01-introduction.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "from preamble import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": false
   },
   "source": [
    "## Introduction\n",
    "### Why machine learning?\n",
    "### Problems that machine learning can solve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Knowing your task and knowing your data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why Python?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scikit-learn\n",
    "#### Installing Scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Essential Libraries and Tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "uuid": "e2b8e959-75f0-4fa9-a878-5ab024f89223"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x:\n",
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "print(\"x:\\n{}\".format(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### SciPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumPy array:\n",
      "[[1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "from scipy import sparse\n",
    "\n",
    "# create a 2d NumPy array with a diagonal of ones, and zeros everywhere else\n",
    "eye = np.eye(4)\n",
    "print(\"NumPy array:\\n{}\".format(eye))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SciPy sparse CSR matrix:\n",
      "  (0, 0)\t1.0\n",
      "  (1, 1)\t1.0\n",
      "  (2, 2)\t1.0\n",
      "  (3, 3)\t1.0\n"
     ]
    }
   ],
   "source": [
    "# convert the NumPy array to a SciPy sparse matrix in CSR format\n",
    "# only the non-zero entries are stored\n",
    "sparse_matrix = sparse.csr_matrix(eye)\n",
    "print(\"\\nSciPy sparse CSR matrix:\\n{}\".format(sparse_matrix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COO representation:\n",
      "  (0, 0)\t1.0\n",
      "  (1, 1)\t1.0\n",
      "  (2, 2)\t1.0\n",
      "  (3, 3)\t1.0\n"
     ]
    }
   ],
   "source": [
    "data = np.ones(4)\n",
    "row_indices = np.arange(4)\n",
    "col_indices = np.arange(4)\n",
    "eye_coo = sparse.coo_matrix((data, (row_indices, col_indices)))\n",
    "print(\"COO representation:\\n{}\".format(eye_coo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "uuid": "30faf136-0ef7-4762-bd82-3795eea323d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10f619320>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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zNDM4IDAwMDAwIG4gCjAwMDAwMDM2MDggMDAwMDAgbiAKMDAwMDAwMzc2MCAw\nMDAwMCBuIAowMDAwMDAzODgxIDAwMDAwIG4gCjAwMDAwMDQwMjEgMDAwMDAgbiAKMDAwMDAwNDM0\nMiAwMDAwMCBuIAowMDAwMDA2Nzc3IDAwMDAwIG4gCnRyYWlsZXIKPDwgL0luZm8gMjQgMCBSIC9S\nb290IDEgMCBSIC9TaXplIDI1ID4+CnN0YXJ0eHJlZgo2OTMxCiUlRU9GCg==\n",
      "image/png": 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VBqMlnE4Hf/nL6QC89dYB27YmCYYwFAED/O73Bw60cu3VnBBGklIe8P7cCawETm/phVLK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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b804dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate a sequence numbers from -10 to 10 with 100 steps in between\n",
    "x = np.linspace(-10, 10, 100)\n",
    "# create a second array using sinus\n",
    "y = np.sin(x)\n",
    "# The plot function makes a line chart of one array against another\n",
    "plt.plot(x, y, marker=\"x\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "uuid": "ad1b06f7-e03a-4938-9d59-5bb40e848553"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Location</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>New York</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Anna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Peter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>London</td>\n",
       "      <td>Linda</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Location   Name\n",
       "0   24  New York   John\n",
       "1   13     Paris   Anna\n",
       "2   53    Berlin  Peter\n",
       "3   33    London  Linda"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "\n",
    "# create a simple dataset of people\n",
    "data = {'Name': [\"John\", \"Anna\", \"Peter\", \"Linda\"],\n",
    "        'Location' : [\"New York\", \"Paris\", \"Berlin\", \"London\"],\n",
    "        'Age' : [24, 13, 53, 33]\n",
    "       }\n",
    "\n",
    "data_pandas = pd.DataFrame(data)\n",
    "# IPython.display allows \"pretty printing\" of dataframes\n",
    "# in the Jupyter notebook\n",
    "display(data_pandas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Location</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Peter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>London</td>\n",
       "      <td>Linda</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Location   Name\n",
       "2   53   Berlin  Peter\n",
       "3   33   London  Linda"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# One of many possible ways to query the table:\n",
    "# selecting all rows that have an age column greate than 30\n",
    "display(data_pandas[data_pandas.Age > 30])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### mglearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python2 versus Python3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Versions Used in this Book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version: 3.6.3 |Anaconda custom (64-bit)| (default, Oct  6 2017, 12:04:38) \n",
      "[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "pandas version: 0.20.3\n",
      "matplotlib version: 2.1.0\n",
      "NumPy version: 1.14.2\n",
      "SciPy version: 1.0.0\n",
      "IPython version: 6.1.0\n",
      "scikit-learn version: 0.19.1\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(\"Python version: {}\".format(sys.version))\n",
    "\n",
    "import pandas as pd\n",
    "print(\"pandas version: {}\".format(pd.__version__))\n",
    "\n",
    "import matplotlib\n",
    "print(\"matplotlib version: {}\".format(matplotlib.__version__))\n",
    "\n",
    "import numpy as np\n",
    "print(\"NumPy version: {}\".format(np.__version__))\n",
    "\n",
    "import scipy as sp\n",
    "print(\"SciPy version: {}\".format(sp.__version__))\n",
    "\n",
    "import IPython\n",
    "print(\"IPython version: {}\".format(IPython.__version__))\n",
    "\n",
    "import sklearn\n",
    "print(\"scikit-learn version: {}\".format(sklearn.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A First Application: Classifying iris species\n",
    "![sepal_petal](images/iris_petal_sepal.png)\n",
    "#### Meet the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris_dataset = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys of iris_dataset: dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])\n"
     ]
    }
   ],
   "source": [
    "print(\"Keys of iris_dataset: {}\".format(iris_dataset.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Plants Database\n",
      "====================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive att\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "print(iris_dataset['DESCR'][:193] + \"\\n...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target names: ['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "print(\"Target names: {}\".format(iris_dataset['target_names']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature names: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "print(\"Feature names: {}\".format(iris_dataset['feature_names']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of data: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(\"Type of data: {}\".format(type(iris_dataset['data'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data: (150, 4)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of data: {}\".format(iris_dataset['data'].shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First five rows of data:\n",
      "[[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]\n",
      " [4.6 3.1 1.5 0.2]\n",
      " [5.  3.6 1.4 0.2]]\n"
     ]
    }
   ],
   "source": [
    "print(\"First five rows of data:\\n{}\".format(iris_dataset['data'][:5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of target: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(\"Type of target: {}\".format(type(iris_dataset['target'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of target: (150,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of target: {}\".format(iris_dataset['target'].shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target:\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "print(\"Target:\\n{}\".format(iris_dataset['target']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Measuring Success: Training and testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    iris_dataset['data'], iris_dataset['target'], random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (112, 4)\n",
      "y_train shape: (112,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train shape: {}\".format(X_train.shape))\n",
    "print(\"y_train shape: {}\".format(y_train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test shape: (38, 4)\n",
      "y_test shape: (38,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_test shape: {}\".format(X_test.shape))\n",
    "print(\"y_test shape: {}\".format(y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First things first: Look at your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
      "0                  5.9               3.0                4.2               1.5\n",
      "1                  5.8               2.6                4.0               1.2\n",
      "2                  6.8               3.0                5.5               2.1\n",
      "3                  4.7               3.2                1.3               0.2\n",
      "4                  6.9               3.1                5.1               2.3\n",
      "5                  5.0               3.5                1.6               0.6\n",
      "6                  5.4               3.7                1.5               0.2\n",
      "7                  5.0               2.0                3.5               1.0\n",
      "8                  6.5               3.0                5.5               1.8\n",
      "9                  6.7               3.3                5.7               2.5\n",
      "10                 6.0               2.2                5.0               1.5\n",
      "11                 6.7               2.5                5.8               1.8\n",
      "12                 5.6               2.5                3.9               1.1\n",
      "13                 7.7               3.0                6.1               2.3\n",
      "14                 6.3               3.3                4.7               1.6\n",
      "15                 5.5               2.4                3.8               1.1\n",
      "16                 6.3               2.7                4.9               1.8\n",
      "17                 6.3               2.8                5.1               1.5\n",
      "18                 4.9               2.5                4.5               1.7\n",
      "19                 6.3               2.5                5.0               1.9\n",
      "20                 7.0               3.2                4.7               1.4\n",
      "21                 6.5               3.0                5.2               2.0\n",
      "22                 6.0               3.4                4.5               1.6\n",
      "23                 4.8               3.1                1.6               0.2\n",
      "24                 5.8               2.7                5.1               1.9\n",
      "25                 5.6               2.7                4.2               1.3\n",
      "26                 5.6               2.9                3.6               1.3\n",
      "27                 5.5               2.5                4.0               1.3\n",
      "28                 6.1               3.0                4.6               1.4\n",
      "29                 7.2               3.2                6.0               1.8\n",
      "..                 ...               ...                ...               ...\n",
      "82                 5.8               4.0                1.2               0.2\n",
      "83                 7.7               2.8                6.7               2.0\n",
      "84                 5.1               3.8                1.5               0.3\n",
      "85                 4.7               3.2                1.6               0.2\n",
      "86                 7.4               2.8                6.1               1.9\n",
      "87                 5.0               3.3                1.4               0.2\n",
      "88                 6.3               3.4                5.6               2.4\n",
      "89                 5.7               2.8                4.1               1.3\n",
      "90                 5.8               2.7                3.9               1.2\n",
      "91                 5.7               2.6                3.5               1.0\n",
      "92                 6.4               3.2                5.3               2.3\n",
      "93                 6.7               3.0                5.2               2.3\n",
      "94                 6.3               2.5                4.9               1.5\n",
      "95                 6.7               3.0                5.0               1.7\n",
      "96                 5.0               3.0                1.6               0.2\n",
      "97                 5.5               2.4                3.7               1.0\n",
      "98                 6.7               3.1                5.6               2.4\n",
      "99                 5.8               2.7                5.1               1.9\n",
      "100                5.1               3.4                1.5               0.2\n",
      "101                6.6               2.9                4.6               1.3\n",
      "102                5.6               3.0                4.1               1.3\n",
      "103                5.9               3.2                4.8               1.8\n",
      "104                6.3               2.3                4.4               1.3\n",
      "105                5.5               3.5                1.3               0.2\n",
      "106                5.1               3.7                1.5               0.4\n",
      "107                4.9               3.1                1.5               0.1\n",
      "108                6.3               2.9                5.6               1.8\n",
      "109                5.8               2.7                4.1               1.0\n",
      "110                7.7               3.8                6.7               2.2\n",
      "111                4.6               3.2                1.4               0.2\n",
      "\n",
      "[112 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "iris_dataframe = pd.DataFrame(X_train, columns=iris_dataset.feature_names)\n",
    "print(iris_dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x110f5b198>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x111336b70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x111365a90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1113a0b00>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1113d9a90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1113d9ac8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1114474e0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x111481470>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1114bd320>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1114ccb70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x111520d30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x111556f60>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x111591390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1115cbe10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x111603550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11163a5c0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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+M7DNxGVHQO3Tapyf0hNi8yFO6bfbVNW5II8IQzgW1zOHrb7rJGGOKWO0NF/E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\nZgoyNjUzNgolJUVPRgo=\n",
      "image/png": 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wYWM3uznCERJIYCADOcYx13lqLGOJJTbKn0D0NKc5zQ520EBDh76HzTSzhz0c5CBxxDGe\n8fSnfxhzHB0azZd8yT72oVCMZWyHWuClgieEEB3wMR/zPu+7Xm9mM2tYw73cSwop7W7fQANP8AQn\nOelat451zGc+V3JlWPLcmh07r/AKO9npWreRjYxiFHdwh0TZE6ITsmLlGZ7hIAdd69aznilM4Squ\n4gmeoJRSAGqooYoq0hzLZjbzCZ9wL/d6BKQQIpzWsIZ/8S/X62C/hw008CRPcoITrnWf8ilzmMM1\nXBOWPEdDM808x3PsY59r3QY2MJ7xLGZxUEHgpIumEEIE6SQnPSp3Tqc5zbu8G9A+3uM9j8qd02pW\nc5SjF5zHQOxgh0flzmk/+/mMzyKSByFEcNay1qNy57SFLTzN067KXRNNVFEFQDXVNNEEwBnO8A7v\nRC7DokcrpdSjcud0hjO8zdsB7WMVqzwqd05rWMNhDl9wHjuLDWzwqNw57WIXO9gR1L6kgieEEEHy\nd6ItpBArVr/ba7TXipXTdrZ3OG/B8Pc5IpUHIURwfJVbjeZzPne9rqfeI9399W52t3ueEiIU/F1L\ndrPb9eCho/voTteqUH5OqeAJIUSQ6qjzmWbD1u6Nkw0bFiw+01vfmIWLv+NEKg9CiOD4Ov9oNDZs\nrtd27B7p7q9t2AK6sRbiQvm7ljTT7PdaCMb3tpFGn+n+rsddjb/PEuw1WSp4QggRJGcwA2i5UdJo\nAPLII4kkv9vHEstFXOQzfRCDQpLP9vg7jvtnFEJ0Hr7KpkaTRpqrIhdHnEe6++sccto9TwkRCv6u\nM9lktztm3YTJ7zy1ob5WNdHECU5QRllI9xsIf58l2PsCqeAJIUSQJjCBZJI5y1lOc5qznOUUp6ih\nhiu4IqB9XMEVXiNVppPOJCaFOstezWIW8cS3WW/GzGxmRyQPQojgzGWuRwCkZpopo4wznMGKlVJK\nqaSSRBJdUX1jiPGY9Hk+8yVSroiICUwgm2yvab6ug635+r6mkcZkJl9wHp1Ws5pf8kv+yB/5L/6L\nP/NnznI2ZPtvzxzmeI3EHU88s5gV1L6kgieEEEFqphkbNo+brBhiMGEKuBtFPvncxm3kkguAQjGa\n0SxnecSerGeTzb3cyxCGuNYNYAD3cI/fFkYhRPQMYhB3cZdrSpUyytBosskmlVQyyaSRRmqoIYcc\n+tOfPPJQKLLJZjGLmcjEKH8K0VPEEsu93MtYxrqiQPaiF7dwS8Dfw9GMZglLyCMPMK6XoxjFcpZ7\nPLi4EJ/yKR/wAQ00uNYVU8wKVrTbjTRU+tGPe7jHo8VyCEO4l3vJISeofck0CUIIEaRtbMOChV70\nwo4djcaECYViLWuZxrSAnkqOdSzVVBNLLIkkRiD3nvrTn+Usp4467NgldLoQXcBwx7KNbbzMyx7h\n05MciwkTP+EnZJBBI41YsJBGmrTciYjLIIMlLLmg7+EYxxKO66UdO+tY5zWtmmp2sIPpTA/Z8fwZ\nylDu535qqMGEqcMVWKngCSFEkEoocf3eel6aCipooCGoVrg00kKWt44K1VNQIUTknOe8z7mx7Nip\nppoMMkhwLEJEUyi+h+G4Xjrni/TF/ZofKRf6sFW6aAohRJD8XWDiHYsQQoSbv5tAheoUD4+E6OwS\nSSSWWJ/pXbEcSQueEEIEaTKTWcc6V+RMdwUUeIzN8+vcOXj7bSgqgoQEmD0b5s8Hs5yahegUmprg\nww9h/XqwWGDcOLjuOsjKinbOABjHON7lXa9jhIYznAwyopAr0e0cOWJcq44ehcxMuPJKmDo12rkK\nmTjiGM94trK1TZoJU8QCn4VS2O4ilFJXAg86Xo4A7tNav+lI6wu8CCQA/6G1Xh2ufAghRKjlkssi\nFrGSlTTT7Fo/lKFczdWB7eTkSXjwQaiublm3bx9s2QI/+xmYpIOFEFFltcIjj8CePS3rjhyBtWvh\nt7+FvLxo5cwlkUSWsITned6jkpdHHotYFMWciW5j+3b4zW/A5phj8ehR2LkTvvY1WLIkunkLoa/w\nFc5ylmKKXetiiOFmbiaLzvFAJxhhq+BprT8APgBQSm0B3CtxDwIPA7uBd1ulCSFEpzeZyYxkJJ/z\nOQ00MIQhXMzFge/ghRc8K3dOO3bA5s0wPTIDuoUQPqxb51m5c6qogJdfhu99L/J58mIEI3iYhymk\nkBpq6EtfRjPa59g8IQJmt8MTT7RU7tytXAkLFkDv3pHPVxgkksi3+BYHOUgxxSSRxKVc2mUDj4W9\nH5BSaghwRmtd67Z6LPBdrbVWStUopVK11jXhzosQQoRSGmnMZGbwGzY3Gy11vmzYIBU8IaJt82bf\naRs3dpoKHhg3p9OYFu1siO7m6FE4c8Z7mtawaRPceGNk8xRGCuWKUNvVReLxzleBN1qti9FaOwev\nVAGZrTdSSi1TSm1XSm0/d+5cuPMohBCRpduO3wsoTQgRGc3NvtPs9sjlQ4hoae97LuWg04rESP6v\nYFTy3LmfNdOAytYbaa2fBJ4EKCgokLsdIUSXY9fNrD/wDGdqD5GbPJjLhi/FbIqDmBgYPx527uRM\nHhy6GOItkL8HEhuBSV1vQLcQ3c6kSUaXaW8mTzb+LSszxig539+rl+st5zjHQQ4SSyz55PucOqWB\nBvayFwsWhjKU3njv8mbHzgEOUEYZ2WQznOHSDVOE15Ahxne6vNx7urMcgNHSt3OncX2bMgXS0yOT\nxxAroYRiikkkkXzyg46K3UgjH/AB5ZQzghFcxmVhyql/Ya3gKaV6A01a69bfjN1KqWkYY/DStNZe\nBqIIIUTXdeTcNp498QsqU+3GYyyKWP35Oyzp8yDDe8+k+bZv8OqYInaObXneFdcEX9t8ERMui84F\nQQjhZt48+OgjOHzYc31KCixeDH//O/zjHy2tGCYT3Hwz9ltvYSUr2cpWV6TdWGK5gRuYwhSPXe1i\nF6/xGk00udZNYAJf5+se0XjPcY6neZoyylzrcsnlbu7ukgEgRBcREwN33gl/+EPbniVXXgn9+xu/\nP/UUvPNOy3tWrIDbb4frr49sfi+AFSsv8AL72Odal0ACt3Iroxkd0D42sYm/8ldXef6Ij/g7f+cX\n/IIccsKSb1/C/ejneuAt5wul1J8cv/4O+DVGcJXfhDkPQggRcS8c+0+jcuemOkXzwqnfYtfNfDz0\nKDu/0g/SUo1pEeLiaOrTi1fuTOB0rI+npUKIyImPh1//Gm65Bfr1g+xsuOIK+O//hhMn4JVXPLuo\n2e3wyivs3vQEW9jiMY2KFSv/5J+c4IRr3RnO8DIve1TuAHayk4/52PVao3mWZz0qdwBnOctzPBfi\nDy1EK7Nmwa9+BQUFRmvesGHw7W/DffcZ6R9+aEyh4F4BtNng6adh9+7o5LkD/sW/PCp3YLTGPc/z\nVLbtaNhGLbX8hb+0Kc8VVPA7fhfSvAYirC14WusnWr3+juPfk8DccB5bCCGiZUfx65Snex+/U5Vq\nZ9OhF9g07BjExbUJtW4HtrCF6+k6Tz6F6LaSkuDWW40fd3/9q89Nmt97B6a1feKv0WxmM/0xWj02\nsxk73scwbWQjC1gAwBGOcAbvgS5KKOEYxxjIwEA+jRAdM2aM8ePNe+/53u7992Hs2PDkKYSsWNnG\nNq9pNmxsZzvzme93H2/zNlasXtNOOBZn2Y8EmU23i1DqtajsT+vOO49Od/gMons6W3PEmOXTh9P1\nxdRQ6zP9POfDkCshRMicPeszKe5clc8097Ltr1WgllqsWIkltt3zwXnOSwVPRI+/QIh+yklnUk+9\nxzySrQVyTT6L/89aQklEK3h+u2gqpaYppR5XSu1WSp1TSh1XSr2nlPqWUqprjp4UQogQsmPHhucc\nQQOzxvndZnDGOHrREoxBOxYnX0EWhBCdRH/fN2qW/rk+0/LI8/p7a5lkEktsu+9zT9dony0IQoSN\nn7LAwK7x4CHFsTi5X4+h/TIIMIhBHtu770OhGMrQC89oEHy24Cml3gdOYYyh+zVwFuOZ9HBgDvCW\nUup/tNZvRyKjQgjRmdRRx3u8xy520UQT/enPQhYykpGM7jefvlse51RW2yeCuedjmTD5BhrI4WVe\npppqmmhCoUgkkRxymMrUKHwiIUTArr8etm1rG3hCKRKvvwW8dPcyY2Y6LfNbTmUq61nv0XJQRx01\n1FBLLT/n50xlKvOZz2AGc5SjbfY5jGFkk827vMtWtlJPPbnkMoc5TEKi8YoIuOEG2Lev7XqTCa65\nJvL56YAYYpjBDN7kTaqpxooVhSKJJHrTmwIK2t3H1VzNm7xJHXU0OyYLUChMmBjN6E4VZOU2rfVd\nWuu3tdantNY2rXWt1nqn1voPWuvZwMYI5VMIIToNK1ZWsIItbHENqD7BCZ7madcg7btHP8pF5Yko\nt+36VsRz1whjsHUOOVgdCxhP/CxYiCOORBIj+nmEEEEaMwbuvx/S0lrWpaXBd79L/iVf52qu9giv\nnkEGd3CHx01eBhksZakrCmYNNVRSSSKJpJBCHXV8zMe8yIvczu0eky8rFCMZyW3cxnM8x1rWUk89\nYHQVe5VXWc/6MP8RhACmToVlyyA5uWVdZib86EcwNLKtVhcihxwsWNpck5NICmiqhDjiyCffY51G\nY8bMLGaFJc/++GzB01p7hGtSSqW5v19rXdH6PUII0RMUUkgppW3WazSrWMVoRpOdOpAfTV3JodMb\nOFlZRJ/0UYwYdrnrvR/yIamkkkyyqwUvnngaaGA725nBjEh+JCFEsObNg5kzW1ovRo82AicBc5nL\nDGZQTDGxxDKIQV7nrBvKUB7kQQ5zmBWsIJnkNu/by17mM59lLOMc5yinnGzHcpSjfMEXXrP3MR8z\nlamurp5ChM211xoRZvftg9hYGDXKmGKhC/mIj8ggg1RSsWLFhIk44jjPeYooYhz+h14c5ShnOMNA\nBlJPPTZsJJBAHHF8yqfMYlZEy2K7QVaUUv8G/CfQAK4OpRoYEsZ8CSFEp3WIQz7TSiihgQZXK9zF\nvWdwcW/PypoNG8UUA2DCREKriCyHOCQVPCG6grg4GOf9xi+eeEYwot1dmDBhxoxyLN4c4hD96U+O\nY3Ff70sttZzmdEQDO4geLD4exo+Pdi46pJpqV6TaGMfi7hCH2q3gHaZlvswkkjzS6qiLeFkMJIrm\nvwP50lonhBAGf901YojB3M6p1YSJWGJ9BkRoXeETQnRvccT5Tfd1zmmv61ggXcuE6OliiUWh2gRX\ncQq0i6Y/kS6LgVTwDoOjY7cQQkRAI43sYAfHOEYKKUxiEn3oE+1suUxgAhvZSNaBcoZ9coL4ehun\n8jM5MnsAl8Rf6uqGUVVRzNZDL3Gu+QzZplwmD11MRvZQTJgYxzif8+6Mp+Up6DrWsZa1WLEyhjFc\nz/VSARTiQjU1wbp1sGcPJCbC5Zcb3couxJkzxqTP584ZkQWvuAIyMgAopZRtbKOWWgYwgIlM9Bhr\n249+5JHnMd+dcwxQI40c5jB11LGHPZzjHDnkkE8+pZRSSSUJjsVdP/qRi++InkIErKYGVq+Go0eN\n8XULFkC/fqE9xvnz8NFHcPKkMT/sFVdAbsv314qVz/mcgxwkjjjGM54hIepMmEgioxjFHvZQTz1N\nNGHCRBJJrmO151Iu5V3exYKFOuqwYyeWWJJJpj/9XWWxgQa2sY2TnCSVVCYxySNyth07e9nLPvah\nUIxhDCMZ6bN135dAKngPARuVUlugJdST1vr+oI4khBABKKOMv/JXqmiZS2o967mO65jJzCjmrMUg\nBrHgpXMMfXWHa13+2jLOv1lC398sg0w4fHAVT9tW0JTpfCJ4nDVlO7i97HZGjryBq7iKoxylDM/O\nEdOY5gqm8AiPeIyvOcQh1rCG/8f/I5PMsH9OIbql6mr4yU/g+PGWde+9BzfeCHfe2bF9btoEv/89\n2NymTFm5Eh55hM9GlvEWb7laB3aykzWs4T7uI5ts19tv5mb+j/+jkUY0mgoqaKCBTDJZz3rKKEOh\niCGGL/mS9awnm2zMmCmjjCSSXAFbEklkETIHrAiBo0fhZz8zyo3Tm2/Cd78Lc+eG5hh798IvfgGN\njS3rXn8dHnwQJk2innpWsIJTnHIlb2ITl3EZN3BDSLIwl7l8yqc00pKHWmqZwhQu4qJ2t08nnXzy\neZ/3PdbXUcdt3AYYAZBWsIJqWv6Wn/IpN3ETU5mKFSvP8AwHOehK38pWxjCG27jN6zheXwJ55xPA\nJ8BmYIfbjxBChNxKVnpU7sAGcOKnAAAgAElEQVR4kv02b7epDEVL6YF1jHp1N7GYicGECYWZGPJK\nrFQ88980Nzfx94anaDJ7dvewxmhetr2AtameNNJ4gAe4kRsZwxgmMYllLOMmbgLgPd7zGjyhkkpW\nsCIin1OIbunZZz0rd05vvGG06AWroQEefdSzcgdQX0/T//6Wt/Sbbbp+VVHFSlZ6rBvIQH7ID1nA\nAnLIIYYY8sgjkUTKKQeMc2Ezza79lVNOEknkkotCkUsuC1nID/lhQDelQrTrscc8K3dgTA/y+ONQ\nVeV9m2DY7fA//+NZuQOwWuF//xcsFt7nfY/KndNnfMYBDlx4HjB6y2SRRTrpJJJIMsnkkEMJJV6P\n3VoNNexjH7nkkkIKCSSQRho55PApnwLwGq95VO7AKNOv8zqVVPIZn3lU7pyKKPLZ48eXQCp4Nq31\n97XWf9NaP+f8CeooQggRgGqqfQYN0Gh2sSvCOfKu/JN/Arieppsxu56spW4o4ssv36Eq0fv4urr4\nZr48+C5g9MmfwQxu53a+ztc9wqCvY53P4zunYhBCBMluh/V+pg9Ysyb4fW7ZYlTyvGgsPUrvL7zf\nBB/iUJubvXTSWcACMhxLLLHUUONRQbRjd/2u0dRSSxxxZJJJFllcwRWkkYYQF+zECTh82Hua1Qqf\nfXbhx9i71+jW7E1tLezYwU52+tx8RwjanBppZC97MWEilVR60YtMMl3j5vwd36mQQpppJo44Msgg\nm2zSSCOGGE5wgoMc9DqXJRhlupBCv58l2M8ZSAVvjVJqmVKqj1Iqy/kT1FGEECIAFiw+BzmD0Xe9\nM9D1dT7TTLZmai3lfrdvsFb7TQc8uom0ZsPmM00I4YfNBhaL7/Q632W7I9vYsRNX7728arTPcu5+\nrnNOmuyLe3pnOUeKbqK21n96R8pLkPuw19ZgwXeZDcV3vokmjwcnHTmGv2s20KZnkrft/e0j2M8Z\nSAXvVhzj8Gjpnrk9qKMIIUQAnN0jfAnVgOqANTXB/v1w8KDRJcUhLt9twLVdG60CjuS6gTkMHzAf\nk496qgKG9L2s3UMPxfcEse5h0oUQQYiLg4svNn63242WN/cK3yWXGP/W1BjdNU+ebH+f+fk+k+Ji\nkzkzzHtrWhpp9KKX1zT3c10yyR5prYMtuKf7O28I4dfJk8Z3vqamZd3gwZDkCPnf3GyUl6amlnRn\nebkQI0f6njNPKUz5lzCQgT43D8V9QSqpHuNhO3IMf+9JJJF88kkhxe/2gxl8QXlw124FT2s92MuP\nzIEnhAi5GGKYz3yvaf3pz2hGRy4zq1bB0qXw4x/DD34Ay5bBLqOL6ODZd1Cfl2Zc6JqaoMkKTRZo\nbiZ28RIyeg1hasVwr7udWD6IXrkj2z38LdziM6yyc5yeEKIDFi+GykooLoaSEqMb2rFjxo3s7Nnw\n1FNwxx1GIJbly+FHP4JTfsbgDBoE06d7TUq4dhE5ad5vmeYzv818W04zmemKshnvWJzct3FOpAyQ\nQgrT8Z4PIXw6fdq4zi1fbnzn77gDnnzSqNAlJMANN8DZs0awlZISY/xqSQkMHw6jQ3BNzsiAq6/2\nnjZrFvTrxwIWeI0imUEGU5hywVlQKBawwGtaHnmMZWy7+xjqWLyZwxwSSWQu3oPSDGYwwxjGXOZ6\nnQw9kURmMavdPLhrt4KnlPqWUirD7XWmUmp5UEcRQogATWMaN3Oz62laLLFMYQrLWBZUBKkLsmWL\nMYDcfWD5mTPwq1/BiRPEWTR9bblU9UvBHmNcdOoy42jqk83QMuNp/Q2Tf8PC85NIaTSCFSdbYphf\nMZ5Fk34XUBZyyeWn/NRjYtQMMribu7mcy0P0QYXogcrLITYWzG6BxOPjjTFFL7wAb79t/O70xRdG\nFEH3lovWvv99IwpniuMJfVYW3H47pjvuZBnLmMIU141bNtnczM1+K2NZZLGc5a7w6H3pSx55JJBA\nDDEkkkgeefShDwrFaEZzH/eRQYbPfQrRhtUKDz9s9FRxX/fuu/D888br8+eNlm9neTGZjNe1tUYr\neCjcdRd885uuaUVITYVFi4xIncAIRrCUpa7AQSZMjGUsy1nuMd3IhZjABL7JN8kjDwAzZiYykfu4\nz2uly5ulLGUGM1wPZLLI4kZudFXsZjGLm7jJFe02nnimMY27uRuFog99uJd7XRVFhWIEI7iXe322\n9vuitPY93gVAKVWotR7Xat0urXXEpqsvKCjQ27d3jl6hSr0W0Pu0Dm144kCPG2rR+hyBHDda/xdd\nUUFBAZ2lDAVKo2mggTji2p04POQefBD2+QhkctVVxtw8zxmxpqwmO1azJqnJ8VQ9O9toATAZlVG7\n3UZjXQUJyVmYTB37HLXUYsUqUyNEUVcsQ8KHf/s3KC01fm9uBqWM8mq3G+OBUlO9b/fAAzBnjv99\nNzdDfT0kJ7vOAU42bDTRRCKJQc1p5RybnEACNmxUUkkGGZgx00gjCtVlJjSXctTJrFsHf/iD97SE\nBPjTn4yWPavVGKZgtxvfa+X4/v70pzDlwlvQXJxlMCnJZ7fNBhowYw640tUR9dQT61g6oplmLFh8\nlvVA7m+c4w5bl22l1A6tdUF7eQjkbsOklFLaURNUSsVAO9O1C6l8CHGBFIokkqJz8OJi/2luEfNi\n7SZi3R/sl5V53CSaTGaSUi9ssmF//faFEEGwWFoqd+B5E2mzGa0Svip4/s4L7vvzsb3ZsQTL/QbP\njNljrFDryc2FCIq/73RjIxQVtbRmK9W20nXsWGgreCaT7/LnEKoWO38u9N4jhhi/+wjk/uZCH9oE\n0t9pFfAPpdQ8pdRc4GXggws6qhBCdGa9/HSFyM72n56UBInhvwAJITogLg7SfEwhEBNjpPvir9wL\n0RX5+04rBQMHtrTWBbu9iKpAHiX9GFgG3IcRAO5D4KlwZkoIIaLqyivh//7Pe9rChdCrF/Y3VnLa\nfopGGtFozJjpRS9S5l/nObbHj3LKWctaDnGIeOKZwASmMS2sXU+E6NGUggUL4J//bJtmNhtdMHfv\nbpsWH28EYAnQec6zjnUc4ABmzKSTTi21NNDAAAZwOZfTj35ttiummHWso5RS0klnGtMYx7g276um\nmnWsYz/7MWNmLGOZycwu01VTdBKXX26MtWs9yTjAtGkwbBiMHw87vcwDl5ICl7UfETpUKqhgLWs5\nyEHiiGM845nBjKCul+/yLh/xETXUkEkm13Itc2in27WbBhpYz3qKKKKZZkYzmlnM6pTzTrZ7F6K1\ntgMrHD9BUUotAW4HYoBvaK1LHOufBUYBDcCTWuu/B7tvIYQIm2uuMSKGrV7dss5kgttug7FjaaKJ\nxx7QXPtYI2arMcjcipV1E2ox35bLwgAOcZrTPM7jHnPbnOQk+9nP3dztM7qeEOIC3XKLEQlw69aW\ndbGxcO+9MHUq/PKXRmAVp8REI5Kmr5a/Vsoo48/8mVqMOcQqqKCeeuKII4ccyihjN7tZylKG0xJt\ndze7eYEXXHOBllHGYQ5zkpNcy7Wu91VSyZ/4k8e8Wqc4RRFFLGe5VPJE4FJT4aGH4Le/NcaOOg0f\nboy9AyPQySOPGNdE9+1+8hPjwUcEnOEMf+bPHtfLEkrYz36WsSyg6+VjPMZGNrpe11PPEzzBKU7x\nDb7R7vYWLPyFv1BKSxfvs5ylkEK+w3f8TvEUDT4reEqpd4AngQ+01tZWaUOAO4BirfUzPrbvB1yu\ntZ7n4xDf0Fof6lCuhRAinEwmuP9+Izz0jh3Gzd/UqUb3TOA5nmPj5bHsmjCUsRtqSKxr5sglSRwf\nkUgS/2Ah17V7iPd4z+vEpQc5SBFFXp/aCyFCIC7OiBx48KDRWpeUBDNmtFTgfvc7Y/3Bg5CebqQF\n0e16Fatclbsmmqin3uP3ZJKxYeMd3uEH/AAwJkR/i7dclTt361jHNKa5ouitZrXXSZNLKGErW5nJ\nzKD+HKKHGz8e/vY32LjRmD7k4ovh0ktb0jMz4dFHjWmCjh41IsROnx6xyh3AB3zg9Xp5mMMUUshE\nJvrdvpRSNrHJa9r7vM+N3NjumLiNbPSo3DlVUsknfMKN3Oh3+0jz14J3D/B94FGlVAVwDkgABgOH\ngD9rrd/ys/1CIEYp9TGwD/ie1rrZkaaB55VS5cC3tdbHLvBzCCFE6A0YYPy0sh0jClxDagxbrvQM\nS15PPec453cy8maa2c9+n+l72CMVPCHCbdgw48ebsWONnw7Ywx7X761vShtocE1MXkop5ZTTi16c\n5KTXShsYEff2stc1D9Ze9vo8dhFFUsETwUtMhHm+2mMwujZPmGD8RJjz++/LXva2W8FbwxqvD0/A\niG67nvUsbKfvjXu59paHLlPB01qfBn4E/EgpNQjog9Gl8oDWut7Xdm7ygDit9Tyl1G+B64HXHWk/\n0FpXKKUuA/4AfK31xkqpZRhj/xjg5QZLCCGEEKIrc950+rr5bP2+QN4rRE8SSHkId5npjGUyoFmD\ntdbFWutNWuvCACt3AFXAOsfvn2CMuXPur8Lx72dAbx/HfFJrXaC1LsjJ8f0kXAghvLFgYRvbWMMa\nDnO4TbpG8wVfsIY17GIXVqxtd3LiBLz1ljHpa3m5a3UBvqegSSLJb+sdGCGURzlOiU00UUMNtdTS\njNHJ4RIuCeQjCiGCYbEY836tXGl0N2tnHuCOyiff9XvrkO7O184yv4pVnOc8/envdQyPDRu1juUc\n5wD/5wc5d4gOaWyETz4xyoa3IENRpFCMZrTPdPfvfA01bGITa1nLKU651s9lrs+5J82YPVq9iylm\nLWvZwhaPFnj3cu0vD000sYMdrGENBzgQtcpfOGcP3ojRzRNgHOAanamUStNaVyulRgCVYcyDEKIH\n+oIveJEXaaQlMtggBrGUpSSRRBVVPM3THheAFFK4gzsYxCBjstXHH4ePPmrZ6VNPGUFWbrqJ27md\nz/iMGmo8jqtQAQ3WBljAAjaxyWMflVQymtGMYUzHPrgQwrv9++HXv4bq6pZ1gwfDz39ujCkKoYUs\n5BCHqKWWOOJIIskVZCWOOI5zHBs2zJh5h3d4j/e4mZu5jut4kRddN4TVjiWFFNawhrWsZTrTmcc8\n9rGPaqo9jtuPfkwhhHOSiZ6hsNAIslJX17JuxAj42c8CDiwUbldxFYc53KbL8xCGcCnGeMFNbOJN\n3nQ9KAUYxzgWs5g+9GEa0zyCrDgtZCFJJGHBwrM8y0EOutLe4i1u4RbGMpbpTGcXu9qMw8sgwxWJ\n8zCHeY7nXONuAS7iIu7iLlLxP79fqAXUgtcRWutCoEEptRaYBHymlPqpI/klpdRnGNMtPBiuPAgh\nep466nie5z0qd2A8lXuDNwB4hVc8KncAtdTyLM8aLXnvvedZuQOj0vfcc7B7N3HEsYIVjGMc8cRj\nxkwOOXyf73MVVwWUzyKKSHYsZszEEks66VRTzVnOdvwPIITwZLG0rdyBETDij38M+eFyyOF+7mcG\nM8ghh3zymc98JjKRSiqxY8eMGZPjFqyZZl7hFTLJZDnLGcMY4onHgoUsssjAGOer0WxgAwc4wHf5\nLrOYRS659KEPV3Il93GfRNAUwampgd/8xrNyB/Dll/DXv0YnT17kkcf3+B7TmU422fSjH9dyLfdw\nD2bMnOAEr/O6R+UOoJBC1rAGgPu5n2/wDfLII4kk+tGPZSzjNm4DjCkU3Ct3YLTGvcRLnOc8CSSw\nnOUsYAF96EMuucxmNvdzPxlkuCqI7pU7MKJjv8ZrYfzreBfOFjy01v/eatWvHeu/Es7jCiF6rh3s\noIkmr2m72c0sZrU5iTvVUksRRUx4/33fB1i1CsaOJZFE/oP/6HA+t7AFM2YyyfSadgM3dHjfQgg3\nGze2rdw5FRbCmTOQlxfSQ2aR1SboQi21rnLfmkbzJm9yP/czmME8wzNYsHjd92Y2M4UpXOdYhOiw\ndeu8z4EHRrmpqjIiyXYCvejFV/mq17QtbPHZFXITm5jPfAC+4lhas2JlBzu8bt9MM9vYxgIWkEgi\nCxxLa5/zuddInwD72U8lla6HNZHQbgueUmqGUuojpdQBpdQRpdRRpdSRSGROCCGC5SsSHRgn6tOc\n9rt9JZUe4+3aKCvraNZcrFhdYdR95kEIERr+yrPW/tNDqIIK7Nj9pjv5O4/J+UGETHtlo6LCd3on\n4q9MVFPd7ji4eup9PhgG/+UxkDxodED7CKVAumg+DfwPcBlGV8sCx79CCOGbxQIN3p9mBaqhtoxm\nm/en2L70dovbpNEeN1TxxDOUoR6Totqxe5z8+9IXBg50vbbEgdX9gfugQUHlx5tYYskm22d6X/p6\nrmhq8vu3tGOngQa/N49C9Fhu5bkNpSA3t/19+CmDlsYqLI3GzVvrsmjB4grg1JvexBLr8xD96e/6\nvQ99XL/bHIu3NCEuiPv1TGtobm4JPpSQAL29xkH0ydJQSZOlxvcbmpradgcNUiWVbVrK3MtE6+t+\nb3r7DLDilOJYfAmkzPl7j3MYh3seG2ho06U0lALpolmltfbTX0kIIdyUlBiTpm7bZlwoRo0ygpNc\nEnh0twMfr0C/9ipJpyppjo+ldvZEht7+SxJTfFeKnMYxjvd5n2Mco556NBozZlJJZS5zySKLCUxg\nDWuooYZmmlEokklmuGPhxhs5bNnP+9coigcplIZR+zTXfhBD7jXXXMhfx+VyLmclK9usjyeeyUw2\nXpw+bfwtt2wxxgAOHw7f/CaMM+bIs2LlQz5kM5tpoIF00pnJTC7n8nYvaEL0GBMnQv/+RlRcp7o6\no3UiPh6WLYNp0+DOOyG71TnGTxk8fXgD5c/+gbTdxQCUjM1m/R1DODM0xTXGzoKFGGLIJ5+v8BUm\nMclroIdYYrmJm1yvZzKTz/iMc5xzVRDNmMkii8u5POR/ItFDTZsGvXoZQYhqaoxrdmysMbn5DTcY\n8+MFoGTfaiqff4y0fSfQSlFdMJy8O35MTv/xxhsqK41y9NlnYLUa88suXgwzZgSc1VWs4g3eoJJK\nFIr+9Gc5yxnEIKYxjXWs4xznXJU/57j2QMpLDDHMYhbv8V6btBRS2p1nD2A0o8khxxXt1t1kJrsm\nUt/MZtawhnLKiSeeAgq4mqtDPn7WZwueUmqCUmoCsEYp9Xul1DTnOsd6IYTwVFEBDz4IW7e2PAXc\nvx/+4z+MQdsB+PKDP5L4xydIOmV0d4ixWElftZmjP1+C3W5rZ2vjRJ1AAlasrpY5GzasWF3939NJ\np4km19MzjcaKlWSSMWHi+NS+/N/P+1I81HgGphXsGxfLX343iOoBoelDP41pXM3VJJDgWpdLLvdw\nj5HP6mp46CHYtMm4sQQ4cAAeeQT2GBOuvszLrGGN64JWRRXv8q7Xi5QQPZbJBL/4BYxxRKetqzMq\nbnFxRoXOZoP1643y5t5K56cMln36Jg0PPUDa50dBa2zaSs7np7j2oY3ElZRRTDFHOEI11dixU0QR\nj/M4d3InE5noCrACkEYaP+SHHuNzLFgop9yj5c6GjfOcp44LawERwiU2FlJSjJY15zXbZjN64LR+\n2OFD6aH12H72IGn7jAcoSmvSt31J9UPLqSo7Yuz7pz+FNWuMyh3A8eNG5M716wM6xmpW8zf+5uoG\nqdEc5ziP8AgVVJBAAmbMHtMdOVu+vU0/4s0c5jCPecQR51rXl74sY1mb6U68iSGGe7iHIQzxWDeV\nqa6xsutZzz/5J+UYXWMtWNjABp7m6ZBPp+CvBe8PrV67T/ykgbkhzYkQouv717+MQdmt2Wzw2mvw\n8MN+N29ubsL06j+9pqUeLOXYtpUMnvJ1v/vYz37OcpZccrFixY6dWGIxYeJjPmYc41jPerLIIp10\nbNiIIQYzZo5ylGMc42M+xpaZAunJYGk0unHFJ1CrYAMbAo6U2Z65zGUGMzjJSRJIoB/9WhI/+MD7\n+Ai7HV59ldJLerEb7/MVfcZnzGGO64mhED1edrYRSbOkxAj/Hh8P5la3QGfOwMcfw7XXGq/9lMHT\nr/+Z9EbjZtLoEmbcnMU12hn7xmGOfNsoyzXUkEoqJkxUUcVWtvJDfkgFFRRRRCaZjGVsm0O8wito\nNLHEurqbKcfyD/7BBOQ5uwiBnTvh2DG46CKjItbcbDz4iIkxrtlXX922nLRS/toK0q1tuxrGVTdy\n4p2/kD7gKs/Wc3cvvwwzZ3pPc/M6r3td30gjr/EawxlOE03kked6uOu87n/ERwxjWLvHUCiu4ipm\nM5tTnCKJpKC7Q2eRxXKWc45zVFNNHnmurp82bHzMx163O8IRDnLQ6EEUIj7/17TWcwCUUkO01h5B\nVZRSQ7xvJYTo0fbu9Z1WVNTu5pWnvySh3Hfwkbo9m6GdCt4RWk5Xrce7VFHFPva5otPFOJbW2x91\nTttpUm26qLjvPxSc4wLbcLTSebVnj998WLFynOOMZGQIcihEN5KTYwRK8nXTumdPSwXPTxmMPVUG\njm7QrZ+8D9rjOQapiSZXS/0RjjCPee12tTzGMdfvpladrVpP8SJEh7lfs+PiPNOqquDkyXbHnSfu\n9X0tMu3dDzUDfG988qTRfTPDd88YGzaPAEStHeCAKzKtQnm0wAEc5SgaHfCwhUQSvV+Tg5DjWNyd\n4Yzf4GpHOBLSCl4gQVa8PU6P/IQOQojOLznZd1qK7wHMTnFJ6UZrmQ+m5PYnCvXXlUKhSMP/xK0J\njsVfekT4+1smJ7ebj0C6lAjR45jNRuudL+7lzk8ZtMf5btVoTPZ8aOR+YxlouWx9k+rOX6AWIYLi\n7zoTSDrQnOS7POnkREjy05MkJsYI5uKHGXObB7HuArlmd4Yx6e2V/VDfW/gbgzdSKXUTkK6U+qrb\nzx0QqTscIUSn09AA774L//Vf8Nhjnk+5Z8/2vZ2/NIfUzAFUjx3sPVEp+sxe3O4+JjDB58n8Yi5m\nOMN9drswY+ZSLvU7oDqQwdYh0c7f8hIuIZ54rFippJJyyqmiimaaySabAfh5aipET2Uyee8S1tAA\nZ89CcTG89RbU1votgyq/JWiUCZPHGWfPnJYn92bMHpU1b10rD3CAV3mV53meDWzAgoWpTPV57Eu5\n1GeaEEGZNcv3Q9X8fKPFux3W2Zeh7JBQbSX9dAPpZxqJr7WBhrjZC2HOHN8bT53abgUP8NuyNYc5\nfq/Lke7OXEIJb/AGz/Ecq1lNDUaLfhZZDMb7/Y0JE+MYF9J8+GvBGwFcC2QAX3H7mQDcE9JcCCG6\nhrIyuP9+ePJJYxLU1avhJz+BZ54x0i+7zPtN0ciR8LWvBXSIPvf9gsZerVr7lKLurlvI7D2q3e2z\nyOI6rmtTyUsn3TVJ6tf5epvxaSZMLGIRSSQxhzkMpG1o9QlMiNzN1ZQpsHBh2/VDh8Lixa7oW2c5\nSy21NNBADTWc5SwzmNEpnlgK0SndfrsRVdOpvNwYm2cywcGD8PTTxnlu0CBY0HZCY4YOZdjy/6Fq\nYsu4HmcLw+GJGRRfOZJYYlEoMsl0lcWpTGU0oz129Tqv8yRPso1t7GY3b/AGj/IoV3O114c0OeRw\nj9yCiVDJyTGiyLau5GVmwre+FdAuhl19P8n1kH62kYRaGwk1VjJON5BgTmHozNuNa9Ytt7TdsE8f\nuOuugI7xLb5FKm178OSTz3zmM5KRTGNam/S+9GUhXq6jYbKJTTzKo2xgA0UU8QEf8Dt+RwklAHyN\nr7X5HArFjdwY8knQldb+o7YopaZprTeF9KhBKigo0Nu3b49mFlyUCm3vVK0XReW4gQo0f4EK9HME\nctxQ7qu7KygoICRl6Pe/9x316re/NaZEACgsbAmHPHEiTJ/e7kBtd431FRR/8gy2L/dCejo58xaT\nN3hKUFktpZStbKWOOvrTnwIKPLpI1FHHVrZSSinppDOZyR595ptppogi9rPf1bIXyv7xASsqgk8/\nNaKaTZhghJWOjaWRRv6T/6Seeuqow4aNWGJJJpkccniIh6SSF0IhK0Oic7BYjHL16afGg6qUlLYh\n4SdPNgJD+SiDdruNY9tWUrtlNQBqyjSOT8rlvKmKHHJIIonjHCeOOMYzvs24ngMc4Eme9Jq9SUxi\nEYv4iI/YwAbs2F3h1P113+zspBx1UsePw0cfGePhhg2DuXMDGlYBwEsvoV95GUtdBbquFq0gJiWd\nuMR01PcegHnzjPcdPAiffGK0jufnGw+DA2i9c2qggTd5kyKKiCeeOcxhFrM83nOYwxRSiAULIxjB\nWMZGrEtzNdX8il95nY+2H/14gAcAY1L17WznJCdJJZVJTPKYv7c9SqkdWuuC9t4XyB3XrUqp1v2i\nqoDtWuu3As6REKJra2oywoX7sm5dSwVv3DjXXG0dkZCUxchr/93oQ9BBfejD9VzvMz2ZZObgu+tI\nDDGMcyxRNWZMS3h3N3vZSxNNmDG3CQNdQQXHOMYgBkUok0J0MfHxcMUVRpAHX93Qtm0zbkZ9lEGT\nyWxE9XUL/BT4bJ+wi10+0wopZBGLWOhYhAirAQMCbk1rY+1alDKRkJINreeqXbu2pYI3bJjx00GJ\nJLLYsfgy1LFEw+d87rVyB0a3TWd07ySS2lRMwyGQICvxwDjgoONnLJAF3KWUejSMeRNCdCY2m/Hj\nS3195PIiaKTxgtKFEHjOe9ea1karXZg4o/l645ziRYhOz18Zauw51yF/5TmQ9FALpIJ3MTBXa/0n\nrfWfgPnAKOBGwEvndCFEt/T/2Tvv+Kiq9A8/Z2Yy6YWQEAi9R5qUAAERgiAgRUUFdWUVRcXurm0t\nWBbXgmV/tnVd175rL4isqCigoYhSRXrvLYX0MpmZ8/vjZDKZTA2kTJLz5DOfZO6Ze++ZyZx7z3ve\n9/2+ERHQxUeFFA8r3E0eKVUB9w0boKDA/+trEV+rlCGEeMwh1Gg01ejjxedmtSoFwdzcOju1rzHc\nkY6V0u8aTVCQleWsm1cVX/f+ZjQv8DWeI4ioURhmbRDI1aMtEIkKy6Ti72QppU0IUb/mqEajaVj+\n8AdVLLh67m779kqNq0s4bdMAACAASURBVDmxYwe88IISZwBVQ2jKFLj6ap+lHmqL1rRmAAM8hnmN\nYpQuk6DRBMLw4Wrham9FLS8pITMT8vMhKQn+/GfVfvfdrsIstUAqqSxnOVlkuWwXCB2WqQkeLBb4\nxz9UuKXj3t+zJ9xzjxoj06bB2rXu3u7YWJg0qd6721B0pjMppLCd7W5tYxlb7+VNAvHgPQNsFEK8\nLYR4B9gAPCeEiAR+qMvOaTSaIGPIEHj4YWccfWgojB0LTz7pu7ZUUyM3Fx57zGncgboJfv45zJ9f\nb924gisYxziiUMnw8cQzlalMYEK99UGjadSYTPD44zBhghJ8yM5WE9XWrSG6Qu1u71545JFaD9cM\nJZRbuIVUUiu9dR3pyA3c0DCCThqNJ157DZYtc13Y3bEDHn0UbDa1APLEE3D22Wpx02BQCydPPw0t\nWzZcvxuAa7iGdNIrF1gTSWQ60+sl5646fj14Uso3hRCLgCGAAB6UUh6taL63Ljun0WiCkNRU9bBY\n1OTIEMg6URPj+++hqMhz28KFcPHF9fK5GDEyjnGcz/mVKpoajaaGREfDLbeo8gnXXKOubdXJzlYK\nwmPH1uqpY4jhCq5gOtOxYdNjWBNc5OUpz50njh5VQkRpadCjh1ooKS9X9z6j98LkTZkQQpjMZCYx\nqcHvyYHOQAxAJpADdBNCNLNYLI1G44bZ3DyNO4BDh7y3ZWd7N/7qCIHQE0ON5kzJzvZs3DnwNe7P\nEAMGPYY1wcfRo77F1Q4edH0eEtJsjbuqBMM92a8HTwgxD7gc2AKVkk4SyKjDfmk0Gk3w4k1WHZQw\nQ0SE93aNRhOctGihJqc2m+d2X+Neo2mKJCSosEtvNbP1mAhaAhFZuRjoKaXUgioajYZjHGMZy9jL\nXsIIYxCDGMGIBl+tqlfOP5/yhV+wYpiddalQGibovFdy3lJJm7Rx9buC+eOP8M03cPKkEoG4+GJV\njFmj0QSG1Qpff62KMJ86pUq+tGjhmlccFgajRrGd7WSQwQlO0IIWDGc4A1HjbT3rWcUqTnGKJJIY\nyUhSSGmgN6XR1AKJiTB4MPz6q3tbTIzKtQNOcYplLGM72zFgoB/9SCedCCoWO8vLVfrCsmUqwuWs\ns+DSS30rc1fjJCdZxjJ2sQszZgYwgJGMJJRmlP9fAwIx8PYCIVDzAg5CiKuBawAjcJWU8kjF9j7A\na6icvpullJtqemyNRlP/HOQgr/EaFpxhTF/zNTvZyQ3cgCHgqO/Gjb11K956sT+7in8Duwps2DBQ\nsHl4DDe1Prf+ChS8+64SdnGQnQ0bN8Ktt8J4rcKn0fhFSnjqKZVLBMoDX1CgCqAnJ0N4uMrR+8tf\n+Dl6M5/jHG955LGf/ZzgBBLJUpa6tO1kJ5dxGWmk1fe70mhqj9tvh7/9TQmrOIiLg4cegtBQcsjh\nZV6mAGepoKUsZQtbuI3bCLeHKhGW9eud+y9fDr/8AnPnQq9efrtwjGP8g3+41Hf9ju/YznZu4qbm\ntcAcIIEYeMUoFc0lVDHypJR3+NpJCNEWGCWlHOOh+XHgSlTI56vARQH3WKPRNBhf87WLcedgF7vY\nwhb60jxq3mxhC7vaFIC9ExQWKSMvPIzy0FAW8T03063uO5GZCV984bnt3Xdh9GiVJ6nRaLyzYYPT\nuAPlfW/bVhVobtFCLZYMHUp5qIFFzPV4iB/4ATt2jLh77r/mawYxSE9ANY2X2Fh49lnYvBn27VPK\nmIMHq3w7YAlLXIw7Byc4wWpWM3ptpKtx58BiUfeqefP8duEbvnEx7hwc4AAb2chgBtf8fTVxAjHw\nvqp41JTxgLHCMNwK/ElK6Qhsj5dSHgIQQsSexrE1TRwhPm3oLmiqUUYZe9jjtX0rW5uNgbeVreoP\ngwFiol3a9rCHMsrqPmxk3TrveRGFhbB1K/TvX7d90GgaO55Cz0CFZJaUQL9+EBrKPnZSQonHlxah\nRJUc5UqqUkIJ+9inyx5oGj99+qhHNSrvhx7YwhZGr/FRF3bbNuUxj472+hI7draxzWv7VrZqA88D\ngZRJeFcIEQ50kFLu8Pf6KiQBZinlmAqhlosAx3Jz1TgujzFdQogbgRsBOnToUIPTajSaukBU/Eg8\nGxWCui/uHSz4eq/19jn4K6beXBVONZqaUAvjyN+Yb07XRk3zw9f324ABv19/P2NQIDBgwIZn8SM9\nvjzj98olhJgCbAS+rXjeXwgRiEcvD/ip4u+lwFlV2uxe/q5ESvm6lDJVSpmaqFV6NJoGx4yZnvT0\n2t5cvHfgfK82q4XCohPkFx2jrDQfgB70qJn3butW+PhjWLBA5dAFypAh3sVcYmNVErtG09zZtw8+\n/VTlqh496t5eIRLhkV69lJAE0IUuRBGFwWqn28qTDP1wH2ctOYapzEZkxU91LFgoo4zDHCaX3Np6\nRxpNUFF57y8rg5xTSqiovNzZNmyY95379YMod893VQSC3vQGoOW2TPp+vI2UBTsJzypWh6Dfmb+J\nJkggIZqPoYqc/wggpdwohOgcwH6rgBsq/u4P7KvSliOEaIcy7vIC7axGo2lYJjGJAxxwC1XqQx+f\nxl9TI4UUEk7Y2Rh7CIwOj2YREfmnGGu+HsICOIjFAk8+6Zqb8PbbcMMNMGmS//1btICrroL33nPd\nLgRcf31lfoRG0yyx2+GVV+CHH5zb3ntPKfddfbVzW9++MHIkZFSr/BQWBrNmVT41YeLSo8MxPPoY\nMSec179z397NqYdvpahnOxaxCACJJIccSighnni+5msWsYhJTCKd9Lp4txpNgzHGPppt+/5Hjr3K\nAmV2Nu0NHRnSZQj0N8OIEbBiheuO4eFw7bUBneN8Szpt5r1BlzU5ldvk23vZOGsQfac0n8XlmhCI\ngWeVUuYJVxeql8SPKi9QhmCJEOJHIAv4UAjxkJTyCeBR4COU4/bWmndbo9E0BG1ow5/5Mz/xE3vZ\nSzjhDGQgQxjSbBQ0ATKP/U5WwV5a2KAoEqQBzGUQnV/OxpNv0znNf9I4H3zgnnhut8PrryvvWyDy\n0ZddBp06OcskdOgAU6ZAipZm1zRzvv3W1bgDlbP62WfQsycMHercftddKl916VKVv5qSAhddpMRW\nqtD3uW8pOxFNIYJyyjFhIiE/ih5PLYU33iDZlMwqVlXmCyWSWOnNl0j+x//oVPGj0TQVYhat4I73\nM1kxUrDtLDDaoN9vMGzVPkLv/x1SU+Gee2DAADXGioqUd/zCC5VSbQDkfPwqPdfkY8eAHYkADHYD\nQ9/4nZMpq2jT/dy6fZONkEAMvM1CiD+gBFO6A3egvHN+kVLeU23TExXbNwEjatJRjUYTHMQTz1Sm\nNnQ3GpRfD3wKLSGySD2qsjZmFxdayzCafIRpSgnff++9bfFiuOmmwDqTmqoeGo3GyeLFvtuqGngG\nA4wdqx7e2LsXdu8mtOLHhZwcWLuWlLQ0Ukjhb/zNa67yL/yiDTxN0+K774gqggnfSCZ8495Gaqoa\nY+efrx6nQdjiDAQCY3WtWinJXvyBNvA8EMiS++1Ab1SJhA+BfOBPddkpjUajCWbyfUSWl4XYKSvx\nE3leXq6Uw7yRk+O9TaPR+MdXPmtNcl0d+BuTVY6Z5+P64KtNo2mU+BobtXAvs9utmPM8K9gCKudP\n44ZfA09KWSylfEhKObhC9OQhKaV7MQqNRqOpb/Lz1cMLBbmHOLpnBVZr7V6y2po6eW2LLzITHpXg\n+wBmM7Rr5709kPDMJkhxsZXMzDKkt/IPQUx2dhkFBeUN3Y3mg92ujKqyivK8BQWQWyFkUl6uQr/s\nHjXcTm98dezoW+2vyjHb4RzbFixYsVY+b4tr2KfGN7m5FvLy9LjCYlHfd6vV/2u9ceQIHD5ce31y\n4Gs81cK9zGAwUdTRu9ii6NLV5XkOORzFg6BSM8NriKYQYiE+cu2klBfWSY80Go3GH9u3K0GSbRW1\ncc46SyVrV+SeZR35nd1/v5mkbTkYrZLdsSZOpZ/NsFv/WyunH9xzBsv2r6IwzP1mO8p2LkIEEBxx\nySXw0kvu26OiYPz4Wuhl4yE7u4znn9/J0qUnsVolbduGc8MNnZk8ObD8jIZk+fJMXn11D7t2FWIw\nQFpaS+65pycdOkQ0dNeaLl9+qR45OcqIM5nUxFcIpSxrs6kadsePKxXMli2dxllIiMr9qSmJiTBq\nFPz4o3tbr14uqrWjGc1LvEQeeZXS7iGEkEwyw/ChKKipZNOmXF54YRebNimP54ABcfzpT93p3buZ\nlU4uK1P3uqVLobRUKSRPmQLTpvkv8eHghx/gqafg2DH1PCkJ7r4bJk+unT5ecgls2uRelzUkRPW1\nFjBeMg3+71W37eWRobQffz0A29nO67xeadxFE81FXMRkaul9NjJ85eA9V2+90Gg0mkA5eBAefti5\ncg/K0Hv4YXj+eazJrTj6wB9JPu5sj8izErFgHausVzL8zg/PuAsR0a2YHf8Anxx9gUNxKtQy3GJk\ndPFQzhl8e2AHGTsWiovhk0+cXsguXeD225VCZjPBYrFz003r2bfPmcx45EgJjz22FSlhypTgNfJ+\n/TWHu+/+rdJRZLfDqlXZ3HDDWj78MI34eHPDdrAp8vHH8P776u/ycjh0SH3wYWFqQukIfW7bVhll\n2dnK4EtKUl692bOVMNHpcNtt6hzLljkNyrQ0tb0KmWSST75L3S4rVk5ykjLKqh9VU43duwu5+eb1\nlJU5PbAbNuRy883ree+9IXTq5F6Sosny9NOwbp3zeV4e/Pe/SqgkEAXKtWvhz39WY8DBiRNw//3K\nWDy3FnLXBgxQ53j3XWeoctu2aqzVUh3r7qNvYHtRPiEffY45X4VrFnVuRextc4hp2ZmTnORJnsSC\npXKfAgr4L/8lhBDG07wWTcGHgSel/Mlbm0aj0TQYX37patw5KCuD+fPZmHSIVsc9T6ISfvody+x8\nzGExZ9yNNm0HcWfb/5B5/HdKSk/RpsNAQkJ91/Nx48ILYcIEOHBASUb7Cttsonz//QkX464qb7yx\nj8mT2yACXamuZ954Y6/HKMDsbAtffHGY669vnqG2dUZpKcyf73yel+cMwywpUQsmjsLkubnQpo3y\niFssMGcODB4cuNfDE2azWoC55hrlDUlIUN7BasxnPgYMhBCCRCIqfmzY+IRPuJM7T78PzYD33tvv\nYtw5KC628d//HmDOnF4N0KsGYNcuV+OuKl9/rVSUo6N9H+PFF12NOwd2u4ogqQ0DDyA9XR1r3z7l\nUT/dRRQfpEy+G+v4W8jcvxZTWBTt2w+obPuYj12Mu6osYIE28DQajSbo2brVe9u2bVhy9nptDiu0\ncWjrYroOvKzWupPY+gxr8JjN0L177XSmEfLbb94LQB85UkJWloXExBoUjq9HfvvNu2CGrzbNaXLw\noDLiHJRWya2121WImMPAK6kQZRACQkOVx6O2FgpiYioLoHsiiyx16oqfquxzKQms8YQeVxU4UhA8\nYbHAnj2qvIcvdu/23rZ//2l1yytGI3TrVrvHrIYpJNyjYqavcZVDDnbszaqUEwSmoqnRaDTBQ5QP\nL1lUFESEe28XEBHfvvb7pDltoqO9rzMajYLISKPX9oYmJsZ73321aU6TyGqheYYqU5jqxpux2vfG\nn6ejFgkhxGtbGGH11o/Gih5XFfi610Fg3+lwH/fDsKbzXfQ1rkyYmp1xB9qDp9FogpB88lnNag5y\nkGiiSSWVrlQoZY0ZAzt2qFyboorQvshIdbMbPZp2Xcchl96J8CARldk5ikGdKupfbd0Kr7yiVjFb\ntlRhV1XrYJWVwZIlqhi5yQTnnKMeVSeV69apfJziYujdG8aNq9eJZFNg8uRk3nvvgFt+PsDo0YlE\nRNTfbWrPnkI+//wwR46U0LFjJJdd1s6nWMrkycn85z8HPLZNmtTG5bmUkhUrsvjmm+OUlNhITW3B\nRRe1JSqqGd+GS0uVAMTGjWqMjRgBw4e7jrGqtG2rhJS2b1fPo6OdHj2DQeXHlZerkDSDQeUaWa3K\n2zF3rqrBNXu28ppXZ+NGJWRRUKAEU8aPVzlKNeAIR1jNaiKJJIssjBjdPHijGV2jYzZHJk9OZtu2\nHR7bqo8rXxw9WsJnnx1mz55CkpLCuOSStqSknHl4fr0xbBi89poKRc7LU99ls1l9L7t1g65d/R9j\nwgQl0mK3O8OZDQb1OO885+tee03lthYVqby5OXMaVX3VMYxhL56jd3rhDOk9ylFWs5occkgiiWEM\nIwE/qteNFOFNjjqYVDRTU1Pl2rVr6+t0PhHi01o9npTTGuS8gRJo/wKlId5Hbb+HxkhqairBMob8\ncYxjvMZrFOGalzWu4oeSEqXadaDaxLpDB/jiC4iI4Od/Xk3yl2tcjLyiOBMRjz1Hx17jYeFCePBB\n99yEK69UYi0FBfDQQ+4hLEOGwAMPKO/Aa6/BokWu7QkJSq0sKenMPoRmxvvvH+CFF3a5GHkdOkTw\n+uuDSEion/DMxYuP8/DDW7DZnJ0wmw0880w/RoxI8DiGiout3HbbhkqlPwdXXtmeu+/u6bJt7tyt\nfPWVq3R327bh/Pvfg2jVqumspAdMfr4aS4cOuW4fNgz+8hfvRt7hw2rymZOjQjJPnlTjNSlJGXLH\njytvXkiIWqSRUhmPIRVetSrXiUrefts1tw8gLk6N5baBlTVYwxo+4RMkEhs2DnMYGzYX70Ff+vIQ\nDwV0vKZKIPcim01y772/kZGR5bJ97NhWPPlkXwwG/6G269ef4s47N1JS4rzGGwxw//0pXHJJI8p1\nfuUV+Oc/XRUqTSaYNw8uuMD//sXFMHKkez262Fi1OBkXpxQ5q+f6GQzw+OPqnthIeJqn2chGl21x\nxPE0TxNHHOtYx0d8hKxi2pgwMZOZpJBS3909bYQQ66SUfq1vXwbeKF871qcIizbwtIF3JmgDr3EZ\neP/gH17j6e/jPlp9sUJNyEpKXD144eEwcyZceikAezfO5/jC1xGFxdCpM32vepKomApFxrQ0z/Xz\nhFBG29Kl7hM+B3/6E7RqpQxETwwbpiaumhqxd28hX399jPx8K2efHcu4ca0xm+snrKa42MoFFyyn\nqMhdjCAhwcz//jeCtLQhHseQzSb56adMVq/OJizMyLhxSfTp4+r5WbUqizvu2Oi2LyiPxF//2rt2\n3khj4t//Vgstnrj7blWSwBslJWqM7t2rvO/t2sGWLfDRR2oMW62QleUUYzIYlOfDEcb5xz86x+iu\nXep8nhg4EB57zO9bKaGEucylHGe9Njt28snHgoWe9GQMY0gjze+xmjqB3ouklPzySw4//ZSJEJCe\n3oohQ+IDOoeUkqlTV3H4sHtxbLPZwDffnEtsrPcw2qAhP18pZZaUqL8dHrzoaLXw8K9/+c8rXbAA\n3nxTGXh5FQtRsbHKsLv6ajV+brjB874REbB5c+2+pzrmV37lB37AgoX+9GciEzFjppRSHudxjyq2\nscTyIA9iJHjTAaoSqIGnVTQ1Gk3QkEuuz2TpDWxgfEaGuqlFRLiuwgMsX15p4HXpP5Uu/ae6H+TH\nH70XR5cS3ntPTRy9kZHh20P3yy9qYhkanMIgwUqXLlHcfnvDiM2sXJnt0bgDyMqysG7dKa/7Go2C\n885rxXnntfL6msWLT3ht+/77Ezz6aK+AvBJNiowM322+DLzwcJg0yX3bN984n5844Zz8SqnC0xx5\neT/84DTwli/3fp4NG5R30E/Y9Ra2uBh3AAYMxBEHwJVcSRe0ompNEEKQltaStDR3lVJ/bN2a79G4\nA1WWZenSk0yd2ggKzq9erUKOTSaIr2bcHj8OO3dCz56e93Xg+H7Hx7sfIyNDHccbxcXw00++x2KQ\nMaTipzpb2eq1REkeeexjH92oW4GY+sZv8L8QojvwFNALnFmMUsomdbVqKA+ZRhPod685eCKrT5Kq\nY8HiuUSCg6qqet5w1MnyRnGx/3P4Oo/drm7K2sBrNFQN4zqd9jM5vsVix2aTzc/AO9Nx7G8fL9FJ\ngBqfgZxLShX26Qdv8uyBtmtql7oez/WGrzECgY0TX68pLfV/juqhnY2U5jhGA4l/eRv4J2AFRgPv\nAf+py05pNJrmSUtaEk/FKmO5FQqLoKS0Mhu4O93h7LO9H6CqZLSUSmZ69WrIzHRuT0935uN4YsIE\n5zlsNhUGWlzsnDD27+88j5QqfKawUIXPgEp+96d+pgkqBg9u4TXly2w2MGDAmRWeHzrUe2jZwIFx\nhIQ0P4U3r+O4pER5zE6e9L3/iRNqbO/cqZ737ev00FksyutR1ciT0vm8b5XSJr5k5tu391jnrjrd\n6e4mpuLAjJlOdPJ7DE3t0bt3rE/13bS0wEI9Gxxf97rwcFfvXVaWGg9bt7p+7319vwcMUKJlDhxj\nxLG/yeTuKW+k+BqjIYTQmc713KO6J5C7SriUcgkqX++AlPIx4Dw/+2g0Gk2NMWBgvHWsmrwd2K+K\nCR8+DAcP0DU/kZ70hKlTPdegio5WbaBCLG+5RYk1PPkkXH89vPCCmvhFR8NFF3nuQEqKCkeZPl0Z\nbfsr+nD0qCrgajDAxIlKTTM+Xgm9HDmiwlz271c32UaUlK5RtGkTzqWXehZemDGjwxnn60yc2IYu\nXSLdtptMgptuCkAJrylyxRWuapYWixpPJ06osLIbboDnn3f3oJWVwTPPwI03qrF9zz1wxx1qvJ5/\nvhqPBw86J6mOCatDTdNohLvuch5v6FDo0cO9f0KoXL0ASCSRVDynxJzHebo0Qj0THm5k1izPE/YJ\nE1rTpUsjWYDr0EEtSHpi+nRV5sBqVQXLZ81S4+H+++Hmm5317y66yPP9MipK3S//9CdlLFb3eEup\n7oWeFGcbIS1p6TF0E5SybTg+ykk0UgIx8EqFEAZglxDiNiHEVMB7soFGo9GcAYPe2cTMF/Jof1Ai\ngKgiSP/Owqw//Y4oKVX5b08/rSTVTSZnCYN581RbaSk8+qia6DmQUokyvPOOej53rpJLb1HhmQkP\nV4pkH3ygnh86pI7rqCEkhBJyKS9XRlxRkQr1jIx0qv2FhamcwH26kHFj5N57e/LnP3cnOVlNxjt2\njOD++1O45ZYzz8sICzPy+uuDuPTStkRGGjEYYMiQeP75z4EMHHhm3sFGS9euSqVy0CD1/NgxNYba\ntVNGmJQq/+eNN1z3e+01WLHCdUK6f78a8wcOqDHp8NAL4czDE0Idv3Vrp7cd1LnmzoULL1STXiHU\nQs8jjygxpgCZxjQmMrEy764VrZjGNMYy1s+emrrg6qs78eijvejaNRIhoFWrUG65pWvjEzS6806Y\nMUMpNIPyKt9xR2WuOf/5j8oprToejh5V4kDFxZCYqO6N556rxoXJpITAnn4akpMhO9t7mOaePXX6\n1uqbS7mUSUyqjBJqRSsu5VKlzt0E8aqiWfkCIQYD24A44HEgFnhGSrm67runqA8VzWBXqQz2/gWK\nVtF0pz5y8BqNimZJiapHV5E3IME1qOKWW1QIpS8WL1bS0p4IDVUiKlWLv9rt7pLs99zjDP2S0lWp\nbOxYNUn873+d26q+Ji5OGZLeYv40QY+UElFNna42x5Cn4zdrliyBF1/03BYSAu++q4yvvDyllFu9\nvAmoSarDQw/K8181/6hdO2dh55Ej1Rj3RPXxfhpIpNdwsOZOQ9yLmsx4q/7dLCtTSpglngVlmD3b\nf4jl7bfD1197bhNCLYx27Hh6/Q1iGvMYPWMVTQdSyjUVBzQAd0gp/SgUaDQazWmSne0yKXO7/B4+\n7P8Yvl5TVqY8cO3bO7d5MsSqHqP6xODwYVcPQPXX5OYqD58ueN5oqevJYJOYbNYmVb3t1SkvV/l4\nUVEqFNqTcQfKuKsqnlJe7t7uMPB8XSNq4X/TWCeOTZUmM96qv4+cHO/GHQR2v/SlGC0l/PprkzTw\nmsMY9bvELIRIFUL8DmwCfhdC/CaEGFT3XdNoNM2O+HjfAiitW/s/RisfEeQhIe5S0Z7wVQahdWvf\n7dHR7uUbNBqNd3yNJ6PRGZ6WmOjdAHOEn1V9Xr09kPNpNI2FuDjfas2B3C/b+igXIQT061fzfmmC\nAr8ePOAt4BYp5XIAIcQIlLKmz/+6EKIT8AsqvNMipRxXpe0xYCpwCvhKSvn30+i7RqMJRvLz4dNP\nYeVKtYo+aBBMm+a8kdjtKhn822+VMEJCggrL/OMflWGUng5ffQWnTilvntGoksSTk70nnFclPR3e\nf99ZBL0qo0apHB1/TJrkOcxTCJWr17IlfPGFu5cAVAipMXgKpm7alMs77+znt9/yiI0NYcqUNlx1\nVcd6KyLenLBa7Xz44SG++uooOTkWeveOYebMTs03zy5QRo5UYZiFhc5tpaXqGhAWpkRRoqNV3qsj\nZ6hFC9Vmsaico5ISNT6zstTENibGGQ1gNruGZTcRZcCmzPHjpbz11j4yMjIRQpCensi113aiVatm\nJlgjJXz3narxmJWlhFcuvliJA4WHw+jRMH+++/0yKUm1+ePRR1UYZnXPuBBqHPmrs6cJWgK5wxc4\njDsAKeUKINAwze+llOlVjbsq3F3Rpo07jaapUFSkVLwWLFA3o7w8dfO4915nuMg11ygxk5wc5+Ts\nqaeUMh4oWefcXJUg7qgpl5urDLxAyg9ERcHDDzsFVBwMHqyU+QJh3DilMFY1fDM0VOUAnnWW8hLe\nf79rf4RQBmQQqWiuXp3NjTeuIyMji7y8cg4eLOYf/9jDPff8hr/8a03Nuf/+33nxxV3s21dEXl45\nq1Zlc9NN68jIyPS/c3MmPFyJmjhKEpSUqOuFlGqyun69muRu2KCe2+0qrLOgQAlBFBWpia3RqCa5\n+/crb16LFmrctmmjjms2K/VN7ZUIak6eLOXaa9fwxRdHyMqykJlZxqefHua669aSne2nbltT41//\ngldfVeJdBQWwZQs88YRaIAVV6iAvz/1+2batZ/XM6rRt69mjLaUyJDWNlkA8eL8KIf4FfIjSPLgc\n+FEIMRBASrne4MB3hgAAIABJREFUx76jhRDLgS+klP9XrW2eEOIUcI+UcuNp9F2j0QQb337rOe6/\nsBA++UQpea1b53nfDz6A225Tv9u0Uav0paVqohYRoSZymzdDnz7++9GrF7z5pjpXXh507w6da1jn\n5tprYcoUNak0myE11dX7N3gwvP22OkdhIfTu7TvcpQF46aVdWK3uhtyqVdn8/HM2w4cnNECvmibr\n15/ixx/dDTm7HV58cRcjRyY2QK8aESkpSjFz3TpV0sRgUMZZQYHTE1dSov5u21b9zstTrzOZnKGb\nZrP60I1GpbjZrp06ptGoxrDOjQ16/vOfA2Rmuhtyx4+X8sEHB7n99u4N0KsG4Ngx5bnzxH/+A+ed\np8S+Wrd23i+NRnWf2rcPNm70XQcPYNEi5f0LDVW55VKqY5hM6p59991aMKyREoiB5/h2PFpt+3CU\nweetJt4xoAdQBiwQQiyRUm6qaHtJSvmYEKI7KgT03Jp1W6PRBCXejDeAtWvVyqI3LBb4/HOn4EJo\nqHt+wZo1gRl4oG5QQ4cG9lpvJCSo2lreCA2F4cPP7Bx1RGZmGTt3FnptX7EiSxt4tciKFVle2w4c\nKObQoWLat9e5mT4xGpWHvLDQOfaLi11fU1ysPOdhYaosgsHgnpdnMKh6eo6C5mN1qYLGxMqV2V7b\nVqzIaj4G3rp17vXpHBQUwM8/q5I+4Pl+uXatfwPP4Qk0GNxr3hUVKZGVGpQL0QQPgahoBhDE63G/\nMpRxhxDif0AflFALUsqcit+7vKkbCSFuBG4E6NChw+l0QaPR1De+cs8cq4K+CPOTX+Fvf00lJpNv\nlbCQEL0qW5voz7uWMBqVweZtYlt1zuBLHTGI8mA1NcPXWGpW48jf/c5fEfJA7pf+XuNLxEUT1ASi\nopkkhHhTCPFNxfNeQohZAexXNQ7iHGBPlbaYit8JeDEypZSvSylTpZSpiYk6tEWjaRScc47vtiuu\n8D4pi4xUeW/dfBSW9nX8Zsrx46Xce+9vTJv2M3/96xYKC1UJhxYtzAwcGOd1vzFjfKiNViM318KH\nHx7klVd288MPJ7Ba7TXu5+bNebz22h5ef30ve/Z49yw2VsaO9a7M2KtXDK1bh7F+/Smuu24NV1zx\nM2+/vQ+73fk5Ll16ghkzfuEPf1jN/PkByJs3VSIi4Oyznc+r591WDZPu3t379WTAgNrvm6ZecFyb\n8vIs7NtXyL59ReTnl7u0BUJhoZW5c7cwbdrP3HXXRo4eLfa/UzAxdKj3hYqEBNWekuJ9/xEj/J9j\n+nTvbfHxehw1YgJZCnkH+A5Irni+E/hTAPudK4RYJ4RYBRyVUv4ihHi5ou1ZIcRKYCFwfw37rNFo\ngpXzzlO5aNVJSoLLL4cePWDiRPd2IeCOO9Rq4o03evbkTZoEXbrUfp8bMe+/f4CUlG959dU9LFp0\njGee2UGXLl+zYoXKBbvrrh5ERbmvoV18cTL9+nk3/qqSkZHJpEkreP75nbzzzn7uv/93Lr98NSdP\nlvrfGbDbJQ899DszZ67hjTf28frre7n88tU8//yOwN9oI6BHj2iuuKK92/aICCP33NODW25Zx8iR\ny/j440MsXHiM227bQJ8+i8nJsTBlygqmTFnJ/PlHWLDgKDNm/MqIEUtPy5BuElx3ndOwi4hw/l21\nBElMDDz/vGcp+OhoJbSkaZRMn96OQ4eK2bu3iNzccnJzLezZU8iRI8VMmxZYnvPPP2fTtesi5s3b\nwaJFx/jXv/Zy1lnf8e67++u287VJixYwY4b7dqNRFTE3GJRwWFWFWAcXXOB7sdRBWprKjfd0jvvu\nq3mfNUGD8KekJoRYI6UcLITYIKUcULFto5TST2Bv7ZGamirXrl1bp+cQ4tM6Pb43pJwW0OuCvX+B\n0hDvo7bfQ20T6GdyJu8jNTWVuh5DlVgs8MMPrmUSLrjAVdHro4/gww9VTl67dnDTTa43maNHYeFC\n2LULYmNhzJigzXVrKAoLrXTs+D9KS92NgMTEUPbvV1LwR4+W8NFHh9i0KZfY2BAmT05m7NhWARX/\nzcsrZ+LE5ZSVuZ9j+PCWvPSS/9Xdjz8+xLPPejbmnnmmH+edF/iKfEMS6BhatuwkCxeqMgm9esVw\n5ZUd2LAhlyuvXO3x9Z07R7Jvn4eSHsCNN3bh//6v3m61wUVmJvzvf0o1MDJSeSxOnlRCK336wOTJ\nalthIfz975CRoaTeU1NVWQWHcqYmqAhkHD3wwCZefHEXNpusVO9XIqmCv/wlhUcf9bCIWI2uXRdx\n/Lj7IpTZbGDfvonExfkJbwwmNm5UuXKZmaro+OTJroudx46psbJjh7rPjhlT82iXd96Bzz5T46lz\nZyV4NkiXvA5GhBDrpJSp/l4XSEJLkRCiJUpQBSFEGpB3hv3TaDRNFbNZeek8eeocXHGFengjOVmt\nUGq88uqruz0ad6AEVr755hgXXNCG5ORw7rqrx2md47vvjns07kCtkJ88Weq3LtWCBUe8tn355ZFG\nY+AFyujRrRg92vU93XCD9wnttm35hIYaPBrc8+cfab4GXmKiUrL1R1SUKrGgaTJ89tkRhBCYTMIt\nReyjjw75NfCWLj3h0bgDsFjsvPLKbubM6VVb3a17+vf3LZbSpk3gJYC8MXOmemiaDIEYeHcBXwFd\nK8IqE4HL6rRXtUhDeb40wUN9eMg0mvrm2DHfIZIHD555vkl2tsVrm5Rw6lS5XwPP1zFycry3NSV8\nvU9fQTSOfEqNpjlRUFDuo83/mDhwwPe1z9+1U6NpCvjNwauoczcKVRZhNtC7SrkDjUaj0TQAI0Z4\nL3EgBKSnn7k4Va9ezrBam01isdgrC6RHRhpp395D7oePY9SkrSnRp09s5d9Suhp1ZrNn7x1Ax466\nrIKm+dGpk1NIp/p46dIl0sMerqSnJ/os3TZiRMsz6Z5G0ygIREVzGhAupdwCXAx87ChyrtFoNJqG\n4dJL29Ghg2cDYOjQeHr2PHPj6dxzE+jQIYIjR0rYtauAPXsK2bWrkKysMqZPb0dEhP8gkKuv7uhx\nshUaavAoStIUefjhszCbDVgsdsrKbBUPOzab5KqrOmA0uht4QsA995xeaK1G05h54IEUpISyMtfx\nAvDQQ2f53b9z5yjOOcfzAljbtuFceWXHWu2vRhOMBKKi+bCUskAIMQIYD7wL/LNuu6XRaDQafyxb\nls6AAXGVSvFGo2D06FYsXBiAPHaAmEwCu11WrqI7VtRDQgKrMzZgQAueeaafi7eve/coXnppAF26\nRPnYs+nQsmUo3bpFuRlycXEhXHVVR159dSAtWzpFH2JjQ/jb3/roiaimWZKSEkPr1qEuC0MGA7Rp\nE0a3boFdM778cjhjxyZV1tQTAs4+O46lS0fVRZc1mqAjkBy8Cg0jJgH/lFIuEEI8Vndd0mg0Gk0g\ntG4dxooV53HgQBE7dxZw9tlxfnPiasKqVdns3VtE+/YRWK3K4+QIKfzww4NcfXVHwsL8G3rp6a0Y\nNSqR/fuLMRqFV89jU+XLL48gpQrVLC62YrVKoqNNCCF48819vP/+UGbM6MjPP2dTXm5nxIiWGHzF\nmGk0TZj33ttPfHwo8fGhFBaqfLyoqBAA3n//II884l8gJSzMxIIF55CVVcqGDbl06xZF587NY0FJ\no4HADLwjQoh/AWOBeUKIUALz/Gk0Go2mHujYMZKOHf3nptSU3393CiabTAYXRbuCAisHDhTTs2d0\nQMcSQtC5c+33sTGweXN+5d/Vw1p37CjAYrFjNhsYNkznBmk0Va87DsPOU1sgJCSEcf75HmolajRN\nnEAMtemoQucTpJS5QDxwb532SqPRaDQNTlxciM/22Fjf7RqFr88xMtJISIj/moQaTXPBV406f9ck\njUaj8OvBk1IWA19UeX4MOFaXndJoNBpNYGzenMfnnx/m2LFSunSJZNq09rXmKRs/vjUvv7wbi8W9\nFt7gwS1o3br2wkH9sXdvIZ9+eph9+4pITg7n0kvb0rt3rP8dG5DMzDI+++wwGzfmcuhQESEhBsrL\nVT5jRISRuLgQJk9uH1DReY2mKXHsWAmffnqYrVvziY83c9FFyQwdqjzYF16YzObNeeTllVeWRYiO\nNhEbG8KUKckN2W2NptEQSIimRqPRaIKQL744zFNPba8UQFm79hRffnmUZ5/t51VFribEx5t57LFe\nPPLIFqxWp1Z5cnIYDz9cf4WCly/P5L77NlFe7ujDKRYuPMqDD57F1Klt660fNWHv3kJuvHEdubnl\nSCkpLbWTlWXBaBSEhBgoKrJis0kuuSQ4+6/R1BVbt+Zzyy3rXeo8Ll58glmzOnPzzV2ZMCGJxx7b\n4lKsvKjISnS0ibFjWzVElzWaRofOpdNoNJpGSF5eOc89t9OtULbFYueJJ7Zht/uooF0Dxo1rzVdf\nncOtt3bl8svb89hjvfjss+EkJ/uvgVcb2GySJ57YVsW4U0gJzz23w2dR5Ibkued2kpur+lZYqIRV\nHAI10dEm2rcPJykpjLff3t+wHdVo6pmnn97uYtw5ePPNfezfX8RHHx0iLMxIx44RxMebadnSTKdO\nEZhMBj777HAD9FijaXxoA0+j0WgaIT/9lOkxdBLg5MkyNm7MrbVztWoVxrXXdubee3syeXIyZnP9\n3To2bDhFVpbFY1tZmZ2MjKx660ug5OWVs2ZNTuVzR5iZwSAwmZQHzyEesWTJiVozxjWaYKe83M7W\nrfle27///gTff38CUIJESUlhtGoVRni4qbJdo9H4Rxt4Go1G0wjxZtwF2t5Y8Pc+yspsPtsbAovF\n7uJZrW7AVW2zWqU28DTNBrufy5LFYvc55h0FzzUajW90Dp5GowlOjhyB3bshNhb69YNmWhfMYrGz\nenU2paU2UlPjiY9XCnNpafEIoYyF4mIr5eWS0FADYWFGIiON9OvnFCBZuzaHb789TqtWYVx3XSdM\nppp9lna7ZN26U2RnW0hJiaZTJ3cRl4yMTDIyMunQIYIZMzrUWh23s8+OIyLCSHGxuyFnMEBaWvCV\nFkhMVIXNd+8upLjYisVix2qVCGHHaoWCAguRkQbMZiMdO0ayaVMePXtG8csvp7DbJUOGxBMT0wjU\nAo8dg507ISYGzj672Y5RTeCEhhpITg7j6NFSj+3DhrWkqMjKoUOHKS21cfx4KUKomp+hoUaGD3eO\nd6vVzrvv7ufYsVLGj09i8GD3a8GuXQXs2VNEUlIoAwa0cGs/dcrCmjU5hIYaSUuLJzTUf13PoESP\nRU01tIGn0WiCC4sFXnwRVqxwujpatYL77oMePRq2b/XMkiUneOKJbeTnqxC/kBDBH/7Qgdtv7067\ndhGMGdOK11/f67KqHRFh5G9/60NEhIniYisTJixn/fpTlR/lnDm/88orA5k+vX1Afdi5s4C//GUT\nhw6VVG5LT0/k8cf7EB5uJCurlPHjl7N9e0Fl+/33/8677w6ulfpTkZEmZs3qzMsv73Zru+yydvWW\nC1hTZs3qxKWX/kxRkRUpXb12+fk28vOLEAIsFivTp/9MXl555SQ2NNTAjTd24ZprOjVY/31iscBL\nL8Hy5c43lpgI994LKSkN2zdN0HPrrd2YM2ezW/7wiBEJDBzYgjZtwpg3bzs5Oc782qwsCwkJZv7w\nhw4AzJ9/mJtucgq1PPXUdvr3j+O770YSFWUiP7+cBx74nV9+cYZKd+0aybPPnk2HDhEA/POfe3jv\nvf2V+b0xMSb+8pcUxo9vRHXz9FjUeEGb+BqNJrh46y3XmxXAyZPw179CcXHD9aue2bOnkAcf3Fxp\n3AGUl0veffcAn39+GLtdsnt3IXFx5grxDggLM9KihZktW1SOy4wZv7Ju3SmXj7KoyMaNN67j4EH/\nn2VZmY3bb9/gYtwB/PhjJs88sx2ASy752cW4A5WD9oc//OJRSOF0uOaaTsyd25uePaMxmw106hTB\nvff25N57e9bK8euC117bi8Vix2AQbhNZB1LC4cNlHDtWSnGxrfJzLiuz8/LLu1m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Ng0F5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\nQAGJJDKOcYx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RceOmjjpAexA/ghEtrqMLFzvYob9249ZH59RRx3a2cz3X041uftsmkcSN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\ndG/dSgu1n3uO1jjWIwpRMMDgc6a8vmMwYImJNDLpq5O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jx471scq0A5owgQ70upR6XJ06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\nJ+j40etbrm2MMcZYa7FmDbB0qft3h4NyRBw/Drz8Mg2Qsg6p0SGaUsplUsq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lKivt8DxV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E/4VRZZxBNv2d7e54SiiaY3vS3bhzCkXfMVwhdyyLHM5Kqg+KRu4mAGo6CYtkUR5ZMkRkKI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\nSillO3gnnJDtc50InHFGqwaOSAF07JhK9+5Nfa5LSoph1KicBo5IK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Vqjs3Tp\nXp+DYgAHD1Yyb553Mfj68tVXu/z+X7dsKWb5cjsIeeBAOfPnV8e1e7f/chKbNhVRVqblaZRSSoVO\n1HTwDh6s8LuusND/ulC+l6tena9ZrRUVDhyOwDWLiosrCfSUUP8eSqn6EWhfdThg+/bigK93DRap\n2h08GLjz05DHzdrey7W+5rHeX91SsO1JbXURlVJKqUMRNR28fv38Jybo3z+09y90755GkybeKa9d\ny3zVr2rXLoWYQIWtgKysRPLy/N+n17+/Jl9QKhoMG+Y/u2N2diKXXdbebyZFERgzprXvlcpLr17p\nXuVi3AVqG0It0DE6MTGGo4+2ybKysxNp27Y6eUpysv8SCqmpcaSnB5+kSymllKpN1HTwrrqqA4mJ\n3uE2axbP+ed717eri+TkWK64Is9reatWySQmxnoldBGBv/zlqKC2fe21vosTd+mS2mDJApRSdXPP\nPd381qAbN64L3bql+S2GPmhQpmbSPATp6fF+63OeempL8vK8awXWl44dUznlFN+1Vy+5pB1paba+\nqIh4HOvz8lI8Ovzuj2+4IXDBeqWUUupQRU0Hr1u3prz44rFVI6gxMTBiRDaTJvUnJyepllcfuquu\n6sA993QjN9duOy0tjquv7sDXX4+gT5+Mqga6detknnuuL5de2j6o7Z58cgueeKI3XbqkApCUFMPo\n0blMmNCP+Pio+XcodURLS0tg7twRDByYWTXgk52dyOOP92LcuK4AvP/+YC6+uB0pKfbqTVJSLGPH\ntuazz4aHLe5oddNNnbntti60aGFLETRrFs8f/9iB8eN7NHgsDz3Uk6uuyiMjw3bmWrZM4vbbu3oV\nfD/11JY8/ngvOndOJSEhlp49m5KeHlc1AyQlJY477ujKY4/1buhfQSmlVCMXNWUS3NPqlpRUEhMj\nAafthIoxhuLiSpKSPK/cFRVVUFLi8DuKH4yiogoSE2M9Ch0rVR+0TEL9KSmpoLCwwqP+nTuHw8Hu\n3WVkZCQQF6eDOIV9B8EAABL5SURBVHXh73jcEGruQw6HoaSkkuTk2Fprsbof6/ftK6OwsLKqkLxS\nR5JIKpMQaqHMotlY/iaqbhp9HTwR2QX8Gu44DkEW0HCp3RqW/m7R6VhgcY1l0fL7RkOc0RAjREec\nkRqjr30onCL17xRO+jfxFml/k0jbj1wi7e/kSzTECNERZzTECL7jbG+Mya7thVHRwYs2IrIwmN51\nNNLfrfGIlt83GuKMhhghOuKMhhgjgf6dvOnfxJv+TYITDX+naIgRoiPOaIgR6hanzhdSSimllFJK\nqUZCO3hKKaWUUkop1UhoB69+TAx3APVIf7fGI1p+32iIMxpihOiIMxpijAT6d/KmfxNv+jcJTjT8\nnaIhRoiOOKMhRqhDnHoPnlJKKaWUUko1EnoFTymllFJKKaUaCe3gKaWUUkoppVQjoR28EBKRo0Xk\nQhEZEO5YQkFEWjm/i4iMEZH/5/z94sIdWyiIyFkikhLuOJRSqjYi0lNEutdYNihc8UQiEbkx3DGE\nU2Nvs5VSwdN78OpIRD41xpwqIuOAUcBHwBAg3xjz5/BGVzci8oUxZqSI/B9QDHwBHAP0N8acH97o\n6k5EtgK/AjuAGcBMY8ye8EZVv0SkCdAM2GuMKQx3PEqp2onIk0ALoAJoDlxljNnlOkaHN7rwEJF5\ngOsERpzfewIrjDHDwxNVeDX2NlupujqSzoF0VKfuEpzfzwZONMY4gAkiMj+MMYWKw/m9pzHmJOfj\nWSLyZbgCCrFVxpgTRaQDcA4wQ0RKgf8aY14Ic2whJSIjgfuA/c6vNBFpCjxqjJkT1uDciMg4Y8wz\nItIHeA57AhcH/NkYMy+80VkicjTwNyANOwvCAHuBB4wxy8IZmzsRudAYM01E2gFPAi2BPdi/5c/h\njc6Klr9lBOhvjDkBQER6A9NF5K4wxxRuM4DewFRjzFwAEfnEGHNaWKMKr8beZodENBwbITqOj1H0\nt4z4c6BQn//oFbw6EpHtwCxgJNDFGFPsXH7Y1ecjhYhcBpwAxALxwFfYBrXEGBP1Jxci8qUx5sQa\ny1oAo40x0ZJCNyjOAYdTjDFFbsuaALOMMUPCF5kntxHoWcANxpi1IpKF7XRHRJzOKwfnG2O2uS3L\nBd42xgwLX2Se3P6W/wWeMMYsEJFuwERXZyHcouVvGW4isgA7gFjm/LkZ8Dq249cirMGFkYgkAFcD\nw4E3geuP5A5eY2+zQyUajo0QHcfHKPpbRvw5UKjPf/QKXt257oG4Dzt9BhFJdf4c1Ywxr4nI58Dv\nsNOD4oBJxpil4Y0sZB6vucAYs4PoqY9yKEqBXsB3bst6ASXhCcevTOdIW6YxZi2AMaZARCJtJEpq\n+TkSJItIRyDLGLMAwBizSkQi7d7raPhbhtttQAawE8AYs0dEzgLOC2tUYebs8L4gIhOBy4DG0jYd\nliOgzQ6VaDk2QuQfH6PlbxkN50AhPf/RDl4dGWN+9bGsEPgkDOGEnDFmKzAl3HHUB2PMZ+GOoQFd\nCvxZRB7HTvWoBJYBl4c1Km8zgGHAByKSYYzZ65xGsSLMcbm7DnjeeRXFNW3mN+CGsEbl7RfsQNOq\nGn/LSLrPNFr+lmFljPnex7JKYFoYwok4xpgKGmk7dagac5sdQtFwbIToOD5Gy98yGs6BQnr+o1M0\nlVJKKaWUUqqRiLRLqEqpBuTMthbxoiFOEfl/4Y4hGNEQZzTEqJRqXKLluBMNcUZDjBA15xaHFaN2\n8I5QIjJCRD4MdnkI3m+MiPRw+3muiNSahEZEWoUiHhHJFpFP67qdaCY+6mhhExNEFD9xvhWWYPwQ\nkX4ikiMisSIyWkROIQKnRUVLnO7E1jKbHO441OETkSudySBqe95UETk32OUhiOsvbo/zRCSoqU8i\nMk5E6jyVS0RuEpE/1HU7qu6cx8YbReSvzv9Lf6LnuBMNWdojLsZoOAcK5fmP3oOnGsoY4EPgUNPm\n3g68VNc3d9aM2iYiQ1w3Ah9JxK2OlohU1dECHsNmgI0IAeJ8lAiJU0QmY290LwWyga3YtMvnAteE\nMTQP0RCn+K9ldgE2M6KKTldi7xvZGuY4avoL9lgSNLFFwq8Cjg3B+78MLCDCB1kaOxF5GkgE5mDv\nIUsD/oC9L+uWMIbmwU+iEgHuB05u4HB8ioYYITrOgUJ9/qMdvAjlTN/6DtAGm/L4YWPM2yLSD3gK\nSAUKgCuNMdtEZC6wBBiIPVhdZYz5XkQGAs8AydjCp38wxqw6hBiew2YaigPGG2P+KyJXAmcBKUAn\nYIYx5m7na/4I3INt2NdgTy7fdD7/BBH5KzDW+RbnicgL2Oxwf/RT52Ms8FfntmOBv2MzhBngJWPM\ncyKy0fkeJ2JTQ1+D3Wk7A/8wxkxwbut94BJsA3ukiZY6WtEQZ2e3GJcbY851Po60WlPREKfWMotw\nIpIHfIrNPtcXWA1cbowp8tUeAUOA/sAbIlIMHA/cBZyJbYe+Aa41QSYAqKXN+w573K9qQ0QkBZgK\ndAdWAnnAjdiBjWQRWQL8BNwLxIrIS8BgIB9bIqe4RggjgcXORC6ISGdgAnbQpBKbybQt8CCwA1tY\n/D1gOXCr83ceY4xZ5/ybbRSRgb4S56gG088YU3MAaYaIfB2WaPwrBP6H7TC5D4T1DltE3qIhRoiO\nc4vQxmiM0a8I/MJ2bF5y+zkd23n5Bsh2LrsAeNn5eK7r+diR7xXOx2lAnPPxScC7zscjgA99vG/V\ncuyowaXOxxnYhr0JthFf74wpCfgV28DlAhuBTGes84Dnna+fCpzr9j5zgSedj08H5viIpQOwyO3n\n64F33X6fTOf3jdj6RwBPYzMjNcU2wDvdXt8aWB7u/22YPk8LgAS3n5sBHwE7wh1btMUJLHB7fKbb\n47nhji1K40zAZoWbhh0I+iTcMemXx/8nD3viNsT588vAnUG0R/3dtpHp9vg11+exZrvg9pyp2A5Z\nbe/h1YY4Y/u38/HR2PJF/Z0/F9b4vSqAY5w/v4OzvasRy4PAzW4/fwec7XychB3oHIEtQN0Ke2Uo\nH3jQ+ZxbgWfcXn8vcEe4/69H8hd2wGCC8zN2ivP7i+7/p0j4AhYB6T6Wzw53bNEUozOeqDi3CGWM\negUvci0H/ikif8d2uOaJyNHYBmu2iIC9srfN7TVvARhjvhaRNBHJwHZ0XhGRLthGOv4QYjgFOEtE\n7nT+nAS0cz7+3BizD0BEfgbaA1nAV8aY3c7l04GuAbb/nvP7ImxjW1MrYJfbzycBE4xzJNX1Pk4z\nnd+XA6nGmAPAAREpcaWbxdaQqvW+kEYqWupoRUOc14hIrDGm0hjzAVQVXH4qzHHVFBVxGq1lFg02\nm+qp7a9jp7F9SuD2yN2JInI3tjOUib2C9kEQ79utlvfw1YYMBf4PwBizQkSWBdj+BmPMEh/bcNcK\neyUQsSnLWxtjZji3X+JcDvCDcRakFpF1wCzn65djrzK67MReXVRhYoy5XUT6Yq8ud8V2zicaY34M\nb2Refo+deVVTJM1wiIYYITrOLUIao3bwIpQxZrVzasrpwGNiK9vPAH4yxhzv72U+fn4Y+NIYc7Zz\nqs3cQwhDgLGmxpROERmEnXrpUon9LB1qAU7XNlyvr6kY26l0j8fftB7Xthw1YnO4bTsJ3weiRs9E\nSR2taIjTGPOTj2VlVA8yRIRoidPFaC2zSOarbRECt0cAiEgS8AL2KtpmERmP53E94MtreQ9fbcih\ntEM127FkH89xb4cCbbtmu+PeJrm3b0dsOxRJnJ25SOvQeXANGPhYXtHQsfgTDTFC1JxbhDRGzaIZ\noZwZyIqMMa8D/8Te4L0KyBaR453PiReRnm4vu8C5fCiwz3mFLR07XQTs1MpD8RlwsziHJ50jXoF8\nj73PrpnzxvSxbusOYK8mHorVeI6ozgKuc24bEck8xO11JbIKZiulVDRo52p3gIuwGfICtUfux3tX\n56hARFKx0+GCVVub58t84Hzn83tg7yF3KReRQ5nFAvbqXWcAY8x+YIuIjHFuP9F5z9+h0HZIKVXv\ntIMXuXoB3ztvCL8X+Jtz9P1c4O8ishSbVGWw22v2iMg32Lnlf3QuewJ7BXABdnrLoXgYO6Vzmdh0\n0g8HerIxJh9739532OxUPwP7nKunAXeJyI8i0imYNzfGHATWOW9qB5gEbHLGsxS4+BB/nxOx85mV\nUkoFbyVwhXO6YybwYi3t0VRggrP9KsVmQl6OTXT1Q7BvGkSb58sL2E7hMmzCr2VUt0MTse3HG8HG\nAHyCZ0bXy4BbnNv/Bmh5CNsCm4RmziG+RimlDok4b+RTUc6ZUexOY8zCMMeRaowpdF5lm4G9IX5G\nHbZ3Njbj1V9DENvX2Cxpe+q6LaVcRGQEdt/7/SG+Lhd41jizW9ZYN9e5zYUi8hdjzKPO5XnYe3KP\nDmL744DdxphXDyUuH9u5CThojNHpk0egQ/nMRQJntuV4Y0yJczDxc6Crs7N4uNucAdxtjFlTx9j6\nArcbYy6ry3bUkcOZtXyWMSZgyRERmYrdT/9ziNu/Djtb7NUay/Oc2ztaRI4Bco0xHzvXjccmLPpn\nLdsW7P43xnn1+7CJyBzgPD1/C55ewVOhNt45arsC2IAdsT1szs7hxroGJSLZwFN6cFCRwhiz1Vfn\nzoe/1P4UT1JduysURVxfJoJqQylVixRgvvOK3wxshuXD7tw5/RmbbKWusoD7QrAddeS4knpMDmeM\nmRDEIOAx2HwQh+p0YGldO3dOr2GzLasgaQevkTDGjAj31TtnHHcaY44xxnQ3xtxiQnCJ2BgzKQTb\n2GWMqVNnU0UnEWkiIh+JyFIRWSEirntV+4nIVyKySEQ+E5FWzuVzReQZEfnG+fyBzuUDnct+dH7v\nVsv7fiy2lg3O19zvfPywiFwtInnOqc+ISLKITBORZSLyNs5kDyLyOM7aXW7TymJF5CUR+UlEZomI\nr8QQXrW7RGSO82+wWEQ6icgI5+//joisFpHHReQSEfleRJa7plIbY4qAja6/gzqyGGM2RsvVOwBj\nzAFjTH9jTB9jTG9jzCch2OYqY0yda6QZY2YbYzbWdTsqOjmP+b+IyCvOY/1/XPdw+mqPRORcqmtK\nLnG2E/eLyA/Otmmi8yqZv/fLEZFFzsd9RMSISDvnz+tEJEVExoszU7ozhqUi8i22dqQr8/JDwAXO\nGC5wbr6Hs61cLyL+BgAvAf7rFs/lzt97qYi85lw2VUReFJEvnds6QUReFpGVzquSLjOx9/+qIGkH\nTynV2J0KbHWe8B0NfCo20cJz2Bpc/bBXqR5xe00TY8xg7Ijhy85lvwDDjTF9gfux95sG8jUwTETS\nsPW2hjiXD8XWiHR3PXaaTG9nHP0AjDF/BoqdgyaXOJ/bBfiXMaYnNr33WLwNwaZ9d3nD+Zo+2HuY\nXJnP+mDrdPXC3lvU1RgzEHu/681ur18IDKvl91VKKRVYN2xJht7AfuAGf+2Rc7rlQuASZxtQjK0t\nPMDZliVjyxT4ZIzZCSQ526Bhzm0NE5H22BrBRTVeMgW4xT1rrfPq9/9v7+5dowqiMIw/Y2MjxI9C\ni4DBSvCjtIpBRfE/sNHGwloE0ULR2kaxEAs7tQlaqakkSIxKNI0kWoighUUKRWIjBMRjMbPhZt1k\ns2zIJjfPr1qyk3tnq+HcmXvea8BwmcNw+WovcBI4BFxPrZsXza9DKTdHugIcK+vQ+cq4beSHkhfI\n8Sm3gH3AgXI8lHL6anNKacdiv1cLWeBJqrtp4HhK6UZK6XDpLlvN13oPXAX6K/8znykJNDIl+4BH\nZdetsQAtZZzcnGGQ3NxnS3laO9AcPVLGPSz3nCI3hljM11hedtd3aJ3dVVnYJyNiJiLmgObsrup1\nN3KGpCStlOZMyUHar0dVR1NKb1NK0+SiqN069IZcaA2RH0oOkYu9BQ8ZU0p9wNaIGCt/etDmuiMR\nMRcRP8jrw84WY7ZHziSmzPVxGd+cY/y0nPaaJod6T0fEX3Je5kBlnOtQB8zBk1Rr0btMyUny8Zov\nwHPy+zfnWLizttQ9F2N2lyStT6udKTlOLuh2k49LXi73fNZ8+RZzW0qrLORmf1JKm0qx1m2OMbgO\ndcQdPEm1lnqUKVmOtnwjZ3JNkBfai/x/PBPycc7T5Z77gYOV78zukqR6WO1MyZfAGeBzKbR+kh92\nvq4OiohZ4FdZ86CsRy3m0IlPwJ7yeRQ41ThimTrMMS7vGu5iBZrubRQWeJLqrpeZkuPkIye/y+d+\nWhd4d8lHOKeAS8C7yndmd0lSPaxqpmSlqU+jSdArYHaRjuJngTulyUp1p+wFualKtcnKcowAR8o8\nPpLfLx8rv/FmB9eB/F76RKNxmNozB0+SKtIayZTsVjK7S5LWjLTOMiW7lXJn6vsRcWIFrnUbeBIR\no93PbGNwB0+S6snsLklST0TEDHCvdPHs1geLu864gydJkiRJNeEOniRJkiTVhAWeJEmSJNWEBZ4k\nSZIk1YQFniRJkiTVhAWeJEmSJNWEBZ4kSZIk1cQ/mz56MWMyxNUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110e53978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create dataframe from data in X_train\n",
    "# label the columns using the strings in iris_dataset.feature_names\n",
    "\n",
    "# create a scatter matrix from the dataframe, color by y_train\n",
    "pd.plotting.scatter_matrix(iris_dataframe, c=y_train, figsize=(15, 15), marker='o',\n",
    "                           hist_kwds={'bins': 20}, s=60, alpha=.8, cmap=mglearn.cm3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building your first model: k nearest neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Making predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_new.shape: (1, 4)\n"
     ]
    }
   ],
   "source": [
    "X_new = np.array([[5, 2.9, 1, 0.2]])\n",
    "print(\"X_new.shape: {}\".format(X_new.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: [0]\n",
      "Predicted target name: ['setosa']\n"
     ]
    }
   ],
   "source": [
    "prediction = knn.predict(X_new)\n",
    "print(\"Prediction: {}\".format(prediction))\n",
    "print(\"Predicted target name: {}\".format(\n",
    "       iris_dataset['target_names'][prediction]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set predictions:\n",
      " [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n",
      " 2]\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn.predict(X_test)\n",
    "print(\"Test set predictions:\\n {}\".format(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.97\n"
     ]
    }
   ],
   "source": [
    "print(\"Test set score: {:.2f}\".format(np.mean(y_pred == y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.97\n"
     ]
    }
   ],
   "source": [
    "print(\"Test set score: {:.2f}\".format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluating on training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "traing set score 1.0\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = knn.predict(X_train)\n",
    "print('traing set score {}'.format(np.mean(y_train_pred == y_train)))  # we train a knn with 1 neighbor# "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### using sklearn score function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary and Outlook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.97\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    iris_dataset['data'], iris_dataset['target'], random_state=0)\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "print(\"Test set score: {:.2f}\".format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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