week1_Assignment.ipynb

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    "---\n",
    "\n",
    "_You are currently looking at **version 1.1** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-machine-learning/resources/bANLa) course resource._\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 1 - Introduction to Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this assignment, you will be using the Breast Cancer Wisconsin (Diagnostic) Database to create a classifier that can help diagnose patients. First, read through the description of the dataset (below)."
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "\n",
    "print(cancer)\n",
    "\n",
    "print(cancer.DESCR) # Print the data set description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The object returned by `load_breast_cancer()` is a scikit-learn Bunch object, which is similar to a dictionary."
   ]
  },
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     "text": [
      "Breast Cancer Wisconsin (Diagnostic) Database\n",
      "=============================================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 569\n",
      "\n",
      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
      "\n",
      "    :Attribute Information:\n",
      "        - radius (mean of distances from center to points on the perimeter)\n",
      "        - texture (standard deviation of gray-scale values)\n",
      "        - perimeter\n",
      "        - area\n",
      "        - smoothness (local variation in radius lengths)\n",
      "        - compactness (perimeter^2 / area - 1.0)\n",
      "        - concavity (severity of concave portions of the contour)\n",
      "        - concave points (number of concave portions of the contour)\n",
      "        - symmetry \n",
      "        - fractal dimension (\"coastline approximation\" - 1)\n",
      "\n",
      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
      "        largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 3 is Mean Radius, field\n",
      "        13 is Radius SE, field 23 is Worst Radius.\n",
      "\n",
      "        - class:\n",
      "                - WDBC-Malignant\n",
      "                - WDBC-Benign\n",
      "\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ===================================== ====== ======\n",
      "                                           Min    Max\n",
      "    ===================================== ====== ======\n",
      "    radius (mean):                        6.981  28.11\n",
      "    texture (mean):                       9.71   39.28\n",
      "    perimeter (mean):                     43.79  188.5\n",
      "    area (mean):                          143.5  2501.0\n",
      "    smoothness (mean):                    0.053  0.163\n",
      "    compactness (mean):                   0.019  0.345\n",
      "    concavity (mean):                     0.0    0.427\n",
      "    concave points (mean):                0.0    0.201\n",
      "    symmetry (mean):                      0.106  0.304\n",
      "    fractal dimension (mean):             0.05   0.097\n",
      "    radius (standard error):              0.112  2.873\n",
      "    texture (standard error):             0.36   4.885\n",
      "    perimeter (standard error):           0.757  21.98\n",
      "    area (standard error):                6.802  542.2\n",
      "    smoothness (standard error):          0.002  0.031\n",
      "    compactness (standard error):         0.002  0.135\n",
      "    concavity (standard error):           0.0    0.396\n",
      "    concave points (standard error):      0.0    0.053\n",
      "    symmetry (standard error):            0.008  0.079\n",
      "    fractal dimension (standard error):   0.001  0.03\n",
      "    radius (worst):                       7.93   36.04\n",
      "    texture (worst):                      12.02  49.54\n",
      "    perimeter (worst):                    50.41  251.2\n",
      "    area (worst):                         185.2  4254.0\n",
      "    smoothness (worst):                   0.071  0.223\n",
      "    compactness (worst):                  0.027  1.058\n",
      "    concavity (worst):                    0.0    1.252\n",
      "    concave points (worst):               0.0    0.291\n",
      "    symmetry (worst):                     0.156  0.664\n",
      "    fractal dimension (worst):            0.055  0.208\n",
      "    ===================================== ====== ======\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
      "\n",
      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
      "\n",
      "    :Donor: Nick Street\n",
      "\n",
      "    :Date: November, 1995\n",
      "\n",
      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
      "https://goo.gl/U2Uwz2\n",
      "\n",
      "Features are computed from a digitized image of a fine needle\n",
      "aspirate (FNA) of a breast mass.  They describe\n",
      "characteristics of the cell nuclei present in the image.\n",
      "\n",
      "Separating plane described above was obtained using\n",
      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
      "pp. 97-101, 1992], a classification method which uses linear\n",
      "programming to construct a decision tree.  Relevant features\n",
      "were selected using an exhaustive search in the space of 1-4\n",
      "features and 1-3 separating planes.\n",
      "\n",
      "The actual linear program used to obtain the separating plane\n",
      "in the 3-dimensional space is that described in:\n",
      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
      "Optimization Methods and Software 1, 1992, 23-34].\n",
      "\n",
      "This database is also available through the UW CS ftp server:\n",
      "\n",
      "ftp ftp.cs.wisc.edu\n",
      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
      "\n",
      "References\n",
      "----------\n",
      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
      "     San Jose, CA, 1993.\n",
      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
      "     July-August 1995.\n",
      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
      "     163-171.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys(['target_names', 'data', 'target', 'feature_names', 'DESCR'])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "\n",
    "# print(cancer)\n",
    "\n",
    "print(cancer.DESCR) # Print the data set description\n",
    "\n",
    "\n",
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 0 (Example)\n",
    "\n",
    "How many features does the breast cancer dataset have?\n",
    "\n",
    "*This function should return an integer.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "       'smoothness error', 'compactness error', 'concavity error',\n",
       "       'concave points error', 'symmetry error', 'fractal dimension error',\n",
       "       'worst radius', 'worst texture', 'worst perimeter', 'worst area',\n",
       "       'worst smoothness', 'worst compactness', 'worst concavity',\n",
       "       'worst concave points', 'worst symmetry', 'worst fractal dimension'], \n",
       "      dtype='<U23')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You should write your whole answer within the function provided. The autograder will call\n",
    "# this function and compare the return value against the correct solution value\n",
    "def answer_zero():\n",
    "    # This function returns the number of features of the breast cancer dataset, which is an integer. \n",
    "    # The assignment question description will tell you the general format the autograder is expecting\n",
    "    return ((cancer['feature_names']))\n",
    "\n",
    "# You can examine what your function returns by calling it in the cell. If you have questions\n",
    "# about the assignment formats, check out the discussion forums for any FAQs\n",
    "# ['target_names', 'data', 'target', 'feature_names', 'DESCR']\n",
    "\n",
    "answer_zero() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "\n",
    "Scikit-learn works with lists, numpy arrays, scipy-sparse matrices, and pandas DataFrames, so converting the dataset to a DataFrame is not necessary for training this model. Using a DataFrame does however help make many things easier such as munging data, so let's practice creating a classifier with a pandas DataFrame. \n",
    "\n",
    "\n",
    "\n",
    "Convert the sklearn.dataset `cancer` to a DataFrame. \n",
    "\n",
    "*This function should return a `(569, 31)` DataFrame with * \n",
    "\n",
    "*columns = *\n",
    "\n",
    "    ['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
    "    'mean smoothness', 'mean compactness', 'mean concavity',\n",
    "    'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
    "    'radius error', 'texture error', 'perimeter error', 'area error',\n",
    "    'smoothness error', 'compactness error', 'concavity error',\n",
    "    'concave points error', 'symmetry error', 'fractal dimension error',\n",
    "    'worst radius', 'worst texture', 'worst perimeter', 'worst area',\n",
    "    'worst smoothness', 'worst compactness', 'worst concavity',\n",
    "    'worst concave points', 'worst symmetry', 'worst fractal dimension',\n",
    "    'target']\n",
    "\n",
    "*and index = *\n",
    "\n",
    "    RangeIndex(start=0, stop=569, step=1)"
   ]
  },
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   "cell_type": "code",
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.990</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.300100</td>\n",
       "      <td>0.147100</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.71190</td>\n",
       "      <td>0.26540</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.570</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.086900</td>\n",
       "      <td>0.070170</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.24160</td>\n",
       "      <td>0.18600</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.690</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.197400</td>\n",
       "      <td>0.127900</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.45040</td>\n",
       "      <td>0.24300</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.420</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.241400</td>\n",
       "      <td>0.105200</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.68690</td>\n",
       "      <td>0.25750</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.290</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.198000</td>\n",
       "      <td>0.104300</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.40000</td>\n",
       "      <td>0.16250</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.450</td>\n",
       "      <td>15.70</td>\n",
       "      <td>82.57</td>\n",
       "      <td>477.1</td>\n",
       "      <td>0.12780</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>0.157800</td>\n",
       "      <td>0.080890</td>\n",
       "      <td>0.2087</td>\n",
       "      <td>0.07613</td>\n",
       "      <td>...</td>\n",
       "      <td>23.75</td>\n",
       "      <td>103.40</td>\n",
       "      <td>741.6</td>\n",
       "      <td>0.17910</td>\n",
       "      <td>0.52490</td>\n",
       "      <td>0.53550</td>\n",
       "      <td>0.17410</td>\n",
       "      <td>0.3985</td>\n",
       "      <td>0.12440</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18.250</td>\n",
       "      <td>19.98</td>\n",
       "      <td>119.60</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>0.09463</td>\n",
       "      <td>0.10900</td>\n",
       "      <td>0.112700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.1794</td>\n",
       "      <td>0.05742</td>\n",
       "      <td>...</td>\n",
       "      <td>27.66</td>\n",
       "      <td>153.20</td>\n",
       "      <td>1606.0</td>\n",
       "      <td>0.14420</td>\n",
       "      <td>0.25760</td>\n",
       "      <td>0.37840</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>0.3063</td>\n",
       "      <td>0.08368</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13.710</td>\n",
       "      <td>20.83</td>\n",
       "      <td>90.20</td>\n",
       "      <td>577.9</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0.16450</td>\n",
       "      <td>0.093660</td>\n",
       "      <td>0.059850</td>\n",
       "      <td>0.2196</td>\n",
       "      <td>0.07451</td>\n",
       "      <td>...</td>\n",
       "      <td>28.14</td>\n",
       "      <td>110.60</td>\n",
       "      <td>897.0</td>\n",
       "      <td>0.16540</td>\n",
       "      <td>0.36820</td>\n",
       "      <td>0.26780</td>\n",
       "      <td>0.15560</td>\n",
       "      <td>0.3196</td>\n",
       "      <td>0.11510</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13.000</td>\n",
       "      <td>21.82</td>\n",
       "      <td>87.50</td>\n",
       "      <td>519.8</td>\n",
       "      <td>0.12730</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>0.185900</td>\n",
       "      <td>0.093530</td>\n",
       "      <td>0.2350</td>\n",
       "      <td>0.07389</td>\n",
       "      <td>...</td>\n",
       "      <td>30.73</td>\n",
       "      <td>106.20</td>\n",
       "      <td>739.3</td>\n",
       "      <td>0.17030</td>\n",
       "      <td>0.54010</td>\n",
       "      <td>0.53900</td>\n",
       "      <td>0.20600</td>\n",
       "      <td>0.4378</td>\n",
       "      <td>0.10720</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.460</td>\n",
       "      <td>24.04</td>\n",
       "      <td>83.97</td>\n",
       "      <td>475.9</td>\n",
       "      <td>0.11860</td>\n",
       "      <td>0.23960</td>\n",
       "      <td>0.227300</td>\n",
       "      <td>0.085430</td>\n",
       "      <td>0.2030</td>\n",
       "      <td>0.08243</td>\n",
       "      <td>...</td>\n",
       "      <td>40.68</td>\n",
       "      <td>97.65</td>\n",
       "      <td>711.4</td>\n",
       "      <td>0.18530</td>\n",
       "      <td>1.05800</td>\n",
       "      <td>1.10500</td>\n",
       "      <td>0.22100</td>\n",
       "      <td>0.4366</td>\n",
       "      <td>0.20750</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16.020</td>\n",
       "      <td>23.24</td>\n",
       "      <td>102.70</td>\n",
       "      <td>797.8</td>\n",
       "      <td>0.08206</td>\n",
       "      <td>0.06669</td>\n",
       "      <td>0.032990</td>\n",
       "      <td>0.033230</td>\n",
       "      <td>0.1528</td>\n",
       "      <td>0.05697</td>\n",
       "      <td>...</td>\n",
       "      <td>33.88</td>\n",
       "      <td>123.80</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>0.11810</td>\n",
       "      <td>0.15510</td>\n",
       "      <td>0.14590</td>\n",
       "      <td>0.09975</td>\n",
       "      <td>0.2948</td>\n",
       "      <td>0.08452</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15.780</td>\n",
       "      <td>17.89</td>\n",
       "      <td>103.60</td>\n",
       "      <td>781.0</td>\n",
       "      <td>0.09710</td>\n",
       "      <td>0.12920</td>\n",
       "      <td>0.099540</td>\n",
       "      <td>0.066060</td>\n",
       "      <td>0.1842</td>\n",
       "      <td>0.06082</td>\n",
       "      <td>...</td>\n",
       "      <td>27.28</td>\n",
       "      <td>136.50</td>\n",
       "      <td>1299.0</td>\n",
       "      <td>0.13960</td>\n",
       "      <td>0.56090</td>\n",
       "      <td>0.39650</td>\n",
       "      <td>0.18100</td>\n",
       "      <td>0.3792</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19.170</td>\n",
       "      <td>24.80</td>\n",
       "      <td>132.40</td>\n",
       "      <td>1123.0</td>\n",
       "      <td>0.09740</td>\n",
       "      <td>0.24580</td>\n",
       "      <td>0.206500</td>\n",
       "      <td>0.111800</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07800</td>\n",
       "      <td>...</td>\n",
       "      <td>29.94</td>\n",
       "      <td>151.70</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>0.10370</td>\n",
       "      <td>0.39030</td>\n",
       "      <td>0.36390</td>\n",
       "      <td>0.17670</td>\n",
       "      <td>0.3176</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15.850</td>\n",
       "      <td>23.95</td>\n",
       "      <td>103.70</td>\n",
       "      <td>782.7</td>\n",
       "      <td>0.08401</td>\n",
       "      <td>0.10020</td>\n",
       "      <td>0.099380</td>\n",
       "      <td>0.053640</td>\n",
       "      <td>0.1847</td>\n",
       "      <td>0.05338</td>\n",
       "      <td>...</td>\n",
       "      <td>27.66</td>\n",
       "      <td>112.00</td>\n",
       "      <td>876.5</td>\n",
       "      <td>0.11310</td>\n",
       "      <td>0.19240</td>\n",
       "      <td>0.23220</td>\n",
       "      <td>0.11190</td>\n",
       "      <td>0.2809</td>\n",
       "      <td>0.06287</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13.730</td>\n",
       "      <td>22.61</td>\n",
       "      <td>93.60</td>\n",
       "      <td>578.3</td>\n",
       "      <td>0.11310</td>\n",
       "      <td>0.22930</td>\n",
       "      <td>0.212800</td>\n",
       "      <td>0.080250</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.07682</td>\n",
       "      <td>...</td>\n",
       "      <td>32.01</td>\n",
       "      <td>108.80</td>\n",
       "      <td>697.7</td>\n",
       "      <td>0.16510</td>\n",
       "      <td>0.77250</td>\n",
       "      <td>0.69430</td>\n",
       "      <td>0.22080</td>\n",
       "      <td>0.3596</td>\n",
       "      <td>0.14310</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14.540</td>\n",
       "      <td>27.54</td>\n",
       "      <td>96.73</td>\n",
       "      <td>658.8</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.15950</td>\n",
       "      <td>0.163900</td>\n",
       "      <td>0.073640</td>\n",
       "      <td>0.2303</td>\n",
       "      <td>0.07077</td>\n",
       "      <td>...</td>\n",
       "      <td>37.13</td>\n",
       "      <td>124.10</td>\n",
       "      <td>943.2</td>\n",
       "      <td>0.16780</td>\n",
       "      <td>0.65770</td>\n",
       "      <td>0.70260</td>\n",
       "      <td>0.17120</td>\n",
       "      <td>0.4218</td>\n",
       "      <td>0.13410</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14.680</td>\n",
       "      <td>20.13</td>\n",
       "      <td>94.74</td>\n",
       "      <td>684.5</td>\n",
       "      <td>0.09867</td>\n",
       "      <td>0.07200</td>\n",
       "      <td>0.073950</td>\n",
       "      <td>0.052590</td>\n",
       "      <td>0.1586</td>\n",
       "      <td>0.05922</td>\n",
       "      <td>...</td>\n",
       "      <td>30.88</td>\n",
       "      <td>123.40</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>0.14640</td>\n",
       "      <td>0.18710</td>\n",
       "      <td>0.29140</td>\n",
       "      <td>0.16090</td>\n",
       "      <td>0.3029</td>\n",
       "      <td>0.08216</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.130</td>\n",
       "      <td>20.68</td>\n",
       "      <td>108.10</td>\n",
       "      <td>798.8</td>\n",
       "      <td>0.11700</td>\n",
       "      <td>0.20220</td>\n",
       "      <td>0.172200</td>\n",
       "      <td>0.102800</td>\n",
       "      <td>0.2164</td>\n",
       "      <td>0.07356</td>\n",
       "      <td>...</td>\n",
       "      <td>31.48</td>\n",
       "      <td>136.80</td>\n",
       "      <td>1315.0</td>\n",
       "      <td>0.17890</td>\n",
       "      <td>0.42330</td>\n",
       "      <td>0.47840</td>\n",
       "      <td>0.20730</td>\n",
       "      <td>0.3706</td>\n",
       "      <td>0.11420</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.810</td>\n",
       "      <td>22.15</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1260.0</td>\n",
       "      <td>0.09831</td>\n",
       "      <td>0.10270</td>\n",
       "      <td>0.147900</td>\n",
       "      <td>0.094980</td>\n",
       "      <td>0.1582</td>\n",
       "      <td>0.05395</td>\n",
       "      <td>...</td>\n",
       "      <td>30.88</td>\n",
       "      <td>186.80</td>\n",
       "      <td>2398.0</td>\n",
       "      <td>0.15120</td>\n",
       "      <td>0.31500</td>\n",
       "      <td>0.53720</td>\n",
       "      <td>0.23880</td>\n",
       "      <td>0.2768</td>\n",
       "      <td>0.07615</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13.540</td>\n",
       "      <td>14.36</td>\n",
       "      <td>87.46</td>\n",
       "      <td>566.3</td>\n",
       "      <td>0.09779</td>\n",
       "      <td>0.08129</td>\n",
       "      <td>0.066640</td>\n",
       "      <td>0.047810</td>\n",
       "      <td>0.1885</td>\n",
       "      <td>0.05766</td>\n",
       "      <td>...</td>\n",
       "      <td>19.26</td>\n",
       "      <td>99.70</td>\n",
       "      <td>711.2</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.17730</td>\n",
       "      <td>0.23900</td>\n",
       "      <td>0.12880</td>\n",
       "      <td>0.2977</td>\n",
       "      <td>0.07259</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>13.080</td>\n",
       "      <td>15.71</td>\n",
       "      <td>85.63</td>\n",
       "      <td>520.0</td>\n",
       "      <td>0.10750</td>\n",
       "      <td>0.12700</td>\n",
       "      <td>0.045680</td>\n",
       "      <td>0.031100</td>\n",
       "      <td>0.1967</td>\n",
       "      <td>0.06811</td>\n",
       "      <td>...</td>\n",
       "      <td>20.49</td>\n",
       "      <td>96.09</td>\n",
       "      <td>630.5</td>\n",
       "      <td>0.13120</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.18900</td>\n",
       "      <td>0.07283</td>\n",
       "      <td>0.3184</td>\n",
       "      <td>0.08183</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>9.504</td>\n",
       "      <td>12.44</td>\n",
       "      <td>60.34</td>\n",
       "      <td>273.9</td>\n",
       "      <td>0.10240</td>\n",
       "      <td>0.06492</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020760</td>\n",
       "      <td>0.1815</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>...</td>\n",
       "      <td>15.66</td>\n",
       "      <td>65.13</td>\n",
       "      <td>314.9</td>\n",
       "      <td>0.13240</td>\n",
       "      <td>0.11480</td>\n",
       "      <td>0.08867</td>\n",
       "      <td>0.06227</td>\n",
       "      <td>0.2450</td>\n",
       "      <td>0.07773</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.340</td>\n",
       "      <td>14.26</td>\n",
       "      <td>102.50</td>\n",
       "      <td>704.4</td>\n",
       "      <td>0.10730</td>\n",
       "      <td>0.21350</td>\n",
       "      <td>0.207700</td>\n",
       "      <td>0.097560</td>\n",
       "      <td>0.2521</td>\n",
       "      <td>0.07032</td>\n",
       "      <td>...</td>\n",
       "      <td>19.08</td>\n",
       "      <td>125.10</td>\n",
       "      <td>980.9</td>\n",
       "      <td>0.13900</td>\n",
       "      <td>0.59540</td>\n",
       "      <td>0.63050</td>\n",
       "      <td>0.23930</td>\n",
       "      <td>0.4667</td>\n",
       "      <td>0.09946</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>21.160</td>\n",
       "      <td>23.04</td>\n",
       "      <td>137.20</td>\n",
       "      <td>1404.0</td>\n",
       "      <td>0.09428</td>\n",
       "      <td>0.10220</td>\n",
       "      <td>0.109700</td>\n",
       "      <td>0.086320</td>\n",
       "      <td>0.1769</td>\n",
       "      <td>0.05278</td>\n",
       "      <td>...</td>\n",
       "      <td>35.59</td>\n",
       "      <td>188.00</td>\n",
       "      <td>2615.0</td>\n",
       "      <td>0.14010</td>\n",
       "      <td>0.26000</td>\n",
       "      <td>0.31550</td>\n",
       "      <td>0.20090</td>\n",
       "      <td>0.2822</td>\n",
       "      <td>0.07526</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16.650</td>\n",
       "      <td>21.38</td>\n",
       "      <td>110.00</td>\n",
       "      <td>904.6</td>\n",
       "      <td>0.11210</td>\n",
       "      <td>0.14570</td>\n",
       "      <td>0.152500</td>\n",
       "      <td>0.091700</td>\n",
       "      <td>0.1995</td>\n",
       "      <td>0.06330</td>\n",
       "      <td>...</td>\n",
       "      <td>31.56</td>\n",
       "      <td>177.00</td>\n",
       "      <td>2215.0</td>\n",
       "      <td>0.18050</td>\n",
       "      <td>0.35780</td>\n",
       "      <td>0.46950</td>\n",
       "      <td>0.20950</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.09564</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>17.140</td>\n",
       "      <td>16.40</td>\n",
       "      <td>116.00</td>\n",
       "      <td>912.7</td>\n",
       "      <td>0.11860</td>\n",
       "      <td>0.22760</td>\n",
       "      <td>0.222900</td>\n",
       "      <td>0.140100</td>\n",
       "      <td>0.3040</td>\n",
       "      <td>0.07413</td>\n",
       "      <td>...</td>\n",
       "      <td>21.40</td>\n",
       "      <td>152.40</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>0.15450</td>\n",
       "      <td>0.39490</td>\n",
       "      <td>0.38530</td>\n",
       "      <td>0.25500</td>\n",
       "      <td>0.4066</td>\n",
       "      <td>0.10590</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>14.580</td>\n",
       "      <td>21.53</td>\n",
       "      <td>97.41</td>\n",
       "      <td>644.8</td>\n",
       "      <td>0.10540</td>\n",
       "      <td>0.18680</td>\n",
       "      <td>0.142500</td>\n",
       "      <td>0.087830</td>\n",
       "      <td>0.2252</td>\n",
       "      <td>0.06924</td>\n",
       "      <td>...</td>\n",
       "      <td>33.21</td>\n",
       "      <td>122.40</td>\n",
       "      <td>896.9</td>\n",
       "      <td>0.15250</td>\n",
       "      <td>0.66430</td>\n",
       "      <td>0.55390</td>\n",
       "      <td>0.27010</td>\n",
       "      <td>0.4264</td>\n",
       "      <td>0.12750</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>18.610</td>\n",
       "      <td>20.25</td>\n",
       "      <td>122.10</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>0.09440</td>\n",
       "      <td>0.10660</td>\n",
       "      <td>0.149000</td>\n",
       "      <td>0.077310</td>\n",
       "      <td>0.1697</td>\n",
       "      <td>0.05699</td>\n",
       "      <td>...</td>\n",
       "      <td>27.26</td>\n",
       "      <td>139.90</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>0.13380</td>\n",
       "      <td>0.21170</td>\n",
       "      <td>0.34460</td>\n",
       "      <td>0.14900</td>\n",
       "      <td>0.2341</td>\n",
       "      <td>0.07421</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>15.300</td>\n",
       "      <td>25.27</td>\n",
       "      <td>102.40</td>\n",
       "      <td>732.4</td>\n",
       "      <td>0.10820</td>\n",
       "      <td>0.16970</td>\n",
       "      <td>0.168300</td>\n",
       "      <td>0.087510</td>\n",
       "      <td>0.1926</td>\n",
       "      <td>0.06540</td>\n",
       "      <td>...</td>\n",
       "      <td>36.71</td>\n",
       "      <td>149.30</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>0.16410</td>\n",
       "      <td>0.61100</td>\n",
       "      <td>0.63350</td>\n",
       "      <td>0.20240</td>\n",
       "      <td>0.4027</td>\n",
       "      <td>0.09876</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>17.570</td>\n",
       "      <td>15.05</td>\n",
       "      <td>115.00</td>\n",
       "      <td>955.1</td>\n",
       "      <td>0.09847</td>\n",
       "      <td>0.11570</td>\n",
       "      <td>0.098750</td>\n",
       "      <td>0.079530</td>\n",
       "      <td>0.1739</td>\n",
       "      <td>0.06149</td>\n",
       "      <td>...</td>\n",
       "      <td>19.52</td>\n",
       "      <td>134.90</td>\n",
       "      <td>1227.0</td>\n",
       "      <td>0.12550</td>\n",
       "      <td>0.28120</td>\n",
       "      <td>0.24890</td>\n",
       "      <td>0.14560</td>\n",
       "      <td>0.2756</td>\n",
       "      <td>0.07919</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>7.691</td>\n",
       "      <td>25.44</td>\n",
       "      <td>48.34</td>\n",
       "      <td>170.4</td>\n",
       "      <td>0.08668</td>\n",
       "      <td>0.11990</td>\n",
       "      <td>0.092520</td>\n",
       "      <td>0.013640</td>\n",
       "      <td>0.2037</td>\n",
       "      <td>0.07751</td>\n",
       "      <td>...</td>\n",
       "      <td>31.89</td>\n",
       "      <td>54.49</td>\n",
       "      <td>223.6</td>\n",
       "      <td>0.15960</td>\n",
       "      <td>0.30640</td>\n",
       "      <td>0.33930</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.2790</td>\n",
       "      <td>0.10660</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>11.540</td>\n",
       "      <td>14.44</td>\n",
       "      <td>74.65</td>\n",
       "      <td>402.9</td>\n",
       "      <td>0.09984</td>\n",
       "      <td>0.11200</td>\n",
       "      <td>0.067370</td>\n",
       "      <td>0.025940</td>\n",
       "      <td>0.1818</td>\n",
       "      <td>0.06782</td>\n",
       "      <td>...</td>\n",
       "      <td>19.68</td>\n",
       "      <td>78.78</td>\n",
       "      <td>457.8</td>\n",
       "      <td>0.13450</td>\n",
       "      <td>0.21180</td>\n",
       "      <td>0.17970</td>\n",
       "      <td>0.06918</td>\n",
       "      <td>0.2329</td>\n",
       "      <td>0.08134</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>14.470</td>\n",
       "      <td>24.99</td>\n",
       "      <td>95.81</td>\n",
       "      <td>656.4</td>\n",
       "      <td>0.08837</td>\n",
       "      <td>0.12300</td>\n",
       "      <td>0.100900</td>\n",
       "      <td>0.038900</td>\n",
       "      <td>0.1872</td>\n",
       "      <td>0.06341</td>\n",
       "      <td>...</td>\n",
       "      <td>31.73</td>\n",
       "      <td>113.50</td>\n",
       "      <td>808.9</td>\n",
       "      <td>0.13400</td>\n",
       "      <td>0.42020</td>\n",
       "      <td>0.40400</td>\n",
       "      <td>0.12050</td>\n",
       "      <td>0.3187</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>14.740</td>\n",
       "      <td>25.42</td>\n",
       "      <td>94.70</td>\n",
       "      <td>668.6</td>\n",
       "      <td>0.08275</td>\n",
       "      <td>0.07214</td>\n",
       "      <td>0.041050</td>\n",
       "      <td>0.030270</td>\n",
       "      <td>0.1840</td>\n",
       "      <td>0.05680</td>\n",
       "      <td>...</td>\n",
       "      <td>32.29</td>\n",
       "      <td>107.40</td>\n",
       "      <td>826.4</td>\n",
       "      <td>0.10600</td>\n",
       "      <td>0.13760</td>\n",
       "      <td>0.16110</td>\n",
       "      <td>0.10950</td>\n",
       "      <td>0.2722</td>\n",
       "      <td>0.06956</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>13.210</td>\n",
       "      <td>28.06</td>\n",
       "      <td>84.88</td>\n",
       "      <td>538.4</td>\n",
       "      <td>0.08671</td>\n",
       "      <td>0.06877</td>\n",
       "      <td>0.029870</td>\n",
       "      <td>0.032750</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.05781</td>\n",
       "      <td>...</td>\n",
       "      <td>37.17</td>\n",
       "      <td>92.48</td>\n",
       "      <td>629.6</td>\n",
       "      <td>0.10720</td>\n",
       "      <td>0.13810</td>\n",
       "      <td>0.10620</td>\n",
       "      <td>0.07958</td>\n",
       "      <td>0.2473</td>\n",
       "      <td>0.06443</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>13.870</td>\n",
       "      <td>20.70</td>\n",
       "      <td>89.77</td>\n",
       "      <td>584.8</td>\n",
       "      <td>0.09578</td>\n",
       "      <td>0.10180</td>\n",
       "      <td>0.036880</td>\n",
       "      <td>0.023690</td>\n",
       "      <td>0.1620</td>\n",
       "      <td>0.06688</td>\n",
       "      <td>...</td>\n",
       "      <td>24.75</td>\n",
       "      <td>99.17</td>\n",
       "      <td>688.6</td>\n",
       "      <td>0.12640</td>\n",
       "      <td>0.20370</td>\n",
       "      <td>0.13770</td>\n",
       "      <td>0.06845</td>\n",
       "      <td>0.2249</td>\n",
       "      <td>0.08492</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>13.620</td>\n",
       "      <td>23.23</td>\n",
       "      <td>87.19</td>\n",
       "      <td>573.2</td>\n",
       "      <td>0.09246</td>\n",
       "      <td>0.06747</td>\n",
       "      <td>0.029740</td>\n",
       "      <td>0.024430</td>\n",
       "      <td>0.1664</td>\n",
       "      <td>0.05801</td>\n",
       "      <td>...</td>\n",
       "      <td>29.09</td>\n",
       "      <td>97.58</td>\n",
       "      <td>729.8</td>\n",
       "      <td>0.12160</td>\n",
       "      <td>0.15170</td>\n",
       "      <td>0.10490</td>\n",
       "      <td>0.07174</td>\n",
       "      <td>0.2642</td>\n",
       "      <td>0.06953</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>10.320</td>\n",
       "      <td>16.35</td>\n",
       "      <td>65.31</td>\n",
       "      <td>324.9</td>\n",
       "      <td>0.09434</td>\n",
       "      <td>0.04994</td>\n",
       "      <td>0.010120</td>\n",
       "      <td>0.005495</td>\n",
       "      <td>0.1885</td>\n",
       "      <td>0.06201</td>\n",
       "      <td>...</td>\n",
       "      <td>21.77</td>\n",
       "      <td>71.12</td>\n",
       "      <td>384.9</td>\n",
       "      <td>0.12850</td>\n",
       "      <td>0.08842</td>\n",
       "      <td>0.04384</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.2681</td>\n",
       "      <td>0.07399</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>10.260</td>\n",
       "      <td>16.58</td>\n",
       "      <td>65.85</td>\n",
       "      <td>320.8</td>\n",
       "      <td>0.08877</td>\n",
       "      <td>0.08066</td>\n",
       "      <td>0.043580</td>\n",
       "      <td>0.024380</td>\n",
       "      <td>0.1669</td>\n",
       "      <td>0.06714</td>\n",
       "      <td>...</td>\n",
       "      <td>22.04</td>\n",
       "      <td>71.08</td>\n",
       "      <td>357.4</td>\n",
       "      <td>0.14610</td>\n",
       "      <td>0.22460</td>\n",
       "      <td>0.17830</td>\n",
       "      <td>0.08333</td>\n",
       "      <td>0.2691</td>\n",
       "      <td>0.09479</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>9.683</td>\n",
       "      <td>19.34</td>\n",
       "      <td>61.05</td>\n",
       "      <td>285.7</td>\n",
       "      <td>0.08491</td>\n",
       "      <td>0.05030</td>\n",
       "      <td>0.023370</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0.1580</td>\n",
       "      <td>0.06235</td>\n",
       "      <td>...</td>\n",
       "      <td>25.59</td>\n",
       "      <td>69.10</td>\n",
       "      <td>364.2</td>\n",
       "      <td>0.11990</td>\n",
       "      <td>0.09546</td>\n",
       "      <td>0.09350</td>\n",
       "      <td>0.03846</td>\n",
       "      <td>0.2552</td>\n",
       "      <td>0.07920</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>10.820</td>\n",
       "      <td>24.21</td>\n",
       "      <td>68.89</td>\n",
       "      <td>361.6</td>\n",
       "      <td>0.08192</td>\n",
       "      <td>0.06602</td>\n",
       "      <td>0.015480</td>\n",
       "      <td>0.008160</td>\n",
       "      <td>0.1976</td>\n",
       "      <td>0.06328</td>\n",
       "      <td>...</td>\n",
       "      <td>31.45</td>\n",
       "      <td>83.90</td>\n",
       "      <td>505.6</td>\n",
       "      <td>0.12040</td>\n",
       "      <td>0.16330</td>\n",
       "      <td>0.06194</td>\n",
       "      <td>0.03264</td>\n",
       "      <td>0.3059</td>\n",
       "      <td>0.07626</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>10.860</td>\n",
       "      <td>21.48</td>\n",
       "      <td>68.51</td>\n",
       "      <td>360.5</td>\n",
       "      <td>0.07431</td>\n",
       "      <td>0.04227</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1661</td>\n",
       "      <td>0.05948</td>\n",
       "      <td>...</td>\n",
       "      <td>24.77</td>\n",
       "      <td>74.08</td>\n",
       "      <td>412.3</td>\n",
       "      <td>0.10010</td>\n",
       "      <td>0.07348</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.2458</td>\n",
       "      <td>0.06592</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>11.130</td>\n",
       "      <td>22.44</td>\n",
       "      <td>71.49</td>\n",
       "      <td>378.4</td>\n",
       "      <td>0.09566</td>\n",
       "      <td>0.08194</td>\n",
       "      <td>0.048240</td>\n",
       "      <td>0.022570</td>\n",
       "      <td>0.2030</td>\n",
       "      <td>0.06552</td>\n",
       "      <td>...</td>\n",
       "      <td>28.26</td>\n",
       "      <td>77.80</td>\n",
       "      <td>436.6</td>\n",
       "      <td>0.10870</td>\n",
       "      <td>0.17820</td>\n",
       "      <td>0.15640</td>\n",
       "      <td>0.06413</td>\n",
       "      <td>0.3169</td>\n",
       "      <td>0.08032</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>12.770</td>\n",
       "      <td>29.43</td>\n",
       "      <td>81.35</td>\n",
       "      <td>507.9</td>\n",
       "      <td>0.08276</td>\n",
       "      <td>0.04234</td>\n",
       "      <td>0.019970</td>\n",
       "      <td>0.014990</td>\n",
       "      <td>0.1539</td>\n",
       "      <td>0.05637</td>\n",
       "      <td>...</td>\n",
       "      <td>36.00</td>\n",
       "      <td>88.10</td>\n",
       "      <td>594.7</td>\n",
       "      <td>0.12340</td>\n",
       "      <td>0.10640</td>\n",
       "      <td>0.08653</td>\n",
       "      <td>0.06498</td>\n",
       "      <td>0.2407</td>\n",
       "      <td>0.06484</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>9.333</td>\n",
       "      <td>21.94</td>\n",
       "      <td>59.01</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.09240</td>\n",
       "      <td>0.05605</td>\n",
       "      <td>0.039960</td>\n",
       "      <td>0.012820</td>\n",
       "      <td>0.1692</td>\n",
       "      <td>0.06576</td>\n",
       "      <td>...</td>\n",
       "      <td>25.05</td>\n",
       "      <td>62.86</td>\n",
       "      <td>295.8</td>\n",
       "      <td>0.11030</td>\n",
       "      <td>0.08298</td>\n",
       "      <td>0.07993</td>\n",
       "      <td>0.02564</td>\n",
       "      <td>0.2435</td>\n",
       "      <td>0.07393</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>12.880</td>\n",
       "      <td>28.92</td>\n",
       "      <td>82.50</td>\n",
       "      <td>514.3</td>\n",
       "      <td>0.08123</td>\n",
       "      <td>0.05824</td>\n",
       "      <td>0.061950</td>\n",
       "      <td>0.023430</td>\n",
       "      <td>0.1566</td>\n",
       "      <td>0.05708</td>\n",
       "      <td>...</td>\n",
       "      <td>35.74</td>\n",
       "      <td>88.84</td>\n",
       "      <td>595.7</td>\n",
       "      <td>0.12270</td>\n",
       "      <td>0.16200</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.06493</td>\n",
       "      <td>0.2372</td>\n",
       "      <td>0.07242</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>10.290</td>\n",
       "      <td>27.61</td>\n",
       "      <td>65.67</td>\n",
       "      <td>321.4</td>\n",
       "      <td>0.09030</td>\n",
       "      <td>0.07658</td>\n",
       "      <td>0.059990</td>\n",
       "      <td>0.027380</td>\n",
       "      <td>0.1593</td>\n",
       "      <td>0.06127</td>\n",
       "      <td>...</td>\n",
       "      <td>34.91</td>\n",
       "      <td>69.57</td>\n",
       "      <td>357.6</td>\n",
       "      <td>0.13840</td>\n",
       "      <td>0.17100</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>0.09127</td>\n",
       "      <td>0.2226</td>\n",
       "      <td>0.08283</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>10.160</td>\n",
       "      <td>19.59</td>\n",
       "      <td>64.73</td>\n",
       "      <td>311.7</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.07504</td>\n",
       "      <td>0.005025</td>\n",
       "      <td>0.011160</td>\n",
       "      <td>0.1791</td>\n",
       "      <td>0.06331</td>\n",
       "      <td>...</td>\n",
       "      <td>22.88</td>\n",
       "      <td>67.88</td>\n",
       "      <td>347.3</td>\n",
       "      <td>0.12650</td>\n",
       "      <td>0.12000</td>\n",
       "      <td>0.01005</td>\n",
       "      <td>0.02232</td>\n",
       "      <td>0.2262</td>\n",
       "      <td>0.06742</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>9.423</td>\n",
       "      <td>27.88</td>\n",
       "      <td>59.26</td>\n",
       "      <td>271.3</td>\n",
       "      <td>0.08123</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1742</td>\n",
       "      <td>0.06059</td>\n",
       "      <td>...</td>\n",
       "      <td>34.24</td>\n",
       "      <td>66.50</td>\n",
       "      <td>330.6</td>\n",
       "      <td>0.10730</td>\n",
       "      <td>0.07158</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.06969</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>14.590</td>\n",
       "      <td>22.68</td>\n",
       "      <td>96.39</td>\n",
       "      <td>657.1</td>\n",
       "      <td>0.08473</td>\n",
       "      <td>0.13300</td>\n",
       "      <td>0.102900</td>\n",
       "      <td>0.037360</td>\n",
       "      <td>0.1454</td>\n",
       "      <td>0.06147</td>\n",
       "      <td>...</td>\n",
       "      <td>27.27</td>\n",
       "      <td>105.90</td>\n",
       "      <td>733.5</td>\n",
       "      <td>0.10260</td>\n",
       "      <td>0.31710</td>\n",
       "      <td>0.36620</td>\n",
       "      <td>0.11050</td>\n",
       "      <td>0.2258</td>\n",
       "      <td>0.08004</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>11.510</td>\n",
       "      <td>23.93</td>\n",
       "      <td>74.52</td>\n",
       "      <td>403.5</td>\n",
       "      <td>0.09261</td>\n",
       "      <td>0.10210</td>\n",
       "      <td>0.111200</td>\n",
       "      <td>0.041050</td>\n",
       "      <td>0.1388</td>\n",
       "      <td>0.06570</td>\n",
       "      <td>...</td>\n",
       "      <td>37.16</td>\n",
       "      <td>82.28</td>\n",
       "      <td>474.2</td>\n",
       "      <td>0.12980</td>\n",
       "      <td>0.25170</td>\n",
       "      <td>0.36300</td>\n",
       "      <td>0.09653</td>\n",
       "      <td>0.2112</td>\n",
       "      <td>0.08732</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>14.050</td>\n",
       "      <td>27.15</td>\n",
       "      <td>91.38</td>\n",
       "      <td>600.4</td>\n",
       "      <td>0.09929</td>\n",
       "      <td>0.11260</td>\n",
       "      <td>0.044620</td>\n",
       "      <td>0.043040</td>\n",
       "      <td>0.1537</td>\n",
       "      <td>0.06171</td>\n",
       "      <td>...</td>\n",
       "      <td>33.17</td>\n",
       "      <td>100.20</td>\n",
       "      <td>706.7</td>\n",
       "      <td>0.12410</td>\n",
       "      <td>0.22640</td>\n",
       "      <td>0.13260</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0.2250</td>\n",
       "      <td>0.08321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>11.200</td>\n",
       "      <td>29.37</td>\n",
       "      <td>70.67</td>\n",
       "      <td>386.0</td>\n",
       "      <td>0.07449</td>\n",
       "      <td>0.03558</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1060</td>\n",
       "      <td>0.05502</td>\n",
       "      <td>...</td>\n",
       "      <td>38.30</td>\n",
       "      <td>75.19</td>\n",
       "      <td>439.6</td>\n",
       "      <td>0.09267</td>\n",
       "      <td>0.05494</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1566</td>\n",
       "      <td>0.05905</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>15.220</td>\n",
       "      <td>30.62</td>\n",
       "      <td>103.40</td>\n",
       "      <td>716.9</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0.20870</td>\n",
       "      <td>0.255000</td>\n",
       "      <td>0.094290</td>\n",
       "      <td>0.2128</td>\n",
       "      <td>0.07152</td>\n",
       "      <td>...</td>\n",
       "      <td>42.79</td>\n",
       "      <td>128.70</td>\n",
       "      <td>915.0</td>\n",
       "      <td>0.14170</td>\n",
       "      <td>0.79170</td>\n",
       "      <td>1.17000</td>\n",
       "      <td>0.23560</td>\n",
       "      <td>0.4089</td>\n",
       "      <td>0.14090</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>20.920</td>\n",
       "      <td>25.09</td>\n",
       "      <td>143.00</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>0.10990</td>\n",
       "      <td>0.22360</td>\n",
       "      <td>0.317400</td>\n",
       "      <td>0.147400</td>\n",
       "      <td>0.2149</td>\n",
       "      <td>0.06879</td>\n",
       "      <td>...</td>\n",
       "      <td>29.41</td>\n",
       "      <td>179.10</td>\n",
       "      <td>1819.0</td>\n",
       "      <td>0.14070</td>\n",
       "      <td>0.41860</td>\n",
       "      <td>0.65990</td>\n",
       "      <td>0.25420</td>\n",
       "      <td>0.2929</td>\n",
       "      <td>0.09873</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.560</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.243900</td>\n",
       "      <td>0.138900</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.41070</td>\n",
       "      <td>0.22160</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.130</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.144000</td>\n",
       "      <td>0.097910</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.32150</td>\n",
       "      <td>0.16280</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.600</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.092510</td>\n",
       "      <td>0.053020</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.34030</td>\n",
       "      <td>0.14180</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.600</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.351400</td>\n",
       "      <td>0.152000</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.93870</td>\n",
       "      <td>0.26500</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.760</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0         17.990         10.38          122.80     1001.0          0.11840   \n",
       "1         20.570         17.77          132.90     1326.0          0.08474   \n",
       "2         19.690         21.25          130.00     1203.0          0.10960   \n",
       "3         11.420         20.38           77.58      386.1          0.14250   \n",
       "4         20.290         14.34          135.10     1297.0          0.10030   \n",
       "5         12.450         15.70           82.57      477.1          0.12780   \n",
       "6         18.250         19.98          119.60     1040.0          0.09463   \n",
       "7         13.710         20.83           90.20      577.9          0.11890   \n",
       "8         13.000         21.82           87.50      519.8          0.12730   \n",
       "9         12.460         24.04           83.97      475.9          0.11860   \n",
       "10        16.020         23.24          102.70      797.8          0.08206   \n",
       "11        15.780         17.89          103.60      781.0          0.09710   \n",
       "12        19.170         24.80          132.40     1123.0          0.09740   \n",
       "13        15.850         23.95          103.70      782.7          0.08401   \n",
       "14        13.730         22.61           93.60      578.3          0.11310   \n",
       "15        14.540         27.54           96.73      658.8          0.11390   \n",
       "16        14.680         20.13           94.74      684.5          0.09867   \n",
       "17        16.130         20.68          108.10      798.8          0.11700   \n",
       "18        19.810         22.15          130.00     1260.0          0.09831   \n",
       "19        13.540         14.36           87.46      566.3          0.09779   \n",
       "20        13.080         15.71           85.63      520.0          0.10750   \n",
       "21         9.504         12.44           60.34      273.9          0.10240   \n",
       "22        15.340         14.26          102.50      704.4          0.10730   \n",
       "23        21.160         23.04          137.20     1404.0          0.09428   \n",
       "24        16.650         21.38          110.00      904.6          0.11210   \n",
       "25        17.140         16.40          116.00      912.7          0.11860   \n",
       "26        14.580         21.53           97.41      644.8          0.10540   \n",
       "27        18.610         20.25          122.10     1094.0          0.09440   \n",
       "28        15.300         25.27          102.40      732.4          0.10820   \n",
       "29        17.570         15.05          115.00      955.1          0.09847   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "539        7.691         25.44           48.34      170.4          0.08668   \n",
       "540       11.540         14.44           74.65      402.9          0.09984   \n",
       "541       14.470         24.99           95.81      656.4          0.08837   \n",
       "542       14.740         25.42           94.70      668.6          0.08275   \n",
       "543       13.210         28.06           84.88      538.4          0.08671   \n",
       "544       13.870         20.70           89.77      584.8          0.09578   \n",
       "545       13.620         23.23           87.19      573.2          0.09246   \n",
       "546       10.320         16.35           65.31      324.9          0.09434   \n",
       "547       10.260         16.58           65.85      320.8          0.08877   \n",
       "548        9.683         19.34           61.05      285.7          0.08491   \n",
       "549       10.820         24.21           68.89      361.6          0.08192   \n",
       "550       10.860         21.48           68.51      360.5          0.07431   \n",
       "551       11.130         22.44           71.49      378.4          0.09566   \n",
       "552       12.770         29.43           81.35      507.9          0.08276   \n",
       "553        9.333         21.94           59.01      264.0          0.09240   \n",
       "554       12.880         28.92           82.50      514.3          0.08123   \n",
       "555       10.290         27.61           65.67      321.4          0.09030   \n",
       "556       10.160         19.59           64.73      311.7          0.10030   \n",
       "557        9.423         27.88           59.26      271.3          0.08123   \n",
       "558       14.590         22.68           96.39      657.1          0.08473   \n",
       "559       11.510         23.93           74.52      403.5          0.09261   \n",
       "560       14.050         27.15           91.38      600.4          0.09929   \n",
       "561       11.200         29.37           70.67      386.0          0.07449   \n",
       "562       15.220         30.62          103.40      716.9          0.10480   \n",
       "563       20.920         25.09          143.00     1347.0          0.10990   \n",
       "564       21.560         22.39          142.00     1479.0          0.11100   \n",
       "565       20.130         28.25          131.20     1261.0          0.09780   \n",
       "566       16.600         28.08          108.30      858.1          0.08455   \n",
       "567       20.600         29.33          140.10     1265.0          0.11780   \n",
       "568        7.760         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0             0.27760        0.300100             0.147100         0.2419   \n",
       "1             0.07864        0.086900             0.070170         0.1812   \n",
       "2             0.15990        0.197400             0.127900         0.2069   \n",
       "3             0.28390        0.241400             0.105200         0.2597   \n",
       "4             0.13280        0.198000             0.104300         0.1809   \n",
       "5             0.17000        0.157800             0.080890         0.2087   \n",
       "6             0.10900        0.112700             0.074000         0.1794   \n",
       "7             0.16450        0.093660             0.059850         0.2196   \n",
       "8             0.19320        0.185900             0.093530         0.2350   \n",
       "9             0.23960        0.227300             0.085430         0.2030   \n",
       "10            0.06669        0.032990             0.033230         0.1528   \n",
       "11            0.12920        0.099540             0.066060         0.1842   \n",
       "12            0.24580        0.206500             0.111800         0.2397   \n",
       "13            0.10020        0.099380             0.053640         0.1847   \n",
       "14            0.22930        0.212800             0.080250         0.2069   \n",
       "15            0.15950        0.163900             0.073640         0.2303   \n",
       "16            0.07200        0.073950             0.052590         0.1586   \n",
       "17            0.20220        0.172200             0.102800         0.2164   \n",
       "18            0.10270        0.147900             0.094980         0.1582   \n",
       "19            0.08129        0.066640             0.047810         0.1885   \n",
       "20            0.12700        0.045680             0.031100         0.1967   \n",
       "21            0.06492        0.029560             0.020760         0.1815   \n",
       "22            0.21350        0.207700             0.097560         0.2521   \n",
       "23            0.10220        0.109700             0.086320         0.1769   \n",
       "24            0.14570        0.152500             0.091700         0.1995   \n",
       "25            0.22760        0.222900             0.140100         0.3040   \n",
       "26            0.18680        0.142500             0.087830         0.2252   \n",
       "27            0.10660        0.149000             0.077310         0.1697   \n",
       "28            0.16970        0.168300             0.087510         0.1926   \n",
       "29            0.11570        0.098750             0.079530         0.1739   \n",
       "..                ...             ...                  ...            ...   \n",
       "539           0.11990        0.092520             0.013640         0.2037   \n",
       "540           0.11200        0.067370             0.025940         0.1818   \n",
       "541           0.12300        0.100900             0.038900         0.1872   \n",
       "542           0.07214        0.041050             0.030270         0.1840   \n",
       "543           0.06877        0.029870             0.032750         0.1628   \n",
       "544           0.10180        0.036880             0.023690         0.1620   \n",
       "545           0.06747        0.029740             0.024430         0.1664   \n",
       "546           0.04994        0.010120             0.005495         0.1885   \n",
       "547           0.08066        0.043580             0.024380         0.1669   \n",
       "548           0.05030        0.023370             0.009615         0.1580   \n",
       "549           0.06602        0.015480             0.008160         0.1976   \n",
       "550           0.04227        0.000000             0.000000         0.1661   \n",
       "551           0.08194        0.048240             0.022570         0.2030   \n",
       "552           0.04234        0.019970             0.014990         0.1539   \n",
       "553           0.05605        0.039960             0.012820         0.1692   \n",
       "554           0.05824        0.061950             0.023430         0.1566   \n",
       "555           0.07658        0.059990             0.027380         0.1593   \n",
       "556           0.07504        0.005025             0.011160         0.1791   \n",
       "557           0.04971        0.000000             0.000000         0.1742   \n",
       "558           0.13300        0.102900             0.037360         0.1454   \n",
       "559           0.10210        0.111200             0.041050         0.1388   \n",
       "560           0.11260        0.044620             0.043040         0.1537   \n",
       "561           0.03558        0.000000             0.000000         0.1060   \n",
       "562           0.20870        0.255000             0.094290         0.2128   \n",
       "563           0.22360        0.317400             0.147400         0.2149   \n",
       "564           0.11590        0.243900             0.138900         0.1726   \n",
       "565           0.10340        0.144000             0.097910         0.1752   \n",
       "566           0.10230        0.092510             0.053020         0.1590   \n",
       "567           0.27700        0.351400             0.152000         0.2397   \n",
       "568           0.04362        0.000000             0.000000         0.1587   \n",
       "\n",
       "     mean fractal dimension   ...    worst texture  worst perimeter  \\\n",
       "0                   0.07871   ...            17.33           184.60   \n",
       "1                   0.05667   ...            23.41           158.80   \n",
       "2                   0.05999   ...            25.53           152.50   \n",
       "3                   0.09744   ...            26.50            98.87   \n",
       "4                   0.05883   ...            16.67           152.20   \n",
       "5                   0.07613   ...            23.75           103.40   \n",
       "6                   0.05742   ...            27.66           153.20   \n",
       "7                   0.07451   ...            28.14           110.60   \n",
       "8                   0.07389   ...            30.73           106.20   \n",
       "9                   0.08243   ...            40.68            97.65   \n",
       "10                  0.05697   ...            33.88           123.80   \n",
       "11                  0.06082   ...            27.28           136.50   \n",
       "12                  0.07800   ...            29.94           151.70   \n",
       "13                  0.05338   ...            27.66           112.00   \n",
       "14                  0.07682   ...            32.01           108.80   \n",
       "15                  0.07077   ...            37.13           124.10   \n",
       "16                  0.05922   ...            30.88           123.40   \n",
       "17                  0.07356   ...            31.48           136.80   \n",
       "18                  0.05395   ...            30.88           186.80   \n",
       "19                  0.05766   ...            19.26            99.70   \n",
       "20                  0.06811   ...            20.49            96.09   \n",
       "21                  0.06905   ...            15.66            65.13   \n",
       "22                  0.07032   ...            19.08           125.10   \n",
       "23                  0.05278   ...            35.59           188.00   \n",
       "24                  0.06330   ...            31.56           177.00   \n",
       "25                  0.07413   ...            21.40           152.40   \n",
       "26                  0.06924   ...            33.21           122.40   \n",
       "27                  0.05699   ...            27.26           139.90   \n",
       "28                  0.06540   ...            36.71           149.30   \n",
       "29                  0.06149   ...            19.52           134.90   \n",
       "..                      ...   ...              ...              ...   \n",
       "539                 0.07751   ...            31.89            54.49   \n",
       "540                 0.06782   ...            19.68            78.78   \n",
       "541                 0.06341   ...            31.73           113.50   \n",
       "542                 0.05680   ...            32.29           107.40   \n",
       "543                 0.05781   ...            37.17            92.48   \n",
       "544                 0.06688   ...            24.75            99.17   \n",
       "545                 0.05801   ...            29.09            97.58   \n",
       "546                 0.06201   ...            21.77            71.12   \n",
       "547                 0.06714   ...            22.04            71.08   \n",
       "548                 0.06235   ...            25.59            69.10   \n",
       "549                 0.06328   ...            31.45            83.90   \n",
       "550                 0.05948   ...            24.77            74.08   \n",
       "551                 0.06552   ...            28.26            77.80   \n",
       "552                 0.05637   ...            36.00            88.10   \n",
       "553                 0.06576   ...            25.05            62.86   \n",
       "554                 0.05708   ...            35.74            88.84   \n",
       "555                 0.06127   ...            34.91            69.57   \n",
       "556                 0.06331   ...            22.88            67.88   \n",
       "557                 0.06059   ...            34.24            66.50   \n",
       "558                 0.06147   ...            27.27           105.90   \n",
       "559                 0.06570   ...            37.16            82.28   \n",
       "560                 0.06171   ...            33.17           100.20   \n",
       "561                 0.05502   ...            38.30            75.19   \n",
       "562                 0.07152   ...            42.79           128.70   \n",
       "563                 0.06879   ...            29.41           179.10   \n",
       "564                 0.05623   ...            26.40           166.10   \n",
       "565                 0.05533   ...            38.25           155.00   \n",
       "566                 0.05648   ...            34.12           126.70   \n",
       "567                 0.07016   ...            39.42           184.60   \n",
       "568                 0.05884   ...            30.37            59.16   \n",
       "\n",
       "     worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0        2019.0           0.16220            0.66560          0.71190   \n",
       "1        1956.0           0.12380            0.18660          0.24160   \n",
       "2        1709.0           0.14440            0.42450          0.45040   \n",
       "3         567.7           0.20980            0.86630          0.68690   \n",
       "4        1575.0           0.13740            0.20500          0.40000   \n",
       "5         741.6           0.17910            0.52490          0.53550   \n",
       "6        1606.0           0.14420            0.25760          0.37840   \n",
       "7         897.0           0.16540            0.36820          0.26780   \n",
       "8         739.3           0.17030            0.54010          0.53900   \n",
       "9         711.4           0.18530            1.05800          1.10500   \n",
       "10       1150.0           0.11810            0.15510          0.14590   \n",
       "11       1299.0           0.13960            0.56090          0.39650   \n",
       "12       1332.0           0.10370            0.39030          0.36390   \n",
       "13        876.5           0.11310            0.19240          0.23220   \n",
       "14        697.7           0.16510            0.77250          0.69430   \n",
       "15        943.2           0.16780            0.65770          0.70260   \n",
       "16       1138.0           0.14640            0.18710          0.29140   \n",
       "17       1315.0           0.17890            0.42330          0.47840   \n",
       "18       2398.0           0.15120            0.31500          0.53720   \n",
       "19        711.2           0.14400            0.17730          0.23900   \n",
       "20        630.5           0.13120            0.27760          0.18900   \n",
       "21        314.9           0.13240            0.11480          0.08867   \n",
       "22        980.9           0.13900            0.59540          0.63050   \n",
       "23       2615.0           0.14010            0.26000          0.31550   \n",
       "24       2215.0           0.18050            0.35780          0.46950   \n",
       "25       1461.0           0.15450            0.39490          0.38530   \n",
       "26        896.9           0.15250            0.66430          0.55390   \n",
       "27       1403.0           0.13380            0.21170          0.34460   \n",
       "28       1269.0           0.16410            0.61100          0.63350   \n",
       "29       1227.0           0.12550            0.28120          0.24890   \n",
       "..          ...               ...                ...              ...   \n",
       "539       223.6           0.15960            0.30640          0.33930   \n",
       "540       457.8           0.13450            0.21180          0.17970   \n",
       "541       808.9           0.13400            0.42020          0.40400   \n",
       "542       826.4           0.10600            0.13760          0.16110   \n",
       "543       629.6           0.10720            0.13810          0.10620   \n",
       "544       688.6           0.12640            0.20370          0.13770   \n",
       "545       729.8           0.12160            0.15170          0.10490   \n",
       "546       384.9           0.12850            0.08842          0.04384   \n",
       "547       357.4           0.14610            0.22460          0.17830   \n",
       "548       364.2           0.11990            0.09546          0.09350   \n",
       "549       505.6           0.12040            0.16330          0.06194   \n",
       "550       412.3           0.10010            0.07348          0.00000   \n",
       "551       436.6           0.10870            0.17820          0.15640   \n",
       "552       594.7           0.12340            0.10640          0.08653   \n",
       "553       295.8           0.11030            0.08298          0.07993   \n",
       "554       595.7           0.12270            0.16200          0.24390   \n",
       "555       357.6           0.13840            0.17100          0.20000   \n",
       "556       347.3           0.12650            0.12000          0.01005   \n",
       "557       330.6           0.10730            0.07158          0.00000   \n",
       "558       733.5           0.10260            0.31710          0.36620   \n",
       "559       474.2           0.12980            0.25170          0.36300   \n",
       "560       706.7           0.12410            0.22640          0.13260   \n",
       "561       439.6           0.09267            0.05494          0.00000   \n",
       "562       915.0           0.14170            0.79170          1.17000   \n",
       "563      1819.0           0.14070            0.41860          0.65990   \n",
       "564      2027.0           0.14100            0.21130          0.41070   \n",
       "565      1731.0           0.11660            0.19220          0.32150   \n",
       "566      1124.0           0.11390            0.30940          0.34030   \n",
       "567      1821.0           0.16500            0.86810          0.93870   \n",
       "568       268.6           0.08996            0.06444          0.00000   \n",
       "\n",
       "     worst concave points  worst symmetry  worst fractal dimension  target  \n",
       "0                 0.26540          0.4601                  0.11890       0  \n",
       "1                 0.18600          0.2750                  0.08902       0  \n",
       "2                 0.24300          0.3613                  0.08758       0  \n",
       "3                 0.25750          0.6638                  0.17300       0  \n",
       "4                 0.16250          0.2364                  0.07678       0  \n",
       "5                 0.17410          0.3985                  0.12440       0  \n",
       "6                 0.19320          0.3063                  0.08368       0  \n",
       "7                 0.15560          0.3196                  0.11510       0  \n",
       "8                 0.20600          0.4378                  0.10720       0  \n",
       "9                 0.22100          0.4366                  0.20750       0  \n",
       "10                0.09975          0.2948                  0.08452       0  \n",
       "11                0.18100          0.3792                  0.10480       0  \n",
       "12                0.17670          0.3176                  0.10230       0  \n",
       "13                0.11190          0.2809                  0.06287       0  \n",
       "14                0.22080          0.3596                  0.14310       0  \n",
       "15                0.17120          0.4218                  0.13410       0  \n",
       "16                0.16090          0.3029                  0.08216       0  \n",
       "17                0.20730          0.3706                  0.11420       0  \n",
       "18                0.23880          0.2768                  0.07615       0  \n",
       "19                0.12880          0.2977                  0.07259       1  \n",
       "20                0.07283          0.3184                  0.08183       1  \n",
       "21                0.06227          0.2450                  0.07773       1  \n",
       "22                0.23930          0.4667                  0.09946       0  \n",
       "23                0.20090          0.2822                  0.07526       0  \n",
       "24                0.20950          0.3613                  0.09564       0  \n",
       "25                0.25500          0.4066                  0.10590       0  \n",
       "26                0.27010          0.4264                  0.12750       0  \n",
       "27                0.14900          0.2341                  0.07421       0  \n",
       "28                0.20240          0.4027                  0.09876       0  \n",
       "29                0.14560          0.2756                  0.07919       0  \n",
       "..                    ...             ...                      ...     ...  \n",
       "539               0.05000          0.2790                  0.10660       1  \n",
       "540               0.06918          0.2329                  0.08134       1  \n",
       "541               0.12050          0.3187                  0.10230       1  \n",
       "542               0.10950          0.2722                  0.06956       1  \n",
       "543               0.07958          0.2473                  0.06443       1  \n",
       "544               0.06845          0.2249                  0.08492       1  \n",
       "545               0.07174          0.2642                  0.06953       1  \n",
       "546               0.02381          0.2681                  0.07399       1  \n",
       "547               0.08333          0.2691                  0.09479       1  \n",
       "548               0.03846          0.2552                  0.07920       1  \n",
       "549               0.03264          0.3059                  0.07626       1  \n",
       "550               0.00000          0.2458                  0.06592       1  \n",
       "551               0.06413          0.3169                  0.08032       1  \n",
       "552               0.06498          0.2407                  0.06484       1  \n",
       "553               0.02564          0.2435                  0.07393       1  \n",
       "554               0.06493          0.2372                  0.07242       1  \n",
       "555               0.09127          0.2226                  0.08283       1  \n",
       "556               0.02232          0.2262                  0.06742       1  \n",
       "557               0.00000          0.2475                  0.06969       1  \n",
       "558               0.11050          0.2258                  0.08004       1  \n",
       "559               0.09653          0.2112                  0.08732       1  \n",
       "560               0.10480          0.2250                  0.08321       1  \n",
       "561               0.00000          0.1566                  0.05905       1  \n",
       "562               0.23560          0.4089                  0.14090       0  \n",
       "563               0.25420          0.2929                  0.09873       0  \n",
       "564               0.22160          0.2060                  0.07115       0  \n",
       "565               0.16280          0.2572                  0.06637       0  \n",
       "566               0.14180          0.2218                  0.07820       0  \n",
       "567               0.26500          0.4087                  0.12400       0  \n",
       "568               0.00000          0.2871                  0.07039       1  \n",
       "\n",
       "[569 rows x 31 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_one():\n",
    "    \n",
    "    # Your code here\n",
    "#     print(len(cancer['feature_names']))\n",
    "    columns = ['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
    "    'mean smoothness', 'mean compactness', 'mean concavity',\n",
    "    'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
    "    'radius error', 'texture error', 'perimeter error', 'area error',\n",
    "    'smoothness error', 'compactness error', 'concavity error',\n",
    "    'concave points error', 'symmetry error', 'fractal dimension error',\n",
    "    'worst radius', 'worst texture', 'worst perimeter', 'worst area',\n",
    "    'worst smoothness', 'worst compactness', 'worst concavity',\n",
    "    'worst concave points', 'worst symmetry', 'worst fractal dimension',\n",
    "    'target']\n",
    "    \n",
    "    \n",
    "    index = range(0, 569, 1)\n",
    "#     print(cancer['data'].shape)\n",
    "    df = pd.DataFrame(data=cancer['data'], index=index, columns = columns[:30])\n",
    "#     print(cancer['target'])\n",
    "    df['target'] = cancer['target']\n",
    "    \n",
    "#     print(df.head())\n",
    "    ans = df\n",
    "    \n",
    "    return ans\n",
    "\n",
    "\n",
    "answer_one()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "What is the class distribution? (i.e. how many instances of `malignant` (encoded 0) and how many `benign` (encoded 1)?)\n",
    "\n",
    "*This function should return a Series named `target` of length 2 with integer values and index =* `['malignant', 'benign']`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "malignant    212\n",
       "benign       357\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_two():\n",
    "    cancerdf = answer_one()\n",
    "    \n",
    "#     cancerdf = pd.DataFrame(cancerdf)\n",
    "    \n",
    "    # Your code here\n",
    "    malignant_count = len(cancerdf[cancerdf['target'] == 0])\n",
    "    benign_count = len(cancerdf[cancerdf['target'] == 1])\n",
    "    \n",
    "    index = ['malignant', 'benign']\n",
    "    \n",
    "    target = pd.Series(data=[malignant_count, benign_count], index=index)\n",
    "    \n",
    "#     print(target)\n",
    "    ans = target\n",
    "    \n",
    "    \n",
    "    return ans\n",
    "\n",
    "\n",
    "answer_two()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "Split the DataFrame into `X` (the data) and `y` (the labels).\n",
    "\n",
    "*This function should return a tuple of length 2:* `(X, y)`*, where* \n",
    "* `X` *has shape* `(569, 30)`\n",
    "* `y` *has shape* `(569,)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       " 0         17.990         10.38          122.80     1001.0          0.11840   \n",
       " 1         20.570         17.77          132.90     1326.0          0.08474   \n",
       " 2         19.690         21.25          130.00     1203.0          0.10960   \n",
       " 3         11.420         20.38           77.58      386.1          0.14250   \n",
       " 4         20.290         14.34          135.10     1297.0          0.10030   \n",
       " 5         12.450         15.70           82.57      477.1          0.12780   \n",
       " 6         18.250         19.98          119.60     1040.0          0.09463   \n",
       " 7         13.710         20.83           90.20      577.9          0.11890   \n",
       " 8         13.000         21.82           87.50      519.8          0.12730   \n",
       " 9         12.460         24.04           83.97      475.9          0.11860   \n",
       " 10        16.020         23.24          102.70      797.8          0.08206   \n",
       " 11        15.780         17.89          103.60      781.0          0.09710   \n",
       " 12        19.170         24.80          132.40     1123.0          0.09740   \n",
       " 13        15.850         23.95          103.70      782.7          0.08401   \n",
       " 14        13.730         22.61           93.60      578.3          0.11310   \n",
       " 15        14.540         27.54           96.73      658.8          0.11390   \n",
       " 16        14.680         20.13           94.74      684.5          0.09867   \n",
       " 17        16.130         20.68          108.10      798.8          0.11700   \n",
       " 18        19.810         22.15          130.00     1260.0          0.09831   \n",
       " 19        13.540         14.36           87.46      566.3          0.09779   \n",
       " 20        13.080         15.71           85.63      520.0          0.10750   \n",
       " 21         9.504         12.44           60.34      273.9          0.10240   \n",
       " 22        15.340         14.26          102.50      704.4          0.10730   \n",
       " 23        21.160         23.04          137.20     1404.0          0.09428   \n",
       " 24        16.650         21.38          110.00      904.6          0.11210   \n",
       " 25        17.140         16.40          116.00      912.7          0.11860   \n",
       " 26        14.580         21.53           97.41      644.8          0.10540   \n",
       " 27        18.610         20.25          122.10     1094.0          0.09440   \n",
       " 28        15.300         25.27          102.40      732.4          0.10820   \n",
       " 29        17.570         15.05          115.00      955.1          0.09847   \n",
       " ..           ...           ...             ...        ...              ...   \n",
       " 539        7.691         25.44           48.34      170.4          0.08668   \n",
       " 540       11.540         14.44           74.65      402.9          0.09984   \n",
       " 541       14.470         24.99           95.81      656.4          0.08837   \n",
       " 542       14.740         25.42           94.70      668.6          0.08275   \n",
       " 543       13.210         28.06           84.88      538.4          0.08671   \n",
       " 544       13.870         20.70           89.77      584.8          0.09578   \n",
       " 545       13.620         23.23           87.19      573.2          0.09246   \n",
       " 546       10.320         16.35           65.31      324.9          0.09434   \n",
       " 547       10.260         16.58           65.85      320.8          0.08877   \n",
       " 548        9.683         19.34           61.05      285.7          0.08491   \n",
       " 549       10.820         24.21           68.89      361.6          0.08192   \n",
       " 550       10.860         21.48           68.51      360.5          0.07431   \n",
       " 551       11.130         22.44           71.49      378.4          0.09566   \n",
       " 552       12.770         29.43           81.35      507.9          0.08276   \n",
       " 553        9.333         21.94           59.01      264.0          0.09240   \n",
       " 554       12.880         28.92           82.50      514.3          0.08123   \n",
       " 555       10.290         27.61           65.67      321.4          0.09030   \n",
       " 556       10.160         19.59           64.73      311.7          0.10030   \n",
       " 557        9.423         27.88           59.26      271.3          0.08123   \n",
       " 558       14.590         22.68           96.39      657.1          0.08473   \n",
       " 559       11.510         23.93           74.52      403.5          0.09261   \n",
       " 560       14.050         27.15           91.38      600.4          0.09929   \n",
       " 561       11.200         29.37           70.67      386.0          0.07449   \n",
       " 562       15.220         30.62          103.40      716.9          0.10480   \n",
       " 563       20.920         25.09          143.00     1347.0          0.10990   \n",
       " 564       21.560         22.39          142.00     1479.0          0.11100   \n",
       " 565       20.130         28.25          131.20     1261.0          0.09780   \n",
       " 566       16.600         28.08          108.30      858.1          0.08455   \n",
       " 567       20.600         29.33          140.10     1265.0          0.11780   \n",
       " 568        7.760         24.54           47.92      181.0          0.05263   \n",
       " \n",
       "      mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       " 0             0.27760        0.300100             0.147100         0.2419   \n",
       " 1             0.07864        0.086900             0.070170         0.1812   \n",
       " 2             0.15990        0.197400             0.127900         0.2069   \n",
       " 3             0.28390        0.241400             0.105200         0.2597   \n",
       " 4             0.13280        0.198000             0.104300         0.1809   \n",
       " 5             0.17000        0.157800             0.080890         0.2087   \n",
       " 6             0.10900        0.112700             0.074000         0.1794   \n",
       " 7             0.16450        0.093660             0.059850         0.2196   \n",
       " 8             0.19320        0.185900             0.093530         0.2350   \n",
       " 9             0.23960        0.227300             0.085430         0.2030   \n",
       " 10            0.06669        0.032990             0.033230         0.1528   \n",
       " 11            0.12920        0.099540             0.066060         0.1842   \n",
       " 12            0.24580        0.206500             0.111800         0.2397   \n",
       " 13            0.10020        0.099380             0.053640         0.1847   \n",
       " 14            0.22930        0.212800             0.080250         0.2069   \n",
       " 15            0.15950        0.163900             0.073640         0.2303   \n",
       " 16            0.07200        0.073950             0.052590         0.1586   \n",
       " 17            0.20220        0.172200             0.102800         0.2164   \n",
       " 18            0.10270        0.147900             0.094980         0.1582   \n",
       " 19            0.08129        0.066640             0.047810         0.1885   \n",
       " 20            0.12700        0.045680             0.031100         0.1967   \n",
       " 21            0.06492        0.029560             0.020760         0.1815   \n",
       " 22            0.21350        0.207700             0.097560         0.2521   \n",
       " 23            0.10220        0.109700             0.086320         0.1769   \n",
       " 24            0.14570        0.152500             0.091700         0.1995   \n",
       " 25            0.22760        0.222900             0.140100         0.3040   \n",
       " 26            0.18680        0.142500             0.087830         0.2252   \n",
       " 27            0.10660        0.149000             0.077310         0.1697   \n",
       " 28            0.16970        0.168300             0.087510         0.1926   \n",
       " 29            0.11570        0.098750             0.079530         0.1739   \n",
       " ..                ...             ...                  ...            ...   \n",
       " 539           0.11990        0.092520             0.013640         0.2037   \n",
       " 540           0.11200        0.067370             0.025940         0.1818   \n",
       " 541           0.12300        0.100900             0.038900         0.1872   \n",
       " 542           0.07214        0.041050             0.030270         0.1840   \n",
       " 543           0.06877        0.029870             0.032750         0.1628   \n",
       " 544           0.10180        0.036880             0.023690         0.1620   \n",
       " 545           0.06747        0.029740             0.024430         0.1664   \n",
       " 546           0.04994        0.010120             0.005495         0.1885   \n",
       " 547           0.08066        0.043580             0.024380         0.1669   \n",
       " 548           0.05030        0.023370             0.009615         0.1580   \n",
       " 549           0.06602        0.015480             0.008160         0.1976   \n",
       " 550           0.04227        0.000000             0.000000         0.1661   \n",
       " 551           0.08194        0.048240             0.022570         0.2030   \n",
       " 552           0.04234        0.019970             0.014990         0.1539   \n",
       " 553           0.05605        0.039960             0.012820         0.1692   \n",
       " 554           0.05824        0.061950             0.023430         0.1566   \n",
       " 555           0.07658        0.059990             0.027380         0.1593   \n",
       " 556           0.07504        0.005025             0.011160         0.1791   \n",
       " 557           0.04971        0.000000             0.000000         0.1742   \n",
       " 558           0.13300        0.102900             0.037360         0.1454   \n",
       " 559           0.10210        0.111200             0.041050         0.1388   \n",
       " 560           0.11260        0.044620             0.043040         0.1537   \n",
       " 561           0.03558        0.000000             0.000000         0.1060   \n",
       " 562           0.20870        0.255000             0.094290         0.2128   \n",
       " 563           0.22360        0.317400             0.147400         0.2149   \n",
       " 564           0.11590        0.243900             0.138900         0.1726   \n",
       " 565           0.10340        0.144000             0.097910         0.1752   \n",
       " 566           0.10230        0.092510             0.053020         0.1590   \n",
       " 567           0.27700        0.351400             0.152000         0.2397   \n",
       " 568           0.04362        0.000000             0.000000         0.1587   \n",
       " \n",
       "      mean fractal dimension           ...             worst radius  \\\n",
       " 0                   0.07871           ...                   25.380   \n",
       " 1                   0.05667           ...                   24.990   \n",
       " 2                   0.05999           ...                   23.570   \n",
       " 3                   0.09744           ...                   14.910   \n",
       " 4                   0.05883           ...                   22.540   \n",
       " 5                   0.07613           ...                   15.470   \n",
       " 6                   0.05742           ...                   22.880   \n",
       " 7                   0.07451           ...                   17.060   \n",
       " 8                   0.07389           ...                   15.490   \n",
       " 9                   0.08243           ...                   15.090   \n",
       " 10                  0.05697           ...                   19.190   \n",
       " 11                  0.06082           ...                   20.420   \n",
       " 12                  0.07800           ...                   20.960   \n",
       " 13                  0.05338           ...                   16.840   \n",
       " 14                  0.07682           ...                   15.030   \n",
       " 15                  0.07077           ...                   17.460   \n",
       " 16                  0.05922           ...                   19.070   \n",
       " 17                  0.07356           ...                   20.960   \n",
       " 18                  0.05395           ...                   27.320   \n",
       " 19                  0.05766           ...                   15.110   \n",
       " 20                  0.06811           ...                   14.500   \n",
       " 21                  0.06905           ...                   10.230   \n",
       " 22                  0.07032           ...                   18.070   \n",
       " 23                  0.05278           ...                   29.170   \n",
       " 24                  0.06330           ...                   26.460   \n",
       " 25                  0.07413           ...                   22.250   \n",
       " 26                  0.06924           ...                   17.620   \n",
       " 27                  0.05699           ...                   21.310   \n",
       " 28                  0.06540           ...                   20.270   \n",
       " 29                  0.06149           ...                   20.010   \n",
       " ..                      ...           ...                      ...   \n",
       " 539                 0.07751           ...                    8.678   \n",
       " 540                 0.06782           ...                   12.260   \n",
       " 541                 0.06341           ...                   16.220   \n",
       " 542                 0.05680           ...                   16.510   \n",
       " 543                 0.05781           ...                   14.370   \n",
       " 544                 0.06688           ...                   15.050   \n",
       " 545                 0.05801           ...                   15.350   \n",
       " 546                 0.06201           ...                   11.250   \n",
       " 547                 0.06714           ...                   10.830   \n",
       " 548                 0.06235           ...                   10.930   \n",
       " 549                 0.06328           ...                   13.030   \n",
       " 550                 0.05948           ...                   11.660   \n",
       " 551                 0.06552           ...                   12.020   \n",
       " 552                 0.05637           ...                   13.870   \n",
       " 553                 0.06576           ...                    9.845   \n",
       " 554                 0.05708           ...                   13.890   \n",
       " 555                 0.06127           ...                   10.840   \n",
       " 556                 0.06331           ...                   10.650   \n",
       " 557                 0.06059           ...                   10.490   \n",
       " 558                 0.06147           ...                   15.480   \n",
       " 559                 0.06570           ...                   12.480   \n",
       " 560                 0.06171           ...                   15.300   \n",
       " 561                 0.05502           ...                   11.920   \n",
       " 562                 0.07152           ...                   17.520   \n",
       " 563                 0.06879           ...                   24.290   \n",
       " 564                 0.05623           ...                   25.450   \n",
       " 565                 0.05533           ...                   23.690   \n",
       " 566                 0.05648           ...                   18.980   \n",
       " 567                 0.07016           ...                   25.740   \n",
       " 568                 0.05884           ...                    9.456   \n",
       " \n",
       "      worst texture  worst perimeter  worst area  worst smoothness  \\\n",
       " 0            17.33           184.60      2019.0           0.16220   \n",
       " 1            23.41           158.80      1956.0           0.12380   \n",
       " 2            25.53           152.50      1709.0           0.14440   \n",
       " 3            26.50            98.87       567.7           0.20980   \n",
       " 4            16.67           152.20      1575.0           0.13740   \n",
       " 5            23.75           103.40       741.6           0.17910   \n",
       " 6            27.66           153.20      1606.0           0.14420   \n",
       " 7            28.14           110.60       897.0           0.16540   \n",
       " 8            30.73           106.20       739.3           0.17030   \n",
       " 9            40.68            97.65       711.4           0.18530   \n",
       " 10           33.88           123.80      1150.0           0.11810   \n",
       " 11           27.28           136.50      1299.0           0.13960   \n",
       " 12           29.94           151.70      1332.0           0.10370   \n",
       " 13           27.66           112.00       876.5           0.11310   \n",
       " 14           32.01           108.80       697.7           0.16510   \n",
       " 15           37.13           124.10       943.2           0.16780   \n",
       " 16           30.88           123.40      1138.0           0.14640   \n",
       " 17           31.48           136.80      1315.0           0.17890   \n",
       " 18           30.88           186.80      2398.0           0.15120   \n",
       " 19           19.26            99.70       711.2           0.14400   \n",
       " 20           20.49            96.09       630.5           0.13120   \n",
       " 21           15.66            65.13       314.9           0.13240   \n",
       " 22           19.08           125.10       980.9           0.13900   \n",
       " 23           35.59           188.00      2615.0           0.14010   \n",
       " 24           31.56           177.00      2215.0           0.18050   \n",
       " 25           21.40           152.40      1461.0           0.15450   \n",
       " 26           33.21           122.40       896.9           0.15250   \n",
       " 27           27.26           139.90      1403.0           0.13380   \n",
       " 28           36.71           149.30      1269.0           0.16410   \n",
       " 29           19.52           134.90      1227.0           0.12550   \n",
       " ..             ...              ...         ...               ...   \n",
       " 539          31.89            54.49       223.6           0.15960   \n",
       " 540          19.68            78.78       457.8           0.13450   \n",
       " 541          31.73           113.50       808.9           0.13400   \n",
       " 542          32.29           107.40       826.4           0.10600   \n",
       " 543          37.17            92.48       629.6           0.10720   \n",
       " 544          24.75            99.17       688.6           0.12640   \n",
       " 545          29.09            97.58       729.8           0.12160   \n",
       " 546          21.77            71.12       384.9           0.12850   \n",
       " 547          22.04            71.08       357.4           0.14610   \n",
       " 548          25.59            69.10       364.2           0.11990   \n",
       " 549          31.45            83.90       505.6           0.12040   \n",
       " 550          24.77            74.08       412.3           0.10010   \n",
       " 551          28.26            77.80       436.6           0.10870   \n",
       " 552          36.00            88.10       594.7           0.12340   \n",
       " 553          25.05            62.86       295.8           0.11030   \n",
       " 554          35.74            88.84       595.7           0.12270   \n",
       " 555          34.91            69.57       357.6           0.13840   \n",
       " 556          22.88            67.88       347.3           0.12650   \n",
       " 557          34.24            66.50       330.6           0.10730   \n",
       " 558          27.27           105.90       733.5           0.10260   \n",
       " 559          37.16            82.28       474.2           0.12980   \n",
       " 560          33.17           100.20       706.7           0.12410   \n",
       " 561          38.30            75.19       439.6           0.09267   \n",
       " 562          42.79           128.70       915.0           0.14170   \n",
       " 563          29.41           179.10      1819.0           0.14070   \n",
       " 564          26.40           166.10      2027.0           0.14100   \n",
       " 565          38.25           155.00      1731.0           0.11660   \n",
       " 566          34.12           126.70      1124.0           0.11390   \n",
       " 567          39.42           184.60      1821.0           0.16500   \n",
       " 568          30.37            59.16       268.6           0.08996   \n",
       " \n",
       "      worst compactness  worst concavity  worst concave points  worst symmetry  \\\n",
       " 0              0.66560          0.71190               0.26540          0.4601   \n",
       " 1              0.18660          0.24160               0.18600          0.2750   \n",
       " 2              0.42450          0.45040               0.24300          0.3613   \n",
       " 3              0.86630          0.68690               0.25750          0.6638   \n",
       " 4              0.20500          0.40000               0.16250          0.2364   \n",
       " 5              0.52490          0.53550               0.17410          0.3985   \n",
       " 6              0.25760          0.37840               0.19320          0.3063   \n",
       " 7              0.36820          0.26780               0.15560          0.3196   \n",
       " 8              0.54010          0.53900               0.20600          0.4378   \n",
       " 9              1.05800          1.10500               0.22100          0.4366   \n",
       " 10             0.15510          0.14590               0.09975          0.2948   \n",
       " 11             0.56090          0.39650               0.18100          0.3792   \n",
       " 12             0.39030          0.36390               0.17670          0.3176   \n",
       " 13             0.19240          0.23220               0.11190          0.2809   \n",
       " 14             0.77250          0.69430               0.22080          0.3596   \n",
       " 15             0.65770          0.70260               0.17120          0.4218   \n",
       " 16             0.18710          0.29140               0.16090          0.3029   \n",
       " 17             0.42330          0.47840               0.20730          0.3706   \n",
       " 18             0.31500          0.53720               0.23880          0.2768   \n",
       " 19             0.17730          0.23900               0.12880          0.2977   \n",
       " 20             0.27760          0.18900               0.07283          0.3184   \n",
       " 21             0.11480          0.08867               0.06227          0.2450   \n",
       " 22             0.59540          0.63050               0.23930          0.4667   \n",
       " 23             0.26000          0.31550               0.20090          0.2822   \n",
       " 24             0.35780          0.46950               0.20950          0.3613   \n",
       " 25             0.39490          0.38530               0.25500          0.4066   \n",
       " 26             0.66430          0.55390               0.27010          0.4264   \n",
       " 27             0.21170          0.34460               0.14900          0.2341   \n",
       " 28             0.61100          0.63350               0.20240          0.4027   \n",
       " 29             0.28120          0.24890               0.14560          0.2756   \n",
       " ..                 ...              ...                   ...             ...   \n",
       " 539            0.30640          0.33930               0.05000          0.2790   \n",
       " 540            0.21180          0.17970               0.06918          0.2329   \n",
       " 541            0.42020          0.40400               0.12050          0.3187   \n",
       " 542            0.13760          0.16110               0.10950          0.2722   \n",
       " 543            0.13810          0.10620               0.07958          0.2473   \n",
       " 544            0.20370          0.13770               0.06845          0.2249   \n",
       " 545            0.15170          0.10490               0.07174          0.2642   \n",
       " 546            0.08842          0.04384               0.02381          0.2681   \n",
       " 547            0.22460          0.17830               0.08333          0.2691   \n",
       " 548            0.09546          0.09350               0.03846          0.2552   \n",
       " 549            0.16330          0.06194               0.03264          0.3059   \n",
       " 550            0.07348          0.00000               0.00000          0.2458   \n",
       " 551            0.17820          0.15640               0.06413          0.3169   \n",
       " 552            0.10640          0.08653               0.06498          0.2407   \n",
       " 553            0.08298          0.07993               0.02564          0.2435   \n",
       " 554            0.16200          0.24390               0.06493          0.2372   \n",
       " 555            0.17100          0.20000               0.09127          0.2226   \n",
       " 556            0.12000          0.01005               0.02232          0.2262   \n",
       " 557            0.07158          0.00000               0.00000          0.2475   \n",
       " 558            0.31710          0.36620               0.11050          0.2258   \n",
       " 559            0.25170          0.36300               0.09653          0.2112   \n",
       " 560            0.22640          0.13260               0.10480          0.2250   \n",
       " 561            0.05494          0.00000               0.00000          0.1566   \n",
       " 562            0.79170          1.17000               0.23560          0.4089   \n",
       " 563            0.41860          0.65990               0.25420          0.2929   \n",
       " 564            0.21130          0.41070               0.22160          0.2060   \n",
       " 565            0.19220          0.32150               0.16280          0.2572   \n",
       " 566            0.30940          0.34030               0.14180          0.2218   \n",
       " 567            0.86810          0.93870               0.26500          0.4087   \n",
       " 568            0.06444          0.00000               0.00000          0.2871   \n",
       " \n",
       "      worst fractal dimension  \n",
       " 0                    0.11890  \n",
       " 1                    0.08902  \n",
       " 2                    0.08758  \n",
       " 3                    0.17300  \n",
       " 4                    0.07678  \n",
       " 5                    0.12440  \n",
       " 6                    0.08368  \n",
       " 7                    0.11510  \n",
       " 8                    0.10720  \n",
       " 9                    0.20750  \n",
       " 10                   0.08452  \n",
       " 11                   0.10480  \n",
       " 12                   0.10230  \n",
       " 13                   0.06287  \n",
       " 14                   0.14310  \n",
       " 15                   0.13410  \n",
       " 16                   0.08216  \n",
       " 17                   0.11420  \n",
       " 18                   0.07615  \n",
       " 19                   0.07259  \n",
       " 20                   0.08183  \n",
       " 21                   0.07773  \n",
       " 22                   0.09946  \n",
       " 23                   0.07526  \n",
       " 24                   0.09564  \n",
       " 25                   0.10590  \n",
       " 26                   0.12750  \n",
       " 27                   0.07421  \n",
       " 28                   0.09876  \n",
       " 29                   0.07919  \n",
       " ..                       ...  \n",
       " 539                  0.10660  \n",
       " 540                  0.08134  \n",
       " 541                  0.10230  \n",
       " 542                  0.06956  \n",
       " 543                  0.06443  \n",
       " 544                  0.08492  \n",
       " 545                  0.06953  \n",
       " 546                  0.07399  \n",
       " 547                  0.09479  \n",
       " 548                  0.07920  \n",
       " 549                  0.07626  \n",
       " 550                  0.06592  \n",
       " 551                  0.08032  \n",
       " 552                  0.06484  \n",
       " 553                  0.07393  \n",
       " 554                  0.07242  \n",
       " 555                  0.08283  \n",
       " 556                  0.06742  \n",
       " 557                  0.06969  \n",
       " 558                  0.08004  \n",
       " 559                  0.08732  \n",
       " 560                  0.08321  \n",
       " 561                  0.05905  \n",
       " 562                  0.14090  \n",
       " 563                  0.09873  \n",
       " 564                  0.07115  \n",
       " 565                  0.06637  \n",
       " 566                  0.07820  \n",
       " 567                  0.12400  \n",
       " 568                  0.07039  \n",
       " \n",
       " [569 rows x 30 columns], 0      0\n",
       " 1      0\n",
       " 2      0\n",
       " 3      0\n",
       " 4      0\n",
       " 5      0\n",
       " 6      0\n",
       " 7      0\n",
       " 8      0\n",
       " 9      0\n",
       " 10     0\n",
       " 11     0\n",
       " 12     0\n",
       " 13     0\n",
       " 14     0\n",
       " 15     0\n",
       " 16     0\n",
       " 17     0\n",
       " 18     0\n",
       " 19     1\n",
       " 20     1\n",
       " 21     1\n",
       " 22     0\n",
       " 23     0\n",
       " 24     0\n",
       " 25     0\n",
       " 26     0\n",
       " 27     0\n",
       " 28     0\n",
       " 29     0\n",
       "       ..\n",
       " 539    1\n",
       " 540    1\n",
       " 541    1\n",
       " 542    1\n",
       " 543    1\n",
       " 544    1\n",
       " 545    1\n",
       " 546    1\n",
       " 547    1\n",
       " 548    1\n",
       " 549    1\n",
       " 550    1\n",
       " 551    1\n",
       " 552    1\n",
       " 553    1\n",
       " 554    1\n",
       " 555    1\n",
       " 556    1\n",
       " 557    1\n",
       " 558    1\n",
       " 559    1\n",
       " 560    1\n",
       " 561    1\n",
       " 562    0\n",
       " 563    0\n",
       " 564    0\n",
       " 565    0\n",
       " 566    0\n",
       " 567    0\n",
       " 568    1\n",
       " Name: target, dtype: int64)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_three():\n",
    "    cancerdf = answer_one()\n",
    "#     from sklearn.model_selection import train_test_split\n",
    "    \n",
    "#     # Your code here\n",
    "#     X = fruits[['height', 'width', 'mass', 'color_score']]\n",
    "#     y = fruits['fruit_label']\n",
    "#     X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "    \n",
    "#     return X, y\n",
    "    X = cancerdf.iloc[:,:30]\n",
    "    y = cancerdf.iloc[:,30:32]\n",
    "#     y = cancerdf['target']\n",
    "    y = cancerdf.target\n",
    "#     print(y)\n",
    "    return X, y\n",
    "\n",
    "answer_three()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "Using `train_test_split`, split `X` and `y` into training and test sets `(X_train, X_test, y_train, and y_test)`.\n",
    "\n",
    "**Set the random number generator state to 0 using `random_state=0` to make sure your results match the autograder!**\n",
    "\n",
    "*This function should return a tuple of length 4:* `(X_train, X_test, y_train, y_test)`*, where* \n",
    "* `X_train` *has shape* `(426, 30)`\n",
    "* `X_test` *has shape* `(143, 30)`\n",
    "* `y_train` *has shape* `(426,)`\n",
    "* `y_test` *has shape* `(143,)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       " 293       11.850         17.46           75.54      432.7          0.08372   \n",
       " 332       11.220         19.86           71.94      387.3          0.10540   \n",
       " 565       20.130         28.25          131.20     1261.0          0.09780   \n",
       " 278       13.590         17.84           86.24      572.3          0.07948   \n",
       " 489       16.690         20.20          107.10      857.6          0.07497   \n",
       " 346       12.060         18.90           76.66      445.3          0.08386   \n",
       " 357       13.870         16.21           88.52      593.7          0.08743   \n",
       " 355       12.560         19.07           81.92      485.8          0.08760   \n",
       " 112       14.260         19.65           97.83      629.9          0.07837   \n",
       " 68         9.029         17.33           58.79      250.5          0.10660   \n",
       " 526       13.460         18.75           87.44      551.1          0.10750   \n",
       " 206        9.876         17.27           62.92      295.4          0.10890   \n",
       " 65        14.780         23.94           97.40      668.3          0.11720   \n",
       " 437       14.040         15.98           89.78      611.2          0.08458   \n",
       " 126       13.610         24.69           87.76      572.6          0.09258   \n",
       " 429       12.720         17.67           80.98      501.3          0.07896   \n",
       " 392       15.490         19.97          102.40      744.7          0.11600   \n",
       " 343       19.680         21.68          129.90     1194.0          0.09797   \n",
       " 334       12.300         19.02           77.88      464.4          0.08313   \n",
       " 440       10.970         17.20           71.73      371.5          0.08915   \n",
       " 441       17.270         25.42          112.40      928.8          0.08331   \n",
       " 137       11.430         15.39           73.06      399.8          0.09639   \n",
       " 230       17.050         19.08          113.40      895.0          0.11410   \n",
       " 7         13.710         20.83           90.20      577.9          0.11890   \n",
       " 408       17.990         20.66          117.80      991.7          0.10360   \n",
       " 523       13.710         18.68           88.73      571.0          0.09916   \n",
       " 361       13.300         21.57           85.24      546.1          0.08582   \n",
       " 553        9.333         21.94           59.01      264.0          0.09240   \n",
       " 478       11.490         14.59           73.99      404.9          0.10460   \n",
       " 303       10.490         18.61           66.86      334.3          0.10680   \n",
       " ..           ...           ...             ...        ...              ...   \n",
       " 459        9.755         28.20           61.68      290.9          0.07984   \n",
       " 510       11.740         14.69           76.31      426.0          0.08099   \n",
       " 151        8.219         20.70           53.27      203.9          0.09405   \n",
       " 244       19.400         23.50          129.10     1155.0          0.10270   \n",
       " 543       13.210         28.06           84.88      538.4          0.08671   \n",
       " 544       13.870         20.70           89.77      584.8          0.09578   \n",
       " 265       20.730         31.12          135.70     1419.0          0.09469   \n",
       " 288       11.260         19.96           73.72      394.1          0.08020   \n",
       " 423       13.660         19.13           89.46      575.3          0.09057   \n",
       " 147       14.950         18.77           97.84      689.5          0.08138   \n",
       " 177       16.460         20.11          109.30      832.9          0.09831   \n",
       " 99        14.420         19.77           94.48      642.5          0.09752   \n",
       " 448       14.530         19.34           94.25      659.7          0.08388   \n",
       " 431       12.400         17.68           81.47      467.8          0.10540   \n",
       " 115       11.930         21.53           76.53      438.6          0.09768   \n",
       " 72        17.200         24.52          114.20      929.4          0.10710   \n",
       " 537       11.690         24.44           76.37      406.4          0.12360   \n",
       " 174       10.660         15.15           67.49      349.6          0.08792   \n",
       " 87        19.020         24.59          122.00     1076.0          0.09029   \n",
       " 551       11.130         22.44           71.49      378.4          0.09566   \n",
       " 486       14.640         16.85           94.21      666.0          0.08641   \n",
       " 314        8.597         18.60           54.09      221.2          0.10740   \n",
       " 396       13.510         18.89           88.10      558.1          0.10590   \n",
       " 472       14.920         14.93           96.45      686.9          0.08098   \n",
       " 70        18.940         21.31          123.60     1130.0          0.09009   \n",
       " 277       18.810         19.98          120.90     1102.0          0.08923   \n",
       " 9         12.460         24.04           83.97      475.9          0.11860   \n",
       " 359        9.436         18.32           59.82      278.6          0.10090   \n",
       " 192        9.720         18.22           60.73      288.1          0.06950   \n",
       " 559       11.510         23.93           74.52      403.5          0.09261   \n",
       " \n",
       "      mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       " 293           0.05642        0.026880             0.022800         0.1875   \n",
       " 332           0.06779        0.005006             0.007583         0.1940   \n",
       " 565           0.10340        0.144000             0.097910         0.1752   \n",
       " 278           0.04052        0.019970             0.012380         0.1573   \n",
       " 489           0.07112        0.036490             0.023070         0.1846   \n",
       " 346           0.05794        0.007510             0.008488         0.1555   \n",
       " 357           0.05492        0.015020             0.020880         0.1424   \n",
       " 355           0.10380        0.103000             0.043910         0.1533   \n",
       " 112           0.22330        0.300300             0.077980         0.1704   \n",
       " 68            0.14130        0.313000             0.043750         0.2111   \n",
       " 526           0.11380        0.042010             0.031520         0.1723   \n",
       " 206           0.07232        0.017560             0.019520         0.1934   \n",
       " 65            0.14790        0.126700             0.090290         0.1953   \n",
       " 437           0.05895        0.035340             0.029440         0.1714   \n",
       " 126           0.07862        0.052850             0.030850         0.1761   \n",
       " 429           0.04522        0.014020             0.018350         0.1459   \n",
       " 392           0.15620        0.189100             0.091130         0.1929   \n",
       " 343           0.13390        0.186300             0.110300         0.2082   \n",
       " 334           0.04202        0.007756             0.008535         0.1539   \n",
       " 440           0.11130        0.094570             0.036130         0.1489   \n",
       " 441           0.11090        0.120400             0.057360         0.1467   \n",
       " 137           0.06889        0.035030             0.028750         0.1734   \n",
       " 230           0.15720        0.191000             0.109000         0.2131   \n",
       " 7             0.16450        0.093660             0.059850         0.2196   \n",
       " 408           0.13040        0.120100             0.088240         0.1992   \n",
       " 523           0.10700        0.053850             0.037830         0.1714   \n",
       " 361           0.06373        0.033440             0.024240         0.1815   \n",
       " 553           0.05605        0.039960             0.012820         0.1692   \n",
       " 478           0.08228        0.053080             0.019690         0.1779   \n",
       " 303           0.06678        0.022970             0.017800         0.1482   \n",
       " ..                ...             ...                  ...            ...   \n",
       " 459           0.04626        0.015410             0.010430         0.1621   \n",
       " 510           0.09661        0.067260             0.026390         0.1499   \n",
       " 151           0.13050        0.132100             0.021680         0.2222   \n",
       " 244           0.15580        0.204900             0.088860         0.1978   \n",
       " 543           0.06877        0.029870             0.032750         0.1628   \n",
       " 544           0.10180        0.036880             0.023690         0.1620   \n",
       " 265           0.11430        0.136700             0.086460         0.1769   \n",
       " 288           0.11810        0.092740             0.055880         0.2595   \n",
       " 423           0.11470        0.096570             0.048120         0.1848   \n",
       " 147           0.11670        0.090500             0.035620         0.1744   \n",
       " 177           0.15560        0.179300             0.088660         0.1794   \n",
       " 99            0.11410        0.093880             0.058390         0.1879   \n",
       " 448           0.07800        0.088170             0.029250         0.1473   \n",
       " 431           0.13160        0.077410             0.027990         0.1811   \n",
       " 115           0.07849        0.033280             0.020080         0.1688   \n",
       " 72            0.18300        0.169200             0.079440         0.1927   \n",
       " 537           0.15520        0.045150             0.045310         0.2131   \n",
       " 174           0.04302        0.000000             0.000000         0.1928   \n",
       " 87            0.12060        0.146800             0.082710         0.1953   \n",
       " 551           0.08194        0.048240             0.022570         0.2030   \n",
       " 486           0.06698        0.051920             0.027910         0.1409   \n",
       " 314           0.05847        0.000000             0.000000         0.2163   \n",
       " 396           0.11470        0.085800             0.053810         0.1806   \n",
       " 472           0.08549        0.055390             0.032210         0.1687   \n",
       " 70            0.10290        0.108000             0.079510         0.1582   \n",
       " 277           0.05884        0.080200             0.058430         0.1550   \n",
       " 9             0.23960        0.227300             0.085430         0.2030   \n",
       " 359           0.05956        0.027100             0.014060         0.1506   \n",
       " 192           0.02344        0.000000             0.000000         0.1653   \n",
       " 559           0.10210        0.111200             0.041050         0.1388   \n",
       " \n",
       "      mean fractal dimension           ...             worst radius  \\\n",
       " 293                 0.05715           ...                   13.060   \n",
       " 332                 0.06028           ...                   11.980   \n",
       " 565                 0.05533           ...                   23.690   \n",
       " 278                 0.05520           ...                   15.500   \n",
       " 489                 0.05325           ...                   19.180   \n",
       " 346                 0.06048           ...                   13.640   \n",
       " 357                 0.05883           ...                   15.110   \n",
       " 355                 0.06184           ...                   13.370   \n",
       " 112                 0.07769           ...                   15.300   \n",
       " 68                  0.08046           ...                   10.310   \n",
       " 526                 0.06317           ...                   15.350   \n",
       " 206                 0.06285           ...                   10.420   \n",
       " 65                  0.06654           ...                   17.310   \n",
       " 437                 0.05898           ...                   15.660   \n",
       " 126                 0.06130           ...                   16.890   \n",
       " 429                 0.05544           ...                   13.820   \n",
       " 392                 0.06744           ...                   21.200   \n",
       " 343                 0.05715           ...                   22.750   \n",
       " 334                 0.05945           ...                   13.350   \n",
       " 440                 0.06640           ...                   12.360   \n",
       " 441                 0.05407           ...                   20.380   \n",
       " 137                 0.05865           ...                   12.320   \n",
       " 230                 0.06325           ...                   19.590   \n",
       " 7                   0.07451           ...                   17.060   \n",
       " 408                 0.06069           ...                   21.080   \n",
       " 523                 0.06843           ...                   15.110   \n",
       " 361                 0.05696           ...                   14.200   \n",
       " 553                 0.06576           ...                    9.845   \n",
       " 478                 0.06574           ...                   12.400   \n",
       " 303                 0.06600           ...                   11.060   \n",
       " ..                      ...           ...                      ...   \n",
       " 459                 0.05952           ...                   10.670   \n",
       " 510                 0.06758           ...                   12.450   \n",
       " 151                 0.08261           ...                    9.092   \n",
       " 244                 0.06000           ...                   21.650   \n",
       " 543                 0.05781           ...                   14.370   \n",
       " 544                 0.06688           ...                   15.050   \n",
       " 265                 0.05674           ...                   32.490   \n",
       " 288                 0.06233           ...                   11.860   \n",
       " 423                 0.06181           ...                   15.140   \n",
       " 147                 0.06493           ...                   16.250   \n",
       " 177                 0.06323           ...                   17.790   \n",
       " 99                  0.06390           ...                   16.330   \n",
       " 448                 0.05746           ...                   16.300   \n",
       " 431                 0.07102           ...                   12.880   \n",
       " 115                 0.06194           ...                   13.670   \n",
       " 72                  0.06487           ...                   23.320   \n",
       " 537                 0.07405           ...                   12.980   \n",
       " 174                 0.05975           ...                   11.540   \n",
       " 87                  0.05629           ...                   24.560   \n",
       " 551                 0.06552           ...                   12.020   \n",
       " 486                 0.05355           ...                   16.460   \n",
       " 314                 0.07359           ...                    8.952   \n",
       " 396                 0.06079           ...                   14.800   \n",
       " 472                 0.05669           ...                   17.180   \n",
       " 70                  0.05461           ...                   24.860   \n",
       " 277                 0.04996           ...                   19.960   \n",
       " 9                   0.08243           ...                   15.090   \n",
       " 359                 0.06959           ...                   12.020   \n",
       " 192                 0.06447           ...                    9.968   \n",
       " 559                 0.06570           ...                   12.480   \n",
       " \n",
       "      worst texture  worst perimeter  worst area  worst smoothness  \\\n",
       " 293          25.75            84.35       517.8           0.13690   \n",
       " 332          25.78            76.91       436.1           0.14240   \n",
       " 565          38.25           155.00      1731.0           0.11660   \n",
       " 278          26.10            98.91       739.1           0.10500   \n",
       " 489          26.56           127.30      1084.0           0.10090   \n",
       " 346          27.06            86.54       562.6           0.12890   \n",
       " 357          25.58            96.74       694.4           0.11530   \n",
       " 355          22.43            89.02       547.4           0.10960   \n",
       " 112          23.73           107.00       709.0           0.08949   \n",
       " 68           22.65            65.50       324.7           0.14820   \n",
       " 526          25.16           101.90       719.8           0.16240   \n",
       " 206          23.22            67.08       331.6           0.14150   \n",
       " 65           33.39           114.60       925.1           0.16480   \n",
       " 437          21.58           101.20       750.0           0.11950   \n",
       " 126          35.64           113.20       848.7           0.14710   \n",
       " 429          20.96            88.87       586.8           0.10680   \n",
       " 392          29.41           142.10      1359.0           0.16810   \n",
       " 343          34.66           157.60      1540.0           0.12180   \n",
       " 334          28.46            84.53       544.3           0.12220   \n",
       " 440          26.87            90.14       476.4           0.13910   \n",
       " 441          35.46           132.80      1284.0           0.14360   \n",
       " 137          22.02            79.93       462.0           0.11900   \n",
       " 230          24.89           133.50      1189.0           0.17030   \n",
       " 7            28.14           110.60       897.0           0.16540   \n",
       " 408          25.41           138.10      1349.0           0.14820   \n",
       " 523          25.63            99.43       701.9           0.14250   \n",
       " 361          29.20            92.94       621.2           0.11400   \n",
       " 553          25.05            62.86       295.8           0.11030   \n",
       " 478          21.90            82.04       467.6           0.13520   \n",
       " 303          24.54            70.76       375.4           0.14130   \n",
       " ..             ...              ...         ...               ...   \n",
       " 459          36.92            68.03       349.9           0.11100   \n",
       " 510          17.60            81.25       473.8           0.10730   \n",
       " 151          29.72            58.08       249.8           0.16300   \n",
       " 244          30.53           144.90      1417.0           0.14630   \n",
       " 543          37.17            92.48       629.6           0.10720   \n",
       " 544          24.75            99.17       688.6           0.12640   \n",
       " 265          47.16           214.00      3432.0           0.14010   \n",
       " 288          22.33            78.27       437.6           0.10280   \n",
       " 423          25.50           101.40       708.8           0.11470   \n",
       " 147          25.47           107.10       809.7           0.09970   \n",
       " 177          28.45           123.50       981.2           0.14150   \n",
       " 99           30.86           109.50       826.4           0.14310   \n",
       " 448          28.39           108.10       830.5           0.10890   \n",
       " 431          22.91            89.61       515.8           0.14500   \n",
       " 115          26.15            87.54       583.0           0.15000   \n",
       " 72           33.82           151.60      1681.0           0.15850   \n",
       " 537          32.19            86.12       487.7           0.17680   \n",
       " 174          19.20            73.20       408.3           0.10760   \n",
       " 87           30.41           152.90      1623.0           0.12490   \n",
       " 551          28.26            77.80       436.6           0.10870   \n",
       " 486          25.44           106.00       831.0           0.11420   \n",
       " 314          22.44            56.65       240.1           0.13470   \n",
       " 396          27.20            97.33       675.2           0.14280   \n",
       " 472          18.22           112.00       906.6           0.10650   \n",
       " 70           26.58           165.90      1866.0           0.11930   \n",
       " 277          24.30           129.00      1236.0           0.12430   \n",
       " 9            40.68            97.65       711.4           0.18530   \n",
       " 359          25.02            75.79       439.6           0.13330   \n",
       " 192          20.83            62.25       303.8           0.07117   \n",
       " 559          37.16            82.28       474.2           0.12980   \n",
       " \n",
       "      worst compactness  worst concavity  worst concave points  worst symmetry  \\\n",
       " 293            0.17580          0.13160               0.09140          0.3101   \n",
       " 332            0.09669          0.01335               0.02022          0.3292   \n",
       " 565            0.19220          0.32150               0.16280          0.2572   \n",
       " 278            0.07622          0.10600               0.05185          0.2335   \n",
       " 489            0.29200          0.24770               0.08737          0.4677   \n",
       " 346            0.13520          0.04506               0.05093          0.2880   \n",
       " 357            0.10080          0.05285               0.05556          0.2362   \n",
       " 355            0.20020          0.23880               0.09265          0.2121   \n",
       " 112            0.41930          0.67830               0.15050          0.2398   \n",
       " 68             0.43650          1.25200               0.17500          0.4228   \n",
       " 526            0.31240          0.26540               0.14270          0.3518   \n",
       " 206            0.12470          0.06213               0.05588          0.2989   \n",
       " 65             0.34160          0.30240               0.16140          0.3321   \n",
       " 437            0.12520          0.11170               0.07453          0.2725   \n",
       " 126            0.28840          0.37960               0.13290          0.3470   \n",
       " 429            0.09605          0.03469               0.03612          0.2165   \n",
       " 392            0.39130          0.55530               0.21210          0.3187   \n",
       " 343            0.34580          0.47340               0.22550          0.4045   \n",
       " 334            0.09052          0.03619               0.03983          0.2554   \n",
       " 440            0.40820          0.47790               0.15550          0.2540   \n",
       " 441            0.41220          0.50360               0.17390          0.2500   \n",
       " 137            0.16480          0.13990               0.08476          0.2676   \n",
       " 230            0.39340          0.50180               0.25430          0.3109   \n",
       " 7              0.36820          0.26780               0.15560          0.3196   \n",
       " 408            0.37350          0.33010               0.19740          0.3060   \n",
       " 523            0.25660          0.19350               0.12840          0.2849   \n",
       " 361            0.16670          0.12120               0.05614          0.2637   \n",
       " 553            0.08298          0.07993               0.02564          0.2435   \n",
       " 478            0.20100          0.25960               0.07431          0.2941   \n",
       " 303            0.10440          0.08423               0.06528          0.2213   \n",
       " ..                 ...              ...                   ...             ...   \n",
       " 459            0.11090          0.07190               0.04866          0.2321   \n",
       " 510            0.27930          0.26900               0.10560          0.2604   \n",
       " 151            0.43100          0.53810               0.07879          0.3322   \n",
       " 244            0.29680          0.34580               0.15640          0.2920   \n",
       " 543            0.13810          0.10620               0.07958          0.2473   \n",
       " 544            0.20370          0.13770               0.06845          0.2249   \n",
       " 265            0.26440          0.34420               0.16590          0.2868   \n",
       " 288            0.18430          0.15460               0.09314          0.2955   \n",
       " 423            0.31670          0.36600               0.14070          0.2744   \n",
       " 147            0.25210          0.25000               0.08405          0.2852   \n",
       " 177            0.46670          0.58620               0.20350          0.3054   \n",
       " 99             0.30260          0.31940               0.15650          0.2718   \n",
       " 448            0.26490          0.37790               0.09594          0.2471   \n",
       " 431            0.26290          0.24030               0.07370          0.2556   \n",
       " 115            0.23990          0.15030               0.07247          0.2438   \n",
       " 72             0.73940          0.65660               0.18990          0.3313   \n",
       " 537            0.32510          0.13950               0.13080          0.2803   \n",
       " 174            0.06791          0.00000               0.00000          0.2710   \n",
       " 87             0.32060          0.57550               0.19560          0.3956   \n",
       " 551            0.17820          0.15640               0.06413          0.3169   \n",
       " 486            0.20700          0.24370               0.07828          0.2455   \n",
       " 314            0.07767          0.00000               0.00000          0.3142   \n",
       " 396            0.25700          0.34380               0.14530          0.2666   \n",
       " 472            0.27910          0.31510               0.11470          0.2688   \n",
       " 70             0.23360          0.26870               0.17890          0.2551   \n",
       " 277            0.11600          0.22100               0.12940          0.2567   \n",
       " 9              1.05800          1.10500               0.22100          0.4366   \n",
       " 359            0.10490          0.11440               0.05052          0.2454   \n",
       " 192            0.02729          0.00000               0.00000          0.1909   \n",
       " 559            0.25170          0.36300               0.09653          0.2112   \n",
       " \n",
       "      worst fractal dimension  \n",
       " 293                  0.07007  \n",
       " 332                  0.06522  \n",
       " 565                  0.06637  \n",
       " 278                  0.06263  \n",
       " 489                  0.07623  \n",
       " 346                  0.08083  \n",
       " 357                  0.07113  \n",
       " 355                  0.07188  \n",
       " 112                  0.10820  \n",
       " 68                   0.11750  \n",
       " 526                  0.08665  \n",
       " 206                  0.07380  \n",
       " 65                   0.08911  \n",
       " 437                  0.07234  \n",
       " 126                  0.07900  \n",
       " 429                  0.06025  \n",
       " 392                  0.10190  \n",
       " 343                  0.07918  \n",
       " 334                  0.07207  \n",
       " 440                  0.09532  \n",
       " 441                  0.07944  \n",
       " 137                  0.06765  \n",
       " 230                  0.09061  \n",
       " 7                    0.11510  \n",
       " 408                  0.08503  \n",
       " 523                  0.09031  \n",
       " 361                  0.06658  \n",
       " 553                  0.07393  \n",
       " 478                  0.09180  \n",
       " 303                  0.07842  \n",
       " ..                       ...  \n",
       " 459                  0.07211  \n",
       " 510                  0.09879  \n",
       " 151                  0.14860  \n",
       " 244                  0.07614  \n",
       " 543                  0.06443  \n",
       " 544                  0.08492  \n",
       " 265                  0.08218  \n",
       " 288                  0.07009  \n",
       " 423                  0.08839  \n",
       " 147                  0.09218  \n",
       " 177                  0.09519  \n",
       " 99                   0.09353  \n",
       " 448                  0.07463  \n",
       " 431                  0.09359  \n",
       " 115                  0.08541  \n",
       " 72                   0.13390  \n",
       " 537                  0.09970  \n",
       " 174                  0.06164  \n",
       " 87                   0.09288  \n",
       " 551                  0.08032  \n",
       " 486                  0.06596  \n",
       " 314                  0.08116  \n",
       " 396                  0.07686  \n",
       " 472                  0.08273  \n",
       " 70                   0.06589  \n",
       " 277                  0.05737  \n",
       " 9                    0.20750  \n",
       " 359                  0.08136  \n",
       " 192                  0.06559  \n",
       " 559                  0.08732  \n",
       " \n",
       " [426 rows x 30 columns],\n",
       "      mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       " 512       13.400         20.52           88.64      556.7          0.11060   \n",
       " 457       13.210         25.25           84.10      537.9          0.08791   \n",
       " 439       14.020         15.66           89.59      606.5          0.07966   \n",
       " 298       14.260         18.17           91.22      633.1          0.06576   \n",
       " 37        13.030         18.42           82.61      523.8          0.08983   \n",
       " 515       11.340         18.61           72.76      391.2          0.10490   \n",
       " 382       12.050         22.72           78.75      447.8          0.06935   \n",
       " 310       11.700         19.11           74.33      418.7          0.08814   \n",
       " 538        7.729         25.49           47.98      178.8          0.08098   \n",
       " 345       10.260         14.71           66.20      321.6          0.09882   \n",
       " 421       14.690         13.98           98.22      656.1          0.10310   \n",
       " 90        14.620         24.02           94.57      662.7          0.08974   \n",
       " 412        9.397         21.68           59.75      268.8          0.07969   \n",
       " 157       16.840         19.46          108.40      880.2          0.07445   \n",
       " 89        14.640         15.24           95.77      651.9          0.11320   \n",
       " 172       15.460         11.89          102.50      736.9          0.12570   \n",
       " 318        9.042         18.90           60.07      244.5          0.09968   \n",
       " 233       20.510         27.81          134.40     1319.0          0.09159   \n",
       " 389       19.550         23.21          128.90     1174.0          0.10100   \n",
       " 250       20.940         23.56          138.90     1364.0          0.10070   \n",
       " 31        11.840         18.70           77.93      440.6          0.11090   \n",
       " 283       16.240         18.77          108.80      805.1          0.10660   \n",
       " 482       13.470         14.06           87.32      546.3          0.10710   \n",
       " 211       11.840         18.94           75.51      428.0          0.08871   \n",
       " 372       21.370         15.10          141.30     1386.0          0.10010   \n",
       " 401       11.930         10.91           76.14      442.7          0.08872   \n",
       " 159       10.900         12.96           68.69      366.8          0.07515   \n",
       " 14        13.730         22.61           93.60      578.3          0.11310   \n",
       " 364       13.400         16.95           85.48      552.4          0.07937   \n",
       " 337       18.770         21.43          122.90     1092.0          0.09116   \n",
       " ..           ...           ...             ...        ...              ...   \n",
       " 500       15.040         16.74           98.73      689.4          0.09883   \n",
       " 338       10.050         17.53           64.41      310.8          0.10070   \n",
       " 427       10.800         21.98           68.79      359.9          0.08801   \n",
       " 406       16.140         14.86          104.30      800.0          0.09495   \n",
       " 96        12.180         17.84           77.79      451.1          0.10450   \n",
       " 490       12.250         22.44           78.18      466.5          0.08192   \n",
       " 384       13.280         13.72           85.79      541.8          0.08363   \n",
       " 281       11.740         14.02           74.24      427.3          0.07813   \n",
       " 325       12.670         17.30           81.25      489.9          0.10280   \n",
       " 190       14.220         23.12           94.37      609.9          0.10750   \n",
       " 380       11.270         12.96           73.16      386.3          0.12370   \n",
       " 366       20.200         26.83          133.70     1234.0          0.09905   \n",
       " 469       11.620         18.18           76.38      408.8          0.11750   \n",
       " 225       14.340         13.47           92.51      641.2          0.09906   \n",
       " 271       11.290         13.04           72.23      388.0          0.09834   \n",
       " 547       10.260         16.58           65.85      320.8          0.08877   \n",
       " 550       10.860         21.48           68.51      360.5          0.07431   \n",
       " 492       18.010         20.56          118.40     1007.0          0.10010   \n",
       " 185       10.080         15.11           63.76      317.5          0.09267   \n",
       " 306       13.200         15.82           84.07      537.3          0.08511   \n",
       " 208       13.110         22.54           87.02      529.4          0.10020   \n",
       " 242       11.300         18.19           73.93      389.4          0.09592   \n",
       " 313       11.540         10.72           73.73      409.1          0.08597   \n",
       " 542       14.740         25.42           94.70      668.6          0.08275   \n",
       " 514       15.050         19.07           97.26      701.9          0.09215   \n",
       " 236       23.210         26.97          153.50     1670.0          0.09509   \n",
       " 113       10.510         20.19           68.64      334.2          0.11220   \n",
       " 527       12.340         12.27           78.94      468.5          0.09003   \n",
       " 76        13.530         10.94           87.91      559.2          0.12910   \n",
       " 162       19.590         18.15          130.70     1214.0          0.11200   \n",
       " \n",
       "      mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       " 512           0.14690        0.144500             0.081720         0.2116   \n",
       " 457           0.05205        0.027720             0.020680         0.1619   \n",
       " 439           0.05581        0.020870             0.026520         0.1589   \n",
       " 298           0.05220        0.024750             0.013740         0.1635   \n",
       " 37            0.03766        0.025620             0.029230         0.1467   \n",
       " 515           0.08499        0.043020             0.025940         0.1927   \n",
       " 382           0.10730        0.079430             0.029780         0.1203   \n",
       " 310           0.05253        0.015830             0.011480         0.1936   \n",
       " 538           0.04878        0.000000             0.000000         0.1870   \n",
       " 345           0.09159        0.035810             0.020370         0.1633   \n",
       " 421           0.18360        0.145000             0.063000         0.2086   \n",
       " 90            0.08606        0.031020             0.029570         0.1685   \n",
       " 412           0.06053        0.037350             0.005128         0.1274   \n",
       " 157           0.07223        0.051500             0.027710         0.1844   \n",
       " 89            0.13390        0.099660             0.070640         0.2116   \n",
       " 172           0.15550        0.203200             0.109700         0.1966   \n",
       " 318           0.19720        0.197500             0.049080         0.2330   \n",
       " 233           0.10740        0.155400             0.083400         0.1448   \n",
       " 389           0.13180        0.185600             0.102100         0.1989   \n",
       " 250           0.16060        0.271200             0.131000         0.2205   \n",
       " 31            0.15160        0.121800             0.051820         0.2301   \n",
       " 283           0.18020        0.194800             0.090520         0.1876   \n",
       " 482           0.11550        0.057860             0.052660         0.1779   \n",
       " 211           0.06900        0.026690             0.013930         0.1533   \n",
       " 372           0.15150        0.193200             0.125500         0.1973   \n",
       " 401           0.05242        0.026060             0.017960         0.1601   \n",
       " 159           0.03718        0.003090             0.006588         0.1442   \n",
       " 14            0.22930        0.212800             0.080250         0.2069   \n",
       " 364           0.05696        0.021810             0.014730         0.1650   \n",
       " 337           0.14020        0.106000             0.060900         0.1953   \n",
       " ..                ...             ...                  ...            ...   \n",
       " 500           0.13640        0.077210             0.061420         0.1668   \n",
       " 338           0.07326        0.025110             0.017750         0.1890   \n",
       " 427           0.05743        0.036140             0.014040         0.2016   \n",
       " 406           0.08501        0.055000             0.045280         0.1735   \n",
       " 96            0.07057        0.024900             0.029410         0.1900   \n",
       " 490           0.05200        0.017140             0.012610         0.1544   \n",
       " 384           0.08575        0.050770             0.028640         0.1617   \n",
       " 281           0.04340        0.022450             0.027630         0.2101   \n",
       " 325           0.07664        0.031930             0.021070         0.1707   \n",
       " 190           0.24130        0.198100             0.066180         0.2384   \n",
       " 380           0.11110        0.079000             0.055500         0.2018   \n",
       " 366           0.16690        0.164100             0.126500         0.1875   \n",
       " 469           0.14830        0.102000             0.055640         0.1957   \n",
       " 225           0.07624        0.057240             0.046030         0.2075   \n",
       " 271           0.07608        0.032650             0.027550         0.1769   \n",
       " 547           0.08066        0.043580             0.024380         0.1669   \n",
       " 550           0.04227        0.000000             0.000000         0.1661   \n",
       " 492           0.12890        0.117000             0.077620         0.2116   \n",
       " 185           0.04695        0.001597             0.002404         0.1703   \n",
       " 306           0.05251        0.001461             0.003261         0.1632   \n",
       " 208           0.14830        0.087050             0.051020         0.1850   \n",
       " 242           0.13250        0.154800             0.028540         0.2054   \n",
       " 313           0.05969        0.013670             0.008907         0.1833   \n",
       " 542           0.07214        0.041050             0.030270         0.1840   \n",
       " 514           0.08597        0.074860             0.043350         0.1561   \n",
       " 236           0.16820        0.195000             0.123700         0.1909   \n",
       " 113           0.13030        0.064760             0.030680         0.1922   \n",
       " 527           0.06307        0.029580             0.026470         0.1689   \n",
       " 76            0.10470        0.068770             0.065560         0.2403   \n",
       " 162           0.16660        0.250800             0.128600         0.2027   \n",
       " \n",
       "      mean fractal dimension           ...             worst radius  \\\n",
       " 512                 0.07325           ...                   16.410   \n",
       " 457                 0.05584           ...                   14.350   \n",
       " 439                 0.05586           ...                   14.910   \n",
       " 298                 0.05586           ...                   16.220   \n",
       " 37                  0.05863           ...                   13.300   \n",
       " 515                 0.06211           ...                   12.470   \n",
       " 382                 0.06659           ...                   12.570   \n",
       " 310                 0.06128           ...                   12.610   \n",
       " 538                 0.07285           ...                    9.077   \n",
       " 345                 0.07005           ...                   10.880   \n",
       " 421                 0.07406           ...                   16.460   \n",
       " 90                  0.05866           ...                   16.110   \n",
       " 412                 0.06724           ...                    9.965   \n",
       " 157                 0.05268           ...                   18.220   \n",
       " 89                  0.06346           ...                   16.340   \n",
       " 172                 0.07069           ...                   18.790   \n",
       " 318                 0.08743           ...                   10.060   \n",
       " 233                 0.05592           ...                   24.470   \n",
       " 389                 0.05884           ...                   20.820   \n",
       " 250                 0.05898           ...                   25.580   \n",
       " 31                  0.07799           ...                   16.820   \n",
       " 283                 0.06684           ...                   18.550   \n",
       " 482                 0.06639           ...                   14.830   \n",
       " 211                 0.06057           ...                   13.300   \n",
       " 372                 0.06183           ...                   22.690   \n",
       " 401                 0.05541           ...                   13.800   \n",
       " 159                 0.05743           ...                   12.360   \n",
       " 14                  0.07682           ...                   15.030   \n",
       " 364                 0.05701           ...                   14.730   \n",
       " 337                 0.06083           ...                   24.540   \n",
       " ..                      ...           ...                      ...   \n",
       " 500                 0.06869           ...                   16.760   \n",
       " 338                 0.06331           ...                   11.160   \n",
       " 427                 0.05977           ...                   12.760   \n",
       " 406                 0.05875           ...                   17.710   \n",
       " 96                  0.06635           ...                   12.830   \n",
       " 490                 0.05976           ...                   14.170   \n",
       " 384                 0.05594           ...                   14.240   \n",
       " 281                 0.06113           ...                   13.310   \n",
       " 325                 0.05984           ...                   13.710   \n",
       " 190                 0.07542           ...                   15.740   \n",
       " 380                 0.06914           ...                   12.840   \n",
       " 366                 0.06020           ...                   24.190   \n",
       " 469                 0.07255           ...                   13.360   \n",
       " 225                 0.05448           ...                   16.770   \n",
       " 271                 0.06270           ...                   12.320   \n",
       " 547                 0.06714           ...                   10.830   \n",
       " 550                 0.05948           ...                   11.660   \n",
       " 492                 0.06077           ...                   21.530   \n",
       " 185                 0.06048           ...                   11.870   \n",
       " 306                 0.05894           ...                   14.410   \n",
       " 208                 0.07310           ...                   14.550   \n",
       " 242                 0.07669           ...                   12.580   \n",
       " 313                 0.06100           ...                   12.340   \n",
       " 542                 0.05680           ...                   16.510   \n",
       " 514                 0.05915           ...                   17.580   \n",
       " 236                 0.06309           ...                   31.010   \n",
       " 113                 0.07782           ...                   11.160   \n",
       " 527                 0.05808           ...                   13.610   \n",
       " 76                  0.06641           ...                   14.080   \n",
       " 162                 0.06082           ...                   26.730   \n",
       " \n",
       "      worst texture  worst perimeter  worst area  worst smoothness  \\\n",
       " 512          29.66           113.30       844.4           0.15740   \n",
       " 457          34.23            91.29       632.9           0.12890   \n",
       " 439          19.31            96.53       688.9           0.10340   \n",
       " 298          25.26           105.80       819.7           0.09445   \n",
       " 37           22.81            84.46       545.9           0.09701   \n",
       " 515          23.03            79.15       478.6           0.14830   \n",
       " 382          28.71            87.36       488.4           0.08799   \n",
       " 310          26.55            80.92       483.1           0.12230   \n",
       " 538          30.92            57.17       248.0           0.12560   \n",
       " 345          19.48            70.89       357.1           0.13600   \n",
       " 421          18.34           114.10       809.2           0.13120   \n",
       " 90           29.11           102.90       803.7           0.11150   \n",
       " 412          27.99            66.61       301.0           0.10860   \n",
       " 157          28.07           120.30      1032.0           0.08774   \n",
       " 89           18.24           109.40       803.6           0.12770   \n",
       " 172          17.04           125.00      1102.0           0.15310   \n",
       " 318          23.40            68.62       297.1           0.12210   \n",
       " 233          37.38           162.70      1872.0           0.12230   \n",
       " 389          30.44           142.00      1313.0           0.12510   \n",
       " 250          27.00           165.30      2010.0           0.12110   \n",
       " 31           28.12           119.40       888.7           0.16370   \n",
       " 283          25.09           126.90      1031.0           0.13650   \n",
       " 482          18.32            94.94       660.2           0.13930   \n",
       " 211          24.99            85.22       546.3           0.12800   \n",
       " 372          21.84           152.10      1535.0           0.11920   \n",
       " 401          20.14            87.64       589.5           0.13740   \n",
       " 159          18.20            78.07       470.0           0.11710   \n",
       " 14           32.01           108.80       697.7           0.16510   \n",
       " 364          21.70            93.76       663.5           0.12130   \n",
       " 337          34.37           161.10      1873.0           0.14980   \n",
       " ..             ...              ...         ...               ...   \n",
       " 500          20.43           109.70       856.9           0.11350   \n",
       " 338          26.84            71.98       384.0           0.14020   \n",
       " 427          32.04            83.69       489.5           0.13030   \n",
       " 406          19.58           115.90       947.9           0.12060   \n",
       " 96           20.92            82.14       495.2           0.11400   \n",
       " 490          31.99            92.74       622.9           0.12560   \n",
       " 384          17.37            96.59       623.7           0.11660   \n",
       " 281          18.26            84.70       533.7           0.10360   \n",
       " 325          21.10            88.70       574.4           0.13840   \n",
       " 190          37.18           106.40       762.4           0.15330   \n",
       " 380          20.53            84.93       476.1           0.16100   \n",
       " 366          33.81           160.00      1671.0           0.12780   \n",
       " 469          25.40            88.14       528.1           0.17800   \n",
       " 225          16.90           110.40       873.2           0.12970   \n",
       " 271          16.18            78.27       457.5           0.13580   \n",
       " 547          22.04            71.08       357.4           0.14610   \n",
       " 550          24.77            74.08       412.3           0.10010   \n",
       " 492          26.06           143.40      1426.0           0.13090   \n",
       " 185          21.18            75.39       437.0           0.15210   \n",
       " 306          20.45            92.00       636.9           0.11280   \n",
       " 208          29.16            99.48       639.3           0.13490   \n",
       " 242          27.96            87.16       472.9           0.13470   \n",
       " 313          12.87            81.23       467.8           0.10920   \n",
       " 542          32.29           107.40       826.4           0.10600   \n",
       " 514          28.06           113.80       967.0           0.12460   \n",
       " 236          34.51           206.00      2944.0           0.14810   \n",
       " 113          22.75            72.62       374.4           0.13000   \n",
       " 527          19.27            87.22       564.9           0.12920   \n",
       " 76           12.49            91.36       605.5           0.14510   \n",
       " 162          26.39           174.90      2232.0           0.14380   \n",
       " \n",
       "      worst compactness  worst concavity  worst concave points  worst symmetry  \\\n",
       " 512            0.38560          0.51060               0.20510          0.3585   \n",
       " 457            0.10630          0.13900               0.06005          0.2444   \n",
       " 439            0.10170          0.06260               0.08216          0.2136   \n",
       " 298            0.21670          0.15650               0.07530          0.2636   \n",
       " 37             0.04619          0.04833               0.05013          0.1987   \n",
       " 515            0.15740          0.16240               0.08542          0.3060   \n",
       " 382            0.32140          0.29120               0.10920          0.2191   \n",
       " 310            0.10870          0.07915               0.05741          0.3487   \n",
       " 538            0.08340          0.00000               0.00000          0.3058   \n",
       " 345            0.16360          0.07162               0.04074          0.2434   \n",
       " 421            0.36350          0.32190               0.11080          0.2827   \n",
       " 90             0.17660          0.09189               0.06946          0.2522   \n",
       " 412            0.18870          0.18680               0.02564          0.2376   \n",
       " 157            0.17100          0.18820               0.08436          0.2527   \n",
       " 89             0.30890          0.26040               0.13970          0.3151   \n",
       " 172            0.35830          0.58300               0.18270          0.3216   \n",
       " 318            0.37480          0.46090               0.11450          0.3135   \n",
       " 233            0.27610          0.41460               0.15630          0.2437   \n",
       " 389            0.24140          0.38290               0.18250          0.2576   \n",
       " 250            0.31720          0.69910               0.21050          0.3126   \n",
       " 31             0.57750          0.69560               0.15460          0.4761   \n",
       " 283            0.47060          0.50260               0.17320          0.2770   \n",
       " 482            0.24990          0.18480               0.13350          0.3227   \n",
       " 211            0.18800          0.14710               0.06913          0.2535   \n",
       " 372            0.28400          0.40240               0.19660          0.2730   \n",
       " 401            0.15750          0.15140               0.06876          0.2460   \n",
       " 159            0.08294          0.01854               0.03953          0.2738   \n",
       " 14             0.77250          0.69430               0.22080          0.3596   \n",
       " 364            0.16760          0.13640               0.06987          0.2741   \n",
       " 337            0.48270          0.46340               0.20480          0.3679   \n",
       " ..                 ...              ...                   ...             ...   \n",
       " 500            0.21760          0.18560               0.10180          0.2177   \n",
       " 338            0.14020          0.10550               0.06499          0.2894   \n",
       " 427            0.16960          0.19270               0.07485          0.2965   \n",
       " 406            0.17220          0.23100               0.11290          0.2778   \n",
       " 96             0.09358          0.04980               0.05882          0.2227   \n",
       " 490            0.18040          0.12300               0.06335          0.3100   \n",
       " 384            0.26850          0.28660               0.09173          0.2736   \n",
       " 281            0.08500          0.06735               0.08290          0.3101   \n",
       " 325            0.12120          0.10200               0.05602          0.2688   \n",
       " 190            0.93270          0.84880               0.17720          0.5166   \n",
       " 380            0.24290          0.22470               0.13180          0.3343   \n",
       " 366            0.34160          0.37030               0.21520          0.3271   \n",
       " 469            0.28780          0.31860               0.14160          0.2660   \n",
       " 225            0.15250          0.16320               0.10870          0.3062   \n",
       " 271            0.15070          0.12750               0.08750          0.2733   \n",
       " 547            0.22460          0.17830               0.08333          0.2691   \n",
       " 550            0.07348          0.00000               0.00000          0.2458   \n",
       " 492            0.23270          0.25440               0.14890          0.3251   \n",
       " 185            0.10190          0.00692               0.01042          0.2933   \n",
       " 306            0.13460          0.01120               0.02500          0.2651   \n",
       " 208            0.44020          0.31620               0.11260          0.4128   \n",
       " 242            0.48480          0.74360               0.12180          0.3308   \n",
       " 313            0.16260          0.08324               0.04715          0.3390   \n",
       " 542            0.13760          0.16110               0.10950          0.2722   \n",
       " 514            0.21010          0.28660               0.11200          0.2282   \n",
       " 236            0.41260          0.58200               0.25930          0.3103   \n",
       " 113            0.20490          0.12950               0.06136          0.2383   \n",
       " 527            0.20740          0.17910               0.10700          0.3110   \n",
       " 76             0.13790          0.08539               0.07407          0.2710   \n",
       " 162            0.38460          0.68100               0.22470          0.3643   \n",
       " \n",
       "      worst fractal dimension  \n",
       " 512                  0.11090  \n",
       " 457                  0.06788  \n",
       " 439                  0.06710  \n",
       " 298                  0.07676  \n",
       " 37                   0.06169  \n",
       " 515                  0.06783  \n",
       " 382                  0.09349  \n",
       " 310                  0.06958  \n",
       " 538                  0.09938  \n",
       " 345                  0.08488  \n",
       " 421                  0.09208  \n",
       " 90                   0.07246  \n",
       " 412                  0.09206  \n",
       " 157                  0.05972  \n",
       " 89                   0.08473  \n",
       " 172                  0.10100  \n",
       " 318                  0.10550  \n",
       " 233                  0.08328  \n",
       " 389                  0.07602  \n",
       " 250                  0.07849  \n",
       " 31                   0.14020  \n",
       " 283                  0.10630  \n",
       " 482                  0.09326  \n",
       " 211                  0.07993  \n",
       " 372                  0.08666  \n",
       " 401                  0.07262  \n",
       " 159                  0.07685  \n",
       " 14                   0.14310  \n",
       " 364                  0.07582  \n",
       " 337                  0.09870  \n",
       " ..                       ...  \n",
       " 500                  0.08549  \n",
       " 338                  0.07664  \n",
       " 427                  0.07662  \n",
       " 406                  0.07012  \n",
       " 96                   0.07376  \n",
       " 490                  0.08203  \n",
       " 384                  0.07320  \n",
       " 281                  0.06688  \n",
       " 325                  0.06888  \n",
       " 190                  0.14460  \n",
       " 380                  0.09215  \n",
       " 366                  0.07632  \n",
       " 469                  0.09270  \n",
       " 225                  0.06072  \n",
       " 271                  0.08022  \n",
       " 547                  0.09479  \n",
       " 550                  0.06592  \n",
       " 492                  0.07625  \n",
       " 185                  0.07697  \n",
       " 306                  0.08385  \n",
       " 208                  0.10760  \n",
       " 242                  0.12970  \n",
       " 313                  0.07434  \n",
       " 542                  0.06956  \n",
       " 514                  0.06954  \n",
       " 236                  0.08677  \n",
       " 113                  0.09026  \n",
       " 527                  0.07592  \n",
       " 76                   0.07191  \n",
       " 162                  0.09223  \n",
       " \n",
       " [143 rows x 30 columns],\n",
       " 293    1\n",
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       " 206    1\n",
       " 65     0\n",
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       " 126    0\n",
       " 429    1\n",
       " 392    0\n",
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       " 334    1\n",
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       " 230    0\n",
       " 7      0\n",
       " 408    0\n",
       " 523    1\n",
       " 361    1\n",
       " 553    1\n",
       " 478    1\n",
       " 303    1\n",
       "       ..\n",
       " 459    1\n",
       " 510    1\n",
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       " 244    0\n",
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       " 99     0\n",
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       " 87     0\n",
       " 551    1\n",
       " 486    1\n",
       " 314    1\n",
       " 396    1\n",
       " 472    1\n",
       " 70     0\n",
       " 277    0\n",
       " 9      0\n",
       " 359    1\n",
       " 192    1\n",
       " 559    1\n",
       " Name: target, dtype: int64,\n",
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       " 89     1\n",
       " 172    0\n",
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       " 283    0\n",
       " 482    1\n",
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       " 372    0\n",
       " 401    1\n",
       " 159    1\n",
       " 14     0\n",
       " 364    1\n",
       " 337    0\n",
       "       ..\n",
       " 500    1\n",
       " 338    1\n",
       " 427    1\n",
       " 406    1\n",
       " 96     1\n",
       " 490    1\n",
       " 384    1\n",
       " 281    1\n",
       " 325    1\n",
       " 190    0\n",
       " 380    1\n",
       " 366    0\n",
       " 469    1\n",
       " 225    1\n",
       " 271    1\n",
       " 547    1\n",
       " 550    1\n",
       " 492    0\n",
       " 185    1\n",
       " 306    1\n",
       " 208    1\n",
       " 242    1\n",
       " 313    1\n",
       " 542    1\n",
       " 514    0\n",
       " 236    0\n",
       " 113    1\n",
       " 527    1\n",
       " 76     1\n",
       " 162    0\n",
       " Name: target, dtype: int64)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def answer_four():\n",
    "    X, y = answer_three()\n",
    "    \n",
    "    # Your code here\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "#     print(y_test.shape)\n",
    "    \n",
    "#     return 'ans'\n",
    "    return X_train, X_test, y_train, y_test\n",
    "answer_four()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5\n",
    "Using KNeighborsClassifier, fit a k-nearest neighbors (knn) classifier with `X_train`, `y_train` and using one nearest neighbor (`n_neighbors = 1`).\n",
    "\n",
    "*This function should return a * `sklearn.neighbors.classification.KNeighborsClassifier`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "def answer_five():\n",
    "    X_train, X_test, y_train, y_test = answer_four()\n",
    "    \n",
    "    # Your code here\n",
    "    knn = KNeighborsClassifier(n_neighbors = 1)\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    \n",
    "    return knn\n",
    "answer_five()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6\n",
    "Using your knn classifier, predict the class label using the mean value for each feature.\n",
    "\n",
    "Hint: You can use `cancerdf.mean()[:-1].values.reshape(1, -1)` which gets the mean value for each feature, ignores the target column, and reshapes the data from 1 dimension to 2 (necessary for the precict method of KNeighborsClassifier).\n",
    "\n",
    "*This function should return a numpy array either `array([ 0.])` or `array([ 1.])`*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_six():\n",
    "    cancerdf = answer_one()\n",
    "    knn = answer_five()\n",
    "    means = (cancerdf.mean()[:-1].values.reshape(1, -1))\n",
    "#     print(means.shape)\n",
    "    # Your code here\n",
    "    prediction = knn.predict(means)\n",
    "#     print(prediction)\n",
    "    ans = np.array(prediction)\n",
    "    return ans\n",
    "answer_six()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7\n",
    "Using your knn classifier, predict the class labels for the test set `X_test`.\n",
    "\n",
    "*This function should return a numpy array with shape `(143,)` and values either `0.0` or `1.0`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(143,)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_seven():\n",
    "    X_train, X_test, y_train, y_test = answer_four()\n",
    "    knn = answer_five()\n",
    "    \n",
    "    # Your code here\n",
    "    \n",
    "    prediction = knn.predict(X_test)\n",
    "#     print(prediction)\n",
    "    ans = np.array(prediction)\n",
    "    return ans\n",
    "    \n",
    "#     return # Return your answer\n",
    "answer_seven()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 8\n",
    "Find the score (mean accuracy) of your knn classifier using `X_test` and `y_test`.\n",
    "\n",
    "*This function should return a float between 0 and 1*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91608391608391604"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_eight():\n",
    "    X_train, X_test, y_train, y_test = answer_four()\n",
    "    knn = answer_five()\n",
    "    \n",
    "    \n",
    "    # Your code here\n",
    "    ans = knn.score(X_test, y_test)\n",
    "    \n",
    "    return ans\n",
    "answer_eight()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optional plot\n",
    "\n",
    "Try using the plotting function below to visualize the differet predicition scores between training and test sets, as well as malignant and benign cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def accuracy_plot():\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    %matplotlib notebook\n",
    "\n",
    "    X_train, X_test, y_train, y_test = answer_four()\n",
    "\n",
    "    # Find the training and testing accuracies by target value (i.e. malignant, benign)\n",
    "    mal_train_X = X_train[y_train==0]\n",
    "    mal_train_y = y_train[y_train==0]\n",
    "    ben_train_X = X_train[y_train==1]\n",
    "    ben_train_y = y_train[y_train==1]\n",
    "\n",
    "    mal_test_X = X_test[y_test==0]\n",
    "    mal_test_y = y_test[y_test==0]\n",
    "    ben_test_X = X_test[y_test==1]\n",
    "    ben_test_y = y_test[y_test==1]\n",
    "\n",
    "    knn = answer_five()\n",
    "\n",
    "    scores = [knn.score(mal_train_X, mal_train_y), knn.score(ben_train_X, ben_train_y), \n",
    "              knn.score(mal_test_X, mal_test_y), knn.score(ben_test_X, ben_test_y)]\n",
    "\n",
    "\n",
    "    plt.figure()\n",
    "\n",
    "    # Plot the scores as a bar chart\n",
    "    bars = plt.bar(np.arange(4), scores, color=['#4c72b0','#4c72b0','#55a868','#55a868'])\n",
    "\n",
    "    # directly label the score onto the bars\n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        plt.gca().text(bar.get_x() + bar.get_width()/2, height*.90, '{0:.{1}f}'.format(height, 2), \n",
    "                     ha='center', color='w', fontsize=11)\n",
    "\n",
    "    # remove all the ticks (both axes), and tick labels on the Y axis\n",
    "    plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='on')\n",
    "\n",
    "    # remove the frame of the chart\n",
    "    for spine in plt.gca().spines.values():\n",
    "        spine.set_visible(False)\n",
    "\n",
    "    plt.xticks([0,1,2,3], ['Malignant\\nTraining', 'Benign\\nTraining', 'Malignant\\nTest', 'Benign\\nTest'], alpha=0.8);\n",
    "    plt.title('Training and Test Accuracies for Malignant and Benign Cells', alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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0oZZ9dPpl3/b/mcdM9NSAu2Xd5OZ/Y+ZXZWDkfxLe0VpJlK8tbG0NhjWUoSftqNrvAmAWionPhqwq03ioz12JBTIj9SuFD9a0szeYGYS0NQXtV+LBl3af1DX62Q+MVe/iU/mjD+u+pmiSPFCM8Jqt5L6qP5fIuBn23/TvjJ/M8Cm0fI3XadnwiZmtryZ6W86mU4MbjCzX7c/SPq1V8+117dPbl2pbRst4XyovefNFe3npfpmnPJ+fKUMWrvleVU7omx7M1Md9TxT+zr3U9Pyyh3N7NVmpogrPdPlH+HEPp3UJ19Tf8qTxsJ7kD0Ay+4vqWxie/W7ple/0n5a6ifyRdlXy+T1/NT7Tc899R318V4pS0BROSpXfU7PZb035Q9/b2Z39qtwxu+dAqDexf/Szne8melE0KwURwKHPq+lZikCoK7VR2U4iVLt0PJDjc3l13e33y/aZ1Tv035pGKcAy181RlDf07tWzwqNEX7XjqDRcueU50W3PPq79qPVuEF7b0lwlf0ubovO8/s1ui3U6kRPnVCt+qoc+YfGOHrWXdcuSzz7pay9tfQuUh31LNUecdorTuNDPXMkJHQ7KTouq999x7q8UFFKGsuKgd6fikxXPZ9tL0m/vh3Fe3O/ipiZopz0DlIKEawaF2t8t397ebvGktozTeUqUk5j5dSkZZjqp7JZ7FN6t8vXxTbFp1Lvp3yDCIDy+6PaN5HvvD3hhnoGiZHGMXoG6P2t/qxng8Yv2m85bKfQrbhh9JWUd3RnffRs1ZhM45Aw7hUHjed14JHGr1m+0lnOsP2pl1nE/iftMYPy/cnM3ttlyW+CeXtm0b30baXvvHCysJYO630nAVnjGu2d2+t7LtVOqXsA6v77tuulbwv5o56NeufKJ+9rM7kscUw5VkZcXxECCIAVMQTVaBEoSgDUAFIvHH0gdqZYADzUzP6xLQz2M4Eejtr4XIOpbmkQAfCYdrkaMHRLGrx+tMe9BxUANUCZ1hY8s+6ttiqaQfsx9koaZJ5kZrv3yHSLmX28vddckQJg0TbsFAA1UPucmUmk6JZ+2I70ytpDJL5GH6u9eAe/vSc6QGJYe8kVsQeghBPZ9KA+fUgDDZ2iJ7G0W9JHsIQzCQQpKe7HZQiAEis1ANZHkZIiBfQxKCYa4AehXf1Hz47UtHc78ijldLoTzUwHM2QlffxJtJCo1i9JUFQ/ilPej6+UQWtnHoke4qbBuISQOHUKgPrI7tUHw7UvmNlXzezsfo1u/z5W3nkEwKr0l0Q0rSVwf0jMnCUAqo9oyZxEk15JApLE8177a3UKgNPN7JTooy4uvygBUM9hiX7yzavN7J+7NCLuK3oXaEJSk2f9BED5g8TPlL6u6EO9Y/XM6ZWKFgDf2d4DsfNk41AHCWwaF6nf9Vuu2flM0ZhG4kqv5eT6aFZ0pcTQbklbL+iju1/SR7eisnWqc68P8M4Pa9n/Mz3sJBFEtpEtO9OoBEBtPaHxZ0rShLbeHeH07qxrOgWbW9vjIAnU3dLXzewbCRVQ//xI+12ZlV221oEbKT6VcLuXswwiAOr74APtEvR+73eQoXxcY3qJxL2SxFJNcHWbGB1GX0l5R4c6a3yocW+v8YO+S45sfyN0isWdbR+mP/XzAY0VvxZl0t6OGqMVnSSuacylf/dKElA1btb7JSul2ilFANTydb2PevXbuA56FmuLD1IDCCAANsDIjppYhACoQYiW4oTZX72kHm9HE+lFEG/qqxeBNndVUqSaHsiaqdGMsz7g9cEZPzg106k6dhs85hUANYOuwaMGr/q3BgWajdFgW1Eu8SBCLyzVNysNIgDqg0uDLD0D9PGmWWEJUFrqpRnk8EGuAf5b2zNFWfdWPu2XpAFLSJpVkmioKDDNSivyQ21SxIfaGJgXEQFYtA1jAfCb7Vlw2UEfYGqTZqY1E6qBkf4dkj4Qsk6DDb9rBk6D7sBV9pZvir18bdv2zJx8S2KZBvRKVRUA9XEvuyu6JyT5roRe+YzYSNSLP2714aWN8juTfORHZqYyldT/5I9iI/FQIpvyaENmHUagFAuAErIVSSDRK4jQEhUU8dqZ1L/Pyvh73j/FS0oUcaaTQoMArA/isMG49gPTIDklvTuKOFJ+lXd7O7pE/qcoFDEIm1hrgK6Z7c6k6A99jMcTIOqT6nuK2NCzUQcUaFZfIoQ2wNaHfpxGIQCe2RYXdF/5jj72ZW/ZWvWMn3fiqPYr+kjRNsqv54ueX/IxnSCvyKmQUpb4FME7VQCsUn9J8UXl0bNKEfVKekaFaNesEzQ7+5X6rPpZEMFUhvqk3qHhPaPnQ3geqs9LDMrqs7o2FgD1rNB7PEQO6bmsiDQ9GxR1IXGhiAhARXarH6kv6LmsPp8VXRpHAv9D+xmYIgDKJzQGUFL/ll8/0J7o0/tSzzO1JzwXlU+igcTxbqlIAfCQ9sd9uJf6nlZCiIGe73pnhT4pThL0lMRN9upMnc8UiR/yKbFVVI7GX/IHvVPijfrFKLwPs9odWOudGp4Negfp//UMVOSwfCWkfid5xh/WesYqClT+rGeo/FeRmCpPYwA9P5XUBo079QyLk8aEetbq+aR6KKmtnfn0dz2fBz1ZW/VUn1A9FCWu8azqqX4lG+lZH48n+zGNBRu95zVpqwhbcQ1jakUSiUF4J6f4pyLo4vehRD75lPqvfErvdf1bvqb3g/pyL5/K8oduf8srAMoX9awJhwFJ3NT3RbekbQJOaI91lUfvMkX8KQJVST4t3w7PPP1dS4r1rutMw+grqcKSfFptVURuSN2e3eoP+k3jW6Vu+50Oy59S7K+JmzDBKb/SGFF+V2TSs1Bjjs4xrMYp6oMa0+h9F54XErk18am+2plS7dRPANQ7W+PD8OzTs0Hjcz1n9Q5W4IZ+k2CpZ4QSAmCRXlHxshAAK26ghlWvCAFQgz49QP+z/bKOl7bp4Rv/v5Zi6uGsKC+JhFlJgzZFMWzQ/rHbR7d+zisAqi6qkz5gNHMUz7xokPC+jhlHDcIkCHSmQQRA3Vv/6OOl84NCbZZYEz6oe+1lpg+eMPBXvfTRpxeT7BCSXjISvyQEhjbrtyIEwKJtGAuAoa56iWoQHO8BpRe92qRlz0q9lnuq/dogPgyUJW5J3NDHXkjyWc00a78ZDU4Utq9UVQFQbQ9RqxpQSADtjBRVmyQeKwpO7dEARIOeziVI+qiVHZX0MaAZ9KyZcZWnQZSWYklk60x5RauMIpL/pA8BDfqUtNxP/hGS/DyIjPIhceoVwaLr5EeaqQ3vZEVe6VmTNUDUh5yiLsVdH/xx0rNAH8ThBFJ9XChCRsvpOiNU9VGrpSr66Orc2Dovy5RBa2dEmfxBYpKefUEICW3pfFarv0gcUhRG53JlXaOyJcJqZl0DW/Uh+Yk+LLNSUbxTBcAq9pdkZ29PDP2sfUHKMnr1YfFX0rtAvq1nYOyDmlzTcyNEdsoXDu5is1gADO94TYopCjBeGqv3pv5JWTba2f7OJcASsiRQ6R2ppMjS73RcpD6keujfEp/CcyxFAJQQLzFEUYYSf7LqrH6gcYXar3aprfJzfcBlpaIEQI13FHUbIv+0lYPGQXrPhaRnuj6uJWLE7/UUATDkl0CiciVYhaRnoMTHeFyhsVC3JaYfbotqiiDN2qdR5emjX++MMKbRe0j3zkrxh7XqqWeLJuUkHMaTv9ruQuOkIBDpma0To7PSsPcA1GSGxrCKNu+2VFzvJbEOQmCvSKhYsAm2ku9rUjSOHJRwoPeLylaSHcMEbycHRSNpLBlEEEXVSjDSpGpI+k2in6IE43FQN5/qgjvzz3kEQImQmpAIwpaeTVoO3C1iTxNz+uZQ/fWM00SnxgUSTOOk97KeJ2HitJsQG79/i+orKe9o1TXezzRMSEsIjX1fgQJ6dmuCJ+77KQJgUf6UanuNxYJ/6hmiZ0mRSatWFPkdni2KBNa7ovObUgK63ovhm0Fje30/dQaUpNqpnwAYf5tpQkxjo/g5Gz9v9c2nMaWE37g/FsmJsipGAAGwYgZpeHWKEACFUIPvLxfIUrPIiu7SjEqvAU5eAVBV1EtAM8dZH7X6PUQg6L87hYbQxEEEQL10JDZ1G1QrpD+IAnqRaXazM+kDRjPWur+SBkCnd+GuDyQNirR3V0hFCICpZk61YSwAqmwtW9VpYVlJ/qB9e8LsmSIF9KHUmfSRF/aOkcirj5vOgWG4RoPfOBqrigJgvKRC/UH+20vgigfe+kCRIBgnfRSEZRNjmYHMK1ql+k5nPu3jqEFeeH/qoz/eA0p/1+/KpyT/6bX8WR/Z8qNwmrAmJCSKxiJ6al3lg/oYVJJNZJusQV+/8vKyTBm0ZgmAvZ5B/eqY9Xv83Pp2tCwxzlsk7xQBsKr9JQ/fPIeAKNJFezGG/tErOlrPTvX/4PsSGbWcsjN1LvO/vP0c6bWUM0/7lDdLANR7S8tuNeGTtQeY9sdS1I9SHOGcIgDmqZ8+WkNUnfgEMbazjKIEwBBRpvIVNaL7dxujxO835U8RAJVPExh613UTM7VXsiYolDQJpzqNJWlVhT6alTRuyZpE0m+dy3Yl2GQt71XeeL9e+aLGSVkRXcMWAFO5SPyTEC+hqpcYEguAKlvCX7yMMr6fIrD1/tJ4UEk2VVR5Z9IWH9pTTkkT2Xo3dVuGLKFSEbchFS0AZkUx616aPNJ7W74ShEpFuEmM1lisW4pFJgmiEou7JT1L9HwIq4uyeMXvX5VTRF9JeUfreawl4qHt+o5SXbOSRFIJg3F0bYoAWJQ/9UC8yE+/iMZi2jJFk3FFJj3/9R5Q0qSJIgG7JdlAka1BkMyK6E6xk8rvJwDGe+NrKXvKPvZFcqGsihNAAKy4gRpWvSIEQAkrirjptb/JIFg16A4imB72WUuBBhEANYOumdBuSfcMH0Td9iEaRADUh4Je1t2SZve17C6EtGe1Of7Y1hIw5enFXTNf8amooxQA1c4UG8YCoKIJFAGiwWK3FC9B1gBZA+U4aSCl6KsQ/aePjl7LfDQAVURImE2sogCoAU446VYDY23k3yvpPSNBQMKAZse1VCkWQPVxpSUSSiq32wdhv36bV7TqV1633yVahb2A9HGsmdbOFH/waclXOCE0q8y4H8nnFOHTL2Iwqxx9gCkaKfharz0C+7U9L8uUQWunANjrI7xf/br9rnsoqkIsFGkaxNA4f1G8VWaKAFjV/pKHcR4BUM+E0Ceylpd33lfv6/BRpveHRILOSK5OAVBRRoMI273anCUAKr8iVNUnlRTtFi/fjJcI6/0XoieKFgAVZaIPcyW9T7SXbFYqQgDUu0d9M0T/hf1Nu7HTRJjuG8YKqQKglvV2Rv7G94jHC936ch4fVl5N5Oo9pEjGEN3VWUb8Ya297zRh1ytpv1EtsVVSNKIOVupMVREAVS9FJ2n/VQmWiozMet/GAqBYybd7LZuMBeOsCS8JS/KpsLJBUWa99vxUfvlUEBWLFgBT/EZ8FNmlPt5rXKsouCCSpZ4UHEcWZ62w6RQAi+grKe9orTIKBxNqhYqiuHtNsmjCNv6OSBEAi/CnFPuFPHrGhGdT0cEhiurTgSh6VkooVhRdv+XFWsUSvhO0l60i8+KUYifl7ycA6nskbJXTOUmdhx95a0oAAbCmhnXarCIEQD2MNXuYN2mArX1atExBgpqEmDhpXzyF+St1exnnFQC1VKPfBumqT4ge0gtZL5jONIgA2G9AoXso4lBMlLQ8VXu/xCne60wDa0V69Ep6senjJeydV7QAWIQNYwEw6yTSzvZpiWv4GAsnwcZ5tNRDkUhKGmjL3t0iKcJ1Wnqi5SZKVRMANYBXhFo4lVaHKKRE4cRLAhXlqAiekOLZcy21Sj3AodMWeUWrPu7a9WdF30gQUermw/rIjPeElJ9kLedVGfEeYhJKwynCeesXt18CqyYP+vlat3vkZZkyaO0UAFOeQVn1U9SNlo0qUkNiZ/ioDHkVOaLILbVdJyl3pqJ4q9x+AmCV+0se/8ojAMYHtsFENX4AACAASURBVPTaMiPcX4z0XggH50gs6TxZPRZQuonuedqTlbebAKh9zhS1oRRHoilqUdElWporMUOiRkh5BUCVISFBfq33mPw6PnhDv4d3Qq9VCEUIgBKFFD2ipI9aiT/9DriKJ9dSBEC9CxXd1yvKWZFFYYuDXoJdbEs9d7WcTUst5U8hkink0ftXH+1KEp6zlnTGH9ZZy747fUd7KYdxWbdo71EKgFqarP3bFGEmBp1jWZ3mHPZY7La0OhYAU8Z22mMtHJKjlR6Kho1TLObGez/26rNiqfGFUhkCYKibovs1QdFttUw8IZg6po0F/SzRMH7/FtVXUt7RcdRttwj62GYS1hQoEN7BKQJgEf6U51mvwImgdWisWcQe0OH+sZCraEjx65f0LNekuZ5NihYOUbHhuhQ7KW8/ATCOTCwigrpfu/jdGQEEQGcGq3l1ixAA9QDWgzg1acCtWVsN8jtPouxWRrclIXkFQO2Zpjb3StpoWR9HSoqyCwOi+JpBBMCUpZYK/9+5faOsSC8N9MLG1r2WycR1jZc0pw6W+tmySBvGAmDKYEH2CEuEsw580MybwvyVtGGyBov9UjyoqJoAqA8LLfVW0uAl+Ga/NkkIDVESnUsCY0YqR3spKVpA/9YHaGrKK1qllhvni5eRSfjUR2S8L1acV5zCRtraP0kfk1kpFhQlJqcy7SwrXiaoKBQ91wZNeVmmDFo7BUBF4qXuN6OxioQ9RVLGBwT0a58+PDujyYrirXv3EwCr3F/6sYt/TxUAgxgW3qWKnlIUVb8UvxeyDguKBZRuy4T73aPf790EQPmeorIl+OkdrD6v/fq0j1zY800iUIjQ031SBUD1CY0bFHkTL6XrVVdFB8eHmcV5ixAA43FYVoRKVt3iyKEUATAlMlTRX2GiqJuYH+qifq5IxX4ncMZ1136TOpisM8Uf1imnuOs5q8hQJW2BEt6PcbmjEAD1btIWEIowSk3dJmFiATBFxJc4rYlLpaxJrNintJdrOFyoVz3j8XTRAmC3fUwlZGnMrcN3dH8xVVJ/ly9kbfGi51WYaJKwk3KKatgfWWVnbbETv3+L6isp7+h4NUbqBF28hUuKAFiEP6X6t/INMwIwnqxXgETWHu1ZddWEQYhule/EE7UpdlKZ/QTAXdp7l4b7ayWKAmTkw/J/UsMJIAA23AEq1vwiBEBFGmnPnpSk2WCJEXrg5kndRMa8AmC/k9hUpxRxLyWPytLAK0RZ6AOi3zLDeA8JDdg6T2iM99bQh5AEm34p3tulCAGwaBvGAmBK/RTFEGb9FBkXlk8EDrFPpy55jA+RqJoAGLe3n627/a4DMxRdGpL6n/bNCZsjx9fppDwNrHT4g/h22+Bc1+QVrQapfxzJ2E9ki4VNCZlaRpgVLRkPUDuXGOapo5aSaPN0pbHO+OZlmTJo7RQAFYmScliDxCQtwwyn0OZhkiUyFsVb9egnAFa5v+ThmCoAavnoJVHBKe8ZZY+jybMiiGIBJSUyJU/bQt5uAqB+1/tNgp+SnvF6FmkvNe1pK4FZy0l16mdIKQKgIkAU8ay+liepz6jvZKUiBMD4UKbUqBbZOezRlyIAdtvOJG5TyjNF+TuXh6ey7Bb9Fn9YZ6186Cw/RdxLyZNa76x8Wqqp7UjyftN125IkFgC7iTpxPfo9B4flU3mY5TkEROV2vnc04amtBzr3b44FsDz1CXmzxO34/VtUX0npT9qjWdHzSqmTN/EWFykCYBH+lIfzMPcAjL+R8tQpzquVGvGBISl20vX9BEDliQXK+J66nwIStC+gvuvyTLQP2k6uqxiBvC+LilWf6tSMQBECYJZQlYVJyyQUih6WiGhZjf7/hvbprNr3LZ6VSRGG8gqAWYJRZ11TxL2UPCo3FgD37HEQRahDPwFQEW/hAAwtARG7fikeCKYIbL3KG4YNU+wc16mfABjvh5W6AbGiKvURrFQ1AVAilsSYsSQtpwubsYdyNNjWrKj6UHxQTHwfffhqMKcoiyzxOq9olbcNelZoj70govfbz7FzL62spY2qQ7xERZEU2vB7kBSL670O5EkpOy/LlEFrSp6suukAHR00EJKigjTJo6WgiiCU8BLvu9NPBCmKt+rT78O3yv0lxQ9CnlQBUHt5xgcmdEY3dLtn/NzNiiAatoCievUSALWcUvVS0tYQmsAIz+isrR9SBMA4ckwTA4r8Vdk6sVF+rX3HgkCe2nf6+b7q30/cifc87HbwWKcdd4oOAEsRAFMiulLarBUK8YFvGoPoHaFlldqnWcsn43FcykdzSp64/Sm+mZInT3+M827YPkgnRN3KfzSW1SnHmkB7toNBP/un+EhnXfs9BwfxKT07wlLiFH/pxy+vAKjytF2NJm7Dd0LWmDWOmutXh6zf1fc7JwHKeP9KD4j3I++1bUncDgVRhP00qygADvMU4K+b2baDGD26RhObsQCX8tzT5anPKU3OaOVEWK3VWV35n9478m2EwDEa09PlCICerFX/uo5SAIwH3xo0aoDWa6Nfvdi0bFapm3DVNAFwkAjAFI6pnj4MGxYtANYtAlARl6e0DTSsvbj0sa1BlZbfaDmTTi6Mk8R6ReN0RgPmHTSn+lnIp0Fuv30ue5XZ7eCdoiLSyowA1NKpsDwq64NGXFIHtp0M4w8sLRUNe2p2Y62JlXAQSufgWtcUxVtl9fvwrXJ/yeP/qQLgKCIAsyYQ8rSlW95eAqCuCUv6JTZr8ktLgZWyTljsJwBq+ZfKCPuz9ZtMkBChjzSlbv1LvxUhAA4rWivv8znleRFvdC/RS/un9tqvUIc6hL2ctR2HomA6U+qHdbguRdxLyTOoD8djKk2OaAzTK7Ja49cQbT8qwWZYPpWH2SACoMr/hplpD3AlRchpKXCcFG2/fvsPqctm+9V7GH0lpT8NEgFYhj/14xf/Hvc9feNp0r7fQR2p5SuCW0ttlfqd/pxaZoqdVFbe55T2TNa4WtvxyJ87J9oV4apxNcuDUy3lPB8CoHMD1qz6oxQA45PbukXmxHhT9q5rmgAYf+Sk7gEYTqAT27FGAA7DhkULgHXbA1ADBw2IlRSloiWWw046CEdLb7RnU4hy0Idc2Kg+3D/voDlvveO9fvJeq/yKVJOI2GtPOi3ZiJdQ5rlPvAegPgR12MqgKV6Gnnev0iIFwDiiTIKvNszudehMLJSo7VkCYLwH4Fh4q/x+AmCV+0se30gVADv3ANQ7UVHM/VKePQDLEgA79ypVmxRhpYnBTtGpnwCo0xklXimlRHlrbzstNVQatgAYj8NSDsJSnfRsDvu6jSoCUJMO2nNNH8xiomdDry0i9L0j0TUcQlYXAVD7U2o1hFJKf8u7Z1sRSza97AGY9ZySqBwOatCekfL1OOl5pD3ElXQYjt4vY015xzIpolFKnnhSP1XMjA99GpWgnIdvfIiTrhvLPsud943F96K2pkixk+qRVwDsrLueGXp3vTvajzBlVVoe9uStMAEEwAobp4FVG6UAqCVk4TQ4fZR328hfZlD4vz7Kw1HyRAC+5Jzx6XcpJ3vp41DLnMKy4bEKgMOwYdECYHwKsIQfHRrS72RWLXfUskellI/DQR4V8bJxXZ/y4aB82h9GG8OHEypTDpMZpH5Z18TPB220HSJyQ968g+Y89VqxHV0TBEjZpZ8dQ/laohUifbKE8qJOpS3yFGAdrKOBvZKWlIW9BbsxiwfZRQqA8aErKYfoxKdNqq5ZAmBRvFV+PwGwyv0lj/+nCoAqM/4gVLSw9srrlSTkaGm9nklK/U4BLksA1HtLywHjU6e7ffT1EwA1caKlmErdIoNjZrHANmwBMN7WQkvCUvbejJeVjkoAjE8JTqmnIv80cRTSKAVAnZCrbVKUivZfnUAd3sev7XOyssRPjWXDe2xUgk38XNbEoXyq38nSsfBW1hJg2Sve4y5rPBbvDyoxdqxbpOieeccyKaJRSp54258UQUvvN4nqYYn0qPwpz7tLOofeQZpIVtJpzuqL/fwv5R6amNbem0opE6UpZabYSeWMVQAMdYm3KdGYVn01ZW/mlLaQp8IEEAArbJwGVm2UAqBO7FupzbifiKG9ySR2hYQA+BKJ+LRahY+/oY8osmt7MNWPY6rrD8OGRQuAGhhJ9AxLEjVY0Mdut6SlYZqFndrOUDUBUNWKozizNuxPtV/efLEopWi6sPQilBOLRUUNxkLZOpwjnKqryQJ9wC9IbIBmnLWfjlJWvbSU8MT279qvSafd9jugJ+vW8h2J4mGPQn2Un5tYx85scSSdlstoz9D4kIPO/PJrDYaVihQAYwE9Zcl5Z5RmlgBYFG+1tZ8AWOX+ksc18giA8b6n2ostnJDa7X7x0not0VK0TWeU7DCXUIZ69VsCrHw68EkCWUjyeW1J0Jn6CYDa+F3RQkr9ouwk1ugwjrDMcNgCoE5BldAZDkfTuEzPrW5JE6PKHyZIRyUAalJG91VS5J+W2/dK8XNY+UYpAMbR2Yqg195hRSUdRhUEmKxTz+P7KNpHJwWHNCrBRuK53k1BqOy36kbvH/XHMHFWlgCob2SdnBpO6M6KrI/fUXpH6v2t8fBYUlkCYLyK6b72uKVXxL3aqr2HR+1PednqG0++HpKEzngyIKU8+a7GmDo4IyRF0WmLEj2jJShqovSulMJ65Bm1ANi5akLv37H67xgRcPkoCCAAjoIy90glMEoB8Jvt/cVUt14vA734tWRCexuFhAD4EolOwaHXjKEGchKLwkeMrh9rBOAwbFi0AKh2xhF9D7RPV9MhM1lJg3MN0kOqogAYD041U/ih9im9Kf1cUbfxiWd6BynyJ0XwiqMIsiIANcP7P+1KaB8TiTNFpXifnx+393tJLTsWJjVI1ISC6h+SBpYaRIaPDB3Woz2Teg28u9079h8x1ebPWQJFSt0V1bt2O2MvMVGbS6vvh4+7IgVACeFBMFe5Yqdll1kpjqoKv2cJgEXyThEAq9pfUnwg5MkjAMpndGBGGF/28h19fChiUPsTKWn5XBDG4vpVRQCUP4bllnr2dVve3E8A3CI6CV39VFEYnaJnaH8sHulvwxYAdQ/tZyXRX0kiriJmuu2bFe89qvyjEgD10a1nZRAeex1Epi0NtOQ6RL6pnqMUADUBJAFSSZMyIfozTx/sllfvvBDdpIlqTYxmJe2tK78MvJRnVAKg7hVH9Knf6N3ULYo+Xl3Sy6fy8BtkD0CJIap3SIoGDJHx8b3jsagEWS2fTYmiknCrcXTnycJlCYD6zpH/hCjnXmN0jdskooXn4aj9KY/t9S7SYUE6WCY8Q/WeSZ0g1VYkWjlwb0bfjVcUyK/1XOn2LI/rrGeR3n9PdTSkKAFQtuwsO4tZHBmtSW0xSp3czmMD8laMAAJgxQzS8OqMUgCMI3r0sNYslpY2xkkze4rM0cNfp8kp3F0JAfCvlGKO+quWt+jkwPgFokhLDXi175EGfL1OVMvTBYZhw2EIgKu0lyCEgbdO5zumQwTSS18fMOoD+tAKA7AqCoCyUbzkSzbVx5WWWaifdCYNcHVao2aLFQmgD9qQ1G7tO6JZdkUIaJlnlvC1eXsgrr6opI+ekzMGTtrEOvjXP7RPis3jU1l5N21/OIXf9OHypxyF6j2rgWaou6I/wj6KoZjdO/Y0/IOZfa494Oy8lcrRx8zdGR97GpTrAyXcSzO5KkfLvjqXvOh5pmgmbQwdIhDje8XPY4kUiuzS8pk4Kar3hHakRuBepACoe2niIJxgJzFCBybEoqYG0tqfTZuzi7U+vEJdsgRAlVkU7xQBsKr9JYcLWx4BUOXq+fbm9g1kDwlKEpRjH5RQqEN1Nmnn06SIlro+nFGxqgiAqcz6CYDyWX1oh21IdICO+tHM6AbyYX1M6nkZjz9GIQDqo1BtCKK+Ip9Uv3irFL2j9IzQxvHxe31UAqBQ6R2gbTWUNLkmkU3PiDgpwlTPDL1/Y46jFAB1mJVEIiUtV9Zp7ykiQYq/xYehKRJS0dh6f8RJS4NlP0VNxgxGKQBq8lfvpjC2kZ/o/p0+rwlFRaLF46BRRwCqf0r803MsjNv0DtTzPmuyUnt0ahIsRCzqPalo4U5fDDaRGCu/lB/ILhIN41SWAKg6dJ5OrgAJTYDGz24dzCZhVO/luO+P0p9S+kacR2NPHeS0ZvRHHQimftnNTtq+Re8xRXrLb7PEe03caj/ysGrnnvaYSz6blfQ9oGeWtvnRMzacLh/yFiUAalJZB1zqPaO6ZAnSmjjQ2C+8g+WHYaVLXr7kd0YAAdCZwWpe3VEKgFqSKcEiRN0IrTb41T8SrzQA1ktdSS9zLT/TR6YSAuBfHVEDJW3gLnEvJH0kKExeyxkV2aHZd708xVYsw1JBDZC0tGnQNAwbDkMAVPskRGigF5658jEtq9JHi9ohEUYDCH3c6WQxzSArVVUA1Mep6qmBakha/iJxUx/wGrxLjJJwsEE06JdwJgEtpHiwo7/JZ25tl6HyNIupQUocOSr/klCQtW9nHGWg6zWgUX2CqKgZUQnUeVIc4aJlMUHYyFNGLGB0i07sjPTRgFv215ISfbRpAKsBqT4elCTsaWDemfTc0jJtLeULSVGX6nsSBCXIyi4a9MmOfzGzd2aUo9lpCa1hYKv6SKDVLLjKkCgbIgTl22EpUNEC4E5mpqW9oe/It9QW9R19nOn5EuqofPKN8FzvJgCquUXwThUAq9pfUn04rwAocfksM5N4HpKEj+vavqzydEBKiMjSx4nEm85JuHBt3QRAtUvPEQkMIUkQ0vNTEa567umdEPZFlIAVRPpRCICqU+fknj70rzGzR9qRK6qf6qm/awygCQIliU/aF60zDUPU0PNQEwTx5IMYaoJAf9symgzRs0zjOkVkK41SAJSfa5IrbDujffD0Ua73URBW9G4c5AAoich6D4S9ldU2PdM1QaSk53x4f+rQFAnt4QTrUQs28V6Wqpt8R2NFvaNVf40j9d4Jk4pBjChaABQDLTHuTBK89d5UhG6wlfJovKaVHIo47ZY0qaQJDb0bQ9J4Qd8OivDT31W2fDAuW22skgCoekoU63x2692vZ5QEND27NXbT3/Rcl5ip1O0083jCuIhDZXqYoedPel7pu0OCfJwkQstOGh+pP6pPadwaRzcqv1aD6fuvM+k5owjDcMCQfheXm9tl6vtH91aZYXJWeTReHZYAGG9poa01NJbUmEk2VD0l4sY21nhZz/yxLmEe1DZcN2ICCIAjBs7tehIYpQCoiuhDWSJGLAJ2VlCDWb3U9LH4jvaPCICLUtLHnsLptUSzW5Koo1MC9YIJB1xINNB+d2NJRdtwWAKg2qgIOA0iwyxxZ7v1AtZHnl7A4cTHqgqAqrs+av6pvaQ5HvR2s6c+8iW6x6f3qgxFAIbo2n6+ID+SUNBtWasGVxL4giDUWV7epcH6INA+U2ELAIka+idvWq/jMIRuH5/6MJMPxuJdt3v1Onk7LFnpHOhmldVrv0TN8J/e8XEZl6EPNQ2oNTMeok6KFgB1P02+6PkRL+GL66F7KqpSHy7xnk29BEBdP1beqQJgVftLqh/nFQBVrsRZbYi/V5+bSAyRLysKrluqowCotnZu+dDZfn24KXpS78l+/UvXxh993Xw/z8e4tqPQARYhErCzfhI2ND7SR7PEd6VuexoOQwDU/eRfalOvd5DEP713FKlehgCoeiraWhGL3ViOZWmw2qRxaSwCdtpKdtGYSyefa7sEpVELgLqnovvk9/FhOnFd5VNaAhyEZf1WtACY+txTPomT8q/OqMqsMrRPsZjq3ylJgoze95ocj9Mw+kpqZJnqIYFIzx2J/N2SJnM0ntUEaTglWVH4WoXRmfI8c3RtnvdqCuc4j/xOkZeaiA4R2P3KkJCrCE+NLbptzaIxl2wfB0P0KleTsvLzzkjBVDv1OwQk3hqgX/s0LtZzPM/Kln5l8nvFCSAAVtxADaveqAXA8KLTrKQGZ5rZ0sNXD2YJDQqdVkSCBrcpwlC8ga72xoiFjmDK+IS9lCPX49NaNWsZb0AeykzJo7yavQwHBGh/n859RzrdLT4RTB/fvWY/da1mQCVyKTJIgomWo0iokYCi09E0oNMMaZgtTCkzpQtosFKUDVPsHNcprz01SNBGwVoSq6UAGkwookIzwJoJ1EBDoqYHATBwkNimPaw08NEMpz5C9JGjaD7tdafBrSJHtIwsa08SRWpoRlmDTc1IKsJNZervinwTH83O6rQ5DS777Y2na8V4x3akm3w+CEd5BcDYvmpvt03/U/w0Ph2118eehBMdqKO9WBQtIDFQ9Vd/1fISzborUqTb/mNxXbT0S5vjSwjU8i+x0AywoowUJaKIEDHttr+XylJfPsTMtNxX/qtxg2a3dfqkBF3VKWXQmpKnF0f1C03CyM/UFgnmEo+ubguQgUeKCBLfZyy8B/lQqVp/SfHdQQTAUK7ECYlR6t+ym4QaPQf0XJDvaalSrwNmVE5dBUC1TRGsmhRTH1Vf13NTzzw9L/Wc0DMrte+k+H7ej3FFj0mA17NEk6V6j+v5IdvpnaVnQbz/Zrdn2zBEjeBjimbRM0rPfEXtaLJJzwYtgZN4KrFCqd9Hc2qeuM/k8U09z4OtVU9NfIXvsLEIgKqPnitioMOxxENJkU1a3ii/0LNeKcX+KXliBnmfg3rHh3e0xkHyKQltwafk/3n9pd9zLK5jt7waW6j/hYgw1UffAL3ej1ll6d2t8bCefYr20xhdYr4izPS+lNCi96eiVbNOo83b9pTnQ0qeuC3ySwl7GosoilTjbNVfkaVhyxZFRir6N6wE6TaxOWx/6mf7rN/D1jR6riniU/1HY1f5gJZ5aywuO8kHNOZKTdo6Skt89VzX81Lc9DxSmVo9oXGXbK8xcdZYNtVO/Z5lGjPK//Te1TeZ+pzevwpA0PtWPq4xk77rNDnQbT/O1HaTzxkBBEBnBqO6EHBOID5MQRtj64VIggAEIAABCEDAJ4F43zCtqtCeWCQIQKD+BHRIV1hxoclGTfyTIACBihNAAKy4gageBGpEQNFhOkFVSdGMikLMmv2sUZNpCgQgAAEIQKC2BBRpotObQ9SZVnJoWwESBCBQbwKKLtOhZkpa7fHGejeX1kGgPgQQAOtjS1oCgSoT6DwsRCdCajkwCQIQgAAEIAABnwTiw0K0jFMiQL9tGny2lFpDAAKBgLZo+U6036H2yNNyYBIEIOCAAAKgAyNRRQhUnIA2/tVef1oKkLWvoE4K1WbB2mtDSXtNaL9E7YVCggAEIAABCECgWgS0V6/2/vp5l0OXtJ+oNtLXQSHhW0IHB4Qo/2q1htpAAAKpBI5u70+ofZe1Z3Bn0l6WOoxN+90pKY8O1tB+oCQIQMABAQRAB0aiihCoOIFwWIg2utWmstrXTwMCba6vZb8bdpze2e0U5Yo3k+pBAAIQgAAEGkEgPjRBE3w6tEWHt+gUzdXMbMuOk3d1SuqH2NajEb5BI+tNIBwwocl6HYioAzF0cMRS7YPJdDBQSNrG57j2QTP1pkLrIFAjAgiANTImTYFASQTi04J7VUGioDYI1/JfEgQgAAEIQAAC1SSQcmqqai4BQKeCaksPTpKspi2pFQTyEIhPmO11nU62Pbm9+idP+eSFAARKJoAAWLIBuD0EakBgipntYWbbmdm6Zra8melver7oRLC7zUzRAee2Iwhq0GSaAAEIQAACEKgtAUX67WhmO7WXAuukT73XJ7e3+tB+f9eZ2fntyP/agqBhEGgYgRXbh/RtY2brtPu9+r7Efo3pFQ38ezM7r32gX8Pw0FwI+CeAAOjfhrQAAhCAAAQgAAEIQAACEIAABCAAAQhAAAJdCSAA4hwQgAAEIAABCEAAAhCAAAQgAAEIQAACEKgxAQTAGhuXpkEAAhCAAAQgAAEIQAACEIAABCAAAQhAAAEQH4AABCAAAQhAAAIQgAAEIAABCEAAAhCAQI0JIADW2Lg0DQIQgAAEIAABCEAAAhCAAAQgAAEIQAACCID4AAQgAAEIQAACEIAABCAAAQhAAAIQgAAEakwAAbDGxqVpEIAABCAAAQhAAAIQgAAEIAABCEAAAhBAAMQHIAABCEAAAhCAAAQgAAEIQAACEIAABCBQYwIIgDU2Lk2DAAQgAAEIQAACEIAABCAAAQhAAAIQgAACID5QKoEXX3zxxVIrwM0hAAEIQAACEIAABCAAAQhAAAINIDBu3Dg0oAbYuVsTMX6DjV+FpiMAVsEK1AECEIAABCAAAQhAAAIQgAAE6k4AAbDuFu7dPgTAZtu/9NYjAJZuAioAAQhAAAIQgAAEIAABCEAAAg0ggADYACP3aCICYLPtX3rrEQBLNwEVgAAEIAABCEAAAhCAAAQgAIEGEEAAbICREQCbbeQqtx4BsMrWoW4QgAAEIAABCEAAAhCAAAQgUBcCCIB1seRg7SACcDBuXFUQAQTAgkBSDAQgAAEIQAACEIAABCAAAQhAoAcBBMBmuwcCYLPtX3rrEQBLNwEVgAAEIAABCEAAAhCAAAQgAIEGEEAAbICRewnAzW4+rS+bAAJg2Rbg/hCAAAQgAAEIQAACEIAABCDQBAIIgE2wcvc2EgHYbPuX3noEwNJNQAUgAAEIQAACEIAABCAAAQhAoAEEEAAbYOQeTUQAbLb9S289AmDpJqACEIAABCAAAQhAAAIQgAAEINAAAgiADTAyAmCzjVzl1iMAVtk61A0CEIAABCAAAQhAAAIQgAAE6kIAAbAulhysHUQADsaNqwoigABYEEiKgQAEIAABCEAAAhCAAAQgAAEI9CCAANhs90AAbLb9S289AmDpJqACEIAABCAAAQhAAAIQgAAEINAAAgiADTByLwG42c2n9WUTQAAs2wLcHwIQgAAEIAABCEAAAhCAAASaQAABsAlW7t5GIgCbbf/SW48AWLoJqAAEIAABCEAAAhCAAAQgAAEINIAAAmADJQz/xQAAIABJREFUjNyjiQiAzbZ/6a1HACzdBFQAAhCAAAQgAAEIQAACEIAABBpAAAGwAUZGAGy2kavcegTAKluHukEAAhCAAAQgAAEIQAACEIBAXQggANbFkoO1gwjAwbhxVUEEEAALAkkxEIAABCAAAQhAAAIQgAAEIACBHgQQAJvtHgiAzbZ/6a1HACzdBFQAAhCAAAQgAAEIQAACEIAABBpAAAGwAUbuJQA3u/m0vmwCCIBlW4D7QwACEIAABCAAAQhAAAIQgEATCCAANsHK3dtIBGCz7V966xEASzcBFYAABCAAAQhAAAIQgAAEIACBBhBAAGyAkXs0EQGw2fYvvfUIgKWbgApAAAIQgAAEIAABCEAAAhCAQAMIIAA2wMgIgM02cpVbjwBYZetQNwhAAAIQgAAEIAABCEAAAhCoCwEEwLpYcrB2EAE4GDeuKogAAmBBICkGAhCAAAQgAAEIQAACEIAABCDQgwACYLPdAwGw2fYvvfUIgKWbgApAAAIQgAAEIAABCEAAAhCAQAMIIAA2wMi9BOBmN5/Wl00AAbBsC3B/CEAAAhCAAAQgAAEIQAACEGgCAQTAJli5exuJAGy2/UtvPQJg6SagAhCAAAQgAAEIQAACEIAABCDQAAIIgA0wco8mIgA22/6ltx4BsHQTUAEIQAACEIAABCAAAQhAAAIQaAABBMAGGBkBsNlGrnLrEQCrbB3qBgEIQAACEIAABCAAAQhAAAJ1IYAAWBdLDtYOIgAH48ZVBRFAACwIJMVAAAIQgAAEIAABCEAAAhCAAAR6EEAAbLZ7IAA22/6ltx4BsHQTUAEIQAACEIAABCAAAQhAAAIQaAABBMAGGLmXANzs5tP6sgkgAJZtAe4PAQhAAAIQgAAEIAABCEAAAk0ggADYBCt3byMRgM22f+mtRwAs3QRUAAIQgAAEIAABCEAAAhCAAAQaQAABsAFG7tFEBMBm27/01iMAlm4CKgABCEAAAhCAAAQgAAEIQAACDSCAANgAIyMANtvIVW49AmCVrUPdIAABCEAAAhCAAAQgAAEIQKAuBBAA62LJwdpBBOBg3LiqIAIeBcADP3ZuQa2nGAj4IfCLLxzkp7IdNaXPujUdFR8DAc99dgzN5lIIQAACEIAABHoQQABstnsgADbb/qW3HgGwdBNQAQgkEfAsJiAAJpmYTDUj4LnP1swUNAcCEIAABCBQGQIIgJUxRSkVQQAsBTs3DQQQAPEFCPgg4FlMQAD04WPUslgCnvtssSQoDQIQgAAEIACBQAABsNm+gADYbPuX3noEwNJNQAUgkETAs5iAAJhkYjLVjIDnPlszU9AcCEAAAhCAQGUIIABWxhSlVAQBsBTs3DQQQADEFyDgg4BnMQEB0IePUctiCXjus8WSoDQIQAACEIAABAIBBMBm+wICYLPtX3rrEQBLNwEVgEASAc9iAgJgkonJVDMCnvtszUxBcyAAAQhAAAKVIYAAWBlTlFIRBMBSsHPTQAABEF+AgA8CnsUEBEAfPkYtiyXguc8WS4LSIAABCEAAAhAIBBAAm+0LCIDNtn/prUcALN0EVAACSQQ8iwkIgEkmJlPNCHjuszUzBc2BAAQgAAEIVIYAAmBlTFFKRRAAS8HOTQMBBEB8AQI+CHgWExAAffgYtSyWgOc+WywJSoMABCAAAQhAIBBAAGy2LyAANtv+pbceAbB0E1ABCCQR8CwmIAAmmZhMNSPguc/WzBQ0BwIQgAAEIFAZAgiAlTFFKRVBACwFOzcNBBAA8QUI+CDgWUxAAPThY9SyWAKe+2yxJCgNAhCAAAQgAIFAAAGw2b6AANhs+5feegTA0k1ABSCQRMCzmIAAmGRiMtWMgOc+WzNT0BwIQAACEIBAZQggAFbGFKVUBAGwFOzcNBBAAMQXIOCDgGcxAQHQh49Ry2IJeO6zxZKgNAhAAAIQgAAEAgEEwGb7AgJgs+1feusRAEs3ARWAQBIBz2ICAmCSiclUMwKe+2zNTEFzIAABCEAAApUhgABYGVOUUhEEwFKwc9NAAAEQX4CADwKexQQEQB8+Ri2LJeC5zxZLgtIgAAEIQAACEAgEEACb7QsIgM22f+mtRwAs3QRUAAJJBDyLCQiASSYmU80IeO6zNTMFzYEABCAAAQhUhgACYGVMUUpFEABLwc5NAwEEQHwBAj4IeBYTEAB9+Bi1LJaA5z5bLAlKgwAEIAABCEAgEEAAbLYvIAA22/6ltx4BsHQTUAEIJBHwLCYgACaZmEw1I+C5z9bMFDQHAhCAAAQgUBkCCICVMUUpFUEALAU7Nw0EEADxBQj4IOBZTEAA9OFj1LJYAp77bLEkKA0CEIAABCAAgUAAAbDZvoAA2Gz7l956BMDSTUAFIJBEwLOYgACYZGIy1YyA5z5bM1PQHAhAAAIQgEBlCCAAVsYUpVQEAbAU7Nw0EEAAxBcg4IOAZzEBAdCHj1HLYgl47rPFkqA0CEAAAhCAAAQCAQTAZvsCAmCz7V966xEASzcBFYBAEgHPYgICYJKJyVQzAp77bM1MQXMgAAEIQAAClSGAAFgZU5RSEQTAUrBz00AAARBfgIAPAp7FBARAHz5GLYsl4LnPFkuC0iAAAQhAAAIQCAQQAJvtCwiAzbZ/6a1HACzdBFQAAkkEPIsJCIBJJiZTzQh47rM1MwXNgQAEIAABCFSGAAJgZUxRSkUQAEvBzk0DAQRAfAECPgh4FhMQAH34GLUsloDnPlssCUqDAAQgAAEIQCAQQABsti8gADbb/qW3HgGwdBNQAQgkEfAsJiAAJpmYTDUj4LnP1swUNAcCEIAABCBQGQIIgJUxRSkVQQAsBTs3DQQQAPEFCPgg4FlMQAD04WPUslgCnvtssSQoDQIQgAAEIACBQAABsNm+gADYbPuX3noEwNJNQAUgkETAs5iAAJhkYjLVjIDnPlszU9AcCEAAAhCAQGUIIABWxhSlVAQBsBTs3DQQQADEFyDgg4BnMQEB0IePUctiCXjus8WSoDQIQAACEIAABAIBBMBm+wICYLPtX3rrEQBLNwEVgEASAc9iAgJgkonJVDMCnvtszUxBcyAAAQhAAAKVIYAAWBlTlFIRBMBSsHPTQAABEF+AgA8CnsUEBEAfPkYtiyXguc8WS4LSIAABCEAAAhAIBBAAm+0LCIDNtn/prUcALN0EVAACSQQ8iwkIgEkmJlPNCHjuszUzBc2BAAQgAAEIVIYAAmBlTFFKRRAAS8HOTQMBBEB8AQI+CHgWExAAffgYtSyWgOc+WywJSoMABCAAAQhAIBBAAGy2LyAANtv+pbceAbB0E1ABCCQR8CwmIAAmmZhMNSPguc/WzBQ0BwIQgAAEIFAZAgiAlTFFKRVBACwFOzcNBBAA8QUI+CDgWUxAAPThY9SyWAKe+2yxJCgNAhCAAAQgAIFAAAGw2b6AANhs+5feegTA0k1ABSCQRMCzmIAAmGRiMtWMgOc+WzNT0BwIQAACEIBAZQggAFbGFKVUBAGwFOzcNBBAAMQXIOCDgGcxAQHQh49Ry2IJeO6zxZKgNAhAAAIQgAAEAgEEwGb7AgJgs+1feusRAEs3ARWAQBIBz2ICAmCSiclUMwKe+2zNTEFzIAABCEAAApUhgABYGVOUUhEEwFKwc9NAAAEQX4CADwKexQQEQB8+Ri2LJeC5zxZLgtIgAAEIQAACEAgEEACb7QsIgM22f+mtRwAs3QRUAAJJBDyLCQiASSYmU80IeO6zNTMFzYEABCAAAQhUhgACYGVMUUpFEABLwc5NAwEEQHwBAj4IeBYTEAB9+Bi1LJaA5z5bLAlKgwAERkHg7378z6O4DfeAQKUI/OTvz6xUfVIqgwCYQqm+eRAA62tbFy1DAHRhJioJAfMsJiAA4sBNJOC5zzbRXrQZAt4JIAB6tyD1H4QAAuAg1LimTAIIgGXS596GAIgTQMAHAc9iAgKgDx+jlsUS8NxniyVBaRCAwCgIIACOgjL3qBoBBMCqWYT69COAANiPEL8PlQAC4FDxUjgECiPgWUxAACzMDSjIEQHPfdYRZqoKAQi0CSAA4gpNJIAA2ESr+24zAqBv+7mvPQKgexPSgIYQ8CwmIAA2xElp5iIEPPdZTAkBCPgjgADoz2bUeOwEEADHzpASRksAAXC0vLlbBwEEQFwCAj4IeBYTEAB9+Bi1LJaA5z5bLAlKgwAERkEAAXAUlLlH1QggAFbNItSnHwEEwH6E+H2oBBAAh4qXwiFQGAHPYgICYGFuQEGOCHjus44wU1UIQKBNAAEQV2giAQTAJlrdd5sRAH3bz33tEQDdm5AGNISAZzEBAbAhTkozFyHguc9iSghAwB8BBEB/NqPGYyeAADh2hpQwWgIIgKPlzd06CCAA4hIQ8EHAs5iAAOjDx6hlsQQ899liSVAaBCAwCgIIgKOgzD2qRgABsGoWoT79CCAA9iPE70MlgAA4VLxDKXz8OLM1V1nGNlprim2w5hTbaM0ptt7qy9qkiRNa97v06hl22o+uG8q9d9h8Vdtz2zVto7WWt+WXmWTPzZ1vDz3+rF1500P2qyvvsTnz5iffd7UVlrL9d1rHtt1kFVtxyhI2fvw4e3zWXLvh9sfswt/fY3c/+HRyWU3I6FlMQAA0o982oZcu2kbPfbZ51qLFEPBPAAHQvw1pQX4CCID5mXFFuQQQAMvl3/i7IwD6c4Fp797eXrfV6l0rPgwBcPLExexjh2xrO26xWtf7Pvbkc3bK966xW2c82RfqfjuuYx84aIuXRcvOCxYsWGg/uvhW+9HFt/UtqykZPIsJCIBm9Num9NS/ttNzn22etWgxBPwTQAD0b0NakJ8AAmB+ZlxRLgEEwHL5N/7uCID+XOCY9+6wiBD39LPP2zPPPW9rrLR0qzFFC4CKXDr20B1t201XaZX/5NNz7cI/3Gv3PfKMLb3kRNt96zVss/VWaP2mevz76ZfZ/Y/O7gp2j23WbImJSgsWvmiXXX+/3XD7TFuw4EXbbL2pttd2a9nExRdr/f7d82+2n06/w5+RhlBjz2ICAqAZ/XYInaLiRXrusxVHS/UgAIEMAgiAuEUTCSAANtHqvtuMAOjbfu5rjwDoz4Rvf/1GtsSkCXbH/U/ZnffPskeeeM5ev/1a9tGDtxmKALj/juvY4W9/TavsGQ8/bceceYU9NXveIuDed+Dm9uY9Nmz97U93zrRpZ1yeCXbZpSbaWdP2tqWWWLwl/n3mu1fZVTc/vEjeV629vJ34T6+zyZMm2PwFC+1Dn59uDzzWXVD0Z8HBauxZTEAANKPfDub3nq/y3Gc9c6fuEGgqAQTAplq+2e1GAGy2/T22HgHQo9VqVGcEwHoYc1gCoKL/vv3J/WyF5Sa3QH30i7+xOx+Y9QpoyvfFI3a3DdaY0vrt2K9fYdfd9tgr8r37DZvZ2/baqPX3X1x2l53185syDXDQbhvY+w/aovXbb/94v536X9fWw1BjaIVnMQEBMNvw9NsxdAgHl3rusw7wUkUIQKCDAAIgLtFEAgiATbS67zYjAPq2n/vaIwC6N2GrAcMSErbcYEX7zGE7t+5x0x0z7egzsyP7XqrD2vbRg7du5b3kqhn25R+/8iCSbx6zj60ydclWnvefdHErejErKcLxP4/brxXpqINF/uHYC+z5+QvrYawBW+FZTEAAHK0ASL8dsJMVfJnnPlswCoqDAARGQAABcASQuUXlCCAAVs4kVKgPAQRAXKRUAgiApeIv7ObDEgDfc8Bm9tY9X4rY+875N9s5Pfbjm7LMJPv+8fu38mqfwHedcOEi7VtrlWXsjH/fq/W3GQ8/Y4d//tc923/8+/+67+BxZ11pf7z10cJ4eSzIs5iAADhaAZB+W40e7rnPVoMgtYAABPIQQADMQ4u8dSGAAFgXSzanHQiAzbF1JVuKAFhJs+Su1LAEwFiEO/qMy+2mO2f2rNu3P7GPrbT8SxF+hxx7gemAkpB2ec3qduQ7t2/9b8pBJYfst4kdvO+rWvm//Yub7We/afZhIJ7FBATA0QqA9Nvcj9ChXOC5zw4FCIVCAAJDJYAAOFS8FF5RAgiAFTUM1epKAAEQ5yiVAAJgqfgLu/mwBMBvHL23rbrCUq16HnriRfbok3N61vmzh+1sW2ywYivPkf9xmf357idezn/wPhvbIftv2vr/H1x4i/3wolt7lhUvKb7oD/fa6T+5vjBeHgvyLCYgAI5WAKTfVqOHe+6z1SBILSAAgTwEEADz0CJvXQggANbFks1pBwJgc2xdyZYiAFbSLLkrNSwB8Aef/htbZsmJrfq8fdr5Nvf5BT3rNu3d29vrtlq9ledT3/y9Xf2XR17Of+gbN7c37f7SScFfO+dG++Xld/csa4fNV7VPvu+1rTyX3/Cgnfy9q3NzqdMFnsUEBMDRCoD022r0fM99thoEqQUEIJCHAAJgHlrkrQsBBMC6WLI57UAAbI6tK9lSBMBKmiV3pYYlAJ5zyoG2+ITxrfoc9PHzbOHCF3vW7WOHbGN7bLNWK8/nz77GfnfdAy/nP/xtr7b9d1q39f+n/+Q6u+gPM3qW9ZqNV7JP/+PrWnm0/5/2AWxy8iwmIACOVgCk31bjSeG5z1aDILWAAATyEEAAzEOLvHUhgABYF0s2px0IgM2xdSVbigBYSbPkrhQCYG5k7i7wLCYgACIAuutwBVTYc58toPkUAQEIjJgAAuCIgXO7ShBAAKyEGahEDgIIgDlgkbV4AgiAxTMto8RhCYAsJSzDmtn39CwmIACOVgCk31aj33rus9UgSC0gAIE8BBAA89Aib10IIADWxZLNaQcCYHNsXcmWIgBW0iy5KzUsAZDDBHKbYmgXeBYTEABHKwDSb4fWDXMV7LnP5moomSEAgUoQQACshBmoxIgJIACOGDi3GzMBBMAxI6SAsRBAABwLvepcOywB8Pj372jbbrpKq6FHn3G53XTnzJ6N/vYn9rGVll+yleeQYy+wp599/uX8u7xmdTvyndu3/v/Sq2fYaT+6rmdZh+y3iR2876taeb79i5vtZ7+5ozrAS6iJZzEBAXC0AiD9toQOmnFLz322GgSpBQQgkIcAAmAeWuStCwEEwLpYsjntQABsjq0r2VIEwEqaJXelhiUAvueAzeyte27Uqs93zr/ZzpneXYSbsvQk+/4J+7fyPvnMXHvX8Rcu0o61VlnGzvj3vVp/m/HwM3b453/ds52xiHH8N660a295NDeXOl3gWUxAABytAEi/rUbP99xnq0GQWkAAAnkIIADmoUXeuhBAAKyLJZvTDgTA5ti6ki1FAKykWXJXalgC4FYbrmgn/fPOrfrceMdjdsyZV3St2+u3X9s+evDWrd8vuWqGffnHr4zw+9Yx+9jKU1+KEDz0xIvs0SfnZJY3eeJi9r3j97clJk2wufPm2z8c9yub98KC3FzqdIFnMQEBcLQCIP22Gj3fc5+tBkFqAQEI5CGAAJiHFnnrQgABsC6WbE47EACbY+tKthQBsJJmyV2pYQmA48eZfefY/WzqspNbdfroF39jdz4w6xX1U74vHrG7bbDGlNZvx551hV1362OvyBdHJv3isrvsrJ/flNnWg3bbwN5/0Bat33573f126tnX5mZStws8iwkIgKMVAOm31ej9nvtsNQhSCwhAIA8BBMA8tMhbFwIIgHWxZHPagQDYHFtXsqUIgJU0S+5KDSIAxtfcdMdMO/rMyzPv+zc7rWuHve3Vrd/ufehpO+Zrl9us2X/d209/f+8Bm9lb2kuF/3z343bkf/xfZlnLLT3Rzpq2ty05eXFbsPBF+8x3r7Krbn54kbwbr728nfRPr7PJkybY/AUL7V9OnW73Pzo7N5O6XeBZTEAALE4ApN/66dme+6wfytQUAhAIBBAA8YUmEkAAbKLVfbcZAdC3/dzXHgHQnwlXmbqk7bPD2otUfN3Vl7XXbr5a6293PzjrFaLajXfMNP0Tp1QhYfz4cab9+LZ+1cqty594eq5d9Pt7bcYjz9gySy5uu229pm2+/gqt32Y/93xL/NNv3dJe261lR7xjm9bPEgEvu/5+u/62x2zhwhdt03Wn2l7br22TFl+s9fv3/vfP9t+X3u7PSEOosWcxAQHQjH47hE5R8SI999mKo6V6EIBABgEEQNyiiQQQAJtodd9tRgD0bT/3tUcA9GfCLTZYwT572C65Kv6DC2+xH15060ACoC7SXnz/dsi2tsPmq3a972NPzbHPff9qu+WeJ/vWTVGFhx60xctCX+cFCxYstJ9cepv94MJF69y34Bpn8CwmIACa0W9r3Dm7NM1zn22etWgxBPwTQAD0b0NakJ8AAmB+ZlxRLgEEwHL5N/7uCID+XKAMISFQeu3mq5oi+DZaa4ott/QkmzNvvj30+LN25U0P2a+uvMeemzs/GehqKy5lEgK33WRlW3HKEjZu3LhWdOENtz9mF/7+XrsrY6/B5MJrmNGzmIAAWI4ASL8t90Hguc+WS467QwACgxBAAByEGtd4J4AA6N2Czas/AmDzbF6pFiMAVsocVAYCXQl4FhMQAHHsJhLw3GebaC/aDAHvBBAAvVuQ+g9CAAFwEGpcUyYBBMAy6XNvQwDECSDgg4BnMQEB0IePUctiCXjus8WSoDQIQGAUBBAAR0GZe1SNAAJg1SxCffoRQADsR4jfh0oAAXCoeCkcAoUR8CwmIAAW5gYU5IiA5z7rCDNVhQAE2gQQAHGFJhJAAGyi1X23GQHQt/3c1x4B0L0JaUBDCHgWExAAG+KkNHMRAp77LKaEAAT8EUAA9Gczajx2AgiAY2dICaMlgAA4Wt7crYMAAiAuAQEfBDyLCQiAPnyMWhZLwHOfLZYEpUEAAqMggAA4Csrco2oEEACrZhHq048AAmA/Qvw+VAIIgEPFS+EQKIyAZzEBAbAwN6AgRwQ891lHmKkqBCDQJoAAiCs0kQACYBOt7rvNCIC+7ee+9giA7k1IAxpCwLOYgADYECelmYsQ8NxnMSUEIOCPAAKgP5tR47ETQAAcO0NKGC0BBMDR8uZuHQQQAHEJCPgg4FlMQAD04WPUslgCnvtssSQoDQIQGAUBBMBRUOYeVSOAAFg1i1CffgQQAPsR4vehEkAAHCpeCodAYQQ8iwkIgIW5AQU5IuC5zzrCTFUhAIE2AQRAXKGJBBAAm2h1321GAPRtP/e1RwB0b0Ia0BACnsUEBMCGOCnNXISA5z6LKSEAAX8EEAD92Ywaj50AAuDYGVLCaAkgAI6WN3frIIAAiEtAwAcBz2ICAqAPH6OWxRLw3GeLJUFpEIDAKAggAI6CMveoGgEEwKpZhPr0I4AA2I8Qvw+VAALgUPFSOAQKI+BZTEAALMwNKMgRAc991hFmqgoBCLQJIADiCk0kgADYRKv7bjMCoG/7ua89AqB7E9KAhhDwLCYgADbESWnmIgQ891lMCQEI+COAAOjPZtR47AQQAMfOkBJGSwABcLS8uVsHAQRAXAICPgh4FhMQAH34GLUsloDnPlssCUqDAARGQQAB8K+Ut119K9tt3R1sg6nr2pTJy9qcF+baw7Mftavuv8EuufMymzN/bqEmWX2ZVWzfDXezzVbe2FZccqpNXGxxmzX3abvnqfvtihnX2BUzrrUX7cWB77n5yhvbJ/f4iI0fN75VxqPPPm4fOv8TA5dXpwsRAOtkzWa0BQGwGXaubCsRACtrGioGgUUIeBYTEABx5iYS8Nxnm2gv2gwB7wQQAM0mTZhkH97xvbb9Gq/uas6Zzz5hX7rym3b743eP2eTjxo2zd2x5kL1xk31eFueyCr3lsTvsS1d8056cOyv3PSUmnrr/J23VpVd6+VoEwL9iRADM7VJcUDIBBMCSDdD02yMANt0DaL8XAp7FBARAL15GPYsk4LnPFsmBsiAAgdEQaLoAKDHuqF0Pt61X27wF/Kk5s+zSuy63+59+yJaeuJTtvPZ2tslKG7Z+mz3vWfvkpafaA888PCbjvH/bd7Qi/5QWLFxgV9x3rf3pkVtt7vy5tvJSK9qu6+xga09Zo/X7jFkP2rGXnmrPvTAn1z3fs/Xb7W833svmvjDXJi8+uXUtAiACYC4nInOlCCAAVsoczasMAmDzbE6LfRLwLCYgAPr0OWo9NgKe++zYWs7VEIBAGQSaLgDuvcEu9sHtDmmhv2/Wg/ap6afZrHnPLGKKd776LXbgJvu0/vbnR2+346d/cWBTbbnKJq1luUoS50763X/YrTPvXKQ8iZIf2PYdtvcGu7b+fuEdv7VvXfuj5HtuvML69qm9Pmbjx4+371733yYxEAFwUXxEACa7ExkrQgABsCKGaGo1EACbanna7Y2AZzEBAdCbt1HfIgh47rNFtJ8yIACB0RJosgAooe3MAz9jU5eY0oJ+5EWfsbufvO8VBlC+k/eZZustv1brtxN/8xW78ZG/DGQoRRtus/oWrWslzv3vbb/OLEf79n1h/0/aGsuu2ooS/PAvj7XHnnui7z0XHz/BPrffMa3rfn/fH+371//UvnrgSQiAHeQQAPu6EhkqRgABsGIGaVp1EACbZnHa65WAZzEBAdCr11HvsRDw3GfH0m6uhQAEyiHQZAFQh2Qct+cRLfA3P3qbnTD9S12NsMd6O9lhO7yr9fv0u6+wM6/6fm6DjbNx9r23nmaTJky0hS8utEN//nF79vnnupbzls3+xg7e8o2t3//rhp/Zubdc1Peeh2z1Jjto0/1s9vPP2REXnGATx09AAMyghgDY15XIUDECCIAVM0jTqoMA2DSL016vBDyLCQiAXr2Oeo+FgOc+O5Z2cy0EIFAOgSYLgIds9WY7aNN9W+DPvuEcO++Wi7saYbnJy9o3Djql9bv2CfzgeUflNtgyk5a2b73p8y+VMfdp++C5R/YsY8c1t7F/3fkDrTy3zLyztRdgr7T+8mvbSXv/uy02fjH7+tVnt/YyXGnJqQiACIC5fZULqkcAAbB6NmlUjRAAG2VuGutFappXAAAgAElEQVSYgGcxAQHQseNR9YEJeO6zAzeaCyEAgdIINFkAnLbbh14+/EPRf4oC7JXOOOAkW3Gpqa0sit57Zt7sXHZbdtIy9s03fW4gAVCHgLznnH/ter/Fxo23k/edZutMWbN1oMinfnNaKy8CYDYyIgBzuS6ZK0AAAbACRmhyFRAAm2x92u6JgGcxAQHQk6dR16IIeO6zRTGgHAhAYHQEmiwAnv6GT9sqS6/Ygn34L47pu8fe8XseYZutvHErv04D7jy8o5/VtK/f2W/9sk1YbMJLS4B/9nF79oXuS4DfvOn+9o6tDnq52H887yh7cs6szNu8ffMD7O1bvMGen/+8/duFJ9rDsx9DAOxhEATAft7K71UjgABYNYs0rD4IgA0zOM11S8CzmIAA6NbtqPgYCHjus2p2k8WEMZidS50T8CgmBORN7rPfftOptvSkpVoo3vnTj9q8+fN6euLHdv6gvXbNrVt5Tv7dV+2PD/0pt+fGIuJ3/vgTu+D26Zll6OARHQKy5rKrvfz7v17wKbv/6YdekX+dKWvYZ/c+qiUs/uDGn9vP/3Lhy3mIAMw2kcc+O05OQWosAYzfWNNXo+EIgNWwA7WAQD8CnsUEBMB+1uX3OhLw3GcRAOvokbQphYBHMQEB0OwHbzu9JZopHfyTw1tReb3Sv+z4Xtt1nR1aWb585bfs8hnXpLjHInl2WWd7+/CO72v9bc4Lc+3E337Fbn/87kXy6LCQ923797bfhrsv8vdjLvncK/IqqvAzex9p609d2+558j476uKTF2kHAiACYG4n5YJKEkAArKRZmlMpBMDm2JqW+ibgWUxAAPTte9R+MAKe+ywC4GA25yr/BBAAfdqwDAFQ4t603Q6316y2eQva/IUL7MoZ19ifHr3V5rwwz1ZeegXbZZ0dbN0pa9qsuc/Y4otNsCUXX6KV9+iLT7E7nrhnEdhhmfCChQtMAuFdT85Y5HcEQARAn72TWncSQADEJ0olgABYKn5uDoFkAp7FBATAZDOTsUYEPPdZBMAaOSJNyUUAATAXrspkLmMJsBo/ecIkO/y17355OXEWkCfmPGWf/7+v2TG7f9iWnrhkK8tHfnmcPTT70Zezr7HMqnbKfkfbxMUWt/NvvcS+d/1PX1EUAiACYGU6HBUZEwEEwDHh4+KxEkAAHCtBrofAaAh4FhMQAEfjI9ylWgQ891kEwGr5ErUZHQEEwNGxLvJOoz4EpLPuW66yie2x7o628Yob2JTJy7aW7j767Ey76v4b7H9v/7XNfWGunf22r9hi4xdr/fben32stWxYSZGEn379v9nGK65vj86eaR/71adt3oLnEQATHcRjn2UPwETj1jQbAmBNDeulWQiAXixFPZtOwLOYgADYdO9tZvs991kEwGb6LK028ygmBLs1+RCQabt9yLZuL8U9YfqX7OZHb+vpzmcccJKtuNTUVp5Df/5xe2be7KG6/1rLrd46CETpgacftiMuOOHl++ngj8/v94nW/1/74E2v2BswZFxq8SXswE32af3v7Oefa0UKhnTeLRfb/IXzh9qGqhbusc8iAFbVm0ZTLwTA0XDmLl0IIADiGhDwQcCzmIAA6MPHqGWxBDz3WQTAYn2B0vwQ8CgmIACaHbLVm+2gTfdtoTj7hnNMgli3tNykZewbb/pc6+en5j5tHzz3yKE76P4b7WHv2+bvW/e55M7L7KxrfvDyPdeZsqZ9fr9jxlSH95zzr/bcC3PGVIbXiz32WQRAr95WTL0RAIvhSCkDEkAAHBAcl0FgxAQ8iwkIgCN2Fm5XCQKe+ywCYCVciEqUQMCjmIAAaLb5yq+y4/b8aAvFnx651T71m9O6es8e6+1kh+3wrtbv0+++ws686vtD97ST951m6y+/dus+0y4+2e584l4EwIKoe+yzCIAFGd9pMQiATg1Xl2ojANbFkrSj7gQ8iwkIgHX3TtqXRcBzn0UAxKebSsCjmIAAaDZu3Dj72oGfteWXWK6F48iLPmN3P3nfK9xY+U7eZ5qtt/xard9O+u1X7IaH/zJUd99ng13tA9v9v9Y9tDRZS5QHSRwCkk3NY59FABykB9TnGgTA+tjSZUsQAF2ajUo3kIBnMQEBsIEOS5PNc59FAMSBm0rAo5iAAPgSgX022M0+sN07Wv89Y9aD9qnpp9nT855ZxJX/4dVvsTe299G75bE77NhffyHT1Xdfd8fW6b79RLuNVlivJTR223/v9evvYodue7BNGL9Y6yCQj194kj3y7MyBuhcCIALgQI7DRZUjgABYOZM0q0IIgM2yN631S8CzmIAA6NfvqPngBDz3WQTAwe3Olb4JIAD6td/4ceNt2m6H26tX3azViCfnzLJL7/o/u3/Ww7b0pCVt57W3t01X2rD1mw7ROPbSU+3+px8akwB45K6H2atW3MCuf+hmu/OJe+yJOU/ZhPETbJWlV7Id1nyNrTtlzVb58+Y/b6de/nW74eE/DwwYARABcGDn4cJKEUAArJQ5mlcZBMDm2ZwW+yTgWUxAAPTpc9R6bAQ891kEwLHZnqv9EkAA9Gs71XzyhEn2kR3fZ9uusVXXhsx87gk77Ypv2W2P39U1T2oEoATAbVffsie0+2Y9aF+/+r963i+FOgIgAmCKn5Cn+gQQAKtvo1rXEAGw1ualcTUi4FlMQACskSPSlGQCnvssAmCymclYMwIIgPUw6Harb2US8TaYuo4tO3mZ1vLbh5+daVfdf51dfOdlNueFuT0bmioAKsJv29W3ss1W3shWXmpFW27yMmYvvmhPzXvG7n5ihl31wPV25X1/tIUvLhwzWARABMAxOxEFVIIAAmAlzNDcSiAANtf2tNwXAc9iAgKgL1+jtsUQ8NxnEQCL8QFK8UcAAdCfzahxswl47LMcAtJsn0UAbLb9S289AmDpJqACEEgi4FlMQABMMjGZakbAc59FAKyZM9KcZAIexYTQuL/78T8nt5OMEKgLAY99FgGwLt43WDsQAAfjxlUFEUAALAgkxUBgyAQ8iwkIgEN2DoqvJAHPfRYBsJIuRaVGQMCjmIAAOALH4BaVJeCxzyIAVtadRlIxBMCRYOYm3QggAOIbEPBBwLOYgADow8eoZbEEPPdZBMBifYHS/BDwKCYgAPrxL2paPAGPfRYBsHg/8FQiAqAna9WwrgiANTQqTaolAc9iAgJgLV2SRvUh4LnPIgDi3k0l4FFMQABsqrfSbhHw2GcRAJvtuwiAzbZ/6a1HACzdBFQAAkkEPIsJCIBJJiZTzQh47rMIgDVzRpqTTMCjmIAAmGxeMtaQgMc+iwBYQ0fM0SQEwBywyFo8AQTA4plSIgSGQcCzmIAAOAyPoMyqE/DcZxEAq+5d1G9YBDyKCQiAw/IGyvVAwGOfRQD04FnDqyMC4PDYUnICAQTABEhkgUAFCHgWExAAK+BAVGHkBDz3WQTAkbsLN6wIAY9iAgJgRZyHapRCwGOfRQAsxVUqc1MEwMqYopkVQQBspt1ptT8CnsUEBEB//kaNx07Ac59FABy7/SnBJwGPYgICoE9fo9bFEPDYZxEAi7G911IQAL1arib1RgCsiSFpRu0JeBYTEABr7540MIOA5z6LAIhLN5WARzEBAbCp3kq7RcBjn0UAbLbvIgA22/6ltx4BsHQTUAEIJBHwLCYgACaZmEw1I+C5zyIA1swZaU4yAY9iAgJgsnnJWEMCHvssAmANHTFHkxAAc8Aia/EEEACLZ0qJEBgGAc9iAgLgMDyCMqtOwHOfRQCsundRv2ER8CgmIAAOyxso1wMBj30WAdCDZw2vjgiAw2NLyQkEEAATIJEFAhUg4FlMQACsgANRhZET8NxnEQBH7i7csCIEPIoJCIAVcR6qUQoBj30WAbAUV6nMTREAK2OKZlYEAbCZdqfV/gh4FhMQAP35GzUeOwHPfRYBcOz2pwSfBDyKCQiAPn2NWhdDwGOfRQAsxvZeS0EA9Gq5mtQbAbAmhqQZtSfgWUxAAKy9e9LADAKe+ywCIC7dVAIexQQEwKZ6K+0WAY99FgGw2b6LANhs+5feegTA0k1ABSCQRMCzmIAAmGRiMtWMgOc+iwBYM2ekOckEPIoJCIDJ5iVjDQl47LMIgDV0xBxNQgDMAYusxRNAACyeKSVCYBgEPIsJCIDD8AjKrDoBz30WAbDq3kX9hkXAo5iAADgsb6BcDwQ89lkEQA+eNbw6IgAOjy0lJxBAAEyARBYIVICAZzEBAbACDkQVRk7Ac59FABy5u3DDihDwKCYgAFbEeahGKQQ89lkEwFJcpTI3RQCsjCmaWREEwGbanVb7I+BZTEAA9Odv1HjsBDz3WQTAsdufEnwS8CgmIAD69DVqXQwBj30WAbAY23stBQHQq+VqUm8EwJoYkmbUnoBnMQEBsPbuSQMzCHjuswiAuHRTCXgUExAAm+qttFsEPPZZBMBm+y4CYLPtX3rrEQBLNwEVgEASAc9iAgJgkonJVDMCnvssAmDNnJHmJBPwKCYgACabl4w1JOCxzyIA1tARczQJATAHLLIWTwABsHimlAiBYRDwLCYgAA7DIyiz6gQ891kEwKp7F/UbFgGPYgIC4LC8gXI9EPDYZxEAPXjW8OqIADg8tpScQAABMAESWSBQAQKexQQEwAo4EFUYOQHPfRYBcOTuwg0rQsCjmIAAWBHnoRqlEPDYZxEAS3GVytwUAbAypmhmRRAAm2l3Wu2PgGcxAQHQn79R47ET8NxnEQDHbn9K8EnAo5iAAOjT16h1MQQ89lkEwGJs77UUBECvlqtJvREAa2JImlF7Ap7FBATA2rsnDcwg4LnPIgDi0k0l4FFMQABsqrfSbhHw2GcRAJvtuwiAzbZ/6a1HACzdBFQAAkkEPIsJCIBJJiZTzQh47rMIgDVzRpqTTMCjmIAAmGxeMtaQgMc+iwBYQ0fM0SQEwBywyFo8AQTA4plSIgSGQcCzmIAAOAyPoMyqE/DcZxEAF/WubVffynZbdwfbYOq6NmXysjbnhbn28OxH7ar7b7BL7rzM5syfW6g7rrTkVNtr/Z1t85U3ttWXXdWWXHwJm7/gBZs1b7bd89R9dtX919sVM66xBS8u7HrfQT+Kj//1F+3Pj91eaHs8FTYotyq08e9+/M9VqAZ1gMBICXjsswiAI3WRyt0MAbByJmlWhRAAm2VvWuuXgGcxAQHQr99R88EJeO6zCIAv2X3ShEn24R3fa9uv8equjjDz2SfsS1d+025//O7BnSW68g0bv97esdVBNnGxxXuW98DTD9sXr/iG3Tfrwcx8g3wUL3xxoX3o/E/azOeeKKQtHgsZhFtV2okAWBVLUI9REvDYZxEAR+kh1bsXAmD1bNKoGiEANsrcNNYxAc9iAgKgY8ej6gMT8NxnEQDNxo0bZ0fterhtvdrmLR94as4su/Suy+3+px+ypScuZTuvvZ1tstKGrd9mz3vWPnnpqfbAMw8P7C+6cL8Nd7dDtz345TJumXmnXfvAjTbzuSdtycUn25rLrWZ7rLuTLbH45Faep+c+Yx+78ESbNffpV9y3l2gZZ95prW1tl3W2b/3pxodvsRN/++UxtcH7xR7FhMAcAdC791H/QQh47LMIgINYuj7XIADWx5YuW4IA6NJsVLqBBDyLCQiADXRYmmye+ywCoNneG+xiH9zukJYnK8ruU9NPs1nznlnEs9/56rfYgZvs0/rbnx+93Y6f/sWBPX/xxRa3bxx0Smu5r9LXrj7bfn3X5a8ob5lJS9uxe3zE1pmyZuu382+9xL53/U8Hvu/n9j3a1l1+rdb1X77yW3b5jGsGLqsOF3oUExAA6+B5tGFQAh77LALgoNaux3UIgPWwo9tWIAC6NR0VbxgBz2ICAmDDnJXmtgh47rNNFwAV/XfmgZ+xqUtMadnyyIs+Y3c/ed8rPFv5Tt5nmq3XFtBO/M1X7MZH/jJQD9hylU3sk3t8pHXtHY/fY0dfckrXcrZebQubttvhrd/vfOJem3bxyQPdc73l17ZT9p3WulZRjP943lH2wsL5A5VVl4s8igkIgHXxPtoxCAGPfRYBcBBL1+caBMD62NJlSxAAXZqNSjeQgGcxAQGwgQ5LkxEAHfuADt84bs8jWi24+dHb7ITpX+ramj3W28kO2+Fdrd+n332FnXnV9wdq+c5rb28f2el9rWsvu/cqO/333+lazmpLr2xffsMJrd+1F+ARF7z033mTlhtr2bHSBbdPt+/88Sd5i6hdfo9iQjACS4Br5440KIGAxz6LAJhg2BpnQQCssXE9NA0B0IOVqCMEfEcTIQDiwU0k4Fm0l72aLCYcstWb7aBN92257dk3nGPn3XJxVxdebvKyraW7Ston8IPnHTWQuy8SAfjEPXb0xd0jALdZbQs7qh0B+If7rrMvXHFW7nsuPn6Cff2gU2zpiUu2rv34hSfZvU/dn7ucul3gUUxAAKybF9KePAQ89lkEwDwWrl9eBMD62dRVixAAXZmLyjaYgGcxAQGwwY7b4KZ77rNNFwCn7fahlw//UPSfogB7pTMOOMlWXGpqK8uhP/+4PTNvdm7PlyCnZcfLTl6mdW3KHoALFy6046Z/wW6deVfu+8URh3c9McOOuvizucuo4wUexQQEwDp6Im1KJeCxzyIAplq3nvkQAOtpVzetQgB0Yyoq2nACnsUEBMCGO29Dm++5zzZdADz9DZ+2VZZeseW5h//iGHvsuSd6evHxex5hm628cSuPTgO+deadA3n9a9fc2j6y06E2YfxiretveewOu+bBG+3x555snfy71nKr2+7r7tg6KGTOC3NbIuGV91070L2036CiDpW+ee0P7aI7fjdQOXW7yKOYgABYNy+kPXkIeOyzCIB5LFy/vAiA9bOpqxYhALoyF5VtMAHPYgICYIMdt8FN99xnmy4AfvtNp9rSk5Zqee87f/pRmzd/Xk9P/tjOHzSJd0on/+6r9seH/jSw52+60kamvfnWXm71zDLmL5hv595ysV1y52X2+JwnB7rPSktOtdMP+LSNHzfenp//fGvZ8nMvzBmorLpd5FFMQACsmxfSnjwEPPZZBMA8Fq5fXgTA+tnUVYsQAF2Zi8o2mIBnMQEBsMGO2+Cme+6zTRcAf/C2023CYhNa3nvwTw63hS8u7OnJ/7Lje23XdXZo5fnyld+yy2dcMybP32yljexdW7/N1l9+7cxyZj//nP327ivtBzeday8seCH3vd6++QH29i3e0Lrusnv+YKf/4bu5y6jrBR7FBATAunoj7Uoh4LHPIgCmWLa+eRAA62tbFy1DAHRhJioJAdcniiIA4sBNJIAA6NfqZQmAy0xcyo543Qdsi1VeZbPnPWv/8+f/tWse0BLgJ2zihIm2/vLr2IGver1ts/qWLbi3P363ffZ3X7XZzz+bDHucjbP/OODTttJSK7SuOWH6aXbzo7cmX1/3jB7FBATAunsl7etFwGOfRQBstk8jADbb/qW3HgGwdBNQAQgkEfAsJiAAJpmYTDUj4LnPyhRNPgW4jCXAExdb3E7ed5qtuexqLfHv6EtOsYdnP5bZK963zd/b/hvt0frt/+692r7y+28n956tVtnUPrHHh1v5H5k90/7ll59MvrYJGT2KCQiATfBM2tiNgMc+iwDYbH9GAGy2/UtvPQJg6SagAhBIIuBZTEAATDIxmWpGwHOfbboAWMYhIAe+am9752ve2uoFP7zxXPvZX37VtUdMWmyinfnGz9rSE5c0nQT8j7+YZrPmPp3Ug3TIyM5rb9fK+6ObzrNz/nxB0nVNyeRRTEAAbIp30s4sAh77LAJgs30ZAbDZ9i+99QiApZuACkAgiYBnMQEBMMnEZKoZAc99tukC4LTdPmRbr7Z5yyNPmP4lu/nR23p65xkHnGQrLjW1lefQn3/cnpk3O7c3H7fnEbZ5+yThoy76rN315IyeZRy162EvLwU+5bIz7NoHb+p7z6UmLmlff+PJpmhDCYeHnX+MPTHnqb7XNSmDRzEBAbBJHkpbOwl47LMIgM32YwTAZtu/9NYjAJZuAioAgSQCnsUEBMAkE5OpZgQ899mmC4CHbPVmO2jTfVseefYN59h5t1zc1TuXm7SMfeNNn2v9/tTcp+2D5x45kCef9rfH2+rLrNK69sO/PLbr8t9Q+CAHj2jZsJYPK/1/9u48hvttu+v6t6WlVCpQZHLAQqsBNCnRVgYbAdGSkAgxJBpRxERQCSQMQYMogqAioyIRBRKMCWCMwx+KBgOUIUCNlEEQBxBLhAKtlKEFShlazO/0FC/X3713//rb+7vXZ+3XSUgId5/1Xeu9Pus853nzPOf53X/8918/57f+km9Sr53/pkSZQAB2TqTZPhGBxJslAD/RVnv/7wRg7/2Wn44ALL8iDSLwAYFkmUAACvGJBJJv9nQB+Pd+h+92/Yx/+Cd+ENvf/xV/4PpZv/kXfcwI/4Dv+n2vH/u9fuQH//tv+sNffP3Hv+NXfpPi/nO+8Kden/1tv+FP/R35gzl+1g/8ydd3//Z/1wfv/+3f/Iuv3/cV//sn/O7P/UH/2vVdP/M7f/DuF/72X379T1/2ez7h33Pag0SZQACellLzfiSBxJslAM/OMAF49v63T08Abl+BBhAYIpAsEwjAoRV71IxA8s2eLgA/6ZM+6fqlP+TfvT7z07/1B6n8Kb/uZ19/+M/80f9fQh/vHuLuG6Xav/NbfvH1e7/8E4u4Z1F/SMSHTHz89Ru/9Ldfv/RLftXHvIjv+Bnf/vr3f/DPuD7lk7/ZB7+V90f9N//K9Rf+8td83At69PgQgI+/vupr/9z1Y/7bf/X6ur/29c2u7v1xEmUCAfj+3lXIJZB4swRgbt5mdE4AzqCoxjeZAAH4TUbnb0TgVgLJMoEAvDUqPlaEQPLNni4AH/N/4ed8v+tf+Pwf/kGa/shX/fHrZ/2mX3R99V/6c39Dun7E9/xh1w/97l/4wf/b//En/9D103/jL3yavu//Xb7P9eO+9z/3wf/2+O8JPv67gh/910f+6bxf/9e+/vplX/KrP/gVhR/917f+Ft/q+qnf78ddn/2Z3/CrBb/kj/3e6+f/tl/6CVP/kX9y8K/5A7/h+pX/83/9Cf+eEx8kyoRv3NPJf3L3iVk18zcQSLxZAvDs9BKAZ+9/+/QE4PYVaACBIQLJMoEAHFqxR80IJN/sYxWny4RP/qRP/kC0fc/v9Pd8kMw/8xe/6vqiL/1t15d91Zdfn/Fpf9P1BX/nP3B9jw9/C+6f/8tfc/30L/oF15d99Z/4JgvAx9/4k/7BH3193+/8eX+9xkMW/s4/9nuvP/U1f/b65p/yqdfnfOZnXf/Qd/neH/zpv4+/vvov/fnrX/8NP+/6ij//Jz/u9XzqJ3/KB3/4x2d82rf84N1P+rU/8/pjX/3lzS5uzjiJMoEAnLN7VTIJJN4sAZiZtVldE4CzSKrzTSJAAH6TsPmbELidQLJMIABvj4sPFiCQfLME4DcE6Ft8yqddP+H7/PPX5/3tn/sxE/WVX/Onr1/0xb/i+oN/6ks/5puRXwH4+Js/5ZM/5frRn/dPXT/ws7/gEyb4IfB+0f/4K67/+89+2Sd8+wV/5+dfP+H7/qgP3v3Br/zS66d90c//hH/PqQ8SZQIBeGpazf0gkHizBODZ2SUAz97/9ukJwO0r0AACQwSSZQIBOLRij5oRSL5ZAvBvDOPn/22fez0k3ud828+6vtW3+Juvr/0rX3t9+V/4yut3fNnvuX79//Vbr7/4V77246Z3VAB+Y5HP+jZ/xwf/PcDv9u0++/qO3/LbXZ/+qZ9+/dWv/6vXV3/tn7u+9M/8kQ9+2+8X/9HfdX3d13/d0NX8tO//46/P/U7f44O3v+xLftX1RV/624f+vhMfJcoEAvDEpJr5Gwkk3iwBeHZ+CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzaI0xXsAACAASURBVBKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1q0RhsJAAAIABJREFUFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0gaqBJvAAAgAElEQVS+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSmEDld8AACAASURBVJmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3v316AnD7CjSAwBCBZJlAAA6t2KNmBJJvlgBsFkbjDBNIlAkE4PB6PWxIIPFmCcCGQXxhJALwBViezidAAM5nqiICKwgkywQCcEUi1KxOIPlmCcDq6dLfKgKJMoEAXJUGdRMIJN4sAZiQrHU9EoDr2Ko8QIAAHIDkCQIFCCTLBAKwQIC0cDuB5JslAG+Piw8WIZAoEwjAIuHRxhYCiTdLAG6JSpmPEoBlVnFmIwTgmXs3dR6BZJlAAOblTcfvE0i+WQLw/f2rkEkgUSYQgJlZ0/UcAok3SwDO2X1qFQIwdXNN+iYAmyzSGO0JJMsEArB9PA34hEDyzRKAIn0qgUSZQACemlZzPwgk3iwBeHZ2CcCz9799egJw+wo0gMAQgWSZQAAOrdijZgSSb5YAbBZG4wwTSJQJBODwej1sSCDxZgnAhkF8YSQC8AVYns4nQADOZ6oiAisIJMsEAnBFItSsTiD5ZgnA6unS3yoCiTKBAFyVBnUTCCTeLAGYkKx1PRKA69iqPECAAByA5AkCBQgkywQCsECAtHA7geSbJQBvj4sPFiGQKBMIwCLh0cYWAok3SwBuiUqZjxKAZVZxZiME4Jl7N3UegWSZQADm5U3H7xNIvlkC8P39q5BJIFEmEICZWdP1HAKJN0sAztl9ahUCMHVzTfomAJss0hjtCSTLBAKwfTwN+IRA8s0SgCJ9KoFEmUAAnppWcz8IJN4sAXh2dgnAs/e/fXoCcPsKNIDAEIFkmUAADq3Yo2YEkm+WAGwWRuMME0iUCQTg8Ho9bEgg8WYJwIZBfGEkAvAFWJ7OJ0AAzmeqIgIrCCTLBAJwRSLUrE4g+WYJwOrp0t8qAokygQBclQZ1Ewgk3iwBmJCsdT0SgOvYqjxAgAAcgOQJAgUIJMsEArBAgLRwO4HkmyUAb4+LDxYhkCgTCMAi4dHGFgKJN0sAbolKmY8SgGVWcWYjBOCZezd1HoFkmUAA5uVNx+8TSL5ZAvD9/auQSSBRJhCAmVnT9RwCiTdLAM7ZfWoVAjB1c036JgCbLNIY7QkkywQCsH08DfiEQPLNEoAifSqBRJlAAJ6aVnM/CCTeLAF4dnYJwLP3b3oEEEAAAQQQQAABBBBAAAEEEEAAgeYECMDmCzYeAggggAACCCCAAAIIIIAAAggggMDZBAjAs/dvegQQQAABBBBAAAEEEEAAAQQQQACB5gQIwOYLNh4CCCCAAAIIIIAAAggggAACCCCAwNkECMCz9296BBBAAAEEEEAAAQQQQAABBBBAAIHmBAjA5gs2HgIIIIAAAggggAACCCCAAAIIIIDA2QQIwLP3b3oEEEAAAQQQQAABBBBAAAEEEEAAgeYECMDmCzYeAggggAACCCCAAAIIIIAAAggggMDZBAjAs/dvegQQQAABBBBAAAEEEEAAAQQQQACB5gQIwOYLNh4CCCCAAAIIIIAAAggggAACCCCAwNkECMCz9296BBBAAAEEEEAAAQQQQAABBBBAAIHmBAjA5gs2HgIIIIAAAggggAACCCCAAAIIIIDA2QQIwLP3b3oEEEAAAQQQQAABBBBAAAEEEEAAgeYECMDmCzYeAggggAACCCCAAAIIIIAAAggggMDZBAjAs/dvegQQQAABBBBAAAEEEEAAAQQQQACB5gQIwOYLNh4CCCCAAAIIIIAAAggggAACCCCAwNkECMCz9296BBBAAAEEEEAAAQQQQAABBBBAAIHmBAjA5gs2HgIIIIAAAggggAACCCCAAAIIIIDA2QQIwLP3b3oEEEAAAQQQQAABBBBAAAEEEEAAgeYECMDmCzYeAggggAACCCCAAAIIIIAAAggggMDZBAjAs/dvegRmEfgh13X9jA+L/dDruv74RxX+N6/r+seu6/oT13U93voLAQQQeBDwzwY5QGCcgB9rx1l5iQAC30DAj7OSgAACf50AASgMCGQQ+Lzrun7ZR7T6Ndd1/aDrur72E7T/add1/brrur7lR7z7l67r+l2Tx/aTkslAlUPgCYGP/ufARz/5i9d1/cnrun7/dV2/5rquLwmg6CcmAUs6qEU/1h60bKMi4MdZGUAAgdMIEICnbdy8qQSe/cT/p13X9T98goEekvBnf9QbAjAnBb/zw1Z/+XVdj//jr7MJfCIB+NF0/vvrun7mdV1fXxgbAVh4OQe25sfaA5d+XZcfa8/c+7Op/TgrCwgg0JoAAdh6vYZrROAj/4XkL1/X9c2v6/ri67p+/CeY8T+4rusLruv6xr/n8XyHAGy0iltH8ZOSW3GX/9hH/nPgv7qu67/8iI4fP55/q+u6Pve6rn/6uq5v++H/9p9c1/UflZ9MgwjUIODH2hp7uLsLP9beTbzu9/w4W3c3OkMAgQkECMAJEJVA4AYCH/kvJL/+uq4v/PBX9fzg67r+1Mf4/kMA/Nrrur7Zh78N+PGrAQnAG5Y18RN+UjIRZoNSH/nPgY/3q0I/+7quX/Xh/0fB4z8X8I9c1/VXGsxvBARWE/Bj7WrCNev7sbbmXnZ05cfZHdR9EwEEbiNAAN6G2ocQeIvAR/4Lyb91XdePva7rb7mu69+7rus/+xiVf/h1XT/5Q0H4+BVA/wYB+NYOdvzNflKyg3rdb47+xOQxwc+7rusHfjjK458F/2fdsXSGQBkCfqwts4pbG/Fj7a24S3/Mj7Ol16M5BBB4lwAB+C5Bfz8C9xD4yH8hefw3vf7uD3+b3x+4ruuf+RgtPH4F0He/rutXX9f1hz7iT+l99luAP+e6rh9wXdffd13X41cPfeZ1XX/1uq6vvK7r913X9fjthv/Lxxl11h8C8v2u6/onruv6Htd1ffp1XV9xXddv+fBXMz1+pePjDzb4W6/r+u8+/FPNPrKlj+7hy6/r+sc//NOHv+t1XZ96XdeXffirIR/S9GP9ASqffF3Xg/fjt04/fjvlZ13X9RnXdT3+gIXHn278O67r+s+v63rU/1h/PX511t9/Xdfvvq7rX7yu6ztc1/Ujrut6zPf4vz++/b99KG8fv5X7o//6xjk/XrqeMbgnjb6yi8ArPzH5Cdd1/bMfNvojP8zbs74f/4z4Ydd1ff51Xd/+uq7Hvxf8Px/+ASKPf3b8kY8x7Kx7G/1vAFb4Z8OuvfvufQT8WPsNv6vAj7X/X+b8WHvf/VX4kh9n9/47eIUM6AGB1gQIwNbrNVwjAh/9k5I/+KHYe4z4T17X9aUfNetD4v0XH/6/Pf57YN/t4wjA0f/g8X96Xdd/OCgDHqLsI/8a+Un+T/lQ/j37xOMnJI//3uEvGBSADyb/8nVd3+tj9Pu/Xtf1Yz6Ueh/95CHsHv/n4/31EHiPX1H5mz7Go48UgL/kuq5feF3Xt/kYbx//ncZf+VH/GwHY6HgnjvLKT0x+znVd/+iH33789v8//VF9PET3T7yu6/GrAz/Wvwt83XVdP/e6/t/2zgR4v2u845+xi63W2KsY1K61hRJBa62d2Ilqq4i9aquWomMdlZlaokxQxBKEGBSxhiiGqhgqliqx7/uWzjf/c/X2dd/3vff3vvf93+VzZjJJfu99zz3nc85zzr3f9znPwzENfagLgJvY25jWhi0OpVUNlIB7rXvt4tRUAByosfbULPfZ3wa7y2fwnobVaiUggYqAAqBzQQLjILD4UhKBKAJfhL6jgCMWuvFA4N7AycChQP1lfdEDMCJZBKr3Fa+fLwA/KkkEUv+di+iWWzwROHaNGHCr4ilXv2zdS348lKqEJvH6i9gYD7kkOzmoeDnGG/GA4p24zgMwXotXBN4MJGZiHl4uCOQ+8epLWSZo5nh1+hBxL16P8RpMEpUDgasAdyjtyN/iffn5Bh6VABjvqSRmSBbWeFN9rMRiuyrw58A5gIgsEWHqIu7Fi8fi0aXuxYQP+fMPiqfWOGawrdwGgbYvJpco8+3MZQ4f1nDzuuAeT9XYVOZ6xO3LFA/j2H/Kw4D3LNRRX1M2sbcxrQ3bGEPrGDYB99p9nv/utf83T91rh22z226d++z+fQbf9nhanwQksEBAAdApIYFxEGh6KalEswhmtwROLV2JXedlPoLVc4CXrBEA45kWESoPuU0lR2efDVwLOAW4dRG06tducgQ4sQwjaEbs+xIQseK7Cw2JaPf8Iorlo3UCYK55fBEA61XlHvG2y5Hn7wHxjErf6+XCRVjLEeimkiO8EQ/z7wiMuc9iqQTA/D3M/qxBrIsIeGTxvsqR4ng3LhbjEo3DPnfVynXZCSMoX6kI07GrHwKHNxzfjy3HMzUlMUXf0NCB2Eq8U69R5nCO09dtpW7zm9jbKgFwaGvDrsbZ++w/Au617rXZvy3zJeA+u3+fwec78+y5BHZEQAFwR6C9jQQ2JND0UhIBKkJYjvLlOGslFiWW1/OKSBdhMPG8VnkAtmlaPIKqZCOJK/aphS9tIgDeq4gUqTJHEuOJ2FTihZTjzCnrBMB3Ao9cUk/inT2mfLbX5AhVgpV4SiZ2YiW+VresC4BN3lPVdS8ugs2yWI4KgG1m53yuaXtcPx6nrytegE0x/J5bhL1VdhKqiZ356oL3AcCJNdR1m9/E3lYJgGNcG+YzG6fZU/fafR6/7rXTnN/2aj0B99nhP4OvH0WvkIAElhJQAHRySGAcBJpeStLyZPfNEd4cy83x3JR4pOUIa5JV5DhrShcBMJ4/5ylHgKo14vQ1AbDpGPAmAmDVh3j9xSMv4kVTqYuQ6wTAiH8RJZrK5YtXZD5LnMB3rZkCZwPOBZylFistHomPLd+LR+SXF+qoBMB4Vd5oRZ/SzsRPi6dWhMTFogA4DvvcVSvbvpikPZl78axN3M4cV69K5nOOt+eHgwjhb1vT+LeX+JXxwI3HalXqNr+Jva0SAIe+Nuxq3L3P7gi41+4LAVD94Odeu7u5552GQcB9tnkc9te6MIxZYSskMCECCoATGky7MmkCy15KbgEkK3A80SKepSTmXeL31I/ArhMAk3E3sf5SR47HRhxYVqpjxfXPNxEA3wrkqF9dsGy6d9r0/nIMeN1LSVNilKrOi9SOPDYdE851yTQcT8fr1eIfLuMRL6UkFamXSgBMHMMc1V5W4rl53yIQNiUsUQCctFl37ty62ESJ+Xcx4OblGHCE+8SdTEzQKut1slPv5Yhb4lAmsUhVFpOALCYiamtvqwTAIa4NnQfNL4yKgHvtvv3fvXZU09bGbpGA+2wzzF2uC1scTquSgAQWCSgAOickMA4Cy15KIvTFgyfeafHmiU0/uWS3jZj3k9K9VQJgYt7lyHD+3aZEPFgUEDYRAE8o8f/ysl951S1rRyUIrBMAmxKRVHWmn1Uik4in8ZKql+sATytM2/BIxuAkUaiXehbgVRmF6xmHc3R7sSgAthmB+Vyz7sWkTuK2NXt6UfEWzufJDFwX8trSW7S5dTbf1t5WCYBDWxvasvK68RJwr903du61453DtnwzAu6zy/ntYl3YbPT8tgQksJaAAuBaRF4ggUEQWPZSksYliP/Nyi/2sekIWIvJKVYJgC8EkpAicewihmWDT2bbHMn9Rfl7fvmLh17KlAXAJERJ7LQkU/gx8DLgAyU7ao7phkdKEiMkjlrKYlblilE8rSIMKgAOwoQm0YguLyax2RzfTRbqZPW8aSGQHwaeUv47//54SzKLmTAVAFuC87JREXCv3Tdcfb/ou9eOyixm8KAzQQAAF0VJREFU1Vj32eXD3fe6MKuJZmclsL8IKADuL/LeVwLdCKx6Kbl2ifNVZejMsb9VAfvrgtUlgBztS6l7CS22Lg/rERMqcWubHoBdjvklQUhiFPblAXh74NGln4mfWImeizwS1++p5Y8KgN3mslfvnUCXF5PcJdmqr1hulzmbzNf1DMDLjsC3aeEuBMAhrQ1tmHjN+Am41+47AuxeO/65bA/2RsB9tpnbLtaFvY2Y35KABDoRUADshMuLJbDfCKx6KcmmHI+/85XWfQNIbMB6Mo1lHoA3LMdd89XEAPzskh7GqzCx//oQAPsI9L/XI8BVUo7vA2GzrBwOJPZfigLgfjOL2d2464vJS4HfL5Ti+fdt4NwlbED2/9cC/7hHirsQAIe0NuwRk18bGQH32m5JQNxrRzbBbe5aAu6zzYi6JAHZ67qwdnC8QAIS2JyAAuDmDK1BArsgsOqlJPd/UBHw8t+vAI5YaNQyAbB+HPAw4BNLOvNM4ODy2baPAOe+8VhMeUjxPGhqxsOAu5YP+vIAjPdfvACTMOH6S7L3Jt5ijgmfv7SlTwGwioEWT65kc7XMm0CXF5PM03cASQzys5LQpvpRIN6+yWSd5EG3Ab6zB6y7EACHtDbsAZFfGSEB91pwrx3hxLXJWyPgPtuMchfrwtYG0YokIIHlBBQAnR0SGAeBdS8l63qxTAC8XIlzl+8fU4sNVq/vDsCjan/YtgAYIe0N5Wjvl4C89Cf+YL1ErHh+yQCcv/clAN4NeGi5cZKqJMFKvcTbMjEXb1L7Y58CYJKVJGlJjl/Xx2DdePv5NAl0eTHJjwJVBup3AY+oIbku8E/l/xOn8uFAYvw1lRy5j0j4euDntQt2IQAOaW2Y5oyyV4sE3Gvda91r570uuM/uv2fwec88ey+BHRFQANwRaG8jgQ0J9PVSkjXglcClSvv+rRwnTtKACwA3BxI7LIkCrlKu2bYAmGrrnj5fLbHLTiqi4EHA3YFvAWctRxiTrCQZfOtlG4JE+hyhI6JHxI54U54IJAHIJYFDy5HKOo8+BcAqwUva8owyDpUIE++tHOm0zIdAfR1I7M5XL3Q93n4XKyEAcmw/JfMlQuDi8f6Ifncp18S2chz4Y0V8j52lnqsBh5REIvGITWKcqmzD3lLXqizAQ1ob5jPL5t1T91r3Wvda14D84JziPgu7fAaf98yz9xLYEQEFwB2B9jYS2JBAXy8laVbiejyvvOQ3NTPCwQOBt5QP+xAAsxbl+O3tlnCKR2A8mp4OHLgkdtm2BInELnkc+wKhN5V4BUYkTHyylD4FwIxNjv9GkFwsTV6QG04zvz5wAvV1oE1Tc7T3b4EPNlwcm7tv+SeJg1aVnwA3LkeJq+u2ZW/rBMAhrQ1tmHvNuAm417rXuteO24Y3bb377G8T3OUz+Kbj5/clIIE1BBQAnSISGAeBPl9KQuCCxQsvXkM5dhdPnxzHjUfgq4oX0YcLqj4EwGoU4mV0J+DyQGKYfQ14P/AS4OtAjjKeHThqTZzDTQMQ58hxvKbi9XiO4hX1GSCeh2FSH48+BcBwuWytLeepiYEKgOOw3W22ct2LyS+AJLA5udhN5mv+f1XJEfMc8786cNFiX4mBGU/cTxfx8PgF77/UtysBcGhrwzbH07qGR8C91r02+7577fBsc1ctcp/d/8/guxpr7yOBWRJQAJzlsNtpCYySQI7nJttxSo7GJm6gRQISkIBrg3NAAtsjoD1tj6U1SWAqBFwXpjKS9mP2BBQAZz8FBCCB0RCIR16OAafES/Bzo2m5DZWABPok4NrQJ13rnhsB7WluI25/JbCegOvCekZeIYFREFAAHMUw2UgJTJ5Akg6cDUjykaaSY7BHAgcAnwLuMXkidlACEggB1wbngQS2R0B72h5La5LAVAi4LkxlJO2HBFoQUABsAclLJCCB3gkkDlmyrSXG3wnAF0vcwcQjTFzCWwPJcHpqSbrx0d5b5A0kIIEhEHBtGMIo2IapENCepjKS9kMC2yPgurA9ltYkgcETUAAc/BDZQAnMgkAePo5d09MkOHgScNwsiNhJCUggBFwbnAcS2B4B7Wl7LK1JAlMh4LowlZG0HxJoQUABsAUkL5GABHoncAbgEOAg4ArAuYFzAslG+hXgQ8DRwCm9t8QbSEACQyLg2jCk0bAtYyegPY19BG2/BLZPwHVh+0ytUQKDJaAAONihsWESkIAEJCABCUhAAhKQgAQkIAEJSEACEticgALg5gytQQISkIAEJCABCUhAAhKQgAQkIAEJSEACgyWgADjYobFhEpCABCQgAQlIQAISkIAEJCABCUhAAhLYnIAC4OYMrUECEpCABCQgAQlIQAISkIAEJCABCUhAAoMloAA42KGxYRKYFYFzAMeXHj8LeHlPvX8mcDDwGeCuPd3DaiUwBwLa7BxG2T5KQAISkIAEJCABCUyGgALgZIbSjkiACwPHboHD1bdQR9cqFBO6EvP6KRDQZtePoqL9ekZeMV0CY14jpjsq9kwCywlos84OCUhg0AQUAAc9PDZOAp0IjPmhQwGw01B78UQIaLPrB1IBcD0jr5gugTGvEfVR2dUeP92ZYM/GQkCbHctI2U4JzJSAAuBMB95uT5LAGYDfXdGzo8tnJwFPWHHdyZOkY6ckMDwC2uzwxsQWSWBIBKayRigADmlW2ZY+CWizfdK1bglIYGMCCoAbI7QCCYyGwIdLSz8K/MVoWm1DJTBfAtrsfMfenkugDYGxrBEKgG1G02vmQECbncMo20cJDJiAAuCAB8emSWDLBMby0LHlbludBEZLQJsd7dDZcAnshMBY1ggFwJ1MB28yAgLa7AgGySZKYMoEFACnPLr2TQL/n0CXh47FuFsXAu4CXBe4AHDWkkU32XRTfge4IZAEIpcFDgROD3wP+DTwNuAtwK+WDMq6l4Nk7H1Y+e4hwE+A2wO3KMeeTwf8d7nHK4FfLLnPqnhiTW24Wun3lYFzAd8GTgT+Bfjymgl2NuBeQNqbmDBp8+eAY0o7bwA8o9SR/lUsnbcSqAhos6DNag8SWE6gyxpR1ZJn/z8G/gS4PHDusj9lD30X8CrgxyugXwo4FPiDstfnyON3ge8AnwQ+ALy7tt+nzrOvGcRXsM/WLRKYOgFtduojbP8kMHACCoADHyCbJ4EtEujy0FF/6X428LSGB/i6aNXmAf/jwEOB7zf0qYsAeCvgSUBEuaaSfh6+RATsIiacCbg/EHFxsfywfJZ4ik3lYsBzgQsu+TyC6NsL11yiALjFiT6hqrTZbgKgNjuhyW9XWhHoskakwvMUoe1KK2r/GvAQ4L8arrk18JjyA9+qBt4c+Hq5oM3zgQJgq+H2ogkQ0GYnMIh2QQJjJqAAOObRs+0S6Eagy0NHJZTlAf4sQNaKfwU+Avy8ePmdAHy1NOE9xdPv/cWTLZ4A+V483yLY/WHtusqTr976LgLgfwBXAN4AHF+88i4K3Ke0K/UeARzVgKetAJh7RGD8z+IN8YXi9RiPiXgepsSbL14Qpy7cJ/2OF2LalPIO4E3ANwqPOwLXKHVfsVyjANhtLs/lam22vQCozc7FKuxnnUCXNeIA4KXFa/6XwBuBDxVv9uxb8eDPXhRvvW+W/47Xe1UuArwGOGMR95JY7FPF0z/fTxKy7PUHA3eqCYC/B2SPf1GpKHvzmxeGMR6E33JoJTADAtrsDAbZLkpgyAQUAIc8OrZNAtsl0OWhoxLK0oIc4z2sHLFd1qKLr/m8foT3HuWloV5XFwHw18AjgIiO9ZKXmwhvER0jTN6yobFtBcB8NV56jwNyv3p5MJA+pNwPqLhW1yTBSpVk5QVA/qmXrLvJwhwPiaooAG53rk+lNm22vQCozU5l1tuPLgS6rBF/A+QHqIh6fwl8vuFGEfFeDJwTeDXw1No1dyte/NkTbwN8ZUlDsxcnDEc9FMe6Pb5Ln71WAmMmoM2OefRsuwQmQEABcAKDaBck0JJAl4eOugD4dCC/9G9SstbEYy/iXOLn5Xhsvax7OagLiMcCT1zSmMTcy/HflAiAlYdidXlbAfAHJb5gUxyk8wJvLRU+Hziy1pYcFz4OOD/wxfKytSgg5vJ4WMT7Iv1OUQDcZHZN97vabHsBUJudrh3Ys+UE2q4RifOXvSnH5B/f4IFXv0N+8HtAiQOYWLXVHvYg4J5F+Itnf5eybo/vUpfXSmDMBLTZMY+ebZfABAgoAE5gEO2CBFoSaPvQkerqAuCNihdgy9ucdlw4IlmSYCQ4eFUeBSSpRjz3Fo8Br3s5qAuAfwX8+5LGXBP45/JZPBxyZLle2gqAOZ6Ul6RlJZ8nGUqO9v597aJLliPD+dOiOLhYV75XeSkqALadXfO6TpttLwBqs/OyDXu7j0DbNeJmwD8UMS9HdJOUalm5SvmhLp8n+VcVC7DahyMIRiBctg831btuj3c8JTAXAtrsXEbafkpgoAQUAAc6MDZLAj0QaPvQkVvXYwDWj6qualaEwtsCeXlIluBlJbG6Eq+vXta9HNQFwLzIJJ5eU7kc8LLyQUTGxWPCbQXAiIhVvKKm++So8aWBdwKPrF2QGIFPKf+fIOrvW8EhMZKq7yoA9jDhJ1ClNtteANRmJzDh7UJnAm3XiOyH2We6lgcCHyxfyo9eyWKfeH8RAU8E3gt8DPhsQ7iMLnt813Z5vQTGSkCbHevI2W4JTISAAuBEBtJuSKAFgbYPHamqEspOLokuVlV/euDJwI1btCGXfKbhRaSLAHgIkON+TeUywMvLB4kTmOyD9dJWAHxWrZ6m++Qeude7gYfXLkiCkEeX/89x5E+uYFIXCxUAW06emV2mzbYXALXZmRmH3T2NQNs14knATffAbHEfPajEsE024XrJnhyh8PVFGFy81bo9fg9N8ysSGCUBbXaUw2ajJTAdAgqA0xlLeyKBdQTaPnSknlVC2eJ97g7E2y0lWXPjHXdSySL405pXQF7Qr68A+Bt8CoDrZqyfa7MKgFqBBFYRaLtGxDM9e87Pa0ms2pA9pcQCrF8bD//84HedEtbjfAsVxTP+sSYBaYPXa2ZIQJud4aDbZQkMiYAC4JBGw7ZIoF8CbR86ugqArwWSOTCefcmO+6sl3UjyjxwPnrIHoEeA+53Dc6tdm+1fANRm52ZV0+pv2zWiygCc3ueHuKYEV3slc3HgeuW0QBJ9pbyg/FPVqQfgXun6vakR0GanNqL2RwIjI6AAOLIBs7kS2IBA24eOLgJgjv/m2E/WkucBL1zSviQDiVfAARMXAC9Vy5hsEpANJqtfPY2ANtu/AKjNamxjJtB2jbgN8LjS0cOBD/TQ6WQafg1wLuBzQOLcViWZ76uQHOuO6/fQNKuUwGAIaLODGQobIoF5ElAAnOe42+t5Emj70NFFADwTcELBeRRwxBK0fwr8Xflsyh6ApwOSjTRHor4I3HFJYPS8DL0RiFdEijEA52mT63qtzfYvAGqz62ahnw+ZQNs1Igk8jgXyY1z27Af11Kn8EHh14NvlyHF1m/qzQp4T8rxgkcAcCWizcxx1+yyBARFQABzQYNgUCfRMoO1DRxcBMNceBxwI/E8RshaPFsXD5kjgnDMQANPF+wH3LX1dPAZVDfETgXp2ZQXAnif/SKvXZvsXALXZkRqHzT6NQJc14jHA7Qq37MnxUl9WIhheE3hT7YIc8/0E8N0lX0pikHgAZq9PPOB7L1yXUwD57HUlcZhDKIE5EtBm5zjq9lkCAyKgADigwbApEuiZQJeHji5JQO4P3Ke0PVmDXwJ8Hkig8GsDdwZOBb4CXHriR4CDIf0+GqhiIb29ePt9q/wtXoHXKBmCr1C4KQD2PPlHWr02uxsBUJsdqYHY7E4CYEJwvBjIj3IpEeniFfhZ4GdFnMsenUy/1wI+AmR/r0qeC/JZjg+fWI75JvtvPNovU/b6i5SLn1D2vfoQPRv4oxJ/8OllD/xFuSD1LBMWHWYJTIlAl31dm53SyNsXCQyEgALgQAbCZkhgBwS6PHR0EQDPDDwXuPKSPsQj8FHA7YGDZyAABkOCoucoVLwomko8Id4KPLV8GDY5MmyRQJ2ANrsbAVCb1e7GSqDLGpE+xgPvyUXIW9fn44G/rl1UPRes+l5+7Hsp8JyGi65WvA5z7H6xvIJ9tm6RwNQJaLNTH2H7J4GBE1AAHPgA2TwJbJFAl4eOLgJgmnhG4C7ATUtG4F8D3yixhvJg/+XycD8XATBM4hVxT+CGxfPvp8VjIh4Xif93y1pcxJsA8RC0SKBOQJvdnQCozWp7YyTQZY2o9y/He28GXBU4L5Af8uKFl1AeOeb73nK8OHt5VZLkIx58ifEXL8J8L3/7JfBV4GPAMcBJK0Dmh8K7AVcq382zQ4oC4Bhnn23eCwFtdi/U/I4EJLA1AgqAW0NpRRKQgAQ6EXgIcHfgR8ANyjHpThV4sQQksFMC2uxOcXszCUhAAhKQgAQkIIFtElAA3CZN65KABCTQjsDpgdcDFwI+tBBnqV0NXiUBCeySgDa7S9reSwISkIAEJCABCUhg6wQUALeO1AolIAEJkEDopwD141MVlqy7jwAOLX94PPBmmUlAAvuVgDa7X/F7cwlIQAISkIAEJCCBvgkoAPZN2PolIIE5Eng4cL2S6OPjJb7fmUrcpFvVEqZ8EjhsiVA4R272WQL7i4A2u7/Ie18JSEACEpCABCQggZ0QUADcCWZvIgEJzIxAxIQkRVlVPg08GPjmzNjYXQkMkYA2O8RRsU0SkIAEJCABCUhAAlsjoAC4NZRWJAEJSOA3BHKc8EbAteG048DJlJhsh98DIvy9AzgO+JXMJCCBQRDQZgcxDDZCAhKQgAQkIAEJSKAvAgqAfZG1XglIQAISkIAEJCABCUhAAhKQgAQkIAEJDICAAuAABsEmSEACEpCABCQgAQlIQAISkIAEJCABCUigLwIKgH2RtV4JSEACEpCABCQgAQlIQAISkIAEJCABCQyAgALgAAbBJkhAAhKQgAQkIAEJSEACEpCABCQgAQlIoC8CCoB9kbVeCUhAAhKQgAQkIAEJSEACEpCABCQgAQkMgIAC4AAGwSZIQAISkIAEJCABCUhAAhKQgAQkIAEJSKAvAgqAfZG1XglIQAISkIAEJCABCUhAAhKQgAQkIAEJDICAAuAABsEmSEACEpCABCQgAQlIQAISkIAEJCABCUigLwIKgH2RtV4JSEACEpCABCQgAQlIQAISkIAEJCABCQyAgALgAAbBJkhAAhKQgAQkIAEJSEACEpCABCQgAQlIoC8CCoB9kbVeCUhAAhKQgAQkIAEJSEACEpCABCQgAQkMgIAC4AAGwSZIQAISkIAEJCABCUhAAhKQgAQkIAEJSKAvAgqAfZG1XglIQAISkIAEJCABCUhAAhKQgAQkIAEJDICAAuAABsEmSEACEpCABCQgAQlIQAISkIAEJCABCUigLwIKgH2RtV4JSEACEpCABCQgAQlIQAISkIAEJCABCQyAgALgAAbBJkhAAhKQgAQkIAEJSEACEpCABCQgAQlIoC8CCoB9kbVeCUhAAhKQgAQkIAEJSEACEpCABCQgAQkMgIAC4AAGwSZIQAISkIAEJCABCUhAAhKQgAQkIAEJSKAvAgqAfZG1XglIQAISkIAEJCABCUhAAhKQgAQkIAEJDICAAuAABsEmSEACEpCABCQgAQlIQAISkIAEJCABCUigLwIKgH2RtV4JSEACEpCABCQgAQlIQAISkIAEJCABCQyAgALgAAbBJkhAAhKQgAQkIAEJSEACEpCABCQgAQlIoC8CCoB9kbVeCUhAAhKQgAQkIAEJSEACEpCABCQgAQkMgIAC4AAGwSZIQAISkIAEJCABCUhAAhKQgAQkIAEJSKAvAgqAfZG1XglIQAISkIAEJCABCUhAAhKQgAQkIAEJDICAAuAABsEmSEACEpCABCQgAQlIQAISkIAEJCABCUigLwL/C49l0J7EtVabAAAAAElFTkSuQmCC\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Uncomment the plotting function to see the visualization, \n",
    "# Comment out the plotting function when submitting your notebook for grading\n",
    "\n",
    "accuracy_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "python-machine-learning",
   "graded_item_id": "f9SY5",
   "launcher_item_id": "oxndk",
   "part_id": "mh1Vo"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
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