lab_bias_variance_CV.ipynb

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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Bias, Variance, and Cross Validation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from matplotlib import rcParams\n",
      " \n",
      "pd.options.display.max_columns = 10 \n",
      "pd.options.display.width = 134\n",
      "pd.options.display.max_rows = 20\n",
      " \n",
      "rcParams['figure.figsize'] = (10, 6)\n",
      "plt.style.use('bmh')\n",
      "colors = [\"#348ABD\", \"#A60628\", \"#7A68A6\", \"#467821\", \"#CF4457\", \"#188487\", \"#E24A33\"]\n",
      "\n",
      "import brewer2mpl\n",
      "#colorbrewer2 Dark2 qualitative color table\n",
      "dark2_cmap = brewer2mpl.get_map('Dark2', 'Qualitative', 7)\n",
      "dark2_colors = dark2_cmap.mpl_colors\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import random\n",
      "import copy\n",
      "def scatter_by(df, scatterx, scattery, by=None, figure=None, axes=None, colorscale=dark2_cmap, labeler={}, mfunc=None, setupfunc=None, mms=8):\n",
      "    cs=copy.deepcopy(colorscale.mpl_colors)\n",
      "    if not figure:\n",
      "        figure=plt.figure(figsize=(8,8))\n",
      "    if not axes:\n",
      "        axes=figure.gca()\n",
      "    x=df[scatterx]\n",
      "    y=df[scattery]\n",
      "    if not by:\n",
      "        col=random.choice(cs)\n",
      "        axes.scatter(x, y, cmap=colorscale, c=col)\n",
      "        if setupfunc:\n",
      "            axeslist=setupfunc(axes, figure)\n",
      "        else:\n",
      "            axeslist=[axes]\n",
      "        if mfunc:\n",
      "            mfunc(axeslist,x,y,color=col, mms=mms)\n",
      "    else:\n",
      "        cs=list(np.linspace(0,1,len(df.groupby(by))))\n",
      "        xlimsd={}\n",
      "        ylimsd={}\n",
      "        xs={}\n",
      "        ys={}\n",
      "        cold={}\n",
      "        for k,g in df.groupby(by):\n",
      "            col=cs.pop()\n",
      "            x=g[scatterx]\n",
      "            y=g[scattery]\n",
      "            xs[k]=x\n",
      "            ys[k]=y\n",
      "            c=colorscale.mpl_colormap(col)\n",
      "            cold[k]=c\n",
      "            axes.scatter(x, y, c=c, label=labeler.get(k,k), s=40, alpha=0.3);\n",
      "            xlimsd[k]=axes.get_xlim()\n",
      "            ylimsd[k]=axes.get_ylim()\n",
      "        xlims=[min([xlimsd[k][0] for k in xlimsd.keys()]), max([xlimsd[k][1] for k in xlimsd.keys()])]\n",
      "        ylims=[min([ylimsd[k][0] for k in ylimsd.keys()]), max([ylimsd[k][1] for k in ylimsd.keys()])]\n",
      "        axes.set_xlim(xlims)\n",
      "        axes.set_ylim(ylims)\n",
      "        if setupfunc:\n",
      "            axeslist=setupfunc(axes, figure)\n",
      "        else:\n",
      "            axeslist=[axes]\n",
      "        if mfunc:\n",
      "            for k in xs.keys():\n",
      "                mfunc(axeslist,xs[k],ys[k],color=cold[k], mms=mms);\n",
      "    axes.set_xlabel(scatterx);\n",
      "    axes.set_ylabel(scattery);\n",
      "    \n",
      "    return axes\n",
      "\n",
      "def make_rug(axeslist, x, y, color='b', mms=8):\n",
      "    axes=axeslist[0]\n",
      "    zerosx1=np.zeros(len(x))\n",
      "    zerosx2=np.zeros(len(x))\n",
      "    xlims=axes.get_xlim()\n",
      "    ylims=axes.get_ylim()\n",
      "    zerosx1.fill(ylims[1])\n",
      "    zerosx2.fill(xlims[1])\n",
      "    axes.plot(x, zerosx1, marker='|', color=color, ms=mms)\n",
      "    axes.plot(zerosx2, y, marker='_', color=color, ms=mms)\n",
      "    axes.set_xlim(xlims)\n",
      "    axes.set_ylim(ylims)\n",
      "    return axes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random.seed(42)\n",
      "\n",
      "def rmse(p,x,y):\n",
      "    yfit = np.polyval(p, x)\n",
      "    return np.sqrt(np.mean((y - yfit) ** 2))\n",
      "\n",
      "def generate_curve(x, sigma):\n",
      "    return np.random.normal(10 - 1. / (x + 0.1), sigma)\n",
      "x = 10 ** np.linspace(-2, 0, 8)\n",
      "intrinsic_error=1.\n",
      "y=generate_curve(x, intrinsic_error)\n",
      "plt.scatter(x,y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "<matplotlib.collections.PathCollection at 0x10c463350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10be11250>"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_new=np.linspace(-0.2, 1.2, 1000)\n",
      "plt.scatter(x,y, s=50)\n",
      "f1=np.polyfit(x,y,1)\n",
      "plt.plot(x_new,np.polyval(f1,x_new))\n",
      "print \"d=1, rmse=\",rmse(f1,x,y)\n",
      "f2=np.polyfit(x,y,2)\n",
      "plt.plot(x_new,np.polyval(f2,x_new))\n",
      "print \"d=2, rmse=\",rmse(f2,x,y)\n",
      "f4=np.polyfit(x,y,4)\n",
      "plt.plot(x_new,np.polyval(f4,x_new))\n",
      "print \"d=4, rmse=\",rmse(f4,x,y)\n",
      "f6=np.polyfit(x,y,6)\n",
      "plt.plot(x_new,np.polyval(f6,x_new))\n",
      "print \"d=6, rmse=\",rmse(f6,x,y)\n",
      "plt.xlim(-0.2, 1.2)\n",
      "plt.ylim(-1, 12)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "d=1, rmse= 1.70524942832\n",
        "d=2, rmse= 0.89214139212\n",
        "d=4, rmse= 0.496552676012\n",
        "d=6, rmse= 0.132980700607\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "(-1, 12)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZbCM9IZgXzh3OI2cM7VNJ1KH4xUYx6tFbOen3D0m6egGSXkf5Z2tYd/IVbLv1\nMYwlFV2+3tHaExopz+DUrj1FURRJkoR5Beq39gS+iy8LzT1JfXYeu//xCrXrsgAwxESScttiBl15\n7jFnmFVUSCxeHExRkYbR/hasQE5+Mt9cE8a33zYRFycuT76ATVb4aW8dK7LL2L2jitCUAQyPDmTJ\npAQyBoT0Gwd6//gY0v5xO0NuuoJ9L6zg4LufU7Lya8o++4Hkay9m6C1XoQ/7o7Fom6kzwWxocOeK\nBdC7RKpCkqR4RVHKJUlKACqP9qQ9e/Zw4403kpSUBEBYWBhjx47t6H877tr6yuNt2TupLTJiGWhW\n5XiO33nLf5+vPfZk/JqNDdQWGdGFWw9bizfFp6vHM2fOdOv7tRaX8cFt91O7Los0TRC6sBBq500h\n7qxTSD59Vpev12pPpqhIS1z0JzSU1hI0FHYXJVB7cC2fftrC9dfP6PPx8+XHJ5xwAhuLG3nqnS8p\nbzITmjKe0RlTmUgxY6OCmDhwhFet112Ps/YXwLnTmXnDZRT841V+/OwLtv/7NdLf+ZyU2xZTPDwO\njV7PzJkzyc3VcOml2xg1s5rIYQE8+089Nef9RkqK4jX/Pd762PFzcbF9A8ukSZOYNWsWPUVSlK7v\n2No1Ul8csmvvaaBGUZSnJEm6FwhXFOUPGqk1a9YoGRkZPV6Qr1JQuo0H315MSvxoHl+44vgvEPRZ\nthdt4rGVN5A2aCIPXfa6p5fjtVibWtj77xUUvb4S2WRG42cg+bqLGXrzlejDu6ct27BBy1lnhXLF\nOWtoHfEoLboD/L7iIxpKM/jqq0amTxfzx7yV3NImlmaWkl9pdyOPDdZzVUYCp6dGusWN3Jeoz85j\n199fom7DFgACBiUw7P+uRzN9NnPnhlJUpGX+zRfRHJzF/jWPY668WFRke0F2djazZs3q8YevS42U\nJEnvAeuBEZIkHZAkaQnwJHCGJEm7gdPaH/+B/qaRMujsPWoxa8878GT8OoTmPrhjD1wfO9lqpXjF\np/w67SL2/+ctZJOZhPmzOXHde4x44MZuJ1EAQ4fKTEqvIHlAJYpsF5tLGivJyTaGDpVd9Z/QJeLc\n7Zrd1a3c980e7vp6D/mVrYT567hh2gCWXpTGnOFRbFj/m6eX6HWEZ6Qx5ZMXyXjrGYJHDMF4oIyt\nN/yNbZf+GU1xPgAWq5baIiP+fiaKirTs2yc2W7iLLlt7iqJcdow/ne6Ctfg0wkdK4KDD+kBopP5A\n9S+b2Pkx+eIGAAAgAElEQVTQCzTvsu/EC588lpGP/IXwjNG9Ol5cnMJlF2ylphyaW4LQGCA+wcor\nTzeLu3Evo7i+jeVZZazdb/dYC9RrWDAujvmjYwg0eJ+RprchSRKxZ5xAzGnTKFn5NbufeA3zzq38\nXXcxv8gXUGK1J07+fhYPr7T/0WUi5Qz91kdKpV17wovGOTwZvw6xuY9WpFwRO+PBcnY+/G8qvvoZ\nsLcmRjx4E3HnnOqUkLikqI6a8mr0Bi2D4gMoaYQnn2oiPd0z1SgQ5+6RVDabeSu7jNUFtcgKGLQS\n56XFcEl6HKH+f/wKEvHrGkmrZeDl5xB/zmlsfXwZpctWcqr2E761RWNNDsDUYPRoRbY/4rJEqr/h\nSKQsYtdev6fD1VxUpJBNZva/9j77nluGzdiGNsCflNsXk3zdJWj9nTdU/O2HPQBMPGEwzXVaShoh\nLMx6nFcJ3EG90cJ7uRV8mVeNRVbQSHDWyCiumBBPdNDRd2EKuo8uJIiMJ29COfE81t74IlqbXT81\nLjCTe2/6nri4aR5eYf9BzNpTCb3WfmFQy5BT6Cycw6MaKR92NQf1Ylf100bWnXoVBf94FZuxjfhz\nTmPmuvcYestCVZKoA/tqKd5bg5+/jkkzB6OR7O0hWfbsnXh/P3dbzDZWZJWx6IM8Vm2vwiIrnJoS\nwRsLRnHrzKTjJlH9PX49ZeJZA7ls01PoEpOpLTKiMUjUPXA7W669n7ayKk8vr18gKlIqYdCrO7RY\n4Lt0is37p6u5saSCnQ+90NHGC0pNYtQ/7iD6pMmqvYeiKPz2QwFgr0b5B+g7RsTIimhpeAKTVeaL\nvCrey62gyWTfLTl1UCiLJyWQEhXo4dX1beLiFKIHh0MRaGJj0Qb4U/HlT1T//DvD7v0TSYvno9GJ\nr3tXITRSKqHV6NBIWmTFhtVmQad1bpCm0Ak4hyfj19zmGA8T7rE1OENvY6fYbBS/+Qm7n3gNW0sr\n2sAAUm5fwuA/XXLYTDw1OLCvloOFdfgH6Jl4QjIAGsleYLfJnm3t9bdz1yorfLe7hneyy6lutQud\nx8QHcfWkRMbEB/f4eP0tfmqh02uJTA7AatUzc+275D/wHJXfrmXnA89T+sE3jH7qLsImpHl6mX0S\nkaKqiF5nwGQxYrGanU6kBL6Lr4vNe0NT/l623/kkDVk7AIibdzIj/34bAQOOOkHKKQ6tRk06cTB+\n/vZzTaOxX85ERco9yIrCL/vqWZFVRkmjCYCUqACWTEpg8sDQfuNG7i3odPYbCbPJhi4uhoxlT1Hx\n7a/k3/8cjVt3sWHedSQtns+w+65HH9rzBFdwbIRGSkU6x8SYnD6W0Ak4h2c1Ur49sLgnsbO1mSh4\n6r+sn72Ehqwd+MVFM2HpE0xY+oRLkiiAoj01lBTVExCoJ2N6csfvHRUpWfasCWdfP3cVRWHTgQZu\n+nQXT/xUSEmjiQGhfvzfqYN56fwRTBkU5lQS1dfj5yoUlI5Ze82N9k1PcWeexMxf32HIjVcgaTQU\nv/kx606+gsrVwqtLTURFSkX0jp17NucTKYFvoigKzUbfFpt3l9qNOey480la9tjHKwxaeD7D77/h\nqLPA1OLQatTkk4Zg8Ou8hGk03pFI9WW2lzezdHMp2ytaAIgO1HNlRjyzh0ehE27kXoJEU0MbEVFB\nAOiCAhnx0E0kLpjD9jufoiF7B9lX3UXigjmMfPQ2DJF9+zrlDoRGSkUMKu7cEzoB5/BU/IzmZmTF\nRoAhyGfbu8eLnbWllV2PvsSB5asACBqWzOhn7iFymuvP+b35lZQdaCAgyMD4aUmH/U3rJa29vnju\n7q1p5c3MMjYdsN8khPhpuTQ9jnPTYvDTqdvY6IvxcxeRyQHQAs0Nf7yZD0lLZdoXr1L43w8oePI1\nSj/6juqfN5H2xB3En3OaB1bbdxAVKRXp3LknKlL9FYc+KshH23rHo3b9Frbd9jjG4lIkvY6htywk\n5daFaPxc7wsk22R+/W43ANNPTcFgOPzy1Sk2FxUptShpMLE8q5Sf99m90fx1Gi4cG8uCsbEECTdy\nr6W56ejfQZJWy5A/X0bsnBPZfvsT1G3YQs51DxB31imkPXEHfrFRbl5p30BopFRErxUaKW/BU/Fz\ntPV8WWh+tNjZWtvIf+gFNl14M8biUkLGDGPGd0sZdve1bkmiAHZsKaW2qoWwiADSpwz6w987NFKK\n0Eg5S3WLmRfWFXPNR3n8vK8evUbigtExLL8kjUUTE1yaRPWF+HkKh0aqpalrY+igIQOZ8vF/SHvq\nLrRBgVR89TPrTrqcsk9/cMcy+xyiIqUiYt6eoLmt77ma12dtZ+tfHqN1bzGSVsvQWxeRctsi1S0N\nusJitnVoo2bOHob2KO2kjtaehw05fZnGNisrcyv4LK8Ks83uRj5neCRXTkggLkS4kfsKzY3Hv5mX\nNBqSFl1AzKzp7LjrKap/+p3cPz9E5XdrSXvijh4ND+/vCI2Uiqi5a0/oBJzDU/Fr6jDj9N1EyhE7\n2WSm4Jn/sf/ld0GWCR4xhLH/fpCw9JFuX1P2hiKaG03EJoYycmzCUZ/jEJvbPFyR8sVz12ix8cn2\nKj7cWkGrxZ6InjgknEUTE0gK93frWnwxft5Ch0aqG4mUg4CB8Ux8918ceOszdj38b8pWraZ2Yw5j\nn7+f6JOnuHC1fQdRkVIRsWtP0NQ+Zy8kwDfNOB005e8l94aHad65DzQahtx0Bal3XavKaJeeYmw1\ns+mXfQCcNGc40jF2h2kczuZCI9VtzDaZr/KreS+ngvo2u5HpxAEhLJmUyPAY4UbuqzQfp7V3JJIk\nkbTwfKJOnMS2Wx6lPnM7mZfcRtLVCxjxwI1oA92bTPsaQiOlIo55e2q09oROwDk8Fb+mVnsiFRoY\n4ZH3dxZFUfj4vsfYcOY1NO/cR+DQQUz9/BVGPHiTR5IogN9/3oepzUpyahSDh0Uf83mds/aERup4\n2GSF73fXcPWHebyysYT6NiujYgN5Zl4qT8xN9WgS5Qvx80qUQzRSjSYURenxIYKGDGTKpy8z7L7r\nkXRaipd+xPrZi2nYkqf2avsUoiKlIo5de2q09gS+iS9XpExVtWy/7XGKVv9AmiaIgVeey8hHbkUX\nFOCxNTXUGdmyoQiAE+cM7/K5nWJzoZE6FoqisK6wgeVZZRTX26sWgyP8WTIpkWlJwo28L6DTa7G2\nyrQZLQQE9lzXptHpSLl1EdGnTmPbzY/SvHs/G8++ntS7rmHoLVchacVuzSMRGikVMai4a0/oBJzD\nU/FrNNYBvpdIVf24kW1/+Tvm6jrSI+MZ/ex9xJ91iqeXxW+rC7DZFEaOiyd+QNe6s86hxUIjdTSy\nSxpZurmM3dWtAMSHGFiYkcCpKRFovchM01vj5wtEJgfgr9VBq10n1ZtEykHYuBFM/34pu594laLX\nVlLw5OvUrM1k3EsP4x8fo+KqfR9RkVIRsWtP4GjthQT6RiJlazOx+/FXKPrvBwBEnpDBuP88hH9i\nrIdXBqXF9eTllKLVSsyc3XU1Cjo1UsJH6nDyK1t4M7OUnNJmACIDdFw+IZ65I6LQa12m7hB4CH9/\nPQrQ0mQiJt65KQNafz9GPXIr0adMZdstf6f2t2x+O20RY1+4n9gzTlBnwX0AoZFSETFrz3vwmEaq\nvbUXGuD9GqnmgkI2zruOov9+gKTTMvz+G5j8wQtk7tvt6aWhyAo/fZUPwMSZgwmPPL5mp3PWnmdb\ne95y7hbWGXl49T5u/Xw3OaXNBBu0XD05gTcvTuPctBivTaK8JX6+SG2REf8Auy2JY96eGsScOo0T\nflxB1MmTsdTWk33VXeQ//AKySRQNQFSkVKVj157QSPVbfEUjVfrJ9+y48ylsrUYChwwk/eW/ETYh\nzdPL6iA/t4yyAw0Ehfgx7ZSUbr1G4yWtPU9T1mTirawy1uypQwH8dBouGB3DReNiCfETl/y+jl+A\nHiPHdjfv9XFjo5j03nPsf/ldCp58jaLXVlK3IYf0Vx8laOgfDXL7E0IjpSIdrT2bmLXnaTwRP0VR\nDkmkvNNHytZmYudDL3BgxacAJMyfzein70IXHNTxHE9/9sxmK79+t8u+ltnDDhtM3BXesmvPU/Gr\nbbXwbk45X++swSor6DQS80ZGcfn4eCIDfWfuo6c/f75MZHIA/gH286UnXlLdRdJoGHrzlUTOmEDu\n9Q/RuHUX689Ywuhn7iZx/mzV389XELcnKqJma0/ge7SamrHJ9oHFjqTam2jZf5Cc6+6naXsBGj8D\nI/9+G4OuOs/rdmpt/nU/zY0m4gaEMmbCgG6/zmHI2d927TWbrHywtZJVO6owWWUk4PTUCK7KSCAh\n1DOWFQL3o2C3O3C09lpckEg5CM8YzYw1y9lx51OUf76GrTf+jbrfcxn16K1uGxnlTQiNlIo4Zu2p\n0doTOgHn8ET8OqpRXig0L//iR9afsZim7QUEDh7AtC9fI2nh+UdNojz52WusN7L51/0AnHrWqGOa\nbx4NreQdYnN3xa/NKvN+bjkLV+bxfm4FJqvM9OQwXp0/krtPGeyzSZS49vWe2iIj/u079XpqytlT\n9KHBpL/2KGlP341k0HNg+Sp+P+8GjAfKXPq+3oioSKmI8JHq33ijPko2mdn56IsUv/ERAHFnn8qY\nf92HPjTYwys7Oj99tROrVWbE2HgGDu6ZYL+/aKQsNplvdtXw7pZyao12N/L0hGCunpzIqNig47xa\n0NdxZWvvSByO6GHjRrDl2vtpyMln/ewljHvpb8ScNs3l7+8tCI2UiohZe96DJ+LX2Gr3kAr1kkSq\nrayKLdf+Hw1ZO5D0OkY+fAtJ1yw4bivPU5+9fbuqKNhRgd6g5eS5I3r8+s4RMZ5t7bkqfjZZ4ae9\ndbyVXUZZk12HOSw6gKsnJZIxIMTrWrS9RVz7ek9kcgB+/vaKVEuTCUVWelTV7S1h40cx4/s32Xrz\no1T/uIGsK+4g5fYlpN6+pF8YeIqKlIr46ezziEwW15ZUBd5JZ2vP89YHdb/nsuXa+zFX1eI/II7x\n/32c8Azv2ZV3JBaLjTVf2MdQzJiVSmh4z93UO+0P+lZFSlEUNhY38mZmKYV19mvLoDA/Fk9KZObg\nsD6TQAnUQauVCAjUYWy10NpqJijYPS1eQ2QYE99+hr3PL2fPM/9j77NLqc/aTvpLf8MQ5R03l65C\naKRUxKC3J1JmFRIpoRNwDo9opFo939pTFIXiNz9m04U3Y66qJfKEDGZ8t7RHSZQnYrfpl3001BqJ\nig0mY0Zyr47RsWvPw609NeOXW9rEX78o4OHV+yisayM2WM8dJyXx+oWjOHFIeJ9MosS1r/c4Zu0F\ntevjXCk4PxqSRkPq7UuY9P5z6CPDqPl5E+tnL6Fh6y63rsPdiIqUivi1J1Imq6hI9Ucc42E81dqz\ntZnIu+cZSlZ+DcDgP1/G8AduQKPz7tO8rrqFTe0C8zPOS0PbS6PIzhExvr9rb3d1K29uLiWrpAmA\nMH8dl4+P46xR0Ri81EhT4D0Eh/hTXd5Mc5MJT8woiD55CjNWLyPnugdoyN7B7+f9mbHP/R8J55/h\ngdW4HqGRUhGhkfIePBE/T4rNjQfL2XLN/9GYuxNNgB9j/nUfiRf0ztfFnbFTFIU1X+Rjs8qMzkhk\n4JDIXh/LYX/g6V17zsSvuL6N5VllrN1v/ywF6jUsGBfH/NExBBr6vtYExLXPGSKT7S3x4PaKlJru\n5j0lYEAcU1e9xI57/0nJe1+S++eHadxewPD7ru9zuinvvlX1MQw69Vp7At/D0doLdbNGqua3bHL/\n9ADmmnoCkhKZ8OYThI4e5tY19JZd28opLKjGP0DPyWeOdOpY3mLI2Rsqm828lV3G6oJaZAUMWolz\n02K4ND2OUH9xmRYcH0VROn4ODnEkUp7dQa7xMzDmX/cROmY4Ox96gf0vvk1T3l7SX/kb+jDn5gB6\nE0IjpSJqtvaETsA5PBG/Rg9UpA689SmZl9yKuaaeqFOmMP27pU4nUe6KXWuLmTVf2OfpnThnOIHB\nzhn5ab1kaHFP4ldvtPDKxoMs+SCP73bXAjBvZBTLLk7jT1MH9MskSlz7eo9dIyURFGr/LvJkRcqB\nJEkkX7OASSvtuqnqHzewYd51NBcUenppqtH/zlIX4qei2FzgezS12x+4I5GSrVZ2PfIiRf/9AIDB\nN1zOiAdu8KmS+U9f5mNsMTNoaCTjJg90+ngdu/Z8QCPVYrbx8bZKPt5eidFiX+8pQ8NZNDGBAWH+\nHl6dwNfpaO2pPG/PGaJmTmL6N2+wZcm9NOXtYeO86xj30t+InX2Cp5fmNEIjpSIdrT0VKlJCJ+Ac\nHtVIudjZ3NLYTO71D1L90+9Ieh2jn7mHgZeepdrx3RG7vTsryc8tQ6fXMOeCMarsPnP4SCke3rXX\nVfxMVpkv8qp4P7eCRpN9nVMGhbJkUgIpUYHuWqJXI659vadDIxXimV17xyMwOZGpX7zKtlsfp+LL\nn8hedDcjHryJwTdc5tM7UEVFSkW0Gh0aSYtNtmG1WdBpfWdQqMA5rDYLLaYmJElDkH+oy96ntfAg\nWVfdTUtBIfrIcDLefIKIqekuez9XYGqzsPrTHQDMPGMY4SolEBovGRFzNGyywne7a3g7u5zqVgsA\nY+KCWDI5kbHx3ukyL/Bdgh2tPS+qSDnQBQUy/r+Pse/5ZRQ89V92PfoiLXuLSHvyLjR630xJhEZK\nRSRJ6mzvOVmVEjoB53B3/JqNDQCEBIR1tJjUpnb9FjbMvZaWgkKCRwxh+jf/c0kS5erY/fLNLpob\nTSQMCiNjxmDVjustQ4sPjZ+sKPy8t45rP8rn+XUHqG61MDQygMfmDOXZs4eJJOooiGtf7+nwkQo2\ngAQtzSZkm/e1uiVJIuWvSxj/+mNo/A0cfOcLsi6/HUt9o6eX1it8M/3zYgx6f4zmFkyWNgL9+s6u\nBEHXuNr64OC7X7Dj7qdRrDZiTp9B+iuPoAvxvblqhQXVbN18EI1WYs78MWhUHF/R4SPlBRUpRVHY\nfLCRZZll7Kmxf7klhvqxaGICJw8NR+PDbQyB96PRaggK9qOlyURLs5kQL9XdxZ97Gv4D48hedA81\nazPZeM71THzrGQIHO6+ZdCdCI6UyanlJCZ2Ac7g7fq7asafIMrufeI39/3kLsJtsjnjwRpeKyl0V\nO2OrmW8/3gbAjNNSiY5T90bDW1p7EanjueOrAraXtwAQFajnyox45gyPQueGuWe+jrj29R6HRgrs\nOqmWJhPNTSavTaQAwjNGM/3r/5J11V0079zHhnnXkfHmkz4lWRAVKZXxE15S/RJHRUpNDynZZGbb\nbY9Ttmo1klZL2tN3MeiKc1U7vjtRFIXVn+6gudFEYlI4U04aovp7eHrX3t6aVpZllvH7AXt7IsRP\ny6XpcZybFoOfTriRC1yNctijoFA/KIWWxjYgzDNL6iYBgxKY9sVr5PzpQap/2simi/7C2H/dR+KC\nMz29tG4hNFIqY1DJS0roBJzD3fFrVNn6wFLfyOZL/0rZqtVogwLJePsZtyVRrohd3pZSdm+vQG/Q\nMu/icWhcMOZEo7HfF7p71l5Jg4knfirkhlW7+P1AI22FW7liQjwrLhnNRePiRBLVQ8S1r/fUFhlx\ndI29xZSzu+hCgsh462mSrl6AYraw9eZH2fPs0sOMRr0VUZFSmY7WnsU3PrwCdVBTI9VaXEbWFXfQ\nUlCIX1w0E9/5J6Fjhjt9XE/RUNfKmi/yAJh1zijCI12zzb9DbO6m1l51i5l3tpTz7a4abAroNRJn\nj4pm8JBk5k5McMsaBIJj4c07946FRqcj7R+3EzR0EPkPvcCeZ/5HW1klaU/e6dUzQ4VGSmXUcjcX\nOgHncHf81DLjbMjdSdaVd2KuqiV4xBAmvvMsAQPj1Vhit1EzdrKs8PUH2zCbbAwbHcfojAGqHftI\nOsXmrm3tNbZZWZlbwWd5VZhtChoJ5gyP5MoJCcSFGADfEsp6G+La13sO00h5wby93pJ87UX4D4gl\n94aHOfj255gqakh/9VF0QQHHf7EHEDVnlRHz9vonje1z9sKc0EhVrv6NTeffiLmqlsiZE5n6+atu\nT6LUZsOPeygpqiMoxI8zzh/tUtO9TrG51SXHN1psvLOlnIUrd/DhtkrMNoWZg8N5ff4o7jgpuT2J\nEgi8g6AQ73M37wlxc09m8of/QR8RStXq39i84BbM1XWeXtZR6XUiJUnSfZIk7ZAkaZskSe9KkuR3\n6N/7q0ZKrYqU0Ak4h7vj19BaA0BYUFSvXn/gnc/JXnQPNmMbiRfNZdK7//LYUE+1YldYUM2Gn/aC\nBGddPI7AINcmGq4Sm5ttMqu2V7JoZR7Ls8potchkDAjhxfNG8NDpQ0iKOHxHlDh3nUPEr/c4fKSg\nM5Fq9dFECiBi8limfv4q/gPjadiSx8Zzrqe18KCnl/UHepVISZI0GLgOyFAUZSygBS5Vb1m+i9BI\n9U8a2lt7oYGRPXqdoijs/fcKdtzxJMgyKX9dzNh/P4DG4Nuu+E0NbXz1wVZQ7FYHSSm9SzB7Qkdr\nT6VEyiYrfL+7hms+zOeVjSXUt1kZGRPI0/NSeXJuKsNjxEgXgfcSFNw+JqbZ7OGVOEfwsMFM++p1\nQsYMo3X/QTaefT0NOfmeXtZh9FYj1QhYgEBJkmxAIFBy6BP6q0bKoJKzudAJOIfbfaRaa4GetfYU\nWWbn3/5D0esrQZIY9fjtJF99oauW2G2cjZ1sk/lqZS7GFjPJqVFMOzVFpZV1jaZDI+Vca09RFH4r\nbGBZVhnF9fbzODnCnyWTEpieFHbc9qQ4d51DxK/3HKqRCmx3N29tNiHLiqrmt+7GPy6aqateZss1\n/0fNr5vZNP9mxv/vcWJOm+bppQG9rEgpilILPAsUA6VAvaIoP6i5MF/F4SNlEhqpfoNNttLUWo+E\n1O2BxbLFytZbHqXo9ZVIeh3przziFUmUGvz6/W4OFtp1UfMuHue2C7jkaO05ITbPLmnkL5/v5tE1\n+ymubyM+xMDdJyfz6gUjmZEc7tODVQV9G+UIHymtVkNAoAFFAWOLb1elwG6PMPHtf5K4YA62ViPZ\nC++idNX3nl4W0PvWXgpwGzAYSASCJUm64tDn9FeNlFoVKaETcA53xq/J2ICCQnBAGFrN8Yu81hYj\n2Yvuoezj79EGBjDx7X+ScP7pblhp93Amdnk5pWSuLUSjkTj70vSO9oI7cKa1l1/Zwt1fF3DvN3vZ\nVdVKRICOm2cM5I0Fozh9WCTaHiSD4tx1DhG/3lNbZESi87Ma1L4BosWHdVKHojHoGfufhxh8w+Uo\nVhtbb3yE4mWfeHpZvW7tTQLWK4pSAyBJ0ifADOAdxxN++eUXMjMzSUpKAiAsLIyxY8d2lG0dJ0tf\ne2ww2L84dmzZyTppXa+Pt23bNq/47/HVx+6MX2NrLbVFRrRhnW29Yz1/yuhxZF15Bxs2b0IXEsyi\nj/5D2IQ0j8dLjce1VS3s22K/iEckNVNUksegIe57//oWu+DfJlu7/fqBoyeyLLOMb3/8BYDEURO5\naFwssfW78attQK/1nviKx+JxV48Ld5biwPH3oGA/qmnm519+JXFQuFett7ePJUmi6rR0GhsqCH13\nDXn3/pONuVtImD+bE088sUfHc/xcXFwMwKRJk5g1axY9ReqNa6gkSenYk6bJQBuwDNikKMpLjues\nWbNGycjI6PGxfZ3vt3zA0tVPcfr4C7l29v95ejkCN7Ct8Hce/+BG0gZN5KHLXj/m89pKK8m89K80\n796P/4A4Jq18nuDUZDeu1HW0NJl4++UNNDW0MXbSQGZf4Fqrg6NR21TJja/MJSI4hldu/LbL55Y3\nmViRXc6agloUwE8rcf6YWC4eF0uIX2/vLwUCz/HcZ3fz+6413Hbuk0wbeQYAX3+4lbwtpcy5cAxj\nJw708ArVp3jFp+Td8wwoin0O6cM3O3Xdyc7OZtasWT0+QK+uGIqi5EqStALIBGQgGzj2N0g/QvhI\n9T8aHELzLqwPWotK2LzgLxgPlBE8YgiT3n8e/4QYdy3RpVgsNj57ZwtNDW0kJoUz69w0j2iJOuwP\nunA2r2218F5OOV/trMEqK+g0EvNGRnHZ+HiiAn17p6RAcCR9wQKhK5IWno8+NJitNz9C4avvYWlo\nYvQzd7vdBb3XPlKKojytKMpoRVHGKoqySFEUy6F/768aKeEj5R24M36OOXvH2rHXXFDI7+fdgPFA\nGWET0pjy6StenUT1JHaKrPDNh1spLa4nJMyf866YgM5Ds+U6du0dZdZes8nKm5tLWfRBHp/lVWOT\nFWalRvDGglHcPGOQqkmUOHedQ8Sv9xzqIwWdFgi+asrZHRLOP52MFc+gCfCj5L0vyb3+IWSTe8X1\nwtlcZTorUn33gys4HEdF6mgeUo07Cth0/o2YyquJmDaeyR++gCEi1N1LdBk/f7uL3dsrMPjpmL9o\nYscdsCfQHGVETJtVZmVuBYs+yOO93ApMVpnpyWG8On8k95wymIRQz61XIHA1jsHFLU2+v2uvK2JO\nm8bklS+gCw2m4qufybrqLqwtxuO/UCVcVv8SPlLCR8qTuDN+DS2O1t7hiVR99g4yL7sda0MT0adO\nZcIbT6AN9D/aIbyK7sYue30hWesK0Wglzr9yAjHxnnFid+Bo7dlkGxabzLe7anhnSzm1RisA6QnB\nXD05kVGxQS5dhzh3nUPEr/cc6iMFENi+a6+1ue/f2EdMGceUVS+Reelfqfl1M1lX3M7Et/+JLti1\n5zuIipTqCB+p/kenGWdnIlW7YQubL7oVa0MTsXNPImPZUz6RRHWXnbll/PjVTgDmzB/jFufy4+GY\ntWeRbVz7UT7/WX+QWqOVYdEBPHFmCk/PS3V5EiUQeIqj7RvrD629QwkdPYypn76MX0IMdRtz2XzJ\nbVgamlz+vi5LpPq7Rkr4SHkWd8bvyNZe1U8bybz8dmwtrSRccAbjX38MjZ/vDLQ9Xuz25FXw1Yf2\n8ZuGAgUAACAASURBVC8nzh7G6AkD3LSyY6MoCpsPNgNgtdkoazIzKMyPB2cN4cXzRjBxYKjbBPDi\n3HUOEb/eU1tkhEM+58GhjtZe/0ikAIJSkpj66cv2+XxZO9h80V8w1za49D1FRUpl9GLWXr/D0doL\nDYyg4ptfyF50D7LRxMDLz2Hciw+h0fed7fSFBdV88V4Oiqww9eShTD3FPeNfuiK3tIm/flHA39cU\nASBh446Tknj9wlGcOES4kQv6LwY/HTqdBovZhtlk9fRy3EZg8gCmfvoygYMH0Lh1F5suvBlTVa3L\n3s9liVR/1UiptWtP6AScw13xUxSlo7XX9tNWcq59AMVsIfnaixj9z3uQtFq3rENNjhW7A/tq+fTt\nbGw2hQnTk5g5e5ibV3Y4u6tbue+bPdz19R7yKlsI83dU/RRm99CNXE3EuescIn6950iNlCRJBDoE\n5/1AJ3UoAQPjmfLpywQNS6Y5fy+b5t9EW3mVS95LVKRUxk/4SPUrTBYjZqsJvaQn/+Z/oNhsDP3L\nQkb+/TYkTd85vQoLqvl4eSZWi8yYiQM47axRHqv0FNe38fc1+7n5011klTQRqNewcGICyy8Z3Tlv\n7ygWCAJBf6S/7Nw7Gv7xMUz55CWCRw6lpaCITRfchLGkQvX3ERoplTEIHymvwF3xc4wlMTRYkWSF\nlL8uYdh91/t0O+nI2O3Jr2TViiysFrndtXwMkgeqPZXNZp79tYg/fZzP2v31GLQSC8bGsuKS0Vw5\nIZ5Ag/YQU87eDy52FnHuOoeIX+850kcKOgXn/UkndSh+MZFM+fhFQscOp3X/QTadfyOtxWWqvkff\nEW94CXqtAQkJq82CLNs6vG0EfZP9334HQEALpNx+Nal3XePTSdSR7NxaxtcfbEWW7e28084a5fYk\nqt5o4b3cCr7Mq8YiK2gkmDcyiismxBMTdLiIX6vRYpOtoiIlELQT2McGF/cGQ1Q4kz/8N5mX30FD\n9g42zb+JqateImBQgirHFxoplZEkCYO+XXBu7f0HV+gEnMMd8Sv7dDXbXnoDgKiEJIbdfW2fSKJm\nzpyJoihkrtvPlytzkWWFKScN4bSz3ZtEtZhtrMgqY9EHeazaXoVFVjhlaDhvLBjFbTOT/pBEQacF\ngq2LMTGuRpy7ziHi11uUP2ik4JDWXj/TSB2JPjyUSe8/R1jGaNoOlrNp/s0YD5arcmxRkXIBBp0/\nJksbJksb/oZATy9H4AJKV33P1psexTja3kJKGNt3bhxkWeHHL/PJ2WifiH7inOFMOWmI25JEk1Xm\ni7wq3s+toNFkT4imDAplyaQEUqK6Pp80GodGynOtPYHAk0gcfp4GhfTv1t6h6EODmfT+c2RechsN\nW/LYNP9mpqx6iYABcU4dV2ikXIAaO/eETsA5XBk/RxKFLBN4RgbwR1dzX8VssvLUw2+Ss7EYrVbi\n7EvTmXryULckUTZZ4eud1Sz5II/XN5XSaLIxJi6IZ88exmNzUo6bRIG9tQddDy52NeLcdQ4Rv94j\nNFLHx5FMhY0fhbG41L6br7TSqWOKipQLMLR7SVmcaO0JvJNDk6jUO6+hfHwNZG86zNXcV6mpbOaz\nd7ZQWlzPiNRkzr8qg4GDjz6IWU1kReHXffUszyqjpNF+zgyNDODqyQlM7qGRpqO158lESiDwJjrt\nD/rfrr1joQ8LYdLK59l88a005u5k0/ybmPLJS70+npi15wIMKoyJEToB53BF/Mo+W3NYEpV65zV8\n9fl9gN2M05fZta2cbz/ehsVsI2PCFM6/cgIR0a4dp2J3I29kWWYZe2rsd9KJoX4smpjAyUPD0fSi\nCtaxa8+DrT1x7jqHiF/v6VIjJSpSh6EPC2HyyufZfPFtNG7dyaYLbyb4pXt7dSxRkXIBHa094SXV\nZ6j8bi1bb/rbYUkUQEO7/YGvVqQsFhu/fruLLRvseqiR4xKYPX80BoNrLw07ypt5I7OU7eUtAEQF\n6rkyI545w6PQOSFod+yS9aTYXCDwJgKDOgcXy7KCxkNGtd6IPjyUyR+0V6a27iK4l8cRGikXYOiY\nt/fHfnV3EToB51AzflU/bWTLdQ+gWG0MueUqUu64uuNvjjl7vqiRKi9p4K0X17NlQzEajcRpZ4/k\nrEvGsWnTRpe9596aVh78bi9//bKA7eUthPhpuW5KIssuTuOskdFOJVHQmUh50v5AnLvOIeLXe46m\nkdLqNAQE6lEUMLaI9t6R6MNDmbTyBULG9H5Sg6hIuQA1WnsC76Dmt2y2LLm3Y+zL8P/7c4dmp6JC\norqhGgBLa4wnl9kjbFaZ33/Zx8af9iLLCpExQZx18TjiBoS57D1LGkysyC7j5711KIC/TsOFY2NZ\nMDaWIIN6XmvaDo2U2LUnEDgICvHD2GqhpdnUsYtP0IkhIpTJH/yb7YV7e/V6oZFyAf56e59aaKQ8\nhxrxq8vcRvZVdyG3mRl45f+zd97xbZXX/39fSZY1vOI9Etux4wwnsTOchCRAgACl7E2hhYzS8aN8\naSlQWlq6odACLbSF0jLCLgTKaNkziyTgJM5y4njFjvdeGta6vz9kywlxEulKsqT4eb9evIgs3Xsf\nHUvy0Tmf53Mudo99GUqidu5UserbGqZe3Y/LqeHKSyeyZo2Z4uLw/gNeX93JR2+V09XubqnNW5zD\naedNJSpqJJkJ5Guv02Tn+R3NvFfRiVOGKJXEhTOS+cacNCboowJ2nWFG7A+Ej1SkIuKnDFke3UcK\n3IlUR+uAWycVGA/Kkw5tYjwcVHasqEgFgeihRMpqM4d4JQKl9O6qYNt1t+E0W8i88mvMvP+OIypR\nK1fG0NrTzFTAZkqhri6KlStjeO+9ftLS5NAufhT6e61seP8A5WVNAExINnDOJTPJzk8KyvX6rA5e\n3tnKm+Xt2JxuN/KvTU3kW3MzSIs92kgzUIhde4Lxzmi7XIUFQnARGqkgMGzCOWgXGqlQ4U/8+vdV\nU/qNH+HoGyDtgjOY9ZefI6lHKjY1NSrq6tREG93eI4MD7rZeXZ2amprwGlRsMdtY924FTz64nvKy\nJjQaFUvPLmDFLaceM4nyJ3YWu5MXdrRww8t7Wbu7DZtT5tTcBP55+QxuOz0nqEkUhIchp3jv+oeI\nn3JG00jBYaacwgIhKIiKVBAY3rVn9SOREoQGU3U9X179Q+xdvaScvYTix36DSjP62yQ6ZiiRMqWO\n5RK9wjxgY8eWOrZ/Xseg1QHAtNnpnHbuVBK8MLb0FZvTxdv7OniprJWeoevNy4pldUkmU1PGzt1f\nVKQEgqMxDs/b6xMVqWAgNFJBIBAVKaET8A8l8bMcaubLq27B1t5F0ukLmPPEPai0R+t48vJc5OQ4\nccUcWZHKyXGSlxdajVR3h4nSTQfZu60Rh8O9lpwpSZz2tamkeykm9yV2TpfMx1VdPLe9hdahb7vT\nUwysXpDJnMxY35+An3jsD4RGKmIR8VPOMTVSMWLeXjARFakgIDRSkcdgexdffuNWrE1tJCwsYu7T\n96HWjb67JS1NZs2aAW570J1I2Uwp5OQ4WbNmICT6KJvNQeWeVnZva6Chttvz87xpKSw4fTKTJgfe\nmkGWZTYd7GXNtmbqe9ybKnIm6FhVksHi7PiQDW/2GHKKXXsCgQcxby+4BC2RKisrY968ecE6fVgz\nsmvPP42U+GamHF/i5+g3se2bt2Gurid2ZgHzn/sTGuPo3+yGKS52ceFlTWyphO+sjOOSpWMrMrcN\nOqipaKeqvJWainZsQ8N9NVEqphdlUHLqZJLTlNnLnSh22xv7eLq0mYp29xeFtBgtK+ZncGb+BNQh\nNvsLFx8p8d5Vjoifck6okRKJVFAQFakgMJxICY1U6HC5ZCxmG1aLHavZjtPhwiXLyC4ZWQaVWiIq\nSo3kclLxkz/SV9VETH4u8196iKh471pSg852AObNTgx6EuV0uGhu6OVQTReHartorOvG6RipumRM\nimd2yUSmzc4gWhect/W+NhNPlzZR1jQAwAS9hm/OTefr05KIUoeHyF5UpASCoxkRm4tEKhgIjVQQ\niBYaqTFBlmX6eqy0NvbS1txPT6eJni4LvV1mLBY7X7z3iXcnyj/T/R9Q9tdSdAYtOn0UekMUOkOU\n+99GLXpDFHrD0P+NWjp63K29eEPgLATsdid93Rb6eix0tplob+mjvbmfjrYBXM7DkjUJsnISmFKY\nRkFhWkAF5F997R3strCmtJnP63oBiNGquaoolUtnpqCPCpyZZiAYmbUnNFKRioifUo7tIxWt06DW\nqLANOrHZHEEfATXeENEMAjqhkQoavd0WDh5o52BlJw113cceeSC5PzyGEyGNRoUkSUgqCUmScDpd\n9Fc3MNg7gKyNRpOShM0h47C7MPUPelUCb4lrBhWsfWwPcdGtngRLZ4giKkqNWqNCo1Gh0bj/LeOu\niLlcMrILHA6nu2JmsTNocWDqH8R8nBEOSakxTMpLZNJk93+GmOBaCbT0D/Ls9hY+ruxCBqLVEpfO\nSuXqolRio8Pzo2OktScqUgLBMJIkYTBq6e+1Yh6woU0Mz/dvpCI0UkEgEM7mQicwwkCflX07m9m/\ns5nWpr4j7tPpo0ifGE9aVhyJyUbiEw0kJOrZXvYlp59+2qjnk2WZit/8jYPPvIRar6Nk7cNMKJkN\nuCtCVrO7HWix2Nz/N9uxmm2Yh/5vMdsxD1gpHXC7g2tcMZ6EqLvTv+RZpZaIjdcRl6AnMdlISnos\nqZmxJKfFoh2j5OXdjz6jxpDP2/s7cbhk1BKcPz2Z6+amk2QIvBt5IAmH1p547/qHiJ9yjqWRAjDE\njCRSCYljZ0kyHhBpaRAQu/b8R5ZlGg92s21THVX7WpGHulraaDU5U5LJLUgmJz+J+ET9qDvEjjfh\nvPZvz3HwHy8hRWmY89S9niQKICpKTVS8mth43XHX1zPQwf8elYnVJ3D77RdgtdixmNyaLIvZjsPm\nxOFw4nC4cDpcHisClUry/KfWqNDp3RWzaH0UBqMWY2x0yKazDww6WLurjac/q0OXG48ELJ8ygRvm\nZZARFxnzucJBbC4QhCPDFgjHq3oLlCE0UkFA+EgpR5ZlDlZ2sPHDSlob3dUnlVpiyrRUZszJIG9a\nChovdDnHit+h59/kwD3/AEmi6K+/JOXMUxSts9vkHlY8ISYZlcpdNjcYg9tqCxZWh4s397bzyq5W\n+ged6HKLWJwdz8qSDCYnHn/3YrgRDhWp8freDRQifso5lkYK8EgBzEJwHnBERSoIRItde4poPtTD\nuvcqPF5IekMUxYuymbNoEjFxx68QeUPb+xvY+5M/AVB43+1kXHq24nP1DLgTqQRjst/rChUOl8y7\n+zt4oayFLrPbjbw4I4ZVJZkUphlDvDplhIPYXCAIR4YTKVO/qEgFGqGRCgKHj4iRZVmROWE46gRk\nWWZ/Qxmb9r1LZ18ryXHpLJy2nFnZC/wyYLRa7Gx4/wA7vzgEuHVPi87IY84p2UQp3BX21fh1l+6m\n7Pu/BJeLKbd/m+wVlyleL0CPqROABGNwhv4GE5cs82l1N89ua6Z56EO1IFnPqpJM5mfFsmnTJkgL\nr9eet4SD2Dwc37uRhIifco6nkfK09kRFKuCIilQQ0Kij0KijcDjt2B2DaKP8r6aEGsugiX+9fw+f\n73//iJ9/WPYqc/NO5QcX/o4YXZzP560qb+XDN8sx9Q+iUkuUnJrLwtPz0OkDJ2o2Vdez/Yaf4LIM\nMvFbF5N/22q/z9kz1NpLiImcipQsy2yp72NNaRO13e6NEBPjo1lZksFpuQkhcyMPJGLWnkAwOp6K\nlBhcHHCERipI6KIMDDh7sdotihKpcPpGZrVZuHftzVQ27UIXZeC8+d8gL30GdW0HeG/bv9lRs5Ff\nPr+SX137BPFG78aROOxO1r1bwY4t9QBkZidw7mUzSU4LzHy24fgNtnVSeu2PPUOIC++7PSAJQ3eE\ntfZ2Nffz1JfNlLe5dxqmGKO4fl4G5xQkHuVGHk6vPV9RqYY1UsJHKlIR8VOGLB/bRwrAYBwWm4uK\nVKARFakgER2lY8DqTqTimBDq5ShGlmX+/vbdVDbtIjkunbuu+juZSbkALJx6FmcWXcIfX/sR9e1V\n/PG1H/Gra/95wsSxp9PMWy+V0dbUh0otcfrXpjF/SQ5SgHerOQZMbPvW7Vjqm4ifM4Pix3+HShOY\nl3zPYWLzcKayw8zTpU2UNvQDEK/TcO2cNC6cnoxWEx5u5IHEU5ESPlKCccqxviiOiM1FRSrQBO2T\ntKysLFinjgj83bm3cePGQC5HMZ/sep0vKz9FrzUekUQNkxyXwV1X/Z3U+CyqW/bywrpHjnu+xrpu\nXnhsM21NfcQn6rnue6dQcmpuwJOo9Z+to+w7v6BvVwWG3CzmeTE/zxdGKlIpATtnIDnUY+X3H9fy\ngzcqKG3oxxCl4oZ56TxzdSGXz0o9bhIVLq89JXgqUiHWSAmUI+KnHO80UiKRCjSiIhUkTgYvqV5T\nF89/+hcAvn3uz45KooZJiEnm1kvu5xfPr+T97S9TMmUZs3MXHfW4fTubeO/V3TidMpOnJnPhN4qJ\n1gXe4FGWZWoffZG09bvRJiUw/6U/E53iXcvRW3rDVGzeNmDj+e0tfFDZiUuGKLXEJYUpXFOcRnyQ\nZvCFE0IjJRCMjt4QhaSSsFrcs0fVJ2FFOlQELZJCIzXsbq6sIhUOOoG1G/+BxWZiTt5STi38+nEf\nOzl9Blcu/R4AT390Pw6n/Yj7t39+kLdf3oXTKTNnUTaXXT8vKEkUQNUf/0Xa+t2o9TrmPfcAxskT\nA3p+WZaP8JEKB3osdv6xpYFVa8t574A7yfv6tCTWXF3Idxdl+ZREhcNrTykj9gfCRypSEfFTzvE0\nUtKQ3x0IU85Ac/J/RQ0RIxWpyPSSauk+xMe7Xkclqbn+zFu9OuaihdezYe//aOqq44Mdazm/5DoA\nSjfW8tk7FQAs+/o0dysvSDvE6p99g+o/r0FSqyn+5+9ImFcY8GuYBvuxOwbRRRk8LdxQYbI5eW13\nG6/tacNidycPy/ISWDE/g4kncGc/GRlp7YmKlEDwVQxGrXum58DgCac3CLxHaKSCRKRrpN7a+gyy\n7OL0WReQlTTZq2M06ii+ecaPAHhz6xoG7Ra+WD+SRJ1zSSELTpsctCSq/dMt7PvZgwCYbryQ1HOW\nBuU63f1tACTGpgbl/N5gc7h4dXcbK17ey/M7WrDYXSyYGMejl07j52dN9iuJCvVrzx9GWntCIxWp\niPgp53gaKRAWCMFCVKSCxIi7eeRppHoGOli3579ISFy8cIVPx87LP4289EJqWsp55s0n6NteAMC5\nl82kaMGkYCwXgP591ZR95xfITid5P7yBttNmBe1anUOJVFJsWtCucSycLpkPDnTy3I4WOkzu9unM\nNCOrF2QyOz1mzNcTboSD2FwgCFeEKWdwED5SQUKnHdZIWRUdH0qdwGd73sLpclAyZdkxBebHQpIk\nrlr6Pe5/7Yesr1pLEXfwtUvnBDWJGmzrZNu3bsc5YCb9kuUU3PldpqqCJ6Ts6m8FxrYi5ZJlNtT2\n8My2Zhp63R+CeYl6VpVksHBSXECrfJGsUQkHsXkkxy8cEPFThszxNVJwmAWC0EgFFFGRChKRumvP\nJbv4ZOcbACyfc4Wic6RqCjE6szCpG0koaqJ44UWBXOIROM1Wtt/wE6yNrcTPn8nsv/wCKYhJFIxU\npMYikZJlmdKGfp4ubaKq0122z4zTsmJ+BsvyJqA6CdzIA0k4iM0FglAicezPBNHaCw6K/+JIkpQg\nSdKrkiTtkySpXJKkUw6/f9xrpKIiUyO1++AW2nobSY7LoDj3lBMf8BV6uy288dwOUq1LAKiyfIos\ny4FeJgCyy8Wum39Db9k+9NmZzFtzP2q9u3QdzPgNV6SC3drb2zrA7W9X8fP3q6nqtJBkiOKWpZN4\n4spCzsxPDFoSFckaFfXQrD1nCCtSkRy/cEDETzkn1kiJ1l4w8Kci9TDwjizLV0qSpAEic1x8kDh8\ncHEk8fFwNar4Ms8AWG9x2J289eIOLGY7c6acTpf5Y+rbK9l3aDuF2fMDvtaK3z1K6zvr0MTFMP/5\nBwLuFXUsugaCW5Gq6bTwdGkTWw/1ARAbreaa4jQuKUwhWni/HJeRocVi155A8FWMwt08KChKpCRJ\nigdOk2V5BYAsyw6g9/DHCI2UfxWpUOgEzIMD7KjegITEslm+teNkWeajt8ppbewjfoKei79Rgmr7\nFbz2+b94b/vLAU+kDj33BgcfexFJo2buk/cSMzX3iPuDGb9gic2b+gZ5Zlszn1V3IwM6jYrLZ6Vw\nVVEaRq1vSa0/RLJGxdPaC+GuvUiOXzgg4qecE2uk3BUpk6hIBRSlFanJQLskSU8DxcA24IeyLEeW\nICiI6CJQI7W9aj12p43pE+f6XG3ZXdrAnm2NaDQqLv7mXPQGLcuLr+A/m59kW9Vn9Jm7iTMEZuZg\nx2dbKf+p2+Zg5h9/QtJpJQE5r7cEWmzeabLzwo4W3q3owClDlErighnJXDsnjQn64JiWnqyMaKRE\nRUog+CoeQ05RkQooSvsEGmAe8Kgsy/MAE/DTwx8w3jVS0X46m4dCJ7B5/4cALJ5+rk/HdbYN8Ml/\n9wFwzqUzScuMAyAxNoU5kxfjdDnZWP5uQNbYv7/GY3Mw+f+uZ+J1o1fOghU/y6AJ8+AAUZpoYnTx\nfp2rz+rgiS8aWfnKXv63vwMZ+NrURJ66qpCbFk8MWRIVyRqVkdae8JGKVET8lOOtj5TFZMPlCo52\ndTyitCLVADTIsvzl0O1X+UoitW7dOkpLS8nOzgYgPj6e2bNne8q2w2+Wk/X2vp0H6KqzYJ1oUXT8\n7t27x3S9H33yAZ+s+5iEiVoWTT3L6+MXn7KEt1/ZRXXdHnKnJjFz3nlH3L9s9sXsqNnES288Q5w1\n26/12vsGUP36CRz9JlpOmYbxtJlMgzGN3+QZWQDYW7Vs2rRJ0fksdif3P/82n9V0o80pAiDHVMl5\nU5O54vS5AV3veLutGtKQVeyqZqNqY8jXI26L22N1u25/M8Mc7/E6fRQVVTv55ONozj7nzLBZfyhu\nD/+7vr4egJKSEpYvX46vSEp3VEmStB64UZblA5Ik/RrQy7J85/D9H3/8sTxv3jxF5z4ZqG7ey8+f\nu4G8tBncu+L5UC/nhKzf8z8efedXFE6azy+v/afXx214/wBb19UQN0HPiv9bSvRXZrrZHTb+36Pn\nMWDt5b4VL5KbNu0YZzo+LpudL6/5Ed2bdxBXPJ1Frz+K2jD2Iw52H9zKPa/c5HOcAGxOF+/s7+TF\nHS30WB0AzMuKZVVJBtNSxF6NQPDfrc/ywrqHuWDBt7webSQQnAz86bVb2Va9ntsve4iSgmXHfNxT\nf95AV7uJFbcsJSU9dgxXGP5s376d5cuX+7wdWmlFCuD/gBckSdIC1cAqP8510hFpzubbqzcAsHDq\nWV4f01Dbxdb1NUgSnH9V0VFJFECURsvSwvN4f/vLbNj7tqJESpZlyn/+EN2bdxCdluy2OQhBEgXQ\n6dFHeS80d7pkPq7q4rntLbQOaROmpRhYvSCTuZnigyyQeJzNQyg2FwjCGWNMNF3tJqGTCiCK91LL\nsrxTluUFsiwXy7J8uSzLR+zaG+8aqWFnc6VDi8dKJ9DaKrFxk4vtVVsAmJvv3Y4Zh93J+6/vARkW\nnp7HxNxjC8mXzPgaAFsqPlKkXal/6jUannsTVbSWuU/fhy4j5YTHBCt+XR4zzhOvQZZlNtb28L3/\n7OeB9fW0DtjImaDj1+dM5pGLp4ZtEjVWr71gIA2JzeUQis0jOX7hgIifck6kkYLD3M3Fzr2A4U9F\nSnAc/BWbjwU7d6pYuTKGPvlLFlw3wGBvHi112aQlnDjZ2bquhu4OM4kpRhYvn3LcxxZkziYxNo3O\n/laqmvYwNavI6zV2bihl/y8fBmDWn+8iYV6h18cGg5FE6vgVqR2N/TxV2kRFu7simRbjdiM/M38C\napVwIw8W4SA2FwjCGeFuHniC5u437n2khpzNrXazImfvYHuptLZKrFwZQ12dmuS8zwBo3n8WK1fG\n0Np6/D/0nW0DbF1XA8C5l85EcwKTSJWk4pRpZwMjOwO9wVRziLLv/Nw9iPiWG8i83PvdhMGKX6fH\n1Xx064P9bSbufKeSO9+toqLdzAS9hpuXTOSpq2ZwdkFiRCRRkezjI3ykIh8RP+WcyEcKxODiYCAq\nUkEiSqNFrdLgdDmwO21oNdGhXtIR1NSoqKtzf3tPyf8UgPbqM+muV1NToyItbfTWiOyS+fCNvbic\nMrNLJjJxsndu4ounn8M7pS+wYfdHFOhvJz8f0tKOnWDa+wbYvuIn2Hv6Sf3aqRT89Ls+PsPg4HE1\njzmyInWw28Ka0mY+r3N3uI1aNVcXpXLpzBT0UWNnpjneGRkR4wjxSgSC8EQMLg48QatIjXeNFIAh\n2r0TyzJo8vnYsdIJ6OIaiUmpxDEYQ0/DiY0t95Y10XCwG4NRy7Kvey8cN7UVYR/IYsDWxre+X8l5\n58Wyc+foLz/Z6WTn936JqbKOmOl5FP39Vz4PIg6eRurIilRL/yB/XFfH9/+zn8/reolWS1xTnMaz\n1xRy7Zz0iEyiIlmjopJC39qL5PiFAyJ+yvFOIzXsbi4SqUAhBncFEb02BgCLzfdEKtjk5bnIyXGS\nmLMZgK76U5BdUeTkOMnLG/2PkG3QwYb3DwCw7Pxp6Lw0jGxtlVi1KpaGPRcAkD7jberq1MdsI1b8\n9u90fLqFqMR45j3zRzQx4WENYHMM0m/pRa1S45Ri+fvnh1i9dh8fVXYhARfNSGbNNTP59oJMYqNF\nsTcUeHbtCY2UYJwh452ExCjE5gFHaKSCiH64IqUgkQq2TiAtTWbNmgEmFQ4lUnWnkJPjZM2agWO2\n3L5YX4upf5D0ifEUFmd6fa3hNmJrxdcBSJnyISBTV+duIx5O4yvvcvDxf7tn6D1xL4Yc769zEYvP\n4AAAIABJREFUOMGI37DQPEqbxMq1+3mzvAOnS2b5lAk8dVUh/7d0EkmGyB/pEskaFU9FyhW6XXuR\nHL9wQMRPOYk5eiTp+DpMgxhcHHDE1+Ygotcqb+2NBUVFTnJmbabHBL+9YzaLivqPmUT19Vgo3VgL\nwJkXTEdSIJrua57N4EAK+vhmYlP30d925A683p372XvH/QDMuPc2EpfM9fkawcLqcPFq2X4A+h2x\nDDpcLM6OZ2VJBpMTTyzwFIwNI7P2REVKIBgNw2Fic1mWT5h4CU6M0EgFEb3WvXNPSUVqLHQCrT0N\n9JhaiNXHc+HZeccVf2/44AAOu4ups9LJyvFt+PBwGxFUtFe57fdTCj46oo1o6+hmx+qf4Rq0MfH6\nS8i+4VLFzwsCFz+HS+a/5e2sfGUv75W725rxxjT+ctFUfnNu3kmZREWyRmWktSd8pCIVET/leKOR\niopSo41W43TKDFrFpoxAIDRSQcSjkRocCPFKRqe8vhSAGZPme77Jj0ZrYy/7yppRqyVOP2+qz9cZ\nbiPm5Dhpq3QnUlmFH3vaiC6Hg7Lv3o21sZX4+TMp/H3oR3u4ZLcb+bfXlvPXzxvoMjtI0bl35J09\nfSqFaeGh2xIcyUhrT1SkBIJjYTAKC4RAErTWntBIHa6R8n1MzFjoBPYOJVKF2cffrbfpoyoA5izO\nISHRoOhaxcUu3nuvnwOVs3l8iw590m4m5bUAqVT87u90fb6d6NQk5j55L6poraJrHI7S+MmyzJb6\nPtaUNlHbbQVgYnw0K0syKN9v57NOSInL8Ht94Uwka1TCQWweyfELB0T8lOONjxS4dVI9XWZMAzYS\nTzykQXAChEYqiHg0UmG4a0+WZU9FauZxEqmm+h5qKtqJ0qpZdHqeX9dMS5NJS4tiS9sitlWtY3v1\nBgqrY6h7/GUkjZo5T9yDLj107+pdzf089WUz5W3u31eKMYrr52VwzpCR5rqt7unqKfHKBPCC4BMO\nYnOBINwZMeUUgvNAIDRSQcSfXXvB1gk0d9XRbeog3pDIxKRjJ0ibPqoEYN7iHM9uD3+ZP+V0ALbs\neIc9t98HwIzf38qEhd6PjjkRvsSvssPMXe9VcfvbVZS3mYjXafj+KVk8fVUh501L8riRd/QNJ1In\nd0UqkjUq4SA2j+T4hQMifsrxRiMFwpQz0IiKVBAZ2bUXfhqpfQ3bAbc+6li7Ng7VdlFX1Yk2WkPJ\nabkBu/a8/NOQkNjXUkaxQ0vutRcxacVlATu/txzqsfLMtmbW1/YAYIhSceXsVC6flYpBe6SRpkt2\n0dHXAkByXPqYr1XgHSOz9kRFSjC+8GUUmd4ovKQCidBIBZHhRMochj5SBxp3ATB94rF/T8PVqJJT\nc9EbAlONAoiLjid9IIbmmH56lmVS+IfbAr4F93jxaxuw8fz2Fj6o7MQlQ5Ra4pLCFK4pTiNeN/pb\nomegA6fLQZxhgmcg9clKJGtUPImU8JGKWET8lOO1RmookbKY7MFczrhBVKSCyHBrz6pAbB5shhOp\ngszR22mNdd001HYTrdMwf2lOQK9dee/jZJSZaT4V+i+bhlo3NnMIeyx2/r2zlf/u68DulFFJ8PVp\nSXxrXjopxuMniu1Dbb3kk1xoHumEw9BigSDcGU6kRGsvMAiNVBDxx5AzmDqBPnM3zd11aDXR5KQW\njPqYL9bVADD3lGyidYFz625+4yNqH32BrEPuc+5pK/OpJO0th8fPZHPy7LZmVrxSzn/2tGN3yizL\nS+CJK2dw62nZJ0yiANp7m4CTXx8Fka1RGZm1J3ykIhURP+X4rpESrb1AICpSQcQQ7faRMtvCSyNV\n2bQbgPz0mWjURydJHa39VO9vR6NRMXdJ4KpRAxW17PnxHwBYfNMtrLc9SVd/Kw2dNUxKzg/YdYax\nOVy8ta+Df5e10Dfo/sO6YGIcq0oymJLsm41Dh6hIRQThYH8gEIQ7etHaCyhCIxVE/KlIBVMnMJxI\nFWTNHvX+Lze4R8HMnJ/l2SbrLw6TmR03/hyn2ULG5ecy+carKX5nL+v3vs3Oms8Dmkg5XTL9ydNZ\nubacjqEPiplpRlYvyGR2eoyic3b0uoXm48H6IJI1KuHQ2ovk+IUDIn7K8VUjJVp7gUFUpIKIbmhE\nTLhppA407gRgambxUff19VjYV9aMJMGC0yYH5HqyLLPntvswVR4kZupkZv7pTiRJonjyEnciVbuZ\nCxde7/d1XLLMhtoentnWTEOvu2Sdl6hjVUkmCyfF+SVob+8bau2JilRYEw6tPYEg3NEPDVe3mG24\nXDIqBbNTBSMIjVQQMYShj5TT5aC6ZS8ABZlHV6S2bTqIyyUzdVa6Yhfzr1L/1Gu0vPERaqOBOU/e\ng8bo/tY0O3eR2wahYTtWm3e9/dGQZZkvD/Vx8xsV3PPJQRp6B9E27+VnZ+bw6GXTWZQd7/euwPbe\n8eEhBZGtUQmH1l4kxy8cEPFTjrcaKZVahU4fBTJYzaK95y+iIhVEdFEjQ4tdsuu48+zGivq2Sgbt\nVtITJhFvTDziPtugg92lDQAsPD0w1aiebXvY/+tHAJj10M+IKcj13BdnmEBeRiHVzXspP1TKvPzT\nfD7/3tYBnvqymd0tbh1akiGKb85NJ7Z9gGX5iSc42jtkWRYaqQhBOJsLxi++bdoxGLVYLXbMpsGA\nmS2PV4RGKoioVGp0UQasdjNWm9kjPveGYOkEKpqGbA+yjrY92LujCdugk6ycCaRlxft9LVtnD2Xf\nvRvZ7iDnxqvIuGT5UY+ZM3kJ1c172Vm72adEqqbTwtOlTWw91AdAbLSaa4rTuKQwhWiNCmb4npQd\ni+6BDmyOQWL18T79DiOVSNaohENFKpLjFw6I+CknMUfvdfXdEKOlq8MkdFIBQFSkgow+2ojVbsZi\nM4XFH+Gqpj3A0W09WZbZsbkOgLmLs/2+jux0svMHv8ba2EpCySym/fLmUR9XPHkJr33+L3bWfO7V\neZv6BnlmWzOfVXcjAzqNistnpXBVURrGr7iRB4rWHneVLjVhYlDOLwgc4SA2FwgiAbFzL3AIjVSQ\n0SsUnAdLJ1DTUg64rQ8Op766i652EzFx0RTMTPP7OtV/XkPnZ18QlZhA8eO/Q6Ud3YsqP6MQoy6O\nlp5DtHQfOub5Ok12Htl4iG+vLefT6m40KolLZ6bwzNWFrCzJPCqJCmT8Wnvc60obJ4lUJGtU1Cr3\nd0On7AjZGiI5fuGAiJ9yvNVIwWE798SYGL8RFakgo9cOeUmFwbw98+AAzV11qFUaslOmHHHfcDWq\neOEk1Gr/8uv2T7dQ9eBTIEkUP/Zr9FnHTszUKg2zcxaxpeJDdh3cTPqESUfc32d18MquVt7c287g\nkBv5uQWJXD8vg7TYsenrD1ekxksiFcmIipRA4B16YYEQMIRGKsjoFe7cC4ZO4GBrBTIyOSkFRGlG\nkpDebjPV+9tQqSWKFkw6zhlOjKWhhV0/+A3IMlPuuJHkZQtPeMycvCVsqfiQsprPOXfu1e7z2J28\nvqedtbvbMNncwuFTc+NZMT+DnAkn9koJZPzGWyIVyRoVoZGKfET8lOOtjxSIeXuBRFSkgow/ppyB\nZritl5deeMTPd35xCFmG6bPTMcYqN+B02eyUffdu7F29JJ95Cvm3rvTquKLcxQDsrS/FbBvkg8o+\nXtzRQo/V3Z6ZmxnL6gUZTEsxKl6bP4wkUv4lmYLgM7xrTxa79gSC4zJiyilae/4iNFJBRmlFKhg6\ngZqWfQDkpc/w/MzpdLF3u9tscs4i/0TmB+55jN7te9FlpVH0918hqbx7eSXGpjAxOZ9Bu4XvvPgW\nj25uoMfqYFqKgfvPn8L950/xOYkKZPzaxllFKpI1Kp7WnvCRilhE/JQh46NGKka09gKFqEgFGU9F\nSoEpZ6AZrSJVW9GOqX+QxBQjmdkJis/d9sFGDj7+bySNmjn//B3aRO/sE2RZZlNdL632aUA1A31l\n5GTOYmVJBkty/DfS9BfzYD/9ll60mmgmxCSHdC2CE6NSCWdzwfhGwrvPTLFrL3AIjVSQUdraC7RO\nwGTtp6XnEFFqLROT8zw/HzbgnF0yUXHSYmlsZfcPfw/A1J99n4T5s7w6bkdjP0+VNlHRbkbjmk4c\n75AVXclfL5+O2s+RBYGKX2v3iPVBqJO6sSKSNSrhIDaP5PiFAyJ+ylGikRK79vwn9FbbJzlKW3uB\nprbV3dbLSZ2KRu22Ihjos1JzoAOVSqJwrrJhvC6Hg53/71fYu/tIWb6Y3P937QmP2d9m4s53Krnz\n3Soq2s1M0Gu4cekZaDXRdPVW02/uVLSWYDDehOaRTjiIzQWCSMAzb89ix+XyzRVdcCRCIxVklFak\nAq0TGNFHjbT19m5vRHbJ5M9IxRijTGRe9acn6PliF9Hpycx+5O7j6qLqui385sMabnnrADuaBjBq\n1awqyWDN1YVcXpRFYXYJALsOblG0lsMJVPzGYyIVyRqVcBhaHMnxCwdE/JTji0ZKzNsLHEIjFWQM\nQ4mU2RZaH6kRfZRbaC7LMru3NQLutp4SOj7bSs0jz4FKRfFjv0GbNLrGqqV/kOe2t/BxVRcuGaLV\nbjPNq4rSiNONvASLck+hrGYTO2s3c/qsCxWtKdCMx0QqkgmH1p5AECmIeXuBQWikgow+WpkhZ6B1\nAl/dsddQ201Pp5nYeB25Bb6LqK2tHey6+bcgyxT85EYSF8896jHdZjsvlrXy9v4OHC4ZtQQXzkjm\nm3PTSTIc7XRePNltg7Dr4Ba/hzwHKn5NXW6j0szEnICcLxKIZI1KOIjNIzl+4YCIn3J80UiBmLcX\nKERFKsjE6OIAMFn7QraGfksPbb2NaDXRZCVNBqC8zG15UDg3E5WPwm7Z6WTXTb/G1tFN0mkl5N1y\nwxH3Dww6WLu7jdf3tGN1uJCAs/IncMP8DDLjjt1CzEzMJTkunY6+Fg62Vhxh0xAqmroOApAxjhKp\nSGY4kXIKHymB4ISInXuBQWikgozRk0j1+3RcIHUCw9Wo3LTpqFUaHHYnB/a0AFA4x3eRefVfnqFr\n03a0yRPcflFq9x8vq8PFKztbWfFKOS+VtWJ1uDglO45/XD6dn56Ze9wkCkCSJI85587azT6v63AC\nEb8BSy995m6io/Qkxqb6fb5IIZI1KodXMUMlOI/k+IUDIn7KkGXZJ40UiJ17gUJUpIKMIToWANOg\nb4lUIDnYuh+AyWnTAaipaGfQ6iAtM46k1BifztW5abtnjl7Ro78mOjUJh0vmvYpOnt/RTJfZ7UZe\nlB7DqgUZzEzz7fzFkxfzya7X2VW7mcsWr/bp2EAz3NbLmJDtV5tRMLaoJDUu2YnL5UTl59xIgSDi\n8MGmRczbCwxCIxVkYnRDiZS1D1mWvfYiCqROoK6tEnBbHwDsK2sGYMacDJ/OY+voZtdNvwaXi7wf\nrSDxtBI+qeri2e3NNPW534hTkvSsXpDJ/KxYRb5Ls3IWopLUHGjaiXlwAEO0b4nYMIGI33BbLzMp\n1+9zRRKRrlFRqVS4nM6QVaQiPX6hRsRPOT5rpERrLyCIilSQ0UbpiFJrsTtt2BxWoqN8e6EHgrr2\nAwDkpk7DarFTU9EGEkwv8j6RkmWZ3T/8PYOtHUxYVEzn1Vfx4H/2U9ttBWBifDQr52dw6uQEVH4Y\nVxp1sUzJnMWBxp2U15dSUnCG4nP5y3AilZWYG7I1CHxH7NwTCLxjZEyMaO35g9BIjQFGT1XK+517\ngdIJ2OxWmrrqUElqJibncWBPC06nTHZeEjFxOq/PU//kq7R/vBkpLpbXL7uBX31SR223lWRjFLee\nls2/rpjB6XkT/EqihhnevVdW+7nicwQifk2dBwHIGGeJVKRrVELtJRXp8Qs1In7KUayREq09vxAC\ngjHAIzgfHPude4c6qpFlF5mJOWg10SO79Xxo6/WXV7H/t38D4M0LvsF2h454nYbvLcpizVWFfH1a\nkt8jXQ7HY4NQuwVZDp3j7rBGKmuctfYiHeFuLhB4h9i1FxiERmoM8AjOfbBACJROoK7N3dbLTi2g\nv9dKQ203Go2KgpnpXh1f39rLtut/ht5mZ1fJUprmzOP62alcMSsVg1YdkDV+lby0GcTo4mnrbaSl\n+xAZidk+n8Pf+Dmcdlp7DiEhkT5hkl/nijQiXaPiqUiFyAIh0uMXakT8lKNUIyV27fmHX4mUJElq\noBRokGX5osAs6eRjRHA+9jv36trdQvPc1Gkey4PJ01KI1h3/V982YOOFHS3YHniU4sZGulLSSLj9\nezxzSi7xJzjWX1QqNbNzF7F5/wfsPLhZUSLlL209jThdTpLjMkKiaxMoR1SkBALv+Oq8PV89BQVu\n/G3t/RAoB47qvwiN1AhGBaacgdIJDFekclILOLCnFYBps45djeq1Onh8SwOr1pZT8canFG9dj6xW\ns+SJ3/PdM6YEPYkaZqS9p8xPyt/4eXbsjUMjzkjXqIRabB7p8Qs1In5K8d1HSszbCwyKEylJkiYC\n5wNPACKNPQ4jGqmxrUjJsuxJpJL1OTTWd6PWqMibnnLUY802J89tb2bFy3t5bU872u5uLnzzBQCm\n330TuYtmjunai3JPAWBvfSkO59i/wevbqwCYlDJlzK8t8I9Qi80FglAi+fjnWOzc8x9/KlJ/Bu4A\nRv3aJzRSIxijfW/tBUIn0N7bhMVmIt6QSGutA2SYXJCMNnqkqmRzuHhtdxsrXinnue0tmO0uSjKN\n/Gj9q2gGBkg6YyG5373G77X4SmJsKpOS8xm0W6ho8L266W/86odaotnjMJGKdI2KZ96e0EhFJCJ+\nyvFVIwVi514gUJRISZJ0IdAmy/IORDXqhBh1vovNA8Gwf1RO6lSPPmrqUFvP6ZJ5t6KTVWvLeXxr\nI71WB4WpRh64oIDv1H6JZcsOtEkJFD1yN5IqNJs7i4baezsP+jcuRgnDFanslIIxv7bAP4RGSiDw\nHo+7+YBIpJSiVPCyBLhYkqTzAR0QJ0nSs7Ise6bXPvzwwxiNRrKz3ULh+Ph4Zs+e7fm2MdwHHw+3\njbo4uuos7FaVw3K8Ov6xxx7zO17r9rwHQGZCPpve2YSkkpg8/SzW13Tzpxffpt1kJy5/DnmJOubL\n9UyfYCCn1cmWP/yDcpeJgu/cQHRqUsji5+o0Am4bhGz1PJ+O9yd+Nscge8sqADxDnsPp9RTs24dr\nVMJhPb7eVklquuosbNm8lUvPnyTiF2G3RfyU3a6vaKWr2eLz8QajlrrGcjZvNjG96IqweT5jcdsT\nu/p6AEpKSli+fDm+Ivnr0yNJ0jLg9q/u2nvwwQfl1atDOystXPiy8lMefP125k9Zxh2XP+TVMRs3\nbvS7xP3g67fxZeVnXDT9RzRvSSExZwKlqfFUdbrfbJlxWlbMz2DZkJGmw2Tm83NWYa45RPa3r6Tw\nnh/7dX1/sTkGufGRM7E5BvnHTe+TEJPs9bH+xK+2ZR8/e/ZbZCbm8tCNryk6RyQTiNdeKPnxE1fQ\n1HWQB1avZWJy3phfP9LjF2pE/JTxh7U38+lnn/DH255gTt4Sr4/b+GElWz6tZvFZ+Sw9e3xX4Ldv\n387y5ct97rIFqmdzVDYmNFIjGKN937UXiA+S4Rl7nfUGANabnVR1Wkg0aLhl6SSeuLKQM/MTPW7k\n+37xF8w1h4iZnse0u3/g9/X9RauJZsak+QDsOrjFp2P9iV99x/hu60X6HzGPRipEYvNIj1+oEfFT\njtBIhQa/EylZltfJsnxxIBZzsqLE/sBfzIMDtPU2IklRWJp0uADLBAM3LsxkzdUzuXBGMprDPENa\n31lH40v/Q6XTUvzYb1Drosdsrcdj2AZhp0IbBCXUt41fofnJQKjtDwSCSGJ4155FJFKKEbP2xgCj\nLgbwbdfe4T1cX2nqG+Se9zcM3UpHjZrolBieum42VxelodMc+WsfbOtkz+33AzD1FzcROyNf8bUD\njcdP6uAWn8TD/sRvpCI1PhMpf2IXDngSqRCJzSM9fqFGxE8Zsuy7jxSIilQg0IR6AeMBT2svyD5S\nnSY7L+xo4d2KDjSWcoxAsioLgFMXTsQ4ykgXWZbZ8+M/YO/qIen0BeSsvjKoa/SVzMRckuPS6ehr\n4WBrBXnpM4J+zUNix15EE+rWnkAQSnydG683uBMpYcipnKBVpIRGagSd1oBKUjNot3htLumLTqDP\n6uCJLxpZ+cpe/re/AxnIMbYBYLS4zTfzp6eOemzD82/S/tHnaOJjmf2Xn4fM6uBYSJJEUa7v7T2l\nOouu/nZ6TJ3otUaS470f7HwyEekalVC39iI9fqFGxE85SjRSeqN7TIyoSCknvP5qnqRIknSYl1Tg\nqlIWu5MXd7Sw4pVyXtnVxqBT5tTceB6/fDrx6mYAogfTSEqNISHJcNTxptoG9v/yEQBm3n87uszR\nk61QM7wDZUdN8Ev+NS17AchLL/T8QRZEFqIiJRB4j26oImUx2/F3F/94RWikxogRd3PvBOfH0wnY\nnC7e2NvOipfLWbOtGZPNydzMWP56yVR+eXYek+K1HBrS+ehd6eTPOHokjMvhYNfNv8FpsZJx2Tlk\nXHqOgmc1NhTlnkKUWktl4y56Bjq8OkapzqK6pRyA/IxCRcefDES6RiXUFalIj1+oEfFTjhKNlEaj\nQhutRnbJDFodQVjVyY/QSI0RgZi353TJfFzVxXPbW2gdcqGdlmJg9YJM5mbGeh7X3F2PzTGIjglo\nZD1TZhxdaar963P0bttLdEYKhX+4TfGaxgKd1sCsnIXsqNnItur1LC++PGjXqm52V6Ty08d2tqAg\ncKiHKlJOUZESCLxCb9BiG7RgMdvcQ4wFPhG0REpopI7E19be4ToBWZbZVNfLM6XN1PVYAchJ0LGy\nJIMlOfFIX1EXDg8qjranYTBqSZ+YcMT9vWX7qHrwKQBmP/wLohLilD2pMWRBwRnsqNlIaeU6rxIp\nJToLWZapadkHjO+KVKRrVDxDi8WsvYhExE85SjRS4B4T09ttwWKyMyEpwIsaB4iK1BgRo4sHYMDa\n69NxOxr7eaq0iYp2MwBpMVpumJ/OWfmJqFWjb88YTqQMzgzyZqagOuxxTrOVXf/3W2SHk5zvXE3y\n6QuUPJ0xZ96U05Hel9hT9wWWQRP6aGPAr9HW28iAtZd4QyJJsekBP79gbBCz9gQC3xiet2cxC8G5\nEoRGaoyINbirQv2WHq8e/9L/PuLOdyq5890qKtrNTNBr+MHiiTx51QzOKUgaNYlqbZXYvFlN2QG3\noaTBmU7+V9p6Ffc8iqmyDmNBLlPv+n9+PquxI8GYREFWEXanzashxkp0FtXNbn1UXnrhUVW+8USk\na1RGNFKhqUhFevxCjYifMmSU+UgB6A3udp4w5VSGqEiNEbH6oUTKfPxEqq7bwprSZt7ddIi4/CSM\nWjVXF6Vy6cwU9FFH+0ANs3OnipUrY6irU7Ps5kqiY8DgyiQnf6RO2/HZVuqffBVJo6bo779CrQ8P\n93JvWTDlDA407qS0ch2nTDs74OevHtqxl58h9FGRzHBrTxYVKcG4xPcvgSMVKeElpQShkRojPInU\nMSpSLf2DPLe9hY+runDJkDJ1LpfOTOGqojTidMf/NbW2Sp4kKkrfSXRMGypZS1fbVLp7okhLk7F1\n97H7R/cAMOWOG4kvmhbYJzgGlBScwQvrHmZH9QYcTjsa9bFFkUp0FgcadwIwJWOW4jWeDES6RiXU\nrb1Ij1+oEfFTjlKNlEFUpPxCVKTGiGMlUt1mOy+WtfL2/g4cLhm1BBfOSOabc9JJMnq3e6KmRkVd\nnftbeGyqWyytd6azrzKLmhoVaWlO9v3iIQZbOkhYMJvJP/hmAJ/Z2JGRmE1W0mQaO2vZ17CD2TkL\nA3Zuq81MTUs5KknN1KyigJ1XMPZIIRabCwSRhqhI+YfQSI0RX9VImWxOni5tYsUr5bxZ3o7TJXNW\n/gSevKqQW5ZOYt+Orcquk+ZOpAzOdGoOuZ25W99ZR/NrH6DSRzP7kbtRaSI3f15QcCYAW/d/dNzH\n+aqzqGjcidPlZHLadAzRMYrXdzIQ6RoVMWsvshHxU45yjZQYXOwPwrp5jIgbqkj1mrp5ZWcrN7y8\nl5fKWrE6XJySHcdjl03np2fmkhnnu24pL89FTo7723fKxF0AqMzZ6GNimZTQyd473AOJp/38JoyT\nJwbmCYWIJTPOBWBLxUdej9vxhn2HtgMwY9K8gJ1TEBo8rT1RkRIIvMIjNhe79hQhNFJjhD7abX/Q\n0N3Bri+bAChKj2HVggxmph1dAfFFJ5CWJrNmzQArV8YQn+7eeWbumsWaNSY6HnoAW2cPiUvmkb36\nigA8k9CSnVLApOR8DnVUs7N2M/OnnD7q43zVWew7tA0QiRREvkbF4yMlNFIRiYifcvzxkQKwmERr\nTwmiIhVkXLLMJ1Vd3PGee/ad7BogP1HHvefl86cLpoyaRCmhuNjF/97uRBNXB7LEimtnk1r7AS3/\n/QS10cCsP98VdgOJlbK08DwANu17LyDnG7RbqGrei4TE9IlzA3JOQehQq0KbSAkEIcGPMXme1p6o\nSClCaKSChCzLbKnv5abX93PfZ3U098sg6ZBw8qfzJ1IyMe64XkVKdAJmVyUyLnSuJGZN1lP+swcA\nmParmzHkZCp+LuHGkulfA2Bb1TqsttE1Ab7E70DTbpwuBzmpUz0O9OOZSNeoDGuknK7QzA2L9PiF\nGhE/5XTVWRR54On0GpDAYrHjconBxb5ycpQowoxdzQP8+H+V/PKDGmq6rCQbo7j1tGxS4tyeTgNe\nmnL6Sln5DgAmRE+i5lcPYe/uI2nZAiZdf0lQrhcqUhOymJpVzKDdyraqdX6fr6xmEwCzArgLUBA6\nVMMVqRANLRYIIg2VWoVOFwUyWC2ivecrQUukxqNGqqrDzF3vVXH725XsbTURr9PwvUVZrLmqkK9P\nS/IIzr1xN1eiE6ioc+uj0kmi7b0NaGKNzHrorpPSpXvpDHd7b/3et0e935f47ah2fwMr5ucGAAAg\nAElEQVSemy+0GRD5GpVQ79qL9PiFGhE/5SjVSAHojcJLSimRuw8+jGjotfJMaTPrat0JkiFKxRWz\nU7l8VipG7Ygb+bAFQp+5OyjraOypdl9nQxUA03/7Q/RZaUG5VqhZMuNcnv/0z+yq3UxbbxOp8cpa\nl609DTR1HUSvNTItqzjAqxSEAk9FSha79gQCb9EbtHRjFl5SChAaKT9oN9n484Z6bnx1H+tqe4hS\nS1wxK4VnrpnJ9fMyjkii4MTu5ofjq06gv9dCn7MBgNQ9LaScvYSsb1zg0zkiiVh9AoumLUdG5pOd\nrx91v7fx21a1HoCiyacc1yl9PBHpGpWRWXvCRyoSEfFTjlIfKTh8556oSPmKqEgpoNfq4N9lLby1\nrwO7U0YlwdenJfHNuemkxmiPeZy38/aUsKe8AqfKQpQjmgS1kZkP3HlStvQO5+w5V7Cx/F0+2/0m\nVy79rqJEaEuF29hz2OhTEPmMtPZERUog8BbhJaUc4SPlA2abk9f2tPHa7jbMdve33WWTE1hRksHE\neN0Jj08wusXmPebOEz7WV53Azt2lAMT36im85zZ06Sk+HR+JTMuaw8SkPBo6a/jiwKces07wLn4d\nfS0caNyJVhNNyZRlwVxqRBHpGhVViO0PIj1+oUbETzn+aaRERUopoiLlBTaHi//u6+DfO1vptbq3\nVJdMjGVVSSYFyQavzxNvTASg13TiRMoXXC4XNY3lYIQsVTIZl5974oNOAiRJ4mvzruHJD//AW1vX\nsHj6OT5V4Tbv/wCAefmnodN6/3sUhDehbu0JBKFA9sdIihEvKbPQSPmM0EgdB6dL5t2KTlatLefx\nrY30Wh0Uphp54IIC7j1vik9JFIxUpHpNXSd8rC86gYpn36EvqgOARRdfftK39A5n2eyLiDcmcbCt\ngl0HN3t+fqL4ybLMJzvfAEYMPgVuIl2jEmqxeaTHL9SI+Cmnq86ChLLPf4PYtacYUZEaBZcss7G2\nhzXbmmnoHQQgL1HHypJMFk06vpHm8Yg3DLX2TB0BW6u1pZ2d//4MyzK3c3rBlPkBO3ckoNVEc37J\ndby07q+8sWUNxZOXeHXcnvovae6uIzEmlXn5pwV5lYKxRFSkBALf8bT2REXKZ4RG6jBkWWZbYz9P\nfdlEVad790NmnJYb5mVwRv4EVH5WejwaKS9ae97oBGRZpvynD9CVmMygugu1pCEjMcevNUYi58y5\nkre2rGHfoW2U1XzOnLwlJ4zfe9v+DcBZxZehVonvE4cT6RqVUFekIj1+oUbETzl+aaQMQiOlFOFs\nPkR5q4k73q7irveqqeq0kGjQcMvSSTxxZSFnTUn0O4kCiDNMQJJUDFh6cTj9z/pb3vyY1vc20DHJ\n/ebJTMwbl1v4DdExXLp4NQDPf/aXE8a2pmUf26rWodVEc3bx5WOxRMEY4hla7BK79gQCb/EYcopd\nez4z7jVStV0WfvlBNT/67wF2tQwQG63mxgWZrLl6JhfOSEajCpzeSKVSE6dPQEam7wQWCCfSCdg6\nutn384ewJqYxoHdrrvIzpwdsrZHGefO+QWpCFg0d1fz3i2ePGT9Zlvn3+r8BcM7cq0iISR7LZUYE\nka5RUalC62we6fELNSJ+yvHLR8pTkRKtPV8ZtxWppr5B7vv0IN//z3621Peh06i4dk4az1xdyNXF\naeg0wQlNvEdw7p9Oat/df8HW2QOLT8WsduujclKn+r2+SCVKo+U75/4cgFc3/ZOGjppRH7ex/F12\nHdyCITqGixeuGMslCsaIEY2UqEgJBN4SrdMgqSRsgw6cDqEv9IVxN2uv02znkU2H+Pbacj6p7kat\nkrikMIVnri5kVUkmMdHB1csMWyD0mI+/c+94OoG2DzbS/PqHqPTRyIsWY1a3AJCbOi1wC41AZucu\n4rx51+B0Ofi04TkaO2uPuL+2ZR9PfXgfADecdZvndyE4kkjXqHhae8JHKiIR8VOOPxopSZKEKadC\nxo3Kts/qYO2uVt7Y287gkBv5OQWJXD8vnfTY6DFbR4LR3UpS6iVl7+1n70/+CEDBT7/P220DWKJa\nAchJLQjMIiOYb515K42dB9ldt5VfPr+Ka5fdQmH2fPYd2saLnz2CxWZiyfSvsWzWRaFeqiBIhLq1\nJxCEAln2z0cK3O0984ANi9lOTNyJTaYFbk56jZTF7uSlshZWvFLOy7vaGHTKLM2J5/HLp3PHspwx\nTaLg8J17x2/tHUsnUPHbvzHY0kFCySxiLz6PbmszsuQgNT4LQ3RswNcbaWjUUdx22YOkOqdhGuzn\niQ/u4cdPXM6/3r8H02A/CwrO4KYLfjOuvLZ8JdI1KqEWm0d6/EKNiJ9yuuos4Mdnm154SSnipK1I\n2Zwu3t3fyYtlLXRb3G7kczNjWVWSwfRUY8jW5YsFwlfpWP8lDS/8F0kbxayH7qK6vvcwfZSoRg2j\n0+q58tTvo0m28lHZq7T1NpKWMJFlsy7itJkXiCTqJGdk1p6oSAkEvuARnAsvKZ846XyknC6ZT6q7\neHZbC60D7qx6WoqB1SWZzM0KfcVmQkwqAF397cd93Fd1Ag6Tmb23ufU9U25bTczUXBpe3nlYIjW+\n9VFf5fTTTgc4Yv6ewDsiXaPiae2FqCIV6fELNSJ+yvFHIwVgEPP2FHHSVKRkWebzul7WbGumrtsK\nQE6CjhUlGSzNiQ+bKkRSrDuR6uxv8em4yj88juVQM7GzCph80zeRZZlDtV1YPELz8btjTyA4nFCL\nzQWCSEWIzZVxUmikdjT1c8tbB/jNR7XUdVtJi9Fyx7Js/nH5dE7NTQibJAogMTYNgK7+tuM+7nCd\nQPcXu6h78lUktZrZf74LVZSG3m4L/X1WzBp3RSpbJFJHIHQWyon02IVabB7p8Qs1In7K8cdHCg4b\nEyO8pHwioitSFe0mnvqymR1N/QBM0Gu4bk46X5+ehFYdnhZZE2KSkZDoGejA4bSf0IncNWhjz233\ngSwz+eZvEjfb3cJrONiNTerBIZmI1ceTEpcxFssXCMIe9VBFyulyhHglAkFkMaKREhUpX4hIjVRd\nt4U1pc1squsFwKhVc3VRKpfOTEEfpQ7adQOBRh1FvDGRHlMnPaZOkuPSR33csE6g5m/PY6o8iCE/\nm/xbV3nub6jtwqxpBCAvvTCsqm7hgNBZKCfSY+eZtReiocWRHr9QI+KnHH81UmJMjDIiqiLV0j/I\n89tb+KiqC5cM0WqJS2emcFVRGnG6yHkqSbHp9Jg66epvO2YiBTBQeZDqh58BYNaf7kStG7FqaKjt\nxqQeSaQEAoEbsWtPMD4JjI8UgFm09nwiIjRS3WY7f/+8gdVr9/FBZRcScOGMZNZcPZNvL8yKqCQK\nIDF2eOde6zEfs2H9evbecT+yzc7E6y4icclcz30DfVZ6usxYopoAyEufEdwFRyBCZ6GcSI/dcEVK\nloWPVCQi4qecrjoL/vQmhI+UMsI6AzHZnLyyq5XX97RjdbiQgLPyJ3DD/Awy48bWSDOQjOzcO7bg\nvP3jzRi27ESbPIGpd//giPua6nuQkTFrmkCG/PSZQV2vQBBJjMzaExUpgcAXPGJz0drzibDUSFkd\nLt7a287Lu1rpH3R/qzwlO46V8zPJS/KvBxwODO/c6zxGRcra2kHcSx/jAGb8/la0E+KOuL+xvodB\nVRd22UyCMYkJMSnBXnLEIXQWyon02Eme1p7wkYpERPyU469GKipKjUajwmF3YbM50GrDutYSNiiK\nkiRJk4BngVTcjdl/yrL8iL+Lcbhk3qvo5IUdLXQOOasWpcewakEGM9Ni/D192JDkSaRG95Laf/fD\nOPoGSFm+mPRLlh91f3N9D2a1EJoLBKPhEZsLjZRA4BOSJKEzRDHQN4jVbBeJlJco1UjZgVtlWZ4J\nnAL8QJKkI4Q6vmikXLLMJ1Vd3PhqOY9sOkSn2c6UJD33npfPny6YclIlUQAp8ZkAtPc2H3Vf2web\naHnrY/ZH2Sm87/ajkiSHw0VrY68Qmp8AobNQTqTHLtStvUiPX6gR8VOOvz5SMCI4t4oxMV6jKN2U\nZbkFaBn694AkSfuATGCfj+dh66E+1pQ2UdPldiOfGB/NyvkZnDo5AdVJWmlJTcgCoLWn4YifO0xm\nyn/2AAATr7sI/aSjvaHamnpxOmVscS0gC6G5QPBVRipSoWntCQSRjE4/bIEgEilv8btuJ0lSLjAX\n2Hr4z0+kkdrVPMDTpU3sbTUBkGyM4vp5GZxbkIhadXImUMPEGxKJjtJjsvYxYO0jRufWQFXe/y+s\nja3EFU3n3HvuGvVYt9DcRT/uJCwvTSRSoyF0FsqJ9NiFuiIV6fELNSJ+yvFXIwWgE2NifMavREqS\npBjgVeCHsiwPeHNMVYeZp0qbKG1wu5HHRau5dk46F81IRqsJTzfyQCNJEmkJWdS3V9He00hMehy9\nO8qpe2ItklrNrAfvRKUZ/VfTWNeDVdWBQ7aSGJtGQkzyGK9eIAhvRmbtiYqUYPwgy/77SMHIvD3R\n2vMexYmUJElRwGvA87Isv/HV+x9++GGMRiPZ2dkAyNEG6tQZ1BimAGCr28XpkxO485oLMWrVnr74\n8LeRk/22pVlFV6OF1t5GcpIKeOl7d2B29HPBzd8lbvY0HnvsMWbPnn3E8bIs03zIwYCmjq46C+nZ\nSZ54h/r5hNvt0eIXTusL59uHa1TCYT2+3lar1HTVWagfGNkVK+IXObdF/JTdPnSgja42CxKSX+fT\nG7TUNZaz9Ys+5pySHTbPLxi3h/9dX18PQElJCcuXH73B60RISrJYya2AfgbolGX51tEe8+CDD8qr\nV6+m3WTj+e0tvH+gE5cMUWqJi2ck84056cRHmJFmIHn2k4d4p/QFrl32f8z+UsOB3z+KPjuTpZ8+\nh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       "text": [
        "<matplotlib.figure.Figure at 0x10abdafd0>"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Generating a dataset\n",
      "N = 200\n",
      "x = np.random.random(N)\n",
      "y = generate_curve(x, intrinsic_error)\n",
      "plt.scatter(x, y, color=colors[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "<matplotlib.collections.PathCollection at 0x10c425390>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PzdtMuFRxdvX+0qs2BRJ0W5vix/7t8Kim882NazucqKdvGvXcS2q6MUx+vk/V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\nAODg/wPHKTejKIgQwAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10beceb50>"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cross_validation import train_test_split\n",
      "xtrain, xtest, ytrain, ytest = train_test_split(x, y, train_size=0.6)\n",
      "plt.scatter(xtrain, ytrain, color=colors[1], label='Train')\n",
      "plt.scatter(xtest, ytest, color=colors[0], label='Test')\n",
      "plt.legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "<matplotlib.legend.Legend at 0x10c631c90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PyEbFtQXIyUjFps+2YtJN34Zr2y64tu3C4IkTNOexvfk06lr6TlcWj8jWdH6Y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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c05cc10>"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ds = np.arange(21)\n",
      "train_err = np.zeros(len(ds))\n",
      "test_err = np.zeros(len(ds))\n",
      "\n",
      "for i, d in enumerate(ds):\n",
      "    p = np.polyfit(xtrain, ytrain, d)\n",
      "\n",
      "    train_err[i] = rmse(p, xtrain, ytrain)\n",
      "    test_err[i] = rmse(p, xtest, ytest)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots()\n",
      "\n",
      "ax.plot(ds, test_err, lw=2, label = 'test error')\n",
      "ax.plot(ds, train_err, lw=2, label = 'training error')\n",
      "ax.legend(loc=0)\n",
      "ax.set_xlabel('degree of fit')\n",
      "ax.set_ylabel('rms error')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "<matplotlib.text.Text at 0x10c4510d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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16uRCinL87DraSf2Rjl7Pac9MERERySSeS8ySqTED8PsMZ1XG9s480us5LZkx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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ca3ced0>"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_learning_curve(d=3):\n",
      "    sizes = np.linspace(2, N, 50).astype(int)\n",
      "    train_err = np.zeros(sizes.shape)\n",
      "    crossval_err = np.zeros(sizes.shape)\n",
      "\n",
      "    for i, size in enumerate(sizes):\n",
      "        # Train on only the first `size` points\n",
      "        p = np.polyfit(xtrain[:size], ytrain[:size], d)\n",
      "        \n",
      "        # Validation error is on the *entire* validation set\n",
      "        crossval_err[i] = rmse(p, xtest, ytest)\n",
      "        \n",
      "        # Training error is on only the points used for training\n",
      "        train_err[i] = rmse(p, xtrain[:size], ytrain[:size])\n",
      "\n",
      "    fig, ax = plt.subplots()\n",
      "    ax.plot(sizes, crossval_err, lw=2, label='validation error')\n",
      "    ax.plot(sizes, train_err, lw=2, label='training error')\n",
      "    ax.plot([0, N], [intrinsic_error, intrinsic_error], '--k', label='intrinsic error')\n",
      "\n",
      "    ax.set_xlabel('training set size')\n",
      "    ax.set_ylabel('rms error')\n",
      "    \n",
      "    ax.legend(loc=0)\n",
      "    \n",
      "    ax.set_xlim(0, 99)\n",
      "\n",
      "    ax.set_title('d = %i' % d)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_learning_curve(d=5)\n",
      "# plt.ylim(0, 10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NGzYUeyo0NTUN6o+85EdZWRkjR44c8PN8n3QVo6arqcD7LnoqIiEqoyHaY0m2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\nbgmRkWzEAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c3f9bd0>"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_learning_curve(d=20)\n",
      "plt.ylim(0, 10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n",
        "//anaconda/lib/python2.7/site-packages/numpy/lib/polynomial.py:588: RankWarning: Polyfit may be poorly conditioned\n",
        "  warnings.warn(msg, RankWarning)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "(0, 10)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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F8J8y9Jvy858yjIfIF10iIiIicRD5oks9Xf5TL4L/lKHflJ//lGE8RL7oEhER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\nvYiIxId6ukTakdraEBRP7RRS2Z8Nn2T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       "text": [
        "<matplotlib.figure.Figure at 0x10c3f9450>"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#k-Nearest Neighbors"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv('data/lab_bias_variance_CV/olive.csv')\n",
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Unnamed: 0</th>\n",
        "      <th>region</th>\n",
        "      <th>area</th>\n",
        "      <th>palmitic</th>\n",
        "      <th>palmitoleic</th>\n",
        "      <th>...</th>\n",
        "      <th>oleic</th>\n",
        "      <th>linoleic</th>\n",
        "      <th>linolenic</th>\n",
        "      <th>arachidic</th>\n",
        "      <th>eicosenoic</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1.North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1075</td>\n",
        "      <td> 75</td>\n",
        "      <td>...</td>\n",
        "      <td> 7823</td>\n",
        "      <td> 672</td>\n",
        "      <td> 36</td>\n",
        "      <td> 60</td>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2.North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1088</td>\n",
        "      <td> 73</td>\n",
        "      <td>...</td>\n",
        "      <td> 7709</td>\n",
        "      <td> 781</td>\n",
        "      <td> 31</td>\n",
        "      <td> 61</td>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3.North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  911</td>\n",
        "      <td> 54</td>\n",
        "      <td>...</td>\n",
        "      <td> 8113</td>\n",
        "      <td> 549</td>\n",
        "      <td> 31</td>\n",
        "      <td> 63</td>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4.North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  966</td>\n",
        "      <td> 57</td>\n",
        "      <td>...</td>\n",
        "      <td> 7952</td>\n",
        "      <td> 619</td>\n",
        "      <td> 50</td>\n",
        "      <td> 78</td>\n",
        "      <td> 35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5.North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1051</td>\n",
        "      <td> 67</td>\n",
        "      <td>...</td>\n",
        "      <td> 7771</td>\n",
        "      <td> 672</td>\n",
        "      <td> 50</td>\n",
        "      <td> 80</td>\n",
        "      <td> 46</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 11 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "       Unnamed: 0  region  area  palmitic  palmitoleic     ...      oleic  linoleic  linolenic  arachidic  eicosenoic\n",
        "0  1.North-Apulia       1     1      1075           75     ...       7823       672         36         60          29\n",
        "1  2.North-Apulia       1     1      1088           73     ...       7709       781         31         61          29\n",
        "2  3.North-Apulia       1     1       911           54     ...       8113       549         31         63          29\n",
        "3  4.North-Apulia       1     1       966           57     ...       7952       619         50         78          35\n",
        "4  5.North-Apulia       1     1      1051           67     ...       7771       672         50         80          46\n",
        "\n",
        "[5 rows x 11 columns]"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.rename(columns={df.columns[0]:'areastring'}, inplace=True)\n",
      "df.areastring = df.areastring.map(lambda x: x.split('.')[-1])\n",
      "acidlist=['palmitic', 'palmitoleic', 'stearic', 'oleic', 'linoleic', 'linolenic', 'arachidic', 'eicosenoic']\n",
      "dfsub=df[acidlist].apply(lambda x: x/100.0)\n",
      "df[acidlist]=dfsub\n",
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>areastring</th>\n",
        "      <th>region</th>\n",
        "      <th>area</th>\n",
        "      <th>palmitic</th>\n",
        "      <th>palmitoleic</th>\n",
        "      <th>...</th>\n",
        "      <th>oleic</th>\n",
        "      <th>linoleic</th>\n",
        "      <th>linolenic</th>\n",
        "      <th>arachidic</th>\n",
        "      <th>eicosenoic</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10.75</td>\n",
        "      <td> 0.75</td>\n",
        "      <td>...</td>\n",
        "      <td> 78.23</td>\n",
        "      <td> 6.72</td>\n",
        "      <td> 0.36</td>\n",
        "      <td> 0.60</td>\n",
        "      <td> 0.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10.88</td>\n",
        "      <td> 0.73</td>\n",
        "      <td>...</td>\n",
        "      <td> 77.09</td>\n",
        "      <td> 7.81</td>\n",
        "      <td> 0.31</td>\n",
        "      <td> 0.61</td>\n",
        "      <td> 0.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  9.11</td>\n",
        "      <td> 0.54</td>\n",
        "      <td>...</td>\n",
        "      <td> 81.13</td>\n",
        "      <td> 5.49</td>\n",
        "      <td> 0.31</td>\n",
        "      <td> 0.63</td>\n",
        "      <td> 0.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  9.66</td>\n",
        "      <td> 0.57</td>\n",
        "      <td>...</td>\n",
        "      <td> 79.52</td>\n",
        "      <td> 6.19</td>\n",
        "      <td> 0.50</td>\n",
        "      <td> 0.78</td>\n",
        "      <td> 0.35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10.51</td>\n",
        "      <td> 0.67</td>\n",
        "      <td>...</td>\n",
        "      <td> 77.71</td>\n",
        "      <td> 6.72</td>\n",
        "      <td> 0.50</td>\n",
        "      <td> 0.80</td>\n",
        "      <td> 0.46</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 11 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "     areastring  region  area  palmitic  palmitoleic     ...      oleic  linoleic  linolenic  arachidic  eicosenoic\n",
        "0  North-Apulia       1     1     10.75         0.75     ...      78.23      6.72       0.36       0.60        0.29\n",
        "1  North-Apulia       1     1     10.88         0.73     ...      77.09      7.81       0.31       0.61        0.29\n",
        "2  North-Apulia       1     1      9.11         0.54     ...      81.13      5.49       0.31       0.63        0.29\n",
        "3  North-Apulia       1     1      9.66         0.57     ...      79.52      6.19       0.50       0.78        0.35\n",
        "4  North-Apulia       1     1     10.51         0.67     ...      77.71      6.72       0.50       0.80        0.46\n",
        "\n",
        "[5 rows x 11 columns]"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfsouth = df[df.region==1]\n",
      "dfsouthns = dfsouth[df.area != 4]\n",
      "dfsouthns.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>areastring</th>\n",
        "      <th>region</th>\n",
        "      <th>area</th>\n",
        "      <th>palmitic</th>\n",
        "      <th>palmitoleic</th>\n",
        "      <th>...</th>\n",
        "      <th>oleic</th>\n",
        "      <th>linoleic</th>\n",
        "      <th>linolenic</th>\n",
        "      <th>arachidic</th>\n",
        "      <th>eicosenoic</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10.75</td>\n",
        "      <td> 0.75</td>\n",
        "      <td>...</td>\n",
        "      <td> 78.23</td>\n",
        "      <td> 6.72</td>\n",
        "      <td> 0.36</td>\n",
        "      <td> 0.60</td>\n",
        "      <td> 0.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10.88</td>\n",
        "      <td> 0.73</td>\n",
        "      <td>...</td>\n",
        "      <td> 77.09</td>\n",
        "      <td> 7.81</td>\n",
        "      <td> 0.31</td>\n",
        "      <td> 0.61</td>\n",
        "      <td> 0.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  9.11</td>\n",
        "      <td> 0.54</td>\n",
        "      <td>...</td>\n",
        "      <td> 81.13</td>\n",
        "      <td> 5.49</td>\n",
        "      <td> 0.31</td>\n",
        "      <td> 0.63</td>\n",
        "      <td> 0.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  9.66</td>\n",
        "      <td> 0.57</td>\n",
        "      <td>...</td>\n",
        "      <td> 79.52</td>\n",
        "      <td> 6.19</td>\n",
        "      <td> 0.50</td>\n",
        "      <td> 0.78</td>\n",
        "      <td> 0.35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> North-Apulia</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10.51</td>\n",
        "      <td> 0.67</td>\n",
        "      <td>...</td>\n",
        "      <td> 77.71</td>\n",
        "      <td> 6.72</td>\n",
        "      <td> 0.50</td>\n",
        "      <td> 0.80</td>\n",
        "      <td> 0.46</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 11 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "     areastring  region  area  palmitic  palmitoleic     ...      oleic  linoleic  linolenic  arachidic  eicosenoic\n",
        "0  North-Apulia       1     1     10.75         0.75     ...      78.23      6.72       0.36       0.60        0.29\n",
        "1  North-Apulia       1     1     10.88         0.73     ...      77.09      7.81       0.31       0.61        0.29\n",
        "2  North-Apulia       1     1      9.11         0.54     ...      81.13      5.49       0.31       0.63        0.29\n",
        "3  North-Apulia       1     1      9.66         0.57     ...      79.52      6.19       0.50       0.78        0.35\n",
        "4  North-Apulia       1     1     10.51         0.67     ...      77.71      6.72       0.50       0.80        0.46\n",
        "\n",
        "[5 rows x 11 columns]"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "akeys=[1,2,3]\n",
      "avals=['North-Apulia','Calabria','South-Apulia']\n",
      "amap={e[0]:e[1] for e in zip(akeys,avals)}\n",
      "ax=scatter_by(dfsouthns, 'palmitic', 'palmitoleic', by='area', labeler=amap, mfunc=make_rug, mms=20)\n",
      "ax.grid(False)\n",
      "ax.legend(loc='upper right');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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T6hZ8nbtSag1wCvB0yUMaeLtS6gWl1P1KqS0L3TchhFho6wItdHgC2JTCphQd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\nMAruIiIiCaPgLiIikjAK7iIiIgnz/wFJnBpLqWbgaAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d0d1550>"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib.colors import ListedColormap\n",
      "#cm_bright = ListedColormap(['#FF0000', '#000000','#0000FF'])\n",
      "#cm = plt.cm.RdBu\n",
      "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
      "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
      "\n",
      "def points_plot(X, Xtr, Xte, ytr, yte, clf, colorscale=cmap_light, cdiscrete=cmap_bold):\n",
      "    h = .02\n",
      "    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
      "    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
      "    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 50),\n",
      "                         np.linspace(y_min, y_max, 50))\n",
      "\n",
      "    plt.figure()\n",
      "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
      "    Z = Z.reshape(xx.shape)\n",
      "    plt.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=0.2)\n",
      "    plt.scatter(Xtr[:, 0], Xtr[:, 1], c=ytr-1, cmap=cdiscrete, s=50, alpha=0.2,edgecolor=\"k\")\n",
      "    # and testing points\n",
      "    yact=clf.predict(Xte)\n",
      "    print \"SCORE\", clf.score(Xte, yte)\n",
      "    plt.scatter(Xte[:, 0], Xte[:, 1], c=yte-1, cmap=cdiscrete, alpha=0.5, marker=\"s\", s=35)\n",
      "    plt.xlim(xx.min(), xx.max())\n",
      "    plt.ylim(yy.min(), yy.max())\n",
      "    return ax"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.neighbors import KNeighborsClassifier\n",
      "subdf=dfsouthns[['palmitic','palmitoleic']]\n",
      "subdfstd=(subdf - subdf.mean())/subdf.std()\n",
      "X=subdfstd.values\n",
      "y=dfsouthns['area'].values\n",
      "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, train_size=0.6)\n",
      "Xtr=np.concatenate((Xtrain, Xtest))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = KNeighborsClassifier(20, warn_on_equidistant=False).fit(Xtrain, ytrain)\n",
      "points_plot(Xtr, Xtrain, Xtest, ytrain, ytest, clf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "SCORE 0.913043478261\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10c6d0250>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5dculWp08Vem5ruvcvDk7tFqSyWSoVF7/PDvLUvR62lD13KD9wMWX\nvyeJxuJijjiOaLdNdH0wP5o2qNbLZGZ4+nSZd9452IVxamqSON5iZWWZILCBiEIh4Pbt01fzpQld\nHx1chWHwWu0dBOG8kKBJEIRz5ZtvmmQyi7s/K6XIZqs8e7ZCpTJ84KsAzabiz/7MxHUTwtDZzdm5\nfz9icnIwpt83Rh6YOz1dZnoagiBA1/Xdg4bTgO/71OttlIJMZhLQdntkvcr+lcZKxaDb9TCMg0nu\nQbDJzMwFdcIUhBFI0CQIwrnheR6umyEz3DgbGLQEAMlHOYyjWxcM94jaz3l0RD9Pnj9fZ3VVxzRL\nJElCp7PF3buKZnOKn/7UGhq/f6VxYaHKysoyjYaNYRSI4xClGty/n5fWA8IbRVoOXBEf49ROi49x\naouP89H2PIXrGoxaTfJ9nU5Hnaj0PIoioijCNE2azfO/x53y/35fO1CFd1gp/mm0948NgkGLgeMC\nmiBQL5PSDfr9AE3bOS9uUKqfJAmWFdPr6Wd+z5Mk2V2VerWf0s74OI4JwxDTNA8N1I7ysbW1zbNn\nE9j2BC+ngCCY59GjTbLZ4OVW4kH2v//b2zrz8wtMTXk0Gi0sy6BUGlTNneVw39OOT4t2WnyMUzst\nPo5DWg5cIR/j1E6Lj3Fqi4+za+dyFoVCHV0f7syt1Dbz82UajfjQ0nPHCXn8eI1WyyJJdEzTo1Cw\nhqrlzup5p/y/2x01/qiWAyebj06ny9dfN3DdQXDgOB63bhXJ50efDFsqady/P6iE87wI13XRdZN8\nHkzTI47XePvtKSzrZIfqHuZ5ZWWTtbWQILBRKqRYDLh9e+ZlXySF74c8edJ6GahplMttZmdDFhdH\nZ14f5uPp0y6l0gKvVjJmMiW2t5uY5vA8vNp6YPD7ZDI5ufPeJ5xHm4TTjk+Ldlp8jFM7LT6OQtY5\nBUE4V27cyPLo0QaZzN4Xres2WVhQ6Lp+ZOn555+vEkXzB5J9V1Z6ZLP1kceJpBHP8/jyyw6Tk4u7\n25RhmPDv/l2HhYUIXd/72N3J43EcmNptxWQSBD7tdgvT9Jme7jM7O3PmLtsrK5vUajksK8fODlev\nF/GLX6xgGIv89KcWtVobpSq7eVE/+pHP6mqPJNng2rWTl6zF8eG7C3F8+u7pgpAWJGgSBOFcmZws\n8s47PZaWlvE87WVvoSyl0iAqOKz0vNVq47rFoeoo285TqzUOOU7kcOp1WF9PaDS6eN7gWiajuH07\nQ7U6vmTppaUtbHuRMIxpNgcBQqej+OKLEp9/7lEu69Trg3vcyePJZnv88i8HRJHCMBJKJRvLsqlW\nLSqV/Jk9JUnC2lqIZR1c4dF1nX6/gG17hKHC9y0ymYNzY9tZNjaaLCzEJ04yd5yYfn84B2uw5Xq2\nexGEN4kETYIgnDvZbJb79093YGur1cc0R69mhKFFGIanSgJeX0/4p//UB8q7X/ZxHPOjH23wgx9M\njK3KzPM0gsCjVvP42c9sNA10Pcs335hAwh/8wcFAolarU6vZlMt7W5quW2durkulMjnyNUZVoL3a\n52o/YRgShhajps808/T7PTzPGDoDb+/5Dr7v4zijH3+VxcUSDx9u4DjTB677/hp37lQpl0/W5FIQ\n0oYETYIgpIJMxiSKfHR9uPRO06JTb081mz1g8kBwpGkaSVJgdbXO/PzU4U8+BXEcs769Rjdsk9Gz\nbG+3+Ppri2y2QhzbRFGC63pEkTYiaElYXo6w7RL7838cp8LS0jJTU6Mr5jY21G4FWhSFdDouf/tv\ne7z9dkSpdDCfLAxDVle3WFmpMzmpUSpNEkUhm5sNwjDGsmImJ4sYhkaShMBwZZtSAYYxOh9rFJlM\nhvv3I54+fbGb1wUB775bJJPRmZ6WAEm4nEjQJAjCWOh0OrRaLtmsOfRFPopyucTTpzVg8cD1KAqp\nVOJjyvGHcd1k5GqSrus0GiHz86eSO+Q1+jxsfYxe1TBsk62a4suVGM9NMIyQKLJQCizLpt/3qFQO\nHvPS73vAxEjtIMjjun0ymcNX7DqdHs0mGEaeILB59KhDJrPMd787h6ZptFptlpY6WFYV0zRZWcnw\n9ddfYJoFMpl5NE1na+spxWKXH//Y4fnzJo6z914ViwlRFFMo+Kcu9S8U8vzSL+UJggClFJ5nDZ0X\nKAiXDWk5cEV8jFM7LT7Gqf1t8pEkCb1e92XTycNXD17XdxiGfPXVGv1+AdOcIgxddL3G/fsVHMc5\nUvfatTKPHq0Qx5PoukUYdkiSHtPTsyf6HNmvHQTq0NYGnqfR7Wpnfm8+2/wKFjPEMQR9ePafsvz8\nX14j9Azu3zL44osEXVfcuRO/XOVSRBG7vsJQ0e+rkceGeJ6i19OHkqq3t3V8P8J1I+p1hWHkCMPB\nfWWzBdrtLF98sc716zN8/HGPcnkB34dyeYpWa5XnzzOYZsT0dAPLCrhxY5YwtAiCGnfvTrG6uvxy\n/m3a7S6ZTJtbt+aG5v/kc2efcnz6/mbetHZafIxTOy0+jkNaDlwhH+PUTouPcWp/G3x0Opu8eOET\nRRNAjGkuc/NmjsnJwom0wzAkSZKRfYd2xn755RqWNYfj7HzJZoEsS0tLvP/+wpGeczmHanWG7e0m\nrtuhWMyQJHMvx8f4vj+yv9AoH8WiQtcDNO3gx5ym+SwuGrvjXve9cV0XKt3dM/XWl5p43Zs8f54l\niWG2otNuGyRJhGUpvvtdxcRExDvvRExPD9oLVKsmT5+uEkX5oVUYy2pRqcwz6gDidjvBdfs4Th6l\nkpfjEzKZBNDwvJggaJDPF8lkYsIwQCmNUinAtiNarZjp6QjbhiBoUi5X8TzFzIzB7dtzbG836fe7\nFAoOudxOBv6wj2/D30xatNPiY5zaafFxFLI9JwjfElqtNi9eaDjO/n2pAo8erfHeex62PdxwcAfP\n83j0qE6/bwMKy/K4fn042ArDkFbL2hcw7REERTqdDkqNDtB2UEoxObl3rly3CxsbDZaXXXzfRqmY\niYmA27enjmwaeft2hg8/3CIMcxiG+dJDQLncZ2urzNaWwvd3zps7OpF6FHEcHfgEbWzwcgsrAjUI\n6n7wg5Akgd/6LZdKpcPCQuGV11FoWpZPPlnDtgel/nEc43nrzM2V+eKL4ZWyQiF5+fqHdwiPIvD9\nmHa7y9pagyCwiWOPr756ysTEb1IuexQKNqZpsLXVwTTrZLP6y2NaFKVSkdLrH+0nCFcWCZoE4VtC\nrdZ+JWAaYNtVlpdXuXVr9CGvURTx8GEdy1pkf/HUo0cbPHjQYWJiryQ+DAOSZHQgo+s2/X6b7OmK\n6qjXt1ld1chkFnb7HvX7ET/72afMzVVJEkUul3D9ehHY81KpKH7ndybY3GxQr/soBVNTNhsb5d0k\nas8bbFV6Hvzwhx4LC21KpTKt1sEqMcdJaLX2AqwBOZQzBYVBi+o40lGawrQS3G7E55/vnSH3wx/6\nlEqDj9uNDcXGxkBhEEAVefddn0ZjlSBQ2HZCqeTwyScB/+Jf6Gga2DaUSlmU0vjgg4CFhZi/9/di\nNjc7mObgXorFBBi8XiYTo2kJy8tdKpW3cN1Nlpa2aDRu8vz5V9y8mcM0bwCD6rl6vcnkZPxyBU/y\njgThMCRoEoRvCb6vj+yRo2kavn94kvXq6haaNjN0PZOp8uLFMg8e7AUqlmWj6y1geJkiijpMTGSJ\notP5Xl3tk8nsNWlKkoRvvqkTBPfodl0mJ8uEIXz22Sq3bunkcnsrZkopqtUy1X2dDHYCFkjY2HAJ\ngvzLRGWb9fUCjx55fPyxxsTE3sfjr/xKwF/9lTXUyfw3fzCD1/kFVs6m22+g+gFBYGEoDQhIEgXE\nxLHBv/23w9HiTp8m23a4fXsQtHa7PT77rEuSlDGMneAuZn29zczMIGm8UoFy2ebTT5eJ44WXwc7g\nPXTddR48KPLsWYtKZYL19SW63QphWCWfn6ffX6bdDqjXG7stDbrd9sjmlf1+n6WlbVxXQ9NiZmcd\npqZGt0EQhG8DEjQJwrcEyxodrcRxjGUdXgLe6cS721uv4roHt+E0TaNaVWxs9LGsvdYBURQyMdHH\ncSaHzg47Dt/XDhwAvL3dJoomsW2bbrfF5Mvv8ExmlqWlFaamRq+YvUqv5+K6Ds+eDT4Gr1/X2dzU\nmZrS2doKDgRNh1HIlJjs3uQ/fPwRQdNmQoX88LcMNGWhlEcmY6KUR7F48o/a58+bZDIL9Hp71zRN\nIwyz9PsuOx/bSinefnuOZ8/WaDYHSeXZbMyDBwXy+Rye12FubpK1tQaa5qNUjzCsc+tWmTjeoNHY\nIp8PcZyEt94aNCXd/960Wh2+/NLFtgdnviUJPH3aodNZ4+bN4SBaEL4NSNAkCN8S5uYmePGigeMc\nXCnw/U0WFg5fPTDNBNcd3Q1a14e3cq5fn0bTNlhfbxBFgzPOJicj3nrrZMHMca/RavmYZokoCrGs\ngwnh/f7hFXY7DSHX1xWbm9DraUTR4DDcV3ODTnPUh6NlmeO3eWt+gmazg3rQpt1OUCokn2/z7ruL\nux3JXyWOI5aWNmm1YH7eYnKyRK+nsIZbJWEYJv1+n/0f27quH7qtqusx/X6XUmke285x7doET5+u\nouslstkJCoVJbt0ajFtYGD7b79mzNo6zcOCabedZX+8zP+8DJ2t0KQhXCWk5cEV8jFM7LT7GqX0Z\nfSRJwrNnbTIZj2Ixh+MMN4XcTxyXmJpaZ3l5lTieABIMo82NGznCMEMYjvZSKpVZWtoikznY3TkI\neszPOyNL9ycnZyiVEoIgwDAMNE2j3z/9PcIgWGi3AwxjsO0Whjquq14Ge1MHAqVuVzv082Z5Gf7k\nTyympiIWFgbHfNh2xLVrEaap0LRBi4IoUoMWAvvaFUQRuO7o9gXPnvVJkgX6ffA8Hc8LCMM8hYJB\nv9+j1VrHcWaH2h/0en2ePnUpl2fodk0ajTamuUIYKqJIw7ICfu3XWmiahm0P+j3ZdkAmkznyM7XZ\nTNjcVDQaWV688AgCkyRJyOU0pqbKLC39gnx+hmbTpd12uXbNYWKiSre7996EYcjW1vCRKoO5qLC0\nVMdxTt7sEtLxN3NZtdPiY5zaafFxHNJy4Ar5GKd2WnyMU/sy+eh0unz5ZZN+fwrfd6jX2xQKde7d\nmzuyCWQuN8nNmwndbgelFLnczjbLYS0AYnI5iwcP4NmzFXR9EqU0gmCb+fng5erRUaX7Ox8xBx87\nzXw8eFBhZWWZ7e0MllWkWoWvv/6Mu3fnyee1Xe1+v0e12sGyiiOr6ixLYZoJ9XpEkgxK89vtgPV1\nZzc53TQTNC3GtjmQv6Trg2TwV3OaLGtwTamYOE548qRLoTCNbQ8Sui3LIpebYXu7hWnurczEcUyn\nE5HN5snlQNNiMpkMUTSH6z6k0dDodHLoeo4kien360xNdfi1X6sSRcmR87e0BP/sn3l4XoFmM6Je\n76PrPf7wDxW23eMHP1jAtnWiaJ3vf//WUPJ3LhcTRQnZbILjDL9OEIRks1AoRJfqb+aya6fFxzi1\n0+LjKGR7ThAuGXEc8+WXLSxr8eXRHDGGUabfn+DZs/Vj802UUuTzo7tQH8b0dJlKJaJebxJFCeVy\n/sgWBeeJUop79+ZxXZetrS0sS+P69SK1mk8cxyRJwqNHz0mSLjMzb/HRRy1KJZ87d2YPBJBhGLK2\n5hKGFqChVEC5HL/cojNRShGGHrbt83f/rsX09N75aI6TvMzJAqX2Vl+q1QTHmeCTTzbxfR2l9rpp\nx3FMPj/wb9vwd/6Ou/vcVqtDs+lQKMTAnp6u63S7Gq7bRakihmESxzG+D57XQ9O0YxPpNzfbhGEF\n09QoFhX5vMnGRh/fb/K9702TJAFxvME77ywe2u9K13UyGZ9kRKpbFG1TrZYO3XIUhKuMBE2CcMnY\n2NhG04bPTdN1k62thJs3x/O6uq4zPT2c+3JROI7D/Pzeak216rK6usYvfrHK/PxbFApv0e9rOE6O\nbjfg669XuXNnUHUXxzG1WhelpvdVENrYdsDv/36fQqFFkkA2a5HJ2Ad6KS0t9Xn8OKTfNzDNBNOM\nmJrKMjenY9tdvvmmyfp6h+fPY6KowMzMFFFkYhirzM0NAlhNC7l9O8CyLJ4/X+fx4zXa7Zs0GhHF\nIpRKew2imk2N7373Nt1um+3tZQxDY2qqhKaVWF3doFI5PDdsczNha8t5ebZbRBQpHMfAsopMTobE\n8VfMzVWYmZk99sDiO3fKPHy4jGHM7gZXrrvF4qLCMAwJmoRvJRI0CcIlw3UDdH1EpjAQRQZxPDpp\n+7IRBAFPn26yvq7jODH5fMLNm+XdFS7HcZiZiVlfd8hkDrY40HWTRsMkigYH/W5ubqNpw20QVlYs\n3ntvm/n5Ho1GQhwroijGNCeAPJ1Ol1/8QuPP/7xKEKjd7bkwbPEP/kGM63bJZBa5dQuKxQYffdRi\ndfUJt2+XuH59EGwkScLm5gs+/7zK0tI2vp9lcnJwLIxpTtJuB3S7de7eLb/0HqNpiomJAhMTBxuB\n+v5gi6FWq7O+7hOGGqYZMztrMz1dZn094ec/t3n6dOcw30GPqIFentnZMnNzJ+vg6TgO7703Ra22\nQbebEMcephmwspKjVlvFMBQPHpSPbDAqCFcNCZoE4ZJRLDqsr3dxnPzQY6YZXImAKYoiPv10HV1f\nwDQNLCvG8xI++ugFU1MJvd5O2X0Hpa6N1EgSG9/3yGSydLs+5bLOj37kHxjj+4p+v0GjMb/bEymO\n4csvN7h3L6FW6+A48xSLPp2OSxyDUlAoaKyvd5ie3tsKrVQmuXMnpNebI44bu6szX331BeXyIjBJ\nr5fBsiqsrW0AK0RRFl03abVMgiAgilpcuzY6IA5Dj1zOZGlpnW43h2VV0bSErS3F2ppHsdjDdXNH\nVAT6FAqnq3gzTZPr16fxfZ9PPqlj2zfZ2dHrduHTT5d4773ZI4+1OSs7VY+vctoO7oJwHkjQJAiX\njFKpiG0vE8dZ9ufDeF6bxcWLyTMaN6urWyh1cAspjhOePbPY3Ay4fn3Q2bzdbvLs2QsePLiDpimS\nJKFeH4wPAp0nTzJomkLXHWq1VaLIxjASqtUJbNtic7PD5mbCjRsHAxXHqbK0tEwYamxuhvzzf26S\nJJPs2InjkIWFbarVQRC1w/XrU6yvb7O+voHrJkRRi8nJAtVqBdftkyTOrn4c95mY2GR7OyaKQly3\nw4MHc8AMjx9v4Th7W6FJkhDH61QqVR4/blEqDSrXmk3Fz35mARZR1Ob2bYNWS3HtWohtD/Kf7t+P\neP/9gHK5Qz7/eturz5/XMc25A9c0TUOpWVZXt1hYGG6MeV5sbKjdDu772WkMKggXyYUETdoJu9np\n2yAOVwIAACAASURBVBEaJ/sXy2nG/v/svdmTY+l5p/ec/RzsSGy51tZV3VXdZHWTHC4SR25TLYos\nimrHREjjcIQvHL72X+Ar3/rO4fDNRNj3novRzFgekZSGMjUKxYxEUWQv7K6uri0rKxdkJjKxA2f9\nfIHcUIlcUJWoPFn1PREMdh18ePE7B0jkm995398L0K/bwOm2kS9qm+Tr0Ar6qp/jaddevjzDo0dr\n1GoWyaSGrvepVHTS6eKR5pEX6RzX1gRhaOH70GoNMpVqtY7vV2g2q3s2A5qWJwi2WV5uUCxOsb6u\n8Dd/oyNEhGEYTE05+L7Pu+9q1GohllVCCFhfrzMz47G2tk2xODXS36nf1zEMj2bTQ4g0Qih7hdGK\nouN5BisrzT1X7V0ymQKm6fH22xUWF0FVZ3fsCHTW16soSh/HsdB1k/n5CrmcwsZGkxs3pjDNJJ7n\nEYaLfPnlU2w7TyplY5ptHEfjn/5piY2NApY10Ot5Ys/KIAgsPC/EdR3K5R6K4uH7BprWoVBocu1a\n5UT7l6Pem81N7dDg41ZLJZUy+fzzBLXacNxiUZDNDu8OPe9nxPM4ZNcwOK7Q6Six+VxPMnZcdEwy\ndlx0nMRLSZqySe/kRUCeDlHydN4f46zdJUqe/sJd1DbJ16EV9FU/x9OtVXn33QpTUw0UpUOxmMNx\nHE6aG3ZRzjGVivbqd2AwSy0IQhIJFUUROM7+Y1/5yjxPnnwKhGhaZmd9yNRUCkURbG/3SKWyVCom\ni4tP8P00lmVQrS6ysBCQz8+ONOnUtJBMxufLL0OuXxdEUXhgp8knmzXY2lphfj479Lxer86bbyZI\nJiOyWQXP8+j1PJaX+0SRg6IUaTT6RFGVd9/No+sGhUKDUmmGtbVNnjwR2PY1btzQaLVqdDpPsKx5\noICqutRqTUxzicuXB67h+zYIIamUyve/73HrFpTLOr1eyPy8Tam0O8Ll5Pdo1HuTTkc7w3yH6XYF\nv/iFSaEw/AfpnTses7OH1z/PZ2TXKuJZTFOQTIqx4z6vjvOOHRcdk4wdFx3HIW/PSSQXkF2fpm63\nQDKZYnW1RTa7faRPU6/XpdtVSCTsY32cnpcwDOl0Oui6cSZWBJWKw5dfNrHt/UJoRQHX7VGpDH9t\naZrOjRtlrl51+OSTkOnpxF6NTRiGBMFgvW07vPXWHP1+lzD0iaISiURAtdrBsoYLrlOpENvukUrZ\neF6Dfj+NEBaaNiiITiQUwKRUatHvb6DrgyLzIKgzOxuRTleAiHI5x9OnGywtGVhWmfn5kOXlLaLI\nxranePBgkTfesHjrrTKu67K4KEgk9m916XqC9fXLCOGTSKg4joPjdOl282xsbKCqB9f61OsDK4ly\nOeDmTeh0dJLJ6IXrgqamDFZWhkfjAPh+n2RydA2WRPIqIpMmieSCEUURd+82sKyBT5NhRBhGgV7P\nP+TT1Gq1efCgieumcF2dVKrK7KzJ9PTZWQc8flxlaUnDshygSyJR4803i5ijZoGcklwuS7G4xsZG\niBADrYbRJggaJJPDtTWe16NSMbFtG8tSnylKFoe8hgzDYmVlm1ZLJ5Fw+Ou/VvC8Bvl8Yce5vMvv\n/M4aly7laLc1VHUbywoQAnR9MJR4UGvV5PbtSxQKGTY36wAUixkMw9i7RWoYBrlcl3v3LDRt0Mk3\nPW3h+0uUy1kMw+X27Wn6fYPV1VVse9hja2OjQyJRptlcZmbntOfnU6ytNdna6lMsDhJDITqUy0cX\neb9oXdDMTIFWa5lGI4ttZxBC0O9vk0wq2PZ4nl8SyUVGJk0SyQXjtD5Nvu9z924X254nkRiYMhpG\niidPtrHtJrlc5lCMcXnyZJ2trTyWldi7ZRZFgs8/f8q7786d8OzjuXp1mkqly6NHq2iaSxAs8eCB\nwt27AsfxuHbN4dKlCo6zSak0OzKGpulo2vC8tsXFKkEwh643SCTSpNMWQeDhuuskkzZhGFIuz5Pa\naU68cUOj11slmayQTNoI4eP7Ta5ebVGpDHb2jmvjz2QyvPVWgnp9nSAQlEoWmcwbKIpCv68ShiFg\nEATiUOdjFKlD/w+QSDjcvGmwufkFpVKNP/1Ti1TKQVEEMCiFKJXOvkD6zTfnaDZbbGysoCgwM5Oj\nVjt+dM9ZUCoJ7tw5XOIxiXOUSE5CJk0SyQWj39+fw/YsB32alpe3MIzyoTWOk2d5efmFkyYhBPfv\nN2g0dDqd3k4dkUGxmMV189Tr4yVmB28hhWHI1lYHz1NwHItPPvmUTOa73LiRoNls0m4HPHiwTrH4\nGV//+nt7txyLxcO/YHs9QV+vsRi4RFFINx8BS5hmiClMwNqxG0ijqgHJ5BSK4u89P5dL881vanzx\nxT+QSOQwTcHbbxeZnT1+ZM0u+XySpaXentHlQXS9j2nm8X3I5y3q9S6Wldh7PJlUqdV8LGu4HkNV\nFS5dyvDmmweL0CefRGQyaTKZwc5Sp6NSq03+NQsFZJecJDbIpEkiuWCc1qfJdTnSP8fzXtzLaXl5\nneVlh1SqjK4rqKpgfb2P521RqWTpdDbJHfaTPJLdW0hRFLK25qJpRRRF4dvfbrC19R7dbp1Ll2yy\n2SzZLECB5eVfDyUu2awyogDZ4v+7t86/+stFPC/E9zVMU0PXdf6Hdw+29Wt43sAI8lkcJ8GNG2/y\nne+M31pv2za53BadjrfnBQXgug0WFvaT36mpHMvLy4Th3N77VihkWVn5nIWFS0Mx+/3qMwnTePR6\nPdrtgLU1D01rcenS1HPVosldIMnrRswsB+qnjjnOWgC93iU4edlO7NPbGYxjZQAXswUzLjomGfsi\n6TCMPFG0TKeTGmohH/g02XvHwlCl02Evidpt3YdBUfXztp/DYJdpcRHAwnWVnVgR4LC21sOyupRK\n9sjXOCrubmt5vd4nijIIMUhetrZcVLWI5wnW17fI5/d3z9rtLBsbfRI7E3eP1jwoUDeMkE5HoCg6\nYQhCcKBlP0TXtZ05b4LFxU36/Qhdh2IxTxCIE6/Zs+fYaAg2NxVgnlarTbfbRwiFfN5ndlYhlRpY\nROyuv3JljsXFdRoNBSEUbDvi/fdn2NpqUa+3d+bjCd55J0MYJo60l3hWx8G2/Xq9Ta9noWkDe4Ot\nrSxra2u89VYa3z99fVK9rpHLwcJIb1HlkLZX/Wd3krHjomOSseOi4yReOGlSFOWHwP8GaMD/KYT4\nX59dM441wKTWpukQTcD6YKBjvDfkIrZgxkXHJGNfJB3f+EaJ+/eX8TwTRRn4NC0sGDu1NYPnX7+e\n5Te/qeI4+7eFHCei32/wxhvWqV6n349Gdl1ls30Mw6FScWk0+iSTNpY12F1Q1QTwJXNzNxnV4t5o\nCJaWRr2awDAEUSQY1JAP4qXTKrVaD8tKEQTB3usABEGPfF5H1/dfZ9R5aZrY+Z+KYbgIoaEoCk4q\n5Mc/dgnDCMvqksslWFraxnUVer0iqqri+4Ivv6xy+XKHzc1Vpqdzp9qVSSYjlpYUfv7z3T+q9neG\n7tzxuHRJDF2fgW6Fr3718G28hYV9W4NOR91Ze7rPVDIZMTcHH37oEgQBKyv+TpG+SzYr0HWFRKLC\n+voyV68mXtmfmYseOy46Jhk7LjqO44WSJkVRNOD/AP4AWAZ+qSjK/yOE+PwsxEkkktHous7NmzPM\nzETYto9pZg/V15imyZtvWjx8uEwQZHBdHVVtMDenMjV1uJB8FEd1Xf3hH4ZAh+npMkGwxsaGiaIk\nEcJDiCpvv310d97m5sFEYp9vfGNQR6SqDHW8ZTIZouhL4CYH66R932NmxkXXx/saSyRMul2PMFTx\ntDZBsEUq1ePrX7+OaWo8fdrmz/+8sFN8rdDp1NG0HN//fgIhfBYXa7zxhjFRF+yzZrcuaGmpRhCU\n2L9ru/+Z6fWMkV5ML8purZrnDTyXdpFjUCQXkRfdafoWcF8I8RhAUZT/G/hvAJk0SSQvAV3X99yh\nR5HLZfja19J0Oh3abZ9yuXgms+k0TcNx+oDCwsIMuVwItDEMA01LUCo9f0KRSGg0mz6aNkisBnVN\nRf7u735FuVzA9z36/Q2y2RXef/+rY8UejCOJcBwd3w8Iwx7J5DSGYfLRR02KRQ9VzVMoOPh+QLfb\nRogsnU5Eq6Xw5Em4V0j/R39kkcm8eAfiy2RQr3W43igMQ/r9Dp6XAc7Wd2k38T448BjkGBTJxeRF\nk6Y54OBG+1Pg2y8YUyKRnCGKopBKpVAUFVU9u52EGzem+O1vn6KqZXTd3hkBssEbb6Sey0AzmYz4\n4Q/7KIrGxsY2/b6NYdg4joKiKPzLf1kAfJrNe8zP56lUvnaquEII5qcs/sffK+O6g4JvIQJ6vYC5\nXAHHS+7MUSuztrZJv99HURRM06Dd1ul2IxTFQdNcFMXFstKE4Q3+y3/5iD/8w9tjn+d5UiplWVnZ\nRtMGSa0QgqdPazSbOqoa7Pg91bhxozzRIbwSyUXlRZMm+WeCRPKaYts2X/vaNNXqNhsbW0xNKUxP\nF8a+XRYEAVtbPba2AqKoh227fO1rWaLIZX29Rr+vcfVqkmRytO/TQauCg7eASqWIVmudWk2wvt5l\ne1unUkkyNVXk0aMWbtPk15+0mZryUdWQcllQKBR4+nR1L/ZgN8pG00AIl1RqUOuoaRqtloXv+xjG\n6RtBzhvTNJmZiVhZqeM4OZaXt2i3MyhKjWvXFoiiBP2+z927q7zzzmjvK4nkdeZFk6Zl4GDvxAKD\n3aY9fvOb3/DzP/uzvX//3je/ye9961sjg02ye25SscfpyoPJDQ4ed/1F7Wp41c8xLjp21w9a9I8e\nlgoqmUyJKNLI5UJcd2B1cBy9nroXMwxDqtU+up7dqYfK0OvBP/3TKpcv5+l2s/R6Kg8eDHbIRg2C\nXV6Gn/50cEup31ew7cHfcv/sn21i2yV03aRW20TXS6ysdGk0uvybf6PgummsAhj5Fpqm8/vf77Lp\nfIK6UKD/SQ2tWyQMI6JII4oCVFUQBAZBAEHgkUw6NBohjnO4KHz3Wmcygg8+8A89nskMdy8efG/2\nO+6G2T33F33P8/kKut5iZWWZWq1JJhNSLJaJIoNWSyWdNmg0UtRqLrZ9tHnlaXXsdu71+8ozx3c/\nQ88fe9y1FzV2XHRMMvZ56viHf/hbfvnLv93797VrBT744IORa180afpH4IaiKFeAFeC/Bf67gwve\ne+89vvknfzL0pONuEEyqe25SscfpytuPPZnBweOuv6hdDS/7HPv9HkvNRVy1jxbpzDhz5HKF1+Ja\n23bEhx8ezoJKJcGzPyKnjb2wIPjww5AoilhcXKfZTKBpHrYtsO00iqJg22WWlpr88pfJoVqYUYNg\ndV3Q6bTwPAgChSiK8LyQ9XUPx/HI500yGQvXjdA0G9ftoaomtq1gpSK+NL/AMAweJls8tv8T7165\nwYf//X+FthbR6Xg8fLhFqTSDbTu4bgQIFKVNMjmFECbJZES/39/pulPRtIhsNk0ymSKZhNnZozbk\nh4/vXr/hjrt9Dp77i77nyWSSbFYhCFI4zm5n3mCd4wyuE3RIJo/vEjyNjoMDdw/WNB0cuPu8sZ9n\n7UWNHRcdk4x9Xjq+973v8r3vfXfv3/fv//LItS+UNAkhAkVR/ifgZwwsB/4v2TkneZVotOvc8z/D\nmhsMug3wuVf/nKmty1w/4nbRq8Q4bsynHQqbzSqUSi4ffbROv29jmoNW/LW1PvX6Clevzu34EZ1c\nFzVooe/S7+/aA4QsLnYBn9u380SRwcpKh263w9///aCjr1IJefLEwHV9rqUEgRXuFceHoUf98mfM\nXPkKN98cbKL/+tcPuX+/w7/+1x5h6GMYEfl8knweLl3SsKwWX3zRx7Jmd3TD/fstomiD+fn4dtgZ\nhomijN5VD8P+yB2052HXANPzFExTDB2XSC4aL+zTJIT4CfCTM9AikcSOx90H2PPDtyicnMNKY4kr\nQWXs+p1XmXGGwi4ubmIYcxjGFpomaLdBUSy63TLVqoeimPR6JolERBiCpoHjHP4l++TJJqo6c8DA\n08U0M7iuiut2sawsimLQ6UQ0my1M0yAIfPr9CEWxgQDQcN2Qfr9JJiNwHBvC/de4ffsyT56so2lF\nbDuJooT4vkewc1/+8eMmtj0/pMu2s6yu9pieDmL7GdF1nWzWo9sN0LR9jUIIHKdFInE2fxTsJt6d\njnLszpJEchGI50+zRBIDPM/DczxsDk+P17Ia2+0tSrnDs91eJw568NTrConE7i0eQa129G3oVktB\n11WKxSSPHrn84he7TtQmCwsdqlUVx7FZXDQIQwVNE/z+7x+uDWo2IZ+HH/zAI4pgeTnAtj3AxPOW\nWV+HVksnnS5Tr28RhjaFgsvv/m4SRWnz3n8dcL/YRFFUzEJIR+2j65mhpGljo04qVaFUclDVCE0z\nUBQHz/NpNhuAjTOi9EfTstRqTSqVoz2rzpvr16f58ssqjYaFogyKwB2nyc2br/fnWiI5Cpk0SSTH\nII5oED3q+OvGQQ+eRkNw//4gURqV4OzS7/dw3T66DsmkQzrdJwh6qOrgdlAYuui6gmEcTlZHoesK\nhQJEkWBpqY+uD0aq9HpttrcjFKVAux1hGAnSacGjRxGJREijAVf/+TafZ39FFPnoYYfL5uFaolrN\nR9N0FEVF15UDr2vQ6YSkDowADAJBo6HgeQJN0xDCYXtbia2Ro6qqvPXWDK7r0uv1d5LKV/+2s0Ty\nvMikSSI5AtM0seuju4dEPWKqEsPfgjGm0Wjx8GGLZjPN1laA51WZm0uQSqUpFnU2Nxt0ux66nsD3\nNYKgxxtv2ESRiqYJbt0KD9XBZLPQaIRomoaqKjjOYKfLdVv0egGmmaDTadPv++h6nlQqR7dr0m53\nSSaLJMM+f9B+n1TKwdBM1JWnfGVqllK4v9MijsmPFUXDsvaHrDUaCj/72SCJVBSX6ekMqqqOZeR4\nHkNwLcvCsqwDMwQlEskoXpuBvZOKPcnBwXEZBhyXltRJxj5qbUV5ky8efYxZtvfqZvrbPdL1a/Sz\nZ28dEZfrcZr1DWeV1co61rc1tABUD65+VUFdnyYMy3u2Ap6nsL3t8cknLrZ9iSBQmZ5O8+BBlS+/\n9CmXA5rNiCCwSad1DCMBqIShSRA0yWaz2DbkcmDb6tAg2KmpMmtrawgxja4bpNM5lpeXMIwW29tF\ndH0eTWuSzfb5/veniKIGuh5Rr/tkMgH+VgG/ehutWMAPfWw7SSk/h2077L7MwHU94v33AyxrP6GI\nopBiUVAq5bl3r4phVPA8Bd9X6Hb7FIsqYagRhse31z97rW37+CG4r+rn6aLrmGTsuOiYZOy46DiJ\nl5I0xWFgb1x0jGtREJdhwHFpSZ1k7KNas3Pu11nefkJf7aFHBnOpN+ByJlbXemurzspKD89T0fWI\nSsUaWUvzItdaCMHS0gZbWxFRBO3rX/KXif/Ew/yg7iiKBH4evlf+I76iFyiXB38elEqCra0aqVSR\nanWFWk3BsgTZbIhh9Oh2+4Shw9RUBttOoWkhmgaGAUFgAz0MwzqiRV3lW98aOHnX6yG6rvLeezqf\nfurheR6uW2VmJsHGho7vQxQlCYJFms1NZmYKtFoBhUIKVQ1RlBap1AKdjk+hsH/eN27k+eijZUql\nhT2bhTAMEWKFW7em0TSNfF7j6dMVXDeFbZvkcjbJpMOurcBJ7fWnfW9qxiq19CYtc3htKSxT8Gee\nO+7zro9L7LjomGTsuOiYZOy46DgOeXtOIjkBy7K4Vr4xdOyUm6cvhbW1LZaWdGx7jl1z6qdPO7ju\nOpcunV1B72efreD702iagaZBFG3QagWEoQZoGMYg0UkGUM4Kbt7cTxJWViIePtxAUebQdQ1dF/i+\noNtd5soVlT/9UxPD0AAPVRW891648xoCw+hSKilH3p7SNI25uRJzcwPDyGQyYm0tolAocu9eE8tK\nUih0qFY7mGaSMKzz1ltzBEGTX/wiJJdLYlkeyWSBMOwxP28fin/7doUvvljD9wdfvNksLCxU9kaN\nmKbJtWvTeJ5CoWDu7LIN660Zq2xo64f0l8IyNqerI9rQ1vmp8ZMhvyOAO+6PjkyaJBLJ2SGTJonk\nAiOEYGXFw7aLQ8dNM0m12mF29mxa3huNJt1uDtseviWZTJrMz/exbRtNG/wiv9ULKfnimedvI8Qt\nXLfN5mYHw4BMxsFxZmk2f8PUVIRp7u7Y7t/Gct0elUrAwsJ49TyGESGEwfXraVZW1jEMjXy+Q7e7\nyOXLAk3bJAxtLl2qYO64JERRiG2HaJrFswmPrutcuTL9An+tCh71HvPv+H9RFEE6bWEYgxe+4/6I\nhVMmTRKJ5HyRSZNEcoHp93sEQZJR488UJU2r1Safz73w69RqXWz78CwyVVUwDJ+pKXtPQ9kVFPrD\n65LJFCsrDwjDBaJoDkURrK93gHt85zsJCoVB3ZOuP+vz1GV7u0inM5zEnNSNNj1t8/hxE8vKcPVq\nESEEipKn16vy7ruXME2T//yfO0RRB9/XUJQA247I51PA0Z1/JzHKyFGIiE5ni0bDIkwNrBXW13uk\nUh2y2fFKDCQSyfkikyaJ5AKjqiqKEo58TIgQTVNHPjb+6wzGk+wWwx9EOdm4G9ftk0qVaDRCgsBD\nUSIgIJMp4fsrXL5cJgjW2N42UZQUUeRhmk1se5q/+Avz8O2oZ7rRgiCg0WjhujqJRIpiMU+/v8Ha\nWg8h0kCEpjW4cSOJZQ2sDaam0lQqBpoWoGkOinL0tWq1m2w1PDQthW0fbYUwysjx4cMqrltB01b2\n1hmGs9PB9/wJmkQiefnIpEkiiSFBEOD7HoZhHnt7zbJsLGsTOFz0rap10umzqXOZmclRrdZIJPbH\ngmR7Zf4g/CGW1SLTz2FGg1tXB9v1d1EUhWQyRT7v0Gh4GEaI46SJIg8hVBRF4fr1GTzPo9PpYpoG\nyeQcd++enJE9ebJOtaqiqhm6XcHqapVr1xzm50vMzIS0Wm1UVSGdnkZ5JsNTVRVdP7oLst/vcbfx\nW/ysj5fXWfcDMs0sN4o3RyaQo2g0QNcPN3ToukOr1YbEqcJIJJIYIC0HYqxjHHuCQewQldN1242z\nNi7WB5OMHRcdW1sK9ze+oGFvIyxQ6wo5b4qrhTcP/cLfjT07W+Czz1bQ9Qq6bhCGAb6/wY0bWbpd\nbWjtOAyvdygU2qysbGFZBRRFwXswRVIPefvtd2ktmeRy+ztez/7E63oOy+rSbAqiKIOuR3Q6XRKJ\nFrad2/EHArAxzcFOTqcDngf9/uHz3m3h39zc4smTDJaV2mntV7GsJB9/vMbt2z6maWEYg9l23e5w\njExG8P77wZ630/7xQUG5EIKPNn+LsWAABv2WipGOqBk9gqVHXCkONwccde06HRXTVPE88PWD57Jj\nyaCrp35vMtE07/s/PjRSJhOU6fQOJ3Fx+VxPMnZcdEwydlx0TDJ2XHSchLQciLGOce0J8nROHXuc\ntRAf64NJxo6Djnsbn6Fc65DX94elhn6DtepdrpffOiK2xXe/W6JardHpBNi2yvR0HsMweNao8EXO\n8fr1AvPzPVZWlglDQS5nUSzOoCgKmhYeG3tmRkVRUnheyMpKFcuKmJpycJw8udz6kc81TQXbFodu\nz+228D961COXmxo6T8eJsKwijUaVK1cqx5wbZLPREa8tqDe3MWd9bGf/s+84ETgqvc4GjnP12N2m\n3bjFYojvRxhhmR8HP9p73PddisWQOa2InTv++u3FpEI2mCEZPbNWBY54fhw+15OOHRcdk4wdFx2T\njB0XHcchb89JJDHBdV1aieZQwgSgGRrbxhZBcHQnnKZpzM4WRz52lti2w7Vro13Sj2N6eopqdQXb\nnmN+PonjRERRhO8vMz9/dGJzEr6vMuqSqKqK6z53WAC6fgcjN/p6C1MQBAGmeXhA8bNcuZLn449X\nsTemKSiD26VB4GGaVd4pzYF/eGdOIpHEE5k0SSQvkd0Bt89SKglUvYOSGF3DIxIRnueeiX3AeaCq\nKrdvz/DkSZW1NZUwjEilBJcvl489p1JJ8MMfenudaAePA5jmwGjzWcIwIHHEtRzFqPfF88q4XofE\n5e6h9ZqrYqROd8vatm1u34YnT1bodBRUFYpFlfn5w92IEokk3lzMb2CJ5IKyO+D2We7c8bhyxSFq\nj95CVlwVwzl5VyPOaJrG1avTlMvqqbfKC4XBWJGj3LTn51Pcu7eFbQ8Xwvt+lZmZ0sjnjGL0+2Ly\ntX+exl5oD92G87o+FWVmZI3ZUdi2zZtvSvNJieSiczb9yBKJ5IWxbYdEK4l4ZkJsGIZk+tmdGiXJ\nQbLZNFeuCIJgmW53m16vhhBP+cpXcmeyK1dOzmCt2PQ2evQbPbxVl1K9zHzh0hmol0gkFw250ySR\nxIgb+XdYWvqEXrqL5qhEXUGyk+J6cXQRuARKpTyl0sAeoNfTyeezZxZbVVVult/B932azYh83ji1\n1YBEInn1kJYDr4iOScYe1/pgHIuCuLSZviwdnsfOXLJhdlvoOx2Da7mv0+t16TW6OGYCNGi1wDBe\nrKW87vdBPb2ZYr0/mffxRa51oyHY3Dx8/YpFQTabpNvVMM3RZp/HxT7pfQGLfl+j1ztd7Nftc32e\nseOiY5Kx46JjkrHjouMkpOXAK6RjUrHHtT4YxD79hzQubaYvQ4dpKofa5wfHxV7dTjIZkUzaLC42\nebzSJIocFKVDKuVy48Zw4bQQAscJT7f7YfbGP8doMu/j817rpSWFn//8cCJ3547H7Gz03LFP876M\nG/t1+lyfd+y46Jhk7LjomGTsuOg4Dnl7TiJ5iezOJht1/CBPnqyzuZnFsvbb+z0v4vPPl/nqV+eI\nooiHD6usrmrYtopp+szPOxQKLz5nLu4U3lmml60OHfOmCsD0c8c87fsikUheb2TSJJG8RHZnkx2H\nEIKNjQjTHPZDUlWVfj9Du93m8eMGQTCLZRnY9uAvqAcPtoE6hUKOtbUt1tddwlDFNCPm51Popsil\njAAAIABJREFURzST1aixoWwcOp4xZki65zNQ9qAFgOcNdugG/y3oZav8deY/DK2/Zf+QF0maTvO+\nSCQSiUyaJJKY4fs+UWSNfMwwkjx+fJ/7KyE4fYKezrwokUykcJw8T58u02z2qdVyWFYRXYcognv3\nalSUFslk+lDMDWWDn5g/OXT8g/4fM8uVsz69I9mor7PhVRFKRPvJZf7ub0ooiorv7986+8Y3fBh9\naSQSiWTiyKRJIokZuq6jqqNL75utbVb4FH3ubTQtIugHPPYfUW6UKWUrdDrQ72skEsNTYG27wPKT\nTS5dPpw0xYF71c9oFppYiUFG1N5usdYNqDgzcMyMRKfUIrDbtNMr3LUEnjrYWSuFZQq+9EWSSCRn\ni0yaJJKYoaoquVxIs+mj68NFz1X/Y6bfnWJjJQJtkExYaZN1r8pUWKTdblEoXB0Z1/McXNfFsuK1\nVVNvbdPINbET+7o0VUVNKzRa26TM/fEwqZTg1vWQJ86gi62TbvHAuMdDMc8X2q/x1cGu1B33RzJp\nkkgkZ460HHhFdEwy9rg6xrEo6PshqGO0jmqnXz+ptS9DR/mNJK0vH7PdcFDVJFHkoyg1cl/1sCtJ\nekurmMY0HTUENIJsyPL2Etlilx4dQuVwp4g3vcZnZhPtGS19+vjKYSuCntqjo57uZ3esc3zGymBx\nax0x79DrHdDqqwShih966GEAOzYCesojN+2SNQdF26EeYGkhqhfiC49+oAManqfS6ZzcUTiODcOk\nLBjGXX9R27jlOV58HZOMHRcdJyEtB14hHZOMPc7asSwKzM5455jvkIxOuX5Sa1+Sjve+NYXrurRb\n21iWjmXn+XXzMU424vrXApbuPsVKJzHVFGG7QSrX5Du/e5Pf/OMGlnV4rlm9UOUX9t8fOv4N/xsY\n4nAy4FiCpOFRWzXYWB/+0imVQwozB5KNMa/fQSuDREegOsNJXteIMHRBpEfYSR9jZ/acaQeUrDwf\nKn8IwLpY53Mxj6NbuFobAEOomFF0uhbjMW0YJmXBMO76i9rGLc/x4uuYZOy46DgOeXtOIokxlmUN\n3U6zXRuA9JTDW98OWduoona3mcpE/O7C76DrOlevGzy8V8W0SqiqShD4hGGVYskZ+Rq5KMcd786h\n4xkyAGysa/zkL4Zv6d35kTucNL0AJafCvdZdnLS9dyxb8vj+HbAaFumch2kOvvBK5ZACBQqisLf2\nV+qv6HJ4qK5EIpGcNTJpkkguENey1/hs7TPsio2maUzNpmALZt3Zvdl0hWKWVNpl9ekqng9TCZXp\nuQK/NdZGxjQVk5vi5qHjHbUDL6ELP5fOk61maOttzJ2hxEp+k2xfcPvK1/CS3TP7K1EikUheBJk0\nSSQXiFQyxbv6uzxdfUpf6WMIg6uJq+RKw6aWlmVx5Y0jjJliyFuVd6hur1GrbRApIVNKidnCPLqu\ns3ujd5SflCc83vfeJ8GgW9DzdMzIpBSWX/IZSCSS1wGZNEkkFwzLsnhj+g1gsBs0Vi1WjKnkp6kc\nY1B5lJ/UHe8ON8VNhBA8XuzQ2zBYE+DmN5menpIDdiUSyZkhkyaJ5DWhKIoja5dK4uXtSNWosWxv\nY0bmsIYX9FUSQvD5p1W21mfIGlMArK15bGys8JWvzKBpZ9c9I5FIXl+k5cAromOSsSepo6/U8cfY\nCKhrp489qbUXVYfQBAvhwuEHFOhw+Gd0N3ZmOuKDHw93Q2bKAR21d2jtSSyzzL+zf4od2UPHf+j9\nCDvMjHzOk+4iaq9LPV3HMz0URRl63FM8ntRW2OrncBMhvWBHlwlumOf+2hLzC6MTw7pWP71Vgt7l\ntF4ap7VgaBgbbGob9CID58D6YlAm2xudRF7UNu7XoVVdnuPF13ES0nLgFdIxydiT0mEbYI55e2m8\n1vbJrH0ddOyuT1ZgtvJsIbYKz8Q6TWxTMbEtMEQ4fFwNSFrDiVkQBPx27bc0C00yToZ22KbWqlG0\ni3tF7wCmMOlVDbJ6np7Zw4mGMxsv8Ehmj7DAULXTX5N0h2R0SiuNU1owLCmr/Nz8Cb6pYYj93bc7\n7o+YVStHPu+itnG/Dq3q8hwvvo7jkDf7JRJJLLm/cR/mwUpbqKqKbuhoGY3N3uZYcYScwyuRSM4I\nWdMkkUhiRxAEtMwWtnrAu0lk+YH3A3zNp9QqYVuDx0qiRFhUWXrcg+G7foRBQL7IBUIAyomrJBLJ\n+SCTJolEEjuCICDSh7fTdUWnQAFf+My15yiZ+3VKoizYqFZpeBnQnJ0YPrDK/KX4Wy80e0280EWo\nEWqk0fIbMneSSGKITJokEslLoyRK/ND7IaYwDx0/iGVZqD2VGjU84e05fmdFlqAVkE6kh9YrisLb\nX63wcG2Z/noHIaBYVpidL8W+c67ertO1PSzNZLdiotGu83T1CfOFS+crTiKRDCG7514RHZOMPUkd\nerd+6uG+AH27ftqZqbHpWruIOiYV28amoBXIhcNmnKM6+Fzb5d8r/57QCveSrO/3vs+0N01YDEd2\n/NmXFKbnEnv/7tM/E92TWpsIEnw7+DahamD5+6Nq0nqWx+oDsuQP+UxdxMHBk4wdFx2TjB0XHZOM\nHRcdJyG7514hHZOMPSkd6Q5E3pixtVe7ay0uOiYZ+zRri4kiU94USS9JoAYoQkEEAmvaYkksURIl\nChQOPe8inaPf8ilZJYQlcKLh2YC9soemNEkkE4eedxEHB08ydlx0TDJ2XHRMMnZcdByHvD0nkUie\ni4NjTTw8TGWwG7SbzIwaewKQERmSnC75SCVSWJHFL/RfoCgK9bDO53wODJzADw7uPQrP89hobKAo\nCsVMEdM0T3zOy0LXdIR/RHufD1oi3rcWJZLXDZk0SSSS5+LgWBNf8THE4JbRbjJz1NiTD/wPmBWz\nY73Ws4aWp2WxukhVr2IWBonS0+2nzEazzJfmnyveUYngUbteJ5FKpTCWDfy8P3RcCIHTdbDy1hHP\nlEgk54FMmiQSyVj4vk8Yhofa++PGVn2L9eQ6Tmb/tpdTclipr5BpZcikRzuQH8dx8+9Os+s1iptT\nN/nV01+hT+kYtoHX81A2Fd4pvvNc8SQSyeSQSZNEIjkVnudxb/MePauH0AUtv0U7apNyUhN5vZIo\ncce7w6qyyrK6DHDq23oAa7017LnDmZ2Tc1hdWX2upOk01KixzPLe7cpdjtqNchyHd5136W516W51\nSZkpirPF595dk0gkk0MmTRKJ5ESEEHy68Sn6vI6tDBKRLl2aURO1p2IkTtnS+Az9fp9H249oa20Q\nkIkyXC1cxTRNChQoiAKe4lFkfIfKSD268DNUwiMfex66dLmr3AVgXVnnU+1TNDQc4VDTasDxu1Gq\nqlKZOnpsikQiiQfScuAV0THJ2HHRAeNZFEzKnmDc9RfNFmDU+o3aBr1KD0Pbv6Ce8BCOYLu5TUbZ\n37VxhctnG5+xlFjiqfUUC4tMIrPXOt9Te3To4HkenzQ+wV7Y3w2q9qr8+u6vWSguYGgGBa2AXbb5\nwP/gkL4MGTrq8HfLQc0RET2lN7RjI4SgLurYKZuP1Y/pqT0cHIqiSFbJnng9vNDDV/xDjzWUBv/R\n/I8AbLHFXe0uOjq/7/3+3npP8egoo78Lx3ofxxkcPIY9AVzMFvG46Jhk7LjomGTsuOg4CWk58Arp\nmGTsuOgY16JgUvYE466/iLYAB9evuWtkChk4sHljCIMfuz/G93wqbmXgpSSgXWsjCoKyVuaPlT8m\nEhHRZsRsehZVVcmQISmSrG2ukZ3OokaDZKrf67PSWEH7qka30WU6P03TbcJT+NrM10YLHNF4tqv5\nrdxbfLT6Ec70fk1TjRp/5f4V09lpNE3bK2C/491hNjq5OL0lWnsF7wfR0PaOa2jo6GhCGzpuCpOk\nOPr6T2RwMOPZE8DFbBGPi45Jxo6LjknGjouO43jupElRlD8F/hfgJvBNIcQ/nYkiiUQSO2zNpuk3\n0Y39r4zdsSZBM+CN9BskRZKVzRW6uS6aPvhFXaAACoTpkMR6giuVK4PdIQFdpTtk3Li6vYo1P+gW\n64qBA7hu6Wxntmm1W6RTwy7gJ2FZFu+k3uHh04f0zR2TSwsq2cqxLuGjOuRKooSNvVdn9SyeOH0S\nI5FILi4vstP0CfAvgH91RlokEklMqeQrrGyuoM8Mf2X4fZ+Kvl+LUw/qGOaInRhNoyVaQ8dUoRId\n2LrqqT1MBsXTmthPauyszcbTjbGTJoBkIslb+ls86j6irtdRUgoZbX/HzMCgoTaGnjOqQ+6Od4cF\nFvbqrJ5lt54JBsXq18PraELjVniLclAGDo+KkUgkF4/nTpqEEHfh+f1TJBLJ0QghaLfbCCFIp9Pn\n/nOm6zpvJt7k/tP7iLxA1VWCZsCUN8XczNzeSBMVlZDRRdYqw+NAymaZxc4iVnKwu6TsTKh12y7T\n9vTeuiiKUJXh556WJ+tPqGpVmoUmf2n8Jd2wyxJLWMbgNd/333+uuMfh4KCjY2BQFmVuiptn/hoS\nieR8kN1zEknM2NjeYMldIswMkg+1qnLJukQpf747Fdl0lq+nvk6j2cDtueTT+UPu2iWrxKPOo71E\naJfADYZ2pABKUyWaq01qbg07b5MIEzS3mhSjIqmpfRsDb8tjOjvNuGw3tqkmqtgZmw4dVE1F1VTc\nyEULNXTtbL7+XNdlSp/iDvu37TzFwxSm3F2SSF4xjv3WUBTlr4BR31b/sxDizycjSSJ5fel2uzwS\nj3BmHYzdrqcUPNp8RKKTIDlmQf5ZoygKuWzuyMcL+QK15RoN0cBODbri3K5LopZgZnbm0Po3Zt5g\npjvD+uo6t6JbbNY2Mef3E7Hedo9KUMG2x3fSXO2uHvJpsoXNm+qbqK5KwkxwK7zFTDDzXMnNdmOb\nJ90nuLYLETiuw/X8dRzHoaN0ji36lkgkF5NjkyYhxPdf9AV+85vf8PM/+7O9f//eN7/J733rWyPX\nXsQ2+LjomGTsuOgA6Pt1/FPeqalrdbQx1o7DpFr9v2h9gTlj0qM3/EAZvlz+khvpGy9Fx4vEnluY\nI9lIsrE6KKau2BXy83m6Snd03BSUUoOkpRyVWa+t0wyaqKhcyVzBL/p0wtPZlhyM3dE7CHXQXueJ\nHasAZVDHpPoqWStLQk2wEC2Awt4tRo/DtgKe4g3Fbnfa3BP3sBb2d9R69PjH5X/ktnWbttE+ld5R\nus907Rj2BIPYIain67aLi51BnFrV5TleTB3/8A9/yy9/+bd7/752rcAHHxy2OYGzuz13ZMHFe++9\nxzf/5E+Gjh3X+HcR2+DjomOSseOio2eefr2mXrxWfxsbKxo9b0xFHRnn2WOe57FYW6SjdFBRKWgF\nZouzY2sed/3Btcl0krn03P6DgiF7gGfjttotnrae4qoueqSzYC9QzA8MLTth57l05IIcvXDg09Sl\nu9f6H0URtrAxhIETOYdizzHHh+6HQ8d2u+d21z6pPyE3l4MIAhHQUAbF5GEl5G7nLo7uYEf2WPPo\nJvJZHdOegPy41zoedgZxalWX53jxdHzve9/le9/77t6/79//5ZFrX8Ry4F8A/ztQBP6Doii/FkIc\n7sWVSCSnxogMhBCHCr+FEJiRecSz9nFdl4+3PsaatdCUwS+0dW+dxkqDywuXJ6L5RanVazyIHuDM\nOWhoCASPWo/ob/Sfe7AuwKWpS3xUHfg0ZUWWH3g/AMBreswl5tA8jQyHR6kc1SG3uxMF4KouGoPr\n21Aa/Mz82d5jqq+SNbN86H743PPoJBJJPHmR7rl/C/zbM9Qikbz2zOXmeLD5gEQpMXS8v9nnevb6\nic9/XHuMNWcNJV26qbOd3KZ5v0nBKVB0iuRz+TPXfloaosGSsrT372VtGW1qYAKpK4OvJDtts9pb\nZTqYhpNzxZFYlsXbybd59PQRgRWQFElszx7UHakOCPY8o8ZFj3TEiCcKIfaSKYlE8uohu+ckkhjh\nOA5v9t/k0fIj/NSgrsZoG1x3rpNIJE549iAJMJThOpPNxiZVrYqe1EnOJHnQeYD11OKd2XeGzCVH\nMcroEY4ePnsaNpVNfm78HIAgCKimqhimwQ+8HwzFVDMqjVYDuzB+EfguqWSKrya/ShAEKIpyrKnl\nOFSsCovtRazUM12C3YCCXRiZUEkkkouPTJokkpiRz+bJZ/N0O4PC6cTMycnSLsoz5YWe71GlipW3\nUPqDx6ykRWiFLK4vcnX66rHxRhk9wvHDZ8dBQUERo0siRSSe25/pWXT95K+6cRLE0lSJbrXLem+d\nMB8SKiGiL8hreXRdx+fwfDqJRHLxkUmTRBJTEsnTJ0u7ZMnSDJt7OyqbrU2MKQN322Ums9/yr+ka\ndcbrppsEmq6h9Y7Y/alDtpw93Ek4IcZNEC9XLjPrz/KR+xE5cthJ+9xNSCUSyWR5KUmT2jldu/BF\nbIOPi45Jxo6LDgC9Wz91B3Xfrp+6IzoulgMvqqNYKVJdrSIqAt3S6dKl3W6T6+cICgG9aD8BcVV3\nUNNzTGwvPNx+D4MW/I7SGVp7Wnpqbyhm2kmz0drAtVx6ao8oinA3XK46V+npvZd2/UZZDcD+uY6M\nbcGsOcuPlR8Ptfb31B4ZMsde3+fVHRubiZjYGcSlZX6SseOiY5Kx46LjJF5K0vSqt8HHRcckY8dF\nR7oDkTdGbO1iWQ68sA4Fvj39barbVRp+A7Wp0s61KUwX6EU9nMjZf16QPPG1WqK116oPg+68hteg\n6lcJWyE5clwuXx5Lt4MzFNPQDRacBbKNLJl2BkMYzOXmBoaW0YhzPIHnvX6mYg7p2sUU5p5R5UjL\nB5LMitmhYx12zC3HKG2K5efpOGJkZxCXlvlJxo6LjknGjouO45C35ySSVwxVVZkpzDDDDEzDJ8uf\nEAbhUBdaf73PreytseK6rstmuImRMTA8Az2t0wgafLb6GVcXjq+NAgjDkOXaMl2ry7vau9jY5JK5\nvWL0UrJEISlb9CUSSXyRSZNE8orzzsw7LK4vsqau4UYuCZHgVuYWqWTqxOeWRIk73sB+ba21BlnA\ng6zIEhDQ0Bv4ZZ9ev0faSh/ZVRdFEb9d/S3RbIRqqpSiEmEY0l/pc7ty+1SF2hKJRHLeyG8qieQV\nR1VVrk5fpayWx75Vc9DosdVrYWd32v+VQbfZz8yfgQlRL0I3dX7g/4CyKO89fzeJqm5VCafDoeRI\n0zSYheX1ZS5Xzt9482CC+OxxiUQiAZk0SSSSU6IdUWMSRRGe5vFYe8yl6BK/Un+199hu59l2sI1u\nHv660TSNpmhOTPM4HOUELpFIJLvIpEkikZyKHLkhO4Ndol6E6Rxv262iEhKOfGzXW+oon6SMyJBk\nvB0yiUQimQTScuAV0THJ2HHRMe76SdkTwAVtER9jbRAEbIttDk4EKVaK1NZqtPItrJSFG7n0Oj2y\napaO1iFUQgIR4Ap3r7h7t13fsR02+5uYCZOW2tp/HS+gYlToqB2WWean5k8PaXk/eJ9slD3zcxx3\n/UV8H+OiY+zYY9gZjGNPABezDT4uOiYZOy46TkJaDrxCOiYZOy46xlk/SXsCOLo121itoa3v75ik\nVQ8zahGWS/gzJ9/+OU/rg36/z/2t+3StLn2tT6PX4FLiEvlsHhT45sw3qTfqbNW3EGnBfHYeTdNo\neS16So9O0KEW1jBDk6nE1F67fjKXpL/cpzXVIp1O40QOXs8jsZng2uw1lEg5suXfiZyX2mI/aser\nJErkyMWj1X+Ca2MTe0w7g3HsCeBitsHHRcckY8dFx3HI23MSyRmjrW9g/cW+s7Sq+BjCwP3RnVMl\nTedFGIZ8uvUp1ryFgwMqqJHKlxtfcqt9i3QqDUAumyOXzZElSzbM0m13eWI+Yc6awzAMdFsnFCHr\nzXU4MJrtrbm32Kpv8aT5BEMYTJvTFGeLsXPRHuUMfse7wwIL56RIIpHEBZk0SSQSANa21tAqh/9i\nd0oOT5efcis17Ou0Wzj98fbHzMzPkAtzANwObwPg6z5224YDzgZTuSks1Rp7h+I4Du4MeXiYyqC+\n6kWGCkskEskoZNIUE/r9PvXlZRTfB9smPzvL8892l0jGpx210Y3RXwl9tX/k81zNRVGUw51nBkQb\n0VDSNAkO7gz5O7t6cHZDhSUSiWQXmTTFgHqtRvjgATP2YOBn5Lqsr6+TunSJxJj1PhLJ82IIg37U\n3yviPogeHf1VoYUawYiq3cAPsPXTp/5H+SRlyJw6hkQikUwSmTSdM0IIeo8fM+fszwRTVZVpx2Hl\n0SMS09PnqE7yPITlEu6P9n/5e6pHFJmE5XibJM7mZvmo9hGJUmLouNtxWbCOrucpakWeek8PfZsE\nmwHlQnn0k0ZwlE9SR+2MNcPtRRmVvJVECeJVeiWRSM4BaTlwzjqarRb5Tgc1POxhk97awq3VcOzT\n/bUe13O8CLH7Sh3/8AbLkRzbPj1nw9zC0Nrdeh84/mfhXFvEEzDtTvOk+gQlr9AxOvS3+hT9Iqnp\nFJ0jtE9VplisLrJlbWFkDKIgQmwJrqeu45ouLu5zaz7Neg8PX/EB6Cv7txF37Q7GjW1jHy76VmLU\njn8BdUwy9jj2BDCeRUFc2uDjomOSseOi4ySk5cA56wh9Hz2RILKswwtTKcJEgujALtRZarmI13pS\nsW0DzJi0Zp9ni3gym2Q+nGersUWLFvOpeUzThBO6db8+/XW0rsb21jamapIv5Qe3+Y543lme47NW\nBbv/vWt38CKxX9ba10HHxGKPaU8wiH36X6JxaYOPi45Jxo6LjuOQt+fOmXQmwyaQGPFY2zAon3KX\nSSI5KzRNozRVIqEmMKPjnb4PYts2M/bMBJWN5uDtNE/xMMV+95xEIpGcJTJpOmdUVUWbm6P+9Cm5\nAwlSrd/HnpuLnYeNRBI3DtZCdZTOqXaXJBKJ5HmQSVMMKMzM0HQclldWUH2fyDRJ37xJRlVPuisi\nkUgkEonkJSGTppiQyeXI5HLDB09ZQC+RxJlut8tyY5lQDUkqSTLFDIxRdC+RSCRxQXbPvSI6Jhk7\nLjomGXuc4b4w3oDfi9iRdFax1zbXeKo9xZ4feJBtB9vU1+t8J/8dTOt09VJxP0ep43xjj63jAg4D\njlNn2etwjschu+deIR2TjB0XHZOKPe5wXxhvwO+F60g6g9i+77MVbTFVnBr4LAlABW1WY3Vplbdn\n3n4pOs4rttTx8mKPpeOCDgOOU2fZ63CORyE3ySUSyUTYaGyg50f/XdYxOkSRrNiTSCQXC5k0SSSS\niSCEQFGP6P5UBo9LJBLJRUImTRKJZCIU0gW87dG3QWzPRtPOrs5AIpFIXgYyaZJIJBPBtm1KXgm3\nMzxGpbfW43L68jmpkkgkkudHWg5IJJKJcXX6KomtBBv1DUI1xI5s3pl6h4yVOW9pEolEMjbScuAV\n0THJ2HHRMcnY4+oYx6JgUvYE464/r9ipYooUqaG1nfD0HmQX4RyljvOLPVEdMRkGHKd2fGk58BJ4\n1dvg46JjkrHjomOSscdZO65FwaTsCcZdH5fYcdExydhSx8uLPTEdMRoGHKd2fGk5IJFIJBKJRCI5\nFpk0xRzXdWl3OgTBOH7VEolEIpFIzhpZCB5TfN+n9vnnmFGEBdQVhaBQoHL1KopyhPeNRCKRSCSS\niSF3mmLK+t27zIUhRcch7TiUbZtCo8H64uJ5S5NIJBKJ5LVE7jTFkHarRbbfR1EUDnomW7oOtRrR\npUuo6uuV726tr+OuraG3WgTpNNb0NFPl8nnLkkgkEslrhLQciKEOb2ODvO+j9nqHHnP6fcJGA908\nPCH+Ip3jOGs3VldJrK5StizUfp/IMGh/8QW1ep3S3NxL0/G86/tKHf+UOW5c2rgnGTsuOiYZW+p4\nebHjogPGsyiYlD3BuOvjEjsuOk5CWg7EUIdRLNKt1TB0nchxhh7rqyqpbJboiJ2mi3KOp10bRRFh\nvU4yl2O3YTRyHBKOQ7PZxH/jDYQQbK2uIrpdhGGQm53Ftu0z1fEi620DzDi0T8codlx0TDK21PHy\nYsdFx7gWBZOyJxh3fVxix0XHccjbczEkncmwbJpk+v2h434YEk1NvVa35jrdLslo9Ic9FUVsbW0R\nPH5MxTDQNY2o36f28cf0rl2j8EzCKRmPIAhYri3jhR55J08umztvSRKJRHKuvD6/fS8YpVu3eKoo\nbHe7dPt9Nvp91pNJKlevnre0l4qmaYRCjHwsBBqPHzNn2+g7w19VVaXkOLiPHxMdkWxJTma7sc3H\ntY9Zn1qnPdvmS+NLPln+hDAMz1uaRCKRnBtypymmmKbJ3Dvv0FZVXNclnUhg7tQxua5LEIY4tv3K\n7zolHIcV22bUHseWojB1REKVA5qNBpl0eqL6XkXCMOR+/z7WnIUeDb4i7JRN6IQ8XHvIjZkb56xQ\nIpFIzgeZNMWchOPAzm0mz/PY/OILnF4PA9hUVSiXKV+6dL4iJ0zu+nWWP/+csqZhAX4QsB4EpK5e\nRX34cORzNFUlkrsiz8VGfQOtoBE8U9GqaRoNrYEQYmyvMCEEYRii6/IrRyKRXFzkN9gFQQjBxm9/\ny7ymoewkURmgu77Opq5TnJ09X4H/f3t3FhvXeZ5x/P/OcIbD4b6aWmhZsiVLdiyrceMmSAzXUA1I\nQZukaIqiF0WToDcJ0PaiDbIYaC+CIEFzkaC96kW6w+1FGgQ16qh1naUyIC9NLSepLS8CooVauA7F\nbTQzZ75ecERR5JA6M5wz55zh8wMMcIbHL585puzP53zvewKUzefp6OxkeX6e/OIiZLOM9vRgZlyt\n0kUIMOd59Pf2Njlpayh6RZJtGxdNAC7pKJfLJJP+NrCWy2Wm3j1PcnaOlHPkUylK9/bROVLbxl0R\nkSjQyIGY5JidmWF4aYlk6s4W1S5g7sIFEr29sf+MmxofJ33qFB2A5fO4Smdc4cQJOgcGmPnFLxiq\nvAewcPMmDA+TWVzES/lr6dXIgdvau9qZW5yj2F/c8L1yqUw+ld/w/ma1J372JnuKHsnruYb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nHHjh3jA5/85B3vlbeoG4Uuo7jmCLJ2VHIEWbvVc3QvQrlQY+1k63fJtHonUFRyBFk7KjmCrB2V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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c08a9d0>"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = KNeighborsClassifier(10, warn_on_equidistant=False).fit(Xtrain, ytrain)\n",
      "points_plot(Xtr, Xtrain, Xtest, ytrain, ytest, clf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "SCORE 0.913043478261\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10c6d0250>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9RS59ca8gSPWcIAhni+d5PHy4Q7erkySQy0XcuDE9Ujn2qmxuBtj26OGgmlZl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\nRMSH/wcUGkH0c//b+gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c08a050>"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8)\n",
      "X_tr = np.concatenate((X_train, X_test))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.grid_search import GridSearchCV\n",
      "from sklearn.metrics import classification_report\n",
      "\n",
      "parameters = {\"n_neighbors\": np.arange(1,80,1)}\n",
      "clf = KNeighborsClassifier(warn_on_equidistant=False)\n",
      "gs = GridSearchCV(clf, param_grid=parameters, cv=10)\n",
      "gs.fit(X_train, y_train)\n",
      "y_true, y_pred = y_test, gs.predict(X_test)\n",
      "\n",
      "print gs.best_params_, gs.best_score_\n",
      "print(classification_report(y_true, y_pred))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'n_neighbors': 22} 0.938864628821\n",
        "             precision    recall  f1-score   support\n",
        "\n",
        "          1       1.00      0.80      0.89         5\n",
        "          2       0.93      0.87      0.90        15\n",
        "          3       0.95      1.00      0.97        38\n",
        "\n",
        "avg / total       0.95      0.95      0.95        58\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "points_plot(X_tr, X_train, X_test, y_train, y_test, gs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "SCORE 0.948275862069\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "//anaconda/lib/python2.7/site-packages/matplotlib/axes/_axes.py:476: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
        "  warnings.warn(\"No labelled objects found. \"\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 90,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10c6d0250>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9e1bBslRu3AhiF9AKwnGRoEkQhFOlVoNqdTxHplSKKJ5i6yjf15j03WsYJoPB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\nQogOyKJJCCGEEKID/x8BdzeaLqv9GAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e17ff90>"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gs.grid_scores_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "[mean: 0.89520, std: 0.07647, params: {'n_neighbors': 1},\n",
        " mean: 0.88646, std: 0.07681, params: {'n_neighbors': 2},\n",
        " mean: 0.91703, std: 0.06566, params: {'n_neighbors': 3},\n",
        " mean: 0.92140, std: 0.06919, params: {'n_neighbors': 4},\n",
        " mean: 0.91703, std: 0.06566, params: {'n_neighbors': 5},\n",
        " mean: 0.92140, std: 0.06397, params: {'n_neighbors': 6},\n",
        " mean: 0.91703, std: 0.06566, params: {'n_neighbors': 7},\n",
        " mean: 0.91703, std: 0.06547, params: {'n_neighbors': 8},\n",
        " mean: 0.93450, std: 0.06836, params: {'n_neighbors': 9},\n",
        " mean: 0.92576, std: 0.06803, params: {'n_neighbors': 10},\n",
        " mean: 0.92140, std: 0.06448, params: {'n_neighbors': 11},\n",
        " mean: 0.93013, std: 0.06813, params: {'n_neighbors': 12},\n",
        " mean: 0.92576, std: 0.06489, params: {'n_neighbors': 13},\n",
        " mean: 0.92576, std: 0.06489, params: {'n_neighbors': 14},\n",
        " mean: 0.93013, std: 0.06518, params: {'n_neighbors': 15},\n",
        " mean: 0.92576, std: 0.06489, params: {'n_neighbors': 16},\n",
        " mean: 0.92576, std: 0.06489, params: {'n_neighbors': 17},\n",
        " mean: 0.92576, std: 0.06489, params: {'n_neighbors': 18},\n",
        " mean: 0.93013, std: 0.06518, params: {'n_neighbors': 19},\n",
        " mean: 0.93013, std: 0.06518, params: {'n_neighbors': 20},\n",
        " mean: 0.93013, std: 0.06518, params: {'n_neighbors': 21},\n",
        " mean: 0.93013, std: 0.06518, params: {'n_neighbors': 22},\n",
        " mean: 0.93013, std: 0.06518, params: {'n_neighbors': 23},\n",
        " mean: 0.92576, std: 0.06489, params: {'n_neighbors': 24},\n",
        " mean: 0.92576, std: 0.06511, params: {'n_neighbors': 25},\n",
        " mean: 0.92576, std: 0.06489, params: {'n_neighbors': 26},\n",
        " mean: 0.92576, std: 0.06511, params: {'n_neighbors': 27},\n",
        " mean: 0.92576, std: 0.06489, params: {'n_neighbors': 28},\n",
        " mean: 0.93450, std: 0.06516, params: {'n_neighbors': 29},\n",
        " mean: 0.93013, std: 0.06518, params: {'n_neighbors': 30},\n",
        " mean: 0.93013, std: 0.06541, params: {'n_neighbors': 31},\n",
        " mean: 0.93013, std: 0.06541, params: {'n_neighbors': 32},\n",
        " mean: 0.92140, std: 0.06387, params: {'n_neighbors': 33},\n",
        " mean: 0.92140, std: 0.06387, params: {'n_neighbors': 34},\n",
        " mean: 0.92140, std: 0.06387, params: {'n_neighbors': 35},\n",
        " mean: 0.89083, std: 0.07366, params: {'n_neighbors': 36},\n",
        " mean: 0.86900, std: 0.06732, params: {'n_neighbors': 37},\n",
        " mean: 0.86026, std: 0.06058, params: {'n_neighbors': 38},\n",
        " mean: 0.86026, std: 0.06058, params: {'n_neighbors': 39},\n",
        " mean: 0.85590, std: 0.06188, params: {'n_neighbors': 40},\n",
        " mean: 0.86026, std: 0.06058, params: {'n_neighbors': 41},\n",
        " mean: 0.85590, std: 0.06188, params: {'n_neighbors': 42},\n",
        " mean: 0.85590, std: 0.06389, params: {'n_neighbors': 43},\n",
        " mean: 0.85590, std: 0.06188, params: {'n_neighbors': 44},\n",
        " mean: 0.85590, std: 0.06389, params: {'n_neighbors': 45},\n",
        " mean: 0.85153, std: 0.06188, params: {'n_neighbors': 46},\n",
        " mean: 0.85590, std: 0.06389, params: {'n_neighbors': 47},\n",
        " mean: 0.85590, std: 0.06188, params: {'n_neighbors': 48},\n",
        " mean: 0.85153, std: 0.06188, params: {'n_neighbors': 49},\n",
        " mean: 0.86026, std: 0.06058, params: {'n_neighbors': 50},\n",
        " mean: 0.85153, std: 0.06188, params: {'n_neighbors': 51},\n",
        " mean: 0.86026, std: 0.06058, params: {'n_neighbors': 52},\n",
        " mean: 0.86026, std: 0.06058, params: {'n_neighbors': 53},\n",
        " mean: 0.86026, std: 0.06058, params: {'n_neighbors': 54},\n",
        " mean: 0.86026, std: 0.04978, params: {'n_neighbors': 55},\n",
        " mean: 0.86463, std: 0.04903, params: {'n_neighbors': 56},\n",
        " mean: 0.86026, std: 0.06058, params: {'n_neighbors': 57},\n",
        " mean: 0.86463, std: 0.04903, params: {'n_neighbors': 58},\n",
        " mean: 0.86026, std: 0.04978, params: {'n_neighbors': 59},\n",
        " mean: 0.85590, std: 0.05017, params: {'n_neighbors': 60},\n",
        " mean: 0.85590, std: 0.05017, params: {'n_neighbors': 61},\n",
        " mean: 0.85590, std: 0.05017, params: {'n_neighbors': 62},\n",
        " mean: 0.85153, std: 0.05139, params: {'n_neighbors': 63},\n",
        " mean: 0.85590, std: 0.05017, params: {'n_neighbors': 64},\n",
        " mean: 0.84716, std: 0.05435, params: {'n_neighbors': 65},\n",
        " mean: 0.84716, std: 0.05435, params: {'n_neighbors': 66},\n",
        " mean: 0.84716, std: 0.05435, params: {'n_neighbors': 67},\n",
        " mean: 0.84716, std: 0.05435, params: {'n_neighbors': 68},\n",
        " mean: 0.84279, std: 0.05841, params: {'n_neighbors': 69},\n",
        " mean: 0.84279, std: 0.05841, params: {'n_neighbors': 70},\n",
        " mean: 0.83406, std: 0.05625, params: {'n_neighbors': 71},\n",
        " mean: 0.82533, std: 0.06181, params: {'n_neighbors': 72},\n",
        " mean: 0.82096, std: 0.06263, params: {'n_neighbors': 73},\n",
        " mean: 0.81223, std: 0.06868, params: {'n_neighbors': 74},\n",
        " mean: 0.79913, std: 0.06629, params: {'n_neighbors': 75},\n",
        " mean: 0.80349, std: 0.07563, params: {'n_neighbors': 76},\n",
        " mean: 0.79039, std: 0.06697, params: {'n_neighbors': 77},\n",
        " mean: 0.77293, std: 0.06724, params: {'n_neighbors': 78},\n",
        " mean: 0.76856, std: 0.05512, params: {'n_neighbors': 79}]"
       ]
      }
     ],
     "prompt_number": 84
    }
   ],
   "metadata": {}
  }
 ]
}