Mobile_Price_Classification.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMp919eW4bnb4BVPPkgK4Cz"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"phaE4xUV8OAU","executionInfo":{"status":"ok","timestamp":1675738827628,"user_tz":-540,"elapsed":3269,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"f591a424-6ef0-4254-d72a-6aa481e6fae0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","source":["from google.colab import files\n","files.upload()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":96},"id":"x4lxY4LG8RWK","executionInfo":{"status":"ok","timestamp":1675506079400,"user_tz":-540,"elapsed":12789,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"10b7bd85-83a9-4c22-be6d-be357451022e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","     <input type=\"file\" id=\"files-5ce51cd1-8031-4863-be48-ea46004f236d\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-5ce51cd1-8031-4863-be48-ea46004f236d\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. 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_uploadFiles,\n","  _uploadFilesContinue,\n","};\n","})(self);\n","</script> "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving kaggle.json to kaggle.json\n"]},{"output_type":"execute_result","data":{"text/plain":["{'kaggle.json': b'{\"username\":\"dahanjunho\",\"key\":\"82f15e7af2a751864ef444d7710905a7\"}'}"]},"metadata":{},"execution_count":2}]},{"cell_type":"code","source":["!mkdir -p ~/.kaggle\n","!cp kaggle.json ~/.kaggle/\n","!chmod 600 ~/.kaggle/kaggle.json"],"metadata":{"id":"qmsd-UV58us6"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!pip install kaggle"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"km4ZP3xi8aGU","executionInfo":{"status":"ok","timestamp":1675506120833,"user_tz":-540,"elapsed":4294,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"53cfdb59-ebb9-477d-de95-ffb7d810b138"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: kaggle in /usr/local/lib/python3.8/dist-packages (1.5.12)\n","Requirement already satisfied: python-slugify in /usr/local/lib/python3.8/dist-packages (from kaggle) (8.0.0)\n","Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.15.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.25.1)\n","Requirement already satisfied: python-dateutil in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.8.2)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from kaggle) (4.64.1)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.8/dist-packages (from kaggle) (2022.12.7)\n","Requirement already satisfied: urllib3 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.24.3)\n","Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.8/dist-packages (from python-slugify->kaggle) (1.3)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (2.10)\n","Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (4.0.0)\n"]}]},{"cell_type":"code","source":["!kaggle datasets download -d iabhishekofficial/mobile-price-classification"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ls4-gxSC8mPt","executionInfo":{"status":"ok","timestamp":1675506202038,"user_tz":-540,"elapsed":787,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"48c81a1f-b87c-4c21-bcbc-62aa96db39a3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading mobile-price-classification.zip to /content\n","\r  0% 0.00/70.6k [00:00<?, ?B/s]\n","\r100% 70.6k/70.6k [00:00<00:00, 36.8MB/s]\n"]}]},{"cell_type":"code","source":["!unzip mobile-price-classification.zip"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"POO-FpLr8rGp","executionInfo":{"status":"ok","timestamp":1675506216459,"user_tz":-540,"elapsed":20,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"d6cd6ff3-2ce8-4844-8a01-9cf98a65b955"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Archive:  mobile-price-classification.zip\n","  inflating: test.csv                \n","  inflating: train.csv               \n"]}]},{"cell_type":"code","source":["!mkdir /content/drive/MyDrive/ML_Data/kaggle/mobile_price_classification\n","!cp train.csv /content/drive/MyDrive/ML_Data/kaggle/mobile_price_classification\n","!cp test.csv /content/drive/MyDrive/ML_Data/kaggle/mobile_price_classification"],"metadata":{"id":"tQP1lKWk8-r4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!rm mobile-price-classification.zip\n","!rm train.csv\n","!rm test.csv"],"metadata":{"id":"a7Ff1tl59PzZ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","from sklearn.model_selection import train_test_split\n","from sklearn.model_selection import cross_val_predict, cross_val_score\n","\n","from sklearn.multiclass import OneVsRestClassifier\n","\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.linear_model import SGDClassifier\n","from sklearn.svm import SVC\n","from sklearn.ensemble import RandomForestClassifier\n","\n","from sklearn.model_selection import GridSearchCV\n","from sklearn.model_selection import RandomizedSearchCV\n","\n","from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, classification_report"],"metadata":{"id":"q0e-PDX89VzG"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train = pd.read_csv('/content/drive/MyDrive/ML_Data/kaggle/mobile_price_classification/train.csv')\n","train.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":299},"id":"4ZUVtBsa-EFh","executionInfo":{"status":"ok","timestamp":1675738834731,"user_tz":-540,"elapsed":13,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"63d2b606-c7ae-4523-de1e-01b9f2b3392e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n","0            842     0          2.2         0   1       0           7    0.6   \n","1           1021     1          0.5         1   0       1          53    0.7   \n","2            563     1          0.5         1   2       1          41    0.9   \n","3            615     1          2.5         0   0       0          10    0.8   \n","4           1821     1          1.2         0  13       1          44    0.6   \n","\n","   mobile_wt  n_cores  ...  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n","0        188        2  ...         20       756  2549     9     7         19   \n","1        136        3  ...        905      1988  2631    17     3          7   \n","2        145        5  ...       1263      1716  2603    11     2          9   \n","3        131        6  ...       1216      1786  2769    16     8         11   \n","4        141        2  ...       1208      1212  1411     8     2         15   \n","\n","   three_g  touch_screen  wifi  price_range  \n","0        0             0     1            1  \n","1        1             1     0            2  \n","2        1             1     0            2  \n","3        1             0     0            2  \n","4        1             1     0            1  \n","\n","[5 rows x 21 columns]"],"text/html":["\n","  <div id=\"df-95078c2a-a11c-46e7-a744-264af64bd4f1\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    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<tr>\n","      <th>0</th>\n","      <td>842</td>\n","      <td>0</td>\n","      <td>2.2</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>7</td>\n","      <td>0.6</td>\n","      <td>188</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>20</td>\n","      <td>756</td>\n","      <td>2549</td>\n","      <td>9</td>\n","      <td>7</td>\n","      <td>19</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1021</td>\n","      <td>1</td>\n","      <td>0.5</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>53</td>\n","      <td>0.7</td>\n","      <td>136</td>\n","      <td>3</td>\n","      <td>...</td>\n","      <td>905</td>\n","      <td>1988</td>\n","      <td>2631</td>\n","      <td>17</td>\n","      <td>3</td>\n","      <td>7</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>2</td>\n","   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  <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1821</td>\n","      <td>1</td>\n","      <td>1.2</td>\n","      <td>0</td>\n","      <td>13</td>\n","      <td>1</td>\n","      <td>44</td>\n","      <td>0.6</td>\n","      <td>141</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>1208</td>\n","      <td>1212</td>\n","      <td>1411</td>\n","      <td>8</td>\n","      <td>2</td>\n","      <td>15</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 21 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-95078c2a-a11c-46e7-a744-264af64bd4f1')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 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EDA"],"metadata":{"id":"ucc6fzgnllt6"}},{"cell_type":"code","source":["train.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"muazPPbn-K7q","executionInfo":{"status":"ok","timestamp":1675738836520,"user_tz":-540,"elapsed":5,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"a6b44ba5-abea-4800-ad88-3210c4836c0d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2000, 21)"]},"metadata":{},"execution_count":71}]},{"cell_type":"code","source":["train.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"blCh8cnt-NtF","executionInfo":{"status":"ok","timestamp":1675738836919,"user_tz":-540,"elapsed":7,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"64e1f730-18b5-460e-a08d-de22856c9097"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 2000 entries, 0 to 1999\n","Data columns (total 21 columns):\n"," #   Column         Non-Null Count  Dtype  \n","---  ------         --------------  -----  \n"," 0   battery_power  2000 non-null   int64  \n"," 1   blue           2000 non-null   int64  \n"," 2   clock_speed    2000 non-null   float64\n"," 3   dual_sim       2000 non-null   int64  \n"," 4   fc             2000 non-null   int64  \n"," 5   four_g         2000 non-null   int64  \n"," 6   int_memory     2000 non-null   int64  \n"," 7   m_dep          2000 non-null   float64\n"," 8   mobile_wt      2000 non-null   int64  \n"," 9   n_cores        2000 non-null   int64  \n"," 10  pc             2000 non-null   int64  \n"," 11  px_height      2000 non-null   int64  \n"," 12  px_width       2000 non-null   int64  \n"," 13  ram            2000 non-null   int64  \n"," 14  sc_h           2000 non-null   int64  \n"," 15  sc_w           2000 non-null   int64  \n"," 16  talk_time      2000 non-null   int64  \n"," 17  three_g        2000 non-null   int64  \n"," 18  touch_screen   2000 non-null   int64  \n"," 19  wifi           2000 non-null   int64  \n"," 20  price_range    2000 non-null   int64  \n","dtypes: float64(2), int64(19)\n","memory usage: 328.2 KB\n"]}]},{"cell_type":"markdown","source":["* battery power: Total energy a battery can store in one time measured in mAh\n","* blue: Has bluetooth or not\n","* clock speed: speed at which microprocessor executes instructions\n","* dual sim: has dual sim support or not\n","* fc: front camera mega pixels\n","* four_g: has 4g or not\n","* int_memory: internal memory in gigabytes\n","* m_dep: mobile depth in cm.\n","* mobile_wt: weight of mobile phones\n","* n_cores: number of cores of processor\n","* pc: primary camera mega pixels\n","* px_height: pixel resolution height\n","* px_width: pixel resolution width\n","* ram: ram in mega bytes\n","* sc_h: screen height of mobile in cm\n","* sc_w: screen width of mobile in cm\n","* talk_time: longest time that a single battery charge will last when you are\n","* three_g: has 3g or not\n","* touch_screen: has touch screen or not\n","* wifi: has wifi or not\n","* price_range: This is the target variable with value of 0(low cost), 1(medium cost), 2(high cost) and 3(very high cost)."],"metadata":{"id":"wGVsKxlcsYa9"}},{"cell_type":"markdown","source":["* there is no null data"],"metadata":{"id":"9upXxkZU-ijR"}},{"cell_type":"code","source":["train.describe()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":393},"id":"H8dYYAx--bV0","executionInfo":{"status":"ok","timestamp":1675738840116,"user_tz":-540,"elapsed":21,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"170f2612-5a26-45c9-fa19-75c76842be6d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       battery_power       blue  clock_speed     dual_sim           fc  \\\n","count    2000.000000  2000.0000  2000.000000  2000.000000  2000.000000   \n","mean     1238.518500     0.4950     1.522250     0.509500     4.309500   \n","std       439.418206     0.5001     0.816004     0.500035     4.341444   \n","min       501.000000     0.0000     0.500000     0.000000     0.000000   \n","25%       851.750000     0.0000     0.700000     0.000000     1.000000   \n","50%      1226.000000     0.0000     1.500000     1.000000     3.000000   \n","75%      1615.250000     1.0000     2.200000     1.000000     7.000000   \n","max      1998.000000     1.0000     3.000000     1.000000    19.000000   \n","\n","            four_g   int_memory        m_dep    mobile_wt      n_cores  ...  \\\n","count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000  ...   \n","mean      0.521500    32.046500     0.501750   140.249000     4.520500  ...   \n","std       0.499662    18.145715     0.288416    35.399655     2.287837  ...   \n","min       0.000000     2.000000     0.100000    80.000000     1.000000  ...   \n","25%       0.000000    16.000000     0.200000   109.000000     3.000000  ...   \n","50%       1.000000    32.000000     0.500000   141.000000     4.000000  ...   \n","75%       1.000000    48.000000     0.800000   170.000000     7.000000  ...   \n","max       1.000000    64.000000     1.000000   200.000000     8.000000  ...   \n","\n","         px_height     px_width          ram         sc_h         sc_w  \\\n","count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n","mean    645.108000  1251.515500  2124.213000    12.306500     5.767000   \n","std     443.780811   432.199447  1084.732044     4.213245     4.356398   \n","min       0.000000   500.000000   256.000000     5.000000     0.000000   \n","25%     282.750000   874.750000  1207.500000     9.000000     2.000000   \n","50%     564.000000  1247.000000  2146.500000    12.000000     5.000000   \n","75%     947.250000  1633.000000  3064.500000    16.000000     9.000000   \n","max    1960.000000  1998.000000  3998.000000    19.000000    18.000000   \n","\n","         talk_time      three_g  touch_screen         wifi  price_range  \n","count  2000.000000  2000.000000   2000.000000  2000.000000  2000.000000  \n","mean     11.011000     0.761500      0.503000     0.507000     1.500000  \n","std       5.463955     0.426273      0.500116     0.500076     1.118314  \n","min       2.000000     0.000000      0.000000     0.000000     0.000000  \n","25%       6.000000     1.000000      0.000000     0.000000     0.750000  \n","50%      11.000000     1.000000      1.000000     1.000000     1.500000  \n","75%      16.000000     1.000000      1.000000     1.000000     2.250000  \n","max      20.000000     1.000000      1.000000     1.000000     3.000000  \n","\n","[8 rows x 21 columns]"],"text/html":["\n","  <div id=\"df-ef2ed88f-a1f4-43d2-8e06-ddf5656c3a3f\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>battery_power</th>\n","      <th>blue</th>\n","      <th>clock_speed</th>\n","      <th>dual_sim</th>\n","      <th>fc</th>\n","      <th>four_g</th>\n","      <th>int_memory</th>\n","      <th>m_dep</th>\n","      <th>mobile_wt</th>\n","      <th>n_cores</th>\n","      <th>...</th>\n","      <th>px_height</th>\n","      <th>px_width</th>\n","      <th>ram</th>\n","      <th>sc_h</th>\n","      <th>sc_w</th>\n","      <th>talk_time</th>\n","      <th>three_g</th>\n","      <th>touch_screen</th>\n","      <th>wifi</th>\n","      <th>price_range</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>2000.000000</td>\n","      <td>2000.0000</td>\n","      <td>2000.000000</td>\n","      <td>2000.000000</td>\n","      <td>2000.000000</td>\n","      <td>2000.000000</td>\n","      <td>2000.000000</td>\n","      <td>2000.000000</td>\n","      <td>2000.000000</td>\n","      <td>2000.000000</td>\n","      <td>...</td>\n","      <td>2000.000000</td>\n","      <td>2000.000000</td>\n","      <td>2000.000000</td>\n","      <td>2000.000000</td>\n","      <td>2000.000000</td>\n","      <td>2000.000000</td>\n","      <td>2000.000000</td>\n","      <td>2000.000000</td>\n","      <td>2000.000000</td>\n","      <td>2000.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>1238.518500</td>\n","      <td>0.4950</td>\n","      <td>1.522250</td>\n","      <td>0.509500</td>\n","      <td>4.309500</td>\n","      <td>0.521500</td>\n","      <td>32.046500</td>\n","      <td>0.501750</td>\n","      <td>140.249000</td>\n","      <td>4.520500</td>\n","      <td>...</td>\n","      <td>645.108000</td>\n","      <td>1251.515500</td>\n","      <td>2124.213000</td>\n","      <td>12.306500</td>\n","      <td>5.767000</td>\n","      <td>11.011000</td>\n","      <td>0.761500</td>\n","      <td>0.503000</td>\n","      <td>0.507000</td>\n","      <td>1.500000</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>439.418206</td>\n","      <td>0.5001</td>\n","      <td>0.816004</td>\n","      <td>0.500035</td>\n","      <td>4.341444</td>\n","      <td>0.499662</td>\n","      <td>18.145715</td>\n","      <td>0.288416</td>\n","      <td>35.399655</td>\n","      <td>2.287837</td>\n","      <td>...</td>\n","      <td>443.780811</td>\n","      <td>432.199447</td>\n","      <td>1084.732044</td>\n","      <td>4.213245</td>\n","      <td>4.356398</td>\n","      <td>5.463955</td>\n","      <td>0.426273</td>\n","      <td>0.500116</td>\n","      <td>0.500076</td>\n","      <td>1.118314</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>501.000000</td>\n","      <td>0.0000</td>\n","      <td>0.500000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>2.000000</td>\n","      <td>0.100000</td>\n","      <td>80.000000</td>\n","      <td>1.000000</td>\n","      <td>...</td>\n","      <td>0.000000</td>\n","      <td>500.000000</td>\n","      <td>256.000000</td>\n","      <td>5.000000</td>\n","      <td>0.000000</td>\n","      <td>2.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>851.750000</td>\n","      <td>0.0000</td>\n","      <td>0.700000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>16.000000</td>\n","      <td>0.200000</td>\n","      <td>109.000000</td>\n","      <td>3.000000</td>\n","      <td>...</td>\n","      <td>282.750000</td>\n","      <td>874.750000</td>\n","      <td>1207.500000</td>\n","      <td>9.000000</td>\n","      <td>2.000000</td>\n","      <td>6.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.750000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>1226.000000</td>\n","      <td>0.0000</td>\n","      <td>1.500000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>1.000000</td>\n","      <td>32.000000</td>\n","      <td>0.500000</td>\n","      <td>141.000000</td>\n","      <td>4.000000</td>\n","      <td>...</td>\n","      <td>564.000000</td>\n","      <td>1247.000000</td>\n","      <td>2146.500000</td>\n","      <td>12.000000</td>\n","      <td>5.000000</td>\n","      <td>11.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.500000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>1615.250000</td>\n","      <td>1.0000</td>\n","      <td>2.200000</td>\n","      <td>1.000000</td>\n","      <td>7.000000</td>\n","      <td>1.000000</td>\n","      <td>48.000000</td>\n","      <td>0.800000</td>\n","      <td>170.000000</td>\n","      <td>7.000000</td>\n","      <td>...</td>\n","      <td>947.250000</td>\n","      <td>1633.000000</td>\n","      <td>3064.500000</td>\n","      <td>16.000000</td>\n","      <td>9.000000</td>\n","      <td>16.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>2.250000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>1998.000000</td>\n","      <td>1.0000</td>\n","      <td>3.000000</td>\n","      <td>1.000000</td>\n","      <td>19.000000</td>\n","      <td>1.000000</td>\n","      <td>64.000000</td>\n","      <td>1.000000</td>\n","      <td>200.000000</td>\n","      <td>8.000000</td>\n","      <td>...</td>\n","      <td>1960.000000</td>\n","      <td>1998.000000</td>\n","      <td>3998.000000</td>\n","      <td>19.000000</td>\n","      <td>18.000000</td>\n","      <td>20.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>8 rows × 21 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ef2ed88f-a1f4-43d2-8e06-ddf5656c3a3f')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 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pd.read_csv('/content/drive/MyDrive/ML_Data/kaggle/mobile_price_classification/test.csv')\n","test.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"je6ztXY0-SZ0","executionInfo":{"status":"ok","timestamp":1675738840117,"user_tz":-540,"elapsed":21,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"20898155-0389-4951-ee21-9b69b70ab911"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1000, 21)"]},"metadata":{},"execution_count":74}]},{"cell_type":"code","source":["test.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"noTF8EW9ul3H","executionInfo":{"status":"ok","timestamp":1675738840567,"user_tz":-540,"elapsed":7,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"f58b0e44-eb75-46ba-cd2e-9a77d0f9614a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1000 entries, 0 to 999\n","Data columns (total 21 columns):\n"," #   Column         Non-Null Count  Dtype  \n","---  ------         --------------  -----  \n"," 0   id             1000 non-null   int64  \n"," 1   battery_power  1000 non-null   int64  \n"," 2   blue           1000 non-null   int64  \n"," 3   clock_speed    1000 non-null   float64\n"," 4   dual_sim       1000 non-null   int64  \n"," 5   fc             1000 non-null   int64  \n"," 6   four_g         1000 non-null   int64  \n"," 7   int_memory     1000 non-null   int64  \n"," 8   m_dep          1000 non-null   float64\n"," 9   mobile_wt      1000 non-null   int64  \n"," 10  n_cores        1000 non-null   int64  \n"," 11  pc             1000 non-null   int64  \n"," 12  px_height      1000 non-null   int64  \n"," 13  px_width       1000 non-null   int64  \n"," 14  ram            1000 non-null   int64  \n"," 15  sc_h           1000 non-null   int64  \n"," 16  sc_w           1000 non-null   int64  \n"," 17  talk_time      1000 non-null   int64  \n"," 18  three_g        1000 non-null   int64  \n"," 19  touch_screen   1000 non-null   int64  \n"," 20  wifi           1000 non-null   int64  \n","dtypes: float64(2), int64(19)\n","memory usage: 164.2 KB\n"]}]},{"cell_type":"markdown","source":["* Since there are 4 classes, it would be the multiclass classification problem.\n","* OvR (One vs Rest)"],"metadata":{"id":"7LR4ZfkSus3b"}},{"cell_type":"code","source":["num_features = ['battery_power', 'clock_speed', 'fc', 'int_memory', 'm_dep',\n","                'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram',\n","                'sc_h', 'sc_w', 'talk_time']\n","cat_features = ['blue', 'dual_sim', 'four_g', 'three_g', 'touch_screen', 'wifi']"],"metadata":{"id":"glL8d0XLumhJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# num features plotting -> boxplot\n","\n","fig, ax = plt.subplots(3, 5, figsize=(20,12))\n","\n","for i, col in enumerate(num_features):\n","  sub = sns.boxplot(x='price_range', y=col, data = train, ax=ax[i//5][i%5])\n","  sub.set_title(col, fontweight='bold')\n","  sub.set_xlabel('')\n","  sub.set_ylabel('')\n","\n","desc = ax[2][4]\n","desc.text(0.5, 0.5, horizontalalignment='center', verticalalignment='center',\n","          s = 'Num Features vs Price Range', fontweight='bold', fontsize=11)\n","\n","desc.set_xticks([])\n","desc.set_yticks([])"],"metadata":{"id":"-tyzrJMg5iLg","colab":{"base_uri":"https://localhost:8080/","height":706},"executionInfo":{"status":"ok","timestamp":1675738843694,"user_tz":-540,"elapsed":2671,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"4e5522c9-c127-42c1-a43b-070dab0fb19e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[]"]},"metadata":{},"execution_count":77},{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x864 with 15 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["* ram seems to be the most significant feature"],"metadata":{"id":"UisMaz53f1zf"}},{"cell_type":"code","source":["# plotting heatmap\n","\n","num_features_with_price = ['battery_power', 'clock_speed', 'fc', 'int_memory', 'm_dep',\n","                'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram',\n","                'sc_h', 'sc_w', 'talk_time', 'price_range']\n","\n","plt.figure(figsize=(10,10))\n","sns.heatmap(data = train[num_features_with_price].corr(), cmap='PiYG', vmin=-1, vmax=1, annot=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":676},"id":"0RlYNTNZgEYK","executionInfo":{"status":"ok","timestamp":1675738844520,"user_tz":-540,"elapsed":855,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"b2a5ecab-5637-417f-9279-5dc82628073a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7ff56035fbe0>"]},"metadata":{},"execution_count":78},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAngAAAKACAYAAAAPVgVOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeVxUVf/A8c8BREFEJWG0sNwKt8wlyTXAXZtBQRhxabUys8XdSsVyQbTcMrUsrZ4yYVxQ4HnEXCAVKzVTXMpSUxIVSERwYz2/P2YYGTYxNvV33q8XL507957vOd977p0z596ZEVJKFEVRFEVRlPuHVVVXQFEURVEURSlfaoCnKIqiKIpyn1EDPEVRFEVRlPuMGuApiqIoiqLcZ9QAT1EURVEU5T6jBniKoiiKoij3GTXAUxRFURRFKSMhxGohRJIQ4mgxzwshxMdCiJNCiDghRPt8zz0vhPjT9Pd8edRHDfAURVEURVHK7iugXwnP9wceNf29CqwAEEI4ATOApwB3YIYQom5ZK6MGeIqiKIqiKGUkpdwFpJSwykDgP9LoJ6COEKIB0BfYJqVMkVJeBrZR8kCxVGzKWoBStOavPFjpPxHy69KTlR0SgFyyqiSuNdWrJG4OGZUe0xrbSo8JIHKqJCzZ1plVEteKapUeU8gqep8tqihsThX9elIVtDdL3Kj8oEC6/LtK4jawf6JSs1zZr7MnvrgwCuPMW56VUsqVd1DEQ0D+nXPOtKy45WWiBniKoiiKoii3YRrM3cmArkqpS7SKoiiKoigVLwFomO+xq2lZccvLRA3wFEVRFEW55wiryv0rB+HAc6ZP03YCrkgpLwBbgT5CiLqmD1f0MS0rE3WJVlEURVEUpYyEEGsBT6CeEOIcxk/GVgOQUn4K/A8YAJwErgMvmp5LEULMAvabipoppSzpwxqlogZ4iqIoiqLcc6ysquiTQsWQUg69zfMSGFPMc6uB1eVZH3WJVlEURVEU5T6jZvAURVEURbnniLtrAu+uo2bwFEVRFEVR7jNqBk9RFEVRlHuOlZqiKpFKj6IoiqIoyn1GzeApiqIoinLPEXfZp2jvNmoGT1EURVEU5T6jBniKoiiKoij3GTXAu4vMeX4hsQviCH9/Z5nLio2NZaD3IHRab1avKvzdiZmZmUyeNAWd1psRw58lIeE8AKmpqbw88hU6d+rC3KBgi22WLv2Evn360blTlxLi/oiPtx/eWl++XPV1kXGnTHoPb60vzw1/kfOmuEePHCNAP5wA/XCG+A9j545oADIyMnh22AsM8R+Gn88QViwv+neepZQEBwej1Wrx8/Pjt99+K3K948ePM3jwYLRaLcHBwRi/dxKuXLnCqFGj0Ol0jBo1irS0NIvtjh49Svv27dm2bVuFtTVPTk4OQ/UjeOuNcflixeLtPRCtVseqYvbnpEmT0Wp1DB8+goSEWz9juGrVKrRaHd7eA4mN3WteHhg4A09PL3x9B1uU9fvvvzNixLPo9XoChg/jyNEjheJJKQmeP49nvHUM1vtzvIR8++r9eMZbR/D8eeZ8f7J8GYP1/vgH6Bn1+mskJScVub2x7eWf5/cDZ9HTsy/+vgHFxCz/42fkyJcZ6D0IvX4Iev0QUi6l3MrlvGC0Oi1+/rfpu36D0eq0BM+7fd9NT0/nzbfexF/vj4+vD5s2bbKI5evri6enJ0FBQUXHuoPjpKTj78KFC4waNYpBgwbh4+NDwnlj35w2Yzr9tAPwD9DjH6Dnt99/q5Q+ZcxBKeP4m+LMuxVnwaKFePsMYrDen7Hjx5GWbsxBamoqI195mae6dCYoeG6h8vbG/ojvwCEM1Pnx5er/FHo+MzOTdyZPY6DOj+dGjOR8wgUAzidcoMtTHgzVP8dQ/XMEzZ5n3uaN18cSoH8Wf99hBM2eR05OTpFtKcrPsYd4dtDbDPN+kzWrNxV63vBNJM/7juMl/UTGj5rJxfPJpS67slhZVe7fvea2VRZCNBJCHC1tgUKIF4QQD+Z7PFYIYf9vK/j/SdjeUF5ZMrzM5eTk5DA3KJhlyz9hY9gGoqKiOHXqlGWssE04OtYiIjKcESOGs2TxEgCq21ZnzJjXGT9+XKFyPTye5ts135QYd17QfJYuX8KGsFCiorZy+tRpi3U2hYXj6FiL8MiNDB8xlCWLPwGgabOmfPvd14QY1vDJ8o+ZMyuY7OxsbG1t+eyL5YSu+461hjX8GPsjcXGFBxx79uwhPj6eiIgIAgMDmT17dpF1nD17NjNmzCAiIoL4+HhiY2MBWL16Ne7u7kRERODu7s6qVass2rV48WI6d+5coW3Ns3ZNCI2bNLKIFRQ0l+XLlxEWtrGY/RmGo6MjkZERjBgxgsWm/Xnq1CmiorayceMGli9fTlBQkPlFYOBAb1asWF4oR4sWLea110ZhMBgYM3o0i5YsLpzv2D2cjY8ncnM4gdOmM3vunKLzPXcOM6YFErk5nLPx8ezZa8z3C889zwbDOtaFGHi6+9N8trLogXtF5Vk38Bk+WbGk2JgVcfwABM2dg8EQisEQitMDTsZc5vXd8AgCpwcye04xfXfObGYEziAivIi++5Sp7z7lzqrVxr4bGhpKkyZNWGdYx6ovVrFg4QJifogxx2ratCm5ublFx7rD46Sk42/atGm88MILbNq0iTVr1uBU18n83Pix41gXYmBdiIF//vmnUvrUnj0F+m5QMXGC5jBjer44phx07tSJjevWs8GwjkceeYRVq41vAGyrV2fM62OYMG58obJycnIInruAj5ctZP3GtWyN2sbpU39ZrLMpLAJHx1psjljP8BEBfLxkmfk5V1dX1hr+w1rDf3hv2hTz8uD5cwgxfINhwxouX05l+7bSTQ7k5OSyJHgV8z55j683LGJnVCxnTp2zWOfR5o34bE0wqw0f4dGzE58t+bZUZSt3j4oYk74APJjv8VjgjgZ4Qgjr8qxQRRBClPsHVA78+TNXrl0uczlHjx6lYcOGuLq6Uq1aNfr260tMTIzFOjHRMei8dQD06t2Lffv2IaXEzt6Odu3bYVu9eqFy27Rpg7Ozcwlxj+Ha0BVX14dMcfsQE7OrQNwf0Ho/A0DP3j3Yv2+/Ma5dDWxsjCnNzMhAmL7BUgiBvb2x+2RnZ5OdnY2g8I210dHR6HQ6hBC0adOG9PR0kpMt33EmJydz7do12rRpgxACnU7Hzp07zdt7e3sD4O3tTXT0rdmetWvX0qtXL5ycbr0wVURbARITE9m9O5ZBPgMtYuXfn/2K2J/R0TF4m/Zn73z7MyYmhn79+mJra4ur60M0bNiQo0eN79c6dOiAo6NjoVwKIbh69RoA6VevFrnPo2Ni0Gm1CCF4ooR8X712jSfy8q3VmvPq4OBgXu/GjRvFfmNpReW5Q4f21C6i7caYFXP8FCc6Jhqd9g77rlbHzuid5u29daa+q7vVd4UQXL92HSkl129cp3bt2uzetRudVsdvv/2GEAIbGxtj/kuKVYrjpLjj79SpU2RnZ5vfHNnb22NnZ1dMHiqnT0X/8C/jxBjjdOncxdyv2jzehsTERGPb7Oxo364d1avbFop57OhxGubrx3369irUj3+I2Y1WNwCAnr282LfvgHnWsDgODjUByM7OISsry6KPl+T3oyd5qGF9HnTVUK2aDT36diE2Zr/FOu06tqaGnbEft2zzKMmJZf5p1HInrCr3715T2irbCCHWCCF+E0KsF0LYCyEChRD7hRBHhRArhZEf8CSwRghxSAjxNsbBXrQQIhpACNFHCPGjEOKgEGKdEMLBtPyMEGKeEOIg8I7pX0zPPZr/cUGmbecLIY4IIfYJIZqZljcSQuwUQsQJIXYIIR4WQlgLIf4y1beOECJHCPG0af1dplg1hRCrTWX9KoQYaHr+BSFEuBBiJ7DjjrNdSZKSkqhfX2N+rHHRkJSYXMQ69QGwsbHBwcGB1NTUMsVNTkq2iOvi4lIobv51bsW9AsCRuKP4+QxB7zeM96ZNMZ9Ec3JyCNAPp5dXX57q5M7jbVoX2WaNJl+bNRqSkpJKvU5KSop5IFOvXj1SUowns8TERHbu3Iler6+Utn40fxFvj3sTq3zXA4zl1M8XS0NiYuG2FbU/ExOT0GhubVtUXgqaPHkSixYtok+fvixctJC333ir0DpJSUnUz1+ui6bQJbGk5CQ0LgX6Yb7YH3+ylN79+/LfLf9jzOjRRdalovJckoo8fmYEvo9eP4SVn600v3gnJSWhqV+GvnupQN81XfoNCAjg9F+n6dW7F35+fkyeNJmk5CRcXFxYsGABEyZMwNHRsdAA798cJ8Vtc/bsWWrVqsW4cePQ6/UsXLjQ4jLi0mWfMFjvz/yPPuRi4sVK6VP5912J+XYpeZ8AhG3eRLeu3YqMY1leMpr6LvnKcyE5qXA/1hTTjxMSzjNsyHO8MnI0vx48ZLHdmNFj6d1jAPb29vTs5XXbuhhjpeCsecD82FnzAMnJxQ/g/rtpJ+5d25aqbOXuUdoBnhuwXErZAkgDXgc+kVJ2lFK2BuwArZRyPXAAGC6lbCulXAKcB7yklF5CiHrANKCXlLK9ad3889mXpJTtpZRzgCtCiLwe9SLw5W3qeEVK+TjwCZB3TWkp8LWUsg2wBvhYSpkDnABaAt2Ag0B3IUR1oKGU8k9gKrBTSukOeAEfCiFqmspsD/hJKT0KVkAI8aoQ4oAQ4kDq79dvU12loMfbtGZ9WCjffPcVX676moyMDACsra0JMawh6vtIjh09zsk/T92mpLLJ/y74ww8/ZOzYsRYDrvJQVFt3/bAbJ6e6tGzZolxj3SmDYR2TJk3k+++3MmnCRGbM/KBC4rz1xpts27KVZ/oPYG1ISIXEKK5PVYWgoCDWb1jHl1+u5uDBX4mMjCz3GEII8ia49+7dS3O35mzfth1DqIG5wXPJyclhx84ddOvWzWJA9q9j3UZOTg6//vorEyZM4LvvvuPcuXNsjggH4O033iJ84ybWfruGK2lX+Pvvc7cp7fYqo0/lWfnF59hYW/PMgAEVGqee8wP8N2oT34X+h/ET3mbquzPMM+wAy1YsZuv2CLKysti/75dyj//9f3dx4vhpAp73Lveyy8pKiEr9u9eU9lXrbyllrOn/32IcGHkJIX4WQhwBegCtSlFOJ4wDq1ghxCHgeeCRfM+H5vv/F8CLpsu1Q4DvblP22nz/5t0s1Tnfdt+Y6g2wG3ja9DfXtLwjkDdH3QfjLOIhIAaoATxsem6blLLItzpSypVSyiellE/WaV51tx26uLhw8WKi+XFiUiIuGuci1rkIGC99Xr16lTp16pQprrOLs0XcpKSkQnHzr3Mrbm2LdZo0aYydvR2nTloO5Go51uLJjh3Yu/dHAEJCQtDr9ej1epydnc2XSsA48+bi4mKxvYuLS7HrODk5mS/TJCcnmy/HHjt2jClTptC/f3+2bdvGnDlziN4ZUyFtPXwojh9idvNM/4G8O2UqB/YfYOq7gaZyLuaLlYhGU7htRe1PjcaFxMRb2xaVl4IiIiLo2bMnAH169+HoMeMl3ZDQEPMN8fWc63Exf7lJibg4F6iTswuJSQX6YRGxn+k/gO07i54Qr+g+VZSKOn7y9lnNmjVxesCJBR8tRK8fgnM9ZxIvlqHvPlB03928eTM9e/YkNDSUiRMncvnyZWxtbTl29BghoSH079+fs2fPsmfPHhYvXly6WMUcJ8Vto9FocHNzw9XVFRsbG7y8vMwfwHB2dibUEMrw50Zw6NAhMjIyKqxPhYSG4D9Ej/8QPfXq1bM4norNd1Lx+2Rz+GZ27drN3DlBpRrourg4k3jx1gxgYmISzi6F+3FiEf3Y1tbW3J9btGyOq+tDxJ+Nt9i2evXqeHh254cCl32L4+ziRHLiJfPj5MRLODs7FVrvwE9xfLsqjKDFk7G1rVaqspW7R2kHeAVvBJDAcowzWY8Dn2McBN2OwDhAamv6aymlHJnv+Wv5/r8B6A9ogV+klJcomSzm/0XZBXQH3IH/AXUAT4wDv7x6Ds5Xz4ellHkftbpWsLC7TatWrYiPjyfhXAJZWVlsjdqKh4enxToenh5EhEcAsH3bdjq6dyz1/RvFx23J3/F/54v7PR4e3QvEfZrI8P8CsGPbTjq6P4kQgoRzCeYb4M+fv8CZM2dp8OCDXE65THpaOgA3b97kp59+plEj43uCgIAADAYDBoMBLy8vIiIikFISFxeHg4NDoXvHnJ2dqVmzJnFxcUgpiYiIwMvLeEnD09OT8HDjzEJ4eLh5+ZYtW8x/vXv3ZurUqXj18KyQtr759hiitkXy3y2bmTtvDk92fJI5c2fSqlVL4uPjOWeKFRW1FQ8PywlkT08Pwk37c9u27bib9qeHhwdRUVvJzMzk3LkE4uPjad268CXugnk6cOAAAD/v28fDDY3vbQKGBJhviO/h6UVEZCRSSg7HxVGrmHw71KzJ4bx8R0bi5ekJwNn4s+b1on+IoXGjxkXWpSLyfDsVcfxkZ2dz+bLx/tqsrCwyMzIYM+Z1DIZQY9+NvMO+GxmBl6ep73p4Em6aFQuPCDcvr9+gPj///DMBAQGsWLECBwcH+vbpS02HmkRtiWLevHk0aNAAb29vxo4dW3ysUhwnnp6eRR5/rVq1Ij093Xwpd9++fTRt0gQwDhADhgRgWBvK092f5vHWrSusTwUMCWBdqIF1oQZ6eP3LvmvqA3tiY/nyq6/5ePHiYu8nLKhlqxbGfpxwnqysLL7fur1wP/boRmTE/wDYsT2ajh07IITgcspl82Vt4zH8Nw+5Psj169dJTv4HMPavPbv30qjxI5SGW6umnIu/wIWEJLKystm5dS9dPJ+0WOfP3/9i4ZzPCVo0mbpOtYspqWqpe/BKVtoPCjwshOgspfwRGAbsAboA/5juofMD1pvWTQdq5ds27/E/wE/AMiFEMynlSdNlz4eklH8UDCilvCmE2AqsAEYWfL4IQ4Bg078/mpbtBQIwzt4N59YAbp9p2WlTnEPAKIyDSYCtwJtCiDellFII0U5K+Wsp6lAmC15ZTsfHOlPXwYmY+QdYGr6ADXvW3n7DAmxsbHjn3SmMHv06ubm5DBw0kGbNmrJ82XJatmqJp6cnPj6DmDp1GjqtN46Ojsybf+srHfr3H8C1q9fIysoiOjqaFZ8up2nTpixatJgt/9vCzZs36dO7Lz6+Powe/ZpF3CnvTmLM6LfIzc3Fe5COps2asmLZZ7Rs1QIPz6cZ5OPN9Kkz8Nb6UtvRkbnzjZ9g+/XXw3y1+mtsqtlgJax4973J1K1bhz/++JMZ0z4gJzcXmZtL7z69eLrAiRGge/fu7NmzB61WS40aNZg5c6b5Ob1ej8FgAGDq1KlMnz6djIwMunbtSrduxkndl156iUmTJrFp0yYaNGjAhx9+eNscl3dbS4r17rvvMHr0aHJzcxk0aCDNmjVj2bLltDLvTx+mTp2KVqvD0dGR+fONX6XQrFkz+vTpjY+PL9bW1rz33rtYWxs/wzRlyjscOHCA1NRUevfuw+jRo/H19SEwMJD58+eTk5ODra0tM6ZNL5zvbt3ZvWcPzwzUUaNGDWa9f+syrn+AnnUhpny/+x7TZgSSkZFBty5dzfcrLf74Y86cPYOVsKJBgwZMnzq1UvP87pRp/HLgF1JTU+nXW8tro19hkO9Ac8zyPn4ebPAgr48eQ3Z2Njk5OTzV6Sl8B/sac5nXd3WmvvtBMX33valMDyym706exKawTTR4sAEfzjf23VdfeZXpgdMZ7DcYKSVjx46lf//+HDp0yBxLq9Wa7x0sy3FS3PFnbW3N+PHjefXVV5FS0rJlSwabvpbnnanvcTn1MlJKmj/mxsz3P2Dxx0sqvE+Z+653EXGG6FkXWkScfDmYOy+YzMxMRpnOfW0eb8P0adMA6DegP1evGff9zuholq1YTJOmjbGxsWHyOxN4Y/RYcnJzGThQS9NmTVixfCUtW7bAw7M7A310TJ/6AQN1ftR2dCRo3iwADh48xKfLP8fGxgZhJXhv2mRq167NpUspjH97MplZmchcyZMd2zPYz6fINhdkY2PN21NeYtLrc8jNzaX/QC8aN23I6uWhuLVsSlfPJ1mx6FtuXL/JjMkLAdDUr0fQkim3KVm5m4jbfUpHCNEIiMJ4v1wH4DjwLPAeMBS4CPwBnJVSvi+EGAwEATcwXiJ9GXgDOG+6D68HMA/I+5jZNClluBDiDPCklPKffLE7YRw4PmK6d664Op7BeHm3P5ABDDUNIB/BeO9ePSAZeFFKGW/aZjewW0r5nhBiGMYZSScpZa4Qwg7jfXxdMM5y/iWl1AohXjDV8Y0SkwY0f+XB280ilrtfl56s7JAA5JJVJXGtKf0nFctTDpV/H5c1hT+ZVxlE6b9Wq1xlW2dWSVwrKv8ylJBVNDVQRbcUiZxKPzWaAld+yCxx4/YrVYB0+XeVxG1g/0SlZvmpKQ0rtTP9PO/ve+pGvNsO8KqSEGIiUFtKWXgKwXK9MxQYHFY1NcCreGqAV/HUAK/iqQFeZQWu/JBqgFex1ACvZOX+XW7lRQgRBjTF+AEORVEURVEUs3vxvrjKdNcO8KSUhW4mMA36Ct6NPUVK2ahSKqUoiqIoinIPuGsHeEUpatCnKIqiKMr/P1ZW99QV00qnJjgVRVEURVHuM/fUDJ6iKIqiKAoU+3PDiomawVMURVEURbnPqAGeoiiKoijKfUZdolUURVEU5Z5jpaaoSqTSoyiKoiiKcp9RM3iKoiiKotxzhPqalBKpGTxFURRFUZT7jJrBUxRFURTlnqPuwSuZGuBVkF+Xnqz0mO3ebFbpMQEOLT1dJXGhan603FpWr/SYUuRWekwArKvmDGpN5ecYqKouVUX+XzUWWQWX82ykfaXHBKhr5VYlcZW7ixrgKYqiKIpyzxFqBq9EKj2KoiiKoij3GTWDpyiKoijKPcdK/VZZidQMnqIoiqIoyn1GzeApiqIoinLPUffglUylR1EURVEU5T6jZvAURVEURbnnqO/BK5lKj6IoiqIoyn1GzeApiqIoinLPUb9FWzI1g6coiqIoinKfUQM8RVEURVGU+4y6RKsoiqIoyj1HfciiZCo9iqIoiqIo9xk1g6coiqIoyj1H/VJZySp8Bk8I8b4QYuK/2M5TCBFZEXUqCyFEIyHE0dKuHxsby0DvQei03qxetbrQ85mZmUyeNAWd1psRw58lIeE8AKmpqbw88hU6d+rC3KBgi22WLv2Evn360blTlzK2xmjO8wuJXRBH+Ps7S72NlCBPVkfuq4mfnx+//fZbkesdP36cwYMHo9VqCQ4ORkoJwJUrVxg1ahQ6nY5Ro0aRlpZmKlcSHByMVqststyrV6/Su3dv3nzzTby9B6LV6tDpvPH31+Pj48usWbPJyckhMzOTSZMmo9XqGD58BAkJCeYyVq1ahVarw9t7ILGxe83L09LSmDBhIgMHDmLQIB8OHz4MwIoVK+jVqzd6vR69Xs/u3btv1XVeMFqdFj//2+TAbzBanZbgebdy8P333+Pj60Pbdm05duxYoe0uXLhAp86d+Prrry2Wl3efunHjBm+88SaDBvrg6zOYJYuXmJ+73f6waOMd7Of9+/fTtWtXc04//fRTAM6cOYNer8ff3x8PDw/atm1Lz549KzzuxYsXGTlyJD6+Pvj4+vDtt99WyL5NSEjA/Sl3c/xZs2eVuR8V19a0tDTGjhuLn78fw4YP48+Tf5rL6t+/P/369efJJzvy5JMdWVVMP7qTYygjI4Nhw4abj8Xly5cXKjM4eB5Pde1sfiylJHj+PJ7x1jFY78/xEtruq/fjGW8dwfPnmdu+YNFCvH0HMVjvz9gJ40hLN7Y9KyuL6TMC8dX74TdEz779+yqkH3/11Vfmfenr60u7du24cuWKubycnBz0Q/S88eYbFXa+SE1NZeTLI+nUuRNBc4Nu5bUC2gvGY0iv1+Pj48NLL71kXp6Wloabm9t6Nze3393c3H5zc3PrXCigUqnUJdoKlJOTw9ygYJYt/4SNYRuIiori1KlTFuuEhW3C0bEWEZHhjBgx3PziWt22OmPGvM748eMKlevh8TTfrvmm3OoZtjeUV5YMv7ONLlvDDSvoeI3AwEBmz55d5GqzZ89mxowZREREEB8fT2xsLACrV6/G3d2diIgI3N3dWbVqFQB79uwhPj6eiIiIIstdtmwZ7du355dffmH58mWEhW3E1rYawcFz2bhxA5cvX+b777cRFhaGo6MjkZERjBgxgsWmvJ46dYqoqK1s3LiB5cuXExQURE5ODgDz58+na9cubN68iXXrDDRu3Ngc99lnR2AwGDAYDHTv3t2yruERBE4PZPacYnIwZzYzAmcQEW6Zg2bNmrFo4SI6tO9Q5HYfLfiIbl27WSyrqD71/HPPsWlzGKGGEA4dOsyePXtKtT/MbbzD/QzQrl07c05fe+01ABo1aoTBYOCtt96iZcuW1K1bl/fee6/C41pbWzNx4kTCNobx7Tff8tXXX/H7779XyL51dXU1x58+bXqZ+9Hq1atxf8rU1qfcWbXa2NYvvviC5m7NWb9uPXNmz2H+/PnmsqSUCCHYtCmMH3/cW0w/urNjyNbWli+++Jx16wwYDKHExu4lLi7OXN6xY8csBgoAe2L3cDY+nsjN4QROm87suXOKbvvcOcyYFkjk5nDOxsezZ6+x7Z07dWKjYT0bDOt45OFHWLXaOFDdsHEDABsN6/lsxad8MGsWZ8+eLfd+/MILL5j35VtvvUWHDh2oXbu2ubw1362hSeMmxrZW0PnCtrotY8aMYfz48bfyWkHHbVpaGkFBQSxZsoSwsDA+/PBDc1mm/hV14sSJ5sATQNGjynJkZSUq9e9eU+4DPCHEc0KIOCHEYSHENwWeayuE+Mn0fJgQoq5peTMhxHbTNgeFEE0LbNdRCPFrweX5nvcQQhwy/f0qhKhlmgHcJYT4rxDihBDiUyGMv1wnhOgjhPjRFGudEMLBtLyDEOIHIcQvQoitQogG+ZYfFkIcBsaUNhdHjx6lYcOGuLq6Uq1aNfr260tMTIzFOjHRMei8dQD06t2Lffv2IaXEzt6Odu3bYVu9eqFy27Rpg7Ozc2mrcVsH/vyZK9cu39lG/9iAJgshjCbSBBYAACAASURBVPVJT08nOTnZYpXk5GSuXbtGmzZtEEKg0+nYudM4SxgdHY23tzcA3t7eREdHm5frdDqEEIXKPX78OJcuXcLV1ZVatWqZ8zpgwABiYmLIzs4mKysLIQTR0TF4m/LaO19eY2Ji6NevL7a2tri6PkTDhg05evQo6enp/PLLQXx8fACoVq0ajo6OJaYgOiYanbbouhabA62OndHGHDRp0oRGjRoVWfbOnTt56MGHaNrUsstXRJ+ys7Ojo3tHc7ubt2hOYmLSbfdHsW0sxX6+nejoaJo3b87DDz9Mz549Kzyus7MzLVq0AKBmzZrY2NjQvl37Ctm3hdpaxn4UHRONt87UVt2ttp4+fRp3d3cAGjduzPnz57l06RJgnOF66KEHzf2oXxH96E6PISEE9vb2AGRnZ5OdnQ0YXxRzcnJYuHAR48aNLdD2GHRaLUIIniih7VevXeMJc9u15jZ26dwFGxvjnUZtHm9DYlIiAKdOn8a9o7HtDzg5cTPjJm3btq3QfhwVFUX//v3NjxMTE9m9ezc+vj7lsp+L61P2dva0b9ee6ra3juuKOm63bNlCz549adCggTG3DzwAYDp//gKwCuDEiROZJ06cSC1UWaVSlesATwjRCpgG9JBSPgG8XWCV/wBTpJRtgCPADNPyNcAy0zZdgAv5yuwCfAoMlFKeomgTgTFSyrZAd+CGabk78CbQEmgK+Aoh6pnq2EtK2R44AIwXQlQDlgJ+UsoOwGog7+3kl8CbpvqVWlJSEvXra8yPNS4akhKTi1inPgA2NjY4ODiQmnoPHBeZVlBdmh9qNBqSkpIsVklKSkKj0RS5TkpKinmQWq9ePVJSUkrcJjc3lwULFjBhwgTS09Oxs7Mzr+PioiE01ICXVw9q1rSnd+9exeY1MTEJjaZ+ofITEhKoW7cugYGB6PVDeP/9D7h+/YZ5vZCQEPz8/AkMnGGehUhKSkJTv+j2lSYHxbl+/TpffvWleXapYHkV2afS0tLZ9cMunnrKvdT1/zf7GSAuLg5/f39ef/11Tp48WajMP/74g379+lVqXDBeRk1JSaFDh1szJeW1b/PK1w/R89LIlzh48GCZ+1HKpQJtvWRs62OPPcaOHTsAOHLkCBcuXCAx0TgAysnN4Y8//iQgYCjr16/HxUVjHtTnj3knxxCYLknq9Xh59aBTp060afM4YDx+PD09Cr0xTUpKon7+slw0JCUXqEdyEhqXAn2+iDyHbd5Ety7GGW+3xx4jZpfxTd850/4U+W7YKs/+BMbbHGJjY+nVq5d52fz58xk3dhxWxnmFCjtfFKWijtuzZ8+SlpbGyJEjCQgIICIiAsB8/gS+dHNz+9XNze0LNze3mndc8TskrCr3715T3lXuAayTUv4DIKU0HwVCiNpAHSnlD6ZFXwNPCyFqAQ9JKcNM29yUUl43rdMCWAnopJTxJcSNBRYKId4yxcg2Ld8npTwtpcwB1gLdgE4YB3yxQohDwPPAI4Ab0BrYZlo+DXAVQtQxlbnLVGax10aFEK8KIQ4IIQ4UdU+LUrT8J97ihIaG0q1bN4sTUn6enp7s2LGdzMws9u3bd8d1yMnJ4ffff8ffX4/BEIqdXQ1Wmy736PV6IiMjMRhCcXaux0cLPrrj8u/Eik9XMGL4CPNsSGXJzs7m3XfeYeiwobi6upZ7+fn3c4sWLYiKimLdunUMHTqUceMsLxtLKTl06BB9+vSp1LjXr19nwsQJNGnSBDt7u4JFlZmzszNbo7ZiCDUwccJE3nn3HfMtAuVBCJE3acZLL71EWnoaer2etSFrae7WHCvT90qMGTOGHj16sGzZMkJDDZw581e5xLe2tsZgMPD991s5evQof/55kqSkJL7/fhtDhw4tlxhFWfnF59jYWPPMgAEADBo4CI2LhqEjhjH/ow9xdHQ0t72sijpf/fDDD7Rt29Z8efaHH37AycmJli1blkvMqpS/vdnZ2Rw/fpylS5eyYsUKVq5cyZkzZ8znT2DFiRMn2gHXgHeqqMqKyd3+KdoLQA2gHXC+uJWklMFCiP8CAzAO3PrmPVVwVYynv21SSouzjRDiceCYlLJzgeV1SltZKeVKjANSbty8Lg8fPszFi4nm5xOTEnHRWL6DdXFx4eLFi2g0GrKzs7l69Sp16pQ6ZKWS56vBhWrGB7VyIOPWgZ+YmIiLi4vF+i4uLuYZg4LrODk5kZycjLOzM8nJyTg5OZW4TVxcHAcPHsRgMJCWlsaNGzdYvHgJY8e+TVJSIhqNC9WrV8fLy5Po6Jhi86rRuJCYeLFQ+RqNBo3GxTzj0Lt3b/MAL+8yBEC1arZs2bKF33/7nVatWpF4sej2lSYHxTly5Ajbt21n8eLFpKenI6wEttWrETA0wNSuiulTs2bO5uGHH8bGxga9fggArVu1vm39/81+dnBwMK/fvXt3goKCWL16NVFRUQDUrVuX+vXrm3NfkXEvX75M3bp1WbNmDZ988gk1a9ake7fuFbJvbW1tsbW1JSQkhI0bN5KWlkZ12+pliuX0QPFtnTVzFmAcMA8YMMA8cH/sUePs3gMPONGjhxdHjx6lffv2hWLeyTGUn6OjIx07dmTv3lgaN27M33//jU5nvNx748YNOjzVkSaNG9OqVSsu5i8rKREX5wJtd3YxX3o1r5Mv3ubwzezavZvPP/3MPBixsbHh4YYN2X9gPxcunEcIYb6U+69yXEx/ylPw8uyhQ4eIiYlha9RWrl67Sm5uLq6urhXSp/L8/PPPxO6N5dCvh2jVulWFHLcajYY6depgb2+Pvb097du3548//qB9+/ZoNBqio6N/NhW3nkoY4N2L98VVpvKewdsJ+AshHgAQQpiPAinlFeCyEKK7adGzwA9SynTgnBBikGmb6kKIvKmLVOAZYK4QwrO4oEKIplLKI1LKecB+oLnpKXchRGPTvXdDgD3AT0BXIUQz07Y1hRCPAScAZyFEZ9PyakKIVlLKVCBVCJF3t3upP43QqlUr4uPjSTiXQFZWFlujtuLhYdkMD08PIsKN09zbt22no3vHUs1oVQXxYBaiw3VEh+vwQDYkVkNK4yUvBweHQpdfnJ2dqVmzJnFxcUgpiYiIwMvLCzDOuIWHhwMQHh5usTwiIgIppUW5c+fOZevWrWzZsoUJEyZQo0YN/Pz8uHLlCpGR/8XDw4Ps7Gx27dpN48aN8fT0INyU123btuNuyquHhwdRUVvJzMzk3LkE4uPjad26NfXq1UOjqc+ZM2cA48mySRPjzdH5712pWdMeLy8vDAYDXl5eREQWrmuJOYiMwMvTq8Q8f/XlV2zZsoUtW7YwfPhwXh75MgFDA4CK61OffLKMq1fTmTR5EgEBQzAYQjEYQo1tLGJ/lHU///PPP+ZP7B05coTc3FxefPFF8w3rmZmZAJUSt06dOkgpOX78OD6DfNi+bXuF7duUlBRycnIICAhg4cKFODg40K9fvzLF8vTwJDzC1NaIcPPytLQ0srKyANi4cSPtO7THwcGB6zeu06hRI+Lj4zl58hSxsbH89dcZPDw8LGLe6TGUkpJivn3h5s2b/PTTTzRq1Jinn36anTt3mPu0nZ0dv/y8n3UhBnp4ehERGYmUksNxcdQqpu0ONWty2Nz2SLw8PQHYExvLl19/zceLF1vctnHjxg28vQeyLsTA+LHjqFunrvkewvLsT3Dr/jNPU50A3n77bbZt28auXbv47NPP6NatG5MnTa6QPpXnqaeeom/fvrfOTRVw3Hp5efHrr7+SnZ3NjRs3OHLkCI0bNzadPzW4ubm5mYrvCRwvVcWVCiPyTnblVqAQzwOTgBzgV+AMcFVK+ZEQoi3G++nsgdPAi1LKy0KIR4HPgHpAFuAPPAxMlFJqhRAPA1uAl6SUPxcRcyngBeQCx4AXgM7ATCAdaAZEA69LKXOFED2AeUDeXanTpJThpvp9DNTGOLu5WEr5uRAi7548CXwPDJBSti4pDzduXpcAu3fv5sP5H5Gbm8vAQQN55ZWXWb5sOS1btcTT05OMjAymTp3Gid9P4OjoyLz5weZ32f37D+Da1WtkZWVRq1YtVny6nKZNm7Jo0WK2/G+L+R2Wj68Po0e/Rrs3m5V6P+W34JXldHysM3UdnLiUnszS8AVs2LO2xG2kBE5Wh8s2NHN9jJkzZ9KqVSvAeEnTYDAAxk/OTZ8+nYyMDLp27cq7776LEILU1FQmTZrExYsXadCgAR9++CG1a9dGSsncuXOJjY2lRo0aFuXm2bx5M9u2bePs2bNkZWWRk5NN7dp1+Oeff2jdujWLFy8iJyeHqVOn8rspr/PnzzPn9fPPP2fTps1YW1szefIkunUzjt1///13PvhgJllZWbi6PsTMmTNxdHTkvfemcuLECYQQPPjgg0yfNh1nZ+dbdd1rqusHJeQgMF8O3jHmYMfOHQQHB3P58mVq1aqFm5sbn6741KKtK1aswN7enudeeNa8rLz7lIODA3379KNx48ZUszXO0AYEDMHX1xekKHZ/lGU/r127FoPBgI2NDdWrV2fixIm0bdsWMF4m7du3Lz179mT//v2VEvfgwYO8+OKLPProo1gJK6SUaOpr+Ouvv8p1327fvp1ly5dRzaYawkowevRoPJ72KFM/Sk1NZdLkSVy8cJEGDzbgw/nGth4+fJhp06chhKBp06Z88P4HODo6cu7cOcaNH8fVq1dJTk7Gzs6O5557lldeeYVly5bTyqIflf4Y+uOPP5g2bTq5ubnk5ubSp08fXnttVKFzR6dOnfk59kfTeUQSFDyX2B/3UqNGDWa9/wGtWhrb7h+gZ12Iqe3HjzFtRiAZGRl069KVd6e8gxCCZ7x1ZGZlUsd0abTN422YPnUaCecTeG3M61gJK1xcXHh/eiBfffufcu/Heeej2NhYi08pm0nj14p8/Z+vWfrx0go7X/Tv35+r167eOq5XrMBgMFRIe7/66is2b96MEAJfX19GjBgBGM+fQ4YM+QWwxfT6fuLEiTv89N6d8VnWrHwHMLcRNubk3Tn7UoxyH+DdLUwzfhOllNqqiJ83wKtM/3aAV1aHlp6ukriFr8BXVtjKP8alyK30mADi/9s3Kd2fp8OiiapprCi/Ww7viLSugtfmqupPVTQMqVGjRqVGVgO8kt3t9+ApiqIoiqIUou7BK9k9NcATQrxI4a9eiZVSFvpuOillDBBTCdVSFEVRFEW5q9xTAzwp5ZcYv5NOURRFUZT/x9QMXsn+n91goyiKoiiKcv9TAzxFURRFUZT7zD11iVZRFEVRFAUw/wzc3UII0Q9YAlgDX0gpgws8vwjjV7qB8eviXKSUdUzP5WD8CVeAeCmld1nrowZ4iqIoiqIoZSCEsAaWAb2Bc8B+IUS4lNL8hc9SynH51n8T46905bkhpWxbnnVSAzxFURRFUe45d9mHLNyBk1LK0wBCiBBgIMX/osdQYEZFVujumt9UFEVRFEW5CwkhXhVCHMj392q+px8C/s73+JxpWVHlPAI0xvjzrnlqmMr8Ke+nW8tKzeApiqIoinLPqewZPCnlSmBlORQVAKyXUub/XZdHpJQJQogmwE4hxBEp5amyBFEzeIqiKIqiKGWTADTM99jVtKwoAYDFD75LKRNM/57G+CMN7QpvdmfUAE9RFEVRlHuOlRCV+ncb+4FHhRCNhRC2GAdx4QVXEkI0B+oCP+ZbVlcIUd30/3pAV4q/d6/U1CXaCpJLVqXHPLT0dKXHBGj7ZpMqiXt4cZlmr/81aV0FMamaX2gXsmreA07b3qNK4s7utfP2K5W3KrpPXFKpv9N+i1UVzStURXOraN/mkl01gf8fk1JmCyHeALZi/JqU1VLKY0KImcABKWXeYC8ACJFS5u+RLYDPhBC5GCfegvN/+vbfUgM8RVEURVHuOVZV9WahGFLK/wH/K7AssMDj94vYbi/weHnX5+7KjqIoiqIoilJmagZPURRFUZR7zl32PXh3HTWDpyiKoiiKcp9RM3iKoiiKotxzSvHJ1v/X1AyeoiiKoijKfUbN4CmKoiiKcs9R9+CVTM3gKYqiKIqi3GfUAE9RFEVRFOU+oy7RKoqiKIpyz7nbvuj4bqOyoyiKoiiKcp9RM3iKoiiKotxzhPqalBKpGTxFURRFUZT7jJrBUxRFURTlnqO+JqVkagavBEKIt4QQvwkh1vzbMmJjf8TH2w9vrS9frvq60POZmZlMmfQe3lpfnhv+IucTzgNw9MgxAvTDCdAPZ4j/MHbuiAYgIyODZ4e9wBD/Yfj5DGH5ss+YH/wR3lpf/Pz8+O2334qsx/Hjxxk8eDBarZbg4GCklABcuXKFUaNGodPpGDVqFGlpaQBIKQkODkar1RZZ7tWrV+nduzdBQUHmZfKIHfIXe+QBe+Sf1TGFKNKc5xcSuyCO8Pd3lj6ZJZBSEjx/Hs946xis9+d4CXnw1fvxjLeO4PnzzHlYsGgh3r6DGKz3Z+yEcaSlG/OQlZXF9BmB+Or98BuiZ9/+fSXmJX+cO8n3X3/9xbPPPsuTTz7J119b9pNvvvmGwT56/Hz1vDPlPWJifmCQty/e2kGsXvVVodjGPvUu3tpBPDv8eXOf+unHnxgWMAL/wUMYFjCCfT/vL7Tt22+Nw89XX3yO5wWj1Wnx879N2/0Go9VpCZ53q+3ff/89Pr4+tG3XlmPHjpnXP3LkCHq9Hr1ej7/enx07dxRZLoCb81NM9lrLOz0MeDV7tsh1nmjQg0mea5jo+S3D2r1v8Vx1G3um9dqET+vxxcaozLaby66APrV//366du1qzu2nn34KwMWLFxk5ciS+Pr74+gxmzZrviI2NZaD3IHRab1avWl0odmZmJpMnTUGn9WbE8GdJMPWp1NRUXh75Cp07dWFuULDFNiNHvsxA70Ho9UPQ64eQciml0vKamprKyJdH0qlzJ4LmBlmUVVExf/zxRwKGBjDYbzABQwP4ed/PFbZvwbh/9Xo9Pj4+vPTSS+blxvOFfxHni4GsXvVlodjG88U7eGsH8uzw5wqcL4bjP1jPsIDh7Pt5HwA3btzgzTfewmegL4N9/Fmy+OMi26RUPTXAK9nrQG8p5fB/s3FOTg7zguazdPkSNoSFEhW1ldOnTlussyksHEfHWoRHbmT4iKEsWfwJAE2bNeXb774mxLCGT5Z/zJxZwWRnZ2Nra8tnXywndN13rDWsYdvWbRw7dpzNERsIDAxk9uzZRdZl9uzZzJgxg4iICOLj44mNjQVg9erVuLu7ExERgbu7O6tWrQJgz549xMfHExERUWS5y5Yto0OHDpZBWtxAdLgOHa5DloDk4ieIw/aG8sqSf5XWIu2J3cPZ+HgiN4cTOG06s+fOKXK92XPnMGNaIJGbwzkbH8+evcY8dO7UiY2G9WwwrOORhx9h1Wrji9yGjRsA2GhYz2crPuWDWbM4e/ZssXkxx7nDfDs6OjJlyhSef/55i3ISExP57rvvWLP2P6zfaCAnJ4eZ78/mk+UfsyFsHVFRWzlVqE9tppZjLcIjNzF8xDCWLF4KQJ06dVj88SLWbQhl5qz3mTY10GK7Hdt3Ym9vX3yO8/pEeASB0wOZPaeYts+ZzYzAGUSEW7a9WbNmLFq4iA7tLftNs2bN+O677zAYDCxftpxZs2aRm1P43YHACp/HJ/LFzxP4MHoY7R7shcahkcU69Wq60uPR5/gk9jU+ihlB+LElFs/3c3uV05cOFdvGym67Rdnl3KcA2rVrh8FgwGAw8NprrwFgbW3NxIkT2Ri2kW++/Q8ha0OY+cEsli3/hI1hG4iKiuLUqVMWscPCNuHoWIuIyHBGjBjOksXGvFa3rc6YMa8zfvy4IuscNHcOBkMoBkMoTg84VVpebavbMmbMGMaPLzyQr6iYderW4eMlH7Nh/QZmzZrF1KlTK2zfpqWlERQUxJIlSwgLC+PDDz8E8p8vvjGdL7KZ+f4s0/lifTHni03UcnQkPHIzw0cMNw/YjOeLxazbYGDmrA8szhfPPfcsYZs3EmL4jsOHDrNnT2yR7apoVlaiUv/uNWqAVwwhxKdAE2CLEGK6EOJLIcQRIUScEGJwaco4evQYrg1dcXV9iGrVqtG3Xx9iYnZZrBMT/QNa72cA6Nm7B/v37UdKiZ1dDWxsjAOkzIwM882kQgjzi3B2djZXrlyha7cuCCFo06YN6enpJCcnW8RITk7m2rVrtGnTBiEEOp2OnTuNM2fR0dF4e3sD4O3tTXR0tHm5Tqcrstzjx49z6dIlOnfubJmzvPGcBHJLPhgO/PkzV65dLk0aSyU6JgadVosQgidKyMPVa9d4Ii8PWq25vV06dzHnu83jbUhMSgTg1OnTuHd0B+ABJyduZtykbdu25Z7vBx54gNatW5vrkF9OTg4ZGRlkZ2eTnJRM/foaXF1d8/WpHyzWj4n+AZ23FoBevXuyb98+pJQ0b9EcFxdnwPgGIiMjg8zMTACuX7/Ot9+s4eVXRpaQ42h02qL7RLFt1+rYGW1se5MmTWjUqFGhcu3s7MztzsjMKPbG6YfrtuTStXOkXD9Pjszm0PnttKrf3WKdpx72JvbMBm5kpQNwNfNWH3uothsO1Z34I3lfsW2s7LZDycdasWWXok8Vx9nZmRYtWgBQs2ZN6tV7AKcHnPL1qb7ExMRYbBMTHYPOWwdAr969zH3Kzt6Odu3bYVu9+u2TWFTbKyiv9nb2tG/Xnuq2hetVUTFbNG+Bi4sLAM2aNiMjI4MdO3ZUyL7dsmULPXv2pEGDBoDx/JHn9ueLGIv4d3q+sLOzo6N7RwCqVatG8xbNSUpMLJQPpeqpAV4xpJSvAecBL8ABuCKlfFxK2QYo1XXFvIMrj4uLC0mJycWuY2Njg4ODA6mpVwA4EncUP58h6P2G8d60KeYXwZycHAL0w+nl1RcHBwc6dnzSXJ5GoyEpKckiRlJSEhqNpsh1UlJScHY2HsT16tUjJSWlxG1yc3NZsGABEyZMKDpvR+zgJwewluCcXZo0lYukpCTqa+qbH2tcNCQlF8hDchIaF43lOgVyBRC2eRPdunQDwO2xx4jZFUN2djbnEhJISUmxGICUV76Lo9FoeP755+nfV0vvXv0QVla4NXfL1wYXkhMLxy+uT+XZvn0HzVs0x9bWFoDly1bw7HMjsKtRo9i6JCUloalfdLtK0/aSxB2Jw8fXBz8/P6ZNm4aVdeFBXu0azqTeuPVCknozmdo1nC3WcXZ4GOeaDRnT9VPe7LYSN+enABAIvFu+SeTxpbetS1Eqsu2l2e7f9qm4uDj8/f15/fXXOXnyZKHYCQnn+euvMzRt0uRW2S6aQucpY58yHl+3+lTqbds2I/B99PohrPxspfmyY6F2VVBei1MZMbdv306LFi34559/KmTfnj17lrS0NEaOHElAQAARERHmbY3ni2fo3atvEecLDcmF9m1Rr0GW+7bg+SJPelo6u37YjftT7qXOTXmyElaV+nevufdqXDV6AcvyHkgpi5x6EkK8KoQ4IIQ4UNS9UXfq8TatWR8WyjfffcWXq74mIyMDMF5iCTGsIer7SNLTr5JwLqHMsaB0HzkPDQ2lW7duFickizIevwGdrhpn8VKty6VelWnlF59jY2PNMwMGADBo4CA0LhqGjhjG/I8+xNHRsdy+XLM0+U5LSyM6OprI/4Xz/bYoMjMz+Tv+7zLFPXXyFB8vXsq06e8BcOL3E/z99zl69PQqU7ll0ebxNoRtDOO7Nd+xatUqcrJKuIGzBFbCmno1G7Ji7xjW/DID/yfeoYaNA10a+fJb0o9cuZl8+0LuYfn7VIsWLYiKimLdunUMHTqUceMsL6Nev36diRMm4u2to1q1auVel6CgINZvWMeXX67m4MFfiYyMLPcYd6OTJ0+yeMlipk+bXq7l5t+32dnZHD9+nKVLl7JixQpWrlzJmTNn8p0vIvKdL86VKa7xfPGx+XyRvw7vvPMeQ4cF4OrqWqYYSsVQn6ItR1LKlcBKgGs3r8jDh+O4ePHWjENSUhIumgIzDi7OXLyYiEajITs7m6tXr1KnTm2LdZo0aYydvR2nTp6iZauWhIasI2zjJgAaPNiAvT/+ZL7Mm5iYaL5MkMfFxYXEfFPo+ddxcnIiOTkZZ2dnkpOTcXJyKnGbuLg4Dh48iMFg4Pr162RlZSGdbBGNM83rCiuQD2TDJRuom/PvklkKIaEhbAjbCECrVq24mHjxVn2TEnFxLpAHZxfzpVfzOvlytTl8M7t27+bzTz8zn0xtbGx4uGFD9h/Yz4UL5xFCWFxGLa98F+enn37ioYcewsmpLgBdunYmYvOtF8rEpCScNYXjF9enEhMTGT9uErNmf0DDhsaT8uG4Ixw//hsD+uvIyc4hJSWFl0e+yherVhISEsLGjbdynHix6HaVpu2l0aRJE+zt7Um/mEOdhpanpys3k6ljd+uNRZ0azoUGbFduJBGfepxcmUPKjQskX/0b55oNeaRuaxo/8ARdGvlS3cYOa1GNjOwb/O/3FcXWpSLbblF261a33e7f9CkHBwfz+t27dycoKIjLly9Tt25dsrKymDB+IgMG9KfNE234dMVnt8pOSix0njL2qYsF+lSd4lIHgMbUL2vWrEn/Af05euQYOp2u0vsUVOy+zC8xMZGXX3kZBwcHJkyYUGH7VqPRUKdOHezt7bG3t6d9+/b88ccfAAXOF12I2Bxxq+ykRJwL7duiXoPqmOsyftxEZs2eScOGDS22mz1zDg8/3JDhI4bdNi8V5V68L64yqRm80tkGjMl7IISoW5qNWrVqyd/xf5NwLoGsrCy2Rn2Ph4flPUMenk8TGf5fAHZs20lH9ycRQpBwLoHsbOMlzvPnL3DmzFkaPPggl1MuM2BAP0IMa/jqP6vIyMjgzF9nkVISFxeHg4ODeUo/j7OzMzVr1iQuLg4pJREREXh5GWdrPD09CQ8PByA8PNxieURERKFy586dy9atW9myZQvjx49Hq9UiGmciM2U2EwAAIABJREFUc0BmGA82KYEUG7DLvdM835GAIQGsCzGwLsRAD08vIiIjkVJyOC6OWsXkwaFmTQ7n5SEyEi9PTwD2xMby5ddf8/HixdjZ2Zm3uXHjBt7eA1kXYmD82HHUrVPXfI9Keea7OPXr1ycuLo4bN24ipeR8wnlu3rxp0ac8PZ622MbD82kiwo2DwO3bdtDRvSNCCNLT0nnzjbG89fYbtG3X1ry+Xu/Htu1R/G9LBF9+9QWP/B97dx4XVdU/cPxzWFQUcUlAE83tCZMiyyS3EnfTYdhHFNQ2s8WtxdRU3BEpl0yxUkhbVAYVWUrcABU0tU3FrR57FMUESolFZb2/P2YcGDY1BcLfeb9evmRmzj3f+z3n3sOZc+8Mj7RlXcjnujb28THcpN+vXz+iY8ofE1XmHhNNP+eqc7yUeqnUsX6Z8+fPY9G8/NB0MfM0LRrZ0dyiFabCjK4PD+TklUSjMslX9tPxoacAaFivCdaWbfjreiobf57Hoj0eBOz1JPrkKn68tKPKyV11516u7grOtSrrvoNj6s8//zRcFj1x4gTFxcU0bdoURVGYO3cu7Tu0Z/SY0Tg4OJCSklLqmNpJ377ORvH7OvclOko3Udize4/hmKpMYWEh167pLnQUFBRwYP9+OnXqWO3tWpmaiJmVlcWEiROYNWsWMdEx1dq3/fr14+eff6awsJAbN25w4sQJ2rdvX2q8uKEfL1IrGC/6GsXX9W1l48VkJk2eaDReAKxeFUx2Tg5T33/vrvpBqlmiovsiJB0hxHngGeAmuku03YAiYJ6iKNuq2jb35t8KQOKBJD4KWkZxcTFqNxdeHfcya1Z/RheHx+jr/Dx5eXnMnjmHM2d+pYmVFYuDFmFn15qY6O9YH7oBM3MzTIQJ48a/Qr/+zvz662/MmTWPouJilOJiBg4awNVr1ziUdIgGDSyYP38+Dg4OAGg0GrRaLQAnT55k9uzZ5OXl0bt3b2bMmIEQgszMTKZOncqVK1do1aoVH374IU2aNEFRFBYvXkxSUhINGjQwqveWyMhITp48ifaPdSj5ApItdJdmFaBpEXTMo7LfAUvHBdP90Z40s2zOX9kZfBK1lK2Jm+6qf46tKPmkn6IoBAQuJunQQRo0aMCCufNw6KLbX28fDeGb9e1w6iSz5viTl5dHn169mTFtOkIIhqtdyC/Ip2kT3UqX4xOOzJ45i9TLqbz+1puYCBNsbGyYO9uf9V9/WWG73Et7//nnn4wcOZLc3FxMTEywsLAgIiICS0tLgoOD2bkzFlNTUzp3tmfAoAGsWLaS4uIiXN3UvDruFYJXf0oXh8dwdu5LXl4es2b6c/bMWaysrAgMCsDOzo61n68jNGQ9bR9pa2i3NWtWGX268XLqZSZNnMKWbbo8TJSSS3eGY+KgPvd5VeTuXyr36brc98btJTAwkGvXrtG4cWPs7e35dM2nRMdEExoairmZOcJEMP618cQVV/xpw842PXF1mIwQphy9GMPe3zYwxP5VLmae4VSabrLn0mUSnW2epVgpZu9vG/jl8h6jOp6xG0abpp2JSF5Wrv6FAyu+vba6cgdQqPxcu5djatOmTWi1WszMzKhfvz7vvfceXbt25aeffuKll17iP//5D0K/AtK/fz+++3YHxcXFuLq5Mm7cqwSvDqaLQxecnZ3Jy8tj5sxZhmNqSVCg4bLcCy8MIzcnl4KCAho3bsyaT4N5uNXDvPzyKxQWFlJUVMSzPZ7lvffexdTUFKGY1Ei7vvDCC+Tk5hj269M1n9KxY8dqi/n52s8JCQnhkbaPGPILXhPM559/ft/7FmD9+vVERkYihMDDwwM/Pz9dTMN4YVZqvPhYP1646seLNfq+vTVezNb3bZMy48UXZcaL1RQUFjB08DDat2+Huf6evBE+Gjw83GnYwLJGl9TeiexZoxOYZa6H6tSSoZzgVZNbE7yaZMo/+yTbveo6scPtC1WD0hO8mqRU8AGA6lZMQY3HBOMJXk2atad/rcStbIJXrWrpV4ZC9a6wV6b0BO+BV0t9W0zNfcCtNDnB+3eR9+BJkiRJklTnyHvwqvb/6K2UJEmSJEnS/w9ygidJkiRJkvSAkZdoJUmSJEmqc+7Xd5I+qGTrSJIkSZIkPWDkCp4kSZIkSXWOyR38NaD/z+QKniRJkiRJ0gNGruBJkiRJklTnyK9JqZpcwZMkSZIkSXrAyBU8SZIkSZLqHLmCVzW5gidJkiRJkvSAkSt4kiRJkiTVOSZCrlFVRbaOJEmSJEnSA0au4FUTU+rXQlSlFmLCsRXnaiXuk1M61krcXz6p+XxNC2vpVBW1c0wt6re3VuIWi+IajymU2nmfXSwKaiUutXTblAnmNR5T1NIaSjGFtRK3psl78KomV/AkSZIkSZIeMHIFT5IkSZKkOsdEmNb2LvyryRU8SZIkSZKkB4xcwZMkSZIkqc4xMZFrVFWRrSNJkiRJkvSAkSt4kiRJkiTVOabyHrwqyRU8SZIkSZKkB4yc4EmSJEmSJD1g5CVaSZIkSZLqHBMTeYm2KnIFT5IkSZIk6QEjV/AkSZIkSapz5BcdV02u4EmSJEmSJD1g5AqeJEmSJEl1jvyi46pVS+sIIQ7eQZkpQoiG1RH/30ZRFAIDA1GpVHh5eXH69OkKy506dQpPT09UKhWBgYEoigLA33//zfjx43FxcWH8+PFkZWUZbZecnMzTTz/NJ598glrtikrlQkhIaLn68/PzmTr1fVQqF3x9/UhNTTW8FhISgkrlglrtSlJSSfdlZWXx7rvv4erqhpubO8eOHQNgzZo1DBw4CI1Gg7ePhgOJB0pyDVrCcLULnhpvTlWRq4fGi+FqFwKDlhhyXbp8GWoPNzw13kx5922ysnW5FhQUMHuOPx4aL7xGaDj6w9E7avuyFo1dRtLS40TNjftH2ytXTWusjc+ePcvo0WPw9PRiwpRJ5OTk6PbhVhu7uuA5ooo2Pq1vY1fjNl4VvBrPEd54j9Qw/s3XSc9IN9ou+WQyTzl1Y9ee3ca5V2PfzvSfhYfGC1cPd9aFhlQc9z7nG58Qb3jex28UP/38c4X1AiQlJeGqdsNFpSa0kn5/f+o0XFRq/HxHk5p6GYDMzExefWUcPXv0YnFAYIV1K4pC4JJAVC4qvLxvMz54eaJyURG45PbjQ3Z2NhMnTcRb4427hzvbt28H4PLly4zwGYGPxhcv9xFs0W4lKekQ7mov1CoPvgjZUGF+06Z+gFrlwRjfl7iszy/5xEl8NL74aHwZ4T2KuL3xAOTl5TF61IuM8B6Fl/sI1gR/Xkm73t+4AF9/tREv9xF4e/gwY9os8vLyysS8v31548YNJkyYiJurOx7unny84mPDa9U19n/77bd4eXnh6enJmDFjOHv2rKGur776Cm93HzQeI/lg2iz2JezHQ+2Nq8qz0jaePnUmripPxvi+bGjjW/744wp9ejjz5YavjZ4vKipilGY0kye8U2FOUu2rlgmeoii97qDYFKBOTPCEEPe00pmYmEhKSgrR0dH4+/uzcOHCCsstXLiQOXPmEB0dTUpKCklJSQCEhobi5OREdHQ0Tk5OhISU/AIsKipixYoV9OjRg4iICIKDVxMRsY3Y2FjOnTtnVH9ERARWVlbExETj5+fHCv1AdO7cOWJjd7Jt21aCg4MJCAigqKgIgKCgIHr37kVk5HbCw7W0b9/eUN/o0X5otVrCN2t5rs9zulyTErmQkkJMZBT+s2azcPGiinNdvIg5s/yJiYziQkoKiQd1ufbs0YNt2i1s1YbzSNtHCAnVDcBbt20FYJt2C5+t+ZSPli1DPwbelYiDYYz72PfuNwRdvP82qLE2njdvHpMnT2Lr1i0M6Nef9V/qBufEpEQuXEwhZvsdtPFsf2K2R3HhYkkbvzhmLFvDwgnfpOX5557ns7Ulv3yLiopYvvJjevboUa6+6urbXXt2U5BfwDbtFjZ/s5EtW7eQejnVOG415Pus07Ns2awlfJOW+XPmMnfBvArrLSoqYnFAIKuDV7EtYmsl/b4dK6vGRMdE4efna/glX79efd56603eeeftCuuGUuNDVDT+s/1ZuKiS8WHRQub4zyE6qoLx4Vn9+PCsEyH6CXJYWBgdOnQgXBtOyLoQli5bSkFBAdbW1nz15Vds1n7Dl998QWjoBgIWLOaT4I/ZGhFGbOxOfj/3u1Hs7RFRWFk1JipmG75+I/l4xSoAOnbqyNcbN7BZ+w2rgleyaEEghYWF1KtXj8/WBRMWvpFN2m84lHSI48dPlGvXJQFB9zVuelo6mzeG8fWmDYRv20xxcRE7Y3cbxayOvhw7ZgzbIyMI027ml1+OkZiYaNy393nsb926NaGhoWzdupXXXnuN+fPnA5CWlsbGjRv5atN6tNs2UVhUxIK5AawMXsGWiM3sjN1VaRtHxmzF18+HlStWG72+/KMV9OrTs9w+b/omjHYd2lWYT00xFaY1+q+uqa4VvBz9/85CiAQhxBYhxBkhxDdCZxLwMBAvhIivqh4hxIdCiJNCiD1CCCd9fb8LIdT6Mqb6MkeFEMeFEONLxd4nhIjUlw8UQvgKIY4IIU4IITrqy7UTQsTpt90rhGirf369EOJTIcRhIEgI8ZsQwlr/mokQ4r+3Ht9OfHw8Li4uCCFwdHQkOzubjIwMozIZGRnk5ubi6OiIEAIXFxfi4uIM26vVagDUajXx8SVNtmnTJgYOHIgQgoceaoGdnR3m5uYMHTqEhISEMvuRgFrtAsCgQQM5cuQIiqKQkJDA0KFDqFevHnZ2rWnTpg3JyclkZ2fz448/4e7uDoC5uTlWVlZV55qQgItKhRCCJ6vINSc3lydv5apSGXLq1bMXZma6+bTjE46kpacBcO7333Hq7gTAQ82b07hxY8i++8P3h98O83futbveDtDFsyiusTa+cCGFbt26AdDz2R7siduri7EvAZfh+jZ+wpHsnEraOCeXJ5/Qt/FwFfEJuja2tLQ0lLtx4wYgDI83hm1i0IABNG/WvFz61dW3Qgiu37hBYWEheXl5mJubY9moZB+rK9+GDRsihDA8f+vnspKTk2nTpo2h34dU0O8J8Qm46Pt9YKl+t2howVNPP0W9+vUrrFvXrvG4qO5yfFC5EBcfZ9he7aIfH1xKxgchBNdzr6MoCtdvXKdJkyaYmppibm5OvXr1AN3qTX5eHg8//DB2dq31+Q0mIWF/mfz2oVIPB2DAoP4cPXJUl59FA0Of5uflGdpQCEHDhrr374WFhRQWFiIwbt/k5JPYtbG7r3FBN4nLy8ujsLCQGzduYm3dolr70sLCgu5O3QHd+dv5sc6kpelXiatp7O/atathnHB0dCQtLa3C/DPSM7BtaWto48FDB5Vr433x+43a+Ii+jQHi4/bxcOuH6dixg9E2aWlpJB5Iws3dFenfqyYuYD+FbrWuC9AB6K0oykrgMtBPUZR+VWzbCIhTFMUByAYWAoMAd2C+vswrwN+KonQHugPjhBC3lpmeBF4HHgNGA48qiuIErAMm6st8AmxQFMUR+AZYWSq+HdBLUZR3gK+BW0s/A4FjiqIYn6mVSE9Px9bW1vDY1taW9PT0Oy5z9epVrK11c8kWLVpw9epVQHeSxcXFodFouHnzJk2bNjVsb2NjaxhkSsdo2bIlAGZmZlhaWpKZmUlaWjq2ti3LxU5NTaVZs2b4+/uj0Yxg7tx5XL9+w1Bu8+bNeHl54z93juHSQXp6Oi1L12VjW+4SYHpGOrY2tsZlyrQHQETkdvr06gOA/aOPkrA/gcLCQi6lpnL69CnIq+H7L/JMoH6x4WF1t3HHjh0MA/quPbu5knalJMadtHEVx9zK1Z8waNgQvo39jrfeeAOAtPQ04uLj0XhpKky/uvp20ICBNLSwYMDgQQweNpSxo8fQpEmTu497l/kC7I2LQ+3hxluTJzJ/ztzK825ZJqe0jArKlO/3O5Geno5ty3sYH/4qMz78pRsffHx8+P1/vzNw0EC8vLx4f+r7hnuWrly5gsZrFMOGuPBc3+do09bOULeNjU25/DLSMwxtUJLf3wCcOJ6Ml/sINF6j+GDWNMPEq6ioCB+NLwP7DeHZHk484fh4pXXer7g2tjaMHuvHsCFqBg8cRuPGlvTsVbIaXd19mZWVzf59+3n2WSdDXdUx9pcWERFBnz59DNuOHTuW4UNcGTJwOCYmJth3frRUvjZkVNDGti1tyuT7N9evX2fDF1/y2uuvlou5NGg5k9+egIlJxW+KaoqJiWmN/qtrauI35BFFUS4pilIM/AK0u4tt84FY/c8ngH2KohTof75Vz2BgjBDiF+Aw8BDwH/1rRxVF+UNRlDzgHLCrVF23tu8JbNT//BXQp1T8cEVRivQ/hwJj9D+/DHxRdmeFEK8JIX4QQvxQ+jLq/VT6neqHH37IlClTqu1G06KiIs6cOYO3twatNgwLiwaE6i+raTQaYmJi0GrDaNGiBR8tW3pfY3++bi1mZqYMHzYMADdXN2xtbBnpN4qgjz7kySefhNodW+6Lqtp43rx5hIVp8fEZSe71XMzNze9b3ElvTWT3dzsZPnQYm8I2AxD00YdMmTS52m9cLtu3ySeTMTE1Yc/OXeyI+Y4NX3/FpUuX7mvMivIFGNC/P1HbtrNi6XJWrQm+rzFrgxDCcF4cPHiQzvad2bN7D9owLYsDFxvu42zZsiXaLRuJjN7Gj0d/4maZ+9TuxhOOj7MlIoyvNq7ni5ANhnveTE1N2az9hthdMZxMPsV/fzt3m5ruPW5WVhYJ8fuI+W47O3d/x40bN/g2Zsd9jVuZwsJCZkyfzshRI7Gzs7v9BnepohXmI0eOEBERwZQpUwDd/bzx8fFEfxdB7O5vyc/P51LKxX8U77M1axnlN9KwEnvL/n2JNGvenMe6PPaP6pVqTk18irb0yFF0lzELFMVwp1XxrboURSkudV+cACYqirKz9IZCCOcysYtLPS6+w/3IvfWDoigXhRBpQoj+gBMlq3mUKvM58DnA+vXrFY1GtxLi4OBgtISelpaGjY2N0bY2NjaVlmnevDkZGRlYW1uTkZFB8+a6y2cnT55k2rRpAPz5558UFxcTFxdH//79SU9Pw9a2fIwrV65ga2tLYWEhOTk5NG3aFFtbG9L0q0OlY9va2mJra4Oj4xMADBo0yDD5eOihhwzl69Wrx3exOzh95jQODg6GlSbQrQrZWJfZD2sbw+U5Q5lS7REZFcn+AwdY++lnhkHNzMyM99+baigz+sUxYFGymlYj6hdDXskkq7rbuH379nz22acABK8KJi8vD++RGhy63GEb3+aYAxj+wjDenDyBt15/k5OnTzFthu54upaZyYGkRA59f4jkk8kA1da33+3YQe+evTE3N+eh5s156smufB6yltNndDekV1e+pT3zdDdmp17i2rVrNGvWzLhuGxuuXCmTk611BWXK93tlNm8OY9u2bQA83uVx0q7cw/jwUMXjQ2RkJC+//DJCCNq2bUvr1q353//+xxNPPGGox9rGmnbtH+H3/5bcl5Wenl4uP2sba65cSSuTXxOjMh06tMeioQXn/nuOLg5dDM83tmrMM927cfDgITr9p2O5Ou9n3NTUy7Ru/TDNmuv6sP+Afhw/dpzhqhcM7Xi/+/KWBfMX0rZtW8zMzNBoRgDwuMPj1TL2A/z666/MmzeP1atXG/bv+++/p3Xr1ob8e/XuSXRkTKl807GuoI3TrqSXa+PkEyfZuyeelStWkZ2djYkwoX69+qSnp7M/YT9JiQfJz8sjJzeXWTPmsHBxxfewVicTIT9FW5XabJ1soPF9qGcn8IYQwhxACPGoEKLRXWx/EPDR/+wLHKii7Dp0l2pLr+xVyMfHB61Wi1arpV+/fkRHR6MoCsePH8fS0tKw7H6LtbU1jRo14vjx4yiKQnR0NP366a5eOzs7ExUVBUBUVJTh+R07dhj+DR48GCsrKx591J6CggJiY3fSt29foxjOzn2JiooGYPfuPTg5dUcIQd++fYmN3al7t3cplZSUFB5//HFatGiBrW1Lzp8/D8Dhw4fp0EF3L0bp+0gaNWxIf+d+hG/W0t+5H9ExMSiKwrHjx2lcSa6WjRpx7FauMTH0c3YGIDEpiS82bGDlihVYWFgYtrlx4wbXb+guXR76/hCmpmaIRjU8wWtcDDdMuHQptUba+C/9pbbi4mJSL19mxvvTCd+kb+Nv9W18ooo2tmzEsRP6Nv42hn59nQG4kHLBUC5+XwLt2+nuaIiN/o7YmB3Exuxg0ICBzJz+AXNm+xO+WVutfduqVSuOHD0CwPUbNzh+4gR+o3wJ36St1nxTLqYY7jU6dfo0Bfn5Ff4id3BwICUlhVR9v++M3Ulffd239HXuS7S+3/fs3kN3fb9XxsdnBFptGFptmG58iLnL8SEmmn7O+vGhrzNR0frxITrK8HzLVi05fPgwAH/99Rfnz5/Hzs6OtLQ0bt68CehWfC6cv0BmZmap/HbRt+9zZfJ7npiobwHYuzuO7k7PIIQg9VIqhYWFAFy+/Afnz1+g1cMPc+3qNbKzsgG4efMm339/mHbtHinTrl24mHLxvsZt2bIlJ44nc+PGTRRF4cjho7Rv365a+xJg1arV5ORkM/X9qeX7thrG/j/++IN33nmHRYsW0a5dSX4tW7bk+PHjhvxTUy9z82YeqZcuU1BQwK7Y3fTt+3yZfJ+rsI1D1n9OzI7txOzYzihfH156dSwjRnozcfJb7NgdQ8yO7QQsWUj37s/UyuROur3a/B68z4FYIcTl29yHdzvr0F1u/UnozsIMwO0utp8IfCGEmKrf9qUqykahuzRb7vJsVZ577jkSExNRqVQ0aNDA8Ikn0F3q1Gq1AMycOZPZs2eTl5dH7969DfdVvPzyy0ydOpXt27fTqlUrPvzww3IxTExMcHd344033qC4uBg3N1c6derE6tXBODh0wdnZGXd3d2bOnIlK5YKVlRVBQUsA6NSpE4MHD8Ld3QNTU1M++GAGpqa6+w2mT5/GjBkfUFBQgJ1da8O+L1++grNnzyKE4OFWD+M/c5Yu1z7PcSAxkeGuLjRo0IAFc0tOfG8fDeGb9bnO+IBZc/zJy8ujT6/e9Omty3XxkkDyC/IZ/8brgO5m/NkzZ3H12lVef+tNTIQJNjY2BCxYyAtBxr8M7sTSccF0f7QnzSybkxD0A59ELWVr4qY72lYIUDrdrLE2jo3dwebNYQAM7DcAN7VrSRsnVdLGIzWEb9K38fQPmDXXn7ybefTpXdLGKz5ZyfkL5zERJrRq1YrZH8y8o/yrq299NCOYPdcfdy8PFAVc1Woe/c+jxnGrId89e/cS/W00ZmZm1K/fgKDFQRX+IjczM2P6jGm88cabFBcX4+rmSqdOHQleHUwXQ7+7MXPmLFxUaqysrFgSVPI1Gi+8MIzcnFwKCgqIj49nzafBdOxYspJlGB9c9OPDvErGhw9mMtu/kvHh/alsj9hOq4db8WGQbnx4bdxrzPafjaeXJ4qiMGXKFJo1a8ahQ4dYumwpCAVFgTEvjsbW1oa33phEcXExajcXOnbqyJrVn9HF4TH6Oj+Pm7ua2TPnoFZ50MTKisVBuk8y//zzMdaHbsDM3AwTYcKMD96nWbOm/Prrb8yZNY+i4mKU4mIGDR7I82Umb2ZmZkybMfW+xm3WrCkDBg3A12c0pqam2He2x8PLvVr70tLSknVr19G+fXt8fEYCugm8h4dHtY39n332GZmZmQQEBAC6y+GbNm3C0dGRQYMG4eszBjNTU+w7P4r/vJlMeGMSRcXFuLq50LFTB6M2dnVXM3vmXFxVnjSxsiIgqOJP+v4b1cX74mqSKLkCKt2OEOIZYLmiKLedWdy8ebMWGrZ2+lJUuZZZfZ6c0vH2harBL5/c33uJ7oQorPGQ+sC1FLeWhqVis5oPLJTauZBSJP75fXd1kQn37x7WOyVq6SJZITdrJa5lg6Y1OmIE/zC6Rk/YN5/5qk7d+S3/ksUdEkJMB96ggnvvJEmSJEmqWXXxu+lq0r/iDkUhxGEhxC9l/j1x+y1rjqIogYqiPKIoSmJt74skSZIkSf8uQoihQoiz+u/JnV7B6y8KITJKzXNeLfXaWP337f4mhBh7P/bnX7GCpyjKs7W9D5IkSZIkSf+EEMIUWI3uu3ovAUeFEFGKopwqUzRMUZQJZbZtDswBnkF3g8qP+m3/4bfy6/wrJniSJEmSJEl341/2NSlOwH8VRfkdQAixGXAFyk7wKjIE2K0oylX9truBocCdfQKwEv+q1pEkSZIkSfo3Kv3HDPT/Xiv1cmug9LdKX9I/V5an/k+jbhFCtLnLbe+KXMGTJEmSJKnOqemvSSn9xwz+oWhgk6IoeUKI8cAGoP992bkKyBU8SZIkSZKke5MKtCn12E7/nIGiKH/p/3Qq6L7Dt9udbvtPyBU8SZIkSZLqHJN/19ekHAX+I4Roj25y5gOMKl1ACNFKUZQ/9A/VwGn9zzuBACHErb+TOBiYca87JCd4kiRJkiRJ90BRlEIhxAR0kzVTIFRRlJNCiPnAD4qiRAGThBBqoBC4Cryo3/aqEGIBukkiwPxbH7i4F3KCJ0mSJElSnWP6L/tTZYqifAd8V+Y5/1I/z6CSlTlFUUKB0Pu5P/IePEmSJEmSpAeMXMGTJEmSJKnO+Zd9D96/jmwdSZIkSZKkB4xcwasmReTdvtB9ZqrUr/GYAEot3QbxyyfnaiVu14kdazzmT5/8WuMxAUxqaYgopqBW4tYGRRTVStzaGi9EsVIrcRE1H7LIpLDmgwJm1E7f1rSa/h68ukau4EmSJEmSJD1g5AqeJEmSJEl1zr/se/D+deQKniRJkiRJ0gNGruBJkiRJklTnmJjINaqqyNaRJEmSJEl6wMgJniRJkiRJ0gNGXqKVJEmSJKnOMZUfsqiSXMGTJEmSJEl6wMgVPEmSJEmS6hz5NSlVkyt4kiRJkiRJDxhb3x42AAAgAElEQVS5gidJkiRJUp0j/1RZ1eQKniRJkiRJ0gNGruBJkiRJklTnmAi5RlUV2TrVLCnpEO5qL9QqD74I2VDu9fz8fKZN/QC1yoMxvi9xOfUyAMknTuKj8cVH48sI71HE7Y032q6oqIiRGj8mTXi7wriKohC4JBCViwovby9Onz5dYblTp07h6eWJykVF4JJAFEUBYNeuXbh7uNP1qa6cPHmy3HZ//PEHPXr2YMOGDbpYgYGoVCq8vG4Ty9MTlUpFYGBJrL///pvx48fj4uLC+PHjycrKAuB///sfo0eP5plnnmHDBuO2mz9/Ps88051u3Z7B21tDXl5euXadOvV9VCoXfH39SE1NNbwWEhKCSuWCWu1KUtJBw/NZWVm8++57uLq64ebmzrFjxwA4e/Yso0ePwdPTi4kTJ6EUVphelRaNXUbS0uNEzY27+43LSEo6iJvaA7XKldCQL8q9rjumpqNWuTLad4zhmPr+0PeM8vHF21PDKB9fjhw+AsCNGzeYOGES7q4eeLp78/GKlYa6qqtv4+Pj8fLyQqPRMHLkSH766SdDXU899RQjNKMYoRnF5Elvl8rXjdCQ9ZXkOwO1yo3RvmPL5OuHt+cIRvn4ceTwUcM2BQUFLJi/CFcXD9xdPdmzZ28lbXz/Yr76ymu4qT0MuV3962q5eqsj7i2TJ72Nl4fG8Phex4jK+jYrK4spb0/By9uLUb6j+O2/vxnVV1RUhGbkCCZMmqjbh6AlDFe74Knx5lQV++Ch8WK42oXAoCWGfVi6fBlqDzc8Nd5MefdtsrJ1+5B6OZXuPZ/F20eDt4+GBYsWGtWny/0O43rr4y4pibtr9y7cPT148umnjMbHgoICZs6ehYe3F64e7oSEhBpe+6fnbWZmJuNeeY1ePfoQGLDEUD43N5cRmpGGf316PcfzfZxRq1yN4pau/27HxKSkJNRqV1QqF6M6Dx8+zIgRPmg0GsaOfZGUlBQA7O3tX7S3t8+wt7f/Rf/v1QobVqoRcoIHCCHOCyFa3O96i4qKWBIQxCfBH7M1IozY2J38fu53ozLbI6KwsmpMVMw2fP1G8vGKVQB07NSRrzduYLP2G1YFr2TRgkAKC0tmFpu+2Uz7Du0qjZ2YmEhKSgrRUdH4z/ZnYZkB7paFixYyx38O0VHRpKSkkJSUBECnTp1Yvmw53Z7uVuF2Hy39iD69+xjHio7G39+fhQsribVwIXPmzCE62jhWaGgoTk5OREdH4+TkREhICABWVlZMmzaNsWPHGtVz+fJltm/fTljYZr7//hCXL1/myy+/NCoTERGBlZUVMTHR+Pn5sWLFxwCcO3eO2NidbNu2leDgYAICAigqKgIgKCiI3r17ERm5nfBwLe3btwdg3rx5TJ48ia1bt9C/f3+4VK/Sdq9MxMEwxn3se9fblVVUVERgQCCrgleyNWILsbE7OVfumNpOYysromIi8fXzNUzYmjZtyoqVKwjfqmX+gnnMmulv2GbMmNFERG5js3Yjx345RmKirm+qq2+fffZZwsPD0Wq1zJs3j3nz5hnqql+/PmHajYRpN7Js+UcEBizR5xteSb6RNLZqTFTMdnz9RvHxik9K5buc8K1hzF8w1yjfdWtDad68GZHR29gaEU63biXHua6N739MgEWLFxpya/5Q8wr6tnri7t0TR8OGDY2eu9cxIjQ0FKdn9X37rBMhobq+XbduHZ3tO7MlfAuLFi4iKCjIqL5vNm00nFuJSYlcSEkhJjIK/1mzWbh4UcX7sHgRc2b5ExMZxYWUFBIP6vahZ48ebNNuYas2nEfaPkJIaMkkxM7OjvDNWsI3a5k9c1a53I3iBlQSN2ARc2aXintrfOzYiWVLl9Ht6aeNyu/as5uC/AK2hW9h8zcb2bplG5dTL9/TeVu/Xn3efOsN3n5nilH5Ro0aEabdRJh2Exs3fU1+fgEfzJyhrz+Wc+fOGZW/2zGxqKiIgIDFBAevJiJim1GdCxcuYvHiALRaLcOGvcDatWtLhwo7e/ZsV/2/dRU27H1iamJao//qGjnBq0bJySexa2OHnV1rzM3NGTJ0MAkJ+43KJMTvQ6UeDsCAQf05euQoiqJgYdEAMzPdFfT8vDyEEIZt0tLSOHAgCTd310pjxyfE46JyQQiBo6Mj2dnZZGRkGJXJyMggNzcXR0dHhBC4qFyIi9etMHXo0IF27dpVWHdcXBytH25Nx44ddbHi43FxuctYLi7ExcUZtler1QCo1Wri43WrlQ899BCPP/64oR1uOXv2LKamprRo0QIhBC1atODixYvG+ccnoFa7ADBo0ECOHDmCoigkJCQwdOgQ6tWrh51da9q0aUNycjLZ2dn8+ONPuLu7A2Bubo6VlRUAFy6kGCYAPXv2gD/v/s6GH347zN+51+56u7KSk0/Spk0b7OzsSh1TCUZlEuL34aJWATBw0ABD7p0f64yNjTWgewORl5dHfn4+FhYWdHfqDujy7vxYZ9LT0oDq69uGDRsajukbN24YHd+3z3ffPeULELk9ipdffgnQ/T3LZs2aVnvM26muuNevX+frr77h1XGvGNV1r2NEfEI8ahd937qU9O3vv/+Ok5MTAO3bt+fy5cv89ddfgG7s2n/gAB5uHvo6EnBRqRBC8GQV+5CTm8uThn1QGWL16tnLMD44PuFIWnraHbV1/L5/GDdBF7dDhw60r2B8FAiu37xBYWEheXl5mJuZ08iy0T2dtxYNLXjq6aeoX7/yN5Z7du9FCMGQoYMxNzdn6NAh5eq/2zExOTnZaJ9L1ymEICcnF4CcnBysra3vqN2lmlWnJ3hCiHZCiDNCiPVCiF+FEN8IIQYKIZKEEL8JIZwq2e4hIcQuIcRJIcQ6QJR6zU8IcUQI8YsQ4jMhdF+0I4TIEUIs12+zVwhx2yM6Iz2Dli1tDY9tbGxIT8uotIyZmRmWlpZkZv4NwInjyXi5j0DjNYoPZk0zDGQfBS1n8tsTq/xDy+np6diWim1ra0t6enr5MrZVlynr+vXrfLH+C15//fW7qqeqMlevXjUMEC1atODq1fKXrkorLCzE3t6eIUOGMnDgIJo0saJBA4ty8Vq2bAmUbtdM0tLSsbVtWW4/UlNTadasGf7+/mg0I5g7dx7Xr98AoGPHDoZfKLt27Ya82jttyvWrjS0ZZY6p9AqPqUyjMnv27KXzY52pV8/4l0Z2Vjb79x3A6VmnknjV1Ld79+7F1dWVCRMmGK3g5efnM2rkaMb4vUj83vgy+dqQkVY+fmXnUEX5ZmdlA7B69RpGjvBl6nvTDBMQQz73OeYtc/3nMUIzis8/W2e43FfdcYNXr2H0GD8sGjSoOt7d9u1fZfpWf8n50UcfZe9e3SXvEydO8Mcff5Cmf8MQ9GEQ70yegomJKMmn9PloY0t6Rpl9yEjH1sb4mK9onIqI3E6fXn0Mj1NTU9GMHMFLr77Cj6VuATDEbVl+HCiXu83djY+DBg6kYQMLBgwaxOAXhjJm7GiaNGly387byuzdG0fbtm0Mb5RsbGxJq/DYufMxsWwbla5z7tw5TJgwgUGDBhMT8y0vv/xy6VCe9vb2x+3t7bfY29u3uaME/iETYVqj/+qaOj3B0+sELAU66/+NAvoA7wEfVLLNHCBRURQHIAJoCyCEeAwYAfRWFKUrUATcuq7WCPhBv80+fR1GhBCvCSF+EEL8UNH9M3frCcfH2RIRxlcb1/NFyAby8vLYv+8AzZs3o0uXx+65/n9izadr8PP1K3e5536pbCWntOvXr/PHH3/w3Xffsnv3LvLz8zl//vw9xS0qKuLMmTN4e2vQasOwsGhAqP5yz7x58wgL0+LjM5Lr13NLvR2om8799xwrV6xk1mzj06OwsJDp0z9g5Cgf7Ozs7nvcsn07YMAAIiMjWbFiBatXrzY8v2PHDjZu+oqAwIVERkWTm5t7T3F1+X5iyLewqIi0tDSe7OrIprBvcHR8guVLV9xTjNvFBAgIWEj41jBCv1jLzz/9TEzMt/c1ZkVxz545y8WLl+g/oN99j1WaEMJwXrz88stkZWeh0WjYtHkTne07Y2Jiwr79+2jerDldunS57/E/X7cWMzNThg8bBoB1C2t2fReLdlMYU995l+kzZ5CTk3Pf45aVfDIZE1MT9uzaxY5vv+OrL7/m0qVL1R732C/HsGtz/8/Zynz11desWrWK3bt34eqq5qOPlt56KRpod/bsWUdgN1D+xnOpxjwIn6L9n6IoJwCEECeBvYqiKEKIE0C7SrZ5HvAAUBTlWyHErWtnA4BuwFH9LyML4NbboGIgTP/z18C2spUqivI58DlA7s2/lWPHjnPlSsklg/T0dGxsjRf+rG2suXIlDVtbWwoLC8nJyaFp0yZGZTp0aI9FQwvO/fccx345zr6EAyQmHiQ/L4/c3FxmzvBn0eL5bN68mW3bdLvl4OBAWqnYaWlp2NjYGNVrY2NjeGddWZmyTpw4wZ7de1i0aBHZObqVkCeeeOK29VQVq3nz5mRkZGBtbU1GRgbNmxvfm1RWWloaJiYmhnJ2dnZcuXKlXLwrV66Uadem2NrakJZ2xaguGxsbbG1tsbW1wdHxCQAGDRpkmOC1b9+ezz77FIDz5y+w5uvV1BYbGxvjfk1Pw7rMMWVT4TGluwSZlpbGO2+/x4KF82nTxvjN9cL5i2jbtg1mZmaM0IwE4HGHx6u9b7t168alS5e4du0azZo1w9bWlmIKsLOzw8HBgQvnL5TKNx1r2/LxKzuHdPlOZcHCebTR/wJs2rQJDRo0YMCA/gAMGjyQ7RFRVbTxvccEsNHX0ahRI14YNpSTJ07i4qKq1rjHjp/g1KnTDHvBhaLCIv7880969XiONm3b8HiXx+9pjGj+UMV9a2lpyYL5CwDdhxmGDRuGnZ0dO3ftJGFfAjt3xpKTm0txcTF2re24Uvp8TE/DxrrMPljbGF16TUs33s/IqEj2HzjA2k8/M7yJqFevnmEFs0uXLrSxs2PturUcPHQI0I2PpceMSnNPv7vx8bsdO+jdqzfm5uY81Lw5Xbs+yamTp7Bt2fKeztuqnD37K0II8m6WfNAsPT0N2wqPnTsfEwGjNrpV59WrV/n1118NY+WQIUN488239Pty9q9SIdcBxjdg3mfyU7RVexBap/THJ4tLPS7m7iewAtigKEpX/T97RVHmVlJWqeR5AweHLlxMuUjqpVQKCgrYGbuLvn2fMyrT1/l5YqJ07+T37o6ju9MzCCFIvZRq+FDF5ct/cP78BVo9/DATJ79F7O4Yvt0RyeIli3im+zMsWjwfAB8fH7RaLVqtln79+hEdE42iKBw/fhxLS8ty90lYW1vTqFEjjh8/jqIoRMdE08+56nf6679Yz44dO9i/fz+vjXuNKZOn8OqrrxIdfZexoqPp108Xy9nZmago3S/YqKgow/OV6d69O1evXuXcuXPk5+dz+PBhunc3vhrv7NyXqKhoAHbv3oOTU3eEEPTt25fY2J3k5+dz6VIqKSkpPP7447Ro0QJb25aGlcDDhw/ToUMHAP7SX3oqLi7W3Uzc6s7uqaoODg5dSClzTDn37WtUpq9zX6KjYgDdvTnd9blnZ2UzccJkJk2eSNenuhpts3pVMNk5OUx9/z1G+GgMN2/369evWvo2JSXFcIny9OnT5Ofn07RpU7Kysgz3jl27lsmFCxfI/PvvMvk+Xybf56vIdwqTJk8wylcIwfN9n+OHoz8CcOTwUTp0bH+bNr63mIWFhVy7prvcVlBQyP79B+jYqeMd9O29xdVovNi9J5bvdkTzxfp1tG/fjoPfHyBMu/Gexwjnvs5ERev7NjrK8HxWVhYFBQUAbNu2jae7PY2lpSWTJ01m967dHEjYz+drPuW53n2YNnUq0TExKIrCsePHaVzJPlg2asQxwz7E0M/ZGYDEpCS+2LCBlStWYGFRcpvG1WtXDR+eunTpEikpKbz80suEh2kJD9PSv1+/fxa3rzNVadWyFUeO6j6dfv3GDY6fOEG79u3v6by9ndgdsbioVUb1x8bupG+Z+u92THRwcCAlJYVLZeq0srIiJyeH8/o3XocOfW/40Iy9vX2rUiHVQMUfT5ZqhCh7H0hdIoRoB8QoivK4/vF6/eMtZV8rs91KIF1RlIVCiBeA7wBrwAaIRHeJNl0I0RxorCjKBSGEAoxUFGWzEGIWYKsoysTK9i335t8KQOKBJD4KWkZxcTFqNxdeHfcya1Z/RheHx+jr/Dx5eXnMnjmHM2d+pYmVFYuDFmFn15qY6O9YH7oBM3MzTIQJ48a/Qr/+zkYxfjj6I19u+JqVq5YDYKrUN7ymKAqLFy8m6WASDRo0YP68+Tg4OACg0WjQarUAnDx5ktn+s8nLy6N3797MmD4DIQR74/YSGBjItWvXaNy4Mfb29ny65lOj+GvWrKFhw4aMGTtGFytJH2t+FbFml4o1QxcrMzOTqVOncuXKFVq1asWHH35IkyZN+PPPPxk5ciS5ubmYmJhgYWFBREQElpaWTJ8+nd27dwO6G56/+eZr1q5dh4NDF5ydncnLy2PmzJmcOXMWKysrgoKWGC47rl27lu3bIzE1NeX996fSp4/uvp0zZ84wb958CgoKsLNrzfz587GysuKbb75h82bd4u2AAQP44r8ruYNx18jSccF0f7QnzSyb81d2Bp9ELWVr4qY73v6nT341/HzgQCIfBS2luLgIVzdXXh33CsGr19DFoQvOzn3Jy8tj1szZnD1zFiurJgQGBWBnZ8faz9cRGvIFbR9pW6oPV1NQWMDQwcNo374d5vpVjxE+Gjw83BGKabX0bWhoKNHR0Zibm1O/fn3efvttnn76aX755RcWLFiAMBEoxcWM8h1JC+sW+nOoCFc3tT7fT+ni8FipfP31+VqVyXd9mXxX0fyh5ly+/AezZvqTk51Ns2bNmDt/Dq1atSzTxvcvpoWFBa+8PI7CwkKKiop5tocT7773Nqamxvf13O+4pT+pezn1MpMmTmHLNl2fiWKzexojMjMzmfr+VK78cYVWD7fiwyBd3x47doxZs2chhKBjx47MmzvP8IElXVyFoz8cZcOXX/LJxysJCFxM0qGDNGjQgAVz5+HQRbcP3j4awjfr9+HUSWbN8ScvL48+vXozY9p0hBAMV7uQX5BP0ya6VUzHJxyZPXMWu/fuIXhNMGZmZggTE94c/wbOziUTHkVRdHEPloqrz917hIbwsJLcDXF7l8TdGxfH4iUl42Nne3s+DV7D9evXmT3Hn99//x1FAbWbC2NfHHNP5y3AsBdU5ObkUlBQQOPGjQn+dDUdO+regKqGqflk9cdcupRqqN/NzY1x48axenXwPY2JBw4cICjoQ4qLi3Fzc2XcuHGA7p6/4OBgTExMsLJqzLx587Czs+PJJ7sGopvYFQJXgTfOnj17hmqy5+KHNTqBGdhmap26Qef/6wTvIWAT0Bo4CAwGuimK8qcQYgQwA93qZgHwlqIo3wshctBdfh2M7rLtCEVRMsrWfcutCV5NKj3Bq1G1dsjXzrHbdWLH2xe6z0pP8GqSSS3dxVFMQa3E/f/ERDGvlbiiuJZ+59TCOFVkUlTzQQETaucDAQ0aWNRoK8sJXtXq9D14iqKcBx4v9fjFyl4rs91f6CZqFb0WRsm9dmVfe+cf76wkSZIkSVINqdMTPEmSJEmS/n+qi19dUpMe6AmeEOIlYHKZp5MURXnrbutSFMXy/uyVJEmSJElS9XqgJ3iKonwBlP+jf5IkSZIk1WlCruBV6UH4mhRJkiRJkiSplAd6BU+SJEmSpAeTvAevanIFT5IkSZIk6QEjV/AkSZIkSapzauv7/uoKuYInSZIkSZL0gJEreJIkSZIk1TnyHryqyRU8SZIkSZKkB4xcwZMkSZIkqc6RK3hVkyt4kiRJkiRJDxi5gldNTKlX4zEVUVzjMQEUimolrmlh7Ry+P33ya43HfHriozUeE+DYx+dqJS4monbC/n/6VF7tNDEotRS3FvKtreNJ1M6QXOPkX7KomlzBkyRJkiRJesDIFTxJkiRJkuqc/1cr7v+AXMGTJEmSJEl6wMgJniRJkiRJ0gNGXqKVJEmSJKnOMRFyjaoqsnUkSZIkSZIeMHIFT5IkSZKkOkd+0XHV5AqeJEmSJEnSA0au4EmSJEmSVOfIFbyqyRU8SZIkSZKkB4xcwZMkSZIkqc6Rf6qsanIFT5IkSZIk6QEjV/AkSZIkSapz5J8qq5pcwZMkSZIkSXrA1LkJnhBirhDivQqef1gIsUX/s7MQIqYaYrcTQoy6XTl7e/uharUrKpULISGh5V7Pz89n6tT3Ualc8PX1IzU11fBaSEgIKpULarUrSUkHDc/7+8/B2bkfHh6eRnWdOXMGP7/RaDQaRo0cxYkTyQAkJSXhqnbDRaUmtJJ9eH/qNFxUavx8R5OaehmAzMxMXn1lHD179GJxQKCh/I0bN5gwYSJuru54uHvy8YqPK8w9KekgbmoP1Co3QkPWVxh32tQZqFVujPYdy2V93O8Pfc8oHz+8PUcwysePI4ePltt28qS38fLQVBhXURQCg5Yw3NUFzxHenDp9usJyp06fwkPjxXBXFwKDlqAoCgCrglfjOcIb75Eaxr/5OukZ6UbbJZ9M5imnbuzas7uCXF0JDfmiklyno1a5Mtp3TJlcffH21DDKx5cjh48AujaeOGES7q4eeLp78/GKlRXmcDcWjV1G0tLjRM2Nu+e6FEUhcMkShqtd8NRU0canTuHh7cVwtQuBS0raeNfuXbh7evDk009x8uRJQ/mCggJmz/HHw9sLL42Goz8Y9/0/befMzEzGvfIavXr0ITBgidE2O2N3ofEawQtDhtP3uX5V1n2352pSUhIVnf+HDx9hxAgfPDw8mTVrFoWFhUaxkpOT6dr1KQYPHoK7uwdff/11he1bmVOnTuHp6YVK5UJgYEm7r1mzhoEDB6HRaNBoNBw4cADQ92dgICqVCi8vL05X0Z+enp6oVCoCAwMN9f7999+MHz8eFxcXxo8fT1ZWFgDffvstXl5eeHp6MmbMGM6ePWuoKysri3fefw+1hxuunu78cuyXGj9v7/U4Xrp8GWp3Nzw13kx5522ysnV5Z2Zm8sq4V3m2V08CAhcDlR8Lt/yT4ysrK4t3330PV1c33NzcOXbsGADLli3D1dUNLy9vprxbsl+lGcbJO8ldo8+9bHtrvPH2qbi9a5qJMK3Rf3VNnZvgVUZRlMuKonhVc5h2QJUTPHt7e1NgdXDwaiIithEbG8u5c+eMykRERGBlZUVMTDR+fn6s0E+Wzp07R2zsTrZt20pwcDABAQEUFRUB4OqqZs2a4HLxli9fweuvj0er1fLGm2+wYsUKioqKWBwQyOrgVWyL2FrJPmzHyqox0TFR+Pn5GiZs9evV56233uSdd94uF2vsmDFsj4wgTLuZX345RmJiotHrRUVFBAYsYVXwSrZGhBMbu5Nz5343KrM9IpLGVo2JitmOr98oPl7xCQBNmzZlxcrlhG8NY/6Cucya6W+03d49cTRs2LDSdk9MSuTCxRRitkfhP2s2CxcvqrDcwsWLmDPbn5jtUVy4mELiwSQAXhwzlq1h4YRv0vL8c8/z2drPjfJavvJjevboUSbXQH2uWyrJdTuNrayIionE18/XMGHT5bqC8K1a5i+YZ5TrmDGjiYjcxmbtRo79cozExKRKc74TEQfDGPex7z3VcUtiYiIXUlKIidS3cUAlbRygb+PIKC6kpJCYpMuhU8dOLFu6jG5PP21Ufuu2rQBsC9/CZ59+ykfLllFcXAzcWzvXr1efN996g7ffmWJUPjMzkxXLV7B6zWrMzEzp9kw3ps2Ypq/73s7VoqIiAgIWU/b8Ly4uZvbs2SxZsoRt27bSqtXDREVFG+Lc2s7CwoLJkycRHq5l//4DpKSk3HH/LFy4iDlz/ImOjiIlJYWkpJJjZ/RoP7RaLVqtlueeew7Q9WdKSgrR0dH4+/uzcOHCSupdyJw5c4iOjjaqNzQ0FCcnJ6Kjo3FyciIkJASA1q1bExoaytatW3nttdeYP3++oa6goCB69+xF1LbtbNmsJS09rUbP21t538tx3LNHD7aFb2GrNpxHHnmEkFDdxK1e/fq89eZbvPv2O4b4FR0Lpf2T3wVBQUH07t2LyMjthIdrad++PQA9evRg69YtbNkSziNtS/bLKPekMrlX1d6zSuVeur214YRv1rf3559XuL3071ArEzz9StgZIcR6IcSvQohvhBADhRBJQojfhBBOQojmQojtQojjQojvhRCOpap4UghxSF92XKk6kyuI1UgIESqEOCKE+FkI4VrFfn17K46+rL/+5/n6OIHAc0KIX4QQ5WdAOk7Af+3s7DA3N2fo0CEkJCQYFYiPT0CtdgFg0KCBHDlyBEVRSEhIYOjQIdSrVw87u9a0adOG5GRdSt26dcPKyqqifSYnJxeAnJwcrK2tSU5Opk2bNtzahyEV7ENCfAIu+n0YWGofLBpa8NTTT1Gvfn2j8hYWFnR36g6Aubk5nR/rTFpamXfLySfLxB1MQsK+MnH34aJW6eMOMMTt/FhnbGysAejYqSN5eXnk5+cDcP36db7+6hteHfdKJU0O8fsScBmuQgjBk084kp2TTUZGhlGZjIwMcnJyefIJR4QQuAxXEZ8QD4ClpaWh3I0bNwBheLwxbBODBgygebPmt8m1bBvfXa4VtXF6WlqlOd+JH347zN+51+6pjlvi9yXgotK3saMj2dmVtHFuLk866ttYVdLGHTp0oH27duXqPff77zh1dwLgoebNady4MadOngLurZ1vHcv169czKp96KZW2bdty+fJl2rRpQ//+/diXsK/Cuu/2XC177t06/zMzMzE3N6ddu0cA6NmzB3v37jHE2bRpE//5z39o0eIh6tWrh5mZGd26dWPv3r1cvHiRN954Ex+fkbz44kv873//K9eGGRkZ5Obm4nir3V1UxAxGcYAAACAASURBVMXFV9WdxMfH4+LighACxyr607heF+Li4gzbq9VqANRqNfHxunhdu3Y1jFWOjo6k6Y/h7OxsfvzxRzzc3AHdMX74yJEaPW/h3o/jXj17YWamu33d8YmS/BpaWPD0UyXHW2XHgnEf3N3xpWvDn3B3L2nDW23dq1eZ/UovP3bEJ/zD3OMraW8hqE1yBa9qtbmC1wlYCnTW/xsF9AHeAz4A5gE/K4riqH/8ZaltHYH+QE/AXwjxcBVxZgJxiqI4Af2AD4UQjSopewDdBK4JUAj01j//HLAfmA4cUBSlq6IoyyupozVw8dYDGxvbchOh9PR0WrZsCYCZmRmWlpZkZmaSlpaOrW1LQzlbW1vS06teAn///aksX76cwYOHsGzpciZNmqiv37akHhtb0tOMT+LK9uFOZGVls3/ffp591qlcnbZGcW3IqDB32zJx/zYqs2fPXjo/1pl69XQDZfDqNYwe44dFgwaV7lN6ejotS7edjW25ywfpGenY2pbavzLtu3L1JwwaNoRvY7/jrTfeACAtPY24+Hg0XsaXhsvnaktGuTbOqCBX4zYum+st2VnZ7N93AKcybVybSh8zUPHxmZ6ejq1N5W1cEftHHyVhXwKFhYVcSk3l9KlT/B975x1XZfU/8PcBVFBU1IAwNEffTHGX+s0FuAdbueKqr6lZppkl7m0iYJaVK831K1MxF1huxRTN0RDF1VIUYziQdWWe3x/3cuGyXIyw8+51X3Gf55zzOZ/P+Tzn+dzPOc9jtP6mWVx2zk2dunW4evUaly9dxsbGhsOHQ4mJjsG2GK7VvDbKvv5r1KhBZmamYWl6//79REfrdIyJieHQocMMHTqE27fvkJycjFar5dixY0RHxzB37jwmT57Epk0bef/995k/3y+fTrGxRfv2pk2b6N/fm5kzZxmWUh9U50Fl7ty5g7W17ofKM888w507d/L1a/v27XTs2BGAqKgoatSowYzZM9EMGsCsuXP4O/rvUr1us3UqLj/evnMHHTt0zHdc14eCfaGwvjyMf2XbcObMmWg0A5g9ew4pKdqC+9U+f78eep60yXP/yG3vJZ/TvXdPvtudY2/FP5OyDPD+klKek1JmARHAQalb6D+Hbim0I/AVgJTyEFBLCJGdwtoppdRKKW8Bh9FlzQqjBzBZCPErEAqYA3ULKXsU6IwusPsOsBRCVAbqSykvF1LHgBDizejo6IDExET37OWKkiYoaAu+vhPYt28vE3wnMGf2nBKVl5GRwZTJkxk4aCD29vbF3v4fv//BZ4s/Z/qMqQBcvnSZ69dv0KWrc7HLysu774xl//d76durDxs3bwIg8KOFvPfuOExMiv9S0en6mUHXbDIyMpg8eSoDB/mUiI3/aXi4e2Bra8vAwYMIXLiQFi1aYFoC9s6mWrVqTJ02ha+/+poffjhK7dp2mJiW7FQohCAgwJ+FCz9i0KDBVKlSBVO9zIULF/Lee+N44YUXeOGFF1i5chWjR79Do0aNSE1N5ezZs/j6+qLRaPjww3ncunXrkWRrNBp27dpFUNBmrK2f4aOPFpWIfnk5deoU27dv5733dEvkmZmZXLp0CU1/DUHfbMbCwpzr16/nq/eolPZ1m83KL1dhZmpK3z59SkxGXrJt6O2tIShIZ8M1eZZiV61ahZlZyfXr3TFj2b97L31792Hjpk0lIuNhURm8oinL16Sk5vo7K9f3LHT9Si+irnzA99wIoN/DBGjAaeAV4E9gP/AMMBL46SHqIqVc2ahRo3PA7OHDh/cAiI2NwdbWxqicjY0N0dHR2NrakpGRQVJSElZWVtja2hATE20oFxMTg42Ncd28hISEMGnSRAB69OjO3DlzGf3OaEN2AHS/Zm1srR+qDw9i3twPqVu3LkOG5N/XZWNjQ4yR3FisC9Q9Jo/c6gZ93x/vy7wP51Cnji6wORt+jgsXLtKntyuZGZncuXOHEcPf5MvVK9kUtImt27cB4NDEgejctouNwcY6j2xrG8NySra8guzbt3cfRo8bwztvjSbi4gUmTZkEwN34eI6GHcOkoihA1xis89nYugBdrXLpOoF5H86lTp06RvU+nDufunXrMHjIA5/nKXE2bd7E1m16Gzs4EB1dtH/a2NgYLQ09jA+bmZkxcYKv4fvQ11+j7vPP57T3BHYuDEenzljVsOKL5V9Qr149TE1NiSmmazW3jXJf/y1atGDdOt2DHMePH+fatWsARERcYNIkvY/djcfc3JyhQ4dw/vx5atV6hqpVqxIUFGTUr8zMTAYOHKjTxdEJjca7UN+uVauW4XiFChXZvXs3ly5dxMGh6QOvBxubwq+ZmjVrEhcXh7W1NXFxcdSsmbMUeuXKFebMmcPSpUsNY2Fra4utrS0XLkYwz28eKVot6WlppXLd7j94gI8//YSqlpbF4sc7g3fyww9HWfXFFwUGt6BbwSjMF3LLeRT/0tnQhubNmwHQvXt3owBv5059v5bn9GvT5lzzpMNDzpOxee4fhdn73TG88/boAvX/NyKE6AV8CpgCX0op/fOcfx8YgW6FMA54Q0p5TX8uE12CCyBSSun2pP35Jz9kcRQYDLqnYoFbUsrsx4LchRDmQohagBO6wKww9gJjhd7bhRCtCisopUxDt7zqDZzQ92ECuuVZgESg6gP6fRr4z40bUaSnp7Nnz14cHR2NCjg5ORo2WO/ff4C2bdsghMDR0ZE9e/aSlpbGjRtRREZG0rRp0yKFWVtbc+bMGUD3i7lu3bo4ODgQGRlJlL4Pe/fsxdHRyaieo5MjIfo+HNh/gDb6PhTFkiVLSUpKxHeib4HnHRyaEBl5PZfcfTg5ds4jtzMhwbv0cg8a5CYmJDJ2zHu8O24MLVu1NJTXaPqz/8Aevt8dwtp1X/L883X5crVuY6+PxoctG4PYsjGILk7OhHy3CyklZ8+FU9XS0rB8lNtWlpZVOHsuHCklId/twllvl2uR1wzlDh8JpX493cblPSHfs2fXbvbs2k33rt2YNnkqzl2cC9HVeJx1Ni5M13G8O26ska4AS5csIzEpCd+J+R4ULxN8BviwZXMQWzYH0cXZmZBdehuHF2HjKlU4G6638a4cGxeGVqslRatbZjrx4wlMTc1o2LABUJhPPZydi+LO7Ts4ODTh6tVrbPj6G1xc+rJ3z74nvlazr72Crv/bt3VLmGlpaaxdu47+/b0B2L37e3bv3s3u3bvp3LkT06ZNpXHjxhw8eAhXVxeee642+/btA3RPQV6+fBlTU1PDQxPvvDMaa2trqlSpQni23UN24eyss3vuPVZVqlTG2dmZoKAgnJ2dCQkJQUpJeHg4loWMp3G7ITg7O+tt40RwcDAAwcHBhuN///23fil5PvVy7bl85plnsLW1pV3b/7JlYxB9e/fBoYlDqVy382bPZc933xeLHx8LC2PtuvV8tngxFhYWhfpYUb7wuP6ls+GzXL16FYCTJ0/SoIHuWgkLC2PduvV8+qlxv3wG+LBlUxBbNunnycfR3aloeytA6P5ZjaVAb6AJMFAI0SRPsV+AV/Rbz74FAnOd0+q3f7UsjuAOQGQ//lyaCCHqAbuklE3139fpv3+bfQ7dUukaoAGQArwppQwXQszWH/sPugxboJRyVe429QHhBCmlixDCAlgMtEcX0P4lpXQpom/zgK5Syvb6vX1RwMtSyp+FEBXQBYy1gHWF7cNr1KhRn7p1636XlZWFh4c7I0eOZOnSZTg4NMHJyYnU1FSmTZvGpUuXqVatGoGBAYaluFWrVrFjx05MTU2ZONHXsH9l0qTJnDlzhvj4eGrWrMnbb7+Nl5cnP//8C4GBgWRmZlKxYkWmTptCkyZNOHr0KAsDPyIrKwt3D3dGjhzBsqXLaGLUh+lc1vchINDf0IfevfuQnJRMeno6VatWZfmKZVhaWtKzRy/q169PhYoVAPDxGYCXlxeSTIPuR48e46PAj8nKysTdw40RI4ezbOkKmjg0xsnJkdTUVKZPm2mQ6x/oh729PatWfsma1euo+3zO6vny5UuoWSsnK3Az6ibvjn2Pb7fpshmmGTkJaCklfgELCDt+HHNzc+bNnoNDEwcAvAdq2LJRVyfiQgTTZ88k9X4qHTt0YMrEyQghGO/7AVevXcVEmGBnZ8eMqdOM9qEATJ81g86dOtO1V5dcui7S6+qu13W53sbZus7Q61o9j65r8+i6lPSMdHr16EP9+vWooN+TN8BHg5eXJ63HvliYyxbJopHLaPPiq9SwrMntxDg+D17E1mMbH7r+2U9znvqTUuLnn8fGDnobD9CwZbPexhERTJ81k9RUvY0n6Wx88NAhFgT4c/fuXapWrcpLjRqxYtlyom5G8dbo0ZiYmGBjbcOcWbOwtc+x/ePaGaBPbxcjX162YikNGzZg8qSpXLlyhZSUFDIyMrAwN8fdw503R775xNfq0aNHCQxcSO7rH3Svsfjhh6NkZWWh0XgzZMiQfPZ2du6CmZkZVlZWTJjwAe3atePGjSjmz5/PrVu3yMjIoGfPnrz11qh8dSMiIpgxQ2f3Dh06MGWKzu5Tp07j8uXLCCGoXbs2M2ZMx9raGilhwYIFhIWFYW5uzty5cw3jqdFoDFlDXbszcrU7BSEE8fHx+Pr6Eh0djZ2dHQsXLqR69erMnj2bAwcOULu2bmu0qakpGzfqfO7SpUvMmT2H9PR07J97jrmz5rBk+dJSuW579OheLH7c182VtLQ0rKrrVh6aN2vOjOnTAejVpzdJyTn+NnLkCL766muK815w6dIl5syZq7Oh/XPMnTuXatWq4eKi75dVdZD6fk2bbmQLg+4nCrC3j4Ytm3LZO1v39jm6j5+Qx97TjO1dqYpFqT51ceXewVINYF6s3rVQ/YQQrwKzpZQ99d+nAEgpFxRSvhWwRErZQf89SUppWVDZx6VMArx/A/fva0vdsLLIleqSlJv54EIlQO4ArzTJNCt9fR83wHtScgd4pUmmSdn41L/rzfhl8wSkyCije04ZrFfJMlojE2Vz+Tz1AV4jq26jgDdzHVoppVwJIIToD/SSUo7Qfx8KtJNSjimoLSHEEiBaSvmh/nsG8Cu65Vt/KeWOJ+2v+qfKFAqFQqFQlDtK+weZPph74pf/CSGGoNvvn3vN/nkpZZQQogFwSAhxTkr5RL+w/5UBnhCiJxCQ5/BfUkrPsuiPQqFQKBSKck0UkPuJOXv9MSOEEN3Qvb7NUUppeNhUShml//+fQohQoBWgArxHRUq5F91eOoVCoVAoFOUQE/GPek70NPAfIUR9dIGdD3n+5Sv9vrsv0C3lxuY6XgNIkVKmCiGeQfeqttwPYDwW/8oAT6FQKBQKhaK4kFJmCCHGoEsemQJrpJQRQoi5wBkpZTCwELAEtuif9M9+HUpj4AshRBa63aL+UsoLT9onFeApFAqFQqEod/zTXj4spfwe+D7PsZm5/u5WSL3jQLPi7s8/Kr+pUCgUCoVCoXhyVAZPoVAoFApFueOflsH7p6EyeAqFQqFQKBRPGSqDp1AoFAqFotwh/lUvJn90VAZPoVAoFAqF4ilDZfAUCoVCoVCUO9QevKJRGTyFQqFQKBSKpwyVwSshyuQfezYtm3hdlNm/qF02/2i5SRlcNmc/faJ/seaxaTGuYZnI/fXzP8tEblmQRXqZyC0LPwaQZqX679EbKAs7l9keMdOysXFpozJ4RaMyeAqFQqFQKBRPGSrAUygUCoVCoXjKUEu0CoVCoVAoyh1C5aiKRFlHoVAoFAqF4ilDZfAUCoVCoVCUQ/4dD5M8LiqDp1AoFAqFQvGUoTJ4CoVCoVAoyh1qD17RKOsoFAqFQqFQPGWoDJ5CoVAoFIpyh1B78IpEZfAUCoVCoVAonjJUBk+hUCgUCkU5ROWoikJZR6FQKBQKheIpQ2XwFAqFQqFQlDvUHryiURk8hUKhUCgUiqcMFeDlQghR4hlNKSX+gQH0dXOln8abCxcvFljuwoULeGn609fNFf/AAKSUACxZtpR+Gm+8fTSMGv0WsXGxxm37++Pi4kL//v25WETb/fr1w8XFBX9/f0Pb9+7dY9SoUbi6ujJq1CgSEhIAOH36NB06dECj0aDRaFixYgUAV69eNRzTaDS079Cer776Cv8Af1xcXejv/YA+9O+Hi6sL/gE5fdi3bx+eXp60bNWSiIgIQ/lz584Z5HhrvDl46GCx2nXRJx/j5uVBP403730wnoREne7p6elMmzkdL01/3L08WbX6yxKx8eHDh+nfvz8ajYaBAwfy888/G9pq1aoV3gM0eA/QMHbcOJ2uAQ+pq7de14AcXfft34dnPy9atG5lZOP09HRmzJqJl3d/+ms0nD5zusB2H8T81z8mbFE4wbMPPVZ9KDlf/uuvvxg6dCivvPIK69evN7QTHR3NG2+8QadOnXj55Zfp1q1bsckE3TWk0Wjw9PTkjTfeMBzfsGED/b009PPUsOHrbwgLO46HmxduLh6sWb0un+y0tDQm+U7BzcWDoYNf52bUTQB+PPEjg3yG4N1vAIN8hnDqZP6xG/fuePp7aQzfw8LCcHNzx8XFldWr1xQoy9d3Ii4urgwePISoqCjDudWrV+Pi4oqbmzthYceN6mVmZqLRDGDMmLGGYz/+eBInJ2defvkVOnbsyLlz54rFtoX5yc2bNxkwYIDB5kFBQQYZkydO4ZXW7Wjdsk2x2Tg9PZ15c+fj7uqFp3s/Dhw4mK/dsLAw3N08cHVxY00h9p7oOwlXFzeGDB5KlF5ufHw8I4aP5NX/tmeBn79RndFvv4PGW4OXZz8+nPchmZmZBlmlNbbZ+PsH0KhRo6R8J0oQIUxK9VPukFKWiw9QD7gIrAIigH2ARSFlXwAOAGeBn4GG6P5Nk4XAeeAcMEBf1gk4CgQDVwBTfbnTQDgwSl/ODvgB+FXfRqei+ns/KUUW9Nm/Z68c9r9hUpuYLE8e/1F6eXkVWM7L01OePP6j1CYmy2H/Gyb3790n7yelyFvRsYYyq1d9KadNmWr4vm/fPjls2DCZkpIiT548Kb28vKRWq8338fT0lCdPnpQpKSly2LBhcv/+/VKr1Uo/Pz+5ZMkSqdVq5ZIlS6Sfn5/UarXyhx9+kMOHDy+wLa1WK7UpWpmUmCRfffVVuXHjRl0fklPkyR/1fUjR5vt4enrKkz+elCnJ+j7s2y+1KVoZcT5CXrxwUQ4aOEieOX3GUP7O7TsyMSFRalO0MvJapGzXrp1Mik8oNrse2n/A0N6C+X5ywXw/eT8pRW7d8q18d8xYeT8pRd6Nuy3/266dHDhwYLHb+Pbt2zIlJUVqtVp59uxZ2aNHD0NbLVq0kPeTUwwfg65JyfLkCb2uuc5nf7w8PeXJEz9KbVIuXZNT5IVz5+XFiAty0MCB8qdTpw3l161ZI30nTJD3k1Nk1PUb0t3dXb443E42GvFon8EBHtJzbg95+cbFR66b/SkpX75x44Y8ffq0DAwMlCtWrDC0ExkZKVevXi2HDRsm4+LiZKdOnWTfvn2LRWZMTIzs1auX/PPPPw190Gq1Mjw8XPbu3VveuhMr7yXekYOHDJadO3eSl3+7IOMTbsu+Ln1l+PlfZbI2wfBZu261nDJ1skzWJsit27bIMWPekcnaBPnTz6fkX9f+kMnaBHk2/GfZoUMHo3rBITvku+PGyt69e8lkbYJMSLoru3TpIn/77YpMSLgnXVxc5Pnz56RWm2L4rFu3Vk6dOlVqtSly27ZtcsyYMVKrTZHnz5+TLi4u8t69ePnbb7/JLl26yKSkREO9L774Qo4b965+zkiRyclJsm3bttLHx0empCRLX19f6ezsXCy2LcxPEhIS5L179wzXlpOTk/zr2h8yIemu7NCxgzzxY5hs3rx5sdn4o0ULZeBCf5msTZCJyfHyxs1ImaxNkCnaZJmiTZaJSQmyS5cu8spvl+W9hHjp4tJXnjsfbjifok2Wa9etlVOnTpEp2mS5bdtWOWbMGJmiTZa3b9+SYWHH5Pr16+SMGTOM6sTGxcgUbbJMTkmSb7/9lty2batBVmmMbfbnzJnTcvz48fLFF19MKs24IC7lN1man7KOgx71U95C0v8AS6WUDkA80K+Qchv05VoA7YG/AS+gJdAC6AYsFELY6cu3BsZJKV8EhgP3pJRtgDbASCFEfWAQsFdKmd3Gr4+jwOHQUFxdXBBC0KJ5cxITE4mLizMqExcXR1JyMi2aN0cIgauLC4cPHwbA0tLSUE6r1YLI2YNw+PBhXF1dEULQvIi2k5OTaZ7dtqsrhw4dMtR3c3MDwM3NzSDzYTh58iR17OsQHh6Oq8sj9sHFlUOHdX1o0KAB9erVy9e+hYUFZma6BGtqWipCGO+9eFK7tn+1vaH95s2aExMbA4AQghStloyMDFJTU0lLT6dv377FbuPKlSsbdNJqtfn0M9L1yGPqGnrYYOP6Bdj4jz//pG2btgDUqlmTqlWrQuKjTxFnfjvJveS7j1wvNyXly7Vq1aJp06aGsc7G2tqa33//HVdXVywtLWncuDEJCQnFInP37t107doVOzs7Qx9Al01s1qwZFhbmmJmZYW//HBUrVsLe3p4KFSrQs1cPQkOPGMkPPXwEVzcXALp178qpU6eQUvJS45ewsbEGoOELDXW+mpYGQEpKCl9/tYERI4cb2jl/PoI6deoYZPXq1ZPQ0NA8YxCKm5srAN27dzPICg0NpVevnlSsWBF7++eoU6cO58+fByAmJoajR4/i6ellaCc+Pp709HQ0Gm+EEHh4eHDnzp1isW1hflKhQgUqVqwI6LJVWVlZBr1faNiQ5i2aIYQoNhvv3BHMG28MA8DExIQaNayM2jx//ryRvXsWYO/Qw6G46u3dLZe9LSpb0Kp1KypWqkResu8HGRkZpKdnIITIJ6skxxZ0Wb2PP/6E8ePfy9e/kkeU8qd8Ud4CvL+klNmB1U/osnpGCCGqAs9JKbcDSCnvSylTgI7ARillppQyBjiCLoADOCWl/Ev/dw/gNSHEr8BJoBa6wPI0MEwIMRtoJqVMfBwFYmNjedb2WcN3Wxtbo2VWgNi4WGxtbI3LxOaU+WzJ53Tv3ZPvdn/PO2+/bdS2rW2uerbG9R5U5s6dO1hb6yawZ555hjt37hjKhYeH4+3tzejRo/n999/z6bVn7x569e6la//Zx+9DUYSfC8fTy5P+/fszffp0o5t0cdg1m+07d9CxfUcAunftRmULC7r26E6PPr2we/ZZ6tev/9j6FWXjgwcP4u7uzpgxY5gzZ47heFpaGj6DBjH4taEcOnxIp+uzzxbYvlEfbB7Nxo1efJHQI6FkZGRwIyqKixcuQGrZTBEl6csPkhkVFcWlS5ewt7cvFpnXrl0jISGB4cOH4+PjQ0hICAAvvPACP//8M/Hx8Wi19/npzM+YmpnmtG1jQ1xMfvnP6q8vMzMzLC0tiY+/Z1TmwIGDvNT4JUOAs2zpcoa+NgQLc/M87eT4kI2NLTEFyno2j6x4YmJisbUt2P8CAxcyfvx7mJjk3Axr1KhBRkYGKSlaAPbv329o/0ltW1Sd6Oho+vfvT8+ePRk2bBg2Ntb556disHFigu5WsHTpcgYOGIzvhEncvn270DZ1cm2JjYkroEx+ez+It98aTRfnrlSuUplu3buV6tgCbNq0CScnR8P4KP45lLcALzXX35kU31PAybn+FsBYKWVL/ae+lHKflPIHoDMQBawTQryWtxEhxJtCiDNCiDNfrlldTF3Lz7tjxrJ/91769u7Dxk2bSkRG7gxS48aN2bNnD1u2bGHgwIGMHz/eqGx6ejpHjhyhR/ceJdKXbJo3a872bdv5ZsM3rF69mtTU1AdXekRWfrkKMzNT+vbpA8D5iPOYmJpwYO8+du/6nhtRUfkyD49L3ixd165d2blzJ4sXL2bp0qWG47t372bTN98Q4LeAwIULdZnbEsDD3QNbW1sGDh5E4MKFtGjRojz+aM1HUdnQvKSmpvLBBx/g6+uLqanpgys8hMyMjAwuXLjA559/zvLly1m5ciVXr16lQYMGDBs2jNFvjeGd0WOp/ZwdJo/Q14L44/c/+Gzx50yfMRWAy5cuc/36Dbp0dX6idh+GI0d+oGbNGjRp0sTouBCCl156iY0bNzJo0GCqVKnySGOSl4et++yzz/Ltt98SEhJCcHBwvqDrcclr44zMTGJiYmjRsjkbN2+gefNmfLJocbHIehiWr1jGgYP7SU9L49Spx9s3+yAKG9vY2Fj27dvPwIEDS0Su4sl46l6TIqVMFELcEEJ4SCl3CCEqodtXdxQYJYRYD9REF6z5Ai/laWIv8LYQ4pCUMl0I8SK6oO4Z4IaUcpW+zdbA/+WRvRJYCZCarJXZxzdt3sTW7dsAcHBwIDom2lAnJjYGG2sbow7YWNsYlggNZWyMywD07d2Hwa8PJfRIqK7tpk2JiclVLyZ/PRsbm0LL1KxZk7i4OKytrYmLi6NmzZqA8bJwp06d8PPz4+7du9SoUQOABf4LSEtL4+2338bBwYGY6Mfvw8PQoEEDKleuzLIVyzh+4oRO92Kw687gnfxw9CirVnxhuIl8v3s3piamDBo62NDGL7/8Qs+ePR9Lv8JsnJuXX36ZGzduGGxsa2vLpo0b2bptG0nJyZiYmBAdHV1g+0Z9iH00G5uZmTFxgq/h+9DXXwOLrCLrFCfyZgX4uwIA1j2tS8SX87Jp0ya2bdNdm40bN2bRokW4ubnRrVs3Pvvss2KRaWtri5WVFZUrV6Zy5cq0bt2aK1euUK9ePby8vPDw0i2VzZg2y/jaiY3F2ja//OjoGGxtbcnIyCApKQkrq+qGvrw/3pd5H86hTh17AM6Gn+PChYv06e1KZkYmd+7cYcTwNxn77hgjH4qNjcG2QFnReWRZYWtrQ0xMfv8LDT1CaOgRjh07RmpqGvfu3aNjx07Urm2Hg4MDbdu2pXfv3hw/foINGzYUi20fZi6xsbHRZ0t/0ZUvCUp3/QAAIABJREFUZhtbWVXH3Nycrl27ANC9Rzd2bA8usM0cuTHY2FoXUCa/vR+GSpUq4eTsROjhUPr07V3iY5ucnMyUKVPp3bsX169fx9XVNbtK5UaNGv1++fLlFx6q40+IKHc5qtLlabXOUOBdIUQ4cBx4FtiO7qGJs8AhYKKUMrqAul8CF4CfhRDngS/QBcJOwFkhxC/AAODTh+2MzwAftmwKYsumILo4OROyaxdSSs6Gh1PV0jJfatva2hrLKlU4Gx6OlJKQXbtwdnIC4FrkNUO5w0dCadmipaFtZ2dnQkJCkFISHh6OZSFtV6lShfDstkNCcHbW/bp3cnIiOFg3MQUHBxuO37p1K/vhFc6dO0dWVpbRxJOUlMSkiZMICtL3Ydcj9mFXCM5ORWcYbkTdICMjA9A9IXf16lX+99r/is2ux8LCWLt+PZ8tXoyFhYWhjp2dHSYmJmzZFMT6tetJTU3l4sWLxW7jyMhIg40vXrxIWloaVlZWJCQk6JZoB/iwcsUXWFWvTreu3R5PV0enIm2s1WpJ0WcHT/x4AlNTM0SV0gvwRO10xMspiJdTSsyX8+Lj40NQUBCbN28mKiqKpKQkhg4dWqwynZ2d+eWXX8jIyECr1XLu3DnDMn92Vunvv6M5d+4c9++nEnUjivT0dPbu2YeTY2cj+Y5OnQkJ3gXAgf0HadO2DUIIEhMSGTvmPd4dN4aWrVoayms0/dl/YA/f7w5h7bovef75uny5eiUODk2IjIzkhl7Wnj17cXR0NJLl5ORIcLBuOXn//gO01ctydHRkz569pKWlceNGFJGRkTRt2pRx495l//597N69m4AAf1599VWOHTtKUFAQr7zShpCQXaSmpvL5559jY2NTLLZ1cnIq0E9iYmK4f/8+AAkJCfzyyy/Uq1dPr/d1om5EIaUsFhsLIejs2Ikzp38C4NTJ0zRoWN+oTQcHByIjI3ON7V4c81yPjk6OhOjtfWD/AYPcwkhJSTGsJmRkZHD0h2PUr1/PIKskx7ZNmzYsWOBH586dOXToILt372b37t0AKaUV3CkejMi+qSiKl9wZvNxIKfHzX0DYieOYm5szb/YcHJo4AODto2HLJt3j/BEXIpg+ayapqal0bN+BKZMmI4Rg/IQPuHrtKibCBDs7O2ZMm2bYa5VlAgsWLCAsLAxzc3Pmzp2Lg4OubY1GY3hVQEREBDNmzCA1NZUOHTowZcoUhBDEx8fj6+tLdHQ0dnZ2LFy4kOrVq7Nx40aCgoIwMzOjUqVKTJgwgZYtdRNcSkoKvXr14rtd31G1alWklLo+HNf3YU4RfZiZqw+TdX04eOgg/v7+3L17l6pVq9KoUSNWLF9ByK4Q1qxZQwWzCggTwag3R9E1V1D4pHbt6+ZKWnoaVtV1v9SbN2vOjGnTSUlJYcbsmfz5559ICW6urvwdE13sNl6zZg0hISFUqFCBSpUqMX78eFq3bs2vv/7KvHnzMBEmZMkshgwajKeHh07X47l01ffBe4CGLZtz+mDQtUOOrgcPHWJBQI6NX2rUiBXLlhN1M4q3Ro/GxMQEG2sb5syaRe+ATo/s+4tGLqPNi69Sw7ImtxPj+Dx4EVuPbXykNn757I8S8eVbt24xcOBAkvWZUAsLC7Zv386VK1cYNmwY1atXJyUlBSEE48aNY8iQIU8sE2DdunXs3LkTIQReXl6Gdv/3v/8Rfy8eMzMzPpgwnrS0ND4K/JisrEzcPdwYMXI4y5auoIlDY5ycHElNTWX6tJlcvnSZatWq4R/oh729PatWfsma1euo+3xdgw2XL19CzVo5mcubUTd5d+x7fLtNp0fY0RMEBi4kKysLDw93Ro4cydKly3BwaIKTkxOpqalMmzaNS3pZgYEB2NvrslarVq1ix46dmJqaMnGiLx07djQav9OnT7N+/f+xZMnnOp9YtIjt23eg1WqxsrLis88+K5bxNMw3efzkxIkTLFq0CCEEUkp8fHzw6u8OwIQPJhJ6+AiZmZlUqVKFIUMHkZUln8jGN2/+zfRpM0lKTKRGjRrMnjsLO7tnEeQs8x89epSFgR+RlZWFu4c7I0eOYNnSZTQxsvd0g9yAQH+DvXv37kNyUjLp6elUrVqV5SuWYWVlxdix75Kelk5WVhZt2rzCBN8JmJmZcezosVIb29y0aNEy+fLly5b5TpQQd7RXSzWAqWlRr1xtWlEBXglRWIBXkkjTMvK9MnIhkVU2gsvCzmWla4txDctE7q+f/1kmcsuCLNLLRK5Jme3QKZt5qizsnDvAK125ZWNjc3OLUhWsAryiKdd78IQQS4EOeQ5/KqVcWxb9USgUCoVCUVo8rbvMiodyHeBJKd8p6z4oFAqFQqFQ/NMo1wGeQqFQKBSKfydltRRdXlD5TYVCoVAoFIqnDJXBUygUCoVCUe5Q78ErGmUdhUKhUCgUiqcMlcFTKBQKhUJRDlF78IpCZfAUCoVCoVAonjJUBk+hUCgUCkW5Q+3BKxplHYVCoVAoFIqnDJXBUygUCoVCUe5Q78ErGpXBUygUCoVCoXjKUAGeQqFQKBQKxVOGkFKWdR+eSpLv3yt1w5pSqbRFAjB9f5cykTvf+WCZyM00yygDqWWzFGFSRrs4Wo5tUCZyz376R6nLlGX0MzuLsvDjsvMpkVn6MrNMy+b+mkV6mcitYl69VCeqBG1cqRq4moV1uVoTVhk8hUKhUCgUiqcM9ZCFQqFQKBSKcod6TUrRKOsoFAqFQqFQPGWoDJ5CoVAoFIpyh3pNStGoDJ5CoVAoFArFU4bK4CkUCoVCoSh/CJWjKgplHYVCoVAoFIqnDJXBUygUCoVCUe5Qe/CKRmXwFAqFQqFQKJ4yVAZPoVAoFApFuUO9B69olHUUCoVCoVAonjJUBk+hUCgUCkU5RO3BKwqVwVMoFAqFQqF4ylABXgkTFnYCT7f+uLl4sXb1+nzn09LSmOQ7FTcXL14bPIybUTcBOH8uAh/NYHw0gxngPYhDBw8b6syeOY+uTj3x9vIxaktKib+/Py4uLvTv35+LFy8W2KcLFy7Qr18/XFxc8Pf3R0oJwL179xg1ahSurq6MGjWKhIQEAE6fPk2HDh3QaDRoNBpWrFgBQHR0NMOHD+dwYDyHF8bz51GtkZxG1u2Y6LyRyV2CcH5haIF9aWHXBV+nDUxw+ppBrWYbnatkVpnp3Xbg2fT9wsxrrHtgAH3dXek3wJsLhel+8QJemv70dXfFPzDAoPuSZUvpN8Ab74EaRo1+i9i4WAAOhx42HPcZMoiff/nF0FZY2HE83Lxwc/Fgzep1+WTpxnYKbi4eDB38umFsfzzxI4N8huDdbwCDfIZw6uRpQ5309HTmzZ2Pu6sXnu79OHDgYL52c+S6s2b12kLkTsbNxZ2hg18zyI2Pj2fk8Ddp/9+O+PsFGNXZu2cfmv4D6OfpzaeffGZs1xLwqb/++ouhQ4fyyiuvsH59znWR7VPyTGXdJ6pCgfIKYv7rHxO2KJzg2Yceuk5RSCnxDwigr5sr/TRF+NSFC3h596evmyv+ATk+tW//Pjz7edGidSsiIiIM5dPT05k2Yzpe3v1x9/JkxoyZuLm54+LiyurVa/K1n5aWhq/vRFxcXBk8eAhRUVGGc6tXr8bFxRU3N3fCwo4b1cvMzESjGcCYMWMNx6ZMmYKbmzteXv2YPXMO6ekZQMn4ss6nfOjnqTHyqbCwsFLT9+TJUwwY4IOXVz+mzZxORoZOX8N88TBjq9GPba75YtEnH+Pm5UE/jTfvfTCehMQEo3p///037Tq8yvr1/2ekt7ubB64ubqwpRO+JvpNwdXFjyOChROW6bkcMH8mr/23PAj9/ozrDh4/A3c0DjWYAGs0A7ty+k6/dkrgHZdt7oGYI744ZX6DtSgOBSal+yhvlr8fliMzMTAL8Avl82ads3b6ZPXv28ucffxqV2bE9mGrVqhK8axuDhwzk08VLAGj4QkO+/mY9m4I2sGTZZ8yf52+YnFzd+7Jk+af55B07dozIyEhCQkKYOXMmH374YYH9+vDDD5k1axYhISFERkYSFhYGwJo1a2jbti0hISG0bduW1atXG+q0atWKoKAggoKCeOuttwAwNTVlwoQJOE+0otPY6lwNu09itK6PAhM8m03gy5MfsPDwIFrV7oatZT2jfjxTxZ4u/3mNJWFv8VHoEIIjjHXq1ehN/rz960PZ+ljYMa5dj2TXjmBmTp/BhwvmF6z7gvnMmjGTXTuCuXY9kmPHdbr/77XX2bp5C1s2BtG5U2e+WLUSgHZt2/HtpiC2bAxi7qzZzJ43B9CNrb9fAEuWfcbW7VvYs2cvf+Qb251UrVaV4F07GDxkEJ8u/hwAKysrFn/2CVu2bmbuvNlMnzbTUOfLVWuoWbMGO0O2sXX7Fl5++WWjNnVy/fVyvy1E7g6qVqtG8K6dDB4ymE8X626ulSpWYvQ7bzP+/feMysfHx7P4k8WsWLmCrdu3cOv2LU6ePKWzawn5VLVq1Zg0aRKvv/66UTvZPiVeSYGWKXCzIjL54aap7cc3M/LTwQ9V9mE4duwY1yIj2bVT71N+hfiUn96ndgZzLTKSY3rdX2j4Ah8v+piXW7c2Kr/vwH7S09LZtuVbNvzfV3z33XfMnDmD7du3sWfPHv744w9jvbZvp1q1auzaFcKQIUNYvFh3nfzxxx/s2bOXbdu2smzZMvz8/MjMzDTU27DhGxo0qG/UVp8+fdi5cwdbt37L/dRUtm/fUSK+rPOpT1mxcjlbtwdx6/ZtTp48RWZmJn5+C1i2bGmJ65uVlcWMGTMICAhg27at1LarTfCuEN3YhuUZ26Lmi+m5xlY/X7z63/+yLehbtgZt4fm6z7N6jXHAtvDjRXTs0MHwPTMzkwV+/ixdtoRt27cWovcOqlWrSsiuYIYMGcyner0rVazEO++M5v33Cw6k/BbMJyhoM0FBm6lZq6bRuZK6BwFs3LCJ+g3qFdinfytCiF5CiMtCiN+FEJMLOF9JCLFZf/6kEKJernNT9McvCyF6Fkd/VICXByFEPSHEJSHEBiHERSHEt0KIykKINkKI40KIs0KIU0KIqg9q6/z5COzr2GNv/xwVKlSgZ68ehIb+YFQm9PARXNz6AtC1exdOnzqNlBILC3PMzHRbJNNSUxEiZ6/Byy+3pnq1avnkHT58GFdXV4QQNG/enMTEROLi4ozKxMXFkZycTPPmzRFC4OrqyqFDhwz13dzcAHBzc+Pw4cP5ZOTG2tqaxo0bA2BmLrC0NeV+QhYAdWs04XbyDe6k3CRTZvDrzQM4PNvJqH67um6EXd2KNj0RgKS0u4Zzz1VvhGWlmlyJO1VkHwy6HwnFta8LQghaNGtOYlLBuiclJdOimV73vi4cDtXpaGlpaSin1WrJ3ttRuXJlg+21Wq3h7/PnI6hTpw729va5xvaIkbzQw0dwdXMBoFv3rpw6dQopJS81fgkbG2tAN4mmpqaSlpYGwM4dwbzxxjAATExMqFHDyqjNguWGPpRci8oWtGrdikqVKhqVj7oRRd26dalZswYA7dq146A+c1hSPlWrVi2aNm1q8PFscvuUMAMqZ0Law+2zOfPbSe4l331wwYfk8JFQXF30PlWE7knJybTI1t0lx6caNGhA/Xr18rUrEKTc15KRkcGvZ89SoUIF/vOf/1ChQgV69eqZbzwPHw7Fzc0VgO7duxnGMzQ0lF69elKxYkXs7Z+jTp06nD9/HoCYmBiOHj2Kp6eXUVudOnVCCIEQgqZNHYiNiSkRX87vU205eOBQPlklqW98fDwVKlSgXr3nAfhvu/9y4OABnYzQxxxbvf+2f7W9wXebN2tOTGyMod6hw4d4rnZtGjZoaDh2/vz5PDbOr3fo4VBc9Xp3y6V39nVbsVIlHpWSugfp7B2Gh6f7I/epOBGl/F+RfRHCFFgK9AaaAAOFEE3yFBsO3JVSvgB8AgTo6zYBfAAHoBewTN/eE6ECvIJpBCyTUjYGEoAxwGZgnJSyBdAN0BZRH4C42DiefdbW8N3GxobYmLhCy5iZmWFpaUl8/D0AzoWfp7/nADT9BzF1+qR8N8O8xMbGYmubI8/W1pbY2NiHLnPnzh2srXWT9TPPPMOdOznp/vDwcLy9vRk9ejS///57PtkpdzK5F5WJVV1dH6ubWxOvzZn04u/HUd3c2qiOtWVdrKvU4Z0OKxjbcSWNrNsBuovWrclYdl34vEh98+r1rO2zOXrZ2BqWWQ1l4oq2z2dLP6d7n558t+d73nn7bcPxg4cO4eblwTvjxjJ31myDPNtcY2trY0NcTH5bFza22Rw4cJCXGr9ExYoVSUzQBbpLly5n4IDB+E6YxO3bt/O1aSzXlrg8PhVboE/FF2Q2AOrUrcPVq9e4GXWTjIwMDh8OJSY6JkdeCfnUg5D3BSSZQtXMBxcuAXTjl8unCtPdpmj75KV7t25UNrega/fujBv/Hi+99BLVq1cHwMbGlpgC/UjXj9zjGRMTi61twf0LDFzI+PHvYWJS8E0pPT2d73Z9T/sO7UvElwvzqbw2LUl9a9SoQWZmpmF5fP/B/UTH5Pj1Q80XNsbXWkFju33nDjq27whASkoKa9at4+1RbxVqP0Nb+a7bgvV+ELNmzkajGcDKL1YalpCzKal70EeBnzBu/FhMTFQIkYu2wO9Syj+llGnAJiBvBOwOZK+Tfwt0FbrI2R3YJKVMlVL+Bfyub++JUKNTMNellGH6v78GegJ/SylPA0gpE6SUGXkrCSHeFEKcEUKcKWgfy6PSrHlTvt2+ma++Wcfa1etJTU194jYflty/1ho3bsyePXvYsmULAwcOZPx446WCjFTJmfWJNHWvTAXzh3cpE2HKM1XqsPz4O2z4aRbeLSZjbmZJ+3peXIw9wb37cQ9upBh5952x7P9+L3179WHj5k2G4127dCF42w4WL/qEJcuXFZu8P37/g88Wf870GVMByMjMJCYmhhYtm7Nx8waaN2/GJ4sWF5u8wqhWrRpTp01h0sTJvDFsBLVr22FiWvxTQ26fehAyE7hgAQ1TdZm8p4jzEecxMTXhwL59TJsyld9//50bN24UW/tHjvxAzZo1aNIkb/IgBz8/P1q/3JrWrVsVi8y8vqzzqclMmjiFN4aNpHbt2iXiU1C4vkIIAgL8WbjwIwYNGkyVylUwLeaAZOWXqzAzM6Vvnz4ALPtiBUMHD6Zy5crFKqcw/Pz8+HbrFtauXcPPP//Crl27irX9gu5BPxw5qrd342KVVR7IfY/Xf97Mdfo54Hqu7zf0xyiojD6GuAfUesi6j8xTNnUWGzLP9wTA/IGVpFwJrARIvn9Pnj0bTnR0ThYrNjYWG9s8WSwba6KjY7C1tSUjI4OkpCSsrKoblWnQoD4WlS344/c/aOJgPIlt3rSF7dt2ANDUoRkxMTnyYmJisLGxMSpvY2NTaJmaNWsSFxeHtbU1cXFx1Kyp28+Re/myU6dO+Pn5cffuXWrUqEF6ejpn1ifyXOtK2DXLWUK4dz8OK4ucX45W5tb5ArZ72lgi4y+QJTO5o/2buKTrWFepw/M1mlK/Vgva1/OikpkFpqICqRlavr+03Kj+pqBNbN2+DQCHJg5Ex0Tn6BUbg411Ht2tC9c9N31792H0uDG889Zoo+OvtH6ZGVE3uHs3XmfHXGMbExuLtW1+Wxc2tjExMbw/3pd5H86hTh17nY2sqmNubk7Xrl0A6N6jGzu2B+dr01huDNZ5fMqmQJ8yXurNi6NTZxydOgMwZdJUzv4azgDNQJo6NC0RnyqK9PR0XXBnk454Jt/vqBJl0+ZNbN2m9ykHB6Kjc/lUYbrHPtincvP97t10aN+BChUq0LBBAywsLIiIiMDe3p7Y2BhsC/Sj6HzjaWtrQ0xM/v6Fhh4hNPQIx44dIzU1jeTkZKZMmcqCBX4ArFixgrt37/LRjMAcHYrZl8HYp7Z+uw1TUxODLtmUtL4tWrRg3Trdg0gL5i/g5s2bePtodGP7MPNFrPG1lntsdwbv5IejR1m14gvDj5dz585x4MB+Pvl0MYmJiQgTEypVrEjjJo2N7gUxsTH57gWF6V0U2barUqUKvfv05vy5CFxdXQ3ns+8vOfZ+8nvQ2V/DORJ6lGPHjpOWmkpycjLTpsxk/oK5Rfa1ZCjd16TkvseXB1QGr2DqCiFe1f89CPgRsBNCtAEQQlQV4sF5BQeHJlyPvE7UjSjS09PZu2cfjo7G+9AcnTqzK/g7AA7uP0Sbtq8ghCDqRpRhQ+vNm39z9eo17GrXzidjgI83m4I2sCloA87OzoSEhCClJDw8HEtLS8PyWDbW1tZUqVKF8PBwpJSEhITg7OwMgJOTE8HBuoAiODjYcPzWrVuG1P+5c+fIysrCysoKKSWzZ8/G0taUho4WRnKux1/kmSr21LSww1SY0bJ2NyKijxmVOR/9Aw1r6TIIlStWx9qyDrdTovjmlznMP+CF38F+hEQs4acbu/MFdwA+Gh+2bNQ9ANHFyZmQ73YhpeTsuXCqFqK7pWUVzp7T6/7dLpwdnQC4FnnNUO7wkVDq19Nt1o68HmnQ/cLFi6SnpWFlVR0HhyZE5hlbJ8fO+cY2JFj3i/rA/oO0adsGIQSJCYmMHfMe744bQ8tWLQ3lhRB0duzEmdM/AXDq5GkaNDTeJF+wXMc8ch0LlFsU2U/fJSQk8Odff7F23Wo2B20sMZ8qjGyfonIWwj69yLIlgc8AH7ZsDmLL5iC6ODsTskvvU+FF+FSVKpzN1n1Xjk8Vht2zdpw6rdtbWr9BA+7evUvlylVIT09nz569OOYZTycnR4KDdQ8H7N9/gLb68XR0dGTPnr2kpaVx40YUkZGRNG3alHHj3mX//n3s3r2bgAB/2rRpYwjutm3bxvHjx/H39zcsr5WEL4OxTwUFfYunp4deViQ39LJKWt/b+j6kpaXx519/8fFHH7Nlk36+eJyxddKN7bGwMNauX89nixdjYZEz961fs5Y93+1mz3e7GTxoMMNHDMdnoA8ODg5ERkbmsvFeHPP4ie66DdHb+MADr9uMjAzu3tXtOU1PT+foDz/wwgsNjcqUxD1o7Lh32LN/F9/t3smCgPm80uaVMgru/nFEAXVyfbfXHyuwjD6GqA7cfsi6j4zIu2b/b0f/VMse4AzwMnABGIpu8+PngAW6/XfdpJRJhbWTfP+eBDh2NIyPAj8mKysLNw9XRox8g+VLv6CJQ2McnTqTmprKjGmzuHTpCtWrVWNB4Hzs7Z9jV8j3rFuzHrMKZpgIE0aOGo5zFycApkyazk9nfiI+Pp6aNWvx1tsj8fByx0RWZMGCBYSFhWFubs7cuXNxcHAAQKPREBQUBEBERAQzZswgNTWVDh06MGXKFIQQxMfH4+vrS3R0NHZ2dixcuJDq1auzceNGgoKCMDMzo1KlSkyYMIGWLVvy888/M2zYMKramZI9D73UuzK2jXWb+F+yeRV3h3EIYcrp67s4+Nt6ejYawfX4S1yI0QV7rk3e5SWbdmTJLA7+tp5fbx4wsuMr9n2oY/US289/nM/G851zXiEipcQvYAFhx49jbm7OvNlzcGii0917oIYtG/W6X4hg+uyZpN5PpWOHDkyZOBkhBON9P+DqtauYCBPs7OyYMXUatja2rFm3lpDvQvS6m/P+uPG0aNMMgKNHj+nHNhN3DzdGjBzOsqUraOLQGCcnR1JTU5k+bSaXL12mWrVq+Af6YW9vz6qVX7Jm9TrqPl/X0P/ly5dQs1ZNbt78m+nTZpKUmEiNGjWYPXcWdnbPkvuXqk7uIr1cd73c5TRxaJJL7gy93OoGuQB9eruQnJRMeno6VatWZdmKpTRs2IDJk6Zy5coVAN58cyS9euse4hLStER86tatWwwcOJDk5GRMTEywsLBg+/btXLlyhWHDhkGVXPvu6qciaj54H96ikcto8+Kr1LCsye3EOD4PXsTWYxsfWC83Zz/NebJRSomffx6f0uvuPUDDls05uk+fNZPUVL1PTdL51MFDh1gQ4M/du3epWrUqLzVqxIply0lJSWHGrJn8+eefSAnNWzTj559/ISsrCw8Pd0aOHMnSpctwcGiCk5MTqampTJs2jUt6PwoMDDCM56pVq9ixYyempqZMnOhLx44djfQ5ffo069f/H0uW6Paztm79MnZ2dlSpUhkJdOnizKi3RpaIL+t86jcA3nxzhMGnwo6eIDBwYano+/HHH/PDD0fJyspC09+boYOHGI/tiQLmCx8NWzblmi+yx7Z9ztj2dXMlLT0NK/3eyebNmjNj2nSjvixbsRwLy8q8/vprABw9epSFgR+RlZWFu4c7I0eOYNnSZfrrNlvv6QYbBwT6G/Tu3buP0XW7fMUyatvV5o03hpORkUFmZibt/tuOCRM+wNTUlCxyfhyVxD0omzOnf+L/1n/NZ0s+AaCKefVSTand194v1QDG3MK8UP30AdsVoCu64Ow0MEhKGZGrzDtAMynlW0IIH8BLSqkRQjgA36Dbd1cbOAj8R0r5RBuQVYCXB32At0tK2fRJ2skO8EoTUx79KaviYPr+LmUiN3eAV5pkmpXusqGOsnlju0kZ7eJoObZBmcjNHeCVFrKM1lGyKAs/LjufEmXwrE6WadncX3MHeKXJvznAAxBC9AEWA6bAGinlfCHEXOCMlDJYCGEOfAW0Au4APlLKP/V1pwFvABnAe1LK3U/aX7UHT6FQKBQKRblD/MMSVFLK74Hv8xybmevv+4B3IXXnAwW/kPExUQFeHqSUV4Enyt4pFAqFQqFQlCUqwFMoFAqFQlH++Gcl8P5xqKdoFQqFQqFQKJ4yVAZPoVAoFApF+UNl8IpEZfAUCoVCoVAonjJUBk+hUCgUCkX54x/2FO0/DZXBUygUCoVCoXjKUBk8hUKhUCgU5Q6hEnhFojJ4CoVCoVAoFE8ZKoOnUCgUCoWi/KEyeEWiMngKhUKhUCgUTxkqwFMoFAqFQqF4yhBSPWZcImj/XRAgAAAgAElEQVTvp5S6YYX8d8XrUmSVjVwyS12mSZntphBlIzWrbOalFuMalrrM8W/1KHWZAP9rvLxM5KaJlDKRK4Rpqcs0k+alLhMgUUaWiVzryi+W6oSRFp9cqhNFRasqZTMhPib/rohAoVAoFAqF4l+AeshCoVAoFApF+UMtQBaJyuApFAqFQqFQPGWoAE+hUCgUCoXiKUMFeAqFQqFQKBRPGWoPnkKhUCgUinKHUG8BKRKVwVMoFAqFQqF4ylAZPIVCoVAoFOUPlcArEpXBUygUCoVCoXjKUBk8hUKhUCgU5Q+VwSsSlcFTKBQKhUKheMpQGTyFQqFQKBTlD/UUbZGoDJ5CoVAoFArFU4YK8EqYsLAw3N08cHVxY83qNfnOp6WlMdF3Eq4ubgwZPJSoqJsAxMfHM2L4SF79b3sW+Pkb1Rk+fATubh5oNAPQaAZw5/adfO1KKfEP8MfF1YX+3v25ePFigf27cOEC/fr3w8XVBf8Af6T+F9G+ffvw9PKkZauWREREGMpHRUXRtl1bNBoNGo2GeR/OK1T3kurDgygJmxcs5zgebl64uXiwZvW6AuVM8p2Cm4sHQwe/zk29nB9P/MggnyF49xvAIJ8hnDp52lBnxPA38XDzYoBmEC593XHt64GbiwerC9HD13ciLi6uDB48hKioKMO51atX4+LiipubO2Fhx41s4+bmjouLq1GbJ0+eYsAAH7y8+jF9+nQyMjIA/Rj6+9OjRw9atGjB2rVrC7TFhQsX6NevHy4uLvj754zhvXv3GDVqFK6urowaNYqEhARDndOnT6PRaPD09OSNN94wHN+wYQOe/fvh2c+LrzZ8rfejAPq6udJP482FIvzIy7s/fd1c8Q8IyPGj/fvw7OdFi9atjPwoPT2daTOm4+XdH3cvT75cvbrAdh/E/Nc/JmxROMGzDz1W/cL46+xd1n7wC6vH/8yp4Kh85yOOxLJ81Gm+mnKWr6ac5dzhGMO5rf4XWDriFNsXFmyronhSey/65GPcPD3op/HmvffHk5CYUGD9vBwP+xEvdx88XL1Zt+b/8p1PS0tjysQZeLh68/qQEdyM+ttw7rcrvzPstZFovAYzoP8QUlNTHyDrBF5u3ri79GPt6vUFyprsOw13l368NvgNw7Wbzd9/R9Pxv0783/qvAUhNTeW1QcP4f/bOPCyq6o3jn8OmKCCiMC5gboWKorngmkCpWQEuKa6VS7YvammLiqmIommuWObW4gIKKNBPXCEFS0tLRdMUFxRkEURwYxnu748ZBoYB1BJQO5/nmcfh3vec79numfe+59zr0MEjGDxgKF8HrDTIs6Lmw8zMTMa+PpYuXbvgN8ev3Hr/GnuYYf3fYojXG/ywZrPB+T8PxzFm2Ie4duxH1K5YvXM9O/Rj1JAPGDXkAz75sOx5vzIRSuV+HjUeeQdPCHHjPu29hBCf3sXGTQgRUca58UKIGveipVarmeM3l+UBywgJDSYyMpL4+Hg9m9DQrVhZWRIeEcbIkSNYvGgxANXMqvHuu+8wceKEUvP2mzOboKBAgoICsaljY3A+JiaGhIQEwsPC8Znmg+9s31Lz8Z3ty3Sf6YSHhZOQkEBsrOaibt68OV8t/IoO7TsYpLG3tycoKIigoCCmTZ1WZv0rsgxlUZFtXlJnrp8/ywKWEBy6mcjIHcTHn9Oz2Rq6DUsrS8IitjJi5HAWL1oKgLW1NYuWfMXm4EBmzvqCqVN89NLNnuPLho0/IIAV3yzT5l9aPUKxsrIiIiKckSNHskhbj/j4eCIjdxASEkxAQAB+fn6o1WrUajV+fnMICFhOaGiILs+CggKmTZuGv78/ISHB1K/fgLCwcEDThxcvXsTBwQFnZ2eCg4NLbQ9fX1+mT59OeLh+H65ZswYXFxfCw8NxcXFhtdaJysrKws/Pj8WLFxMaGsr8+fMBOHPmDMHBwWz44Uc2Bwaxb99+QkJDuZiQQMS2MHymTsPXb3bpZfCbzfRpPkRsC+NiQgIxheOoWXMWLlhIh/bt9ex37t5FXm4eIZu3sGn9BrYEb0G5I8ru9DIIPRDIuMUj7jtdeRQUKOxde54Bk1syan47Th24SvrlWwZ2T3Wpwytz2vLKnLa0cVfpjnfyaEjft5v/I+2YmJh/1d5du3QhZPMWgoM288QTT7B6jeHNSUnUajX+c75kyfIFbA7ZwI7I3ZyLP69nsy00HEsrS7aGb2b4yCEsXRwAQH5+PtOmzOCzKZMJClnPN6uWY2JS9u4jzbU7nyUBi9gSuokdkTs5Z3DthmFlZcm2iGBGjBzKkkXL9c5/9eUiuvXoqvvbzMyMr1ctZ9Pm9WwI+pEDsb9y/NhxvTQVNR+aVTPj3XffZeLEiWXWubDeC+d+zZfLvuDH4OXsjtzH+fgEPRtVfVs+nzGeXn1dDdJXq2bGusAlrAtcgv/isud9ycPDI+/g3S+KooQpinL38EzZjAfuycGLi4vDwcEBe3t7TE1Neb7v80RHR+vZREdF4+nlCUCv3r04dOgQiqJgXsOcp9s/jVm1av+okFHRUXh6eCKEwNnZmezsbNLS0vRs0tLSuHnzJs7Ozggh8PTwZG+UJgrRtGlTGjdu/I+0q7IMldXmcXEnSuj0ITr65xI6P+Pp5aHVeU6n06JlC+zsbAFo1rwZOTk55Obmlpt/31LqERUVjZe2Hr2L1SM6Opq+fZ/HzMwMe/uGODg4EBcXZ9A2hXlmZmZiampK48ZPANC1axf27Nmt1YjCxsaG3r1707hxY+7cuXP3PvT0ZO/evbr0Xl5eAHh5eREVFQXA9u3bee6556hfvz4AderUAeD8+fO0adMGc3NzTExM6NihA8GhIXh6eCCEoG054+jGzZu01Y0jD6KiNVpNmzalSSnjSCC4dec2+fn55OTkYGpqCsb3f5v++5mDXL957b7TlUfy2RtYq6pjraqOsYkRLbrWJf7wvWs0al0LM3Pjf6Qd9XP0v2rvbl276Rws5zbOpKSkGGiU5ETcSRwc7LG3b4ipqSl9nu/Fz9H79Wx+jt6Ph+cLADzXy51Dh35HURR+/eUQTz7ZjKccnwTA2roWxsZl191Aq29voqP36WtF7cPD6yWNVu9nOXToN10kLWrvzzRo2IBmzZrq7IUQ1Kih+VnIz8/XRsD1bxYqaj6sYV6D9k+3p5pZ+fPWX3FnsHeoT0P7epiamtLr+Z7ERB/Us6nfQEXzp5pgZHT/NzqSh48qdfCEEI2FEKeEEOuFEH8JIbYIIWoJIU4LIRy1NhuFEOPuks9sIcRRIcSvQgiV9pitECJYCPGb9tNde3yUEGKZ9nszbZrjQgjfEtFAC215CssnhBAfAA2AKCFE1N3ql5qaSr16RXfVKjsVqSlppdjUA8DExAQLCwsyMzPv2nbTfb7A23sIK79ZqZt4SuarKq6tUpGammpooyrfpjQSExPxHuLNmLFjOHLkSJl2FVmG8jQrqs0Nyq2nY0daimHdCstSpHNdz2b37j20aNkCMzMz3bEvfGbw2adTuJaZqetbOzsVKaXmb1iPlJRUVKp6RWXTtmlx++J51q5dG7VarVv22bVrF8nJmh/ly5cvc+bMGby9vQGoVavWffVhRkYGtrYaZ7Zu3bpkZGi2E1y8eJGsrCzGjh3L0KFDCQ/XRAybN2/OkSNHyMzM5Pbt2+yPiSEjPUOv3GWOI7v7G0e9e/WiRnVznuvdmz4v9OW1V19FmJabpNK4cS0XyzpFP9gWNmZkZxguO579LYPvPzlK+KLTZKeXvyx5r5QcJ/+mvUO3baVH9x73oJmmdz3ZqWxJTS153RbZaMZ7Ta5nXifh4iUQgvfeHs+IoaP4bu2Pd62f4bVbwtFKTUNVz66YlubavXXrFt+t/Z433nrdIF+1Ws0w75H0du9Lly4utHFuXb5uJcyH+nVKx05VV/e3raoOaWnp95w+NzeXscMn8MarH7Mv6pcHUqZ/jVLJn0eMh+EpWkdgrKIosUKINcA44D1gnRBiMVBbUZRvy0lfE/hVUZQpQoh52vS+wGLgK0VRYoQQjYAdQMsSaRcDixVF2SiEeKvEuacBJyAJiAW6K4qyRAgxEXBXFOVqyYIIId4A3gBYumwpjRo53E873DN+fn6oVHbcvHmTjyZ+TEREBJ6enhWiVRJbW1t2RO7A2tqakydPMn7CeEKCQ7CwsKgU/ceJ+LPxLFm0lICvi5Z//Px8sVPZERH+E0sWLyMi4ic8PT0qtBxCCPz95zJ//pfk5ubSrVtXjI01937x8fG89dZbGBn9+3tBIYqiAvn5+Zw8eZKVK1dq9i+9+ipt2rShadOmjB49mjffeRvz6uY4OjpyNd3gUnsgxJ2Iw8jYiN07d5KVnc2oMaNR7ATC/NGYyZu2r41jt7qYmBpxbE8KkSvOMniqU1UXS8fKVd9iYmzMSy++WKE6arWao38c4/v1q6levTpvv/k+LVu1wKVzxweu9c2Kbxk+cpguWlccY2NjNgb9SHZWNh9NmMzZM/G0aP7w9Me/Zcv/1mBrV4fEy8l8+MYUmjVvTEOH+lVdLEk5PAwO3iVFUQp3c/4IfKAoypdCiMHAcqDtXdLnAoX75Q4DvbXfewGtiv2oWAkhSnohXYH+2u8bgC+LnTukKMplACHEn0BjIKa8giiKshJYCXD7zi3l6NGjukgIQEpqCnYqW700dnZ2JCcno1KpyM/P58aNG1hbW5dbYZVKc2dZs2ZNXnjxBeKOn8DT05NNmzYREhICgJOTEynFtVNSsLOzM9AuvnxSmk1JzMzMdNGmVq1a4WDvwMWLF3Fy0kxklVGG8tC054Nv89J09OqWmoqtyrBuyckpJXRqaexTUpg4YRKzfGfg4GBflEabh0MjB6ysrDhx/ASenh6kpqbo+v1u9VCp7EhJSS4qW7E2TU4uOl48z7Zt27JuneYBCj+/OSQlJeHt7c2tW7dYtmwZa9as4dq1a+Tk5PD333/r+lvXFmX0oY2NDWlpadja2pKWloaNjWa/qEqlwtramho1alCjRg3at2/P33//TePGjRk4cCB5OTkEh4SwZ+8eGjk46JW7zHGUen/j6H/bt9O9W3dMTU2pY2PD0+3acfHvU2CeX266ysCitpleRO5GRi6WNvpLcOaWReHG1u527Ntw8R/rbQrcRHCx6/bftve2sG3s27efb7/5Rs+xLws7O1u96yk1JU23jaGkjUplpx3vN6llXQs7lS1Pt2+HdW3NNdy9RzdO/XW6TAev9GtXX8vWzpaU5FSDazfu+An27I5iyaJlZGdnYySMqGZWjSHDBuvSWlpZ0rFTBw4c+IU/fz9epfOhfp3qkJpSdLOUlpKOrW2d+0oP0NC+Hk93bM3fp85VvYP3aNyLVRkPwx68kl2kCCGM0ETbbgG175I+Tylao1RT5LQaAV0URWmn/TRUFOV+Hsgovt5RPN97xsnJiYSEBBIvJ5KXl8eOyB24urrp2bi6uRKu3dC+e9duOrl0KndCzM/P59o1zV6cvLw89u/bR/PmzQAYOnSo7uEHd3d3wiPCURSFY8eOYWFhoVsqK8TW1paaNWty7NgxFEUhPCIcdzf3cuuUkZGBWq0GNMt3FxMuYm9f5KBURhnKoyLavHSdViQkXCqmsxM3154ldHoSHhah1dmj08nOyub998bzwYfv0e7pdjp7Td9qloqfeuopEpMSqVO3Dnl5eURG7sDVVX/js5ubq+5hiF27duOizd/V1ZXIyB3k5uZy+XIiCQkJtG7dWtc2l7VlLp5nuvZJ7NzcXM6fP8+CBQsJCgrC398fJycn/ve//9GhQwcaNmzIgAED9Mph0Ifh4bi7u2vL6EZYWBgAYWFhuuPu7u788ccf5Ofnc/v2bY4fP06TJk20ZUln6JChLFm0GCsrK4YPH054RASKonD02DEsyxhHFjVrclQ3jiJwL9HvJalfrz6HfjsEwK3btzl27DiYF5SbprKo18yCzOQ7XE+9gzq/gFO/XKVpB/2p8Ma1on2b8YczsGlo/o/1hg4ZyubAIDYHBvGsu/u/au+Y2FjWrvuOJYsWYW5+b2Vq5dSSSwmXSUxMIi8vj507dtPTVX9pt6frM0SEbwdgz+4oOnXqgBCCrt06c/ZsPHdu3yE/P58jh/+gadPGd9G6ROJlrVbkLlwNrt1niAj7SaO1ay+dXDoihGD1upVEbN9KxPatDB8xlNGvv8aQYYO5lnGN7KxsAO7cucPBXw/RuHHjKp8Pi9PC6UkuJSSRlJhMXl4eu3fso7ubyz2lzcq6QW5uHgCZ165z/M+/aNy0YlaoJA8OUdr+rUoTF6IxcB7opijKL0KIVUDhs+OOwA/AV0BXRVHyysjjhqIoFtrvgwAPRVFGCSE2AH8oijJfe66doih/CiFGAR0VRXlPCPET8L2iKIHa5dWFiqJYCCHcgI8VRfHQpl0G/K4oyjohxHHAS1GU84alKeL2nVsKwP79+5k/70sKCgro178f48a9TsDyAFo5tcLNzY2cnBymTJnK6VOnsbKywn/eXJ3D9MILL3Lzxk3y8vKwtLRkxdcBNKjfgDFjxpKfn49araZzl858/PFHGBsbI5Qif11RFObMmUPsgViqV6/OzBkzdVEXb29vgoKCADhx4gTTfKaRk5ND9+7d+ezTzxBCsGfvHubOncu1a9ewtLTE0dGRr1d8ze7du1kesBxTE1OEkeDtt9/GrYwf0ooqgy5/UfqP8YNu82bNmunXC7VWJ4Yv5y2koEBNv/5evD5uLAHLv6aVU0vc3FzJyclh6hQfnc7ceX7Y29vz7cpVrFm9jkZPNNLluWLFMszNzRk7Zpy2bwtwcLDn/PnzFBQU0L9/f8aNG8fy5QE46dVjCqe0+c+b56+rx7fffsvWrdswNjZm8uRJ9OjRQ9c28+bN1+bZj3HjNNtbFy5cyL59+ykoKMDbezAjR47U9iGaPoyN5fr164wZM0b3ShODPpxWrA8/0/RhZmYmkyZNIjk5mfr16zN//nxq1dJEMdetW8e2bdsQQjBw4ECd5qhRo7ieeR0TExM+/ugjOru44Dd3DrEHDlC9enVmfTFDN44GD/Fmc2BRGaZO9yEnJ4ce3bvz2SefasfRXub4F42jFo6OfB2wglu3bjFtug/nzp1DUaBfPy8WHbn/1z8sGBdAp6e6UtvChvTsNJaGLSA4ZuM9p5/wVp9Sj5/74xrRP1xAKVBo7WZH5/72xG5OoF5TC5p1sGH/poucO3wNYSyobmFCr9FNdU5e4Iw4MpJuk3tHjbmlKX3GNaNxW/0o9aiWK0rVVRTlX7X3S16e5ObmYq3tZ+c2zkybOlWXf64wfBoYIGb/ARbOX4y6QI1XPw/GjhvF1wHf0rJVC1zdniEnJwefKTM5ffpvrKys8POfib19QwD+91Mk61b/AEITwftwwrsG+QthXEwrlgXzvkJdUEC//p6MHTeaFcu/oZVTS1zdepKTk8O0KV9w+tTf1LKywm+er06rkG9WfIt5DXNefW0kZ/4+w/SpM1EXFKAUFNCrz3O88dbrmCjV9dq1oubDF154gRs3b+jmrQUB02nSrBEl+WX/7yz+8lsKCgp4qV8vXnt9CKsCfqRFqyfp4daZv078zecT/cjOuoFZNTNs6ljzY3AAx//8i/mzlyOEQFEUvId74THAcNza1niqUp/OyL+SVakOjEl9q0fq6ZOHwcGLBH4HOgAngZnAJsBFUZRsIcRCIFtRlOll5FGWg1cXzRJvSzTRt32KorxVwsF7Es2ysLm2HCMURWl4FwfvfTR7BJMURSnz1qrQwatMijt4/wXKcvAqXFfr4FUmRlW2m6Jq5jNRUDXzUtsPm93d6AFTloNX0ZTl4FU0ZTl4FU1xB6+yKO7gVSbZSsLdjSoA6eA9XDwMe/DyFUUZWeKY7mEIRVHKfblPoXOn/b4F2KL9fhUYUor9OmCd9s9ENMu4ihBiKJqoIYqiRAPRxdK8V+z7UmDpXWslkUgkEomkwngUXz5cmTwMDl5V0gFYJjQbsDKBMXexl0gkEolEInnoqVIHT1GUC0Dru9kBCCEOAiXf5PiKoijHS7O/R/393P0pXYlEIpFIJJJHikcmgqcoSueqLoNEIpFIJBLJo8Aj4+BJJBKJRCKR6KjCh0QfBf5bj11KJBKJRCKR/AeQETyJRCKRSCSPHjKAVy4ygieRSCQSiUTymCEjeBKJRCKRSB455HvwykdG8CQSiUQikUgeM6SDJ5FIJBKJRPKYIZdoJRKJRCKRPHrI16SUi4zgSSQSiUQikTxmyAheBSGU/5DvLKpItoraWBHqKtGtCgrIqxJdI6OqmZomvNWn0jW/+npnpWsCvLa0SmQxNvgfJysHhSq4bqvoKQBF+Y/MUTKAVy7/IS9EIpFIJBKJ5L+BjOBJJBKJRCJ59CiQIbzykBE8iUQikUgkkscMGcGTSCQSiUTyyKHIp2jLRUbwJBKJRCKRSB4zZARPIpFIJBLJo0dBVRfg4UZG8CQSiUQikUgeM2QETyKRSCQSySOHIp+iLRcZwZNIJBKJRCJ5zJARPIlEIpFIJI8e8inacpERPIlEIpFIJJIKRAhhI4TYJYQ4o/23dik27YQQvwghTgghjgkhhhQ7t04IcV4I8af20+5umtLBk0gkEolE8sihFCiV+vmXfArsURTlSWCP9u+S3AJeVRTFCegLLBJCWBc7P0lRlHbaz593E5QOnkQikUgkEknF0g/4Tvv9O6B/SQNFUf5WFOWM9nsSkArY/lNBuQevAnB0dBTDhg0jJiaG6tWrM2vmLFq2bGlgd/LkSab5TCMnJ4cePXrwyeRPEEJw/fp1Jk+eTFJSEg0aNGD+/PlYWVmRnZ3N51M+Jzk5mfz8fF579TX69y8aI9nZ2fTu0xtjY2Pq16//wHSzsrLwme7D5cuXMTMzY8aMGTzZ/EkAXnjhBcxrmJOens7Nmzdp3Lgxs2aVozutmO4n5ev+9ttvjB8/noYNGwLw7LPP8tZbb5GcnMyUKVNIT08nPT0dIyMjbG1tK7ydk5KSmDBxAgVKAfl5+XRy6civv/xKQUEB/Qf0Z8zYUXq6ubm5TJsynb/++otatWrhP28ODRo24NdffmXJ4mXk5eVhamrK+Akf4tK5k17aDz+YQOLlRLaEBBnUZ/369QQHh6AoCi+/PJCRI0caDsIy0PSBT7E+mIwQghUrVhAcHIKNjWbV4P333+eZZ57RSxsbe4D5/l9WSn1jY2Px959HQUEBAwYMYOzYMQZaU6ZM1WnNm+evGyerV68mNHQrRkZGfPLJJ3Tv3k2XTq1WM2zYcOzs7Fi2bCkAn332GSdOnMTExITq9ln0GtsUYxP9e9/zR68R/f0FCgoU2rircPFqqHf+xM+p7NtwEQsbMwDa9alHG3cVAMFzT5J89gYNHC0ZMMlwfP5TZr+2EDfnXqRnX8Xri2fvO31ltvHGjZtYv349ly5dYm/0bmrXrq0tQ+GYUmvH1GiDMkyb4lNsTM0tNqaWlhhTLty+fZvJkz7h8qXLGBkZ09P1GT4c/0Epdf+FL/0XoC4oYMCAfowe+1opul/w11+nsK5Vi7nzZtOgYQPijp/Ad5YfoPlfFN58axzPPucOwEsv9KNmjRoYGRthbGzM+o3fV0gbZ2VlMWPGTM6ePYsQghkzvqBt27ZMmjSZixcvAHA96xoWljVZG7hIT+9g7BEWz19FQUEBHv17M3LMy3rn/zx8giVfrubcmQtMn/Mx7r01mkd+O87SL1fr7BIuJDJ97kf0dO9i0LaPM0KIN4A3ih1aqSjKyntMrlIU5Yr2ezKguouWC2AGxBc7PFsI4YM2AqgoSk55efznInhCiFVCiFalHB8lhFim/d6/uI0QIloI0fE+ZF5ISEggPCwcn2k++M72LdXId7Yv032mEx4WTkJCArGxsQCsWbMGl84uhIeH49LZhdVrNBdWYGAgTZs2ZXPQZlavWs2ChQvIy8vT5Tdl6hRq1qzJiy+++EB1V61aRQvHFmzZvIXZvrOZN2+eXn7jxo3DycmJ3377DR8fH3x9y9D19WX69OmEh5ei66LVdXFh9eqiieTpp58mKCiIoKAg3nrrLQCMjY35+OOP+WjiR7Rq1QpLS0vGjBlT4e1sa2vLD9//QGDQBtZ9v5otm4OZMesLgkM3Exm5g/j4c3q6W0O3YWllSVjEVkaMHM7iRZofO2traxYt+YrNwYHMnPUFU6f46KXbs3svNWrUKLUuZ86cJTg4hPXrf2Tz5iD27dtPQkJCqbal98Fspk/3ITw8TK8tAF55ZaSurUs6d2q1mrl+/iwLWFLh9VWr1fj5zSEgYDmhoSFERkYSHx+vlyY0NBQrKysiIsIZOXIkixYtBiA+Pp7IyB2EhAQTEBCAn58farVal279+g00bdpEL68XX3yRbdu2Ehy8hfzcAuKiUvXOFxQo7F17ngGTWzJqfjtOHbhK+uVbBm37VJc6vDKnLa/Maatz7gA6eTSk79vNDez/LaEHAhm3eMQ/SqsoVGobt2vXjm+++ZoGDerrjmnG1FztmNpSxpjaiqWVFWER2xgxcgSLFy0BCsfUIjYHBzFz1gy9MfXqq68Qui2ETUEbOPrnUWJiYvXyVKvV+PvNY2nAYoJDA4mM3ME5A90wrKwsCYsIYcTIYSxetAyAZs2b8eOG79gUtJ5lAUuYPWsu+fn5unTfrFrBpqD1rN/4fYWN43nz5tG9eze2bdvK5s1BNGmiaev58+fprl/X57rS89muBvVeOPcbvlzmww/BS9kduZ/z8Zf0bFT16/L5jA/o1ben3vH2ndqwNnARawMXsXjlLKpVr4ZLl6epcgqUSv0oirJSUZSOxT56zp0QYrcQIq6UT7/idorm/1grc81XCFEf+AEYrShK4eucPwNaAJ0AG+CTuzXPf87BUxTldUVRTt7FrD9g4ATeB/08PTwRQuDs7Ex2djZpaWl6Bmlpady8eRNnZ2eEEHh6eL5Lni4AACAASURBVLI3ai8AUdFReHl6AeDl6UVUVBQAQghu3byFoijcun2LWrVqYWxsDGgiM/Hx8bi5uT1w3XPnzuHi4gJAkyZNSEpKIj09XZdnbGwsnp73WV9PT/bu1epGReHlpdX1KtItC1tbW1q2bElUdBT9+/WnWbNm1LauXeHtbGpqipmZJkJz7OhxXaTU1NSU5/v2ITr6Zz3t6Kif8fTyAKBX7+c4dOgQiqLQomUL7Ow0UfdmzZuRk5NDbm4uALdu3eLHH9bz+rixpdb9/PlztGnTBnNzc0xMTOjQoQN79uzh0qVLvP32OwwdOoxRo0Zz/vx5g7SGfeDB3r3lt3UhcXEncHBwwN7evsLrW1Krb9/niY6O1tOKiorGy8sTgN69e+m0oqOj6dv3eczMzLC3b4iDgwNxcXEApKSksH//fgYMGKiX1zPPPIMQAiEE9ZpZkJ2Rq3c++ewNrFXVsVZVx9jEiBZd6xJ/+No9tRtAo9a1MDM3vmf7e+X3Mwe5fvPey6FHtlGltnHLli10kalCSh9T+mW43zFlbm5OJxdNdNjU1JQWLVuQmpJioGvvYI+9fcNiuvsMdD28XgLgud7P8tuh31AUBXPz6piYaBa+cnNyEEKU2cQVMY6zs7M5fPgIAwYM0NXRyspKL09FUYjaFUuvvvo3aX/FnaGhQ30a2NfD1NSU557vQUz0QT2b+g1UNH+qMcKo7HpF7z5Al+7tqW5erUyb/yqKovRSFKV1KZ9tQIrWcSt04FJLy0MIYQX8BExRFOXXYnlfUTTkAGsBl7uV56Fy8IQQjYUQp4QQ64UQfwkhtgghagkhTgshHLU2G4UQ48pIP1gIsVD7/UMhxDnt96ZCiFjtd100TggxWgjxtxDiENBde6wb4AXM1z6p0kyb/WAhxCGt/TMltUvQUFWv6A5epVKRmqrfl6mpqahUpdtkpGdga6uZvOrWrUtGegYAQ4cO5dz5c/Tq3YtBgwYxedJkjIyMKCgoYMGCBTRs0JBaVrUeuO5TTz3Fnj17ADh+/DhXrlwhpXDSFLB3715WrFjBli1b/pluRgndjAyd3bFjxxg8eDDvvPMOZ8+eNcjT2NiYU6dO0aZNmwpvZ4Dk5GS8Bw1lwviJtGjpqPuRUdnZkZZiqF1POw5MTEywsLAgM/O6ns3u3Xto0bKFznEMWL6CV14diXn16pRG8+bNOXLkCJmZmdy+fZuYmBiSk1OYOXMWn376CZs2bWTixInMnu1nkLa8tgDYtGkTgwYNxsdnOllZWYZpi4/pCqyvJp96ur/t7FSklKpVr4RWJikpqahURWmL13HevPlMmDAeozJ+vPLy8vgr5iqN21rrHb9xLRfLOkU/ZhY2ZmRnGK6MnP0tg+8/OUr4otNkp5e7clL15BhVSRuXzF9/TKlIS0krYZNWypjK1LMpOaYKyc7KZt/P+3HprP87mFYsT03d7UgtoZtWqq5mLB8/FsegAUPwHjScz6d+onP4BPDuW+8zfOirBG8J1ebxYNs4MTGR2rVr4+Pjg7f3EL74Yga3bt3Wy/PIkSPUtrHG4YkGJeqUgZ2qru5vW1UdrqZlcL/s2RHDc33v9hNYOSiKUqmff0kYULgX4DVgW0kDIYQZEAp8ryjKlhLnCp1DgSYIFXc3wYfKwdPiCAQoitISyALGAe8B64QQQ4HaiqJ8W0ba/UDhyHsGSBdCNNR+17tF0zbWDDSOXQ+0ETtFUQ6g6YjCp1UKY+omiqK4AOOB6Q+kpveAEEIzcwAHDhyghWMLdu/aTVBgEHPmzuHGjRsEBgXSo0cPzKqZlZ/ZP9QdM2YMWdlZeHt7s3HTRlo4ttA5POvWrqN9+/ZMnDiRwMBADh8+/O91tbRs2ZLIyEg2b97MsGHDmDBhgp6tWq1m2fJlTJo0CQsLi3+ve5d2BqhXrx5BWzbxyaeTSbh4SS+Seb/En41nyaKlTJ32OQCnT53m0qXLuj09pdG0aVNGjx7NW2+9zTvvvIujoyM5OTkcPXqUSZMm4e3tja/vLK5evXpfZfH29iYiIoKgoEBsbevy5ZcL/nG9yuKf1PdB8fPP+7CxqU2rVmUH5v38/GjYwhL7FlZl2pRF0/a1Gbu4Pa/6t+WJNtZErjh790SPGffSxg8azZhaohtTheTn5/Ppp58zbPhQ7O3tH6hmG+fWbAkN5IcN61i7+jtycjTO/Jp137Ih8AeWLV9EUOBmzp07d5ec7h+1Ws2pU6cYPNiboKBAzM2rs2bNGj2b7dsjDaJ3D4qraRnEn7lI564PwfLso8dcoLcQ4gzQS/s3QoiOQohVWhtvoCcwqpTXoawXQhwHjgN1gdL3JBXjYXzI4pKiKIWbJn4EPlAU5UshxGBgOdC2rISKoiQLISyEEJaAA7ABTWM9A4SUMO8MRCuKkgYghAgEniqnXIXpDwONS550dHR8Nz8//9OCgoI6OTk5Nzdt2kT7p9sDmmULOzs7PXs7O7uiKFgJG5s6NqSlpWFra0taWho2NjYAbNu2jTFjxiCEoFGjRjRs2JCVK1cSEhrC7du3MTM14/DhwwghqFGjxgPTtbCwYNbMWYVtzIsvvsjBgwfx8dHse3Fq7cTt27d59tlniYuLu39dm7J1C3nmmWfw8/NjzZo1REZGoigKmZmZdOzYkV7P9arwdj5//jxt2rTR5dP8yeaYmppy5Mgf9O7di5TUVGxVhtrJySmoVCry8/O5ceMG1ta1dOWYOGESs3xn4OCg+QE6euw4J0/+xYsveKLOV5ORkcHrY99g1Wr9PbwDBw5g4EDNEs2SJUuoU6culpaWBAXpP5Ch2ew+DABXVze8vQeX2RZ16tQplv9A3n9ff2O6nZ0dKcnF0lZgfd//4D2Sk5N1+aampqAqVSu5hJY1KpUdKSlFaQvrGB39M9HRPxMTE0NOTi43b97ks88+Z84cTaTz66+/5tq1a7iNbUxJLGqb6UXkbmTkYmmjvzxlbmmq+97a3Y59Gy4a5PNQUa2g0tu4JIZjKgVblW0JG9tSxpS1TnfihI+Z5TsTBwcHvXS+M2fTqJEDI0YON9C11eZZVPdU7Ero2paqW0vPpmnTJpjXMCf+bDytnFphpyqaV9yfdSMjI/OBt7FKpUKlssPZWTMX9e7dW8/By8/PZ8+ePXy7QX+ftKZONqSmFN34paWkU9fWxsCuPKJ2xdLz2c6YmD4krkPB3U0eFhRFSQeeK+X478Dr2u8/ovF7Skt/309SPYwRvJJxUEUIYQS0RPOOGIOXA5bgADAaOE1RRK8rEFteonugcIZXU4pjfPr06eXx8fEO58+fr2FpaTnqxo0bKIrCsWPHsLCw0C0FFmJra0vNmjU5duwYiqIQHhGOu5smkuHm6kZYeBgAYeFhuuP16tfj4EHNnon09HQuXLjA6NGjidkfw+HfD+Pv70+DBg146aWXeNb92Qemm5WVpXuYIyQkhPYd2vPaa6+x7rt1rF27Fnd3d7Zu3cqBAwcwNja+N93wcNzdtbpuboSFaXXDwnTHr169qguLHz9+nIKCAkaPHk1gYCBPPfUUrVq24vr165XSzvb29qSkpHDnzh0AHBwcyMjIoIZ5DfLy8tgRuRM3V/2Nya5uPQkPiwBg9649dHLphBCC7Kxs3n9vPB98+B7tni56V6W39yB27Y7kf9vDWbtuFU880cjAudOUSbOscuXKFfbs2YunpwcNGzZg586dgMYJP336NMbGxrpN1++++04pfRCBu7sbgN7exb1799K8uf5DAU5OrUhIuETi5cQKr69GK4HLWq3IyB24urrqabm5uRIWFg7Arl27cdFqubq6Ehm5g9zcXC5fTiQhIYHWrVvz4YcfsGvXTrZv346//1w6deqkczxCQkI4cOAAc+fOLXXvUb1mFmQm3+F66h3U+QWc+uUqTTvoT0M3rhXt24s/nIFNQ3ODfB4qLAsqtY1Lo/QxpV8GVzfXcsbUh3zw4ft6Ywpg+bIAsm/cYNLkj8vUvVRC19VVP+Ll6taTiLCfANizay+dXDoihCDxcqLuoYqkpCtcuHCR+g0acPvWbW7evAnA7Vu3+fWXg3Tr3vWBt3HdunVRqepx4cIFAA4ePEjTpk11+R08eJAmTZroLcUW0sLpSS4nXCEpMYW8vDz27Iihh9tdt3HpsTtyv8EDGJKHF/EA1pUfGEKIxsB5oJuiKL9ow5Z/aU87onmq5Cugq6IoeWXkMQqYqf2sRbNOfVtRlPba89HAx0Ai8CvQHs1S8F7gqKIo7wkhlgJHFEVZWzyNoii/CyHqAr8ritK4rHo4OjqKId5DCmIPxFK9enVmzpiJk5MToFkKK4y0nDhxQvf6ju7du/PZp58hhCAzM5NJkyeRfCWZ+g3qM3/efGrVqkVqairTfKbpHJ8xY8bg8ZKHTldRFN544w1OnDxB/fr1H5ju0aNHmTptKkIImjVrxowvZmBlZcXly5eZMHECiqLoIkN2dnbMnFmO7rRiup8V0500ieTkZOrXr8/8+RrdjRs3EhQUhImJCdWqVePjjz+mXbt2HDlyhNGjR9O8eXOupl3l5q2b1K1blwVfLqjQdv7ll19YsHCBZilXUejQoT0HDvxKQYGafv29eH3cWAKWf00rp5a4ubmSk5PD1Ck+nD51GisrK+bO88Pe3p5vV65izep1NHqika7vVqxYhk2dorvppMQkPnh/vO61IUbF7ilGjRrN9evXMTEx4eOPP6Jz585cvpzI7NmzuXr1Kvn5+Tz//PO89dabBmNT0wc+xfrgU4QQfP75FE6fPo0QggYNGjBt2lRsbW0poOgJwf37Y/hy3sJKqW/s/l+YN2++5pUs/fsxbtw4li8PwMmpFW5ubuTk5DBlyhROabXmzfPXLcV9++23bN26DWNjYyZPnkSPHj302uC3337ju+++173Co337DtSvX5+aNWuQcecyzTvZ0HWgfkTo3B/XiP7hAkqBQms3Ozr3tyd2cwL1mlrQrIMN+zdd5NzhawhjQXULE3qNbqpz8gJnxJGRdJvcO2rMLU3pM66Z3j6/r77eadBP98KCcQF0eqortS1sSM9OY2nYAoJjNt5z+qXe6yqtjdev38C6detIT0+ntk1tevTozvQvfLRjaoF2TPXTjqkVtHJqVWxMTdOOqVolxtTaEmNqOXn5efTt8yJNmjTGVLsnb8hQbwYOHIBC0ZO+MftjtWO5AK/+nrw+bgwrln9DK6eWuLr1JCcnh2lTpnPq1N/UsrJizrzZ2Ns3JCL8f6xb8x0mpiYYCSPGvTkW92fduHw5kY8mTAJAna+m74vP8/q4MRzYf/CBt/GpU6eYMWMmeXl52Ns3ZObMmboHLaZNm0abNs70HaT/GqJCftn/O0u+XENBgZqX+vXi1dcHsypgAy1aNaeHmwt/nTjDlIlzyc66gVk1M2zqWPNDsKYPrySl8M6ozwiOXKXbolMSuxot77758gGSczS5Uh2Yam3rVWr9/i0Po4MXCfwOdABOonHUNgEuiqJkax+iyFYUpdR9cNqHIs4Cjoqi/C2E2AmcUhTlA+35aIqctdFoHj3OBP4EcrUOXnfgWzRRu0HAau7DwQO4c/vOw9OwFU1VDfkqauECUeq9RYViVEW7KYo7eJVJVdX3uxNvV7rmP3Xw/i1/Lo2/u1EFUFDM0apMlCrQNebB7Yu+H7IKLlSJrnTwHi4ekoV0PfIVRSn55lbd20EVRZlYXmLtQxGi2N99Spx3K/Z9LZooX8k8YtF/TUrxNFcpZQ+eRCKRSCSSSuTf//dhjzUP4x48iUQikUgkEsm/4KGK4CmKcgFofS+2QoiDQMk3Lb6iKMrxB10uiUQikUgkDxeKjOCVy0Pl4N0PiqJ0ruoySCQSiUQikTyMPLIOnkQikUgkkv8wD9FDog8jcg+eRCKRSCQSyWOGjOBJJBKJRCJ55JB78MpHRvAkEolEIpFIHjOkgyeRSCQSiUTymCGXaCUSiUQikTx6FFR1AR5uZARPIpFIJBKJ5DFDRvAkEolEIpE8cijyNSnlIiN4EolEIpFIJI8ZQnrAFcOdO3eqoGGrpi+VKtItIK9KdI2Vkv9DXiUgKl9SQ1X1bX6V6BoXVP6ihlJFt9nt3m9WJbpHlv5dJboK6krXNMK00jUB3KY7VonuQf9LlTpT3YpNqNQJqkb3RlU2E/8TZARPIpFIJBKJ5DFD7sGTSCQSiUTy6CFfdFwuMoInkUgkEolE8pghI3gSiUQikUgeOeQzBOUjI3gSiUQikUgkjxkygieRSCQSieTRQ/5PFuUiI3gSiUQikUgkjxkygieRSCQSieSRQ5FP0ZaLjOBJJBKJRCKRPGbICJ5EIpFIJJJHD/kUbbnICJ5EIpFIJBLJY4Z08CQSiUQikUgeM+QSrUQikUgkkkcO+ZBF+cgInkQikUgkEsljhnTwHhCOjo7C0dFxiaOj41lHR8djf/31V6l2J0+e5OWXX8bDw4O5c+fq/quV69ev8+abb+Lp6cmbb75JVlYWoPmvWObOnYuHhweDBg2ieL5XrlzhzTffpH///gwYMIBt27bh5dWPrl270aPHM3h7e+Pt7c2pU6cAyM3NZdKkyXh4eDJixEgSExN1ea1evRoPD0+8vPoRG3sAgJycHIYPH8Hgwd4MGDCQgIAAg/rMnetP1y7ddH/HxsbSz6s/nh5erFm9xsA+NzeXyZM+wdPDi5EjXiExMQmAzMxMXh87jq5dujHHb65emrFjX6efV3+8vYfg7T2EjPQMg3xjY39hgNcgvDwGsnb1d6XqfjLpc7w8BvLqiNEkaXXjjp9gqPcIhnqPYMjg4ezdE6Wr+yvDRzFk8HAGDRjCioCVRf3hPxcPTw8GDdbvj+KcPHmSlwe9jIenB3P9797PWVlZjJ8wnkGDBzF8xHDOnD2jl19+fj6urq507tzZYBwY6N7H+Prpp58YNGgQL7/8Mq+++iqnT5/W5ZWVlcVrr42iQ4eOdOjQEV/f2aW26/2MqULUajXe3kN47733dccOHjzEkCFDGTjwZaZNnU5+fj4AsbEH6O81EC+P/qxZva7UMnwy6TO8PPrzyojXdH376y+/MnzoSAa/PIThQ0dy6OBvujQ7InfiPWgoLw/wZvFXS0pty+Jo+t2fl7w8edl7MCfLaf+Bgwfxkpcnc/39de2/4KuFeA3oz8vegxk/cQJZ2Vm6NLGxsXh59cPDw5PVZVwzD6qNN27chIeHJ23btkPJE3etd0lmv7aQ2AXHCPti732nLY2ivu3HmtVrDc5r+vZTvDz68cqIV3V9m5mZybixb9CtSw/m+vnrpcnLy2PWTF/6eQ5gQL+B7N69pxTdBztfAGRnZTPpo08Z2G8wA/t7c/TosRKaD35uXLp0Gc/36as3B5dHl6fcCPo4mi2T9vOq2zsG5+tZN2TZuI38OH4nAW8EYVerHgBP1m/Fqne2snHibn4cv5Nezp73pFfhFCiV+3nE+E87eELDg2qDF4AntZ83fH19SzXy9fVl+vTphIeHk5CQQGxsLABr1qzBxcWF8PBwXFxcWL16NQAxMTEkJCQQHh6Oj48PxfOdOnUqo0aNYuvWrXz//fd8881KAgKW8+yz7lhYWDBnzhyCgoJo0aIFAKGhoVhZWREREc7IkSNZtGgxAPHx8URG7iAkJJiAgAD8/PxQq9WYmZmxatW3bN4cRFBQILGxBzh2rGjSOnHihM5RAM0Pyhy/uSwPWEZIaDCRkZHEx8fr1T80dCtWVpaER4QxcuQIFmvLUM2sGu+++w4TJ04otd385swmKCiQoKBAbOrY6J1Tq9X4+81jacBigkMDiYzcwbn4c3o2W0PDsLKyJCwihBEjh7F40TIAmjVvxo8bvmNT0HqWBSxh9qy55OfnY2ZmxjerAgjcvIGNQev5JfYXjh07TmzMAU1/hIXjM80H39ll9PNsX6b7TCc8rJR+7qzt584urF6j6edVq1bRwrEFWzZvYbbvbObNm2cwbkxMTOjYsaPBOChpdz/jq2HDhqxZs4bg4GDeeOMNZs6cqcvL39+fixcvsm3bVvbv38eRI0dK6c/7G1OFrF+/gaZNm+j+LigoYNq0afj7+xMSEkz9+vUJD4tArVYz18+fZQFLCA7dTGTkDuIN+nYbllaWhEVsZcTI4SxetBQAa2trFi35is3Bgcyc9QVTp/gAmh/MRV8t5uuVKwgODeJqejoHDx4qtT0LiYmJ4WJCAhHbwvCZOg1fP0NnF8DXbzbTp/kQsS2MiwkJxGjbv2uXLoRs3kJw0GaeeOIJVq/R/MCr1Wr8/OYQELCc0NCQMq6ZB9PGAO3ateObb76mQYP65da3LEIPBDJu8Yh/lLYkmr6dq+3bLWX07VYsrawIi9jGiJEjWLxI44xXM6vGO+++zYSJ4w3yXfXtamxsbNgWHkpw6BY6dGhvoPug5wuA+fMW0K17F0K2bSZw83qaNmmip1kRc6Ora09+XP/DPbW3kTBiUn9fxq95laELn6VP2340sXtSz+aDl6byv8PBjFzUh9V7FvFO308BuJN3mxmB4xm2sBfjV7/CBM/pWFS3uiddSdXxn3PwhBCNhRCnhRDfA3HAaiHE70KIE0KIGcXsLggh5ggh/tSeby+E2CGEiBdCvFVK1v2A70+fPq2cPn361+zsbNLS0vQM0tLSuHnzJs7Ozggh8PT0ZO9ezZ1wVFQUXl5eAHh5eREVFaU77unpiRACZ2dnCvONj48nPz+frl27AnDu3DkaNWqEvb09RkZGtGvXjujoaD39qKhovLw0d169e/fi0KFDKIpCdHQ0ffs+j5mZGfb2DXFwcCAuLg4hBDVq1AA0ESTNRKa561er1Sxc+BUTJhRNsHFxcTg4OGBvb4+pqSnP933eoAzRUdF4asvQq1gZzGuY83T7pzGrVu2e+rE4cXEnsHewx96+oVa3D9HR+0ro/oyH10sAPNf7WX479JtG17w6Jiaarai5OTkIoalfaXUXCKKj9uHpYdgfxTHoZw9P9kZp+zk6Ci9PbT97FvXzuXPncHFxAaBJkyYkJSWRnp4OQEpKCjExMfTv3//+dO9hfLVr1w4rK81E7ezsTEpKCgDZ2dn8+uuvtGjRAnt7e2rUqMFLL734r8dUYX3279/PgAEDdflkZmZiampK48ZPANCla2f27NlLXNyJEmOqD9HRPxv0raeXBwC9ej+nK0OLli2ws7MFND/MOTk55Obmkng5kUaNGmFjUxuAzp1d2LO7/IhU1M/ReHp4IISgbTntf+PmTdrq+t2DqGhNO3fr2k03zpzbFLVzyWumbynXzINqY4CWLVvQsGHDcutaHr+fOcj1m9f+cfrilN630Xo2ZfVt4XxRrZqZQb7btoYxZsxoAIyMjKhdu7aB7oOeL7Kzb3Dk8B/0H9APAFNTUyytLItpVszc6OzsjK2tbdmNXIxWDu24nH6BpIwE8tV57DoaRs9WffRsmqie5Pd4zU3J4fgDuvOXrp7nUvoFAK5mp3DtRjq1a+rfaFcFiqJU6udR4z/n4Gl5EghQFMUJ+EhRlI6AM+AqhHAuZpegKEo7YD+wDhgEdAFmYEhD4FLhHyqVitTUVD2D1NRUVCoVpdlkZGToLtS6deuSkZFRbpqLFy9iaWnJhAkT8Pb2ZvXq1ahUdjq7Q4cO8d133zN//nxyc3N1edWrpwm5m5iYYGFhQWZmJikpqahU9Uotl2aZxxt392fp0qULzs5tANi0aRNubq56k4sm/2JltVORmqL/I1hWGe7GdJ8v8PYewspvVhpcaGmpaXq6dnZ2BrrFbYp0rwNw/FgcgwYMwXvQcD6f+oluAler1Qz1HkEv9+fp3MWFNs6tNf1Rr/Q+LF7HMvs5vUQ/a5ebn3rqKfbs0SwlHT9+nCtXruicgHnz52Fvb0+dOnX+uW4Z46s4oaGh9OjRA4DExESqV69OYmIi3t5D+OKLGVhb1yYlxVDzfsfUvHnzmTBhPEZGRUuEtWvXRq1Wc+LECQB279pDSnKKYXvb2ZFWahlK79tCdu/eQ4uWLTAzM8OhkQMXLlwkKTGJ/Px8oqKiSUlOMWiPsupZsj7FbVR25Y8NgNBtW+nRXdPOKSXytbNTVVgbP2wY9q2KNIP5orTrtuz5IjsrG4Dly1cwbMhwJn08WXejVEhFzBdJiUnUrl2bL3xmMsx7JDO/8OX2rdt6da2oufFesatVj5TMpCK961ewrVVPz+ZM0l+4t34BADenvtSsbolVDWs9m1b27TAxMeVyxsUHVjZJxfBfdfAuKoryq/a7txDiCPAH4AS0KmYXpv33OHBQUZRsRVHSgBwhhP6oB1JTU7200b7fk5KSSp6+ZwrvCstDrVbzxx9/8NFHH7FhwwbS09O5eDEBgA8++IAJE8bTp08frl+/zpo1hntb7hVjY2OCgoLYuXMHcXFxnDlzltTUVHbu3MWwYcP+cb73g5+fH1uCN7N27RqOHPmDiIiIB5p/G+fWbAkN5IcN61i7+jtycnIATd03Ba0ncmcEJ+JOcvZM/F1yuj+EEIUBUcaMGUNWdhbe3t5s3LSRFo4tMDIy4ud9P2NT2wYLC4sHq1uCQ4cOERoayvjxmoisWq0mKSmJJk2aEBQUiLl5dfbt22eQ7n75+ed92NjUplWrVnrHhRD4+89l/vwvGT58BDVr1sDI2Phf6wHEn41nyaKlTJ32OQBWVlZ8PuVTPpn8GWNGj6NBgwYYGVfOVLhy1beYGBvz0osvVphGWW38XyBfnU9KSgpt2zmzMXADzs7OfLVg0QPVKG2+UKvzOXXqNIMGv8zGoB8xNzdn7RrDvX0PO0t+8uXppl34/oPttG/ahdTrVygoKNCdr2NpxxdDF+G7+aOHI6Il9+CVy3/1NSk3AYQQTYCPgU6KolwTQqwDqhezy9H+W1Dse+HfJo6Oju8C47THfrOzs/vj2rVrkwH69Omj2NnZURw7OztdVAY0yyiFNjY2NqSlpWFra0taWho2NjbloaitiQAAIABJREFUplGr1Tg6OmJvbw9At27dCAvbBoCtrS2pqanUr18PZ2dnvvvue11eycnJqFQq8vPzuXHjBtbW1qhUdqSkJJdarkKsrKzo1KkTBw7E0qRJEy5duoSnp2Y54c6dO3h6eOE7exbJxSIhKakp2Kn0lw/KKkN5FEYma9asyQsvvkDc8RM6bQBbO1s93dTUVAPdQht93Vp6Nk2bNsG8hjnxZ+Np5VT04/i//0Vy/vwF3nn7fZ55prtetKe0tiq3n+uU3s8WFhbMmjkL0Cw7vPjiixw8eJB1360jKysLMzMz/vjjD/Lz8/nss8/uX7eM8QXw999/M2PGDJYvX67rC5VKhY2NjS7627t3b2bMmKGL4BbXvJ8xFR39M9HRPxMTE0NOTi43b97ks88+Z84cP9q2bcu6dZqbkdgDMVy8mKCpk96YSsVWZVjvsvo2JSWFiRMmMct3Bg4O9ro0rm49cXXrCUDwlhCMS3HwNgVuIjgkBAAnJyeSk8u/Ruzs7EhJLXtsbAvbxr59+/n2m290TrZK236FpKam6EXiH3QbP0wY9m0KtgbzRWnXbdnzhbW1NdWrV+e5554FoHefXmwN3aZnUxHzhZ3KDjuVHW2cWwOaZd11a77Xq2tFzI33Q+r1ZFTWDYr0atUn7Xqyns3V7BQ+/eENAMzNauDe5kVu3NHss65ZzYKFo9fx9Y55xCX88cDKJak4/qsRvEKs0Dh714UQKjQPStwzp0+fXn769Ol2p0+fbgdsBV7VPk3bxcLCwmBvhK2tLTVr1uTYsWMoikJ4eDju7u4AuLm5ERamCRiGhYXpHQ8PD0dRFI4dO0Zhvk5OTmRnZ+uW2pKSkrhz5w6XLyeSlJREZOQOevZ0JSoqiubNm2vzciUsLByAXbt24+LSCSEErq6uREbuIDc3l8uXE0lISKB169ZkZGToHqK4c+cOv/76K40bN6Fnz57s3buH7du3s337dqpXr054RBhOTk4kJCSQeDmRvLw8dkTuwNXVTa8NXN1cCdeWYfeu3XTSlqEs8vPzuXZNs+cnLy+P/fv20bx5Mz0bJ6dWXEq4VEx3J66uz5TQ7UlE2E8A7Nm1l04uHRFCkHg5UbdJOinpChcuXKR+gwZcy7imW+7p198Th0b2TJ32GW7uroRHGPZHuf0cEY67m7Y/Xd0IC9f2c3iY7nhWVhZ5eXkAhISE0L5De1577TWi9kZx+PfD+Pv707hxYzp27MiwYcPuTfcexteVK1eYOHEis2fPpnHjxrq86tati729PefOnePy5UQOHDjA9etZuLq66mne75j68MMP2LVrJ9u3b8fffy6dOnXSOR7p2uXq3Nxc1q39jkGDXsbJqRUJJfrWzbWnQd+Gh2miurt37dGNqeysbN5/bzwffPge7Z5up5emcGk8KyuLoKAtDBjQn5IMHTKUzYFBbA4M4ll3d8IjIlAUhaPHjmFZRvtb1KzJUV2/R+CuHf8xsbGsXfcdSxYtwtzcXJem8Jq5rK1fZOSOCm3jh4nS+1a/7pr5wrBvy0IIQU/Xnvz+2+8AHPo/e/cdFsXxP3D8PTRBARHlsGBi+8YWsUVjS8QWG0UsiCWmWFI0RRNbBDSKCBi7otFINIkFEFHAiBVU0FhS7KaYKFEj8LOi4gHH/v6487ij2SjBzOt57oG7nZ3PTtllbrZw+Aj16hvfaFISx4tq1arh6KjiwoWLurhHqWtwg0tJHBsf19lLx6ldtQ41qtTGzNScHs3d2X92l1GayhWr6GO+0WUcMUfDADAzNSdoxCq2/xTJ3pPfF9s2PS1Fk1Oqr/JG/CumWUuREKIOEKsoyou692uADmivn7sFRCuKskYIcQF4SVGU/xNCvKn7fZxuHf2yB/k2bNhQAEuBXsC99evXv9i0aVMAvLy8CA8PB7R3nvr6+qJWq+nYsSNTp05FCMHNmzeZOHEiV69epUaNGsydO5fKlSujKApz5swhKSkJS0tLZs6cyYN8Dx06xLx581AUhSZNmtC1a1fmz59PSkoKlSpVokoV7c46ZsxoevbsiVqtZtq0aZw79yu2trYEBwfpZwBXrVrFli1bMTU1ZdKkiXTq1InffvsNHx9fcnJyyMnJ4bXXXuPdd9/JV6ft2rXn0A/aRzQcOHCAucFfkJOTg0c/D0aPHkXIshCaNG2Ci4uLbht8+FW3DUHBgfpt6N27D3fv3CUrKwsbGxuWrwihZo2avP32SLKzs9FoNLzc7mU+/fQTTE1NySFLvw2JB5L4Ing+OTk5uPdzY9Tot1m+7EuaNG1MZ5dXUavV+E6bzrlzv1HZ1pY5wbNxcqpFbMz3rAldi5m5GSbChNHvjKRLVxd+++13pvt8jiYnByUnhx6vdWfMu6NQFIXggPkkHdS1x+e57ZGvnf0M2nmKQTtPmsjVf65So2YN5gZr2/n48eP4+PoghKB+/fp8PuNz/c0PAAoK48eP59ChQzg5ORn1g6fpXzNmzGD37t3UrKn9Zm9qasqGDRsAOHfuHJ9++ilXr17F1NSU118fzrhx41i2LISmRu356H3K0NGjR1m79huWLtXe9Tp//nz27z9ATk4Og7wGMGz4UF2fStS1rQaPfu6MGj2SkGUraNK0MS4unVGr1fhM89P3qcDgAJycnFi18itCV6/hueef08dcvnwp9lXtmTL5M377TfsomjFjRtGrd09t+XMKPqmhKAoBgXNIOngQS0tLZs34XF//gwZ7ERGWW/8+0/1Qq9V06tiRqZOnIISgr7sbmZmZ2FXWzgI5N3PG18cHgP1JBwgOnktOTg79+nkwevToEqvjdevWs2bNGq5du4bGNAvssxEvqHlU80aH0OaF9lSxtudaehpLoucRmbjhkdcH+GnJb/rftW07T9e2Hrq2Xa47XjxoW19d21bWty1An96uRseLkBXLqF+/Hleu/IPPNF/upKdrr4ubOZ0aNWqgkHuHcXEfLwB+PfcbMz/3JysrGyenmsyY6YedbVWDshbvsbF+/fosWLCQ7d9v18/Oe/b35L333sVlesMC675Dwy6Md5uBiYkpMUfDWBO/hDE9PuHspRMcOLuLrs368H6vKSiKws9/HWbuFh+yNJn0aumJ76B5/JmS23Yzwyfw+z9njPI/HPR3qV70eSvmXKkOYCq7Nfr3XtRagP/cAK+03L9/vwwqtmzaUimjuIYDvNJkqjz+nb5PrcwOK2XVttllErewAV5JUsroPEqLD+o/PFEJMBzglSbDAV5pMcG81GMChQ7wSlppD/Bubj1bqgcoO4/G5WqA918/RStJkiRJkvTM+a/eZCFJkiRJUjlWHq+LK01yBk+SJEmSJOkZI2fwJEmSJEkqf3LkDF5R5AyeJEmSJEnSM0YO8CRJkiRJkp4x8hStJEmSJEnljqKRj3kripzBkyRJkiRJesbIGTxJkiRJksodRd5kUSQ5gydJkiRJkvSMkTN4kiRJkiSVO/JBx0WTM3iSJEmSJEnPGKEo8i6UkqC+m/GfqVjFpGz+/7JGqMskrpnGovSDllFvUszK6n9rl02Bs5R7ZRLXVFQok7hlodUHL5RJ3E/e61UmcUc0WlbqMTUmmlKPCVDR0rpUDxj/982xUj1QVBvxUlkdEJ+InMGTJEkqQ/+lwd1/TVkM7iTpAXkNniRJkiRJ5Y6S8585UfZE5AyeJEmSJEnSM0bO4EmSJEmSVO7Iu2iLJmfwJEmSJEmSnjFyBk+SJEmSpHJH/ieLoskZPEmSJEmSpBIkhLAXQuwSQvyu+1mlkHQaIcQvule0wed1hRCHhRB/CCHChBAPfV6XHOBJkiRJklT+aHJK9/V0pgB7FEX5H7BH974gGYqitNC93A0+DwIWKIrSALgBjHxYQDnAkyRJkiRJKlkewFrd72uBfo+6ohBCAF2BTY+zvhzgSZIkSZIkPYQQYowQ4pjBa8xjrO6oKMo/ut+vAo6FpLPU5f2DEOLBIK4qcFNRlGzd+0tArYcFlDdZSJIkSZJU7pT2g44VRVkJrCxsuRBiN1C9gEXT8uSjCCEK2/jnFUW5LISoB+wVQpwEbj3J9soBniRJkiRJ0lNSFKV7YcuEEClCiBqKovwjhKgBpBaSx2Xdzz+FEAlASyASsBNCmOlm8ZyAyw/bHnmKVpIkSZKkckfR5JTq6ylFA2/ofn8D2Jo3gRCiihDaf04thKgGdATOKIqiAPHAwKLWz0sO8CRJkiRJkkpWINBDCPE70F33HiHES0KIr3RpGgPHhBDH0Q7oAhVFOaNbNhmYIIT4A+01easfFlCeon0EQog3gZcURRn3tHkpikLQ3GAOJCZiaWnJrM9n0qRx43zpzpw5g88MP9T31bzSqROTJ05CCMHSkGXEJyRgYiKwt7dn1uczUTmoij3OvAXz2XdgP+Zm5tSu7cTMGZ9ja2NLVlYWM/1ncfrsGUyECZMnTuSltm2N4wYHkfgg7sxZNC4krq+fL2q1mk6dOjF50mSEEOzcuZPlK5bz119/se67dTRt2hSAmzdv8smnn3D69Gnc3d35bOpnRvklJR3ii6B5aHJy8PT04K2Rbxgtz8zMxHfaDM6ePYdd5coEBs+mZq2anDp5Gv9ZAfptf+fd0XTt1gWA775dz5bNWxFC0OB/DZgx05cKFSqUeB1fvnKZfgP6U+f55wFwbuaM72c++eMm6eLOKCTu2TP4TPdDrVbzSsc8fWifrg9Vyd+HTp46yfA3R2Bvb6/tY7OKaENfgzacrG3DW7duMWnSJK5cuULNmjWZO3cutra22u0OMugbunyvXLnC+PHjURSFrKwshgwZQq1aNQkKCubGjevk5ChoNBp++OGQUXtOm+bD2bNnqVy5MsHBQdSqpb3mePXq1URFbcHExITJkyfTsWMH/XoajYYhQ4aiUqlYunQJAIcPH2H+/PlkZWXRqPEL+M6YipmZ8aHxYNIPfBG8kJwcDf083Xjz7RH5+td0n1mcPXuOypUrMydoFjVr1QDg99/+IMA/iLt37iFMBN+sW23UjwwlJR1kbtAXujj9eHvkW/ni+E7z05c7KDiQmrVq8sOhH1i8aAlZWVmYm5vz8fiPaPtyWzIyMpg0cTKX/r6EiYkpr3Z+hY8+/rDY4t68eZOJn0zi9OkzuLu7MeWzyfp1srKyCJwTxLGjP2JiIhj7wVi6d+9WYLkfZvYb83Fx7s619P/DfUbXJ8qjMH/9coO93/yJkgPNujjysoeT0fJT+1LYt+4C1vbaNmv5WnWcu2ovs9o05zT//JFOrYa29J/UpMg42mOjwX5b1PHCcL+dpN1vd+7ayfIVK/jzr79Y/+13+mPjtu+3sWbtWv36v/3+Oxs2rqNho4ZA8fcpgLHvjSPt//4PTbaGlq1aMvWzyZiamj5OtReL8vSgY0VRrgH5dgBFUY4Bo3S/HwSaFbL+n0DbgpYVRs7glbLEpEQuJicTuzUaPx9f/OfMLjCd/5zZTPfxI3ZrNBeTk0k8mATAmyPeIDI8goiN4bz6yqt8ubLg6z2fNk77du3YHL6JyPAInn/ueVaHhgIQuTkSgM3hm/hy+Qq+mD+fHIOdLDExkeTkZGKiY/Dz9cN/tn/BcWf7M91vOjHRMSQnJ5OUpI3boEEDFsxfQOtWrY3SW1SwYOzYsUyYMCFfXhqNhqCAYJaELCIyKoy4uB38ef5PozRboqKxtbUhOnYzw4YPYdHCpQDUb1Cf79avZWP4OpaGLGb2rECys7NJTUll4/owvtuwlojNG8nJ0bAjblep1DGAk5MTERvDidgYju80H6P8EpMSufh3MrFbHiGurx+xW6K5+HeePhQWQcQGXR9alduHNBoNM2bNxK5yZaZMnIyfnx/+/oW0ob8/06dPJybGuA1DQ0Np27YtMTExtG3bltWrtV809X0jJsYoXwcHB7799lvCw8NZt24doaGhzJrlT0jIMhYtWoRKpTLqYwBRUVHY2toSGxvD8OHDWbhwEQDnz58nLm4HmzdHEhISQkBAABqNRr/eunXrqVevrv59Tk4Ovr6+BAUFsXlzJDVqVic2ZrtRLI1GQ9CcL1i8bB4Rm9ezI243f57/yyjN1qgYbGxt2BITwdDhg1myKASA7OxsfKd9ztRpkwjfvI4vv1qWb/BoGCcwIJClIYuJjNpEXNwOzufrx1uwsbUlOnYrw4YPY9HCxQDY2dmxcPFCIiLDmTnrc3ym+enXGTHidaK2bmZj+HqO/3KcxMSkYotbwaIC7499j/ETPs5Xnq9Wrcbe3p6tMVFERm2idetWBZb7UUQdDGP0omFPvH5hcnIUdn/9JwMmN+WtL1py7mAa/3fpXr50DdtX443AFrwR2EI/uANo41aLPu+/8EixEhPzHC8CCtlvA3T77YPjxYNjY/0GzJ83n9atjOuxb5++RISFExEWzmz/2dSqVUs/uCupPhU0N5DwiI1s2hzOjRs32LVz9yPVgVS6/jMDPCFEJSHENiHEcSHEKSHEYCFEGyHEQd1nR4QQNkVkUVMIEad7CnXwk25HfEICbq6uCCFo7uxMeno6aWlpRmnS0tK4c/cuzZ2dEULg5upKfHw8ANbW1vp0GRkZIESJxOnQvoP+D5FzM2dSUlMAOP/nn7Rto/0SUdXeHhsbG06fPm0QNx43VzeEEDgXEffu3bs46+O6sTd+LwD16tWjTp06+cpT0aoirVq2ooJF/pmPU6dO41TbCSenWpibm9Oz12skJOw3SpMQvw9X974AdOvRlaNHjqIoClZWlvpyZqrVCIP61Gg0qNVqsrOzyci4j4NDtVKp44eJ35eAW19d3GbOpN8pJO6duzRvpovb15X4hEL6ELllXh+2gUqVKlG3br3Ha0M3N/bu1bZhfHw87u7a53O6u7vryxsfH4+bW/6+YW5ujoWF9qHsmZmZqNVqatasiZOTE61atcLVta/RIE2bVwLu7m4A9OjRnSNHjqAoCgkJCfTq1RMLCwucnGpRu3ZtTp06BUBKSgoHDhzA07O/Pp+bN29ibm5OnTra2dKX27Vh7+4Eo1inT52htkH/eq1nd/YlHDBKsy/hAK5uvQHo1r0LR44cQ1EUfjh0hP/9rz4vNPwfAHZ2lQud6Th16jS1a9fGycnJoB8bb0tC/D7c3F0B6N6jm77cjRo3QqVyALRfWtRqNZmZmVhZWdGmbRsAzM3NadS4EakpKcUW16qiFS1btaRChfwP1d+6JZq339bOFpmYmFClSoEP7n8kx34/zK27N554/cJc/SOdKtUtsXO0xNTMhEbtHTh/7Pojr//8i3aYWz3azFX8vic8Xuj223r16lG3gGOjoe1x2+nVs6f+fUn0Kcg9hmRnZ5OdlWV03CxVGqV0X+XMf2aAB/QCriiK0lxRlBeBOCAM+EhRlOZoz4lnFLF+C2Aw2unTwUKI2k+yEampqVR3zP0G6KhyJDXN+Gaa1LRUHFWOxmlSc9MsXrqEHr17sm3794x9770Si/NA1NYtdOrQCYCGL7xAwv4EsrOzuXT5MmfPniHF4A9GamoqjtUN8nTMn2dqaiqOjkWneRxpqWlUN4ipUqlITUkrNI2ZmRnW1tbcvKm98/zkiVMM9ByM18ChfOYzGTMzM1SOKl5/Yzh9errzWvc+2NhY075Du3zlKIk6Brh8+TJeQwbz1qiR/PjTT08Wt4g6XrxsCT369GRbXG4fSklNYW98PDY21lhZWRa67oNtKCz/69ev4+Cg/cNQrVo1rl+//tB1rl69ysCBA+nZsyedO3fmueee06dTqRzRXmOcpw6qa+sgtz1vkpKSiqNh3RjECA6ey/jxH2NikvvHqEqVKmg0Gv2XlD274o36szZWmlGfVjk6kJqaVmga7fZU4tbNWyRf/BuEYNx7HzPM+03Wfv0dhcm376gcSUvJHyd/P75plGb37j00atxIP2h+IP12Ovv3HdCfZivuuHljASxbtpwhg4cy8dNJXLt2rdD0ZSX9RiY2VXPrybqqBek31PnS/X7kGmsm/czWBee4fS3/8kdh2GehiP1K9eTHxh07d9K7V2/j/EqoT73/7li6delBxUoV6d7jyU69SyXrvzTAO4n2AscgIcQrwHPAP4qiHAVQFOW2wUMEC7JHUZRbiqLcB84Az+dNYPgQxK9CH3r94xP7cNwH7Nq+g769+7Bh48YSiwOw8qtVmJmZ0rdPHwD6efTDUeXIkOFDCf5iLs2bN8fEpHx3o2bOL7IpKoxv16/h69VrUavV3L59m4T4fcR+v4Udu74nIyODbbHbH57ZE8hbxw7VHNj5fRzhG8KYOOETpkybyp07d4o15odjP2DX9zvo26sPG8K0fSj4i7l8/OFHCIrv2/ijfrOvXr06mzZtIiYmhqNHj6JW3y+2bQDYt28/9vZVaNLE+DopIQRBQYHMnfsFQ4cOo2KlipiaFN+1RBqNhuM/n8A/YAarv15BQvw+jhw+Vmz553X+j/MsXrgYH1/ja1Szs7OZMuUzhgz1xsnJqZC1i0+2JpuUlBSat3BmQ9h6nJ2dWTBvYYnHLQn1W9kzevFLvBnckjrN7Nge8ntZb1KBTpw8iaWlJf9r0KBY8y2sT4WsWMauPTvIzMzi6JGjxRrzUSk5OaX6Km/+MzdZKIrymxCiFdAH8Af2PmYWhl/bNBRQd4YPQVTfzdBPOWwM20hk1GYAmjZtytWUq/p1UlJT8t0koXJQGZ2uS0lNQaXKfyNF3959eP/DcYx97/0SibM1eiv7Dxxg1Yov9X+ozczMmPTpRH2a198cwZkzZ1ixYoU+bspVgzxT8m+7SqUymiUpKM3jcFA5cPWq8SyiytGhwDSOjo5kZ2dz584d7OwqG6WpV68uVhWtOP/HeS5fvkKtWjWpYq89rdS1WxdOHD/BnTt32BK5VV/WkqhjCwsL/TflJk2aUNvJiVWrV3FQd5NB0yaPGPcR6rhv7z68/9E4qtrbE78vgfh9CViYm5OVnc2Zs2cxtTB77Da0t7cnLS0NBwcH0tLSsLe3f+g6hvnWrVuXP/44r/8sNTUl30BRpVJx9erVPO1ph6OjihTDutHFSEjYR0LCPhITE1GrM7l79y5Tp37GnDkBNG/enDVrvgbgQFK8dtbNKJaDUZ9OTUnTn7rKm8bRUaXbnrtUtquMytGBlq1aYFfFDoCOnTpw7uyvtH35pXxtoVKpjPed1BQcHPPHyd+P7fRlnTD+U2b5z6R2beMTDP4zZ/Pcc7UZNnxoscctiJ2dHZaWlnTrpr0hosdr3dkS9dCnOpQ6myoWpF/L1L+/cy0TmyrGl4FY2Zjrf2/W1ZF96y88cv4bwzYSudngmHw1f980pFLlOV48xrExbkccvXv1yp9fCfUpgAoVKuDSpTMJ8fto175dvuVS2SrfUy+PQQhRE7inKMp3wFzgZaCGEKKNbrmNEKJEBrzeg731F8x3delCTGwsiqJw/MQJbKyt9aezHnBwcMC6UiWOnziBoijExMbSxcUFgIvJF/Xp4vclULdO3RKJk5iUxNdr17J44UKsrKz062RkZHAvQ3sm+9APhzA1NWPcuHGEh4cTHh5Oly5diImNQVEUTpw4gXUhcStVqsQJfdwYurh0eeL6bdq0CX8n/83lS5fJyspiR9xOOnd+xShNZ5dXiY3eBsCeXXtp0/YlhBBcvnSZ7GztxO2VK/9w4cJFatSsSfXq1Tl54hQZGfdRFIUjh49St24dBnsPKvE6vn7juv6as0uXLpGcnMzbb75NxIZwIjbo4m7TxT1ZRFzrShw/qYu7LZYunbVxC+pD3l7e/HT4GD8dPkbwnCCq2Nnx2eSpVKtW7dHaMCaGLl20beji4kJ0dDQA0dHRRp/HxOTvGykpKdy/r52xu337NhcuXODmzZtc0rVnXNyOfNetubh0Jjo6BoBdu3bTtm0bhBB07tyZuLgdZGZmcunSZZKTk3nxxRf56KMP2bVrJ9u3bycoKJA2bdowZ4727ulr17SnkDMzM1m75jsGDDL+F49Nmjbm7+RLXL58haysLHbu2M2rnTsZpXm18yv6mzP27I6nTZvWCCFo3+Fl/vjjPPcz7pOdnc1PP/5MvXp1KEjTpk1IztOPXTp3NkrT2aUzMdGxAOzetYc2unKn307ng3Ef8eFHH9CiZQujdZYtDSH9zh0mTvq02OMWRgjBq51f5dhR7WzlkcNHqFe/bqHpy0r1+jbcuJrBzdT7aLJzOHcojfqt7Y3S3LmROwA8/+N1qtayyptNobwHe+tvgOja5QmPF7r9tig5OTns3LmT3j2NB3gl0afu3bunv3YwOzubxP2J1Klb55HrpDiVs+fglTqR99qWZ5UQoifagV0OkAW8h/bq8iWAFdrr77oripLvXFjex6QIIWKBLxRFSSgsnuEMniFFUQgInEPSoYO6R1x8TtMm2tvdB3l7EbExHIDTZ07rb5Xv1KEjUydPQQjB+E8/4cLFC5gIE2rUqIHvtGlG12wUV5y+7m5kZmViV1k7y+XczBnfaT5cvnKZd8e+j4kwQaVS8bnfdGo41TKKO2fOHJIOJmFpacnMz2fqb+f38vIiPFwX9/Rp/WNSOnbsyNQpUxFCsGfvHgIDA7lx4wY2NjY0bNiQFcu1s4O9e/fmzt07ZGVlYWNjQ8iKxdSrXw+AxANJfBGsvaPXvZ8bo0a/zfJlX9KkaWM6u7yKWq3Gd9p0zp37jcq2tswJno2TUy1iY75nTehazMzNMBEmjH5nJF26ugCwPGQlu3bswtTUlIaNGuI3YxoWFhaYaSxKtI537dlNyPIQzMzMECYmvP/Oe7i8mntQVhSFgKA5JB0sIO4QLyI2GMTVPZ6lU8eOTJ2k60MT8/Shz4z7kKIoeAzwJD39NlXs7Zk5s4g29DVow6naNrx58yYTJ07k6tWr1KhRg7lz51K5cuXcvpGk6xu6fA8dOsS8efMQQqAoCt7e3jg6qggOnqt/TMq9e/ewsrLi1VdfISgoCLVazbRp0zh37ldsbW0JDg7Sn3pctWqHcN7SAAAgAElEQVQVW7ZsxdTUlEmTJtKpk/Fg7OjRo6xd+43+MSnz589n//4D5OTkMGBQP4YOH5xvf0o8cJD5cxehydHg7uHKyNFvsiJkFY2bNKKzyyuo1Wr8ps3k119/w9bWloCgmTjp9ovvt8WxZvW3ILQzeB+NH2uUt6nInTE6cCCRL4LnkZOjwaOfB6NGjyRk2XKaNG2Ci0tn1Go1PtN8+fXcr9jaViYwOAAnJydWrfyK0NVf89zzudcuLl++jKzsLHq91oe6detgrpsVHuztRf/+nkbb8KRxAfr0duXunbsG++Uy6tevx5Ur/+AzzZc76elUqVKFGTOnU6NGDVp98Gh3nRqaNzqENi+0p4q1PdfS01gSPY/IxA2Plccn7/Uq8PM/f75O/Dd/kZMDzVxUtPOsTWLERarXtabBS1XZv+EC53+8jompwNLajO5v16dqrYoAbJhxkutX7pF1PwdLGzN6jmlA3ea5N5OMaLRM/7v+eGG43+r2q0GDvYgIy92v9MeLjrnHiz179zInKPfY2KhhQ1aELAfg6LGjLFy8mHXffAuAxiT3pqTi7lMKCh9+8DFZmZnk5Ci81OYlPp04ATMzMypaWpfq3RaXgnaV6gDGaXKPMrqb5Mn8ZwZ4pa2wAd6zSDEpmz6vEU92sfPTejDAK1Vl1JsUs7I6npVNgbOU/I/IKGmGA7z/gicZ4BWHwgZ4JclwgFeaDAd4pam0B3h/z9lRqgeK2lN7lqsB3n/mFK0kSZIkSdJ/xX/mJotHoTuNG5Tn478URfEsKL0kSZIkSWWkHD6brjTJAZ4BRVF2ADvKejskSZIkSZKehjxFK0mSJEmS9IyRM3iSJEmSJJU75fHhw6VJzuBJkiRJkiQ9Y+QMniRJkiRJ5U55fPhwaZIzeJIkSZIkSc8YOYMnSZIkSVK5I6/BK5qcwZMkSZIkSXrGyBk8SZIkSZLKH/mg4yLJGTxJkiRJkqRnjJzBkyRJkiSp3JHX4BVNDvBKiij9kIpJGQQFKKNZchPMyyZwWVRzGTVtDlllEtdUUzaHJmFqWuoxFTSlHrMsffJerzKJO295XKnHHLGo1EMC/70+JRVMDvAkSZIkSSp3lGw5g1cUeQ2eJEmSJEnSM0bO4EmSJEmSVO7I/2RRNDmDJ0mSJEmS9IyRM3iSJEmSJJU78hq8oskZPEmSJEmSpGeMnMGTJEmSJKnckdfgFU3O4EmSJEmSJD1j5ABPkiRJkiTpGSNP0UqSJEmSVO7ImyyKJmfwJEmSJEmSnjFyBk+SJEmSpHJHzuAVTc7gSZIkSZIkPWPkAK+EKYpCYFAQfd3dGOA1iDNnzxaY7syZM/QfNJC+7m4EBgWhKAoA8xbMx92zHwO8BvHxhPHcTr8NwM2bNxk5ehQvd2hPQOCc3FiBgbi6ujJw4EDOFhFrwIABuLq6EhgYqI9169Yt3nnnHdzc3HjnnXe4fVsba82aNXh5eeHl5UX//v1p2bIlt27d0uen0WjwGuzFuA/G6cobiKubKwMHPWQbBg7A1c2VwKDcbdi5cyee/T1p0bIFp0+f1qc/dOgQ3kO8GTBwAN5DvDl85LBRfklJSXi498PN1Z3Q1aH54mVmZjJp4mTcXN0ZPux1Ll++oq/HUSNH075dB+YEBOrTZ2RkMG7cB/Tz8KS/5wAWLVyUL8+nbdudu3biOaA/zVu1NCprVlYW03x96D9oIB79Pflq9epijfs4fcq4jg/Sz70/7q79CF29psA6njxxKu6u/Xh92Btc0dXxD4d+YKj3cAYNGMxQ7+EcOXzUqKyzZs7Gw60/nh4D2L17T8H1HPyI5fXSlTc4T3n768r7SW55H/jnn394uWN71nyz1ujzg0mH6O8+CA/XAXy92njZg/JOmTgND9cBjBj2tr68uflepVM7F75Z+x0AarWaEUPfwnvQMAZ5erMiZGW+PJOSDuHpPhB31/6Fxpw88TPcXfszYthb+pinTp7G22sY3l7DGDxoKHv3xOvX6dvbA68BQ/D2GsawISMKrLuSiJt+O52Jn0yhv8cg+vfz4vjxEwXGfuCvX26wesKPfPXxjxzeeinf8lP7Ulg25jBrp/zC2im/cGLvVf2yTXNOs2TkD2wOPlNkjMc1+435JM07QfSMvU+dV0nttydPnWTQYC8GDfZioJcXe/Yab2tJtO36dRsZ1N+bgZ6DWffdhqeumyelaJRSfZU3coBXwhITE7mYnEzs1mj8fHzxD5hdYDr/gNlM9/Ujdms0F5OTSUxKAqB9u3ZsjthEZHgEzz//PKtDtYMXiwoVGPv+WD4ZP8EoVnJyMjExMfj5+eHv719wLH9/pk+fTkxMDMnJySTpYoWGhtK2bVtiYmJo27Ytq3UDizfffJPw8HDCw8P58MMPad26NZUrV9bnt279OurVrWe8DdEx+Pn64T+7kG2Y7c90v+nERBtvQ4MGDVgwfwGtW7U2Sm9XxY7FixYTuSmSWbNmMW3aNP0yjUbDnIBAloUsZXNUJHFxcZw/f95o/aioLdja2hATG83w4cP0A7YKFhUYO/Z9JkwYn28b3xgxgi1bowgL38gvvxwnMTHRaPnTtm2D+g2YP28+rVu1Mkq/c/cusjKz2ByxiY3r1rMpchOXr1wutriP06cM6zgwIIilIYuJjIogLm4H58//aZRmS9RWbGxtiI7dwrDhQ1m0cAkAdnZ2LFy8gIjIMGbOmoHPND/9Ol+tCsXevgpbYzYTGRVB69bG7Q6QmJSnvHMKKe+c2Uz3MSjvQYPyhuvK+1xueR+YO38enTp2LKC8c1kcspBNURvZEbeTP/OVNxpbWxu2xkYybLg3ixcuM1q+4IuFdOjUXv/ewsKCFV8tY2PEOtaHf8fBpB84eeKkUcyggGCWhCwiMiqMuLgdhcaMjt3MsOFDWLRwKQD1G9Tnu/Vr2Ri+jqUhi5k9K5Ds7Gz9el9+tZyN4etYt+GbfPVWUnHnBs+jQ8d2bN4aQVjEOurVrZsv9gM5OQq7v/6TAZOb8tYXLTl3MI3/u3QvX7qG7avxRmAL3ghsgXPX6vrP27jVos/7LxSa/5OKOhjG6EXDiiWvktpvG9RvwIZ164kIC2f5smXM9J+lb4OSaNs/fj9PVOQWvlm3ho0R6ziwP5Hk5L+LpY6k4vXMD/CEEJWEENuEEMeFEKeEEIOFEG2EEAd1nx0RQtgUsu42IYSz7vefhRB+ut9nCiFGP0r8+H0JuLm6IoSgubMz6enppKWlGaVJS0vjzt27NHd2RgiBm6sr8Qnab0sd2nfAzEx7qaRzM2dSUlIAqGhlRauWLalQwSI3Vnw8bm5uCCFwLiLW3bt3cX4Qy82NvbpvfPHx8bi7uwPg7u5OfHw8ecXFxdG7d2/9+5SUFA4cOIBnf09tHgnxuLk+5ja4urE3XrsN9erVo06dOvniNm7UGJVKBWgPaGq1mszMTABOnTpF7dq1cXJywtzcnJ69epKQkGC0fkJ8Am7ubgB079GdI0eOoCgKVhWtaNmqJRYVKhilt7Kyok3bNgCYm5vTqHEjUlJSjdI8bdvWq1ePugWUVSC4dz+D7Oxs1Go15ubmWFeyLra4j9OnHjh16nSeOn6NhIR9eep4H27urro67qav40aNG6FSOQDaPxqGbbd1SzRvv/0WACYmJlSpYpcvdnzCE5Y3vpDypqbo19sbv5daNWtSv159o/xOnzpD7dpOODnVwtzcnNd69SAhYb9Rmn3x+3F17wtAtx5dOXLkqH62JX7vPmrWqkn9+vX06YUQVKxYEYDs7GzdH2FhVMdOBjG1dWwcMyF+n1HMo7qYVlaW+jJmqtUIIXhUJRE3Pf0OP/34M/08PQDtPmRjW+BhFoCrf6RTpboldo6WmJqZ0Ki9A+ePXX/kMjz/oh3mVqaPnP5RHfv9MLfu3iiWvEpqv7WystJ/rs7MNGr7kmjbv/76ixebNdUvb926ldHsXmlSsnNK9VXePPMDPKAXcEVRlOaKorwIxAFhwEeKojQHugMZhax7AHhFCFEZyAYefM1/BdhfyDpGUlNTqV4995umo6Mjqamp+dI4qhyLTAMQtXULnTp2KjKWo2PR+RSV5vr16zg4aP8QV6tWjevXjQ+wGRkZJCUl0b17d/1nwcHBjP94PCbCJDf/6k++DY9i9+7dNG7cGAsLC31+1Q1jqhxJTTE+cBq2g5mZGdbW1ty8efOR4t2+nc7+fft5+eW2heZZWDketW0N9ejenYqWVnTr0YPXevfijREjjGZMS7NPGeVnVMcq0lLyx3zQDrl1fMsoze7de2jUuBEWFhak304HYNmy5QwZPIyJn07m2rVrBcau7mhQXpUjqWl5YqflKa+qiPJ20Jb33r17hK5Zw3vvvPuI5c3zxzg1DcfqqnzlvXfvHmu//oYx747Kl69Go2GI13B6dOlFu3Ztaeb8olF+hv1YpVLl68eGafLW8ckTpxjoORivgUP5zGey/o+zAMa++wFDvUcQuSkq3zaVRNwrl69QpUoVZvjNZIjXcGbO8CfjXmGHWUi/kYlN1dwvFtZVLUi/oc6X7vcj11gz6We2LjjH7Wv5l/+bleR+e+LkSTwH9GfAoIH4TvPRt31JtG39BvX5+adfuHnzJhkZ90lMTCLlagrSv89/YYB3EughhAgSQrwCPAf8oyjKUQBFUW4ripJdyLoHgFfRDuy2AdZCiIpAXUVRfs2bWAgxRghxTAhx7KvQ1XkXP5WVX63CzNSUvn36FGu+hSloBmDfvn20aNFCP9jYt28f9vb2NGnSpFS2CeCPP/5g4aKF+Pr4lkq87Oxspk6ZwpChQ3ByciqVmKdOn8LE1ITdO3eyfdv3rP32Wy5dyn9N0tMq7T51/o/zLF64BB/fzwDI1mhISUmheQtnNoStw9m5GQvmLSyx+Cu/WoWZWW55Q75cwevDhuln1YrLl8tXMXT4kALzNTU1ZUP4d2zfGcOpU6f54/fzBeTwZJo5v8imqDC+Xb+Gr1evRa3WDoBC16xifdi3LF22kPCwCH788adii1lYXI0mm3PnfmXgoAFsCP8OKysrvg7Nf/3X46jfyp7Ri1/izeCW1Glmx/aQ34upBOVLQfutc7NmREVuZsN361gdulrf9k+roLatV68ub741gvff/ZBx739Iw4YvYGJa/LOnj0LR5JTqq7x55h+ToijKb0KIVkAfwB94nKtljwIvAX8Cu4BqwGjgx0JirQRWNmzYcOyOHTta79ixg6ZNm3L1au7FwCkpKfpTjQ+oVCqj00Z502yN3sr+/QdY9eWX+QZehw8fJvHgQX7++Weavviiftq+yFiFpLG3tyctLQ0HBwfS0tKwt7c3Wjfv6dlffvmFhIQEdsTt4M7dO+Tk5ODk5GT0be5xt6EoKSkpjJ8wHv9Z/tSuXRuFHH1+Vw1jpqagcnTIF/Pq1as4OjqSnZ3NnTt3sLPLfzowr1kz/XnuuecYPlx7Hc7GjWFERW4GKJa2Lcj327fTsUNHzM3NqWpvT8sWLVj51Sr9DSsl3acKolKpjNs1NRUHx/wxr15NyVPHlfXxJ4yfyCz/z6ldWztQtrOrjKWlJd26dQWgx2vd2RIVDcDGsI1ERhnUc4pBeVNTUDnkie2Qp7ypBZT3wAFWrcgt78mTJ9m9excLFi0kPT0dYWKCmaUpg4cMKqS8xn3KQeVAytXUfOU9dfI0e3bHs3jhUtLT0zERJlSwqMDgIYP069rY2vBSm9YcPHiI//2vgT4/w36cmpqarx8/SFNQHT9Qr15drCpacf6P8zRp2gSVrp3sq9rTpasLp0+doXXrVvnyLM64KkcVKkeVfoayW4+urAnNf/2fvj6qWJB+LVP//s61TGyq5LlswsZc/3uzro7sW3+h0Pz+LTaGbSRyc/EdLx6239arVw+rihX1bV9Sfapffw/69deefl+yOARHx4cfv6XS98zP4AkhagL3FEX5DpgLvAzUEEK00S23EUIUONBVFCUT+BsYBBxCO6P3KQ85Pfvrr78uiwgLJyIsnK5duhATG4uiKBw/cQIba2v9adAHHBwcsK5UieMnTqAoCjGxsXTp7AJAYlISX69Zy+KFC7GyssoX6+WXX6ZXz55EhIXTpUsXYmJiUBSFEydOYF1IrEqVKnHiQayYGLp06QKAi4sL0dHaP7DR0dH6zwHS09P58ccfcXFx0X/20UcfsWvXLvbv38+XK76kU6dOTJo4iZjYx9yG2Bi6uHShKLdv32bcB+P46KOPaNmypdGypk2bkpyczOVLl8nKymJH3A46d3YxStPZpTMx0TEA7N61mzZt2zx0YLN06TLu3Eln4qSJ+s+8vQdTXG1bmBrVa3Dk6BEA7mVkcOLESYYPHVZscR/WpwrStGkTkpP/Nqjjnbh0ftUoTWeXV4mJjgVg9649+jpOv53OB+M+5sOPxtGiZQt9eiEEr3Z+hWNHtd+Xjhw+Sr362gvxvQd7E7ExnIiN4XR1ecLyuhiUd23+8q4N/Zq4bduJ27adYUOHMertkfpBWJOmjfk7+W8uX7pCVlYWO+N20TlfeV8hNnobAHt27aVN25cQQrB6zUpit28hdvsWhg7z5q1RbzB4yCBuXL+hPy19//59Dv9wxOh606ZNm+hi5tZx586v5KvjgmJevnRZf2H9lSv/cOHCRWrUrEnGvQzu3r0LQMa9DH44dJj6DYyvNyyJuNWqVcPRUcWFCxf1bVu3XuE3WVSvb8ONqxncTL2PJjuHc4fSqN/a+AvmnRu5A8DzP16naq1H67tlyXuwd4nvt5cuG7bBFS78dYEaNWsCJdO2ANevaS/f+eefq8Tviad3757FXHOPRl6DVzTx4KLgZ5UQoifagV0OkAW8h/aylCWAFdrr77orinKnkPVnAd0URemgGyxeBlorilLkeQ71vQwFtLfGBwTOIengQSwtLZk143OaNm0KwKDBXkSEhQNw+vRpfKb7oVar6dSxI1MnT0EIQV93NzIzM7HTnRZ1buaMr48PAL369ObO3btkZWVhY2PD8uXLCQ8PJykpCUtLS2bOnKmP5eXlRXh4bixfX1/UajUdO3Zk6tSpCCG4efMmEydO5OrVq9SoUYO5c+fqT8du3bqVpKQkgoOD8xdWgaNHj7L2m7UsWbyEOXPmkHRQtw2fF7ENfgbbMEW7DXv27iEwMJAbN25gY2NDw4YNWbF8BStXrWT16tU8/9zz+rDLV4RgX1X7R+DAgQPMDf6CnJwcPPp5MHr0KEKWhdCkaRNcXFxQq9VMm+bDr+d+xdbWlqDgQP0p1969+3D3jkE9rgjB2tqanq/1om7duphbaGcOvL0H079/f0xytAPDp23bPXv3Micot6yNGjZkRchy7t27h+90P/78808UBTw83HnrjTdzq7sU+1TIiqX6GwUOHEjki+D55ORo8OjnzqjRIwlZtoImTRvj4tIZtVqNzzQ/fR0HBgfg5OTEqpVfEbp6Dc89/1xu2y1fin1Ve65c+QefaX7cSU/XXrM1czo1alTHVGOWv7yHDMrbRFdeby8iNurKe8agvB3ylDcrT3mn+Rh14ZAVy6lYsSLD3vLWf5Z4IIl5wQvQ5OTg0c+NkaPfYvmyL2nStDGdXV5FrVbjO20Gv577jcq2tgQE++PkVMso3y+Xr8KqohUj3hjO77/9znSfmWhyclBycuj+WjfGvDsKYXCjReKBJF0d5+Dez41Ro98uIOZ0zulizgmejZNTLWJjvmdN6FrMzM0wESaMfmckXbq6cOnSZT4Zr/2CosnW0KtPT0aNfpu8ijsuwK/nfmPm5/5kZWXj5FSTGTP9sLW1Zf2Z/HesA/z583Xiv/mLnBxo5qKinWdtEiMuUr2uNQ1eqsr+DRc4/+N1TEwFltZmdH+7PlVraU+Db5hxkutX7pF1PwdLGzN6jmlA3eZVjPKftzyuwLhFmTc6hDYvtKeKtT3X0tNYEj2PyMRHfyzI8UW5p+BLar+NiY0l9OtQzMzMECYmvDtmDK92y70+ryTa9u03R3Pr1m3MzEyZ8OnH+uuTK1lWfvS7e4rB6YFfleoApummUaVavqf1zA/wysqDAV5pUkzKqO+VURdSRNl8o3owwPsv0JgUdnlqyTIc4JWmLNPSv3DfcID3X1DYAK+kPckA72kZDvBKU7ZJ5sMTlYDSHuCd6reyVP/6vLhlTLnaWZ/5U7SSJEmSJEn/Nc/8TRaPQncaNyjPx38piuJZFtsjSZIkSVLRyuOdraVJDvAARVF2ADvKejskSZIkSZKKgzxFK0mSJEmS9IyRM3iSJEmSJJU75fHRJaVJzuBJkiRJkiQ9Y+QMniRJkiRJ5Y6cwSuanMGTJEmSJEl6xsgZPEmSJEmSyh35mJSiyRk8SZIkSZKkZ4ycwZMkSZIkqdyR1+AVTc7gSZIkSZIkPWPkDJ4kSZIkSeWOvAavaHKAV0KyREapxzRTKpZ6TABEWYUtmwlojUl2qcc0wbTUYwKIMoqbY6qUSVwzxbL0g4qyKatC2cQd0WhZ2cRdVPoxm39Uv/SDAj8t+a1M4kr/LnKAJ0mSJElSuSOvwSuavAZPkiRJkiSpBAkh7IUQu4QQv+t+VikgTRchxC8Gr/tCiH66ZWuEEH8ZLGvxsJhyBk+SJEmSpHKnnM3gTQH2KIoSKISYons/2TCBoijxQAvQDgiBP4CdBkkmKoqy6VEDyhk8SZIkSZKkkuUBrNX9vhbo95D0A4HtiqLce9KAcoAnSZIkSVK5o2iUUn0JIcYIIY4ZvMY8xuY6Koryj+73q4DjQ9J7AxvyfDZbCHFCCLFACFHhYQHlKVpJkiRJkqSHUBRlJbCysOVCiN1A9QIWTcuTjyJE4bfPCyFqAM2AHQYfT0U7MLTQbcNkYGZR2ysHeJIkSZIkSU9JUZTuhS0TQqQIIWooivKPbgCXWkRWXkCUoihZBnk/mP1TCyG+Bj592PbIAZ4kSZIkSeVOObvJIhp4AwjU/dxaRNohaGfs9AwGhwLt9XunHhZQXoMnSZIkSZJUsgKBHkKI34HuuvcIIV4SQnz1IJEQog5QG9iXZ/11QoiTwEmgGuD/sIByBk+SJEmSpHKnPP2rMkVRrgHdCvj8GDDK4P0FoFYB6bo+bkw5gydJkiRJkvSMkTN4kiRJkiSVO+XsGrxSJ2fwJEmSJEmSnjFygFfCDiYdor/HYDzcBvJ16Df5lmdmZjJlkg8ebgMZMXwkVy5r74S+cvkfOrzcmSFeIxjiNYIA/yD9OuPe/xhvr9cZ1H8oAf5BaDSafPkqikJgUCCubq4MHDSQs2fPFrh9Z86cYcDAAbi6uRIYFIiiaB/Ns3PnTjz7e9KiZQtOnz6tT3/z5k1GjhpJu/btCJgTkBsrMBBXV1cGDnxIrAEDcHV1JTAwN9atW7d45513cHNz45133uH27dv6dY4ePYqXlxeenp68/fbb+s+//fZb+vXrx6uvvkr79u0ZMGBAscXdtm0bAwcOZMCAAYwYMYJff/3VKO4Az0EM7O/FyLdH4+HmiburB6Grv84XNzMzk8kTp+Du6sHrw0Zw5fIVfR2OHjmGDu06ERiQ2653795lsNcQ/atL567MDf4CgKSkJNzdPXB1dWP16tACY02cOAlXVzeGDRvO5cuX9ctWr16Nq6sb7u4eJCUd1H9++/ZtPvnkUzw8+tGvnyfHjx8HYP78+Xh49GPgwEGM/3gCt2+n67fBw70fbq7uhBayDZMmTsbN1Z3hw17nskF5R40cTft2HZgTEGi0zvvvjcVrkBf9PQfgP8u/wL5cEnFHjhyFh3s/vLwG4+U1mOvXrhstL839pzTbduLESXh5eeHl5UXv3n3w8hpcYnW8ZMlSer7Wi/btOhRYd7n1HERfdzcGeA3iTBH13H/QQPq6uxEYFJRbz7t24jmgP81btTSq523fb2PQYC/9q3mrlpz79VyxxZ23YD7unv0Y4DWIjyeM53a69thx8tRJfcyBXl4o//dkJ8lmvzGfpHkniJ6x94nWN5SUdJB+7v0f+zj1w6EfGOo9jEEDvBjqPYwjh48AkJGRwQfjPsTToz8DPAexaOHip97GJ6Vk55Tqq7z5VwzwhBB2Qoj3HyHdHd1PFyFE7CPm7SKE6GDw/l0hxIgn39pHp9FoCJwzj8XL5rNp8wZ2xO3iz/N/GaXZEhWDra0NW2M2MWy4N4sXLdMvc3JyYkP4N2wI/4bPfHL/ZV1g8Gw2hn9LeOQ6bty4ye5d+Q8CiYmJJCcnExMdg5+vH/6zC77hxn+2P9P9phMTHUNycjJJSUkANGjQgAXzF9C6VWuj9BYVLBg7diwTJkzIHysmBj8/P/z9C4nl78/06dOJiTGOFRoaStu2bYmJiaFt27asXr0a0P6RCggIYNGiRURFRTF37lwAUlJSWL9+PR988AEvvvgiHTt2pEuXLsUWt1atWoSGhhIZGcmYMWOYOXOmUdx1G74lLGIDZ8+cxbN/PyKjNhEXt4Pz5/80irslags2trZEx25l2PBh+gNhBYsKvD/2PcZP+NgofaVKlQgL36B/1ahRg67duqLRaAgImENIyDKiojYTFxfH+fPnjdaNiorC1taW2NgYhg8fzsKFiwA4f/48cXE72Lw5kpCQEAICAvSDqODgYDp27MDWrVuIiAinbt26ALRr147IyE1s2hTB888/T+jqUDQaDXMCAlkWspTNUZGFbMMWbG1tiImNZvjwYSzSbUMFiwqMHfs+EyaMz9c2wXODCI8IJ3LzJm7cuMGunbuMlpdUXICAObMJDw8jPDwM+6r2RstKa/8p7badOzeY8PBwwsPD6d6tG926di2xOu7c+VW+W/dtgfVmWM8Xk5OJ3RqNn48v/gGzC67ngNlM9/Ujdms0F5OTSXxQz/UbMH/efFq3amWUvm+fvkSEhRMRFs5s/9nUqlWLRg0bFVvc9u3asTliE5Hh2n1kdWiofns2rFtPRFg4y5ctg98roBT6ONvCRR0MY/SiYY+/Yh4ajYbAgMhJIvIAACAASURBVECWhix+7OOUnZ0dCxcvJCIynJmzPsdnmp9+nREjXidq62Y2hq/n+C/HSUxMeuptlYrfv2KAB9gBDx3gPSEXQD/AUxRlhaIo+afSSsDpU2eoXdsJJ6damJub81rP7iQk7DdKsy/hAK5ufQDo1r0LR44c039LLIy1dSUAsrM1ZGVloX0sjrH4hHjcXN0QQuDs7Ex6ejppaWlGadLS0rh79y7Ozs4IIXBzdWNvvHawWK9ePerUqZMv34pWFWnVshUVLHL/S0p8fDxubo8Zy82NvXv36td3d3cHwN3dnfj4eAC2b99Ot27dqFGjBgBVq1bV56fRaIiPj6dPnz7cv3+fVq1aFVvcFi1aYGtrC4CzszMpKSlGcdVqNcePn6BChQo0bPgC5ubm9Oz1GgkJCUaxE+L34ebuCkD3Ht04cuQIiqJgVdGKlq1aUqGCRb76feDihYtcv36DVq1acurUaWrXro2TkxPm5ub06tUzX6z4+ATc3d0A6NGjuz5WQkICvXr1xMLCAienWtSuXZtTp06Rnp7Ojz/+hKenJwDm5ub6Mnfo0AEzMzNd+ZuRkprCqVOnjLahZwHbkBCfgJtuG7obbMOD8lpUyP+fdaytrQHIzs4mKys7X18uqbgPU1r7T97ylXTbPqAoCjt37qJX714lVsfOzs44ODgUXc/7EnBzdUUIQfMi6vnO3bs019ezK/EJ8fp6rltAPRvaHredXj17FmvcDu0N9pFmuccIKysr/efqzMwit6sox34/zK27N554/QfyHjse5zjVqHEjVCpt+9VvUB+1Wk1mZiZWVla0adsG0PatRo0bkWpwjCxNiianVF/lzb9lgBcI1BdC/KL7H2t7hBA/CSFOCiE8ilpRCNFGCPGzEKJ+AcvqAO8C43V5vyKEmCGE+FS3PEEX75gQ4qwur81CiN+FEP4G+QwXQhzR5fGlEML0UQqVmpqGY3WV/r2jo4q01DwHkdQ0HKtr/yWdmZkZ1tbW3Lx5C4DLl68wdPAIRo98j59/+sVovbHvfUyPrn2oWLEi3bp3KSB2qj5fbWxHUlNT86dxLDrNo5Xz4fkUleb69ev6PwTVqlXj+nXt6bKLFy9y+/ZtRo4cibe3NzExMfp133jjDbZt20ZgYCA2NjZ06NCh2OIaioqKolOnTkZxe/fsywfjPsTa2pr2Hdprl6kcSUsxbtvU1DSq52vbmw+vUCAubgev9eyBEILU1FSqV8/97zcqlSMpKfnL+SCNYayUlFQcHXPXfVD+y5cvU6VKFfz8/PDyGsyMGZ9z715Gvu3YsmUrnTp21OVvUI8qR1LzlbfgbXiY9959n65dulGxUkW69zB+EHxJxp3uNwMvr8Gs/HJlvi9VpbX/lFXb/vTTT1Stas/zzz9fonX8uOUvtJ5VT17PO3bupHev3iUWN2rrFjp17KR/f+LkSTwH9GfAoIHwPzUFfP8uNfn68RMep3bv3kOjxo2wsDD+Upp+O539+w7Q9uW2JVQC6Wn8WwZ4U4DziqK0ACYCnoqitAK6APNEQVNUgO7U6wrAQ1GU83mX654nswJYoChKC0VRDhSQTaaiKC/p0m0FxgIvAm8KIaoKIRoDg4GOuu3TAE8/d/4Q1Ryqsi1uC+vDvmHCJx8xbep07ty5q1++bPlCduyOISsri6NHfizpzSk1hk2dnZ3NmTNnWLJkCcuXL2flypVcuHCB27dvEx8fT9u2bVmwYAEZGRnExj7SGftHivvAkSNHiIqK4uOPtadSH8SN/T4GX18fsjXZbIv9/qniFmbHjp306t2rRPIG7UzkuXPnGDTIi/DwMKysLAkNNb72atWqVZiamtKnb58S2w6A5StC2L1nF1mZmRw5crREYz0QEBDApsgIvv46lJ9++vmp+8+/yaO07fbtcfTqVXL969/ixMmTWFpa8r8GDUok/5VfrcLM1JS+fXL3EedmzYiK3MyG79bB3xYo5W/ix8j5P86zeOFifHw/M/o8OzubKVM+Y8hQb5ycnMpk2+QMXtH+LQM8QwIIEEKcAHajfeCfYwHpGqP9h7tuiqIkP0W8aN3Pk8BpRVH+URRFDfyJ9mnS3YDWwFEhxC+69/UK3HAhxuhmA4+Frl6LSuVAytXcb3wpKak4qIxPWTioHEi5qp3ezs7O5s6dO9jZVcbCwgI7u8ragjZphJNTLZIvGhezQoUKdHZ5hX26074bN27UX0DtUC03X23sFFQqldH6KpXK6PRjQWkKc/jwYXbs+P/2zjs8qnL5458hgNRQhIQuzQaKioIFJEFERekKouL12q7XAlhQVHpvFjoKYvnZKCJSlC4tIIqiNCnKFUNNEJEupMzvj3MSdlNBcnY3YT7Ps09y6vc9Zc/OmXdm3vmOVtmy2e4nK63SpUundo/s37+f0qWdeKjIyEhuuukmihQpQqlSpahbty7vvfce7du3Z+vWrVSoUIE//viDJk2asG7duhzTBdi2bRt9+/ZlxIgRlCxZEoDVq1dTsWJFSpcuRfkK5SlatGhq8HpcfBxlI/2vbUREWfalu7Ylsz23W7duIykxiVq1Lk89hn379qUuj4+PIzIy/XGmrOOrFRkZQVzc6W1Tjj8yMpLIyAjq1LkSgKZNm7Jly+lA85kzZ7J8+QoGDR6IiLj79zmP8XFEpDvejNtwJlxwwQVEN45m6ZKlGewz53VTzl/RokVpdmczNm7YxOTJUwL2/cmo7RCYa5uYmMjixYu5/Y7bffbv3bVNy+Qpk1MTEcqUKeN3/Jme5/h/dp7nzZ9HM9eQzWndmbOc78jggYMyfEGsXr065FM4Fryf2YiICP/7+CyfU3FxcTz/XFf6D+hH5cqV/bYb0G8gVapU5oGO93t8FMY/JRQNvAeAssC1rscsDiiUwXp7gb+Ba85R76T7N9nn/5Tp/DgG5weuB/BqVb1UVftktCNVnaCq16nqdY88+hC1al/Oztid7N69h4SEBBbMX0RU1M1+20RFNWTObMcLtHjREurVuxYR4eCfB1MDpnft2k1s7E4qVqrA8ePH2b//D8D5MsasWEXVahcB0KFDh9QA6saNGzN7zmxUlfXr11OsWLF08TBly5alaNGirF+/HlVl9pzZNI5O392bEddffz233377aa3ZZ6k1ezaNGzta0dHRzJrl2NmzZs1Knd+4cWN+/PFHEhMTOXHiBBs2bKBjx44MGzaMMmXK0KBBA2bPns3q1aspWLBgjunu3buX559/noEDB/rFUZUrV47169dz4sQJatW6nL179lCqVCkSEhKYP28B0VFR/tc2OorZsxzP0KKFi6lXv16GPwRpmTd3Hnc0Ox0zVLt2LWJjY9m1azcJCQnMmzefqDRa0dFRzJrldGEvXLiI+q5WVFQU8+bN59SpU+59FMsVV1xBmTJliIwsx44dOwDHYK9e3XlvWblyJe+//wEjR46gcOHCbhtqExsby263DfPnzScqKjqD453tHu+ibI/XuZcdAzsxMZEVy2OoVq2q3zpe6CYmJnLwoBPflJCQwIrly6lZswYdOtwbsO9P2uML1LVNma5WrVpq97IX5zgrOtzbITUB4pbGjZk9Zw6qyrr16ymeyXkuVrQo61LP8xwap2lfRiQnJ7NgwQKa3X5HjuvGrFzJe+9/wKgRp78jALt27yYxMRGAPXv2wIl8UOgfZFnkEM6zY6fPtT3z59SRw0fo9EwXOnfpxNXXXO23zdgx4zhy9CgvvpTtePeekqzJAf3kNiS7gP6ANELkQmCtql4kIl2AmqraSUQaA18D1VR1h4gcVdViIhINdAUeBRYCnVV1aSb7fgEIV9Xe7nQf4KiqviYiS4Guqvp9yj5Vtbm73lJX4zhO120DVY0XkdJAcVX9PatjOnriTwWIWbGK14ePICk5mVatmvPo4/9m/LgJ1Kp1OVHRN3Py5El6du/L1q3bKBEezqCh/alUqSKLFy3hrXETyZ8/P5JP+O+Tj9Eo6mYOHPiTZzt15VTCKTRZua5eXZ7v2oX8+fOTnyKp+qrK4MGDWblqJYUKFaJf337Url0bgPbt2zN16lQANm3aRM9ePTl58iQNGjTglZdfQURY/PVihgwZwsGDBylevDiXXnopb41/C4BmzZpx9NhREhISKF68OOPHj2fq1KmsXOlq9ctCq6eP1iuO1l9//cWLL77Ivn37KF++PMOHD6dECcd7+f777zNz5kxEhLZt29KxY0cAxo0bx7x58zh48CCJiYmUL1+e/v3754hunz59WLRoERUqVAAgLCyMTz/9NFV3/vx5hIXlp3TpUuyLi0OTk2nVuhWPPf4o48aOp1btWkRHR3Hy5El6dO/J1i1bCQ8vwZBhg1K7Mu5s1pxjR4+lnsNxb42lRg3nR7j5nS0ZPXZkauYjwMoVqxg2bDjJycm0bt2Kxx9/nLFjx1G7di2io6M5efIk3bt3Z8uWrYSHhzNs2NBUrYkTJ/LFFzMJCwvjpZdeTI0p3LJlC3379iMhIYFKlSrSr18/wsPDad68BadOnaJkyRIoTpdTj549WLFiBcOHvUaye7yPP/4Y48aOc483pQ093OMNZ+iwIaltaNbsTr/jHf/WOEqWLEmnTp1JOJVAcnIy9epdR9cXu6YGqaeQ07oVylfgkUceJTExkaSkJK6/4Xq6dn2BsLAwRPMF/Pvz+OOP8eGHHwXk2gL07NmTK6+sQ7v293h2jmvUqMGbb45g7ldz2b9/P2XLlqVN2zY8+eR/yZd82jBUVQYNGczKVasoVKgQ/fv0TT3P7e5tz7Qpp89zj969OHnyJA0bNOCVbi+75/lrBg89fZ4vu/RS3ho3HoA1369hxKhRfPx/6TN5z1X3rpbud8R9TtW5sg49e/Rg9pw5vPveu+5zOx9b8v2IlElMp58drz8+jnqX3EipYqU5cGQ/o2e9zvSYT894+7Wjt6X+v2JFDK8Ne53k5KSzek5NnPAO7056jyoXVUnd1/jxY0lITOCO2+6kWrWqFHBj8u7t0J62bdtQpFCxgEYcrqjQPaAGzM17BgYxovLsCQkDD0BEPgHqAGuAy4BiwPfADUCzjAw8VW0uIlWAucAjqvptBvu9BPgMxyPXCaeL9YwNPHfZvcArOB7PBOBpVV2d1fGkGHiBxNfACyi56pY/d5I5+wf2uZKPM8rryXGU0Hg+BIoUAy/AooHXJHjX1tfAy+tc1SVd7l9A8DXwAkmgDbxl5V8N6E0ctXdQrrp5Q2aoMlXNtiNfVYu5f5cCS93/Y4HaWWyzDcdwTGGFz7Jon/9T95nBsinAlOzaZxiGYRiGEQqEYgyeYRiGYRiGcQ6EjAfvXBGRh4EuaWavVNWng9EewzAMwzC8IykXJj4Ekjxj4Knqe0D6gfYMwzAMwzDOM/KMgWcYhmEYxvlDbixdEkgsBs8wDMMwDCOPYR48wzAMwzByHckhUuYtVDEPnmEYhmEYRh7DPHiGYRiGYeQ6LAYva8yDZxiGYRiGkccwD55hGIZhGLkO8+BljXnwDMMwDMMw8hjmwTMMwzAMI9dhWbRZYwaeRxzRnQHXLJXv0oBrAiSTeF7p5ueCgGtKUsAlHcIkKLJJnAqK7nGNC7imanAubou+twVFd0nfzUHRVQJ/nteO3hZwTYC6nS4Jiu6WiXuComtkjBl4hmEYhmHkOiwGL2ssBs8wDMMwDCOPYR48wzAMwzByHUnmwcsS8+AZhmEYhmHkMczAMwzDMAzDyGNYF61hGIZhGLkOK5OSNebBMwzDMAzDyGOYB88wDMMwjFyHlUnJGvPgGYZhGIZh5DHMg2cYhmEYRq7DPHhZYx48wzAMwzCMPIZ58AzDMAzDyHVYFm3WmAfPMAzDMAwjj2EePMMwDMMwch0Wg5c1QTXwRKQfsFxVFwWzHcHi25U/MWb4eyQlJ3NX6yY88Ehrv+VTP5zDlzMWE5Y/jJKlwnmp95OUq1A20/2pKkOHDiUmJoZChQrRv39/Lr/88nTr/fzzz/Ts2ZOTJ0/SsGFDunXrhohw6NAhXnrpJfbs2UOFChUYPnw44eHhAKxZs4bhw4eTkJBAqVKlePfddwE4fPgwffr2YfuvvyIitLm7LdOnTSc5OYnWbVrzyKMP+2mfOnWKnt17sXnzZkqUKMHQYUOoULECq79ZzaiRo0lISKBAgQI8+1wX6l9fnxMnTvDSi93YtXMX+fKF0SjqZro82zndMa1a+Q2vDX2DpORkWrdpycOPPpROt1f3vmzevIUSJUowZNgAKlSskLp87959tGvTgf88+Rj/eqhj6vykpCTatmzHgQMHuLBMGVq3acnjj/4n3b67d++RekzDhg2lYsWKAEyaNIkZM74gX758dOvWjQYNbgJg5cqVDB06jOTkZNq0acOjjz7i3BPffssbb7yJajKFCxehf/9+VKlShZmzZvLGiBFERDjXv8O9Hbi7TVvnmg8fxoqUa963H7UyueY9+vTi5N8nublhQ7q9+BIiwphxY1mydCn58gmlS5emf99+RJSNSN0us3aey7GnnNf77rufiIgIxowZ7bfPIUOG8sUXX7By9TK3Dd/w2tDXSUpOpk2bVhle257d+7B58xZKlijBkGEDqVCxAhs3bGJA/0GA89144r+Pc0uTxn5t6HjfQ5SNKMuoMW+mO2e+rF75AyOHTyQ5OZnmrZvy4CPt/Jb/9MNGRr02ke2/7KDP4Jdo3LRB6rJG17aies2LAIgsV5ahI3tmqZXCtyvXMnL4O6maHR+5O43mJka9Non//bKD3oO70ripc37XrtnA6Ncmpa4Xu2M3vYe8QKPGN5yR7g2XRPN8yz7kkzBmrfmU/1s6zm95uZIV6dHuNUoWvZDDx/+iz5TOxB/ax8Xla9GtzSCKFipGUnIy7389mkXrZ2eptXLlKoYPfS3HnhcATz/5DPv/+IOkxCSuqXsNr7zajbCwsDS6OX9PffLxZGZM/wJVpc3drXmg432eHuuZPhvPhoEPvUF0nVs5cOQPWva55Zz2ZQSfoHXRikiYqvbywrgTkZD3TCYlJTNyyCSGjnmVD6a/ydfzVrJj+y6/dS6+rCpvfzyEd6e+RlSTG3h75EdZ7jMmJobY2Fhmz55Nr169GDBgQIbrDRgwgN69ezN79mxiY2NZuXIlAO+++y7169dn9uzZ1K9fn0mTnB+Jw4cPM2jQIEaOHMmMGTMYPnx46r6GDRvGTQ1uZMbMz/lk8sdM/mQyY8aNYvqMz5g3bz7bt//PT/uLGV9QPDycWXNm8kDHBxg5YhQAJUuWZMSoEUybPpV+/fvSo3uv1G3+9a8HmTHzcyZP/YR1P60jJmZlmnOZxJBBwxk1bgSfzZjM/HkL+F863VmEhxdn5pzpPNCxA6NGjPVb/uZrI7ip4Y3pztXHH37KgQN/Urt27dR9b9++3W+dGTNmEB4ezpw5s+nYsSMjRowEYPv27cybN5/PP5/OuHHjGDRoEElJSSQlJTFo0GDGjRvLjBmfM2/evNR9DhgwkMGDBzF16lTuvLMZEydOTNW5/bbbmDZ5KtMmT+XuNm2da74yht9jY5kzcxa9evRkwOCBGV/zwQPp3aMXc2bO4vfYWGJWOefw3/96iOlTpzFt8lQa3dyItydM8DuvmbXznx576nn9+BOqV6+Wrp2bNm3i8OHDfm0YOmgYo8eNZPqMKcybNz/Taztrzuc80PE+Ro4YA0CNmjX46JMPmDz1Y8aMG8XA/kNITExM3e7TjydTrXrVDM+XL0lJSbwx5C1eG9OHj6aPZdG85fy2PdZvncjyZXm177PcekdUuu0vuKAg708ZxftTRp2xcedovs1rY3rx4fTRLJq3gt+270yjWYZX+3bm1jsa+c2vW+9K3psygvemjGDkhP5cUOgC6t9wzRnp5pN8vNh6AM+++y86vHELt13VimoRF/ut0/muHnz1w3Q6jriNSYtH8NQdLwPwd8IJ+k55lvveuJVnJz3Icy16U6xQeJbHOGTQkBx/XgwdPoSp0ybz2edTOXjwIAsXLEqnm9P31K+/bGfG9C/4v4/fZ/K0j1mxPIbY2J1+ml4ca3bPxrNlxqopPD7ygXPaRyBJ0uSAfnIbnhh4IlJVRLaIyMcisllEPhORIiKyQ0SGishaoJ2IvC8i97jb1BORVSKyTkS+E5HiIhImIsNFZI2IrBeRJ7LQjBaRFSIyC/jZnfeFiPwgIptE5D8+6x4VkYGu1moRiXTn13CnN4jIABE56rPNiz7t6Huu52jLxl+pWLkcFSpFUqBAfm65/SZWLl3jt8419a6gUOELAKhV52L2x/2Z5T6XLFlCixYtEBHq1KnDkSNH2L9/v986+/fv59ixY9SpUwcRoUWLFnz99dep27ds2RKAli1bsmTJEgDmzp1LkyZNKF++PAAXXnghAEeOHOGHH36gTRvH87h16zYuuqgKlSpVokCBAtx+x20sXbrUT3/pkmW0aNkcgFubNuG7775DVbns8stSvVM1atbg5MmTnDp1isKFC1Ovfj0AChQowGWXX0Z8XJzfPjdt/JnKlStRqVJFChQowG13NGXp0uV+6yxbspzmLe8CoEnTW/juuzWoG6C75OtlVKhYgRo1qvttExcXx4J5C6larSqFChfy2bf/MS1ZspSWLVsA0LTpranHtHTpUu6443YKFixIpUoVqVy5Mhs3bmTjxo1Urlw59TzdccftqfsUEY4ePQbA0aNHKVs2c48twJKlS2nRvDkiwlVZXPOjx45xVco1b9489doWK1Ysdb0TJ06ASOp0Vu38p8eecl5XrFhBG9dITSEpKYk33niT55571qcNm6jkc22de8r/2i5dsszv2q5xr23hwoXIn9951zt18iTic2xOG1bSuk2rLM8vwOaNv1CpcnkqVipHgQIFuPX2RsQs/dZvnfIVIql5STXy5ZNM9nJ2bN74CxUrl6eCq9nk9oaZaFZFstBcumgVNzSom/ocyY5ala9m14Ed7PkzlsSkBBaum0WjWrf5rVMt8mK+3+4YEj9sX5W6fOcfv7HzwA4A/jgSx8GjByhVtHSmWhs3bvK7v3LieQGn7+nExEQSExL8rnuKbk7fU7/99htXXFk7dfm119bl68VLPD3WM3k2ni3f//Ith44dPKd9GKGDlx68S4Fxqno5cBh4yp1/QFXrqurklBVFpCAwBeiiqlcBtwIngEeBQ6paD6gHPC4i6V/7T1PX3ccl7vQjqnotcB3QWUQudOcXBVa7WsuBx935I4GRqnolkOpOE5HbgIuB+sDVwLUi4v/afJbsj/+TspEXpk6XjbyQ/fszN+C+/OJr6je4Ost9xsfHExkZmTodGRlJfHz8Ga/z559/phoUZcqU4c8/nfb8/vvvHD58mEcffZQOHTowe7bT7bJ7925KlSpF71596ND+fsaOGUeZMmVO7zsikv1x/sZGfPx+ypVz9PPnz0+xYsX466+//NZZtGgxl11+GQULFvSbf+TwEZYvW5HaFeN3TOV8jikiIp3u/vj9RJaLSKN7iOPHj/PBe//Hf/77WLrz+fqwN2l6exNKly7lt++4uPTntFy5cumOKS4unsjIcqe3dc+17/oAERGRqfvs06c3zzzzDE2b3sacOV/yyCOnu0QXfb2Yu9u34/kXu7Jv377T2r4aEZHE70/Tvv3xREZE+q/jc1+MGjOaps1u58u5X/H0k0+mzo/bn3k7/+mxAwwbNpznnns2nTE0efJkoqOj/Iza/T73i9OGCOIzuLbp76lDAGxYv5F72txL+3vu59Ue3VJ/nF8b9iZdnutEvnzZPwL3xx8gIvL0fe18Vw9ku10Kp06d4tH7n+M//+rK8iXfnNE2++P/TKf5RxbPh8xYPD+GJnfcfMbrR5QoR9xfe1Kn4w/tpWyJcn7r/LJnM42vaAZAdO07KFqoOOFFSvqtU6vS1eTPX4Bdf/6eqVb6723OPS+e+u/TNGnclCJFi3Br0yZ+63txT9WoWYMf1/7EX3/9xYkTfxMTs5K4faeNLS+PFTJ/NuZ1kjU5oJ/chpcG3k5VTfEXfwQ0dP+fksG6lwJ7VXUNgKoeVtVE4DbgXyLyE/AtcCGOoZUZ36nqbz7TnUVkHbAaqOyz7Slgjvv/D0BV9/8bgWnu/5/47Oc29/MjsBa4LKN2iMh/ROR7Efn+o3c/y6KZZ8eCL5ez9ef/0eGhljm2z+zwfetNTEzk559/ZvTo0YwfP54JEyawY8cOkpKS2LJlC+3a3cPkqZ9QsGBBtm7bdk6623/dzqgRo+jR81W/+YmJibz88qvcd38HKlWqdE4avrw9fiL3d7yPIkWK+M1fviyGUqVLU7FSxRzTOhM+/PAjxowZw8KFC2jVqiWvvfY6AFGNopg35yumT53GjdffQPdeZ9bVdyZ0fqYTC+fO565md/Lp5MnZb3AOLFu2nNKlS1GrVi2/+fHx8SxYsJD77rsvky3/GVfWuYLPZkzhw0/e571JH3Dy5EmWL1vhtiF9rKIXfPbVu0z65E16D+rKqOHvsHvn3oDo/rH/T7b/8jvX33hm3bNnyqgvB3BN9Rv4v85zqVv9BuIP7SU5+fSP34XFI+jTYQQDpr2Q6iX3isyeF+PeGsvCxfM5dSqBNd+tyWTrf0ZG91T16tX498P/4qn/duaZpzpz6aWXkC9N3N+5Euhno5H78TJWLe03O2X62FnsQ4BOqjr/DNdP3beIRON4Am9U1eMishQo5C5O0NNPniSyPw8CDFbVt7NaSVUnABMA9h5fl+WTrWxEafbHnfYC7I87QNmy6bszvl+9no8mzWDkO30oWLBAuuUzpsxjzueLAbjqimuJ83HRx8XFERER4bd+REREpuuULl2a/fv3U7ZsWfbv30/p0k57IiMjKVmyJEWKFKFIkSLUrVuXbdu2UbduXSIjI7myzpUANIpqxPix40/vOz6OspH+XYwREWXZty+OyMhIEhMTOXr0KCVLlkxty/PPdaX/gH5UrlzZb7sB/QZSpUplHuh4E/wt9wAAIABJREFUf7pzEBER4fe2HBcfn063bERZ4vbFp9EtwcYNm1i8aAmjRozhyJEj5JN8XFDwAuLj41m+dDlLvl7K4UOHyBcWRo9XelO9ZjUiI9Of03379qU7psjICOLi9mV4rlM8cADx8XFERkbw559/sm3bNuq45/P222/nqaeeBkg9RwAJCQn8sPYH2nVoT+3atdnnqxEf55ckARBRNoK4+Dj/ddLcFwB3NbuTpzo/w9NPOs72yLIRGbbzXI596dJlLF26jJiYGE6ePMWxY8d45ZVXadbsDnbu3EmLFk53799//03L5m3pP7AP+9J4QiIyuLbp76kSfutUr16NwkUKs/3X7az7aT3Llq4gJmYVp06e5NixY3R/pRcDB/dLd06c/V9IfNwfqdPOd/XCDNfNbHuAipXKcc11V7Bty/+oWLl8NtuUTqdZJoPnQ1YsWbiSRrdcT/4CZ/6Yjz+0j8iSp5OPIkqUZ/+hfX7r/HEkjpc/dCJeChcsQuMr7+To307cZNELivHGw+/z1vxhbIz9MUut9N/bnHteAFxwwQVEN45i6ZJl3HDj6QSTlPsl9Zhz4J6qVbsWrdu2onVbp8t/9Khxft8VL481q2djXsfq4GWNlx68KiKSErV+PxCTxbpbgfIiUg/Ajb/LD8wHnhSRAu78S0Sk6BnqlwAOusbdZcCZpJCtBlJS1Tr4zJ8PPCIixdx2VBSR9L+QZ8GltWuwK3Yve3fHk5CQyNfzV3FT9HV+6/yy5TfeGDiRQW++RKnSJTLcT5t772DSlOFMmjKcxo0bM3v2bFSV9evXU6xYsXQxXGXLlqVo0aKsX78eVWX27Nk0buxkgUVHRzNr1iwAZs2alTq/cePG/PjjjyQmJnLixAk2bNhAtWrVKFOmDJGRkezYsQOAP/bvJzExkd27dpOQkMD8eQuIjvIPOo+KjmL2LMd5umjhYurVr4eIcOTwETo904XOXTpx9TX+XdFjx4zjyNGjvPhS1wzPQa3al7Mzdie7d+0hISGBBfMWEhXl34MeFX0zc2Z9CcDihV9Tr/51iAiT3p/AnLlfMGfuF9z/QAcefuwh7r2vHZ26PM3chXP4av4sSpQowZVXXkHvfj3cffsfU3R0FLNmOd3WCxcuor57TFFRUcybN59Tp06xa9duYmNjueKKK6hduzaxsbHscs/TvHnziYqKIjw8nKNHj7Jjh9Ot9c03q6lWzYlI8I2ri4yMpHat2kybPJVbohsze84cVJV169dTPJNrXqxoUdalXPM5c2gcHQ3A77Gnu9CWLFtKtaqnIyAya+e5HHuXLp1ZuHABc+fOZejQIdSrV4/BgwfRqFEjvv56MXPnzmXu3LkUKlSIWXM+p3btWu61PX1PRUX5dzlGRTfK8Nru3rU7Naliz5697NjxO+UrVKBTl6eZt3AOX86dyeChA7mu3nWZGncAl9W+mJ2xe9izex8JCQksmr+cBtFn1hV2+PBRTp1KAOCvg4fY8NNmqlZPb4xkpLkrdi97dseRkJDA4vkxNDxDzRQWzVuRLgEjOzbvWkflC6tSvlRl8ocVoOlVLVm+eaHfOiWKlEr18D/U+Blmr3E6ZfKHFWDovyYyd+10vt7wVbZatWvXIjbNtT3X58Xx48dTvyuJiYnELI+harWq6XRz+p4C+POA04W+d+8+lixeQrNmt3t6rJD9s9E4v/HSg7cVeFpE3sVJehgPdMpoRVU9JSL3AqNFpDBO/N2twDs43adrxXmi7AdaZ7SPDJgH/FdENrttWX0G2zwLfCQi3d3tD7ntWyAilwPfuA+2o0BHID6zHWVH/vxhdOn2CC8+NZDk5GSatWpMtRqVeXfcFC6tVYMG0dcx/s2POHH8b3q/9AYAkeXKMGhkt0z3efPNNxMTE0Pz5s0pVKgQ/fqd/tFq3749U6dOBaB79+6pZVIaNGhAw4ZO7/kjjzzCiy++yBdffEH58uVTs2WrV69OgwYNaNeuHSJC27Ztufhip4f65Zdf5tVXepCYkEDFShXp2as7Tz35DMnJSbRq3YoaNWswbux4atWuRXR0FK3btKJH9560bN6K8PASDBnmlByYPHkKO2N3MmHCRCZMcDJHx48fS0JiAu9MnES1alW5r4OT3XVvh/a0bdvG51zm56VXuvLMk51JSk6mVesW1KhZnfFj36ZW7cuJim5EqzYt6dm9D62a302J8HAGDcs4wzj9dcpPh/vv5Z2J73J363tp1boFNWvWZOzYcdSuXYvo6GjatGlD9+7dad68BeHh4QwbNhSAmjVrctttTWnTpi1hYWG8+uorqeUaXnnlZZ588kmSk5Np3boVNWvWBKBXr1688MIL5MuXj/Dw4vTt6+TzfDL5U5YuW0pYWH5KlAhnQF/n2t7c8GZWxMRwV6sWTpmUPqfzf9p1aM+0ye41f+VVevTu5ZTGuakBDRs413zEqFHs+H0H+SQf5cuXp2f37n7HnlE7z/XYz4b8+fPT7ZUXefrJziQnJ9OydQtq1Kzhd21bt2lJz+69adm8LSXCwxk8zMkk/vHHdbz/7gfkL5CffJKPV159iVKlSmajmFEbwni+2395/qneJCcnc1erW6le4yLeGfcRl9W6mIbR17N50zZefX4QRw4fZeXyNUx662M+mj6O3/+3k+EDxyIiqCodH76HajWqnJHmc90e54Wn+pKcnMRdrW6lWo0qvDPuEy6rVZOG0fXZvOkXuj8/hCOHj7Jq+fe8+9anfDjdKTmzd08c8fv+4Opra5/VsSYlJ/HazJ6MevQj8uULY/aaKfwWt43/NH2BzbvWs2LzQq6tcSNP3fEyqsqPv33L8C96AHBrneZcU+16ShQpxV3XOmVk+k19nl/2/pzJMean2ysv5ejzQlGe7fI8CadOkZysXFfvOu5pd3cGujl/T3V9oRuHDh0mf/4wur36IsXDi3t6rGfybDxbXn98HPUuuZFSxUqzdNj3jJ71OtNjPv3H+zOCi3gRIyEiVYE5qnpFju/cQ0SkCHBCVVVEOgD3qWr2aXYZkF0XrReUyndpoCUBSCYx+5XykG5+ziwjMSeRpOzX8QLN2TCiMyaJU0HRPZ58blmI/wQlOBe3Rd/bsl/JA5b03RwU3WCcZyE4X6C6nS7JfiUP2DJxT86kkZ8h7xR6MKC/s4/9/WFAj+9cCfl6cQHmWmCM6y38C3gkm/UNwzAMwzBCDk8MPFXdAXjivRORK4EP08w+qarXn+u+VXUFcNW57scwDMMwDG/JjaVLAkmu8+Cp6gacWnSGYRiGYRhGBuQ6A88wDMMwDMPKpGRN0MaiNQzDMAzDMLzBPHiGYRiGYeQ6kiwGL0vMg2cYhmEYhpHHMA+eYRiGYRi5DsuizRrz4BmGYRiGYeQxzINnGIZhGEauw7Jos8Y8eIZhGIZhGB4iIu1EZJOIJIvIdVmsd4eIbBWRX0XkZZ/51UTkW3f+FBEpmJ2mGXiGYRiGYeQ6kjU5oJ9zZCPQFlie2QoiEgaMBZoBtYD7RKSWu3go8Kaq1gQOAo9mJ2gGnmEYhmEYhoeo6mZV3ZrNavWBX1X1f6p6CpgMtBIRAW4BPnPX+wBofSai9gmxD/Cf80X3fDpW0827mqabdzVN1z6+5wX43udz1ucJWApcl8mye4B3fKYfBMYAZVzDL2V+ZWBjdlrmwQtN/nMe6Z5Px2q6eVfTdPOupukaAKjqBFW9zuczwXe5iCwSkY0ZfFoFo72WRWsYhmEYhnGOqOqt57iL3TjeuRQqufMOACVFJL+qJvrMzxLz4BmGYRiGYQSfNcDFbsZsQaADMEudftklOF24AA8BM7PbmRl4ocmE7FfJM7rn07Gabt7VNN28q2m6xjkjIm1EZBdwI/CliMx351cQka8AXO/cM8B8YDMwVVU3ubvoBjwvIr8CFwKTstV0A/YMwzAMwzCMPIJ58AzDMAzDMPIYZuAZhmEYhmHkMczAMwzDMAzDyGOYgWecV4jIhcFug2EYhmF4jRl4IYCIhInIkmC3I1C42UQlfKZLikj2w67kDKtFZJqI3OkO/+I5InKhiIwWkbUi8oOIjAyEoSkiXc5kXg7qNRCRou7/HUXkDRG5yCu9NNpFRSSfz3Q+ESkSAN3FZzIvLyEi4SJSOuUTIM2KInKTiDRK+QRA81ERudhrnQx0xf3+9HKnq4hI/QBpNxSRh93/y4pItUDoGt5gWbQhgvuj0FZVDwVIbwOQ6cVX1Toeav+kqlenmfejql7jlaaPjgC3Ao8A9YCpwPuqus1DzYU4A0x/5M56AIjOgaKY2emuVdW6aeZ5dp5FZD1wFVAHeB94B2ivqlFe6KXRXg3cqqpH3eliwAJVvckjvUJAEZzaVNFAystCODBPVS/zQtdHvwHQB7gIp2C9AKqq1T3UfALoC/zN6WeHp5qu7lDgXuBnIMlHt6XHun2Bm4GqwA843+EVqvqTx7rjgWTgFlW9XERK4dzL9TzW7Q1cB1yqqpeISAVgmqo28FLX8A4bySJ0OApscI2BYykzVbWzR3rN3b9Pu38/dP8+4JGeLxl5jgNyL7oFIxcCC0WkMY7R9ZSIrANeVtVvPJAtr6r9faYHiMi9HugAICL3AfcD1URkls+i4sCfXukCiaqq7rA8Y1R1kog86qGeL4VSjDsAVT3qsQfvCeBZoALOj3+KgXcYZ+xIr5kEPOdqJ2Wzbk7RFbhCVf8IkF4KrXGMjpOBFFXV3gAiUhh4HHgRGAGEeSx9varWFZEf3XYcdIveek0b4Bpgrau7R0SKB0DX8Agz8EKHz91PQFDV3wFEpGkaj87LIrIWeNlD+e9F5A1grDv9DM4Plee4XaMdcQZxjgM6AbOAq4FpgBddEgtEpAOOtxCcauTzPdBJYRWwF2eA6td95h8B1nuoe0REXsE5tze7XaYFPNTz5ZiI1FXVtQAich1wwisxVR0JjBSRTqo62iudLDikqnMDrLkdOB5gTYD/4dxHATXwRKQH0AAoBvyIY+CuCIB0goiE4XpJRaQsjkfPa065L2gpukUDoGl4iHXRhhDum2IVVd0aQM2fgKdVdaU7fRMwLm0Xag5pfaiqD7oPzmI4XaXgeNQGqOqxzLfOsTZsw/FWvqequ9Is66aqQz3QPAIUxXlIK44HIOVYVVXDc1rT1X0MWKaqv3ix/wz0yuF4Dteo6goRqYLTFf1/AdCuB0wG9rizygP3qqrnLw7ud6YqPi/MXh2ziKR0ubfHuY8+x8fwSTFwPdK+BngP+DaNpie9DCIyGuf7UhGn639xIHR99NcCicCXwDLgm0B4EUXkAZwu6brABzgvhD1UdZrHul2Bi4GmwGCcMJZPgvQCY+QAZuCFCCLSAngNKKiq1UTkaqBfAOJMrgXeBVKSHv4CHvHih0JEfsYx6uYCjXHjhlKWq6qX3Ye4b8XDVPUFL3VChWDEELlJFRer6iK3izRMVY94peejWwjHG3s7TjfpN8BoVf3bY90PgRrAT/jHh3ll9GSVjKWqeosXuq72d0AMsAEfj5KqfuCR3kNZLfdKN00bwnG8eA2BdkC8qjYMgO5lQBOcZ+RiVd3staar2xS4zdWdr6oLA6FreIMZeCGCiPwA3AIsTekyFZGNqnpFgPRLAHiZ5CEinYEngerAbt9FBCBY223DN6p6o9c6aTQFJ7axmqr2F5HKOHF53wVIPyWGqCtQUVU9iSESkceB/wClVbWGm4H4lqo28UIvjfZUHMPuY3fW/UBJVW3nse5moJYG+EEqItVV9X/ZzcthzYAkQp0tIjJdVe/2YL9X4LwgReEkH+zEeUHqldNaGWiXAirj7xX2zDtr5E3MwAsRRGS1qt7g+xAVkfVeZrO6GpHAIKCCqjYTkVrAjaqa7UDG56A5XlWf9Gr/2WnjdPlMwz+ZxbP4xyBmxaWNIYrB+YHa65HeT0B94Fufe3iDql7phV4a7Z9VtVZ28zzQnQZ09uqcZqGbUYb0D6p6rYeag4AdwGz8u0o99bxnh1eGp4jMwfF6x+CEHSTktEYmuv2Bf+PEPPpmK3vmnXV1j/jopXAI+B54wcuXB8MbLMkidNgkIvcDYa7nozNOsLzXvI8TV9Pdnd4GTMHJ0vOEYBl3LoWAAzje0hQUbxNcgpUV15bAxhCdVNVT4pYXFJH8ZFGKJ4dZKyI3qOpqV/t6nB8mTxCR2TjHVhz42e2+9DV6PAmtcLvuagMlRKStz6JwnHvbS+5z/77iM09xPPLBxJN7TFWbZ7XcK88hTnxlDVU95cG+s2IEsAv4BKdXpQNO+MFanDCe6AC3xzhHzMALHTrhGFkngU+BecCAAOiWUdWpbvYjqpooIoEquxBwVPXhIMgGJSvONSpTYoiaAhNExMsYomUi8ipQ2I3leQrH2xMIrgVWiUisO10F2CpuvUcPPOGv5fD+zpRLcUoclQRa+Mw/gtMN7xmqakVv/fHKsN2Ic33jPdp/ZrRU1at8pieIU7O0m/u9NnIZZuCFDuVVtTunPWmB4phbOiTF+LgBxy2fJxGRSsBoHKMHnLIHXdJm1OYwo4AZQISIDMTNivNQD8g8hshDyW7AYzhB+E8AX+EUOw4EdwRIBwBVXRZIPR/dmcBMEblRvanZmCXuPVULH29hILKksyEgI9JkgFfe6cHAjyKykQB4hX04LiLtgc/c6XtwilpD4DzxRg5iMXghgogsAyoBa3B+hJer6oYA6NbFMXiuwHlzLAvco6pe1ksLGuIUkv6E04WdOwIPqGpTj3UDnhUXyBgi10O5ST0ewSHUCHTckk/pkAzxsnSIOCMdROMYeF8BzYAYVb3HK01XtyhwQlWT3el8OIWtj7vTt6nqAi/bkEm70sVB5tB+NwFvkz5b2dOXChGpDowEbsS5x1bjFNPeDVyrqjFe6hs5jxl4IYQbl1UP5yH6BFBMVT0f69GNlboUx/jYGqhg4mAgGQ+Tlm5eDmllee1CIDg9R2OIRGQm0ElVY7NdOY/gBsRnFrf0pKpG57BeSumQBjiG1hR3uh3ws6r+Nyf10mhvwKlH96OqXuUmaH0UgJejgA5Ddxbt8iq5Y43XCVjG+YF10YYIItIQpzvtZpz4izkEoGq6W6vseeAiVX1cRC4WkUtVdY7X2kHigIh0xIlzBCdw/IBHWj/gvAkLTkzYQff/kkAs3oyacTbkdAxRKZxkoe/wz1D2umspmAQ0biml9puIPAk0VNVEd/otvH9e/K2qySKS6MZ2xuOU8vCaQA9DB2TvOcQJSfCCFSIyGGeEnYAUsYbU2ODHSV+0+xEvdQ3vMAMvdFiKYxAMBr4KYAbVe65uSm243TglRPKqgfcITpf0mzjG1yrAk8SLlKB0EZkIzFDVr9zpZjjjawabnHbf98zh/eUGghW3VAonczbFC1zMnecJbi3H9SJSEpiI88w4ilNQ2msCOgydD4txCrOnGJdFgAXATQAedguneAVv8Jmn+Gf+e8FMnJeERQRufGPDQ6yLNkRwH5wNgEY43bTJOGUtPP3RFJHvVfW6NPX31qXxShjnQEa14AJVHy4rvIghcrvtUrqXvlPVQGcCBpRgxS2JyMNAH2AJjle4EdBHPRzdwfeeFZGqQHggYnVdg24KAR6GLpDhHKFAXj628xXz4IUIqvqXiPwPp8ujEs5bYiAGaj8lzkgHKVm0NQjwoN6BRESq4ZSkqYp/N4SX3Yh73KLDH7nTD3D6xyqY5Gj2oevJGo7jjRZgtIi8qKqfZblhLsZNomiRyWLPgtJV9T0RmQtc787qpqr7vNJzWSsi9VR1jaru8FjLl2o4Xq0qOLUdrycwWZ0B9RyKSEdV/UhEns9ouaq+4ZW2yxwRuTOlp8HI/ZgHL0RwjbstOD8Ky3G8H55307r1ynrgBGwvwPEi/ltVl3qtHQxEZB1OEeeAZai5yRa9cbwsinN9+wUiycI13quo6tYMluVo9qF7bpumeO3cmJ5FedEbLCIvqeqwzLJavcpmFZHLVHWLm/2eDi/jtERkC1AT+B0nxjJliEGvR9tZr6p13Djl/jg1CHup6vXZbHquugH1HIrIE6r6tputnBZV1X5e6ProHwGK4rzgJ3D6+oZ7qWt4h3nwQoeaKcG8gURVF4rIWpx4D8GpCfdHoNsRQP5W1VGBFHQNuS6ZLReR0araKad1RaQFzo9hQaCaiFyNY1i2dNuV0zFE+dJ0yR4A8uWwRqiQUubGs9EyMuF5nPF+X89gmddxWrd7uO+sSIkHuwuYqKpfikggisAH1HOoqm+7/y5S1ZW+y0SkQQab5LR+ca81jMBiHrwQIUgFeFO02wINcR5eMao6w2vNYCHOcHAX43grA5ahlk2bvKqn9QPOD/5SDcDYsCIyHKjD6Qzle4ENqvqSF3qhhIgU8cmuNHIQt57jbpzRWOridJN+57VnOIiew4zGGfbkGZGBdimc56NvIevlXusa3mAevNDhPZxaWu3c6Y7uPK9rTI3D6XZJ+VF+QkRuVdWnvdQNIlcCD+IYPike00BkqAWDBFU9JOIXauelB+JFn5cFgAl5+WUBQERuxOnyLwZUEZGrgCdU9SmPdWNwxhdeAaxU1SNe6gWZ9jgjlbzmxiqXB14MgG5APYfuvXQTUDZNHF44EOaVro/+Yzg9DZWAn3B6db4hbz4bzwvMwAsdyqrqez7T74vIswHQvQW4XF1Xroh8AGwKgG6waAdUD2AZmmCyyfVYhonIxUBnnLIwnuAmsHylqp+704VFpGqAA/IDzQicrstZAKq6TkQaBUD3QZyamXcDw0XkJLBCVZ8LgHZAcT2jn/tM7wX2BkB6t4i8jfOSPVRELsDbkIOCOC8K+QHf7tLDOOV3vKYLTgb8alVtLM7oO4MCoGt4hBl4oUMgC/D68itOjMnv7nRld15eJVgDeWeFV2NpdsIZ2/gkzn01H6erySum4dYIc0ly5+XpqvyqujONl9TzGmKq+puI/A2ccj+Ngcu91j3PCKjn0E30WiYi76vq75mt51XMLk588t8igohc4CbzXOqBjhEgzMALHXwL8AKsxKMCvGkoDmx2Rx8A58f4exFJ8UjktVEISgJbRGQNARrI283G6w5chPOdS5t9ONILXdfz0d39BIL8vp5RVT0lzvB7eZmdInIToCJSAMcLEohxhrcDf+CEdUzCGSIu4ElaeZlgeQ6zMu5cvEq42OXWY/0CWCgiBzn94m/kQizJ4jxHRKKyWu5l+ZBgkNnxelwmZSvOm3/a0iyePDxFZDZZD0jviTErIguB0ao6y51uBXRW1SZe6IUCIlIGx0C/FcdwX4CTHOWp911EuuDEOlbGKa+0DFiuqtu91DWCTyASLtznZAlg3nkSzpInMQMvRPCpiH8Dzo/zN8BzbiFVL3VTx1sUkUuAy4C5qprgpW4wEZGLgItVdZE4Y1qGeRmkLiIxqtow+zVzTC8oRrtbJPtjoII7axfwoBkd3iEixXA8/V2BSqrqeTC+EVy8MPBEJAzYpKqX5eR+jeBiBl6IICKrgbGcjsHrgNPt4nVK/g84wdqlcLqF1wCnVPUBL3WDhYg8jlNHrLSq1nCTD97y0sskIk1wYioX498t/HmmG+ViXKMD9Rkg3p3/kHo4lFYwkCAN0C4ir+N48IrhJM7E4CRZePpCaAQf8RlWMof3OxPnNyc2p/dtBAeLwQsdiqjqhz7TH4lIIEoBiKoeF5FHgXFudf51AdANFk8D9YFvAVT1FxGJ8FjzYRzPaAH8S7N4YuCJyFRVbS8iG/Dvqg3IyANpDTsfugB5ysAjeAO0fwMMU9W4jBaKSG1VzcvZ8HkWEammqr+lmVdPVde4k57E7OK85G9y47GPpczMg3HY5w1m4IUOc0XkZWAyzo/yvcBX7jBXKaMheIG49ZceAB515+XV0QcATrrB/wCISH68H9eynqoGMhstZdSM5gHUPBO8yhYOJkVUtVugRTX78X0/xCkKbOQ+potIC1XdDakhF2Nwaniiqu97pNvTo/0aQcIMvNChvfv3iTTzO+AYINU90u0CvALMUNVNbizgEo+0QoFlIvIqUFiccXifAmZ7rLlKRGqp6s8e6wCp2X6o6u8iUg7HY6nAGvV+QPosmxZEba8I1QHa86Ixfb7wBPCFO9RgXWAwcKfXotnF5orIN6p6o9ftMHIOi8HLJYhIU1VdGARdr2ouBQURyYfjqbwN50dwPvCOevhFEJHNQA3gN5wYvEAN0v4Y0Av42tWMwhmL9l0vdbNojyexQ8FAnIHZFee8htwA7YEa2srwBrdX5W3gb+AuVd0f5Cblqe/v+YIZeLmEYD2w7Yfi3HGzdtPhVZkUH92twE0pJTtE5EJglVfdxW6l/7tJn3DQz10+RlWf8UI7VAlWLJx9b3MfGZQ3qoVTd+8gBD8Wzu6p3Id10eYerMslBxCR5jijOaQtOuyZx8VrQy4LDgC+5V+O4O3oKDOBQ8AP+GQLp3C+GXcuwYqFs9pluY/Xgt0AI29hBl7uwVytOcMIoC2wwctu2WDiM1D5r8C3bvkDBVoB6z2UrqSqd3i4/9yIJy9mIvKoqk7ymQ4DeqhqXwBVvcELXcM7ckFReXMy5DLycrakkTPktS/1TmBjXjXuXIq7n+04ww6lHOtMnDhAr1glIld6uP/ciFf3WRMR+UpEyotIbWA1/gPUG7kMETkiIocz+BwRkcMBasNFInKr+39hEfG9px4MRBuMnMM8eLmHHV7sNIg1l4LFSzjlZ5bhX3T4jeA1KWdJ8eKkkFnhYQ9oCPxbRAKaTHI+oqr3i8i9OMPfHQPuV9WVQW6WcQ6oalANdN8i8DhJYZWAt4AmAKq6MXitM/4JlmQRIrgjSrwLfKKqBwOouxZIV3NJVfOkJ0ZEFgBHST8ubN9MN8qliMgVODFgpd1ZfwD/8iroP1jJJKGMiKz2orvUHYHlA5z7+HLgZ+B5VT2e01pGcHALsBdKmfZ6hAkR+Qm3CHxKtqyIbMirvwXnA+bBCx3uxRnxYI2IfA8jHCPVAAAHtUlEQVS8BywIQFdiUGouBZEKqnpFsBsRICbg/OgvARCRaGAicJMXYuejIRfEWLjZwNOqulicqt3P4QwzWNsjPSNAiEhL4HWcMZ3jcRLCNuP9tQ1GEXjDQywGL0RQ1V9VtTtwCfAJjjfvdxHpmzKahUe6a4DOwAKgD3Crqu70Si8E+EpEbgt2IwJE0RTjDkBVl+LUbDNyjmDFwtUHrhKRz4HPcH6I7wuAruE9/YEbgG2qWg2ni3R1AHTTFoGfhvdF4A0PsS7aEEJE6uB48e7EKcD7MU5c04OqenUOa4V0zSWvcAvUhlxhWi8QkRnAWpxuWoCOwLWq2iZ4rcp7uLFwYwlgLJyITMUpe/ORO+t+oISqts98KyM3ICLfq+p17pjg16hqsoisU9WrPNYNeBF4w1vMwAsR3Bi8v4BJwHRVPemz7HNVbZvDelFZLc8FKfuekJcGaReRUkBfoIE7awXQR1X/Cl6r8hbBioUTkZ9VtVZ284zch4gsAlrjhMuUwemmvU5VG2S54bnrFgX+VtUkdzoMuMDiOnMvFoMXArhvTtNVdVBGy3PauHP3uczVrgbsVdW/3enCQGRO6+Ui8tIg7TWAyjihGPlxunpuASyrNecIVizcWhG5QVVXA4jI9cD3HmsagWEdcBznXnoAKAEUC4DuYuBWnCQ0gMI4oTuexOwa3mMevBAhxS0fDF2c4axOudMFgZWqWi/QbQkF8tJ4i+5QZV2BjfhnDJ93yRBeISLhwGM4oRQKxACLVdXLgtIp4xtfCqRkVlYBtgKJWGmaXE1GQ4KJyPoAjF39U9pQoIzmGbkH8+CFDotEpCswBSeWBwBV/dNj3fwpxp2rd8o18s5X8tIbz35VtSBpb3kHJxZutDt9P3Aj4HUsnI0YkscQkSeBp4AaIuL7glAcCESNw2MiUldV17rtuRY4EQBdwyPMgxciuMVh06KqWt1j3YXAaFWd5U63AjqrahMvdUOVvDSgtog0wcmsXIx/UefPg9aoPIbFwhk5hYiUAErhxN697LPoSABe9BGResBkYA9OkkU54F5V/cFrbcMbzIMXIrjp8MHgv8DHIjLWnd7J+T0kTV4apP1h4DKgAKe7aBUwAy/nsFg4I0dQ1UPAIYJU7kZV14jIZThd/wBbVTUhGG0xcgbz4IUIIlIEeB6ooqr/cbPzLlXVOQHSD9RwVkFFRBan9U5mNC8vICJbVfXS7Nc0/ikWC2fkdkTkFlX9WkQyTOYzj3/uxTx4ocN7wA+czljajVNo0lMDz+0W6A00cqeXAf3ct8k8g4gUAooAZdzyIeIuCgcqBq1h3rJKRGqp6s/BbkgexmLhjNxOFPA10CKDZebxz8WYBy9E8ClumZrFGaDiltNxsiw/cGc9CFzlRWmWYCIiXYBncYb/2c1pA+8wMFFVxwSrbV7hepdqAL/hxOClFHU2r5JhGKm4pbruUdWpwW6LkXOYgRciiMgqnDplK1W1rojUAD5V1foe655XqfEi0klVR2e/Zu5HRC7KaL6VSTEMIy3BKtVleId10YYOfYB5QGUR+Rhn9IGHA6B7QkQaqmoMgIg0IA+nxqvqaBG5CaiKz/2vqv8XtEZ5hBlyhmGcBcEq1WV4hHnwQggRuRBnkGkBVqvqHwHQvBqne7aEq/sn8G9VXee1djAQkQ9xui1/ApLc2aqqnYPXKsMwjODilupKZxB4XarL8A4z8EKEYGd3uhX5UdXDgdALFm5cWi0bQNswDOM07jCVT3F6VJYVwFuqmmd7dPI61kUbZIKV3Skiz2cyHwBVfcMr7SCzEaeA595gN8QwDCOE+AAn6WyUO32/O8/rUVkMjzADL/g8wenszh/wz+70MrOzuPtXfTTxmZdXKQP8LCLf4T+6Q8vgNckwDCPoXJFmBJYlImIllnIxZuAFGVUdCYwUkc6qOsp3mYhc4KFuX1fjA6CLqv7lTpcCXvdKNwToE+wGGIZhhCA2Kksew2LwQoSMxkANxLiovnX3sppnGIZh5F1sVJa8h3nwgoyIlMOJtSssItfgH4NXJABNyCcipVT1oNue0uTB+0JEYlS1oYgcwb8LOqX4b3iQmmYYhhEK2KgseYw890OeC7kd+DdQCfBNbDgCvBoA/deBb0RkmjvdDhgYAN2AoqoN3b/Fs1vXMAzjfMPqZuY9rIs2RBCRu1V1epC0awG3uJNf29ilhmEYhpG7MQMvhBCRu4DaQKGUearaL3gtMgzDMAwjN5Iv2A0wHETkLeBeoBNOXFg7IMOxRA3DMAzDMLLCPHghgoisV9U6Pn+LAXNV9eZgt80wDMMwjNyFefBCh5ThYI6LSAUgASgfxPYYhmEYhpFLsSza0GGOiJQEhuGMaAHwThDbYxiGYRhGLsW6aEMEd6DnJ4GbOT3Q83hV/TuoDTMMwzAMI9dhBl6IICJTcWrffeTOuh8ooao20LNhGIZhGGeFGXghgoj8nGag5wznGYZhGIZhZIclWYQOa0XkhpQJG+jZMAzDMIx/iiVZBBkR2YATc1cAWCUise70RcCWYLbNMAzDMIzciXXRBhkRybKYsY0PaBiGYRjG2WIGnmEYhmEYRh7DYvAMwzAMwzDyGGbgGYZhGIZh5DHMwDMMwzAMw8hjmIFnGIZhGIaRx/h/01+5MJre5EUAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["* pc,fc / px_height,px_width / sc_h,sc_w are have strong relationships"],"metadata":{"id":"YeVYCwhuo9Xk"}},{"cell_type":"code","source":["# cat features plotting -> countplot\n","\n","fig, ax = plt.subplots(2, 3, figsize=(15,10))\n","\n","for i, col in enumerate(cat_features):\n","  sub = sns.countplot(x=col, hue='price_range', data=train, ax=ax[i//3][i%3])\n","  sub.set_title(col, fontweight='bold')\n","  sub.set_xlabel('')\n","  sub.set_ylabel('')\n","  sub.legend(loc='upper right')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"id":"Jp_YBJzXdPZ7","executionInfo":{"status":"ok","timestamp":1675738844962,"user_tz":-540,"elapsed":457,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"641bb42b-9e45-4961-863f-0f8ab8370b52"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x720 with 6 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["plt.figure(figsize=(8,8))\n","\n","sns.countplot(x='price_range', data=train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":517},"id":"-QSaKLIM_yY-","executionInfo":{"status":"ok","timestamp":1675738844964,"user_tz":-540,"elapsed":22,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"5d5a6e3d-b04c-448b-9351-36aec64d84b1"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7ff554542fd0>"]},"metadata":{},"execution_count":80},{"output_type":"display_data","data":{"text/plain":["<Figure size 576x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["* perfectly balanced dataset"],"metadata":{"id":"m5407XhxAKTy"}},{"cell_type":"markdown","source":["# Data Preprocessing"],"metadata":{"id":"Pe5xdvNC_rIe"}},{"cell_type":"markdown","source":["* There is nothing to preprocess."],"metadata":{"id":"SFSaQDyh_tWH"}},{"cell_type":"markdown","source":["# Modeling"],"metadata":{"id":"9v78NQV1l9th"}},{"cell_type":"code","source":["X = train.drop(columns='price_range')\n","y = train['price_range']"],"metadata":{"id":"7GLDrSthoGR0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=42)"],"metadata":{"id":"S2OzkWosn9Jo"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## 1. LogisticRegression\n","* Use OvR strategy for multilclass classification"],"metadata":{"id":"Ifg9_zOOmqcI"}},{"cell_type":"code","source":["ovr_log_clf = OneVsRestClassifier(LogisticRegression(random_state=42), n_jobs=-1)\n","ovr_log_clf.fit(X_train, y_train)\n","\n","y_pred_log = ovr_log_clf.predict(X_test)"],"metadata":{"id":"XzBhrKcmk2Bc"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(classification_report(y_test, y_pred_log))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hiHwZUaCmHGI","executionInfo":{"status":"ok","timestamp":1675738846188,"user_tz":-540,"elapsed":19,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"15b92925-6798-41e3-cae9-865c8a28e294"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.85      0.90      0.87       105\n","           1       0.62      0.56      0.59        91\n","           2       0.62      0.49      0.55        92\n","           3       0.73      0.88      0.79       112\n","\n","    accuracy                           0.72       400\n","   macro avg       0.70      0.70      0.70       400\n","weighted avg       0.71      0.72      0.71       400\n","\n"]}]},{"cell_type":"code","source":["print(confusion_matrix(y_test, y_pred_log))\n","print(f'accuracy: {accuracy_score(y_test, y_pred_log): .4f}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MPaAnd2ooe3A","executionInfo":{"status":"ok","timestamp":1675738846189,"user_tz":-540,"elapsed":16,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"106819e5-6c41-4d6e-8fe8-b2f76e102193"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[94 10  1  0]\n"," [16 51 17  7]\n"," [ 0 17 45 30]\n"," [ 0  4 10 98]]\n","accuracy:  0.7200\n"]}]},{"cell_type":"markdown","source":["* drop features having strong relationships with others."],"metadata":{"id":"T3pUnqFypIJ9"}},{"cell_type":"code","source":["X_new = train.drop(columns=['fc','px_height','sc_h','price_range'])\n","\n","X_new_train, X_new_test, y_new_train, y_new_test = train_test_split(X_new,y,test_size=0.2,random_state=42)"],"metadata":{"id":"HBZI5QJTonRV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["ovr_log_clf = OneVsRestClassifier(LogisticRegression(random_state=42), n_jobs=-1)\n","ovr_log_clf.fit(X_new_train, y_new_train)\n","\n","y_pred_log = ovr_log_clf.predict(X_new_test)"],"metadata":{"id":"_NsOd6cHpuPK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(classification_report(y_new_test, y_pred_log))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qUtB02_XpxVT","executionInfo":{"status":"ok","timestamp":1675738846190,"user_tz":-540,"elapsed":12,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"7c538535-5cb8-4f77-92ed-fc3ba7893eef"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.84      0.87      0.85       105\n","           1       0.58      0.53      0.55        91\n","           2       0.58      0.48      0.52        92\n","           3       0.71      0.84      0.77       112\n","\n","    accuracy                           0.69       400\n","   macro avg       0.68      0.68      0.67       400\n","weighted avg       0.68      0.69      0.69       400\n","\n"]}]},{"cell_type":"code","source":["print(confusion_matrix(y_test, y_pred_log))\n","print(f'accuracy: {accuracy_score(y_test, y_pred_log): .4f}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"o6cpUfUwpzCc","executionInfo":{"status":"ok","timestamp":1675738846976,"user_tz":-540,"elapsed":795,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"3e036075-46c3-451b-d37c-64529f88bb41"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[91 13  1  0]\n"," [17 48 17  9]\n"," [ 0 18 44 30]\n"," [ 0  4 14 94]]\n","accuracy:  0.6925\n"]}]},{"cell_type":"markdown","source":["## 2. SGDClassifier"],"metadata":{"id":"DQmIDywzqgEs"}},{"cell_type":"code","source":["ovr_sgd_clf = OneVsRestClassifier(SGDClassifier(random_state=42), n_jobs=-1)\n","ovr_sgd_clf.fit(X_train, y_train)\n","\n","y_pred_sgd = ovr_sgd_clf.predict(X_test)"],"metadata":{"id":"DJzCC4mhqAzK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["y_pred_sgd"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"24j9VPhxukOa","executionInfo":{"status":"ok","timestamp":1675738846978,"user_tz":-540,"elapsed":45,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"248d4377-dbc1-40b9-e809-41902f7941b4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 2, 2, 3, 2, 2, 3, 0, 3, 2, 0, 2, 2, 3, 2, 2, 2, 2, 2, 0, 0, 2,\n","       2, 2, 0, 2, 3, 3, 2, 0, 2, 2, 3, 0, 2, 3, 2, 2, 3, 0, 2, 2, 2, 0,\n","       3, 2, 3, 2, 2, 2, 3, 2, 0, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0,\n","       3, 3, 2, 2, 2, 2, 0, 2, 2, 0, 0, 3, 2, 2, 3, 2, 2, 0, 2, 3, 2, 3,\n","       3, 0, 3, 3, 3, 0, 3, 2, 2, 3, 2, 2, 0, 2, 2, 0, 2, 3, 3, 0, 2, 2,\n","       0, 0, 2, 2, 2, 2, 3, 3, 0, 2, 2, 3, 2, 2, 3, 3, 0, 3, 0, 2, 3, 2,\n","       2, 2, 0, 2, 2, 2, 0, 2, 3, 2, 3, 3, 0, 0, 2, 2, 2, 2, 3, 2, 2, 0,\n","       2, 3, 0, 2, 0, 2, 3, 3, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 0, 3, 2,\n","       2, 2, 2, 2, 0, 0, 0, 2, 3, 2, 0, 2, 0, 0, 0, 0, 2, 3, 3, 2, 0, 2,\n","       2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 0, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2,\n","       3, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 0, 2, 3, 2, 2, 0, 2, 3, 2, 3, 3,\n","       2, 3, 0, 3, 2, 2, 2, 3, 3, 2, 2, 3, 2, 2, 2, 3, 3, 3, 3, 2, 2, 2,\n","       2, 2, 2, 0, 3, 3, 2, 2, 2, 0, 2, 2, 0, 2, 3, 2, 3, 2, 2, 2, 2, 3,\n","       0, 0, 3, 0, 2, 2, 3, 2, 2, 0, 2, 0, 0, 3, 3, 0, 0, 2, 2, 0, 3, 3,\n","       3, 3, 2, 3, 2, 0, 2, 2, 2, 2, 0, 0, 2, 3, 2, 3, 2, 2, 2, 0, 3, 3,\n","       2, 0, 2, 2, 2, 2, 3, 2, 0, 3, 2, 2, 2, 0, 2, 2, 2, 2, 3, 3, 2, 2,\n","       2, 2, 2, 0, 3, 0, 0, 2, 0, 2, 2, 2, 2, 3, 0, 2, 2, 2, 3, 3, 2, 3,\n","       2, 2, 0, 2, 2, 3, 3, 0, 2, 3, 2, 3, 2, 3, 2, 0, 2, 2, 0, 0, 3, 3,\n","       2, 2, 2, 2])"]},"metadata":{},"execution_count":91}]},{"cell_type":"code","source":["ovr_sgd_clf.predict([X_test.iloc[0]])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EX2ajZaDs-gw","executionInfo":{"status":"ok","timestamp":1675738846979,"user_tz":-540,"elapsed":41,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"9993a6b7-7ef1-48fb-8c13-4c3fd25bfe51"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but SGDClassifier was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but SGDClassifier was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but SGDClassifier was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but SGDClassifier was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"execute_result","data":{"text/plain":["array([0])"]},"metadata":{},"execution_count":92}]},{"cell_type":"code","source":["print(classification_report(y_test,y_pred_sgd))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UV9RCFLorQOy","executionInfo":{"status":"ok","timestamp":1675738846979,"user_tz":-540,"elapsed":36,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"c067e60b-b88d-472a-83f9-a90678c1655b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.90      0.72      0.80       105\n","           1       0.00      0.00      0.00        91\n","           2       0.31      0.75      0.44        92\n","           3       0.73      0.62      0.67       112\n","\n","    accuracy                           0.54       400\n","   macro avg       0.49      0.52      0.48       400\n","weighted avg       0.51      0.54      0.50       400\n","\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n"]}]},{"cell_type":"markdown","source":["* there is no sample classified as label 1"],"metadata":{"id":"g-RxBjeaupaa"}},{"cell_type":"markdown","source":["## 3. SVM"],"metadata":{"id":"6O-2kITiuy1T"}},{"cell_type":"code","source":["ovr_svc_clf = SVC(random_state=42)\n","ovr_svc_clf.fit(X_train, y_train)\n","\n","y_pred_svc = ovr_svc_clf.predict(X_test)"],"metadata":{"id":"Z9-9WqWBrUKH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(classification_report(y_test,y_pred_svc))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OXc8G01ivJWP","executionInfo":{"status":"ok","timestamp":1675738846980,"user_tz":-540,"elapsed":30,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"21f99390-ef6b-49f2-e23d-d31bb159d7d3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.99      0.98      0.99       105\n","           1       0.95      0.99      0.97        91\n","           2       0.94      0.95      0.94        92\n","           3       0.98      0.95      0.96       112\n","\n","    accuracy                           0.96       400\n","   macro avg       0.96      0.97      0.96       400\n","weighted avg       0.97      0.96      0.97       400\n","\n"]}]},{"cell_type":"code","source":["print(confusion_matrix(y_test, y_pred_svc))\n","print(f'accuracy: {accuracy_score(y_test,y_pred_svc): .4f}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ziGzq5YFvMsy","executionInfo":{"status":"ok","timestamp":1675738846981,"user_tz":-540,"elapsed":23,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"4f646b3b-b344-4bb4-cf9c-747af349b41d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[103   2   0   0]\n"," [  1  90   0   0]\n"," [  0   3  87   2]\n"," [  0   0   6 106]]\n","accuracy:  0.9650\n"]}]},{"cell_type":"markdown","source":["* There is no sample incorrectly classified **as 2 or 3 from 0 and vice versa**\n","=> quite good classification"],"metadata":{"id":"TSQwZCpvvoCB"}},{"cell_type":"markdown","source":["## 4. RandomForestClassifier"],"metadata":{"id":"XcgWlUyGwZV9"}},{"cell_type":"code","source":["forest_clf = RandomForestClassifier(random_state=42, n_jobs=-1)\n","forest_clf.fit(X_train, y_train)\n","\n","y_pred_forest = forest_clf.predict(X_test)"],"metadata":{"id":"aqsHEmDsvcRg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(classification_report(y_test, y_pred_forest))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ll9g-FXkw0lK","executionInfo":{"status":"ok","timestamp":1675738847276,"user_tz":-540,"elapsed":18,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"f4016381-ead4-48f0-8611-2c1010eeb132"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.95      0.96      0.96       105\n","           1       0.89      0.87      0.88        91\n","           2       0.78      0.87      0.82        92\n","           3       0.94      0.87      0.90       112\n","\n","    accuracy                           0.89       400\n","   macro avg       0.89      0.89      0.89       400\n","weighted avg       0.90      0.89      0.89       400\n","\n"]}]},{"cell_type":"code","source":["print(confusion_matrix(y_test, y_pred_forest))\n","print(f'accuracy: {accuracy_score(y_test, y_pred_forest): .4f}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aaW9gpJ7w4dh","executionInfo":{"status":"ok","timestamp":1675738847276,"user_tz":-540,"elapsed":14,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"483bae08-9f8d-48b5-e237-ed746e66c8cd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[101   4   0   0]\n"," [  5  79   7   0]\n"," [  0   6  80   6]\n"," [  0   0  15  97]]\n","accuracy:  0.8925\n"]}]},{"cell_type":"markdown","source":["* There is no sample incorrectly classified as 2 or 3 from 0 and vice versa => quite good classification"],"metadata":{"id":"U_XIVGWbxITZ"}},{"cell_type":"markdown","source":["# Hyperparameter tuning"],"metadata":{"id":"9kuWgMMnxNb3"}},{"cell_type":"markdown","source":["* I would compare SVM and RandomForestClassifier."],"metadata":{"id":"JQztRgLvxc1U"}},{"cell_type":"code","source":["# SVM\n","\n","param_grid = {\n","    'C':[1e-3,1e-2,1e-1,1,1e+1,1e+2,1e+3],\n","}\n","\n","grid_svm = GridSearchCV(SVC(random_state=42), param_grid, cv=5, verbose=3, n_jobs=-1)\n","grid_svm.fit(X_train, y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6fPl3YaexbRJ","executionInfo":{"status":"ok","timestamp":1675738849116,"user_tz":-540,"elapsed":1850,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"31dd192c-91ab-4ea7-d03e-f809c07943d6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 7 candidates, totalling 35 fits\n"]},{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=5, estimator=SVC(random_state=42), n_jobs=-1,\n","             param_grid={'C': [0.001, 0.01, 0.1, 1, 10.0, 100.0, 1000.0]},\n","             verbose=3)"]},"metadata":{},"execution_count":100}]},{"cell_type":"code","source":["grid_svm.cv_results_"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qP_aj5-p9D0W","executionInfo":{"status":"ok","timestamp":1675738849117,"user_tz":-540,"elapsed":61,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"807c234c-3aa6-4b22-f9da-ce53066c5ae1"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'mean_fit_time': array([0.15380406, 0.14509802, 0.08756924, 0.05872455, 0.0396172 ,\n","        0.03974981, 0.02980723]),\n"," 'std_fit_time': array([0.00916998, 0.01150697, 0.01877344, 0.00822615, 0.00439316,\n","        0.01402107, 0.00373402]),\n"," 'mean_score_time': array([0.03433571, 0.04109702, 0.03992658, 0.03329005, 0.0186923 ,\n","        0.01341214, 0.00856385]),\n"," 'std_score_time': array([0.00183874, 0.00904759, 0.01631592, 0.00280518, 0.00253309,\n","        0.00620307, 0.00428568]),\n"," 'param_C': masked_array(data=[0.001, 0.01, 0.1, 1, 10.0, 100.0, 1000.0],\n","              mask=[False, False, False, False, False, False, False],\n","        fill_value='?',\n","             dtype=object),\n"," 'params': [{'C': 0.001},\n","  {'C': 0.01},\n","  {'C': 0.1},\n","  {'C': 1},\n","  {'C': 10.0},\n","  {'C': 100.0},\n","  {'C': 1000.0}],\n"," 'split0_test_score': array([0.25625 , 0.353125, 0.88125 , 0.953125, 0.95625 , 0.96875 ,\n","        0.9625  ]),\n"," 'split1_test_score': array([0.25625 , 0.340625, 0.9125  , 0.946875, 0.9625  , 0.975   ,\n","        0.965625]),\n"," 'split2_test_score': array([0.428125, 0.453125, 0.878125, 0.959375, 0.971875, 0.965625,\n","        0.971875]),\n"," 'split3_test_score': array([0.403125, 0.421875, 0.896875, 0.953125, 0.9375  , 0.953125,\n","        0.95625 ]),\n"," 'split4_test_score': array([0.253125, 0.3375  , 0.896875, 0.946875, 0.9625  , 0.978125,\n","        0.971875]),\n"," 'mean_test_score': array([0.319375, 0.38125 , 0.893125, 0.951875, 0.958125, 0.968125,\n","        0.965625]),\n"," 'std_test_score': array([0.07899268, 0.04726918, 0.0124059 , 0.00467707, 0.01145644,\n","        0.00870524, 0.00592927]),\n"," 'rank_test_score': array([7, 6, 5, 4, 3, 1, 2], dtype=int32)}"]},"metadata":{},"execution_count":101}]},{"cell_type":"code","source":["grid_svm.classes_"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DO77vRKK9Xgo","executionInfo":{"status":"ok","timestamp":1675738849117,"user_tz":-540,"elapsed":46,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"720d6491-3328-46db-d529-d6327baf95d9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 1, 2, 3])"]},"metadata":{},"execution_count":102}]},{"cell_type":"code","source":["print(f'accuracy: {grid_svm.best_score_: .4f}')\n","print(f'best estimator: {grid_svm.best_estimator_}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0B5BqM849vVF","executionInfo":{"status":"ok","timestamp":1675738849118,"user_tz":-540,"elapsed":38,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"58d507bb-9b6d-47d4-838b-83e59cfbb868"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["accuracy:  0.9681\n","best estimator: SVC(C=100.0, random_state=42)\n"]}]},{"cell_type":"code","source":["best_svm_clf = SVC(C=100.0, random_state=42)\n","best_svm_clf.fit(X_train, y_train)\n","\n","y_pred_svm_best = best_svm_clf.predict(X_test)"],"metadata":{"id":"MZIj2e6u9zda"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(classification_report(y_test, y_pred_svm_best))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qJ8k9h-v-7Ba","executionInfo":{"status":"ok","timestamp":1675738849121,"user_tz":-540,"elapsed":35,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"c37ef42b-99f8-4e30-8720-c1c9cd011052"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       1.00      0.99      1.00       105\n","           1       0.97      1.00      0.98        91\n","           2       0.97      0.98      0.97        92\n","           3       1.00      0.97      0.99       112\n","\n","    accuracy                           0.98       400\n","   macro avg       0.98      0.99      0.98       400\n","weighted avg       0.99      0.98      0.99       400\n","\n"]}]},{"cell_type":"code","source":["print(confusion_matrix(y_test, y_pred_svm_best))\n","print(f'accuracy: {accuracy_score(y_test, y_pred_svm_best): .4f}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6rkolLa0--tD","executionInfo":{"status":"ok","timestamp":1675738849121,"user_tz":-540,"elapsed":26,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"cd8e4903-ffa3-407f-a066-7cd9a4cb7270"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[104   1   0   0]\n"," [  0  91   0   0]\n"," [  0   2  90   0]\n"," [  0   0   3 109]]\n","accuracy:  0.9850\n"]}]},{"cell_type":"markdown","source":["* 96.50% -> 98.50%\n","* accuracy has increased markedly after hyperparameter tuning."],"metadata":{"id":"9kUec4n4_fW1"}},{"cell_type":"code","source":["max_depth = [x for x in np.arange(10,110,10)]\n","max_depth.append(None)"],"metadata":{"id":"Itdt4iNaBC0b"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# RandomForestClassifier\n","\n","# for dealing with not showing verbose message error\n","from joblib import parallel_backend\n","\n","param_distib = {\n","    'n_estimators':np.arange(100,2000,200),\n","    'max_depth':max_depth,\n","    'min_samples_split':[2,5,10],\n","    'min_samples_leaf':[1,2,4],\n","    'bootstrap':[True,False]\n","}\n","\n","rnd_forest_clf = RandomizedSearchCV(RandomForestClassifier(random_state=42),\n","                                    param_distib, n_iter=100, cv=5, verbose=10, n_jobs=-1)\n","\n","with parallel_backend('multiprocessing'):\n","  rnd_forest_clf.fit(X_train, y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IlYPkEPM_OSz","executionInfo":{"status":"ok","timestamp":1675739939710,"user_tz":-540,"elapsed":161242,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"db2009f8-ae30-4abb-f151-43002ad44c54"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[CV 1/5; 1/100] START bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1500\n","[CV 2/5; 1/100] START bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1500\n","Fitting 5 folds for each of 100 candidates, totalling 500 fits\n","[CV 1/5; 1/100] END bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1500;, score=0.884 total time=   7.8s\n","[CV 3/5; 1/100] START bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1500\n","[CV 2/5; 1/100] END bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1500;, score=0.900 total time=   8.3s\n","[CV 4/5; 1/100] START bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1500\n","[CV 3/5; 1/100] END bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1500;, score=0.881 total time=   7.5s\n","[CV 5/5; 1/100] START bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1500\n","[CV 4/5; 1/100] END bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1500;, score=0.856 total time=   7.7s\n","[CV 1/5; 2/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1700\n","[CV 5/5; 1/100] END bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1500;, score=0.847 total time=   5.8s\n","[CV 2/5; 2/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1700\n","[CV 1/5; 2/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1700;, score=0.878 total time=   7.0s\n","[CV 3/5; 2/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1700\n","[CV 2/5; 2/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1700;, score=0.912 total time=   8.6s\n","[CV 4/5; 2/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1700\n","[CV 3/5; 2/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1700;, score=0.884 total time=   8.1s\n","[CV 5/5; 2/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1700\n","[CV 4/5; 2/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1700;, score=0.859 total time=   6.7s\n","[CV 1/5; 3/100] START bootstrap=True, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=500\n","[CV 5/5; 2/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1700;, score=0.847 total time=   6.8s\n","[CV 2/5; 3/100] START bootstrap=True, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=500\n","[CV 1/5; 3/100] END bootstrap=True, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=500;, score=0.872 total time=   1.5s\n","[CV 3/5; 3/100] START bootstrap=True, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=500\n","[CV 2/5; 3/100] END bootstrap=True, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=500;, score=0.903 total time=   1.9s\n","[CV 4/5; 3/100] START bootstrap=True, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=500\n","[CV 3/5; 3/100] END bootstrap=True, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=500;, score=0.878 total time=   1.9s\n","[CV 5/5; 3/100] START bootstrap=True, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=500\n","[CV 4/5; 3/100] END bootstrap=True, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=500;, score=0.844 total time=   2.8s\n","[CV 1/5; 4/100] START bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=700\n","[CV 5/5; 3/100] END bootstrap=True, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=500;, score=0.850 total time=   2.7s\n","[CV 2/5; 4/100] START bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=700\n","[CV 2/5; 4/100] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=700;, score=0.906 total time=   2.6s\n","[CV 3/5; 4/100] START bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=700\n","[CV 1/5; 4/100] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=700;, score=0.887 total time=   2.7s\n","[CV 4/5; 4/100] START bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=700\n","[CV 3/5; 4/100] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=700;, score=0.875 total time=   2.6s\n","[CV 5/5; 4/100] START bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=700\n","[CV 4/5; 4/100] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=700;, score=0.841 total time=   2.6s\n","[CV 1/5; 5/100] START bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n","[CV 1/5; 5/100] END bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=0.872 total time=   0.9s\n","[CV 2/5; 5/100] START bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n","[CV 2/5; 5/100] END bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=0.900 total time=   0.9s\n","[CV 3/5; 5/100] START bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n","[CV 5/5; 4/100] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=700;, score=0.847 total time=   2.7s\n","[CV 4/5; 5/100] START bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n","[CV 3/5; 5/100] END bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=0.881 total time=   0.9s\n","[CV 5/5; 5/100] START bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n","[CV 4/5; 5/100] END bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=0.844 total time=   0.9s\n","[CV 1/5; 6/100] START bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=1700\n","[CV 5/5; 5/100] END bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=0.850 total time=   0.9s\n","[CV 2/5; 6/100] START bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=1700\n","[CV 1/5; 6/100] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=1700;, score=0.872 total time=   7.1s\n","[CV 3/5; 6/100] START bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=1700\n","[CV 2/5; 6/100] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=1700;, score=0.900 total time=   7.1s\n","[CV 4/5; 6/100] START bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=1700\n","[CV 3/5; 6/100] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=1700;, score=0.878 total time=   5.4s\n","[CV 5/5; 6/100] START bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=1700\n","[CV 4/5; 6/100] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=1700;, score=0.847 total time=   5.6s\n","[CV 1/5; 7/100] START bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n","[CV 1/5; 7/100] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=0.856 total time=   0.9s\n","[CV 2/5; 7/100] START bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n","[CV 2/5; 7/100] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=0.903 total time=   1.7s\n","[CV 3/5; 7/100] START bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n","[CV 3/5; 7/100] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=0.884 total time=   1.8s\n","[CV 4/5; 7/100] START bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n","[CV 4/5; 7/100] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=0.838 total time=   1.0s\n","[CV 5/5; 7/100] START bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n","[CV 5/5; 7/100] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=0.838 total time=   0.9s\n","[CV 1/5; 8/100] START bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=1900\n","[CV 5/5; 6/100] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=1700;, score=0.856 total time=   7.2s\n","[CV 2/5; 8/100] START bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=1900\n","[CV 1/5; 8/100] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=1900;, score=0.881 total time=   7.3s\n","[CV 3/5; 8/100] START bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=1900\n","[CV 2/5; 8/100] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=1900;, score=0.909 total time=   7.2s\n","[CV 4/5; 8/100] START bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=1900\n","[CV 3/5; 8/100] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=1900;, score=0.875 total time=   8.8s\n","[CV 5/5; 8/100] START bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=1900\n","[CV 4/5; 8/100] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=1900;, score=0.844 total time=   8.6s\n","[CV 1/5; 9/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 5/5; 8/100] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=1900;, score=0.847 total time=   8.5s\n","[CV 2/5; 9/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 1/5; 9/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.881 total time=   8.4s\n","[CV 3/5; 9/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 2/5; 9/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.909 total time=   6.8s\n","[CV 3/5; 9/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.878 total time=   6.6s\n","[CV 4/5; 9/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 5/5; 9/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 4/5; 9/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.856 total time=   8.1s\n","[CV 1/5; 10/100] START bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=900\n","[CV 5/5; 9/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.853 total time=   8.2s\n","[CV 2/5; 10/100] START bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=900\n","[CV 1/5; 10/100] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=900;, score=0.863 total time=   2.8s\n","[CV 3/5; 10/100] START bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=900\n","[CV 2/5; 10/100] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=900;, score=0.903 total time=   2.7s\n","[CV 4/5; 10/100] START bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=900\n","[CV 4/5; 10/100] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=900;, score=0.841 total time=   2.7s\n","[CV 5/5; 10/100] START bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=900[CV 3/5; 10/100] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=900;, score=0.884 total time=   2.8s\n","\n","[CV 1/5; 11/100] START bootstrap=False, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1500\n","[CV 5/5; 10/100] END bootstrap=True, max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=900;, score=0.838 total time=   2.8s\n","[CV 2/5; 11/100] START bootstrap=False, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1500\n","[CV 1/5; 11/100] END bootstrap=False, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1500;, score=0.887 total time=   7.4s\n","[CV 3/5; 11/100] START bootstrap=False, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1500\n","[CV 2/5; 11/100] END bootstrap=False, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1500;, score=0.909 total time=   7.1s\n","[CV 4/5; 11/100] START bootstrap=False, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1500\n","[CV 3/5; 11/100] END bootstrap=False, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1500;, score=0.875 total time=   5.6s\n","[CV 5/5; 11/100] START bootstrap=False, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1500\n","[CV 4/5; 11/100] END bootstrap=False, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1500;, score=0.844 total time=   5.7s\n","[CV 1/5; 12/100] START bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=900\n","[CV 1/5; 12/100] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=900;, score=0.878 total time=   4.0s\n","[CV 2/5; 12/100] START bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=900\n","[CV 5/5; 11/100] END bootstrap=False, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1500;, score=0.847 total time=   7.2s\n","[CV 3/5; 12/100] START bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=900\n","[CV 2/5; 12/100] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=900;, score=0.897 total time=   2.7s\n","[CV 4/5; 12/100] START bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=900\n","[CV 3/5; 12/100] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=900;, score=0.878 total time=   2.7s\n","[CV 5/5; 12/100] START bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=900\n","[CV 4/5; 12/100] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=900;, score=0.831 total time=   2.8s\n","[CV 1/5; 13/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1100\n","[CV 5/5; 12/100] END bootstrap=True, max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=900;, score=0.838 total time=   2.8s\n","[CV 2/5; 13/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1100\n","[CV 1/5; 13/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1100;, score=0.881 total time=   4.1s\n","[CV 3/5; 13/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1100\n","[CV 2/5; 13/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1100;, score=0.903 total time=   4.0s\n","[CV 4/5; 13/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1100\n","[CV 4/5; 13/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1100;, score=0.847 total time=   5.3s\n","[CV 5/5; 13/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1100\n","[CV 3/5; 13/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1100;, score=0.875 total time=   5.9s\n","[CV 1/5; 14/100] START bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=900\n","[CV 1/5; 14/100] END bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=900;, score=0.875 total time=   3.3s\n","[CV 2/5; 14/100] START bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=900\n","[CV 5/5; 13/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1100;, score=0.847 total time=   4.0s\n","[CV 3/5; 14/100] START bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=900\n","[CV 2/5; 14/100] END bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=900;, score=0.906 total time=   3.3s\n","[CV 4/5; 14/100] START bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=900\n","[CV 3/5; 14/100] END bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=900;, score=0.872 total time=   3.3s\n","[CV 5/5; 14/100] START bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=900\n","[CV 4/5; 14/100] END bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=900;, score=0.850 total time=   4.9s\n","[CV 1/5; 15/100] START bootstrap=False, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=1300\n","[CV 5/5; 14/100] END bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=2, n_estimators=900;, score=0.847 total time=   4.6s\n","[CV 2/5; 15/100] START bootstrap=False, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=1300\n","[CV 1/5; 15/100] END bootstrap=False, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=1300;, score=0.887 total time=   5.2s\n","[CV 3/5; 15/100] START bootstrap=False, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=1300\n","[CV 2/5; 15/100] END bootstrap=False, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=1300;, score=0.909 total time=   5.0s\n","[CV 4/5; 15/100] START bootstrap=False, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=1300\n","[CV 3/5; 15/100] END bootstrap=False, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=1300;, score=0.875 total time=   4.8s\n","[CV 5/5; 15/100] START bootstrap=False, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=1300\n","[CV 4/5; 15/100] END bootstrap=False, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=1300;, score=0.841 total time=   4.9s\n","[CV 1/5; 16/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=1900\n","[CV 5/5; 15/100] END bootstrap=False, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=1300;, score=0.850 total time=   6.5s\n","[CV 2/5; 16/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=1900\n","[CV 1/5; 16/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=1900;, score=0.881 total time=   9.5s\n","[CV 3/5; 16/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=1900\n","[CV 2/5; 16/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=1900;, score=0.909 total time=   8.1s\n","[CV 4/5; 16/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=1900\n","[CV 3/5; 16/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=1900;, score=0.881 total time=   9.4s\n","[CV 5/5; 16/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=1900\n","[CV 4/5; 16/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=1900;, score=0.863 total time=   9.4s\n","[CV 1/5; 17/100] START bootstrap=False, max_depth=60, min_samples_leaf=2, min_samples_split=10, n_estimators=1300\n","[CV 5/5; 16/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=1900;, score=0.856 total time=   7.6s\n","[CV 2/5; 17/100] START bootstrap=False, max_depth=60, min_samples_leaf=2, min_samples_split=10, n_estimators=1300\n","[CV 1/5; 17/100] END bootstrap=False, max_depth=60, min_samples_leaf=2, min_samples_split=10, n_estimators=1300;, score=0.887 total time=   6.0s\n","[CV 3/5; 17/100] START bootstrap=False, max_depth=60, min_samples_leaf=2, min_samples_split=10, n_estimators=1300\n","[CV 2/5; 17/100] END bootstrap=False, max_depth=60, min_samples_leaf=2, min_samples_split=10, n_estimators=1300;, score=0.909 total time=   6.4s\n","[CV 4/5; 17/100] START bootstrap=False, max_depth=60, min_samples_leaf=2, min_samples_split=10, n_estimators=1300\n","[CV 3/5; 17/100] END bootstrap=False, max_depth=60, min_samples_leaf=2, min_samples_split=10, n_estimators=1300;, score=0.875 total time=   5.2s\n","[CV 5/5; 17/100] START bootstrap=False, max_depth=60, min_samples_leaf=2, min_samples_split=10, n_estimators=1300\n","[CV 4/5; 17/100] END bootstrap=False, max_depth=60, min_samples_leaf=2, min_samples_split=10, n_estimators=1300;, score=0.841 total time=   4.9s\n","[CV 1/5; 18/100] START bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=700\n","[CV 5/5; 17/100] END bootstrap=False, max_depth=60, min_samples_leaf=2, min_samples_split=10, n_estimators=1300;, score=0.850 total time=   4.8s\n","[CV 2/5; 18/100] START bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=700\n","[CV 1/5; 18/100] END bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=700;, score=0.866 total time=   2.2s\n","[CV 3/5; 18/100] START bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=700\n","[CV 2/5; 18/100] END bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=700;, score=0.897 total time=   3.3s\n","[CV 4/5; 18/100] START bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=700\n","[CV 3/5; 18/100] END bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=700;, score=0.869 total time=   3.6s\n","[CV 5/5; 18/100] START bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=700\n","[CV 4/5; 18/100] END bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=700;, score=0.856 total time=   2.4s\n","[CV 1/5; 19/100] START bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 5/5; 18/100] END bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=700;, score=0.856 total time=   2.2s\n","[CV 2/5; 19/100] START bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 1/5; 19/100] END bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.875 total time=   5.5s\n","[CV 3/5; 19/100] START bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 2/5; 19/100] END bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.903 total time=   5.4s\n","[CV 4/5; 19/100] START bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 4/5; 19/100] END bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.850 total time=   6.9s\n","[CV 5/5; 19/100] START bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 3/5; 19/100] END bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.881 total time=   7.2s\n","[CV 1/5; 20/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1300\n","[CV 1/5; 20/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1300;, score=0.875 total time=   4.2s\n","[CV 2/5; 20/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1300\n","[CV 5/5; 19/100] END bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.859 total time=   5.5s\n","[CV 3/5; 20/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1300\n","[CV 2/5; 20/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1300;, score=0.903 total time=   4.3s\n","[CV 4/5; 20/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1300\n","[CV 3/5; 20/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1300;, score=0.881 total time=   5.2s\n","[CV 5/5; 20/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1300\n","[CV 4/5; 20/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1300;, score=0.844 total time=   5.8s\n","[CV 1/5; 21/100] START bootstrap=False, max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=1500\n","[CV 5/5; 20/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1300;, score=0.856 total time=   4.9s\n","[CV 2/5; 21/100] START bootstrap=False, max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=1500\n","[CV 1/5; 21/100] END bootstrap=False, max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=1500;, score=0.878 total time=   5.6s\n","[CV 3/5; 21/100] START bootstrap=False, max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=1500\n","[CV 2/5; 21/100] END bootstrap=False, max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=1500;, score=0.906 total time=   5.4s\n","[CV 4/5; 21/100] START bootstrap=False, max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=1500\n","[CV 3/5; 21/100] END bootstrap=False, max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=1500;, score=0.872 total time=   6.9s\n","[CV 5/5; 21/100] START bootstrap=False, max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=1500\n","[CV 4/5; 21/100] END bootstrap=False, max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=1500;, score=0.853 total time=   7.1s\n","[CV 1/5; 22/100] START bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=900\n","[CV 1/5; 22/100] END bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=900;, score=0.875 total time=   2.7s\n","[CV 2/5; 22/100] START bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=900\n","[CV 5/5; 21/100] END bootstrap=False, max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=1500;, score=0.850 total time=   5.5s\n","[CV 3/5; 22/100] START bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=900\n","[CV 2/5; 22/100] END bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=900;, score=0.897 total time=   2.8s\n","[CV 4/5; 22/100] START bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=900\n","[CV 3/5; 22/100] END bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=900;, score=0.875 total time=   2.8s\n","[CV 5/5; 22/100] START bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=900\n","[CV 4/5; 22/100] END bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=900;, score=0.841 total time=   3.3s\n","[CV 1/5; 23/100] START bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=500\n","[CV 1/5; 23/100] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=500;, score=0.869 total time=   2.5s\n","[CV 2/5; 23/100] START bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=500\n","[CV 5/5; 22/100] END bootstrap=True, max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=900;, score=0.847 total time=   4.5s\n","[CV 3/5; 23/100] START bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=500\n","[CV 2/5; 23/100] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=500;, score=0.897 total time=   1.6s\n","[CV 4/5; 23/100] START bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=500\n","[CV 3/5; 23/100] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=500;, score=0.878 total time=   1.6s\n","[CV 5/5; 23/100] START bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=500\n","[CV 4/5; 23/100] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=500;, score=0.844 total time=   1.6s\n","[CV 1/5; 24/100] START bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1300\n","[CV 5/5; 23/100] END bootstrap=True, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=500;, score=0.838 total time=   1.6s\n","[CV 2/5; 24/100] START bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1300\n","[CV 2/5; 24/100] END bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1300;, score=0.903 total time=   4.0s\n","[CV 3/5; 24/100] START bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1300\n","[CV 1/5; 24/100] END bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1300;, score=0.872 total time=   4.3s\n","[CV 4/5; 24/100] START bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1300\n","[CV 4/5; 24/100] END bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1300;, score=0.847 total time=   5.2s\n","[CV 5/5; 24/100] START bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1300\n","[CV 3/5; 24/100] END bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1300;, score=0.881 total time=   5.7s\n","[CV 1/5; 25/100] START bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=700\n","[CV 1/5; 25/100] END bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=700;, score=0.875 total time=   2.4s\n","[CV 2/5; 25/100] START bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=700\n","[CV 5/5; 24/100] END bootstrap=True, max_depth=80, min_samples_leaf=2, min_samples_split=10, n_estimators=1300;, score=0.850 total time=   4.4s\n","[CV 3/5; 25/100] START bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=700\n","[CV 2/5; 25/100] END bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=700;, score=0.900 total time=   2.3s\n","[CV 4/5; 25/100] START bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=700\n","[CV 3/5; 25/100] END bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=700;, score=0.887 total time=   2.4s\n","[CV 5/5; 25/100] START bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=700\n","[CV 4/5; 25/100] END bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=700;, score=0.847 total time=   2.4s\n","[CV 1/5; 26/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=500\n","[CV 5/5; 25/100] END bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=700;, score=0.856 total time=   2.3s\n","[CV 2/5; 26/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=500\n","[CV 1/5; 26/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=500;, score=0.875 total time=   2.0s\n","[CV 3/5; 26/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=500\n","[CV 2/5; 26/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=500;, score=0.903 total time=   2.3s\n","[CV 4/5; 26/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=500\n","[CV 3/5; 26/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=500;, score=0.884 total time=   2.7s\n","[CV 5/5; 26/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=500\n","[CV 4/5; 26/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=500;, score=0.850 total time=   3.0s\n","[CV 1/5; 27/100] START bootstrap=True, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n","[CV 5/5; 26/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=500;, score=0.853 total time=   2.8s\n","[CV 2/5; 27/100] START bootstrap=True, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n","[CV 1/5; 27/100] END bootstrap=True, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=0.866 total time=   1.1s\n","[CV 3/5; 27/100] START bootstrap=True, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n","[CV 2/5; 27/100] END bootstrap=True, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=0.903 total time=   1.1s\n","[CV 4/5; 27/100] START bootstrap=True, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n","[CV 3/5; 27/100] END bootstrap=True, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=0.875 total time=   0.9s\n","[CV 5/5; 27/100] START bootstrap=True, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n","[CV 4/5; 27/100] END bootstrap=True, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=0.841 total time=   0.9s\n","[CV 1/5; 28/100] START bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=10, n_estimators=1300\n","[CV 5/5; 27/100] END bootstrap=True, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=0.847 total time=   1.0s\n","[CV 2/5; 28/100] START bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=10, n_estimators=1300\n","[CV 1/5; 28/100] END bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=10, n_estimators=1300;, score=0.869 total time=   4.3s\n","[CV 3/5; 28/100] START bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=10, n_estimators=1300\n","[CV 2/5; 28/100] END bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=10, n_estimators=1300;, score=0.903 total time=   4.2s\n","[CV 4/5; 28/100] START bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=10, n_estimators=1300\n","[CV 3/5; 28/100] END bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=10, n_estimators=1300;, score=0.887 total time=   5.7s\n","[CV 5/5; 28/100] START bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=10, n_estimators=1300\n","[CV 4/5; 28/100] END bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=10, n_estimators=1300;, score=0.838 total time=   5.6s\n","[CV 1/5; 29/100] START bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=1900\n","[CV 5/5; 28/100] END bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=10, n_estimators=1300;, score=0.853 total time=   4.2s\n","[CV 2/5; 29/100] START bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=1900\n","[CV 1/5; 29/100] END bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=1900;, score=0.869 total time=   5.9s\n","[CV 3/5; 29/100] START bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=1900\n","[CV 2/5; 29/100] END bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=1900;, score=0.903 total time=   6.0s\n","[CV 4/5; 29/100] START bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=1900\n","[CV 3/5; 29/100] END bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=1900;, score=0.878 total time=   7.4s\n","[CV 5/5; 29/100] START bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=1900\n","[CV 4/5; 29/100] END bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=1900;, score=0.841 total time=   7.2s\n","[CV 1/5; 30/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=1300\n","[CV 5/5; 29/100] END bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=1900;, score=0.847 total time=   5.9s\n","[CV 2/5; 30/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=1300\n","[CV 1/5; 30/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=1300;, score=0.866 total time=   4.1s\n","[CV 3/5; 30/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=1300\n","[CV 2/5; 30/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=1300;, score=0.897 total time=   4.3s\n","[CV 4/5; 30/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=1300\n","[CV 3/5; 30/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=1300;, score=0.878 total time=   5.6s\n","[CV 5/5; 30/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=1300\n","[CV 4/5; 30/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=1300;, score=0.841 total time=   5.2s\n","[CV 1/5; 31/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=1500\n","[CV 5/5; 30/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=1300;, score=0.847 total time=   4.1s\n","[CV 2/5; 31/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=1500\n","[CV 1/5; 31/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=1500;, score=0.872 total time=   4.9s\n","[CV 3/5; 31/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=1500\n","[CV 2/5; 31/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=1500;, score=0.897 total time=   5.0s\n","[CV 4/5; 31/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=1500\n","[CV 3/5; 31/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=1500;, score=0.875 total time=   6.3s\n","[CV 5/5; 31/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=1500\n","[CV 4/5; 31/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=1500;, score=0.850 total time=   6.5s\n","[CV 1/5; 32/100] START bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=1500\n","[CV 5/5; 31/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=1500;, score=0.856 total time=   5.1s\n","[CV 2/5; 32/100] START bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=1500\n","[CV 1/5; 32/100] END bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=1500;, score=0.872 total time=   4.9s\n","[CV 3/5; 32/100] START bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=1500\n","[CV 2/5; 32/100] END bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=1500;, score=0.903 total time=   5.5s\n","[CV 4/5; 32/100] START bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=1500\n","[CV 3/5; 32/100] END bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=1500;, score=0.881 total time=   6.6s\n","[CV 5/5; 32/100] START bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=1500\n","[CV 4/5; 32/100] END bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=1500;, score=0.850 total time=   5.6s\n","[CV 1/5; 33/100] START bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=1700\n","[CV 5/5; 32/100] END bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=1500;, score=0.859 total time=   4.9s\n","[CV 2/5; 33/100] START bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=1700\n","[CV 1/5; 33/100] END bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=1700;, score=0.875 total time=   6.7s\n","[CV 3/5; 33/100] START bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=1700\n","[CV 2/5; 33/100] END bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=1700;, score=0.906 total time=   7.7s\n","[CV 4/5; 33/100] START bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=1700\n","[CV 3/5; 33/100] END bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=1700;, score=0.872 total time=   7.3s\n","[CV 5/5; 33/100] START bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=1700\n","[CV 4/5; 33/100] END bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=1700;, score=0.850 total time=   6.3s\n","[CV 1/5; 34/100] START bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=10, n_estimators=1900\n","[CV 5/5; 33/100] END bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=1700;, score=0.853 total time=   7.2s\n","[CV 2/5; 34/100] START bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=10, n_estimators=1900\n","[CV 1/5; 34/100] END bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=10, n_estimators=1900;, score=0.878 total time=   8.5s\n","[CV 3/5; 34/100] START bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=10, n_estimators=1900\n","[CV 2/5; 34/100] END bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=10, n_estimators=1900;, score=0.906 total time=   7.2s\n","[CV 4/5; 34/100] START bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=10, n_estimators=1900\n","[CV 3/5; 34/100] END bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=10, n_estimators=1900;, score=0.878 total time=   6.8s\n","[CV 5/5; 34/100] START bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=10, n_estimators=1900\n","[CV 4/5; 34/100] END bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=10, n_estimators=1900;, score=0.834 total time=   8.4s\n","[CV 1/5; 35/100] START bootstrap=True, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=500\n","[CV 1/5; 35/100] END bootstrap=True, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=500;, score=0.869 total time=   1.6s\n","[CV 2/5; 35/100] START bootstrap=True, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=500\n","[CV 2/5; 35/100] END bootstrap=True, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=500;, score=0.897 total time=   1.6s\n","[CV 3/5; 35/100] START bootstrap=True, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=500\n","[CV 5/5; 34/100] END bootstrap=False, max_depth=80, min_samples_leaf=4, min_samples_split=10, n_estimators=1900;, score=0.853 total time=   8.5s\n","[CV 4/5; 35/100] START bootstrap=True, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=500\n","[CV 3/5; 35/100] END bootstrap=True, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=500;, score=0.878 total time=   1.6s\n","[CV 5/5; 35/100] START bootstrap=True, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=500\n","[CV 4/5; 35/100] END bootstrap=True, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=500;, score=0.844 total time=   1.6s\n","[CV 1/5; 36/100] START bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=10, n_estimators=1900\n","[CV 5/5; 35/100] END bootstrap=True, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=500;, score=0.838 total time=   1.6s\n","[CV 2/5; 36/100] START bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=10, n_estimators=1900\n","[CV 1/5; 36/100] END bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=10, n_estimators=1900;, score=0.872 total time=   7.5s\n","[CV 3/5; 36/100] START bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=10, n_estimators=1900\n","[CV 2/5; 36/100] END bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=10, n_estimators=1900;, score=0.897 total time=   7.4s\n","[CV 4/5; 36/100] START bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=10, n_estimators=1900\n","[CV 3/5; 36/100] END bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=10, n_estimators=1900;, score=0.884 total time=   5.9s\n","[CV 5/5; 36/100] START bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=10, n_estimators=1900\n","[CV 4/5; 36/100] END bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=10, n_estimators=1900;, score=0.847 total time=   5.9s\n","[CV 1/5; 37/100] START bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=700\n","[CV 1/5; 37/100] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=700;, score=0.872 total time=   2.2s\n","[CV 2/5; 37/100] START bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=700\n","[CV 2/5; 37/100] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=700;, score=0.897 total time=   3.8s\n","[CV 5/5; 36/100] END bootstrap=True, max_depth=90, min_samples_leaf=2, min_samples_split=10, n_estimators=1900;, score=0.844 total time=   7.4s\n","[CV 3/5; 37/100] START bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=700[CV 4/5; 37/100] START bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=700\n","\n","[CV 3/5; 37/100] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=700;, score=0.875 total time=   2.1s[CV 4/5; 37/100] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=700;, score=0.844 total time=   2.1s\n","\n","[CV 5/5; 37/100] START bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=700\n","[CV 1/5; 38/100] START bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n","[CV 1/5; 38/100] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.869 total time=   0.4s\n","[CV 2/5; 38/100] START bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n","[CV 2/5; 38/100] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.894 total time=   0.4s\n","[CV 3/5; 38/100] START bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n","[CV 3/5; 38/100] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.869 total time=   0.4s\n","[CV 4/5; 38/100] START bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n","[CV 4/5; 38/100] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.844 total time=   0.4s\n","[CV 5/5; 38/100] START bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n","[CV 5/5; 38/100] END bootstrap=False, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.838 total time=   0.4s\n","[CV 1/5; 39/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=700\n","[CV 5/5; 37/100] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=700;, score=0.850 total time=   2.2s\n","[CV 2/5; 39/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=700\n","[CV 1/5; 39/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=700;, score=0.872 total time=   2.2s\n","[CV 3/5; 39/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=700\n","[CV 2/5; 39/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=700;, score=0.897 total time=   2.2s\n","[CV 4/5; 39/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=700\n","[CV 3/5; 39/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=700;, score=0.875 total time=   2.1s\n","[CV 5/5; 39/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=700\n","[CV 4/5; 39/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=700;, score=0.844 total time=   2.1s\n","[CV 1/5; 40/100] START bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=900\n","[CV 5/5; 39/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=700;, score=0.850 total time=   3.5s\n","[CV 2/5; 40/100] START bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=900\n","[CV 1/5; 40/100] END bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=900;, score=0.881 total time=   4.9s\n","[CV 3/5; 40/100] START bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=900\n","[CV 2/5; 40/100] END bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=900;, score=0.900 total time=   3.9s\n","[CV 4/5; 40/100] START bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=900\n","[CV 3/5; 40/100] END bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=900;, score=0.878 total time=   3.4s\n","[CV 5/5; 40/100] START bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=900\n","[CV 4/5; 40/100] END bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=900;, score=0.856 total time=   3.5s\n","[CV 1/5; 41/100] START bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=5, n_estimators=500\n","[CV 5/5; 40/100] END bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=900;, score=0.853 total time=   3.5s\n","[CV 2/5; 41/100] START bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=5, n_estimators=500\n","[CV 1/5; 41/100] END bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=5, n_estimators=500;, score=0.872 total time=   1.6s\n","[CV 3/5; 41/100] START bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=5, n_estimators=500\n","[CV 2/5; 41/100] END bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=5, n_estimators=500;, score=0.903 total time=   1.5s\n","[CV 4/5; 41/100] START bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=5, n_estimators=500\n","[CV 3/5; 41/100] END bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=5, n_estimators=500;, score=0.878 total time=   1.6s\n","[CV 5/5; 41/100] START bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=5, n_estimators=500\n","[CV 4/5; 41/100] END bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=5, n_estimators=500;, score=0.844 total time=   2.4s\n","[CV 1/5; 42/100] START bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=900\n","[CV 5/5; 41/100] END bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=5, n_estimators=500;, score=0.850 total time=   2.5s\n","[CV 2/5; 42/100] START bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=900\n","[CV 1/5; 42/100] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=900;, score=0.869 total time=   4.0s\n","[CV 3/5; 42/100] START bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=900\n","[CV 2/5; 42/100] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=900;, score=0.909 total time=   3.7s\n","[CV 4/5; 42/100] START bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=900\n","[CV 3/5; 42/100] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=900;, score=0.875 total time=   3.2s\n","[CV 5/5; 42/100] START bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=900\n","[CV 4/5; 42/100] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=900;, score=0.838 total time=   3.3s\n","[CV 1/5; 43/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 5/5; 42/100] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=900;, score=0.850 total time=   3.3s\n","[CV 2/5; 43/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 1/5; 43/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.881 total time=   8.3s\n","[CV 3/5; 43/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 2/5; 43/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.909 total time=   8.0s\n","[CV 4/5; 43/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 3/5; 43/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.878 total time=   6.6s\n","[CV 5/5; 43/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 4/5; 43/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.856 total time=   7.3s\n","[CV 1/5; 44/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=900\n","[CV 1/5; 44/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=900;, score=0.878 total time=   4.4s\n","[CV 2/5; 44/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=900\n","[CV 5/5; 43/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.853 total time=   8.1s\n","[CV 3/5; 44/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=900\n","[CV 2/5; 44/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=900;, score=0.906 total time=   3.3s\n","[CV 4/5; 44/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=900\n","[CV 3/5; 44/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=900;, score=0.875 total time=   3.3s\n","[CV 5/5; 44/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=900\n","[CV 4/5; 44/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=900;, score=0.838 total time=   3.3s\n","[CV 1/5; 45/100] START bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=700\n","[CV 5/5; 44/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=900;, score=0.834 total time=   3.3s\n","[CV 2/5; 45/100] START bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=700\n","[CV 1/5; 45/100] END bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=700;, score=0.878 total time=   3.8s\n","[CV 3/5; 45/100] START bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=700\n","[CV 2/5; 45/100] END bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=700;, score=0.900 total time=   3.6s\n","[CV 4/5; 45/100] START bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=700\n","[CV 3/5; 45/100] END bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=700;, score=0.884 total time=   2.6s\n","[CV 5/5; 45/100] START bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=700\n","[CV 4/5; 45/100] END bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=700;, score=0.853 total time=   2.4s\n","[CV 1/5; 46/100] START bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=500\n","[CV 1/5; 46/100] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=500;, score=0.866 total time=   1.8s\n","[CV 2/5; 46/100] START bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=500\n","[CV 5/5; 45/100] END bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=700;, score=0.859 total time=   2.4s\n","[CV 3/5; 46/100] START bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=500\n","[CV 2/5; 46/100] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=500;, score=0.909 total time=   1.9s\n","[CV 4/5; 46/100] START bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=500\n","[CV 3/5; 46/100] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=500;, score=0.872 total time=   1.8s\n","[CV 5/5; 46/100] START bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=500\n","[CV 4/5; 46/100] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=500;, score=0.856 total time=   1.8s\n","[CV 1/5; 47/100] START bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=900\n","[CV 5/5; 46/100] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=500;, score=0.850 total time=   1.9s\n","[CV 2/5; 47/100] START bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=900\n","[CV 1/5; 47/100] END bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=900;, score=0.875 total time=   4.1s\n","[CV 3/5; 47/100] START bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=900\n","[CV 2/5; 47/100] END bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=900;, score=0.906 total time=   4.7s\n","[CV 4/5; 47/100] START bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=900\n","[CV 3/5; 47/100] END bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=900;, score=0.872 total time=   4.0s\n","[CV 5/5; 47/100] START bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=900\n","[CV 4/5; 47/100] END bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=900;, score=0.850 total time=   3.5s\n","[CV 1/5; 48/100] START bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=1900\n","[CV 5/5; 47/100] END bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=2, n_estimators=900;, score=0.847 total time=   3.3s\n","[CV 2/5; 48/100] START bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=1900\n","[CV 1/5; 48/100] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=1900;, score=0.869 total time=   5.7s\n","[CV 3/5; 48/100] START bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=1900\n","[CV 2/5; 48/100] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=1900;, score=0.903 total time=   7.4s\n","[CV 4/5; 48/100] START bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=1900\n","[CV 3/5; 48/100] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=1900;, score=0.878 total time=   7.3s\n","[CV 5/5; 48/100] START bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=1900\n","[CV 4/5; 48/100] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=1900;, score=0.841 total time=   5.9s\n","[CV 1/5; 49/100] START bootstrap=False, max_depth=40, min_samples_leaf=1, min_samples_split=5, n_estimators=1100\n","[CV 5/5; 48/100] END bootstrap=True, max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=1900;, score=0.847 total time=   5.8s\n","[CV 2/5; 49/100] START bootstrap=False, max_depth=40, min_samples_leaf=1, min_samples_split=5, n_estimators=1100\n","[CV 1/5; 49/100] END bootstrap=False, max_depth=40, min_samples_leaf=1, min_samples_split=5, n_estimators=1100;, score=0.884 total time=   5.1s\n","[CV 3/5; 49/100] START bootstrap=False, max_depth=40, min_samples_leaf=1, min_samples_split=5, n_estimators=1100\n","[CV 2/5; 49/100] END bootstrap=False, max_depth=40, min_samples_leaf=1, min_samples_split=5, n_estimators=1100;, score=0.903 total time=   5.8s\n","[CV 4/5; 49/100] START bootstrap=False, max_depth=40, min_samples_leaf=1, min_samples_split=5, n_estimators=1100\n","[CV 3/5; 49/100] END bootstrap=False, max_depth=40, min_samples_leaf=1, min_samples_split=5, n_estimators=1100;, score=0.875 total time=   5.2s\n","[CV 5/5; 49/100] START bootstrap=False, max_depth=40, min_samples_leaf=1, min_samples_split=5, n_estimators=1100\n","[CV 4/5; 49/100] END bootstrap=False, max_depth=40, min_samples_leaf=1, min_samples_split=5, n_estimators=1100;, score=0.856 total time=   4.3s\n","[CV 1/5; 50/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=1500\n","[CV 5/5; 49/100] END bootstrap=False, max_depth=40, min_samples_leaf=1, min_samples_split=5, n_estimators=1100;, score=0.847 total time=   4.5s\n","[CV 2/5; 50/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=1500\n","[CV 1/5; 50/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=1500;, score=0.884 total time=   7.6s\n","[CV 3/5; 50/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=1500\n","[CV 2/5; 50/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=1500;, score=0.906 total time=   7.3s\n","[CV 4/5; 50/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=1500\n","[CV 3/5; 50/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=1500;, score=0.875 total time=   5.8s\n","[CV 5/5; 50/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=1500\n","[CV 4/5; 50/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=1500;, score=0.859 total time=   5.9s\n","[CV 1/5; 51/100] START bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=1900\n","[CV 5/5; 50/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=1500;, score=0.859 total time=   7.4s\n","[CV 2/5; 51/100] START bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=1900\n","[CV 1/5; 51/100] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=1900;, score=0.872 total time=   8.2s\n","[CV 3/5; 51/100] START bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=1900\n","[CV 2/5; 51/100] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=1900;, score=0.903 total time=   6.5s\n","[CV 4/5; 51/100] START bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=1900\n","[CV 3/5; 51/100] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=1900;, score=0.872 total time=   6.5s\n","[CV 5/5; 51/100] START bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=1900\n","[CV 4/5; 51/100] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=1900;, score=0.844 total time=   8.2s\n","[CV 1/5; 52/100] START bootstrap=False, max_depth=70, min_samples_leaf=4, min_samples_split=10, n_estimators=700\n","[CV 5/5; 51/100] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=1900;, score=0.844 total time=   8.1s\n","[CV 2/5; 52/100] START bootstrap=False, max_depth=70, min_samples_leaf=4, min_samples_split=10, n_estimators=700\n","[CV 1/5; 52/100] END bootstrap=False, max_depth=70, min_samples_leaf=4, min_samples_split=10, n_estimators=700;, score=0.869 total time=   2.6s\n","[CV 3/5; 52/100] START bootstrap=False, max_depth=70, min_samples_leaf=4, min_samples_split=10, n_estimators=700\n","[CV 2/5; 52/100] END bootstrap=False, max_depth=70, min_samples_leaf=4, min_samples_split=10, n_estimators=700;, score=0.906 total time=   2.5s\n","[CV 4/5; 52/100] START bootstrap=False, max_depth=70, min_samples_leaf=4, min_samples_split=10, n_estimators=700\n","[CV 3/5; 52/100] END bootstrap=False, max_depth=70, min_samples_leaf=4, min_samples_split=10, n_estimators=700;, score=0.872 total time=   2.5s\n","[CV 5/5; 52/100] START bootstrap=False, max_depth=70, min_samples_leaf=4, min_samples_split=10, n_estimators=700\n","[CV 4/5; 52/100] END bootstrap=False, max_depth=70, min_samples_leaf=4, min_samples_split=10, n_estimators=700;, score=0.831 total time=   2.6s\n","[CV 1/5; 53/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=1700\n","[CV 5/5; 52/100] END bootstrap=False, max_depth=70, min_samples_leaf=4, min_samples_split=10, n_estimators=700;, score=0.850 total time=   2.5s\n","[CV 2/5; 53/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=1700\n","[CV 1/5; 53/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=1700;, score=0.881 total time=   8.0s\n","[CV 3/5; 53/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=1700\n","[CV 2/5; 53/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=1700;, score=0.912 total time=   7.9s\n","[CV 4/5; 53/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=1700\n","[CV 3/5; 53/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=1700;, score=0.875 total time=   6.6s\n","[CV 5/5; 53/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=1700\n","[CV 4/5; 53/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=1700;, score=0.847 total time=   7.1s\n","[CV 1/5; 54/100] START bootstrap=False, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300\n","[CV 1/5; 54/100] END bootstrap=False, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300;, score=0.875 total time=   5.5s\n","[CV 2/5; 54/100] START bootstrap=False, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300\n","[CV 5/5; 53/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=10, n_estimators=1700;, score=0.850 total time=   7.5s\n","[CV 3/5; 54/100] START bootstrap=False, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300\n","[CV 2/5; 54/100] END bootstrap=False, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300;, score=0.903 total time=   4.8s\n","[CV 4/5; 54/100] START bootstrap=False, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300\n","[CV 3/5; 54/100] END bootstrap=False, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300;, score=0.869 total time=   4.8s\n","[CV 5/5; 54/100] START bootstrap=False, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300\n","[CV 4/5; 54/100] END bootstrap=False, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300;, score=0.853 total time=   6.5s\n","[CV 1/5; 55/100] START bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=900\n","[CV 5/5; 54/100] END bootstrap=False, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300;, score=0.844 total time=   6.2s\n","[CV 2/5; 55/100] START bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=900\n","[CV 1/5; 55/100] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=900;, score=0.884 total time=   3.1s\n","[CV 3/5; 55/100] START bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=900\n","[CV 2/5; 55/100] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=900;, score=0.900 total time=   3.0s\n","[CV 4/5; 55/100] START bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=900\n","[CV 3/5; 55/100] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=900;, score=0.891 total time=   3.0s\n","[CV 5/5; 55/100] START bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=900\n","[CV 4/5; 55/100] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=900;, score=0.847 total time=   3.0s\n","[CV 1/5; 56/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=500\n","[CV 1/5; 56/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=500;, score=0.881 total time=   2.9s\n","[CV 2/5; 56/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=500\n","[CV 5/5; 55/100] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=900;, score=0.856 total time=   4.0s\n","[CV 3/5; 56/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=500\n","[CV 2/5; 56/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=500;, score=0.912 total time=   2.5s\n","[CV 4/5; 56/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=500\n","[CV 3/5; 56/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=500;, score=0.884 total time=   2.5s\n","[CV 5/5; 56/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=500\n","[CV 4/5; 56/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=500;, score=0.850 total time=   1.9s\n","[CV 1/5; 57/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1100\n","[CV 5/5; 56/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=500;, score=0.856 total time=   2.0s\n","[CV 2/5; 57/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1100\n","[CV 1/5; 57/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1100;, score=0.878 total time=   3.6s\n","[CV 3/5; 57/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1100\n","[CV 2/5; 57/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1100;, score=0.894 total time=   3.7s\n","[CV 4/5; 57/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1100\n","[CV 3/5; 57/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1100;, score=0.894 total time=   3.7s\n","[CV 5/5; 57/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1100\n","[CV 4/5; 57/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1100;, score=0.850 total time=   4.2s\n","[CV 1/5; 58/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=500\n","[CV 1/5; 58/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=500;, score=0.881 total time=   2.8s\n","[CV 2/5; 58/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=500\n","[CV 5/5; 57/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1100;, score=0.853 total time=   5.2s\n","[CV 3/5; 58/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=500\n","[CV 2/5; 58/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=500;, score=0.912 total time=   2.0s\n","[CV 4/5; 58/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=500\n","[CV 3/5; 58/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=500;, score=0.884 total time=   2.1s\n","[CV 5/5; 58/100] START bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=500\n","[CV 4/5; 58/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=500;, score=0.850 total time=   2.0s\n","[CV 1/5; 59/100] START bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=1900\n","[CV 5/5; 58/100] END bootstrap=False, max_depth=40, min_samples_leaf=2, min_samples_split=2, n_estimators=500;, score=0.856 total time=   2.0s\n","[CV 2/5; 59/100] START bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=1900\n","[CV 1/5; 59/100] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=1900;, score=0.869 total time=   6.3s\n","[CV 3/5; 59/100] START bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=1900\n","[CV 2/5; 59/100] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=1900;, score=0.900 total time=   7.7s\n","[CV 4/5; 59/100] START bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=1900\n","[CV 3/5; 59/100] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=1900;, score=0.881 total time=   7.0s\n","[CV 5/5; 59/100] START bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=1900\n","[CV 4/5; 59/100] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=1900;, score=0.844 total time=   6.1s\n","[CV 1/5; 60/100] START bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=1700\n","[CV 5/5; 59/100] END bootstrap=True, max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=1900;, score=0.847 total time=   6.5s\n","[CV 2/5; 60/100] START bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=1700\n","[CV 1/5; 60/100] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=1700;, score=0.884 total time=   8.0s\n","[CV 3/5; 60/100] START bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=1700\n","[CV 2/5; 60/100] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=1700;, score=0.900 total time=   7.6s\n","[CV 4/5; 60/100] START bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=1700\n","[CV 3/5; 60/100] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=1700;, score=0.881 total time=   6.4s\n","[CV 5/5; 60/100] START bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=1700\n","[CV 4/5; 60/100] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=1700;, score=0.856 total time=   8.0s\n","[CV 1/5; 61/100] START bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n","[CV 1/5; 61/100] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=0.872 total time=   1.1s\n","[CV 2/5; 61/100] START bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n","[CV 2/5; 61/100] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=0.916 total time=   1.1s\n","[CV 3/5; 61/100] START bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n","[CV 5/5; 60/100] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=1700;, score=0.847 total time=   8.1s\n","[CV 4/5; 61/100] START bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n","[CV 3/5; 61/100] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=0.872 total time=   1.1s\n","[CV 5/5; 61/100] START bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n","[CV 4/5; 61/100] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=0.825 total time=   1.1s\n","[CV 1/5; 62/100] START bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=1700\n","[CV 5/5; 61/100] END bootstrap=False, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=0.853 total time=   1.1s\n","[CV 2/5; 62/100] START bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=1700\n","[CV 1/5; 62/100] END bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=1700;, score=0.872 total time=   5.4s\n","[CV 3/5; 62/100] START bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=1700\n","[CV 2/5; 62/100] END bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=1700;, score=0.903 total time=   5.4s\n","[CV 4/5; 62/100] START bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=1700\n","[CV 3/5; 62/100] END bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=1700;, score=0.891 total time=   6.8s\n","[CV 5/5; 62/100] START bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=1700\n","[CV 4/5; 62/100] END bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=1700;, score=0.831 total time=   7.0s\n","[CV 1/5; 63/100] START bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=1300\n","[CV 1/5; 63/100] END bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=1300;, score=0.866 total time=   3.9s\n","[CV 2/5; 63/100] START bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=1300\n","[CV 5/5; 62/100] END bootstrap=True, max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=1700;, score=0.853 total time=   5.4s\n","[CV 3/5; 63/100] START bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=1300\n","[CV 2/5; 63/100] END bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=1300;, score=0.900 total time=   4.9s\n","[CV 4/5; 63/100] START bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=1300\n","[CV 3/5; 63/100] END bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=1300;, score=0.881 total time=   5.4s\n","[CV 5/5; 63/100] START bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=1300\n","[CV 4/5; 63/100] END bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=1300;, score=0.847 total time=   4.4s\n","[CV 1/5; 64/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=10, n_estimators=900\n","[CV 5/5; 63/100] END bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=1300;, score=0.856 total time=   4.0s\n","[CV 2/5; 64/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=10, n_estimators=900\n","[CV 1/5; 64/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=10, n_estimators=900;, score=0.881 total time=   3.4s\n","[CV 3/5; 64/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=10, n_estimators=900\n","[CV 2/5; 64/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=10, n_estimators=900;, score=0.909 total time=   3.4s\n","[CV 4/5; 64/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=10, n_estimators=900\n","[CV 3/5; 64/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=10, n_estimators=900;, score=0.875 total time=   3.5s\n","[CV 5/5; 64/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=10, n_estimators=900\n","[CV 4/5; 64/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=10, n_estimators=900;, score=0.847 total time=   3.9s\n","[CV 1/5; 65/100] START bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=700\n","[CV 1/5; 65/100] END bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=700;, score=0.872 total time=   3.1s\n","[CV 2/5; 65/100] START bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=700\n","[CV 5/5; 64/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=10, n_estimators=900;, score=0.844 total time=   4.9s\n","[CV 3/5; 65/100] START bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=700\n","[CV 2/5; 65/100] END bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=700;, score=0.897 total time=   2.2s\n","[CV 4/5; 65/100] START bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=700\n","[CV 3/5; 65/100] END bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=700;, score=0.875 total time=   2.2s\n","[CV 5/5; 65/100] START bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=700\n","[CV 4/5; 65/100] END bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=700;, score=0.844 total time=   2.2s\n","[CV 1/5; 66/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=700\n","[CV 5/5; 65/100] END bootstrap=True, max_depth=90, min_samples_leaf=4, min_samples_split=5, n_estimators=700;, score=0.850 total time=   2.2s\n","[CV 2/5; 66/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=700\n","[CV 1/5; 66/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=700;, score=0.875 total time=   2.3s\n","[CV 3/5; 66/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=700\n","[CV 2/5; 66/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=700;, score=0.903 total time=   2.3s\n","[CV 4/5; 66/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=700\n","[CV 3/5; 66/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=700;, score=0.878 total time=   2.2s\n","[CV 5/5; 66/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=700\n","[CV 4/5; 66/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=700;, score=0.847 total time=   3.0s\n","[CV 1/5; 67/100] START bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=2, n_estimators=500\n","[CV 5/5; 66/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=700;, score=0.853 total time=   3.8s\n","[CV 2/5; 67/100] START bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=2, n_estimators=500\n","[CV 1/5; 67/100] END bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=2, n_estimators=500;, score=0.875 total time=   2.4s\n","[CV 3/5; 67/100] START bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=2, n_estimators=500\n","[CV 2/5; 67/100] END bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=2, n_estimators=500;, score=0.909 total time=   1.7s\n","[CV 4/5; 67/100] START bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=2, n_estimators=500\n","[CV 3/5; 67/100] END bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=2, n_estimators=500;, score=0.887 total time=   1.7s\n","[CV 5/5; 67/100] START bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=2, n_estimators=500\n","[CV 4/5; 67/100] END bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=2, n_estimators=500;, score=0.853 total time=   1.9s\n","[CV 1/5; 68/100] START bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=1300\n","[CV 5/5; 67/100] END bootstrap=True, max_depth=40, min_samples_leaf=1, min_samples_split=2, n_estimators=500;, score=0.856 total time=   1.7s\n","[CV 2/5; 68/100] START bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=1300\n","[CV 2/5; 68/100] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=1300;, score=0.906 total time=   5.1s\n","[CV 1/5; 68/100] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=1300;, score=0.881 total time=   5.3s\n","[CV 3/5; 68/100] START bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=1300\n","[CV 4/5; 68/100] START bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=1300\n","[CV 3/5; 68/100] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=1300;, score=0.884 total time=   6.4s\n","[CV 5/5; 68/100] START bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=1300\n","[CV 4/5; 68/100] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=1300;, score=0.853 total time=   6.9s\n","[CV 1/5; 69/100] START bootstrap=True, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=1900\n","[CV 5/5; 68/100] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=1300;, score=0.850 total time=   5.2s\n","[CV 2/5; 69/100] START bootstrap=True, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=1900\n","[CV 1/5; 69/100] END bootstrap=True, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=1900;, score=0.875 total time=   6.0s\n","[CV 3/5; 69/100] START bootstrap=True, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=1900\n","[CV 2/5; 69/100] END bootstrap=True, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=1900;, score=0.903 total time=   7.6s\n","[CV 4/5; 69/100] START bootstrap=True, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=1900\n","[CV 3/5; 69/100] END bootstrap=True, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=1900;, score=0.891 total time=   7.5s\n","[CV 5/5; 69/100] START bootstrap=True, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=1900\n","[CV 4/5; 69/100] END bootstrap=True, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=1900;, score=0.838 total time=   6.0s\n","[CV 1/5; 70/100] START bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=10, n_estimators=1100\n","[CV 5/5; 69/100] END bootstrap=True, max_depth=90, min_samples_leaf=1, min_samples_split=10, n_estimators=1900;, score=0.850 total time=   6.1s\n","[CV 2/5; 70/100] START bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=10, n_estimators=1100\n","[CV 1/5; 70/100] END bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=10, n_estimators=1100;, score=0.869 total time=   5.4s\n","[CV 3/5; 70/100] START bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=10, n_estimators=1100\n","[CV 2/5; 70/100] END bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=10, n_estimators=1100;, score=0.903 total time=   4.8s\n","[CV 4/5; 70/100] START bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=10, n_estimators=1100\n","[CV 3/5; 70/100] END bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=10, n_estimators=1100;, score=0.891 total time=   3.5s\n","[CV 5/5; 70/100] START bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=10, n_estimators=1100\n","[CV 4/5; 70/100] END bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=10, n_estimators=1100;, score=0.834 total time=   3.5s\n","[CV 1/5; 71/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1500\n","[CV 5/5; 70/100] END bootstrap=True, max_depth=60, min_samples_leaf=1, min_samples_split=10, n_estimators=1100;, score=0.850 total time=   3.5s\n","[CV 2/5; 71/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1500\n","[CV 1/5; 71/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1500;, score=0.872 total time=   4.9s\n","[CV 3/5; 71/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1500\n","[CV 2/5; 71/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1500;, score=0.903 total time=   6.5s\n","[CV 4/5; 71/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1500\n","[CV 3/5; 71/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1500;, score=0.881 total time=   6.5s\n","[CV 5/5; 71/100] START bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1500\n","[CV 4/5; 71/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1500;, score=0.850 total time=   5.0s\n","[CV 1/5; 72/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n","[CV 1/5; 72/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.869 total time=   0.3s\n","[CV 2/5; 72/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n","[CV 2/5; 72/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.897 total time=   0.3s\n","[CV 3/5; 72/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n","[CV 3/5; 72/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.881 total time=   0.3s\n","[CV 4/5; 72/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n","[CV 4/5; 72/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.838 total time=   0.3s\n","[CV 5/5; 72/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n","[CV 5/5; 72/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.859 total time=   0.3s\n","[CV 1/5; 73/100] START bootstrap=False, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=500\n","[CV 5/5; 71/100] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=1500;, score=0.859 total time=   5.1s\n","[CV 2/5; 73/100] START bootstrap=False, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=500\n","[CV 1/5; 73/100] END bootstrap=False, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=500;, score=0.875 total time=   2.0s\n","[CV 3/5; 73/100] START bootstrap=False, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=500\n","[CV 2/5; 73/100] END bootstrap=False, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=500;, score=0.903 total time=   2.0s\n","[CV 4/5; 73/100] START bootstrap=False, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=500\n","[CV 3/5; 73/100] END bootstrap=False, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=500;, score=0.884 total time=   3.0s\n","[CV 5/5; 73/100] START bootstrap=False, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=500\n","[CV 4/5; 73/100] END bootstrap=False, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=500;, score=0.850 total time=   3.2s\n","[CV 1/5; 74/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=500\n","[CV 1/5; 74/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=500;, score=0.878 total time=   1.9s\n","[CV 2/5; 74/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=500\n","[CV 5/5; 73/100] END bootstrap=False, max_depth=90, min_samples_leaf=2, min_samples_split=5, n_estimators=500;, score=0.853 total time=   2.4s\n","[CV 3/5; 74/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=500\n","[CV 2/5; 74/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=500;, score=0.903 total time=   1.7s\n","[CV 4/5; 74/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=500\n","[CV 3/5; 74/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=500;, score=0.881 total time=   1.6s\n","[CV 5/5; 74/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=500\n","[CV 4/5; 74/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=500;, score=0.841 total time=   1.7s\n","[CV 1/5; 75/100] START bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300\n","[CV 5/5; 74/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=500;, score=0.850 total time=   1.6s\n","[CV 2/5; 75/100] START bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300\n","[CV 1/5; 75/100] END bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300;, score=0.866 total time=   4.0s\n","[CV 3/5; 75/100] START bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300\n","[CV 2/5; 75/100] END bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300;, score=0.897 total time=   3.9s\n","[CV 4/5; 75/100] START bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300\n","[CV 3/5; 75/100] END bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300;, score=0.878 total time=   5.5s\n","[CV 5/5; 75/100] START bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300\n","[CV 4/5; 75/100] END bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300;, score=0.841 total time=   5.6s\n","[CV 1/5; 76/100] START bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1900\n","[CV 5/5; 75/100] END bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1300;, score=0.847 total time=   4.0s\n","[CV 2/5; 76/100] START bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1900\n","[CV 1/5; 76/100] END bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1900;, score=0.869 total time=   5.8s\n","[CV 3/5; 76/100] START bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1900\n","[CV 2/5; 76/100] END bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1900;, score=0.903 total time=   6.7s\n","[CV 4/5; 76/100] START bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1900\n","[CV 3/5; 76/100] END bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1900;, score=0.878 total time=   7.4s\n","[CV 5/5; 76/100] START bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1900\n","[CV 4/5; 76/100] END bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1900;, score=0.841 total time=   6.4s\n","[CV 1/5; 77/100] START bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n","[CV 1/5; 77/100] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=0.884 total time=   1.0s\n","[CV 2/5; 77/100] START bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n","[CV 5/5; 76/100] END bootstrap=True, max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=1900;, score=0.847 total time=   5.9s\n","[CV 3/5; 77/100] START bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n","[CV 2/5; 77/100] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=0.903 total time=   1.2s\n","[CV 4/5; 77/100] START bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n","[CV 3/5; 77/100] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=0.884 total time=   1.0s\n","[CV 5/5; 77/100] START bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n","[CV 4/5; 77/100] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=0.853 total time=   1.0s\n","[CV 1/5; 78/100] START bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n","[CV 5/5; 77/100] END bootstrap=True, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=0.859 total time=   1.0s\n","[CV 2/5; 78/100] START bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n","[CV 1/5; 78/100] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=0.875 total time=   1.1s\n","[CV 3/5; 78/100] START bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n","[CV 3/5; 78/100] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=0.869 total time=   1.5s\n","[CV 2/5; 78/100] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=0.903 total time=   1.6s[CV 4/5; 78/100] START bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n","\n","[CV 5/5; 78/100] START bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n","[CV 5/5; 78/100] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=0.828 total time=   1.8s\n","[CV 4/5; 78/100] END bootstrap=False, max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=0.838 total time=   1.9s\n","[CV 1/5; 79/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1300[CV 2/5; 79/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1300\n","\n","[CV 2/5; 79/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1300;, score=0.906 total time=   5.1s\n","[CV 3/5; 79/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1300\n","[CV 1/5; 79/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1300;, score=0.875 total time=   5.2s\n","[CV 4/5; 79/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1300\n","[CV 3/5; 79/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1300;, score=0.881 total time=   5.3s\n","[CV 5/5; 79/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1300\n","[CV 4/5; 79/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1300;, score=0.853 total time=   5.6s\n","[CV 1/5; 80/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=500\n","[CV 1/5; 80/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=500;, score=0.875 total time=   2.8s\n","[CV 2/5; 80/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=500\n","[CV 2/5; 80/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=500;, score=0.906 total time=   2.1s\n","[CV 3/5; 80/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=500\n","[CV 5/5; 79/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=5, n_estimators=1300;, score=0.847 total time=   6.0s\n","[CV 4/5; 80/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=500\n","[CV 3/5; 80/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=500;, score=0.884 total time=   2.0s\n","[CV 5/5; 80/100] START bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=500\n","[CV 4/5; 80/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=500;, score=0.853 total time=   2.0s\n","[CV 1/5; 81/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=700\n","[CV 5/5; 80/100] END bootstrap=False, max_depth=60, min_samples_leaf=1, min_samples_split=2, n_estimators=500;, score=0.847 total time=   2.0s\n","[CV 2/5; 81/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=700\n","[CV 1/5; 81/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=700;, score=0.878 total time=   2.3s\n","[CV 3/5; 81/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=700\n","[CV 2/5; 81/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=700;, score=0.900 total time=   2.4s\n","[CV 4/5; 81/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=700\n","[CV 3/5; 81/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=700;, score=0.884 total time=   2.9s\n","[CV 5/5; 81/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=700\n","[CV 4/5; 81/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=700;, score=0.853 total time=   3.6s\n","[CV 1/5; 82/100] START bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=5, n_estimators=1500\n","[CV 5/5; 81/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=700;, score=0.859 total time=   3.1s\n","[CV 2/5; 82/100] START bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=5, n_estimators=1500\n","[CV 1/5; 82/100] END bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=5, n_estimators=1500;, score=0.878 total time=   6.0s\n","[CV 3/5; 82/100] START bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=5, n_estimators=1500\n","[CV 2/5; 82/100] END bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=5, n_estimators=1500;, score=0.906 total time=   5.9s\n","[CV 4/5; 82/100] START bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=5, n_estimators=1500\n","[CV 3/5; 82/100] END bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=5, n_estimators=1500;, score=0.881 total time=   7.3s\n","[CV 5/5; 82/100] START bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=5, n_estimators=1500\n","[CV 4/5; 82/100] END bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=5, n_estimators=1500;, score=0.856 total time=   7.2s\n","[CV 1/5; 83/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1300\n","[CV 1/5; 83/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1300;, score=0.875 total time=   4.3s\n","[CV 2/5; 83/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1300\n","[CV 5/5; 82/100] END bootstrap=False, max_depth=90, min_samples_leaf=1, min_samples_split=5, n_estimators=1500;, score=0.847 total time=   6.1s\n","[CV 3/5; 83/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1300\n","[CV 2/5; 83/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1300;, score=0.894 total time=   4.9s\n","[CV 4/5; 83/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1300\n","[CV 3/5; 83/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1300;, score=0.894 total time=   5.6s\n","[CV 5/5; 83/100] START bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1300\n","[CV 4/5; 83/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1300;, score=0.850 total time=   4.8s\n","[CV 1/5; 84/100] START bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=1500\n","[CV 5/5; 83/100] END bootstrap=True, max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=1300;, score=0.847 total time=   4.3s\n","[CV 2/5; 84/100] START bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=1500\n","[CV 1/5; 84/100] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=1500;, score=0.878 total time=   6.0s\n","[CV 3/5; 84/100] START bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=1500\n","[CV 2/5; 84/100] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=1500;, score=0.906 total time=   6.1s\n","[CV 4/5; 84/100] START bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=1500\n","[CV 3/5; 84/100] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=1500;, score=0.881 total time=   7.3s\n","[CV 5/5; 84/100] START bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=1500\n","[CV 4/5; 84/100] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=1500;, score=0.856 total time=   6.7s\n","[CV 1/5; 85/100] START bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=500\n","[CV 1/5; 85/100] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=500;, score=0.875 total time=   1.8s\n","[CV 2/5; 85/100] START bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=500\n","[CV 2/5; 85/100] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=500;, score=0.906 total time=   1.8s\n","[CV 3/5; 85/100] START bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=500\n","[CV 5/5; 84/100] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=1500;, score=0.847 total time=   6.0s\n","[CV 4/5; 85/100] START bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=500\n","[CV 3/5; 85/100] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=500;, score=0.866 total time=   1.8s\n","[CV 5/5; 85/100] START bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=500\n","[CV 4/5; 85/100] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=500;, score=0.831 total time=   3.0s\n","[CV 1/5; 86/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=500\n","[CV 5/5; 85/100] END bootstrap=False, max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=500;, score=0.847 total time=   3.1s\n","[CV 2/5; 86/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=500\n","[CV 1/5; 86/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=500;, score=0.875 total time=   2.0s\n","[CV 3/5; 86/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=500\n","[CV 2/5; 86/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=500;, score=0.903 total time=   1.9s\n","[CV 4/5; 86/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=500\n","[CV 3/5; 86/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=500;, score=0.884 total time=   1.9s\n","[CV 5/5; 86/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=500\n","[CV 4/5; 86/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=500;, score=0.850 total time=   2.0s\n","[CV 1/5; 87/100] START bootstrap=True, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=500\n","[CV 1/5; 87/100] END bootstrap=True, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=500;, score=0.869 total time=   1.6s\n","[CV 2/5; 87/100] START bootstrap=True, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=500\n","[CV 5/5; 86/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=500;, score=0.853 total time=   2.0s\n","[CV 3/5; 87/100] START bootstrap=True, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=500\n","[CV 2/5; 87/100] END bootstrap=True, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=500;, score=0.897 total time=   1.6s\n","[CV 4/5; 87/100] START bootstrap=True, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=500\n","[CV 3/5; 87/100] END bootstrap=True, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=500;, score=0.878 total time=   1.5s\n","[CV 5/5; 87/100] START bootstrap=True, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=500\n","[CV 4/5; 87/100] END bootstrap=True, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=500;, score=0.844 total time=   1.6s\n","[CV 1/5; 88/100] START bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=700\n","[CV 5/5; 87/100] END bootstrap=True, max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=500;, score=0.838 total time=   1.6s\n","[CV 2/5; 88/100] START bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=700\n","[CV 2/5; 88/100] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=700;, score=0.906 total time=   3.9s\n","[CV 3/5; 88/100] START bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=700\n","[CV 1/5; 88/100] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=700;, score=0.881 total time=   4.3s\n","[CV 4/5; 88/100] START bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=700\n","[CV 3/5; 88/100] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=700;, score=0.881 total time=   2.8s\n","[CV 5/5; 88/100] START bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=700\n","[CV 4/5; 88/100] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=700;, score=0.856 total time=   2.8s\n","[CV 1/5; 89/100] START bootstrap=False, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n","[CV 1/5; 89/100] END bootstrap=False, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.866 total time=   0.4s\n","[CV 2/5; 89/100] START bootstrap=False, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n","[CV 2/5; 89/100] END bootstrap=False, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.909 total time=   0.4s\n","[CV 3/5; 89/100] START bootstrap=False, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n","[CV 3/5; 89/100] END bootstrap=False, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.884 total time=   0.4s\n","[CV 4/5; 89/100] START bootstrap=False, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n","[CV 4/5; 89/100] END bootstrap=False, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.844 total time=   0.4s\n","[CV 5/5; 89/100] START bootstrap=False, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n","[CV 5/5; 89/100] END bootstrap=False, max_depth=90, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.838 total time=   0.4s\n","[CV 1/5; 90/100] START bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1900\n","[CV 5/5; 88/100] END bootstrap=False, max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=700;, score=0.850 total time=   2.9s\n","[CV 2/5; 90/100] START bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1900\n","[CV 1/5; 90/100] END bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1900;, score=0.881 total time=   8.4s\n","[CV 3/5; 90/100] START bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1900\n","[CV 2/5; 90/100] END bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1900;, score=0.900 total time=   8.3s\n","[CV 4/5; 90/100] START bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1900\n","[CV 3/5; 90/100] END bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1900;, score=0.881 total time=   7.3s\n","[CV 5/5; 90/100] START bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1900\n","[CV 4/5; 90/100] END bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1900;, score=0.856 total time=   7.2s\n","[CV 1/5; 91/100] START bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n","[CV 1/5; 91/100] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=0.863 total time=   1.1s\n","[CV 2/5; 91/100] START bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n","[CV 2/5; 91/100] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=0.900 total time=   2.1s\n","[CV 3/5; 91/100] START bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n","[CV 3/5; 91/100] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=0.875 total time=   1.0s\n","[CV 4/5; 91/100] START bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n","[CV 4/5; 91/100] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=0.838 total time=   1.0s\n","[CV 5/5; 91/100] START bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n","[CV 5/5; 91/100] END bootstrap=True, max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=0.844 total time=   1.0s\n","[CV 1/5; 92/100] START bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=10, n_estimators=1300\n","[CV 5/5; 90/100] END bootstrap=False, max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=1900;, score=0.847 total time=   8.7s\n","[CV 2/5; 92/100] START bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=10, n_estimators=1300\n","[CV 1/5; 92/100] END bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=10, n_estimators=1300;, score=0.878 total time=   4.8s\n","[CV 3/5; 92/100] START bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=10, n_estimators=1300\n","[CV 2/5; 92/100] END bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=10, n_estimators=1300;, score=0.903 total time=   4.7s\n","[CV 4/5; 92/100] START bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=10, n_estimators=1300\n","[CV 3/5; 92/100] END bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=10, n_estimators=1300;, score=0.878 total time=   5.9s\n","[CV 5/5; 92/100] START bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=10, n_estimators=1300\n","[CV 4/5; 92/100] END bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=10, n_estimators=1300;, score=0.844 total time=   6.0s\n","[CV 1/5; 93/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=1700\n","[CV 5/5; 92/100] END bootstrap=False, max_depth=60, min_samples_leaf=4, min_samples_split=10, n_estimators=1300;, score=0.850 total time=   4.8s\n","[CV 2/5; 93/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=1700\n","[CV 1/5; 93/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=1700;, score=0.863 total time=   5.1s\n","[CV 3/5; 93/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=1700\n","[CV 2/5; 93/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=1700;, score=0.903 total time=   6.1s\n","[CV 4/5; 93/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=1700\n","[CV 3/5; 93/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=1700;, score=0.875 total time=   6.5s\n","[CV 5/5; 93/100] START bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=1700\n","[CV 4/5; 93/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=1700;, score=0.844 total time=   5.3s\n","[CV 1/5; 94/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=1700\n","[CV 5/5; 93/100] END bootstrap=True, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=1700;, score=0.853 total time=   5.2s\n","[CV 2/5; 94/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=1700\n","[CV 1/5; 94/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=1700;, score=0.878 total time=   8.1s\n","[CV 3/5; 94/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=1700\n","[CV 2/5; 94/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=1700;, score=0.906 total time=   7.8s\n","[CV 4/5; 94/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=1700\n","[CV 3/5; 94/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=1700;, score=0.878 total time=   6.6s\n","[CV 5/5; 94/100] START bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=1700\n","[CV 4/5; 94/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=1700;, score=0.859 total time=   6.6s\n","[CV 1/5; 95/100] START bootstrap=True, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1500\n","[CV 5/5; 94/100] END bootstrap=False, max_depth=70, min_samples_leaf=2, min_samples_split=2, n_estimators=1700;, score=0.859 total time=   8.0s\n","[CV 2/5; 95/100] START bootstrap=True, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1500\n","[CV 1/5; 95/100] END bootstrap=True, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1500;, score=0.872 total time=   6.0s\n","[CV 3/5; 95/100] START bootstrap=True, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1500\n","[CV 2/5; 95/100] END bootstrap=True, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1500;, score=0.903 total time=   6.1s\n","[CV 4/5; 95/100] START bootstrap=True, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1500\n","[CV 3/5; 95/100] END bootstrap=True, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1500;, score=0.881 total time=   6.9s\n","[CV 5/5; 95/100] START bootstrap=True, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1500\n","[CV 4/5; 95/100] END bootstrap=True, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1500;, score=0.850 total time=   6.9s\n","[CV 1/5; 96/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=1300\n","[CV 5/5; 95/100] END bootstrap=True, max_depth=70, min_samples_leaf=2, min_samples_split=5, n_estimators=1500;, score=0.859 total time=   7.0s\n","[CV 2/5; 96/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=1300\n","[CV 1/5; 96/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=1300;, score=0.878 total time=   4.7s\n","[CV 3/5; 96/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=1300\n","[CV 2/5; 96/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=1300;, score=0.906 total time=   4.8s\n","[CV 4/5; 96/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=1300\n","[CV 3/5; 96/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=1300;, score=0.878 total time=   5.9s\n","[CV 5/5; 96/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=1300\n","[CV 4/5; 96/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=1300;, score=0.838 total time=   6.0s\n","[CV 1/5; 97/100] START bootstrap=False, max_depth=40, min_samples_leaf=4, min_samples_split=5, n_estimators=500\n","[CV 1/5; 97/100] END bootstrap=False, max_depth=40, min_samples_leaf=4, min_samples_split=5, n_estimators=500;, score=0.869 total time=   1.8s\n","[CV 2/5; 97/100] START bootstrap=False, max_depth=40, min_samples_leaf=4, min_samples_split=5, n_estimators=500\n","[CV 2/5; 97/100] END bootstrap=False, max_depth=40, min_samples_leaf=4, min_samples_split=5, n_estimators=500;, score=0.909 total time=   1.8s\n","[CV 3/5; 97/100] START bootstrap=False, max_depth=40, min_samples_leaf=4, min_samples_split=5, n_estimators=500\n","[CV 5/5; 96/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=1300;, score=0.841 total time=   5.1s\n","[CV 4/5; 97/100] START bootstrap=False, max_depth=40, min_samples_leaf=4, min_samples_split=5, n_estimators=500\n","[CV 3/5; 97/100] END bootstrap=False, max_depth=40, min_samples_leaf=4, min_samples_split=5, n_estimators=500;, score=0.872 total time=   1.8s\n","[CV 5/5; 97/100] START bootstrap=False, max_depth=40, min_samples_leaf=4, min_samples_split=5, n_estimators=500\n","[CV 4/5; 97/100] END bootstrap=False, max_depth=40, min_samples_leaf=4, min_samples_split=5, n_estimators=500;, score=0.856 total time=   1.9s\n","[CV 1/5; 98/100] START bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=2, n_estimators=1100\n","[CV 5/5; 97/100] END bootstrap=False, max_depth=40, min_samples_leaf=4, min_samples_split=5, n_estimators=500;, score=0.850 total time=   1.9s\n","[CV 2/5; 98/100] START bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=2, n_estimators=1100\n","[CV 1/5; 98/100] END bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=2, n_estimators=1100;, score=0.866 total time=   3.4s\n","[CV 3/5; 98/100] START bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=2, n_estimators=1100\n","[CV 2/5; 98/100] END bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=2, n_estimators=1100;, score=0.897 total time=   4.8s\n","[CV 4/5; 98/100] START bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=2, n_estimators=1100\n","[CV 3/5; 98/100] END bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=2, n_estimators=1100;, score=0.875 total time=   4.4s\n","[CV 5/5; 98/100] START bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=2, n_estimators=1100\n","[CV 4/5; 98/100] END bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=2, n_estimators=1100;, score=0.841 total time=   3.4s\n","[CV 1/5; 99/100] START bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=1500\n","[CV 5/5; 98/100] END bootstrap=True, max_depth=70, min_samples_leaf=4, min_samples_split=2, n_estimators=1100;, score=0.847 total time=   3.5s\n","[CV 2/5; 99/100] START bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=1500\n","[CV 1/5; 99/100] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=1500;, score=0.863 total time=   4.6s\n","[CV 3/5; 99/100] START bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=1500\n","[CV 2/5; 99/100] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=1500;, score=0.897 total time=   4.6s\n","[CV 4/5; 99/100] START bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=1500\n","[CV 3/5; 99/100] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=1500;, score=0.875 total time=   5.8s\n","[CV 5/5; 99/100] START bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=1500\n","[CV 4/5; 99/100] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=1500;, score=0.838 total time=   5.9s\n","[CV 1/5; 100/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 5/5; 99/100] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=1500;, score=0.853 total time=   4.6s\n","[CV 2/5; 100/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 1/5; 100/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.881 total time=   6.2s\n","[CV 3/5; 100/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 2/5; 100/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.909 total time=   7.3s\n","[CV 4/5; 100/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 3/5; 100/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.875 total time=   7.4s\n","[CV 5/5; 100/100] START bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1700\n","[CV 4/5; 100/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.831 total time=   6.4s\n","[CV 5/5; 100/100] END bootstrap=False, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=1700;, score=0.844 total time=   5.6s\n"]}]},{"cell_type":"code","source":["!jupyter --version"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vviCa4hb7_bx","executionInfo":{"status":"ok","timestamp":1675739940495,"user_tz":-540,"elapsed":778,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"5654a3b3-0907-4cf0-e713-7d8706abe934"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Selected Jupyter core packages...\n","IPython          : 7.9.0\n","ipykernel        : 5.3.4\n","ipywidgets       : 7.7.1\n","jupyter_client   : 6.1.12\n","jupyter_core     : 5.2.0\n","jupyter_server   : not installed\n","jupyterlab       : not installed\n","nbclient         : not installed\n","nbconvert        : 5.6.1\n","nbformat         : 5.7.3\n","notebook         : 5.7.16\n","qtconsole        : not installed\n","traitlets        : 5.7.1\n"]}]},{"cell_type":"code","source":["print(f'accuracy: {rnd_forest_clf.best_score_: .4f}')\n","print(f'best estimator: {rnd_forest_clf.best_estimator_}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rU-YYg0HDCnJ","executionInfo":{"status":"ok","timestamp":1675739940500,"user_tz":-540,"elapsed":15,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"a8efc766-1a33-492b-d78d-fefcc3c0e1b6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["accuracy:  0.8781\n","best estimator: RandomForestClassifier(bootstrap=False, max_depth=60, n_estimators=1900,\n","                       random_state=42)\n"]}]},{"cell_type":"code","source":["rnd_forest_clf_best = RandomForestClassifier(bootstrap=False, max_depth=90, n_estimators=1300, random_state=42)\n","rnd_forest_clf_best.fit(X_train, y_train)\n","\n","y_pred_forest_best = rnd_forest_clf_best.predict(X_test)"],"metadata":{"id":"YFK-lMByFHJT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(classification_report(y_test, y_pred_forest_best))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"g9nHkAlIFvvd","executionInfo":{"status":"ok","timestamp":1675739946081,"user_tz":-540,"elapsed":9,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"7a1f0615-2f9e-416b-8c5f-9e73e6b64b46"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.94      0.95      0.95       105\n","           1       0.87      0.86      0.86        91\n","           2       0.80      0.85      0.83        92\n","           3       0.93      0.89      0.91       112\n","\n","    accuracy                           0.89       400\n","   macro avg       0.89      0.89      0.89       400\n","weighted avg       0.89      0.89      0.89       400\n","\n"]}]},{"cell_type":"code","source":["print(confusion_matrix(y_test, y_pred_forest_best))\n","print(f'accuracy: {accuracy_score(y_test, y_pred_forest_best): .4f}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WQNSaKMVFsai","executionInfo":{"status":"ok","timestamp":1675739946082,"user_tz":-540,"elapsed":7,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"c24d582f-5d43-45cc-b406-caf7014b297f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[100   5   0   0]\n"," [  6  78   7   0]\n"," [  0   7  78   7]\n"," [  0   0  12 100]]\n","accuracy:  0.8900\n"]}]},{"cell_type":"markdown","source":["* 89.25% -> 89.00%\n","* SVC(C=100.0, random_state=42) is the best estimator"],"metadata":{"id":"-koGghLRGArL"}},{"cell_type":"markdown","source":["# Make a submission file"],"metadata":{"id":"j66wJt5XoBiN"}},{"cell_type":"code","source":["test.head()"],"metadata":{"id":"D_uIIe4DUg1J","colab":{"base_uri":"https://localhost:8080/","height":299},"executionInfo":{"status":"ok","timestamp":1675739946603,"user_tz":-540,"elapsed":5,"user":{"displayName":"한준호","userId":"12870242508044632368"}},"outputId":"53e039c8-d966-4cdd-b015-0df61ec79b8e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   id  battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  \\\n","0   1           1043     1          1.8         1  14       0           5   \n","1   2            841     1          0.5         1   4       1          61   \n","2   3           1807     1          2.8         0   1       0          27   \n","3   4           1546     0          0.5         1  18       1          25   \n","4   5           1434     0          1.4         0  11       1          49   \n","\n","   m_dep  mobile_wt  ...  pc  px_height  px_width   ram  sc_h  sc_w  \\\n","0    0.1        193  ...  16        226      1412  3476    12     7   \n","1    0.8        191  ...  12        746       857  3895     6     0   \n","2    0.9        186  ...   4       1270      1366  2396    17    10   \n","3    0.5         96  ...  20        295      1752  3893    10     0   \n","4    0.5        108  ...  18        749       810  1773    15     8   \n","\n","   talk_time  three_g  touch_screen  wifi  \n","0          2        0             1     0  \n","1          7        1             0     0  \n","2         10        0             1     1  \n","3          7        1             1     0  \n","4          7        1             0     1  \n","\n","[5 rows x 21 columns]"],"text/html":["\n","  <div id=\"df-78e0c03b-644d-44f7-a946-696974008e68\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>battery_power</th>\n","      <th>blue</th>\n","      <th>clock_speed</th>\n","      <th>dual_sim</th>\n","      <th>fc</th>\n","      <th>four_g</th>\n","      <th>int_memory</th>\n","      <th>m_dep</th>\n","      <th>mobile_wt</th>\n","      <th>...</th>\n","      <th>pc</th>\n","      <th>px_height</th>\n","      <th>px_width</th>\n","      <th>ram</th>\n","      <th>sc_h</th>\n","      <th>sc_w</th>\n","      <th>talk_time</th>\n","      <th>three_g</th>\n","      <th>touch_screen</th>\n","      <th>wifi</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>1043</td>\n","      <td>1</td>\n","      <td>1.8</td>\n","      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<td>2.8</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>27</td>\n","      <td>0.9</td>\n","      <td>186</td>\n","      <td>...</td>\n","      <td>4</td>\n","      <td>1270</td>\n","      <td>1366</td>\n","      <td>2396</td>\n","      <td>17</td>\n","      <td>10</td>\n","      <td>10</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1546</td>\n","      <td>0</td>\n","      <td>0.5</td>\n","      <td>1</td>\n","      <td>18</td>\n","      <td>1</td>\n","      <td>25</td>\n","      <td>0.5</td>\n","      <td>96</td>\n","      <td>...</td>\n","      <td>20</td>\n","      <td>295</td>\n","      <td>1752</td>\n","      <td>3893</td>\n","      <td>10</td>\n","      <td>0</td>\n","      <td>7</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>1434</td>\n","      <td>0</td>\n","      <td>1.4</td>\n","      <td>0</td>\n","      <td>11</td>\n","      <td>1</td>\n","      <td>49</td>\n","      <td>0.5</td>\n","      <td>108</td>\n","      <td>...</td>\n","      <td>18</td>\n","      <td>749</td>\n","      <td>810</td>\n","      <td>1773</td>\n","      <td>15</td>\n","      <td>8</td>\n","      <td>7</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 21 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-78e0c03b-644d-44f7-a946-696974008e68')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 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