gvREfk5aWv05cVrFkaTZMncWTLVrndSpZKuLpxdxX3+TJ6ZOo9N4ALoSF8nHPXhliXJ1dWPhGP7p9NonaIwfzy6H9TOv9EgBd6tSj76PtePSTj6g6dCBuLi6EPPa4zcdRuKA7U7v05JUfFtB02kT+io5i+KPtM8T8dPgAj34xhUe/mEK7L6cRdvMmw39ZTkR8HA8V8SM6McGy/9Evpti82Cnk5sbo1u1459cVdF74NVdu3GBAo+YZYlydnJjZuQfzDuzlqR/mM3vfTsa1Tf951wkqTttyFXn6h/k8+d03uLu48HSNh206BrGOfFvw7Np/ksoVSlIi2B+AJzs1Zc2GfZjN5hzHDe7bnbf7dAEgIuoGKcZUS4fo6Inz7Dv0J0+9Mo6XB0zm4JEzVh/TwVPhlCvhQ5C/BwAdmpRk8/6rmcbk7ORA/6er4+udXoCVK+FDdGwyxtT0D6KVWy7yznO18PWybVcE4Ni+fZSpVJHA4sEAtOrSiZ2/r88whtIVKzBx0XcU9PAgJTmZ6PAIPLy9AKhYswZPPP8sDg4OODg6UrJ8OSKuh9p0DH8eOEjx8uXwCw4CoGHH9hzasDnDGILLl+W9eV/h5u6OMSWF2IhI3D09M9xPqtHIoglT6NTnVXz8/aye9649B6lSqRwlS6Tn3b1rB9aszZh3djGr1mzk2We64u3tiYODA8Pf60uHx1oC6Z2dxKQkPho50OrjuFdvdenB178s46fNv+d1KllqU7U6+86f42zodQBmblzHMw2bZohxdHDAgAHvgundNg9XV5KM6V2HZxs3Z/KaVUTHx2E2m+kzbzYLd2y17SCA5mXLc/jqZS5ERQAwf+8uutaolWV836aPEBEfx8L9uwGoU7wkJrOJ5S/3YcNbAwlp0RoHg8Emuf9PwxKlORF6nUs3ogH46dgh2lWofEdMKa7ciGH7X+lfgDefP8u7a1YC4GAwUMDJiQJOTjg5OODi6ERyHnTa5MHL8ZRWamoqp0+fxtHRkQoVKmCw8os4NCwmw3STv58P8fFJxCckZZjW+rc4J0dHRoybx4Yth3ikSQ1KFg8AwNvLncda1aFVs1ocPn6OQSO/4ofZQwnwu3VfD1p4dBJ+hW51kYr4uJKQlEpiUmqGaa2AwgUJKJz+pmg2m5m99A/qVwvA2Sm9Pv3ozfpWy/HfRIWGUfi2D3dfPz8S4+NJSkjIMK3l5OTE/q3bmPPJRJydXej2Svo32Wr16lpiIq5fZ+2PS3jp3UG2GwAQEx6Bj18Ry7a3XxGSEhJITkjMMK3l6OTE8R27+GnydJycnWjzfMZv7Ht/+x2vwr5Ua9LIJnmHhoUTGHDrZx/gX4S4+ATiExItU1bZxVy6dJWoyjG8+fZIwsOjqFWzCiF905+Xls0b0rJ5Q/YdOGqTsdyLflM/AaBNvYZ5nEnWihcuwpV/igSAK1GReBcsiKerm2VaKz45iT7zvmLHyDFExt3E0cGBJh+NBKB8YDH2eZ1l9TvDKeZTiO1/nuTdRd/afBzFvH24diPGsv137A28XN3wKFAgw7QWgG/BgrzeuBltZ06zXOfk4MDWc2cYu241zo6OLOz9MnHJSczetd1mYwjw8OR63E3LdmjcTTwLFMDdxcUyrVXSx5eIhHg+aPUY5Yv4czM5iak7tgCw98oldl+6yG8vvoHRlMbF6CiWHD9ss/xzTX9aIks56vDs2LGDFi1aMHLkSIYMGUKrVq04etS6b4xm892fNEcHh3uOGzPsBTYs/4QbNxOYvXA1AJM+fI3WzWtjMBioVa0s1SuXZs/+zNNLD5LZbL5roejgcPfiMSk5lfFzD/J3RDz9n65h1dxyymw2w13GYHDI/FKq06wpM39dSZeXXmDCwMGYbvtFvHDqNGPe7M+j3bpQq7FtCob/MZvuPgaHu4yhauOGjP75e9o89wxfD30/wxi2/byc1r16WjXX25lMZgxkzvv213p2McbUNHbvPcSEsUP5ft5UYmNvMv3LBVbN+f8LB4OBOxq1AKTd9nqpGlyCkZ27U2VoCMEDXmfcyqUs6Z9e7Ds7OtK6SnV6zphM3Q+GUMjdg7Hdn7ZV+hYOBgN3GUaGcfxP7zoNWHvyDy5FR1mu++7AXkb8uoJEo5HYpCS+2rmVdpWqWjHjzNKfi8yjSDPdus7JwYEmJcvw8/Ej9Fq8gEVHDzKjUzecHR15onI1inl503rOF7Se8wXXYm8wqOkjthyCWEmOCp7x48fz9ddfs3TpUpYvX860adMYNWrUA09m5je/8PSr43j61XEsX72TiMhbC9/Cw2Pw8iyIm1vGaZxAf98s43bu+4PwiPRvKwXdXGnb8mFO/XmZm3EJzP3utwy/FGbAycnxgY9p4a+n6fvxVvp+vJW1uy4TeSPJsi/yRhIeBZ1xLZC50RYWlcg7k3fg6GBgfL+GeBTMvLA5LxQO8CcmItKyHR0RgbunJ65ut3Xdrlzh9JFbBXHzDu2ICA0l/mb6t65d6zfwScg79HjjNTo919t2yf/Dx9+P2Mhbb9KxEZG4eXrg4nar+xZx9RoXjp+wbNdt+yjRYeEkxsUBcPXsOUxpaZSpXs1meRcN8CP8tp99WHgkXl4euN2Wd3Yx/n6+tGrRCA/3gjg7O9P+sUc4euykzfK3Z5ciIyha6FZ3OKiQL1FxcSSk3OqKtK1Wgx1nTlkWKX++fi1Vg0tQ2MOTazHRLDuwh5tJiRjTUvlu5zYalC1v83FcvRFDgKeXZbuopxfRCQkkGo2ZYjtVq8HiQ/syXPdkjdpUCih66woDGG3ccfj7Zix+Hh6WbX8PT24kJZKUemsM4fFxXIiO5Hho+sEHm8+fxcHBgWAvb1o9VJ41p/8gwZiCMS2Nn48foW5wCZuOITfM5rQ8v+RXOSp4XFxcqFixomW7WjXrvMn3efFxywLieTMGc+zkRS5dCQNgyartNG9UPdNtGtSplGXc+s0H+WrBasxmMykpRtZvPkjdWhUo6ObKj8u3snFbepvy1JnLnDh1kYb1Kme6/9x6tkMFZgxpxowhzZg8qDGnL0ZzNSz9Q3P19r9oUC0g020SklIZ8tkuGtUoynsv1qaAy4MvxO5X1Xp1OXviD65fvgLAhuUrqd20cYaYmMgoPh/1ITdj0ovNnevWE1y6NJ7e3hzcvpOFU6fz7pSJNGqTN4tQKzxci0snTxN+5SoAu35ZTZWGDTLExEZF8d3YCcTfSC+mD27cTGCpErh7pX8YnD96nLI1a1h9avd2DevX5ujx0/x1KT3vJctW06JpgxzHtH6kMes2bCcpKRmz2cymLbupUtn2H6r2aN2xIzR4qBxlAwIBeKNlG1YczFgMHLx4geYVKuPv5Q1A54frciE8jMi4m/y8bxfd6zXC1dnFsm//+XO2HQSw+expHi5egtK+6VO+z9VryNpTJzLFebu6Udq3CPsuXcxwfYWAQN5t1QYHgwFXJydeqt+YlcdsOx2069JFqgcWo4R3egH6ZLWabD5/NkPM9r8uEOTlTSW/9Pff2sWCwWzmauwNToaF0rJseRz/+d1u+VB5jl7/G/nvy9Eanjp16jB8+HB69OiBo6Mjv/76K0FBQezbl/4LXbdu3X+5h3vnW8iTDwb35t1RX2NMTSW4mB8fDnkOgD9O/8VHk77jh9nDso0L6dOVcVMW0fPlsQC0aFKDp7u1wMHBgcljXmfC9J+YNe9XHB0dGD/yZQp5e2SZz4Pg41mAt3vVYPycAxjTzBQtUpBBz9YE4MylGKZ9f5QZQ5rxy9aLhEclsPPodXYevW65/bh+DfByd7Fqjv/Gu1AhXh32Hp+N+IC0VCP+QcV4fcQwzp86xZyPJzJ23hwq1KhOp+eeZWy/t3F0dMSnSBHeHj8GgB8+nwmYmfPxRMt9lqtWjRcGvW2zMXgU8qHHOwNY+NF40oypFC5WlKfeHcjl02f4afJnDJw1nTLVqtLymZ7MfGcoDg6OeBf25YVRIyz3EX7lGoUC/W2WM4Cvrw+jR77N4GHjMRqNBAcXZcz7gzhx8gyjx03jx4UzsowB6NGtAzdi43jmhQGkmUxUqvAQgwa8YtMx2Kvwm7G8NPsLfuo3CBcnJ86FhfL8rBk8XLoMs1/qQ+2Rg9l08jiTVq9k07BRpKSmEhUXR+d/1id9sX4dvu4e7P/wExwdHDh48TyDvv/qXx71wYuMj+ftpT8y++lncXF05GJUJP1/XkSNYsFM6tydR7+YAkCpwoUJvRlL6h3dm8mbfmdsh85s6jsIJ0cHfjl+lO8O7LXpGKITE/jg9zVMbP8Ezo6OXLkRw4h1v1LZP5APWrWl5w/ziUyIJ+SXZQx75FHcnJ1JSUtj4K/LSUlLY87+XbzTtCVLe79MSloaf0aEMT4fL5iXnDOY7zbZeYdnn3026zswGFiwIGfrAOKurs95ZvmUR1Brzq6z7SLbB61sm08B2Bv+3/7WUs+vKCsvWf/oOmvqVKIcAInRZ/8lMn9zK1QWQ9Osj+b5rzBvO4TDc93zOo1cMS34CYCiIwfncSa58/dHE6n52YS8TiNXDvd/1+aPGXNuqc0f804+D3XN6xTuKkcdnoUL8+eJvkRERERyIts1PCaTiW+//ZY///wTgAULFtCxY0fee+894v5ZvCkiIiL5Q14vWP7PLlr+9NNP2bFjBwULFuTAgQNMmzaNoUOHUrZsWT766CNb5SgiIiKSK9lOaW3dupVly5bh5OTE/Pnzadu2LY0aNaJRo0a0a9fOVjmKiIiI5Eq2BY+DgwNOTukhe/fu5fXXX7fsM+lsjiIiIvmKWZ/NWcq24HFzc+PatWvEx8dz7tw5GjVKPyPuqVOn8PCw7iHcIiIiIg9KtgVPSEgIPXv2JC4ujr59++Lj48P333/P559/zvjx422Vo4iIiORAfl40nNeyLXjq16/Phg0bSEpKwuufM8xWqVKF7777jlKlStkiPxEREZFc+9fz8Li4uODi4sLGjRvZu3cvTk5ONGrUSAWPiIiI/Gfk6MSDn376KQcOHKBdu3aYTCamTZvGsWPHMixiFhERkbxlNmlKKys5Kng2b97M0qVLcXZO/4vdTz31FN26dVPBIyIiIv8JOSp4vL29iY+Px8fHBwCj0aijtERERPIZLVrOWrYFz9ChQ4H0c+488cQTtGzZEkdHR7Zu3UqZMmVskqCIiIhIbmVb8NSrVy/Dv/9TpUoV62UkIiIi8oBlW/A0adIEPz8/rl27Zqt8RERE5D7pTMtZy7bgGTFiBLNmzaJ3794YDIZM+zds2GC1xEREREQelGwLnlmzZrFp0ybmzZtHiRIl+P3331myZAmVK1emT58+tspRREREckCLlrPmkN3OuXPnMmPGDFJSUjh16hSDBw+mdevW3Lhxg0mTJtkqRxEREZFcybbDs3z5chYvXoybmxuTJk2iZcuWdO/eHbPZTPv27W2Vo4iIiEiuZFvwGAwG3NzcANizZw/PPPOM5XoRERHJZ3Sm5SxlW/A4OjoSGxtLQkICJ0+epHHjxgBcvXoVJ6ccnbNQREREJM9lW7W89tprdO7cmdTUVJ588kn8/f1ZvXo1U6ZM4a233rJVjiIiIiK5km3B89hjj1GrVi2io6OpWLEiAO7u7owZM4b69evbJEERERHJGR2llbV/nZcKCAggICDAst28eXOrJiQiIiLyoGkhjoiIiJ3QmZazlu15eERERETsgQoeERERsXua0hIREbETWrScNXV4RERExO6pwyMiImInzDrTcpbU4RERERG7p4JHRERE7J6mtEREROyEFi1nTR0eERERsXvq8IiIiNgJnWk5a+rwiIiIiN0zmM1mc14nISIiIrl3effHeZ0CxRsMyesU7sqmU1qJEQdt+XBW4VakNhe3f5DXaeRKqSajAVhy8SeW+ScAACAASURBVFQeZ5I7T5aqiOfAN/M6jVy5OfkLABKj/tvPhZtvRRye657XaeSaacFPGJrWyus0csW87RAADr275XEmuWP69mfc334jr9PIlfipX9r8MbVoOWua0hIRERG7p0XLIiIidkJnWs6aOjwiIiJi91TwiIiIiN3TlJaIiIid0KLlrKnDIyIiInZPHR4RERE7oUXLWVOHR0REROyeCh4RERGxe5rSEhERsRPmNE1pZUUdHhEREbF7KnhERETE7mlKS0RExE7oKK2sqcMjIiIidk8dHhERETuhRctZU4dHRERE7J4KHhEREbF7mtISERGxEyYtWs6SOjwiIiJi99ThERERsRNatJw1dXhERETE7qngEREREbunKS0RERE7oSmtrKnDIyIiInZPHR4RERE7YTal5nUK+ZY6PCIiImL3VPCIiIiI3dOUloiIiJ0wadFyltThEREREbunDo+IiIidMOtvaWVJHR4RERGxeyp4RERExO5pSktERMRO6EzLWVOHR0REROxevu3wbN15kOlfLiIlJZVyZUswauhreLgXvKeY66GRPPvaSH6c/zGFfLw4d+EKQ0fNsOw3mUycPX+ZT8eG0KpFPZuMa8+Ra3yz9AhGo4nSwT6EvFgPdzfnu8aazWYmzd1DqSAfuj9WEYA0k4nPvzvIsdNhANStVpRXe9TEYDDYJH+AU3v2s+6bBaQZjQSWLkWXkH643vHc7FrxK3t/WQMGA75FA+kS8hYePj4kxN5k5fSZ/H3+As6urjzcphUNn3jcZrn/T9tKVRnV4QkKODlx/NpV3lr8LTeTkyz7n65Tn77NW1q2vVzdCPIpRIXRw0g1pTGl29NUDwomPiWZb/fuZtb2zTYfA8DWHfuZPnMBKf/H3n2HNXW+DRz/hr0REVDBLdZtte5tVVy1zmq12v5crbbiaLWlbsVdd61WrdvWXUdbxYlacOIeWBcOUNmyhUDy/kGNRojBIgny3p/rynVxTu6c3A8n487zPOccpRLPcqWZNNY76/vkFTHN2/bB1bWIJvazTzrToU1zQzaB9jVqMf2j3liam3PpwT0G/rKUhKcpWjGd36vLpC49UKnVxCQl8vmqn7kTEQ7AkJZeDGjWEmsLC86G3GHgyqWkpeffs82uGTOFy3duMnfTemOnkq3279Zieo8+WJqbcen+PQb+soSElJf2R+26TOr6MSq1ipjERD5fuZQ7EeFsGTaK8m5FNXFlXFw5ev0anefNNHQzNNpUrsqUDzpj8e97/cuN67Xe6wAdq73LuHYfoFKriU1K4qvNGwiJjjJSxiKv5MsenpjYeCZOW8acaSPZtWkeHsVdWbh042vF/LH3GP2/mkxkVKxmXbkyHmxZO1Nza1C3Gm1bNzRYsfMk4SlzV59i/JeNWTm9A0VdbFm17WK2sfcfxvHdHH/+Dnqgtf7Q8buEPo7n5yltWTqpLZdvRGaJyUtJT+L4fe4ieo/3YeTKpTgVLcq+Veu0YsJu3iJg+06+WDCL4ct/pIh7cQ6u/Q2APctWYmFtzfDlixm8YDY3zpzj+skzBssfoIitHUs/7kufNcupNXMyd2OimPxBZ62YjUGnaDR3Bo3mzqDZ/FlEJMTzze+biUxMYGan7iSlpVJ71hTeX/gDrStVpm3lqgZtA0BMbBwTpy1izgwfdm1eiod7URYuWZfjmLv3QnFwsGfLugWam6GLnSL2Dqwa9CXdf5xDpe+GExIRzsyen2jFWJlbsH6wN90WzaHW+NH8eT6IhX36A9Cldl2Gtm5H61m+VP3+a6wtLBjZ1vAFdE5ULFWGQwuW0b15K2OnolPm/hhK94U/UGn0sH/3Rx+tmMz9MZxuC2dTa+yozP3x6QAAeiyaQ62xo6g1dhSfr1zKk+Rkhq5ZYYymAJnv9WW9PqX3quXUnD6Ju9FRTOnYRSvGytyclX360WvVMhr8MI09Vy8zp2tPI2Wce2pVhtFv+VW+LHhOnL5ElUplKVWiGAAfdWnN3v2BqNXqHMVERMbgfyyIpfO+1/kc5y5c56D/acaNHpC3jXnxOa8+5p3ShXF3swfggxblOXzqnla7ntntf4u2TcrStHYJrfUqtZqnqekolSqU6Rko01VYmJsaJH+Am+fO4/5OeYq4Fweg3gdtuXj4qFYb3D3L8/WqpVjZ2qJMSyM+Ohob+8w2h928zbstm2NiaoqZuTnv1H2PKwHHDZY/wPvvVOLcg3vcjooE4JfAY/SoVUdn/Mj3vYhMTGD1iQAA3vUoycagU6jUapQZGey7doXO1WsaJPcXnTh9niqVylOqROa++KhrW/buO/rS+0R3zIXL1zE1MaHf4O/5qM8wlq3cRIaBx/+9qlbnzJ3b3Ap/DMDSw/vp3aCJVoypiQkKFDjaZPZK2VlZ8VSZBkDfRs2Yt/cPYpMSUavVDFmzgvWBxwzahpz6qksPfvlzB1uPHDB2Kjp5VavBmZBb3Ap/BMDSQ/vo3TCb/aFQ4Gj9wv5IU2rFmJuaseYLb0ZuWEVoTLRhks9Gy4qVOXv/HrejMnvEVwQeo+d72j9wTRWZ7XGwsgbA1tKSp+nKLNsSb798OaQVHhFNUVdnzbKbS2ESk1JISk7RdMW/KsbVpTDzZnz9yueY/9OvDP2iR5bu/7wUGZNMkcLPn8/FyYbkFCXJT9OzDGsN/eQ9ILNIelHrRmU4FvSAT0btIiNDTa0qRan/rnveJ/+vuMgoHIs8HwJxcClCanIyqckpWsNapmZmXDt+kh3zF2Nqbk7LT3sDUKJiBS4cOkKpKpVIVyq5GnACEzPDFWwAHoWcCH3yvOcvLO4JjtbW2FtaZenqdra1xbt5K5rOm6FZF3T/Lr1q1+NkyG0szczpVL0mSiNMFAwPj6LoC8NRbi5FSExK1n6fvCImI0NFvTo1GP7lpyjT0/H+xhdbWxv6fPyhwdpQwrkIoTHPhw5CY6JxtLHB3spaM6yVlPqUIWuWEzh+KtGJCZiamNDYdzwAFYoW54zDLfaMGkvxQk4E3Ajm200bDJb/6/BeMAsAr7oNjJyJbiWcixAa/fL+sMXe2lozrJWU+pQhq5cROHH68/0xZazWdgY0b8nD2Fh2Bp02aP4vy/JefxKb5b2elJbK8K2/cnjEaGKSkjA1MaHlwh+MlXKuyZmWddNb8Ny5c4ctW7YQFxentX7GjBk6HpF7KpU62zkppiYmrxWjy4XLN4h9Ek+71o1yl+hrUql15Zzz+Tcbdl+lkJ0lm+Z3Ji0tg0mLA9i27zrd21R8k6nqpNbxfzcxzfp/r9ywPpUb1ufMnv2sGTOJr1f/TLvP+7F3xWoWfzkSe6dClK/1LveuXTdE6s9zVSiy7VXLUKuyrOtXvzF7rlzk7gu/Usfs2s60D7sS+M0YwhPi8b9xnXqly+ZpztnJ7fukWycvzbI10LdXJzZu+dOgBU/mvsi6PkP1fF9U9SjJ+M4fUeX7kdyJCMe7dTu2DfuGmuNGY25qSqsq1em8YDZPlUrWfP4V0z7qxchf1xisDQVJjvdHl4+o8t3wzP3h1Z5tw0ZTc+w3mpgRbT/gi1U/GyLlV1IoFKh59Xu9SrHi+Hh14L0ZkwmJjmJI0xb81u8L6v8w1ZCpCgPQWx0MHToUOzs76tatq3XLS8WKOmvNvYmIisHB3hZra6vXitFl36ETfNCuCSY5KI5ya+3OywyZ5MeQSX74HbtD9JPnk/+iYlOws7HAyjLnHW2BZ0PxalIWczNTbG0saN2wNBevR+RF6tkq5OpCfHSMZjk+KhprOzssrJ7/36PDHnH3yjXN8nttWvIkIpKniYmkJqfQdsD/GL78R/rP8kWtVuNcvCiG9OBJLMUcHTXLxR0LEZOcRHJaWpbYrjXfY/2Zk1rr7K2sGP/nDur9MJUPf16EAjRd5oZUrKgLkVHP90VEZDQO9nYvvU90x/y5158bt+5q7lOr1ZgZuLftfnQUxZycNMvuToWJSUwkOS1Vs65NtRoE3ryumaT808F9VPUoibOdPQ+fxLLj7CkSnqagzEjn1+N/U798BYO2oSDJuj+ciUlMIDn1hf1R/V0Cb7ywPw74UbVECZztMoet3y1VBjNTU44GXzVs8tkIjY2hmEMhzXJxx0LEJGm/11tVrMLJkNuaScrL/j5C5WLFcba1NXi+Im/p/cZ3cHBg6NChdOnSReuWlxrUrc6lqze59yBzHHnbjoM0b1L7tWN0OXs+mHrvGWaS6Wedq7F0UuYE44VjW3P9ThRh4QkA/HX0Fg1qvt5wVPlSThw7kzlJOT1dxcmLD6lU1lnPo96c8u+9y4Pr/xAV9hCA03/5UamBdgGcEBPD5hlzSIqLB+Di4aO4lSqJjYMDp//04+C6zAnMibFPCPI7QI0WzQyWP8Chf65Rp1QZyhVxAWBAwybsuXIpS1wha2vKOrtwKuS21voBDZsw9t+JsS529nxavxFbzwXlfeIvaVD3XS5d+Yd7DzL3xbYdfjRvWjfHMbfu3GfJit/IyMjg6dNUNm3bg1cr7fkaeW3/5YvUL+epObJn8Pte7DqnPYn93N0Qmr1TGVeHzCK183t1CImMIDoxge1nTvBR3YZYmVto7gu6o72/RM7tv3yB+uUrUN4tc27k4JbZ7Y87NKtY5fn+qF1Xsz8AmlWsjP+1y4ZNXIdD/wRTt3QZyhVxBWBgo6b8dUX7QJELofdpXL4Crv8WbB2rvcvd6Ciik5IMnu+bYOwJy/l50rLeroUuXbowf/586tevj5nZ8/A6dXRP8sytwk6OTB4zmNHjFqBUpuPh7sbU8V9yNfg2k2euYMvamTpjcuJ+6GOKF3PJs/x1KeRgxTf96uG7JJD0DBXFXOwYPaAeADfuxjB/zWmWTmr7ym0M/rgmP/16lgFj/8LEREHNSm581M4ww1kAdoUK0e2bYWz0nUVGejqFixWl++gRhN64yY75P+G9dAGlq1Wh+ccf8cvosZiYmuLgXJhPJo0BoNnH3dg6ewELP/cGtZpWn/bG4x1Pg+UPEJWYyJBN61n/v0FYmJoREhXJ5xvXUtOjJIt7fkKjuZnDtWWLuPI4IY50lfZQ19xD+1jR+3+cGj0OhQKm+f3JuQf3DNoGgMKFCzF53DBGj5n173ugKFMnjOBq8E0mz/iJLesW6IwB+GLAx8ycs4yP+gxDmZ5B6/cb0fXD1gZtQ2RCPP1XLGGr9zdYmJlxOyKcz5Yt5r0yZVnRfwi1xo/GP/gKc/bsxn/MJNLS04lJTKTzv/NhlhzcT2FbO4KmzMLUxIRzd+/wzW/LDdqGgiQyPp7+y39i67BR/+6Px3z284+8V6YcKwYOodbYUfhfu8Kcv3bhP3ZK5v5ISqTzvFmabXgWLcbdSMP3eGYnMjGBwb+t49d+n2NuZkpIVCSDfl1DzRIlWfJxXxr8MI2jN/9hweH97PX+GmV6BjHJSfRcudTYqYs8oFBnN5nhBT4+Ppw7dw43N7fnD1IoWLdu3Sselb2UqHOvn2E+Y12kFncDJho7jVwp3XgyANvuGnbuzJvWvXRF7L/OWZGbXyXMWwJASszbvS+sC1fE5NOPjJ1GrqnWbUXRxPBH3L1J6r/PA2DSp5uRM8kd1Ybt2I4YbOw0ciVpgeHnMZ1blbcjMDlRq/8OY6eQLb09PFevXmX//v2GyEUIIYQQIk/oncPj6enJ9etv969PIYQQQvz/lqPD0rt06YKLiwvm5uao/z20+tChQ4bITwghhBA5pM7Iv5dVMTa9Bc9PP/1kiDyEEEIIIfKM3oLnzJnsr3Pk7m64s/sKIYQQQj9VPj4sPKcePnzI6NGjiY6OpkyZMsyZMwfbl86LFBERwbfffktMTAyWlpZMmTKFSpUqvXK7eufwnDp1SnMLCAhg4cKFBAYG5q41QgghhBDZmDx5Mr1798bPz4+qVauyZMmSLDHz58+nTZs27N69G29vbyZPnqx3u3p7eF6+hMSTJ08YOXLka6QuhBBCCKGfUqnkzJkzmuk0Xbt2pU+fPowePVorbtq0aZq/Q0NDcXBw0Lvt1754qI2NDWFhYa/7MCGEEELkMXU+uHhofHw88fHxWdY7ODjoLUxiY2Oxs7PTnOjYxcWF8PDwLHHPLg3Vtm1bwsLCsu0Fepnegqdv376aiw+q1WpCQ0Np1sywlwIQQgghxNth7dq1LF68OMv6oUOH4u3trVneu3dvllGkUqVKZbngcXYXQH7Gz8+P4OBg+vfvz969eylUqJDOWL0Fz4vJKRQKnJycKF++vL6HCSGEEMLA8sO1rD777LNsr7n5cu9Ou3btaNeundY6pVJJvXr1yMjIwNTUlMjISFxdXbNs68iRI9SpUwdbW1sqVapE8eLFefDgwX8reB4+zLzYoIeHR7b3FS9eXOdGhRBCCPH/U06GrnQxNzendu3a7Nmzh44dO7Jz506aNm2aJW7Hjh2EhITQr18/bt26RVRUFGXLln3ltnUWPH369EGhUPDipbYUCgWRkZEolUqCg4P/U2OEEEIIIXSZOHEiPj4+LF26lGLFijFv3jwANm7cSEREBMOHD2fMmDGMGTOGHTt2YGlpydy5c7Mcuv4ynQXP4cOHtZaTkpKYNWsWAQEB+Pr6voEmCSGEEOJNyg+TlnPL3d2d9evXZ1nfq1cvzd9ubm6sXLnytbar9zw8ACdOnODDDz8EYPfu3TRq1Oi1nkQIIYQQwpheOWk5OTmZmTNnanp1pNARQggh8q+CcKblvKKzh+fEiRN07NgRgD/++EOKHSGEEEK8tXT28PTr1w8zMzMCAgK0LiUhV0sXQgghxNtGZ8EjBY0QQgjxdikIk5bzis6C59nV0D///HO6du3K+++/j4WFhcESE0IIIYR4U/QepTVw4ECOHTtG27ZtmTx5MpcuXTJEXkIIIYQQb4zeS0vUrVuXunXr8vTpU/z8/PD29sbe3p7u3bvTu3dv6fURQggh8gm1Kt3YKeRbObpa+qlTp9i1axeBgYE0bdqU9u3bc/z4cYYMGfLaJ/4RQgghhDA0vQVPixYt8PDwoFu3bkyYMAErKysA6tWrR7du3fI8QSGEEELkjExa1k1vwbN27VpKliyZZb2JiQk7duzIk6SEEEIIId4knQVP3759USgUOh+4bt26PElICCGEEOJN01nweHt7GzIPIYQQQuSSXFpCN52Hpdva2lK3bl0UCkW2NyGEEEKIt4XOHp5Nmzbh6+vLokWLstynUChkSEsIIYTIZ2TSsm46Cx5fX18A1q9fb7BkhBBCCCHygt6jtIKCgli7di1xcXFa66WHRwghhBBvC70Fj4+PD0OHDqV48eKGyEcIIYQQ/5FKpTZ2CvmW3oLHzc2Nzp07GyIXIYQQQog8obfg6du3L6NGjaJ+/fqYmT0PlyJICCGEyF9UKpWxU8i39BY827dvJzU1lbNnz2qtl4JHCCGEEG8LvQVPVFSUXEJCCCGEEG81nScefKZ69er4+/uTIcf2CyGEEPmaSqU2+i2/UqjV6ldm17hxY6KiorQfpFAQHBycp4kJIYQQ4vXsH1fZ2CngNfWasVPIlt4hrYCAAEPkIYQQQohcys89LMamt+CJiYlh9+7dJCUloVarUalUhIaGMnv27Nd+suSE8P+UZH5iY+9G2Jm5xk4jV9zrfAPAzIvHjZxJ7vjUaEj1BTONnUauXBrhA8CT278bOZPcKVSuK8XGjzZ2Grn2yPcHTPp0M3YauaLasB0ARZOaRs4kd9R/n6f2knnGTiNXgr782tgpiBfoncMzYsQIgoOD2b17NykpKezbtw8TE70PE0IIIYTIN/RWLhEREcyaNYv3338fLy8vNmzYwLVr+XN8TgghhPj/TKVWGf2WX+kteBwdHQEoU6YM169fx8nJKc+TEkIIIYR4k3TO4dmzZw/t27enUqVKDBs2jO+++47+/ftz9epVrKysDJmjEEIIIXJAJi3rprOHZ/78+aSnp3Px4kVGjx6Nu7s78+bNo2zZsixevNiQOQohhBBC5IrOHp7atWtTrVo1ALy8vDTr1Wo1s2bNkvPwCCGEEOKtobPgmTFjBjNmzGDIkCEsXbrUkDkJIYQQ4j+Qi4fqpnfSshQ7QgghhHjbyQl1hBBCCFHg6T3TshBCCCHeDnKUlm7SwyOEEEKIAk96eIQQQogCQnp4dJMeHiGEEEIUeFLwCCGEEKLAkyEtIYQQooCQ8/DoJj08QgghhCjwpIdHCCGEKCBk0rJu0sMjhBBCiAJPCh4hhBBCFHgypCWEEEIUEDKkpZv08AghhBCiwJMeHiGEEKKAUKnlsHRdpIdHCCGEEAWeFDxCCCGEKPBkSEsIIYQoIGTSsm7SwyOEEEKIAk96eIQQQogCQq6lpZv08AghhBCiwJOCRwghhBAFngxpCSGEEAWETFrWTXp4hBBCCFHgScEjhBBCiAJPhrSEEEKIAkKGtHSTHh4hhBBCFHhvRQ/P3wEn+HHxMtLSlHh6lmPi+O+ws7PNEqdWq5kwaTqe5cvyad9eWvc9fhzOp/2GsHnjKpwKFTJU6lmcPH+fX7acJk2ZQdmShRk9sBm2NhbZxqrVamYtO0KZEoXp2aGGZn3nwWtxKWynWe7ZoTqtGnnmee7PPDh3kbO/bSNDmU7hUh40GtwfCxvrbGPvnT7HscUr6LtuKQAnV/1KePA/mvuTYmKxcSpE5zm+Bsn9mSalyzG8UTMsTE25ERXJxIN7SEpL04rxdHbBp0Vr7CwsUalVTDnkR3BEOOampvg0b0Vdj1IkK5UcvXOLpSf/xlC/qwJOX2fpmn2kKdMpX6YoY0d0w87G6rXitv15gt37gkhNU1KxvDtjR3QjNVXJEJ8VWtu4ffcx3v3b0btrkzxrT8sKFRnTuj0WZqYEP37E1zu3kpiaqrn/o3ff4/OGTTXLDlZWFHNwpNYPU4lKSuSqzyQexsdp7l8acITfL53Ps3x1af9uLab36IOluRmX7t9j4C9LSEhJ0YrpXLsuk7p+jEqtIiYxkc9XLuVORDhbho2ivFtRTVwZF1eOXr9G53kzDd2MHFszZgqX79xk7qb1xk4li0alyjC0fmMsTEy5GR2Fr/9+kpTa7+9yhYvwbZMW2FlYkqFWMf3oQa5HRjCqcQtqFnfXxLna2hGVnESvzfmvndmR8/Dolu8LnpjYJ0ycPIPVK3+iVMkSLFy0lEWLlzHG52utuDshd5k5az6XrwTjWb6s1n1//OnHz8tXERkZZcjUs3gSn8LsFUdYNKETHkUdWb7pFCs2n2ZEv8ZZYu+FxbJwbSDXb0dQpkRhzfr7D5/gYGfFiundDJm6xtP4eAKWrKS97xgcixXlzIYtnP1tKw0GfpolNu7RY86s3wzq56VA/f6faP5OiIhiz8TpNBk6yCC5P+NkbY2vV3s+3bKB+09iGdG4OSMaNWea/35NjJWZGT937cnEA3sIuHuH5mU9mdn2QzqtW8GgOg0obu9Itw0rScvIYGLLtvSsUYtNF8/lee6xcYlMnb+N5XMGU9K9CItX7WXJaj++/apzjuP8A6+w9Y8TLJ8zGHtbK76f/hsbdwTwWY/mbFg8TLONLbuPczjgCj0+bJhn7XG2sWVBl558uOInQmKiGOvVnrGt2/P9nzs0MVsvnGXrhbMAmJmYsGPAlyw+5k9UUiLlirgQm5JM6yXz8yzHnChi78CqQUNpPGUst8IfMbNnH2b27MNXa54XkFbmFqwfPJx3x37D7fDHjGj7AQs/HUDHOdPpsWiOJq522XJsHTaaoWtWZPdURlexVBl+GulDvcrVuHznprHTyaKQlTUTW7RhwI5NPIh7gnf9Jgxt0JhZxw5rYizNzPipY1d8/Q8QeD+EZqXLMbVVe7pvXMOcAH9NXDF7B1Z06cnEg37GaIp4w/L9kNbJk6epUrkipUqWAOCj7p3Zu/cAarX27+ktW3bQpfMHtG7VXGt9RGQUR47+zZIf5xoqZZ2CLofyThkXPIo6AvBhy8ocOn4zS1sAdh68SofmFWlWV7t4u3ozHBMTBcOn7Gbg99tYt+MsGQas6MMuXqVIuTI4Fsv8NVrR631u/30ySxvSU1M59uMK6n72sc5tHV+2mqod2uBcumSe5vyyBiXLcCX8EfefxAKw5dJ52lesrB1TqgwPnsQScPcOAEfu3GTUnp0AVHItyt4bwaRlZABw+PZNWntWNEjup87dpFIFD0q6FwGga4f6+PlfyPL/f1XcnkPn6d2lCY72NpiYmODj3Zl279fUevyDh1Gs2uTPpFE9MDMzzbP2NCtfgQthDwiJyfwxsvb0CbrWqKkzfmiTFkQlJbI+6CQAtUuUQqVWsXPAEA599TUjm7fCRKHIs3x18apWgzMht7gV/giApYf20buhdq+YqYkJCoUCR2sbAOysrHiaptSKMTc1Y80X3ozcsIrQmGjDJP+avurSg1/+3MHWIweMnUq26pcoxbXIxzyIewLAtqsXaedZKUtMaHwcgfdDADh69zY++//Msq1xzVvz24Wz3IiOzPvERZ7T28MTFhbGuHHjCAsLY8OGDYwaNYrp06fj4eFhiPx4HB6Bm5urZtnV1YXEpCSSkpK1hrV8vhsJwImTZ7Qe7+pShLk/TDNIrvpERCfh6vx8KMqlsC1JKUqSU5RZhrWGf5bZ6xN0OVRrfYZKRa0q7gzqWZeMDBXfz/HDxtqC7m2r5X0DgKToGGydn/c42To7oUxJQZnyVGtY6/jytbzTqhlO/xaqLws9f4nEqBgqtW+d5zm/rKi9A48TEjTL4Qnx2FtaYWthoRnWKl2oMNHJSUxq1Y53XFxJSE1l/t+Zv/wuP35I2woVOXDzOsqMDNpXrIyLTdYh1rwQHhmHWxFHzbJrEQeSklNJSknVGtZ6Vdz9sChi4xIZPn4VUdEJ1KhSGu8B7bSe5+e1++nRsQFFXfN2+Le4YyEe/vvFBPAoPg4HK2vsLC21hrUACtvY8EWjprRZulCzzszEhGO3bzJt/x7MTU1Z32cAialPGgfsTgAAIABJREFUWXEiIE/zflkJ5yKERj/vQQ6NicbRxhZ7a2vNsFZS6lOGrF5G4MTpRCcmYGpiQuMpY7W2M6B5Sx7GxrIz6LRB838d3gtmAeBVt4GRM8mem5094YnP398RiQnYWVpia26hGdYq5ehEdHIS41t44elchITUVBad+FtrOw1LlqaonT2bLht+eDQ3ZNKybnp7eCZMmMCAAQOwtbXFxcWFDz74gO+++84QuQGgVqlRZPOLzdQ033dOZaFWqyGbH58mJjn/RfpBi0oM+6wR1lbm2Nla8lG7agQE3X1zSeqhVqkhm/2hMHm+P4L3HUZhakqF95tmiXvm6l/7qd6lAyYmht+PmT0AWT8UXvygMDMxoXHpcmy/coFeG9fy24Wz/NT5I8xNTVkVdJLb0VFs6NmXFV17ceFhGEpVhkFyV6t1vB9e+j++Ki49I4NT528x7fverFn4FfGJySxdu08TEx75hJPnbtKzU6M334CXmCgU2c59yq7Xsk/t+uwLvsb92BjNul/PnmbcX7tIUSqJf/qU5ceP0a5S1TzMOHsmCgXZdNRqtaOqR0nGd/mIKt8Nx8N7ENN3bWfbsNFa8SPafsC0XdvyOt0CTee+UD/fF2amJjQqWYbfr17i022/seXyBRZ+0AVzk+e9mb1r1GL1udOostuYeCvp/baJjY2lcePGmg/QHj16kJiYaIjcACha1E1r7k1EZBQODvZYW2c/STa/Wb0tiEFjtjNozHb2HLlOdGyy5r7I2CTsbS2xtjLP8fb2B9zg9v3nXd1qMt+8hmJXpDDJsbGa5eSYWCxsbTG3stSsu3UkgKjbIewaPYEDM+aTkZbGrtETSI7JfNzT+Hgib96hdP06Bsv7RY8S4nGxtdcsu9rZE/c0hZT058MLEUmJhMREc/lx5hDFkTs3MVGY4OFQCEcra9aePU23Davot+1XElKfcv/JkyzP86YsW3+APkMX0WfoInbtO0NkTLzmvsioeBzsrLG20u4hdHMppDPOpbADLRpWwc7GCnNzM9q2qMmV4Pua2MMBV2jWoAq2NpbktbC4J7jZO2iWi9k7EJucTIpSmSX2w2o12Hxeuwe3e41aVHIr9nyFApRGmLR5PzqKYk5OmmV3J2diEhNIfqGXqk31dwm8cZ07EeEA/HTAj6olSuBsl/lafLdUGcxMTTkafNWwyRcwjxMTcLF93uPqYmtH3NOnPE1P16yLTEoiJDaGqxGPgcwhLVOFAnfHzF7RQlbWVHUtxsHbNwyb/BugUqmNfsuv9H5TWllZ8fjxY82vxaCgICwssj+qKC80qF+Hy1euce/+AwC2bd9F82ZZJ/nmV/2612bF9G6smN6NxZM6E3wrgtDHmUeU/HEomIa1Sr3W9u6GxrJmexAZKhWpaens3H+V5vXL6n/gG1K8RlUib94h7lHmB8X1A/6UrKM956LjjAl0mTuVTj9MofX3IzG1sKDTD1OwKZz5hRB+/RZFypfRKpIM6cS9EKoXLU7JQpn5fFS9Jv63tSdfBty9g7ujI5Vc3QB4z70EoCYs/gnNy5ZnQsu2AFibm9OnZh32XM+7L6kv+rZmw+JhbFg8jJXzvuTK9QfcD8v8EfD7nlM0qV85y2Pq1fLUGfd+46oc/PsyT1OVqNVqjp24RqUKz4eoz10Ooc675fKsPS86cusf3itRkjKFM+cafVq3Afuy+V86WllTpnARzty/q7X+HbeifNvSCxOFAiszM/rXa8TuyxcMkbqW/ZcvUL98Bcr/W3wNbunFrnPaxdm5u3doVrEKrg6ZX6qda9clJDKC6H+HX5pVrIz/tcuGTbwAOvngLlXdilHCMXM4tlvVGhwNuaUVc/xeCMUdHKnokjldomYxd9RqNEf71ShWnKsRj7WKJPH20zuHx8fHhy+++IL79+/TqVMn4uLiWLhwob6HvTGFCzsxaYIPo7+bQLpSiYeHO76Tx3L12nWmTJ3N5t9WGSyX3HJytGb0582YtOgA6ekqirs64DO4OQD/3Ilkzi/H9B599WmX91i0NoCBPttIz1DRrG5ZOjQ3zIRZAGtHBxoP6Y//vCWo0tOxd3Ol6dCBRN0OIfDn1XT6YYrebcQ/DsfOxdkA2WYvJiWZ8Qf+Ym6HLpibmvDgyRPG7vuTyq5FmdS6HT1+XU10chIj/vidce+3wdrMnLSMDEb+uYO0jAx2Xr1EtaLF+b3vAEwVJmy/cpEDt/7R/8RvQOFCdowf2Y3vp/9KenoG7kULM3FUDwCCb4QybdHvbFg87JVx3TrUJz4hmc+GLUalUvFO+eL4DOqieY4HD6Mo5uaU7fO/adFJSYz4fQsrevXFwtSUuzHRDNu+iRrFPZjT+SPN0VelnZ0JT4gn/aXem3n+B5jWoTP+Q7/BzNSEP69c4tezhp//EhkfT//lP7F12CgszMy4HfGYz37+kffKlGPFwCHUGjsK/2tXmPPXLvzHTiEtPZ2YpEQ6z5ul2YZn0WLcjYwweO4FTWxKClMO72dWm46Ym5oQGhfHxEN+VHJxY1yL1nyyZQPRKcmM2rsLn6YtNe/v0X67NQcilHR04lFCvJ5nEm8bhTq7Q4ReolQquXv3LhkZGZQtW5bw8HBKlMh+MuqrJCeE/6ck8xMbezfCzhj/iK/ccK/zDQAzLx43cia541OjIdUX5N/zlOTEpRE+ADy5/buRM8mdQuW6Umz8aP2B+dwj3x8w6WOcUz68KaoN2wFQNNF9tNvbQP33eWovmWfsNHIl6Muv9Qe9YUt6G+bHyqt8+Vus/iAj0DukVatWLQ4fPoynpycVK1bEwsKCYcOG6XuYEEIIIUS+obfgcXJyYvXq1cyb97zSzkGnkBBCCCEMTKVWG/2WX+kteBwcHFi3bh2PHz9m0KBBJCQkGOVQYiGEEEKI/0pv5aJWq7GwsGD27NnUr1+fHj16kPDCSduEEEIIIfI7vUdpNWny/PToAwYMwNPTk2nT8seZi4UQQgjxnFw8VDedBU9kZCQuLi706tWLhw8fataXL1+e1atXGyQ5IYQQQog3QWfBM27cOJYtW0afPn1QKBRaE5UVCgWHDh0ySIJCCCGEyJn8fKZjY9NZ8CxbtgyAw4cPGywZIYQQQoi88MpJy/7+/jx4kHlJh4MHDzJ48GAWLVqEMpvr3AghhBBC5Fc6C56VK1eyePFiUlNTuX79OqNGjaJly5Y8efKE2bNnGzJHIYQQQuSAsS8cmp+H1HQOae3atYvNmzdjbW3NnDlzeP/99/noo49Qq9W0b9/ekDkKIYQQQuSKzoJHoVBgbW0NwKlTp+jdu7dmvRBCCCHyn/zcw2JsOgseU1NT4uPjSU5OJjg4mEaNGgEQFhaGmZne0/cIIYQQQuQbOiuXzz//nM6dO5Oenk737t1xdXVlz549zJ8/n6+++sqQOQohhBBC5IrOgqdt27bUrFmT2NhYKlasCICtrS1Tp06lXr16BktQCCGEEDkjI1q6vfKwdDc3N+bOnYufnx9paWk0a9ZMih0hhBBCvHX0Xjx00KBBHDt2jLZt2zJ58mQuXbpkiLyEEEIIId4YvbOP69atS926dXn69Cl+fn54e3tjb29P9+7d6d27NxYWFobIUwghhBB6yJCWbjk63OrUqVPs2rWLwMBAmjZtSvv27Tl+/DhDhgxh5cqVeZ2jEEIIIUSu6C14WrRogYeHB926dWPChAlYWVkBUK9ePbp165bnCQohhBAiZzKki0cnvQXP2rVrKVmyZJb1JiYm7NixI0+SEkIIIYR4k3QWPH379n3lWZXXrVuXJwkJIYQQQrxpOgseb29vQ+YhhBBCiFySES3ddB6WbmtrS926dVEoFNnehBBCCCHeFjp7eDZt2oSvry+LFi3Kcp9CoZAhLSGEECKfkR4e3XQWPL6+vgCsX7/eYMkIIYQQQuQFvUdpBQUFsXbtWuLi4rTWSw+PEEIIId4WegseHx8fhg4dSvHixQ2RjxBCCCH+IxnS0k1vwePm5kbnzp0NkYsQQgghRJ7QW/D07duXUaNGUb9+fczMnodLESSEEELkLyqVsTPIv/QWPNu3byc1NZWzZ89qrZeCRwghhBBvC70FT1RUlFxCQgghhBBvNZ0nHnymevXq+Pv7k5GRYYh8hBBCCPEfZajVRr/lV3p7eA4dOsTmzZu11ikUCoKDg/MsKSGEEEKIN0lvwRMQEGCIPIQQQgiRS3JYum56C56YmBh2795NUlISarUalUpFaGgos2fPNkR+QgghhBC5plCrXz3g9umnn1KsWDEuXLhAq1atOHLkCNWqVWPmzJmGylEIIYQQOeDjZW3sFJi5P8XYKWRLbw9PREQE69atY9asWXh5eTFw4EA+++yz//RkKbG3/tPj8hNrp/I8PLvA2GnkSvH3RgDQY8dvRs4kd7Z06U3jXxYbO41cCRg4FICYG5v1ROZvhSv05N1Fb3+v74Vh32I7YrCx08iVpAU/A1B7yTwjZ5I7QV9+jaJJTWOnkSvqv88b/DkLwnl4Hj58yOjRo4mOjqZMmTLMmTMHW1tbrZi0tDRmz55NUFAQSqWS77//nsaNG79yu3qP0nJ0dASgTJkyXL9+HScnp1w0QwghhBBCt8mTJ9O7d2/8/PyoWrUqS5YsyRLzyy+/EBsby44dO1iwYAHff/89egasdBc8e/bsAaBSpUoMGzaMRo0asWrVKiZMmICVlVUumyOEEEKIN02lNv4tN5RKJWfOnKFNmzYAdO3aFT8/vyxxe/fuZdCgQSgUCjw9PVm9evV/L3jmz59Peno6Fy9eZPTo0bi7uzNv3jzKli3L4sVv9zCCEEIIIfJGfHw8oaGhWW7x8fF6HxsbG4udnZ3mUlYuLi6Eh4dnibt37x5nzpyhd+/e9OzZk6ioKExMXj1opXMOT+3atalWrRoAXl5emvVqtZpZs2bJeXiEEEIIkcXatWuz7RgZOnQo3t7emuW9e/cyY8YMrZhSpUqhUCi01r28DJCRkcHjx4/59ddf+eeffxg4cCB79+7F3t5eZ146C54ZM2YwY8YMhgwZwtKlS3W3TAghhBD5Qn44D89nn31Gly5dsqx3cHDQWm7Xrh3t2rXTWqdUKqlXrx4ZGRmYmpoSGRmJq6trlm0VKVKEDh06oFAoqFixIkWLFiUkJITq1avrzEvvUVpS7AghhBAipxwcHLIUNzllbm5O7dq12bNnDx07dmTnzp00bdo0S1yLFi3Ys2cPlStX5sGDBzx69IgyZcq8ctt6j9ISQgghhDCUiRMnsmXLFtq3b09QUBAjRmSeSmXjxo0sXLgQgFGjRhEREUGHDh0YPHgwU6dOfeVwFuSgh0cIIYQQb4f8fPHOnHJ3d2f9+vVZ1vfq1Uvzt52d3Wtf8UF6eIQQQghR4EkPjxBCCFFAFIQzLecV6eERQgghRIEnBY8QQgghCjwZ0hJCCCEKiPxwHp78Snp4hBBCCFHgSQ+PEEIIUUBID49u0sMjhBBCiAJPCh4hhBBCFHgypCWEEEIUEHIeHt2kh0cIIYQQBZ708AghhBAFREG4llZekR4eIYQQQhR4UvAIIYQQosCTIS0hhBCigJDz8OgmPTxCCCGEKPCkh0cIIYQoIOSwdN2kh0cIIYQQBZ4UPEIIIYQo8GRISwghhCggZNKybtLDI4QQQogCT3p4hBBCiAJCenh0kx4eIYQQQhR4UvAIIYQQosCTIS0hhBCigJCLh+omPTxCCCGEKPCk4BFCCCFEgZdvh7SOBZ7mxyVrSVMq8SxfmkljR2Bna5PjmM3b/mTH7v2kpqZSqWJ5Jo0dgYWFOTdv3eXTQd9QwqOYZjuzp/pQupSHQdp14vw9ftl0EmV6BmVLODP68xbY2lhkG6tWq5n582HKlnCm5wfvZrl/wnw/nAvZMrxfk7xOW0tNt+L0rlIDcxNT7sU/4edzJ0lJT9eK6Vu1Jg3cS5KYlgbAw8R4FpwJBOCjitVo4FESlVpNyJMYlp8/jdLA50NvUKIUX9RpgIWJKbdjopnx9yGSlUqtmLJOzoxs2BRbcwtUajU/BPjzT3QkACs798DC1Ix0VQYA+2/dYOPl8wbJPfDMPyxddxClMp1ypYsydlgnbG2schw3ZsYmQh/FaOIehsdSs2ppfhj/CWcv3WHRyn1kqFQ42lszYlB7PMsUzdP2NCldFu+GTbEwNeNmVASTDvmR9O/r5pnyzkXwadYKO0tLMlRqph7eR3BkOACD6zXCy7MiKrWK4IhwfA/vIy0jI09z1qdN5apM+aAzFmZmXHkYxpcb15OQ+lQrpmO1dxnX7gNUajWxSUl8tXkDIdFRRsr4uUalyjC0fmMsTEy5GR2Fr/9+kpTa+6Nc4SJ826QFdhaWZKhVTD96kOuREYxq3IKaxd01ca62dkQlJ9Fr83pDNyNH1oyZwuU7N5m7KX/m91/IpSV0y5c9PDGxcUycuoA5M8awa8tyPIoXZeFPq3Mcc8g/kE1b/2DZj9PYvnEpqalpbNi0A4CLl4Np59WMLesXa26GKnaexKcwe9lhJo9ow7q5vSnm5sDyTSezjb0XFss303Zz7PSdbO/f+Md5Ll1/lJfpZsvewpIv36vP3FMBjDj4JxFJifSukrUYe8fZhQVnAvnWfy/f+u/VFDuVi7jS0KMk3x32Y9ShPVibmdO23DsGbUMhKyvGNG3JuIN76b3tVx4mxDGkTkOtGEtTM+a1+5BfL56j/87NrDl/hgktvACwMjOjuL0j//t9E/12bKbfjs0GK3Zi45KYtnAnM77/mM0/D8e9qBNL1hx4rbjp33/MukVfsm7Rl/gM7YS9rRWjBn9AYtJTvp++iaH9vdjw41eMHtKRcbM2k6ZMz7L9N8XJ2prJrdox6q9ddF7/C6FxcQxv2EwrxsrMjKWde7Dm7Gk+3riWFWeOM73NBwDUdi9BG8+K9Nq4lu6/rsbWwoJeNd7Ls3xzooitHct6fUrvVcupOX0Sd6OjmNKxi1aMlbk5K/v0o9eqZTT4YRp7rl5mTteeRsr4uUJW1kxs0YZv/f6g28Y1hMXHMbRBY60YSzMzfurYlXXng/hk6wZWBp1iaqv2AMwJ8OeTLRv4ZMsGRu3dTWpGBhMP+hmjKa9UsVQZDi1YRvfmrYydijAgvQXPzZs3s6y7cOFCniTzzIlT56hSyZNSJTN/KXzUtQN79x1B/cJkrFfF/LH3MH17d8XR0R4TExPGfjeUDm3fz8z9cjAhd0P5+NNhfNJ/JIf8A/O0LS86c+kB75R1xaNYIQA6tarCocCbWu16Zuf+K7RvUYlm9cplue/CtTDOXLzPhy2r5HnOL6vhWozbsdE8TkoAYH/ITZqUKK0VY2ZiQmlHJzp5VmLO++35pm5jnK0ze95MFArMTUyxMDXFVGGCuYkpSgP/Gq/jXpLgyAhC4+MA2BF8hdblK2jF1PUowcP4OE6G3gMg4H4IEw5nfnBXcnEjJV3J3LYdWdu1F971GmNhamqQ3E+fv0Ulz+KUKO4MQNd2ddh39FKW11BO4pTKdHwX/M7wQe1wc3HkwcNobG2tqFMj8zVXuoQLttaWXLn+IM/a06BkGa6GP+Z+XCwAWy+fp907lV+KKU1o3BMC7mUW/0fu3OLbvbuBzNeTpZkZlmZmmJmYYGFqRmp63hVoOdGyYmXO3r/H7agIAFYEHqPne3W1YkwVJigUChysrAGwtbTkaboyy7YMrX6JUlyLfMyDuCcAbLt6kXaelbLEhMbHEXg/BICjd2/js//PLNsa17w1v104y41/e0Xzk6+69OCXP3ew9UjWHwtvO5Xa+Lf8SueQ1tmzZ1GpVIwbN45p06ZpPijT09OZNGkS+/bty7OkwiMiKermoll2cy1CYlIySckpmiGrV8Xcvx9GTOUnfDliPJGRMdR8twojh/YHwNrKkratm9K9Szvu3Q9jwJc+FC3qSpVKnnnWnmciYxJxdbbTLLsUtiMpJY3kFGWWYa1nw1RBl0O11kfFJvHjukBmf9eBPw5dy/OcX+ZsY0N0SrJmOTolGRtzC6zNzDTDWk5W1lyJDGfTtUs8SIijo2clvq3flO/8/bgSGc7lyMcsbduJdJWKh4kJHAi5ZdA2uNnaEZGUqFmOTErEzsISG3NzzbBWCcdCRKck49PkfcoXLkJiWipLTh8HwMbcnHMPQ1l48m9S09OZ0NyLwXUasOhkQJ7nHh4Zh2sRR82ySxEHkpJTSU5J1RrWykncHwfOUaSwPc0bZBYYJd2defo0jVPnblGvVnmu3Qjjzv1IomIS8qw9bnb2PE58vv3wxATsLS2xtbDQDGuVKlSYqOQkJrZsS4UiriSkPmVB4FEATofe5+T9u/j1G4xSlcHd2Bi2XcnbH2T6eBRyIvRJrGY57EksjtbW2FtaaYa1ktJSGb71Vw6PGE1MUhKmJia0XPiDsVLWcLOzJ/yF/RGRmICdpSW25haaYa1Sjk5EJycxvoUXns5FSEhNZdGJv7W207BkaYra2bPJQD2fr8t7wSwAvOo2MHImwpAU6uy6F4Aff/yR06dPc+XKFapWrapZb2ZmRpMmTejfv3+eJfXzzz/z6NEjJk+eDGQWWVWqVOH8+fPY2NjojencuTNubm4sXboUCwsLfHx8cHZ2ZuzYsVmey9fXF0dHR4YNG5Zn7Xmddr3Mx8cHT09PBgwYgFKppF+/fnz11Vc0aNCAH3/8kdjYWCZMmJDnuYv8IaevoZzEtWnThilTplCvXj3N486ePcuCBQuIi4ujTp06hIaG0rVrV9q0aWPAVgohxJuns4fH29sbgJ07d9K5c2eDJQRQrFgxLl68qFkODw/H0dFR6wP9VTGurq54eXlhZ5fZm/Lhhx/y008/kZGRwfLly+nbt6/mPrVajZlZ3s3dXrhwIYcPHwYgMTGRChWeD51k165XuXLlCg8ePGDmzJkAREVFkZGRQWpqKtOmTXvzyYt84b+8hvS9h65du0Z6ejp16z4falGpVNja2rJ+/fMJnG3atKFUqVJ50i4hhDAkvXN4mjRpwogRI6hXrx61a9dm6NChREXl7ZEEjRs35uLFi9y9exeATZs20bJlyxzHtGnThr179/L06VPUajUHDx6kWrVqmJqacvjwYbZs2QJAWFgY+/fvz9Nfr8OHD2fXrl3s2rWLLVu26G3Xq9SsWZOjR49qtvfxxx/Tvn17KXYKuP/yGtL3Hjp9+jT169dHoVBo1ikUCgYNGsTly5cB2LNnDxYWFrzzjmEnlQshRF7Q27UxceJEatasydSpU1GpVGzevJmxY8eybNmyPEvK2dmZGTNmMGzYMJRKJSVLlmTWrFlcvnyZcePGsWvXLp0xAL179yYuLo6uXbuSkZFBlSpV8PHxAWDOnDlMnDiRHTt2kJGRwZgxYyhXLuvEYEO2C9BqmxC65PQ19Ko4gHv37uHu7q61bYVCwdy5cxk/fjxKpRIXFxeWLFmiVRQJIcTbSuccnmc6deqU5Uu4Y8eO/PHHH3mamBBCCCHEm6J3SEuhUPDo0fPzvTx8+DBP57wIIYQQQrxpeiuX4cOH07NnT2rUqIFarebixYv4+voaIjchhBBCiDdC75AWQExMDJcuXUKlUlGjRg2cnZ0NkZsQQgghxBvxyiGtEydOcPv2bQoXLkzz5s0JDQ3lxo0bhspNCCGEEOKN0Fnw7Nmzh4kTJ5KSkqJZV6RIEcaPH5+nZ1kWQgghhHjTdA5pde3alaVLl+Lm5qa1PjQ0lGHDhvH7778bJEEhhBBCiNzS2cOjVquzFDsAHh4eqN7y68/fvn2be/fuGTsNIYwqB9P3hPh/KyYmhvj4eGOnId6gVxY8SUlJWdYnJiaiVBr/qr7/lVKpZOHChfz888/5uuh59mWUlpZG2r8XUXxbFaQv1uza8ja2T61Wa04omJiYSLqRrzD+prztP8ayU1Becy/Lz21ISEhg7dq1XLp0iVOnTpGQkHcX0BWGYzpp0qRJ2d2RkpLCL7/8wrvvvoujY+ZVlx8/fsyYMWNo1KiR1jV43hYqlQozMzPatWvHpk2buHv3LuXLl8fBwcHYqWl59mXk7+/PggUL8PPzIywsjPfee8/Yqb22F79Y09PTUSqVmJmZaa1/W8THx2NllXml8cDAQC5fvkyFChXeunYAmpx/++03Vq1aRXJyMp6enm/VObaevYauX7/O3bt3SUtLw8nJydhpvXEKhYKTJ09y7tw54uLicHd3R6FQvFXvoWe5RkdHo1AoMDc3R6FQoFKp8mUbLC0tSUpKYuLEifj5+dG1a1fN9RfF20tnwVOzZk1u3ryJj48PK1euZO3ataxdu5YWLVrg7e2dL1+k+jzLeefOndy4cYPjx48TFhZGhQoVKFSokJGze+7ZB9z8+fPx9fUlJCSEI0eO0KFDBywtLY2d3mt59j9fu3Yt27ZtY/369dja2lK+fHkjZ/Z6QkJCWL16NcWLF+fQoUPMnz+fy5cv8+eff9KiRQusra2NneJr27p1K3/99RdDhgzB1dUVU1NTHjx4gIuLi7FTyxGFQkFgYCDDhw8nKSmJ+fPn4+zsTNmyZTE1NTV2em/MtWvX+Oabb1AqlVy6dImEhASqVKnyVhU9CoWCo0eP4uvry507d1izZg2dOnXKd214MRc3Nzdu3bqFUqnE09MTZ2dnLCwsjJyhyA295+FJTk7mzp07mJiYUK5cOSwtLUlJSXkrP+ABTp06xdSpU9m+fTuJiYnMmTMHW1tb/ve//2W5tpAx7dq1C1dXV5KTk1mxYgULFixg06ZNVKhQgfbt2xs7vdeyadMm9uzZw5IlSzRfrtOnT8fS0jJffdi9ytWrVzXXXwsJCWHZsmVYWlri7e1Namoqs2fPzldFc3Ze/l8vX76csmXLkpqayvnz57ly5QoxMTHMnDmTWrVqGTHTV3vWjuDgYLZt20bbtm2pU6cOBw8e5Oeff2bkyJE0atTI2GnmyrM2PnjwgOPHj+Ph4UGjRo3YuXMnAQEBNGjQgG4VRVuxAAAgAElEQVTduhk7zRy7dOkSY8eOZcGCBRw+fJidO3eyatWqbOeJGsuL74/g4GBsbW2xsbHh/v37zJkzh08++YQOHTqQlpYmhc9bSu+lJZYsWULVqlWpXLkylpaWmp6Gt8XL9ZylpSWlSpUiPT2dwoUL8/3333P27FmmT5+eL+b0/PPPP6SlpaFWq/Hx8WHNmjUsWbKEokWLkpCQgK2trbFT1Ovl/3lGRgZTpkxh69at2NjY4Ovry5gxY7h27Vq+L3aezQmpUqUKn3zyCRYWFkRGRhIUFATAjz/+iJWVFUOGDCEuLs6Yqb7Six/mt27dIi0tDXt7e3bu3MnKlSupVasWixYtomPHjvl2WCsuLo6YmBgePnwIwNKlSwkMDCQ5OZmMjAxatWpFx44dWbNmDRkZGUbO9r97tq8CAgL45ptvWLFiBadPnyYjI4OWLVvSuHFj/P392bJli7FTfaUXPwdSU1MZMGAAjx494sCBA6xatYqAgAAWLFhgxAy1PXt/bNiwgbFjx7J48WL69u2LtbU1I0aM4LfffmPs2LH8+OOPBWbO2/83ej/Z7t+/z8yZMxk4cCC+vr7cunWLmTNnGiK3XHvxQz4+Ph4zMzMKFSqEmZkZly5dokqVKtjb29O2bVsCAwOxt7c3ap63bt1ixowZeHh4MHnyZE6cOMG9e/dwdHQkICCAgIAAPvjgA6Pk+Dqe/c+3bduGubk5//zzDytXrqRmzZosW7YMgKdPn2JjY2PMNPVSq9WYmGT+Jjhz5gyFChWiYcOGKBQKTpw4gbW1taZQGDVqFMnJyZr5bvnNi0OLfn5+VK5cme+++45OnTphY2NDdHQ0x48f58CBA3Tp0sXI2WZ1+/ZtJk2ahImJCdWqVWPYsGFMmjSJadOmERQURPXq1XFycsLT05Pr16+/1UNaCoWC8+fP8/vvvzN9+nSCg4P5/fffOXToEK1bt6Zly5aoVCoqVapk7FR1evaZdvDgQQDNZ1qxYsXYvn071tbW3L9/P1/18AAEBQWxZcsWVq5cSeHChfnrr7/49NNP2blzJyNGjODEiRN06NAh3/4oEK+mcw7PM23atGHv3r2MGzeOtm3bMnv2bEqWLGmg9HLn2Yf8qlWr2Lp1Kzt27MDV1ZW0tDSOHj1KcHAwZ8+exd/fn1mzZlG0aFGj5Xnw4EHmzJlD1apVefDgAadOnWLs2LGcO3eOrVu3EhgYyMiRI2nQoIFRcnxdR48eZfHixfj6+tKoUSN27NhByZIlad68Odu2bcPf35/u3bvn64mAz14/v/zyC6tXr+bSpUt4eXlRsWJF/vnnH+7du4epqSkeHh54eXkZrWDOqcDAQDZu3MjGjRuxtLTkyZMnhIWF8ddff7F7926OHTvGrFmzKFOmjLFT1XLnzh1Gjx5Nnz59GDx4MO+88w6Ojo7Y2NhohnkOHTrEjRs32Lx5Mx9//HG+a8PrSPu/9u48rOb8///4/XSqKVFpkS1JKFuylawVhhIT00Imc31m5kOy5TuWsY0MhkbGOvjMZN8jIoUpWwoRxhKFolITFSklba/fH67OjxkN8/nMOKfmfbsu15ULl+fpdM55vF/v5+v5Ki0lODiYmJgYpk2bhqWlJS9evCAsLAx1dXXat2+PpaUlDRo0UHapb1QVduLj45kyZQpqamqMGDECuVxOamoq7du3JyUlhe3btzN06FBMTU2VVuuzZ88oKiqioqICTU1NUlNTyc3Nxc3NTREqHz16RF5eHi4uLnTp0gUjIyOl1Sv531QbU9esWaP4ulGjRtStW5ebN28qrtAnTJjw91f3F6h6I9+8eTNeXl5cuXKFWbNmERsbS1paGg8ePCAwMFBpIa6yspKKigrCwsKYMmUKdnZ25Ofns3btWlasWMGyZcsAyMvLw9DQUGV7Xl6tKycnh8jISLS0tEhJScHCwoLg4GD8/PyYPHkyOTk5rFy5UuWu7qq8+ljS0tI4duwYu3fvBkBTU5PS0lJatWrFjRs3OHv2LDY2Nmhqaqrc8/Lbn5Xc3FxMTEw4ePAgMTEx3Lt3j/r16+Pt7c3YsWN59uyZSgbQ6Oho3N3dGTJkCPByte2HH37g8ePH2NvbExQUxKxZszh//jzjxo3DyclJyRX/9+7evUtOTg5+fn6kpKQwbtw41q1bh5eXF2VlZezatQs7OzuVbCwvLy9HXV1d0aC8fPlyPDw8ePLkCQAjR45EW1ub7777DgMDA/z9/ZV6AXf69Gm2bt1Kbm4uQ4YMwdPTEwsLC1JSUoiMjFT0Sqqrq1NcXKz4WlJzVbvCc+HCBcXXMpmMDh06vJZsa8q29F9++YXu3bsTExNDdnY2c+fOJSAgAHt7e5ycnOjZsycGBgbvtabMzEyysrKoW7cuGhoaVFRUEBISgrm5Oa1bt0ZdXR1NTU1CQ0O5ffs29vb2itUDVftQhdc/WPPy8tDX16ddu3bcv3+f9PR0jI2NadasGe7u7jg5OeHq6kqjRo2UXPWb/bbXpbi4mIiICD7++GO0tbUpKSlh06ZNfPDBB3z00Ud07twZXV1dlXteXn0cN2/epLS0lC5dunDkyBHS0tJwd3dn2rRppKamUlRURJcuXVS2ETM6OprU1FS6du3KlClTSExMpLS0lEGDBhESEkKHDh1wcXHh0qVLPHz4kFatWqncqIm3qayspLKykuDgYK5evUqTJk0YMWIEJ0+e5Oeff8bZ2Rlra2t69OihkhcKOTk5LFy4kD59+pCTk8MPP/yAv78/zs7OhIeH4+rqiqamJm3atMHFxYXBgwfTqlUrpV3AxcTEsHz5cqZNm8bw4cNp3bo1enp66Ovro6WlxfHjx7l69SrZ2dmEh4czYcKEWjny4J+m2sBja2uLra0tnTt3pqioiBEjRtCyZUtycnL49NNPVe4N/rfi4+NJTEzk2bNnbN26lcLCQtasWUOdOnXYs2cP1tbWNG7cGHi/IaK8vBwfHx8OHTrEiRMnaNy4MSYmJpiamvLjjz9ibm5O06ZNycrKIisri5KSEvT19ZW67Ps2Vd+/n376ibVr17Jx40aGDBlCp06duHDhAhkZGejr69OgQQM0NDRU9oMV/v9jOXToEMuXL2f8+PEkJiZy/fp1OnbsiI6ODufPn+fRo0c4OzurbB9S1ePYtm0bS5cuJSkpSdEHM2jQIOLj40lPT2f//v34+fmp9A4zS0tL1q9fT1hYGNra2owbN46RI0dibW3NnTt3sLCwwNzcnG7dunHy5EkcHBxU9nmpTkFBAdra2tjY2JCYmEhiYiJGRka4u7sTHh7O0aNHcXV1RUdHRyXfe3V0dGjdujXFxcVUVlbi4uJCixYtyM/PZ+/evXh5eXHu3Dn8/Pzo16+f4iLzfT8WIQTPnz9n3bp1+Pr60q1bN/T19dHV1UUulytu8zo4OHDx4kVKS0uZNGlSjRujIXmzt+7Smjt3Lj///LPi9/Hx8cybN+9vLep/IYSgoqKCuLg4EhIScHd3R1tbW9HMeODAAe7fv68IEO/7Baeurs6oUaMYOnQojo6OrFixgjFjxhAfH0/37t2ZMGEC69evZ9q0aYqt8s+ePXuvNb6r+Ph4bt26Bbyc6RIbG8v69esxMjLC09MTNTU1PvvsM7Kzszlx4kSNmdB98uRJduzYgYeHBwAff/wxpaWl+Pj4sH79eg4dOqT4M1Xz6s9KZGQkR48eZdOmTZiamhIVFUVAQAAFBQVcvnyZmJgYFi9eTPPmzZVXcDWqdlmVlpZibGxMWFgY27ZtY+PGjXTp0oW6desqpuBWzaYyNDQkMDCwRvRYZGRkcPXqVeDliq+fnx9nz56lbt26jBs3Dm1tbbZs2cK9e/dYunSpooWgqoleVTx+/JikpCQyMzMxMzNj48aNjBs3TjGZWF1dHWNjY44cOcKKFSv48ssvldoDKpPJ0NLSoqioCA0NDSorKxW7yYQQJCQkcOHCBaytrVmyZAlTp06ldevWSqtX8td666vnxo0bBAYGAmBgYMDSpUu5cuXK317Yn/Hq2O/CwkLkcjljxozh0qVLXL58mcDAQDIzM9m8eTNHjhxh7dq1Sl0WtrKyIiQkhD59+rB792569+7N+vXrSU9PR11dna5du7J48WKEEMTFxWFpaam0WqsTGxvLrFmzePHiBfByF42fnx9ZWVnY2tri5uaGu7s7RUVFeHh44OnpiYaGhpKrfrPfbqNv0aIFderU4fr16+Tl5dG1a1emTp3KiBEjaNiwIf/5z39o0aKFkqqtXkZGBgEBAVy6dAl4ORl91KhRqKmpUVFRwZdffklSUhLfffcdixcvZtGiRVhZWSm56tfl5eWRk5ODXC4nLi6O7777jp9++okbN25gYGDAqVOnWL58OVFRUUybNo3JkyfTtm1bxb9XtUBQndjYWMaOHcvly5epX78+Dg4O/Pjjj5w/f566devi7+9Peno6x48fVzQqq5qUlBTGjBnDt99+y7p163j8+DHz5s3D3t6eKVOmkJGRgaGhIYWFhQQFBTF+/HicnJyUdqRE1f9bWFhIQUEBaWlpqKmpKS6SZTIZjRo1IjU1VdGzI6ld3rpLa8eOHQwYMEAx/yUvL49Dhw4xcuTI91HfW6WkpHD06FFsbGy4cuUKq1atoqKignbt2lGnTh3S0tLo27cvPXr0wNXVFUdHR6X3jzRs2JCioiKuX79OgwYNWLNmDZ988gkODg78+uuvNGzYkLi4OCIjI5k3b57KLafGxsaybNky5s6dS7du3cjPz+fixYt06NCB2NhYLCwscHNzIywsjCtXrvD555+rbE/Fqz0EBw4c4JdffkFbWxsvLy9CQ0PJzs6mZcuW6Onp0b59e6ysrFT2Xn5ubi4PHz7kwoULNG3aFGNjY8rKykhMTMTU1JS+ffty/vx50tPT6dmzp8o9J1W7k06cOIEQglWrVtG3b1+Sk5O5c+cO6enpNG3alMjISJ4/f463t7fiA1QVb/P8kQ4dOqCurs6qVauwsbHBycmJ4uJi9u7di5mZGfCyOfuzzz5TydvZaWlp+Pv78+mnnzJz5kwsLS0VjdR9+vQhKyuLDRs2YGdnx4MHD/Dx8cHBwQFQXh9i1f+roaGBrq4uc+fOpWPHjjRr1gyZTIZMJlNM33dxcanRow0k1RBvcejQIdGjRw8xceJEMXHiRNGnTx9x7Nixt/2z9yYlJUXk5eWJ1NRUsWXLFhEVFSVcXV3FmjVrxOzZs8WoUaNEamqqssv8nTNnzggvLy/h4OAgtm7d+rs/z83NFfn5+Uqo7I+dPXtWdO3aVdy5c0cIIUR6erqYPXu2uHLliigqKhL//ve/RXl5uYiMjBTTp08XGRkZSq743WzatEm4urqKtWvXCldXV3H27FmRm5srxowZI5YsWSJycnKUXWK1KioqFF9nZWWJNWvWiKlTp4pbt24JIYRwdnYWZ86cEfHx8cLb21vk5uYqq9RqZWRkiIcPH4oLFy6IwMBA4eHhIXbs2CGEECIvL08cOHBAzJ8/X5SWlr72eCsrK5VV8p/24sWL39UbHBwsPvzwQ3Hp0iVRWFgoNm7cKPr37y8GDRokoqKilFTp2+3bt08EBwcrfr9//37xf//3f8LDw0MsWLBAVFZWiq+//lp88sknoqSkRAihvOcqPT1d8fo9cuSICAgIEEIIsXr1atGtWzexb98+ERsbK0JDQ8WwYcNEcnKyUuqU/P3eusJjaWmJq6srurq6tG3blgkTJmBtbf2e4tjb1a9fn7S0NPbv38+TJ09wdnbGzc2N58+fk5WVRXR0NBoaGtjb26vUcnezZs04d+4cJSUlfPvtt8DLhuaqGuvUqaM4qFKVpKamcuzYMQYOHIiRkRG+vr7Y2tri7OzMs2fPWLlyJUlJSezZs4eAgACVnYeSnJxMbm4ujx8/RiaTsW7dOrZv387jx4958OABcXFxWFhY4O7urtLnZYlXhiOeO3cOmUyGXC5HLpcTHR2NhYUF9evXJzg4mNOnT/PNN98oVhBUyf79+zE2NqZt27bUr1+f27dvc/PmTXr27ImRkRENGjRgy5Yt2NravrbCVlNWdh49eoSbmxvR0dEUFBRw5coVGjVqRJ8+fVBXV2f9+vXY2NgwcOBAHB0dcXFxoXPnziq7enXt2jV27txJ586dmTp1KqmpqRgYGODi4sK1a9fQ09Nj9OjRtG3bVjHfTBmPIy8vj3Xr1pGamkqnTp1ISUkhJyeH3r17Y2trS8OGDYmIiCA5OZnMzEy++uorqWenFqv2LK09e/bg5eX12jyeV6nKHJ7ExESOHz+Ok5MTERERlJeX4+7uruh7CQ0NpVevXiq1lbPqTezmzZsEBQUREBBA06ZNVSqQ/ZGTJ0+yYMECXrx4wVdffcWQIUOoqKhALpeTnZ3NjRs3sLKyomnTpsou9Y1Onz7NkiVLMDc359dff6Vjx46UlJQwfvx49u3bR+/evYmMjCQsLIyFCxcyaNAglX9uNm3aRFhYGG3btmXkyJHUr1+fw4cPk5GRwdChQxXBU5VeBwC//vord+7cQUNDAz09PRYtWsSSJUsoKioiIiICTU1NvL29KS4uxt/fn5UrV6rsz9UfqaysZNq0aURERDB8+HDKysrIyMggOzsbV1dXgoOD0dHR4YcffqB79+7KLrdaVe9djx8/Jjg4mNjYWExNTZk4cSJmZmZoa2uzcOFCunTpgrOzs0oEtoiICC5cuECzZs0oLy/n3r17TJ8+HQ0NjdeGhZaUlKjkRabkr1PtFKVqcpDKUVNTIyIiAkdHR0aPHs3WrVs5ePAgDg4O2NraquQBe1VvAEZGRuTn51NWVqbyH6ivcnR0RE1NjdmzZ7/2hlFWVkbDhg2VNrH6XcTFxbFixQoCAwNp3rw5hw8f5uLFi3h4eJCVlYWGhgZdu3bl0qVLeHl50aZNG5V8bpKTkxWvUVNTU8LDw9m8eTM6Ojpoamry9OlTGjZsSEVFBZGRkXz99dcqNzQtJSUFf39/2rRpQ0lJCQEBATRp0oRZs2axePFinJyc2Lt3L56enpiYmDBu3LgaGXaqVuHmzp1Lu3btOHbsGDt37kQul3PhwgVKS0upqKhQnMytyqoONF28eDGLFy/G19f3tV6wq1evcvbsWYYOHar4+8rwatAaPHgwderU4erVq8THx3Pt2jXkcjkJCQlYWVlha2vLqFGjFLv9JLVYdfe63Nzc/vb7aX/Wq/eAk5KSREFBgRBCiNDQUDF9+nTx4sULcfPmTREQECCWL1+uuHesygoLC5Vdwn/t+PHjol+/fiIsLEzZpbyTqv6jV3u6fvnlFzFhwgRRXFwsgoKChJeXlzhy5IhwdHQU6enpSqy2eqdOnRKDBg0S48aNEx999JFYunSpGD16tCgrKxNCCFFSUiL27NkjNm7cKB49eiTy8vKUXPHvZWdnC1dXV7F3714hhFD0FeXn54uxY8cKPz8/8eDBA3Hjxg2xZMkScf36dSFEzerZeVV6errw8/MT2dnZYs6cOcLDw0M8efLkjX9XFR9jeXm5oncqIyNDzJ49W2RnZwshhIiKihIzZswQISEhon///krvPXr1+xcWFiaCgoJEVFSUOHz4sJg4caIYP368yMrKEpcvXxaHDx8Wd+/eVWK1kvdJ9S5dqyFeSewxMTHs3r0bHx8frl27RpMmTTAyMiIvL482bdrg6emJj49PjUjsNeH08+o4OTkxZ84cFi5cSGRkpLLLeavS0lLg5Q6TKseOHUMul6Otrc2nn36KpaUloaGhrFu3TiV3x7y6QhUYGIiHhweZmZnIZDJmzZoFwAcffMCjR4/IzMzE2Nj4vU8SfxcPHjzAzs4Od3d3KisrOXDgAL6+vgwbNox69epx69YtvvzyS3R1dfHz81Nsy1b27ZE/o6KigsrKSuBl3VXPw4IFC7C0tMTX15enT58Cr6+oq9JjzM/PB0Aul5OZmQm8PAhUS0tLcYh0w4YN0dXV5eHDh8ybN4/+/fsr9Q5B1fdv69atbN68mXr16qGjo8PgwYNxcXGhSZMm7N+/HzMzMwYPHoyFhYXSapW8X9X28LRv3/6N9/urgsfx48f/9uLeJCIigo0bN7J9+3a2bdtGamoqOjo6HD58mP79+7No0SKl1PVPFhMTg5mZmUo2w/7WyZMnWbRoETNmzCAlJYUrV66wevXq16Y/FxcXq+Sk3nPnzjFp0iTFMSTw8hbCxo0b8ff3Z+3ataSmpvLhhx8SHh7OypUrVfbNPDk5GW9vb3x9fYmMjMTAwIBOnTphZ2fHxYsXady4MdevX2fw4MF07txZ2eX+Kfn5+YrJ1RkZGYrgvGDBAvLy8lixYgVCCGbOnMnt27cJCQlBLperVNCBlz0ts2fPpkmTJkycOBE3Nze6du3KkCFDsLKyYtWqVQwfPlxlZjndu3eP1NRU+vXrR3l5OTNnzsTX11fxGigqKuLEiRMUFRUp5oap6ogJyd+j2pv6ZmZm/Pjjj++zljd6dWXn1KlTbNq0CW9vb7S1tRkzZgxpaWk8ffqU27dvk5+fz9OnT9HT01Ny1f8sffr0UXYJ78zR0REhBHPnzkVHR0cxRby0tBQNDQ1kMplKhh14fYWqKvAcPXoUuVxO8+bNWbp0KTt27EBXV5dVq1ap5HDEKpaWlgQEBBAVFYWNjQ1+fn7Uq1cPLS0trl69Sm5uLnPnzlV2mX9aSUkJCxYsUIQEX19fRUiYMmUKq1ev5vr163To0IFvvvmG+/fvq1xvFbycoFxaWsqwYcPYsWMHmzZtYuvWrezZs4eQkBDu3r1LgwYNuHTpkiLwCCU2KJ87d47k5GQ+/vhjHj58iImJCc+fP2fXrl3MmTMHeBmyo6OjWblypdSg/A9V7bb0vXv3MmbMGHR1dd/4632pegFlZGTQrFkzTp8+TUlJCZaWltSrVw99fX1MTExwcnLC0dFRSuyStzI3N6dly5acOnWKpk2b0qJFC5W8wv6t5s2b06pVKxYtWkSjRo2Iiori1q1bLFu2TPGhaW1tjaWlZY14HVhaWuLs7IyDgwNCCOrUqUNCQgKrV6/G09NTJW8p/pHHjx9TVFSEiYkJp06dIjs7m9mzZ3P//n3i4uLYsGED5eXlaGlpYW1tjVwux9DQUNll/055eTnLly/HxsYGc3NzGjduzOHDhykrK+Pzzz9nwIABFBcXk5uby4kTJ7C1tcXQ0FBpr58zZ86wZMkSRowYgVwuZ9myZVRUVNCrVy8SExNJS0ujU6dO3Lhxg4SEBPr376+yFzWSv1e1gef27dv07dv3PZfzZhkZGUyaNInGjRvzr3/9i3379vH48WNatGih6IHR0tJSyTkpEtXUvHlzmjVrxvTp02natCmtWrVSdknvpOpw2Xnz5pGSkkJISAjq6uqUlpbWyMmwlZWVijO9bty4QXBwMF999VWNWjWEPx8SunXrprJnfqmpqWFnZ6c4eLlbt25YWlpy8OBB7t+/j52dneL2Y1FREXp6eko7H+vMmTP4+vri6+vLwIEDqaioIDc3lzt37lBWVkaHDh3Yv38/R44c4fjx43zzzTeKQ6Ml/zzVBh5VCTvwchS4sbExO3fuxNjYmNGjR7N7927u379PmzZtpLQu+a+Ym5tjZWVFq1atVPq08N+qboWqJpLJZBgZGaGrq4uJiQmenp7Y29urxPyWP+PPhgR9fX2lHqJZnarvu7q6OsXFxWzfvp38/HxsbW1p1aoVx48fJykpiR49eqCtrc3Bgwd5+vQp9vb2773WM2fOEBQURL9+/Th27BitW7emZcuWWFhYkJmZSUZGBg0aNGDy5MnY29vj7u5e41YNJX+tt05aft8KCgoQQqCurq44OM/IyIgmTZpgYGDA5s2bMTc3x93dXTF/R1rZkfy3zMzMalTYqVJTV6jeRFNTk5YtW9K6dWvFRomaFHZqUkh4G5lMxrlz5zhz5gw6Ojr4+Piwe/dusrOz6d69O82bNycqKop27dpRt25dwsPD8fHxee+35oqKili1ahWTJk3C29ubiooKvv/+e9q2bUuLFi0UQ0XPnz+Puro6NjY20ueERLUCz6lTp/j+++/ZvXs3+vr6hIeHs3v3bnr27ImhoSEmJibcv3+fDRs2YG1tzRdffFGjt3VLJP+LmrpCVdvUlJDwLpKSkvD398fAwIBDhw6hra3N2LFjCQ0N5d69e/To0YMhQ4bQuHFj5HI5AwcOVBwa+j5pamri4OCAmZkZlZWV2NjYoKamRlBQkCL0mJmZUVRURK9evaTPCQnwB9vS37dTp06xYsUKvv76a2QyGS1atEBPT48FCxZw584dFi1ahKmpKXv27CE3Nxc3NzeaNGmi7LIlEsk/XFJSEmPHjmXAgAEkJSXh4eFBv379mDlzJo0bN8bb2xs9PT1FKK06hkVVVK1Q3bt3j1u3bqGtrY2joyMxMTEEBwfj6elJ3759mTp1KjNmzFDs/qv66FCl1bjt27ezfft2AgIC6N69O5WVlSo5KV2iHCqxH/L58+ccPHiQuXPn0rlzZwoLC7l06RInT56kadOmREVFMW/ePJo1a0ZcXBybN2+Wwo5EIlGaV0NCamoqAQEBr4UEuVzOt99+y9SpU6moqFCEHfHKYa+qoOpxnDt3joULFyKTyejUqRMdO3ZUNI6vXr2a8vJy1q5d+1pQU6WgU+WTTz6hpKSEwMBAdu3aVSOGz0reH5UIPDKZjPz8fB4+fEhmZibz589HTU2NZ8+eYWpqSq9evbCwsOCDDz7Ax8dHCjsSiURpalNIkMlkJCQk8PPPPxMUFER2djaRkZFER0czYMAA+vTpgxACfX19lVqV+iNffPEFHh4e0pwdye+oRODR0tLCzc2N5cuXU1RURO/evRk+fDh2dnY8fPiQFStWMGrUKOkHWCKRKF1tCQlVt3uCg4NJSEhg6tSptGnThqdPnxIXF8eLFy9wdXVVqR2770oaPit5E5Xp4YGX5+s8fw0YwsQAAAMVSURBVP78tR0n4eHhhISEsGbNGumHWCKRKFVVSPD19SUhIYHTp0+jo6NDWFgYcXFxWFtb4+rqqtKDH6seQ15enqJx2tvbG01NTTZv3gzAvn37iI2NZcaMGTRq1EiJ1Uokfx2VCjwAhYWFxMXFoaamxtOnT9myZQvLli3D0tJS2aVJJJJ/qNoQEgoKCpDL5ejo6HDy5ElWr15Nhw4d+PDDD+nZsyfu7u7o6emxYcMGAMURDRJJbaFygefp06eEhoZy+vRpDA0NGTt2rBR2JBKJUtSWkJCamsr8+fP5/vvvuXfvHvPnz2fmzJns3LmT58+fM3r0aPr27cvgwYMxMDBg27ZtNW74o0TyNioXeOBlU2B5eTlCiNdOsZZIJJL3pbaEhMrKStauXUtWVhZz5sxhyZIligNN58yZg6GhIXfv3mX48OH079+fy5cv17gT6iWSd6E6+yNfIZPJ0NDQkMKORCJRisrKSiIjI2nSpAna2tocOnSIf//739jb26Onp0e7du3Ys2cP0dHRREREMGXKFED1dmHBy2MvPD092b9/P/369cPc3JyKigpCQkJwcHDAx8eHrKwsdu3aRUZGhhR2JLWWSgYeiUQiUabaFhIMDQ2xsrKisLAQPT09hg0bxoEDB6hbty5lZWXI5XJmzJghnTUlqdVUYlu6RCKRqJqqkHD37l1FSBgxYgSTJ0+ucSFBLpdz4MABMjIycHFx4cmTJ/Tr14/Q0FCuX7/OtGnTaN26tbLLlEj+VirZwyORSCSqQAihCAlTpkyhsrKS5ORkRUjo37+/skv8065evcpnn31Gz549GTNmDEVFRdjZ2alk/5FE8leSAo9EIpG8RW0LCdeuXcPT05PTp0+r5K4yieTvIAUeiUQieQe1LSQUFBSgq6ur7DIkkvdGCjwSiUTyjmpTSKhanaqpq1QSyZ8lBR6JRCJ5R1JIkEhqLinwSCQSiUQiqfWkOTwSiUQikUhqPSnwSCQSiUQiqfWkwCORSCQSiaTWkwKPRCKRSCSSWk8KPBKJRCKRSGo9KfBIJBKJRCKp9f4f1V3DaOZOcg4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation map\n",
    "fullSel=full.drop(['Cabin','Name','Ticket','PassengerId','Surname','SurnameNum'],axis=1)\n",
    "\n",
    "corrDf=pd.DataFrame()\n",
    "corrDf=fullSel.corr()\n",
    "corrDf['Survived'].sort_values(ascending=True)\n",
    "\n",
    "plt.figure(figsize=(10,12))\n",
    "sns.heatmap(fullSel[['Survived','Age','Embarked','Fare','Parch','Pclass',\n",
    "                    'Sex','SibSp','Title','familyNum','familySize','Deck',\n",
    "                     'TickCot','TickGroup',]].corr(),cmap='BrBG',annot=True,\n",
    "           linewidths=.5)\n",
    "plt.xticks(rotation=45)\n",
    "#plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "fullSel=fullSel.drop(['familyNum','SibSp','TickCot','Parch'],axis=1)\n",
    "#one-hot编码\n",
    "fullSel=pd.get_dummies(fullSel)\n",
    "PclassDf=pd.get_dummies(full['Pclass'],prefix='Pclass')\n",
    "#TickGroupDf=pd.get_dummies(full['TickGroup'],prefix='TickGroup')\n",
    "familySizeDf=pd.get_dummies(full['familySize'],prefix='familySize')\n",
    "\n",
    "fullSel=pd.concat([fullSel,PclassDf,familySizeDf],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "#build the models and compare\n",
    "#拆分实验数据与预测数据\n",
    "experData=fullSel[fullSel['Survived'].notnull()]\n",
    "preData=fullSel[fullSel['Survived'].isnull()]\n",
    "\n",
    "experData_X=experData.drop('Survived',axis=1)\n",
    "experData_y=experData['Survived']\n",
    "preData_X=preData.drop('Survived',axis=1)\n",
    "\n",
    "#导入机器学习算法库\n",
    "from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,GradientBoostingClassifier,ExtraTreesClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV,cross_val_score,StratifiedKFold\n",
    "\n",
    "#设置kfold，交叉采样法拆分数据集\n",
    "kfold=StratifiedKFold(n_splits=10)\n",
    "\n",
    "#汇总不同模型算法\n",
    "classifiers=[]\n",
    "classifiers.append(SVC())\n",
    "classifiers.append(DecisionTreeClassifier())\n",
    "classifiers.append(RandomForestClassifier())\n",
    "classifiers.append(ExtraTreesClassifier())\n",
    "classifiers.append(GradientBoostingClassifier())\n",
    "classifiers.append(KNeighborsClassifier())\n",
    "classifiers.append(LogisticRegression())\n",
    "classifiers.append(LinearDiscriminantAnalysis())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c05b33bd68>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_results=[]\n",
    "for classifier in classifiers:\n",
    "    cv_results.append(cross_val_score(classifier,experData_X,experData_y,\n",
    "                                      scoring='accuracy',cv=kfold,n_jobs=-1))\n",
    "cv_means=[]\n",
    "cv_std=[]\n",
    "for cv_result in cv_results:\n",
    "    cv_means.append(cv_result.mean())\n",
    "    cv_std.append(cv_result.std())\n",
    "    \n",
    "#汇总数据\n",
    "cvResDf=pd.DataFrame({'cv_mean':cv_means,\n",
    "                     'cv_std':cv_std,\n",
    "                     'algorithm':['SVC','DecisionTreeCla','RandomForestCla','ExtraTreesCla',\n",
    "                                  'GradientBoostingCla','KNN','LR','LinearDiscrimiAna']})\n",
    "\n",
    "cvResDf  \n",
    "sns.barplot(data=cvResDf,x='cv_mean',y='algorithm',**{'xerr':cv_std})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cv_mean</th>\n",
       "      <th>cv_std</th>\n",
       "      <th>algorithm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.828273</td>\n",
       "      <td>0.040199</td>\n",
       "      <td>SVC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.786850</td>\n",
       "      <td>0.043195</td>\n",
       "      <td>DecisionTreeCla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.815888</td>\n",
       "      <td>0.036101</td>\n",
       "      <td>RandomForestCla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.798011</td>\n",
       "      <td>0.044715</td>\n",
       "      <td>ExtraTreesCla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.831694</td>\n",
       "      <td>0.040614</td>\n",
       "      <td>GradientBoostingCla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.805863</td>\n",
       "      <td>0.038105</td>\n",
       "      <td>KNN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.829421</td>\n",
       "      <td>0.029056</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.823841</td>\n",
       "      <td>0.027174</td>\n",
       "      <td>LinearDiscrimiAna</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cv_mean    cv_std            algorithm\n",
       "0  0.828273  0.040199                  SVC\n",
       "1  0.786850  0.043195      DecisionTreeCla\n",
       "2  0.815888  0.036101      RandomForestCla\n",
       "3  0.798011  0.044715        ExtraTreesCla\n",
       "4  0.831694  0.040614  GradientBoostingCla\n",
       "5  0.805863  0.038105                  KNN\n",
       "6  0.829421  0.029056                   LR\n",
       "7  0.823841  0.027174    LinearDiscrimiAna"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvResDf  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 72 candidates, totalling 720 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  76 tasks      | elapsed:    4.1s\n",
      "[Parallel(n_jobs=-1)]: Done 376 tasks      | elapsed:   18.0s\n",
      "[Parallel(n_jobs=-1)]: Done 720 out of 720 | elapsed:   33.5s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 6 candidates, totalling 60 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=None, shuffle=False),\n",
       "       error_score='raise-deprecating',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'C': [1, 2, 3], 'penalty': ['l1', 'l2']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model tunning\n",
    "GBC = GradientBoostingClassifier()\n",
    "gb_param_grid = {'loss' : [\"deviance\"],\n",
    "              'n_estimators' : [100,200,300],\n",
    "              'learning_rate': [0.1, 0.05, 0.01],\n",
    "              'max_depth': [4, 8],\n",
    "              'min_samples_leaf': [100,150],\n",
    "              'max_features': [0.3, 0.1] \n",
    "              }\n",
    "modelgsGBC = GridSearchCV(GBC,param_grid = gb_param_grid, cv=kfold, \n",
    "                                     scoring=\"accuracy\", n_jobs= -1, verbose = 1)\n",
    "modelgsGBC.fit(experData_X,experData_y)\n",
    "\n",
    "#LogisticRegression模型\n",
    "modelLR=LogisticRegression()\n",
    "LR_param_grid = {'C' : [1,2,3],\n",
    "                'penalty':['l1','l2']}\n",
    "modelgsLR = GridSearchCV(modelLR,param_grid = LR_param_grid, cv=kfold, \n",
    "                                     scoring=\"accuracy\", n_jobs= -1, verbose = 1)\n",
    "modelgsLR.fit(experData_X,experData_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "modelgsGBC score：0.842\n",
      "modelgsLR score：0.832\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=4,\n",
       "              max_features=0.3, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=100, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=300,\n",
       "              n_iter_no_change=None, presort='auto', random_state=None,\n",
       "              subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
       "              verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#modelgsGBC模型\n",
    "print('modelgsGBC score：%.3f'%modelgsGBC.best_score_)\n",
    "#modelgsLR模型\n",
    "print('modelgsLR score：%.3f'%modelgsLR.best_score_)\n",
    "\n",
    "modelgsGBC.best_estimator_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ROC curve\n",
    "modelgsGBCtestpre_y=modelgsGBC.predict(experData_X).astype(int)\n",
    "modelgsLRtestpre_y=modelgsLR.predict(experData_X).astype(int)\n",
    "\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr,tpr,threshold = roc_curve(experData_y, modelgsGBCtestpre_y) \n",
    "roc_auc = auc(fpr,tpr) \n",
    "\n",
    "plt.figure()\n",
    "lw = 2\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.plot(fpr, tpr, color='r',\n",
    "         lw=lw, label='ROC curve (area = %0.3f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Titanic GradientBoostingClassifier Model')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingClassifier Confusion Matrix\n",
      " [[507  42]\n",
      " [ 73 269]]\n",
      "LinearRegression Confusion Matrix\n",
      " [[480  69]\n",
      " [ 80 262]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print('GradientBoostingClassifier Confusion Matrix\\n',confusion_matrix(experData_y.astype(int).astype(str),modelgsGBCtestpre_y.astype(str)))\n",
    "print('LinearRegression Confusion Matrix\\n',confusion_matrix(experData_y.astype(int).astype(str),modelgsLRtestpre_y.astype(str)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20175438596491227"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "69 / 342"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "#result and discussion\n",
    "#TitanicGBSmodle\n",
    "GBCpreData_y=modelgsGBC.predict(preData_X)\n",
    "GBCpreData_y=GBCpreData_y.astype(int)\n",
    "#导出预测结果\n",
    "GBCpreResultDf=pd.DataFrame()\n",
    "GBCpreResultDf['PassengerId']=full['PassengerId'][full['Survived'].isnull()]\n",
    "GBCpreResultDf['Survived']=GBCpreData_y\n",
    "GBCpreResultDf\n",
    "#将预测结果导出为csv文件\n",
    "GBCpreResultDf.to_csv('data/TitanicGBSmodel.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "train_hp = pd.read_csv(\"data/trainhouseprice.csv\")\n",
    "#train_hp.info()  \n",
    "\n",
    "x = train_hp[train_hp[\"LotArea\"]<20000][train_hp[\"LotArea\"]>15000][\"LotArea\"]\n",
    "y = train_hp[train_hp[\"LotArea\"]<20000][train_hp[\"LotArea\"]>15000][\"SalePrice\"]\n",
    "plt.scatter(x,y)\n",
    "x=np.array(x).reshape((69,1))\n",
    "y=np.array(y).reshape((69,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(x, y) #training the algorithm\n",
    "y_pred = regressor.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 73709.81663319696\n",
      "Mean Squared Error: 11690911976.829996\n",
      "fraction: 158607.25898977095\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y, y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y, y_pred))  \n",
    "print(\"fraction:\", metrics.mean_squared_error(y, y_pred)/metrics.mean_absolute_error(y, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_1 = train_hp[train_hp[\"LotArea\"]<20000][train_hp[\"LotArea\"]>15000][train_hp[\"SalePrice\"]<350000][\"LotArea\"]\n",
    "y_1 = train_hp[train_hp[\"LotArea\"]<20000][train_hp[\"LotArea\"]>15000][train_hp[\"SalePrice\"]<350000][\"SalePrice\"]\n",
    "plt.scatter(x_1,y_1)\n",
    "\n",
    "x_1=np.array(x_1).reshape((64,1))\n",
    "y_1=np.array(y_1).reshape((64,1))\n",
    "\n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(x_1, y_1) #training the algorithm\n",
    "y_pred = regressor.predict(x_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 50687.2169787383\n",
      "Mean Squared Error: 4029926730.5925207\n",
      "fraction: 79505.78017102317\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_1, y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_1, y_pred))  \n",
    "print(\"fraction:\", metrics.mean_squared_error(y_1, y_pred)/metrics.mean_absolute_error(y_1, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2c05eb927f0>"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def create_linear_data(nums_data, if_plot=False):\n",
    "    \"\"\"\n",
    "    Create data for linear model\n",
    "    Args:\n",
    "        nums_data: how many data points that wanted\n",
    "    Returns:\n",
    "        x with shape (nums_data, 1)\n",
    "    \"\"\"\n",
    "    x = np.linspace(0,1,num = nums_data)\n",
    "    noise = np.random.random_sample(size=nums_data)\n",
    "    k=2\n",
    "    data = k*x+noise \n",
    "    return np.array(x).reshape(-1,1),np.array(data).reshape(-1,1)\n",
    "x,y= create_linear_data(100)\n",
    "\n",
    "y[50]=10\n",
    "\n",
    "plt.scatter(x,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.3115349626213928\n",
      "Mean Squared Error: 0.799645481827698\n",
      "fraction: 2.5667921028802922\n",
      "no outlier\n",
      "\n",
      "Mean Absolute Error: 0.24279057984858762\n",
      "Mean Squared Error: 0.08358180900085795\n",
      "fraction: 0.3442547443685104\n",
      "Lasso \n",
      "\n",
      "Mean Absolute Error: 0.6271955110340786\n",
      "Mean Squared Error: 1.144440758239203\n",
      "fraction: 1.8246953909990917\n",
      "no outlier\n",
      "\n",
      "Mean Absolute Error: 0.5440870803301932\n",
      "Mean Squared Error: 0.42652735239056483\n",
      "fraction: 0.7839321458096667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(x, y) #training the algorithm\n",
    "y_pred = regressor.predict(x)\n",
    "y[50]=10\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y, y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y, y_pred))  \n",
    "print(\"fraction:\", metrics.mean_squared_error(y, y_pred)/metrics.mean_absolute_error(y, y_pred))\n",
    "\n",
    "print(\"no outlier\\n\")\n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(x, y) #training the algorithm\n",
    "y_pred = regressor.predict(x)\n",
    "y[50]=1\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y, y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y, y_pred))  \n",
    "print(\"fraction:\", metrics.mean_squared_error(y, y_pred)/metrics.mean_absolute_error(y, y_pred))\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "print(\"Lasso \\n\")\n",
    "regressor = Lasso()  \n",
    "y[50]=10\n",
    "regressor.fit(x, y) #training the algorithm\n",
    "y_pred = regressor.predict(x)\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y, y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y, y_pred))  \n",
    "print(\"fraction:\", metrics.mean_squared_error(y, y_pred)/metrics.mean_absolute_error(y, y_pred))\n",
    "\n",
    "print(\"no outlier\\n\")\n",
    "regressor = Lasso()  \n",
    "y[50]=1\n",
    "regressor.fit(x, y) #training the algorithm\n",
    "y_pred = regressor.predict(x)\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y, y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y, y_pred))  \n",
    "print(\"fraction:\", metrics.mean_squared_error(y, y_pred)/metrics.mean_absolute_error(y, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5669341894060995"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.7996/0.3115"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}