bro_clustering.ipynb

{
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    "<div style=\"float: right; margin: 0px 0px 0px 0px\"><img src=\"images/clusters.png\" width=\"400px\"></div>\n",
    "\n",
    "# Clustering: Picking the 'K' hyperparameter\n",
    "The unsupervised machine learning technique of clustering data into similar groups can be useful and fairly efficient in most cases. The big trick is often how you pick the number of clusters to make (the K hyperparameter). \n",
    "The number of clusters may vary dramatically depending on the characteristics of the data, the different types of variables (numeric or categorical), how the data is normalized/encoded and the distance metric used.\n",
    "\n",
    "<div style=\"float: left; margin: 20px 50px 20px 20px\"><img src=\"images/picking.png\" width=\"100px\"></div>\n",
    "**For this notebook we're going to focus specifically on the following:**\n",
    "- Optimizing the number of clusters (K hyperparameter) using Silhouette Scoring\n",
    "- Utilizing an algorithm (DBSCAN) that automatically determines the number of clusters\n",
    "\n",
    "\n",
    "### Software\n",
    "- BroThon: https://github.com/Kitware/BroThon\n",
    "- Pandas: https://github.com/pandas-dev/pandas\n",
    "- Scikit-Learn: http://scikit-learn.org/stable/index.html\n",
    "\n",
    "<div style=\"float: right; margin: 20px 20px 20px 20px\"><img src=\"images/scikit.png\" width=\"200px\"></div>\n",
    "### Techniques\n",
    "- One Hot Encoding: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html\n",
    "- PCA: http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html\n",
    "- Kmeans: http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html\n",
    "- Silhouette Score: https://en.wikipedia.org/wiki/Silhouette_(clustering)\n",
    "- DBSCAN: https://en.wikipedia.org/wiki/DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting brothon\n",
      "  Using cached brothon-0.2.5-py2.py3-none-any.whl\n",
      "Collecting pandas\n",
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      "Collecting scipy (from brothon)\n",
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      "Successfully built watchdog PyYAML pathtools\n",
      "Installing collected packages: numpy, scipy, chardet, idna, certifi, urllib3, requests, PyYAML, argh, pathtools, watchdog, scikit-learn, six, python-dateutil, pytz, pandas, brothon\n",
      "Successfully installed PyYAML-3.11 argh brothon certifi-2017.7.27.1 chardet-3.0.4 idna-2.6 numpy pandas pathtools python-dateutil-2.6.1 pytz requests-2.18.4 scikit-learn scipy six-1.10.0 urllib3-1.22 watchdog\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using pip version 8.1.1, however version 9.0.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "pip3 install brothon pandas scikit-learn girder-client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named 'pandas'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-5bf2f5244026>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Third Party Imports\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensemble\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mIsolationForest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: No module named 'pandas'"
     ]
    }
   ],
   "source": [
    "# Third Party Imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.cluster import KMeans, DBSCAN\n",
    "\n",
    "# Local imports\n",
    "import brothon\n",
    "from brothon import bro_log_reader\n",
    "from brothon.analysis import dataframe_to_matrix\n",
    "\n",
    "# Good to print out versions of stuff\n",
    "print('BroThon: {:s}'.format(brothon.__version__))\n",
    "print('Pandas: {:s}'.format(pd.__version__))\n",
    "print('Scikit Learn Version:', sklearn.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pull data from the Girder Data Management Platform\n",
    "\n",
    "The most basic level of integration with Girder can happen directly inside your jupyter notebook -- you can use the [Girder python client library](http://girder.readthedocs.io/en/latest/python-client.html). Rather than referring to a file on the local filesystem as you normally would, you can instead point to data in Girder and download it locally. This makes your jupyter notebook portable, since it can dynamically fetch its own inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import girder_client\n",
    "\n",
    "# Create a Grider Client handle\n",
    "gc = girder_client.GirderClient(apiUrl='https://talonw4.afrl.hpc.mil/api/v1')\n",
    "\n",
    "# Give Girder our DoDRoot cert\n",
    "with gc.session() as session:\n",
    "    session.verify = './DoDRoot3.pem'\n",
    "\n",
    "    # Authenticate through HTTPS with our API Key\n",
    "    gc.authenticate(apiKey='isZW9ijxDPoOYn2vNmgbsfD84zaO6dxs9lyqQ3fl')\n",
    "    \n",
    "    # Now that we have an active girder connection we can look at available data and pull some down\n",
    "    \n",
    "    # TODO: Fix this\n",
    "    # print(list(gc.listResource('/collections')))\n",
    "    gc.downloadItem(itemId='5942de7caefb0b0e98409a1e', dest='.', name='http.log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully monitoring http.log...\n"
     ]
    },
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       "  filename            host      id.orig_h  id.orig_p       id.resp_h  id.resp_p info_code info_msg method orig_fuids   ...    response_body_len status_code         status_msg     tags  trans_depth                         ts                 uid                                                uri                                         user_agent username\n",
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     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load in the Bro IDS dns logs\n",
    "reader = bro_log_reader.BroLogReader('http.log')\n",
    "\n",
    "# Create a Pandas dataframe from reader\n",
    "http_df = pd.DataFrame(reader.readrows())\n",
    "\n",
    "# Print out the head of the dataframe\n",
    "http_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Our HTTP features are a mix of numeric and categorical data\n",
    "When we look at the http records some of the data is numerical and some of it is categorical so we'll need a way of handling both data types in a generalized way. We have a DataFrameToMatrix class that handles a lot of the details and mechanics of combining numerical and categorical data, we'll use below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"float: right; margin: 10px 40px 10px 40px\"><img src=\"images/transformers.png\" width=\"200px\"></div>\n",
    "## Transformers\n",
    "**We'll now use the Scikit-Learn tranformer class to convert the Pandas DataFrame to a numpy ndarray (matrix). The transformer class takes care of many low-level details**\n",
    "* Applies 'one-hot' encoding for the Categorical fields\n",
    "* Normalizes the Numeric fields\n",
    "* The class can be serialized for use in training and evaluation\n",
    "  * The categorical mappings are saved during training and applied at evaluation\n",
    "  * The normalized field ranges are stored during training and applied at evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Changing column method to category...\n",
      "Changing column resp_mime_types to category...\n",
      "Normalizing column id.resp_p...\n",
      "Normalizing column request_body_len...\n",
      "\n",
      "NOTE: The resulting numpy matrix has 12 dimensions based on one-hot encoding\n",
      "(150, 12)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.]])"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We're going to pick some features that might be interesting\n",
    "# some of the features are numerical and some are categorical\n",
    "features = ['id.resp_p', 'method', 'resp_mime_types', 'request_body_len']\n",
    "\n",
    "# Use the DataframeToMatrix class (handles categorical data)\n",
    "# You can see below it uses a heuristic to detect category data. When doing\n",
    "# this for real we should explicitly convert before sending to the transformer.\n",
    "to_matrix = dataframe_to_matrix.DataFrameToMatrix()\n",
    "http_feature_matrix = to_matrix.fit_transform(http_df[features], normalize=True)\n",
    "\n",
    "print('\\nNOTE: The resulting numpy matrix has 12 dimensions based on one-hot encoding')\n",
    "print(http_feature_matrix.shape)\n",
    "http_feature_matrix[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Plotting defaults\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.size'] = 12.0\n",
    "plt.rcParams['figure.figsize'] = 14.0, 7.0\n",
    "\n",
    "# Helper method for scatter/beeswarm plot\n",
    "def jitter(arr):\n",
    "    stdev = .02*(max(arr)-min(arr))\n",
    "    return arr + np.random.randn(len(arr)) * stdev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"float: right; margin: 10px 40px 10px 40px\"><img src=\"images/silhouette.jpg\" width=\"250px\"></div>\n",
    "# Silhouette Scoring\n",
    "\"The silhouette value is a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation). The silhouette ranges from -1 to 1, where a high value indicates that the object is well matched to its own cluster and poorly matched to neighboring clusters. If most objects have a high value, then the clustering configuration is appropriate. If many points have a low or negative value, then the clustering configuration may have too many or too few clusters.\"\n",
    "- https://en.wikipedia.org/wiki/Silhouette_(clustering)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10798d400>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0hZfmbdKjc1dreKdWmnRdluIimUkCAAACly87OWWSjj5npYWk0gbWPicpUlIr\nSbGS5ugYOznW2knW2ixrbVZycrLviQE0GmutHv94jR6du1o/6X2aXr4+m4IDAAACni8lZ52kMGNM\nl3rH+knKa2Btf0lTrbWF1toq1Q0dGGyM4Z3LgJ/xeK1++/YKPf/lRl0zOEPPjhyoyDC307EAAABO\n2XFLjrW2XHU7Mg8bY2KNMadLulTSjAaW50i6zhiTYIwJl3SLpF3W2r2NGRrAqamq9ei2mYs1a+F2\n3XpOJ/35573ldnGRTwAAEBx8HSF9i6RoSQWSZkm62VqbZ4w50xhTVm/d3ZIqVffenD2SLpb080bM\nC+AUlVXVatzUHH208js9cEkP/ebC7jKGggMAAIKHTyffW2sLJV3WwPF5qhtMcOj2PtVNVAPgh/aV\nVen6qTnK21WiJ6/spysGpTkdCQAAoNHxDmMgROzcf0CjJy/QzqID+te1g3RezxSnIwEAADQJSg4Q\nAjYUlGr05IUqq6zV9HGDNaRjK6cjAQAANBlKDhDklm7fr+tfXii3y6XXbhyqXu0SnI4EAADQpCg5\nQBD7av1eTZyRq1ZxEXpl/BC1bxXrdCQAAIAmR8kBgtSHK3br9teWqFNynKaPG6w2LaKcjgQAANAs\nKDlAEHp1wVY98M5KDcxI1JQx2UqICXc6EgAAQLOh5ABBxFqr57/cqCc+WauzuyXrhVGDFB3hdjoW\nAABAs6LkAEHC67V6dO5qTfl6sy7r305PXNlP4W5fr/cLAAAQPCg5QBCo8Xh1z5vL9faSnRo7PFMP\njegpl8s4HQsAAMARlBwgwB2o9ujWmYv1+ZoC3XV+V932484yhoIDAABCFyUHCGDFB2p0w7Qc5W4t\n0iOX9dbooe2djgQAAOA4Sg4QoApKK3Xd5IXauKdM/7hmgEb0bed0JAAAAL9AyQEC0LZ9Fbp28gLt\nKa3S5DHZ+lHXZKcjAQAA+A1KDhBgVu8u0XVTFqrG49XMCUM0ICPR6UgAAAB+hZIDBJCcLYUaNzVH\nsRFhmnnjMHVJiXc6EgAAgN+h5AAB4vM1+br5lcVKbRmt6eMHKy0xxulIAAAAfomSAwSAOYt36Ddv\nLlfPti009fpstYqLdDoSAACA36LkAH5uyleb9fAHqzSsYytNum6Q4qPCnY4EAADg1yg5gJ+y1uqp\nT9fpH59v0EW9TtPTV/dXVLjb6VgAAAB+j5ID+CGP1+rBd1dq5oJtujo7XX/6eR+5XcbpWAAAAAGB\nkgP4marbyMpTAAAgAElEQVRaj+58fZnmrtitm8/upHsu7CZjKDgAAAC+ouQAfqS8qlY3vbJI89bv\n1e8u7qEJP+rodCQAAICAQ8kB/ERRebXGTs3Ryp3FeuIXfXVlVrrTkQAAAAISJQfwA7v2H9B1UxZq\nW2GFXhg1UBf0Os3pSAAAAAGLkgM4bENBma6bvECllbWaPm6whnZs5XQkAACAgEbJARy0fMd+jX05\nRy4jzZo4VL1TE5yOBAAAEPAoOYBD5m/YqwnTc5UYG6EZ44eoQ+tYpyMBAAAEBUoO4ICPV+7Wr2ct\nVYfWsZo+frBSWkQ5HQkAACBoUHKAZvbawm367dsr1D+9paaMzVbLmAinIwEAAAQVSg7QTKy1+ud/\nNumxj9forK7JeuHagYqJ4I8gAABAY+MVFtAMrLX684er9eK8zfpZv3b6f1f2U0SYy+lYAAAAQYmS\nAzSxWo9X9761Qm8t3qExw9rr9z/tJZfLOB0LAAAgaFFygCZUWePRbTOX6N+r83XHeV3163M7yxgK\nDgAAQFOi5ABNpMbj1YTpufpqw149cmkvjR6W6XQkAACAkEDJAZrIIx+s0rz1e/XYFX30y+wMp+MA\nAACEDN75DDSB6d9s0fRvturGH3Wk4AAAADQzSg7QyOat36M/vr9K5/Voo3su6u50HAAAgJBDyQEa\n0cY9Zbrl1cXq0iZOT189QG6mqAEAADQ7Sg7QSPZXVOuGabmKcLv00pgsxUXyljcAAAAn8CoMaAQ1\nHq9ueXWxdhYd0MwJQ5SWGON0JAAAgJBFyQFOkbVWv38vT/M37tOTV/ZTVmaS05EAAABCGqerAado\n2vwtmrlgm24+u5OuGJTmdBwAAICQR8kBTsF/1u3Rwx+s0gU9U/SbC7o5HQcAAACi5AAnbUNBqW6b\nuVjdTmuhv/2yv1xMUgMAAPALlBzgJBSVV2v8tFxFhtVNUotlkhoAAIDf4JUZcIKqa7266ZVF2l1c\nqVkThiq1ZbTTkQAAAFCPTzs5xpgkY8zbxphyY8xWY8zIY6z7yBhTVu9XtTFmReNGBpxTN0ltpRZs\nLtTjV/TVoPaJTkcCAADAUXzdyXlOUrWkFEn9Jc01xiyz1ubVX2St/Un928aYLyV93gg5Ab8w5est\nmrVwu247p7MuG5DqdBwAAAA04Lg7OcaYWElXSHrQWltmrf1K0nuSRh/ncZmSzpQ0/dRjAs77Ym2B\n/jR3lS7slaI7z+/qdBwAAAAcgy+nq3WVVGutXVfv2DJJvY7zuOskzbPWbjnJbIDfWJdfql/NXKLu\nTFIDAADwe76UnDhJJUcdK5YUf5zHXSdp6rHuNMZMNMbkGmNy9+zZ40MMwBmF5dUaPy1H0RFuvTQm\nSzERzOsAAADwZ76UnDJJLY461kJS6bEeYIw5Q9Jpkt481hpr7SRrbZa1Nis5OdmXrECzOzRJraCk\nSi9el6V2TFIDAADwe76UnHWSwowxXeod6ycp7xjrJWmMpDnW2rJTCQc4yVqrB95ZoYWbC/XElf3U\nP72l05EAAADgg+OWHGttuaQ5kh42xsQaY06XdKmkGQ2tN8ZES7pKP3CqGhAIJn+1WW/k7tCvf9xZ\nP+vXzuk4AAAA8JFP18mRdIukaEkFkmZJutlam2eMOdMYc/RuzWWS9kv6ovFiAs3rs9X5+tOHq3Vx\nn9P0f+cxSQ0AACCQ+PQOamttoerKy9HH56luMEH9Y7NUV4SAgLT2u1L9etYS9W6XoCevZJIaAABA\noPF1JwcICfvKqjR+Wo5iI8P04nVZio5wOx0JAAAAJ4hZuMBBVbUe3fTKIu0prdIbNw7TaQlRTkcC\nAADASaDkAKqbpPa7t1cqZ0uRnh05QP2YpAYAABCwOF0NkDTpv5v05qId+r/zumhEXyapAQAABDJK\nDkLep6vy9deP12hE37a6/dwux38AAAAA/BolByFt9e4S3f7aEvVNTdD/u7KfjGGSGgAAQKCj5CBk\n7Smt0g3TctUiKlyTrstSVDiT1AAAAIIBgwcQkipr6iap7Suv0uwbhyulBZPUAAAAggUlByHHWqvf\nzlmhRVuL9PyogeqTluB0JAAAADQiTldDyHnhPxs1Z8lO3XV+V13cp63TcQAAANDIKDkIKZ/kfacn\nPlmrn/Vrp9t+3NnpOAAAAGgClByEjLxdxbrj9aXqm9ZSj/+iL5PUAAAAghQlByGhoLRSE6blKiE6\nXC+OHsQkNQAAgCDG4AEEvcoajyZOX6SiihrNvmmY2jBJDQAAIKhRchDUrLW6963lWrp9v/557SD1\nTmWSGgAAQLDjdDUEtee/3Kh3l+7Sby7spot6n+Z0HAAAADQDSg6C1scrd+uJT9bqsv7tdMvZnZyO\nAwAAgGZCyUFQWrmzWHe8vkwDMlrqr1cwSQ0AACCUUHIQdApKKnXDtFwlxoRr0ugsJqkBAACEGAYP\nIKhU1ng0YcYilVTW6M2bhis5PtLpSAAAAGhmlBwEDWutfvPmci3fsV//unaQerZr4XQkAAAAOIDT\n1RA0/vH5Br2/bJfuubC7LujFJDUAAIBQRclBUJi7fLee+nSdLh+YqpvO6uh0HAAAADiIkoOAt2JH\nse6avVRZ7RP1l8v7MEkNAAAgxFFyENDySyp1w/QctYqN1D9HD1JkGJPUAAAAQh0lBwHrQLVHE6bn\nqqyyVpPHZql1HJPUAAAAwHQ1BCiv1+ru2cu0YmexXhydpe6nMUkNAAAAddjJQUD6+2frNXfFbt3/\nk+46r2eK03EAAADgRyg5CDjvL9ulv3+2XlcOStOEM5mkBgAAgCNRchBQlm3fr7tnL9PgzCQ9+vPe\nTFIDAADA91ByEDB2Fx/QhOm5So6P1AvXDmSSGgAAABpEyUFAqKiu1YTpuaqo9mjymGy1YpIaAAAA\njoGSg4Dw2zkrtGpXif5xzQB1Oy3e6TgAAADwY5Qc+L0Plu/SO0t36f/O66pzurdxOg4AAAD8HCUH\nfq2gpFIPvLNS/dNb6pazOzkdBwAAAAGAkgO/Za3VfXNWqLLGoyev6qcwNz+uAAAAOD5eNcJvvZG7\nXZ+vKdC9F3VXp+Q4p+MAAAAgQFBy4Je2F1bo4fdXaVjHVhozLNPpOAAAAAgglBz4Ha/X6u7Zy2SM\n0RNX9pXLxQU/AQAA4DtKDvzOy/O3aMHmQj30055KS4xxOg4AAAACDCUHfmVDQake+3iNzuvRRlcO\nSnM6DgAAAAIQJQd+o8bj1Z1vLFNcZJj+cnlfGcNpagAAADhxYU4HAA55/ouNWr6jWC+MGqjk+Ein\n4wAAACBAsZMDv7BiR7H+8fl6Xda/nX7Sp63TcQAAABDAKDlwXGWNR3e+sVSt4yL1x5/1djoOAAAA\nAhynq8FxT326TusLyjRt3GAlxIQ7HQcAAAABzqedHGNMkjHmbWNMuTFmqzFm5A+sHWiM+a8xpswY\nk2+Mub3x4iLYLNxcqBfnbdKoIRk6q2uy03EAAAAQBHzdyXlOUrWkFEn9Jc01xiyz1ubVX2SMaS3p\nY0l3SHpTUoQk5gCjQWVVtbpr9lKlJ8botxf3cDoOAAAAgsRxd3KMMbGSrpD0oLW2zFr7laT3JI1u\nYPmdkj6x1r5qra2y1pZaa1c3bmQEiz/NXa0dRQf05FX9FBvJmZMAAABoHL6crtZVUq21dl29Y8sk\n9Wpg7VBJhcaY+caYAmPM+8aYjMYIiuDyxdoCzVq4TRN/1FHZmUlOxwEAAEAQ8aXkxEkqOepYsaT4\nBtamSRoj6XZJGZI2S5rV0JMaYyYaY3KNMbl79uzxPTEC3v6Kat375nJ1S4nXned3dToOAAAAgowv\nJadMUoujjrWQVNrA2gOS3rbW5lhrKyX9UdJwY0zC0QuttZOstVnW2qzkZN5wHkoeejdPheXVevKq\nfooMczsdBwAAAEHGl5KzTlKYMaZLvWP9JOU1sHa5JFvvtm1gDULY3OW79d6yXbr93C7qnfq97gsA\nAACcsuOWHGttuaQ5kh42xsQaY06XdKmkGQ0sf1nSz40x/Y0x4ZIelPSVtba4MUMjMBWUVOqBd1ao\nX3pL3Xx2J6fjAAAAIEj5dJ0cSbdIipZUoLr32Nxsrc0zxpxpjCk7tMha+7mk30qae3BtZ0nHvKYO\nQoe1VvfNWaGKao+evLKfwty+/ugBAAAAJ8anub3W2kJJlzVwfJ7qBhPUP/aCpBcaJR2Cxhu52/X5\nmgI9NKKnOreJO/4DAAAAgJPEP6ejyW0vrNDD76/SsI6tNHZ4ptNxAAAAEOQoOWhSXq/V3bOXyRij\nJ67sK5fLOB0JAAAAQY6Sgyb18vwtWrC5UA/9tKfSEmOcjgMAAIAQQMlBk9lQUKbHP16j83q00ZWD\n0pyOAwAAgBBByUGTqPF4decbSxUT4dafL+8jYzhNDQAAAM3Dp+lqwIl6/ouNWr6jWM+PGqg28VFO\nxwEAAEAIYScHjW7FjmL94/P1urR/O13cp63TcQAAABBiKDloVJU1Ht35xlK1iovQwz/r7XQcAAAA\nhCBOV0OjeurTdVpfUKap12crISbc6TgAAAAIQezkoNEs3FyoF+dt0qghGTq7Wxun4wAAACBEUXLQ\nKMqranX37GVKT4zRby/u4XQcAAAAhDBOV0Oj+NOHq7W9qEJv3DhMsZH8WAEAAMA57OTglH2xtkAz\nF2zTxDM7Kjszyek4AAAACHGUHJyS/RXVuvfN5eqaEqc7zu/qdBwAAACA09Vwah56N0+F5dWaMjZb\nUeFup+MAAAAA7OTg5M1dvlvvLdulX5/bRb1TE5yOAwAAAEii5OAkFZRW6oF3VqhfWoJuObuT03EA\nAACAwyg5OGHWWt3/1gpVVHv05FX9FebmxwgAAAD+g1enOGGzc3foszUFuvei7urcJs7pOAAAAMAR\nKDk4IdsLK/TH9/M0rGMrjR2e6XQcAAAA4HsoOfCZ12t19+xlMsboiSv7yuUyTkcCAAAAvoeSA5+9\nPH+LFmwu1EMjeiotMcbpOAAAAECDKDnwyYaCMj3+8Rqd272NrsxKczoOAAAAcEyUHBxXrceru95Y\nqpgIt/5yRR8Zw2lqAAAA8F9hTgeA/3v+y41atqNYz40cqDbxUU7HAQAAAH4QOzn4QSt3FuuZz9br\n0v7tdEnftk7HAQAAAI6LkoNjqqzx6I7Xl6pVXIQe/llvp+MAAAAAPuF0NRzTU5+u0/qCMk29PlsJ\nMeFOxwEAAAB8wk4OGrRwc6FenLdJI4dk6OxubZyOAwAAAPiMkoPvKa+q1d2zlyk9MUa/u7iH03EA\nAACAE8LpavieP324WtuLKvT6xGGKjeRHBAAAAIGFnRwc4cu1BZq5YJsmnNlRgzskOR0HAAAAOGGU\nHBy2v6Ja9761XF1T4nTn+V2djgMAAACcFM5FwmEPvZunfWXVmjwmW1HhbqfjAAAAACeFnRxIkuYu\n3633lu3Sr8/tot6pCU7HAQAAAE4aJQcqKK3UA++sUL+0BN1ydien4wAAAACnhJIT4qy1uv+tFaqo\n9ujJq/orzM2PBAAAAAIbr2hD3OzcHfpsTYHuuai7OreJczoOAAAAcMooOSFse2GFHv5glYZ2TNL1\nwzOdjgMAAAA0CkpOiPJ6rX7z5jJJ0hO/6CeXyzicCAAAAGgclJwQ9fL8Lfp2U6EeHNFD6UkxTscB\nAAAAGg0lJwRtKCjT4x+v0bnd2+iqrHSn4wAAAACNipITYmo9Xt31xlLFRLj1lyv6yBhOUwMAAEBw\nCXM6AJrXU5+u07IdxXpu5EC1iY9yOg4AAADQ6NjJCSGT/rtRz3+5UVdnp+uSvm2djgMAAAA0CUpO\niJjxzRb9+cM1GtG3rf708z5OxwEAAACajE8lxxiTZIx52xhTbozZaowZeYx1fzDG1Bhjyur96ti4\nkXGi3sjdrgffzdP5PVP0t1/2l5tx0QAAAAhivr4n5zlJ1ZJSJPWXNNcYs8xam9fA2tettdc2VkCc\nmveW7dJ9by3XmV1a69mRAxTuZvMOAAAAwe24r3iNMbGSrpD0oLW2zFr7laT3JI1u6nA4NZ/kfac7\nXl+qrMwkTRqdpcgwt9ORAAAAgCbnyz/rd5VUa61dV+/YMkm9jrH+p8aYQmNMnjHm5lNOiJPyn3V7\n9KuZS9QnNUFTxmYrOoKCAwAAgNDgS8mJk1Ry1LFiSfENrH1DUg9JyZImSHrIGHNNQ09qjJlojMk1\nxuTu2bPnBCLjeL7ZuE8Tp+eqc5s4TRs3WHGRTAoHAABA6PCl5JRJanHUsRaSSo9eaK1dZa3dZa31\nWGvnS/q7pF809KTW2knW2ixrbVZycvKJ5sYxLNpapPHTcpSRFKMZ4wcrITrc6UgAAABAs/Kl5KyT\nFGaM6VLvWD9JDQ0dOJqVxCivZrJyZ7HGvrxQbeIj9eoNQ9QqLtLpSAAAAECzO27JsdaWS5oj6WFj\nTKwx5nRJl0qacfRaY8ylxphEU2ewpF9LerexQ+P71uWXavTkBWoRFa5XJwxVmxZRTkcCAAAAHOHr\nPOFbJEVLKpA0S9LN1to8Y8yZxpiyeuuulrRBdaeyTZf0mLV2WmMGxvdt2lOmkS8uULjbpZkThii1\nZbTTkQAAAADH+PSOdGttoaTLGjg+T3WDCQ7dbnDIAJrO9sIKjXppgay1mjlxqNq3inU6EgAAAOAo\nxm4FsO+KKzXypW9VUe3RrAlD1blNQwPvAAAAgNDi6+lq8DN7Sqs08qVvVVReo2njBqtnu6MH4AEA\nAAChiZITgPZXVGv05AXavb9SL1+frf7pLZ2OBAAAAPgNTlcLMCWVNbpuykJt2luuKWOylZ2Z5HQk\nAAAAwK+wkxNAKqprNe7lHK3aVaIXRg3UGV1aOx0JAAAA8DuUnABRWePRDdNytXhbkZ65ZoDO7ZHi\ndCQAAADAL3G6WgCorvXq5lcW6ZtN+/Tklf10cZ+2TkcCAAAA/BY7OX6u1uPV7a8t0Rdr9+hPl/XR\n5QPTnI4EAAAA+DVKjh/zeK3unr1MH638Tg+O6KmRQzKcjgQAAAD4PUqOn7LW6ndvr9A7S3fpNxd2\n0/gzOjgdCQAAAAgIlBw/ZK3VH99fpddytuu2czrr1nM6Ox0JAAAACBiUHD9jrdXjn6zV1PlbNP6M\nDrrrgq5ORwIAAAACCiXHzzz7+Qa98OVGjRqSoQcu6SFjjNORAAAAgIBCyfEjL83bpCc/XafLB6bq\nkUt7U3AAAACAk0DJ8RMzvt2qR+eu1iV92urxK/rK5aLgAAAAACeDkuMH3ly0Qw++s1Ln9Wijv/2y\nv8Lc/GcBAAAAThavph32/rJduufNZTqzS2s9O3KgIsL4TwIAAACcCl5RO+jTVfm64/WlymqfpH+N\nHqSocLfTkQAAAICAR8lxyH/X7dGtry5Wr9QETR6bpZiIMKcjAQAAAEGBkuOAbzft08QZuerUJk7T\nrx+s+KhwpyMBAAAAQYOS08wWbyvS+Kk5SkuM0Yzxg5UQQ8EBAAAAGhMlpxmt3FmssVMWqnV8pF69\nYYhax0U6HQkAAAAIOpScZrIuv1TXTVmouMgwvXrDEKW0iHI6EgAAABCUKDnNYPPeco16aYHCXEYz\nJwxVWmKM05EAAACAoMVIrya2o6hCo178Vh6v1esThyqzdazTkQAAAICgxk5OE/quuFIjX1ygsqpa\nzRg/WF1S4p2OBAAAAAQ9Sk4T2VtWpVEvfat9ZVWaNm6werVLcDoSAAAAEBI4Xa0J7K+o1ujJC7Vz\n/wFNu36wBmQkOh0JAAAACBns5DSy0soajZmyUBsLyvTidVka0rGV05EAAACAkELJaUQV1bUaNzVH\nebtK9PyogTqzS7LTkQAAAICQQ8lpJJU1Hk2cvkiLthbp6av767yeKU5HAgAAAEIS78lpBNW1Xt3y\n6mJ9tWGvnryyn0b0bed0JAAAACBksZNzimo9Xv3f60v0+ZoCPXpZb10xKM3pSAAAAEBIo+ScAq/X\n6p43l+vDFd/pgUt66Nqh7Z2OBAAAAIQ8Ss5Jstbqd++s1JwlO3X3BV11w5kdnY4EAAAAQLwn56Rs\n3luuv360Wp/k5euWszvpth93cToSAAAAgIMoOSegoKRSf/9svV7P2a6IMJfuuaibbj6rk9OxAAAA\nANRDyfFBSWWNJv1nkyZ/tVk1Hq9GDsnQr37cRcnxkU5HAwAAAHAUSs4PqKr1aMY3W/XcFxtUVFGj\nn/Zrp7vO76rM1rFORwMAAABwDJScBni8Vu8s2amnPl2nnfsP6MwurXXPhd3VJy3B6WgAAAAAjoOS\nU4+1Vl+sLdBjH63V2vxS9UlN0GNX9NUZXVo7HQ0AAACAjyg5By3aWqTHPlqjhVsKldkqRs+OHKCL\ne7eVy2WcjgYAAADgBIR8ydlQUKrHP16r/29VvlrHReqRy3rr6ux0hbu5hBAAAAAQiEK25OwuPqCn\nP12v2Yu2KyYiTHdf0FXjzuigmIiQ/S0BAAAAgkLIvaLfX1GtF77cqKnzt8ha6frTO+jWczorKTbC\n6WgAAAAAGkHIlJzKGo9e/nqLXvhyg0qravXzAam68/yuSkuMcToaAAAAgEbkU8kxxiRJmizpAkl7\nJd1vrZ35A+sjJC2TFG+tTWuMoCer1uPVm4t26Ol/r9d3JZX6cfc2uueibup+WgsnYwEAAABoIr7u\n5DwnqVpSiqT+kuYaY5ZZa/OOsf43kvZIij/1iCfHWqtP8vL1xCdrtHFPuQZmtNQz1wzQ4A5JTkUC\nAAAA0AyOW3KMMbGSrpDU21pbJukrY8x7kkZLuq+B9R0kXSvpTkkvNm5c33y7aZ8e+3iNlmzbr85t\n4vSv0YN0Qc8UGcM4aAAAACDY+bKT01VSrbV2Xb1jyySddYz1/5D0W0kHfuhJjTETJU2UpIyMDB9i\nHN/q3SV6/OM1+mLtHp3WIkqPXdFHVwxMUxjjoAEAAICQ4UvJiZNUctSxYjVwKpox5ueS3Nbat40x\nZ//Qk1prJ0maJElZWVnWp7THsL2wQn/7dJ3eXrpT8ZFhuv8n3TVmeKaiwt2n8rQAAAAAApAvJadM\n0tHv0m8hqbT+gYOntT0u6eLGiXZ8+8qq9OwXG/Tqt9tkjHTjjzrp5rM6KSEmvLkiAAAAAPAzvpSc\ndZLCjDFdrLXrDx7r9/+3d+/BdpXlHce/PxLkEsJFuQgEiBAFxRFQbmMFL2ip9caIII6MogOlOgqt\nStEZTSOoI9bRtkSqKFDUohRKWweV0lohUKZA2ipKMW2EcI8hXEIIJGDy9I+1MrMnPTmE5Jy9Ttb5\nfmbOnLPXXnvv33lmz9rnWe+73gOsu+jAi4GZwA3ttS/PA3ZIshg4sqoWjUliYMWq33LRjXdx4bw7\nefLp33LioXtx5htfzO47bDNWLyFJkiRpM/WsTU5VrUhyFXBOklNpVld7B/DqdXb9JbDXwO1XA3OB\nV9KstLbJnlm9hu/fcg9/8ZOFLH1iFcceuBtnHbs/s3btbBE3SZIkSRPMhi4h/WHgYmAJ8DDwoaq6\nPclRwI+raruq+i2weO0DkjwCrKmqxSM+43OwZk3xw188yJevXcDdDz/J4S96Phe+71W8cu+dNvWp\nJUmSJPXMBjU5VfUIcNwI22+gWZhgpMdcB2zyPwK98X+X8sVr7uCX9z/OAS+cziWnHMbr9t/F5aAl\nSZIkjWhDR3KG7hf3LeO8a37FjQuXsueO2/CVEw/iHQfvyZQtbG4kSZIkrd+Ea3IWLV3Bl69dwNW3\nPchO227J7Le+jPceuTdbTXU5aEmSJEnPbsI0OUuWr+T8nyzke7fcw5ZTtuCMN8zitKP3ZfrWLgct\nSZIkacNNiCbnN4+v5LVfuo5nVq/hPYfvzUePmcWu07fuOpYkSZKkzdCEaHKWLF/FcS/bjY+/6SXM\n3Hla13EkSZIkbcYmRJMza5ftOP89h3QdQ5IkSVIPbNF1AIBtnueiApIkSZLGxoRociRJkiRprNjk\nSJIkSeoVmxxJkiRJvWKTI0mSJKlXbHIkSZIk9YpNjiRJkqRescmRJEmS1Cs2OZIkSZJ6xSZHkiRJ\nUq/Y5EiSJEnqFZscSZIkSb1ikyNJkiSpV2xyJEmSJPWKTY4kSZKkXrHJkSRJktQrqaquM5BkObCg\n6xyTyM7A0q5DTCLWe7is93BZ7+Gy3sNlvYfLeg/P5lrrfapqlw3Zcep4J9lAC6rq0K5DTBZJ5lvv\n4bHew2W9h8t6D5f1Hi7rPVzWe3gmQ62driZJkiSpV2xyJEmSJPXKRGlyLuw6wCRjvYfLeg+X9R4u\n6z1c1nu4rPdwWe/h6X2tJ8TCA5IkSZI0VibKSI4kSZIkjQmbHEmSJEm90lmTk2SrJBcluTvJ8iQ/\nS/LmrvJMJklenGRlku92naXvkpyU5I4kK5L8OslRXWfqqyQzk/woyaNJFieZm2SiLJO/2UvykSTz\nk6xK8tfr3HdMkl8leTLJT5Ps01HMXlhfrZMcmeSfkzyS5KEkVyTZvcOovTDae3tgn9lJKskbhxyv\nd57lWLJtkguSLE2yLMm8jmL2xrPU+8T2b5TlSf47yXEdxRwXXY7kTAXuBV4L7AB8GvjbJDM7zDRZ\nfA24tesQfZfkTcB5wAeA6cDRwJ2dhuq3C4AlwO7AwTTHlg93mqhfHgA+B1w8uDHJzsBVwGeA5wPz\ngcuHnq5fRqw1sBPNxcIzgX2A5cAlQ03WT+urNwBJ9gNOAB4cZqgeG63eF9IcR17afv/jIebqq/Ud\nu/cEvgt8DNgeOAu4LMmuQ084Tjo7y1lVK4A5A5uuTnIX8CpgUReZJoMkJwGPATcBszqO03efBc6p\nqn9vb9/fZZhJ4EXA3KpaCSxOcg1wYMeZeqOqrgJIcigwY+CudwK3V9UV7f1zgKVJDqiqXw09aA+s\nr9ZV9ePB/ZLMBa4fbrr+GeW9vdbXgLNpTqRoE62v3kkOAN4OzKiqx9vN/zH8hP0yyvt7BvDYwHHl\nh0lWAPvRnDDc7E2Ya3KS7Aa8BLi96yx9lWR74Byarl3jKMkU4FBglyQLk9zXTp/aputsPfbnwEnt\ndPDoXOIAAAbkSURBVIc9gTcD13ScaTI4EPj52hvtCaxfY4M5DEfjZ+a4SnICsKqqftR1lkngcOBu\n4LPtdLVfJDm+61A9Nh+4I8nbk0xpp6qtAm7rONeYmRBNTpItgb8BLvXM37g6F7ioqu7rOsgksBuw\nJfAu4Cia6VOH0EzL1PiYR/OH9ePAfTQH8H/oNNHksB2wbJ1ty2imaGqcJHkFMJtmionGQZLpwBeA\nM7vOMknMAF5Oc/zYA/gIcGmSl3aaqqeqajXwbeAymubmMuD09kRVL3Te5CTZAvgO8DTNG1rjIMnB\nwBuBr3adZZJ4qv1+flU9WFVLga8Av99hpt5qjyPX0FwbMg3Ymeb6hfO6zDVJPEEzn3vQ9jTXi2gc\nJJkF/Bg4s6pu6DpPj80BvlNVizrOMVk8BTwDfK6qnq6q64GfAr/bbax+ahfR+BLwOuB5NNexfqv9\ne7EXOm1ykgS4iOas9/FV9UyXeXrudTQXq96TZDHwCeD4JP/ZZai+qqpHaUYTBv/brv95d/w8H9ib\n5pqcVVX1MM0F2TaV4+924KC1N5JMo5nT7TSqcdCuXPcvwLlV9Z2u8/TcMcAZ7WqNi4G9aBZIOrvj\nXH010jQpPzfHz8HAvKqaX1VrqupW4GaaE+K90PVIzl/RrKDxtqp66tl21ia5kOYPj4Pbr68DPwSO\n7TJUz10CfDTJrkl2olkl5uqOM/VSO1J2F/ChJFOT7Ai8nx7NLe5aW9etgSnAlCRbt0t0/z3w8iTH\nt/fPBm5z6vHGW1+t22vN/pWmmf96tyn7Y5T39jE006fWfm4+AJxOsxCBNtIo9Z4H3AN8qt3nd4DX\nA//UYdzN3ij1vhU4au3ITZJDaKbX9+Zzs8v/k7MPzcHiYJqVkJ5ov97bVaY+q6onq2rx2i+aKSYr\nq+qhrrP12Lk0B5H/Ae4A/gv4fKeJ+u2dwO8BDwELaaY9uPzo2Pk0zXSSTwIntz9/uj2GHE/z3n4U\nOAI4qauQPTFirYFTgX2BOQOfmU90F7M31vfefnidz83VwKNVZc03zfrq/QzwDpoR+GXAN4H3ecJk\nk62v3tfTTMm8Msly4O+AL1TVtV0FHWupciRQkiRJUn90PV1NkiRJksaUTY4kSZKkXrHJkSRJktQr\nNjmSJEmSesUmR5IkSVKv2ORIkiRJ6hWbHEnShJTklCQ3dp1DkrT5scmRpEkqyaIkS5JMG9h2apLr\nhpjh2CTzkixP8lCS65O8fYxfo5LMGsvnlCRNbDY5kjS5TQHO7OKFk7wLuAL4NjAD2A2YDbytizwj\nSTK16wySpOfOJkeSJrc/Az6RZMd170gysx0FmTqw7bokp7Y/n5Lk35J8NcljSe5M8up2+73tKNH7\nR3rRJAG+ApxbVd+qqmVVtaaqrq+q0zYiy6x2FGhZkqVJLm+3z2t3/3mSJ5K8u93+1iQ/a3PflOQV\nA8+7KMnZSW4DViSZ2t6+vx1xWpDkmOdcaUnS0NjkSNLkNh+4DvjERj7+COA24AXAZcD3gcOAWcDJ\nwNwk243wuP2BvYArN/J113UucC2wE82o0PkAVXV0e/9BVbVdVV2e5BDgYuD0Nvc3gB8k2Wrg+d4D\nvAXYEdgP+AhwWFVNB44FFo1RbknSOLDJkSTNBj6aZJeNeOxdVXVJVa0GLqdpXM6pqlVVdS3wNE3D\ns64XtN8f3KjE/98zwD7AHlW1sqpGW7DgD4BvVNXNVbW6qi4FVgFHDuzzl1V1b1U9BawGtgJelmTL\nqlpUVb8eo9ySpHFgkyNJk1xV/RK4GvjkRjz8NwM/P9U+37rbRhrJebj9vvtGvOZI/gQIcEuS25N8\ncJR99wE+3k5VeyzJYzTN2R4D+9y79oeqWgj8ETAHWJLk+0kG95UkTTA2OZIkgD8FTgP2HNi2ov2+\n7cC2F47R6y2gaSSO38D9R81SVYur6rSq2oNmGtoFo6yodi/w+araceBr26r63sA+NfiAqrqsql5D\n0yAVcN4G5pYkdcAmR5K0drTicuCMgW0PAfcDJyeZ0o6O7DdGr1fAx4DPJPlAku2TbJHkNUkuHGH/\nUbMkOSHJjPbmozSNyJr29m+AfQee7pvAHyY5Io1pSd6SZPpIWZPsn+QN7TU7K2lGp9aMtK8kaWKw\nyZEkrXUOMG2dbacBZ9FMLzsQuGmsXqyqrgTeDXwQeICmGfkc8I/rechoWQ4Dbk7yBPAD4MyqurO9\nbw5waTs17cSqmt8+11yahmghcMooUbcCvggsBRYDuwKfei6/qyRpuNKcTJMkSZKkfnAkR5IkSVKv\n2ORIkiRJ6hWbHEmSJEm9YpMjSZIkqVdsciRJkiT1ik2OJEmSpF6xyZEkSZLUKzY5kiRJknrFJkeS\nJElSr/wfSTZ2BaETW8UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1122f7048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "scores = []\n",
    "clusters = range(2,20)\n",
    "for K in clusters:\n",
    "    \n",
    "    clusterer = KMeans(n_clusters=K)\n",
    "    cluster_labels = clusterer.fit_predict(http_feature_matrix)\n",
    "    score = silhouette_score(http_feature_matrix, cluster_labels)\n",
    "    scores.append(score)\n",
    "\n",
    "# Plot it out\n",
    "pd.DataFrame({'Num Clusters':clusters, 'score':scores}).plot(x='Num Clusters', y='score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Silhouette graphs shows that 10 is the 'optimal' number of clusters\n",
    "- 'Optimal': Human intuition and clustering involves interpretation/pattern finding and is often partially subjective :)\n",
    "- For large datasets running an exhaustive search can be time consuming\n",
    "- For large datasets you can often get a large K using max score, so pick the 'knee' of the graph as your K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# So we know that the highest (closest to 1) silhouette score is at 10 clusters\n",
    "kmeans = KMeans(n_clusters=10).fit_predict(http_feature_matrix)\n",
    "pca = PCA(n_components=5).fit_transform(http_feature_matrix)\n",
    "\n",
    "# Now we can put our ML results back onto our dataframe!\n",
    "http_df['cluster'] = kmeans\n",
    "\n",
    "# This is just a HACK for displaying 12 dimensional data in 2D\n",
    "http_df['x'] = pca[:, 0] + pca[:, 2]             # Combining the first and third principle components \n",
    "http_df['y'] = pca[:, 1] + pca[:, 3] + pca[:, 4] # Combining the rest of the principle components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ic+eKiIiIyGAxxgWefqHHWvjzP197WEU/641D7+dTn4IvfWntSlmr5a75xS/uWEVqsxSm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111b83b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Jitter so we can see instances that are projected coincident in 2D\n",
    "http_df['jx'] = jitter(http_df['x'])\n",
    "http_df['jy'] = jitter(http_df['y'])\n",
    "\n",
    "# Now use dataframe group by cluster\n",
    "cluster_groups = http_df.groupby('cluster')\n",
    "\n",
    "# Plot the Machine Learning results\n",
    "colors = {-1:'black', 0:'green', 1:'blue', 2:'red', 3:'orange', 4:'purple', 5:'brown', 6:'pink', 7:'lightblue', 8:'grey', 9:'yellow'}\n",
    "fig, ax = plt.subplots()\n",
    "for key, group in cluster_groups:\n",
    "    group.plot(ax=ax, kind='scatter', x='jx', y='jy', alpha=0.5, s=250,\n",
    "               label='Cluster: {:d}'.format(key), color=colors[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 0: 22 observations\n",
      "    id.resp_p method resp_mime_types  request_body_len\n",
      "22         80    GET      image/jpeg                 0\n",
      "24         80    GET      image/jpeg                 0\n",
      "29         80    GET      image/jpeg                 0\n",
      "30         80    GET      image/jpeg                 0\n",
      "32         80    GET      image/jpeg                 0\n",
      "\n",
      "Cluster 1: 40 observations\n",
      "   id.resp_p method resp_mime_types  request_body_len\n",
      "3         80    GET      text/plain                 0\n",
      "4         80    GET      text/plain                 0\n",
      "6         80    GET      text/plain                 0\n",
      "7         80    GET      text/plain                 0\n",
      "8         80    GET      text/plain                 0\n",
      "\n",
      "Cluster 2: 10 observations\n",
      "     id.resp_p method resp_mime_types  request_body_len\n",
      "140         80   POST      text/plain             69823\n",
      "141         80   POST      text/plain             69993\n",
      "142         80   POST      text/plain             71993\n",
      "143         80   POST      text/plain             70993\n",
      "144         80   POST      text/plain             72993\n",
      "\n",
      "Cluster 3: 15 observations\n",
      "    id.resp_p method resp_mime_types  request_body_len\n",
      "18         80    GET       image/png                 0\n",
      "19         80    GET       image/png                 0\n",
      "20         80    GET       image/png                 0\n",
      "21         80    GET       image/png                 0\n",
      "23         80    GET       image/png                 0\n",
      "\n",
      "Cluster 4: 14 observations\n",
      "    id.resp_p method resp_mime_types  request_body_len\n",
      "40         80    GET               -                 0\n",
      "46         80    GET               -                 0\n",
      "48         80    GET               -                 0\n",
      "55         80    GET               -                 0\n",
      "56         80    GET               -                 0\n",
      "\n",
      "Cluster 5: 13 observations\n",
      "    id.resp_p method  resp_mime_types  request_body_len\n",
      "91         80    GET  application/pdf                 0\n",
      "92         80    GET  application/pdf                 0\n",
      "93         80    GET  application/pdf                 0\n",
      "94         80    GET  application/pdf                 0\n",
      "95         80    GET  application/pdf                 0\n",
      "\n",
      "Cluster 6: 14 observations\n",
      "    id.resp_p method resp_mime_types  request_body_len\n",
      "0          80    GET       text/html                 0\n",
      "1          80    GET       text/html                 0\n",
      "2          80    GET       text/html                 0\n",
      "5          80    GET       text/html                 0\n",
      "53         80    GET       text/html                 0\n",
      "\n",
      "Cluster 7: 8 observations\n",
      "     id.resp_p method        resp_mime_types  request_body_len\n",
      "106         80    GET  application/x-dosexec                 0\n",
      "107         80    GET  application/x-dosexec                 0\n",
      "109         80    GET  application/x-dosexec                 0\n",
      "112         80    GET  application/x-dosexec                 0\n",
      "113         80    GET  application/x-dosexec                 0\n",
      "\n",
      "Cluster 8: 7 observations\n",
      "     id.resp_p   method resp_mime_types  request_body_len\n",
      "133         80  OPTIONS      text/plain                 0\n",
      "134         80  OPTIONS      text/plain                 0\n",
      "135         80  OPTIONS      text/plain                 0\n",
      "136         80  OPTIONS      text/plain                 0\n",
      "137         80  OPTIONS      text/plain                 0\n",
      "\n",
      "Cluster 9: 7 observations\n",
      "     id.resp_p method resp_mime_types  request_body_len\n",
      "126       8080    GET      text/plain                 0\n",
      "127       8080    GET      text/plain                 0\n",
      "128       8080    GET      text/plain                 0\n",
      "129       8080    GET      text/plain                 0\n",
      "130       8080    GET      text/plain                 0\n"
     ]
    }
   ],
   "source": [
    "# Now print out the details for each cluster\n",
    "pd.set_option('display.width', 1000)\n",
    "for key, group in cluster_groups:\n",
    "    print('\\nCluster {:d}: {:d} observations'.format(key, len(group)))\n",
    "    print(group[features].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"float: right; margin: 10px 40px 10px 40px\"><img src=\"images/no_hands.jpg\" width=\"350px\"></div>\n",
    "# Look Ma... No K!\n",
    "### DBSCAN\n",
    "Density-based spatial clustering is a data clustering algorithm that given a set of points in space, groups points that are closely packed together and marking low-density regions as outliers.\n",
    "\n",
    "- You don't have to pick K\n",
    "- There are other hyperparameters (eps and min_samples) but defaults often work well\n",
    "- https://en.wikipedia.org/wiki/DBSCAN\n",
    "- Hierarchical version: https://github.com/scikit-learn-contrib/hdbscan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Now try DBScan\n",
    "http_df['cluster_db'] = DBSCAN().fit_predict(http_feature_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1xhs2vc97WFiwalSjYRWlTKZVsYqi1rS8wUG4cgX+3t+DBx6wkJVuwjs6Ct/5jl2zuEYr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itXrqSwsHDI+w6GgikRERERkXGgM52hM5XBN8hAqofPMXSmMnSmM3mdd//+/cTjcVasWJHX/uer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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1124e62e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now use dataframe group by cluster\n",
    "cluster_groups = http_df.groupby('cluster_db')\n",
    "\n",
    "# Plot the Machine Learning results\n",
    "fig, ax = plt.subplots()\n",
    "for key, group in cluster_groups:\n",
    "    group.plot(ax=ax, kind='scatter', x='jx', y='jy', alpha=0.5, s=250,\n",
    "               label='Cluster: {:d}'.format(key), color=colors[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Now try DBScan with different parameters\n",
    "http_df['cluster_db'] = DBSCAN(min_samples=10).fit_predict(http_feature_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JlgWAoUiQ5j0PeP994Nd/Hfjt317+H0SlIs/77rtyTbuNf1tHr1+8KIMv+vokbFUqj//7\nQERERERPxi5qw3vcGKa22uHDsr9TrSZVo8lJmcpn2xKklJKwVSpJmEomm7tDu64Eo3CfqD/+Y7lm\nz57Fm/BWKsCVK3LuajtLh6PXLUtGr7/yioS8ePzJfC+IiIiI6MnYJW14jxvD1FYbHAT+8A+BW7ck\nuHieVInKZWnFCzdH01oeK5VkfVRYSYrFmm2A9bq0B/7szzY34f3LvwS+8Q0ZjblakGrVOnr9Yx+T\nUEVEREREO8suaMN73BimtprjSJAyDKkA3bkjxz1v8S7TSsnXvi8tgMmkXHPnjlSSIsF0lfv3gb/4\nCzlmWfKc1aqsreruliC2HrGYBLdiUQIfEREREREtwjC1lbQG/uAPgBdeAL79bQlWvt+c2NeurGoY\nzUAVj8taKt+XsOQEG7TNzMj+VJ4nwyfm5+XzGzek0tSubU/r5sTAclmes1qVe7h1C3jzTRlEUaks\nbiHs72f5l4iIiIh2JYaprRTu4XTokASUt9+WEOM4EpqW0loeDzdOsyxZNzU3J4EmbAus1+WxULgO\nqliUlsCPf3xxAKpUgLt3F08MDNsKb90C/t7fk6rWD/0Q8NRTzRbCCxckTJ061X6KIBERERHRDraO\n4fL02LTu/9TXJxvvdnc3K0xhcPL9YIdoTwJT2KpXq0mQCkNUGLB8v/ka4TWxGNDVJXtLffe7wAcf\nANevS7i6fl2ePxqVdsGwKgXI8+3fL/d55YqMV08mgQMHJEjNzMhY9pGRJ/7tIyIiIiLaSgxTW2np\nhr25HPDpT0tYCceeax0sDozJuZmMBB/DkIpSWJHSWp4jGl1c1QorVeGGv6YpE/8cR/68e1f+nJuT\n9r7WFsN4vPl88bjc1+XLEqjCc3I5oLcXeOMNYGzsyXzfiIiIiIg6AMPUVqpUFrfjARKWenvleDYr\nYSWblapSuM+U48i5vi9fh+uswtA0NQV85zvA974nlaPpaQlgpinhyDSl2gRIxSls66vXpY3Pspr7\nXrWur2qd8heGN0DWbXV1AUNDi48TEREREe1gDFNbKR6XkNNKKeCTn5T2PM8LNk5zZYhEGIwqFQlP\n4dCISkXOCQNReLxUkmqT58nxWq3ZAjg3J+Eol5OPnh5pMbx3TypVYdtga+UMaE75K5UWH89mm2vA\niIiIiIh2AYaprXT4sFSSlurvl6EU8/MSnmZmZA1T2PIHNMNWuL4qZFkSkkxz8fFw7VUYwopFea7W\nQRSmKaGsWm2uoYou2XcgbCscH19+3LZlHRgRERER0S7AMLWVBgclDC1tjVNKRpiHAyiWajeKPAxG\nrWutAAlI4f5U4THPk8C0tMUQkPPCiYL79rV/rVhMWgmXymRkfDoRERER0S7AMLWV+vubE/FaTU/L\neqd8XtYipVISYML1UWGrXriGCpCAZFnNjX7D9sHWkelhoDKM5rqrVuE4dK1l2ES7/agAuX5peyIg\nr1GpbPjbQERERES0HTFMbSWlZI+mcJIeIEHm6lVpr8tmJWxFoxKUEgkJOKYpLXVhOFKq2dbXWn1q\nrSoZRjNYha9Tr8tzeJ60/4XT/lKpxePVl/L99lUtz1s5gBERERER7TAMU1ttYAA4fRqYnJSKVLEo\nwx1iMXk8HpcKVX+/fF6tSqjKZuVx05RgE45HX2uaXjh5r6tLqki+L9em0/I6YWvfamGqWpWBFUsV\ni8Czz27mu0BEREREtO0wTHWCo0eB11+XgPTNb0pYcRxppavXpXJVqwHPPCNh5eMfl494fHmAWtp+\nFw6e0FoqXJ4ngerQIbn+ox+Vz/ftkwCXzTZHrrcTPl8+v/y448g6MCIiIiKiXaBNrxZtiYEB4OxZ\nqVDduCGT/FxXqk4HD0p4SaeB//Sfmi17XV0SjsKw1LrmKVzX1DqYApDH9+1rTv2rVhe35oX7UNl2\n+/usVuU+0unFx2dmmmvAiIiIiIh2AYapTqKUhJkXX1w+kjxkWc2R55GI7A21sCBft+5NZVkSsiIR\nCVbh4IrubglP9bqEqnq9GaxC2WxzSEXrcceR848fX1y5Kpcl/J05s3JFi4iIiIhoh2GbX6dpt5Fv\nq54eqQ4B0q4XrncyTfk8EpGvM5lmW5/nyWPJZHMT3moVePpp4OWXJQxVKhLCqlVgzx7gR36kedz3\n5c9yWc7P5eQ5tJZ1Xg8eyLqvgYHH+70hIiIiIuogDFOdZqWNfEP5fLOtL5NpVqEyGQlLqVRzn6mu\nLqkspVISsAyjeU247qmvD3jllebeUTMzsi4rlwN+6IekGnX7tvx57JhUrRYWgIkJoFCQr197DXjp\npSf1HSIiIiIi6ghs8+s0g4PAhQvNkedLheuVqlUJQNFoc22VYciapXASICDDKyYmZIBFeP7SdU/Z\nLPDCC8Ddu8AnPiHnVirA3r3SujcwAIyNAR98IMfjceDECeDkSVnPxdY+IiIiItqFGKY6TetGvmE7\nXSulgCNHgIsXJUDt2ycVIt+XoLR0rVUqBezfL9WlREKeV2tZl1UuSxXMceQ1/+E/XLlV78SJR/9e\niYiIiIi2MYapThNu5HvunFSSEonl5+Rysnbp8mVZI7VnD/Dhh/Jnq3ANlNbA3/pbMjzi+9+XNr5k\nUq5lhYmIiIiIaFOUXmuT1x3m2LFj+p133tnq21jbyAhw/rxUlrLZ9kFnakoC1fS07BU1NydT9YDm\nuqlUSgZNJBJSfTp1ioMiiIiIiIhWoZT6ptb62FrnsTLVqcKNfIeGpI3PtmV4hGnKdL6wPe/TnwZ+\n9EflnNFR4P594N49eY69e6Vadfgwq09ERERERI8Yw1QnCzfyLRSA4WHg2jUOgCAiIiIi6hAMU51O\nKRlhns9v9Z0QEREREVEL7jNFRERERES0CQxTREREREREm8AwRUREREREtAkMU0RERERERJvAMEVE\nRERERLQJDFNERERERESbwDBFRERERES0CQxTREREREREm8AwRUREREREtAkMU0RERERERJvAMEVE\nRERERLQJDFNERERERESbwDBFRERERES0CQxTREREREREm8AwRUREREREtAkMU0RERERERJvAMEVE\nRERERLQJ1lbfABER0bajNbBQAO4PA7OjgFcBzDjQfRjYMwgk+wGltvouiYjoMWOYIiIi2oi5MeDG\nkIQpwwbsDGDEAN8BJi8B9y5ImDp0CkgNbPXdEhHRY8QwRUREu89mK0vTI8DoecBKAYml50QBKynP\nXZsBvnsOOHwayB19Ym+LiIieLIYpIiLaXTZbWZobkyAV7QWsxMrPrxQQzQFmDBh9A3j+NVaoiIh2\nKA6gICKi3WN6RCpGtRmpLMUPSDXJDKpK8QNyPKwsTY/IdVpLALNSqwepVlYCsLrkOq0f33siIqIt\nwzBFRES7Q2tlKZpbeUBEWFmK9kplaW5MqlgLBSCS3dhrRrLNa4mIaMdhmx8REe1MreuiSt8D7l8A\nfBdIPA0k80AkDWCViXtWAvCCylLykLQEbnRCn1Jy3f1hoCv/EG+GiIg6EcMUERHtPEvXRWkFuAuA\n2QWUbwIL44CdBrJHgEhu5ecJK0vVSVlbtRl2Bpi9trlriYioo3VcmFJK5QD8DoCfAPAAwD/RWv+7\nNuedBfA6gFrL4Re01tefxH0SEVGHajdxb+ZdCVVmFEAUgAa8KnD/LaDnOBDva/9cYWVpfgzI/tDm\n7keZMi3wUeI+V0REHaHjwhSA3wJQB7AXwIsA/lQpdVVr/V6bc/+D1vrnn+jdERFR52o7cU8D5Qmg\nPgtUHwDwARjBgIgkMPU2sOfVlStUdgZw3gO0CwliG6Q9CTqPCve5IiLqGB0VppRSSQB/B8DzWut5\nAJeUUn8C4BSAX93SmyMios7WbuJefRqYuSptfcoAjAhk9pIGnBJQL0rl6ME3gP0/CVlDpYF6Sa6p\nTUnVpz4NTF0Gsi9Je+BGqj5OEeg9sfI9b6TCxH2uiIg6SkeFKQDPAnC11q3N5VcBDK5w/k8ppaYB\n3AHwhtb6f293klLqcwA+BwAHDx58hLdLREQdI5yal+iXryt3pOpkRIPKkJbgFFKmHPNdqfbMfgDE\nnpLw5ZSC8BWT8+xueb56SQZXZI7IxL+GJQFMO4Cypdpl2kDvq8vvd6MVJu5zRUTUcTptNHoXgNkl\nx0oAUm3O/QqAHwDQC+CXAPzPSqm/2+5JtdZf1Fof01of6+3tfZT3S0REneL+cHPiXn1agpSZlCBl\nJiQ0LaMAZUlwuv2nwPiXpUJUmwbcMqB9CTfJvKyrMiKy1mryIlC+I09RnwbuvQncf1OGW2hPnlN7\nstZqbgy4/n/Kn6GN7nc19U3uc0VE1IE6rTI1D6B7ybFuAHNLT9Rav9/y5X9RSv0bAD8L4N8/vtsj\nIqKONTsaTNzTUl0yohKuACCSkXY7rRe3xvkO4M4Dfl0+N+NAJIFGG2BtRs7PfEKe+95fSsCCB0z8\nPxKynDkJLfaSUevakfD11AlpJwzb7uzujVeY3v+ChLP0Jzb2PWnd54qj2YmIHrlOC1PXAFhKqY9p\nrT8Ijh0B0G74xFIaq24YQkREO5pXkTa5ekmCkJ1uPmZE5UO70n4HAF4NcOcAqCAgBWuptAaMoFrl\nVwGvDNz8vyQYKTMIXTEAPlD8toxbtxcAtU/CmNZynV8Hel4GYj3yemYM+N55wO7aeIWpPiPVso2G\nKe5zRUT0WHVUm5/WegHAVwH8M6VUUin1IwB+GsDQ0nOVUj+tlMoq8TKAMwD++MneMRERdQwzLmEp\nHDbR+vs1pYD4PqnuaC+oSM01z9N+s2LlV+Xx6hTgTMn+VH4d8Bbk+Q1LjtVLwXUm4NWB+RtA9R7g\nliQ49b6yeOS6FVS8Ji9KpWwj3CCcOaWNf1+4zxUR0WPTUWEq8PcBxAHch7Ts/bLW+j2l1CtKqfmW\n8/5rAGOQFsAvAfgXWuvff+J3S0REnaH7sLTy1aakQrWUGZe1SL4DOLPSzwAjGHkeDKdQBuAsANX7\ngF9phiVlysa/XkUqWlASvrQnVSN3Th6r3geSA0Dv4JIBFYF6UdZiOUuXB6/FlXVYC+Mb/a48nn2u\niIgIQOe1+UFrPQ3gb7c5fhEyoCL8uu2wCSIi2qX2DMoEPL/eXCu1lNUF2DkJQNoH/BoAL3hQybW6\nJq2A2gyKW62/dwwGOfjV4DoDgC9te4YVDKd4Eyh9B7BTwUAMC4j2yPqq+pQMmVgYByIvNp+z3STA\n8JpIWr42PHl8ox71PldERNTQcWGKiIhoU5L98lH8bksQauFVgMpdqV7BkJHl2gP8IEz5dQAuABNQ\nUQBBBSoUVqJ0NTwAqWhB1lWpLnkOtwq4FXnMTEh4Kt+UVjunBESfaoaicB+sRaPYg0mA5ZsSsOy0\ntA26s4DexP+2V9vnioiIHgrDFBERbX/h5rdWUtYteZXmWPRIRtY0VW8395YybAkvypCR6WY0qEpB\nHtf14IlbE5kffKiWx7RUuLw64E9LCIIvz2PY0ipo2M2Q5S1I8Io/vXgfrKWTABWCDYa1VLuqk1IN\ns5YOvF3H98V3pGpHRESPHMMUERFtb0s3v839sLT7qZqEmPoDCVdWanH7n/YAaBkMod3gaxWsj3Kk\nQtQ8OZj4t1QYqGrBXk5BS6B2AW3IOiun2Jwg6LsSjvyCnBPJrdySGD6/GZd7mhsFYn2rnNtGfaZZ\nsSMiokeOYYqIiLav6RHZs8lKySa3SgHx/UDlQ6A8IeHFrQLQMiRC66BVD7LGyeoCoIDaJJo7bAQf\n2gsm/IUVo9aNbw0011ot3RBXy4dfbXnYkwpUGNS8OWDhpqyrwmphKqBsmcpXn5FJglZy7Wvcsuyh\n9fEzi/fWIiKiR6YTp/kRERGtbW6sufltNNcSGBTw1CeD472AYQZVJgV487KOyU5K+59hB61/QGOi\nX7vw1KhKKQBBe2AYmhbx2xwDAEda/nyn+Tx+TcapO8v2pV/Or0prYPIjwaCK6aAS1obW8njtgWwS\nnBpY+/mJiGhTWJkiIqLtR2tp7Vtp89tIDnjqOHD7P8p6KDMOCTBBmNFuUIjSwUa+hgytaAgqT+Fo\n9EZICsJU25a/pZaGMr/l66A90HeAygRg9K88cc93gg2Aj0sgS31MqlNhW6OdCSpenrQU+o609X38\nDIMUEdE81V/zAAAgAElEQVRjxjBF66a1RqFUwPD4MEanRlFxK4hbcRzuOYzB/CD60/1QbCUhoidh\noSAfiVXWAsX7gNgeoHpX9oZSCoApFSFlBRP4TAlbkSzgzAfHgGYQ8uWaRcJBFMFY9HUJ2wV18HzB\nde68hJ/Zv5ZQZCXkTzMqp/rBZr09L0v1Ldws+Mjn5f3fH5YpgV5FwljvCWDvSSBxkK19RERPAMMU\nrcvY9BiGrg6hUCrANmxkYhnEzBgcz8Glm5dwYfwC+tP9OHXkFD6a/ShDFxE9XveHgxa9Nf4tMWwJ\nXGHVxi1DhjpEAW0DsV5ZN1W9J+urGoMowmqSxuJ2vqDFTyP4s97mRdtpub4hnAwYVKi8mgS92rSE\nvEhGwmDP8eYGwOEGvEoBXXn5ICKiLcMwRavSWuNPP/hTfOHSF1B1q7CUBduy0RPvQT6TRzqaRjKS\nhNYaM9UZ/OP//I/RE+9B3a+vGboGcmw/IaJNmh2VCs5awj2bzBhg7pNjfl1CSfdzwPRlma5npWW4\ngxFs1huuadL1oKUvaO8z4xJ8VNiqt5ola5qUFTyXi2alypSWRHdOgpwRBwxDzlUG0HW4GaQAbsBL\nRNRhGKZombCd7yvf/Qq+8t5XMDY9hogVQXe0G5lYBspTuFm6ifGZcUTtKFKRFGpeDaVKCXfm78A2\nbfz4oR9HIprAB9MfYKoyBddzYZkWemI9AIDPv/V5nDl+Bkf7jm7xuyWibcmryAa3a4n2yOa3RqR5\nTBkSXBJ9gPkKUAw2zYUh57llqfwoE1DB1DwzKpUjKCzea6rxpGg/eCJ8LPzfbesgCy2vY1iA3S2v\n33WoeZnvADNXAOuVZqDiBrxERB2FYYoWCdv5Ru6MYHRqFHO1OcSsGGzTxkJ9AfP1eUTNKDKxDGYq\nMyjPlaG1xlPxpzBTm4Ft2Ki5NXz1e19F0k7iqcRTSMfSMA0Tnu/h5uxNjJfGkbST+OcX/zm+8ONf\nYIWKiDYurBAhuvp5ybxMv9O62RKo/eYeUtEcsOekhKnSe0Dx24AVB9xKUBkakIBVCdZdaQeN0LRo\nCEXrRr6tQat1IqC35PGWfanCUeytDBvQEQl7e07KMW7AS0TUURimdon1DI/41t1v4fzl8/B8D7dm\nbyEVSWG2NouoFYVSCqZhQmuNilPB/YX76Ip0IWkn4fkePpz7EF2RLmhoVN0q/OCHjKnKFGJWDKlo\nCgAQMSPQWs55b/I9/PqlX8dv/9Rvcw0VEW1M92Fg8tLa+y3Zafnwqs32OL8qAxpCSsn6pN4fAbo+\nAky9LXtTeRVI4HGBxAEg0i17Q/n15nUqHrQNGsvDUCNIWSsUroKqVHiuWjroAlJ9c0ryoX1uwEtE\n1GEYpnaB9QyPSEVSuD13Gx/JfgSXJy4jYkQwVZlCxamg4lSgoaGgYCgDNa8Gy7BQcSuImBEopaCh\nUXbK8LUP0zChtIKnPcTNOCbmJtBv9CNuyw8ySinE7ThMZeLC+AUMF4ZxMn9ya79JRLS97BkE7l1Y\nXHFqRykgcwSYvCjVKCNYt5TMtz8/3gf0/DAwdQXIPCcb7daL8mHG5PHqHdlEt+sjMriiPt3SArh0\n7ylD7mFR0GqtYgWVKd+V0Nfu/pUhE/vi+7gBLxFRh2GY2uFG7ozg/OXzSEVSy6boRRFFMpKE7/v4\ns+//mVShzCjuL9zHQn0BU5UpAJBwBAWtNRbcBWit4SkPlmFhvj4P27ChoFBza7BNG4aSHw4cz0FX\npAu+9nF34S7y6fyi149YEUTMCP71X/1rDPYPsjpFROsXVmhqM4sHNLQTzQG5l2XYhOtLaIm0CS5a\nyxAK3wOO/65ct3T0ePezsn7qvf8tGEShJHD57uIWwHCdVOPftSV7TBm2hDPtA15djkfaDdTQckll\nAnjxHPeNIiLqMAxTO9jY9BjOXz6P3kQvEnabTS0Ds/VZ1LwasrEs/vLGX6LqVZG0kzAMAwaMRsjR\nwQ8JhjKgoVH36vC1D1/7UErBh99o71OQapXWck2xUsSYNybtgspE3I4jE8sgFUnhevE6CqUC8pn8\n4/2GENHOoRRw6BTw3XMSStpt3Nsq0QfgGDB9BYg9BVRur2+z23ajx7UGiu9Ly1/ltqxpstOAOxu0\nAAbrohSweMQ65LgRCapQGojtA2r3gz2lalKFUsGmwH5V/jST0taYe+lhv2tERPSIMUztUFprDF0d\nQiqSWjVIAcB4cRyGMuBpD6V6CaYyYRkWDBiN9j4AcH0XgLTphWHJ8R342oehDChIa1/dq8P1XWho\n1OZrjWsMx0AqmoKvfZSqJRSrRUSMCGJ2DMPjw8i/mH+s3xMi2mFSA8Dh08DoecBLyca77SrcYcVJ\nr1Jx2shmt0oBH/kFCXLZIxKCvKo8tnALcKYBf0klKmztMxPB5D5Iy6GVANQeIPOiBLralEwaVJbc\nS1c+aE9cY9AGERFtCYapHapQKqBQKqA/vfZC5anKFKJmFLfnbkuA0hqe9mCbNqputdG25/leI1iF\nFBQc3wEQVK508zwNDWVIeyA0UHbKSNgJ2KbdGGZR9+qoVCp4a/wtfObFzzz6bwQR7Wy5o8DzrwM3\nhoCFgrTPbbbitBGtQS7ZD8xclc1/uz8GVKeAyi2pKmkdLKUypIJmdzdb+6JPSZjrOS5rsVZSmeA4\ndCKiDsUwtUMNjw/LWqZ1rENyPReO76Dm1RojzKtuFTErhqpbhdZ62fP42ofne9LKt2RElYaGDx9G\nsLDaNEz4vg8fPkq1ElKRVGNCIACkIil84/Y38MHUB7BNe9WJg1xXRUTLpAaAT5yVMPUwFaeNag1y\n9RnZSNjqkvVYpi2DKry6VJUiPdLOF45Xjzwla6QyR1Zf86U1x6ETEXUwhqkdanRqFJlYu8XMy1mm\nhcnyJBQUImYEVb8Kx3OQtJONIGS2jOwNgxTQXBvVqvVrz/dk6UDQGmgoA3P1ORjKgGVY0NDIxrOo\nuTV89k8+i3w2j4gRaTtxsD/dj1NHTnFfKiJaTimpNj1sxWmjWoPcza8AH35NpvsZNpB+EYj1SOte\n9S5k/LkBZI4C6R+UdVNrBbz6DMehExF1MIapHariVhAzY+s6tyfeI1UhQ9rvKm6lMVSiy+5CqVZq\njDx3PKcxZAIAfPiLnisMV+HgirBl0FAGbEMm/WmtMV+fR1ekC1Ezivn6PCpuBZ728OK+F5GNZxvP\nF04c1FpjpjqDcxfP4fTLp3G07+ij+UYRET2sMMg994+AH/ifllfIknmZApjMA9d/D4j2rj0wAwDc\nMuDOcxw6EVEHY5jaoeJWHI7nIIq1Fy3nM3l8/ebXJUwpE6YyG1P4bNNGKpLCXH2uMWACQCMUAYAB\noxGqwoEVYTueUqpRyQrDnaEMuL6LuldHT6IHH85+iEOZQ9CQjYVbw1RIKYVcPIeYFcMbl9/Aa6+8\nxgoVEXWetSpkdmr9AzPceVmXxXHoREQdy9jqG6DH43DPYRSrxXWdm46mEbfjcH0XSilEzSgMw2i0\n8kWtKNLRdGMQBSDTAsMA5cOHgoKlmtnc1z487cHTzXVVvvblumCcesJOYKo8hZgVQ1ekCzErhqny\n1Kr3mrAT6Ip0YejqUCPMERFtG+E6q2gWKBdkuIS7INMA3QX5ulyQx59/jePQiYg6HMPUDjWYH4Tj\nO+sKHEopPNvzrIw091yYholnup+B4ztwPHkO27QRMSMStNTivzYGDCStJHLxXHN/KSx/3YpbQcWt\nAAAysQxqbg0Vt4KnU0/L6PSgYrWWbCzbmFZIRLTthOusPnFWBmQYEdmfyojI1y/8U3mMFSkioo7H\nNr8dqj/dj/50P2aqM8jFV5kUFXh+z/O4WbyJ6eo0ImYEuVgOSTuJuwt3UXNr8LWPhfqCVJqCqpSC\nauxJFY/EseAswFRmoxWwNVCFwyciZgS+9hvTA59OPY1ERNYO+NqHZaz9V1IpBduwuTcVEW1fWzUw\ng4iIHilWpnYopRROHTmFufocyk55zfPT0TR6k73IRDN4pvsZzNZn4fgOsrEsDBiouBW4WjbiNQ1T\nJv0Fwcn1XcxWZ6XVz7AalSsDBixlIWJEYClrUUWr4siAjNb1UVW3ip5Ez7reXyaWwbWpaxv8rhAR\nERERPTqsTO1gA7kBnH75NM5fPo9UJIVsLNt2n6ZwUl5fqg9PJZ7Csz3PwvEdvHvnXfz1g7+GUgqZ\naAYRI4KIKR9KKZSqJZTrZUABnvZgaqlS+dpH3IoDkOOtY9RjVgwxK4aaW1u0CXC4liqfya/rvYVT\nB4mIiIiItgrD1A53tO8oXn/ldQxdHUKhVIBt2MjEMo3NeYvVIhzfQX+6H2eOn8FsbRbnL5+Xx2pF\nDOQGYJs2AODu/F2UqiUAgOM5jfY+x3Nkuh80UtEUFpwF2ZvKMGHCBEzA9d3GoAmtpbqloVF1q7BN\nG1W3inQ0jXQ0va735fleI7AREREREW0FhqldYCA3gLMnz6JQKmB4fBjXpq6h4lYQt+I4cfAETuZP\n4mD6YKNq9dqJ1/DZP/ksam6tEZIMZSBpJzG5MAlf+4hZMRzoPoDr09dRR13CmfZQrpeRtJOYrc/C\n1/6iEeoxS0aju76LqBlF1IziQfkBYlYMda+O408fb1s5a6dYLeLEwROP5xtGRERERLQODFO7hFIK\n+Ux+XQMbbNNGPpvHi/teRKFUwFR5Cq7vImEn0JvoRcyOIR1NQymFiBVpbPJrwICrXRiG0dibKgxS\nlrJkjZUv49Kf7noada+Om6WbWKgv4PjTx9c1KAOQlkDHdzCYH3yYbwkRERHRjqS1RqFQwPDwMEZH\nR1GpVBCPx3H48GEMDg6iv79/3b/AptUxTNEyw+PDiBgRZOPZZRvoTlem8VbhLbi+C9u0YSgDqWgK\n8/X5xvqnqltFV6QLhjIwW5+F67mIRWKoeTIVcF/XPhm77juI23G8sO8F9KX61n1/M9WZxrRCIiIi\nImoaGxvDl770Jbz//vuYnJxEtVqF7/swDANf//rX8bWvfQ3PPfccfuEXfgEDA9yC4WExTNEyo1Oj\nyMQybR/LxXM4fuA43p54G1E/CgMGbMNGOprGfH0eru+i4sqkPh8+4mYcXfEuuNoFNNCT6EFXtAs9\n8R7sSe6BAQM1v4ayU0bCTqx5b2WnjPn6PM4cP8PfqBARERG1GBkZwblz5/Dhhx+iVqvBMAzEYjHY\ntg3f9zE3N4dSqYRbt27hvffew+uvv46jR49u9W1vawxTtEwYhlbSl+rDq/2v4urdq4ACKvUK4nYc\n3dFu1L06Km4FjnZgGzYOZA7g6e6nkc/kG62BoYnZCfyNg38Dn9j7iXVPHJyvz+P0y6cxkONvUoiI\niGjn2mir3tjYGM6ePYsbN24gkUggnU4v+5kqEolAa41qtYpr167h7Nmz+I3f+A1WqB6CCte07BbH\njh3T77zzzlbfRkd77S9eg+M5SEaSq56ntcat2Vv48+t/DkA23dVaIxlJ4m9+7G8uC09Lry2UCjh7\n8izymTzGpsfWNXHw1JFTDFJERES0o42NjWFoaAiFQgG2bSOTycCyLLiui2KxCMdx0N/fj1OnTmFg\nYABaa5w5cwZ//ud/jlwuB9u213wNx3EwMzODH/uxH8Nv/uZvsuNnCaXUN7XWx9Y6j5UpWuZwz2Fc\nunlpzTCllMIz3c/gUOYQqm4VcTuO2dosDqYPrtgmGFq67mmjEweJiIiIdqKRkRGcP38eqVRqWfUp\nGo0imUxKx87MDM6dO4fTp08jm83i4sWLSKVS6wpSAGDbNrq6unDx4kVcunQJ169f57CKTWCYomUG\n84O4MH4BWus1/+NRSuHIviN4q/AWTGWua+PdldY9bWTiIBEREdFOMzY2hvPnz6O3txeJxMpryZVS\nyOVyiMVieOONN7Bnzx6Uy2Wk0+vbr7PVrVu38Cu/8it46aWXkMlkEIvF4DgOLl26hAsXLiyqgNFy\nxlbfAHWesGI0U51Z1/nhUIqZ6gyiZhTdke6252mtMV2ZxoPyA657IiIiImqhtcbQ0BBSqdSqQapV\nIpFAV1cXvvrVryIajW6ogjQ3N4ebN28CAGZnZ3HgwAEkk8lG9evAgQPo7+9vVMBGRkY29b52Olam\naBmlFE4dOYVzF88hZsXWNWUvHUvjuT3PYX/Xftycvbnquqczx88wSBERERG1KBQKKBQK6O9fvvWL\n1hqlUgnj4+OYmpqC67qwLAs9PT3o7+/H9PQ0Dhw4sO7XqlQqmJiYgG3bME0TCwsLbc9bWgF77bXX\nWKFagmGK2hrIDeD0y6c3NGXv1175tcZGv1z3RERERLR+w8PDsG172c9J09PTuHr1KkqlUmPUuWma\n8DwPN2/exPj4OMrlMqanp5HNZld49iatNe7evQvTNGGaJnzfX/OasAI2NDSEs2fP8me5FgxTtKKj\nfUfx+iuvr2vKXmu1ieueiIiIiDZmdHQUmcziAV537tzB5cuXEYlEVh11blkWJicnsWfPHqRSqVVf\np1aroVarIRqNAgBc113XWqtsNtuonuXz+Y29uR2MYYpWxSl7RERERI9fpVJBLNbc53N6ehqXL19G\nIpFYdUJf2Ip39+5dTExMoL+/H/F4fMXzi8UilFJQSkFrDdd11xWOlFKwbRvDw8MMUy0YpmhNnLJH\nRERE9HjF43E4joNoNAqtNa5evYpIJLKuUefZbBaTk5PwfR93795FPp9f8Rfd5XIZliURwHEc2LaN\nH/iBH1jXPWYyGVy7dm39b2oXYJgiIiIiItpihw8fxqVLl5BMJlEqlVAqlTY06nzv3r2YnZ1FpVJB\ntVqFUgrFYhHlchm+78MwDCQSCdTrdUQiEXieh3q9jo9+9KPL2gtXYpomKpXKZt/ijsTR6ERERERE\nW2xwcBCO40BrjfHxcRiGsa5lFFpraK3x6quvoqenB9VqFdeuXcONGzdQKpWgtYZhGI2JgLOzs5ie\nnka5XEZPTw+OHz++7uUanuet2kK4GzFMERERERFtsf7+/sa+TlNTU4vWT62mWq0inU6jv78fzz//\nPAAZMrESy7Lgui48z8Pzzz+PXC637nssFot49tln133+bsAwRURERES0xZRSOHXqFObm5lCpVGAY\na/+Y7jgO6vU6jhw5gpmZGVy/fh2HDh1CLpdDT08PIpEIfN+H67rwfR+RSAR79+5FOp3GoUOHcP36\ndUxPT6/r/rTWcBwHg4ODD/tWdxSumSIiIiIi6gADAwM4ffo0fvEXfxGe5yESiaBUKi1b95ROpxvh\n5uWXX0Y2m8Wbb76JSCQC0zSRSqVw7NixFTf5/da3vtWoXl29ehUnT55cs9VvZmamUT2jJoYpIiIi\nIqIOcfToUfzkT/4kvvSlL6FWq8EwDEQiERiGAdd18eDBA9y/fx+pVAqDg4Po6+tDsVhsDKyYm5vD\nwYMHkclk8OKLL7Z9jRdffBFvvfUWEolEY9jFakMoyuUy5ufncebMGW6HswTDFBERERFRhxgZGcH3\nv/99ZDIZpFIpzM7ONipTlmWhu7u7UZn69re/jXg8jnv37jXaAn3fX3MfqFwuh+PHj+Ptt9+G4zgY\nHx9vG7y01piZmcH8/DxOnz6NgYGBx/GWtzWGKSIiIiKiDjA2Nobz58/j0KFDmJycRLVaxb59+1Y8\n37ZtXL58GaZpIhaLNYZRrGekel9fH1599VVcuXIF169fR29vLzKZDEzThOd5KBaLcBwH/f39OHPm\nDIPUChimiIiIiIi2mNYaQ0NDSKVSSCaTOHLkCC5evAjLslbcuNe2bUQiEXz44Yc4cOAAHMfZ0Kjz\nXC6HV199FVNTUzhx4gSuXbuGSqWCeDyOEydO4OTJkzh48CBb+1bBMEVEREREtMUKhQIKhUJjwEMu\nl8PLL7+My5cvIxKJIBaLtQ010WgUtVoNxWIRn/rUpzY06hyQtsC9e/fiM5/5zCN5H7sNR6MTERER\nEW2x4eFh2La9KDD19fXhlVdeQSwWa2y4W6/X4bou6vU6ZmdnMTs7i2QyiUwmg76+vg2/LveOejis\nTBERERERbbHR0dG2E/VyuRxOnjyJUqm0bNT5wYMHkc/nsbCwgLfffhta6w215HHvqIfHMEVERERE\ntMUqlQpisVjbx5RSq446j0ajSCQSmJmZ2VCbH/eOenhs8yMiIiIi2mLxeByu627qWt/38dJLL2Fu\nbg7lcnld14R7R506dYoDJh4CwxQRERER0RY7fPgwisXipq4tFov45Cc/idOnT2NychLT09PQWrc9\nV2uN6elpPHjwgHtHPQJs8yN6hLTWKBQKGB4exujoaGO86OHDhzE4ONgoo691Dn9DREREtLsMDg7i\nwoULD7XuKZ/P4/XXX8fQ0BAKhQJs2+beUY+ZWim17lTHjh3T77zzzlbfBu1AY2Njy/7xsiwLrus2\n/vFKpVIAgLm5uRXP6e/vx6lTp/gPHBER0S6itcbZs2c3vO5penoa2WwWZ8+ebYSw1l/utu4d9eyz\nz3LvqHVSSn1Ta31szfMYpoge3sjICM6fP49UKoVsNtv2H6jbt2/j4sWLAIBXXnkF+/fvX3aO1hoz\nMzOYm5vD6dOncfTo0cd+70RERNQZxsbGcO7cOfT29iKRSKx5frlcxoMHD/Daa6/xl7CP2HrDFNdM\nET2ksbExnD9/Hr29vcjlcm2D1PT0NC5fvoxcLodcLocrV65genp62XlKKeRyOfT29uKNN97A2NjY\nk3gLRERE1AEGBga47mmbYZgieghaawwNDSGVSq34GyStNa5evYpoNArbtmHbNiKRCK5evbriP5KJ\nRAJdXV0YGhpa8RwiIiLaeY4ePYrXX38d2WwWhUIBExMTWFhYQLVaxcLCAiYmJlAoFJDNZvHaa6/h\npZde2upb3tU4gILoIRQKBRQKhVX3ZyiVSiiVSkin041j4U7mpVKp7QZ9ABr/iBYKBeTz+Ud960RE\nRNShBgYGcPbs2bbrnk6cOMF1Tx2EYYpoE8KFnZ///Ofx7rvvYnR0FJZloaenB/l8Hul0uvEP3Pj4\nOAzDWPQPnlIKhmFgfHx8xQ34lFKwbRvDw8MMU0RERLuMUgr5fJ4/A3S4jgtTSqkcgN8B8BMAHgD4\nJ1rrf9fmPAXgCwA+Gxz6twB+VbMnih7SWuPNHcfBH/zBH6BQKODdd9+FbdtwHAeTk5P44IMP8PWv\nf70xMef555/HgwcP2u5oHovFMDU1teq9ZDIZXLt27XG9VSIiIiJ6CB0XpgD8FoA6gL0AXgTwp0qp\nq1rr95ac9zkAfxvAEQAawH8GcAPA//EE75V2mHbjzWOxGBzHwaVLl/CHf/iHuHXrFl544QUcOnQI\n7777Lu7du4d6vd6oJIXh6lvf+hZu3ryJcrmMvXv3IhKJLHotwzDW3OncNE1UKpXH+ZaJiIiIaJM6\nKkwppZIA/g6A57XW8wAuKaX+BMApAL+65PTPAPiXWusPg2v/JYBfAsMUbVLrePOlG+dGo1HUajXc\nvHkTpmniO9/5DhYWFnDnzh0opRCNRhedb5omLMvC7OwsPM/DrVu38MwzzzT2mQIA3/dhWav/J+h5\nHuLx+KN/s0RERET00Dptmt+zAFytdWtf01UAP9jm3B8MHlvrPKI1rTXePJzIF4vF0N3dDaUULly4\ngFgsBqVU2wWgpmnCNM3GNL6JiYlFVaZqtYqenp5V76tYLOLZZ599BO+QiIiIiB61TgtTXQBmlxwr\nAUitcG5pyXldqs1PtUqpzyml3lFKvTM5OfnIbpZ2hvWMNw8n78ViMWitMTU1Bdu24XkefN9fcXy5\nZVlQSkFrDcMwcPfuXWitobWG7/urLirVWsNxHAwODj6Kt0lEREREj1inhal5AN1LjnUDmFvHud0A\n5tsNoNBaf1FrfUxrfay3t/eR3SztDOH48Ww2u+I5rRP5arUaarUaotEoXNeFZVkrrn1SSsGyLPi+\nDwCNa6vVKtLp9KJx6UvNzMygv79/1bHrRERERLR1OmrNFIBrACyl1Me01h8Ex44AWDp8AsGxIwAu\nr3EedQCtNRZu38b9K1cwOz4Or1aDGY2iO5/Hnh/+YST379+yvRKGh4dh2/aqrz81NdWYyFcsFhuj\nzcP1UuVyGYZhwDTNZdeG1amwgjU1NYWuri4cP358xdcsl8uYn5/HmTNnuIcEERERUYfqqDCltV5Q\nSn0VwD9TSn0WMs3vpwH8V21O/xKA/14p9f9Cpvn9DwDOP7GbpXWbKxRw42tfw8Lt2zBsG3YqBSMS\nge+6mPzWt3Dv8mUk9+/HoZ/6KaS2oAozOjq64sa5IcdxGsMiyuVy43PLsuB5Hg4cOICJiYnGUIl2\ne0r19fXhxo0bmJmZwac+9Snkcrllr6O1xszMDObn53H69GkMDAw8wndKRERERI/SusKUUsrUWnuP\n+2YCfx/A7wK4D2AKwC9rrd9TSr0C4D9qrbuC834bwEcAfCf4+t8Gx6iDTL//Pka//GVYySQSbapP\nVjwOrTVqs7P47he/iMM/93PIPffcE73HSqXSdh+oVuH6KECm8BmGdMgqpeB5XmMC4N27d1Gr1Rrt\nfUopuK4L3/fheR7y+Tx830e9XsfExAQymQxM04TneSgWi3AcB/39/Thz5gyDFBEREVGHW29l6o5S\n6t8DGNJav/M4b0hrPQ3ZP2rp8YuQoRPh1xrAPwo+qAPNFQoY/fKXEc1mYa0y3lsphWg6DTMSweiX\nv4znP/e5J1qhisfjcBwH0Wh0xXN6enpw8+ZNRCIRGIbRGDihtW609sXjceTzedRqNRSLRZTLZfi+\nD9/3sX//frz66quwLAuRSAS/9Eu/hD/6oz/C8PAw7t27BwDYu3cvTp48iZ/5mZ/hbudERERE24Ba\naQrZopOUOgrg5wH8XQBFAEOQYHXr8d7eo3fs2DH9zjuPNQ8SJGR857d+C7XZWURXGbKwVK1UQrS7\nG5/4B/9g02uFNro+6/d///dx6dIlHDhwYMXnLBaLePPNN5FOp3Hv3j0Ui0VYloW5uTlYloV4PA7T\nNJ5Ib8oAACAASURBVBGPx5HJZBr7TmmtUSqVcPLkSWQyGUxMTOBjH/sYFhYWFm0MHA6xaK1OnTp1\nalPVKa01CoUChoeHMTo6ikqlgng8jsOHD2NwcHDZHlpEREREtJhS6pta62NrnbeuypTWegTAiFLq\nfwTwE5Bg9V2l1AgkWP0HrfXCw9ww7SwLt29j4fZtJPbv39B1ke7uxrVdq4SblWxmfdbg4CAuXLgA\nrfWKISOcvFetVhGLxTA/P98YKJFIJGAYBnzfR6lUQrFYRDQaxb59+xZdq7XG3bt3USwW0dfX13Zj\n4GQy2Vg3de7cOZw+fRpHjx5d9/sfGxvD0NDQoqAWi8XgOA4uXbqECxcuPFRQIyIiIqKmDY1G11r7\nAP4awPcATAI4AODnANxSSp169LdH29X9K1dgrDEhrx2lFAzbxv0rVzb8mtPvv4/vfvGLqM3OIrF/\nP+J79sCKx2FGIrDiccT37EFi//7G+qzp998HgMb48ZmZmVXv68iRIygWi7h9+3Zjo95oNNpo/TNN\nE9FotDEy/caNGygWizhy5AiUUrhx4wZu3bqFfD7fdmPg1tfK5XLo7e3FG2+8gbGxsXW9/5GREZw7\nd64xUv3AgQNIJpONkHbgwIHG+zx37hxGRkY2/D0mIiIioqZ1hSmlVFYp9d8qpS4BGIGEqF/QWj+r\ntf4xAJ8G8JuP8T5pm5kdH4cGMPO97+HuN76BO5cu4e43voGZ730P9dlZYJX2UjuVwmyhsKHXa12f\nFU2nVw0q0XQa0WwWo1/+MuYKBSilcOrUKczNzaFcLq/r9WKxGHzfRzweX1dgXFhYwLe//W288MIL\nSCaT63qNRCKBrq4uDA0NrbgpcGhsbAznz59Hb2/vYwlqRERERLTceitTHwL4KUhg2q+1/mWt9X8J\nH9RaXwHwx4/h/mgbmisUcO/ttzH17rso37kD7ftQpgnt+yjfuYP7V67g3pUrqBeLba9XhgGvWl33\n62mtceNrX4OVTK466KKVFY/DSiZx42tfg9YaAwMDOH36NCYnJzE9Pb0svGitcfXqVaTTafT19f3/\n7N17cNR1nu//5/fb90vSnYQAIYSOZxIiq6OI0QG55eye0cMpGQd/jqXlpOZ3ajmWKwfj0SqVpNbN\nWsA461l2zwqWM1u6uyeMWju1bvitHtlz3ANh2EEQWXEGnTAB08xELrl059rd6cv398eHb3dCLiRN\nh4C8H1WpkO7P5/v9dvAPXr4/n/cHi8VCcXExw8PDxGIxkskkyWQyfSiv1WrlpptuwufzcfjwYb78\n8kvKysq46aabpvy5AAoKCtKHCk/2+ZuamsjLy8Ptdk/putMJakIIIYQQYnwT7pnSNG2NYRgHLv74\nOHD24p9XXvJ/vYeBdsMw/t8ZeUJxXTFboaeSSaweD7rNln5PA/SL5zMlYzEufPIJRbfdhqu4eNQ1\njFQKy2ValY+Uq/1Zy5Yto6GhYcyeI4vFQk9PD2fPnsXtduP3+1mzZg0Wi4UTJ06kmzxYLBa8Xi9+\nvx+73U40GiWZTKJpGhUVFXR3d2e17NFms9HS0jJhhz8zbAWm2QFxZFCT7oFCCCGEENM3WQOK14Bb\nL/55K+pg3PH+JagDczRN+yvDMLbk+PnEdWTkUjv3vHkMnT07KkyNZHE4SCUSfPXzn+MsLES3WtGs\nVhx+PxaHg5KVK6d831zszzKbXVRUVNDY2Jjuhnfy5EkikQhnz56lpKSEO+64A9+IZYQrV67knnvu\nobe3l/b2drq7u0kkElgsFhYtWkR5eTmDg4P86le/YsmSJdN6PpPf7+fkyZMTvt/S0oIty89/uaAm\nhBBCCCEmNmGYMgzj1hF/nnRtkqZpxcBJQMLUDerSpXaeBQsY7OgYt0NeMhYj2tVFcngYI5Ui2t2N\nZ8ECjFSKwa++IjE4iLOwkP5ly6Z03lRfezu2vLysnnu8/VmaplFeXj4qYNTX1xOPx8fd76RpGn6/\nn6VLl45/D5uN48eP881vfjOrZ7RYLEQikQnfb21txe/3Z3XtywU1IYQQQggxsake2jspwzA6NU37\ndi6uJWbPdM9nGunSpXa2vDxseXkko9FRS/biQ0NEzp9H03V0ux0NSA4Pk0oksDgcGFYrzrlzSRkG\nv/rJT6h67DEKf+/3JnzO3i+/pGPfPpLDw2i6rvZCud04/H48CxZgz8uDSSo2U92fFYlEcE5j6eFI\n5qG+iURi0oOBJ5JMJnFNshfsSp9tsqAmhBBCCCEmlpMwBWAYhpyEex3L5nymkS5daqdpGv6qKjqP\nHkWzWNBtNpKxGJHz59WSPj3T+0TTNOL9/Wi6TiqRoOj223H4fCQcDlp/+lNuffzx9D1HPmcyFmPw\n7FmiPT1gGGi6TryvD91uZ7i3l8GODmx5eRQsXox9gsrNVPdnuVwu4vF41mFo3rx5hMPhKXfyGykc\nDrNq1aoZe7bJgpoQQgghhJjYtM6ZEl9P2Z7PNNJ4S+0cPh+Ft91GMholGY0S6exE0/VRQQoAXWd4\nYIBkLEbRN7+Jw+cDRnfcS6VS/G7/fg41NNBx4ABdJ05w/sgRol1dWN1uNKsVi8OBxenEMAxi4TDo\nerrRRaSzc9zPHu/vJ38KSwmrqqoIT9B98HLC4TA1NTXE4/Fpd84zDIN4PM7atWtn7NkWL16c1Vwh\nhBBCiBudhKkbXLbnM/UFgwx0dHC6uZlP//IvOXvwIJ3HjqXPkTJDg7u4mOLqatA0EkNDGKkUqWSS\nZDzO8MAAsXCY4d5eEoOD6eYTIwOHPT+f8MmTfNTQwLGXXyY+MICRTDLc04NmsZCMxUgMDpIYHCSZ\nSACgWyzoViuRCxfAMLC4XHR/9tmYVuyGYZCKx5l7112X/T2tXbv2isLQgw8+eNmDgcdjHsA7Wae+\nK322yYKaEEIIIYSYmISpG1i25zMZySSH//iP+WznTjr/7d9IJRJodjupZDJ9jtSFjz8m1tsLqAqV\nc84cXMXFWJxOEoODxAcGSI0IACnDIPyb3/DbDz/k7MGDqrIERLu6CLe1ceGTT7D5fNjy84n19qql\ng1Yrus2GfnGZXryvj2Q8Dqi9UJrFQrS7W42z2wmdPDnqsODhvj48CxbgmUJLdTPQZBuGysvLp30w\n8NDQEAMDA9TW1k7aqe9Kn226LdWFEEIIIYQiYeoGZjaNsOfnT3lOpLOTUGsr0Z4ebHl56SWBrqIi\nuHi2lNXrJRmN0nn0KEMXl9cN9/aqZXfRKLrDgWaxoGla+stqt6NbraTicQZ++1vO/PM/0/3LX9L9\n2WdY7HaSkQhWp5PU8DCp4WG0i00dAHRNw+b1qr1XAwOkkkn1usVCcng43Ugj3t/PcH8/AIlIhMTg\nIDetXz+lluKapl1xGLrcwcAmwzDo6emhq6uLzZs3U1FRMePPJoQQQgghpk/C1A1suuczDYfDdH/2\nGVa3G6vTydDZs+n3zNbmoP5xb3E6sTid9Pzyl8QuLuOLdXdjGAbJSEQ1jLBYVCi6OF7T9fQ+LYDz\nR46QiERIxmLpitJwfz9cDGAj6VYrVo8HDEMtBbw43mxuAapaNdDRQay3l1goRNX3vz+l1uumXIQh\n82Bg88Dcjo4OBgcHiUajDA4O0tHRQTAYpKCggPr6eu64446r9mxCCCGEEGJ6tOnus7jeVVdXG0eP\nSuNBgE//8i9JJRJTW+JnGJz/+ON0lScVj6NZLMxfvnzc901mJWrwd79TVaJoVDWguBiGjFQKTddV\nC/MRUskksXA43e5cs1rxzJ9PfzCIcXFvFJqWXsJnVrqSiQTxgQGsDgea1apCl6bhKSkhMTQEmkbZ\nH/zBhF0Jp6KtrY2mpiaCwSA2mw2/34/FYiGZTBIOh4nH4wQCAWpraycMK4ZhjDkY2OVysXjxYmpq\nali0aFFWFaNcPJsQQgghxI1O07RPDMOovuw4CVM3rk9+9CN0ux2L3X7ZscN9fVz4+ON0x75UIgGG\nQcnKlZkx4TAXPvkEi8uFblVd9w3DIBYKEe/rY3hgYFQ3PyOZJJVIqFbpF0OPbrWiOxykYjESsVh6\nnNXlQrfZSAwOwogwhlkNs1jUGKuV5PAwVqcTzWYjGYlgJBJ4AwFsHg+u4mK+9dJLV7y0babCUC5c\ny88mhBBCCHE9mGqYytk5U+L6Y3E4VCiagsGvvhrd0vziMr2R7H4/RbfdRvdnn2HY7VgcDlUtikTU\nEsBUCiwW1dFveBgjlVLLDEdcNzk8nD7EV7daMVIpjHgcLoYpzWJRS/7MMGCxYBgGRiqlKlJut9p7\nlUziLSlRzTF0nfnLl5OIRKa1rHEymqZRXl5OeXn5FV8r167lZxNCCCGE+DqRPVM3sPzy8vR+osuJ\nhcPoI5bvpYaHcYxzEK6ruJi5d96ZbviQGBwkFY+rKpOuY8TjJKNRACxOJ7rNpoLRxa/0PirDIBmL\nkRoeBk3DSCZVswqrdcx+IE3TVCDTdRJDQ+mqGUAqFks/51TPlBJCCCGEEGIqJEzdwObeddeo9uST\nMRKJdEXHrARN1FLc7vcz7667mHvXXbhLSjBQS/UsTqf6brenG05MflMDDAPdZsO42KEvvR9rnGc2\nlwomo1HMd83nnM6ZUkIIIYQQQkyFLPO7gZlnLMX6+nD4fJOO1S4uudNQ1R5bXl56/9T4EzTs+fnY\n8/OJdnUx0NGRXmKXjEbVtcyK1KXMoGQYqpplGGi6TiqZTFeu0vPHuW8qHsditZK8+Jz2vDxi0zhT\nSgghhBBCiKmQytQNTNM0blq/nsTgIIlIZNKxDr+fVCxGKh4nlUjgr6qa8t4j3W5H03WSsRhWp1Od\nCaXraj9UMqn2UhkGXPzZSKVUkwldR9M0DMPAerGqRSqlug9erFqN95nQNFIX92UVLF5MIhqd1plS\nQgghhBBCTIWEqRtcXiBA1WOPEQuFiPX2Trjkz11SQiIaJRmNUvTNb162kmUyDANHQYFaqnex855u\ntaYrW7rdrvZEXWwqodvt6nWbTVWlIL0fyj1vntoPpWlYPR4VxswgZt7vYhBLxmIUfvObGJqW1ZlS\nQgghhBBCXI4s8xMU/t7vcevjj/PlP/0Tg199hW6zYcvLS1eP4v39pOJxnIWFuEtLcRUXT/naw319\n+L7xDQY6OtRhuoBmflks6G73+BMNA91iSe+V0jQNq9uNp6SEaHc3yYt7sFLDw2r5Hxf3chkGNo8H\ne14eRjyOo7iYmx97TIKUEEIIIYTIOQlTAlAVqm9u2sTgV19x4eOP6QsGSUajWJxOiu+4g3l3301y\neJgTf/3XJCKRKR30m4hESAwOcvNjjzF49iyRc+dIDQ9P6VwrUAHK4nKpJhkXq1oWpxNPaSnJWEx1\nC4xGScXjJGMxNMPAVVyMLS8PZ2Eht/3X/4q7pESW9gkhhBBCiBkhYUqkaZqGt7QUb2nphGOqHnuM\n1p/+lOTwMPb8/HGDimEYDPf1kRgcTC+vc8+dS9HSpXQdPUoSJjzvyTAM1QZd18FiQbfbcc2dS3xw\nkHh/P5quozsc6FYrdr8fayyGkUphy8ujYPFi7H4/kQsXKL7jDmk2IYQQQgghZpSEKTEtU10S6Fmw\nYNTyuvzycmKhEPNWruTCkSMko1G1zM9iUR39DIPUxf1Pus2Gc84cYj09uObORQPyFi3CU1rK4Fdf\nEQuHVat2qxV3SQne0lJsXm9675W0QBdCCCGEEFeDhCkxbVNZEnjp8rq5d93F+SNHyC8vx1FQQM8v\nf0mksxMjHldhStexulxYHA40XVctzb1eABXOSkvTrdYnMywt0IUQQgghxFUiYUpkZSpLAkcaeaaV\n0++nZNUq4v39mUrTxTOkHH4/nouVpuG+Ps4dOoT94llRlzNyj5bskxJCCCGEEDNNwpS4KswzrX71\nk59gsduxulyXrTRZ7Ha8ixZhsVqJ9fVNa4+WEEIIcSMxDAgGoaUFWlshEgGXC6qqYO1aCATUQhAh\nRG5pE50r9HVVXV1tHD16dLYf44bV8/nntP70p1g9nimHI5vbPaU9WjetXy9BSgghxDXjagWctjZo\nalL3stnA7werFRIJCIchHlf3qq2Fioorv58QNwJN0z4xDKP6suMkTImrrT8YnHY4Mgxj3D1a+YHA\nuHu0hBBCiNmUi4AzWRhbs0aN+Z//E/7hH0DXweuFOXOgvBx8vkxQMwwIhaC/HzZvhmXLpn8/qW5l\np6+vjwsXLhCPx2f7UcQ4PB4PCxcuRNf1Me9JmJqAhKlrg4QjIYQQX1fHjsGrr0JeHhQUjB9ALhdw\nJgtjwSCcPAmxGESjKkA5HJBKqZ9TKRWmbr8dCgsz1xwagq4uqK8fG+CkupV7fX19nD9/ntLSUlwu\nl/y75hqTSqXo6OjA4XAwd+7cMe9LmJqAhCkhhBBCzJS2Nti2DYqLwe2+/PjxAs5kYezsWThyRAWd\nr75SgSoQUGNNhqFCVSwG3/oWlJRk3uvpUddsbMxcNxfhT4zV1tbGggULcE/lPwQxK4aHhwkGg1RW\nVo55b6phamxNSwghhBBCTJthqOpOXl4mSBmGqux8+in8y7/A3r3q+6efqtddLrU8r6lJjW1rU8Gm\nuFhVlUYGm54eFaTcbrBYIJkEpxM6OtSSPJOmqet6PGp8T0/mvYICVX0KBtXPk91vJE1T7xcXw86d\nap6YXDwex+VyzfZjiEnYbDYSicQVXUO6+QkhhBBC5IAZUsxeSD09cPw49PaqPU1Op6ooJZNw5gy0\nt6vleLfdpua1t48NYybDUNey29UyvO5uFXCsVvXeuXNqr9TIMGSzqfHHj0NNTea9WAy2blXBaN8+\ntZRv4cLMXitQz9zeru6TSKj7FBWpMR6Pes6R1S0xvuku7TMMg2AwSEtLC62trUQiEVwuF1VVVaxd\nu5ZAICDLBXMoF79LCVNCCCGEEDnQ0qICjKap5XiHD6u9TCObQZjs9sxyvAMHYP582LIFPv9cBSmb\nLRNefD4Vbnp7M2EnElEBB9T3WEx9OZ2j7+N0ZuamUipYdXerOXffDYODqjJmhju7XT1rLJYJgGYV\nbGQALCpSAbC8fGZ/pzeStrY2mpqaCAaD2Gw2/H4/TqeTeDzOwYMH2bdvH4FAgNraWiquwsa1xsZG\n2tra2L1794zf63omy/yEEEIIIXKgtVU1bujpUUHK41HL7SZbOgeZ5XsHDqiANbJ6tX+/+vrVr1S4\nMeckk5k/a5r6CofHv4euq2WFBw6o8Obzqee6cEGFNocD8vPVuC+/hFOn1J/z81W4slrV9/x8NTca\nVc/zd3+X69/gjevYsWNs27aNUChEIBCgtLQUj8eDw+HA4/FQWlpKIBAgFAqxbds2jh07lpP7vvXW\nW1RXV+P1eikpKWHdunUcPHgwJ9cGaG9vR9O0K15KN11///d/zz333IPb7aampmZG7yVhSgghhBAi\nByIRVcU5flwFFJtt8vH9/aq6k0qp8f39aonfeOHll79Uy+1MFosKXiarVTWzuJRhwMCACnenTsGJ\nE+r5vvwSTp9W9wE197e/VSFteBh+/WvVMfDcOXV/817mfiyfD959V/ZO5UJbWxuvvvoqxcXFFBYW\nTrj0TNM0CgsLKS4uZufOnbRd4S9/x44dPP3009TX13P+/HnOnDnDk08+yZ49e67ourmUbQgrLCzk\n6aef5oUXXsjxE40lYUoIIYQQIgdcLlVl6u0du9zuUpGIahxhs6kvsxo1PDx6nBledB3On880mnC5\nRocrTVOhbKSeHtWpr60t00bd/N7drYLVv/2bCk5ffKGW/Jn313UV7sy9U+3to5tcOBxqjNk4Q2TH\nMAyamprIy8ubctc/t9uN1+ulqamJbLty9/b28uKLL7Jr1y4efPBBPB4PNpuN9evX88orr4wZv3//\nfhYuXDjqtfLycj788EMAjhw5QnV1Nfn5+cybN49nnnkGgDUXD0Tz+/14vV4OHToEwJtvvsmSJUso\nKCjgvvvuI2h2REGFxl27dlFZWTlul72p+A//4T/w8MMPs2DBgqzmT4eEKSGEEEKIHKiqUtWckcvx\nxmM2jLBY1BeoIGWzjb9UD9R7uq7mGYZaTmgYmSBjGOp988+nT6uleAMDmXGJhPpKJtX3VEqdH3X+\nvBqXSGSWBZqVL4tFBSfzfKv+fnWPVEotYxzZGVBMXzAYJBgMUlBQMK15BQUF6bnZOHToENFolA0b\nNmQ1/1J1dXXU1dXR19fHqVOnePjhhwE4cOAAAOFwmIGBAVasWMGePXvYvn077777Lp2dnaxevZpH\nH3101PWam5s5fPgwn3/+OQD3338/L7/8ck6eNdekAYUQQgghRA6sXQv//b+rhg6TMZtFOBzqZzPs\n+P0q1IzH7VZBa+RcM+TYbOq7z6eqR7/5jToTajKaNjqMgQpIsVhmnxSoKpbXmwlzHR2qW2E8DosW\nqddbWtRrwaD6c2ureg6XSwXMtWvV+9KEbqyWlhZsNtu0u8ppmobNZqOlpYXyLLqAdHd3M2fOHKzW\n3EQBm81GW1sbXV1dzJkzh+XLl0849vXXX2fLli0sWbIEgPr6erZv304wGCRwsRXmli1bKBxx4vR7\n772Xk+ecCVKZEkIIIYTIgUBAVWsuXap3qXA40zQCVBByOFSr8mRy/GVzfr/6bjaa0DTVAdCsMhmG\nCjZtbZn3JzPePcxrRSIqRMHoz2JW0s6dU2PLy9VzHT6s2qQ3NsLBgypoOZ3q+8GDmfdkf9VYra2t\n+M2/3Gny+/2cPHkyq7lFRUV0dXXlrDHEG2+8wcmTJ7n55pu56667Jg0/wWCQuro6/H4/fr+fwsJC\nDMOgo6MjPaasrGzK937iiSfwer14vV62b99+RZ8jGxKmhBBCCCFyQNNg6VJV3YnHJx43sq15Mqm+\n5s9XFSGXKxNkTGYFKR5Xy+w6OlQDid5emDMn0yDiwgW17+nSitN0jLyXWQUb+VmsVnUPp1NVwrq6\nVDUqFFJhsrRUBUqHQ30vLVWvh0KwbZvawyUyIpFI1tUhi8VCZORGtmlYsWIFDoeD5ubmKY33eDwM\njehwkkwm6ezsTP9cWVnJ22+/zYULF3j++ed56KGHGBwcHLfiVlZWxo9//GPC4XD6KxKJcM8996TH\nTKdS9/rrrzMwMMDAwAD19fVTnpcrEqaEEEIIIXJkxQqorFTd8SKRiStAoEJKPK4ChxmiqqpGB5hI\nRDV/CAbVMrtUSl0zlVJh6sKFzHUHB9XcKzXy37GGoe5jXte8f36+Ckgff6w6EBYWTt4CvrBQVd52\n7pQK1Ugulyvr6lAymcTlcmU11+fz8dJLL7Fp0yaam5sZGhoiHo/zwQcf8Nxzz40Zv3jxYqLRKO+/\n/z7xeJytW7cSG/Ef2+7du+ns7ETX9XSlTdd1iouL0XWd06dPp8c+8cQT/PCHP+TEiROAaobxs5/9\nLKvPMZFkMkk0GiWRSJBKpYhGo8Qn+z8cV0DClBBCCCFEjqxdqyo2q1ZlDszt61PL5RKJzPdYTFV5\n5s9XIej0adWaPBxWS+dCIejsVCHKXAbo8ajgYoYps9FFYaGqWGVZpBjD7AqYSmUaZJjP092tXgsG\n1fI+w1CfYSrcbrX/SjoAZlRVVRGeqOvIZYTDYRYvXpz1vZ999ll27NjB1q1bKS4upqysjJ07d/Ld\n7353zFifz8drr73Gxo0b02dgjezut3fvXm655Ra8Xi91dXW88847uFwu3G43DQ0NrFy5Er/fz0cf\nfcSGDRt4/vnneeSRR8jPz+fWW2/lgw8+mPRZ161bN60lfE1NTbhcLv7oj/6In//857hcLv7Lf/kv\nU//lTIOWbUvF61V1dbVx9OjR2X4MIYQQQnwNGYbaHxQKQUFBprV4d7cKReYyuYGBzDI6sxmE3Q4L\nF6oKVW+v6rJnt6sgZbWqcWYIM6tbRUUqSH31Vea1XNE0dS+zGmW1quCm6+oZUyn1fOvWqf1TI38H\n433uoiK15C8chj/909Fzvo6++OKLdJOFibS3t9PY2EggEJjW0jbDMAgGgzQ2NmbVgEJkTPT3pGna\nJ4ZhVF9uvnTzE0IIIYTIEU2D2lq1P8jpVFWmpUtHjzl5EvbuVe87HJkW5SUlKpzYbCqEeDyZA31T\nqUxYuuUW+L3fU6GlvR3+5V/GnjGVC+b/bzfPwTL3U0UimSpYIqHuv3q1Wt4YCqlDgXt7VehyOlUl\nK5mEM2fU8+o6/P3fwziryW44gUCAQCBAKBQa1b3uckKhUHqumF2yzE8IIYQQIocqKmDzZrUsrqdn\n9JK2nh51WK7Ho35OJEbvmwJV9RkayiwHvHAhs9SvvByWL1chraAAbropUznKNU3LtGAHtUQxFMpU\npTQt071w7174h3+A//N/1Ps+n9pXZbZZt9vVzz6fetbdu6UZBahGC7W1tfT3949q8DCZoaEhBgYG\nqK2tnXZLdZF7EqaEEEIIIXJs2TJoaFCBJxhUHfgGBuCTT1SlJj9fhQ5NU0vf8vLUvEgETp3KLAME\nVcnp68vsv/rnf1Ytx//lX+Ddd8d2/8sVw8g00jADlBnadF19BqdThUCrVe37Mpf1TdaMwuNR4Uqa\nUSgVFRVs3ryZzs5Oenp6mGgLjmEY9PT00NXVxebNm6moqLjKTyrGI2FKCCGEEGIGVFRkzlhatUqF\nknBYhYnKSviP/1G1NgcVUvr7VfAy90WZ+6Q0TS2TKypS+61OnYJ/+zcVrsLhmW3mkEqpylMymVlq\naLOpypjNlnl2M/hZLCo4TtYMI5VSAUyaUWQsW7aMhoYGCgoKCAaDdHR0MDg4SDQaZXBwkI6ODoLB\nIAUFBdTX13PHHXfM9iOLi2TPlBBCCCHEDNE0tTTP7BEwPKyW9JnmzFF7jC5cUF/mkjpQFR5QAcXp\nVA0pvN7MXqqzZ3PTCn2qzGC3cKEKVb29mf1QyaR6Lx5Xn+HcOfWZx6tQRaOwaFGmahcMfv2bUUxF\nRUUFjY2NBINBWlpaOHnyJJFIBJfLxapVq6ipqWHRokWytO8aI2FKCCGEEOIqaG1VFZ2RCgtVnHOW\nnQAAIABJREFUO/V//mcVjMwAZe5XcjpV5SYcVsHFrAZZLJnzqMxugDNF19X9zIpSd7dqh25Wxcxl\nhhaLeh6PJ9Op0OkcfS2zrbsZtGw2deivhClF0zTKy8ulQ991RMKUEEIIIcRVEImMDReg9kHFYlBW\npgLGuXOq6uNwZILUpcWIkSFm5GG+MyGVyjSRMBtIhELqZ7OBhnnmlWFkKljh8NgzqMzmFD6f+tnv\nV90NhbheSZgSQgghhLgKXK7MMriRzHbhZmDy+zNVH3MJ3cj3Qb1mGOr1md5zZJ43ZbVmDvGNxVRQ\nOncuc4CwGaRAjb20OV08rpY5futbmXEWS+4OG/46MAyDYG+QlvYWWrtbiSQiuKwuqoqqWFu+loBv\neudRiZknYUoIIYQQ4iqoqlJd+My26Kbu7tEVK7MdudkaHVRgGTnG7AQIKsSYe5dyHazMKlMioUKe\neU9NU89QWgq//nXmvuYzalrm7CuzijY8DHffrZY2mpLJTEv4G11bTxtNx5sI9gax6Tb8Tj9Oi5N4\nMs7BMwfZ176PgC9A7e21VBTOfCe/xsZG2tra2L1794zf63om3fyEEEIIIa6CtWtVdebSwJNIqMqO\nSdNU1SeZVGHKfG9kmIrHM1Ui8/2ZqFCZ1SabLbNfC1TlKRJRLd3nzVOvmRU0s5oGmZbuTqc62Lek\nZPT1w2FYvDj3z329OXb2GNt+vo1QNETAF6A0vxSP3YPD6sBj91CaX0rAFyAUDbHt59s4djY3h3S9\n9dZbVFdX4/V6KSkpYd26dRw8eDAn1wZob29H0zQS5mbAGfLpp59y55134na7ufPOO/n0009n9H4j\nSZgSQgghhLgKAgH1FQqNft1qzVRxTC6XqvqYwcRiyYQnUCHHas2EHat17FLAXNB1FYT0S/7FaLZr\nB5g7V40pKFBVJ7Orn9utuvbV1KivkRUp8zPE4ypk3sjaetp49cirFLuLKXQVTriMT9M0Cl2FFLuL\n2XlkJ209V3ZI144dO3j66aepr6/n/PnznDlzhieffJI9e/Zc0XVzaSohbHh4mAceeIDvf//7hEIh\nfvCDH/DAAw8wPDx8FZ5QwpQQQgghxFWhaVBbq86TGrmfqKho/IN38/LUwbigqkIjz3syu+KBCjqF\nhZlzqXIVqnRdNZkY73rm0kJznNUKxcWqonbTTbBgAfyn/wRLl45eHjhSKJQJmDcqwzBoOt5Enj0P\nt809pTlumxuv3UvT8aYJD/i9nN7eXl588UV27drFgw8+iMfjwWazsX79el555ZUx4/fv38/ChQtH\nvVZeXs6HH34IwJEjR6iuriY/P5958+bxzDPPALBmzRoA/H4/Xq+XQ4cOAfDmm2+yZMkSCgoKuO++\n+wgGg+nraprGrl27qKyspLKy8rKfZf/+/SQSCZ5++mkcDgdPPfUUhmHwf//v/83qdzNdEqaEEEII\nIa6SigrYvBk6O6GnR4WS8vKx3fgMQy2js9tVl7+KCtUBz9yLZLerMJOfr86eMitHZrAZWcWajpEN\nJJxO9T2RGL3ED9RrTqd6xkhEVZ50XVWaLu3YN56hIRgYUOHyRu6nEOwNEuwNUuAsmNa8AmdBem42\nDh06RDQaZcOGDVnNv1RdXR11dXX09fVx6tQpHn74YQAOHDgAQDgcZmBggBUrVrBnzx62b9/Ou+++\nS2dnJ6tXr+bRRx8ddb3m5mYOHz7M559/DsD999/Pyy+/PO69T5w4wW233Taqonfbbbdx4sSJnHy2\ny5EGFEIIIYQQV9GyZdDQAE1N6sBaq1UtiRsYUI0nolEVmHw++P3fh08/Va+PbDMejUJbm2pmUVQE\nX32lXjeX/ZmhyjSd5hQul3qehQvVQcJdXZlrmGFueDhzmPC3vqUqYz4f/OIXatzdd48fksy26gMD\nKlRWVFz6vpE+tLa1tTV9aG1VVRVr164lEPh6dbNraW/Bptum/Zk0TcOm22hpb6F8afm079vd3c2c\nOXOwWnMTBWw2G21tbXR1dTFnzhyWL18+4djXX3+dLVu2sGTJEgDq6+vZvn07wWCQwMUy5ZYtWygc\nsS70vffem/B6AwMD+C5J7j6fj/7+/iv5SFMmYUoIIYQQ4iqrqIDGRhWmWlpU5efAAfV90SJVrTL/\nffjllyo8jex6Z4Ylv19Vp2w2GBxUFR9z+Z/Zic9sXW7ucRr5+ki6DnPmZNqg+3yqAlZYqBpJmI0y\nkkk17tvfzizhMwwV+KqqVLjr71fP7PdnOg2Gw6pyFQjAU0+NDVJtbW00NTURDAax2Wz4/X6cTifx\neJyDBw+yb98+AoEAtbW1VFw6+TrV2t2K3+m//MBx+J1+TnZnd0hXUVERXV1dJBKJnASqN954gxdf\nfJGbb76Zm266iT/5kz/h/vvvH3dsMBikrq6OZ599Nv2aYRh0dHSkw1RZWdmU7+31eunr6xv1Wl9f\nH3l5eVl8kumTMCWEEEIIMQs0TYWm8nL4wQ/g2DF49VW1V8pc0gdw++0qaJkhx2wz/u//vWpLHo+r\noLVgAfzudyr0pFIq3GiaGmuzZVqUj/y3s3mWlaZlwlM8rqpS5oG8v//7auzx46qN+/Cw6szncqnw\ndmlI+sY3MiHx5Em1DNDlglWr1HLARYvGVq2OHTvGq6++Sl5e3pjqk8PhwOPxYBgGoVCIbdu2sXnz\nZpYtWzbh73a8Ctfw8DCxWAy73Y7dbsftds96xSuSiOC0jHOS8xRYdAuRRHaHdK1YsQKHw0FzczMP\nPfTQZcd7PB6GRmz0SyaTdHZ2pn+urKzk7bffJpVK8e677/LQQw/R3d097u+0rKyMhoYGHnvssQnv\nN52/i1tuuYU///M/xzCM9LzPPvuMTZs2TfkaV0LClBBCCCHENeDS5X82m6rsuN1w223w0UeqyjN3\nbmZpXX4+HDmiQpDPp5blJZPqPbOSFYup9uSgOu6ZZ1SZB+2aTc/M4GX2GRgaypwLZRjqGc6dg1tv\nVeMmC0lmSJyKtrY2Xn31VYqLi3G7x2/CYBgGvb29nDlzhnPnzlFbW8uaNWtYvnz5mDB0aYUL4PPP\nP6e3t5dEIoHFYqGgoICbb76Zzs7OdMXr+9//Pjab7aouMXRZXcSTcRw4Lj/4EslUEpc1u0O6fD4f\nL730Eps2bcJqtXLvvfdis9n48MMP2bdvH3/2Z382avzixYuJRqO8//773HvvvWzfvp2YeQgasHv3\nbu677z6Ki4vx+1WlTdd1iouL0XWd06dPs/hiD/wnnniCP/7jP2bp0qXccsst9Pb28r//9//me9/7\nXlafpaamBovFwl/91V/xxBNP8Nd//dcA/L75fwFm2DUVpjRNKwTeAO4FuoAthmG8NcHYRqABiI14\n+TbDME7P9HMKIYQQQsyES5f/mZWdsjIVYjo6VCUoElHL+goK4M47VVWrt1dVowoK1J4k89+6hqFe\ng8whubGYCkTm8ryRDSf6+9VSvbvvVmGtoyNTeaqrG7s870oYhkFTUxN5eXkTBqmenh6OHz9Ob28v\nuq7jdDpxuVx8+umnJBKJUcv/+vr6RlW4zp07x+HDh3E4HBQVFaFpGoZhEI1G+eSTT7j77rsJBAJ8\n+eWXfO9736OsrIz58+dftSWGVUVVHDxzEI/dc/nBlwhHw6xatCrrez/77LPMnz+frVu38thjj5GX\nl8edd95JQ0PDmLE+n4/XXnuNjRs3kkwmee6550Z199u7dy/PPPMMQ0NDBAIB3nnnHVwX03xDQwMr\nV64kHo+zd+9eNmzYwMDAAI888gjBYBCfz8e3v/3tScPUunXrWL16NfX19WPes9vtNDc3s3HjRl54\n4QWWLFlCc3Mzdrs969/NdGjZtlScCZqmvY3qMPiHwFLgfeAewzDGtOO4GKYqDMP4/nTuUV1dbRw9\nejQHTyuEEEIIcXUZxvhL6CorVcj513+Fn/5UjTOrUfn56kvTVFWqqytTnbLb1dgVK9TyvKIi9Xp3\nd2af1uLFEy/Pu1Lt7e00NjZOWPU5e/YsR44cwW6343Q602PMSlVNTQ0+n49QKERHRwfxeJybb74Z\nt9tNT08PBw4cSLf9vlQ8HmdoaIiqqip+/etfo+s6qVSKNWvWjGp+YN4vFArR399/2SWGpi+++CLd\nZGHCzx9up3F/IwHf9KpehmEQ7A3SWNNIub98yvPEWBP9PWma9olhGNWXm3/NVKY0TfMA/w9wq2EY\nA8BBTdP+P6AWeGFWH04IIYQQ4howcp/VeFatUo0hXn010zI9GFThKJFQSwbvuENVmVIpVd3avFm9\nNhtaWlqw2cbvZtfT08ORI0dwu91jwpCmaei6Tnt7O0uXLqWgoIBPPvmEUChEeXk5LpeL48eP43A4\nxg1SoDrQpVIp9u/fz7/7d/8Ou91OJBLh+PHj1NTUjHomTdMoLCzE6XSyc+dO6uvrc1KhCvgCBHwB\nQtEQha7Cy0+4KBQNpeeK2XUtnTO1GEgYhjGyLclx4JZJ5qzXNK1H07QTmqb90cw+nhBCCCHEtc/c\ne1VYqKpTxcWwfDmsXau+Fxer1wsLob5+9oIUQGtra3qPzUipVIrDhw/T29vLb3/7W9ra2jh9+jTn\nzp0jGo1iGAZOp5Pu7m5AHUI7ODiI1+vl+PHjhMNhent7cTonbu5gVpsSiQSpi60NnU4nvb299Jpl\nvUu43W68Xi9NTdkfmDuSpmnU3l5L/3A/Q/Ghy08AhuJDDAwPUHt77deqTfz16pqpTAFeoO+S13qB\nifoa/j3wE+A88C3gHzRNCxuG8falAzVNexx4HGDRokU5e2AhhBBCiGvRRHuvLtdV72qLRCJjAk9P\nTw+HDx/mN7/5DXa7HZvNhq7r6aV94XAYh8PBnDlz0vti2tvb0XUdl8tFb28vX3zxBbquTxo2YrEY\nw8PD2Gw2wuEw8+fPH1PxGk9BQQHBYJBgMEj5VLtsTKKisILNd2/m1SOvkmfPo8BZMO5zG4ZBKBpi\nYHiAzXdvpqLw69Ee/np31cKUpmn7gbUTvP2vwGYg/5LX84FxT9wyDOPzET/+QtO0/wE8BIwJU4Zh\n/AQVvKiurr52NokJIYQQQsyQyy0JvBa4XC7i8TgOh+pmd/bsWQ4fPkxfXx8OhyP9uslisWAYBolE\ngt/+9reUlpYC6hBac0+VGYbmzJkz6b3D4TCapmG1WolEMi3GR1a8xqNpWrrrXy7CFMCykmU0rG6g\n6XgTwd4gNt2G3+nHoltIppKEo2HiqTgBX4CnvvWUBKlryFULU4Zh1Ez2/sU9U1ZN0yoNw/jNxZdv\nB8Y0n5joFoDUOoUQQgghrhNVVVUcPHgQj8eTrkh5PB5CodCEh8maYSaRSNDd3U1PT0+65TmoMHT+\n/Hnmzp076b2HhoawWq1omkbSPNEY1dI7kUhMOtfv93PyZHYH5k6korCCxppGgr1BWtpbONl9kkgi\ngsvqYtWiVdSU17DIt0iW9l1jrpllfoZhDGqa9i7wkqZpG1Hd/B4A7hlvvKZpDwAHgDBwF/AUMLZf\nohBCCCGEuCatXbuWffv2kUqlRjWMSCaT6PrEW/vNA1oLCws5fvw4Vqs1HYjMeeY+qImkUqn08kEz\niJmvTxTkTBaLZVQ1K1c0TaPcX0750vKcX1vMjGupAQXAk4ALuIBarvdHZlt0TdNWa5o2MGLsI0Ab\nahng/wR+ZBjG313l5xVCCCGEEFkKBAIEAgHOnDkzqmGEuZxvIolEAofDgc/no7e3F5fLRTQaBVQY\n8ng86Z8nYgapRCKRPhMJIBqNUlRUNOncZDI5ao64cV0zlSkAwzB6gO9O8N7PUU0qzJ8fvVrPJYQQ\nQgghck/TNGpra/nP//k/k0ql0kvYzEYSIytGpmQySTKZZOHChek9UqBClHkgb3l5ORcuXEhXsMbj\ndrsJh8MA6Y6ChmGQSqUuuxcqHA6zalX2B+ZOZOQ5Yq2tmaYhVVWqG2MgMPtNQ8Ro11SYEkIIIYQQ\nN5aKigoqKys5cuRIuruf3+8nHA6PCkNmFSmZTFJaWpquDDmdToaGhvD5fEQiEVKpFEuWLCEWixGJ\nRNB1nXA4zNDQUHppn9vtxuVy0dnZidfrTTe6iEaj+Hw+fD7fhM9rGAbxeJy1ayfqq5adtjZoalJh\nymYDvx+cTojH4eBB2LdPhanaWtWtcaY1NjbS1tbG7t27Z/5m17FrbZmfEEIIIYS4wfh8PlatWpU+\n5ykWi2G1WhkeHiaZTBKLxdKvBQIB8vIyJ+fouk4ymeT222+nv78ft9uN3+9PLx88ffo0vb29GIYx\nqsX67373O6LRKC6XC03TiMfjDA8Pc/vtt0/a5CEUCqWXJ+bKsWOwbRuEQiowlZaCxwMOh/peWqpe\nD4XUuGPHcnPft956i+rqarxeLyUlJaxbt46DBw/m5uKolvWapl22oceVevzxx6mqqkLXdf72b/92\nRu91KQlTQgghhBBiVrlcLvLy8qipqaGmpoZAIMD8+fPTB+r6fD7Ky8spLy8fs1fJbBjhdDqprKyk\npKSE1tZWjh8/zpw5c9B1fdwvi8XCggULOH/+POfOnWNoaIi7776bwsLCCZ9zaGiIgYEBamtzd2Bu\nWxu8+qo6TLmwcOJlfJqm3i8uhp071bwrsWPHDp5++mnq6+s5f/48Z86c4cknn2TPnj1XduEcmmoI\nu/3223nttddYtmzZDD/RWBKmhBBCCCHErKqqqkqf++T3+1m6dCn3338/Dz74IHPnzsXn8+FwOMYN\nMJFIBIfDQVdXF3/6p3/Kk08+ya9//WuSySR2u50FCxag6zqRSIT+/n6GhoawWCyUlJSQl5dHYWEh\nFy5cSAe48RiGQU9PD11dXWzevJmKHK2zMwy1tC8vD9zuqc1xu8HrVfMm6dExqd7eXl588UV27drF\ngw8+iMfjwWazsX79el555ZUx4/fv38/ChQtHvVZeXs6HH34IwJEjR6iuriY/P5958+bxzDPPALBm\nzRpA7Unzer0cOnQIgDfffJMlS5ZQUFDAfffdRzAYTF9X0zR27dpFZWUllZWVU/o8mzZt4g/+4A/G\nHAB9NcieKSGEEEIIMavMFumXNowoKSlh9erVHD9+nN7eXnRdx+l0ous6qVSKaDRKf38/a9asYfPm\nzXzjG9+gsbGR1atXY7FYaG9vp7u7G7vdTiqVSp9HZbFYsNlsFBUVUV5eTnd3N2fOnKG9vR273Y7f\n78disZBMJgmHw8TjcQKBAE899VTOghSo/VHBoFrCNx0FBZm52ZwbfOjQIaLRKBs2bJj+5HHU1dVR\nV1dHbW0tAwMD/OpXvwLgwIED3HTTTYTD4XS7+T179rB9+3b+6Z/+icrKSl5++WUeffRRfvGLX6Sv\n19zczOHDh9NVyPvvv59Vq1bxwgsv5OR5c0nClBBCCCGEmFXmHqRQKDRmmV1hYSE1NTX09vamw1Ei\nkcBqtVJUVMTq1av5i7/4C3Rdp729nWAwSCAQQNM0li5dOqX7+3w+LBYLGzdu5NSpU5w8eZJIJILL\n5WLVqlXU1NSwaFHuD8xtaVHNJqZ7WU1T81pasgtT3d3dzJkz57LnaU2VzWajra2Nrq4u5syZw/Ll\nyycc+/rrr7NlyxaWLFkCQH19Pdu3b0//vQFs2bJl1H8H7733Xk6ecyZImBJCCCGEELPKbJG+bds2\nnE4n7kvWvI1c/mcaGhpKL7sz26O3tLRgs9mmHXo0TcNms3Hq1Cl+8IMfXPkHmqLWVtW1Lxt+P5w8\nmd3coqIiurq60qH0Sr3xxhu8+OKL3Hzzzdx00038yZ/8Cffff/+4Y4PBIHV1dTz77LPp1wzDoKOj\nIx2mysrKrviZrhbZMyWEEEIIIWZdRUUFmzdvprOzk56engkP7Z1s/1Jra2v6zKjp8vv9nMw2nWQp\nEoFss4zFouZnY8WKFTgcDpqbm6c03uPxMDQ0lP45mUzS2dmZ/rmyspK3336bCxcu8Pzzz/PQQw8x\nODg4bqgtKyvjxz/+MeFwOP0ViUS455570mNyXQGcSRKmhBBCCCHENWHZsmU0NDRQUFBAMBiko6OD\nwcFBotEog4ODdHR0EAwGKSgooL6+njvuuGPU/EgkknWlxWKxEMk2nWTJ5YJsu4Ynk2p+Nnw+Hy+9\n9BKbNm2iubmZoaEh4vE4H3zwAc8999yY8YsXLyYajfL+++8Tj8fZunUrsVgs/f7u3bvp7OxE1/V0\nmNV1neLiYnRd5/Tp0+mxTzzxBD/84Q85ceIEoJph/OxnP8vug1w0PDxMNBpNnwEWjUZJpVJXdM2p\nkmV+QgghhBDimlFRUUFjYyPBYJCWlpZp7V9yuVzE4/H0IbzTkUwmx7Rdn2lVVepAXo9n+nPDYVi1\nKvt7P/vss8yfP5+tW7fy2GOPkZeXx5133klDQ8OYsT6fj9dee42NGzeSTCZ57rnnRnX327t3L888\n8wxDQ0MEAgHeeeed9O+yoaGBlStXEo/H2bt3Lxs2bGBgYIBHHnmEYDCIz+fj29/+Nt/73vcmfNZ1\n69axevVq6uvrx33/3nvvpaWlBYBf/OIXPP744+zbt4+amprsf0FTpE1UQv26qq6uNo4ePTrbjyGE\nEEIIIXLs7/7u7zh48CClpaXTntvR0cGqVatytmfqiy++SDdZmEh7OzQ2qm5+01nZZhiqk19jY3YN\nKETGRH9PmqZ9YhhG9eXmyzI/IYQQQgjxtbB27Vri8fiE+60mYi4PW7t27Qw92fgCAfUVCk1vXiiU\nmStml4QpIYQQQgjxtTCyxfp0hEKh9NyrSdOgthb6+2FEf4dJDQ3BwICadx31afjakjAlhBBCCCG+\nFswW6/39/aO6z01maGiIgYEBamtrZ6WLXEUFbN4MnZ3Q06OW8I3HMNT7XV1qfA7PDhZXQMKUEEII\nIYT42shFi/WrbdkyaGiAggK1F6qjAwYHIRpV3zs61OsFBVBfD5c0MRSzSLr5CSGEEEKIrxWzxXpT\nUxPBYBCbzYbf78disZBMJgmHw8TjcQKBAE899dSsBilTRYVqKBEMQkuLOpA3ElHtz1etgpoaWLRI\nlvZdayRMCSGEEEKIr50rabE+WzRNdeeTDn3XDwlTQgghhBDia0nTNMrLyymXdCJmiIQpIYQQQggh\nrgXmAVItLdDamlnnV1UFa9dO/0AqMeMkTAkhhBBCCDHb2tqgqUmFKZsN/H5wOiEeh4MHYd8+FaZq\na69KK7/Gxkba2trYvXv3jN/reibd/IQQQgghhJhNx47Btm2Z03hLS8HjAYdDfS8tzZzuu22bGp8D\nb731FtXV1Xi9XkpKSli3bh0HDx7MybUB2tvb0TSNRCKRs2te6uTJkzzwwAMUFxdTWFjIfffdR2tr\n64zd71ISpoQQQgghhJgtbW3w6qtQXAyFhRMv49M09X5xMezcqeZdgR07dvD0009TX1/P+fPnOXPm\nDE8++SR79uy5ouvm0lRCWDgc5jvf+Q6tra2cP3+eu+++mwceeOAqPJ0iYUoIIYQQQojZYBhqaV9e\nHrjdU5vjdoPXq+ZNdMLvZfT29vLiiy+ya9cuHnzwQTweDzabjfXr1/PKK6+MGb9//34WLlw46rXy\n8nI+/PBDAI4cOUJ1dTX5+fnMmzePZ555BoA1a9YA4Pf78Xq9HDp0CIA333yTJUuWUFBQwH333Ucw\nGExfV9M0du3aRWVlJZWVlZf9LHfffTd/+Id/SGFhITabjf/23/4bra2tdHd3Z/W7mS4JU0IIIYQQ\nQsyGYDBzGu90mKf7jggh03Ho0CGi0SgbNmzIav6l6urqqKuro6+vj1OnTvHwww8DcODAAUBVjwYG\nBlixYgV79uxh+/btvPvuu3R2drJ69WoeffTRUddrbm7m8OHDfP755wDcf//9vPzyy1N6lgMHDjB/\n/nyKiopy8tkuR8KUEEIIIYQQs6GlRTWbmG6HPk1T81pasrptd3c3c+bMwWrNTS86m81GW1sbXV1d\neL1eli9fPuHY119/nS1btrBkyRKsViv19fV8+umno6pTW7ZsobCwEJfLBcB7773HCy+8cNnn+N3v\nfsemTZvYsWPHlX+oKZIwJYQQQgghxGxobVVd+7Lh98PJk1lNLSoqoqurK2eNId544w1OnjzJzTff\nzF133cV777034dhgMEhdXR1+vx+/309hYSGGYdDR0ZEeU1ZWNu1n6Ozs5N577+XJJ58cU+maSRKm\nhBBCCCGEmA2RCGRbHbJY1PwsrFixAofDQXNz85TGezwehoaG0j8nk0k6OzvTP1dWVvL2229z4cIF\nnn/+eR566CEGBwfRxqm4lZWV8eMf/5hwOJz+ikQi3HPPPekx482bTCgU4t577+U73/kODQ0N05p7\npSRMCSGEEEIIMRtcLsi2OpRMqvlZ8Pl8vPTSS2zatInm5maGhoaIx+N88MEHPPfcc2PGL168mGg0\nyvvvv088Hmfr1q3EYrH0+7t376azsxNd1/FfrLTpuk5xcTG6rnP69On02CeeeIIf/vCHnDhxAlDN\nMH72s59l9TkA+vr6uO+++1i5cuWU91XlkoQpIYQQQgghZkNVFYTD2c0Nh2Hx4qxv/eyzz7Jjxw62\nbt1KcXExZWVl7Ny5k+9+97tjxvp8Pl577TU2btxIaWkpHo9nVHe/vXv3csstt+D1eqmrq+Odd97B\n5XLhdrtpaGhg5cqV+P1+PvroIzZs2MDzzz/PI488Qn5+PrfeeisffPDBpM+6bt06tm/fPu57//iP\n/8jHH3/M3/zN3+D1etNfZ86cyfp3Mx2akWVLxetVdXW1cfTo0dl+DCGEEEII8TX2xRdfsGTJkskH\ntbdDY6M6kHc6S9sMQ3Xya2yE8vLsH1JM+PekadonhmFUX26+VKaEEEIIIYSYDYGA+gqFpjcvFMrM\nFbNKwpQQQgghhBCzQdOgthb6+2FEg4dJDQ3BwICaN92W6iLnJEwJIYQQQggxWyoqYPNm6OyEnh61\nhG88hqHe7+pS4ysqru5zinHl5qQuIYQQQgghRHaWLYOGBmhqUnuhbDZ1jpTForr2hcNzWfK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ZPKEI/GySQzhKvCVM2rouG1htO2m3nrzEF1QSsxExERkfPNYKr5yeg702p+Z1PPlIyij3/5Mdu+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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112447278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now use dataframe group by cluster\n",
    "cluster_groups = http_df.groupby('cluster_db')\n",
    "\n",
    "# Plot the Machine Learning results\n",
    "fig, ax = plt.subplots()\n",
    "for key, group in cluster_groups:\n",
    "    group.plot(ax=ax, kind='scatter', x='jx', y='jy', alpha=0.5, s=250,\n",
    "               label='Cluster: {:d}'.format(key), color=colors[key])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}