GP Clustering -- Gradient Descent.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "import scipy\n",
      "\n",
      "#plot the scatter plot\n",
      "def plotScatter(X,Y,title,c):\n",
      "    fig = plt.figure()\n",
      "    ax = fig.add_subplot(111)\n",
      "    ax.plot(X,Y,c+'.')\n",
      "    ax.set_title(title)\n",
      "    plt.show()\n",
      "    \n",
      "#init parameters for the model\n",
      "alpha = 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 384
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Calculate Covariance Matrix C\n",
      "def kernelFunction(x1, x2):\n",
      "    c = 0.;\n",
      "    for i in range(len(x1)):\n",
      "        c += alpha * (x1[i] - x2[i]) **2\n",
      "\n",
      "    c = math.exp(- 0.5 * c)\n",
      "    return c\n",
      "\n",
      "#calculate the whole covariance matrix \n",
      "def getCovarianceMatrix(data):\n",
      "    size = len(data[0,:])\n",
      "    C = [[ 0. for i in range (size)] for j in range (size)]\n",
      "    for i in range (size):\n",
      "        for j in range (size):\n",
      "            C[i][j] = kernelFunction(data[:,i], data[:,j])\n",
      "    return matrix(C)  \n",
      "\n",
      "\n",
      "#TODO check how to calculate the variance\n",
      "#calculate variance of dataset\n",
      "def getVariance(data):\n",
      "    return np.var(data)\n",
      "\n",
      "#calculate inversion of Matrix A\n",
      "def getInversionMatrix(data, C):\n",
      "    size = len(data[0,:])\n",
      "    var = getVariance(data)\n",
      "    return  (C + var * matrix(np.eye(size)) ).I \n",
      "\n",
      "#calcalate the variance for a given point x\n",
      "def varianceFunction(x, data, inv):\n",
      "    size = len(data[0,:])\n",
      "    k = matrix( [kernelFunction(x, data[:,i]) for i in range (size)]).T\n",
      "    result = kernelFunction(x,x) - k.T * inv * k\n",
      "    return result[0,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 385
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#normalization of point delta value\n",
      "def normalizationPointDeltaValue(x):\n",
      "    tmp = math.sqrt( x[0] **2 + x[1] **2)\n",
      "    return [x[0]/tmp, x[1]/tmp]\n",
      "\n",
      "#nabla_variance_function\n",
      "def nablaVarianceFunction(x, data, inv):\n",
      "    size = len(data[0,:])\n",
      "    k = matrix( [kernelFunction(x, data[:,i]) for i in range (size)]).T\n",
      "    nablaK_x1 = matrix([ - kernelFunction(x, data[:,i]) * (x[0] - data[0,i]) for i in range (size)]).T\n",
      "    nablaK_x2 = matrix([ - kernelFunction(x, data[:,i]) * (x[1] - data[1,i]) for i in range (size)]).T\n",
      "    delta_x1 =  nablaK_x1.T * inv * k + k.T * inv * nablaK_x1 \n",
      "    delta_x2 =  nablaK_x2.T * inv * k + k.T * inv * nablaK_x2\n",
      "    return [delta_x1[0,0], delta_x2[0,0]]\n",
      "\n",
      "#gradient descent iteration\n",
      "def gradientDescentIteration(x, data, inv, ita):\n",
      "    delta_x = nablaVarianceFunction(x,data,inv)\n",
      "    #print \"delta_x:\" , delta_x\n",
      "    return [ x[i] + ita * delta_x[i] for i in range (len(x))]\n",
      "\n",
      "def getEquilibriumPoint(x,data,inv,ita,maxIteration):\n",
      "    x_old = x\n",
      "    iteration = 1\n",
      "    for i in range(maxIteration):\n",
      "        x_new = gradientDescentIteration(x_old,data,inv,ita)\n",
      "        if( (x_new[0] - x_old[0])/x_old[0] < 0.00001 and (x_new[1] - x_old[1])/x_old[1] < 0.00001):\n",
      "            break\n",
      "        else:\n",
      "            x_old = x_new\n",
      "            iteration += 1\n",
      "    print \"iteration: \" , iteration, \"x0: \", x, \"xt: \", x_new\n",
      "    return x_new\n",
      "\n",
      "def getMaxVariance():\n",
      "    maxVars = 0.\n",
      "    maxIndex = 0\n",
      "    for i in range (size):\n",
      "        if(i == 0):\n",
      "            maxVar = varianceFunction(data[:,i], data, invA)\n",
      "            maxIndex = 0\n",
      "        else:\n",
      "            tmp = varianceFunction(data[:,i], data, invA)\n",
      "            if(tmp > maxVar):\n",
      "                maxVar = tmp\n",
      "                maxIndex = i\n",
      "    return maxVar, data[:,maxIndex]\n",
      "\n",
      "def getMaxVariances(num):\n",
      "    maxVars = [ 0. for i in range (num)]\n",
      "    variances = array([varianceFunction(data[:,i], data, invA) for i in range(size)])\n",
      "    sorted_vars = sort(variances)\n",
      "    for i in range (num):\n",
      "        maxVars[i] = sorted_vars[-1-i]\n",
      "    \n",
      "    return maxVars"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 390
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dataset = loadtxt(\"dataset\\R15.txt\")\n",
      "data = array([dataset[:,0], dataset[:,1]])\n",
      "clusters = dataset[:,2]\n",
      "size = len(clusters)\n",
      "\n",
      "X = data[0,:]\n",
      "Y = data[1,:]\n",
      "dimension = 2\n",
      "alpha = 2\n",
      "plotScatter(X,Y,\"Scatter Plot\", 'g')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ANwCwnOikPu9U9SmM24xEbo+kzFw2hJ/N6CEzcSGEQ+rMPFAfSJm5jCB9EJ9d+ozKq5UA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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x797fa20>"
       ]
      }
     ],
     "prompt_number": 437
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "C = getCovarianceMatrix(data)\n",
      "invA = getInversionMatrix(data, C)\n",
      "SEPs = array([getEquilibriumPoint(data[:,i], data, invA, 0.5, 1000) for i in range (size) ])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "iteration:  23 x0:  [  9.802  10.132] xt:  [10.004066089718279, 10.055327150681562]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 25 x0:  [ 10.35    9.768] xt:  [10.004698361695775, 10.054816896290264]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 10.098   9.988] xt:  [10.004686010772341, 10.054827800415064]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 22 x0:  [ 9.73  9.91] xt:  [10.004095059246584, 10.055296617161813]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 26 x0:  [  9.754  10.43 ] xt:  [10.004060152189483, 10.055334250607425]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 9.836  9.902] xt:  [10.004107388561545, 10.055251535329644]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 24 x0:  [ 10.238   9.866] xt:  [10.004642249157936, 10.05486261485501]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 25 x0:  [ 9.53   9.862] xt:  [10.004092167284531, 10.055303486160444]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23 x0:  [ 10.154   9.82 ] xt:  [10.004696157016458, 10.054817426083751]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 30 x0:  [  9.336  10.456] xt:  [10.004086461543061, 10.055311828142893]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 28 x0:  [  9.378  10.21 ] xt:  [10.004075407655119, 10.055320041452415]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 25 x0:  [  9.712  10.264] xt:  [10.004051447864311, 10.055340062907959]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 26 x0:  [  9.638  10.208] xt:  [10.004102511547069, 10.055298464596794]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 25 x0:  [ 9.518  9.956] xt:  [10.004039144676691, 10.055346689527834]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23 x0:  [ 10.236   9.91 ] xt:  [10.004694765766075, 10.054820594265152]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 27 x0:  [  9.4    10.086] xt:  [10.004055099237448, 10.055335688534658]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 24 x0:  [ 10.196   9.746] xt:  [10.004702031681701, 10.054812773018124]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23 x0:  [ 10.138   9.828] xt:  [10.004672563751338, 10.054836539422954]\n",
        "iteration:  1 x0:  [ 10.062  10.26 ] xt:  [10.035451530635058, 10.198340375758722]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 24 x0:  [ 10.394   9.984] xt:  [10.004686620320852, 10.054829046362336]\n",
        "iteration:  1 x0:  [ 10.284  10.348] xt:  [10.210526622557126, 10.275794350642199]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23 x0:  [ 9.706  9.978] xt:  [10.004072368747234, 10.055318678044031]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 27 x0:  [  9.906  10.588] xt:  [10.004107954536362, 10.055296973644406]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 32 x0:  [ 10.356   9.198] xt:  [10.004647329124587, 10.054857572905652]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 22 x0:  [ 9.954  9.704] xt:  [10.004688421976635, 10.054816933968798]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 25 x0:  [  9.796  10.378] xt:  [10.004041440115182, 10.055349601977831]\n",
        "iteration:  1 x0:  [ 10.386  10.608] xt:  [10.333877588358924, 10.547562009927109]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 25 x0:  [ 10.41    9.912] xt:  [10.004651957636327, 10.05485582159246]\n",
        "iteration:  1 x0:  [ 10.172  10.598] xt:  [10.133275167141539, 10.511394285869338]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 25 x0:  [ 10.286   9.712] xt:  [10.004695430597186, 10.054818687178319]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 22 x0:  [  9.932  10.234] xt:  [10.004051314970946, 10.055343635507509]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23 x0:  [ 10.298   9.948] xt:  [10.004723913986856, 10.054798225061957]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 24 x0:  [ 10.352   9.932] xt:  [10.004686076945308, 10.054828495393981]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 24 x0:  [  9.848  10.328] xt:  [10.004036112661668, 10.055354377583082]\n",
        "iteration:  1 x0:  [ 10.514  10.498] xt:  [10.444978115067819, 10.449981475577447]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 9.944  9.934] xt:  [10.004574900413958, 10.054851609147844]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 18 x0:  [  9.92   10.022] xt:  [10.004099121840737, 10.055286434153516]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 27 x0:  [  9.908  10.606] xt:  [10.004095330872088, 10.055307389591846]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 22 x0:  [ 10.182   9.99 ] xt:  [10.004646914886342, 10.054860664952015]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 30 x0:  [ 10.256   9.25 ] xt:  [10.004657922895991, 10.054848773562915]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13 x0:  [ 12.04   10.028] xt:  [12.080093226978605, 9.9536817909780595]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2 x0:  [ 12.082  10.044] xt:  [12.082156464594444, 9.9929711015205189]\n",
        "iteration:  1 x0:  [ 12.4    10.156] xt:  [12.297420525452809, 10.098513853113872]\n",
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      {
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       "stream": "stdout",
       "text": [
        " 16 x0:  [ 11.988   9.926] xt:  [12.080061311461288, 9.9533444870584731]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 7 x0:  [ 12.34    9.918] xt:  [12.094170832811958, 9.9543481592748329]\n",
        "iteration:  1 x0:  [ 12.228   9.978] xt:  [12.177598953798052, 9.9722324930659187]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 12.348  10.488] xt:  [12.270666203502298, 10.38466584042744]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 12.044   9.358] xt:  [12.080086052352319, 9.9532382108858695]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 11.736  10.122] xt:  [12.080010769946284, 9.9534389972394361]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 14 x0:  [ 12.35    9.798] xt:  [12.080970480393116, 9.9532987888629787]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 25 x0:  [ 11.246  10.122] xt:  [12.080055258092253, 9.9534080525382898]\n",
        "iteration:  1 x0:  [ 12.276  10.99 ] xt:  [12.196339328126603, 10.925004219841304]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 12.374  10.018] xt:  [12.274877759014469, 10.00218231675953]\n",
        "iteration:  1 x0:  [ 12.53  10.  ] xt:  [12.385721745087942, 9.9946539801309822]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 12.27    9.792] xt:  [12.080540968912571, 9.9532811000393657]\n",
        "iteration:  1 x0:  [ 12.364  10.176] xt:  [12.273380571587387, 10.111991031934139]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 12.458  10.18 ] xt:  [12.339404854020554, 10.118806086728661]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 11.952   9.682] xt:  [12.080042392498349, 9.9532244193120718]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 11.772   9.924] xt:  [12.080036773048036, 9.9533571679027872]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 11.502  10.008] xt:  [12.079990645658368, 9.9533806133229223]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 12.134   9.482] xt:  [12.080152817541117, 9.9532835682038741]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 11.628  10.286] xt:  [12.08005213197603, 9.9534714884417799]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 12.064   9.616] xt:  [12.080142922297268, 9.9532837275544122]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 11.906   9.82 ] xt:  [12.080056597896949, 9.9533038420433044]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 11.736  10.29 ] xt:  [12.080068982914138, 9.9535001028741696]\n",
        "iteration:  1 x0:  [ 12.114  10.904] xt:  [12.060547099385129, 10.847510976625713]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 11.59    9.712] xt:  [12.08003095079315, 9.953309560926721]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10 x0:  [ 12.648   9.814] xt:  [12.08974896598453, 9.9539488430070904]\n",
        "iteration:  1 x0:  [ 12.164  11.018] xt:  [12.091659176051261, 10.966247656113131]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 12.22    9.796] xt:  [12.080433554937743, 9.9532499419128282]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 11.846   9.634] xt:  [12.080010135652419, 9.9532271450548571]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 11.808  10.058] xt:  [12.079997976635839, 9.9534214920933195]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 12.096   9.846] xt:  [12.080192500482303, 9.9532436441992314]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 11.594  10.078] xt:  [12.080057694651046, 9.9534055264959278]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6 x0:  [ 12.252   9.938] xt:  [12.094397852959897, 9.9544701416062011]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 11.998   9.676] xt:  [12.080079617784691, 9.9532371693116559]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 11.894  10.012] xt:  [12.080014237379876, 9.9534096655473157]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6 x0:  [ 12.274   9.936] xt:  [12.096132881817063, 9.9546453847304495]\n",
        "iteration:  1 x0:  [ 12.176  10.364] xt:  [12.145856672125046, 10.26545150265656]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 12.104  10.388] xt:  [12.092512952220185, 10.289655976610533]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 11.372  11.466] xt:  [11.206146051431437, 11.619615357308243]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 10.94   11.482] xt:  [11.205575603533319, 11.619683917907864]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 11.084  11.554] xt:  [11.205547604425371, 11.619665485855794]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 11.232  11.374] xt:  [11.205648942331162, 11.619598396773375]\n",
        "iteration:  1 x0:  [ 11.22  11.64] xt:  [11.215989958316245, 11.633393304385837]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 10.962  11.75 ] xt:  [11.205519958263643, 11.619721182768373]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 11.014  11.746] xt:  [11.205523744394331, 11.619741749920891]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23 x0:  [ 11.524  10.982] xt:  [11.205759712696382, 11.619620815028185]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 11.012  11.364] xt:  [11.205526219043785, 11.619622398540539]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 11.2    11.062] xt:  [11.205591315397982, 11.619557727047827]\n",
        "iteration:  1 x0:  [ 11.626  11.894] xt:  [11.508692613568298, 11.823834771846139]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 11.23   11.728] xt:  [11.225315746266016, 11.690681221668159]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10 x0:  [ 11.144  11.91 ] xt:  [11.206182936112656, 11.62374392392756]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [ 11.106  11.868] xt:  [11.205630135863244, 11.620322097448124]\n",
        "iteration:  1 x0:  [ 11.53   11.918] xt:  [11.439585580936567, 11.834079316992783]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 11.21   11.114] xt:  [11.205622450957618, 11.619601440369593]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23 x0:  [ 10.746  11.702] xt:  [11.205563909550436, 11.619703845756577]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [ 11.154  11.692] xt:  [11.205602948675304, 11.619902519969731]\n",
        "iteration:  1 x0:  [ 11.412  11.924] xt:  [11.357635622288456, 11.829731813987397]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 10.948  11.532] xt:  [11.205522815603929, 11.619664463845064]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 10.988  12.298] xt:  [11.205606263122693, 11.620303882686354]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 10.96   11.392] xt:  [11.205566263374516, 11.619664339612816]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 11.656  11.346] xt:  [11.206222028441111, 11.619695824484747]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 x0:  [ 11.178  12.062] xt:  [11.213617009714373, 11.674093339544816]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 11 x0:  [ 11.368  11.56 ] xt:  [11.207996685742083, 11.619840502785161]\n",
        "iteration:  1 x0:  [ 11.264  11.724] xt:  [11.248207075646166, 11.689196709458759]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 8 x0:  [ 11.554  11.576] xt:  [11.22185860452942, 11.621247575908233]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 22 x0:  [ 10.974  11.114] xt:  [11.205536822130492, 11.619606077175504]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 11.12   11.634] xt:  [11.205527977790254, 11.619708467059491]\n",
        "iteration:  1 x0:  [ 11.51   12.052] xt:  [11.437182721744863, 11.932341815820173]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 10.95   11.402] xt:  [11.205561524750044, 11.619664133077361]\n",
        "iteration:  1 x0:  [ 11.864  12.406] xt:  [11.779953104934629, 12.301142687515592]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 24 x0:  [ 11.198  10.854] xt:  [11.205628599905987, 11.61960362434716]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13 x0:  [ 11.65   11.496] xt:  [11.208939528154296, 11.619944349982406]\n",
        "iteration:  1 x0:  [ 11.248  11.722] xt:  [11.237344886958363, 11.687381736949233]\n",
        "iteration:  1 x0:  [ 11.602  11.888] xt:  [11.490076645774751, 11.817680992646654]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 11.424  11.454] xt:  [11.206320330589701, 11.619633691152247]\n",
        "iteration:  1 x0:  [ 11.312  11.718] xt:  [11.280165387952737, 11.686904273443101]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23 x0:  [ 10.736  11.68 ] xt:  [11.205553278087519, 11.619698225849111]\n",
        "iteration:  1 x0:  [ 11.56   11.798] xt:  [11.453861167661399, 11.750482215974097]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 10.028  12.268] xt:  [9.9626637340153312, 12.145502063674302]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 29 x0:  [  9.282  11.976] xt:  [9.6015552934295982, 11.873084591045123]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 32 x0:  [  9.178  11.53 ] xt:  [9.6015514331211875, 11.87308264048351]\n",
        "iteration:  1 x0:  [  9.954  12.398] xt:  [9.902022721424709, 12.248786776051048]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [  9.622  11.558] xt:  [9.6016822310231245, 11.872886116169298]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 11 x0:  [  9.914  11.844] xt:  [9.6287694874437051, 11.873915208372869]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 41 x0:  [  9.07   11.092] xt:  [9.6015056564087224, 11.873078879945442]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 25 x0:  [ 10.578  11.354] xt:  [11.205510070524777, 11.619663951620534]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [  9.582  12.14 ] xt:  [9.6016172357047651, 11.873724704863427]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [  9.622  11.528] xt:  [9.6016637422234172, 11.872935976480084]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 28 x0:  [  9.35  11.71] xt:  [9.6015003046070948, 11.873078456114547]\n",
        "iteration:  1 x0:  [ 10.234  11.974] xt:  [10.175989040832985, 11.925501588176761]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 32 x0:  [  8.986  12.31 ] xt:  [9.6015556222949723, 11.873084886708025]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 26 x0:  [  9.438  12.11 ] xt:  [9.6015581328183242, 11.873088092891935]\n",
        "iteration: "
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      {
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       "text": [
        " 11 x0:  [  9.592  12.012] xt:  [9.6016784425910888, 11.874302638575962]\n",
        "iteration:  1 x0:  [  9.666  11.88 ] xt:  [9.6526264896789371, 11.878081983914129]\n",
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       "stream": "stdout",
       "text": [
        " 27 x0:  [  9.364  12.012] xt:  [9.6015046913803825, 11.873081871371472]\n",
        "iteration: "
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      },
      {
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       "stream": "stdout",
       "text": [
        " 14 x0:  [  9.71   11.772] xt:  [9.6056458593711067, 11.873070871854262]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 12 x0:  [  9.992  11.836] xt:  [9.6310003898849033, 11.87394284391624]\n",
        "iteration:  1 x0:  [  9.916  12.028] xt:  [9.8593226160361986, 11.973599014486666]\n",
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      },
      {
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       "stream": "stdout",
       "text": [
        " 27 x0:  [  9.382  12.226] xt:  [9.6015368668908589, 11.873086516858638]\n",
        "iteration:  1 x0:  [  9.808  12.23 ] xt:  [9.7706652839807511, 12.11024890038542]\n",
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      {
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       "stream": "stdout",
       "text": [
        " 29 x0:  [  9.272  12.152] xt:  [9.6015538989036813, 11.873085358011464]\n",
        "iteration: "
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      },
      {
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       "stream": "stdout",
       "text": [
        " 32 x0:  [  9.392  11.18 ] xt:  [9.6015427717662227, 11.87307893955826]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 29 x0:  [  9.28   11.976] xt:  [9.6015534242688911, 11.873084450641674]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 16 x0:  [  9.848  11.632] xt:  [9.608166125397231, 11.873151916749197]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 30 x0:  [  9.322  11.514] xt:  [9.6015401630978232, 11.873080930563402]\n",
        "iteration:  1 x0:  [  9.718  11.95 ] xt:  [9.6944228826372285, 11.92328889170116]\n",
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      {
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       "stream": "stdout",
       "text": [
        " 31 x0:  [  9.12  11.76] xt:  [9.6015214692444228, 11.873080970975176]\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 32 x0:  [  8.978  12.37 ] xt:  [9.601544821353615, 11.873084236671414]\n",
        "iteration:  1 x0:  [ 10.072  12.202] xt:  [10.002173188070087, 12.096026298295733]\n",
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      {
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       "stream": "stdout",
       "text": [
        " 12 x0:  [  9.966  11.822] xt:  [9.6282777632283825, 11.873819099798661]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 25 x0:  [  9.506  11.648] xt:  [9.60152358327211, 11.873074946333185]\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 18 x0:  [  9.702  11.536] xt:  [9.602986266488907, 11.872956049531368]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 25 x0:  [  9.45  11.96] xt:  [9.6014908552359568, 11.873081571271083]\n",
        "iteration:  1 x0:  [  9.916  11.962] xt:  [9.8599009516332234, 11.930247753006165]\n",
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      {
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       "stream": "stdout",
       "text": [
        " 17 x0:  [  9.96   11.538] xt:  [9.6108968143050628, 11.873234740659273]\n",
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      },
      {
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       "stream": "stdout",
       "text": [
        " 32 x0:  [  9.014  11.744] xt:  [9.6015043895158048, 11.8730795808564]\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 32 x0:  [  9.024  11.846] xt:  [9.6015464078732222, 11.873083132211557]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 21 x0:  [ 10.296  11.61 ] xt:  [9.6272329328702764, 11.873801804464255]\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 14 x0:  [  7.87   10.838] xt:  [8.1212938931633847, 10.846557436351363]\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 20 x0:  [  8.164  10.534] xt:  [8.1213596378405271, 10.84639094576959]\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 19 x0:  [  8.214  10.62 ] xt:  [8.1213604973099009, 10.846396127292365]\n",
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       ]
      },
      {
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       "text": [
        " 17 x0:  [  8.166  10.698] xt:  [8.1213619823827976, 10.846349239542302]\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 16 x0:  [  8.05   10.746] xt:  [8.1213541898645918, 10.846365601418555]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 13 x0:  [  7.978  11.13 ] xt:  [8.1212665897367575, 10.848041979888514]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 11 x0:  [  8.08   10.992] xt:  [8.1212739341254156, 10.84827823717071]\n",
        "iteration:  1 x0:  [  8.472  11.082] xt:  [8.3877743860182399, 11.035376036670449]\n",
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      {
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       "stream": "stdout",
       "text": [
        " 19 x0:  [  8.494  10.584] xt:  [8.1213691895554962, 10.846333285936693]\n",
        "iteration: "
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      },
      {
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       "stream": "stdout",
       "text": [
        " 22 x0:  [  8.354  10.38 ] xt:  [8.1213600961988099, 10.846393404813089]\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 17 x0:  [  8.096  10.714] xt:  [8.1213583301581469, 10.846377868077427]\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 14 x0:  [  7.882  10.832] xt:  [8.1212975575500987, 10.846531382636348]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 14 x0:  [  7.908  11.346] xt:  [8.1212674490287888, 10.848427097679059]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 14 x0:  [  7.814  10.872] xt:  [8.1212754285033633, 10.846701943870377]\n",
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      },
      {
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       "stream": "stdout",
       "text": [
        " 26 x0:  [  8.28   10.104] xt:  [8.1213596768592087, 10.84637882189635]\n",
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       ]
      },
      {
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       "text": [
        " 18 x0:  [  8.082  10.676] xt:  [8.1213582020533668, 10.846395348717449]\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 26 x0:  [  8.068  10.118] xt:  [8.1213593694978421, 10.846388220065238]\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 17 x0:  [  8.116  10.698] xt:  [8.1213596845654177, 10.846352667357518]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 14 x0:  [  8.042  10.79 ] xt:  [8.1213403812087144, 10.846333695236947]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10 x0:  [  8.096  10.878] xt:  [8.1212778505723673, 10.84713725894086]\n",
        "iteration:  1 x0:  [  8.124  10.932] xt:  [8.123079970830176, 10.903347757036162]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [  8.632  11.124] xt:  [8.5558696846802444, 11.097125081090917]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [  8.27   10.716] xt:  [8.1213670392199759, 10.846369936198842]\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 26 x0:  [  7.622  10.148] xt:  [8.1213599039593696, 10.846348664615812]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2 x0:  [  8.198  11.398] xt:  [8.1865162236593978, 11.132421276581544]\n",
        "iteration:  1 x0:  [  8.582  11.064] xt:  [8.4926712819602201, 11.037853392052583]\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13 x0:  [  7.942  11.076] xt:  [8.1212514062239052, 10.847773530279472]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [  8.004  10.574] xt:  [8.1213583444902575, 10.846361405901746]\n",
        "iteration:  1 x0:  [  8.504  11.378] xt:  [8.4780958135701443, 11.289920520506319]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4 x0:  [  8.118  11.012] xt:  [8.1214553244020529, 10.878817163383355]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [  7.874  11.296] xt:  [8.1212569774759942, 10.848217748310805]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 15 x0:  [  7.668  10.924] xt:  [8.1212800783751007, 10.846823695994177]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 17 x0:  [  7.966  10.72 ] xt:  [8.1213527447769156, 10.846393553130969]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 13 x0:  [  7.94   10.996] xt:  [8.1212581718732775, 10.84737178949109]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13 x0:  [  7.988  11.228] xt:  [8.1212683702581998, 10.848579097792017]\n",
        "iteration:  1 x0:  [  8.164  11.112] xt:  [8.1521820680049828, 11.025779980833633]\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [  8.386  10.772] xt:  [8.1214131045086635, 10.84633759046751]\n",
        "iteration:  1 x0:  [  8.248  10.994] xt:  [8.2010770143387965, 10.948551013279989]\n",
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      },
      {
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       "stream": "stdout",
       "text": [
        " 17 x0:  [  8.286  10.734] xt:  [8.1213675436779074, 10.846396587035009]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 22 x0:  [  8.224  10.316] xt:  [8.1213605783968887, 10.846346812296328]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 22 x0:  [ 7.976  9.578] xt:  [8.0947753106737466, 9.0825251185118656]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 19 x0:  [ 7.876  8.796] xt:  [8.0950284353143456, 9.0819849058316375]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 18 x0:  [ 8.172  9.01 ] xt:  [8.0950522725115519, 9.081984411102404]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 17 x0:  [ 8.068  9.202] xt:  [8.0947999504909181, 9.0824752793662888]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 24 x0:  [ 8.416  8.654] xt:  [8.0950685335927446, 9.0819516275040897]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 36 x0:  [ 8.71   8.458] xt:  [8.0950477694595246, 9.0819904878784765]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 24 x0:  [ 8.056  8.434] xt:  [8.0950400781245921, 9.0819994072303327]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 22 x0:  [ 7.304  9.266] xt:  [8.0947812472855052, 9.0824739689752327]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 23 x0:  [ 8.118  8.608] xt:  [8.0950373950538257, 9.0820048052823594]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 22 x0:  [ 7.616  9.446] xt:  [8.0947956645660142, 9.0824687992759099]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 18 x0:  [ 8.092  8.956] xt:  [8.0950500034357642, 9.0819718744533589]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 21 x0:  [ 8.368  8.968] xt:  [8.0950532864425764, 9.0819848233331335]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 19 x0:  [ 8.022  9.334] xt:  [8.0947719071618458, 9.0825295724360551]\n",
        "iteration:  1 x0:  [ 8.32   9.062] xt:  [8.2423371261304865, 9.0540289937844651]\n",
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       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 7.832  8.952] xt:  [8.0948719814277244, 9.0821591946537978]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 7.704  8.672] xt:  [8.0950133856772624, 9.0820058664036853]\n",
        "iteration:  1 x0:  [ 8.236  9.108] xt:  [8.1824487998922457, 9.0926544056555887]\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 22 x0:  [ 8.37   8.904] xt:  [8.0950436164531432, 9.0819996977009971]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 22 x0:  [ 8.352  8.896] xt:  [8.09504114045812, 9.0820038348384742]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 17 x0:  [ 8.046  9.228] xt:  [8.0947546455307808, 9.0825628877125286]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 22 x0:  [ 7.71   9.538] xt:  [8.0947660583468135, 9.0825342060338219]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 27 x0:  [ 8.534  8.55 ] xt:  [8.0950484587889395, 9.0819889938078848]\n",
        "iteration: "
       ]
      },
      {
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       "text": [
        " 17 x0:  [ 7.996  9.172] xt:  [8.0947889349223363, 9.0824722912369875]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 8.046  9.204] xt:  [8.0947836449894002, 9.0825016476816707]\n",
        "iteration:  1 x0:  [ 8.622  9.174] xt:  [8.4870440106909406, 9.1257229679265919]\n",
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      },
      {
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       "stream": "stdout",
       "text": [
        " 16 x0:  [ 7.776  8.898] xt:  [8.0948846796823393, 9.0820825292229088]\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 18 x0:  [ 8.226  9.038] xt:  [8.0950612469482603, 9.0819782399596942]\n",
        "iteration: "
       ]
      },
      {
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       "text": [
        " 18 x0:  [ 7.904  9.194] xt:  [8.09477877585595, 9.0824847211836754]\n",
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      },
      {
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       "text": [
        " 18 x0:  [ 7.874  8.856] xt:  [8.0950101387100251, 9.0819994547562128]\n",
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       "stream": "stdout",
       "text": [
        " 17 x0:  [ 7.992  8.952] xt:  [8.0950164496163186, 9.0819997961445189]\n",
        "iteration:  1 x0:  [ 8.262  9.468] xt:  [8.1934532281396617, 9.3865545578895446]\n",
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       "stream": "stdout",
       "text": [
        " 1 x0:  [ 8.088  9.294] xt:  [8.0827151122487368, 9.2357979693097878]\n",
        "iteration: "
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      {
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       "stream": "stdout",
       "text": [
        " 25 x0:  [ 8.034  9.792] xt:  [8.0947546224869189, 9.0825712152920239]\n",
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       "text": [
        " 20 x0:  [ 8.352  9.016] xt:  [8.0950617971369283, 9.0819738870753408]\n",
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      {
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       "stream": "stdout",
       "text": [
        " 20 x0:  [ 7.85   9.334] xt:  [8.0947870831622524, 9.0824858585774582]\n",
        "iteration:  1 x0:  [ 8.404  9.366] xt:  [8.2956399636272913, 9.2944688754942089]\n",
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      {
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       "stream": "stdout",
       "text": [
        " 19 x0:  [ 7.892  8.808] xt:  [8.0950278367878443, 9.0819888853107855]\n",
        "iteration:  1 x0:  [ 8.202  9.232] xt:  [8.1552220232370196, 9.1847754839558498]\n",
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       "stream": "stdout",
       "text": [
        " 18 x0:  [ 7.668  9.026] xt:  [8.0947882960234558, 9.0823968987345829]\n",
        "iteration:  1 x0:  [ 8.242  9.308] xt:  [8.1793226867427489, 9.2437265551971883]\n",
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      {
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       "stream": "stdout",
       "text": [
        " 1 x0:  [ 9.432  8.61 ] xt:  [9.4169046246892094, 8.4816557272467961]\n",
        "iteration:  1 x0:  [ 10.066   8.19 ] xt:  [9.9894764039205945, 8.154829426357681]\n",
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       "stream": "stdout",
       "text": [
        " 19 x0:  [ 9.146  8.044] xt:  [9.4804563517508029, 7.9604479967257431]\n",
        "iteration: "
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      {
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       "stream": "stdout",
       "text": [
        " 17 x0:  [ 9.662  7.866] xt:  [9.4807596526342355, 7.9601522536287712]\n",
        "iteration: "
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      },
      {
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       "stream": "stdout",
       "text": [
        " 16 x0:  [ 9.6    7.874] xt:  [9.4807751586249847, 7.9601325581673414]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 28 x0:  [ 8.618  8.552] xt:  [8.0950644979761091, 9.0819597621568384]\n",
        "iteration: "
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      },
      {
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       "stream": "stdout",
       "text": [
        " 17 x0:  [ 9.334  7.658] xt:  [9.4806872620227658, 7.9601762489980219]\n",
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      },
      {
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       "stream": "stdout",
       "text": [
        " 15 x0:  [ 9.424  7.83 ] xt:  [9.4806859498310825, 7.9601686473771087]\n",
        "iteration: "
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      {
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       "stream": "stdout",
       "text": [
        " 23 x0:  [ 8.892  8.166] xt:  [9.4804702122028548, 7.9604394366404527]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 16 x0:  [ 9.386  7.746] xt:  [9.4806945740479893, 7.9601620385369793]\n",
        "iteration:  1 x0:  [ 9.878  8.054] xt:  [9.7743554970862032, 8.0278126823629101]\n",
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      {
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       "stream": "stdout",
       "text": [
        " 14 x0:  [ 9.558  7.948] xt:  [9.4807908267774099, 7.9601437689216663]\n",
        "iteration: "
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      },
      {
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       "stream": "stdout",
       "text": [
        " 18 x0:  [ 9.222  8.002] xt:  [9.4804694947984576, 7.9604310499614641]\n",
        "iteration:  1 x0:  [ 9.52   8.282] xt:  [9.4954669111103343, 8.1831337414536129]\n",
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       "stream": "stdout",
       "text": [
        " 17 x0:  [ 9.76   7.932] xt:  [9.480793303068161, 7.9601398572319448]\n",
        "iteration:  1 x0:  [ 9.568  8.052] xt:  [9.5335710815608827, 8.0162019113025202]\n",
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      {
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       "stream": "stdout",
       "text": [
        " 19 x0:  [ 9.736  7.552] xt:  [9.4807588119463766, 7.9601382776464451]\n",
        "iteration:  1 x0:  [ 9.584  8.478] xt:  [9.5487770649721284, 8.3536874308949756]\n",
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      {
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       "stream": "stdout",
       "text": [
        " 19 x0:  [ 9.358  8.242] xt:  [9.4804796509112368, 7.9604387208168061]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 16 x0:  [ 9.404  7.79 ] xt:  [9.4806783983623895, 7.9601884102005682]\n",
        "iteration:  1 x0:  [ 9.458  8.54 ] xt:  [9.441073707074759, 8.4113409227138245]\n",
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      {
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       "stream": "stdout",
       "text": [
        " 17 x0:  [ 9.482  7.766] xt:  [9.4807106547842626, 7.9601755333457866]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 22 x0:  [ 8.844  8.024] xt:  [9.4804451148829791, 7.9604622260096303]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 21 x0:  [ 9.29   8.472] xt:  [9.4804473145720802, 7.960471421613275]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 17 x0:  [ 9.274  7.566] xt:  [9.4807017046634545, 7.9601441345949437]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 9.11   8.014] xt:  [9.4804473624764185, 7.9604549289885416]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 18 x0:  [ 9.542  7.688] xt:  [9.4807224136569879, 7.9601694882000915]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 9.432  8.122] xt:  [9.4804266341589454, 7.9605077479132706]\n",
        "iteration:  1 x0:  [ 9.786  8.066] xt:  [9.6942424946730661, 8.0298375115382719]\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 9.382  7.664] xt:  [9.4807081859218396, 7.96015963049999]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 9.404  8.228] xt:  [9.480454917059955, 7.9604708457693834]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 20 x0:  [ 9.146  8.158] xt:  [9.4804618100884372, 7.9604478495300661]\n",
        "iteration:  1 x0:  [ 9.622  8.004] xt:  [9.5702780466374602, 7.9842518608673014]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 x0:  [ 10.286   7.892] xt:  [9.8391400539997846, 8.0034037460312195]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 9.43   7.676] xt:  [9.4807254091796764, 7.9601472338947321]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 15 x0:  [ 9.44   8.058] xt:  [9.4804415206993138, 7.9604888633326372]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 9.788  7.684] xt:  [9.4807457274891114, 7.9601592980362064]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 9.586  7.91 ] xt:  [9.4807422457619808, 7.9601702249398771]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 9.694  7.448] xt:  [9.4807236801080936, 7.9601731543554459]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 9.576  7.866] xt:  [9.4807612539872981, 7.9601413059157275]\n",
        "iteration:  1 x0:  [ 11.442   8.688] xt:  [11.39692274321256, 8.609709296750891]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 11.466   8.558] xt:  [11.40161224981015, 8.5124473536150287]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 24 x0:  [ 10.674   8.936] xt:  [11.260358330272121, 8.3995008240539288]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 11.23    8.126] xt:  [11.260458843012289, 8.3993702880085213]\n",
        "iteration:  1 x0:  [ 11.614   8.588] xt:  [11.508689940111232, 8.5492565033638162]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 11.59    8.496] xt:  [11.482897779269617, 8.4764348479321292]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 11.536   7.586] xt:  [11.260551613289323, 8.3993855981965684]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 11.638   8.266] xt:  [11.26112114837103, 8.3994108423404459]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 11.16   8.43] xt:  [11.260304392538247, 8.3994908602387639]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 10.904   8.532] xt:  [11.260312510897265, 8.399487962120098]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2 x0:  [ 11.284   8.742] xt:  [11.282713432951397, 8.5618697879403154]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 11.25    8.192] xt:  [11.26046780297809, 8.3993521180907127]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 10.84    8.218] xt:  [11.260338690963039, 8.3994467419037502]\n",
        "iteration:  1 x0:  [ 11.798   8.836] xt:  [11.696375187306099, 8.8103695661425334]\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 11.51    8.094] xt:  [11.260664543328701, 8.399365496786638]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 10.932   7.796] xt:  [11.26034927044844, 8.3993837736030716]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 11.404   8.206] xt:  [11.260639925273834, 8.3993739089267727]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 11.088   8.326] xt:  [11.260339950713274, 8.3994382908682912]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 11.334   8.17 ] xt:  [11.260562116207939, 8.3993445528688575]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 8 x0:  [ 11.272   8.394] xt:  [11.26088579353728, 8.3993709369128684]\n",
        "iteration:  2 x0:  [ 11.59    8.408]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " xt:  [11.402070018880488, 8.4120466436741559]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13 x0:  [ 11.212   8.516] xt:  [11.26032848920766, 8.3997514480430269]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 11.566   8.024] xt:  [11.260637231027479, 8.399384436396149]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 7 x0:  [ 11.246   8.584] xt:  [11.260529813400284, 8.4072224860459919]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6 x0:  [ 11.252   8.566] xt:  [11.260813699752113, 8.4104857738224332]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 10.78    8.294] xt:  [11.26029421117879, 8.3994519797658143]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 11.04    8.322] xt:  [11.260285626438812, 8.3994328197651473]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 11.198   7.886] xt:  [11.26046178905883, 8.3993834009182802]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 11.168   8.262] xt:  [11.260302853103918, 8.3993345536700534]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 11.88   8.08] xt:  [11.260839138587164, 8.3993985488834237]\n",
        "iteration:  1 x0:  [ 11.356   8.586] xt:  [11.32888293019124, 8.5253096490984976]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 11.182   8.342] xt:  [11.260340694342302, 8.3994119533852629]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 10.836   8.664] xt:  [11.260314678731305, 8.3995008848519532]\n",
        "iteration:  1 x0:  [ 11.696   8.906] xt:  [11.628860874608339, 8.862000619978426]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 11.282   8.28 ] xt:  [11.260519698274742, 8.3993688185661846]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 10.718   8.534] xt:  [11.260315541806477, 8.3994788577053523]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 25 x0:  [ 10.444   8.684] xt:  [11.260329466396138, 8.3994807767268949]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 11.124   8.618] xt:  [11.260325508890562, 8.39960591970266]\n",
        "iteration:  1 x0:  [ 11.392   8.94 ] xt:  [11.390480969565505, 8.8380166453649416]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 11.212   8.308] xt:  [11.260395641413544, 8.3993848558333752]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 16.674   9.638] xt:  [16.37336068798205, 9.8906204289937225]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 9 x0:  [ 16.162  10.302] xt:  [16.374268644779626, 9.90654544041214]\n",
        "iteration:  1 x0:  [ 16.612  10.218] xt:  [16.528265091476083, 10.141335073037189]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 16.1     9.702] xt:  [16.37335714935989, 9.8906303739211605]\n",
        "iteration:  1 x0:  [ 16.404  10.072] xt:  [16.39511916251228, 10.019090909474562]\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13 x0:  [ 15.93   10.106] xt:  [16.373358997554732, 9.8922122543049102]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13 x0:  [ 16.128   9.888] xt:  [16.373273206903079, 9.8905819425692858]\n",
        "iteration:  1 x0:  [ 16.41   10.188] xt:  [16.403224568549025, 10.105508515113229]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13 x0:  [ 15.982   9.92 ] xt:  [16.373225463812368, 9.8907302465180802]\n",
        "iteration: "
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      {
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       "stream": "stdout",
       "text": [
        " 10 x0:  [ 16.224  10.02 ] xt:  [16.373368278707467, 9.8934008650547334]\n",
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       "stream": "stdout",
       "text": [
        " 20 x0:  [ 16.296   9.458] xt:  [16.373352555015828, 9.8905793929716435]\n",
        "iteration:  1 x0:  [ 16.586  10.174] xt:  [16.508246461066058, 10.105213345772329]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 x0:  [ 16.314  10.716] xt:  [16.395117587738735, 10.098503011702057]\n",
        "iteration: "
       ]
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      {
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       "stream": "stdout",
       "text": [
        " 20 x0:  [ 16.278   9.452] xt:  [16.373352011337367, 9.8905756691775899]\n",
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       "stream": "stdout",
       "text": [
        " 19 x0:  [ 16.622   9.652] xt:  [16.37336115608521, 9.8906287945944058]\n",
        "iteration: "
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       "text": [
        " 20 x0:  [ 16.22    9.494] xt:  [16.373353805002616, 9.8905919835668925]\n",
        "iteration:  1 x0:  [ 16.626  10.162] xt:  [16.533131978212104, 10.098850447253474]\n",
        "iteration: "
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      {
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       "stream": "stdout",
       "text": [
        " 1 x0:  [ 16.982  10.596] xt:  [16.866930280555163, 10.505704466132993]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 8 x0:  [ 16.27   10.128] xt:  [16.373981104230708, 9.9027177425534596]\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 18 x0:  [ 16.202   9.7  ] xt:  [16.37334974426291, 9.8905726017865732]\n",
        "iteration: "
       ]
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      {
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       "stream": "stdout",
       "text": [
        " 16 x0:  [ 16.532   9.776] xt:  [16.373359228953227, 9.8905651979682556]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 16 x0:  [ 17.124   9.726] xt:  [16.373447838812396, 9.8905963246361637]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 18 x0:  [ 16.47    9.698] xt:  [16.373356261924286, 9.8905952063238942]\n",
        "iteration: "
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      },
      {
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       "stream": "stdout",
       "text": [
        " 11 x0:  [ 16.004  10.28 ] xt:  [16.373614007200658, 9.8975676239265979]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 16 x0:  [ 16.366   9.796] xt:  [16.373351476334758, 9.8905691456413294]\n",
        "iteration: "
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      {
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       "text": [
        " 20 x0:  [ 16.268   9.522] xt:  [16.373355943540229, 9.8906073883226568]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 16.13    9.748] xt:  [16.373353262313124, 9.8906096501375185]\n",
        "iteration:  1 x0:  [ 16.67   10.498] xt:  [16.594907592165768, 10.37715251183114]\n",
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 16.488  10.542] xt:  [16.471383832022045, 10.400410533667149]\n",
        "iteration:  1 x0:  [ 16.57  10.21] xt:  [16.500747613011175, 10.131891567440718]\n",
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      {
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       "stream": "stdout",
       "text": [
        " 1 x0:  [ 16.456  10.112] xt:  [16.426354899377838, 10.050629551447201]\n",
        "iteration:  1 x0:  [ 16.482   9.986] xt:  [16.435828430423115, 9.9601068318829693]\n",
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      {
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       "stream": "stdout",
       "text": [
        " 17 x0:  [ 16.584   9.754] xt:  [16.373362341327599, 9.8906075570791003]\n",
        "iteration: "
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      },
      {
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       "stream": "stdout",
       "text": [
        " 12 x0:  [ 16.1    9.93] xt:  [16.373219435458054, 9.8909029348543598]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 19 x0:  [ 16.226   9.67 ] xt:  [16.373356897810318, 9.8906200464878875]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 16.448   9.566] xt:  [16.373351871495238, 9.8905664367919481]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 19 x0:  [ 16.572   9.624] xt:  [16.373358104108938, 9.8906081538325576]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 16.436   9.41 ] xt:  [16.373359205973038, 9.8906288552584041]\n",
        "iteration:  1 x0:  [ 16.502   9.98 ] xt:  [16.447051134186317, 9.9565641472006821]\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 16.418   9.966] xt:  [16.399420509964319, 9.9438481307883535]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 14.362  14.644] xt:  [13.967550219934108, 14.952013069263355]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 14.138  14.63 ] xt:  [13.96751504044971, 14.951935416312843]\n",
        "iteration:  1 x0:  [ 14.064  15.072] xt:  [14.025325356499069, 15.024717151397988]\n",
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      },
      {
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       "stream": "stdout",
       "text": [
        " 15 x0:  [ 13.692  14.958] xt:  [13.967249148917363, 14.952203732991523]\n",
        "iteration:  1 x0:  [ 14.238  15.296] xt:  [14.1428832871112, 15.176109574969193]\n",
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      },
      {
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       "stream": "stdout",
       "text": [
        " 15 x0:  [ 13.73   15.128] xt:  [13.967249572094357, 14.952307575416848]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 12 x0:  [ 13.952  14.868] xt:  [13.967393384510089, 14.951948819657291]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [ 13.986  14.77 ] xt:  [13.967441077983006, 14.951979794571548]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12 x0:  [ 13.916  14.996] xt:  [13.967273766047864, 14.952300503492662]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13 x0:  [ 13.874  14.954] xt:  [13.967273834691795, 14.952192217504068]\n",
        "iteration:  1 x0:  [ 14.168  15.276] xt:  [14.094594409179647, 15.158552538192637]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 14.278  15.152] xt:  [14.163151869313703, 15.079401682050747]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12 x0:  [ 14.098  14.9  ] xt:  [13.967706550970743, 14.952001701255364]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 13.764  15.212] xt:  [13.967257943440798, 14.952357228366028]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 13.948  14.218] xt:  [13.967423397749377, 14.951986873082506]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13 x0:  [ 14.13   14.838] xt:  [13.967640041597898, 14.951956884738543]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 13.362  14.932] xt:  [13.967234159250893, 14.952207112305958]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 13.546  14.844] xt:  [13.967253137616989, 14.952156579132824]\n",
        "iteration:  1 x0:  [ 14.13  15.11] xt:  [14.066144859109064, 15.048693361752072]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 13.816  14.602] xt:  [13.967375110136274, 14.952024241458943]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 14.386  14.686] xt:  [13.967652789552339, 14.951947581949051]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [ 13.786  14.726] xt:  [13.967274845372076, 14.951945604806673]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [ 14.204  14.822] xt:  [13.967610611663083, 14.952013541717632]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [ 13.856  15.206] xt:  [13.967264532404867, 14.95244151207732]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 17 x0:  [ 14.074  14.384] xt:  [13.967448758029541, 14.951982249712566]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 13.68   14.988] xt:  [13.967236871719091, 14.95222533756975]\n",
        "iteration:  1 x0:  [ 14.204  14.976] xt:  [14.110981824683986, 14.966678436753631]\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 13.388  15.39 ] xt:  [13.967258897044784, 14.952297027712008]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 13.708  15.048] xt:  [13.967247429479004, 14.952259576394068]\n",
        "iteration:  1 x0:  [ 14.114  15.366] xt:  [14.061725804007615, 15.222037707024066]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 14.4   15.04] xt:  [14.246926607758825, 15.010119080694952]\n",
        "iteration:  1 x0:  [ 14.194  15.04 ] xt:  [14.105222486157528, 15.006157930430243]\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [ 13.888  15.436] xt:  [13.967235516491623, 14.952723680965629]\n",
        "iteration:  1 x0:  [ 13.958  15.322] xt:  [13.957466257407315, 15.188402037445998]\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [ 13.922  14.802] xt:  [13.967385575182254, 14.952021579204573]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 13.652  14.602] xt:  [13.967313405844822, 14.952025578688607]\n",
        "iteration:  1 x0:  [ 14.294  14.996] xt:  [14.170057281551051, 14.979864000265557]\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 13.81   14.526] xt:  [13.967375983260224, 14.951978369528938]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 13.408  15.34 ] xt:  [13.967191804043683, 14.952344875409443]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [ 13.834  14.778] xt:  [13.967313108330847, 14.952003445071725]\n",
        "iteration:  1 x0:  [  8.826  16.474] xt:  [8.749853687705631, 16.404414723706598]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [  8.33   16.488] xt:  [8.6306859912152731, 16.269007734967602]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13 x0:  [  8.468  16.378] xt:  [8.6306819799268624, 16.268945796941246]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [  8.904  15.846] xt:  [8.6307365581162205, 16.268557161703612]\n",
        "iteration:  1 x0:  [  8.662  16.354] xt:  [8.6506960998876696, 16.323003171064428]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [  8.684  16.776] xt:  [8.6704266074484995, 16.624868954578663]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [  8.33   16.066] xt:  [8.6306422360699493, 16.268599548619072]\n",
        "iteration:  1 x0:  [  8.904  16.402] xt:  [8.7973313857875581, 16.358723757151242]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [  8.778  16.486] xt:  [8.7211363644072399, 16.41118333169673]\n",
        "iteration:  1 x0:  [  8.81   16.458] xt:  [8.739673817661112, 16.393362206527595]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12 x0:  [  8.398  16.576] xt:  [8.6307389615994801, 16.269849966127651]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [  8.542  15.918] xt:  [8.6306651824457852, 16.268558865652526]\n",
        "iteration:  1 x0:  [  9.064  16.456] xt:  [8.9124992549624853, 16.400046881652937]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13 x0:  [  9.152  16.094] xt:  [8.6312695184618047, 16.268652413636119]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [  8.614  15.908] xt:  [8.6306808538719935, 16.268567586789171]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 7 x0:  [  8.566  17.012] xt:  [8.6358457581936143, 16.315473305285469]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [  8.12  16.11] xt:  [8.6306460292443585, 16.268634535849973]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [  8.844  16.026] xt:  [8.6307648388200704, 16.268621330099052]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [  8.398  16.282] xt:  [8.6306783987592066, 16.26873424580285]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [  8.808  15.59 ] xt:  [8.6307052562956912, 16.268586661979626]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [  8.502  16.166] xt:  [8.6306324721444536, 16.268581159884302]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10 x0:  [  8.942  16.19 ] xt:  [8.6321293147439082, 16.26869865397747]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [  8.376  16.112] xt:  [8.6306672412770702, 16.268644869798962]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [  8.518  15.84 ] xt:  [8.6306825471747679, 16.268605390143406]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [  8.878  16.004] xt:  [8.6307716642510481, 16.268604003721144]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6 x0:  [  8.582  16.774] xt:  [8.6347018817554027, 16.306808951476714]\n",
        "iteration: "
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      {
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       "text": [
        " 16 x0:  [  8.248  16.154] xt:  [8.6306373409796961, 16.268628658620905]\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 12 x0:  [  8.588  16.24 ] xt:  [8.6306587549909022, 16.268646264638896]\n",
        "iteration:  1 x0:  [  8.706  16.374] xt:  [8.6764777125771086, 16.336638793723587]\n",
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       "stream": "stdout",
       "text": [
        " 12 x0:  [  8.524  16.392] xt:  [8.6307271023389802, 16.269159630698645]\n",
        "iteration: "
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      {
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       "stream": "stdout",
       "text": [
        " 13 x0:  [  8.458  16.452] xt:  [8.6307309100879746, 16.269132795391567]\n",
        "iteration:  1 x0:  [  8.83  16.36] xt:  [8.7497800263463255, 16.330048578846633]\n",
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       "stream": "stdout",
       "text": [
        " 14 x0:  [  8.616  16.112] xt:  [8.6306775822719146, 16.268558516084955]\n",
        "iteration:  1 x0:  [  8.844  16.362] xt:  [8.7584541835730327, 16.331569954865586]\n",
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       "stream": "stdout",
       "text": [
        " 16 x0:  [  8.468  15.928] xt:  [8.6306477744021723, 16.268546665763093]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 4 x0:  [  8.62   16.674] xt:  [8.6375381533502367, 16.340365965946436]\n",
        "iteration:  1 x0:  [  8.974  16.53 ] xt:  [8.8490691882487962, 16.44814236787067]\n",
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       "stream": "stdout",
       "text": [
        " 14 x0:  [  8.826  16.084] xt:  [8.6307931278527654, 16.268602017667281]\n",
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      {
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       "stream": "stdout",
       "text": [
        " 18 x0:  [  8.104  15.962] xt:  [8.6306675407857671, 16.268643216089544]\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 15 x0:  [  8.386  16.24 ] xt:  [8.6306588071935852, 16.268687569250766]\n",
        "iteration:  1 x0:  [  4.576  12.878] xt:  [4.4494240411494328, 12.860769366603778]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [  4.46  13.16] xt:  [4.3793027044466157, 13.062427109071791]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 18 x0:  [  3.632  12.862] xt:  [4.2407211388498229, 12.808140113738061]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 20 x0:  [  4.238  12.506] xt:  [4.2407564370290967, 12.808073433612112]\n",
        "iteration:  1 x0:  [  4.348  13.268] xt:  [4.3100812481759894, 13.14281778526759]\n",
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      {
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       "stream": "stdout",
       "text": [
        " 22 x0:  [  3.788  12.372] xt:  [4.240751435571628, 12.808070219722136]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 14 x0:  [  4.19   12.772] xt:  [4.2407293297883282, 12.808023319067727]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [  3.86   12.706] xt:  [4.2407257090019881, 12.808012432730854]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 12 x0:  [  3.978  13.308] xt:  [4.2411943932094687, 12.815109275394914]\n",
        "iteration:  1 x0:  [  4.336  12.854] xt:  [4.2962445787787962, 12.841112420429351]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6 x0:  [  4.218  13.03 ] xt:  [4.2421710809657407, 12.83267022217569]\n",
        "iteration:  1 x0:  [  4.25   13.002] xt:  [4.2487675919102497, 12.943141284686847]\n",
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       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [  4.334  13.06 ] xt:  [4.2980187999408628, 12.985477254991808]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [  4.654  12.566] xt:  [4.2407697777172393, 12.808060249744797]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 9 x0:  [  4.38   12.792] xt:  [4.2417358127889822, 12.808183508474459]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 14 x0:  [  3.968  13.016] xt:  [4.2407973369001004, 12.809310103039595]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [  4.614  12.526] xt:  [4.2407624739245282, 12.808017305575083]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 18 x0:  [  3.95  12.67] xt:  [4.2407303990079317, 12.808001259345788]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 18 x0:  [  4.038  12.67 ] xt:  [4.240740737923927, 12.808035432030254]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 22 x0:  [  4.426  12.238] xt:  [4.2407579374796835, 12.808072798968974]\n",
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       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 20 x0:  [  4.066  12.514] xt:  [4.2407504720680009, 12.8080553327879]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [  4.248  12.392] xt:  [4.2407449138112066, 12.807988667377741]\n",
        "iteration:  1 x0:  [  4.61  12.95] xt:  [4.4762269905146237, 12.914004459810471]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [  4.328  12.99 ] xt:  [4.2934305048599075, 12.935663798297389]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [  4.5    12.522] xt:  [4.2407576528276669, 12.808016825744518]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [  4.176  12.71 ] xt:  [4.2407504122134831, 12.808065675402155]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 20 x0:  [  4.492  12.464] xt:  [4.2407587244981064, 12.808051880251586]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [  4.134  12.834] xt:  [4.2407466378793393, 12.808332917741508]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [  4.316  12.764] xt:  [4.240790037274432, 12.808071739563921]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23 x0:  [  4.454  12.084] xt:  [4.2407575983145014, 12.808072079644047]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [  4.052  12.934] xt:  [4.2407797701141714, 12.808855088996166]\n",
        "iteration:  1 x0:  [  4.26   13.118] xt:  [4.2553786199879893, 13.026875133881623]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 11 x0:  [  4.058  13.718] xt:  [4.2424766762714867, 12.835666885641213]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [  4.24   12.626] xt:  [4.2407505774120038, 12.808028561618675]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 22 x0:  [  3.838  12.232] xt:  [4.2407431266320339, 12.808011610222279]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 9 x0:  [  4.128  13.4  ] xt:  [4.242327868593005, 12.833856409035478]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 22 x0:  [  3.764  12.38 ] xt:  [4.2407508247946923, 12.808068100693273]\n",
        "iteration:  1 x0:  [  4.424  13.186] xt:  [4.3565918068300666, 13.080780171345403]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 x0:  [  4.234  12.994] xt:  [4.2427521066291431, 12.837786005785574]\n",
        "iteration:  1 x0:  [  5.13   13.296] xt:  [5.0183982636925073, 13.245883927281936]\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 3.9    6.742] xt:  [4.2031052480989031, 7.1099363654637697]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 3.994  7.206] xt:  [4.2031035931731848, 7.1099374573660201]\n",
        "iteration:  1 x0:  [ 4.278  7.222] xt:  [4.2527737727950283, 7.1690271419882086]\n",
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       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [ 4.172  6.848] xt:  [4.2030934613061808, 7.1099120633301931]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 3.882  6.894] xt:  [4.2031044509531235, 7.1099367453377686]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 3.936  6.994] xt:  [4.2030909425931364, 7.1099368232103224]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 4.162  6.87 ] xt:  [4.2031009001064206, 7.1099255288170493]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 3.762  7.1  ] xt:  [4.2030811761358517, 7.1099372670562691]\n",
        "iteration:  1 x0:  [ 4.256  7.612] xt:  [4.2461624617817977, 7.4365528020096514]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13 x0:  [ 4.55   6.822] xt:  [4.2047194763392524, 7.1098671827548356]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 4.062  6.984] xt:  [4.2031033953084078, 7.1099363550083057]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 4.026  7.23 ] xt:  [4.2030868683678539, 7.1099379787179586]\n",
        "iteration:  1 x0:  [ 4.364  7.184] xt:  [4.3105856282845636, 7.1495680003768713]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 4.292  7.208] xt:  [4.2619744238606296, 7.1616139526535143]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12 x0:  [ 4.288  6.91 ] xt:  [4.2036422042122448, 7.1098639209468928]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 4.018  7.062] xt:  [4.203086514431666, 7.1099369249795128]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 4.07   7.104] xt:  [4.2031057533906715, 7.1099370905544603]\n",
        "iteration:  1 x0:  [ 4.548  7.654] xt:  [4.4747138929948802, 7.4936577849790913]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 9 x0:  [ 4.402  7.082] xt:  [4.2074456177356385, 7.1098614590218769]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 3.692  7.49 ] xt:  [4.2030829322399406, 7.1099378167213043]\n",
        "iteration:  1 x0:  [ 4.888  7.194] xt:  [4.7375586107768068, 7.1640362691978234]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 4.456  7.146] xt:  [4.3752962082061702, 7.1299070809936396]\n",
        "iteration:  1 x0:  [ 4.732  7.154] xt:  [4.5949994692849376, 7.1362557676052258]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 4.088  7.212] xt:  [4.2030868771130798, 7.1099384409629192]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12 x0:  [ 4.502  6.928] xt:  [4.2050743001372712, 7.1098605552810596]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23 x0:  [ 3.402  7.434] xt:  [4.2030921497699616, 7.1099374589077309]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [ 4.246  6.692] xt:  [4.2032821035168011, 7.1098892223212529]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 4.166  7.256] xt:  [4.2031019041195421, 7.1099439518651275]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [ 4.852  6.83 ] xt:  [4.2057941454875172, 7.1098824922743402]\n",
        "iteration:  1 x0:  [ 4.398  7.428] xt:  [4.3422595233679715, 7.2985870599225953]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12 x0:  [ 5.016  7.054] xt:  [4.2124630126423428, 7.1098903464384602]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13 x0:  [ 4.25  6.76] xt:  [4.2033522322388261, 7.1098647560830663]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 3.738  7.082] xt:  [4.2031045640553906, 7.1099371328810239]\n",
        "iteration:  1 x0:  [ 4.254  7.264] xt:  [4.2372567859089019, 7.1919150061989399]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 4.122  7.238] xt:  [4.2030798888274745, 7.1099402702655699]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 3.878  7.232] xt:  [4.2031014573247489, 7.1099373865890954]\n",
        "iteration:  1 x0:  [ 4.55  7.29] xt:  [4.4490814308294402, 7.2155192602736484]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 4.03   7.126] xt:  [4.2030903571270599, 7.109937300576334]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 11 x0:  [ 4.412  7.022] xt:  [4.2050611675743585, 7.1098715828385295]\n",
        "iteration:  1 x0:  [ 4.276  7.244] xt:  [4.2517090055652593, 7.1810555282945341]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 8.376  3.788] xt:  [8.6486990827478802, 3.7532108093708665]\n",
        "iteration:  1 x0:  [ 8.81   3.864] xt:  [8.7508202877559231, 3.823645821504805]\n",
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       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 8.218  3.548] xt:  [8.6487267231283234, 3.7532169964837374]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 8.374  3.748] xt:  [8.6487281427489329, 3.7532241125328918]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 8.102  4.3  ] xt:  [8.6487119518658968, 3.7532827886550408]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 8.386  3.952] xt:  [8.6487099444575382, 3.7532534046480612]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 8.858  3.274] xt:  [8.6487688002287655, 3.7532192730160308]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 8.884  3.504] xt:  [8.6488825098117843, 3.7532456670881085]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 8.294  3.38 ] xt:  [8.6487180506774237, 3.7532073150188729]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 8.38   3.178] xt:  [8.6487349132453684, 3.7532181352602483]\n",
        "iteration:  1 x0:  [ 8.738  4.294] xt:  [8.7153290914629515, 4.1272152133651785]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 9.1   3.84] xt:  [8.9469423017557563, 3.8180559965317102]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 9 x0:  [ 9.086  3.6  ] xt:  [8.6544590043306986, 3.754180577343516]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 8.616  3.45 ] xt:  [8.6487388332351838, 3.7532144338925546]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 8.624  3.698] xt:  [8.648748130375747, 3.7532247513186352]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 7 x0:  [ 8.632  3.82 ] xt:  [8.649031805779245, 3.7549110686069165]\n",
        "iteration: "
       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 8.286  3.704] xt:  [8.6487296902038029, 3.753222705155673]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [ 8.816  3.58 ] xt:  [8.6488969954457229, 3.753241298233315]\n",
        "iteration:  1 x0:  [ 8.722  3.854] xt:  [8.6966503639395434, 3.8151356076056921]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 8.298  3.378] xt:  [8.6487185315547386, 3.7532076108939214]\n",
        "iteration:  1 x0:  [ 9.014  4.034] xt:  [8.8931808347697245, 3.9405223478850426]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [ 8.87   3.554] xt:  [8.6489557660237804, 3.7532592484978942]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 8.562  3.662] xt:  [8.6487455752978288, 3.7532280908601567]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 11 x0:  [ 8.6    3.828] xt:  [8.6487331552313016, 3.7534313292167871]\n",
        "iteration:  1 x0:  [ 8.94   3.836] xt:  [8.8328056959031311, 3.8109189053488755]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 8.768  4.32 ] xt:  [8.7370769563129631, 4.1510127771539507]\n",
        "iteration:  1 x0:  [ 8.838  3.926] xt:  [8.7702357059294265, 3.8619609515264313]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 8.288  3.466] xt:  [8.6487293058864338, 3.7532170936244151]\n",
        "iteration:  1 x0:  [ 8.652  3.782] xt:  [8.6517792416367403, 3.7703850518540514]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 8.376  3.956] xt:  [8.6487044736948473, 3.7532512699454359]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23 x0:  [ 7.724  3.414] xt:  [8.6487108371008325, 3.7532056434128087]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 8.374  4.136] xt:  [8.6487331236723612, 3.7533766842080505]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 8.6    3.712] xt:  [8.6487337553768242, 3.753219952868216]\n",
        "iteration:  1 x0:  [ 9.026  3.788] xt:  [8.8908055714312688, 3.783886340002133]\n",
        "iteration: "
       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 8.534  3.252] xt:  [8.6487402724776139, 3.7532198478642043]\n",
        "iteration: "
       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12 x0:  [ 8.874  3.602] xt:  [8.6493188969936678, 3.753312102764542]\n",
        "iteration:  1 x0:  [ 8.796  3.888] xt:  [8.7429054429019128, 3.8375626257686846]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 8 x0:  [ 8.592  3.988] xt:  [8.6494338844167284, 3.7568973610950356]\n",
        "iteration:  1 x0:  [ 8.98   4.014] xt:  [8.8677618818507451, 3.9247199363734233]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12 x0:  [ 8.562  3.856] xt:  [8.6486808799098593, 3.7533644064607188]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 26 x0:  [ 13.894   4.16 ] xt:  [14.130972213218264, 5.056183881421588]\n",
        "iteration:  1 x0:  [ 14.278   5.26 ] xt:  [14.220628326366889, 5.189928732382926]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 22 x0:  [ 14.364   4.748] xt:  [14.130993215766804, 5.0561874602839909]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 14.108   4.918] xt:  [14.13097403849264, 5.0561854563018898]\n",
        "iteration: "
       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 13.998   5.498] xt:  [14.130724691543346, 5.0567579652624666]\n",
        "iteration:  1 x0:  [ 14.4     5.296] xt:  [14.301678465246345, 5.2153268100705477]"
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "iteration:  1 x0:  [ 14.3     5.368] xt:  [14.234975382015962, 5.2649530921930703]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 13.958   5.35 ] xt:  [14.130670381283203, 5.0567817119421115]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 13.842   4.984] xt:  [14.130729659176843, 5.0563121808204299]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 26 x0:  [ 13.85    4.246] xt:  [14.130966093116262, 5.056203240910337]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 13.978   5.356] xt:  [14.130685192102193, 5.0567774264175247]\n",
        "iteration:  1 x0:  [ 14.366   5.104] xt:  [14.278658195452916, 5.0839720448755177]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 14.272   4.94 ] xt:  [14.13101471436687, 5.0561676084014255]\n",
        "iteration:  1 x0:  [ 14.336   5.176] xt:  [14.2586380697817, 5.1327213741861266]\n",
        "iteration: "
       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 14.744   5.248] xt:  [14.576100396246664, 5.1965923525872393]\n",
        "iteration:  1 x0:  [ 14.306   5.06 ] xt:  [14.241216452235967, 5.0552818516545575]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 13.986   5.05 ] xt:  [14.130697967373051, 5.0563999596417784]\n",
        "iteration:  1 x0:  [ 14.44   5.33] xt:  [14.330681895277943, 5.2406296988126622]\n",
        "iteration: "
       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 14.004   4.92 ] xt:  [14.130931122113099, 5.0561762351454345]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23 x0:  [ 13.332   4.592] xt:  [14.130849214410784, 5.0562032498828877]\n",
        "iteration:  1 x0:  [ 14.218   5.544] xt:  [14.180886416839018, 5.3969709438161395]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 14.154   4.768] xt:  [14.130986264225848, 5.0561735679430919]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 13.468   4.92 ] xt:  [14.130705690933041, 5.0563341402713915]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 13.67    5.406] xt:  [14.130749076023948, 5.056551215558998]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 13.664   5.016] xt:  [14.130743695248929, 5.0563777141465165]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 14.12   4.87] xt:  [14.130977420339544, 5.0561878790047503]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 24 x0:  [ 13.836   4.51 ] xt:  [14.130960077981102, 5.0562051022343484]\n",
        "iteration:  1 x0:  [ 14.204   5.064] xt:  [14.177282856636241, 5.0599944422801277]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 14.004   5.228] xt:  [14.130687895820168, 5.0567218739779447]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 22 x0:  [ 13.266   4.858] xt:  [14.130752234886518, 5.0563116176161529]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 13.668   5.34 ] xt:  [14.130670532771843, 5.0566222290722136]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 22 x0:  [ 14.528   4.812] xt:  [14.131000688179396, 5.0561913170481274]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23 x0:  [ 14.318   4.592] xt:  [14.130988403877382, 5.0561843435624088]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 14.018   5.182] xt:  [14.130736593928859, 5.0566178247322071]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 14.37    4.884] xt:  [14.130996767687227, 5.0561967720338741]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 14.198   4.804] xt:  [14.130986939019412, 5.0561841333071165]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23 x0:  [ 14.32   4.59] xt:  [14.13098868111785, 5.0561837571160488]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19 x0:  [ 13.636   5.218] xt:  [14.130679784005714, 5.0565446451933242]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23 x0:  [ 14.41    4.656] xt:  [14.130988316761492, 5.0561945166265394]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 14.02    5.614] xt:  [14.130684616502554, 5.0568858921039226]\n"
       ]
      }
     ],
     "prompt_number": 392
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotScatter(X,Y,\"Scatter Plot\", 'g')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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s/9LpuW7jaB1HNVg/x+7GTqlOER5hNBhJGZ8CWKpUFsYu1AK4yWDSAjh4rra7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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7bba550>"
       ]
      }
     ],
     "prompt_number": 393
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotScatter(SEPs[:,0],SEPs[:,1],\"SEP Scatter Plot\" ,\"r\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wwqQm9/8IrF9acq5fzrUD8q/fUgPSJ15dXY1jx45h9uzZA3E4IiLqo36H+OXL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\n4kREMsYQJyKSMYY4EZGM/T/emQhO/ol+zQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8a5ceb8>"
       ]
      }
     ],
     "prompt_number": 394
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print SEPs.shape\n",
      "print data.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(600L, 2L)\n",
        "(2L, 600L)\n"
       ]
      }
     ],
     "prompt_number": 395
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "maxVar, point = getMaxVariance()\n",
      "print \"max variance: \", maxVar, \"data point: \", point"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "max variance:  0.765507339739 data point:  [  5.13   13.296]\n"
       ]
      }
     ],
     "prompt_number": 396
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "maxVars = getMaxVariances(100)\n",
      "print \"max variances: \", maxVars"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "max variances:  [0.76550733973896712, 0.72561117373054673, 0.71377795391802079, 0.68185158536603518, 0.67029289731244368, 0.66455926301081569, 0.6633932607480364, 0.65442288444450192, 0.65374556897738234, 0.64640578618078348, 0.63539899108567688, 0.63434762671919998, 0.63002892357425277, 0.6266724406992713, 0.61461281811137725, 0.60483498244154199, 0.60086723398613018, 0.59933293180891356, 0.59805603846927358, 0.59418897130889525, 0.58776687632998237, 0.58654910216773648, 0.58426136547012941, 0.57545409673873893, 0.57343504367782705, 0.57327230185023759, 0.573130866246361, 0.56803682968524816, 0.56317540594958349, 0.56217924748048986, 0.55392470401786331, 0.55043128528514784, 0.55024372882939976, 0.54755001052216923, 0.54652905283753417, 0.54541479601454457, 0.54410952047110306, 0.5421661285098518, 0.5406195850265525, 0.5374249284661492, 0.53109800350713221, 0.52902139521365943, 0.52726778462647095, 0.52149989879895653, 0.52014266622487038, 0.51764016426527726, 0.5150709163114533, 0.51474625625650972, 0.51158299364672222, 0.51138806332294251, 0.51113922960614455, 0.50874171461589968, 0.50767583942921046, 0.50632751082819805, 0.50231683382978698, 0.50153374425817132, 0.50107599876520825, 0.49599130755637033, 0.49477085164051493, 0.49183209030178876, 0.4914530864918869, 0.49087111116052906, 0.49045095142046691, 0.48978670350787668, 0.48880028800437714, 0.48754657054947115, 0.48743561645251732, 0.48718054756478668, 0.48576216542212303, 0.48567233874912863, 0.48458245418988843, 0.48084332562792853, 0.47965502308894592, 0.47889030520960718, 0.47848354984838659, 0.47710210750777937, 0.47654872412524574, 0.47459304492365584, 0.47297088890494388, 0.47276543632037815, 0.47210360366144966, 0.46839051574992452, 0.46794304338212667, 0.46662751626953658, 0.46611100032489372, 0.46503658379003121, 0.46468481577358256, 0.46310514536820213, 0.4625336655819714, 0.46149984821816659, 0.46116418606751231, 0.46029130444816035, 0.45826509208444843, 0.4571449983334186, 0.45661146574909472, 0.45641473193568327, 0.45604219767072551, 0.45222007887138949, 0.45213684001617438, 0.44969475358672772]\n"
       ]
      }
     ],
     "prompt_number": 397
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def isExistInList(sepList, point, min_accepted_covariance):\n",
      "    for i in range (len(sepList)):\n",
      "        covariance = kernelFunction(sepList[i], point)\n",
      "        if(covariance > min_accepted_covariance):\n",
      "            return i\n",
      "    return -1\n",
      "\n",
      "def reduceSEPs(seps, min_accepted_covariance):\n",
      "    sepList = []\n",
      "    sepIndexMap = {}\n",
      "    for i in range (len(seps)):\n",
      "        index = isExistInList(sepList, [seps[i,0],seps[i,1]], min_accepted_covariance)\n",
      "        if index == -1 :\n",
      "            index = len(sepList)\n",
      "            sepList.append([seps[i,0],seps[i,1]])\n",
      "        sepIndexMap[i] = index\n",
      "    return array(sepList), sepIndexMap\n",
      "            "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 398
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sepList, sepIndexMap = reduceSEPs(SEPs, 0.99)\n",
      "print \"size of reduced SEPs: \" , len(sepList)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "size of reduced SEPs:  91\n"
       ]
      }
     ],
     "prompt_number": 402
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotScatter(sepList[:,0],sepList[:,1],\"reduced SEPs Scatter Plot\" ,\"r\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bkJeX1IXCAO9w7BMnItOqq6UuldWrpXC++za1Cx7YJCJSMM6dQkTUiTDEiYgU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\nJyJSsP8HJDHjtIOhKq8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7bba550>"
       ]
      }
     ],
     "prompt_number": 403
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def getGeometricDistance(x1, x2):\n",
      "    d = 0.\n",
      "    for i in range (len(x1)):\n",
      "        d += (x1[i] - x2[i])**2\n",
      "    return math.sqrt(d)\n",
      "\n",
      "def getAdjacencyMatrix(sepList, maxVar, pointsNumPerDistanceUnit):\n",
      "    A = [[ -1 for i in range (len(sepList))] for j in range (len(sepList)) ]\n",
      "    for i in range (len(sepList)):\n",
      "        for j in range (len(sepList)):\n",
      "            if(i == j ):\n",
      "                A[i][j] = 1\n",
      "            elif( i < j ):\n",
      "                isConnected = True\n",
      "                delta = sepList[i] - sepList[j]\n",
      "                distance = getGeometricDistance(sepList[i], sepList[j])\n",
      "                pointsNum = pointsNumPerDistanceUnit * distance\n",
      "                for m in range ((int)(pointsNum)):\n",
      "                    testPoint = sepList[j] + (m+1) * delta/pointsNum\n",
      "                    testVar = varianceFunction(testPoint, data, invA)\n",
      "                    if(testVar > maxVar):\n",
      "                        isConnected = False\n",
      "                        break\n",
      "                if isConnected is True:\n",
      "                    A[i][j] = 1\n",
      "                else:\n",
      "                    A[i][j] = 0\n",
      "            elif (i > j ):\n",
      "                A[i][j] = A[j][i]\n",
      "    return array(A)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 404
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = getAdjacencyMatrix(sepList, maxVars[30], 50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 405
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def getSEPsClusters(adjacencyMatrix, sepList):\n",
      "    assignedSEPs = []\n",
      "    clusters = [ -1 for i in range (len(sepList))]\n",
      "    clusterIndex = 1\n",
      "    for i in range (len(sepList)):\n",
      "        if not i in assignedSEPs:\n",
      "            cluster_seps = []\n",
      "            for j in range (len(sepList)):\n",
      "                if(adjacencyMatrix[i][j] == 1):\n",
      "                    clusters[j] = clusterIndex\n",
      "                    assignedSEPs.append(j)\n",
      "            clusterIndex += 1\n",
      "    return clusters    \n",
      "\n",
      "def getPointClusters(sepsClusters, sepIndexMap):\n",
      "    clusters = [-1 for i in range (len(sepIndexMap))]\n",
      "    for i in range (len(sepIndexMap)):\n",
      "        clusters[i] = sepsClusters[sepIndexMap[i]]\n",
      "    return clusters"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 432
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sepsClusters = getSEPsClusters(A, sepList)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 407
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print sepsClusters"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 2, 6, 4, 2, 6, 6, 6, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 2, 7, 2, 8, 8, 8, 9, 8, 8, 10, 10, 10, 10, 10, 11, 11, 11, 11, 12, 12, 12, 12, 12, 13, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16]\n"
       ]
      }
     ],
     "prompt_number": 408
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results = getPointClusters(sepsClusters, sepIndexMap)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 435
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plotCluster(X,Y,clusters,title):\n",
      "#    fig = plt.figure()\n",
      " #   ax = fig.add_subplot(111)\n",
      "    \n",
      "    colors = np.arange(len(clusters))\n",
      "    t = [colors[(int)(clusters[i])]  for i in range (len(clusters))]\n",
      "    plt.scatter(X, Y, c=t)\n",
      "    plt.show()\n",
      "#    plt.set_title(title)\n",
      "#    colors = ['r','g','b','c','m','y']   \n",
      "#    c = [colors[((int)(clusters[i])-1) % len(colors)]  + '.' for i in range (len(clusters))]\n",
      "    \n",
      "#    for i in range (len(X)):\n",
      " #       ax.plot(X[i],Y[i],c[i])\n",
      "  #  ax.set_title(title)\n",
      "    plt.show()\n",
      "    \n",
      "def plotCluster2(X,Y,clusters,title):\n",
      "    fig = plt.figure()\n",
      "    ax = fig.add_subplot(111)\n",
      "\n",
      "    colors = ['r','g','b','c','m','y']   \n",
      "    c = [colors[((int)(clusters[i])-1) % len(colors)]  + '.' for i in range (len(clusters))]\n",
      "    \n",
      "    for i in range (len(X)):\n",
      "        ax.plot(X[i],Y[i],c[i])\n",
      "    ax.set_title(title)\n",
      "    plt.show()    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 453
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotCluster(sepList[:,0],sepList[:,1],sepsClusters,\"Scatter Plot\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UhcjISMqX9wa+ub31Erm5UZjNZlxcHLGxSfnbO5Jxd5dLLUXByZG4sGpHjx7l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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x797f550>"
       ]
      }
     ],
     "prompt_number": 428
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotCluster(X,Y,results,\"Scatter Plot\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9JV988cV/DiSvpaWlybCXnpcalSOkbasmcvz48Ttuf+zYMQnydRLHl1oSlwVI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\nz48eTY3z5/EU4TcXF4I1jaMieAAPAlNFSBLhFM6W+c/AcsCK82t9OM6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sWbOsQ/lHf/31\nF2vWrKFx48YkJiayefPmsg6pROPHjy9+Pz3zzDO89dZb/3j9Tctgt/IioMWLFxMUFESdOnVu2VY4\ngM1mIzU1lUcffZTU1FTc3d0ZP358WYd1if379zN58mQOHjzIsWPHyM3NZd68eWUd1hXRNK14dtWt\naty4cRiNRvr161fWoVzCYrHw5ptv8vrrrxffd6u+p2w2G1lZWWzYsIF33nmHPn36lHVIJRo2bBhT\np07l8OHDTJo0qfib+OXclCReVFTEPffcw4ABA+jRo8fNeInrsn79ehYuXEh0dDR9+/Zl5cqVDBo0\nqKzDukR4eDjh4eE0aNAAgF69epGamlrGUV1q8+bNNG3aFH9/f1xcXLj77rtZv359WYd1WcHBwZw4\ncQKA48ePExQUVMYRXd6sWbNITk6+ZT8U9+/fz8GDB6lVqxbR0dFkZGRQr149Tp06VdahXSI8PJy7\n774bgAYNGqDT6cjMzCzjqC61adMmevbsCTjf85s2bfrH6294EhcRhg0bRnx8/CWrOG8Vb775JkeO\nHCE9PZ358+fTpk0b5syZU9ZhXSIkJISIiAj27dsHwPLly6levXoZR3WpatWqsWHDBvLz8xERli9f\nTnx8fFmHdVndu3dn9uzZAMyePfuWbGgALF26lHfeeYcff/wRNze3sg6nRDVq1ODkyZOkp6eTnp5O\neHg4qampt+QHY48ePVi5ciUA+/btw2q14u/vX8ZRXSo2NpbVq1cDsHLlSqpUqfLPT7jRo61r164V\nTdOkVq1aUrt2baldu7YsWbLkRr/MDZOSknJLz07Ztm2b1K9fX2rWrCk9e/a8JWeniIhMmDBB4uPj\nJSEhQQYNGlQ8A6Cs3XfffRIaGioGg0HCw8Nl5syZkpmZKW3btpXKlStLUlKSZGVllXWYl8T56aef\nSmxsrFSsWLH4ffTII4+UdZjFcRqNxuKf5/+Kjo6+JWanlBSn1WqVAQMGSEJCgtStW1dWrVpV1mGW\n+P/z999/l4YNG0qtWrWkcePGkpqa+o9lqMU+iqIo5ditOzVDURRF+VcqiSuKopRjKokriqKUYyqJ\nK4qilGMqiSuKopRjKokriqKUYyqJK4qilGMqiSuKopRj/w/OKNuqyujNIQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x77e3b00>"
       ]
      }
     ],
     "prompt_number": 436
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotCluster2(X,Y,results,\"Scatter Plot\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GQzCKZvmglCL2r5LyEqrr1W+UpdZSNpTGtnMhQVwMKIejnpqauSQmTgioOFHp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\nXbf75qdBUeLi1H+LirrPq/s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1ED916hS33347BQUFTJkyhZdeeima7RJCCNEDvQ7iJpOJ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\nEEIMYRLEhRBiCPv/AQuYeEOrNBN7AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8aa4160>"
       ]
      }
     ],
     "prompt_number": 457
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotCluster(X,Y,clusters,\"Scatter Plot\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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oUQMvNxes2QNORj34uZtIT0+/TxE/3vz8/Jj66YeMf/ctatWsTlpaGmXLliXj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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8a6ea20>"
       ]
      }
     ],
     "prompt_number": 461
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A1 = getAdjacencyMatrix(sepList, maxVars[80], 20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 440
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sepsClusters1 = getSEPsClusters(A1, sepList)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 441
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print sepsClusters1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 2, 4, 5, 2, 6, 6, 6, 6, 7, 6, 6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 13, 12, 12, 14, 14, 14, 14, 14, 15, 15, 15, 15, 16, 16, 16, 16, 16, 17, 18, 18, 18, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 20]\n"
       ]
      }
     ],
     "prompt_number": 442
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotCluster(sepList[:,0],sepList[:,1],sepsClusters1,\"Scatter Plot\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7F+WGTqfD3d29Uha4UurqRcbr1WLPx38AkJF4hSNrTmE2m3F2cCbjdE7R49NP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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8a6e630>"
       ]
      }
     ],
     "prompt_number": 443
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results1 = getPointClusters(sepsClusters1, sepIndexMap)\n",
      "plotCluster2(X,Y,results1,\"Scatter Plot\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OYhbeVUi+PZ+D/99Blk5bypJpS/hgxQcd1sc7C9TaRGUka8qhxpeUdBsQRULC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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x77f6b70>"
       ]
      }
     ],
     "prompt_number": 459
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotCluster(X,Y,results1,\"Scatter Plot\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PL4ZjxwzSG08gc2wK1xt+ybONm5OQkPCv46xSpQr7dm7h4qkjmA2QHFgFTE7a\nxmcacuHMSSpVq8W4ybNJC20Dzp5wcBGOgz+zecs2PPyC2LZtW858iMpT4T+tnTJ69GjMZjMdOnT4\ny+3Dhw+//XNkZCSRkZH/5e3+E6vVygcff/qv9y9UqBAB+QsxcNsJugZnsvSsgViHTU0pfEgyMzNp\n0rIdm3b+hsHND9OtCwx9awA4e0FIY22n2BPw7EgoXkd73uIL+KY9JF6F4ZfB2RMJbQJbJ0LrKeD3\njPao8TqOtaMY1LszH81eREL5rFkhIY1hnT/Hjh3Ldj16/EcTWLnpV7AegXpDwdUXtk/Fw8efoxl+\nZNTtAbtmw975MO8FKBIJFboQv/sbIus14szx38mbN++D+wCVx8aGDRvYsGHDfb/+vpP4V199xS+/\n/MLatWv/dp8/JvHHjcFg4Jc1G+nf+wXabttD0WLFWbNpJjabLbdDeyrMmjWLNYdiyHjtMBjNsOlT\n+r85BBwOuLAX8pXV5nve/MPMkoTL2uwT9NrSk/9jMEL8BfAP0Z7fvAAGKx+Mn6DVzROugKsf3LpM\netx5/P39/zImEeHAgQPEx8dTtmxZRISO3V9k1cpVZGSkw6u7YN98GFtUe0FGKjeMejIMebWTtAcW\nwg9RUK4D+JWELZ9BqZZk7olhx44dtGjRIkc+S+XR9v8HuCNGjMjW6+8ria9YsYJx48axceNGrFbr\n/RziseDr68vcn5bkdhhPpfk/LiSjeCMtgQOENIG1Y9CVboFMioT84XBuJ9jTICMFbF7oN47DkZYE\nZmdtGmHkQLi4B5JvwJzWUOM1SIiBoyuh1kAytn7KS1FdmPFlJfSFq+M4tYlBAwcQGBj4p3jsdjsl\ny1bk2LGjgA6r1UK5sDB2nLiOI7AinN0Oqbcg+iuwZ4DJGawmMoKf1U6ufhoO4S+C3qD9VgBQrA5M\nqoVOrydPnjwP66NVnjD/WBNv3749VapU4ejRowQFBTFz5kz69u1LYmIi9erVIywsjD59+jyMWJWn\nyKmzF7RRbcpNbVS9cyaYbUitwRj1oDNZoPcaeG23ljh3zUZCm6N3zgMGs1YL3zkDjq0Bo0WbjXJp\nH3gUhP47IeEKkp7MmFEj+Pn7WRRxnCL91nU++exzZs/5+k/xdIvqwbFraTDgAAzYT6qLVst2JN2A\nlhO1L5mpDcDqrs16eaYhRPSGTt9C2xnw7Huw5VNw8blzUJs3pCUQUqxArpYalcfbE7uKofJ4q9Oo\nGetOJsMGzDS3AAAgAElEQVT5aC0xigMKVIZSLdAvfBlHsfraKN2eAYnXoNcqLXle+A0+qwyeBbUy\niU4PL62DW5fgmw6QmaqdeExP4fNPPuaVV/rQom0nfjmjJ63J5xB7Cuc5z7Ny0Q9Uq1btdjwFgktz\nrsoIKNVcaziwEL6P0ko1BhOYXaFgZWgzE95yhtBmUPI5qNBF2//YWnRzWiL2DGg9DXyCYcnr+Kae\nYdeOLQQFBT38D1l5JKlVDJVHht1uv+8v8vfeHYTz1d+g8gtQoiEkxWpJeWFfHHYHODKhcE049yt4\nFNASOGilC70BnvsA3joB4T3gx5dg5ywo3RKGX4GeK7G4uBEeXhGAtWvXkPbsWHByh3xhpIR1Y82a\nu8/15PXzgSuH7zRcPQJih7rvQo9l2mi/RCMwGLQTpPEXYO37cOUIxJ2FZYMQ9NqVqFsnwrRn4cZ5\nrrmHEhpWUV1ir9w3lcSV/+TGjRusWrWK7du3374QKiUlheYdO2Bxdsbq6sqw9967ncxPnjxJi04d\nqVyvLiNGj77rgqs/qlKlClvWraJvGQeN/a5g8ckPVfpAi4ngVQi6zIPwKOg4F/Z+D0dXayc55/eG\nwjWgVDNw8YbnP4SYA3Bstfazkzvkr4Q9rDPr1q0DII+nF1zOuuRdBOv13/Hx0aafHjp0iOnTp9Ol\nfStMGz+A77pojzWjwOIGK4ZoV4HGnoS1Y7X6fKsp2knWuDMwoTx8+Iw2Uo9aAkEVITkOitWDd04i\nLyzjVu336NVvQM7+RSlPrCfy9mzKw7Fy5Uqatm0LedzQ24UqYWVZ8dNCXhs8mLVJN7FdPozciOfj\nxh0JLlKEunXrUqlGdVJ7d4U2jfj940mcv3SJ6V9++ZfHDwsLIywsjPXr17M+6jXSyrSC42vBbLsz\n8g4qj8mox2f1K1y6cB4Cy8GtGLBn3pmVkpmm3a7pyu/aLBERzLGH8fYuCcCUzz6iZfuO2Eu3wRh3\nkny6q3Tr1o358xfQtWcfdM88h/7yfiIiqlCrWmHS0tI4F9yM7+YvgqjFULwu/L4U5rSBdzy0q0N1\neq10Y/PWZsX0yVr6tkBleNcDynW804f8lTj964c5/delPKFUTVy5L6dOnaJ4mdIYenVF5+ZO2kdf\nYLDZaBRemQMnT3BtyjiM5csCkDb5K9ocOk1k5cq89sti+HYyAI64G6QWKkdqUtI9746UmZlJtdoN\n2HfTmdTAyrB+HLoa/ZHiDbDsmkaY+TzbNqymV59XmPbDMu2CH6MFClXD+fBPvNW/F14e7rzx9nDs\nZdpiun6UQpabRG/dcHt21cGDB1mzZg3u7u60bdsWJycn3L18Sei6AoLKgz0Dl8kRfDthKE2aNGHs\n2LG8PWE2DD5yJ9DRRbTEnJ4M754DHPBtJ4jZD28e1ralJsBQb3D1h/6/ahf/zO2C7fwmEq/f30Jc\nypMlu7lTjcSV+/LZxIkYe3bFMuodkpt1xlitMqYWz7Nq/mIscXFk7t6HoXQIOpMJw94DBAYWRqfT\nwR/vWpNpR4CjR48SHBz8t4ncaDSyYdUyJk6cxInTZylefzgr129h67xZJN2K54DVxIRPP2fKxC+o\nFhHOgsVLyUhJpGIFGzWHTKdOHe1ioIiICNatW4enZznatWt31/TY0NBQQkND74SWmUnirRuQt4zW\nYDDhCCjD5cuXAWjbti1vDx9zZ475jfNaCaVQVczxJ0mXTK0233YGjAiAr9tB8XqYd80gtHwF9l7T\n4RhdSDthG1yftIQbD/YvSHlqqJG4cl9efPllvsnvg/HZWiQ17YTr79vRGY1IRgZJQaWwJyaDw4Ex\nrx95nV3Yu30HdrudwiElyejSFl1YKdLf/xTOXcDiYqNcSCirFi3GycnprvfZvn077773AYlJyXTv\n0JpePV9Ap9PRp9/rfLXxBCmtZkHSdZxnP8e3k8bTrFmzB9bHsuHVOOhRF3vdIXBpP85fPcuOjWso\nVaoUAA0bN2PFus0QVEmbJ67TYzbqEIeQkZIAOj0G78IEB7jg7+MJBgvNnmtAcPFiNO/yEsk9N4GL\nN/qtn1MqZhF7d255YLErjy81O0V5KDq1aYNuwiQy123RasBZy6pmrtuMw2zG9cAWXI5HoyuQH29P\nT4xGI0NHjcLh70v65u2kvDwIR1o6EvoMqR552GvWM2zUqLveY9++fdRt1IR1Ls+zs1g/3hj1CRM+\n/RyAZStXk1JnhLasrG8wyeF9WbpizQPt49IfvyckbjX6wVZsM+sya/IXtxM4QLmwMEy+RaFMG+ix\nFJ1vcXy9PcnIHwFjEmB4DA69kZde6M7aVStZu3wJ7dq2wcPDgz7d2mIeXwLncUXId2gGP30/52/j\n+Oab76hVrynPNW3L5MmTWbx48e3fCBQlx5YazMFDK7nkxIkTMn36dJk3b56kpqbKjBkzBBeb4OYq\n5t7dxbZukejLlhLLkAHicmib6AIDxPh8AzFGVJSg4OJitFrF7coRMbZ4Xiwj3xL3tBhxS70kpqgO\nYmzaSGo3bXLX+732+kCh/jBtRcCPROi3XQoElxIRkXJVIoUO39zeZqryorzz7tAc6Xd6ero4HI4/\ntYfXrC+8sOxOfF3mCzYv4ZWtd9raTJcqkfVEROSbb74TJzcPcSscJk5unjJj5iw5ffq0ZGZm/u17\nT5kyVZw8CgvhPwhhEwWDTWy2KuLq6is7duzIkf4quSu7uVPVxJV/ZdOmTTRq2QLjs3Xg7HkGDR+G\nl6cnCNi2Lid9/BekDByG4/BR7IEB2A8dwdKnB5YBLwMQ1+8tHF9fABcbcjEG4ysvANqvjsbKFcn4\n6EvKNL177RC9QY/OkcHtXywz02/fSOHLj8ZSt2FjMs9uwpB8DY/4g7z26vYc6bvJZPrL9gL58rLr\n/K/Yn9FW8TRe2EFmZhrsnw8XfwM3fzi1CWdPA1euXOHFPq+Q0nMTKQGhcGkfr7xai/Onjt/u0/93\n5coV+r7+LunlfwDfSK0x7RpJh6+B9KFjx16cOLE3J7qsPEZUElf+lR79+yGTxkOTZxERLjRsy3mT\nEZ2/D8biRTBOnQDArZJVcJy7gFyPxfJS99uvd1QMw2XZKjJadoNC+Un7ZDKGCmUhJZX0z6eR1w4j\nhwy56z1fiOrG5IjqJFvzIK5+OK8bzluj3gWgcuXK7I3ezvLly3FycqJ164d/d6nxY0eyoUoNUi7v\nRhyZuCWcJNXDjbids7QrO8/vghvnGLB4HqdPn8bsW0RL4AB5y2DyzM+ZM2f+dPef/+na/WXSM53+\nYosOqE5MTN8c65vy+FBJXPlXrl2+jKFcaUAbPesrl4f0dBx7D5Dx01KMzRqR8dNS5FIM5lHvkLno\nF1LGfoJp4zYyN2zBceI0DrMZx+9HMaemYbVaueUbDCI0a9GSubNn/2nEW6JECbZvWsfoDyeQkLSb\nbp9/SOvWrW5vL1q0KH375l4iCwoK4sj+PaxevRqdTke9evXwDwyCvr+Cf0mwZ2D+JAyHw0GhQoVI\nv3oSLh/S5o1f2kdG3DkKFiz4t8ffvXs3OPeC6Beg9BhIvw5HPwJZicEwibJlKzy8zj6hNm/ezLr1\n6/D18aVbt25/OrH+WMiZqo6qiT9pnm/bRmxRncQt4axW786XV2y//CBO30wWbM6CXic6Px/BahFd\niWKiKxsquLqIvlwZcV46VywfDBOdn4+4HP1VXCOry5QpUyQxMVFSUlJyu2t/8lf1738jOTlZDCaz\nMM5+uybuEt5BZs+eLSJZNXFXD3ErpNXEv/9h3j2PV75ipOi8Jwk+3wkuTURnKSA6nUEsFk8JDi4n\nFy5cuK84Fc2MmdPFL9BNOrwVJFWf95cK4WUeiX+P2c2dKokr/0pcXJzUbNRQDCaTGJ2cRO/rLc4/\nzRFDjSpiGTlY3BLPiXtajDhNGi94e2knPM1mcT27T9zTYsQ9LUZMnduI9dOxYhk5WIqGhsjy5csf\nSuxnz56VfPkKi05nEKvVTebOnSsiIqtWrZISJcpIYGAh6dv3NTl16pSULx8her1BPD19ZcmSJdl+\nr9By4WJoMEz4IE14ebM45/GWo0eP3t5+9epV+fXXX+95a8D/OXjwoHh6BYqbT21xyVNSatRsKPHx\n8XLlypX7/qJR7vD0dpPp+0vLOomQtY7KUrGOv8yePVtiY2Pv+fkmJSXJK31flNBSBaVuvcqyZ8+e\nBxqXSuJKjkpPTxe73S4TPv1UjD7egmcecZrx6e1E7bzoG8Ezj+BkFZys4no8+vY2Y+umYh03XPQl\ng8XUvYM45w2QH3/8McdjzpPHTyBCYLBAF9HpzDJ37lxxcnIXaCfQW5ycgsXT018MhkiBdwW6icnk\nJAMHDpTNmzf/6/c6f/68hFWuLnqDQTz9AuXnn3/+T7HHxsbK8uXLZfPmzbdnsVy4cEEWLlwomzdv\nVsn8PjkcDjGZDPJLUiVZJxGyTiKkfF0vcbKZxeZqkaLBBSQ6OlqmTp0qH3/8sRw6dOj2a9u1byrP\nt3SVlbtd5KPpTuLt4yrnzp17YLGpJK48NBkZGfJK376i9/cT2/rFYtu2XPShz4g+vJyYurYTXGyi\nDykhTrO/FPMbfQRnJ8HZScz9eopb6iVx/nG2lK1RPdvve+vWLWnXrrP4+ARKSEiYbN269U/72O12\nmTNnjrz++usCOoGhAsOzHiWlUqVKotdX+0NbPwFT1n5DBYIFAsRgqCzOzl7y2WefZytGu92e7X79\nGxs3bhQXFy9xc6sqNlsBadasbY6915Pu+ab1pVFUoMy/VF4GTCssVptepu3VRuavTy4iNnezVGvi\nL81fDhJPbxdZu3atZGZmitlskJOJbhIj7hIj7tKyo7tMnz79gcWV3dyprthU/jM3X1+SPNxBr8Pc\nrQOOpETSP5kCGRnaRUAmE9jtWE1mMsqFYvt5Ljq9nsy1myg0agIHtmZvamDDhk1Zv/48aWlVgcvY\nbGvYv383hQsXBrTbqDVv3oZVq3aQkpIGxAEvAd6AA5hMnTql2Lw5nvT0rPt1cg74FugB3AJWAz0B\nA3ADs3kKSUkJGI0Pby7A5cuXadeuO9HROzCbnahZswqbN28lLu4VIBxIx2Z7g6+/HkPz5s0fWlxP\nivj4eHq+1I11a9djNpspVtHE8IUFb29v6Pwr8y6Wx9XDyNYlcfw4Us+e6EO4ujqx6YiZvPm0ayU7\nNoQenSf+7b2Gs0utnaI8dB3at+e7Iwfh0zE4Ll4irVV3yOMOCQmQngEOB9Yx72IoVZKMV94kuVNv\nzJ3boh84jL4D38zWe9ntdlavXo7dPhAwA96InGXNmjX07NkT0K70XLlyLamp6UAx4AYwHQgFYjCb\nUxg6dCgtW7YjPv4XMjPdgK0YDK7Y7TPR6wNwONzQEjhAHhwOITk5GTc3twfyma1Zs4atW7dqa7Qk\npuDkZKVnzxduz1YREerVa8zhw0Ww26eQnHyAxYsnoH0JJWcdxUxm5jOcPXv2gcT0pEpPT2fhwoXc\nuHGDihUr8t33c9i7fxfBxUOYMnEWHh4ebN68mU5RTUlJtOPkYuDkviTQgbOb9m+gwDNOxMbGoNPp\nGPzWYDo0+IguL6VxYI+By+d9adKkSe518IH9DvD/5OChlUdMamqqvPjKK+KZL1ACg4uLV2Be0bm5\ninXKx+J6dp9Yhg4QfclgcUs6Ly7Ra8Ts7SXla0fKtOnTs13TdTgcYrXassof3QQaiMUSKN98842I\niFy/fl3atm2bVUIpIOAs8KJAD4HSAgbx8ckrVqu7mEzOUqJEiOj1ZoGWWWWVLmI0WsRstgl0FBgs\nBkNNCQkp+8A+r48++kScnfMK1BFwEugken0rcXPzkWPHjt3uh9nsIrBKYHXWI0KgvUDerPZvxcnJ\nTzZs2PDAYnvSpKamSpXqFSWshp88HxUkzq5GCYv0lDHLSkjT3vmkbPkQSUtLk/T0dOnQqbUEFc0j\n1ZvkFauzUSxWvby/vIQsvF5BIlsFSI+eXUVE+zc4d+5c6f1Sdxk67F25cePGA405u7lTJXHlX9mw\nYYP06d9f3nrnnXtObYuJiZGpU6eKrVK52yc03VIviS4or7ge3iG2VT9K4TKl/1Ms48d/LEajq4Cr\nQHnR6bylZct2cu7cOfHxyStQVCBQwEugyB/q3sMFXAS8BSIF3harNb9YLF537ePuXkwmTJggefMW\nEJPJKuHh1eXixYvZivHmzZuyc+dOOXv27F3tWk3VSeBrgfJZJ1u1JK3TdZaePV8WEZGUlBQxmawC\nc7O2rxAoLNBXdDrXrPq9RcAger1JOneOkvT09P/0uT6JZsyYIZXq+claR2VZJxEyfk1JCSxmlXUS\nIT9dKy8BBdzkvffek8LFgsQnwEXMFoPYXM0S3iiP1GrnJRYnvRiMyHNN6ktSUtJDiTm7ufOeC2BF\nRUXh5+d316I/cXFx1KtXj+LFi1O/fn3i4+Nz9DcFJffNnz+fRu3bMdPXlU/jL1MmvBKXLt299rWI\n8OIrr1DwmRL0H/ouKecuImlp2ra4G8i1WFLHfwFRfRk5cNB/iufFF3sA6cCzQAoiXixduoLevfsS\nF1cU6AR0AVKAGO6UH64BGUAzYB9gJjW1JCKpwKGsfU5ht1+nU6dOXLx4hvT0FHbs2ETevHn/dXw7\nduwgKKgodet2ITi4NEOGjLi9LT09nczMdGA+cD4rHoATiMRx9ux5AKxWK0OHDsVieR2tFDQAcMZk\nWoFOZwLaAoWAeTgc85k/fx8jRozOzsf4xIuJieG90cMJCtFryyADRco6E381gxN7k3ix9H7c/Rx8\nPnUUDvN1XLwc6AxCRkYGNVt7MWRucV76qAB5fE2cO3cWZ2fnXO7R37hXht+0aZPs2bNHQkNDb7cN\nHDhQPvjgAxERef/99+XNN998IN8myqOrcJnSYls+7840wl7dZPiIEXft8+2334prWGlxu3pUXFMu\niqF0iOhLh4h5cH/RP1NcjC2eF5Obq3z77bf/OZ6zZ8+KxeKaVSppItBCwFkKFiwq0CprRD1AoHZW\nucIq4JP1s3dWGcNbYIgYjQWlVq3a4unpI0ajRdzcPOXDDz+UrVu33nNhqr/jcDjExydIYETWCHqe\nODsHyLZt22TPnj3SsGHjrDh6CHTOiq2mgGfWjBhnCQmpKK+99oa4uvoKGASMYjDYxN+/iBQsWELg\ndYHqAm//odQyVgoWDFEzVf4golp5adDFT7zymmTGgTKyIjVcnnvRTwIKWcUnyCyDZhaRdRIhq9LD\nJY+PUXqMzifvLS4urd8IEFdPg0zZU1o+3RwixSvYxOpsfGhxZzd3/uPep0+fviuJBwcHy+XLl0VE\n+9U5ODj4gQSiPLoCihUVlz3rbydx69CBMuD/fXm/MXCgWEYOvr2Py8GtgsUs5ndeF+d5M8Ut9ZJ4\nNqwrixYt+s/xZGZmipNTHoFGfyiDtBGdzkn0eheBGlnJ0VPAmFVWyZNVQ++Qtc0p60vARQyGkmIw\nOIurq5eAWfR6m5jNeaRKlZqSlpaWrdiSk5NFrzfdVcu22RrIkCFDxNnZM6skMugPyfeFrDimZMXb\nWeAtgQABm8B4gZUCxbLKJ//7IgoTaPOH43QTg8FHmjdvp+aOi0haWpoYjXpZnVlZXnw/SJzdDKLT\nI/4B3pIvKEBMVp3Mu1he1kmELImvIBZnrWyiNyBeASax2nQSUtVFytdzl6Z9/MQtj/NDiz27uTPb\n64lfuXIFPz8/APz8/Lhy5coD/c1AefR0bNUaXb+3sO89QMay1egnf0Wr/zelLbhYMcxrNyPp6QA4\nVm9AZ7FgieqIqWlDSE8n4+TZv13sKTsMBgPVq1f7U7uILw5HGLAdeAHoB3QDkoDGQEGgOFAbbeaJ\nAXgFu70NdnsPEhLigYo4HGVIT09i27ZtjBw5MluxWa1WvL39gW1ZLTcQ2c/mzdEkJ7cF3IE/znBx\nB2zAXqASWhmoNjAma/twYBhwBW35/+eB9sBpYAkwMGv7Uuz2saxcuZEDBw5kK+YnkclkwubixIpZ\nV5k3PoZKDd2xuRkoXsVBnR4GnF0MfNLnJCLC5AHnKFfbnWUJ4cy7WB53HxMvvl+A80dSSbyRSfSK\nW7z55lu53aW/9Z+mGOp0utu1pr8yfPjw2z9HRkYSGRn5X95OySVjR45EP2IE30e9iouLCx/OnEV4\nePhd+3To0IHxn3/GmSJhGGyu2DLt9O7Vm6l1mmNv/Cz6HbuoWbYsVatWfSAxvfvuIDZvbkxKihHt\nn/FqoBFgAY6jzQkHyIe26l/qH16dkvUIAP53izavrNdWQkusTsBxpk79ilH/72YV96LT6ViyZD4N\nGjRB5DvS068yYMDrREfvBVyASGBK1vHTgWlodfHL3P3f0QAI4Iw2rTAxq389s7YXBYYAJ4HewKvA\nSpKTb1G9ej1ee+0Vhg17957/P59kOp2Ozz+fyEsv92DID0W5cjadzHRh+IJgACIae/B65CFaBewm\nPdXB+DUlMVv1eFrNNO7lx4m9SZSs7EL8aW/GDHuHLp275lisGzZsYMOGDfd/gH8aqv9VOSUmJkZE\nRC5duqTKKYrY7Xap2aCu+DeuKoUmvSreNcOkcasW4nA4ZP369TJu3Dj54YcfHni9dv369VKjRt2s\n8sj/auEvZ83c6Jv1/MWskopFoF5W/dk5q80qECUwLKu27iowJOt1zQSKi82W575iu3nzpkRHR9++\nHHvx4sXi7Own8J5ALQFXcXX1F9Bnvb9TVoy9BUZnlU+sAguySjNGgbZ/KJ9MF22mjbPodN2zyjL5\nBaYJTBebrbh8/vkXD/Ljfiy5ujvJT1crSK9xBaR5X//bl9j/dLWCmJ10UiLcJm5eRun3RcHba6g0\n6OYjLV/1F1cPg3j75pGDBw/+7fEzMzNl3Pj35flm9aRXnx5y6dKl/xxzdnNntpP4wIED5f333xcR\nkbFjx6oTm4rs3r1bPIrml8oZqyVC1kml5OVi8/H80/S6nGIwOIk2dzpSIJ+AW1Yi9836s3lWkgwS\nba54SFbCbJ+V0A1Z+7lnJcPuWTX0YKlVq8EDi/P777+XwoVLZtXuW4jFUk+MRhcxGJplJfc8AiVE\nmyLpKoBoJzu/yqrzO2XVyydk1dZdBIpKvnzFxGBwz/oC+l+SHy5Vq9Z/YLE/rp5vWl9a9M0nU38r\nLc5uBhmztIR8dbiMFHjGSZxc9OLkqpcmL/mJh59JIhp7yDPhLuLsZhAnF7007eMn/b4oKB7eNtm1\na9dfHr93nxekbHUfGTa/uLQbGCQFCwdKfHz8f4r5gSbxdu3aSUBAgJhMJsmXL5/MnDlTYmNjpU6d\nOlKsWDGpV6/e3050V0n8yeRwOCQ+Pv6umRuLFy8Wt5KFssY466SyY6245w+4feFKTuvatYeYTAEC\nZQUqipOTmxQvHiIWi5uAVfR696xEXUi0WSqhWQmwtcCbWQm8mWhzy12zRsAmcXHxzPb88H9SqlT4\nXcnWaGwsefMWzErQRbKStUVglMAvAi9lffm8lbWPl4BH1p/BYjQ+L+PHj5dWrTqKTvei3Jlz3kua\nNGnzQGN/HF27dk1q16smRqNeLM56KRDiJO7eRgmt6ipfnwiTL7aHinc+s7z9bVFpMyBA8vgZxSWP\nXgIKW+SZcBfJW8QiZotObC5mmTf/7qWD09PTxWw2ypL4irdH+FUa5b29Sub9euAj8fulkviT5+TJ\nk1KsVEkx25z/r737jq/5bB84/jkze0sikUViJCGLGLVixJ61KVqjOrSlqlNbnVSrVlUXxdNB12PG\nJrYaEZsQCYIIkSUnJ2fdvz9OnrR+aM0c436/Xn1VzvmeO9d9yJX7XN97CAcXZzF3/jxx8eJFUTko\nQGh8PUSVtwaIOrtmCf/nu4na8XHCZDKJkpIScfHixXs6Y0Kv14uRI0eJoKDqIiamgdi8ebMwm83i\nxIkTIj09XaxevVqo1fbi7wt6NJpaZSWK4LKk+e7fng8RGo2vmDjxk7sea2BgTQGz/jZiflo0b95S\nODrGC+sslA+EdebJmr/95yRAK6yza94r+4RRScBjwsWlksjMzBSHDx8WLi6VhFrdVajV3YSzs9c/\nlgEeNR9++KHwD7UXb/wnTFSLchRf7a5Tnnif/TxY+FWzE/6hdiLyMWcR18pVrDFaFwcN+dA6s8Uv\n1E5UCfK5qs3S0lKh1arF8qK/dkJs2qWK+Omnn+4o1lvNnfK0e+lfGQwGXh33JrUb1MUwsClxRUup\nsWM6L4wdw5QpU1A1rkWd3V+hT8vixJOfcOmndWxIWsWnUybj5ulBYFg16tSvy9mzZ+9JfHZ2dsyY\nMYVTp9LYu3cHTZo0QalUEhoaSrVq1WjdujVVqgSiUPyJ9WZhFhrNeYYMGYxSmQ1ogGSsNw/3AWdJ\nTGzAmDEv3/VYu3XriIPD90AOcAxHx6WEhARSWloN6+wTN6wLlErLXpEDmIF6wG/Y2c3A2VkQFORF\nx47+7NixkeDgYMLDw9m/fxcffNCQ99+vz/79u4iMjLzr8T+omjRpQukVFV+8lMmVPBPZmaXlz53c\npyP3vIFXvgvF2UNNXCs3VGrrDeGGHT1wdlOhVCrIyy24qk2tVkv/J/rwfo9T/Lkij/nvnSMj1UTb\ntm0rtG9yJC79q4HDhgifxHiBWiUamteWl038+7YSrVq1EpUHJIpGYr2IOTZP1Fr+sXB0cxVr1qwR\nblWriLgzC0VDyzrh91w34Vs1SHTp21PM/+E/Fd6HtLQ0ERYWLlQqjXB2di+fr75z504xbtw4ERYW\nIRwdXUW1arXEypUr71kcpaWlYtiwZ4Wrq7fw9g4U3377ndi0aZNwdPQRMLushBIirPPEO5SVf4YJ\nCBfQVQQH17pnsT3sps+YKrR2GqFQIuwdlaLvq/6i/RBv4eGrEq6V1MI32E5ENnYRDs5K8dm6cLHO\n0lB0H1lZePlpRHgDV9Gle7tr2jQYDOLd98aJFomNxIBBve/KfaBbzZ1yK1qpnMViISUlhcLCQurW\nrffDhq4AACAASURBVIubmxtCCOwcHYg+u5DUmoOptexjXBqEk5e0g7Re7+MWFkjBiTNoAr0xXS5E\noVCiNJmJrFad46piTPnFKB3sKM3MpvKL3XEIq0Luxwt5a8QLjBk1usL7WFJSgr29/TVT70pLS1Eo\nFGi12gqPCeCbb75l1KhXKC3VERkZy5kzJ8nPLwAqAyYgHHgRjaYfBkOJTWJ8GBiNRjy8XFHZmWjR\n2wvfEDtcvdT89PFZvt0XjYOTii2LLjNh4HG09iqMBgtmg4K27Vszb87PuLu73/MYbzl33vGvjRu4\nh01L94DJZBIdHu8q3EMDhV+TGOEd4C8OHTokLBaLcHBxFnFnFoqaiz8Q6kpuwr1DfaF0sBMRGz4X\njcR6EXdmoVC5OwltNT/h0qSOCPvxLaEN9hEuj0WKqP3fiVpLPxIqNydRZ9cs0UisF1Gp3wrfkEAh\nxO2fZ3m36PV60aNHX6FSaYRKpRHt2nUSS5YsEQUFBRUei8ViEUajUaxcuVK4uISX1ek9BUwQsFoo\nlc+IqKgGFR7Xw6ZL93Yi4jEX4RtiJ56dHCxqxTuJDkN9yuvaK0saCLVGKU6cOHFbWy/cqVvNnbIm\nLgEwb948dl86Tc3D3xGy+XNc3urNoGeGo1AoGDPmZU51HIfpUiGVOjZC/HkCO0d73BJiALAL8MYp\npjqGMxcJXz0JAPPlK4TOfQ2nOtXw6NSIys91JW+p9fAHpb0Gk8lEYpeOaO3t8fD1Zv4P/7FJv8eN\ne4ekpP2YzWMxm19m5co99Ow5jJo1a5OVlVWhsSgUCtRqNcXFxYAn0AToArwP9MDPbw1//PFjhcb0\nMJo7+ye87CK5eKaUeeOzyM02sm3pZXLPW1cbJ313kaiYCEJDQ1GpVP/Smu3JJC4BkHbiOHatY1Bq\nNQC4d2hARvpJAN5/ZzyTRr9B3MZz9K4UzuH9B7BTaShYlwJA6alsdIczUaiVKNQqLv2wBpWnC8bs\ny+Xtl57JQbc/nfxVu8gaOAl3d3eO+KuJy1tE0KqPGfnqGHbs2FHh/V63bhMlJXWxHjDhADTCYPDm\n4sUwXnxxTIXHA5TdmD0OrACaodEkEB0dTmbmUUJDQ20S08PEw8ODTeu3s2f3XloltKNO9cdo16o7\nT9Y4SP+gwyybbuTnH363dZg37x59IpDllAfMwoULhVd0DRGft0Q0tKwTwe8MFi06/LXQpbi4WAwY\n+qRw86kkqoRVFe+8845QOmiFY51qQuXuLEKmvyDsq1cRnr2aC5dmUcJnRCeh8fcSQROGCZ/hHYXS\n0U6o3ByFR5C/mDT5M+Hg6iLqXfxv+U3SwDG9yxeRVaQuXXoIpbLV36YXNhDQUMAgERUVX+Hx/M++\nfftEXFwT4esbIrp27SNyc3NtFsujIi8vT6Snpwuj0WjTOG41d8rj2SQAevXqxaYd25gd3B87N2d8\nPLyYv3xl+fMjXhrJ+rwMQvfMpDQzm0+7v4tKpcYtsS7egxI5/fq3lJ7LxazTo9BoKE5Jw/uJRPJW\n7uTK9iP4jemFe6s40odP5tjRY3j6VEJ34CRuLWIRQmA8cArvPhU8NQuYOvVTtm17jMLCrLIbhleA\nwdjbr6dp0wTAulf6l199ydrk1VT28eOdN9/Fz8/vpto3m81MnvIZK9etwKeSDx+++zFhYWHlz6Wm\nplJaWkpsbCwODg7lr4uKimLPns13ubfSP3F3d6+QG5d3m5ydIl3l4sWLFBUVERwcfFU90NO/MlW3\nT8EuuDIAp974lnNTfgWFAqVGje+IzlR+vhv5q3ZxavSXqLxc8ezUkKI/j+DapA5Vp78AQOHWgxzr\n9CYLZ89j8IhhePRohiEtiyoGDdvWJWNnZ1fhfc7NzWXlypV8+eXX7Ny5E6VSSfPmLVi06FccHR0Z\n+/or/LFuAY1H1+Tc3jyO/XaB/SkH8fDw+Ne2R415kaQdi0h4I4LzB/L4c8ZJ9qccwN3dnXad23L8\n1FHsnbWYChVM+uhT2rRpc1PtSg8veVCydEe8vb3x9va+5nFXdzf06efKk3jpyfMo1Wo0Ph4YL+aj\nO5iBQgGVR3Tm0s/rKd5zjIC3B3Ju8q8oHf5KzOYiHQLw8/NjR/JmNmzYgGczT3r06GGTBA7g5eXF\ngAEDGDBgAAUFBZjNZjw9PQHrKHzG9C8Yl9ELF19H6vaHvBMbWbJkCYMHD/7HdoUQfPvNd7x+vAeu\nlR2J7BTMpcNXWLx4MZcuX6LQ+TyvHOmCUqVkyas7eG7s06hGa1mdtIaYmJiK6Lr0EJBJXLop0yd+\nyoB+Q3Ad2Ap9xjmKth1CYW+H7zOd8erZjEsLNnC47WvU2T0LY/ZlvAe2IbX2EOxrBFJy4CQKrRqN\ntxtZH/yAMJkwGAxERkbed6sK3dzcsFgsZGRkYG9vb907XwhUdn99KlHbKzGbzf/Yzt69exnwZD9K\nDaUIy1+jKrPJgkKh4EjaIWp09EOpss4tiOpRjePrz9H0xTo8OXwgqbvknuDSzZHlFOmm/fbbbwwe\n+hRU9UUb4ov+WBaxR+aWP58S+gRKJ3swmtC4O2NJz8a/kg9ZjmZc6odj0RtQqFXkLkwmff8hgoOD\n7zgms9nMuXPn8PDwwNnZ+Y7bu3TpEu06tyHzdAaGEiNdu3ZFo9Hy58lkmr8WwbnUy6z/+CA+ft6Y\nTSYGDhjM+LffQ6lUIoTgwIEDXLx4kX4D+9B2UhRnUi6SnnyexLdiyUrNZePnB+jbpx9RtaP4fuWX\nPLmkBWo7Fb+/sBWDzkSXTxowqeYiCi4X3nFfpAfTreZOmcSlm2IwGKgaXhPtyPZc+iWZ0lMXsOiN\n1D37CyoHO8zFJezx70V0jXBS96aistNQIzycxQt/JbFLR84brqD2dEF3MAOnWkEECUfGvzkOpVJJ\nQEAA8fHxt3yAwfHjx2nbuR2X8i5juKLngw/eZ+zLY++on/0G9SHb/Rhdp9XHoDMxp8N6nuv5Mrl5\nl1i9fhUWg4UTp48z4OdmOLhp+X34DoZ0f5axY16la88u7Nm3C62zmounL2PvqkGhUqBUKdAXGnGt\n7EhMr6qkzMsgwDuIjFOZ6PV6VFoFHsHOPLOyAzu+SyNvlZLN67feUT+kB5dM4tI9kZaWRoN2rQg/\nOR+L0UTR9kMc6/o2dlUr49WzObkLN1BF2JNTXEjY5s/R+Hlxbtz3VE25wAdvvk2nQX3xGNUdp/ga\nnHxyEsJgwnipANe6NRFnLtG9TQfmzPr6phJ5SUkJaWlp9BnUD32winPJx9G6O2DIL+H1Ua/Srl07\nGjRocFsLNcKja9Dx+zoExllPBtry5SFc91XnlVGv0vnxTpzOOI1SAwP+05KobiGc3JLNnG5rGPLE\ncNYfXcZTS1ui0ihZ/tZO9vyUjslgpu93zbAYLfw8ZCO+4e6c3n2Jak0q413dlfwMHYXHTFy6mIuL\npxOuTu6sSVpLSEjILccuPRzkjU3pnvDy8kKfm48h+zLayp44x9XA3sGBIJM9unkb6de8JaUlejZU\nMaH1tyZAn5e6syd8KBqNBo2TA34vPs7x/h/i2TuBi98lUWv5BNyaR2MuLuGnWk+yqPIifv95AS1b\ntrxhHIcOHaJ1hzYoXNRczMhGna2lz/FxOPq6sP/zDXw2YQrf/j6X0MohrFm26qppezcjLLQ6R5Oy\nCIyrhMVs4cSqC/R5rBMdu7an/thgRg5rxZk9l/i6/Qr863iSf7YY92AnZs//lnbvx6LSWGvcMb1D\n2f7dUXrNakpE+yAAunxWyqbpB1FpFDh52ZF3ppizBy9Rmmvi8KGjCCEICgpCrZY/ltLNkys2pZvi\n5eXF66+9RvpjL3F25BekN3qJPt26c3j/ATKPHadj+w78tmwxBZsOIEzWm36FG/fhHxRI3bp1CXDx\n5MyQz9DtS8e9dRym3EJcm0UBoHJywLVVLA4DmtOtT69/3LK27+D+1BjXlM4HX6b/mXfRONuRu9e6\nPD786ccwFOnptH80F92uMHX6tFvu5xdTvuTQvGxmNlzJ57WX4l7iz4D+A7h06RKNhtdCoVAQVM+b\nwLqVWPr6n/w+citdP2uIg7MdqQsyMepNCCHY9Z801PYqSq8Yy9suLTJScLYYraOGKzl68k5dQa1R\nYTSZ8PX1pVq1ajKBS7dM/ouRbtq7b46j+WNN2LdvH2Hth9ChQ4fy5z6e/jlBs8dwce4q9sU+jdbP\nE/2fx0hetQaNRsOmVWt598P3+dV8jNzvV2NfK5ALXy2l8rNd0KefpWD1Hmot+whV2nl2795NlSpV\nrhtD2qFjDOg3AAB7TycC29XiUkoWge3CObPyCK5hlVCqlPgkhnJs77Fb7mNwcDAHUw+ze/du7O3t\nqVevHhaLBYtJcP7QZfwiPSm9YuTsvlx8w915ZlUHTv2Zw+XsAoryrzDOZz5KtRKLSYAQLBy+iRPJ\n5/ANd2fdxH0IIWj7dhwJo6OwmC1802klWoMTjo6Ot/eXIj3yZE1cuivimj2G/q1uuCXWpXDjPi7+\nsJaEUjd++eHnq64rKCigbbfO7N9/AIOhFJQKhNlCyOfPUWlgIsejRrB8/gIaNWp03e8THhOJ38sx\nVB9Un8zFB9g0bAHCbEHtYochv4QOq57Bs44/69rP4dV+L/L8s89d00ZpaSl79uxBoVBQr149NBrN\nv/bvhx//w3MvPUtgQy+yD+fhVc2FzO0X8Ap2Iy+riNE7unHhWB4Lh2/GI8iZgT+2xFRq5vseaygt\nNmEsNmExCFT2Cl7c0hX/OtZ56MlTD7Bl0jFOnTgjE7kEyBubko3M/n4Or3w0Ht8pIzAXlZA9+iuS\nfvsvTZs2veZaIQRZWVmoVCq++34On305A49ODSnecYS4oDDOZ5/l9MlTxNSN5YfZ8/Hx8eG1ca+z\ncu1qnBwdSTuShnBWYsgrodnsvjh4O7P5mV8wl5owl5owFpbSJjGRJb8vvubmZm5uLs0SEyiwXEFY\nBN4OnmxcvQE3N7d/7WNUvTpUSlBSu0sw1ZpWJvnzA6wcv4fqzaswbFkbvum4guzDefSa1ZTwdoEA\n7P7hOPv+yOBI0hlqtQokc8cFaj8eTO+vm1JaZGRW4nIUZiXP9hnNa6++dnf+MqQHWoXd2JwwYQI/\n/PADSqWSOnXq8P3339tsxZ1ke0OfGoJKpeLr6XOx02qZ9eOC6yZwsP4jDQy0Jrl33hpHm1atSUlJ\nwa1Zb158ZRTRn7UjNrEXR7/aTpvO7YivW49t2anU+aYdeQfPYxp1GHsXFyKebYxPg2CyVh2lWp9Y\njny9nbrvtuH4F38ydtQr5OTkMOf7OeQXFODr7UNQUBBLVy5D/ZgXHWYMBWDbsF9594PxTP1syr/2\n0cnZkbAWnoQ2+2vflIC6Xpzek4O+0IDZZKGkwEBuxl9zvHMzinCuZI+9q4ZTu7OJGxjGn3OOkfpr\nOsIM9Z+qgVc1FzJOnOTX337l23lfo9VoGfPCWFq0aHEnfyXSI+K2RuKZmZm0bNmSI0eOYGdnR58+\nfejQocNVy5DlSFy6VVOnTuXNj9/GVGrCu24gzeb0ZUWjmRTnFdH/3HjsPZ0AWP/Efzi7No2ANjU5\ns/Ioge3DKckpIntLBnbuDph0BsKCQ7mQnU3lzjVQezlwaOYWfMKrkHv0PPWndKHWUw0BOPn7PpT/\nOc+qRSv+Nb6ffv6J58c8Q+fJ8ZQWGVn+1i4en/YYS0fvRqEWKO0BBRSe09FwWC3MRgv7/8ggplc1\nTm65gFIFVeIqcXDxKUqLjLj6OfD0svYsHLSNNvGd+CPpVzp8GotBZ2LFq3tZ+sdymjRpci/fcuk+\nVCEjcVdXVzQaDTqdDpVKhU6nu+GNKEm6GTk5Obz70Xs8NrMHVVrX4MhX21jV8VuK84oQCoEhr6Q8\niZfmlWDILyFzyUEaTe5GraHWhLy291yy1h6j5pMNMOkMXPn9JHU/6YiduyPe8UHsn7yB2mMS2D9x\nPTUGxoOA0z+m0jO6AyaTiZycHLy8vG74ibJ/v/5kZ2fzzgvj8K7pSuJbMWz5/BijXniZju06smXL\nFmbMmkahQse2b46gALTOGnJPFjHwpxZMb7qUnGMFDPqpJVWbVGb9J/uY0nARw4cMZ++BvXT9Ip7a\nna2rWEuLjHw9Z5ZM4tK/uq0php6enowZM4agoCD8/f1xd3endevWdzs26RGye/dufGIDCe0di72n\nEzFvtKb4bAH+VQOo1juWFR2/4dCXW9j09EJydmTSdaf1JPpKcQEA5B/P4czqI2ic7Dj5eypn1x/H\nM8qf08sOA+Ac6I6xUI/vYyEodBZ+DfyIXwI+INjgTbvWbakSEkB4bCSVKnvz+x83PhDg5dEvs3bZ\neqp71CZ7kYVn+7/E22++Tb169Rg1ahRfffENSqUSs8ECCsAicKviyNftVmLUmQht5kftLiE4edrT\naWJ9VKh4/90PUSgUWMx/jb4sZoFCIWcAS//utkbi6enpTJ06lczMTNzc3OjVqxc//vgjAwYMuOq6\n8ePHl/85ISGBhISEO4lVeoh5eHhQmJmLudSEyk5NyYUiMFqoEVYDY4InwZ0iyVp7jCunL+Pfsjpu\noV5YDGZ2vbmMVr88ycqO3+IR4Ufdd9py+cB59n60GovZRN7hbPKPXmDLc7/hGe3PsWlbGfrEUzw/\n4jkUCgV+fn4EVgsienoHqj4ezaWUMwxpO4z4evEEBQVdN9aGDRuyetla1q1bx4TJH7Jo2R8M7v8U\nw4cOZ+ybY2j9Wgyt3ojmzJ5LfNlqOdGqBF6ZksiLo0dyKb0Qs8mCSq0k/0wxZpPAycmJ0c+NYcTI\nYegLDRiKTSR/dIiVy/69Ti89+JKTk0lOTr79Bm793AkhFixYIIYOHVr+9fz588Vzzz131TW32bT0\niLJYLKJ7n8dFYIMwETu2tfAO8xPvffie2LRpk3D2chUNJnUR8R91EFo3e9H0m94ioFkNobRXC/dI\nX4ESodSqxMALH4inxVTxtJgqQvvFCZWjRjiHeApHfzdh7+sslGqV6NGvl9Dr9eXfNyMjQ3gGVCp/\n3dNiqqjeJkosX778H+Pdtm2bcPd2FQN/bCmeTmonqtTyEVOmTREarVpMsQwvb63hgEgxd+5cIYQQ\nly9fFrVjIkTV+n6i5cvRwjfES3z6+aflbS5dulR07dVZ9Oz3uNi6deu9eaOl+96t5s7bGonXqlWL\nDz74gJKSEuzt7Vm7di3169e//d8k95ELFy5w9uxZQkNDy6ed5ebmMnbkSA6lplKrdm0+nTkTHx8f\nG0f6cFEoFPz206/88ssvZGRkUHfmWNq0aQPAuqQ1fDX7a86dPUd07WjyvzvGhUOncA5050pmHko7\nNZgFFuNf28OadEYUSgUmnQGNsx3mSwZSdu8hOjr6qu/r4+ODvrCEy4fO4xnph/7SFS4eyLrhKPx/\n5v04l2avhlO3v/WUHo2DmrmvzcHewZ7zB/Pwr+OJyWDm/IE8Kg+07sHu4eHB3l37WLBgAVlZWbw1\nu/5VWwx06tSJTp063Y23U3qE3FYSj46OZtCgQdSrVw+lUklcXBxPP/303Y6twn01cyZvvPIKgXZa\nzpstLFi0iObNm9O+WTPqZh5ngsXI4tPptGmSys6Dh9BqtbYO+aGiVCrp27fvNY/Xr1+/fJBQWlpK\nTL1YhAKMRaU0+KQzkc83ZePTC1ieOIu4d9qSm3qWrDVHcanqyeM7x6B21JL64RpefvMV1i1fc1Xb\njo6OfPXlLEa2eBG/BlXJ2XuG559+jtq1a/9jrBq1BlPJX780jDoTWo2Gr2Z9xcjE54noEMTZvbnE\n1apPYmIiAHq9nrFvvMLqtavw8vKiefPmd/qWSdLDv9gnKSmJXbt2ERISwoABA264N0VaWhpNY2PY\nIEqoqoRNZhiocWH1pk30aNaUA+YrKBQgBMSrXZi/fgN169at4N482sxmMy3ateK4/gwXdmSiUCgY\nWvpZ+b+1RQ2mgFKBvZcj55NPEPd2W2Jet95wLzhxkU2JczmXkXXdtk+cOMHBgwcJCQm5qVN1Dh06\nRJOExjR5pRZOnlrWvXeQmVO+onev3uzfv58///wTPz8/OnTogFJpvUH55LBBpF7YRuIH0Vw4nM/S\nl/awa/vu8jM3JQluPXc+FLe/i4uLeXXUKNo3bsyLI0aQn58PwPtvv81LfXpTPOE9vn3heXp0aI/F\nYrluG8eOHSPWTkPVsnckRgmGkhLefOklLpeWUlr2npoBncVyU0u1pbtr69atHD9/kg7rnkOpUaFx\ntSNnRyYA5hIjpXk66r3fDmOeHqVGxd4Ja9g49GdMJQZOLkylVq1aN2w7LCyMbt263fSxaJGRkWxc\nt4lK6bVQbvFj3jc/0LtXb8B6yPHw4cPp1KlTeQIH+HXhb/Se25iAmErU7R9GnR5BJCUl3f4bIkk8\nBBtgWSwWurZJxGvfXoaa9CTt202brVtYvWUrn3zyCYc0RnzVYDQX02DHDrZu3XrdlYTVq1dnb6mR\nMwK8FdC8BNqoTLTcsYlsi5J4i4qxSjPLtQ5Uj4n914/b0t1XXFyMk68raq2aBp90JuWD1axo/zXe\n9YLIO5yNucTEpn4/YTQbabN4GC4hnmwe8Qs/V/sA9GYO7z10V+OJiori+2/m3vT19g52XLmox8nL\nHgDdRcMtb5UrSf/fAz8SP3nyJEdSU/lO6Omohi+EgeIzZ9i2bRsOKhX/u/2oUUCQWlk+Sv//3N3d\n6fvUEOqaNdQ0aTAJmKuFwRr4Q2PhnMnMJGdP8mpGMO+XX64aYUkVo2HDhhQdu8ThmVsIaFOTyo9V\nRSPUtA1uwvKfFnMk5SBDn3iKyOea4t88DJdgT5p+1QuTzsjggYNtftDC2+Pe4ftO60meeoBfn97G\n5QN6evfubdOYpAffAz8Sv95JMAKBl5cXwcHBvH/6BCOEmU0W2KOw8Nn48TwzaBBV/PwoLC7mXHY2\nCpORKxaBI9bfagVAHvBUKXxvB8+UQjMVDCm+zOojV+jcsiXb9u0rv7F5+PBh9uzZQ2BgIM2bN7/l\nY8akm+Ph4UHymg0MG/k0Wz//k7p16/LdiXV4e3uXXxMcHMzqPTvKvy7KuIy9vT2ffzLZFiFfZdSL\no6kaXI2Va1dQx9+Hl7a/dFMbb0nSP3ngb2wKIWjbrCluqXvoZdSTpLbjaNXqbE5J4eLFiwzp04c9\nqalU8fXlcn4+zxfn0V1h4RcTTDJCKdBDBdO00LMUqilhhhZyBDTTQx8VzDHBKUfraF4IaKB0wq1G\nTXQF+Xj7VyFl925a2qlINQuad3ucr+fNk4ncRvLz84lrWA+7OC8cQ9xJn7OLuV/NoVu3brYOTZJu\nyiO5Fa1Op+P9t9/mwK6d1Khdh/ETJlw1x7tVwwakn0jHUwFHy7ZsXmCCUaXQUQVpAvwUkGqB1fYQ\nUlYp+cQAnxmtH1dO/y2JR+khUQX9VTDFCJcErHKAQgtECzueeH4kAwcOvGZOslQx8vPzmTt3LvkF\n+bRv154GDRrYOiRJummPZBK/EYvFQmRICPXPn6GtEl40wDFHcAD8dLDWAWorwVg26i4VMFYL/dRg\nEdBRD9ss4AxEKGGUBlagYqnRQpq9wE5hfa2fDo47wHADnBIQZ69lFSqmzZ5z3XnPkiRJNyKT+N+c\nOnWKuNBQTtuZUQDPGGCvGTqrYJIJChxBWVb1eEIPG8ygBxoq4Zywllo22sNBAb31YAfUiI3BlH6C\njSbrvPE8AVV1MFsLk02wyR7UCkg1Q2e1MxcLC2VpRZKkm/ZIzhO/ETs7OwxYE7NCAV9o4KKAb0zg\nCrxnsI6k/zTDKjPkY50HftwCxwXstQdvJbRQQTuVtcxiKi1FVAlgKHZ8a4QO2OPoYM+TRgVhCmsC\nB6ijhAKdDpPJZLP+S5L08Huok3jlypXp1Lkz7UoVzDZCXwMUAz+UrZb/2QQeOuisBwFogLYqmKQp\nq4OXtWMWcFLAGxrIOXKE1p27UHPMq2xp25nzKg2RdlqCHexZJZTsMlt/MXxoUdEwOkouCpIk6Z56\nqMspYF2qPXXKFJYsXEjK3hQSLBY0SkhQwjtG65bPVRUQroAVFnBSQHUF7LZYk/rjajhsAWcF/KaF\nJnrI1Gg5cPw4zw0eTOMdmxilsmAWkGBWk65QU2Qw0Dg2hp+XLMXf39/Wb4EkSQ8QWU75f1QqFWNe\neYWVmzaBQslIrXVF5hcmMAJCq2G/gBQLeClgnwMsd4D/2Flr4r9rHbEAL6nhHROYgChHe9LS0shI\nP0FLrMv4VQroL0z07dePUoOBjbv3yAQuSdI999An8f9xcHAgOCCAvqXWqYSXBXRXQWLLlvi4OJML\nNFaBQ1lNu7kKSoA9Bw9yWKFinME6lfBrOzhaaqRatWrExsfzvVKDRUChgIVaR+IaNbrmhHVJkqR7\n5ZFJ4gBRMdEMsVPysRa2OYCLnYaAaqGkpB3H4ubGChOcLtsf6ysjuKiUVK1alTkLFnDawZETLm50\nUzgwfuJEqlWrxrRvvuVgjUiqCQdqmLXE9uzN0KFDbdtJSZIeKQ99TfzvTp06RbP4eCIMevRAtpsH\nm/fsoVKlSqxcuZLBXbtwxWDEEWt5JKpJU1Zv2gRAdnY2aWlphISEXHVggMVi4dy5c9jb21OpUiXb\ndEySpIeGnCf+L/Ly8li3bh0qlYrExEScnZ3Ln/ts4kTGvT0OBDSKi+XXpBUyMUuSVKFkEr9DJpOJ\n0tJSnJycbB2KJEmPIJnEJUmSHmByiqEkSdIj5LaTeH5+Pj179iQ8PJyIiAh27Njx7y+SJEmS7qrb\nPhTipZdeokOHDvz222+YTCaKi4vvZlySJEnSTbitmnhBQQGxsbGcPHnyxg3LmrgkSdItq5CaeEZG\nBt7e3jz11FPExcUxfPhwdDrd7TQlSZIk3YHbKqeYTCZSUlL44osviI+PZ9SoUUycOJH333//quvG\njx9f/ueEhAQSEhLuJFZJkqSHTnJyMsnJybf9+tsqp2RnZ9OoUSMyMjIA2LJlCxMnTmTZsmV/cd08\n4QAAFQVJREFUNSzLKZIkSbesQsoplStXJjAwkLS0NADWrl1LZGTk7TQlSdID7MyZM/zxxx9s3rxZ\nDtps5LYX++zbt49hw4ZhMBgIDQ3l+++/Lz+cGORIXJIeduvXr6dHly4EqlRcMptp2qYNU2fOZMuW\nLTg6OpKYmIhWq7V1mA8cuWJTkqQK4VepEom5uYRi3Zt/noMDBQoFISoVxULgWa0aG7dvx9HR0dah\nPlDkik1Jku4aIQRLly7liy++YPv27eWPnzt3jgu5uQSXfa0BKpWUEKzT0aOoiIFXrmBIS+OLL76w\nSdyPktte7CNJ0sNNCEG/Xr1YvXQpjmYzhWo1H06cyIujRvHzzz9jB2wHmmA9ZPwY0L7stQqgsl7P\n1EmTCA4OxmAwUKtWLeLj423Um4eXLKdIknRdmzdvpl1CApUtFgKA/YBOqeRiXh7VAgMxFhaixHoC\nlhFQAdFaLe0NBvTAPKyjxDwgWKHggp0dL7/+OuPefRcAvV7PwYMHcXR0JDw8HIVCYZN+3m9uNXfK\nkbgkSdeVlJSE0mKhBDgPdAZ+tFhYtWoVuqIiEoEtWM+ddQaq1KyJ0sGBj1NTUQC1gSPASMBFCK7o\n9XwyYQKDnnoKpVJJQuPGGPLy0JnNNElI4LfFi1GrZUq6VbImLknSda1JSsIP6AhUB/4oe1yr1YIQ\nrAR0QAhgAc5kZNCsVSsA6mNN4I6AS9nrnAE3pZLs7GxGPPUUQWfPMrSoiGd1Oo4kJ/PVV19VWN8e\nJjKJS5J0DaPRyL6DB+kFBGBNygFAQJUqtG7dGqWDAwB9gYHAC4DCYGDRH3/gCOwCqmEttRwra/M4\nUATUrFmTI0eOUMtsBqzlgKo6HQdTUyusfw8TmcQlSbqGUqlEqVRi+NtjJUDfAQP4ZOJEGjVujBHK\nZ6fYYU3y+adP0xrryP1C2WO/Ae8DvyoULFq+HDc3NyIjIzmiUiGw1tNPOjoSFRdXUd17qMgbm5Ik\nlTtx4gSfTZxIYX4+Obm5pCYn0wg4B6QBJoWCugoFJywW8oE2QF3gMvA1MAAIwlpe+QpoAWwDsoHW\nLVuStG4dYJ2i2KJxY65cukSJ2UzLxEQW/vEHKpWqort835E3NiVJummzZ8/mmxkz0Go0DB4xgtfH\njKFOUREuQrBdrcZXoeCsEDgDw4HpQpAoBDlYR+argbWAAevH+gCsNzr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/dPz4cTZt2kTD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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7b96940>"
       ]
      }
     ],
     "prompt_number": 460
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A2 = getAdjacencyMatrix(sepList, maxVars[99], 20)\n",
      "sepsClusters2 = getSEPsClusters(A2, sepList)\n",
      "print sepsClusters2\n",
      "plotCluster(sepList[:,0],sepList[:,1],sepsClusters2,\"Scatter Plot\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 2, 4, 5, 2, 6, 6, 6, 6, 7, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 14, 13, 13, 15, 15, 15, 15, 15, 16, 16, 16, 16, 17, 17, 17, 17, 17, 18, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FpaHT6TAYDPdlgSul2LFjBwZPN75Zfh6AtIRsjm1NpLCwEEd7J9LO5xW9PvV8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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7b8f358>"
       ]
      }
     ],
     "prompt_number": 463
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results2 = getPointClusters(sepsClusters2, sepIndexMap)\n",
      "plotCluster2(X,Y,results2,\"Scatter Plot\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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h52uZYnAnplaC2b9fLbksWgTbt3ecVXYWqIOHFkZK600hAJKTbweiSEiYi9GY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\nqX0eaTRy+urVkPHnnam0V1J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       "text": [
        "<matplotlib.figure.Figure at 0x7b642b0>"
       ]
      }
     ],
     "prompt_number": 464
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}